From f0b87e28fb9bb322c00f09b7649ed5fbc9211699 Mon Sep 17 00:00:00 2001 From: SDKAuto Date: Tue, 30 Apr 2024 04:13:28 +0000 Subject: [PATCH 01/41] CodeGen from PR 27576 in Azure/azure-rest-api-specs Merge 482ba279cb21fbf7f4e6bbd4ad14b6c14aa979d7 into b8af2fc3af08e42d2df6f48d1c84070ef1f6589b --- sdk/face/azure-ai-vision-face/CHANGELOG.md | 5 + sdk/face/azure-ai-vision-face/LICENSE | 21 + sdk/face/azure-ai-vision-face/MANIFEST.in | 8 + sdk/face/azure-ai-vision-face/README.md | 45 + sdk/face/azure-ai-vision-face/_meta.json | 7 + .../azure-ai-vision-face/azure/__init__.py | 1 + .../azure-ai-vision-face/azure/ai/__init__.py | 1 + .../azure/ai/vision/__init__.py | 1 + .../azure/ai/vision/face/__init__.py | 30 + .../azure/ai/vision/face/_client.py | 272 + .../azure/ai/vision/face/_configuration.py | 183 + .../azure/ai/vision/face/_model_base.py | 887 + .../ai/vision/face/_operations/__init__.py | 23 + .../ai/vision/face/_operations/_operations.py | 20437 ++++++++++++++++ .../ai/vision/face/_operations/_patch.py | 20 + .../azure/ai/vision/face/_patch.py | 20 + .../azure/ai/vision/face/_serialization.py | 1998 ++ .../azure/ai/vision/face/_vendor.py | 91 + .../azure/ai/vision/face/_version.py | 9 + .../azure/ai/vision/face/aio/__init__.py | 27 + .../azure/ai/vision/face/aio/_client.py | 284 + .../ai/vision/face/aio/_configuration.py | 189 + .../vision/face/aio/_operations/__init__.py | 23 + .../face/aio/_operations/_operations.py | 18070 ++++++++++++++ .../ai/vision/face/aio/_operations/_patch.py | 20 + .../azure/ai/vision/face/aio/_patch.py | 20 + .../azure/ai/vision/face/aio/_vendor.py | 50 + .../azure/ai/vision/face/models/__init__.py | 167 + .../azure/ai/vision/face/models/_enums.py | 258 + .../azure/ai/vision/face/models/_models.py | 2763 +++ .../azure/ai/vision/face/models/_patch.py | 20 + .../azure/ai/vision/face/py.typed | 1 + .../azure-ai-vision-face/dev_requirements.txt | 3 + .../azure-ai-vision-face/sdk_packaging.toml | 2 + sdk/face/azure-ai-vision-face/setup.py | 72 + .../azure-ai-vision-face/tsp-location.yaml | 4 + sdk/face/ci.yml | 34 + 37 files changed, 46066 insertions(+) create mode 100644 sdk/face/azure-ai-vision-face/CHANGELOG.md create mode 100644 sdk/face/azure-ai-vision-face/LICENSE create mode 100644 sdk/face/azure-ai-vision-face/MANIFEST.in create mode 100644 sdk/face/azure-ai-vision-face/README.md create mode 100644 sdk/face/azure-ai-vision-face/_meta.json create mode 100644 sdk/face/azure-ai-vision-face/azure/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_client.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_configuration.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_model_base.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_patch.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_serialization.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_vendor.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/_version.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_client.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_configuration.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_patch.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py create mode 100644 sdk/face/azure-ai-vision-face/azure/ai/vision/face/py.typed create mode 100644 sdk/face/azure-ai-vision-face/dev_requirements.txt create mode 100644 sdk/face/azure-ai-vision-face/sdk_packaging.toml create mode 100644 sdk/face/azure-ai-vision-face/setup.py create mode 100644 sdk/face/azure-ai-vision-face/tsp-location.yaml create mode 100644 sdk/face/ci.yml diff --git a/sdk/face/azure-ai-vision-face/CHANGELOG.md b/sdk/face/azure-ai-vision-face/CHANGELOG.md new file mode 100644 index 000000000000..628743d283a9 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/CHANGELOG.md @@ -0,0 +1,5 @@ +# Release History + +## 1.0.0b1 (1970-01-01) + +- Initial version diff --git a/sdk/face/azure-ai-vision-face/LICENSE b/sdk/face/azure-ai-vision-face/LICENSE new file mode 100644 index 000000000000..63447fd8bbbf --- /dev/null +++ b/sdk/face/azure-ai-vision-face/LICENSE @@ -0,0 +1,21 @@ +Copyright (c) Microsoft Corporation. + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/sdk/face/azure-ai-vision-face/MANIFEST.in b/sdk/face/azure-ai-vision-face/MANIFEST.in new file mode 100644 index 000000000000..a8d93584ffcc --- /dev/null +++ b/sdk/face/azure-ai-vision-face/MANIFEST.in @@ -0,0 +1,8 @@ +include *.md +include LICENSE +include azure/ai/vision/face/py.typed +recursive-include tests *.py +recursive-include samples *.py *.md +include azure/__init__.py +include azure/ai/__init__.py +include azure/ai/vision/__init__.py \ No newline at end of file diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md new file mode 100644 index 000000000000..7ba6d76e8240 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/README.md @@ -0,0 +1,45 @@ + + +# Azure Ai Vision Face client library for Python + + +## Getting started + +### Install the package + +```bash +python -m pip install azure-ai-vision-face +``` + +#### Prequisites + +- Python 3.8 or later is required to use this package. +- You need an [Azure subscription][azure_sub] to use this package. +- An existing Azure Ai Vision Face instance. + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.microsoft.com. + +When you submit a pull request, a CLA-bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., label, +comment). Simply follow the instructions provided by the bot. You will only +need to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information, +see the Code of Conduct FAQ or contact opencode@microsoft.com with any +additional questions or comments. + + +[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/ +[authenticate_with_token]: https://docs.microsoft.com/azure/cognitive-services/authentication?tabs=powershell#authenticate-with-an-authentication-token +[azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials +[azure_identity_pip]: https://pypi.org/project/azure-identity/ +[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential +[pip]: https://pypi.org/project/pip/ +[azure_sub]: https://azure.microsoft.com/free/ + diff --git a/sdk/face/azure-ai-vision-face/_meta.json b/sdk/face/azure-ai-vision-face/_meta.json new file mode 100644 index 000000000000..ebf0ddcdb2f0 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/_meta.json @@ -0,0 +1,7 @@ +{ + "commit": "37acfe2967e5e1be1169146ac461eb1875c9476e", + "repository_url": "https://github.com/Azure/azure-rest-api-specs", + "typespec_src": "specification/ai/Face", + "@azure-tools/typespec-python": "0.23.8", + "@autorest/python": "6.13.15" +} \ No newline at end of file diff --git a/sdk/face/azure-ai-vision-face/azure/__init__.py b/sdk/face/azure-ai-vision-face/azure/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/face/azure-ai-vision-face/azure/ai/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py new file mode 100644 index 000000000000..4cc609842e93 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py @@ -0,0 +1,30 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._client import FaceClient +from ._client import FaceAdministrationClient +from ._client import FaceSessionClient +from ._version import VERSION + +__version__ = VERSION + +try: + from ._patch import __all__ as _patch_all + from ._patch import * # pylint: disable=unused-wildcard-import +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "FaceClient", + "FaceAdministrationClient", + "FaceSessionClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) + +_patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_client.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_client.py new file mode 100644 index 000000000000..5f7d7fc0915c --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_client.py @@ -0,0 +1,272 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, TYPE_CHECKING, Union + +from azure.core import PipelineClient +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies +from azure.core.rest import HttpRequest, HttpResponse + +from ._configuration import ( + FaceAdministrationClientConfiguration, + FaceClientConfiguration, + FaceSessionClientConfiguration, +) +from ._operations import ( + FaceAdministrationClientOperationsMixin, + FaceClientOperationsMixin, + FaceSessionClientOperationsMixin, +) +from ._serialization import Deserializer, Serializer + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core.credentials import TokenCredential + + +class FaceClient(FaceClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword + """FaceClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + _endpoint = "{endpoint}/face/{apiVersion}" + self._config = FaceClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: PipelineClient = PipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request(self, request: HttpRequest, *, stream: bool = False, **kwargs: Any) -> HttpResponse: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + def close(self) -> None: + self._client.close() + + def __enter__(self) -> "FaceClient": + self._client.__enter__() + return self + + def __exit__(self, *exc_details: Any) -> None: + self._client.__exit__(*exc_details) + + +class FaceAdministrationClient( + FaceAdministrationClientOperationsMixin +): # pylint: disable=client-accepts-api-version-keyword + """FaceAdministrationClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + _endpoint = "{endpoint}/face/{apiVersion}" + self._config = FaceAdministrationClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: PipelineClient = PipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request(self, request: HttpRequest, *, stream: bool = False, **kwargs: Any) -> HttpResponse: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + def close(self) -> None: + self._client.close() + + def __enter__(self) -> "FaceAdministrationClient": + self._client.__enter__() + return self + + def __exit__(self, *exc_details: Any) -> None: + self._client.__exit__(*exc_details) + + +class FaceSessionClient(FaceSessionClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword + """FaceSessionClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + _endpoint = "{endpoint}/face/{apiVersion}" + self._config = FaceSessionClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: PipelineClient = PipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request(self, request: HttpRequest, *, stream: bool = False, **kwargs: Any) -> HttpResponse: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + def close(self) -> None: + self._client.close() + + def __enter__(self) -> "FaceSessionClient": + self._client.__enter__() + return self + + def __exit__(self, *exc_details: Any) -> None: + self._client.__exit__(*exc_details) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_configuration.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_configuration.py new file mode 100644 index 000000000000..02f03574f8db --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_configuration.py @@ -0,0 +1,183 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING, Union + +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies + +from ._version import VERSION + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core.credentials import TokenCredential + + +class FaceClientConfiguration: # pylint: disable=too-many-instance-attributes + """Configuration for FaceClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "v1.1-preview.1") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-vision-face/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.BearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) + + +class FaceAdministrationClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long + """Configuration for FaceAdministrationClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "v1.1-preview.1") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-vision-face/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.BearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) + + +class FaceSessionClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long + """Configuration for FaceSessionClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "v1.1-preview.1") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-vision-face/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.BearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_model_base.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_model_base.py new file mode 100644 index 000000000000..5cf70733404d --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_model_base.py @@ -0,0 +1,887 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +# pylint: disable=protected-access, arguments-differ, signature-differs, broad-except + +import copy +import calendar +import decimal +import functools +import sys +import logging +import base64 +import re +import typing +import enum +import email.utils +from datetime import datetime, date, time, timedelta, timezone +from json import JSONEncoder +from typing_extensions import Self +import isodate +from azure.core.exceptions import DeserializationError +from azure.core import CaseInsensitiveEnumMeta +from azure.core.pipeline import PipelineResponse +from azure.core.serialization import _Null + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping + +_LOGGER = logging.getLogger(__name__) + +__all__ = ["SdkJSONEncoder", "Model", "rest_field", "rest_discriminator"] + +TZ_UTC = timezone.utc +_T = typing.TypeVar("_T") + + +def _timedelta_as_isostr(td: timedelta) -> str: + """Converts a datetime.timedelta object into an ISO 8601 formatted string, e.g. 'P4DT12H30M05S' + + Function adapted from the Tin Can Python project: https://github.com/RusticiSoftware/TinCanPython + + :param timedelta td: The timedelta to convert + :rtype: str + :return: ISO8601 version of this timedelta + """ + + # Split seconds to larger units + seconds = td.total_seconds() + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + + days, hours, minutes = list(map(int, (days, hours, minutes))) + seconds = round(seconds, 6) + + # Build date + date_str = "" + if days: + date_str = "%sD" % days + + if hours or minutes or seconds: + # Build time + time_str = "T" + + # Hours + bigger_exists = date_str or hours + if bigger_exists: + time_str += "{:02}H".format(hours) + + # Minutes + bigger_exists = bigger_exists or minutes + if bigger_exists: + time_str += "{:02}M".format(minutes) + + # Seconds + try: + if seconds.is_integer(): + seconds_string = "{:02}".format(int(seconds)) + else: + # 9 chars long w/ leading 0, 6 digits after decimal + seconds_string = "%09.6f" % seconds + # Remove trailing zeros + seconds_string = seconds_string.rstrip("0") + except AttributeError: # int.is_integer() raises + seconds_string = "{:02}".format(seconds) + + time_str += "{}S".format(seconds_string) + else: + time_str = "" + + return "P" + date_str + time_str + + +def _serialize_bytes(o, format: typing.Optional[str] = None) -> str: + encoded = base64.b64encode(o).decode() + if format == "base64url": + return encoded.strip("=").replace("+", "-").replace("/", "_") + return encoded + + +def _serialize_datetime(o, format: typing.Optional[str] = None): + if hasattr(o, "year") and hasattr(o, "hour"): + if format == "rfc7231": + return email.utils.format_datetime(o, usegmt=True) + if format == "unix-timestamp": + return int(calendar.timegm(o.utctimetuple())) + + # astimezone() fails for naive times in Python 2.7, so make make sure o is aware (tzinfo is set) + if not o.tzinfo: + iso_formatted = o.replace(tzinfo=TZ_UTC).isoformat() + else: + iso_formatted = o.astimezone(TZ_UTC).isoformat() + # Replace the trailing "+00:00" UTC offset with "Z" (RFC 3339: https://www.ietf.org/rfc/rfc3339.txt) + return iso_formatted.replace("+00:00", "Z") + # Next try datetime.date or datetime.time + return o.isoformat() + + +def _is_readonly(p): + try: + return p._visibility == ["read"] # pylint: disable=protected-access + except AttributeError: + return False + + +class SdkJSONEncoder(JSONEncoder): + """A JSON encoder that's capable of serializing datetime objects and bytes.""" + + def __init__(self, *args, exclude_readonly: bool = False, format: typing.Optional[str] = None, **kwargs): + super().__init__(*args, **kwargs) + self.exclude_readonly = exclude_readonly + self.format = format + + def default(self, o): # pylint: disable=too-many-return-statements + if _is_model(o): + if self.exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + return {k: v for k, v in o.items() if k not in readonly_props} + return dict(o.items()) + try: + return super(SdkJSONEncoder, self).default(o) + except TypeError: + if isinstance(o, _Null): + return None + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, self.format) + try: + # First try datetime.datetime + return _serialize_datetime(o, self.format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return super(SdkJSONEncoder, self).default(o) + + +_VALID_DATE = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" + r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") +_VALID_RFC7231 = re.compile( + r"(Mon|Tue|Wed|Thu|Fri|Sat|Sun),\s\d{2}\s" + r"(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s\d{4}\s\d{2}:\d{2}:\d{2}\sGMT" +) + + +def _deserialize_datetime(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + attr = attr.upper() + match = _VALID_DATE.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + return date_obj + + +def _deserialize_datetime_rfc7231(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize RFC7231 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + match = _VALID_RFC7231.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + return email.utils.parsedate_to_datetime(attr) + + +def _deserialize_datetime_unix_timestamp(attr: typing.Union[float, datetime]) -> datetime: + """Deserialize unix timestamp into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + return datetime.fromtimestamp(attr, TZ_UTC) + + +def _deserialize_date(attr: typing.Union[str, date]) -> date: + """Deserialize ISO-8601 formatted string into Date object. + :param str attr: response string to be deserialized. + :rtype: date + :returns: The date object from that input + """ + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + if isinstance(attr, date): + return attr + return isodate.parse_date(attr, defaultmonth=None, defaultday=None) # type: ignore + + +def _deserialize_time(attr: typing.Union[str, time]) -> time: + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :rtype: datetime.time + :returns: The time object from that input + """ + if isinstance(attr, time): + return attr + return isodate.parse_time(attr) + + +def _deserialize_bytes(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + return bytes(base64.b64decode(attr)) + + +def _deserialize_bytes_base64(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return bytes(base64.b64decode(encoded)) + + +def _deserialize_duration(attr): + if isinstance(attr, timedelta): + return attr + return isodate.parse_duration(attr) + + +def _deserialize_decimal(attr): + if isinstance(attr, decimal.Decimal): + return attr + return decimal.Decimal(str(attr)) + + +_DESERIALIZE_MAPPING = { + datetime: _deserialize_datetime, + date: _deserialize_date, + time: _deserialize_time, + bytes: _deserialize_bytes, + bytearray: _deserialize_bytes, + timedelta: _deserialize_duration, + typing.Any: lambda x: x, + decimal.Decimal: _deserialize_decimal, +} + +_DESERIALIZE_MAPPING_WITHFORMAT = { + "rfc3339": _deserialize_datetime, + "rfc7231": _deserialize_datetime_rfc7231, + "unix-timestamp": _deserialize_datetime_unix_timestamp, + "base64": _deserialize_bytes, + "base64url": _deserialize_bytes_base64, +} + + +def get_deserializer(annotation: typing.Any, rf: typing.Optional["_RestField"] = None): + if rf and rf._format: + return _DESERIALIZE_MAPPING_WITHFORMAT.get(rf._format) + return _DESERIALIZE_MAPPING.get(annotation) + + +def _get_type_alias_type(module_name: str, alias_name: str): + types = { + k: v + for k, v in sys.modules[module_name].__dict__.items() + if isinstance(v, typing._GenericAlias) # type: ignore + } + if alias_name not in types: + return alias_name + return types[alias_name] + + +def _get_model(module_name: str, model_name: str): + models = {k: v for k, v in sys.modules[module_name].__dict__.items() if isinstance(v, type)} + module_end = module_name.rsplit(".", 1)[0] + models.update({k: v for k, v in sys.modules[module_end].__dict__.items() if isinstance(v, type)}) + if isinstance(model_name, str): + model_name = model_name.split(".")[-1] + if model_name not in models: + return model_name + return models[model_name] + + +_UNSET = object() + + +class _MyMutableMapping(MutableMapping[str, typing.Any]): # pylint: disable=unsubscriptable-object + def __init__(self, data: typing.Dict[str, typing.Any]) -> None: + self._data = data + + def __contains__(self, key: typing.Any) -> bool: + return key in self._data + + def __getitem__(self, key: str) -> typing.Any: + return self._data.__getitem__(key) + + def __setitem__(self, key: str, value: typing.Any) -> None: + self._data.__setitem__(key, value) + + def __delitem__(self, key: str) -> None: + self._data.__delitem__(key) + + def __iter__(self) -> typing.Iterator[typing.Any]: + return self._data.__iter__() + + def __len__(self) -> int: + return self._data.__len__() + + def __ne__(self, other: typing.Any) -> bool: + return not self.__eq__(other) + + def keys(self) -> typing.KeysView[str]: + return self._data.keys() + + def values(self) -> typing.ValuesView[typing.Any]: + return self._data.values() + + def items(self) -> typing.ItemsView[str, typing.Any]: + return self._data.items() + + def get(self, key: str, default: typing.Any = None) -> typing.Any: + try: + return self[key] + except KeyError: + return default + + @typing.overload + def pop(self, key: str) -> typing.Any: ... + + @typing.overload + def pop(self, key: str, default: _T) -> _T: ... + + @typing.overload + def pop(self, key: str, default: typing.Any) -> typing.Any: ... + + def pop(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + if default is _UNSET: + return self._data.pop(key) + return self._data.pop(key, default) + + def popitem(self) -> typing.Tuple[str, typing.Any]: + return self._data.popitem() + + def clear(self) -> None: + self._data.clear() + + def update(self, *args: typing.Any, **kwargs: typing.Any) -> None: + self._data.update(*args, **kwargs) + + @typing.overload + def setdefault(self, key: str, default: None = None) -> None: ... + + @typing.overload + def setdefault(self, key: str, default: typing.Any) -> typing.Any: ... + + def setdefault(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + if default is _UNSET: + return self._data.setdefault(key) + return self._data.setdefault(key, default) + + def __eq__(self, other: typing.Any) -> bool: + try: + other_model = self.__class__(other) + except Exception: + return False + return self._data == other_model._data + + def __repr__(self) -> str: + return str(self._data) + + +def _is_model(obj: typing.Any) -> bool: + return getattr(obj, "_is_model", False) + + +def _serialize(o, format: typing.Optional[str] = None): # pylint: disable=too-many-return-statements + if isinstance(o, list): + return [_serialize(x, format) for x in o] + if isinstance(o, dict): + return {k: _serialize(v, format) for k, v in o.items()} + if isinstance(o, set): + return {_serialize(x, format) for x in o} + if isinstance(o, tuple): + return tuple(_serialize(x, format) for x in o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, format) + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, enum.Enum): + return o.value + try: + # First try datetime.datetime + return _serialize_datetime(o, format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return o + + +def _get_rest_field( + attr_to_rest_field: typing.Dict[str, "_RestField"], rest_name: str +) -> typing.Optional["_RestField"]: + try: + return next(rf for rf in attr_to_rest_field.values() if rf._rest_name == rest_name) + except StopIteration: + return None + + +def _create_value(rf: typing.Optional["_RestField"], value: typing.Any) -> typing.Any: + if not rf: + return _serialize(value, None) + if rf._is_multipart_file_input: + return value + if rf._is_model: + return _deserialize(rf._type, value) + return _serialize(value, rf._format) + + +class Model(_MyMutableMapping): + _is_model = True + + def __init__(self, *args: typing.Any, **kwargs: typing.Any) -> None: + class_name = self.__class__.__name__ + if len(args) > 1: + raise TypeError(f"{class_name}.__init__() takes 2 positional arguments but {len(args) + 1} were given") + dict_to_pass = { + rest_field._rest_name: rest_field._default + for rest_field in self._attr_to_rest_field.values() + if rest_field._default is not _UNSET + } + if args: + dict_to_pass.update( + {k: _create_value(_get_rest_field(self._attr_to_rest_field, k), v) for k, v in args[0].items()} + ) + else: + non_attr_kwargs = [k for k in kwargs if k not in self._attr_to_rest_field] + if non_attr_kwargs: + # actual type errors only throw the first wrong keyword arg they see, so following that. + raise TypeError(f"{class_name}.__init__() got an unexpected keyword argument '{non_attr_kwargs[0]}'") + dict_to_pass.update( + { + self._attr_to_rest_field[k]._rest_name: _create_value(self._attr_to_rest_field[k], v) + for k, v in kwargs.items() + if v is not None + } + ) + super().__init__(dict_to_pass) + + def copy(self) -> "Model": + return Model(self.__dict__) + + def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: # pylint: disable=unused-argument + # we know the last three classes in mro are going to be 'Model', 'dict', and 'object' + mros = cls.__mro__[:-3][::-1] # ignore model, dict, and object parents, and reverse the mro order + attr_to_rest_field: typing.Dict[str, _RestField] = { # map attribute name to rest_field property + k: v for mro_class in mros for k, v in mro_class.__dict__.items() if k[0] != "_" and hasattr(v, "_type") + } + annotations = { + k: v + for mro_class in mros + if hasattr(mro_class, "__annotations__") # pylint: disable=no-member + for k, v in mro_class.__annotations__.items() # pylint: disable=no-member + } + for attr, rf in attr_to_rest_field.items(): + rf._module = cls.__module__ + if not rf._type: + rf._type = rf._get_deserialize_callable_from_annotation(annotations.get(attr, None)) + if not rf._rest_name_input: + rf._rest_name_input = attr + cls._attr_to_rest_field: typing.Dict[str, _RestField] = dict(attr_to_rest_field.items()) + + return super().__new__(cls) # pylint: disable=no-value-for-parameter + + def __init_subclass__(cls, discriminator: typing.Optional[str] = None) -> None: + for base in cls.__bases__: + if hasattr(base, "__mapping__"): # pylint: disable=no-member + base.__mapping__[discriminator or cls.__name__] = cls # type: ignore # pylint: disable=no-member + + @classmethod + def _get_discriminator(cls, exist_discriminators) -> typing.Optional[str]: + for v in cls.__dict__.values(): + if ( + isinstance(v, _RestField) and v._is_discriminator and v._rest_name not in exist_discriminators + ): # pylint: disable=protected-access + return v._rest_name # pylint: disable=protected-access + return None + + @classmethod + def _deserialize(cls, data, exist_discriminators): + if not hasattr(cls, "__mapping__"): # pylint: disable=no-member + return cls(data) + discriminator = cls._get_discriminator(exist_discriminators) + exist_discriminators.append(discriminator) + mapped_cls = cls.__mapping__.get(data.get(discriminator), cls) # pyright: ignore # pylint: disable=no-member + if mapped_cls == cls: + return cls(data) + return mapped_cls._deserialize(data, exist_discriminators) # pylint: disable=protected-access + + def as_dict(self, *, exclude_readonly: bool = False) -> typing.Dict[str, typing.Any]: + """Return a dict that can be JSONify using json.dump. + + :keyword bool exclude_readonly: Whether to remove the readonly properties. + :returns: A dict JSON compatible object + :rtype: dict + """ + + result = {} + if exclude_readonly: + readonly_props = [p._rest_name for p in self._attr_to_rest_field.values() if _is_readonly(p)] + for k, v in self.items(): + if exclude_readonly and k in readonly_props: # pyright: ignore + continue + is_multipart_file_input = False + try: + is_multipart_file_input = next( + rf for rf in self._attr_to_rest_field.values() if rf._rest_name == k + )._is_multipart_file_input + except StopIteration: + pass + result[k] = v if is_multipart_file_input else Model._as_dict_value(v, exclude_readonly=exclude_readonly) + return result + + @staticmethod + def _as_dict_value(v: typing.Any, exclude_readonly: bool = False) -> typing.Any: + if v is None or isinstance(v, _Null): + return None + if isinstance(v, (list, tuple, set)): + return type(v)(Model._as_dict_value(x, exclude_readonly=exclude_readonly) for x in v) + if isinstance(v, dict): + return {dk: Model._as_dict_value(dv, exclude_readonly=exclude_readonly) for dk, dv in v.items()} + return v.as_dict(exclude_readonly=exclude_readonly) if hasattr(v, "as_dict") else v + + +def _deserialize_model(model_deserializer: typing.Optional[typing.Callable], obj): + if _is_model(obj): + return obj + return _deserialize(model_deserializer, obj) + + +def _deserialize_with_optional(if_obj_deserializer: typing.Optional[typing.Callable], obj): + if obj is None: + return obj + return _deserialize_with_callable(if_obj_deserializer, obj) + + +def _deserialize_with_union(deserializers, obj): + for deserializer in deserializers: + try: + return _deserialize(deserializer, obj) + except DeserializationError: + pass + raise DeserializationError() + + +def _deserialize_dict( + value_deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj: typing.Dict[typing.Any, typing.Any], +): + if obj is None: + return obj + return {k: _deserialize(value_deserializer, v, module) for k, v in obj.items()} + + +def _deserialize_multiple_sequence( + entry_deserializers: typing.List[typing.Optional[typing.Callable]], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + return type(obj)(_deserialize(deserializer, entry, module) for entry, deserializer in zip(obj, entry_deserializers)) + + +def _deserialize_sequence( + deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + return type(obj)(_deserialize(deserializer, entry, module) for entry in obj) + + +def _sorted_annotations(types: typing.List[typing.Any]) -> typing.List[typing.Any]: + return sorted( + types, + key=lambda x: hasattr(x, "__name__") and x.__name__.lower() in ("str", "float", "int", "bool"), + ) + + +def _get_deserialize_callable_from_annotation( # pylint: disable=R0911, R0915, R0912 + annotation: typing.Any, + module: typing.Optional[str], + rf: typing.Optional["_RestField"] = None, +) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + if not annotation or annotation in [int, float]: + return None + + # is it a type alias? + if isinstance(annotation, str): + if module is not None: + annotation = _get_type_alias_type(module, annotation) + + # is it a forward ref / in quotes? + if isinstance(annotation, (str, typing.ForwardRef)): + try: + model_name = annotation.__forward_arg__ # type: ignore + except AttributeError: + model_name = annotation + if module is not None: + annotation = _get_model(module, model_name) + + try: + if module and _is_model(annotation): + if rf: + rf._is_model = True + + return functools.partial(_deserialize_model, annotation) # pyright: ignore + except Exception: + pass + + # is it a literal? + try: + if annotation.__origin__ is typing.Literal: # pyright: ignore + return None + except AttributeError: + pass + + # is it optional? + try: + if any(a for a in annotation.__args__ if a == type(None)): # pyright: ignore + if len(annotation.__args__) <= 2: # pyright: ignore + if_obj_deserializer = _get_deserialize_callable_from_annotation( + next(a for a in annotation.__args__ if a != type(None)), module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_with_optional, if_obj_deserializer) + # the type is Optional[Union[...]], we need to remove the None type from the Union + annotation_copy = copy.copy(annotation) + annotation_copy.__args__ = [a for a in annotation_copy.__args__ if a != type(None)] # pyright: ignore + return _get_deserialize_callable_from_annotation(annotation_copy, module, rf) + except AttributeError: + pass + + # is it union? + if getattr(annotation, "__origin__", None) is typing.Union: + # initial ordering is we make `string` the last deserialization option, because it is often them most generic + deserializers = [ + _get_deserialize_callable_from_annotation(arg, module, rf) + for arg in _sorted_annotations(annotation.__args__) # pyright: ignore + ] + + return functools.partial(_deserialize_with_union, deserializers) + + try: + if annotation._name == "Dict": # pyright: ignore + value_deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[1], module, rf # pyright: ignore + ) + + return functools.partial( + _deserialize_dict, + value_deserializer, + module, + ) + except (AttributeError, IndexError): + pass + try: + if annotation._name in ["List", "Set", "Tuple", "Sequence"]: # pyright: ignore + if len(annotation.__args__) > 1: # pyright: ignore + + entry_deserializers = [ + _get_deserialize_callable_from_annotation(dt, module, rf) + for dt in annotation.__args__ # pyright: ignore + ] + return functools.partial(_deserialize_multiple_sequence, entry_deserializers, module) + deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[0], module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_sequence, deserializer, module) + except (TypeError, IndexError, AttributeError, SyntaxError): + pass + + def _deserialize_default( + deserializer, + obj, + ): + if obj is None: + return obj + try: + return _deserialize_with_callable(deserializer, obj) + except Exception: + pass + return obj + + if get_deserializer(annotation, rf): + return functools.partial(_deserialize_default, get_deserializer(annotation, rf)) + + return functools.partial(_deserialize_default, annotation) + + +def _deserialize_with_callable( + deserializer: typing.Optional[typing.Callable[[typing.Any], typing.Any]], + value: typing.Any, +): + try: + if value is None or isinstance(value, _Null): + return None + if deserializer is None: + return value + if isinstance(deserializer, CaseInsensitiveEnumMeta): + try: + return deserializer(value) + except ValueError: + # for unknown value, return raw value + return value + if isinstance(deserializer, type) and issubclass(deserializer, Model): + return deserializer._deserialize(value, []) + return typing.cast(typing.Callable[[typing.Any], typing.Any], deserializer)(value) + except Exception as e: + raise DeserializationError() from e + + +def _deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + if isinstance(value, PipelineResponse): + value = value.http_response.json() + if rf is None and format: + rf = _RestField(format=format) + if not isinstance(deserializer, functools.partial): + deserializer = _get_deserialize_callable_from_annotation(deserializer, module, rf) + return _deserialize_with_callable(deserializer, value) + + +class _RestField: + def __init__( + self, + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + is_discriminator: bool = False, + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + ): + self._type = type + self._rest_name_input = name + self._module: typing.Optional[str] = None + self._is_discriminator = is_discriminator + self._visibility = visibility + self._is_model = False + self._default = default + self._format = format + self._is_multipart_file_input = is_multipart_file_input + + @property + def _class_type(self) -> typing.Any: + return getattr(self._type, "args", [None])[0] + + @property + def _rest_name(self) -> str: + if self._rest_name_input is None: + raise ValueError("Rest name was never set") + return self._rest_name_input + + def __get__(self, obj: Model, type=None): # pylint: disable=redefined-builtin + # by this point, type and rest_name will have a value bc we default + # them in __new__ of the Model class + item = obj.get(self._rest_name) + if item is None: + return item + if self._is_model: + return item + return _deserialize(self._type, _serialize(item, self._format), rf=self) + + def __set__(self, obj: Model, value) -> None: + if value is None: + # we want to wipe out entries if users set attr to None + try: + obj.__delitem__(self._rest_name) + except KeyError: + pass + return + if self._is_model: + if not _is_model(value): + value = _deserialize(self._type, value) + obj.__setitem__(self._rest_name, value) + return + obj.__setitem__(self._rest_name, _serialize(value, self._format)) + + def _get_deserialize_callable_from_annotation( + self, annotation: typing.Any + ) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + return _get_deserialize_callable_from_annotation(annotation, self._module, self) + + +def rest_field( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, +) -> typing.Any: + return _RestField( + name=name, + type=type, + visibility=visibility, + default=default, + format=format, + is_multipart_file_input=is_multipart_file_input, + ) + + +def rest_discriminator( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin +) -> typing.Any: + return _RestField(name=name, type=type, is_discriminator=True) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/__init__.py new file mode 100644 index 000000000000..9642d0953f19 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/__init__.py @@ -0,0 +1,23 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._operations import FaceClientOperationsMixin +from ._operations import FaceAdministrationClientOperationsMixin +from ._operations import FaceSessionClientOperationsMixin + +from ._patch import __all__ as _patch_all +from ._patch import * # pylint: disable=unused-wildcard-import +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "FaceClientOperationsMixin", + "FaceAdministrationClientOperationsMixin", + "FaceSessionClientOperationsMixin", +] +__all__.extend([p for p in _patch_all if p not in __all__]) +_patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py new file mode 100644 index 000000000000..1a5deace3dbf --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py @@ -0,0 +1,20437 @@ +# pylint: disable=too-many-lines,too-many-statements +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from io import IOBase +import json +import sys +from typing import Any, Callable, Dict, IO, List, Optional, Type, TypeVar, Union, cast, overload + +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.polling import LROPoller, NoPolling, PollingMethod +from azure.core.polling.base_polling import LROBasePolling +from azure.core.rest import HttpRequest, HttpResponse +from azure.core.tracing.decorator import distributed_trace +from azure.core.utils import case_insensitive_dict + +from .. import _model_base, models as _models +from .._model_base import SdkJSONEncoder, _deserialize +from .._serialization import Serializer +from .._vendor import ( + FaceAdministrationClientMixinABC, + FaceClientMixinABC, + FaceSessionClientMixinABC, + prepare_multipart_form_data, +) + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False + + +def build_face_detect_from_url_request( + *, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("content-type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detect" + + # Construct parameters + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if recognition_model is not None: + _params["recognitionModel"] = _SERIALIZER.query("recognition_model", recognition_model, "str") + if return_face_id is not None: + _params["returnFaceId"] = _SERIALIZER.query("return_face_id", return_face_id, "bool") + if return_face_attributes is not None: + _params["returnFaceAttributes"] = _SERIALIZER.query( + "return_face_attributes", return_face_attributes, "[str]", div="," + ) + if return_face_landmarks is not None: + _params["returnFaceLandmarks"] = _SERIALIZER.query("return_face_landmarks", return_face_landmarks, "bool") + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + if face_id_time_to_live is not None: + _params["faceIdTimeToLive"] = _SERIALIZER.query("face_id_time_to_live", face_id_time_to_live, "int") + + # Construct headers + if content_type is not None: + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_detect_request( + *, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: str = kwargs.pop("content_type") + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detect" + + # Construct parameters + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if recognition_model is not None: + _params["recognitionModel"] = _SERIALIZER.query("recognition_model", recognition_model, "str") + if return_face_id is not None: + _params["returnFaceId"] = _SERIALIZER.query("return_face_id", return_face_id, "bool") + if return_face_attributes is not None: + _params["returnFaceAttributes"] = _SERIALIZER.query( + "return_face_attributes", return_face_attributes, "[str]", div="," + ) + if return_face_landmarks is not None: + _params["returnFaceLandmarks"] = _SERIALIZER.query("return_face_landmarks", return_face_landmarks, "bool") + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + if face_id_time_to_live is not None: + _params["faceIdTimeToLive"] = _SERIALIZER.query("face_id_time_to_live", face_id_time_to_live, "int") + + # Construct headers + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_find_similar_request(**kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/findsimilars" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_find_similar_from_face_list_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/findsimilars" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_find_similar_from_large_face_list_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/findsimilars" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_identify_from_person_group_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/identify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_identify_from_large_person_group_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/identify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_identify_from_person_directory_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/identify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_identify_from_dynamic_person_group_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/identify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_verify_face_to_face_request(**kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/verify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_verify_from_person_group_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/verify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_verify_from_large_person_group_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/verify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_verify_from_person_directory_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/verify" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_group_request(**kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/group" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_face_list_request( # pylint: disable=name-too-long + face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_face_list_request( # pylint: disable=name-too-long + face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_face_list_request( # pylint: disable=name-too-long + face_list_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_update_face_list_request( # pylint: disable=name-too-long + face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_face_lists_request( # pylint: disable=name-too-long + *, return_recognition_model: Optional[bool] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists" + + # Construct parameters + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_face_list_face_from_url_request( # pylint: disable=name-too-long + face_list_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}/persistedfaces" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_face_list_face_request( # pylint: disable=name-too-long + face_list_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: str = kwargs.pop("content_type") + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}/persistedfaces" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_delete_face_list_face_request( # pylint: disable=name-too-long + face_list_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/facelists/{faceListId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "faceListId": _SERIALIZER.url("face_list_id", face_list_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_large_face_list_request( # pylint: disable=name-too-long + large_face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_large_face_list_request( # pylint: disable=name-too-long + large_face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_face_list_request( # pylint: disable=name-too-long + large_face_list_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_update_large_face_list_request( # pylint: disable=name-too-long + large_face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_face_lists_request( # pylint: disable=name-too-long + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_get_large_face_list_training_status_request( # pylint: disable=name-too-long + large_face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/training" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_train_large_face_list_request( # pylint: disable=name-too-long + large_face_list_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/train" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_add_large_face_list_face_from_url_request( # pylint: disable=name-too-long + large_face_list_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/persistedfaces" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_large_face_list_face_request( # pylint: disable=name-too-long + large_face_list_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: str = kwargs.pop("content_type") + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/persistedfaces" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_delete_large_face_list_face_request( # pylint: disable=name-too-long + large_face_list_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_face_list_face_request( # pylint: disable=name-too-long + large_face_list_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_large_face_list_face_request( # pylint: disable=name-too-long + large_face_list_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_face_list_faces_request( # pylint: disable=name-too-long + large_face_list_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largefacelists/{largeFaceListId}/persistedfaces" + path_format_arguments = { + "largeFaceListId": _SERIALIZER.url("large_face_list_id", large_face_list_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_create_person_group_request( # pylint: disable=name-too-long + person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_person_group_request( # pylint: disable=name-too-long + person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_group_request( # pylint: disable=name-too-long + person_group_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_update_person_group_request( # pylint: disable=name-too-long + person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_groups_request( # pylint: disable=name-too-long + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_get_person_group_training_status_request( # pylint: disable=name-too-long + person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/training" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_train_person_group_request( # pylint: disable=name-too-long + person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/train" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_person_group_person_request( # pylint: disable=name-too-long + person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_person_group_person_request( # pylint: disable=name-too-long + person_group_id: str, person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_group_person_request( # pylint: disable=name-too-long + person_group_id: str, person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_person_group_person_request( # pylint: disable=name-too-long + person_group_id: str, person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_group_persons_request( # pylint: disable=name-too-long + person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_person_group_person_face_from_url_request( # pylint: disable=name-too-long + person_group_id: str, + person_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}/persistedfaces" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_person_group_person_face_request( # pylint: disable=name-too-long + person_group_id: str, + person_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: str = kwargs.pop("content_type") + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}/persistedfaces" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_delete_person_group_person_face_request( # pylint: disable=name-too-long + person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_group_person_face_request( # pylint: disable=name-too-long + person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_person_group_person_face_request( # pylint: disable=name-too-long + person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persongroups/{personGroupId}/persons/{personId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "personGroupId": _SERIALIZER.url("person_group_id", person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_large_person_group_request( # pylint: disable=name-too-long + large_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_large_person_group_request( # pylint: disable=name-too-long + large_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_person_group_request( # pylint: disable=name-too-long + large_person_group_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_update_large_person_group_request( # pylint: disable=name-too-long + large_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_person_groups_request( # pylint: disable=name-too-long + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if return_recognition_model is not None: + _params["returnRecognitionModel"] = _SERIALIZER.query( + "return_recognition_model", return_recognition_model, "bool" + ) + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_get_large_person_group_training_status_request( # pylint: disable=name-too-long + large_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/training" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_train_large_person_group_request( # pylint: disable=name-too-long + large_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/train" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_large_person_group_person_request( # pylint: disable=name-too-long + large_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_large_person_group_person_request( # pylint: disable=name-too-long + large_person_group_id: str, person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_person_group_person_request( # pylint: disable=name-too-long + large_person_group_id: str, person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_large_person_group_person_request( # pylint: disable=name-too-long + large_person_group_id: str, person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_person_group_persons_request( # pylint: disable=name-too-long + large_person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_large_person_group_person_face_from_url_request( # pylint: disable=name-too-long + large_person_group_id: str, + person_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_large_person_group_person_face_request( # pylint: disable=name-too-long + large_person_group_id: str, + person_id: str, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: str = kwargs.pop("content_type") + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_delete_large_person_group_person_face_request( # pylint: disable=name-too-long + large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_large_person_group_person_face_request( # pylint: disable=name-too-long + large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_large_person_group_person_face_request( # pylint: disable=name-too-long + large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/largepersongroups/{largePersonGroupId}/persons/{personId}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "largePersonGroupId": _SERIALIZER.url("large_person_group_id", large_person_group_id, "str"), + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_person_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_person_request( # pylint: disable=name-too-long + person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_request( # pylint: disable=name-too-long + person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_person_request( # pylint: disable=name-too-long + person_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_persons_request( # pylint: disable=name-too-long + *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_get_dynamic_person_group_references_request( # pylint: disable=name-too-long + person_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/dynamicPersonGroupReferences" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_person_face_from_url_request( # pylint: disable=name-too-long + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/recognitionModels/{recognitionModel}/persistedfaces" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "recognitionModel": _SERIALIZER.url("recognition_model", recognition_model, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_add_person_face_request( # pylint: disable=name-too-long + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: str = kwargs.pop("content_type") + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/recognitionModels/{recognitionModel}/persistedfaces" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "recognitionModel": _SERIALIZER.url("recognition_model", recognition_model, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if target_face is not None: + _params["targetFace"] = _SERIALIZER.query("target_face", target_face, "[int]", div=",") + if detection_model is not None: + _params["detectionModel"] = _SERIALIZER.query("detection_model", detection_model, "str") + if user_data is not None: + _params["userData"] = _SERIALIZER.query("user_data", user_data, "str") + + # Construct headers + _headers["content-type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_delete_person_face_request( # pylint: disable=name-too-long + person_id: str, recognition_model: Union[str, _models.FaceRecognitionModel], persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/recognitionModels/{recognitionModel}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "recognitionModel": _SERIALIZER.url("recognition_model", recognition_model, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_face_request( # pylint: disable=name-too-long + person_id: str, recognition_model: Union[str, _models.FaceRecognitionModel], persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/recognitionModels/{recognitionModel}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "recognitionModel": _SERIALIZER.url("recognition_model", recognition_model, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_person_face_request( # pylint: disable=name-too-long + person_id: str, recognition_model: Union[str, _models.FaceRecognitionModel], persisted_face_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/recognitionModels/{recognitionModel}/persistedfaces/{persistedFaceId}" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "recognitionModel": _SERIALIZER.url("recognition_model", recognition_model, "str"), + "persistedFaceId": _SERIALIZER.url("persisted_face_id", persisted_face_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_person_faces_request( # pylint: disable=name-too-long + person_id: str, recognition_model: Union[str, _models.FaceRecognitionModel], **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/persons/{personId}/recognitionModels/{recognitionModel}/persistedfaces" + path_format_arguments = { + "personId": _SERIALIZER.url("person_id", person_id, "str"), + "recognitionModel": _SERIALIZER.url("recognition_model", recognition_model, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_dynamic_person_group_with_person_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_create_dynamic_person_group_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PUT", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_delete_dynamic_person_group_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_dynamic_person_group_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_dynamic_person_group_with_person_changes_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_update_dynamic_person_group_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="PATCH", url=_url, headers=_headers, **kwargs) + + +def build_face_administration_get_dynamic_person_groups_request( # pylint: disable=name-too-long + *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_administration_get_dynamic_person_group_persons_request( # pylint: disable=name-too-long + dynamic_person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/dynamicpersongroups/{dynamicPersonGroupId}/persons" + path_format_arguments = { + "dynamicPersonGroupId": _SERIALIZER.url("dynamic_person_group_id", dynamic_person_group_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_session_create_liveness_session_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLiveness/singleModal/sessions" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_session_delete_liveness_session_request( # pylint: disable=name-too-long + session_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLiveness/singleModal/sessions/{sessionId}" + path_format_arguments = { + "sessionId": _SERIALIZER.url("session_id", session_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_session_get_liveness_session_result_request( # pylint: disable=name-too-long + session_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLiveness/singleModal/sessions/{sessionId}" + path_format_arguments = { + "sessionId": _SERIALIZER.url("session_id", session_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_session_get_liveness_sessions_request( # pylint: disable=name-too-long + *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLiveness/singleModal/sessions" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_session_get_liveness_session_audit_entries_request( # pylint: disable=name-too-long + session_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLiveness/singleModal/sessions/{sessionId}/audit" + path_format_arguments = { + "sessionId": _SERIALIZER.url("session_id", session_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_session_create_liveness_with_verify_session_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLivenessWithVerify/singleModal/sessions" + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_session_create_liveness_with_verify_session_with_verify_image_request( # pylint: disable=name-too-long + **kwargs: Any, +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLivenessWithVerify/singleModal/sessions" + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, headers=_headers, **kwargs) + + +def build_face_session_delete_liveness_with_verify_session_request( # pylint: disable=name-too-long + session_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLivenessWithVerify/singleModal/sessions/{sessionId}" + path_format_arguments = { + "sessionId": _SERIALIZER.url("session_id", session_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="DELETE", url=_url, headers=_headers, **kwargs) + + +def build_face_session_get_liveness_with_verify_session_result_request( # pylint: disable=name-too-long + session_id: str, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLivenessWithVerify/singleModal/sessions/{sessionId}" + path_format_arguments = { + "sessionId": _SERIALIZER.url("session_id", session_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, headers=_headers, **kwargs) + + +def build_face_session_get_liveness_with_verify_sessions_request( # pylint: disable=name-too-long + *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLivenessWithVerify/singleModal/sessions" + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_face_session_get_liveness_with_verify_session_audit_entries_request( # pylint: disable=name-too-long + session_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/detectLivenessWithVerify/singleModal/sessions/{sessionId}/audit" + path_format_arguments = { + "sessionId": _SERIALIZER.url("session_id", session_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + if start is not None: + _params["start"] = _SERIALIZER.query("start", start, "str") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +class FaceClientOperationsMixin(FaceClientMixinABC): + + @overload + def _detect_from_url( + self, + body: JSON, + *, + content_type: str = "application/json", + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + @overload + def _detect_from_url( + self, + *, + url: str, + content_type: str = "application/json", + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + @overload + def _detect_from_url( + self, + body: IO[bytes], + *, + content_type: str = "application/json", + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + + @distributed_trace + def _detect_from_url( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in "Identify", "Verify", and "Find + Similar". The stored face features will expire and be deleted at the time specified by + faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying "Identify", "Verify", and "Find Similar" ('returnFaceId' is + true), please use faces that are: frontal, clear, and with a minimum size of 200x200 pixels + (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like "Verify", + "Identify", "Find Similar" are needed, please specify the recognition model with + 'recognitionModel' parameter. The default value for 'recognitionModel' is 'recognition_01', if + latest model needed, please explicitly specify the model you need in this parameter. Once + specified, the detected faceIds will be associated with the specified recognition model. More + details, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Default value is None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. The default value is + true. Default value is None. + :paramtype return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. This is only applicable when returnFaceId = true. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("content-type", None)) + cls: ClsType[List[_models.FaceDetectionResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_detect_from_url_request( + detection_model=detection_model, + recognition_model=recognition_model, + return_face_id=return_face_id, + return_face_attributes=return_face_attributes, + return_face_landmarks=return_face_landmarks, + return_recognition_model=return_recognition_model, + face_id_time_to_live=face_id_time_to_live, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceDetectionResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def _detect( + self, + image_content: bytes, + *, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in "Identify", "Verify", and "Find + Similar". The stored face features will expire and be deleted at the time specified by + faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying "Identify", "Verify", and "Find Similar" ('returnFaceId' is + true), please use faces that are: frontal, clear, and with a minimum size of 200x200 pixels + (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like "Verify", + "Identify", "Find Similar" are needed, please specify the recognition model with + 'recognitionModel' parameter. The default value for 'recognitionModel' is 'recognition_01', if + latest model needed, please explicitly specify the model you need in this parameter. Once + specified, the detected faceIds will be associated with the specified recognition model. More + details, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param image_content: The input image binary. Required. + :type image_content: bytes + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Default value is None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. The default value is + true. Default value is None. + :paramtype return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. This is only applicable when returnFaceId = true. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[List[_models.FaceDetectionResult]] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_detect_request( + detection_model=detection_model, + recognition_model=recognition_model, + return_face_id=return_face_id, + return_face_attributes=return_face_attributes, + return_face_landmarks=return_face_landmarks, + return_recognition_model=return_recognition_model, + face_id_time_to_live=face_id_time_to_live, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceDetectionResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def find_similar( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceIds": [ + "str" # An array of candidate faceIds. All of them are created by + "Detect" and the faceIds will expire 24 hours after the detection call. The + number of faceIds is limited to 1000. Required. + ], + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + def find_similar( + self, + *, + face_id: str, + face_ids: List[str], + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any, + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_ids: An array of candidate faceIds. All of them are created by "Detect" and the + faceIds will expire 24 hours after the detection call. The number of faceIds is limited to + 1000. Required. + :paramtype face_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + def find_similar( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @distributed_trace + def find_similar( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + face_ids: List[str] = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any, + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_ids: An array of candidate faceIds. All of them are created by "Detect" and the + faceIds will expire 24 hours after the detection call. The number of faceIds is limited to + 1000. Required. + :paramtype face_ids: list[str] + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceIds": [ + "str" # An array of candidate faceIds. All of them are created by + "Detect" and the faceIds will expire 24 hours after the detection call. The + number of faceIds is limited to 1000. Required. + ], + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceFindSimilarResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + body = { + "faceid": face_id, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "mode": mode, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_find_similar_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceFindSimilarResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def find_similar_from_face_list( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceListId": "str", # An existing user-specified unique candidate Face + List, created in "Create Face List". Face List contains a set of persistedFaceIds + which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + def find_similar_from_face_list( + self, + *, + face_id: str, + face_list_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any, + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_list_id: An existing user-specified unique candidate Face List, created in + "Create Face List". Face List contains a set of persistedFaceIds which are persisted and will + never expire. Required. + :paramtype face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + def find_similar_from_face_list( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @distributed_trace + def find_similar_from_face_list( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + face_list_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any, + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_list_id: An existing user-specified unique candidate Face List, created in + "Create Face List". Face List contains a set of persistedFaceIds which are persisted and will + never expire. Required. + :paramtype face_list_id: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceListId": "str", # An existing user-specified unique candidate Face + List, created in "Create Face List". Face List contains a set of persistedFaceIds + which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceFindSimilarResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if face_list_id is _Unset: + raise TypeError("missing required argument: face_list_id") + body = { + "faceid": face_id, + "facelistid": face_list_id, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "mode": mode, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_find_similar_from_face_list_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceFindSimilarResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def find_similar_from_large_face_list( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "largeFaceListId": "str", # An existing user-specified unique candidate + Large Face List, created in "Create Large Face List". Large Face List contains a + set of persistedFaceIds which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + def find_similar_from_large_face_list( + self, + *, + face_id: str, + large_face_list_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any, + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword large_face_list_id: An existing user-specified unique candidate Large Face List, + created in "Create Large Face List". Large Face List contains a set of persistedFaceIds which + are persisted and will never expire. Required. + :paramtype large_face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + def find_similar_from_large_face_list( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @distributed_trace + def find_similar_from_large_face_list( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + large_face_list_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any, + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword large_face_list_id: An existing user-specified unique candidate Large Face List, + created in "Create Large Face List". Large Face List contains a set of persistedFaceIds which + are persisted and will never expire. Required. + :paramtype large_face_list_id: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "largeFaceListId": "str", # An existing user-specified unique candidate + Large Face List, created in "Create Large Face List". Large Face List contains a + set of persistedFaceIds which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceFindSimilarResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if large_face_list_id is _Unset: + raise TypeError("missing required argument: large_face_list_id") + body = { + "faceid": face_id, + "largefacelistid": large_face_list_id, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "mode": mode, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_find_similar_from_large_face_list_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceFindSimilarResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def identify_from_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personGroupId": "str", # personGroupId of the target Person Group, created + by "Create Person Group". Parameter personGroupId and largePersonGroupId should + not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_person_group( + self, + *, + face_ids: List[str], + person_group_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_group_id: personGroupId of the target Person Group, created by "Create Person + Group". Parameter personGroupId and largePersonGroupId should not be provided at the same time. + Required. + :paramtype person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace + def identify_from_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + person_group_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_group_id: personGroupId of the target Person Group, created by "Create Person + Group". Parameter personGroupId and largePersonGroupId should not be provided at the same time. + Required. + :paramtype person_group_id: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personGroupId": "str", # personGroupId of the target Person Group, created + by "Create Person Group". Parameter personGroupId and largePersonGroupId should + not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if person_group_id is _Unset: + raise TypeError("missing required argument: person_group_id") + body = { + "confidencethreshold": confidence_threshold, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "persongroupid": person_group_id, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def identify_from_large_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "largePersonGroupId": "str", # largePersonGroupId of the target Large Person + Group, created by "Create Large Person Group". Parameter personGroupId and + largePersonGroupId should not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_large_person_group( + self, + *, + face_ids: List[str], + large_person_group_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword large_person_group_id: largePersonGroupId of the target Large Person Group, created by + "Create Large Person Group". Parameter personGroupId and largePersonGroupId should not be + provided at the same time. Required. + :paramtype large_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_large_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace + def identify_from_large_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + large_person_group_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword large_person_group_id: largePersonGroupId of the target Large Person Group, created by + "Create Large Person Group". Parameter personGroupId and largePersonGroupId should not be + provided at the same time. Required. + :paramtype large_person_group_id: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "largePersonGroupId": "str", # largePersonGroupId of the target Large Person + Group, created by "Create Large Person Group". Parameter personGroupId and + largePersonGroupId should not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if large_person_group_id is _Unset: + raise TypeError("missing required argument: large_person_group_id") + body = { + "confidencethreshold": confidence_threshold, + "faceids": face_ids, + "largepersongroupid": large_person_group_id, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_large_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def identify_from_person_directory( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personIds": [ + "str" # Array of personIds created in Person Directory "Create + Person". The valid number of personIds is between [1,30]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_person_directory( + self, + *, + face_ids: List[str], + person_ids: List[str], + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_ids: Array of personIds created in Person Directory "Create Person". The valid + number of personIds is between [1,30]. Required. + :paramtype person_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_person_directory( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace + def identify_from_person_directory( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + person_ids: List[str] = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_ids: Array of personIds created in Person Directory "Create Person". The valid + number of personIds is between [1,30]. Required. + :paramtype person_ids: list[str] + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personIds": [ + "str" # Array of personIds created in Person Directory "Create + Person". The valid number of personIds is between [1,30]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if person_ids is _Unset: + raise TypeError("missing required argument: person_ids") + body = { + "confidencethreshold": confidence_threshold, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "personids": person_ids, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_person_directory_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def identify_from_dynamic_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "dynamicPersonGroupId": "str", # DynamicPersonGroupId of the target + PersonDirectory DynamicPersonGroup to match against. Required. + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_dynamic_person_group( + self, + *, + face_ids: List[str], + dynamic_person_group_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword dynamic_person_group_id: DynamicPersonGroupId of the target PersonDirectory + DynamicPersonGroup to match against. Required. + :paramtype dynamic_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + def identify_from_dynamic_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace + def identify_from_dynamic_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + dynamic_person_group_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any, + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword dynamic_person_group_id: DynamicPersonGroupId of the target PersonDirectory + DynamicPersonGroup to match against. Required. + :paramtype dynamic_person_group_id: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "dynamicPersonGroupId": "str", # DynamicPersonGroupId of the target + PersonDirectory DynamicPersonGroup to match against. Required. + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if dynamic_person_group_id is _Unset: + raise TypeError("missing required argument: dynamic_person_group_id") + body = { + "confidencethreshold": confidence_threshold, + "dynamicpersongroupid": dynamic_person_group_id, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_dynamic_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def verify_face_to_face( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId1": "str", # The faceId of one face, come from "Detect". Required. + "faceId2": "str" # The faceId of another face, come from "Detect". Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_face_to_face( + self, *, face_id1: str, face_id2: str, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :keyword face_id1: The faceId of one face, come from "Detect". Required. + :paramtype face_id1: str + :keyword face_id2: The faceId of another face, come from "Detect". Required. + :paramtype face_id2: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_face_to_face( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace + def verify_face_to_face( + self, body: Union[JSON, IO[bytes]] = _Unset, *, face_id1: str = _Unset, face_id2: str = _Unset, **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id1: The faceId of one face, come from "Detect". Required. + :paramtype face_id1: str + :keyword face_id2: The faceId of another face, come from "Detect". Required. + :paramtype face_id2: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId1": "str", # The faceId of one face, come from "Detect". Required. + "faceId2": "str" # The faceId of another face, come from "Detect". Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id1 is _Unset: + raise TypeError("missing required argument: face_id1") + if face_id2 is _Unset: + raise TypeError("missing required argument: face_id2") + body = {"faceid1": face_id1, "faceid2": face_id2} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_face_to_face_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def verify_from_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personGroupId": "str", # Using existing personGroupId and personId for fast + loading a specified person. personGroupId is created in "Create Person Group". + Required. + "personId": "str" # Specify a certain person in Person Group. Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_from_person_group( + self, + *, + face_id: str, + person_group_id: str, + person_id: str, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_group_id: Using existing personGroupId and personId for fast loading a + specified person. personGroupId is created in "Create Person Group". Required. + :paramtype person_group_id: str + :keyword person_id: Specify a certain person in Person Group. Required. + :paramtype person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_from_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace + def verify_from_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + person_group_id: str = _Unset, + person_id: str = _Unset, + **kwargs: Any, + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_group_id: Using existing personGroupId and personId for fast loading a + specified person. personGroupId is created in "Create Person Group". Required. + :paramtype person_group_id: str + :keyword person_id: Specify a certain person in Person Group. Required. + :paramtype person_id: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personGroupId": "str", # Using existing personGroupId and personId for fast + loading a specified person. personGroupId is created in "Create Person Group". + Required. + "personId": "str" # Specify a certain person in Person Group. Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if person_group_id is _Unset: + raise TypeError("missing required argument: person_group_id") + if person_id is _Unset: + raise TypeError("missing required argument: person_id") + body = {"faceid": face_id, "persongroupid": person_group_id, "personid": person_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_from_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def verify_from_large_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "largePersonGroupId": "str", # Using existing largePersonGroupId and + personId for fast loading a specified person. largePersonGroupId is created in + "Create Large Person Group". Required. + "personId": "str" # Specify a certain person in Large Person Group. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_from_large_person_group( + self, + *, + face_id: str, + large_person_group_id: str, + person_id: str, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword large_person_group_id: Using existing largePersonGroupId and personId for fast loading + a specified person. largePersonGroupId is created in "Create Large Person Group". Required. + :paramtype large_person_group_id: str + :keyword person_id: Specify a certain person in Large Person Group. Required. + :paramtype person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_from_large_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace + def verify_from_large_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + large_person_group_id: str = _Unset, + person_id: str = _Unset, + **kwargs: Any, + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword large_person_group_id: Using existing largePersonGroupId and personId for fast loading + a specified person. largePersonGroupId is created in "Create Large Person Group". Required. + :paramtype large_person_group_id: str + :keyword person_id: Specify a certain person in Large Person Group. Required. + :paramtype person_id: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "largePersonGroupId": "str", # Using existing largePersonGroupId and + personId for fast loading a specified person. largePersonGroupId is created in + "Create Large Person Group". Required. + "personId": "str" # Specify a certain person in Large Person Group. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if large_person_group_id is _Unset: + raise TypeError("missing required argument: large_person_group_id") + if person_id is _Unset: + raise TypeError("missing required argument: person_id") + body = {"faceid": face_id, "largepersongroupid": large_person_group_id, "personid": person_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_from_large_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def verify_from_person_directory( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personId": "str" # Specify a certain person in PersonDirectory Person. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_from_person_directory( + self, *, face_id: str, person_id: str, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_id: Specify a certain person in PersonDirectory Person. Required. + :paramtype person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + def verify_from_person_directory( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace + def verify_from_person_directory( + self, body: Union[JSON, IO[bytes]] = _Unset, *, face_id: str = _Unset, person_id: str = _Unset, **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_id: Specify a certain person in PersonDirectory Person. Required. + :paramtype person_id: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personId": "str" # Specify a certain person in PersonDirectory Person. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if person_id is _Unset: + raise TypeError("missing required argument: person_id") + body = {"faceid": face_id, "personid": person_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_from_person_directory_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def group(self, body: JSON, *, content_type: str = "application/json", **kwargs: Any) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of candidate faceIds created by "Detect". The maximum + is 1000 faces. Required. + ] + } + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + + @overload + def group( + self, *, face_ids: List[str], content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :keyword face_ids: Array of candidate faceIds created by "Detect". The maximum is 1000 faces. + Required. + :paramtype face_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + + @overload + def group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + + @distributed_trace + def group( + self, body: Union[JSON, IO[bytes]] = _Unset, *, face_ids: List[str] = _Unset, **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of candidate faceIds created by "Detect". The maximum is 1000 faces. + Required. + :paramtype face_ids: list[str] + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of candidate faceIds created by "Detect". The maximum + is 1000 faces. Required. + ] + } + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceGroupingResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + body = {"faceids": face_ids} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceGroupingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class FaceAdministrationClientOperationsMixin( # pylint: disable=too-many-public-methods + FaceAdministrationClientMixinABC +): + + @overload + def create_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def create_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + # pylint: disable=line-too-long + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_face_list_request( + face_list_id=face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def delete_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, **kwargs: Any + ) -> None: + """Delete a specified Face List. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_face_list_request( + face_list_id=face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_face_list( + self, face_list_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.FaceList: + # pylint: disable=line-too-long + """Retrieve a Face List's faceListId, name, userData, recognitionModel and faces in the Face List. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: FaceList. The FaceList is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceList + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "faceListId": "str", # Valid character is letter in lower case or digit or + '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "persistedFaces": [ + { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to + the face. The length limit is 1K. + } + ], + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceList] = kwargs.pop("cls", None) + + _request = build_face_administration_get_face_list_request( + face_list_id=face_list_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceList, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_face_list_request( + face_list_id=face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_face_lists( + self, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> List[_models.FaceListItem]: + # pylint: disable=line-too-long + """List Face Lists' faceListId, name, userData and recognitionModel. + + To get face information inside Face List use "Get Face List". + + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of FaceListItem + :rtype: list[~azure.ai.vision.face.models.FaceListItem] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "faceListId": "str", # Valid character is letter in lower case or + digit or '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.FaceListItem]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_face_lists_request( + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceListItem], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def add_face_list_face_from_url( + self, + face_list_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_face_list_face_from_url( + self, + face_list_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_face_list_face_from_url( + self, + face_list_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace + def add_face_list_face_from_url( + self, + face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_face_list_face_from_url_request( + face_list_id=face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def add_face_list_face( + self, + face_list_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_face_list_face_request( + face_list_id=face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_face_list_face( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a Face List by specified faceListId and persistedFaceId. + + Adding/deleting faces to/from a same Face List are processed sequentially and to/from different + Face Lists are in parallel. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_face_list_face_request( + face_list_id=face_list_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + # pylint: disable=line-too-long + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_large_face_list_request( + large_face_list_id=large_face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def delete_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, **kwargs: Any + ) -> None: + """Delete a face from a Large Face List by specified largeFaceListId and persistedFaceId. + + Adding/deleting faces to/from a same Large Face List are processed sequentially and to/from + different Large Face Lists are in parallel. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_face_list_request( + large_face_list_id=large_face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_face_list( + self, large_face_list_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.LargeFaceList: + # pylint: disable=line-too-long + """Retrieve a Large Face List's largeFaceListId, name, userData and recognitionModel. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: LargeFaceList. The LargeFaceList is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargeFaceList + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "largeFaceListId": "str", # Valid character is letter in lower case or digit + or '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargeFaceList] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_request( + large_face_list_id=large_face_list_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargeFaceList, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_face_list_request( + large_face_list_id=large_face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_face_lists( + self, + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any, + ) -> List[_models.LargeFaceList]: + # pylint: disable=line-too-long + """List Large Face Lists' information of largeFaceListId, name, userData and recognitionModel. + + To get face information inside largeFaceList use "Get Large Face List Face". + + Large Face Lists are stored in alphabetical order of largeFaceListId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of LargeFaceList + :rtype: list[~azure.ai.vision.face.models.LargeFaceList] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "largeFaceListId": "str", # Valid character is letter in lower case + or digit or '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargeFaceList]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_lists_request( + start=start, + top=top, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargeFaceList], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_large_face_list_training_status( + self, large_face_list_id: str, **kwargs: Any + ) -> _models.FaceCollectionTrainingResult: + # pylint: disable=line-too-long + """To check the Large Face List training status completed or still ongoing. Large Face List + training is an asynchronous operation triggered by "Train Large Face List". + + Training time depends on the number of face entries in a Large Face List. It could be in + seconds, or up to half an hour for 1,000,000 faces. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :return: FaceCollectionTrainingResult. The FaceCollectionTrainingResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceCollectionTrainingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the created time of the person group, large person group or + large face list. Required. + "lastActionDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the last modify time of the person group, large person + group or large face list, could be null value when the group is not successfully + trained. Required. + "lastSuccessfulTrainingDateTime": "2020-02-20 00:00:00", # A combined UTC + date and time string that describes the last successful training time of the + person group, large person group or large face list. Required. + "status": "str", # Training status of the container. Required. Known values + are: "notStarted", "running", "succeeded", and "failed". + "message": "str" # Optional. Show failure message when training failed + (omitted when training succeed). + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceCollectionTrainingResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_training_status_request( + large_face_list_id=large_face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceCollectionTrainingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _train_large_face_list_initial( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_train_large_face_list_request( + large_face_list_id=large_face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace + def begin_train_large_face_list(self, large_face_list_id: str, **kwargs: Any) -> LROPoller[None]: + """Submit a Large Face List training task. + + Training is a crucial step that only a trained Large Face List can be used by "Find Similar + From Large Face List". + + The training task is an asynchronous task. Training time depends on the number of face entries + in a Large Face List. It could be in seconds, or up to half an hour for 1,000,000 faces. To + check training completion, please use "Get Large Face List Training Status". + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._train_large_face_list_initial( # type: ignore + large_face_list_id=large_face_list_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace + def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_large_face_list_face_from_url_request( + large_face_list_id=large_face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def add_large_face_list_face( + self, + large_face_list_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_large_face_list_face_request( + large_face_list_id=large_face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a Large Face List by specified largeFaceListId and persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_face_list_face_request( + large_face_list_id=large_face_list_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_face_list_face( + self, large_face_list_id: str, persisted_face_id: str, **kwargs: Any + ) -> _models.LargeFaceListFace: + """Retrieve persisted face in Large Face List by largeFaceListId and persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: LargeFaceListFace. The LargeFaceListFace is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargeFaceListFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargeFaceListFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_face_request( + large_face_list_id=large_face_list_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargeFaceListFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_face_list_face_request( + large_face_list_id=large_face_list_id, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_face_list_faces( + self, large_face_list_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LargeFaceListFace]: + """List faces' persistedFaceId and userData in a specified Large Face List. + + Faces are stored in alphabetical order of persistedFaceId created in "Add Large Face List + Face". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LargeFaceListFace + :rtype: list[~azure.ai.vision.face.models.LargeFaceListFace] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the + face. The length limit is 1K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargeFaceListFace]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_faces_request( + large_face_list_id=large_face_list_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargeFaceListFace], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def create_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def create_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + # pylint: disable=line-too-long + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_person_group_request( + person_group_id=person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def delete_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, **kwargs: Any + ) -> None: + """Delete an existing Person Group with specified personGroupId. Persisted data in this Person + Group will be deleted. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_group_request( + person_group_id=person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_person_group( + self, person_group_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.PersonGroup: + # pylint: disable=line-too-long + """Retrieve Person Group name, userData and recognitionModel. To get person information under this + personGroup, use "Get Person Group Persons". + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: PersonGroup. The PersonGroup is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonGroup + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personGroupId": "str", # ID of the container. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonGroup] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_request( + person_group_id=person_group_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonGroup, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_group_request( + person_group_id=person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_person_groups( + self, + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any, + ) -> List[_models.PersonGroup]: + # pylint: disable=line-too-long + """List Person Groups' personGroupId, name, userData and recognitionModel. + + Person Groups are stored in alphabetical order of personGroupId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of PersonGroup + :rtype: list[~azure.ai.vision.face.models.PersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personGroupId": "str", # ID of the container. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.PersonGroup]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_groups_request( + start=start, + top=top, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.PersonGroup], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_person_group_training_status( + self, person_group_id: str, **kwargs: Any + ) -> _models.FaceCollectionTrainingResult: + # pylint: disable=line-too-long + """To check Person Group training status completed or still ongoing. Person Group training is an + asynchronous operation triggered by "Train Person Group" API. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :return: FaceCollectionTrainingResult. The FaceCollectionTrainingResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceCollectionTrainingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the created time of the person group, large person group or + large face list. Required. + "lastActionDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the last modify time of the person group, large person + group or large face list, could be null value when the group is not successfully + trained. Required. + "lastSuccessfulTrainingDateTime": "2020-02-20 00:00:00", # A combined UTC + date and time string that describes the last successful training time of the + person group, large person group or large face list. Required. + "status": "str", # Training status of the container. Required. Known values + are: "notStarted", "running", "succeeded", and "failed". + "message": "str" # Optional. Show failure message when training failed + (omitted when training succeed). + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceCollectionTrainingResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_training_status_request( + person_group_id=person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceCollectionTrainingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _train_person_group_initial( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_train_person_group_request( + person_group_id=person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace + def begin_train_person_group(self, person_group_id: str, **kwargs: Any) -> LROPoller[None]: + """Submit a Person Group training task. Training is a crucial step that only a trained Person + Group can be used by "Identify From Person Group". + + The training task is an asynchronous task. Training time depends on the number of person + entries, and their faces in a Person Group. It could be several seconds to minutes. To check + training status, please use "Get Person Group Training Status". + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._train_person_group_initial( # type: ignore + person_group_id=person_group_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + def create_person_group_person( + self, person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + def create_person_group_person( + self, + person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + def create_person_group_person( + self, person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @distributed_trace + def create_person_group_person( + self, + person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreatePersonResult] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_person_group_person_request( + person_group_id=person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreatePersonResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_person_group_person( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, person_id: str, **kwargs: Any + ) -> None: + """Delete an existing person from a Person Group. The persistedFaceId, userData, person name and + face feature(s) in the person entry will all be deleted. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_group_person_request( + person_group_id=person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_person_group_person(self, person_group_id: str, person_id: str, **kwargs: Any) -> _models.PersonGroupPerson: + """Retrieve a person's name and userData, and the persisted faceIds representing the registered + person face feature(s). + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: PersonGroupPerson. The PersonGroupPerson is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonGroupPerson + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the person. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonGroupPerson] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_person_request( + person_group_id=person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonGroupPerson, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, person_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_group_person_request( + person_group_id=person_group_id, + person_id=person_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_person_group_persons( + self, person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.PersonGroupPerson]: + """List all persons' information in the specified Person Group, including personId, name, userData + and persistedFaceIds of registered person faces. + + Persons are stored in alphabetical order of personId created in "Create Person Group Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of PersonGroupPerson + :rtype: list[~azure.ai.vision.face.models.PersonGroupPerson] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the + person. + ], + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.PersonGroupPerson]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_persons_request( + person_group_id=person_group_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.PersonGroupPerson], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace + def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_person_group_person_face_from_url_request( + person_group_id=person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def add_person_group_person_face( + self, + person_group_id: str, + person_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a person in a Person Group by specified personGroupId, personId and + persistedFaceId. + + Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_person_group_person_face( + self, person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> _models.PersonGroupPersonFace: + """Retrieve person face information. The persisted person face is specified by its personGroupId, + personId and persistedFaceId. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: PersonGroupPersonFace. The PersonGroupPersonFace is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonGroupPersonFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonGroupPersonFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonGroupPersonFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any, + ) -> None: + # pylint: disable=line-too-long + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_large_person_group_request( + large_person_group_id=large_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def delete_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, **kwargs: Any + ) -> None: + """Delete an existing Large Person Group with specified personGroupId. Persisted data in this + Large Person Group will be deleted. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_person_group_request( + large_person_group_id=large_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_person_group( + self, large_person_group_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.LargePersonGroup: + # pylint: disable=line-too-long + """Retrieve the information of a Large Person Group, including its name, userData and + recognitionModel. This API returns Large Person Group information only, use "Get Large Person + Group Persons" instead to retrieve person information under the Large Person Group. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: LargePersonGroup. The LargePersonGroup is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargePersonGroup + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "largePersonGroupId": "str", # ID of the container. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargePersonGroup] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_request( + large_person_group_id=large_person_group_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargePersonGroup, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_person_group_request( + large_person_group_id=large_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_person_groups( + self, + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any, + ) -> List[_models.LargePersonGroup]: + # pylint: disable=line-too-long + """List all existing Large Person Groups' largePersonGroupId, name, userData and recognitionModel. + + Large Person Groups are stored in alphabetical order of largePersonGroupId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of LargePersonGroup + :rtype: list[~azure.ai.vision.face.models.LargePersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "largePersonGroupId": "str", # ID of the container. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargePersonGroup]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_groups_request( + start=start, + top=top, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargePersonGroup], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_large_person_group_training_status( + self, large_person_group_id: str, **kwargs: Any + ) -> _models.FaceCollectionTrainingResult: + # pylint: disable=line-too-long + """To check Large Person Group training status completed or still ongoing. Large Person Group + training is an asynchronous operation triggered by "Train Large Person Group" API. + + Training time depends on the number of person entries, and their faces in a Large Person Group. + It could be in seconds, or up to half an hour for 1,000,000 persons. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :return: FaceCollectionTrainingResult. The FaceCollectionTrainingResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceCollectionTrainingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the created time of the person group, large person group or + large face list. Required. + "lastActionDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the last modify time of the person group, large person + group or large face list, could be null value when the group is not successfully + trained. Required. + "lastSuccessfulTrainingDateTime": "2020-02-20 00:00:00", # A combined UTC + date and time string that describes the last successful training time of the + person group, large person group or large face list. Required. + "status": "str", # Training status of the container. Required. Known values + are: "notStarted", "running", "succeeded", and "failed". + "message": "str" # Optional. Show failure message when training failed + (omitted when training succeed). + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceCollectionTrainingResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_training_status_request( + large_person_group_id=large_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceCollectionTrainingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _train_large_person_group_initial( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_train_large_person_group_request( + large_person_group_id=large_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace + def begin_train_large_person_group(self, large_person_group_id: str, **kwargs: Any) -> LROPoller[None]: + """Submit a Large Person Group training task. Training is a crucial step that only a trained Large + Person Group can be used by "Identify From Large Person Group". + + The training task is an asynchronous task. Training time depends on the number of person + entries, and their faces in a Large Person Group. It could be in several seconds, or up to half + a hour for 1,000,000 persons. To check training status, please use "Get Large Person Group + Training Status". + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._train_large_person_group_initial( # type: ignore + large_person_group_id=large_person_group_id, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + def create_large_person_group_person( + self, large_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + def create_large_person_group_person( + self, + large_person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + def create_large_person_group_person( + self, large_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @distributed_trace + def create_large_person_group_person( + self, + large_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreatePersonResult] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_large_person_group_person_request( + large_person_group_id=large_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreatePersonResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, person_id: str, **kwargs: Any + ) -> None: + """Delete an existing person from a Large Person Group. The persistedFaceId, userData, person name + and face feature(s) in the person entry will all be deleted. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_person_group_person_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_person_group_person( + self, large_person_group_id: str, person_id: str, **kwargs: Any + ) -> _models.LargePersonGroupPerson: + """Retrieve a person's name and userData, and the persisted faceIds representing the registered + person face feature(s). + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: LargePersonGroupPerson. The LargePersonGroupPerson is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargePersonGroupPerson + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the person. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargePersonGroupPerson] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_person_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargePersonGroupPerson, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_person_group_person_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_person_group_persons( + self, large_person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LargePersonGroupPerson]: + """List all persons' information in the specified Large Person Group, including personId, name, + userData and persistedFaceIds of registered person faces. + + Persons are stored in alphabetical order of personId created in "Create Large Person Group + Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LargePersonGroupPerson + :rtype: list[~azure.ai.vision.face.models.LargePersonGroupPerson] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the + person. + ], + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargePersonGroupPerson]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_persons_request( + large_person_group_id=large_person_group_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargePersonGroupPerson], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace + def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_large_person_group_person_face_from_url_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def add_large_person_group_person_face( + self, + large_person_group_id: str, + person_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a person in a Large Person Group by specified largePersonGroupId, personId + and persistedFaceId. + + Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_large_person_group_person_face( + self, large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> _models.LargePersonGroupPersonFace: + """Retrieve person face information. The persisted person face is specified by its + largePersonGroupId, personId and persistedFaceId. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: LargePersonGroupPersonFace. The LargePersonGroupPersonFace is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.LargePersonGroupPersonFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargePersonGroupPersonFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargePersonGroupPersonFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + def _create_person_initial( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> JSON: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[JSON] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_person_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(JSON, response.json()) + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @overload + def begin_create_person( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> LROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns CreatePersonResult. The CreatePersonResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + def begin_create_person( + self, *, name: str, content_type: str = "application/json", user_data: Optional[str] = None, **kwargs: Any + ) -> LROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of LROPoller that returns CreatePersonResult. The CreatePersonResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + def begin_create_person( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> LROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns CreatePersonResult. The CreatePersonResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @distributed_trace + def begin_create_person( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> LROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of LROPoller that returns CreatePersonResult. The CreatePersonResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreatePersonResult] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._create_person_initial( + body=body, + name=name, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(_models.CreatePersonResult, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[_models.CreatePersonResult].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[_models.CreatePersonResult]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + def _delete_person_initial( # pylint: disable=inconsistent-return-statements + self, person_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_request( + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace + def begin_delete_person(self, person_id: str, **kwargs: Any) -> LROPoller[None]: + """Delete an existing person from Person Directory. The persistedFaceId(s), userData, person name + and face feature(s) in the person entry will all be deleted. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._delete_person_initial( # type: ignore + person_id=person_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @distributed_trace + def get_person(self, person_id: str, **kwargs: Any) -> _models.PersonDirectoryPerson: + """Retrieve a person's name and userData from Person Directory. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :return: PersonDirectoryPerson. The PersonDirectoryPerson is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonDirectoryPerson + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # Person ID of the person. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonDirectoryPerson] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_request( + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonDirectoryPerson, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_person( # pylint: disable=inconsistent-return-statements + self, person_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_person( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_person( # pylint: disable=inconsistent-return-statements + self, person_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_person( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_request( + person_id=person_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_persons( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.PersonDirectoryPerson]: + """List all persons' information in Person Directory, including personId, name, and userData. + + Persons are stored in alphabetical order of personId created in Person Directory "Create + Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of PersonDirectoryPerson + :rtype: list[~azure.ai.vision.face.models.PersonDirectoryPerson] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # Person ID of the person. Required. + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.PersonDirectoryPerson]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_persons_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.PersonDirectoryPerson], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_dynamic_person_group_references( + self, person_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> _models.ListGroupReferenceResult: + """List all Dynamic Person Groups a person has been referenced by in Person Directory. + + Dynamic Person Groups are stored in alphabetical order of Dynamic Person Group ID created in + Person Directory "Create Dynamic Person Group". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: ListGroupReferenceResult. The ListGroupReferenceResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.ListGroupReferenceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "dynamicPersonGroupIds": [ + "str" # Array of PersonDirectory DynamicPersonGroup ids. Required. + ] + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ListGroupReferenceResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_group_references_request( + person_id=person_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ListGroupReferenceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _add_person_face_from_url_initial( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> JSON: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[JSON] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_person_face_from_url_request( + person_id=person_id, + recognition_model=recognition_model, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(JSON, response.json()) + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @overload + def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> LROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns AddFaceResult. The AddFaceResult is compatible + with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> LROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns AddFaceResult. The AddFaceResult is compatible + with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any, + ) -> LROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns AddFaceResult. The AddFaceResult is compatible + with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace + def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> LROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: An instance of LROPoller that returns AddFaceResult. The AddFaceResult is compatible + with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._add_person_face_from_url_initial( + person_id=person_id, + recognition_model=recognition_model, + body=body, + url=url, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(_models.AddFaceResult, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[_models.AddFaceResult].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[_models.AddFaceResult]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + def _add_person_face_initial( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> JSON: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[JSON] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(JSON, response.json()) + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def begin_add_person_face( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> LROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: An instance of LROPoller that returns AddFaceResult. The AddFaceResult is compatible + with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._add_person_face_initial( + person_id=person_id, + recognition_model=recognition_model, + image_content=image_content, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(_models.AddFaceResult, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[_models.AddFaceResult].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[_models.AddFaceResult]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + def _delete_person_face_initial( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + **kwargs: Any, + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace + def begin_delete_person_face( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + **kwargs: Any, + ) -> LROPoller[None]: + """Delete a face from a person in Person Directory by specified personId and persistedFaceId. + + Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._delete_person_face_initial( # type: ignore + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @distributed_trace + def get_person_face( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + **kwargs: Any, + ) -> _models.PersonDirectoryFace: + """Retrieve person face information. The persisted person face is specified by its personId. + recognitionModel, and persistedFaceId. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: PersonDirectoryFace. The PersonDirectoryFace is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonDirectoryFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonDirectoryFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonDirectoryFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any, + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_person_faces( + self, person_id: str, recognition_model: Union[str, _models.FaceRecognitionModel], **kwargs: Any + ) -> _models.ListFaceResult: + """Retrieve a person's persistedFaceIds representing the registered person face feature(s). + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: ListFaceResult. The ListFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.ListFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceIds": [ + "str" # Array of persisted face ids. Required. + ], + "personId": "str" # Id of person. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ListFaceResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_faces_request( + person_id=person_id, + recognition_model=recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ListFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _create_dynamic_person_group_with_person_initial( # pylint: disable=inconsistent-return-statements,name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + add_person_ids: List[str] = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + if add_person_ids is _Unset: + raise TypeError("missing required argument: add_person_ids") + body = {"addpersonids": add_person_ids, "name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_dynamic_person_group_with_person_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @overload + def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> LROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns DynamicPersonGroup. The DynamicPersonGroup is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Array of personIds created by Person Directory "Create + Person" to be added. Required. + ], + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + *, + name: str, + add_person_ids: List[str], + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> LROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Required. + :paramtype add_person_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of LROPoller that returns DynamicPersonGroup. The DynamicPersonGroup is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> LROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns DynamicPersonGroup. The DynamicPersonGroup is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @distributed_trace + def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + add_person_ids: List[str] = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> LROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Required. + :paramtype add_person_ids: list[str] + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of LROPoller that returns DynamicPersonGroup. The DynamicPersonGroup is + compatible with MutableMapping + :rtype: ~azure.core.polling.LROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Array of personIds created by Person Directory "Create + Person" to be added. Required. + ], + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DynamicPersonGroup] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._create_dynamic_person_group_with_person_initial( # type: ignore + dynamic_person_group_id=dynamic_person_group_id, + body=body, + name=name, + add_person_ids=add_person_ids, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + + deserialized = _deserialize(_models.DynamicPersonGroup, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[_models.DynamicPersonGroup].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[_models.DynamicPersonGroup]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + @overload + def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + def _delete_dynamic_person_group_initial( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace + def begin_delete_dynamic_person_group(self, dynamic_person_group_id: str, **kwargs: Any) -> LROPoller[None]: + """Deletes an existing Dynamic Person Group with specified dynamicPersonGroupId. + + Deleting this Dynamic Person Group only delete the references to persons data. To delete actual + person see Person Directory "Delete Person". + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._delete_dynamic_person_group_initial( # type: ignore + dynamic_person_group_id=dynamic_person_group_id, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @distributed_trace + def get_dynamic_person_group(self, dynamic_person_group_id: str, **kwargs: Any) -> _models.DynamicPersonGroup: + """Retrieve the information of a Dynamic Person Group, including its name and userData. + + This API returns Dynamic Person Group information only, use Person Directory "Get Dynamic + Person Group Persons" instead to retrieve person information under the Dynamic Person Group. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :return: DynamicPersonGroup. The DynamicPersonGroup is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.DynamicPersonGroup + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.DynamicPersonGroup] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DynamicPersonGroup, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _update_dynamic_person_group_with_person_changes_initial( # pylint: disable=inconsistent-return-statements,name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + add_person_ids: Optional[List[str]] = None, + remove_person_ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = { + "addpersonids": add_person_ids, + "name": name, + "removepersonids": remove_person_ids, + "userdata": user_data, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_dynamic_person_group_with_person_changes_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @overload + def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> LROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be added. + ], + "name": "str", # Optional. User defined name, maximum length is 128. + "removePersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be removed. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + add_person_ids: Optional[List[str]] = None, + remove_person_ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> LROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Default value is None. + :paramtype add_person_ids: list[str] + :keyword remove_person_ids: Array of personIds created by Person Directory "Create Person" to + be removed. Default value is None. + :paramtype remove_person_ids: list[str] + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> LROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + add_person_ids: Optional[List[str]] = None, + remove_person_ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> LROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Default value is None. + :paramtype add_person_ids: list[str] + :keyword remove_person_ids: Array of personIds created by Person Directory "Create Person" to + be removed. Default value is None. + :paramtype remove_person_ids: list[str] + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be added. + ], + "name": "str", # Optional. User defined name, maximum length is 128. + "removePersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be removed. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._update_dynamic_person_group_with_person_changes_initial( # type: ignore + dynamic_person_group_id=dynamic_person_group_id, + body=body, + name=name, + user_data=user_data, + add_person_ids=add_person_ids, + remove_person_ids=remove_person_ids, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs, + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_dynamic_person_groups( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.DynamicPersonGroup]: + """List all existing Dynamic Person Groups by dynamicPersonGroupId along with name and userData. + + Dynamic Person Groups are stored in alphabetical order of dynamicPersonGroupId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of DynamicPersonGroup + :rtype: list[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. + Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.DynamicPersonGroup]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_groups_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.DynamicPersonGroup], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_dynamic_person_group_persons( + self, dynamic_person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> _models.ListPersonResult: + """List all persons in the specified Dynamic Person Group. + + Persons are stored in alphabetical order of personId created in Person Directory "Create + Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: ListPersonResult. The ListPersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.ListPersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personIds": [ + "str" # Array of PersonDirectory Person ids. Required. + ] + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ListPersonResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_group_persons_request( + dynamic_person_group_id=dynamic_person_group_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ListPersonResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class FaceSessionClientOperationsMixin(FaceSessionClientMixinABC): + + @overload + def create_liveness_session( + self, body: _models.CreateLivenessSessionContent, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + + @overload + def create_liveness_session( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + + @overload + def create_liveness_session( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + + @distributed_trace + def create_liveness_session( + self, body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] + Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreateLivenessSessionResult] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_session_create_liveness_session_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreateLivenessSessionResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_liveness_session( # pylint: disable=inconsistent-return-statements + self, session_id: str, **kwargs: Any + ) -> None: + """Delete all session related information for matching the specified session id. + + .. + + [!NOTE] + Deleting a session deactivates the Session Auth Token by blocking future API calls made with + that Auth Token. While this can be used to remove any access for that token, those requests + will still count towards overall resource rate limits. It's best to leverage TokenTTL to limit + length of tokens in the case that it is misused. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_session_delete_liveness_session_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_liveness_session_result(self, session_id: str, **kwargs: Any) -> _models.LivenessSession: + # pylint: disable=line-too-long + """Get session result of detectLiveness/singleModal call. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: LivenessSession. The LivenessSession is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LivenessSession + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this session was + created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. Required. + "status": "str", # The current status of the session. Required. Known values + are: "NotStarted", "Started", and "ResultAvailable". + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "result": { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + }, + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime when this + session was started by the client. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LivenessSession] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_session_result_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LivenessSession, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_liveness_sessions( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionItem]: + # pylint: disable=line-too-long + """Lists sessions for /detectLiveness/SingleModal. + + List sessions from the last sessionId greater than the 'start'. + + The result should be ordered by sessionId in ascending order. + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionItem + :rtype: list[~azure.ai.vision.face.models.LivenessSessionItem] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this + session was created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. + Required. + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session + should last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each + end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime + when this session was started by the client. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionItem]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_sessions_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionItem], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_liveness_session_audit_entries( + self, session_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionAuditEntry]: + # pylint: disable=line-too-long + """Gets session requests and response body for the session. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionAuditEntry + :rtype: list[~azure.ai.vision.face.models.LivenessSessionAuditEntry] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionAuditEntry]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_session_audit_entries_request( + session_id=session_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionAuditEntry], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def _create_liveness_with_verify_session( + self, body: _models.CreateLivenessSessionContent, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @overload + def _create_liveness_with_verify_session( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @overload + def _create_liveness_with_verify_session( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @distributed_trace + def _create_liveness_with_verify_session( + self, body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: + # pylint: disable=line-too-long + """Create a new liveness session with verify. Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLivenessWithVerify/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + + * + + + * Client access can be revoked by deleting the session using the Delete Liveness With Verify + Session operation. + * To retrieve a result, use the Get Liveness With Verify Session. + * To audit the individual requests that a client has made to your resource, use the List + Liveness With Verify Session Audit Entries. + + + Alternative Option: Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + .. + + [!NOTE] + Extra measures should be taken to validate that the client is sending the expected + VerifyImage. + + :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] + Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] + :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is + compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str", # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "qualityForRecognition": "str" # Quality of face image for + recognition. Required. Known values are: "low", "medium", and "high". + } + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreateLivenessWithVerifySessionResult] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_session_create_liveness_with_verify_session_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreateLivenessWithVerifySessionResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + def _create_liveness_with_verify_session_with_verify_image( # pylint: disable=protected-access,name-too-long + self, body: _models._models.CreateLivenessWithVerifySessionContent, **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @overload + def _create_liveness_with_verify_session_with_verify_image( # pylint: disable=name-too-long + self, body: JSON, **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @distributed_trace + def _create_liveness_with_verify_session_with_verify_image( # pylint: disable=name-too-long + self, body: Union[_models._models.CreateLivenessWithVerifySessionContent, JSON], **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: + # pylint: disable=line-too-long + """Create a new liveness session with verify. Provide the verify image during session creation. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLivenessWithVerify/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + + * + + + * Client access can be revoked by deleting the session using the Delete Liveness With Verify + Session operation. + * To retrieve a result, use the Get Liveness With Verify Session. + * To audit the individual requests that a client has made to your resource, use the List + Liveness With Verify Session Audit Entries. + + + Recommended Option: VerifyImage is provided during session creation. + + :param body: Is either a CreateLivenessWithVerifySessionContent type or a JSON type. Required. + :type body: ~azure.ai.vision.face.models._models.CreateLivenessWithVerifySessionContent or JSON + :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is + compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "Parameters": { + "livenessOperationMode": "str", # Type of liveness mode the client + should follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session + should last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each + end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not + to allow client to set their own 'deviceCorrelationId' via the Vision SDK. + Default is false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a + '200 - Success' response body to be sent to the client, which may be + undesirable for security reasons. Default is false, clients will receive a + '204 - NoContent' empty body response. Regardless of selection, calling + Session GetResult will always contain a response body enabling business logic + to be implemented. + }, + "VerifyImage": filetype + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str", # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "qualityForRecognition": "str" # Quality of face image for + recognition. Required. Known values are: "low", "medium", and "high". + } + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.CreateLivenessWithVerifySessionResult] = kwargs.pop("cls", None) + + _body = body.as_dict() if isinstance(body, _model_base.Model) else body + _file_fields: List[str] = ["VerifyImage"] + _data_fields: List[str] = ["Parameters"] + _files, _data = prepare_multipart_form_data(_body, _file_fields, _data_fields) + + _request = build_face_session_create_liveness_with_verify_session_with_verify_image_request( + files=_files, + data=_data, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreateLivenessWithVerifySessionResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def delete_liveness_with_verify_session( # pylint: disable=inconsistent-return-statements + self, session_id: str, **kwargs: Any + ) -> None: + """Delete all session related information for matching the specified session id. + + .. + + [!NOTE] + Deleting a session deactivates the Session Auth Token by blocking future API calls made with + that Auth Token. While this can be used to remove any access for that token, those requests + will still count towards overall resource rate limits. It's best to leverage TokenTTL to limit + length of tokens in the case that it is misused. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_session_delete_liveness_with_verify_session_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace + def get_liveness_with_verify_session_result( + self, session_id: str, **kwargs: Any + ) -> _models.LivenessWithVerifySession: + # pylint: disable=line-too-long + """Get session result of detectLivenessWithVerify/singleModal call. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: LivenessWithVerifySession. The LivenessWithVerifySession is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.LivenessWithVerifySession + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this session was + created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. Required. + "status": "str", # The current status of the session. Required. Known values + are: "NotStarted", "Started", and "ResultAvailable". + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "result": { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + }, + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime when this + session was started by the client. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LivenessWithVerifySession] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_with_verify_session_result_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LivenessWithVerifySession, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_liveness_with_verify_sessions( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionItem]: + # pylint: disable=line-too-long + """Lists sessions for /detectLivenessWithVerify/SingleModal. + + List sessions from the last sessionId greater than the "start". + + The result should be ordered by sessionId in ascending order. + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionItem + :rtype: list[~azure.ai.vision.face.models.LivenessSessionItem] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this + session was created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. + Required. + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session + should last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each + end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime + when this session was started by the client. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionItem]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_with_verify_sessions_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionItem], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def get_liveness_with_verify_session_audit_entries( # pylint: disable=name-too-long + self, session_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionAuditEntry]: + # pylint: disable=line-too-long + """Gets session requests and response body for the session. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionAuditEntry + :rtype: list[~azure.ai.vision.face.models.LivenessSessionAuditEntry] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionAuditEntry]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_with_verify_session_audit_entries_request( + session_id=session_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionAuditEntry], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_serialization.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_serialization.py new file mode 100644 index 000000000000..2f781d740827 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_serialization.py @@ -0,0 +1,1998 @@ +# -------------------------------------------------------------------------- +# +# Copyright (c) Microsoft Corporation. All rights reserved. +# +# The MIT License (MIT) +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the ""Software""), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS +# IN THE SOFTWARE. +# +# -------------------------------------------------------------------------- + +# pylint: skip-file +# pyright: reportUnnecessaryTypeIgnoreComment=false + +from base64 import b64decode, b64encode +import calendar +import datetime +import decimal +import email +from enum import Enum +import json +import logging +import re +import sys +import codecs +from typing import ( + Dict, + Any, + cast, + Optional, + Union, + AnyStr, + IO, + Mapping, + Callable, + TypeVar, + MutableMapping, + Type, + List, + Mapping, +) + +try: + from urllib import quote # type: ignore +except ImportError: + from urllib.parse import quote +import xml.etree.ElementTree as ET + +import isodate # type: ignore + +from azure.core.exceptions import DeserializationError, SerializationError +from azure.core.serialization import NULL as CoreNull + +_BOM = codecs.BOM_UTF8.decode(encoding="utf-8") + +ModelType = TypeVar("ModelType", bound="Model") +JSON = MutableMapping[str, Any] + + +class RawDeserializer: + + # Accept "text" because we're open minded people... + JSON_REGEXP = re.compile(r"^(application|text)/([a-z+.]+\+)?json$") + + # Name used in context + CONTEXT_NAME = "deserialized_data" + + @classmethod + def deserialize_from_text(cls, data: Optional[Union[AnyStr, IO]], content_type: Optional[str] = None) -> Any: + """Decode data according to content-type. + + Accept a stream of data as well, but will be load at once in memory for now. + + If no content-type, will return the string version (not bytes, not stream) + + :param data: Input, could be bytes or stream (will be decoded with UTF8) or text + :type data: str or bytes or IO + :param str content_type: The content type. + """ + if hasattr(data, "read"): + # Assume a stream + data = cast(IO, data).read() + + if isinstance(data, bytes): + data_as_str = data.decode(encoding="utf-8-sig") + else: + # Explain to mypy the correct type. + data_as_str = cast(str, data) + + # Remove Byte Order Mark if present in string + data_as_str = data_as_str.lstrip(_BOM) + + if content_type is None: + return data + + if cls.JSON_REGEXP.match(content_type): + try: + return json.loads(data_as_str) + except ValueError as err: + raise DeserializationError("JSON is invalid: {}".format(err), err) + elif "xml" in (content_type or []): + try: + + try: + if isinstance(data, unicode): # type: ignore + # If I'm Python 2.7 and unicode XML will scream if I try a "fromstring" on unicode string + data_as_str = data_as_str.encode(encoding="utf-8") # type: ignore + except NameError: + pass + + return ET.fromstring(data_as_str) # nosec + except ET.ParseError as err: + # It might be because the server has an issue, and returned JSON with + # content-type XML.... + # So let's try a JSON load, and if it's still broken + # let's flow the initial exception + def _json_attemp(data): + try: + return True, json.loads(data) + except ValueError: + return False, None # Don't care about this one + + success, json_result = _json_attemp(data) + if success: + return json_result + # If i'm here, it's not JSON, it's not XML, let's scream + # and raise the last context in this block (the XML exception) + # The function hack is because Py2.7 messes up with exception + # context otherwise. + _LOGGER.critical("Wasn't XML not JSON, failing") + raise DeserializationError("XML is invalid") from err + raise DeserializationError("Cannot deserialize content-type: {}".format(content_type)) + + @classmethod + def deserialize_from_http_generics(cls, body_bytes: Optional[Union[AnyStr, IO]], headers: Mapping) -> Any: + """Deserialize from HTTP response. + + Use bytes and headers to NOT use any requests/aiohttp or whatever + specific implementation. + Headers will tested for "content-type" + """ + # Try to use content-type from headers if available + content_type = None + if "content-type" in headers: + content_type = headers["content-type"].split(";")[0].strip().lower() + # Ouch, this server did not declare what it sent... + # Let's guess it's JSON... + # Also, since Autorest was considering that an empty body was a valid JSON, + # need that test as well.... + else: + content_type = "application/json" + + if body_bytes: + return cls.deserialize_from_text(body_bytes, content_type) + return None + + +_LOGGER = logging.getLogger(__name__) + +try: + _long_type = long # type: ignore +except NameError: + _long_type = int + + +class UTC(datetime.tzinfo): + """Time Zone info for handling UTC""" + + def utcoffset(self, dt): + """UTF offset for UTC is 0.""" + return datetime.timedelta(0) + + def tzname(self, dt): + """Timestamp representation.""" + return "Z" + + def dst(self, dt): + """No daylight saving for UTC.""" + return datetime.timedelta(hours=1) + + +try: + from datetime import timezone as _FixedOffset # type: ignore +except ImportError: # Python 2.7 + + class _FixedOffset(datetime.tzinfo): # type: ignore + """Fixed offset in minutes east from UTC. + Copy/pasted from Python doc + :param datetime.timedelta offset: offset in timedelta format + """ + + def __init__(self, offset): + self.__offset = offset + + def utcoffset(self, dt): + return self.__offset + + def tzname(self, dt): + return str(self.__offset.total_seconds() / 3600) + + def __repr__(self): + return "".format(self.tzname(None)) + + def dst(self, dt): + return datetime.timedelta(0) + + def __getinitargs__(self): + return (self.__offset,) + + +try: + from datetime import timezone + + TZ_UTC = timezone.utc +except ImportError: + TZ_UTC = UTC() # type: ignore + +_FLATTEN = re.compile(r"(? None: + self.additional_properties: Optional[Dict[str, Any]] = {} + for k in kwargs: + if k not in self._attribute_map: + _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__) + elif k in self._validation and self._validation[k].get("readonly", False): + _LOGGER.warning("Readonly attribute %s will be ignored in class %s", k, self.__class__) + else: + setattr(self, k, kwargs[k]) + + def __eq__(self, other: Any) -> bool: + """Compare objects by comparing all attributes.""" + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + return False + + def __ne__(self, other: Any) -> bool: + """Compare objects by comparing all attributes.""" + return not self.__eq__(other) + + def __str__(self) -> str: + return str(self.__dict__) + + @classmethod + def enable_additional_properties_sending(cls) -> None: + cls._attribute_map["additional_properties"] = {"key": "", "type": "{object}"} + + @classmethod + def is_xml_model(cls) -> bool: + try: + cls._xml_map # type: ignore + except AttributeError: + return False + return True + + @classmethod + def _create_xml_node(cls): + """Create XML node.""" + try: + xml_map = cls._xml_map # type: ignore + except AttributeError: + xml_map = {} + + return _create_xml_node(xml_map.get("name", cls.__name__), xml_map.get("prefix", None), xml_map.get("ns", None)) + + def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON: + """Return the JSON that would be sent to server from this model. + + This is an alias to `as_dict(full_restapi_key_transformer, keep_readonly=False)`. + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize(self, keep_readonly=keep_readonly, **kwargs) # type: ignore + + def as_dict( + self, + keep_readonly: bool = True, + key_transformer: Callable[[str, Dict[str, Any], Any], Any] = attribute_transformer, + **kwargs: Any + ) -> JSON: + """Return a dict that can be serialized using json.dump. + + Advanced usage might optionally use a callback as parameter: + + .. code::python + + def my_key_transformer(key, attr_desc, value): + return key + + Key is the attribute name used in Python. Attr_desc + is a dict of metadata. Currently contains 'type' with the + msrest type and 'key' with the RestAPI encoded key. + Value is the current value in this object. + + The string returned will be used to serialize the key. + If the return type is a list, this is considered hierarchical + result dict. + + See the three examples in this file: + + - attribute_transformer + - full_restapi_key_transformer + - last_restapi_key_transformer + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param function key_transformer: A key transformer function. + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize(self, key_transformer=key_transformer, keep_readonly=keep_readonly, **kwargs) # type: ignore + + @classmethod + def _infer_class_models(cls): + try: + str_models = cls.__module__.rsplit(".", 1)[0] + models = sys.modules[str_models] + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} + if cls.__name__ not in client_models: + raise ValueError("Not Autorest generated code") + except Exception: + # Assume it's not Autorest generated (tests?). Add ourselves as dependencies. + client_models = {cls.__name__: cls} + return client_models + + @classmethod + def deserialize(cls: Type[ModelType], data: Any, content_type: Optional[str] = None) -> ModelType: + """Parse a str using the RestAPI syntax and return a model. + + :param str data: A str using RestAPI structure. JSON by default. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises: DeserializationError if something went wrong + """ + deserializer = Deserializer(cls._infer_class_models()) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def from_dict( + cls: Type[ModelType], + data: Any, + key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None, + content_type: Optional[str] = None, + ) -> ModelType: + """Parse a dict using given key extractor return a model. + + By default consider key + extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor + and last_rest_key_case_insensitive_extractor) + + :param dict data: A dict using RestAPI structure + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises: DeserializationError if something went wrong + """ + deserializer = Deserializer(cls._infer_class_models()) + deserializer.key_extractors = ( # type: ignore + [ # type: ignore + attribute_key_case_insensitive_extractor, + rest_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + if key_extractors is None + else key_extractors + ) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def _flatten_subtype(cls, key, objects): + if "_subtype_map" not in cls.__dict__: + return {} + result = dict(cls._subtype_map[key]) + for valuetype in cls._subtype_map[key].values(): + result.update(objects[valuetype]._flatten_subtype(key, objects)) + return result + + @classmethod + def _classify(cls, response, objects): + """Check the class _subtype_map for any child classes. + We want to ignore any inherited _subtype_maps. + Remove the polymorphic key from the initial data. + """ + for subtype_key in cls.__dict__.get("_subtype_map", {}).keys(): + subtype_value = None + + if not isinstance(response, ET.Element): + rest_api_response_key = cls._get_rest_key_parts(subtype_key)[-1] + subtype_value = response.pop(rest_api_response_key, None) or response.pop(subtype_key, None) + else: + subtype_value = xml_key_extractor(subtype_key, cls._attribute_map[subtype_key], response) + if subtype_value: + # Try to match base class. Can be class name only + # (bug to fix in Autorest to support x-ms-discriminator-name) + if cls.__name__ == subtype_value: + return cls + flatten_mapping_type = cls._flatten_subtype(subtype_key, objects) + try: + return objects[flatten_mapping_type[subtype_value]] # type: ignore + except KeyError: + _LOGGER.warning( + "Subtype value %s has no mapping, use base class %s.", + subtype_value, + cls.__name__, + ) + break + else: + _LOGGER.warning("Discriminator %s is absent or null, use base class %s.", subtype_key, cls.__name__) + break + return cls + + @classmethod + def _get_rest_key_parts(cls, attr_key): + """Get the RestAPI key of this attr, split it and decode part + :param str attr_key: Attribute key must be in attribute_map. + :returns: A list of RestAPI part + :rtype: list + """ + rest_split_key = _FLATTEN.split(cls._attribute_map[attr_key]["key"]) + return [_decode_attribute_map_key(key_part) for key_part in rest_split_key] + + +def _decode_attribute_map_key(key): + """This decode a key in an _attribute_map to the actual key we want to look at + inside the received data. + + :param str key: A key string from the generated code + """ + return key.replace("\\.", ".") + + +class Serializer(object): + """Request object model serializer.""" + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + _xml_basic_types_serializers = {"bool": lambda x: str(x).lower()} + days = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"} + months = { + 1: "Jan", + 2: "Feb", + 3: "Mar", + 4: "Apr", + 5: "May", + 6: "Jun", + 7: "Jul", + 8: "Aug", + 9: "Sep", + 10: "Oct", + 11: "Nov", + 12: "Dec", + } + validation = { + "min_length": lambda x, y: len(x) < y, + "max_length": lambda x, y: len(x) > y, + "minimum": lambda x, y: x < y, + "maximum": lambda x, y: x > y, + "minimum_ex": lambda x, y: x <= y, + "maximum_ex": lambda x, y: x >= y, + "min_items": lambda x, y: len(x) < y, + "max_items": lambda x, y: len(x) > y, + "pattern": lambda x, y: not re.match(y, x, re.UNICODE), + "unique": lambda x, y: len(x) != len(set(x)), + "multiple": lambda x, y: x % y != 0, + } + + def __init__(self, classes: Optional[Mapping[str, type]] = None): + self.serialize_type = { + "iso-8601": Serializer.serialize_iso, + "rfc-1123": Serializer.serialize_rfc, + "unix-time": Serializer.serialize_unix, + "duration": Serializer.serialize_duration, + "date": Serializer.serialize_date, + "time": Serializer.serialize_time, + "decimal": Serializer.serialize_decimal, + "long": Serializer.serialize_long, + "bytearray": Serializer.serialize_bytearray, + "base64": Serializer.serialize_base64, + "object": self.serialize_object, + "[]": self.serialize_iter, + "{}": self.serialize_dict, + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_transformer = full_restapi_key_transformer + self.client_side_validation = True + + def _serialize(self, target_obj, data_type=None, **kwargs): + """Serialize data into a string according to type. + + :param target_obj: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, dict + :raises: SerializationError if serialization fails. + """ + key_transformer = kwargs.get("key_transformer", self.key_transformer) + keep_readonly = kwargs.get("keep_readonly", False) + if target_obj is None: + return None + + attr_name = None + class_name = target_obj.__class__.__name__ + + if data_type: + return self.serialize_data(target_obj, data_type, **kwargs) + + if not hasattr(target_obj, "_attribute_map"): + data_type = type(target_obj).__name__ + if data_type in self.basic_types.values(): + return self.serialize_data(target_obj, data_type, **kwargs) + + # Force "is_xml" kwargs if we detect a XML model + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + is_xml_model_serialization = kwargs.setdefault("is_xml", target_obj.is_xml_model()) + + serialized = {} + if is_xml_model_serialization: + serialized = target_obj._create_xml_node() + try: + attributes = target_obj._attribute_map + for attr, attr_desc in attributes.items(): + attr_name = attr + if not keep_readonly and target_obj._validation.get(attr_name, {}).get("readonly", False): + continue + + if attr_name == "additional_properties" and attr_desc["key"] == "": + if target_obj.additional_properties is not None: + serialized.update(target_obj.additional_properties) + continue + try: + + orig_attr = getattr(target_obj, attr) + if is_xml_model_serialization: + pass # Don't provide "transformer" for XML for now. Keep "orig_attr" + else: # JSON + keys, orig_attr = key_transformer(attr, attr_desc.copy(), orig_attr) + keys = keys if isinstance(keys, list) else [keys] + + kwargs["serialization_ctxt"] = attr_desc + new_attr = self.serialize_data(orig_attr, attr_desc["type"], **kwargs) + + if is_xml_model_serialization: + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + xml_prefix = xml_desc.get("prefix", None) + xml_ns = xml_desc.get("ns", None) + if xml_desc.get("attr", False): + if xml_ns: + ET.register_namespace(xml_prefix, xml_ns) + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + serialized.set(xml_name, new_attr) # type: ignore + continue + if xml_desc.get("text", False): + serialized.text = new_attr # type: ignore + continue + if isinstance(new_attr, list): + serialized.extend(new_attr) # type: ignore + elif isinstance(new_attr, ET.Element): + # If the down XML has no XML/Name, we MUST replace the tag with the local tag. But keeping the namespaces. + if "name" not in getattr(orig_attr, "_xml_map", {}): + splitted_tag = new_attr.tag.split("}") + if len(splitted_tag) == 2: # Namespace + new_attr.tag = "}".join([splitted_tag[0], xml_name]) + else: + new_attr.tag = xml_name + serialized.append(new_attr) # type: ignore + else: # That's a basic type + # Integrate namespace if necessary + local_node = _create_xml_node(xml_name, xml_prefix, xml_ns) + local_node.text = str(new_attr) + serialized.append(local_node) # type: ignore + else: # JSON + for k in reversed(keys): # type: ignore + new_attr = {k: new_attr} + + _new_attr = new_attr + _serialized = serialized + for k in keys: # type: ignore + if k not in _serialized: + _serialized.update(_new_attr) # type: ignore + _new_attr = _new_attr[k] # type: ignore + _serialized = _serialized[k] + except ValueError as err: + if isinstance(err, SerializationError): + raise + + except (AttributeError, KeyError, TypeError) as err: + msg = "Attribute {} in object {} cannot be serialized.\n{}".format(attr_name, class_name, str(target_obj)) + raise SerializationError(msg) from err + else: + return serialized + + def body(self, data, data_type, **kwargs): + """Serialize data intended for a request body. + + :param data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: dict + :raises: SerializationError if serialization fails. + :raises: ValueError if data is None + """ + + # Just in case this is a dict + internal_data_type_str = data_type.strip("[]{}") + internal_data_type = self.dependencies.get(internal_data_type_str, None) + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + if internal_data_type and issubclass(internal_data_type, Model): + is_xml_model_serialization = kwargs.setdefault("is_xml", internal_data_type.is_xml_model()) + else: + is_xml_model_serialization = False + if internal_data_type and not isinstance(internal_data_type, Enum): + try: + deserializer = Deserializer(self.dependencies) + # Since it's on serialization, it's almost sure that format is not JSON REST + # We're not able to deal with additional properties for now. + deserializer.additional_properties_detection = False + if is_xml_model_serialization: + deserializer.key_extractors = [ # type: ignore + attribute_key_case_insensitive_extractor, + ] + else: + deserializer.key_extractors = [ + rest_key_case_insensitive_extractor, + attribute_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + data = deserializer._deserialize(data_type, data) + except DeserializationError as err: + raise SerializationError("Unable to build a model: " + str(err)) from err + + return self._serialize(data, data_type, **kwargs) + + def url(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL path. + + :param data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :raises: TypeError if serialization fails. + :raises: ValueError if data is None + """ + try: + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + + if kwargs.get("skip_quote") is True: + output = str(output) + output = output.replace("{", quote("{")).replace("}", quote("}")) + else: + output = quote(str(output), safe="") + except SerializationError: + raise TypeError("{} must be type {}.".format(name, data_type)) + else: + return output + + def query(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL query. + + :param data: The data to be serialized. + :param str data_type: The type to be serialized from. + :keyword bool skip_quote: Whether to skip quote the serialized result. + Defaults to False. + :rtype: str, list + :raises: TypeError if serialization fails. + :raises: ValueError if data is None + """ + try: + # Treat the list aside, since we don't want to encode the div separator + if data_type.startswith("["): + internal_data_type = data_type[1:-1] + do_quote = not kwargs.get("skip_quote", False) + return self.serialize_iter(data, internal_data_type, do_quote=do_quote, **kwargs) + + # Not a list, regular serialization + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + if kwargs.get("skip_quote") is True: + output = str(output) + else: + output = quote(str(output), safe="") + except SerializationError: + raise TypeError("{} must be type {}.".format(name, data_type)) + else: + return str(output) + + def header(self, name, data, data_type, **kwargs): + """Serialize data intended for a request header. + + :param data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :raises: TypeError if serialization fails. + :raises: ValueError if data is None + """ + try: + if data_type in ["[str]"]: + data = ["" if d is None else d for d in data] + + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + except SerializationError: + raise TypeError("{} must be type {}.".format(name, data_type)) + else: + return str(output) + + def serialize_data(self, data, data_type, **kwargs): + """Serialize generic data according to supplied data type. + + :param data: The data to be serialized. + :param str data_type: The type to be serialized from. + :param bool required: Whether it's essential that the data not be + empty or None + :raises: AttributeError if required data is None. + :raises: ValueError if data is None + :raises: SerializationError if serialization fails. + """ + if data is None: + raise ValueError("No value for given attribute") + + try: + if data is CoreNull: + return None + if data_type in self.basic_types.values(): + return self.serialize_basic(data, data_type, **kwargs) + + elif data_type in self.serialize_type: + return self.serialize_type[data_type](data, **kwargs) + + # If dependencies is empty, try with current data class + # It has to be a subclass of Enum anyway + enum_type = self.dependencies.get(data_type, data.__class__) + if issubclass(enum_type, Enum): + return Serializer.serialize_enum(data, enum_obj=enum_type) + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.serialize_type: + return self.serialize_type[iter_type](data, data_type[1:-1], **kwargs) + + except (ValueError, TypeError) as err: + msg = "Unable to serialize value: {!r} as type: {!r}." + raise SerializationError(msg.format(data, data_type)) from err + else: + return self._serialize(data, **kwargs) + + @classmethod + def _get_custom_serializers(cls, data_type, **kwargs): + custom_serializer = kwargs.get("basic_types_serializers", {}).get(data_type) + if custom_serializer: + return custom_serializer + if kwargs.get("is_xml", False): + return cls._xml_basic_types_serializers.get(data_type) + + @classmethod + def serialize_basic(cls, data, data_type, **kwargs): + """Serialize basic builting data type. + Serializes objects to str, int, float or bool. + + Possible kwargs: + - basic_types_serializers dict[str, callable] : If set, use the callable as serializer + - is_xml bool : If set, use xml_basic_types_serializers + + :param data: Object to be serialized. + :param str data_type: Type of object in the iterable. + """ + custom_serializer = cls._get_custom_serializers(data_type, **kwargs) + if custom_serializer: + return custom_serializer(data) + if data_type == "str": + return cls.serialize_unicode(data) + return eval(data_type)(data) # nosec + + @classmethod + def serialize_unicode(cls, data): + """Special handling for serializing unicode strings in Py2. + Encode to UTF-8 if unicode, otherwise handle as a str. + + :param data: Object to be serialized. + :rtype: str + """ + try: # If I received an enum, return its value + return data.value + except AttributeError: + pass + + try: + if isinstance(data, unicode): # type: ignore + # Don't change it, JSON and XML ElementTree are totally able + # to serialize correctly u'' strings + return data + except NameError: + return str(data) + else: + return str(data) + + def serialize_iter(self, data, iter_type, div=None, **kwargs): + """Serialize iterable. + + Supported kwargs: + - serialization_ctxt dict : The current entry of _attribute_map, or same format. + serialization_ctxt['type'] should be same as data_type. + - is_xml bool : If set, serialize as XML + + :param list attr: Object to be serialized. + :param str iter_type: Type of object in the iterable. + :param bool required: Whether the objects in the iterable must + not be None or empty. + :param str div: If set, this str will be used to combine the elements + in the iterable into a combined string. Default is 'None'. + :keyword bool do_quote: Whether to quote the serialized result of each iterable element. + Defaults to False. + :rtype: list, str + """ + if isinstance(data, str): + raise SerializationError("Refuse str type as a valid iter type.") + + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + is_xml = kwargs.get("is_xml", False) + + serialized = [] + for d in data: + try: + serialized.append(self.serialize_data(d, iter_type, **kwargs)) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized.append(None) + + if kwargs.get("do_quote", False): + serialized = ["" if s is None else quote(str(s), safe="") for s in serialized] + + if div: + serialized = ["" if s is None else str(s) for s in serialized] + serialized = div.join(serialized) + + if "xml" in serialization_ctxt or is_xml: + # XML serialization is more complicated + xml_desc = serialization_ctxt.get("xml", {}) + xml_name = xml_desc.get("name") + if not xml_name: + xml_name = serialization_ctxt["key"] + + # Create a wrap node if necessary (use the fact that Element and list have "append") + is_wrapped = xml_desc.get("wrapped", False) + node_name = xml_desc.get("itemsName", xml_name) + if is_wrapped: + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + else: + final_result = [] + # All list elements to "local_node" + for el in serialized: + if isinstance(el, ET.Element): + el_node = el + else: + el_node = _create_xml_node(node_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + if el is not None: # Otherwise it writes "None" :-p + el_node.text = str(el) + final_result.append(el_node) + return final_result + return serialized + + def serialize_dict(self, attr, dict_type, **kwargs): + """Serialize a dictionary of objects. + + :param dict attr: Object to be serialized. + :param str dict_type: Type of object in the dictionary. + :param bool required: Whether the objects in the dictionary must + not be None or empty. + :rtype: dict + """ + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_data(value, dict_type, **kwargs) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized[self.serialize_unicode(key)] = None + + if "xml" in serialization_ctxt: + # XML serialization is more complicated + xml_desc = serialization_ctxt["xml"] + xml_name = xml_desc["name"] + + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + for key, value in serialized.items(): + ET.SubElement(final_result, key).text = value + return final_result + + return serialized + + def serialize_object(self, attr, **kwargs): + """Serialize a generic object. + This will be handled as a dictionary. If object passed in is not + a basic type (str, int, float, dict, list) it will simply be + cast to str. + + :param dict attr: Object to be serialized. + :rtype: dict or str + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + return attr + obj_type = type(attr) + if obj_type in self.basic_types: + return self.serialize_basic(attr, self.basic_types[obj_type], **kwargs) + if obj_type is _long_type: + return self.serialize_long(attr) + if obj_type is str: + return self.serialize_unicode(attr) + if obj_type is datetime.datetime: + return self.serialize_iso(attr) + if obj_type is datetime.date: + return self.serialize_date(attr) + if obj_type is datetime.time: + return self.serialize_time(attr) + if obj_type is datetime.timedelta: + return self.serialize_duration(attr) + if obj_type is decimal.Decimal: + return self.serialize_decimal(attr) + + # If it's a model or I know this dependency, serialize as a Model + elif obj_type in self.dependencies.values() or isinstance(attr, Model): + return self._serialize(attr) + + if obj_type == dict: + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_object(value, **kwargs) + except ValueError: + serialized[self.serialize_unicode(key)] = None + return serialized + + if obj_type == list: + serialized = [] + for obj in attr: + try: + serialized.append(self.serialize_object(obj, **kwargs)) + except ValueError: + pass + return serialized + return str(attr) + + @staticmethod + def serialize_enum(attr, enum_obj=None): + try: + result = attr.value + except AttributeError: + result = attr + try: + enum_obj(result) # type: ignore + return result + except ValueError: + for enum_value in enum_obj: # type: ignore + if enum_value.value.lower() == str(attr).lower(): + return enum_value.value + error = "{!r} is not valid value for enum {!r}" + raise SerializationError(error.format(attr, enum_obj)) + + @staticmethod + def serialize_bytearray(attr, **kwargs): + """Serialize bytearray into base-64 string. + + :param attr: Object to be serialized. + :rtype: str + """ + return b64encode(attr).decode() + + @staticmethod + def serialize_base64(attr, **kwargs): + """Serialize str into base-64 string. + + :param attr: Object to be serialized. + :rtype: str + """ + encoded = b64encode(attr).decode("ascii") + return encoded.strip("=").replace("+", "-").replace("/", "_") + + @staticmethod + def serialize_decimal(attr, **kwargs): + """Serialize Decimal object to float. + + :param attr: Object to be serialized. + :rtype: float + """ + return float(attr) + + @staticmethod + def serialize_long(attr, **kwargs): + """Serialize long (Py2) or int (Py3). + + :param attr: Object to be serialized. + :rtype: int/long + """ + return _long_type(attr) + + @staticmethod + def serialize_date(attr, **kwargs): + """Serialize Date object into ISO-8601 formatted string. + + :param Date attr: Object to be serialized. + :rtype: str + """ + if isinstance(attr, str): + attr = isodate.parse_date(attr) + t = "{:04}-{:02}-{:02}".format(attr.year, attr.month, attr.day) + return t + + @staticmethod + def serialize_time(attr, **kwargs): + """Serialize Time object into ISO-8601 formatted string. + + :param datetime.time attr: Object to be serialized. + :rtype: str + """ + if isinstance(attr, str): + attr = isodate.parse_time(attr) + t = "{:02}:{:02}:{:02}".format(attr.hour, attr.minute, attr.second) + if attr.microsecond: + t += ".{:02}".format(attr.microsecond) + return t + + @staticmethod + def serialize_duration(attr, **kwargs): + """Serialize TimeDelta object into ISO-8601 formatted string. + + :param TimeDelta attr: Object to be serialized. + :rtype: str + """ + if isinstance(attr, str): + attr = isodate.parse_duration(attr) + return isodate.duration_isoformat(attr) + + @staticmethod + def serialize_rfc(attr, **kwargs): + """Serialize Datetime object into RFC-1123 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises: TypeError if format invalid. + """ + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + except AttributeError: + raise TypeError("RFC1123 object must be valid Datetime object.") + + return "{}, {:02} {} {:04} {:02}:{:02}:{:02} GMT".format( + Serializer.days[utc.tm_wday], + utc.tm_mday, + Serializer.months[utc.tm_mon], + utc.tm_year, + utc.tm_hour, + utc.tm_min, + utc.tm_sec, + ) + + @staticmethod + def serialize_iso(attr, **kwargs): + """Serialize Datetime object into ISO-8601 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises: SerializationError if format invalid. + """ + if isinstance(attr, str): + attr = isodate.parse_datetime(attr) + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + if utc.tm_year > 9999 or utc.tm_year < 1: + raise OverflowError("Hit max or min date") + + microseconds = str(attr.microsecond).rjust(6, "0").rstrip("0").ljust(3, "0") + if microseconds: + microseconds = "." + microseconds + date = "{:04}-{:02}-{:02}T{:02}:{:02}:{:02}".format( + utc.tm_year, utc.tm_mon, utc.tm_mday, utc.tm_hour, utc.tm_min, utc.tm_sec + ) + return date + microseconds + "Z" + except (ValueError, OverflowError) as err: + msg = "Unable to serialize datetime object." + raise SerializationError(msg) from err + except AttributeError as err: + msg = "ISO-8601 object must be valid Datetime object." + raise TypeError(msg) from err + + @staticmethod + def serialize_unix(attr, **kwargs): + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param Datetime attr: Object to be serialized. + :rtype: int + :raises: SerializationError if format invalid + """ + if isinstance(attr, int): + return attr + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + return int(calendar.timegm(attr.utctimetuple())) + except AttributeError: + raise TypeError("Unix time object must be valid Datetime object.") + + +def rest_key_extractor(attr, attr_desc, data): + key = attr_desc["key"] + working_data = data + + while "." in key: + # Need the cast, as for some reasons "split" is typed as list[str | Any] + dict_keys = cast(List[str], _FLATTEN.split(key)) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = working_data.get(working_key, data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + return working_data.get(key) + + +def rest_key_case_insensitive_extractor(attr, attr_desc, data): + key = attr_desc["key"] + working_data = data + + while "." in key: + dict_keys = _FLATTEN.split(key) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = attribute_key_case_insensitive_extractor(working_key, None, working_data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + if working_data: + return attribute_key_case_insensitive_extractor(key, None, working_data) + + +def last_rest_key_extractor(attr, attr_desc, data): + """Extract the attribute in "data" based on the last part of the JSON path key.""" + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_extractor(dict_keys[-1], None, data) + + +def last_rest_key_case_insensitive_extractor(attr, attr_desc, data): + """Extract the attribute in "data" based on the last part of the JSON path key. + + This is the case insensitive version of "last_rest_key_extractor" + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_case_insensitive_extractor(dict_keys[-1], None, data) + + +def attribute_key_extractor(attr, _, data): + return data.get(attr) + + +def attribute_key_case_insensitive_extractor(attr, _, data): + found_key = None + lower_attr = attr.lower() + for key in data: + if lower_attr == key.lower(): + found_key = key + break + + return data.get(found_key) + + +def _extract_name_from_internal_type(internal_type): + """Given an internal type XML description, extract correct XML name with namespace. + + :param dict internal_type: An model type + :rtype: tuple + :returns: A tuple XML name + namespace dict + """ + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + xml_name = internal_type_xml_map.get("name", internal_type.__name__) + xml_ns = internal_type_xml_map.get("ns", None) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + return xml_name + + +def xml_key_extractor(attr, attr_desc, data): + if isinstance(data, dict): + return None + + # Test if this model is XML ready first + if not isinstance(data, ET.Element): + return None + + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + + # Look for a children + is_iter_type = attr_desc["type"].startswith("[") + is_wrapped = xml_desc.get("wrapped", False) + internal_type = attr_desc.get("internalType", None) + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + + # Integrate namespace if necessary + xml_ns = xml_desc.get("ns", internal_type_xml_map.get("ns", None)) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + + # If it's an attribute, that's simple + if xml_desc.get("attr", False): + return data.get(xml_name) + + # If it's x-ms-text, that's simple too + if xml_desc.get("text", False): + return data.text + + # Scenario where I take the local name: + # - Wrapped node + # - Internal type is an enum (considered basic types) + # - Internal type has no XML/Name node + if is_wrapped or (internal_type and (issubclass(internal_type, Enum) or "name" not in internal_type_xml_map)): + children = data.findall(xml_name) + # If internal type has a local name and it's not a list, I use that name + elif not is_iter_type and internal_type and "name" in internal_type_xml_map: + xml_name = _extract_name_from_internal_type(internal_type) + children = data.findall(xml_name) + # That's an array + else: + if internal_type: # Complex type, ignore itemsName and use the complex type name + items_name = _extract_name_from_internal_type(internal_type) + else: + items_name = xml_desc.get("itemsName", xml_name) + children = data.findall(items_name) + + if len(children) == 0: + if is_iter_type: + if is_wrapped: + return None # is_wrapped no node, we want None + else: + return [] # not wrapped, assume empty list + return None # Assume it's not there, maybe an optional node. + + # If is_iter_type and not wrapped, return all found children + if is_iter_type: + if not is_wrapped: + return children + else: # Iter and wrapped, should have found one node only (the wrap one) + if len(children) != 1: + raise DeserializationError( + "Tried to deserialize an array not wrapped, and found several nodes '{}'. Maybe you should declare this array as wrapped?".format( + xml_name + ) + ) + return list(children[0]) # Might be empty list and that's ok. + + # Here it's not a itertype, we should have found one element only or empty + if len(children) > 1: + raise DeserializationError("Find several XML '{}' where it was not expected".format(xml_name)) + return children[0] + + +class Deserializer(object): + """Response object model deserializer. + + :param dict classes: Class type dictionary for deserializing complex types. + :ivar list key_extractors: Ordered list of extractors to be used by this deserializer. + """ + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") + + def __init__(self, classes: Optional[Mapping[str, type]] = None): + self.deserialize_type = { + "iso-8601": Deserializer.deserialize_iso, + "rfc-1123": Deserializer.deserialize_rfc, + "unix-time": Deserializer.deserialize_unix, + "duration": Deserializer.deserialize_duration, + "date": Deserializer.deserialize_date, + "time": Deserializer.deserialize_time, + "decimal": Deserializer.deserialize_decimal, + "long": Deserializer.deserialize_long, + "bytearray": Deserializer.deserialize_bytearray, + "base64": Deserializer.deserialize_base64, + "object": self.deserialize_object, + "[]": self.deserialize_iter, + "{}": self.deserialize_dict, + } + self.deserialize_expected_types = { + "duration": (isodate.Duration, datetime.timedelta), + "iso-8601": (datetime.datetime), + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_extractors = [rest_key_extractor, xml_key_extractor] + # Additional properties only works if the "rest_key_extractor" is used to + # extract the keys. Making it to work whatever the key extractor is too much + # complicated, with no real scenario for now. + # So adding a flag to disable additional properties detection. This flag should be + # used if your expect the deserialization to NOT come from a JSON REST syntax. + # Otherwise, result are unexpected + self.additional_properties_detection = True + + def __call__(self, target_obj, response_data, content_type=None): + """Call the deserializer to process a REST response. + + :param str target_obj: Target data type to deserialize to. + :param requests.Response response_data: REST response object. + :param str content_type: Swagger "produces" if available. + :raises: DeserializationError if deserialization fails. + :return: Deserialized object. + """ + data = self._unpack_content(response_data, content_type) + return self._deserialize(target_obj, data) + + def _deserialize(self, target_obj, data): + """Call the deserializer on a model. + + Data needs to be already deserialized as JSON or XML ElementTree + + :param str target_obj: Target data type to deserialize to. + :param object data: Object to deserialize. + :raises: DeserializationError if deserialization fails. + :return: Deserialized object. + """ + # This is already a model, go recursive just in case + if hasattr(data, "_attribute_map"): + constants = [name for name, config in getattr(data, "_validation", {}).items() if config.get("constant")] + try: + for attr, mapconfig in data._attribute_map.items(): + if attr in constants: + continue + value = getattr(data, attr) + if value is None: + continue + local_type = mapconfig["type"] + internal_data_type = local_type.strip("[]{}") + if internal_data_type not in self.dependencies or isinstance(internal_data_type, Enum): + continue + setattr(data, attr, self._deserialize(local_type, value)) + return data + except AttributeError: + return + + response, class_name = self._classify_target(target_obj, data) + + if isinstance(response, str): + return self.deserialize_data(data, response) + elif isinstance(response, type) and issubclass(response, Enum): + return self.deserialize_enum(data, response) + + if data is None: + return data + try: + attributes = response._attribute_map # type: ignore + d_attrs = {} + for attr, attr_desc in attributes.items(): + # Check empty string. If it's not empty, someone has a real "additionalProperties"... + if attr == "additional_properties" and attr_desc["key"] == "": + continue + raw_value = None + # Enhance attr_desc with some dynamic data + attr_desc = attr_desc.copy() # Do a copy, do not change the real one + internal_data_type = attr_desc["type"].strip("[]{}") + if internal_data_type in self.dependencies: + attr_desc["internalType"] = self.dependencies[internal_data_type] + + for key_extractor in self.key_extractors: + found_value = key_extractor(attr, attr_desc, data) + if found_value is not None: + if raw_value is not None and raw_value != found_value: + msg = ( + "Ignoring extracted value '%s' from %s for key '%s'" + " (duplicate extraction, follow extractors order)" + ) + _LOGGER.warning(msg, found_value, key_extractor, attr) + continue + raw_value = found_value + + value = self.deserialize_data(raw_value, attr_desc["type"]) + d_attrs[attr] = value + except (AttributeError, TypeError, KeyError) as err: + msg = "Unable to deserialize to object: " + class_name # type: ignore + raise DeserializationError(msg) from err + else: + additional_properties = self._build_additional_properties(attributes, data) + return self._instantiate_model(response, d_attrs, additional_properties) + + def _build_additional_properties(self, attribute_map, data): + if not self.additional_properties_detection: + return None + if "additional_properties" in attribute_map and attribute_map.get("additional_properties", {}).get("key") != "": + # Check empty string. If it's not empty, someone has a real "additionalProperties" + return None + if isinstance(data, ET.Element): + data = {el.tag: el.text for el in data} + + known_keys = { + _decode_attribute_map_key(_FLATTEN.split(desc["key"])[0]) + for desc in attribute_map.values() + if desc["key"] != "" + } + present_keys = set(data.keys()) + missing_keys = present_keys - known_keys + return {key: data[key] for key in missing_keys} + + def _classify_target(self, target, data): + """Check to see whether the deserialization target object can + be classified into a subclass. + Once classification has been determined, initialize object. + + :param str target: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + """ + if target is None: + return None, None + + if isinstance(target, str): + try: + target = self.dependencies[target] + except KeyError: + return target, target + + try: + target = target._classify(data, self.dependencies) # type: ignore + except AttributeError: + pass # Target is not a Model, no classify + return target, target.__class__.__name__ # type: ignore + + def failsafe_deserialize(self, target_obj, data, content_type=None): + """Ignores any errors encountered in deserialization, + and falls back to not deserializing the object. Recommended + for use in error deserialization, as we want to return the + HttpResponseError to users, and not have them deal with + a deserialization error. + + :param str target_obj: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :param str content_type: Swagger "produces" if available. + """ + try: + return self(target_obj, data, content_type=content_type) + except: + _LOGGER.debug( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + @staticmethod + def _unpack_content(raw_data, content_type=None): + """Extract the correct structure for deserialization. + + If raw_data is a PipelineResponse, try to extract the result of RawDeserializer. + if we can't, raise. Your Pipeline should have a RawDeserializer. + + If not a pipeline response and raw_data is bytes or string, use content-type + to decode it. If no content-type, try JSON. + + If raw_data is something else, bypass all logic and return it directly. + + :param raw_data: Data to be processed. + :param content_type: How to parse if raw_data is a string/bytes. + :raises JSONDecodeError: If JSON is requested and parsing is impossible. + :raises UnicodeDecodeError: If bytes is not UTF8 + """ + # Assume this is enough to detect a Pipeline Response without importing it + context = getattr(raw_data, "context", {}) + if context: + if RawDeserializer.CONTEXT_NAME in context: + return context[RawDeserializer.CONTEXT_NAME] + raise ValueError("This pipeline didn't have the RawDeserializer policy; can't deserialize") + + # Assume this is enough to recognize universal_http.ClientResponse without importing it + if hasattr(raw_data, "body"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text(), raw_data.headers) + + # Assume this enough to recognize requests.Response without importing it. + if hasattr(raw_data, "_content_consumed"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text, raw_data.headers) + + if isinstance(raw_data, (str, bytes)) or hasattr(raw_data, "read"): + return RawDeserializer.deserialize_from_text(raw_data, content_type) # type: ignore + return raw_data + + def _instantiate_model(self, response, attrs, additional_properties=None): + """Instantiate a response model passing in deserialized args. + + :param response: The response model class. + :param d_attrs: The deserialized response attributes. + """ + if callable(response): + subtype = getattr(response, "_subtype_map", {}) + try: + readonly = [k for k, v in response._validation.items() if v.get("readonly")] + const = [k for k, v in response._validation.items() if v.get("constant")] + kwargs = {k: v for k, v in attrs.items() if k not in subtype and k not in readonly + const} + response_obj = response(**kwargs) + for attr in readonly: + setattr(response_obj, attr, attrs.get(attr)) + if additional_properties: + response_obj.additional_properties = additional_properties + return response_obj + except TypeError as err: + msg = "Unable to deserialize {} into model {}. ".format(kwargs, response) # type: ignore + raise DeserializationError(msg + str(err)) + else: + try: + for attr, value in attrs.items(): + setattr(response, attr, value) + return response + except Exception as exp: + msg = "Unable to populate response model. " + msg += "Type: {}, Error: {}".format(type(response), exp) + raise DeserializationError(msg) + + def deserialize_data(self, data, data_type): + """Process data for deserialization according to data type. + + :param str data: The response string to be deserialized. + :param str data_type: The type to deserialize to. + :raises: DeserializationError if deserialization fails. + :return: Deserialized object. + """ + if data is None: + return data + + try: + if not data_type: + return data + if data_type in self.basic_types.values(): + return self.deserialize_basic(data, data_type) + if data_type in self.deserialize_type: + if isinstance(data, self.deserialize_expected_types.get(data_type, tuple())): + return data + + is_a_text_parsing_type = lambda x: x not in ["object", "[]", r"{}"] + if isinstance(data, ET.Element) and is_a_text_parsing_type(data_type) and not data.text: + return None + data_val = self.deserialize_type[data_type](data) + return data_val + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.deserialize_type: + return self.deserialize_type[iter_type](data, data_type[1:-1]) + + obj_type = self.dependencies[data_type] + if issubclass(obj_type, Enum): + if isinstance(data, ET.Element): + data = data.text + return self.deserialize_enum(data, obj_type) + + except (ValueError, TypeError, AttributeError) as err: + msg = "Unable to deserialize response data." + msg += " Data: {}, {}".format(data, data_type) + raise DeserializationError(msg) from err + else: + return self._deserialize(obj_type, data) + + def deserialize_iter(self, attr, iter_type): + """Deserialize an iterable. + + :param list attr: Iterable to be deserialized. + :param str iter_type: The type of object in the iterable. + :rtype: list + """ + if attr is None: + return None + if isinstance(attr, ET.Element): # If I receive an element here, get the children + attr = list(attr) + if not isinstance(attr, (list, set)): + raise DeserializationError("Cannot deserialize as [{}] an object of type {}".format(iter_type, type(attr))) + return [self.deserialize_data(a, iter_type) for a in attr] + + def deserialize_dict(self, attr, dict_type): + """Deserialize a dictionary. + + :param dict/list attr: Dictionary to be deserialized. Also accepts + a list of key, value pairs. + :param str dict_type: The object type of the items in the dictionary. + :rtype: dict + """ + if isinstance(attr, list): + return {x["key"]: self.deserialize_data(x["value"], dict_type) for x in attr} + + if isinstance(attr, ET.Element): + # Transform value into {"Key": "value"} + attr = {el.tag: el.text for el in attr} + return {k: self.deserialize_data(v, dict_type) for k, v in attr.items()} + + def deserialize_object(self, attr, **kwargs): + """Deserialize a generic object. + This will be handled as a dictionary. + + :param dict attr: Dictionary to be deserialized. + :rtype: dict + :raises: TypeError if non-builtin datatype encountered. + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + # Do no recurse on XML, just return the tree as-is + return attr + if isinstance(attr, str): + return self.deserialize_basic(attr, "str") + obj_type = type(attr) + if obj_type in self.basic_types: + return self.deserialize_basic(attr, self.basic_types[obj_type]) + if obj_type is _long_type: + return self.deserialize_long(attr) + + if obj_type == dict: + deserialized = {} + for key, value in attr.items(): + try: + deserialized[key] = self.deserialize_object(value, **kwargs) + except ValueError: + deserialized[key] = None + return deserialized + + if obj_type == list: + deserialized = [] + for obj in attr: + try: + deserialized.append(self.deserialize_object(obj, **kwargs)) + except ValueError: + pass + return deserialized + + else: + error = "Cannot deserialize generic object with type: " + raise TypeError(error + str(obj_type)) + + def deserialize_basic(self, attr, data_type): + """Deserialize basic builtin data type from string. + Will attempt to convert to str, int, float and bool. + This function will also accept '1', '0', 'true' and 'false' as + valid bool values. + + :param str attr: response string to be deserialized. + :param str data_type: deserialization data type. + :rtype: str, int, float or bool + :raises: TypeError if string format is not valid. + """ + # If we're here, data is supposed to be a basic type. + # If it's still an XML node, take the text + if isinstance(attr, ET.Element): + attr = attr.text + if not attr: + if data_type == "str": + # None or '', node is empty string. + return "" + else: + # None or '', node with a strong type is None. + # Don't try to model "empty bool" or "empty int" + return None + + if data_type == "bool": + if attr in [True, False, 1, 0]: + return bool(attr) + elif isinstance(attr, str): + if attr.lower() in ["true", "1"]: + return True + elif attr.lower() in ["false", "0"]: + return False + raise TypeError("Invalid boolean value: {}".format(attr)) + + if data_type == "str": + return self.deserialize_unicode(attr) + return eval(data_type)(attr) # nosec + + @staticmethod + def deserialize_unicode(data): + """Preserve unicode objects in Python 2, otherwise return data + as a string. + + :param str data: response string to be deserialized. + :rtype: str or unicode + """ + # We might be here because we have an enum modeled as string, + # and we try to deserialize a partial dict with enum inside + if isinstance(data, Enum): + return data + + # Consider this is real string + try: + if isinstance(data, unicode): # type: ignore + return data + except NameError: + return str(data) + else: + return str(data) + + @staticmethod + def deserialize_enum(data, enum_obj): + """Deserialize string into enum object. + + If the string is not a valid enum value it will be returned as-is + and a warning will be logged. + + :param str data: Response string to be deserialized. If this value is + None or invalid it will be returned as-is. + :param Enum enum_obj: Enum object to deserialize to. + :rtype: Enum + """ + if isinstance(data, enum_obj) or data is None: + return data + if isinstance(data, Enum): + data = data.value + if isinstance(data, int): + # Workaround. We might consider remove it in the future. + try: + return list(enum_obj.__members__.values())[data] + except IndexError: + error = "{!r} is not a valid index for enum {!r}" + raise DeserializationError(error.format(data, enum_obj)) + try: + return enum_obj(str(data)) + except ValueError: + for enum_value in enum_obj: + if enum_value.value.lower() == str(data).lower(): + return enum_value + # We don't fail anymore for unknown value, we deserialize as a string + _LOGGER.warning("Deserializer is not able to find %s as valid enum in %s", data, enum_obj) + return Deserializer.deserialize_unicode(data) + + @staticmethod + def deserialize_bytearray(attr): + """Deserialize string into bytearray. + + :param str attr: response string to be deserialized. + :rtype: bytearray + :raises: TypeError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return bytearray(b64decode(attr)) # type: ignore + + @staticmethod + def deserialize_base64(attr): + """Deserialize base64 encoded string into string. + + :param str attr: response string to be deserialized. + :rtype: bytearray + :raises: TypeError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return b64decode(encoded) + + @staticmethod + def deserialize_decimal(attr): + """Deserialize string into Decimal object. + + :param str attr: response string to be deserialized. + :rtype: Decimal + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + return decimal.Decimal(str(attr)) # type: ignore + except decimal.DecimalException as err: + msg = "Invalid decimal {}".format(attr) + raise DeserializationError(msg) from err + + @staticmethod + def deserialize_long(attr): + """Deserialize string into long (Py2) or int (Py3). + + :param str attr: response string to be deserialized. + :rtype: long or int + :raises: ValueError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return _long_type(attr) # type: ignore + + @staticmethod + def deserialize_duration(attr): + """Deserialize ISO-8601 formatted string into TimeDelta object. + + :param str attr: response string to be deserialized. + :rtype: TimeDelta + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + duration = isodate.parse_duration(attr) + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize duration object." + raise DeserializationError(msg) from err + else: + return duration + + @staticmethod + def deserialize_date(attr): + """Deserialize ISO-8601 formatted string into Date object. + + :param str attr: response string to be deserialized. + :rtype: Date + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + return isodate.parse_date(attr, defaultmonth=0, defaultday=0) + + @staticmethod + def deserialize_time(attr): + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :rtype: datetime.time + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + return isodate.parse_time(attr) + + @staticmethod + def deserialize_rfc(attr): + """Deserialize RFC-1123 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: Datetime + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + parsed_date = email.utils.parsedate_tz(attr) # type: ignore + date_obj = datetime.datetime( + *parsed_date[:6], tzinfo=_FixedOffset(datetime.timedelta(minutes=(parsed_date[9] or 0) / 60)) + ) + if not date_obj.tzinfo: + date_obj = date_obj.astimezone(tz=TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to rfc datetime object." + raise DeserializationError(msg) from err + else: + return date_obj + + @staticmethod + def deserialize_iso(attr): + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: Datetime + :raises: DeserializationError if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + attr = attr.upper() # type: ignore + match = Deserializer.valid_date.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize datetime object." + raise DeserializationError(msg) from err + else: + return date_obj + + @staticmethod + def deserialize_unix(attr): + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param int attr: Object to be serialized. + :rtype: Datetime + :raises: DeserializationError if format invalid + """ + if isinstance(attr, ET.Element): + attr = int(attr.text) # type: ignore + try: + attr = int(attr) + date_obj = datetime.datetime.fromtimestamp(attr, TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to unix datetime object." + raise DeserializationError(msg) from err + else: + return date_obj diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_vendor.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_vendor.py new file mode 100644 index 000000000000..3177cb12a490 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_vendor.py @@ -0,0 +1,91 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from abc import ABC +import json +from typing import Any, Dict, IO, List, Mapping, Optional, Sequence, TYPE_CHECKING, Tuple, Union + +from ._configuration import ( + FaceAdministrationClientConfiguration, + FaceClientConfiguration, + FaceSessionClientConfiguration, +) +from ._model_base import Model, SdkJSONEncoder + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core import PipelineClient + + from ._serialization import Deserializer, Serializer + + +class FaceClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "PipelineClient" + _config: FaceClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" + + +class FaceAdministrationClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "PipelineClient" + _config: FaceAdministrationClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" + + +class FaceSessionClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "PipelineClient" + _config: FaceSessionClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" + + +# file-like tuple could be `(filename, IO (or bytes))` or `(filename, IO (or bytes), content_type)` +FileContent = Union[str, bytes, IO[str], IO[bytes]] + +FileType = Union[ + # file (or bytes) + FileContent, + # (filename, file (or bytes)) + Tuple[Optional[str], FileContent], + # (filename, file (or bytes), content_type) + Tuple[Optional[str], FileContent, Optional[str]], +] + +FilesType = Union[Mapping[str, FileType], Sequence[Tuple[str, FileType]]] + + +def serialize_multipart_data_entry(data_entry: Any) -> Any: + if isinstance(data_entry, (list, tuple, dict, Model)): + return json.dumps(data_entry, cls=SdkJSONEncoder, exclude_readonly=True) + return data_entry + + +def prepare_multipart_form_data( + body: Mapping[str, Any], multipart_fields: List[str], data_fields: List[str] +) -> Tuple[List[FileType], Dict[str, Any]]: + files: List[FileType] = [] + data: Dict[str, Any] = {} + for multipart_field in multipart_fields: + multipart_entry = body.get(multipart_field) + if isinstance(multipart_entry, list): + files.extend([(multipart_field, e) for e in multipart_entry]) + elif multipart_entry: + files.append((multipart_field, multipart_entry)) + + for data_field in data_fields: + data_entry = body.get(data_field) + if data_entry: + data[data_field] = serialize_multipart_data_entry(data_entry) + + return files, data diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_version.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_version.py new file mode 100644 index 000000000000..be71c81bd282 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_version.py @@ -0,0 +1,9 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +VERSION = "1.0.0b1" diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py new file mode 100644 index 000000000000..696348801174 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py @@ -0,0 +1,27 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._client import FaceClient +from ._client import FaceAdministrationClient +from ._client import FaceSessionClient + +try: + from ._patch import __all__ as _patch_all + from ._patch import * # pylint: disable=unused-wildcard-import +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "FaceClient", + "FaceAdministrationClient", + "FaceSessionClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) + +_patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_client.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_client.py new file mode 100644 index 000000000000..33890cfdd37d --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_client.py @@ -0,0 +1,284 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, Awaitable, TYPE_CHECKING, Union + +from azure.core import AsyncPipelineClient +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies +from azure.core.rest import AsyncHttpResponse, HttpRequest + +from .._serialization import Deserializer, Serializer +from ._configuration import ( + FaceAdministrationClientConfiguration, + FaceClientConfiguration, + FaceSessionClientConfiguration, +) +from ._operations import ( + FaceAdministrationClientOperationsMixin, + FaceClientOperationsMixin, + FaceSessionClientOperationsMixin, +) + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core.credentials_async import AsyncTokenCredential + + +class FaceClient(FaceClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword + """FaceClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + _endpoint = "{endpoint}/face/{apiVersion}" + self._config = FaceClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request( + self, request: HttpRequest, *, stream: bool = False, **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = await client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> "FaceClient": + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details: Any) -> None: + await self._client.__aexit__(*exc_details) + + +class FaceAdministrationClient( + FaceAdministrationClientOperationsMixin +): # pylint: disable=client-accepts-api-version-keyword + """FaceAdministrationClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + _endpoint = "{endpoint}/face/{apiVersion}" + self._config = FaceAdministrationClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request( + self, request: HttpRequest, *, stream: bool = False, **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = await client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> "FaceAdministrationClient": + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details: Any) -> None: + await self._client.__aexit__(*exc_details) + + +class FaceSessionClient(FaceSessionClientOperationsMixin): # pylint: disable=client-accepts-api-version-keyword + """FaceSessionClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + _endpoint = "{endpoint}/face/{apiVersion}" + self._config = FaceSessionClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request( + self, request: HttpRequest, *, stream: bool = False, **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = await client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> "FaceSessionClient": + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details: Any) -> None: + await self._client.__aexit__(*exc_details) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_configuration.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_configuration.py new file mode 100644 index 000000000000..997338152abc --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_configuration.py @@ -0,0 +1,189 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING, Union + +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies + +from .._version import VERSION + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core.credentials_async import AsyncTokenCredential + + +class FaceClientConfiguration: # pylint: disable=too-many-instance-attributes + """Configuration for FaceClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + api_version: str = kwargs.pop("api_version", "v1.1-preview.1") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-vision-face/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.AsyncBearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.AsyncRedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.AsyncRetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) + + +class FaceAdministrationClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long + """Configuration for FaceAdministrationClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + api_version: str = kwargs.pop("api_version", "v1.1-preview.1") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-vision-face/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.AsyncBearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.AsyncRedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.AsyncRetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) + + +class FaceSessionClientConfiguration: # pylint: disable=too-many-instance-attributes,name-too-long + """Configuration for FaceSessionClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + api_version: str = kwargs.pop("api_version", "v1.1-preview.1") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-vision-face/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.AsyncBearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.AsyncRedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.AsyncRetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/__init__.py new file mode 100644 index 000000000000..9642d0953f19 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/__init__.py @@ -0,0 +1,23 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._operations import FaceClientOperationsMixin +from ._operations import FaceAdministrationClientOperationsMixin +from ._operations import FaceSessionClientOperationsMixin + +from ._patch import __all__ as _patch_all +from ._patch import * # pylint: disable=unused-wildcard-import +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "FaceClientOperationsMixin", + "FaceAdministrationClientOperationsMixin", + "FaceSessionClientOperationsMixin", +] +__all__.extend([p for p in _patch_all if p not in __all__]) +_patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py new file mode 100644 index 000000000000..2cd629abda47 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py @@ -0,0 +1,18070 @@ +# pylint: disable=too-many-lines,too-many-statements +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +from io import IOBase +import json +import sys +from typing import Any, Callable, Dict, IO, List, Optional, Type, TypeVar, Union, cast, overload + +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod +from azure.core.polling.async_base_polling import AsyncLROBasePolling +from azure.core.rest import AsyncHttpResponse, HttpRequest +from azure.core.tracing.decorator_async import distributed_trace_async +from azure.core.utils import case_insensitive_dict + +from ... import _model_base, models as _models +from ..._model_base import SdkJSONEncoder, _deserialize +from ..._operations._operations import ( + build_face_administration_add_face_list_face_from_url_request, + build_face_administration_add_face_list_face_request, + build_face_administration_add_large_face_list_face_from_url_request, + build_face_administration_add_large_face_list_face_request, + build_face_administration_add_large_person_group_person_face_from_url_request, + build_face_administration_add_large_person_group_person_face_request, + build_face_administration_add_person_face_from_url_request, + build_face_administration_add_person_face_request, + build_face_administration_add_person_group_person_face_from_url_request, + build_face_administration_add_person_group_person_face_request, + build_face_administration_create_dynamic_person_group_request, + build_face_administration_create_dynamic_person_group_with_person_request, + build_face_administration_create_face_list_request, + build_face_administration_create_large_face_list_request, + build_face_administration_create_large_person_group_person_request, + build_face_administration_create_large_person_group_request, + build_face_administration_create_person_group_person_request, + build_face_administration_create_person_group_request, + build_face_administration_create_person_request, + build_face_administration_delete_dynamic_person_group_request, + build_face_administration_delete_face_list_face_request, + build_face_administration_delete_face_list_request, + build_face_administration_delete_large_face_list_face_request, + build_face_administration_delete_large_face_list_request, + build_face_administration_delete_large_person_group_person_face_request, + build_face_administration_delete_large_person_group_person_request, + build_face_administration_delete_large_person_group_request, + build_face_administration_delete_person_face_request, + build_face_administration_delete_person_group_person_face_request, + build_face_administration_delete_person_group_person_request, + build_face_administration_delete_person_group_request, + build_face_administration_delete_person_request, + build_face_administration_get_dynamic_person_group_persons_request, + build_face_administration_get_dynamic_person_group_references_request, + build_face_administration_get_dynamic_person_group_request, + build_face_administration_get_dynamic_person_groups_request, + build_face_administration_get_face_list_request, + build_face_administration_get_face_lists_request, + build_face_administration_get_large_face_list_face_request, + build_face_administration_get_large_face_list_faces_request, + build_face_administration_get_large_face_list_request, + build_face_administration_get_large_face_list_training_status_request, + build_face_administration_get_large_face_lists_request, + build_face_administration_get_large_person_group_person_face_request, + build_face_administration_get_large_person_group_person_request, + build_face_administration_get_large_person_group_persons_request, + build_face_administration_get_large_person_group_request, + build_face_administration_get_large_person_group_training_status_request, + build_face_administration_get_large_person_groups_request, + build_face_administration_get_person_face_request, + build_face_administration_get_person_faces_request, + build_face_administration_get_person_group_person_face_request, + build_face_administration_get_person_group_person_request, + build_face_administration_get_person_group_persons_request, + build_face_administration_get_person_group_request, + build_face_administration_get_person_group_training_status_request, + build_face_administration_get_person_groups_request, + build_face_administration_get_person_request, + build_face_administration_get_persons_request, + build_face_administration_train_large_face_list_request, + build_face_administration_train_large_person_group_request, + build_face_administration_train_person_group_request, + build_face_administration_update_dynamic_person_group_request, + build_face_administration_update_dynamic_person_group_with_person_changes_request, + build_face_administration_update_face_list_request, + build_face_administration_update_large_face_list_face_request, + build_face_administration_update_large_face_list_request, + build_face_administration_update_large_person_group_person_face_request, + build_face_administration_update_large_person_group_person_request, + build_face_administration_update_large_person_group_request, + build_face_administration_update_person_face_request, + build_face_administration_update_person_group_person_face_request, + build_face_administration_update_person_group_person_request, + build_face_administration_update_person_group_request, + build_face_administration_update_person_request, + build_face_detect_from_url_request, + build_face_detect_request, + build_face_find_similar_from_face_list_request, + build_face_find_similar_from_large_face_list_request, + build_face_find_similar_request, + build_face_group_request, + build_face_identify_from_dynamic_person_group_request, + build_face_identify_from_large_person_group_request, + build_face_identify_from_person_directory_request, + build_face_identify_from_person_group_request, + build_face_session_create_liveness_session_request, + build_face_session_create_liveness_with_verify_session_request, + build_face_session_create_liveness_with_verify_session_with_verify_image_request, + build_face_session_delete_liveness_session_request, + build_face_session_delete_liveness_with_verify_session_request, + build_face_session_get_liveness_session_audit_entries_request, + build_face_session_get_liveness_session_result_request, + build_face_session_get_liveness_sessions_request, + build_face_session_get_liveness_with_verify_session_audit_entries_request, + build_face_session_get_liveness_with_verify_session_result_request, + build_face_session_get_liveness_with_verify_sessions_request, + build_face_verify_face_to_face_request, + build_face_verify_from_large_person_group_request, + build_face_verify_from_person_directory_request, + build_face_verify_from_person_group_request, +) +from ..._vendor import prepare_multipart_form_data +from .._vendor import FaceAdministrationClientMixinABC, FaceClientMixinABC, FaceSessionClientMixinABC + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + + +class FaceClientOperationsMixin(FaceClientMixinABC): + + @overload + async def _detect_from_url( + self, + body: JSON, + *, + content_type: str = "application/json", + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any + ) -> List[_models.FaceDetectionResult]: ... + @overload + async def _detect_from_url( + self, + *, + url: str, + content_type: str = "application/json", + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any + ) -> List[_models.FaceDetectionResult]: ... + @overload + async def _detect_from_url( + self, + body: IO[bytes], + *, + content_type: str = "application/json", + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any + ) -> List[_models.FaceDetectionResult]: ... + + @distributed_trace_async + async def _detect_from_url( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any + ) -> List[_models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in "Identify", "Verify", and "Find + Similar". The stored face features will expire and be deleted at the time specified by + faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying "Identify", "Verify", and "Find Similar" ('returnFaceId' is + true), please use faces that are: frontal, clear, and with a minimum size of 200x200 pixels + (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like "Verify", + "Identify", "Find Similar" are needed, please specify the recognition model with + 'recognitionModel' parameter. The default value for 'recognitionModel' is 'recognition_01', if + latest model needed, please explicitly specify the model you need in this parameter. Once + specified, the detected faceIds will be associated with the specified recognition model. More + details, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Default value is None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. The default value is + true. Default value is None. + :paramtype return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. This is only applicable when returnFaceId = true. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("content-type", None)) + cls: ClsType[List[_models.FaceDetectionResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_detect_from_url_request( + detection_model=detection_model, + recognition_model=recognition_model, + return_face_id=return_face_id, + return_face_attributes=return_face_attributes, + return_face_landmarks=return_face_landmarks, + return_recognition_model=return_recognition_model, + face_id_time_to_live=face_id_time_to_live, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceDetectionResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def _detect( + self, + image_content: bytes, + *, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + return_face_id: Optional[bool] = None, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any + ) -> List[_models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in "Identify", "Verify", and "Find + Similar". The stored face features will expire and be deleted at the time specified by + faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying "Identify", "Verify", and "Find Similar" ('returnFaceId' is + true), please use faces that are: frontal, clear, and with a minimum size of 200x200 pixels + (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like "Verify", + "Identify", "Find Similar" are needed, please specify the recognition model with + 'recognitionModel' parameter. The default value for 'recognitionModel' is 'recognition_01', if + latest model needed, please explicitly specify the model you need in this parameter. Once + specified, the detected faceIds will be associated with the specified recognition model. More + details, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param image_content: The input image binary. Required. + :type image_content: bytes + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Default value is None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. The default value is + true. Default value is None. + :paramtype return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. This is only applicable when returnFaceId = true. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[List[_models.FaceDetectionResult]] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_detect_request( + detection_model=detection_model, + recognition_model=recognition_model, + return_face_id=return_face_id, + return_face_attributes=return_face_attributes, + return_face_landmarks=return_face_landmarks, + return_recognition_model=return_recognition_model, + face_id_time_to_live=face_id_time_to_live, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceDetectionResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def find_similar( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceIds": [ + "str" # An array of candidate faceIds. All of them are created by + "Detect" and the faceIds will expire 24 hours after the detection call. The + number of faceIds is limited to 1000. Required. + ], + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + async def find_similar( + self, + *, + face_id: str, + face_ids: List[str], + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_ids: An array of candidate faceIds. All of them are created by "Detect" and the + faceIds will expire 24 hours after the detection call. The number of faceIds is limited to + 1000. Required. + :paramtype face_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + async def find_similar( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @distributed_trace_async + async def find_similar( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + face_ids: List[str] = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a faceId array. A faceId + array contains the faces created by Detect. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target faceId array. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_ids: An array of candidate faceIds. All of them are created by "Detect" and the + faceIds will expire 24 hours after the detection call. The number of faceIds is limited to + 1000. Required. + :paramtype face_ids: list[str] + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceIds": [ + "str" # An array of candidate faceIds. All of them are created by + "Detect" and the faceIds will expire 24 hours after the detection call. The + number of faceIds is limited to 1000. Required. + ], + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceFindSimilarResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + body = { + "faceid": face_id, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "mode": mode, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_find_similar_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceFindSimilarResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def find_similar_from_face_list( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceListId": "str", # An existing user-specified unique candidate Face + List, created in "Create Face List". Face List contains a set of persistedFaceIds + which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + async def find_similar_from_face_list( + self, + *, + face_id: str, + face_list_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_list_id: An existing user-specified unique candidate Face List, created in + "Create Face List". Face List contains a set of persistedFaceIds which are persisted and will + never expire. Required. + :paramtype face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + async def find_similar_from_face_list( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @distributed_trace_async + async def find_similar_from_face_list( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + face_list_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Face List. A 'faceListId' + is created by Create Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Face List. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword face_list_id: An existing user-specified unique candidate Face List, created in + "Create Face List". Face List contains a set of persistedFaceIds which are persisted and will + never expire. Required. + :paramtype face_list_id: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "faceListId": "str", # An existing user-specified unique candidate Face + List, created in "Create Face List". Face List contains a set of persistedFaceIds + which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceFindSimilarResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if face_list_id is _Unset: + raise TypeError("missing required argument: face_list_id") + body = { + "faceid": face_id, + "facelistid": face_list_id, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "mode": mode, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_find_similar_from_face_list_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceFindSimilarResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def find_similar_from_large_face_list( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "largeFaceListId": "str", # An existing user-specified unique candidate + Large Face List, created in "Create Large Face List". Large Face List contains a + set of persistedFaceIds which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + async def find_similar_from_large_face_list( + self, + *, + face_id: str, + large_face_list_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword large_face_list_id: An existing user-specified unique candidate Large Face List, + created in "Create Large Face List". Large Face List contains a set of persistedFaceIds which + are persisted and will never expire. Required. + :paramtype large_face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @overload + async def find_similar_from_large_face_list( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + + @distributed_trace_async + async def find_similar_from_large_face_list( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + large_face_list_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + mode: Optional[Union[str, _models.FindSimilarMatchMode]] = None, + **kwargs: Any + ) -> List[_models.FaceFindSimilarResult]: + # pylint: disable=line-too-long + """Given query face's faceId, to search the similar-looking faces from a Large Face List. A + 'largeFaceListId' is created by Create Large Face List. + + Depending on the input the returned similar faces list contains faceIds or persistedFaceIds + ranked by similarity. + + Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default + mode that it tries to find faces of the same person as possible by using internal same-person + thresholds. It is useful to find a known person's other photos. Note that an empty list will be + returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person + thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used + in the cases like searching celebrity-looking faces. + + The 'recognitionModel' associated with the query faceId should be the same as the + 'recognitionModel' used by the target Large Face List. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: faceId of the query face. User needs to call "Detect" first to get a valid + faceId. Note that this faceId is not persisted and will expire 24 hours after the detection + call. Required. + :paramtype face_id: str + :keyword large_face_list_id: An existing user-specified unique candidate Large Face List, + created in "Create Large Face List". Large Face List contains a set of persistedFaceIds which + are persisted and will never expire. Required. + :paramtype large_face_list_id: str + :keyword max_num_of_candidates_returned: The number of top similar faces returned. The valid + range is [1, 1000]. Default value is 20. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword mode: Similar face searching mode. It can be 'matchPerson' or 'matchFace'. Default + value is 'matchPerson'. Known values are: "matchPerson" and "matchFace". Default value is None. + :paramtype mode: str or ~azure.ai.vision.face.models.FindSimilarMatchMode + :return: list of FaceFindSimilarResult + :rtype: list[~azure.ai.vision.face.models.FaceFindSimilarResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # faceId of the query face. User needs to call "Detect" + first to get a valid faceId. Note that this faceId is not persisted and will + expire 24 hours after the detection call. Required. + "largeFaceListId": "str", # An existing user-specified unique candidate + Large Face List, created in "Create Large Face List". Large Face List contains a + set of persistedFaceIds which are persisted and will never expire. Required. + "maxNumOfCandidatesReturned": 0, # Optional. The number of top similar faces + returned. The valid range is [1, 1000]. Default value is 20. + "mode": "str" # Optional. Similar face searching mode. It can be + 'matchPerson' or 'matchFace'. Default value is 'matchPerson'. Known values are: + "matchPerson" and "matchFace". + } + + # response body for status code(s): 200 + response == [ + { + "confidence": 0.0, # Confidence value of the candidate. The higher + confidence, the more similar. Range between [0,1]. Required. + "faceId": "str", # Optional. faceId of candidate face when find by + faceIds. faceId is created by "Detect" and will expire 24 hours after the + detection call. + "persistedFaceId": "str" # Optional. persistedFaceId of candidate + face when find by faceListId or largeFaceListId. persistedFaceId in face + list/large face list is persisted and will not expire. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceFindSimilarResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if large_face_list_id is _Unset: + raise TypeError("missing required argument: large_face_list_id") + body = { + "faceid": face_id, + "largefacelistid": large_face_list_id, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "mode": mode, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_find_similar_from_large_face_list_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceFindSimilarResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def identify_from_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personGroupId": "str", # personGroupId of the target Person Group, created + by "Create Person Group". Parameter personGroupId and largePersonGroupId should + not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_person_group( + self, + *, + face_ids: List[str], + person_group_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_group_id: personGroupId of the target Person Group, created by "Create Person + Group". Parameter personGroupId and largePersonGroupId should not be provided at the same time. + Required. + :paramtype person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace_async + async def identify_from_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + person_group_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Group (given by personGroupId), and return candidate + person(s) for that face ranked by similarity confidence. The Person Group should be trained to + make it ready for identification. See more in "Train Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_group_id: personGroupId of the target Person Group, created by "Create Person + Group". Parameter personGroupId and largePersonGroupId should not be provided at the same time. + Required. + :paramtype person_group_id: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personGroupId": "str", # personGroupId of the target Person Group, created + by "Create Person Group". Parameter personGroupId and largePersonGroupId should + not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if person_group_id is _Unset: + raise TypeError("missing required argument: person_group_id") + body = { + "confidencethreshold": confidence_threshold, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "persongroupid": person_group_id, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def identify_from_large_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "largePersonGroupId": "str", # largePersonGroupId of the target Large Person + Group, created by "Create Large Person Group". Parameter personGroupId and + largePersonGroupId should not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_large_person_group( + self, + *, + face_ids: List[str], + large_person_group_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword large_person_group_id: largePersonGroupId of the target Large Person Group, created by + "Create Large Person Group". Parameter personGroupId and largePersonGroupId should not be + provided at the same time. Required. + :paramtype large_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_large_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace_async + async def identify_from_large_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + large_person_group_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Large Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Large Person Group (given by largePersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. The Large Person Group + should be trained to make it ready for identification. See more in "Train Large Person Group". + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * Try "Find Similar" when you need to find similar faces from a Face List/Large Face List + instead of a Person Group/Large Person Group. + * The 'recognitionModel' associated with the query faces' faceIds should be the same as the + 'recognitionModel' used by the target Person Group or Large Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword large_person_group_id: largePersonGroupId of the target Large Person Group, created by + "Create Large Person Group". Parameter personGroupId and largePersonGroupId should not be + provided at the same time. Required. + :paramtype large_person_group_id: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "largePersonGroupId": "str", # largePersonGroupId of the target Large Person + Group, created by "Create Large Person Group". Parameter personGroupId and + largePersonGroupId should not be provided at the same time. Required. + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if large_person_group_id is _Unset: + raise TypeError("missing required argument: large_person_group_id") + body = { + "confidencethreshold": confidence_threshold, + "faceids": face_ids, + "largepersongroupid": large_person_group_id, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_large_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def identify_from_person_directory( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personIds": [ + "str" # Array of personIds created in Person Directory "Create + Person". The valid number of personIds is between [1,30]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_person_directory( + self, + *, + face_ids: List[str], + person_ids: List[str], + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_ids: Array of personIds created in Person Directory "Create Person". The valid + number of personIds is between [1,30]. Required. + :paramtype person_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_person_directory( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace_async + async def identify_from_person_directory( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + person_ids: List[str] = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + person directory personIds array. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Person Directory Persons (given by personIds), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword person_ids: Array of personIds created in Person Directory "Create Person". The valid + number of personIds is between [1,30]. Required. + :paramtype person_ids: list[str] + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "personIds": [ + "str" # Array of personIds created in Person Directory "Create + Person". The valid number of personIds is between [1,30]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if person_ids is _Unset: + raise TypeError("missing required argument: person_ids") + body = { + "confidencethreshold": confidence_threshold, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + "personids": person_ids, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_person_directory_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def identify_from_dynamic_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "dynamicPersonGroupId": "str", # DynamicPersonGroupId of the target + PersonDirectory DynamicPersonGroup to match against. Required. + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_dynamic_person_group( + self, + *, + face_ids: List[str], + dynamic_person_group_id: str, + content_type: str = "application/json", + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword dynamic_person_group_id: DynamicPersonGroupId of the target PersonDirectory + DynamicPersonGroup to match against. Required. + :paramtype dynamic_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @overload + async def identify_from_dynamic_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + + @distributed_trace_async + async def identify_from_dynamic_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_ids: List[str] = _Unset, + dynamic_person_group_id: str = _Unset, + max_num_of_candidates_returned: Optional[int] = None, + confidence_threshold: Optional[float] = None, + **kwargs: Any + ) -> List[_models.FaceIdentificationResult]: + # pylint: disable=line-too-long + """1-to-many identification to find the closest matches of the specific query person face from a + Dynamic Person Group. + + For each face in the faceIds array, Face Identify will compute similarities between the query + face and all the faces in the Dynamic Person Group (given by dynamicPersonGroupId), and return + candidate person(s) for that face ranked by similarity confidence. + + .. + + [!NOTE] + + * + + + * The algorithm allows more than one face to be identified independently at the same + request, but no more than 10 faces. + * Each person could have more than one face, but no more than 248 faces. + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * Number of candidates returned is restricted by maxNumOfCandidatesReturned and + confidenceThreshold. If no person is identified, the returned candidates will be an empty + array. + * The Identify operation can only match faces obtained with the same recognition model, that + is associated with the query faces. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of query faces faceIds, created by the "Detect". Each of the faces are + identified independently. The valid number of faceIds is between [1, 10]. Required. + :paramtype face_ids: list[str] + :keyword dynamic_person_group_id: DynamicPersonGroupId of the target PersonDirectory + DynamicPersonGroup to match against. Required. + :paramtype dynamic_person_group_id: str + :keyword max_num_of_candidates_returned: The range of maxNumOfCandidatesReturned is between 1 + and 100. Default value is 10. Default value is None. + :paramtype max_num_of_candidates_returned: int + :keyword confidence_threshold: Customized identification confidence threshold, in the range of + [0, 1]. Advanced user can tweak this value to override default internal threshold for better + precision on their scenario data. Note there is no guarantee of this threshold value working on + other data and after algorithm updates. Default value is None. + :paramtype confidence_threshold: float + :return: list of FaceIdentificationResult + :rtype: list[~azure.ai.vision.face.models.FaceIdentificationResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "dynamicPersonGroupId": "str", # DynamicPersonGroupId of the target + PersonDirectory DynamicPersonGroup to match against. Required. + "faceIds": [ + "str" # Array of query faces faceIds, created by the "Detect". Each + of the faces are identified independently. The valid number of faceIds is + between [1, 10]. Required. + ], + "confidenceThreshold": 0.0, # Optional. Customized identification confidence + threshold, in the range of [0, 1]. Advanced user can tweak this value to override + default internal threshold for better precision on their scenario data. Note + there is no guarantee of this threshold value working on other data and after + algorithm updates. + "maxNumOfCandidatesReturned": 0 # Optional. The range of + maxNumOfCandidatesReturned is between 1 and 100. Default value is 10. + } + + # response body for status code(s): 200 + response == [ + { + "candidates": [ + { + "confidence": 0.0, # Confidence value of the + candidate. The higher confidence, the more similar. Range between + [0,1]. Required. + "personId": "str" # personId of candidate person. + Required. + } + ], + "faceId": "str" # faceId of the query face. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[List[_models.FaceIdentificationResult]] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + if dynamic_person_group_id is _Unset: + raise TypeError("missing required argument: dynamic_person_group_id") + body = { + "confidencethreshold": confidence_threshold, + "dynamicpersongroupid": dynamic_person_group_id, + "faceids": face_ids, + "maxnumofcandidatesreturned": max_num_of_candidates_returned, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_identify_from_dynamic_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceIdentificationResult], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def verify_face_to_face( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId1": "str", # The faceId of one face, come from "Detect". Required. + "faceId2": "str" # The faceId of another face, come from "Detect". Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_face_to_face( + self, *, face_id1: str, face_id2: str, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :keyword face_id1: The faceId of one face, come from "Detect". Required. + :paramtype face_id1: str + :keyword face_id2: The faceId of another face, come from "Detect". Required. + :paramtype face_id2: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_face_to_face( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace_async + async def verify_face_to_face( + self, body: Union[JSON, IO[bytes]] = _Unset, *, face_id1: str = _Unset, face_id2: str = _Unset, **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether two faces belong to a same person. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the both faces should be the same. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id1: The faceId of one face, come from "Detect". Required. + :paramtype face_id1: str + :keyword face_id2: The faceId of another face, come from "Detect". Required. + :paramtype face_id2: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId1": "str", # The faceId of one face, come from "Detect". Required. + "faceId2": "str" # The faceId of another face, come from "Detect". Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id1 is _Unset: + raise TypeError("missing required argument: face_id1") + if face_id2 is _Unset: + raise TypeError("missing required argument: face_id2") + body = {"faceid1": face_id1, "faceid2": face_id2} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_face_to_face_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def verify_from_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personGroupId": "str", # Using existing personGroupId and personId for fast + loading a specified person. personGroupId is created in "Create Person Group". + Required. + "personId": "str" # Specify a certain person in Person Group. Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_from_person_group( + self, + *, + face_id: str, + person_group_id: str, + person_id: str, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_group_id: Using existing personGroupId and personId for fast loading a + specified person. personGroupId is created in "Create Person Group". Required. + :paramtype person_group_id: str + :keyword person_id: Specify a certain person in Person Group. Required. + :paramtype person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_from_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace_async + async def verify_from_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + person_group_id: str = _Unset, + person_id: str = _Unset, + **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_group_id: Using existing personGroupId and personId for fast loading a + specified person. personGroupId is created in "Create Person Group". Required. + :paramtype person_group_id: str + :keyword person_id: Specify a certain person in Person Group. Required. + :paramtype person_id: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personGroupId": "str", # Using existing personGroupId and personId for fast + loading a specified person. personGroupId is created in "Create Person Group". + Required. + "personId": "str" # Specify a certain person in Person Group. Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if person_group_id is _Unset: + raise TypeError("missing required argument: person_group_id") + if person_id is _Unset: + raise TypeError("missing required argument: person_id") + body = {"faceid": face_id, "persongroupid": person_group_id, "personid": person_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_from_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def verify_from_large_person_group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "largePersonGroupId": "str", # Using existing largePersonGroupId and + personId for fast loading a specified person. largePersonGroupId is created in + "Create Large Person Group". Required. + "personId": "str" # Specify a certain person in Large Person Group. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_from_large_person_group( + self, + *, + face_id: str, + large_person_group_id: str, + person_id: str, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword large_person_group_id: Using existing largePersonGroupId and personId for fast loading + a specified person. largePersonGroupId is created in "Create Large Person Group". Required. + :paramtype large_person_group_id: str + :keyword person_id: Specify a certain person in Large Person Group. Required. + :paramtype person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_from_large_person_group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace_async + async def verify_from_large_person_group( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + face_id: str = _Unset, + large_person_group_id: str = _Unset, + person_id: str = _Unset, + **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in a Large Person Group. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The 'recognitionModel' associated with the query face should be the same as the + 'recognitionModel' used by the Large Person Group. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword large_person_group_id: Using existing largePersonGroupId and personId for fast loading + a specified person. largePersonGroupId is created in "Create Large Person Group". Required. + :paramtype large_person_group_id: str + :keyword person_id: Specify a certain person in Large Person Group. Required. + :paramtype person_id: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "largePersonGroupId": "str", # Using existing largePersonGroupId and + personId for fast loading a specified person. largePersonGroupId is created in + "Create Large Person Group". Required. + "personId": "str" # Specify a certain person in Large Person Group. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if large_person_group_id is _Unset: + raise TypeError("missing required argument: large_person_group_id") + if person_id is _Unset: + raise TypeError("missing required argument: person_id") + body = {"faceid": face_id, "largepersongroupid": large_person_group_id, "personid": person_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_from_large_person_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def verify_from_person_directory( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personId": "str" # Specify a certain person in PersonDirectory Person. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_from_person_directory( + self, *, face_id: str, person_id: str, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_id: Specify a certain person in PersonDirectory Person. Required. + :paramtype person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @overload + async def verify_from_person_directory( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + + @distributed_trace_async + async def verify_from_person_directory( + self, body: Union[JSON, IO[bytes]] = _Unset, *, face_id: str = _Unset, person_id: str = _Unset, **kwargs: Any + ) -> _models.FaceVerificationResult: + # pylint: disable=line-too-long + """Verify whether a face belongs to a person in Person Directory. + + .. + + [!NOTE] + + * + + + * Higher face image quality means better identification precision. Please consider + high-quality faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) + or bigger. + * For the scenarios that are sensitive to accuracy please make your own judgment. + * The Verify operation can only match faces obtained with the same recognition model, that + is associated with the query face. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_id: The faceId of the face, come from "Detect". Required. + :paramtype face_id: str + :keyword person_id: Specify a certain person in PersonDirectory Person. Required. + :paramtype person_id: str + :return: FaceVerificationResult. The FaceVerificationResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceVerificationResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceId": "str", # The faceId of the face, come from "Detect". Required. + "personId": "str" # Specify a certain person in PersonDirectory Person. + Required. + } + + # response body for status code(s): 200 + response == { + "confidence": 0.0, # A number indicates the similarity confidence of whether + two faces belong to the same person, or whether the face belongs to the person. + By default, isIdentical is set to True if similarity confidence is greater than + or equal to 0.5. This is useful for advanced users to override 'isIdentical' and + fine-tune the result on their own data. Required. + "isIdentical": bool # True if the two faces belong to the same person or the + face belongs to the person, otherwise false. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceVerificationResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_id is _Unset: + raise TypeError("missing required argument: face_id") + if person_id is _Unset: + raise TypeError("missing required argument: person_id") + body = {"faceid": face_id, "personid": person_id} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_verify_from_person_directory_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceVerificationResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def group( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of candidate faceIds created by "Detect". The maximum + is 1000 faces. Required. + ] + } + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + + @overload + async def group( + self, *, face_ids: List[str], content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :keyword face_ids: Array of candidate faceIds created by "Detect". The maximum is 1000 faces. + Required. + :paramtype face_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + + @overload + async def group( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + + @distributed_trace_async + async def group( + self, body: Union[JSON, IO[bytes]] = _Unset, *, face_ids: List[str] = _Unset, **kwargs: Any + ) -> _models.FaceGroupingResult: + # pylint: disable=line-too-long + """Divide candidate faces into groups based on face similarity. + + > + * + + + * The output is one or more disjointed face groups and a messyGroup. A face group contains + faces that have similar looking, often of the same person. Face groups are ranked by group + size, i.e. number of faces. Notice that faces belonging to a same person might be split into + several groups in the result. + * MessyGroup is a special face group containing faces that cannot find any similar counterpart + face from original faces. The messyGroup will not appear in the result if all faces found their + counterparts. + * Group API needs at least 2 candidate faces and 1000 at most. We suggest to try "Verify Face + To Face" when you only have 2 candidate faces. + * The 'recognitionModel' associated with the query faces' faceIds should be the same. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword face_ids: Array of candidate faceIds created by "Detect". The maximum is 1000 faces. + Required. + :paramtype face_ids: list[str] + :return: FaceGroupingResult. The FaceGroupingResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceGroupingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "faceIds": [ + "str" # Array of candidate faceIds created by "Detect". The maximum + is 1000 faces. Required. + ] + } + + # response body for status code(s): 200 + response == { + "groups": [ + [ + "str" # A partition of the original faces based on face + similarity. Groups are ranked by number of faces. Required. + ] + ], + "messyGroup": [ + "str" # Face ids array of faces that cannot find any similar faces + from original faces. Required. + ] + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.FaceGroupingResult] = kwargs.pop("cls", None) + + if body is _Unset: + if face_ids is _Unset: + raise TypeError("missing required argument: face_ids") + body = {"faceids": face_ids} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_group_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceGroupingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class FaceAdministrationClientOperationsMixin( # pylint: disable=too-many-public-methods + FaceAdministrationClientMixinABC +): + + @overload + async def create_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def create_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create an empty Face List with user-specified faceListId, name, an optional userData and + recognitionModel. + + Up to 64 Face Lists are allowed in one subscription. + + Face List is a list of faces, up to 1,000 faces, and used by "Find Similar From Face List". + + After creation, user should use "Add Face List Face" to import the faces. No image will be + stored. Only the extracted face feature(s) will be stored on server until "Delete Face List" is + called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + Please consider Large Face List when the face number is large. It can support up to 1,000,000 + faces. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_face_list_request( + face_list_id=face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def delete_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, **kwargs: Any + ) -> None: + """Delete a specified Face List. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_face_list_request( + face_list_id=face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_face_list( + self, face_list_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.FaceList: + # pylint: disable=line-too-long + """Retrieve a Face List's faceListId, name, userData, recognitionModel and faces in the Face List. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: FaceList. The FaceList is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceList + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "faceListId": "str", # Valid character is letter in lower case or digit or + '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "persistedFaces": [ + { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to + the face. The length limit is 1K. + } + ], + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceList] = kwargs.pop("cls", None) + + _request = build_face_administration_get_face_list_request( + face_list_id=face_list_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceList, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_face_list( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_face_list( # pylint: disable=inconsistent-return-statements + self, + face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update information of a Face List, including name and userData. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_face_list_request( + face_list_id=face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_face_lists( + self, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> List[_models.FaceListItem]: + # pylint: disable=line-too-long + """List Face Lists' faceListId, name, userData and recognitionModel. + + To get face information inside Face List use "Get Face List". + + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of FaceListItem + :rtype: list[~azure.ai.vision.face.models.FaceListItem] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "faceListId": "str", # Valid character is letter in lower case or + digit or '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.FaceListItem]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_face_lists_request( + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.FaceListItem], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def add_face_list_face_from_url( + self, + face_list_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_face_list_face_from_url( + self, + face_list_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_face_list_face_from_url( + self, + face_list_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace_async + async def add_face_list_face_from_url( + self, + face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_face_list_face_from_url_request( + face_list_id=face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def add_face_list_face( + self, + face_list_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Face List, up to 1,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Face List + Face" or "Delete Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_face_list_face_request( + face_list_id=face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_face_list_face( # pylint: disable=inconsistent-return-statements + self, face_list_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a Face List by specified faceListId and persistedFaceId. + + Adding/deleting faces to/from a same Face List are processed sequentially and to/from different + Face Lists are in parallel. + + :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :type face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_face_list_face_request( + face_list_id=face_list_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + async def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create an empty Large Face List with user-specified largeFaceListId, name, an optional userData + and recognitionModel. + + Large Face List is a list of faces, up to 1,000,000 faces, and used by "Find Similar From Large + Face List". + + After creation, user should use Add Large Face List Face to import the faces and Train Large + Face List to make it ready for "Find Similar". No image will be stored. Only the extracted face + feature(s) will be stored on server until Delete Large Face List is called. + + "Find Similar" is used for scenario like finding celebrity-like faces, similar face filtering, + or as a light way face identification. But if the actual use is to identify person, please use + Person Group / Large Person Group and "Identify". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 64 Large Face Lists. + * S0-tier subscription quota: 1,000,000 Large Face Lists. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_large_face_list_request( + large_face_list_id=large_face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def delete_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, **kwargs: Any + ) -> None: + """Delete a face from a Large Face List by specified largeFaceListId and persistedFaceId. + + Adding/deleting faces to/from a same Large Face List are processed sequentially and to/from + different Large Face Lists are in parallel. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_face_list_request( + large_face_list_id=large_face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_face_list( + self, large_face_list_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.LargeFaceList: + # pylint: disable=line-too-long + """Retrieve a Large Face List's largeFaceListId, name, userData and recognitionModel. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: LargeFaceList. The LargeFaceList is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargeFaceList + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "largeFaceListId": "str", # Valid character is letter in lower case or digit + or '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargeFaceList] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_request( + large_face_list_id=large_face_list_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargeFaceList, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_large_face_list( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update information of a Large Face List, including name and userData. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_face_list_request( + large_face_list_id=large_face_list_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_face_lists( + self, + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any + ) -> List[_models.LargeFaceList]: + # pylint: disable=line-too-long + """List Large Face Lists' information of largeFaceListId, name, userData and recognitionModel. + + To get face information inside largeFaceList use "Get Large Face List Face". + + Large Face Lists are stored in alphabetical order of largeFaceListId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of LargeFaceList + :rtype: list[~azure.ai.vision.face.models.LargeFaceList] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "largeFaceListId": "str", # Valid character is letter in lower case + or digit or '-' or '_', maximum length is 64. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargeFaceList]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_lists_request( + start=start, + top=top, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargeFaceList], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_large_face_list_training_status( + self, large_face_list_id: str, **kwargs: Any + ) -> _models.FaceCollectionTrainingResult: + # pylint: disable=line-too-long + """To check the Large Face List training status completed or still ongoing. Large Face List + training is an asynchronous operation triggered by "Train Large Face List". + + Training time depends on the number of face entries in a Large Face List. It could be in + seconds, or up to half an hour for 1,000,000 faces. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :return: FaceCollectionTrainingResult. The FaceCollectionTrainingResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceCollectionTrainingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the created time of the person group, large person group or + large face list. Required. + "lastActionDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the last modify time of the person group, large person + group or large face list, could be null value when the group is not successfully + trained. Required. + "lastSuccessfulTrainingDateTime": "2020-02-20 00:00:00", # A combined UTC + date and time string that describes the last successful training time of the + person group, large person group or large face list. Required. + "status": "str", # Training status of the container. Required. Known values + are: "notStarted", "running", "succeeded", and "failed". + "message": "str" # Optional. Show failure message when training failed + (omitted when training succeed). + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceCollectionTrainingResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_training_status_request( + large_face_list_id=large_face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceCollectionTrainingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _train_large_face_list_initial( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_train_large_face_list_request( + large_face_list_id=large_face_list_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace_async + async def begin_train_large_face_list(self, large_face_list_id: str, **kwargs: Any) -> AsyncLROPoller[None]: + """Submit a Large Face List training task. + + Training is a crucial step that only a trained Large Face List can be used by "Find Similar + From Large Face List". + + The training task is an asynchronous task. Training time depends on the number of face entries + in a Large Face List. It could be in seconds, or up to half an hour for 1,000,000 faces. To + check training completion, please use "Get Large Face List Training Status". + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._train_large_face_list_initial( # type: ignore + large_face_list_id=large_face_list_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + async def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace_async + async def add_large_face_list_face_from_url( + self, + large_face_list_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_large_face_list_face_from_url_request( + large_face_list_id=large_face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def add_large_face_list_face( + self, + large_face_list_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a specified Large Face List, up to 1,000,000 faces. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large Face + List Face" or "Delete Large Face List" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 faces per Large Face List. + * S0-tier subscription quota: 1,000,000 faces per Large Face List. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_large_face_list_face_request( + large_face_list_id=large_face_list_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, large_face_list_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a Large Face List by specified largeFaceListId and persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_face_list_face_request( + large_face_list_id=large_face_list_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_face_list_face( + self, large_face_list_id: str, persisted_face_id: str, **kwargs: Any + ) -> _models.LargeFaceListFace: + """Retrieve persisted face in Large Face List by largeFaceListId and persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: LargeFaceListFace. The LargeFaceListFace is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargeFaceListFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargeFaceListFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_face_request( + large_face_list_id=large_face_list_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargeFaceListFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + async def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_large_face_list_face( # pylint: disable=inconsistent-return-statements + self, + large_face_list_id: str, + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a specified face's userData field in a Large Face List by its persistedFaceId. + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_face_list_face_request( + large_face_list_id=large_face_list_id, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_face_list_faces( + self, large_face_list_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LargeFaceListFace]: + """List faces' persistedFaceId and userData in a specified Large Face List. + + Faces are stored in alphabetical order of persistedFaceId created in "Add Large Face List + Face". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :type large_face_list_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LargeFaceListFace + :rtype: list[~azure.ai.vision.face.models.LargeFaceListFace] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the + face. The length limit is 1K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargeFaceListFace]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_face_list_faces_request( + large_face_list_id=large_face_list_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargeFaceListFace], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def create_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def create_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create a new Person Group with specified personGroupId, name, user-provided userData and + recognitionModel. + + A Person Group is a container holding the uploaded person data, including face recognition + features. + + After creation, use "Create Person Group Person" to add persons into the group, and then call + "Train Person Group" to get this group ready for "Identify From Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Person Group Person" or "Delete Person Group" is called. + + 'recognitionModel' should be specified to associate with this Person Group. The default value + for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Person + Group will use the recognition model that's already associated with the collection. Existing + face feature(s) in a Person Group can't be updated to features extracted by another version of + recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Person Groups. Each holds up to 1,000 persons. + * S0-tier subscription quota: 1,000,000 Person Groups. Each holds up to 10,000 persons. + * to handle larger scale face identification problem, please consider using Large Person + Group. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_person_group_request( + person_group_id=person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def delete_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, **kwargs: Any + ) -> None: + """Delete an existing Person Group with specified personGroupId. Persisted data in this Person + Group will be deleted. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_group_request( + person_group_id=person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_person_group( + self, person_group_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.PersonGroup: + # pylint: disable=line-too-long + """Retrieve Person Group name, userData and recognitionModel. To get person information under this + personGroup, use "Get Person Group Persons". + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: PersonGroup. The PersonGroup is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonGroup + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personGroupId": "str", # ID of the container. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonGroup] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_request( + person_group_id=person_group_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonGroup, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_person_group( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_person_group( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update an existing Person Group's name and userData. The properties keep unchanged if they are + not in request body. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_group_request( + person_group_id=person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_person_groups( + self, + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any + ) -> List[_models.PersonGroup]: + # pylint: disable=line-too-long + """List Person Groups' personGroupId, name, userData and recognitionModel. + + Person Groups are stored in alphabetical order of personGroupId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of PersonGroup + :rtype: list[~azure.ai.vision.face.models.PersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personGroupId": "str", # ID of the container. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.PersonGroup]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_groups_request( + start=start, + top=top, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.PersonGroup], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_person_group_training_status( + self, person_group_id: str, **kwargs: Any + ) -> _models.FaceCollectionTrainingResult: + # pylint: disable=line-too-long + """To check Person Group training status completed or still ongoing. Person Group training is an + asynchronous operation triggered by "Train Person Group" API. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :return: FaceCollectionTrainingResult. The FaceCollectionTrainingResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceCollectionTrainingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the created time of the person group, large person group or + large face list. Required. + "lastActionDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the last modify time of the person group, large person + group or large face list, could be null value when the group is not successfully + trained. Required. + "lastSuccessfulTrainingDateTime": "2020-02-20 00:00:00", # A combined UTC + date and time string that describes the last successful training time of the + person group, large person group or large face list. Required. + "status": "str", # Training status of the container. Required. Known values + are: "notStarted", "running", "succeeded", and "failed". + "message": "str" # Optional. Show failure message when training failed + (omitted when training succeed). + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceCollectionTrainingResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_training_status_request( + person_group_id=person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceCollectionTrainingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _train_person_group_initial( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_train_person_group_request( + person_group_id=person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace_async + async def begin_train_person_group(self, person_group_id: str, **kwargs: Any) -> AsyncLROPoller[None]: + """Submit a Person Group training task. Training is a crucial step that only a trained Person + Group can be used by "Identify From Person Group". + + The training task is an asynchronous task. Training time depends on the number of person + entries, and their faces in a Person Group. It could be several seconds to minutes. To check + training status, please use "Get Person Group Training Status". + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._train_person_group_initial( # type: ignore + person_group_id=person_group_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + async def create_person_group_person( + self, person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + async def create_person_group_person( + self, + person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + async def create_person_group_person( + self, person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @distributed_trace_async + async def create_person_group_person( + self, + person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Person Group. To add face to this person, please call "Add + Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Person Groups. + + * S0-tier subscription quota: + + * 10,000 persons per Person Group. + * 1,000,000 Person Groups. + * 100,000,000 persons in all Person Groups. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreatePersonResult] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_person_group_person_request( + person_group_id=person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreatePersonResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_person_group_person( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, person_id: str, **kwargs: Any + ) -> None: + """Delete an existing person from a Person Group. The persistedFaceId, userData, person name and + face feature(s) in the person entry will all be deleted. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_group_person_request( + person_group_id=person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_person_group_person( + self, person_group_id: str, person_id: str, **kwargs: Any + ) -> _models.PersonGroupPerson: + """Retrieve a person's name and userData, and the persisted faceIds representing the registered + person face feature(s). + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: PersonGroupPerson. The PersonGroupPerson is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonGroupPerson + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the person. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonGroupPerson] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_person_request( + person_group_id=person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonGroupPerson, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, person_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_person_group_person( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_group_person_request( + person_group_id=person_group_id, + person_id=person_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_person_group_persons( + self, person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.PersonGroupPerson]: + """List all persons' information in the specified Person Group, including personId, name, userData + and persistedFaceIds of registered person faces. + + Persons are stored in alphabetical order of personId created in "Create Person Group Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of PersonGroupPerson + :rtype: list[~azure.ai.vision.face.models.PersonGroupPerson] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the + person. + ], + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.PersonGroupPerson]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_persons_request( + person_group_id=person_group_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.PersonGroupPerson], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace_async + async def add_person_group_person_face_from_url( + self, + person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_person_group_person_face_from_url_request( + person_group_id=person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def add_person_group_person_face( + self, + person_group_id: str, + person_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Person + Group Person Face", "Delete Person Group Person" or "Delete Person Group" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a person in a Person Group by specified personGroupId, personId and + persistedFaceId. + + Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_person_group_person_face( + self, person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> _models.PersonGroupPersonFace: + """Retrieve person face information. The persisted person face is specified by its personGroupId, + personId and persistedFaceId. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: PersonGroupPersonFace. The PersonGroupPersonFace is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonGroupPersonFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonGroupPersonFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonGroupPersonFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + async def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + person_group_id: str, + person_id: str, + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param person_group_id: ID of the container. Required. + :type person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_group_person_face_request( + person_group_id=person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @overload + async def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, _models.FaceRecognitionModel]] = None, + **kwargs: Any + ) -> None: + # pylint: disable=line-too-long + """Create a new Large Person Group with user-specified largePersonGroupId, name, an optional + userData and recognitionModel. + + A Large Person Group is a container holding the uploaded person data, including the face + recognition features. It can hold up to 1,000,000 entities. + + After creation, use "Create Large Person Group Person" to add person into the group, and call + "Train Large Person Group" to get this group ready for "Identify From Large Person Group". + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + 'recognitionModel' should be specified to associate with this Large Person Group. The default + value for 'recognitionModel' is 'recognition_01', if the latest model needed, please explicitly + specify the model you need in this parameter. New faces that are added to an existing Large + Person Group will use the recognition model that's already associated with the collection. + Existing face feature(s) in a Large Person Group can't be updated to features extracted by + another version of recognition model. + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: 1,000 Large Person Groups. + * S0-tier subscription quota: 1,000,000 Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword recognition_model: The 'recognitionModel' associated with this face list. Supported + 'recognitionModel' values include 'recognition_01', 'recognition_02, 'recognition_03', and + 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since + its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall + accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Default value is + None. + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. The 'recognitionModel' associated + with this face list. Supported 'recognitionModel' values include + 'recognition_01', 'recognition_02, 'recognition_03', and 'recognition_04'. The + default value is 'recognition_01'. 'recognition_04' is recommended since its + accuracy is improved on faces wearing masks compared with 'recognition_03', and + its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "recognitionmodel": recognition_model, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_large_person_group_request( + large_person_group_id=large_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def delete_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, **kwargs: Any + ) -> None: + """Delete an existing Large Person Group with specified personGroupId. Persisted data in this + Large Person Group will be deleted. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_person_group_request( + large_person_group_id=large_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_person_group( + self, large_person_group_id: str, *, return_recognition_model: Optional[bool] = None, **kwargs: Any + ) -> _models.LargePersonGroup: + # pylint: disable=line-too-long + """Retrieve the information of a Large Person Group, including its name, userData and + recognitionModel. This API returns Large Person Group information only, use "Get Large Person + Group Persons" instead to retrieve person information under the Large Person Group. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: LargePersonGroup. The LargePersonGroup is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargePersonGroup + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "largePersonGroupId": "str", # ID of the container. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargePersonGroup] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_request( + large_person_group_id=large_person_group_id, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargePersonGroup, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_large_person_group( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update an existing Large Person Group's name and userData. The properties keep unchanged if + they are not in request body. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_person_group_request( + large_person_group_id=large_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_person_groups( + self, + *, + start: Optional[str] = None, + top: Optional[int] = None, + return_recognition_model: Optional[bool] = None, + **kwargs: Any + ) -> List[_models.LargePersonGroup]: + # pylint: disable=line-too-long + """List all existing Large Person Groups' largePersonGroupId, name, userData and recognitionModel. + + Large Person Groups are stored in alphabetical order of largePersonGroupId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :return: list of LargePersonGroup + :rtype: list[~azure.ai.vision.face.models.LargePersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "largePersonGroupId": "str", # ID of the container. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "recognitionModel": "str", # Optional. Name of recognition model. + Recognition model is used when the face features are extracted and associated + with detected faceIds. Known values are: "recognition_01", "recognition_02", + "recognition_03", and "recognition_04". + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargePersonGroup]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_groups_request( + start=start, + top=top, + return_recognition_model=return_recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargePersonGroup], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_large_person_group_training_status( + self, large_person_group_id: str, **kwargs: Any + ) -> _models.FaceCollectionTrainingResult: + # pylint: disable=line-too-long + """To check Large Person Group training status completed or still ongoing. Large Person Group + training is an asynchronous operation triggered by "Train Large Person Group" API. + + Training time depends on the number of person entries, and their faces in a Large Person Group. + It could be in seconds, or up to half an hour for 1,000,000 persons. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :return: FaceCollectionTrainingResult. The FaceCollectionTrainingResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.FaceCollectionTrainingResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the created time of the person group, large person group or + large face list. Required. + "lastActionDateTime": "2020-02-20 00:00:00", # A combined UTC date and time + string that describes the last modify time of the person group, large person + group or large face list, could be null value when the group is not successfully + trained. Required. + "lastSuccessfulTrainingDateTime": "2020-02-20 00:00:00", # A combined UTC + date and time string that describes the last successful training time of the + person group, large person group or large face list. Required. + "status": "str", # Training status of the container. Required. Known values + are: "notStarted", "running", "succeeded", and "failed". + "message": "str" # Optional. Show failure message when training failed + (omitted when training succeed). + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.FaceCollectionTrainingResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_training_status_request( + large_person_group_id=large_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.FaceCollectionTrainingResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _train_large_person_group_initial( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_train_large_person_group_request( + large_person_group_id=large_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace_async + async def begin_train_large_person_group(self, large_person_group_id: str, **kwargs: Any) -> AsyncLROPoller[None]: + """Submit a Large Person Group training task. Training is a crucial step that only a trained Large + Person Group can be used by "Identify From Large Person Group". + + The training task is an asynchronous task. Training time depends on the number of person + entries, and their faces in a Large Person Group. It could be in several seconds, or up to half + a hour for 1,000,000 persons. To check training status, please use "Get Large Person Group + Training Status". + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._train_large_person_group_initial( # type: ignore + large_person_group_id=large_person_group_id, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + async def create_large_person_group_person( + self, large_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + async def create_large_person_group_person( + self, + large_person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + async def create_large_person_group_person( + self, large_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @distributed_trace_async + async def create_large_person_group_person( + self, + large_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.CreatePersonResult: + """Create a new person in a specified Large Person Group. To add face to this person, please call + "Add Large Person Group Person Face". + + .. + + [!NOTE] + + * + + + * Free-tier subscription quota: + + * 1,000 persons in all Large Person Groups. + + * S0-tier subscription quota: + + * 1,000,000 persons per Large Person Group. + * 1,000,000 Large Person Groups. + * 1,000,000,000 persons in all Large Person Groups. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: CreatePersonResult. The CreatePersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreatePersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 200 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreatePersonResult] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_large_person_group_person_request( + large_person_group_id=large_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreatePersonResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, person_id: str, **kwargs: Any + ) -> None: + """Delete an existing person from a Large Person Group. The persistedFaceId, userData, person name + and face feature(s) in the person entry will all be deleted. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_person_group_person_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_person_group_person( + self, large_person_group_id: str, person_id: str, **kwargs: Any + ) -> _models.LargePersonGroupPerson: + """Retrieve a person's name and userData, and the persisted faceIds representing the registered + person face feature(s). + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :return: LargePersonGroupPerson. The LargePersonGroupPerson is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LargePersonGroupPerson + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the person. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargePersonGroupPerson] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_person_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargePersonGroupPerson, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_large_person_group_person( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_person_group_person_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_person_group_persons( + self, large_person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LargePersonGroupPerson]: + """List all persons' information in the specified Large Person Group, including personId, name, + userData and persistedFaceIds of registered person faces. + + Persons are stored in alphabetical order of personId created in "Create Large Person Group + Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LargePersonGroupPerson + :rtype: list[~azure.ai.vision.face.models.LargePersonGroupPerson] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # ID of the person. Required. + "persistedFaceIds": [ + "str" # Optional. Face ids of registered faces in the + person. + ], + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LargePersonGroupPerson]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_persons_request( + large_person_group_id=large_person_group_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LargePersonGroupPerson], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace_async + async def add_large_person_group_person_face_from_url( # pylint: disable=name-too-long + self, + large_person_group_id: str, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_large_person_group_person_face_from_url_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def add_large_person_group_person_face( + self, + large_person_group_id: str, + person_id: str, + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> _models.AddFaceResult: + # pylint: disable=line-too-long + """Add a face to a person into a Large Person Group for face identification or verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until "Delete Large + Person Group Person Face", "Delete Large Person Group Person" or "Delete Large Person Group" is + called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: AddFaceResult. The AddFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.AddFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AddFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> None: + """Delete a face from a person in a Large Person Group by specified largePersonGroupId, personId + and persistedFaceId. + + Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_large_person_group_person_face( + self, large_person_group_id: str, person_id: str, persisted_face_id: str, **kwargs: Any + ) -> _models.LargePersonGroupPersonFace: + """Retrieve person face information. The persisted person face is specified by its + largePersonGroupId, personId and persistedFaceId. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: LargePersonGroupPersonFace. The LargePersonGroupPersonFace is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.LargePersonGroupPersonFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LargePersonGroupPersonFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LargePersonGroupPersonFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + async def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_large_person_group_person_face( # pylint: disable=inconsistent-return-statements + self, + large_person_group_id: str, + person_id: str, + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a person persisted face's userData field. + + :param large_person_group_id: ID of the container. Required. + :type large_person_group_id: str + :param person_id: ID of the person. Required. + :type person_id: str + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_large_person_group_person_face_request( + large_person_group_id=large_person_group_id, + person_id=person_id, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + async def _create_person_initial( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> JSON: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[JSON] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_person_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(JSON, response.json()) + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @overload + async def begin_create_person( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns CreatePersonResult. The CreatePersonResult + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + async def begin_create_person( + self, *, name: str, content_type: str = "application/json", user_data: Optional[str] = None, **kwargs: Any + ) -> AsyncLROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of AsyncLROPoller that returns CreatePersonResult. The CreatePersonResult + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @overload + async def begin_create_person( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns CreatePersonResult. The CreatePersonResult + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + + @distributed_trace_async + async def begin_create_person( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> AsyncLROPoller[_models.CreatePersonResult]: + """Creates a new person in a Person Directory. To add face to this person, please call Person + Directory "Add Person Face". + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of AsyncLROPoller that returns CreatePersonResult. The CreatePersonResult + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.CreatePersonResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "personId": "str" # Person ID of the person. Required. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreatePersonResult] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._create_person_initial( + body=body, + name=name, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(_models.CreatePersonResult, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[_models.CreatePersonResult].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[_models.CreatePersonResult]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + async def _delete_person_initial( # pylint: disable=inconsistent-return-statements + self, person_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_request( + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace_async + async def begin_delete_person(self, person_id: str, **kwargs: Any) -> AsyncLROPoller[None]: + """Delete an existing person from Person Directory. The persistedFaceId(s), userData, person name + and face feature(s) in the person entry will all be deleted. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._delete_person_initial( # type: ignore + person_id=person_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @distributed_trace_async + async def get_person(self, person_id: str, **kwargs: Any) -> _models.PersonDirectoryPerson: + """Retrieve a person's name and userData from Person Directory. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :return: PersonDirectoryPerson. The PersonDirectoryPerson is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonDirectoryPerson + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # Person ID of the person. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonDirectoryPerson] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_request( + person_id=person_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonDirectoryPerson, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_person( # pylint: disable=inconsistent-return-statements + self, person_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_person( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_person( # pylint: disable=inconsistent-return-statements + self, person_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_person( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update name or userData of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_request( + person_id=person_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_persons( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.PersonDirectoryPerson]: + """List all persons' information in Person Directory, including personId, name, and userData. + + Persons are stored in alphabetical order of personId created in Person Directory "Create + Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of PersonDirectoryPerson + :rtype: list[~azure.ai.vision.face.models.PersonDirectoryPerson] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "name": "str", # User defined name, maximum length is 128. Required. + "personId": "str", # Person ID of the person. Required. + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.PersonDirectoryPerson]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_persons_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.PersonDirectoryPerson], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_dynamic_person_group_references( + self, person_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> _models.ListGroupReferenceResult: + """List all Dynamic Person Groups a person has been referenced by in Person Directory. + + Dynamic Person Groups are stored in alphabetical order of Dynamic Person Group ID created in + Person Directory "Create Dynamic Person Group". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: ListGroupReferenceResult. The ListGroupReferenceResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.ListGroupReferenceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "dynamicPersonGroupIds": [ + "str" # Array of PersonDirectory DynamicPersonGroup ids. Required. + ] + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ListGroupReferenceResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_group_references_request( + person_id=person_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ListGroupReferenceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _add_person_face_from_url_initial( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> JSON: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[JSON] = kwargs.pop("cls", None) + + if body is _Unset: + if url is _Unset: + raise TypeError("missing required argument: url") + body = {"url": url} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_add_person_face_from_url_request( + person_id=person_id, + recognition_model=recognition_model, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(JSON, response.json()) + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @overload + async def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: JSON, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> AsyncLROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param body: Required. + :type body: JSON + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns AddFaceResult. The AddFaceResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + *, + url: str, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> AsyncLROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns AddFaceResult. The AddFaceResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @overload + async def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: IO[bytes], + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> AsyncLROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param body: Required. + :type body: IO[bytes] + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns AddFaceResult. The AddFaceResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + + @distributed_trace_async + async def begin_add_person_face_from_url( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + body: Union[JSON, IO[bytes]] = _Unset, + *, + url: str = _Unset, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> AsyncLROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required. + :paramtype url: str + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: An instance of AsyncLROPoller that returns AddFaceResult. The AddFaceResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._add_person_face_from_url_initial( + person_id=person_id, + recognition_model=recognition_model, + body=body, + url=url, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(_models.AddFaceResult, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[_models.AddFaceResult].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[_models.AddFaceResult]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + async def _add_person_face_initial( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> JSON: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[JSON] = kwargs.pop("cls", None) + + _content = image_content + + _request = build_face_administration_add_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(JSON, response.json()) + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def begin_add_person_face( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + image_content: bytes, + *, + target_face: Optional[List[int]] = None, + detection_model: Optional[Union[str, _models.FaceDetectionModel]] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> AsyncLROPoller[_models.AddFaceResult]: + # pylint: disable=line-too-long + """Add a face to a person (see Person Directory "Create Person") for face identification or + verification. + + To deal with an image containing multiple faces, input face can be specified as an image with a + targetFace rectangle. It returns a persistedFaceId representing the added face. No image will + be stored. Only the extracted face feature(s) will be stored on server until Person Directory + "Delete Person Face" or "Delete Person" is called. + + Note that persistedFaceId is different from faceId generated by "Detect". + > + * + + + * Higher face image quality means better recognition precision. Please consider high-quality + faces: frontal, clear, and face size is 200x200 pixels (100 pixels between eyes) or bigger. + * Each person entry can hold up to 248 faces. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * "targetFace" rectangle should contain one face. Zero or multiple faces will be regarded as an + error. If the provided "targetFace" rectangle is not returned from "Detect", there's no + guarantee to detect and add the face successfully. + * Out of detectable face size (36x36 - 4096x4096 pixels), large head-pose, or large occlusions + will cause failures. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + * + * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + * This is a long running operation. Use Response Header "Operation-Location" to determine when + the AddFace operation has successfully propagated for future requests to "Identify". For + further information about Operation-Locations see "Get Face Operation Status". + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param image_content: The image to be analyzed. Required. + :type image_content: bytes + :keyword target_face: A face rectangle to specify the target face to be added to a person, in + the format of 'targetFace=left,top,width,height'. Default value is None. + :paramtype target_face: list[int] + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Default value is None. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword user_data: User-provided data attached to the face. The size limit is 1K. Default + value is None. + :paramtype user_data: str + :return: An instance of AsyncLROPoller that returns AddFaceResult. The AddFaceResult is + compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.AddFaceResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "persistedFaceId": "str" # Persisted Face ID of the added face, which is + persisted and will not expire. Different from faceId which is created in "Detect" + and will expire in 24 hours after the detection call. Required. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: str = kwargs.pop("content_type", _headers.pop("content-type", "application/octet-stream")) + cls: ClsType[_models.AddFaceResult] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._add_person_face_initial( + person_id=person_id, + recognition_model=recognition_model, + image_content=image_content, + target_face=target_face, + detection_model=detection_model, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + response_headers["Location"] = self._deserialize("str", response.headers.get("Location")) + + deserialized = _deserialize(_models.AddFaceResult, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[_models.AddFaceResult].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[_models.AddFaceResult]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + async def _delete_person_face_initial( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace_async + async def begin_delete_person_face( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + **kwargs: Any + ) -> AsyncLROPoller[None]: + """Delete a face from a person in Person Directory by specified personId and persistedFaceId. + + Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting + faces to/from different persons are processed in parallel. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._delete_person_face_initial( # type: ignore + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @distributed_trace_async + async def get_person_face( + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + **kwargs: Any + ) -> _models.PersonDirectoryFace: + """Retrieve person face information. The persisted person face is specified by its personId. + recognitionModel, and persistedFaceId. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :return: PersonDirectoryFace. The PersonDirectoryFace is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.PersonDirectoryFace + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceId": "str", # Face ID of the face. Required. + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.PersonDirectoryFace] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.PersonDirectoryFace, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + body: JSON, + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + + @overload + async def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + *, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + body: IO[bytes], + *, + content_type: str = "application/json", + **kwargs: Any + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_person_face( # pylint: disable=inconsistent-return-statements + self, + person_id: str, + recognition_model: Union[str, _models.FaceRecognitionModel], + persisted_face_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update a persisted face's userData field of a person. + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param persisted_face_id: Face ID of the face. Required. + :type persisted_face_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword user_data: User-provided data attached to the face. The length limit is 1K. Default + value is None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "userData": "str" # Optional. User-provided data attached to the face. The + length limit is 1K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_person_face_request( + person_id=person_id, + recognition_model=recognition_model, + persisted_face_id=persisted_face_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_person_faces( + self, person_id: str, recognition_model: Union[str, _models.FaceRecognitionModel], **kwargs: Any + ) -> _models.ListFaceResult: + """Retrieve a person's persistedFaceIds representing the registered person face feature(s). + + :param person_id: Person ID of the person. Required. + :type person_id: str + :param recognition_model: The 'recognitionModel' associated with faces. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :return: ListFaceResult. The ListFaceResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.ListFaceResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "persistedFaceIds": [ + "str" # Array of persisted face ids. Required. + ], + "personId": "str" # Id of person. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ListFaceResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_person_faces_request( + person_id=person_id, + recognition_model=recognition_model, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ListFaceResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _create_dynamic_person_group_with_person_initial( # pylint: disable=inconsistent-return-statements,name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + add_person_ids: List[str] = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + if add_person_ids is _Unset: + raise TypeError("missing required argument: add_person_ids") + body = {"addpersonids": add_person_ids, "name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_dynamic_person_group_with_person_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @overload + async def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns DynamicPersonGroup. The DynamicPersonGroup + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Array of personIds created by Person Directory "Create + Person" to be added. Required. + ], + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + *, + name: str, + add_person_ids: List[str], + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> AsyncLROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Required. + :paramtype add_person_ids: list[str] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of AsyncLROPoller that returns DynamicPersonGroup. The DynamicPersonGroup + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns DynamicPersonGroup. The DynamicPersonGroup + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @distributed_trace_async + async def begin_create_dynamic_person_group_with_person( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + add_person_ids: List[str] = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> AsyncLROPoller[_models.DynamicPersonGroup]: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Required. + :paramtype add_person_ids: list[str] + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: An instance of AsyncLROPoller that returns DynamicPersonGroup. The DynamicPersonGroup + is compatible with MutableMapping + :rtype: ~azure.core.polling.AsyncLROPoller[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Array of personIds created by Person Directory "Create + Person" to be added. Required. + ], + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + + # response body for status code(s): 202 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.DynamicPersonGroup] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._create_dynamic_person_group_with_person_initial( # type: ignore + dynamic_person_group_id=dynamic_person_group_id, + body=body, + name=name, + add_person_ids=add_person_ids, + user_data=user_data, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): + response_headers = {} + response = pipeline_response.http_response + response_headers["operation-Location"] = self._deserialize( + "str", response.headers.get("operation-Location") + ) + + deserialized = _deserialize(_models.DynamicPersonGroup, response.json()) + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + return deserialized + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[_models.DynamicPersonGroup].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[_models.DynamicPersonGroup]( + self._client, raw_result, get_long_running_output, polling_method # type: ignore + ) + + @overload + async def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + *, + name: str, + content_type: str = "application/json", + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def create_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: str = _Unset, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Creates a new Dynamic Person Group with specified dynamicPersonGroupId, name, and user-provided + userData. + + A Dynamic Person Group is a container that references Person Directory "Create Person". After + creation, use Person Directory "Update Dynamic Person Group" to add/remove persons to/from the + Dynamic Person Group. + + Dynamic Person Group and user data will be stored on server until Person Directory "Delete + Dynamic Person Group" is called. Use "Identify From Dynamic Person Group" with the + dynamicPersonGroupId parameter to identify against persons. + + No image will be stored. Only the person's extracted face feature(s) and userData will be + stored on server until Person Directory "Delete Person" or "Delete Person Face" is called. + + 'recognitionModel' does not need to be specified with Dynamic Person Groups. Dynamic Person + Groups are references to Person Directory "Create Person" and therefore work with most all + 'recognitionModels'. The faceId's provided during "Identify" determine the 'recognitionModel' + used. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Required. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + if name is _Unset: + raise TypeError("missing required argument: name") + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_create_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + async def _delete_dynamic_person_group_initial( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_administration_delete_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @distributed_trace_async + async def begin_delete_dynamic_person_group( + self, dynamic_person_group_id: str, **kwargs: Any + ) -> AsyncLROPoller[None]: + """Deletes an existing Dynamic Person Group with specified dynamicPersonGroupId. + + Deleting this Dynamic Person Group only delete the references to persons data. To delete actual + person see Person Directory "Delete Person". + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._delete_dynamic_person_group_initial( # type: ignore + dynamic_person_group_id=dynamic_person_group_id, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @distributed_trace_async + async def get_dynamic_person_group(self, dynamic_person_group_id: str, **kwargs: Any) -> _models.DynamicPersonGroup: + """Retrieve the information of a Dynamic Person Group, including its name and userData. + + This API returns Dynamic Person Group information only, use Person Directory "Get Dynamic + Person Group Persons" instead to retrieve person information under the Dynamic Person Group. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :return: DynamicPersonGroup. The DynamicPersonGroup is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.DynamicPersonGroup + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.DynamicPersonGroup] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.DynamicPersonGroup, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _update_dynamic_person_group_with_person_changes_initial( # pylint: disable=inconsistent-return-statements,name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + add_person_ids: Optional[List[str]] = None, + remove_person_ids: Optional[List[str]] = None, + **kwargs: Any + ) -> None: + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = { + "addpersonids": add_person_ids, + "name": name, + "removepersonids": remove_person_ids, + "userdata": user_data, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_dynamic_person_group_with_person_changes_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["operation-Location"] = self._deserialize("str", response.headers.get("operation-Location")) + + if cls: + return cls(pipeline_response, None, response_headers) # type: ignore + + @overload + async def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be added. + ], + "name": "str", # Optional. User defined name, maximum length is 128. + "removePersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be removed. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + add_person_ids: Optional[List[str]] = None, + remove_person_ids: Optional[List[str]] = None, + **kwargs: Any + ) -> AsyncLROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Default value is None. + :paramtype add_person_ids: list[str] + :keyword remove_person_ids: Array of personIds created by Person Directory "Create Person" to + be removed. Default value is None. + :paramtype remove_person_ids: list[str] + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def begin_update_dynamic_person_group_with_person_changes( # pylint: disable=name-too-long + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + add_person_ids: Optional[List[str]] = None, + remove_person_ids: Optional[List[str]] = None, + **kwargs: Any + ) -> AsyncLROPoller[None]: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :keyword add_person_ids: Array of personIds created by Person Directory "Create Person" to be + added. Default value is None. + :paramtype add_person_ids: list[str] + :keyword remove_person_ids: Array of personIds created by Person Directory "Create Person" to + be removed. Default value is None. + :paramtype remove_person_ids: list[str] + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "addPersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be added. + ], + "name": "str", # Optional. User defined name, maximum length is 128. + "removePersonIds": [ + "str" # Optional. Array of personIds created by Person Directory + "Create Person" to be removed. + ], + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._update_dynamic_person_group_with_person_changes_initial( # type: ignore + dynamic_person_group_id=dynamic_person_group_id, + body=body, + name=name, + user_data=user_data, + add_person_ids=add_person_ids, + remove_person_ids=remove_person_ids, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + @overload + async def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + + @overload + async def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + *, + content_type: str = "application/json", + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, dynamic_person_group_id: str, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def update_dynamic_person_group( # pylint: disable=inconsistent-return-statements + self, + dynamic_person_group_id: str, + body: Union[JSON, IO[bytes]] = _Unset, + *, + name: Optional[str] = None, + user_data: Optional[str] = None, + **kwargs: Any + ) -> None: + """Update the name or userData of an existing Dynamic Person Group, and manage its members by + adding or removing persons. + + The properties keep unchanged if they are not in request body. + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword name: User defined name, maximum length is 128. Default value is None. + :paramtype name: str + :keyword user_data: Optional user defined data. Length should not exceed 16K. Default value is + None. + :paramtype user_data: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "name": "str", # Optional. User defined name, maximum length is 128. + "userData": "str" # Optional. Optional user defined data. Length should not + exceed 16K. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + + if body is _Unset: + body = {"name": name, "userdata": user_data} + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_administration_update_dynamic_person_group_request( + dynamic_person_group_id=dynamic_person_group_id, + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_dynamic_person_groups( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.DynamicPersonGroup]: + """List all existing Dynamic Person Groups by dynamicPersonGroupId along with name and userData. + + Dynamic Person Groups are stored in alphabetical order of dynamicPersonGroupId. + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of DynamicPersonGroup + :rtype: list[~azure.ai.vision.face.models.DynamicPersonGroup] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "dynamicPersonGroupId": "str", # ID of the dynamic person group. + Required. + "name": "str", # User defined name, maximum length is 128. Required. + "userData": "str" # Optional. Optional user defined data. Length + should not exceed 16K. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.DynamicPersonGroup]] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_groups_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.DynamicPersonGroup], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_dynamic_person_group_persons( + self, dynamic_person_group_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> _models.ListPersonResult: + """List all persons in the specified Dynamic Person Group. + + Persons are stored in alphabetical order of personId created in Person Directory "Create + Person". + > + * + + + * "start" parameter (string, optional) specifies an ID value from which returned entries will + have larger IDs based on string comparison. Setting "start" to an empty value indicates that + entries should be returned starting from the first item. + * "top" parameter (int, optional) determines the maximum number of entries to be returned, with + a limit of up to 1000 entries per call. To retrieve additional entries beyond this limit, + specify "start" with the personId of the last entry returned in the current call. + + .. + + [!TIP] + + + * For example, there are total 5 items with their IDs: "itemId1", ..., "itemId5". + + * "start=&top=" will return all 5 items. + * "start=&top=2" will return "itemId1", "itemId2". + * "start=itemId2&top=3" will return "itemId3", "itemId4", "itemId5". + + :param dynamic_person_group_id: ID of the dynamic person group. Required. + :type dynamic_person_group_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: ListPersonResult. The ListPersonResult is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.ListPersonResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "personIds": [ + "str" # Array of PersonDirectory Person ids. Required. + ] + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.ListPersonResult] = kwargs.pop("cls", None) + + _request = build_face_administration_get_dynamic_person_group_persons_request( + dynamic_person_group_id=dynamic_person_group_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.ListPersonResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + +class FaceSessionClientOperationsMixin(FaceSessionClientMixinABC): + + @overload + async def create_liveness_session( + self, body: _models.CreateLivenessSessionContent, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + + @overload + async def create_liveness_session( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + + @overload + async def create_liveness_session( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + + @distributed_trace_async + async def create_liveness_session( + self, body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], **kwargs: Any + ) -> _models.CreateLivenessSessionResult: + # pylint: disable=line-too-long + """Create a new detect liveness session. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLiveness/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + Client access can be revoked by deleting the session using the Delete Liveness Session + operation. To retrieve a result, use the Get Liveness Session. To audit the individual requests + that a client has made to your resource, use the List Liveness Session Audit Entries. + + :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] + Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] + :return: CreateLivenessSessionResult. The CreateLivenessSessionResult is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessSessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreateLivenessSessionResult] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_session_create_liveness_session_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreateLivenessSessionResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_liveness_session( # pylint: disable=inconsistent-return-statements + self, session_id: str, **kwargs: Any + ) -> None: + """Delete all session related information for matching the specified session id. + + .. + + [!NOTE] + Deleting a session deactivates the Session Auth Token by blocking future API calls made with + that Auth Token. While this can be used to remove any access for that token, those requests + will still count towards overall resource rate limits. It's best to leverage TokenTTL to limit + length of tokens in the case that it is misused. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_session_delete_liveness_session_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_liveness_session_result(self, session_id: str, **kwargs: Any) -> _models.LivenessSession: + # pylint: disable=line-too-long + """Get session result of detectLiveness/singleModal call. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: LivenessSession. The LivenessSession is compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.LivenessSession + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this session was + created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. Required. + "status": "str", # The current status of the session. Required. Known values + are: "NotStarted", "Started", and "ResultAvailable". + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "result": { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + }, + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime when this + session was started by the client. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LivenessSession] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_session_result_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LivenessSession, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_liveness_sessions( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionItem]: + # pylint: disable=line-too-long + """Lists sessions for /detectLiveness/SingleModal. + + List sessions from the last sessionId greater than the 'start'. + + The result should be ordered by sessionId in ascending order. + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionItem + :rtype: list[~azure.ai.vision.face.models.LivenessSessionItem] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this + session was created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. + Required. + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session + should last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each + end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime + when this session was started by the client. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionItem]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_sessions_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionItem], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_liveness_session_audit_entries( + self, session_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionAuditEntry]: + # pylint: disable=line-too-long + """Gets session requests and response body for the session. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionAuditEntry + :rtype: list[~azure.ai.vision.face.models.LivenessSessionAuditEntry] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionAuditEntry]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_session_audit_entries_request( + session_id=session_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionAuditEntry], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def _create_liveness_with_verify_session( + self, body: _models.CreateLivenessSessionContent, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @overload + async def _create_liveness_with_verify_session( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @overload + async def _create_liveness_with_verify_session( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @distributed_trace_async + async def _create_liveness_with_verify_session( + self, body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: + # pylint: disable=line-too-long + """Create a new liveness session with verify. Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLivenessWithVerify/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + + * + + + * Client access can be revoked by deleting the session using the Delete Liveness With Verify + Session operation. + * To retrieve a result, use the Get Liveness With Verify Session. + * To audit the individual requests that a client has made to your resource, use the List + Liveness With Verify Session Audit Entries. + + + Alternative Option: Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + .. + + [!NOTE] + Extra measures should be taken to validate that the client is sending the expected + VerifyImage. + + :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] + Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] + :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is + compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str", # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "qualityForRecognition": "str" # Quality of face image for + recognition. Required. Known values are: "low", "medium", and "high". + } + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.CreateLivenessWithVerifySessionResult] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_face_session_create_liveness_with_verify_session_request( + content_type=content_type, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreateLivenessWithVerifySessionResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @overload + async def _create_liveness_with_verify_session_with_verify_image( # pylint: disable=protected-access,name-too-long + self, body: _models._models.CreateLivenessWithVerifySessionContent, **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @overload + async def _create_liveness_with_verify_session_with_verify_image( # pylint: disable=name-too-long + self, body: JSON, **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @distributed_trace_async + async def _create_liveness_with_verify_session_with_verify_image( # pylint: disable=name-too-long + self, body: Union[_models._models.CreateLivenessWithVerifySessionContent, JSON], **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: + # pylint: disable=line-too-long + """Create a new liveness session with verify. Provide the verify image during session creation. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLivenessWithVerify/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + + * + + + * Client access can be revoked by deleting the session using the Delete Liveness With Verify + Session operation. + * To retrieve a result, use the Get Liveness With Verify Session. + * To audit the individual requests that a client has made to your resource, use the List + Liveness With Verify Session Audit Entries. + + + Recommended Option: VerifyImage is provided during session creation. + + :param body: Is either a CreateLivenessWithVerifySessionContent type or a JSON type. Required. + :type body: ~azure.ai.vision.face.models._models.CreateLivenessWithVerifySessionContent or JSON + :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is + compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "Parameters": { + "livenessOperationMode": "str", # Type of liveness mode the client + should follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session + should last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each + end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not + to allow client to set their own 'deviceCorrelationId' via the Vision SDK. + Default is false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a + '200 - Success' response body to be sent to the client, which may be + undesirable for security reasons. Default is false, clients will receive a + '204 - NoContent' empty body response. Regardless of selection, calling + Session GetResult will always contain a response body enabling business logic + to be implemented. + }, + "VerifyImage": filetype + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str", # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "qualityForRecognition": "str" # Quality of face image for + recognition. Required. Known values are: "low", "medium", and "high". + } + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.CreateLivenessWithVerifySessionResult] = kwargs.pop("cls", None) + + _body = body.as_dict() if isinstance(body, _model_base.Model) else body + _file_fields: List[str] = ["VerifyImage"] + _data_fields: List[str] = ["Parameters"] + _files, _data = prepare_multipart_form_data(_body, _file_fields, _data_fields) + + _request = build_face_session_create_liveness_with_verify_session_with_verify_image_request( + files=_files, + data=_data, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.CreateLivenessWithVerifySessionResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def delete_liveness_with_verify_session( # pylint: disable=inconsistent-return-statements + self, session_id: str, **kwargs: Any + ) -> None: + """Delete all session related information for matching the specified session id. + + .. + + [!NOTE] + Deleting a session deactivates the Session Auth Token by blocking future API calls made with + that Auth Token. While this can be used to remove any access for that token, those requests + will still count towards overall resource rate limits. It's best to leverage TokenTTL to limit + length of tokens in the case that it is misused. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: None + :rtype: None + :raises ~azure.core.exceptions.HttpResponseError: + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + + _request = build_face_session_delete_liveness_with_verify_session_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = False + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + @distributed_trace_async + async def get_liveness_with_verify_session_result( + self, session_id: str, **kwargs: Any + ) -> _models.LivenessWithVerifySession: + # pylint: disable=line-too-long + """Get session result of detectLivenessWithVerify/singleModal call. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :return: LivenessWithVerifySession. The LivenessWithVerifySession is compatible with + MutableMapping + :rtype: ~azure.ai.vision.face.models.LivenessWithVerifySession + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this session was + created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. Required. + "status": "str", # The current status of the session. Required. Known values + are: "NotStarted", "Started", and "ResultAvailable". + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "result": { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + }, + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime when this + session was started by the client. + } + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.LivenessWithVerifySession] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_with_verify_session_result_request( + session_id=session_id, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.LivenessWithVerifySession, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_liveness_with_verify_sessions( + self, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionItem]: + # pylint: disable=line-too-long + """Lists sessions for /detectLivenessWithVerify/SingleModal. + + List sessions from the last sessionId greater than the "start". + + The result should be ordered by sessionId in ascending order. + + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionItem + :rtype: list[~azure.ai.vision.face.models.LivenessSessionItem] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "createdDateTime": "2020-02-20 00:00:00", # DateTime when this + session was created. Required. + "id": "str", # The unique ID to reference this session. Required. + "sessionExpired": bool, # Whether or not the session is expired. + Required. + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session + should last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each + end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "sessionStartDateTime": "2020-02-20 00:00:00" # Optional. DateTime + when this session was started by the client. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionItem]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_with_verify_sessions_request( + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionItem], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def get_liveness_with_verify_session_audit_entries( # pylint: disable=name-too-long + self, session_id: str, *, start: Optional[str] = None, top: Optional[int] = None, **kwargs: Any + ) -> List[_models.LivenessSessionAuditEntry]: + # pylint: disable=line-too-long + """Gets session requests and response body for the session. + + :param session_id: The unique ID to reference this session. Required. + :type session_id: str + :keyword start: List resources greater than the "start". It contains no more than 64 + characters. Default is empty. Default value is None. + :paramtype start: str + :keyword top: The number of items to list, ranging in [1, 1000]. Default is 1000. Default value + is None. + :paramtype top: int + :return: list of LivenessSessionAuditEntry + :rtype: list[~azure.ai.vision.face.models.LivenessSessionAuditEntry] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "clientRequestId": "str", # The unique clientRequestId that is sent + by the client in the 'client-request-id' header. Required. + "digest": "str", # The server calculated digest for this request. If + the client reported digest differs from the server calculated digest, then + the message integrity between the client and service has been compromised and + the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required. + "id": 0, # The unique id to refer to this audit request. Use this id + with the 'start' query parameter to continue on to the next page of audit + results. Required. + "receivedDateTime": "2020-02-20 00:00:00", # The UTC DateTime that + the request was received. Required. + "request": { + "contentType": "str", # The content type of the request. + Required. + "method": "str", # The HTTP method of the request (i.e., + GET, POST, DELETE). Required. + "url": "str", # The relative URL and query of the liveness + request. Required. + "contentLength": 0, # Optional. The length of the request + body in bytes. + "userAgent": "str" # Optional. The user agent used to submit + the request. + }, + "requestId": "str", # The unique requestId that is returned by the + service to the client in the 'apim-request-id' header. Required. + "response": { + "body": { + "livenessDecision": "str", # Optional. The liveness + classification for the target face. Known values are: "uncertain", + "realface", and "spoofface". + "modelVersionUsed": "str", # Optional. The model + version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", + "2022-10-15-preview.04", and "2023-03-02-preview.05". + "target": { + "faceRectangle": { + "height": 0, # The height of the + rectangle, in pixels. Required. + "left": 0, # The distance from the + left edge if the image to the left edge of the rectangle, in + pixels. Required. + "top": 0, # The distance from the + top edge if the image to the top edge of the rectangle, in + pixels. Required. + "width": 0 # The width of the + rectangle, in pixels. Required. + }, + "fileName": "str", # The file name which + contains the face rectangle where the liveness classification was + made on. Required. + "imageType": "str", # The image type which + contains the face rectangle where the liveness classification was + made on. Required. Known values are: "Color", "Infrared", and + "Depth". + "timeOffsetWithinFile": 0 # The time offset + within the file of the frame which contains the face rectangle + where the liveness classification was made on. Required. + }, + "verifyResult": { + "isIdentical": bool, # Whether the target + liveness face and comparison image face match. Required. + "matchConfidence": 0.0, # The target face + liveness face and comparison image face verification confidence. + Required. + "verifyImage": { + "faceRectangle": { + "height": 0, # The height of + the rectangle, in pixels. Required. + "left": 0, # The distance + from the left edge if the image to the left edge of the + rectangle, in pixels. Required. + "top": 0, # The distance + from the top edge if the image to the top edge of the + rectangle, in pixels. Required. + "width": 0 # The width of + the rectangle, in pixels. Required. + }, + "qualityForRecognition": "str" # + Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + } + } + }, + "latencyInMilliseconds": 0, # The server measured latency + for this request in milliseconds. Required. + "statusCode": 0 # The HTTP status code returned to the + client. Required. + }, + "sessionId": "str" # The unique sessionId of the created session. It + will expire 48 hours after it was created or may be deleted sooner using the + corresponding session DELETE operation. Required. + } + ] + """ + error_map: MutableMapping[int, Type[HttpResponseError]] = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[List[_models.LivenessSessionAuditEntry]] = kwargs.pop("cls", None) + + _request = build_face_session_get_liveness_with_verify_session_audit_entries_request( + session_id=session_id, + start=start, + top=top, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), + "apiVersion": self._serialize.url("self._config.api_version", self._config.api_version, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + await response.read() # Load the body in memory and close the socket + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _deserialize(_models.FaceErrorResponse, response.json()) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(List[_models.LivenessSessionAuditEntry], response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py new file mode 100644 index 000000000000..9f2bd2f22400 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py @@ -0,0 +1,50 @@ +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from abc import ABC +import json +from typing import Any, Dict, IO, List, Mapping, Optional, Sequence, TYPE_CHECKING, Tuple, Union + +from ._configuration import ( + FaceAdministrationClientConfiguration, + FaceClientConfiguration, + FaceSessionClientConfiguration, +) +from ._model_base import Model, SdkJSONEncoder + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from azure.core import AsyncPipelineClient + + from .._serialization import Deserializer, Serializer + + +class FaceClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "AsyncPipelineClient" + _config: FaceClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" + + +class FaceAdministrationClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "AsyncPipelineClient" + _config: FaceAdministrationClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" + + +class FaceSessionClientMixinABC(ABC): + """DO NOT use this class. It is for internal typing use only.""" + + _client: "AsyncPipelineClient" + _config: FaceSessionClientConfiguration + _serialize: "Serializer" + _deserialize: "Deserializer" diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py new file mode 100644 index 000000000000..dcbd91525083 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py @@ -0,0 +1,167 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from ._models import AccessoryItem +from ._models import AddFaceResult +from ._models import AuditLivenessResponseInfo +from ._models import AuditRequestInfo +from ._models import BlurProperties +from ._models import CreateLivenessSessionContent +from ._models import CreateLivenessSessionResult +from ._models import CreateLivenessWithVerifySessionResult +from ._models import CreatePersonResult +from ._models import DynamicPersonGroup +from ._models import ExposureProperties +from ._models import FaceAttributes +from ._models import FaceCollectionTrainingResult +from ._models import FaceDetectionResult +from ._models import FaceError +from ._models import FaceErrorResponse +from ._models import FaceFindSimilarResult +from ._models import FaceGroupingResult +from ._models import FaceIdentificationCandidate +from ._models import FaceIdentificationResult +from ._models import FaceLandmarks +from ._models import FaceList +from ._models import FaceListFace +from ._models import FaceListItem +from ._models import FaceOperationResult +from ._models import FaceRectangle +from ._models import FaceVerificationResult +from ._models import FacialHair +from ._models import HairColor +from ._models import HairProperties +from ._models import HeadPose +from ._models import LandmarkCoordinate +from ._models import LargeFaceList +from ._models import LargeFaceListFace +from ._models import LargePersonGroup +from ._models import LargePersonGroupPerson +from ._models import LargePersonGroupPersonFace +from ._models import ListFaceResult +from ._models import ListGroupReferenceResult +from ._models import ListPersonResult +from ._models import LivenessOutputsTarget +from ._models import LivenessResponseBody +from ._models import LivenessSession +from ._models import LivenessSessionAuditEntry +from ._models import LivenessSessionItem +from ._models import LivenessWithVerifyImage +from ._models import LivenessWithVerifyOutputs +from ._models import LivenessWithVerifySession +from ._models import MaskProperties +from ._models import NoiseProperties +from ._models import OcclusionProperties +from ._models import PersonDirectoryFace +from ._models import PersonDirectoryPerson +from ._models import PersonGroup +from ._models import PersonGroupPerson +from ._models import PersonGroupPersonFace + +from ._enums import AccessoryType +from ._enums import BlurLevel +from ._enums import ExposureLevel +from ._enums import FaceAttributeType +from ._enums import FaceDetectionModel +from ._enums import FaceImageType +from ._enums import FaceLivenessDecision +from ._enums import FaceOperationStatus +from ._enums import FaceRecognitionModel +from ._enums import FaceSessionStatus +from ._enums import FindSimilarMatchMode +from ._enums import GlassesType +from ._enums import HairColorType +from ._enums import LivenessModel +from ._enums import LivenessOperationMode +from ._enums import MaskType +from ._enums import NoiseLevel +from ._enums import QualityForRecognition +from ._enums import Versions +from ._patch import __all__ as _patch_all +from ._patch import * # pylint: disable=unused-wildcard-import +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AccessoryItem", + "AddFaceResult", + "AuditLivenessResponseInfo", + "AuditRequestInfo", + "BlurProperties", + "CreateLivenessSessionContent", + "CreateLivenessSessionResult", + "CreateLivenessWithVerifySessionResult", + "CreatePersonResult", + "DynamicPersonGroup", + "ExposureProperties", + "FaceAttributes", + "FaceCollectionTrainingResult", + "FaceDetectionResult", + "FaceError", + "FaceErrorResponse", + "FaceFindSimilarResult", + "FaceGroupingResult", + "FaceIdentificationCandidate", + "FaceIdentificationResult", + "FaceLandmarks", + "FaceList", + "FaceListFace", + "FaceListItem", + "FaceOperationResult", + "FaceRectangle", + "FaceVerificationResult", + "FacialHair", + "HairColor", + "HairProperties", + "HeadPose", + "LandmarkCoordinate", + "LargeFaceList", + "LargeFaceListFace", + "LargePersonGroup", + "LargePersonGroupPerson", + "LargePersonGroupPersonFace", + "ListFaceResult", + "ListGroupReferenceResult", + "ListPersonResult", + "LivenessOutputsTarget", + "LivenessResponseBody", + "LivenessSession", + "LivenessSessionAuditEntry", + "LivenessSessionItem", + "LivenessWithVerifyImage", + "LivenessWithVerifyOutputs", + "LivenessWithVerifySession", + "MaskProperties", + "NoiseProperties", + "OcclusionProperties", + "PersonDirectoryFace", + "PersonDirectoryPerson", + "PersonGroup", + "PersonGroupPerson", + "PersonGroupPersonFace", + "AccessoryType", + "BlurLevel", + "ExposureLevel", + "FaceAttributeType", + "FaceDetectionModel", + "FaceImageType", + "FaceLivenessDecision", + "FaceOperationStatus", + "FaceRecognitionModel", + "FaceSessionStatus", + "FindSimilarMatchMode", + "GlassesType", + "HairColorType", + "LivenessModel", + "LivenessOperationMode", + "MaskType", + "NoiseLevel", + "QualityForRecognition", + "Versions", +] +__all__.extend([p for p in _patch_all if p not in __all__]) +_patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py new file mode 100644 index 000000000000..4ebb4c54d967 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py @@ -0,0 +1,258 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +from enum import Enum +from azure.core import CaseInsensitiveEnumMeta + + +class AccessoryType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of the accessory.""" + + HEADWEAR = "headwear" + """Head wear.""" + GLASSES = "glasses" + """Glasses.""" + MASK = "mask" + """Mask.""" + + +class BlurLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Indicates level of blurriness.""" + + LOW = "low" + """Low blur level.""" + MEDIUM = "medium" + """Medium blur level.""" + HIGH = "high" + """High blur level.""" + + +class ExposureLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Indicates level of exposure.""" + + UNDER_EXPOSURE = "underExposure" + """Low exposure level.""" + GOOD_EXPOSURE = "goodExposure" + """Good exposure level.""" + OVER_EXPOSURE = "overExposure" + """High exposure level.""" + + +class FaceAttributeType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available options for detect face with attribute.""" + + HEAD_POSE = "headPose" + """3-D roll/yaw/pitch angles for face direction.""" + GLASSES = "glasses" + """Glasses type. Values include 'NoGlasses', 'ReadingGlasses', 'Sunglasses', 'SwimmingGoggles'.""" + OCCLUSION = "occlusion" + """Whether each facial area is occluded, including forehead, eyes and mouth.""" + ACCESSORIES = "accessories" + """Accessories around face, including 'headwear', 'glasses' and 'mask'. Empty array means no + accessories detected. Note this is after a face is detected. Large mask could result in no face + to be detected.""" + BLUR = "blur" + """Face is blurry or not. Level returns 'Low', 'Medium' or 'High'. Value returns a number between + [0,1], the larger the blurrier.""" + EXPOSURE = "exposure" + """Face exposure level. Level returns 'GoodExposure', 'OverExposure' or 'UnderExposure'.""" + NOISE = "noise" + """Noise level of face pixels. Level returns 'Low', 'Medium' and 'High'. Value returns a number + between [0,1], the larger the noisier""" + MASK = "mask" + """Whether each face is wearing a mask. Mask type returns 'noMask', 'faceMask', + 'otherMaskOrOcclusion', or 'uncertain'. Value returns a boolean 'noseAndMouthCovered' + indicating whether nose and mouth are covered.""" + QUALITY_FOR_RECOGNITION = "qualityForRecognition" + """The overall image quality regarding whether the image being used in the detection is of + sufficient quality to attempt face recognition on. The value is an informal rating of low, + medium, or high. Only 'high' quality images are recommended for person enrollment and quality + at or above 'medium' is recommended for identification scenarios. The attribute is only + available when using any combinations of detection models detection_01 or detection_03, and + recognition models recognition_03 or recognition_04.""" + AGE = "age" + """Age in years.""" + SMILE = "smile" + """Smile intensity, a number between [0,1].""" + FACIAL_HAIR = "facialHair" + """Properties describing facial hair attributes.""" + HAIR = "hair" + """Properties describing hair attributes.""" + + +class FaceDetectionModel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The detection model for the face.""" + + DETECTION_01 = "detection_01" + """The default detection model. Recommend for near frontal face detection. For scenarios with + exceptionally large angle (head-pose) faces, occluded faces or wrong image orientation, the + faces in such cases may not be detected.""" + DETECTION_02 = "detection_02" + """Detection model released in 2019 May with improved accuracy especially on small, side and + blurry faces.""" + DETECTION_03 = "detection_03" + """Detection model released in 2021 February with improved accuracy especially on small faces.""" + + +class FaceImageType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The type of image.""" + + COLOR = "Color" + INFRARED = "Infrared" + DEPTH = "Depth" + + +class FaceLivenessDecision(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The outcome of the liveness classification.""" + + UNCERTAIN = "uncertain" + """The algorithm could not classify the target face as either real or spoof.""" + REALFACE = "realface" + """The algorithm has classified the target face as real.""" + SPOOFFACE = "spoofface" + """The algorithm has classified the target face as a spoof.""" + + +class FaceOperationStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The status of long running operation.""" + + NOT_STARTED = "notStarted" + """The operation is not started.""" + RUNNING = "running" + """The operation is still running.""" + SUCCEEDED = "succeeded" + """The operation is succeeded.""" + FAILED = "failed" + """The operation is failed.""" + + +class FaceRecognitionModel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The recognition model for the face.""" + + RECOGNITION_01 = "recognition_01" + """The default recognition model for "Detect". All those faceIds created before 2019 March are + bonded with this recognition model.""" + RECOGNITION_02 = "recognition_02" + """Recognition model released in 2019 March.""" + RECOGNITION_03 = "recognition_03" + """Recognition model released in 2020 May.""" + RECOGNITION_04 = "recognition_04" + """Recognition model released in 2021 February. It's recommended to use this recognition model for + better recognition accuracy.""" + + +class FaceSessionStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The current status of the session.""" + + NOT_STARTED = "NotStarted" + """Session has not started.""" + STARTED = "Started" + """Session has started.""" + RESULT_AVAILABLE = "ResultAvailable" + """Session has available result.""" + + +class FindSimilarMatchMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Similar face searching mode.""" + + MATCH_PERSON = "matchPerson" + """Match person.""" + MATCH_FACE = "matchFace" + """Match face.""" + + +class GlassesType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Glasses type of the face.""" + + NO_GLASSES = "noGlasses" + """No glasses on the face.""" + READING_GLASSES = "readingGlasses" + """Normal glasses on the face.""" + SUNGLASSES = "sunglasses" + """Sunglasses on the face.""" + SWIMMING_GOGGLES = "swimmingGoggles" + """Swimming goggles on the face.""" + + +class HairColorType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Name of the hair color.""" + + UNKNOWN_HAIR_COLOR = "unknown" + """Unknown.""" + WHITE = "white" + """White.""" + GRAY = "gray" + """Gray.""" + BLOND = "blond" + """Blond.""" + BROWN = "brown" + """Brown.""" + RED = "red" + """Red.""" + BLACK = "black" + """Black.""" + OTHER = "other" + """Other.""" + + +class LivenessModel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The model version used for liveness classification.""" + + ENUM_2020_02_15_PREVIEW_01 = "2020-02-15-preview.01" + ENUM_2021_11_12_PREVIEW_03 = "2021-11-12-preview.03" + ENUM_2022_10_15_PREVIEW_04 = "2022-10-15-preview.04" + ENUM_2023_03_02_PREVIEW_05 = "2023-03-02-preview.05" + + +class LivenessOperationMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The operation mode for the liveness modal.""" + + PASSIVE = "Passive" + """The operation mode for the liveness modal.""" + + +class MaskType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of the mask.""" + + FACE_MASK = "faceMask" + """Face mask.""" + NO_MASK = "noMask" + """No mask.""" + OTHER_MASK_OR_OCCLUSION = "otherMaskOrOcclusion" + """Other types of mask or occlusion.""" + UNCERTAIN = "uncertain" + """Uncertain.""" + + +class NoiseLevel(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Indicates level of noise.""" + + LOW = "low" + """Low noise level.""" + MEDIUM = "medium" + """Medium noise level.""" + HIGH = "high" + """High noise level.""" + + +class QualityForRecognition(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Indicates quality of image for recognition.""" + + LOW = "low" + """Low quality.""" + MEDIUM = "medium" + """Medium quality.""" + HIGH = "high" + """High quality.""" + + +class Versions(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """API versions for Azure AI Face API.""" + + V1_1_PREVIEW_1 = "v1.1-preview.1" + """v1.1-preview.1""" diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py new file mode 100644 index 000000000000..c917c9209d29 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py @@ -0,0 +1,2763 @@ +# coding=utf-8 +# pylint: disable=too-many-lines +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- + +import datetime +import sys +from typing import Any, List, Mapping, Optional, TYPE_CHECKING, Union, overload + +from .. import _model_base +from .._model_base import rest_field +from .._vendor import FileType + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports + +if TYPE_CHECKING: + # pylint: disable=unused-import,ungrouped-imports + from .. import models as _models +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object + + +class AccessoryItem(_model_base.Model): + """Accessory item and corresponding confidence level. + + All required parameters must be populated in order to send to server. + + :ivar type: Type of the accessory. Required. Known values are: "headwear", "glasses", and + "mask". + :vartype type: str or ~azure.ai.vision.face.models.AccessoryType + :ivar confidence: Confidence level of the accessory type. Range between [0,1]. Required. + :vartype confidence: float + """ + + type: Union[str, "_models.AccessoryType"] = rest_field() + """Type of the accessory. Required. Known values are: \"headwear\", \"glasses\", and \"mask\".""" + confidence: float = rest_field() + """Confidence level of the accessory type. Range between [0,1]. Required.""" + + @overload + def __init__( + self, + *, + type: Union[str, "_models.AccessoryType"], + confidence: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class AddFaceResult(_model_base.Model): + """Response body for adding face. + + All required parameters must be populated in order to send to server. + + :ivar persisted_face_id: Persisted Face ID of the added face, which is persisted and will not + expire. Different from faceId which is created in "Detect" and will expire in 24 hours after + the detection call. Required. + :vartype persisted_face_id: str + """ + + persisted_face_id: str = rest_field(name="persistedFaceId") + """Persisted Face ID of the added face, which is persisted and will not expire. Different from + faceId which is created in \"Detect\" and will expire in 24 hours after the detection call. + Required.""" + + @overload + def __init__( + self, + *, + persisted_face_id: str, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class AuditLivenessResponseInfo(_model_base.Model): + """Audit entry for a response in the session. + + All required parameters must be populated in order to send to server. + + :ivar body: The response body. The schema of this field will depend on the request.url and + request.method used by the client. Required. + :vartype body: ~azure.ai.vision.face.models.LivenessResponseBody + :ivar status_code: The HTTP status code returned to the client. Required. + :vartype status_code: int + :ivar latency_in_milliseconds: The server measured latency for this request in milliseconds. + Required. + :vartype latency_in_milliseconds: int + """ + + body: "_models.LivenessResponseBody" = rest_field() + """The response body. The schema of this field will depend on the request.url and request.method + used by the client. Required.""" + status_code: int = rest_field(name="statusCode") + """The HTTP status code returned to the client. Required.""" + latency_in_milliseconds: int = rest_field(name="latencyInMilliseconds") + """The server measured latency for this request in milliseconds. Required.""" + + @overload + def __init__( + self, + *, + body: "_models.LivenessResponseBody", + status_code: int, + latency_in_milliseconds: int, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class AuditRequestInfo(_model_base.Model): + """Audit entry for a request in the session. + + All required parameters must be populated in order to send to server. + + :ivar url: The relative URL and query of the liveness request. Required. + :vartype url: str + :ivar method: The HTTP method of the request (i.e., GET, POST, DELETE). Required. + :vartype method: str + :ivar content_length: The length of the request body in bytes. + :vartype content_length: int + :ivar content_type: The content type of the request. Required. + :vartype content_type: str + :ivar user_agent: The user agent used to submit the request. + :vartype user_agent: str + """ + + url: str = rest_field() + """The relative URL and query of the liveness request. Required.""" + method: str = rest_field() + """The HTTP method of the request (i.e., GET, POST, DELETE). Required.""" + content_length: Optional[int] = rest_field(name="contentLength") + """The length of the request body in bytes.""" + content_type: str = rest_field(name="contentType") + """The content type of the request. Required.""" + user_agent: Optional[str] = rest_field(name="userAgent") + """The user agent used to submit the request.""" + + @overload + def __init__( + self, + *, + url: str, + method: str, + content_type: str, + content_length: Optional[int] = None, + user_agent: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class BlurProperties(_model_base.Model): + """Properties describing any presence of blur within the image. + + All required parameters must be populated in order to send to server. + + :ivar blur_level: An enum value indicating level of blurriness. Required. Known values are: + "low", "medium", and "high". + :vartype blur_level: str or ~azure.ai.vision.face.models.BlurLevel + :ivar value: A number indicating level of blurriness ranging from 0 to 1. Required. + :vartype value: float + """ + + blur_level: Union[str, "_models.BlurLevel"] = rest_field(name="blurLevel") + """An enum value indicating level of blurriness. Required. Known values are: \"low\", \"medium\", + and \"high\".""" + value: float = rest_field() + """A number indicating level of blurriness ranging from 0 to 1. Required.""" + + @overload + def __init__( + self, + *, + blur_level: Union[str, "_models.BlurLevel"], + value: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class CreateLivenessSessionContent(_model_base.Model): + """Request for creating liveness session. + + All required parameters must be populated in order to send to server. + + :ivar liveness_operation_mode: Type of liveness mode the client should follow. Required. + "Passive" + :vartype liveness_operation_mode: str or ~azure.ai.vision.face.models.LivenessOperationMode + :ivar send_results_to_client: Whether or not to allow a '200 - Success' response body to be + sent to the client, which may be undesirable for security reasons. Default is false, clients + will receive a '204 - NoContent' empty body response. Regardless of selection, calling Session + GetResult will always contain a response body enabling business logic to be implemented. + :vartype send_results_to_client: bool + :ivar device_correlation_id_set_in_client: Whether or not to allow client to set their own + 'deviceCorrelationId' via the Vision SDK. Default is false, and 'deviceCorrelationId' must be + set in this request body. + :vartype device_correlation_id_set_in_client: bool + :ivar device_correlation_id: Unique Guid per each end-user device. This is to provide rate + limiting and anti-hammering. If 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + :vartype device_correlation_id: str + :ivar auth_token_time_to_live_in_seconds: Seconds the session should last for. Range is 60 to + 86400 seconds. Default value is 600. + :vartype auth_token_time_to_live_in_seconds: int + """ + + liveness_operation_mode: Union[str, "_models.LivenessOperationMode"] = rest_field(name="livenessOperationMode") + """Type of liveness mode the client should follow. Required. \"Passive\"""" + send_results_to_client: Optional[bool] = rest_field(name="sendResultsToClient") + """Whether or not to allow a '200 - Success' response body to be sent to the client, which may be + undesirable for security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will always contain a + response body enabling business logic to be implemented.""" + device_correlation_id_set_in_client: Optional[bool] = rest_field(name="deviceCorrelationIdSetInClient") + """Whether or not to allow client to set their own 'deviceCorrelationId' via the Vision SDK. + Default is false, and 'deviceCorrelationId' must be set in this request body.""" + device_correlation_id: Optional[str] = rest_field(name="deviceCorrelationId") + """Unique Guid per each end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this 'deviceCorrelationId' must be + null.""" + auth_token_time_to_live_in_seconds: Optional[int] = rest_field(name="authTokenTimeToLiveInSeconds") + """Seconds the session should last for. Range is 60 to 86400 seconds. Default value is 600.""" + + @overload + def __init__( + self, + *, + liveness_operation_mode: Union[str, "_models.LivenessOperationMode"], + send_results_to_client: Optional[bool] = None, + device_correlation_id_set_in_client: Optional[bool] = None, + device_correlation_id: Optional[str] = None, + auth_token_time_to_live_in_seconds: Optional[int] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class CreateLivenessSessionContentForMultipart(_model_base.Model): + """Dedicated parameter model for multipart/form-data. + + All required parameters must be populated in order to send to server. + + :ivar liveness_operation_mode: Type of liveness mode the client should follow. Required. + "Passive" + :vartype liveness_operation_mode: str or ~azure.ai.vision.face.models.LivenessOperationMode + :ivar send_results_to_client: Whether or not to allow a '200 - Success' response body to be + sent to the client, which may be undesirable for security reasons. Default is false, clients + will receive a '204 - NoContent' empty body response. Regardless of selection, calling Session + GetResult will always contain a response body enabling business logic to be implemented. + :vartype send_results_to_client: bool + :ivar device_correlation_id_set_in_client: Whether or not to allow client to set their own + 'deviceCorrelationId' via the Vision SDK. Default is false, and 'deviceCorrelationId' must be + set in this request body. + :vartype device_correlation_id_set_in_client: bool + :ivar device_correlation_id: Unique Guid per each end-user device. This is to provide rate + limiting and anti-hammering. If 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + :vartype device_correlation_id: str + :ivar auth_token_time_to_live_in_seconds: Seconds the session should last for. Range is 60 to + 86400 seconds. Default value is 600. + :vartype auth_token_time_to_live_in_seconds: int + """ + + liveness_operation_mode: Union[str, "_models.LivenessOperationMode"] = rest_field(name="livenessOperationMode") + """Type of liveness mode the client should follow. Required. \"Passive\"""" + send_results_to_client: Optional[bool] = rest_field(name="sendResultsToClient") + """Whether or not to allow a '200 - Success' response body to be sent to the client, which may be + undesirable for security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will always contain a + response body enabling business logic to be implemented.""" + device_correlation_id_set_in_client: Optional[bool] = rest_field(name="deviceCorrelationIdSetInClient") + """Whether or not to allow client to set their own 'deviceCorrelationId' via the Vision SDK. + Default is false, and 'deviceCorrelationId' must be set in this request body.""" + device_correlation_id: Optional[str] = rest_field(name="deviceCorrelationId") + """Unique Guid per each end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this 'deviceCorrelationId' must be + null.""" + auth_token_time_to_live_in_seconds: Optional[int] = rest_field(name="authTokenTimeToLiveInSeconds") + """Seconds the session should last for. Range is 60 to 86400 seconds. Default value is 600.""" + + +class CreateLivenessSessionResult(_model_base.Model): + """Response of liveness session creation. + + All required parameters must be populated in order to send to server. + + :ivar session_id: The unique session ID of the created session. It will expire 48 hours after + it was created or may be deleted sooner using the corresponding Session DELETE operation. + Required. + :vartype session_id: str + :ivar auth_token: Bearer token to provide authentication for the Vision SDK running on a client + application. This Bearer token has limited permissions to perform only the required action and + expires after the TTL time. It is also auditable. Required. + :vartype auth_token: str + """ + + session_id: str = rest_field(name="sessionId") + """The unique session ID of the created session. It will expire 48 hours after it was created or + may be deleted sooner using the corresponding Session DELETE operation. Required.""" + auth_token: str = rest_field(name="authToken") + """Bearer token to provide authentication for the Vision SDK running on a client application. This + Bearer token has limited permissions to perform only the required action and expires after the + TTL time. It is also auditable. Required.""" + + @overload + def __init__( + self, + *, + session_id: str, + auth_token: str, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class CreateLivenessWithVerifySessionContent(_model_base.Model): + """Request of liveness with verify session creation. + + All required parameters must be populated in order to send to server. + + :ivar parameters: The parameters for creating session. Required. + :vartype parameters: + ~azure.ai.vision.face.models._models.CreateLivenessSessionContentForMultipart + :ivar verify_image: The image stream for verify. Content-Disposition header field for this part + must have filename. Required. + :vartype verify_image: bytes + """ + + parameters: "_models._models.CreateLivenessSessionContentForMultipart" = rest_field(name="Parameters") + """The parameters for creating session. Required.""" + verify_image: FileType = rest_field(name="VerifyImage", is_multipart_file_input=True) + """The image stream for verify. Content-Disposition header field for this part must have filename. + Required.""" + + +class CreateLivenessWithVerifySessionResult(_model_base.Model): + """Response of liveness session with verify creation with verify image provided. + + All required parameters must be populated in order to send to server. + + :ivar session_id: The unique session ID of the created session. It will expire 48 hours after + it was created or may be deleted sooner using the corresponding Session DELETE operation. + Required. + :vartype session_id: str + :ivar auth_token: Bearer token to provide authentication for the Vision SDK running on a client + application. This Bearer token has limited permissions to perform only the required action and + expires after the TTL time. It is also auditable. Required. + :vartype auth_token: str + :ivar verify_image: The detail of face for verification. + :vartype verify_image: ~azure.ai.vision.face.models.LivenessWithVerifyImage + """ + + session_id: str = rest_field(name="sessionId") + """The unique session ID of the created session. It will expire 48 hours after it was created or + may be deleted sooner using the corresponding Session DELETE operation. Required.""" + auth_token: str = rest_field(name="authToken") + """Bearer token to provide authentication for the Vision SDK running on a client application. This + Bearer token has limited permissions to perform only the required action and expires after the + TTL time. It is also auditable. Required.""" + verify_image: Optional["_models.LivenessWithVerifyImage"] = rest_field(name="verifyImage") + """The detail of face for verification.""" + + @overload + def __init__( + self, + *, + session_id: str, + auth_token: str, + verify_image: Optional["_models.LivenessWithVerifyImage"] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class CreatePersonResult(_model_base.Model): + """Response of create person. + + All required parameters must be populated in order to send to server. + + :ivar person_id: Person ID of the person. Required. + :vartype person_id: str + """ + + person_id: str = rest_field(name="personId") + """Person ID of the person. Required.""" + + @overload + def __init__( + self, + *, + person_id: str, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class DynamicPersonGroup(_model_base.Model): + """A container that references Person Directory "Create Person". + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar dynamic_person_group_id: ID of the dynamic person group. Required. + :vartype dynamic_person_group_id: str + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + """ + + dynamic_person_group_id: str = rest_field(name="dynamicPersonGroupId", visibility=["read"]) + """ID of the dynamic person group. Required.""" + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class ExposureProperties(_model_base.Model): + """Properties describing exposure level of the image. + + All required parameters must be populated in order to send to server. + + :ivar exposure_level: An enum value indicating level of exposure. Required. Known values are: + "underExposure", "goodExposure", and "overExposure". + :vartype exposure_level: str or ~azure.ai.vision.face.models.ExposureLevel + :ivar value: A number indicating level of exposure level ranging from 0 to 1. [0, 0.25) is + under exposure. [0.25, 0.75) is good exposure. [0.75, 1] is over exposure. Required. + :vartype value: float + """ + + exposure_level: Union[str, "_models.ExposureLevel"] = rest_field(name="exposureLevel") + """An enum value indicating level of exposure. Required. Known values are: \"underExposure\", + \"goodExposure\", and \"overExposure\".""" + value: float = rest_field() + """A number indicating level of exposure level ranging from 0 to 1. [0, 0.25) is under exposure. + [0.25, 0.75) is good exposure. [0.75, 1] is over exposure. Required.""" + + @overload + def __init__( + self, + *, + exposure_level: Union[str, "_models.ExposureLevel"], + value: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceAttributes(_model_base.Model): # pylint: disable=too-many-instance-attributes + """Face attributes for the detected face. + + :ivar age: Age in years. + :vartype age: float + :ivar smile: Smile intensity, a number between [0,1]. + :vartype smile: float + :ivar facial_hair: Properties describing facial hair attributes. + :vartype facial_hair: ~azure.ai.vision.face.models.FacialHair + :ivar glasses: Glasses type if any of the face. Known values are: "noGlasses", + "readingGlasses", "sunglasses", and "swimmingGoggles". + :vartype glasses: str or ~azure.ai.vision.face.models.GlassesType + :ivar head_pose: 3-D roll/yaw/pitch angles for face direction. + :vartype head_pose: ~azure.ai.vision.face.models.HeadPose + :ivar hair: Properties describing hair attributes. + :vartype hair: ~azure.ai.vision.face.models.HairProperties + :ivar occlusion: Properties describing occlusions on a given face. + :vartype occlusion: ~azure.ai.vision.face.models.OcclusionProperties + :ivar accessories: Properties describing any accessories on a given face. + :vartype accessories: list[~azure.ai.vision.face.models.AccessoryItem] + :ivar blur: Properties describing any presence of blur within the image. + :vartype blur: ~azure.ai.vision.face.models.BlurProperties + :ivar exposure: Properties describing exposure level of the image. + :vartype exposure: ~azure.ai.vision.face.models.ExposureProperties + :ivar noise: Properties describing noise level of the image. + :vartype noise: ~azure.ai.vision.face.models.NoiseProperties + :ivar mask: Properties describing the presence of a mask on a given face. + :vartype mask: ~azure.ai.vision.face.models.MaskProperties + :ivar quality_for_recognition: Properties describing the overall image quality regarding + whether the image being used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + :vartype quality_for_recognition: str or ~azure.ai.vision.face.models.QualityForRecognition + """ + + age: Optional[float] = rest_field() + """Age in years.""" + smile: Optional[float] = rest_field() + """Smile intensity, a number between [0,1].""" + facial_hair: Optional["_models.FacialHair"] = rest_field(name="facialHair") + """Properties describing facial hair attributes.""" + glasses: Optional[Union[str, "_models.GlassesType"]] = rest_field() + """Glasses type if any of the face. Known values are: \"noGlasses\", \"readingGlasses\", + \"sunglasses\", and \"swimmingGoggles\".""" + head_pose: Optional["_models.HeadPose"] = rest_field(name="headPose") + """3-D roll/yaw/pitch angles for face direction.""" + hair: Optional["_models.HairProperties"] = rest_field() + """Properties describing hair attributes.""" + occlusion: Optional["_models.OcclusionProperties"] = rest_field() + """Properties describing occlusions on a given face.""" + accessories: Optional[List["_models.AccessoryItem"]] = rest_field() + """Properties describing any accessories on a given face.""" + blur: Optional["_models.BlurProperties"] = rest_field() + """Properties describing any presence of blur within the image.""" + exposure: Optional["_models.ExposureProperties"] = rest_field() + """Properties describing exposure level of the image.""" + noise: Optional["_models.NoiseProperties"] = rest_field() + """Properties describing noise level of the image.""" + mask: Optional["_models.MaskProperties"] = rest_field() + """Properties describing the presence of a mask on a given face.""" + quality_for_recognition: Optional[Union[str, "_models.QualityForRecognition"]] = rest_field( + name="qualityForRecognition" + ) + """Properties describing the overall image quality regarding whether the image being used in the + detection is of sufficient quality to attempt face recognition on. Known values are: \"low\", + \"medium\", and \"high\".""" + + @overload + def __init__( + self, + *, + age: Optional[float] = None, + smile: Optional[float] = None, + facial_hair: Optional["_models.FacialHair"] = None, + glasses: Optional[Union[str, "_models.GlassesType"]] = None, + head_pose: Optional["_models.HeadPose"] = None, + hair: Optional["_models.HairProperties"] = None, + occlusion: Optional["_models.OcclusionProperties"] = None, + accessories: Optional[List["_models.AccessoryItem"]] = None, + blur: Optional["_models.BlurProperties"] = None, + exposure: Optional["_models.ExposureProperties"] = None, + noise: Optional["_models.NoiseProperties"] = None, + mask: Optional["_models.MaskProperties"] = None, + quality_for_recognition: Optional[Union[str, "_models.QualityForRecognition"]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceCollectionTrainingResult(_model_base.Model): + """Training result of a container. + + All required parameters must be populated in order to send to server. + + :ivar status: Training status of the container. Required. Known values are: "notStarted", + "running", "succeeded", and "failed". + :vartype status: str or ~azure.ai.vision.face.models.FaceOperationStatus + :ivar created_date_time: A combined UTC date and time string that describes the created time of + the person group, large person group or large face list. Required. + :vartype created_date_time: ~datetime.datetime + :ivar last_action_date_time: A combined UTC date and time string that describes the last modify + time of the person group, large person group or large face list, could be null value when the + group is not successfully trained. Required. + :vartype last_action_date_time: ~datetime.datetime + :ivar last_successful_training_date_time: A combined UTC date and time string that describes + the last successful training time of the person group, large person group or large face list. + Required. + :vartype last_successful_training_date_time: ~datetime.datetime + :ivar message: Show failure message when training failed (omitted when training succeed). + :vartype message: str + """ + + status: Union[str, "_models.FaceOperationStatus"] = rest_field() + """Training status of the container. Required. Known values are: \"notStarted\", \"running\", + \"succeeded\", and \"failed\".""" + created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") + """A combined UTC date and time string that describes the created time of the person group, large + person group or large face list. Required.""" + last_action_date_time: datetime.datetime = rest_field(name="lastActionDateTime", format="rfc3339") + """A combined UTC date and time string that describes the last modify time of the person group, + large person group or large face list, could be null value when the group is not successfully + trained. Required.""" + last_successful_training_date_time: datetime.datetime = rest_field( + name="lastSuccessfulTrainingDateTime", format="rfc3339" + ) + """A combined UTC date and time string that describes the last successful training time of the + person group, large person group or large face list. Required.""" + message: Optional[str] = rest_field() + """Show failure message when training failed (omitted when training succeed).""" + + @overload + def __init__( + self, + *, + status: Union[str, "_models.FaceOperationStatus"], + created_date_time: datetime.datetime, + last_action_date_time: datetime.datetime, + last_successful_training_date_time: datetime.datetime, + message: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceDetectionResult(_model_base.Model): + """Response for detect API. + + All required parameters must be populated in order to send to server. + + :ivar face_id: Unique faceId of the detected face, created by detection API and it will expire + 24 hours after the detection call. To return this, it requires 'returnFaceId' parameter to be + true. + :vartype face_id: str + :ivar recognition_model: The 'recognitionModel' associated with this faceId. This is only + returned when 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + :vartype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :ivar face_rectangle: A rectangle area for the face location on image. Required. + :vartype face_rectangle: ~azure.ai.vision.face.models.FaceRectangle + :ivar face_landmarks: An array of 27-point face landmarks pointing to the important positions + of face components. To return this, it requires 'returnFaceLandmarks' parameter to be true. + :vartype face_landmarks: ~azure.ai.vision.face.models.FaceLandmarks + :ivar face_attributes: Face attributes for detected face. + :vartype face_attributes: ~azure.ai.vision.face.models.FaceAttributes + """ + + face_id: Optional[str] = rest_field(name="faceId") + """Unique faceId of the detected face, created by detection API and it will expire 24 hours after + the detection call. To return this, it requires 'returnFaceId' parameter to be true.""" + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = rest_field(name="recognitionModel") + """The 'recognitionModel' associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: \"recognition_01\", + \"recognition_02\", \"recognition_03\", and \"recognition_04\".""" + face_rectangle: "_models.FaceRectangle" = rest_field(name="faceRectangle") + """A rectangle area for the face location on image. Required.""" + face_landmarks: Optional["_models.FaceLandmarks"] = rest_field(name="faceLandmarks") + """An array of 27-point face landmarks pointing to the important positions of face components. To + return this, it requires 'returnFaceLandmarks' parameter to be true.""" + face_attributes: Optional["_models.FaceAttributes"] = rest_field(name="faceAttributes") + """Face attributes for detected face.""" + + @overload + def __init__( + self, + *, + face_rectangle: "_models.FaceRectangle", + face_id: Optional[str] = None, + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = None, + face_landmarks: Optional["_models.FaceLandmarks"] = None, + face_attributes: Optional["_models.FaceAttributes"] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceError(_model_base.Model): + """The error object. For comprehensive details on error codes and messages returned by the Face + Service, please refer to the following link: https://aka.ms/face-error-codes-and-messages. + + All required parameters must be populated in order to send to server. + + :ivar code: One of a server-defined set of error codes. Required. + :vartype code: str + :ivar message: A human-readable representation of the error. Required. + :vartype message: str + """ + + code: str = rest_field() + """One of a server-defined set of error codes. Required.""" + message: str = rest_field() + """A human-readable representation of the error. Required.""" + + @overload + def __init__( + self, + *, + code: str, + message: str, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceErrorResponse(_model_base.Model): + """A response containing error details. + + All required parameters must be populated in order to send to server. + + :ivar error: The error object. Required. + :vartype error: ~azure.ai.vision.face.models.FaceError + """ + + error: "_models.FaceError" = rest_field() + """The error object. Required.""" + + @overload + def __init__( + self, + *, + error: "_models.FaceError", + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceFindSimilarResult(_model_base.Model): + """Response body for find similar face operation. + + All required parameters must be populated in order to send to server. + + :ivar confidence: Confidence value of the candidate. The higher confidence, the more similar. + Range between [0,1]. Required. + :vartype confidence: float + :ivar face_id: faceId of candidate face when find by faceIds. faceId is created by "Detect" and + will expire 24 hours after the detection call. + :vartype face_id: str + :ivar persisted_face_id: persistedFaceId of candidate face when find by faceListId or + largeFaceListId. persistedFaceId in face list/large face list is persisted and will not expire. + :vartype persisted_face_id: str + """ + + confidence: float = rest_field() + """Confidence value of the candidate. The higher confidence, the more similar. Range between + [0,1]. Required.""" + face_id: Optional[str] = rest_field(name="faceId") + """faceId of candidate face when find by faceIds. faceId is created by \"Detect\" and will expire + 24 hours after the detection call.""" + persisted_face_id: Optional[str] = rest_field(name="persistedFaceId") + """persistedFaceId of candidate face when find by faceListId or largeFaceListId. persistedFaceId + in face list/large face list is persisted and will not expire.""" + + @overload + def __init__( + self, + *, + confidence: float, + face_id: Optional[str] = None, + persisted_face_id: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceGroupingResult(_model_base.Model): + """Response body for group face operation. + + All required parameters must be populated in order to send to server. + + :ivar groups: A partition of the original faces based on face similarity. Groups are ranked by + number of faces. Required. + :vartype groups: list[list[str]] + :ivar messy_group: Face ids array of faces that cannot find any similar faces from original + faces. Required. + :vartype messy_group: list[str] + """ + + groups: List[List[str]] = rest_field() + """A partition of the original faces based on face similarity. Groups are ranked by number of + faces. Required.""" + messy_group: List[str] = rest_field(name="messyGroup") + """Face ids array of faces that cannot find any similar faces from original faces. Required.""" + + @overload + def __init__( + self, + *, + groups: List[List[str]], + messy_group: List[str], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceIdentificationCandidate(_model_base.Model): + """Candidate for identify call. + + All required parameters must be populated in order to send to server. + + :ivar person_id: personId of candidate person. Required. + :vartype person_id: str + :ivar confidence: Confidence value of the candidate. The higher confidence, the more similar. + Range between [0,1]. Required. + :vartype confidence: float + """ + + person_id: str = rest_field(name="personId") + """personId of candidate person. Required.""" + confidence: float = rest_field() + """Confidence value of the candidate. The higher confidence, the more similar. Range between + [0,1]. Required.""" + + @overload + def __init__( + self, + *, + person_id: str, + confidence: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceIdentificationResult(_model_base.Model): + """Identify result. + + All required parameters must be populated in order to send to server. + + :ivar face_id: faceId of the query face. Required. + :vartype face_id: str + :ivar candidates: Identified person candidates for that face (ranked by confidence). Array size + should be no larger than input maxNumOfCandidatesReturned. If no person is identified, will + return an empty array. Required. + :vartype candidates: list[~azure.ai.vision.face.models.FaceIdentificationCandidate] + """ + + face_id: str = rest_field(name="faceId") + """faceId of the query face. Required.""" + candidates: List["_models.FaceIdentificationCandidate"] = rest_field() + """Identified person candidates for that face (ranked by confidence). Array size should be no + larger than input maxNumOfCandidatesReturned. If no person is identified, will return an empty + array. Required.""" + + @overload + def __init__( + self, + *, + face_id: str, + candidates: List["_models.FaceIdentificationCandidate"], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceLandmarks(_model_base.Model): # pylint: disable=too-many-instance-attributes + """A collection of 27-point face landmarks pointing to the important positions of face components. + + All required parameters must be populated in order to send to server. + + :ivar pupil_left: The coordinates of the left eye pupil. Required. + :vartype pupil_left: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar pupil_right: The coordinates of the right eye pupil. Required. + :vartype pupil_right: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_tip: The coordinates of the nose tip. Required. + :vartype nose_tip: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar mouth_left: The coordinates of the mouth left. Required. + :vartype mouth_left: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar mouth_right: The coordinates of the mouth right. Required. + :vartype mouth_right: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eyebrow_left_outer: The coordinates of the left eyebrow outer. Required. + :vartype eyebrow_left_outer: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eyebrow_left_inner: The coordinates of the left eyebrow inner. Required. + :vartype eyebrow_left_inner: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_left_outer: The coordinates of the left eye outer. Required. + :vartype eye_left_outer: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_left_top: The coordinates of the left eye top. Required. + :vartype eye_left_top: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_left_bottom: The coordinates of the left eye bottom. Required. + :vartype eye_left_bottom: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_left_inner: The coordinates of the left eye inner. Required. + :vartype eye_left_inner: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eyebrow_right_inner: The coordinates of the right eyebrow inner. Required. + :vartype eyebrow_right_inner: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eyebrow_right_outer: The coordinates of the right eyebrow outer. Required. + :vartype eyebrow_right_outer: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_right_inner: The coordinates of the right eye inner. Required. + :vartype eye_right_inner: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_right_top: The coordinates of the right eye top. Required. + :vartype eye_right_top: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_right_bottom: The coordinates of the right eye bottom. Required. + :vartype eye_right_bottom: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar eye_right_outer: The coordinates of the right eye outer. Required. + :vartype eye_right_outer: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_root_left: The coordinates of the nose root left. Required. + :vartype nose_root_left: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_root_right: The coordinates of the nose root right. Required. + :vartype nose_root_right: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_left_alar_top: The coordinates of the nose left alar top. Required. + :vartype nose_left_alar_top: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_right_alar_top: The coordinates of the nose right alar top. Required. + :vartype nose_right_alar_top: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_left_alar_out_tip: The coordinates of the nose left alar out tip. Required. + :vartype nose_left_alar_out_tip: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar nose_right_alar_out_tip: The coordinates of the nose right alar out tip. Required. + :vartype nose_right_alar_out_tip: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar upper_lip_top: The coordinates of the upper lip top. Required. + :vartype upper_lip_top: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar upper_lip_bottom: The coordinates of the upper lip bottom. Required. + :vartype upper_lip_bottom: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar under_lip_top: The coordinates of the under lip top. Required. + :vartype under_lip_top: ~azure.ai.vision.face.models.LandmarkCoordinate + :ivar under_lip_bottom: The coordinates of the under lip bottom. Required. + :vartype under_lip_bottom: ~azure.ai.vision.face.models.LandmarkCoordinate + """ + + pupil_left: "_models.LandmarkCoordinate" = rest_field(name="pupilLeft") + """The coordinates of the left eye pupil. Required.""" + pupil_right: "_models.LandmarkCoordinate" = rest_field(name="pupilRight") + """The coordinates of the right eye pupil. Required.""" + nose_tip: "_models.LandmarkCoordinate" = rest_field(name="noseTip") + """The coordinates of the nose tip. Required.""" + mouth_left: "_models.LandmarkCoordinate" = rest_field(name="mouthLeft") + """The coordinates of the mouth left. Required.""" + mouth_right: "_models.LandmarkCoordinate" = rest_field(name="mouthRight") + """The coordinates of the mouth right. Required.""" + eyebrow_left_outer: "_models.LandmarkCoordinate" = rest_field(name="eyebrowLeftOuter") + """The coordinates of the left eyebrow outer. Required.""" + eyebrow_left_inner: "_models.LandmarkCoordinate" = rest_field(name="eyebrowLeftInner") + """The coordinates of the left eyebrow inner. Required.""" + eye_left_outer: "_models.LandmarkCoordinate" = rest_field(name="eyeLeftOuter") + """The coordinates of the left eye outer. Required.""" + eye_left_top: "_models.LandmarkCoordinate" = rest_field(name="eyeLeftTop") + """The coordinates of the left eye top. Required.""" + eye_left_bottom: "_models.LandmarkCoordinate" = rest_field(name="eyeLeftBottom") + """The coordinates of the left eye bottom. Required.""" + eye_left_inner: "_models.LandmarkCoordinate" = rest_field(name="eyeLeftInner") + """The coordinates of the left eye inner. Required.""" + eyebrow_right_inner: "_models.LandmarkCoordinate" = rest_field(name="eyebrowRightInner") + """The coordinates of the right eyebrow inner. Required.""" + eyebrow_right_outer: "_models.LandmarkCoordinate" = rest_field(name="eyebrowRightOuter") + """The coordinates of the right eyebrow outer. Required.""" + eye_right_inner: "_models.LandmarkCoordinate" = rest_field(name="eyeRightInner") + """The coordinates of the right eye inner. Required.""" + eye_right_top: "_models.LandmarkCoordinate" = rest_field(name="eyeRightTop") + """The coordinates of the right eye top. Required.""" + eye_right_bottom: "_models.LandmarkCoordinate" = rest_field(name="eyeRightBottom") + """The coordinates of the right eye bottom. Required.""" + eye_right_outer: "_models.LandmarkCoordinate" = rest_field(name="eyeRightOuter") + """The coordinates of the right eye outer. Required.""" + nose_root_left: "_models.LandmarkCoordinate" = rest_field(name="noseRootLeft") + """The coordinates of the nose root left. Required.""" + nose_root_right: "_models.LandmarkCoordinate" = rest_field(name="noseRootRight") + """The coordinates of the nose root right. Required.""" + nose_left_alar_top: "_models.LandmarkCoordinate" = rest_field(name="noseLeftAlarTop") + """The coordinates of the nose left alar top. Required.""" + nose_right_alar_top: "_models.LandmarkCoordinate" = rest_field(name="noseRightAlarTop") + """The coordinates of the nose right alar top. Required.""" + nose_left_alar_out_tip: "_models.LandmarkCoordinate" = rest_field(name="noseLeftAlarOutTip") + """The coordinates of the nose left alar out tip. Required.""" + nose_right_alar_out_tip: "_models.LandmarkCoordinate" = rest_field(name="noseRightAlarOutTip") + """The coordinates of the nose right alar out tip. Required.""" + upper_lip_top: "_models.LandmarkCoordinate" = rest_field(name="upperLipTop") + """The coordinates of the upper lip top. Required.""" + upper_lip_bottom: "_models.LandmarkCoordinate" = rest_field(name="upperLipBottom") + """The coordinates of the upper lip bottom. Required.""" + under_lip_top: "_models.LandmarkCoordinate" = rest_field(name="underLipTop") + """The coordinates of the under lip top. Required.""" + under_lip_bottom: "_models.LandmarkCoordinate" = rest_field(name="underLipBottom") + """The coordinates of the under lip bottom. Required.""" + + @overload + def __init__( + self, + *, + pupil_left: "_models.LandmarkCoordinate", + pupil_right: "_models.LandmarkCoordinate", + nose_tip: "_models.LandmarkCoordinate", + mouth_left: "_models.LandmarkCoordinate", + mouth_right: "_models.LandmarkCoordinate", + eyebrow_left_outer: "_models.LandmarkCoordinate", + eyebrow_left_inner: "_models.LandmarkCoordinate", + eye_left_outer: "_models.LandmarkCoordinate", + eye_left_top: "_models.LandmarkCoordinate", + eye_left_bottom: "_models.LandmarkCoordinate", + eye_left_inner: "_models.LandmarkCoordinate", + eyebrow_right_inner: "_models.LandmarkCoordinate", + eyebrow_right_outer: "_models.LandmarkCoordinate", + eye_right_inner: "_models.LandmarkCoordinate", + eye_right_top: "_models.LandmarkCoordinate", + eye_right_bottom: "_models.LandmarkCoordinate", + eye_right_outer: "_models.LandmarkCoordinate", + nose_root_left: "_models.LandmarkCoordinate", + nose_root_right: "_models.LandmarkCoordinate", + nose_left_alar_top: "_models.LandmarkCoordinate", + nose_right_alar_top: "_models.LandmarkCoordinate", + nose_left_alar_out_tip: "_models.LandmarkCoordinate", + nose_right_alar_out_tip: "_models.LandmarkCoordinate", + upper_lip_top: "_models.LandmarkCoordinate", + upper_lip_bottom: "_models.LandmarkCoordinate", + under_lip_top: "_models.LandmarkCoordinate", + under_lip_bottom: "_models.LandmarkCoordinate", + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceList(_model_base.Model): + """Face list is a list of faces, up to 1,000 faces. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar recognition_model: Name of recognition model. Recognition model is used when the face + features are extracted and associated with detected faceIds. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + :vartype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :ivar face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :vartype face_list_id: str + :ivar persisted_faces: Face ids of registered faces in the face list. + :vartype persisted_faces: list[~azure.ai.vision.face.models.FaceListFace] + """ + + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = rest_field(name="recognitionModel") + """Name of recognition model. Recognition model is used when the face features are extracted and + associated with detected faceIds. Known values are: \"recognition_01\", \"recognition_02\", + \"recognition_03\", and \"recognition_04\".""" + face_list_id: str = rest_field(name="faceListId", visibility=["read"]) + """Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required.""" + persisted_faces: Optional[List["_models.FaceListFace"]] = rest_field(name="persistedFaces") + """Face ids of registered faces in the face list.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = None, + persisted_faces: Optional[List["_models.FaceListFace"]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceListFace(_model_base.Model): + """Face resource for face list. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar persisted_face_id: Face ID of the face. Required. + :vartype persisted_face_id: str + :ivar user_data: User-provided data attached to the face. The length limit is 1K. + :vartype user_data: str + """ + + persisted_face_id: str = rest_field(name="persistedFaceId", visibility=["read"]) + """Face ID of the face. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """User-provided data attached to the face. The length limit is 1K.""" + + @overload + def __init__( + self, + *, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceListItem(_model_base.Model): + """Face list item for list face list. + + All required parameters must be populated in order to send to server. + + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar recognition_model: Name of recognition model. Recognition model is used when the face + features are extracted and associated with detected faceIds. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + :vartype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :ivar face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum + length is 64. Required. + :vartype face_list_id: str + """ + + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = rest_field(name="recognitionModel") + """Name of recognition model. Recognition model is used when the face features are extracted and + associated with detected faceIds. Known values are: \"recognition_01\", \"recognition_02\", + \"recognition_03\", and \"recognition_04\".""" + face_list_id: str = rest_field(name="faceListId") + """Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required.""" + + @overload + def __init__( + self, + *, + name: str, + face_list_id: str, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceOperationResult(_model_base.Model): + """Long running operation resource for person directory. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar operation_id: Operation ID of the operation. Required. + :vartype operation_id: str + :ivar status: Current status of the operation. Required. Known values are: "notStarted", + "running", "succeeded", and "failed". + :vartype status: str or ~azure.ai.vision.face.models.FaceOperationStatus + :ivar created_time: Date and time the operation was created. Required. + :vartype created_time: ~datetime.datetime + :ivar last_action_time: Date and time the operation was last updated. + :vartype last_action_time: ~datetime.datetime + :ivar finished_time: Date and time the operation was finished. + :vartype finished_time: ~datetime.datetime + :ivar message: Message for the operation. + :vartype message: str + """ + + operation_id: str = rest_field(name="operationId", visibility=["read"]) + """Operation ID of the operation. Required.""" + status: Union[str, "_models.FaceOperationStatus"] = rest_field() + """Current status of the operation. Required. Known values are: \"notStarted\", \"running\", + \"succeeded\", and \"failed\".""" + created_time: datetime.datetime = rest_field(name="createdTime", format="rfc3339") + """Date and time the operation was created. Required.""" + last_action_time: Optional[datetime.datetime] = rest_field(name="lastActionTime", format="rfc3339") + """Date and time the operation was last updated.""" + finished_time: Optional[datetime.datetime] = rest_field(name="finishedTime", format="rfc3339") + """Date and time the operation was finished.""" + message: Optional[str] = rest_field() + """Message for the operation.""" + + @overload + def __init__( + self, + *, + status: Union[str, "_models.FaceOperationStatus"], + created_time: datetime.datetime, + last_action_time: Optional[datetime.datetime] = None, + finished_time: Optional[datetime.datetime] = None, + message: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceRectangle(_model_base.Model): + """A rectangle within which a face can be found. + + All required parameters must be populated in order to send to server. + + :ivar top: The distance from the top edge if the image to the top edge of the rectangle, in + pixels. Required. + :vartype top: int + :ivar left: The distance from the left edge if the image to the left edge of the rectangle, in + pixels. Required. + :vartype left: int + :ivar width: The width of the rectangle, in pixels. Required. + :vartype width: int + :ivar height: The height of the rectangle, in pixels. Required. + :vartype height: int + """ + + top: int = rest_field() + """The distance from the top edge if the image to the top edge of the rectangle, in pixels. + Required.""" + left: int = rest_field() + """The distance from the left edge if the image to the left edge of the rectangle, in pixels. + Required.""" + width: int = rest_field() + """The width of the rectangle, in pixels. Required.""" + height: int = rest_field() + """The height of the rectangle, in pixels. Required.""" + + @overload + def __init__( + self, + *, + top: int, + left: int, + width: int, + height: int, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FaceVerificationResult(_model_base.Model): + """Verify result. + + All required parameters must be populated in order to send to server. + + :ivar is_identical: True if the two faces belong to the same person or the face belongs to the + person, otherwise false. Required. + :vartype is_identical: bool + :ivar confidence: A number indicates the similarity confidence of whether two faces belong to + the same person, or whether the face belongs to the person. By default, isIdentical is set to + True if similarity confidence is greater than or equal to 0.5. This is useful for advanced + users to override 'isIdentical' and fine-tune the result on their own data. Required. + :vartype confidence: float + """ + + is_identical: bool = rest_field(name="isIdentical") + """True if the two faces belong to the same person or the face belongs to the person, otherwise + false. Required.""" + confidence: float = rest_field() + """A number indicates the similarity confidence of whether two faces belong to the same person, or + whether the face belongs to the person. By default, isIdentical is set to True if similarity + confidence is greater than or equal to 0.5. This is useful for advanced users to override + 'isIdentical' and fine-tune the result on their own data. Required.""" + + @overload + def __init__( + self, + *, + is_identical: bool, + confidence: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class FacialHair(_model_base.Model): + """Properties describing facial hair attributes. + + All required parameters must be populated in order to send to server. + + :ivar moustache: A number ranging from 0 to 1 indicating a level of confidence associated with + a property. Required. + :vartype moustache: float + :ivar beard: A number ranging from 0 to 1 indicating a level of confidence associated with a + property. Required. + :vartype beard: float + :ivar sideburns: A number ranging from 0 to 1 indicating a level of confidence associated with + a property. Required. + :vartype sideburns: float + """ + + moustache: float = rest_field() + """A number ranging from 0 to 1 indicating a level of confidence associated with a property. + Required.""" + beard: float = rest_field() + """A number ranging from 0 to 1 indicating a level of confidence associated with a property. + Required.""" + sideburns: float = rest_field() + """A number ranging from 0 to 1 indicating a level of confidence associated with a property. + Required.""" + + @overload + def __init__( + self, + *, + moustache: float, + beard: float, + sideburns: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class HairColor(_model_base.Model): + """An array of candidate colors and confidence level in the presence of each. + + All required parameters must be populated in order to send to server. + + :ivar color: Name of the hair color. Required. Known values are: "unknown", "white", "gray", + "blond", "brown", "red", "black", and "other". + :vartype color: str or ~azure.ai.vision.face.models.HairColorType + :ivar confidence: Confidence level of the color. Range between [0,1]. Required. + :vartype confidence: float + """ + + color: Union[str, "_models.HairColorType"] = rest_field() + """Name of the hair color. Required. Known values are: \"unknown\", \"white\", \"gray\", + \"blond\", \"brown\", \"red\", \"black\", and \"other\".""" + confidence: float = rest_field() + """Confidence level of the color. Range between [0,1]. Required.""" + + @overload + def __init__( + self, + *, + color: Union[str, "_models.HairColorType"], + confidence: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class HairProperties(_model_base.Model): + """Properties describing hair attributes. + + All required parameters must be populated in order to send to server. + + :ivar bald: A number describing confidence level of whether the person is bald. Required. + :vartype bald: float + :ivar invisible: A boolean value describing whether the hair is visible in the image. Required. + :vartype invisible: bool + :ivar hair_color: An array of candidate colors and confidence level in the presence of each. + Required. + :vartype hair_color: list[~azure.ai.vision.face.models.HairColor] + """ + + bald: float = rest_field() + """A number describing confidence level of whether the person is bald. Required.""" + invisible: bool = rest_field() + """A boolean value describing whether the hair is visible in the image. Required.""" + hair_color: List["_models.HairColor"] = rest_field(name="hairColor") + """An array of candidate colors and confidence level in the presence of each. Required.""" + + @overload + def __init__( + self, + *, + bald: float, + invisible: bool, + hair_color: List["_models.HairColor"], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class HeadPose(_model_base.Model): + """3-D roll/yaw/pitch angles for face direction. + + All required parameters must be populated in order to send to server. + + :ivar pitch: Value of angles. Required. + :vartype pitch: float + :ivar roll: Value of angles. Required. + :vartype roll: float + :ivar yaw: Value of angles. Required. + :vartype yaw: float + """ + + pitch: float = rest_field() + """Value of angles. Required.""" + roll: float = rest_field() + """Value of angles. Required.""" + yaw: float = rest_field() + """Value of angles. Required.""" + + @overload + def __init__( + self, + *, + pitch: float, + roll: float, + yaw: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LandmarkCoordinate(_model_base.Model): + """Landmark coordinates within an image. + + All required parameters must be populated in order to send to server. + + :ivar x: The horizontal component, in pixels. Required. + :vartype x: float + :ivar y: The vertical component, in pixels. Required. + :vartype y: float + """ + + x: float = rest_field() + """The horizontal component, in pixels. Required.""" + y: float = rest_field() + """The vertical component, in pixels. Required.""" + + @overload + def __init__( + self, + *, + x: float, + y: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LargeFaceList(_model_base.Model): + """Large face list is a list of faces, up to 1,000,000 faces. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar recognition_model: Name of recognition model. Recognition model is used when the face + features are extracted and associated with detected faceIds. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + :vartype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :ivar large_face_list_id: Valid character is letter in lower case or digit or '-' or '_', + maximum length is 64. Required. + :vartype large_face_list_id: str + """ + + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = rest_field(name="recognitionModel") + """Name of recognition model. Recognition model is used when the face features are extracted and + associated with detected faceIds. Known values are: \"recognition_01\", \"recognition_02\", + \"recognition_03\", and \"recognition_04\".""" + large_face_list_id: str = rest_field(name="largeFaceListId", visibility=["read"]) + """Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LargeFaceListFace(_model_base.Model): + """Face resource for large face list. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar persisted_face_id: Face ID of the face. Required. + :vartype persisted_face_id: str + :ivar user_data: User-provided data attached to the face. The length limit is 1K. + :vartype user_data: str + """ + + persisted_face_id: str = rest_field(name="persistedFaceId", visibility=["read"]) + """Face ID of the face. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """User-provided data attached to the face. The length limit is 1K.""" + + @overload + def __init__( + self, + *, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LargePersonGroup(_model_base.Model): + """The container of the uploaded person data, including face recognition feature, and up to + 1,000,000 people. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar recognition_model: Name of recognition model. Recognition model is used when the face + features are extracted and associated with detected faceIds. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + :vartype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :ivar large_person_group_id: ID of the container. Required. + :vartype large_person_group_id: str + """ + + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = rest_field(name="recognitionModel") + """Name of recognition model. Recognition model is used when the face features are extracted and + associated with detected faceIds. Known values are: \"recognition_01\", \"recognition_02\", + \"recognition_03\", and \"recognition_04\".""" + large_person_group_id: str = rest_field(name="largePersonGroupId", visibility=["read"]) + """ID of the container. Required.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LargePersonGroupPerson(_model_base.Model): + """The person in a specified large person group. To add face to this person, please call "Add + Large Person Group Person Face". + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar person_id: ID of the person. Required. + :vartype person_id: str + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar persisted_face_ids: Face ids of registered faces in the person. + :vartype persisted_face_ids: list[str] + """ + + person_id: str = rest_field(name="personId", visibility=["read"]) + """ID of the person. Required.""" + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + persisted_face_ids: Optional[List[str]] = rest_field(name="persistedFaceIds") + """Face ids of registered faces in the person.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + persisted_face_ids: Optional[List[str]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LargePersonGroupPersonFace(_model_base.Model): + """Face resource for large person group person. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar persisted_face_id: Face ID of the face. Required. + :vartype persisted_face_id: str + :ivar user_data: User-provided data attached to the face. The length limit is 1K. + :vartype user_data: str + """ + + persisted_face_id: str = rest_field(name="persistedFaceId", visibility=["read"]) + """Face ID of the face. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """User-provided data attached to the face. The length limit is 1K.""" + + @overload + def __init__( + self, + *, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class ListFaceResult(_model_base.Model): + """Response of list face of person. + + All required parameters must be populated in order to send to server. + + :ivar person_id: Id of person. Required. + :vartype person_id: str + :ivar persisted_face_ids: Array of persisted face ids. Required. + :vartype persisted_face_ids: list[str] + """ + + person_id: str = rest_field(name="personId") + """Id of person. Required.""" + persisted_face_ids: List[str] = rest_field(name="persistedFaceIds") + """Array of persisted face ids. Required.""" + + @overload + def __init__( + self, + *, + person_id: str, + persisted_face_ids: List[str], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class ListGroupReferenceResult(_model_base.Model): + """Response of list dynamic person group of person. + + All required parameters must be populated in order to send to server. + + :ivar dynamic_person_group_ids: Array of PersonDirectory DynamicPersonGroup ids. Required. + :vartype dynamic_person_group_ids: list[str] + """ + + dynamic_person_group_ids: List[str] = rest_field(name="dynamicPersonGroupIds") + """Array of PersonDirectory DynamicPersonGroup ids. Required.""" + + @overload + def __init__( + self, + *, + dynamic_person_group_ids: List[str], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class ListPersonResult(_model_base.Model): + """Response of list dynamic person group person. + + All required parameters must be populated in order to send to server. + + :ivar person_ids: Array of PersonDirectory Person ids. Required. + :vartype person_ids: list[str] + """ + + person_ids: List[str] = rest_field(name="personIds") + """Array of PersonDirectory Person ids. Required.""" + + @overload + def __init__( + self, + *, + person_ids: List[str], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessOutputsTarget(_model_base.Model): + """The liveness classification for target face. + + All required parameters must be populated in order to send to server. + + :ivar face_rectangle: The face region where the liveness classification was made on. Required. + :vartype face_rectangle: ~azure.ai.vision.face.models.FaceRectangle + :ivar file_name: The file name which contains the face rectangle where the liveness + classification was made on. Required. + :vartype file_name: str + :ivar time_offset_within_file: The time offset within the file of the frame which contains the + face rectangle where the liveness classification was made on. Required. + :vartype time_offset_within_file: int + :ivar image_type: The image type which contains the face rectangle where the liveness + classification was made on. Required. Known values are: "Color", "Infrared", and "Depth". + :vartype image_type: str or ~azure.ai.vision.face.models.FaceImageType + """ + + face_rectangle: "_models.FaceRectangle" = rest_field(name="faceRectangle") + """The face region where the liveness classification was made on. Required.""" + file_name: str = rest_field(name="fileName") + """The file name which contains the face rectangle where the liveness classification was made on. + Required.""" + time_offset_within_file: int = rest_field(name="timeOffsetWithinFile") + """The time offset within the file of the frame which contains the face rectangle where the + liveness classification was made on. Required.""" + image_type: Union[str, "_models.FaceImageType"] = rest_field(name="imageType") + """The image type which contains the face rectangle where the liveness classification was made on. + Required. Known values are: \"Color\", \"Infrared\", and \"Depth\".""" + + @overload + def __init__( + self, + *, + face_rectangle: "_models.FaceRectangle", + file_name: str, + time_offset_within_file: int, + image_type: Union[str, "_models.FaceImageType"], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessResponseBody(_model_base.Model): + """The response body of detect liveness API call. + + :ivar liveness_decision: The liveness classification for the target face. Known values are: + "uncertain", "realface", and "spoofface". + :vartype liveness_decision: str or ~azure.ai.vision.face.models.FaceLivenessDecision + :ivar target: Specific targets used for liveness classification. + :vartype target: ~azure.ai.vision.face.models.LivenessOutputsTarget + :ivar model_version_used: The model version used for liveness classification. Known values are: + "2020-02-15-preview.01", "2021-11-12-preview.03", "2022-10-15-preview.04", and + "2023-03-02-preview.05". + :vartype model_version_used: str or ~azure.ai.vision.face.models.LivenessModel + :ivar verify_result: The face verification output. Only available when the request is liveness + with verify. + :vartype verify_result: ~azure.ai.vision.face.models.LivenessWithVerifyOutputs + """ + + liveness_decision: Optional[Union[str, "_models.FaceLivenessDecision"]] = rest_field(name="livenessDecision") + """The liveness classification for the target face. Known values are: \"uncertain\", \"realface\", + and \"spoofface\".""" + target: Optional["_models.LivenessOutputsTarget"] = rest_field() + """Specific targets used for liveness classification.""" + model_version_used: Optional[Union[str, "_models.LivenessModel"]] = rest_field(name="modelVersionUsed") + """The model version used for liveness classification. Known values are: + \"2020-02-15-preview.01\", \"2021-11-12-preview.03\", \"2022-10-15-preview.04\", and + \"2023-03-02-preview.05\".""" + verify_result: Optional["_models.LivenessWithVerifyOutputs"] = rest_field(name="verifyResult") + """The face verification output. Only available when the request is liveness with verify.""" + + @overload + def __init__( + self, + *, + liveness_decision: Optional[Union[str, "_models.FaceLivenessDecision"]] = None, + target: Optional["_models.LivenessOutputsTarget"] = None, + model_version_used: Optional[Union[str, "_models.LivenessModel"]] = None, + verify_result: Optional["_models.LivenessWithVerifyOutputs"] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessSession(_model_base.Model): + """Session result of detect liveness. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar id: The unique ID to reference this session. Required. + :vartype id: str + :ivar created_date_time: DateTime when this session was created. Required. + :vartype created_date_time: ~datetime.datetime + :ivar session_start_date_time: DateTime when this session was started by the client. + :vartype session_start_date_time: ~datetime.datetime + :ivar session_expired: Whether or not the session is expired. Required. + :vartype session_expired: bool + :ivar device_correlation_id: Unique Guid per each end-user device. This is to provide rate + limiting and anti-hammering. If 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + :vartype device_correlation_id: str + :ivar auth_token_time_to_live_in_seconds: Seconds the session should last for. Range is 60 to + 86400 seconds. Default value is 600. + :vartype auth_token_time_to_live_in_seconds: int + :ivar status: The current status of the session. Required. Known values are: "NotStarted", + "Started", and "ResultAvailable". + :vartype status: str or ~azure.ai.vision.face.models.FaceSessionStatus + :ivar result: The latest session audit result only populated if status == 'ResultAvailable'. + :vartype result: ~azure.ai.vision.face.models.LivenessSessionAuditEntry + """ + + id: str = rest_field(visibility=["read"]) + """The unique ID to reference this session. Required.""" + created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") + """DateTime when this session was created. Required.""" + session_start_date_time: Optional[datetime.datetime] = rest_field(name="sessionStartDateTime", format="rfc3339") + """DateTime when this session was started by the client.""" + session_expired: bool = rest_field(name="sessionExpired") + """Whether or not the session is expired. Required.""" + device_correlation_id: Optional[str] = rest_field(name="deviceCorrelationId") + """Unique Guid per each end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this 'deviceCorrelationId' must be + null.""" + auth_token_time_to_live_in_seconds: Optional[int] = rest_field(name="authTokenTimeToLiveInSeconds") + """Seconds the session should last for. Range is 60 to 86400 seconds. Default value is 600.""" + status: Union[str, "_models.FaceSessionStatus"] = rest_field() + """The current status of the session. Required. Known values are: \"NotStarted\", \"Started\", and + \"ResultAvailable\".""" + result: Optional["_models.LivenessSessionAuditEntry"] = rest_field() + """The latest session audit result only populated if status == 'ResultAvailable'.""" + + @overload + def __init__( + self, + *, + created_date_time: datetime.datetime, + session_expired: bool, + status: Union[str, "_models.FaceSessionStatus"], + session_start_date_time: Optional[datetime.datetime] = None, + device_correlation_id: Optional[str] = None, + auth_token_time_to_live_in_seconds: Optional[int] = None, + result: Optional["_models.LivenessSessionAuditEntry"] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessSessionAuditEntry(_model_base.Model): + """Audit entry for a request in session. + + All required parameters must be populated in order to send to server. + + :ivar id: The unique id to refer to this audit request. Use this id with the 'start' query + parameter to continue on to the next page of audit results. Required. + :vartype id: int + :ivar session_id: The unique sessionId of the created session. It will expire 48 hours after it + was created or may be deleted sooner using the corresponding session DELETE operation. + Required. + :vartype session_id: str + :ivar request_id: The unique requestId that is returned by the service to the client in the + 'apim-request-id' header. Required. + :vartype request_id: str + :ivar client_request_id: The unique clientRequestId that is sent by the client in the + 'client-request-id' header. Required. + :vartype client_request_id: str + :ivar received_date_time: The UTC DateTime that the request was received. Required. + :vartype received_date_time: ~datetime.datetime + :ivar request: The request of this entry. Required. + :vartype request: ~azure.ai.vision.face.models.AuditRequestInfo + :ivar response: The response of this entry. Required. + :vartype response: ~azure.ai.vision.face.models.AuditLivenessResponseInfo + :ivar digest: The server calculated digest for this request. If the client reported digest + differs from the server calculated digest, then the message integrity between the client and + service has been compromised and the result should not be trusted. For more information, see + how to guides on how to leverage this value to secure your end-to-end solution. Required. + :vartype digest: str + """ + + id: int = rest_field() + """The unique id to refer to this audit request. Use this id with the 'start' query parameter to + continue on to the next page of audit results. Required.""" + session_id: str = rest_field(name="sessionId") + """The unique sessionId of the created session. It will expire 48 hours after it was created or + may be deleted sooner using the corresponding session DELETE operation. Required.""" + request_id: str = rest_field(name="requestId") + """The unique requestId that is returned by the service to the client in the 'apim-request-id' + header. Required.""" + client_request_id: str = rest_field(name="clientRequestId") + """The unique clientRequestId that is sent by the client in the 'client-request-id' header. + Required.""" + received_date_time: datetime.datetime = rest_field(name="receivedDateTime", format="rfc3339") + """The UTC DateTime that the request was received. Required.""" + request: "_models.AuditRequestInfo" = rest_field() + """The request of this entry. Required.""" + response: "_models.AuditLivenessResponseInfo" = rest_field() + """The response of this entry. Required.""" + digest: str = rest_field() + """The server calculated digest for this request. If the client reported digest differs from the + server calculated digest, then the message integrity between the client and service has been + compromised and the result should not be trusted. For more information, see how to guides on + how to leverage this value to secure your end-to-end solution. Required.""" + + @overload + def __init__( + self, + *, + id: int, # pylint: disable=redefined-builtin + session_id: str, + request_id: str, + client_request_id: str, + received_date_time: datetime.datetime, + request: "_models.AuditRequestInfo", + response: "_models.AuditLivenessResponseInfo", + digest: str, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessSessionItem(_model_base.Model): + """Session data returned for enumeration. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar id: The unique ID to reference this session. Required. + :vartype id: str + :ivar created_date_time: DateTime when this session was created. Required. + :vartype created_date_time: ~datetime.datetime + :ivar session_start_date_time: DateTime when this session was started by the client. + :vartype session_start_date_time: ~datetime.datetime + :ivar session_expired: Whether or not the session is expired. Required. + :vartype session_expired: bool + :ivar device_correlation_id: Unique Guid per each end-user device. This is to provide rate + limiting and anti-hammering. If 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + :vartype device_correlation_id: str + :ivar auth_token_time_to_live_in_seconds: Seconds the session should last for. Range is 60 to + 86400 seconds. Default value is 600. + :vartype auth_token_time_to_live_in_seconds: int + """ + + id: str = rest_field(visibility=["read"]) + """The unique ID to reference this session. Required.""" + created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") + """DateTime when this session was created. Required.""" + session_start_date_time: Optional[datetime.datetime] = rest_field(name="sessionStartDateTime", format="rfc3339") + """DateTime when this session was started by the client.""" + session_expired: bool = rest_field(name="sessionExpired") + """Whether or not the session is expired. Required.""" + device_correlation_id: Optional[str] = rest_field(name="deviceCorrelationId") + """Unique Guid per each end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this 'deviceCorrelationId' must be + null.""" + auth_token_time_to_live_in_seconds: Optional[int] = rest_field(name="authTokenTimeToLiveInSeconds") + """Seconds the session should last for. Range is 60 to 86400 seconds. Default value is 600.""" + + @overload + def __init__( + self, + *, + created_date_time: datetime.datetime, + session_expired: bool, + session_start_date_time: Optional[datetime.datetime] = None, + device_correlation_id: Optional[str] = None, + auth_token_time_to_live_in_seconds: Optional[int] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessWithVerifyImage(_model_base.Model): + """The detail of face for verification. + + All required parameters must be populated in order to send to server. + + :ivar face_rectangle: The face region where the comparison image's classification was made. + Required. + :vartype face_rectangle: ~azure.ai.vision.face.models.FaceRectangle + :ivar quality_for_recognition: Quality of face image for recognition. Required. Known values + are: "low", "medium", and "high". + :vartype quality_for_recognition: str or ~azure.ai.vision.face.models.QualityForRecognition + """ + + face_rectangle: "_models.FaceRectangle" = rest_field(name="faceRectangle") + """The face region where the comparison image's classification was made. Required.""" + quality_for_recognition: Union[str, "_models.QualityForRecognition"] = rest_field(name="qualityForRecognition") + """Quality of face image for recognition. Required. Known values are: \"low\", \"medium\", and + \"high\".""" + + @overload + def __init__( + self, + *, + face_rectangle: "_models.FaceRectangle", + quality_for_recognition: Union[str, "_models.QualityForRecognition"], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessWithVerifyOutputs(_model_base.Model): + """The face verification output. + + All required parameters must be populated in order to send to server. + + :ivar verify_image: The detail of face for verification. Required. + :vartype verify_image: ~azure.ai.vision.face.models.LivenessWithVerifyImage + :ivar match_confidence: The target face liveness face and comparison image face verification + confidence. Required. + :vartype match_confidence: float + :ivar is_identical: Whether the target liveness face and comparison image face match. Required. + :vartype is_identical: bool + """ + + verify_image: "_models.LivenessWithVerifyImage" = rest_field(name="verifyImage") + """The detail of face for verification. Required.""" + match_confidence: float = rest_field(name="matchConfidence") + """The target face liveness face and comparison image face verification confidence. Required.""" + is_identical: bool = rest_field(name="isIdentical") + """Whether the target liveness face and comparison image face match. Required.""" + + @overload + def __init__( + self, + *, + verify_image: "_models.LivenessWithVerifyImage", + match_confidence: float, + is_identical: bool, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class LivenessWithVerifySession(_model_base.Model): + """Session result of detect liveness with verify. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar id: The unique ID to reference this session. Required. + :vartype id: str + :ivar created_date_time: DateTime when this session was created. Required. + :vartype created_date_time: ~datetime.datetime + :ivar session_start_date_time: DateTime when this session was started by the client. + :vartype session_start_date_time: ~datetime.datetime + :ivar session_expired: Whether or not the session is expired. Required. + :vartype session_expired: bool + :ivar device_correlation_id: Unique Guid per each end-user device. This is to provide rate + limiting and anti-hammering. If 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + :vartype device_correlation_id: str + :ivar auth_token_time_to_live_in_seconds: Seconds the session should last for. Range is 60 to + 86400 seconds. Default value is 600. + :vartype auth_token_time_to_live_in_seconds: int + :ivar status: The current status of the session. Required. Known values are: "NotStarted", + "Started", and "ResultAvailable". + :vartype status: str or ~azure.ai.vision.face.models.FaceSessionStatus + :ivar result: The latest session audit result only populated if status == 'ResultAvailable'. + :vartype result: ~azure.ai.vision.face.models.LivenessSessionAuditEntry + """ + + id: str = rest_field(visibility=["read"]) + """The unique ID to reference this session. Required.""" + created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") + """DateTime when this session was created. Required.""" + session_start_date_time: Optional[datetime.datetime] = rest_field(name="sessionStartDateTime", format="rfc3339") + """DateTime when this session was started by the client.""" + session_expired: bool = rest_field(name="sessionExpired") + """Whether or not the session is expired. Required.""" + device_correlation_id: Optional[str] = rest_field(name="deviceCorrelationId") + """Unique Guid per each end-user device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this 'deviceCorrelationId' must be + null.""" + auth_token_time_to_live_in_seconds: Optional[int] = rest_field(name="authTokenTimeToLiveInSeconds") + """Seconds the session should last for. Range is 60 to 86400 seconds. Default value is 600.""" + status: Union[str, "_models.FaceSessionStatus"] = rest_field() + """The current status of the session. Required. Known values are: \"NotStarted\", \"Started\", and + \"ResultAvailable\".""" + result: Optional["_models.LivenessSessionAuditEntry"] = rest_field() + """The latest session audit result only populated if status == 'ResultAvailable'.""" + + @overload + def __init__( + self, + *, + created_date_time: datetime.datetime, + session_expired: bool, + status: Union[str, "_models.FaceSessionStatus"], + session_start_date_time: Optional[datetime.datetime] = None, + device_correlation_id: Optional[str] = None, + auth_token_time_to_live_in_seconds: Optional[int] = None, + result: Optional["_models.LivenessSessionAuditEntry"] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class MaskProperties(_model_base.Model): + """Properties describing the presence of a mask on a given face. + + All required parameters must be populated in order to send to server. + + :ivar nose_and_mouth_covered: A boolean value indicating whether nose and mouth are covered. + Required. + :vartype nose_and_mouth_covered: bool + :ivar type: Type of the mask. Required. Known values are: "faceMask", "noMask", + "otherMaskOrOcclusion", and "uncertain". + :vartype type: str or ~azure.ai.vision.face.models.MaskType + """ + + nose_and_mouth_covered: bool = rest_field(name="noseAndMouthCovered") + """A boolean value indicating whether nose and mouth are covered. Required.""" + type: Union[str, "_models.MaskType"] = rest_field() + """Type of the mask. Required. Known values are: \"faceMask\", \"noMask\", + \"otherMaskOrOcclusion\", and \"uncertain\".""" + + @overload + def __init__( + self, + *, + nose_and_mouth_covered: bool, + type: Union[str, "_models.MaskType"], + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class NoiseProperties(_model_base.Model): + """Properties describing noise level of the image. + + All required parameters must be populated in order to send to server. + + :ivar noise_level: An enum value indicating level of noise. Required. Known values are: "low", + "medium", and "high". + :vartype noise_level: str or ~azure.ai.vision.face.models.NoiseLevel + :ivar value: A number indicating level of noise level ranging from 0 to 1. [0, 0.25) is under + exposure. [0.25, 0.75) is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise level. Required. + :vartype value: float + """ + + noise_level: Union[str, "_models.NoiseLevel"] = rest_field(name="noiseLevel") + """An enum value indicating level of noise. Required. Known values are: \"low\", \"medium\", and + \"high\".""" + value: float = rest_field() + """A number indicating level of noise level ranging from 0 to 1. [0, 0.25) is under exposure. + [0.25, 0.75) is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise level. [0.3, + 0.7) is medium noise level. [0.7, 1] is high noise level. Required.""" + + @overload + def __init__( + self, + *, + noise_level: Union[str, "_models.NoiseLevel"], + value: float, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class OcclusionProperties(_model_base.Model): + """Properties describing occlusions on a given face. + + All required parameters must be populated in order to send to server. + + :ivar forehead_occluded: A boolean value indicating whether forehead is occluded. Required. + :vartype forehead_occluded: bool + :ivar eye_occluded: A boolean value indicating whether eyes are occluded. Required. + :vartype eye_occluded: bool + :ivar mouth_occluded: A boolean value indicating whether the mouth is occluded. Required. + :vartype mouth_occluded: bool + """ + + forehead_occluded: bool = rest_field(name="foreheadOccluded") + """A boolean value indicating whether forehead is occluded. Required.""" + eye_occluded: bool = rest_field(name="eyeOccluded") + """A boolean value indicating whether eyes are occluded. Required.""" + mouth_occluded: bool = rest_field(name="mouthOccluded") + """A boolean value indicating whether the mouth is occluded. Required.""" + + @overload + def __init__( + self, + *, + forehead_occluded: bool, + eye_occluded: bool, + mouth_occluded: bool, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class PersonDirectoryFace(_model_base.Model): + """Face resource for person directory person. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar persisted_face_id: Face ID of the face. Required. + :vartype persisted_face_id: str + :ivar user_data: User-provided data attached to the face. The length limit is 1K. + :vartype user_data: str + """ + + persisted_face_id: str = rest_field(name="persistedFaceId", visibility=["read"]) + """Face ID of the face. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """User-provided data attached to the face. The length limit is 1K.""" + + @overload + def __init__( + self, + *, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class PersonDirectoryPerson(_model_base.Model): + """Person resource for person directory. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar person_id: Person ID of the person. Required. + :vartype person_id: str + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + """ + + person_id: str = rest_field(name="personId", visibility=["read"]) + """Person ID of the person. Required.""" + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class PersonGroup(_model_base.Model): + """The container of the uploaded person data, including face recognition feature, and up to 10,000 + persons. To handle larger scale face identification problem, please consider using Large Person + Group. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar recognition_model: Name of recognition model. Recognition model is used when the face + features are extracted and associated with detected faceIds. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + :vartype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :ivar person_group_id: ID of the container. Required. + :vartype person_group_id: str + """ + + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = rest_field(name="recognitionModel") + """Name of recognition model. Recognition model is used when the face features are extracted and + associated with detected faceIds. Known values are: \"recognition_01\", \"recognition_02\", + \"recognition_03\", and \"recognition_04\".""" + person_group_id: str = rest_field(name="personGroupId", visibility=["read"]) + """ID of the container. Required.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + recognition_model: Optional[Union[str, "_models.FaceRecognitionModel"]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class PersonGroupPerson(_model_base.Model): + """The person in a specified person group. To add face to this person, please call "Add Large + Person Group Person Face". + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar person_id: ID of the person. Required. + :vartype person_id: str + :ivar name: User defined name, maximum length is 128. Required. + :vartype name: str + :ivar user_data: Optional user defined data. Length should not exceed 16K. + :vartype user_data: str + :ivar persisted_face_ids: Face ids of registered faces in the person. + :vartype persisted_face_ids: list[str] + """ + + person_id: str = rest_field(name="personId", visibility=["read"]) + """ID of the person. Required.""" + name: str = rest_field() + """User defined name, maximum length is 128. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """Optional user defined data. Length should not exceed 16K.""" + persisted_face_ids: Optional[List[str]] = rest_field(name="persistedFaceIds") + """Face ids of registered faces in the person.""" + + @overload + def __init__( + self, + *, + name: str, + user_data: Optional[str] = None, + persisted_face_ids: Optional[List[str]] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) + + +class PersonGroupPersonFace(_model_base.Model): + """Face resource for person group person. + + Readonly variables are only populated by the server, and will be ignored when sending a request. + + All required parameters must be populated in order to send to server. + + :ivar persisted_face_id: Face ID of the face. Required. + :vartype persisted_face_id: str + :ivar user_data: User-provided data attached to the face. The length limit is 1K. + :vartype user_data: str + """ + + persisted_face_id: str = rest_field(name="persistedFaceId", visibility=["read"]) + """Face ID of the face. Required.""" + user_data: Optional[str] = rest_field(name="userData") + """User-provided data attached to the face. The length limit is 1K.""" + + @overload + def __init__( + self, + *, + user_data: Optional[str] = None, + ): ... + + @overload + def __init__(self, mapping: Mapping[str, Any]): + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useless-super-delegation + super().__init__(*args, **kwargs) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py new file mode 100644 index 000000000000..f7dd32510333 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py @@ -0,0 +1,20 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/py.typed b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/py.typed new file mode 100644 index 000000000000..e5aff4f83af8 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. \ No newline at end of file diff --git a/sdk/face/azure-ai-vision-face/dev_requirements.txt b/sdk/face/azure-ai-vision-face/dev_requirements.txt new file mode 100644 index 000000000000..105486471444 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/dev_requirements.txt @@ -0,0 +1,3 @@ +-e ../../../tools/azure-sdk-tools +../../core/azure-core +aiohttp \ No newline at end of file diff --git a/sdk/face/azure-ai-vision-face/sdk_packaging.toml b/sdk/face/azure-ai-vision-face/sdk_packaging.toml new file mode 100644 index 000000000000..e7687fdae93b --- /dev/null +++ b/sdk/face/azure-ai-vision-face/sdk_packaging.toml @@ -0,0 +1,2 @@ +[packaging] +auto_update = false \ No newline at end of file diff --git a/sdk/face/azure-ai-vision-face/setup.py b/sdk/face/azure-ai-vision-face/setup.py new file mode 100644 index 000000000000..85c0e4ceb836 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/setup.py @@ -0,0 +1,72 @@ +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# coding: utf-8 + +import os +import re +from setuptools import setup, find_packages + + +PACKAGE_NAME = "azure-ai-vision-face" +PACKAGE_PPRINT_NAME = "Azure Ai Vision Face" + +# a-b-c => a/b/c +package_folder_path = PACKAGE_NAME.replace("-", "/") + +# Version extraction inspired from 'requests' +with open(os.path.join(package_folder_path, "_version.py"), "r") as fd: + version = re.search(r'^VERSION\s*=\s*[\'"]([^\'"]*)[\'"]', fd.read(), re.MULTILINE).group(1) + +if not version: + raise RuntimeError("Cannot find version information") + + +setup( + name=PACKAGE_NAME, + version=version, + description="Microsoft {} Client Library for Python".format(PACKAGE_PPRINT_NAME), + long_description=open("README.md", "r").read(), + long_description_content_type="text/markdown", + license="MIT License", + author="Microsoft Corporation", + author_email="azpysdkhelp@microsoft.com", + url="https://github.com/Azure/azure-sdk-for-python/tree/main/sdk", + keywords="azure, azure sdk", + classifiers=[ + "Development Status :: 4 - Beta", + "Programming Language :: Python", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: MIT License", + ], + zip_safe=False, + packages=find_packages( + exclude=[ + "tests", + # Exclude packages that will be covered by PEP420 or nspkg + "azure", + "azure.ai", + "azure.ai.vision", + ] + ), + include_package_data=True, + package_data={ + "azure.ai.vision.face": ["py.typed"], + }, + install_requires=[ + "isodate>=0.6.1", + "azure-core>=1.30.0", + "typing-extensions>=4.6.0", + ], + python_requires=">=3.8", +) diff --git a/sdk/face/azure-ai-vision-face/tsp-location.yaml b/sdk/face/azure-ai-vision-face/tsp-location.yaml new file mode 100644 index 000000000000..e64b5978f87a --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tsp-location.yaml @@ -0,0 +1,4 @@ +directory: specification/ai/Face +commit: 37acfe2967e5e1be1169146ac461eb1875c9476e +repo: Azure/azure-rest-api-specs +additionalDirectories: diff --git a/sdk/face/ci.yml b/sdk/face/ci.yml new file mode 100644 index 000000000000..66a24e07a1d8 --- /dev/null +++ b/sdk/face/ci.yml @@ -0,0 +1,34 @@ +# DO NOT EDIT THIS FILE +# This file is generated automatically and any changes will be lost. + +trigger: + branches: + include: + - main + - hotfix/* + - release/* + - restapi* + paths: + include: + - sdk/face/ + +pr: + branches: + include: + - main + - feature/* + - hotfix/* + - release/* + - restapi* + paths: + include: + - sdk/face/ + +extends: + template: ../../eng/pipelines/templates/stages/archetype-sdk-client.yml + parameters: + ServiceDirectory: face + TestProxy: true + Artifacts: + - name: azure-ai-vision-face + safeName: azureaivisionface From dae967969e79bb9da94caf9608c7dbf76069a388 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Tue, 30 Apr 2024 11:13:48 +0800 Subject: [PATCH 02/41] Fix SDK error --- .../azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py | 1 - 1 file changed, 1 deletion(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py index 9f2bd2f22400..459a9e05c2b4 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py @@ -14,7 +14,6 @@ FaceClientConfiguration, FaceSessionClientConfiguration, ) -from ._model_base import Model, SdkJSONEncoder if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports From 2673dbda71a2714f2bec416b33047d12a66723aa Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 15 Apr 2024 11:26:01 +0800 Subject: [PATCH 03/41] SDK Customization --- .../azure/ai/vision/face/_patch.py | 959 +++++++++++++++++- .../azure/ai/vision/face/aio/_patch.py | 959 +++++++++++++++++- .../azure/ai/vision/face/models/_patch.py | 38 +- 3 files changed, 1951 insertions(+), 5 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index f7dd32510333..513f42282beb 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -6,9 +6,964 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from typing import List +from typing import Any, IO, List, Optional, Union, cast +import sys -__all__: List[str] = [] # Add all objects you want publicly available to users at this package level +from azure.core.tracing.decorator import distributed_trace + +from . import models as _models +from ._client import FaceClient as FaceClientGenerated +from ._client import FaceSessionClient as FaceSessionClientGenerated +from ._operations._operations import ( + FaceClientOperationsMixin, + FaceSessionClientOperationsMixin, +) + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object + + +class FaceClient(FaceClientGenerated): + """FaceClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + @distributed_trace + def detect_from_url( + self, + url: str, + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + *, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models._models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in Face - Identify, Face - Verify, + and Face - Find Similar. The stored face features will expire and be deleted at the time + specified by faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying Face - Identify, Face - Verify, and Face - Find Similar + ('returnFaceId' is true), please use faces that are: frontal, clear, and with a minimum size of + 200x200 pixels (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like Verify, + Identify, Find Similar are needed, please specify the recognition model with 'recognitionModel' + parameter. The default value for 'recognitionModel' is 'recognition_01', if latest model + needed, please explicitly specify the model you need in this parameter. Once specified, the + detected faceIds will be associated with the specified recognition model. More details, please + refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param url: URL of input image. Required. + :type url: str + :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Required. + :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param return_face_id: Return faceIds of the detected faces or not. Required. + :type return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + # By default, return_face_id is False. + if return_face_id is not True: + return_face_id = False + + return FaceClientOperationsMixin._detect_from_url( + self, + url=url, + return_face_id=return_face_id, + return_face_landmarks=return_face_landmarks, + return_face_attributes=return_face_attributes, + recognition_model=recognition_model, + return_recognition_model=return_recognition_model, + detection_model=detection_model, + face_id_time_to_live=face_id_time_to_live, + **kwargs) + + @distributed_trace + def detect( + self, + image_content: bytes, + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + *, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models._models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in Face - Identify, Face - Verify, + and Face - Find Similar. The stored face features will expire and be deleted at the time + specified by faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying Face - Identify, Face - Verify, and Face - Find Similar + ('returnFaceId' is true), please use faces that are: frontal, clear, and with a minimum size of + 200x200 pixels (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like Verify, + Identify, Find Similar are needed, please specify the recognition model with 'recognitionModel' + parameter. The default value for 'recognitionModel' is 'recognition_01', if latest model + needed, please explicitly specify the model you need in this parameter. Once specified, the + detected faceIds will be associated with the specified recognition model. More details, please + refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param image_content: The input image binary. Required. + :type image_content: bytes + :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Required. + :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param return_face_id: Return faceIds of the detected faces or not. Required. + :type return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + # By default, return_face_id is False. + if return_face_id is not True: + return_face_id = False + + return FaceClientOperationsMixin._detect( + self, + image_content=image_content, + return_face_id=return_face_id, + return_face_landmarks=return_face_landmarks, + return_face_attributes=return_face_attributes, + recognition_model=recognition_model, + return_recognition_model=return_recognition_model, + detection_model=detection_model, + face_id_time_to_live=face_id_time_to_live, + **kwargs) + + +class FaceSessionClient(FaceSessionClientGenerated): + """FaceSessionClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + # TODO + @distributed_trace + def create_liveness_with_verify_session( + self, + body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], + verify_image: bytes, + **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: + # pylint: disable=line-too-long + """Create a new liveness session with verify. Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLivenessWithVerify/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + + * + + + * Client access can be revoked by deleting the session using the Delete Liveness With Verify + Session operation. + * To retrieve a result, use the Get Liveness With Verify Session. + * To audit the individual requests that a client has made to your resource, use the List + Liveness With Verify Session Audit Entries. + + + Alternative Option: Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + .. + + [!NOTE] + Extra measures should be taken to validate that the client is sending the expected + VerifyImage. + + :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] + Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] + :param verify_image: The image for verify. If you don't have any images to use for verification, set it to None. + Required. + :type verify_image: bytes + :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is + compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + # TODO + if verify_image is not None: + request_body = _models._models.CreateLivenessWithVerifySessionContent( + parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), + verify_image=("verify-image", verify_image) + ) + return FaceSessionClientOperationsMixin._create_liveness_with_verify_session_with_verify_image( + self, request_body, **kwargs) + + return FaceSessionClientOperationsMixin._create_liveness_with_verify_session( + self, body, **kwargs) + + +__all__: List[str] = [ + "FaceClient", + "FaceSessionClient", +] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index f7dd32510333..4b87274a1f87 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -6,9 +6,964 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from typing import List +from typing import Any, IO, List, Optional, Union, cast +import sys -__all__: List[str] = [] # Add all objects you want publicly available to users at this package level +from azure.core.tracing.decorator_async import distributed_trace_async + +from .. import models as _models +from ._client import FaceClient as FaceClientGenerated +from ._client import FaceSessionClient as FaceSessionClientGenerated +from ._operations._operations import ( + FaceClientOperationsMixin, + FaceSessionClientOperationsMixin, +) + +if sys.version_info >= (3, 9): + from collections.abc import MutableMapping +else: + from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports +JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object + + +class FaceClient(FaceClientGenerated): + """FaceClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + @distributed_trace_async + async def detect_from_url( + self, + url: str, + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + *, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models._models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in Face - Identify, Face - Verify, + and Face - Find Similar. The stored face features will expire and be deleted at the time + specified by faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying Face - Identify, Face - Verify, and Face - Find Similar + ('returnFaceId' is true), please use faces that are: frontal, clear, and with a minimum size of + 200x200 pixels (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like Verify, + Identify, Find Similar are needed, please specify the recognition model with 'recognitionModel' + parameter. The default value for 'recognitionModel' is 'recognition_01', if latest model + needed, please explicitly specify the model you need in this parameter. Once specified, the + detected faceIds will be associated with the specified recognition model. More details, please + refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param url: URL of input image. Required. + :type url: str + :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Required. + :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param return_face_id: Return faceIds of the detected faces or not. Required. + :type return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "url": "str" # URL of input image. Required. + } + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + # By default, return_face_id is False. + if return_face_id is not True: + return_face_id = False + + return await FaceClientOperationsMixin._detect_from_url( + self, + url=url, + return_face_id=return_face_id, + return_face_landmarks=return_face_landmarks, + return_face_attributes=return_face_attributes, + recognition_model=recognition_model, + return_recognition_model=return_recognition_model, + detection_model=detection_model, + face_id_time_to_live=face_id_time_to_live, + **kwargs) + + @distributed_trace_async + async def detect( + self, + image_content: bytes, + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + *, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models._models.FaceDetectionResult]: + # pylint: disable=line-too-long + """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, + and attributes. + + .. + + [!IMPORTANT] + To mitigate potential misuse that can subject people to stereotyping, discrimination, or + unfair denial of services, we are retiring Face API attributes that predict emotion, gender, + age, smile, facial hair, hair, and makeup. Read more about this decision + https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + + + * + + + * No image will be stored. Only the extracted face feature(s) will be stored on server. The + faceId is an identifier of the face feature and will be used in Face - Identify, Face - Verify, + and Face - Find Similar. The stored face features will expire and be deleted at the time + specified by faceIdTimeToLive after the original detection call. + * Optional parameters include faceId, landmarks, and attributes. Attributes include headPose, + glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Some + of the results returned for specific attributes may not be highly accurate. + * JPEG, PNG, GIF (the first frame), and BMP format are supported. The allowed image file size + is from 1KB to 6MB. + * The minimum detectable face size is 36x36 pixels in an image no larger than 1920x1080 pixels. + Images with dimensions higher than 1920x1080 pixels will need a proportionally larger minimum + face size. + * Up to 100 faces can be returned for an image. Faces are ranked by face rectangle size from + large to small. + * For optimal results when querying Face - Identify, Face - Verify, and Face - Find Similar + ('returnFaceId' is true), please use faces that are: frontal, clear, and with a minimum size of + 200x200 pixels (100 pixels between eyes). + * Different 'detectionModel' values can be provided. To use and compare different detection + models, please refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + + * 'detection_02': Face attributes and landmarks are disabled if you choose this detection + model. + * 'detection_03': Face attributes (mask and headPose only) and landmarks are supported if you + choose this detection model. + + * Different 'recognitionModel' values are provided. If follow-up operations like Verify, + Identify, Find Similar are needed, please specify the recognition model with 'recognitionModel' + parameter. The default value for 'recognitionModel' is 'recognition_01', if latest model + needed, please explicitly specify the model you need in this parameter. Once specified, the + detected faceIds will be associated with the specified recognition model. More details, please + refer to + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + + :param image_content: The input image binary. Required. + :type image_content: bytes + :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default + value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". + Required. + :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', + 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' + is recommended since its accuracy is improved on faces wearing masks compared with + 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and + 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and + "recognition_04". Required. + :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :param return_face_id: Return faceIds of the detected faces or not. Required. + :type return_face_id: bool + :keyword return_face_attributes: Analyze and return the one or more specified face attributes + in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute + analysis has additional computational and time cost. Default value is None. + :paramtype return_face_attributes: list[str or ~azure.ai.vision.face.models.FaceAttributeType] + :keyword return_face_landmarks: Return face landmarks of the detected faces or not. The default + value is false. Default value is None. + :paramtype return_face_landmarks: bool + :keyword return_recognition_model: Return 'recognitionModel' or not. The default value is + false. Default value is None. + :paramtype return_recognition_model: bool + :keyword face_id_time_to_live: The number of seconds for the face ID being cached. Supported + range from 60 seconds up to 86400 seconds. The default value is 86400 (24 hours). Default value + is None. + :paramtype face_id_time_to_live: int + :return: list of FaceDetectionResult + :rtype: list[~azure.ai.vision.face.models.FaceDetectionResult] + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # response body for status code(s): 200 + response == [ + { + "faceRectangle": { + "height": 0, # The height of the rectangle, in pixels. + Required. + "left": 0, # The distance from the left edge if the image to + the left edge of the rectangle, in pixels. Required. + "top": 0, # The distance from the top edge if the image to + the top edge of the rectangle, in pixels. Required. + "width": 0 # The width of the rectangle, in pixels. + Required. + }, + "faceAttributes": { + "accessories": [ + { + "confidence": 0.0, # Confidence level of the + accessory type. Range between [0,1]. Required. + "type": "str" # Type of the accessory. + Required. Known values are: "headwear", "glasses", and "mask". + } + ], + "age": 0.0, # Optional. Age in years. + "blur": { + "blurLevel": "str", # An enum value indicating level + of blurriness. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of + blurriness ranging from 0 to 1. Required. + }, + "exposure": { + "exposureLevel": "str", # An enum value indicating + level of exposure. Required. Known values are: "underExposure", + "goodExposure", and "overExposure". + "value": 0.0 # A number indicating level of exposure + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. Required. + }, + "facialHair": { + "beard": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "moustache": 0.0, # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + "sideburns": 0.0 # A number ranging from 0 to 1 + indicating a level of confidence associated with a property. + Required. + }, + "glasses": "str", # Optional. Glasses type if any of the + face. Known values are: "noGlasses", "readingGlasses", "sunglasses", and + "swimmingGoggles". + "hair": { + "bald": 0.0, # A number describing confidence level + of whether the person is bald. Required. + "hairColor": [ + { + "color": "str", # Name of the hair + color. Required. Known values are: "unknown", "white", + "gray", "blond", "brown", "red", "black", and "other". + "confidence": 0.0 # Confidence level + of the color. Range between [0,1]. Required. + } + ], + "invisible": bool # A boolean value describing + whether the hair is visible in the image. Required. + }, + "headPose": { + "pitch": 0.0, # Value of angles. Required. + "roll": 0.0, # Value of angles. Required. + "yaw": 0.0 # Value of angles. Required. + }, + "mask": { + "noseAndMouthCovered": bool, # A boolean value + indicating whether nose and mouth are covered. Required. + "type": "str" # Type of the mask. Required. Known + values are: "faceMask", "noMask", "otherMaskOrOcclusion", and + "uncertain". + }, + "noise": { + "noiseLevel": "str", # An enum value indicating + level of noise. Required. Known values are: "low", "medium", and + "high". + "value": 0.0 # A number indicating level of noise + level ranging from 0 to 1. [0, 0.25) is under exposure. [0.25, 0.75) + is good exposure. [0.75, 1] is over exposure. [0, 0.3) is low noise + level. [0.3, 0.7) is medium noise level. [0.7, 1] is high noise + level. Required. + }, + "occlusion": { + "eyeOccluded": bool, # A boolean value indicating + whether eyes are occluded. Required. + "foreheadOccluded": bool, # A boolean value + indicating whether forehead is occluded. Required. + "mouthOccluded": bool # A boolean value indicating + whether the mouth is occluded. Required. + }, + "qualityForRecognition": "str", # Optional. Properties + describing the overall image quality regarding whether the image being + used in the detection is of sufficient quality to attempt face + recognition on. Known values are: "low", "medium", and "high". + "smile": 0.0 # Optional. Smile intensity, a number between + [0,1]. + }, + "faceId": "str", # Optional. Unique faceId of the detected face, + created by detection API and it will expire 24 hours after the detection + call. To return this, it requires 'returnFaceId' parameter to be true. + "faceLandmarks": { + "eyeLeftBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeLeftTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyeRightTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowLeftOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightInner": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "eyebrowRightOuter": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "mouthRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseLeftAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarOutTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRightAlarTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseRootRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "noseTip": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilLeft": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "pupilRight": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "underLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipBottom": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + }, + "upperLipTop": { + "x": 0.0, # The horizontal component, in pixels. + Required. + "y": 0.0 # The vertical component, in pixels. + Required. + } + }, + "recognitionModel": "str" # Optional. The 'recognitionModel' + associated with this faceId. This is only returned when + 'returnRecognitionModel' is explicitly set as true. Known values are: + "recognition_01", "recognition_02", "recognition_03", and "recognition_04". + } + ] + """ + # By default, return_face_id is False. + if return_face_id is not True: + return_face_id = False + + return await FaceClientOperationsMixin._detect( + self, + image_content=image_content, + return_face_id=return_face_id, + return_face_landmarks=return_face_landmarks, + return_face_attributes=return_face_attributes, + recognition_model=recognition_model, + return_recognition_model=return_recognition_model, + detection_model=detection_model, + face_id_time_to_live=face_id_time_to_live, + **kwargs) + + +class FaceSessionClient(FaceSessionClientGenerated): + """FaceSessionClient. + + :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: + https://{resource-name}.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a + AzureKeyCredential type or a TokenCredential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: API Version. Default value is "v1.1-preview.1". Note that overriding this + default value may result in unsupported behavior. + :paramtype api_version: str or ~azure.ai.vision.face.models.Versions + """ + + # TODO + @distributed_trace_async + async def create_liveness_with_verify_session( + self, + body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], + verify_image: bytes, + **kwargs: Any + ) -> _models.CreateLivenessWithVerifySessionResult: + # pylint: disable=line-too-long + """Create a new liveness session with verify. Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + A session is best for client device scenarios where developers want to authorize a client + device to perform only a liveness detection without granting full access to their resource. + Created sessions have a limited life span and only authorize clients to perform the desired + action before access is expired. + + Permissions includes... + > + * + + + * Ability to call /detectLivenessWithVerify/singleModal for up to 3 retries. + * A token lifetime of 10 minutes. + + .. + + [!NOTE] + + * + + + * Client access can be revoked by deleting the session using the Delete Liveness With Verify + Session operation. + * To retrieve a result, use the Get Liveness With Verify Session. + * To audit the individual requests that a client has made to your resource, use the List + Liveness With Verify Session Audit Entries. + + + Alternative Option: Client device submits VerifyImage during the + /detectLivenessWithVerify/singleModal call. + + .. + + [!NOTE] + Extra measures should be taken to validate that the client is sending the expected + VerifyImage. + + :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] + Required. + :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] + :param verify_image: The image for verify. If you don't have any images to use for verification, set it to None. + Required. + :type verify_image: bytes + :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is + compatible with MutableMapping + :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult + :raises ~azure.core.exceptions.HttpResponseError: + + Example: + .. code-block:: python + + # JSON input template you can fill out and use as your body input. + body = { + "livenessOperationMode": "str", # Type of liveness mode the client should + follow. Required. "Passive" + "authTokenTimeToLiveInSeconds": 0, # Optional. Seconds the session should + last for. Range is 60 to 86400 seconds. Default value is 600. + "deviceCorrelationId": "str", # Optional. Unique Guid per each end-user + device. This is to provide rate limiting and anti-hammering. If + 'deviceCorrelationIdSetInClient' is true in this request, this + 'deviceCorrelationId' must be null. + "deviceCorrelationIdSetInClient": bool, # Optional. Whether or not to allow + client to set their own 'deviceCorrelationId' via the Vision SDK. Default is + false, and 'deviceCorrelationId' must be set in this request body. + "sendResultsToClient": bool # Optional. Whether or not to allow a '200 - + Success' response body to be sent to the client, which may be undesirable for + security reasons. Default is false, clients will receive a '204 - NoContent' + empty body response. Regardless of selection, calling Session GetResult will + always contain a response body enabling business logic to be implemented. + } + + # response body for status code(s): 200 + response == { + "authToken": "str", # Bearer token to provide authentication for the Vision + SDK running on a client application. This Bearer token has limited permissions to + perform only the required action and expires after the TTL time. It is also + auditable. Required. + "sessionId": "str" # The unique session ID of the created session. It will + expire 48 hours after it was created or may be deleted sooner using the + corresponding Session DELETE operation. Required. + } + """ + # TODO + if verify_image is not None: + request_body = _models._models.CreateLivenessWithVerifySessionContent( + parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), + verify_image=("verify-image", verify_image) + ) + return await FaceSessionClientOperationsMixin._create_liveness_with_verify_session_with_verify_image( + self, request_body, **kwargs) + + return await FaceSessionClientOperationsMixin._create_liveness_with_verify_session( + self, body, **kwargs) + + +__all__: List[str] = [ + "FaceClient", + "FaceSessionClient", +] # Add all objects you want publicly available to users at this package level def patch_sdk(): diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py index f7dd32510333..f240c057460a 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py @@ -6,9 +6,45 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ +from enum import Enum from typing import List -__all__: List[str] = [] # Add all objects you want publicly available to users at this package level +from ._enums import FaceAttributeType + +FaceAttributeTypeDetection01 = Enum("FaceAttributeTypeDetection01", [ + (a.name, a.value) for a in FaceAttributeType if a in [ + FaceAttributeType.ACCESSORIES, + FaceAttributeType.BLUR, + FaceAttributeType.EXPOSURE, + FaceAttributeType.GLASSES, + FaceAttributeType.HEAD_POSE, + FaceAttributeType.NOISE, + FaceAttributeType.OCCLUSION]]) +"""Available attribute options for detection_01 model.""" + +FaceAttributeTypeDetection03 = Enum("FaceAttributeTypeDetection03", [ + (a.name, a.value) for a in FaceAttributeType if a in [ + FaceAttributeType.HEAD_POSE, + FaceAttributeType.MASK]]) +"""Available attribute options for detection_03 model.""" + +FaceAttributeTypeRecognition03 = Enum("FaceAttributeTypeRecognition03", [ + (a.name, a.value) for a in FaceAttributeType if a in [ + FaceAttributeType.QUALITY_FOR_RECOGNITION]]) +"""Available attribute options for recognition_03 model.""" + +FaceAttributeTypeRecognition04 = Enum("FaceAttributeTypeRecognition04", [ + (a.name, a.value) for a in FaceAttributeType if a in [ + FaceAttributeType.QUALITY_FOR_RECOGNITION]]) +"""Available attribute options for recognition_04 model.""" + + +__all__: List[str] = [ + "FaceAttributeTypeDetection01", + "FaceAttributeTypeDetection03", + "FaceAttributeTypeRecognition03", + "FaceAttributeTypeRecognition04", +] # Add all objects you want publicly available to users at this package level def patch_sdk(): From de6d3d9dab144c4e4c93022395f95cb6e1edded3 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Wed, 3 Apr 2024 20:12:41 +0800 Subject: [PATCH 04/41] test-resources.json --- sdk/face/test-resources.json | 105 +++++++++++++++++++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 sdk/face/test-resources.json diff --git a/sdk/face/test-resources.json b/sdk/face/test-resources.json new file mode 100644 index 000000000000..00457548d98b --- /dev/null +++ b/sdk/face/test-resources.json @@ -0,0 +1,105 @@ +{ + "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", + "contentVersion": "1.0.0.0", + "parameters": { + "baseName": { + "type": "string", + "defaultValue": "[resourceGroup().name]" + }, + "location": { + "type": "string", + "defaultValue": "[resourceGroup().location]" + }, + "cognitiveServicesEndpointSuffix": { + "type": "string", + "defaultValue": ".cognitiveservices.azure.com" + }, + "testApplicationOid": { + "type": "string", + "metadata": { + "description": "The principal to assign the role to. This is application object id." + } + }, + "tenantId": { + "type": "String", + "metadata": { + "description": "The tenant id to which the application and resources belong." + } + }, + "testApplicationId": { + "type": "String", + "metadata": { + "description": "The application client id used to run tests." + } + }, + "testApplicationSecret": { + "type": "String", + "metadata": { + "description": "The application client secret used to run tests." + } + } + }, + "variables": { + "authorizationApiVersion": "2018-09-01-preview", + "faceApiVersion": "2022-03-01", + "faceAccountName": "[concat('face', parameters('baseName'))]", + "azureFaceUrl": "[concat('https://', variables('faceAccountName'), parameters('cognitiveServicesEndpointSuffix'))]", + "cognitiveServiceUserRoleId": "[concat('/subscriptions/', subscription().subscriptionId, '/providers/Microsoft.Authorization/roleDefinitions/a97b65f3-24c7-4388-baec-2e87135dc908')]" + }, + "resources": [ + { + "type": "Microsoft.Authorization/roleAssignments", + "apiVersion": "[variables('authorizationApiVersion')]", + "name": "[guid(concat(variables('cognitiveServiceUserRoleId'), variables('faceAccountName')))]", + "dependsOn": [ + "[variables('faceAccountName')]" + ], + "properties": { + "principalId": "[parameters('testApplicationOid')]", + "roleDefinitionId": "[variables('cognitiveServiceUserRoleId')]" + } + }, + { + "type": "Microsoft.CognitiveServices/accounts", + "apiVersion": "[variables('faceApiVersion')]", + "name": "[variables('faceAccountName')]", + "location": "[parameters('location')]", + "kind": "Face", + "tags": { + "Owner": "[resourceGroup().name]" + }, + "sku": { + "name": "S0" + }, + "properties": { + "customSubDomainName": "[toLower(variables('faceAccountName'))]" + } + } + ], + "outputs": { + "AZURE_TENANT_ID": { + "type": "String", + "value": "[parameters('tenantId')]" + }, + "AZURE_CLIENT_ID": { + "type": "String", + "value": "[parameters('testApplicationId')]" + }, + "AZURE_CLIENT_SECRET": { + "type": "String", + "value": "[parameters('testApplicationSecret')]" + }, + "AZURE_FACE_API_NAME": { + "type": "string", + "value": "[variables('faceAccountName')]" + }, + "AZURE_FACE_API_ENDPOINT": { + "type": "string", + "value": "[variables('azureFaceUrl')]" + }, + "AZURE_FACE_API_KEY": { + "type": "string", + "value": "[listKeys(resourceId('Microsoft.CognitiveServices/accounts', variables('faceAccountName')), variables('faceApiVersion')).key1]" + } + } +} From dfc57f5dd5f88f3166b0308168656c9c67ec28a5 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 15 Apr 2024 14:20:44 +0800 Subject: [PATCH 05/41] Samples --- .../azure-ai-vision-face/dev_requirements.txt | 3 +- .../azure-ai-vision-face/samples/README.md | 60 ++++++ .../samples/_shared/__init__.py | 0 .../samples/_shared/constants.py | 61 +++++++ .../samples/_shared/helpers.py | 35 ++++ .../samples_authentication_async.py | 113 ++++++++++++ .../samples_face_detection_async.py | 111 +++++++++++ .../samples_face_grouping_async.py | 77 ++++++++ .../samples_face_liveness_detection_async.py | 100 ++++++++++ ...eness_detection_with_verification_async.py | 112 ++++++++++++ .../samples_find_similar_faces_async.py | 172 ++++++++++++++++++ .../samples/images/Family1-Dad1.jpg | Bin 0 -> 44796 bytes .../samples/images/Family1-Dad2.jpg | Bin 0 -> 46585 bytes .../samples/images/Family1-Dad3.jpg | Bin 0 -> 34562 bytes .../samples/images/Family1-Daughter1.jpg | Bin 0 -> 26921 bytes .../samples/images/Family1-Daughter2.jpg | Bin 0 -> 27307 bytes .../samples/images/Family1-Daughter3.jpg | Bin 0 -> 18559 bytes .../samples/images/Family1-Mom1.jpg | Bin 0 -> 34200 bytes .../samples/images/Family1-Mom2.jpg | Bin 0 -> 26212 bytes .../samples/images/Family1-Son1.jpg | Bin 0 -> 38031 bytes .../samples/images/Family1-Son2.jpg | Bin 0 -> 36035 bytes .../samples/images/Family2-Lady1.jpg | Bin 0 -> 62275 bytes .../samples/images/Family2-Lady2.jpg | Bin 0 -> 40873 bytes 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sdk/face/azure-ai-vision-face/samples/_shared/__init__.py create mode 100644 sdk/face/azure-ai-vision-face/samples/_shared/constants.py create mode 100644 sdk/face/azure-ai-vision-face/samples/_shared/helpers.py create mode 100644 sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py create mode 100644 sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py create mode 100644 sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py create mode 100644 sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py create mode 100644 sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py create mode 100644 sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Dad1.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Dad2.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Dad3.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Daughter1.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Daughter2.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Daughter3.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Mom1.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Mom2.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Son1.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family1-Son2.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family2-Lady1.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family2-Lady2.jpg create mode 100644 sdk/face/azure-ai-vision-face/samples/images/Family2-Man1.jpg create mode 100644 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sdk/face/azure-ai-vision-face/samples/samples_authentication.py create mode 100644 sdk/face/azure-ai-vision-face/samples/samples_face_detection.py create mode 100644 sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py create mode 100644 sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py create mode 100644 sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py create mode 100644 sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py diff --git a/sdk/face/azure-ai-vision-face/dev_requirements.txt b/sdk/face/azure-ai-vision-face/dev_requirements.txt index 105486471444..343daf7b0d6d 100644 --- a/sdk/face/azure-ai-vision-face/dev_requirements.txt +++ b/sdk/face/azure-ai-vision-face/dev_requirements.txt @@ -1,3 +1,4 @@ -e ../../../tools/azure-sdk-tools ../../core/azure-core -aiohttp \ No newline at end of file +../../identity/azure-identity +aiohttp diff --git a/sdk/face/azure-ai-vision-face/samples/README.md b/sdk/face/azure-ai-vision-face/samples/README.md new file mode 100644 index 000000000000..cfaea0419f25 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/README.md @@ -0,0 +1,60 @@ +--- +page_type: sample +languages: + - python +products: + - azure-ai-vision-face +urlFragment: face-samples +--- + +# Azure Face client library for Python Samples + +These are code samples that show common scenario operations with the Azure Face client library. +The async versions of the samples (the python sample files appended with `_async`) show asynchronous operations. + +Several Azure Face Python SDK samples are available to you in the SDK's GitHub repository. These samples provide example code for additional scenarios commonly encountered while working with Face: + +* [samples_authentication.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_authentication.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py)) - Examples for authenticating and creating the client: + * From a key + * From Microsoft Entra ID + +* [samples_face_liveness_detection.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py)) - Examples for performing liveness detection + +* [samples_face_liveness_detection_with_verification.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py)) - Examples for performing liveness detection with face verification + +* [samples_face_detection.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py)) - Examples for detecting human faces in an image: + * From a binary data + * From a URL + +* [samples_face_grouping.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py)) - Examples for dividing candidate faces into groups based on face similarity. + +* [samples_find_similar_faces.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py)) - Examples for searching the similar-looking faces from a set of candidate faces: + * From a faceId array + * From a large face list + +## Prerequisites +* Python 3.8 or later is required to use this package +* You must have an [Azure subscription](https://azure.microsoft.com/free/) and an [Face APIs account](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity) +to run these samples. + +## Setup + +1. Install the Face client library for Python with [pip](https://pypi.org/project/pip/): + +```bash +pip install azure-ai-vision-face +``` + +2. Clone or download this sample repository +3. Open the sample folder in Visual Studio Code or your IDE of choice. + +## Running the samples + +1. Open a terminal window and `cd` to the directory that the samples are saved in. +2. Set the environment variables specified in the sample file you wish to run. +3. Follow the usage described in the file, e.g. `python samples_face_detection.py` + +## Next steps + +Check out the [API reference documentation](https://aka.ms/azsdk-python-face-ref) to learn more about what you can do +with the Azure Face client library. diff --git a/sdk/face/azure-ai-vision-face/samples/_shared/__init__.py b/sdk/face/azure-ai-vision-face/samples/_shared/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/sdk/face/azure-ai-vision-face/samples/_shared/constants.py b/sdk/face/azure-ai-vision-face/samples/_shared/constants.py new file mode 100644 index 000000000000..c4f49c6f325f --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/_shared/constants.py @@ -0,0 +1,61 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +""" +FILE: constants.py + +DESCRIPTION: + This module defines constants required to run the sample codes. +""" + +CONFIGURATION_NAME_FACE_API_ENDPOINT = "AZURE_FACE_API_ENDPOINT" +CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY = "AZURE_FACE_API_ACCOUNT_KEY" + +DEFAULT_FACE_API_ENDPOINT = "" +DEFAULT_FACE_API_ACCOUNT_KEY = "" + +# Test images +IMAGE_FAMILY_1_DAD_1 = "images/Family1-Dad1.jpg" +IMAGE_FAMILY_1_DAD_2 = "images/Family1-Dad2.jpg" +IMAGE_FAMILY_1_DAD_3 = "images/Family1-Dad3.jpg" +IMAGE_FAMILY_1_DAUGHTER_1 = "images/Family1-Daughter1.jpg" +IMAGE_FAMILY_1_DAUGHTER_2 = "images/Family1-Daughter2.jpg" +IMAGE_FAMILY_1_DAUGHTER_3 = "images/Family1-Daughter3.jpg" +IMAGE_FAMILY_1_MOM_1 = "images/Family1-Mom1.jpg" +IMAGE_FAMILY_1_MOM_2 = "images/Family1-Mom2.jpg" +IMAGE_FAMILY_1_SON_1 = "images/Family1-Son1.jpg" +IMAGE_FAMILY_1_SON_2 = "images/Family1-Son2.jpg" +IMAGE_FAMILY_2_LADY_1 = "images/Family2-Lady1.jpg" +IMAGE_FAMILY_2_LADY_2 = "images/Family2-Lady2.jpg" +IMAGE_FAMILY_2_Man_1 = "images/Family2-Man1.jpg" +IMAGE_FAMILY_2_Man_2 = "images/Family2-Man2.jpg" +IMAGE_FAMILY_3_LADY_1 = "images/Family3-Lady1.jpg" +IMAGE_FAMILY_3_Man_1 = "images/Family3-Man1.jpg" +IMAGE_CHILD_1_PERSON_GROUP = "images/child1-person-group.jpg" +IMAGE_CHILD_2_PERSON_GROUP = "images/child2-person-group.jpg" +IMAGE_CHILD_3_PERSON_GROUP = "images/child3-person-group.jpg" +IMAGE_DETECTION_1 = "images/detection1.jpg" +IMAGE_DETECTION_2 = "images/detection2.jpg" +IMAGE_DETECTION_3 = "images/detection3.jpg" +IMAGE_DETECTION_4 = "images/detection4.jpg" +IMAGE_DETECTION_5 = "images/detection5.jpg" +IMAGE_DETECTION_6 = "images/detection6.jpg" +IMAGE_EXTRA_WOMAN_IMAGE = "images/extra-woman-image.jpg" +IMAGE_FINDSIMILAR = "images/findsimilar.jpg" +IMAGE_IDENTIFICATION1 = "images/indentification1.jpg" +IMAGE_MAN1_PERSON_GROUP = "images/man1-person-group.jpg" +IMAGE_MAN2_PERSON_GROUP = "images/man2-person-group.jpg" +IMAGE_MAN3_PERSON_GROUP = "images/man3-person-group.jpg" +TEST_IMAGE_PERSON_GROUP = "images/test-image-person-group.jpg" +IMAGE_WOMAN1_PERSON_GROUP = "images/woman1-person-group.jpg" +IMAGE_WOMAN2_PERSON_GROUP = "images/woman2-person-group.jpg" +IMAGE_WOMAN3_PERSON_GROUP = "images/woman3-person-group.jpg" +IMAGE_NINE_FACES = "images/nine-faces.png" + +DEFAULT_IMAGE_URL = "https://aka.ms/facesampleurl" +DEFAULT_IMAGE_FILE = IMAGE_DETECTION_1 diff --git a/sdk/face/azure-ai-vision-face/samples/_shared/helpers.py b/sdk/face/azure-ai-vision-face/samples/_shared/helpers.py new file mode 100644 index 000000000000..4423317ee5a7 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/_shared/helpers.py @@ -0,0 +1,35 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +""" +FILE: helpers.py + +DESCRIPTION: + This module loads logger and some utility methods required to run the sample codes. +""" + +import json +import logging +import typing + + +def get_logger(name): + # create console handler + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter(fmt='%(asctime)s %(levelname)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')) + + # create Logger + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + logger.addHandler(handler) + + return logger + + +def beautify_json(obj: typing.Dict[str, typing.Any]): + return json.dumps(obj, indent=4) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py new file mode 100644 index 000000000000..a0ccee1c7601 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py @@ -0,0 +1,113 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +""" +FILE: sample_authentication_async.py + +DESCRIPTION: + This sample demonstrates authenticating a client via: + * api key + * Azure Active Directory(AAD) + +USAGE: + python sample_authentication_async.py + + Set the environment variables with your own values before running this sample: + 1) FACE_ENDPOINT - the endpoint to your Face resource. + The environment variable below is used for api key authentication. + 2) FACE_KEY - your Face API key. + The following environment variables are required for using azure-identity's DefaultAzureCredential. + For more information, refer to https://aka.ms/azsdk/python/identity/docs#azure.identity.DefaultAzureCredential + 3) AZURE_TENANT_ID - the tenant ID in Azure Active Directory + 4) AZURE_CLIENT_ID - the application (client) ID registered in the AAD tenant + 5) AZURE_CLIENT_SECRET - the client secret for the registered application +""" +import asyncio +import os +import sys + +from dotenv import find_dotenv, load_dotenv + +# To import utility files in ../_shared folder +sys.path.insert(1, os.path.join(sys.path[0], '..')) + +from _shared.constants import ( # noqa: E402 + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, + CONFIGURATION_NAME_FACE_API_ENDPOINT, + DEFAULT_FACE_API_ACCOUNT_KEY, + DEFAULT_FACE_API_ENDPOINT, + DEFAULT_IMAGE_FILE, +) +from _shared.helpers import beautify_json, get_logger # noqa: E402 + + +class FaceAuthentication(): + def __init__(self): + load_dotenv(find_dotenv()) + self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) + self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.logger = get_logger("sample_authentication_async") + + async def authentication_by_api_key(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceClient + from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel + + self.logger.info("Instantiate a FaceClient using an api key") + async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + from pathlib import Path + + sample_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + result = await face_client.detect( + file_content, + FaceDetectionModel.DETECTION_03, + FaceRecognitionModel.RECOGNITION_04, + False) # return_face_id = False + + self.logger.info(f"Detect faces from the file: {sample_file_path}") + for idx, face in enumerate(result): + self.logger.info(f"----- Detection result: #{idx+1} -----") + self.logger.info(f"Face: {beautify_json(face.as_dict())}") + + async def authentication_by_aad_credential(self): + from azure.identity.aio import DefaultAzureCredential + from azure.ai.vision.face.aio import FaceClient + from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel + + self.logger.info("Instantiate a FaceClient using a TokenCredential") + async with DefaultAzureCredential() as credential, \ + FaceClient(endpoint=self.endpoint, credential=credential) as face_client: + + from pathlib import Path + + sample_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + result = await face_client.detect( + file_content, + FaceDetectionModel.DETECTION_03, + FaceRecognitionModel.RECOGNITION_04, + False) # return_face_id = False + + self.logger.info(f"Detect faces from the file: {sample_file_path}") + for idx, face in enumerate(result): + self.logger.info(f"----- Detection result: #{idx+1} -----") + self.logger.info(f"Face: {beautify_json(face.as_dict())}") + + +async def main(): + sample = FaceAuthentication() + await sample.authentication_by_api_key() + await sample.authentication_by_aad_credential() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py new file mode 100644 index 000000000000..e33d6c25b3c6 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py @@ -0,0 +1,111 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +""" +FILE: sample_face_detection_async.py + +DESCRIPTION: + This sample demonstrates how to detect faces and analyze faces from an image or binary data. + +USAGE: + python sample_face_detection_async.py + + Set the environment variables with your own values before running this sample: + 1) FACE_ENDPOINT - the endpoint to your Face resource. + 2) FACE_KEY - your Face API key. +""" +import asyncio +import os +import sys + +from dotenv import find_dotenv, load_dotenv + +# To import utility files in ../_shared folder +sys.path.insert(1, os.path.join(sys.path[0], '..')) + +from _shared.constants import ( # noqa: E402 + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, + CONFIGURATION_NAME_FACE_API_ENDPOINT, + DEFAULT_FACE_API_ACCOUNT_KEY, + DEFAULT_FACE_API_ENDPOINT, + DEFAULT_IMAGE_URL, + IMAGE_DETECTION_5, +) +from _shared.helpers import beautify_json, get_logger # noqa: E402 + + +class DetectFaces(): + def __init__(self): + load_dotenv(find_dotenv()) + self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) + self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.logger = get_logger("sample_face_detection_async") + + async def detect(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceClient + from azure.ai.vision.face.models import ( + FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) + + async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + from pathlib import Path + + sample_file_path = Path(__file__).resolve().parent / ("../" + IMAGE_DETECTION_5) + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + result = await face_client.detect( + file_content, + FaceDetectionModel.DETECTION_03, + FaceRecognitionModel.RECOGNITION_04, + True, # return_face_id + return_face_attributes=[ + FaceAttributeTypeDetection03.HEAD_POSE, + FaceAttributeTypeDetection03.MASK, + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], + return_face_landmarks=True, + return_recognition_model=True, + face_id_time_to_live=120) + + self.logger.info(f"Detect faces from the file: {sample_file_path}") + for idx, face in enumerate(result): + self.logger.info(f"----- Detection result: #{idx+1} -----") + self.logger.info(f"Face: {beautify_json(face.as_dict())}") + + async def detect_from_url(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceClient + from azure.ai.vision.face.models import ( + FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection01) + + async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + sample_url = DEFAULT_IMAGE_URL + result = await face_client.detect_from_url( + sample_url, + FaceDetectionModel.DETECTION_01, + FaceRecognitionModel.RECOGNITION_04, + False, # return_face_id + return_face_attributes=[ + FaceAttributeTypeDetection01.ACCESSORIES, + FaceAttributeTypeDetection01.EXPOSURE, + FaceAttributeTypeDetection01.GLASSES, + FaceAttributeTypeDetection01.NOISE]) + + self.logger.info(f"Detect faces from the url: {sample_url}") + for idx, face in enumerate(result): + self.logger.info(f"----- Detection result: #{idx+1} -----") + self.logger.info(f"Face: {beautify_json(face.as_dict())}") + + +async def main(): + sample = DetectFaces() + await sample.detect() + await sample.detect_from_url() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py new file mode 100644 index 000000000000..fc674a9f4ab1 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py @@ -0,0 +1,77 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +""" +FILE: sample_face_grouping_async.py + +DESCRIPTION: + This sample demonstrates how to group faces based on face similarity. + +USAGE: + python sample_face_grouping_async.py + + Set the environment variables with your own values before running this sample: + 1) FACE_ENDPOINT - the endpoint to your Face resource. + 2) FACE_KEY - your Face API key. +""" +import asyncio +import os +import sys + +from dotenv import find_dotenv, load_dotenv + +# To import utility files in ../_shared folder +sys.path.insert(1, os.path.join(sys.path[0], '..')) + +from _shared.constants import ( # noqa: E402 + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, + CONFIGURATION_NAME_FACE_API_ENDPOINT, + DEFAULT_FACE_API_ACCOUNT_KEY, + DEFAULT_FACE_API_ENDPOINT, + IMAGE_NINE_FACES, +) +from _shared.helpers import beautify_json, get_logger # noqa: E402 + + +class GroupFaces(): + def __init__(self): + load_dotenv(find_dotenv()) + self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) + self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.logger = get_logger("sample_face_grouping_async") + + async def group(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceClient + from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel + + async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + from pathlib import Path + + sample_file_path = Path(__file__).resolve().parent / ("../" + IMAGE_NINE_FACES) + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + detect_result = await face_client.detect( + image_content=file_content, + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True) + + face_ids = [face.face_id for face in detect_result] + self.logger.info(f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}") + + group_result = await face_client.group(face_ids=face_ids) + self.logger.info(f"Group result: {beautify_json(group_result.as_dict())}") + + +async def main(): + sample = GroupFaces() + await sample.group() + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py new file mode 100644 index 000000000000..c28fd46cab86 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py @@ -0,0 +1,100 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +""" +FILE: sample_face_liveness_detection_async.py + +DESCRIPTION: + This sample demonstrates how to determine if a face in an input video stream is real (live) or fake (spoof). + There is a step during the execution of the program that requires the user to switch to mobile SDK for performing + the liveness detection. + +USAGE: + python sample_face_liveness_detection_async.py + + Set the environment variables with your own values before running this sample: + 1) FACE_ENDPOINT - the endpoint to your Face resource. + 2) FACE_KEY - your Face API key. +""" +import asyncio +import os +import sys +import uuid + +from dotenv import find_dotenv, load_dotenv + +# To import utility files in ../_shared folder +sys.path.insert(1, os.path.join(sys.path[0], '..')) + +from _shared.constants import ( # noqa: E402 + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, + CONFIGURATION_NAME_FACE_API_ENDPOINT, + DEFAULT_FACE_API_ACCOUNT_KEY, + DEFAULT_FACE_API_ENDPOINT, +) +from _shared.helpers import beautify_json, get_logger # noqa: E402 + + +class DetectLiveness(): + def __init__(self): + load_dotenv(find_dotenv()) + self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) + self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.logger = get_logger("sample_face_liveness_detection_async") + + async def livenessSession(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceSessionClient + from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode + + async with FaceSessionClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + # Create a session. + self.logger.info("Create a new liveness session.") + created_session = await face_session_client.create_liveness_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=str(uuid.uuid4()), + send_results_to_client=False, + auth_token_time_to_live_in_seconds=60)) + self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") + + # Get the liveness detection result. + self.logger.info("Get the liveness detection result.") + liveness_result = await face_session_client.get_liveness_session_result(created_session.session_id) + self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + + self.logger.info( + "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + " to download client SDK to run session starts and detcet liveness call.") + input("Press any key to continue when you complete these steps to run sample to get session results.") + + # Get the liveness detection result again. + self.logger.info("Get the liveness detection result again.") + liveness_result = await face_session_client.get_liveness_session_result(created_session.session_id) + self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + + # Get audit entries + self.logger.info("Get the audit entries of this session.") + audit_entries = await face_session_client.get_liveness_session_audit_entries(created_session.session_id) + for idx, entry in enumerate(audit_entries): + self.logger.info(f"----- Audit entries: #{idx+1}-----") + self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") + + # Delete the session + self.logger.info("Delete the session.") + await face_session_client.delete_liveness_session(created_session.session_id) + + +async def main(): + sample = DetectLiveness() + await sample.livenessSession() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py new file mode 100644 index 000000000000..2005cec903a0 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py @@ -0,0 +1,112 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +""" +FILE: sample_face_liveness_detection_with_verification_async.py + +DESCRIPTION: + This sample demonstrates how to determine if a face in an input video stream is real (live) or fake (spoof), and + verify if the face belongs to the particular person. + There is a step during the execution of the program that requires the user to switch to mobile SDK for performing + the liveness detection. + +USAGE: + python sample_face_liveness_detection_with_verification_async.py + + Set the environment variables with your own values before running this sample: + 1) FACE_ENDPOINT - the endpoint to your Face resource. + 2) FACE_KEY - your Face API key. +""" +import asyncio +import os +import sys +import uuid + +from dotenv import find_dotenv, load_dotenv + +# To import utility files in ../_shared folder +sys.path.insert(1, os.path.join(sys.path[0], '..')) + +from _shared.constants import ( # noqa: E402 + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, + CONFIGURATION_NAME_FACE_API_ENDPOINT, + DEFAULT_FACE_API_ACCOUNT_KEY, + DEFAULT_FACE_API_ENDPOINT, + DEFAULT_IMAGE_FILE, +) +from _shared.helpers import beautify_json, get_logger # noqa: E402 + + +class DetectLivenessWithVerify(): + def __init__(self): + load_dotenv(find_dotenv()) + self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) + self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.logger = get_logger("sample_face_liveness_detection_with_verification_async") + + async def livenessSessionWithVerify(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceSessionClient + from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode + + async with FaceSessionClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + from pathlib import Path + + verify_image_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) + with open(verify_image_file_path, "rb") as fd: + file_content = fd.read() + + # Create a session. + self.logger.info("Create a new liveness with verify session with verify image.") + + created_session = await face_session_client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=str(uuid.uuid4()), + send_results_to_client=False, + auth_token_time_to_live_in_seconds=60), + verify_image=file_content) + self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") + + # Get the liveness detection and verification result. + self.logger.info("Get the liveness detection and verification result.") + liveness_result = await face_session_client.get_liveness_with_verify_session_result( + created_session.session_id) + self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + + self.logger.info( + "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + " to download client SDK to run session starts and detcet liveness call.") + input("Press any key to continue when you complete these steps to run sample to get session results.") + + # Get the liveness detection result again. + self.logger.info("Get the liveness detection result again.") + liveness_result = await face_session_client.get_liveness_with_verify_session_result( + created_session.session_id) + self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + + # Get audit entries + self.logger.info("Get the audit entries of this session.") + audit_entries = await face_session_client.get_liveness_with_verify_session_audit_entries( + created_session.session_id) + for idx, entry in enumerate(audit_entries): + self.logger.info(f"----- Audit entries: #{idx+1}-----") + self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") + + # Delete the session + self.logger.info("Delete the session.") + await face_session_client.delete_liveness_with_verify_session(created_session.session_id) + + +async def main(): + sample = DetectLivenessWithVerify() + await sample.livenessSessionWithVerify() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py new file mode 100644 index 000000000000..1148e6130f44 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py @@ -0,0 +1,172 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +""" +FILE: sample_findsimilar_faces_async.py + +DESCRIPTION: + This sample demonstrates how to find similar faces from a specified list of face ids or a largeFaceList. + +USAGE: + python sample_findsimilar_faces_async.py + + Set the environment variables with your own values before running this sample: + 1) FACE_ENDPOINT - the endpoint to your Face resource. + 2) FACE_KEY - your Face API key. +""" +import asyncio +import os +import sys + +from dotenv import find_dotenv, load_dotenv + +# To import utility files in ../_shared folder +sys.path.insert(1, os.path.join(sys.path[0], '..')) + +from _shared.constants import ( # noqa: E402 + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, + CONFIGURATION_NAME_FACE_API_ENDPOINT, + DEFAULT_FACE_API_ACCOUNT_KEY, + DEFAULT_FACE_API_ENDPOINT, + IMAGE_FAMILY_1_MOM_1, + IMAGE_FAMILY_1_MOM_2, + IMAGE_FAMILY_2_LADY_1, + IMAGE_FAMILY_2_LADY_2, + IMAGE_FAMILY_3_LADY_1, + IMAGE_FINDSIMILAR, + IMAGE_NINE_FACES, +) +from _shared.helpers import beautify_json, get_logger # noqa: E402 + + +def _read_file_content(file_path: str): + with open(file_path, "rb") as fd: + file_content = fd.read() + + return file_content + + +def _get_full_file_path(image_file_name: str): + from pathlib import Path + return Path(__file__).resolve().parent / ("../" + image_file_name) + + +class FindSimilarFaces(): + def __init__(self): + load_dotenv(find_dotenv()) + self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) + self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.logger = get_logger("sample_findsimilar_faces_async") + + async def find_similar_from_face_ids(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceClient + from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel + + async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + # Detect faces from 'IMAGE_NINE_FACES' + nine_faces_file_path = _get_full_file_path(IMAGE_NINE_FACES) + detect_result1 = await face_client.detect( + image_content=_read_file_content(nine_faces_file_path), + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True) + + face_ids = [face.face_id for face in detect_result1] + self.logger.info(f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}") + + # Detect face from 'IMAGE_FINDSIMILAR' + find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) + detect_result2 = await face_client.detect( + image_content=_read_file_content(find_similar_file_path), + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True) + + assert len(detect_result2) == 1 + face_id = detect_result2[0].face_id + self.logger.info(f"Detect 1 face from the file '{find_similar_file_path}': {face_id}") + + # Call Find Similar + # The default find similar mode is MATCH_PERSON + find_similar_result1 = await face_client.find_similar(face_id=face_id, face_ids=face_ids) + self.logger.info("Find Similar with matchPerson mode:") + for r in find_similar_result1: + self.logger.info(f"{beautify_json(r.as_dict())}") + + async def find_similar_from_large_face_list(self): + from azure.core.credentials import AzureKeyCredential + from azure.ai.vision.face.aio import FaceAdministrationClient, FaceClient + from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel, FindSimilarMatchMode + + async with FaceAdministrationClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_admin_client, \ + FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + + large_face_list_id = "lfl01" + # Prepare a LargeFaceList which contains serveral faces. + self.logger.info(f"Create a LargeFaceList, id = {large_face_list_id}") + await face_admin_client.create_large_face_list( + large_face_list_id, name="List of Face", user_data="Large Face List for Test", + recognition_model=FaceRecognitionModel.RECOGNITION_04) + + # Add faces into the largeFaceList + self.logger.info(f"Add faces into the LargeFaceList {large_face_list_id}") + await face_admin_client.add_large_face_list_face( + large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_1_MOM_1)), + detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-1") + await face_admin_client.add_large_face_list_face( + large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_1_MOM_2)), + detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-2") + await face_admin_client.add_large_face_list_face( + large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_2_LADY_1)), + detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-1") + await face_admin_client.add_large_face_list_face( + large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_2_LADY_2)), + detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-2") + await face_admin_client.add_large_face_list_face( + large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_3_LADY_1)), + detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady3-1") + + # The LargeFaceList should be trained to make it ready for find similar operation. + self.logger.info(f"Train the LargeFaceList {large_face_list_id}, and wait until the operation completes.") + poller = await face_admin_client.begin_train_large_face_list(large_face_list_id, polling_interval=30) + await poller.wait() # Keep polling until the "Train" operation completes. + + # Detect face from 'IMAGE_FINDSIMILAR' + find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) + detect_result = await face_client.detect( + image_content=_read_file_content(find_similar_file_path), + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True) + + assert len(detect_result) == 1 + face_id = detect_result[0].face_id + self.logger.info(f"Detect 1 face from the file '{find_similar_file_path}': {face_id}") + + # Call Find Similar + find_similar_result = await face_client.find_similar_from_large_face_list( + face_id=face_id, large_face_list_id=large_face_list_id, + max_num_of_candidates_returned=3, mode=FindSimilarMatchMode.MATCH_FACE) + self.logger.info("Find Similar with matchFace mode:") + for r in find_similar_result: + self.logger.info(f"{beautify_json(r.as_dict())}") + + # Clean up: Remove the LargeFaceList + self.logger.info(f"Remove the LargeFaceList {large_face_list_id}") + await face_admin_client.delete_large_face_list(large_face_list_id) + + +async def main(): + sample = FindSimilarFaces() + await sample.find_similar_from_face_ids() + await sample.find_similar_from_large_face_list() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/images/Family1-Dad1.jpg b/sdk/face/azure-ai-vision-face/samples/images/Family1-Dad1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a9baaa7c58a6b574dd8eea1b48a37598f8e1876d GIT binary patch literal 44796 zcmeFZbzD_V*D!qOZV_oDMY_ABK|;Ev4&i}wfOBX8MU(~sK}6~9?owJQ>25^2JHG?Q zbzS#!zt403zW4Y1^*XR;X02JXX3d(|XYaN4T#a7Mk+sXZ*;)YrDk^~c0000TfQCQ_ zK!WEG;C}!FG62d=9sn>$Apf0zf^hFA4I-R|3jmJ+l<><5f%_(Z3!V@6w*?^oteXZ; zzXI6WJ^}zt_`gyqONcp;M%@MigFtN{4m8rzH1~9E!IltLC^LYYlZ#K7lUJBifQE}p zn2%4GixbWRDGA|6yH{5LKoa6_{+&rkzw&F)W7!wf$Fb*ATa*DABYIQ zcp$JN{^m^@@q-;e0iMD&@*}+(z1jd^-}KY7>Ba0|6|bUDI7n0Q>;h=;-Je z=-3z-*aTRZSOmm)*w}c)cZi6HiHPnHVBdtF$q(RPV+0&5EF4@Md|X`oTe!Hmx2_}H zTR*B0{3i^qJ^~2P0Z9OVBm`OjA^`#t0m9W3yptGLEeH~DDNzt^0$kwhfCNB5L`FeH 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z)2m36KI)e8*IiIIs2uX^h4wnN_1%%T`gbol3k|fG!7zTr9jES z$lw?|G$y2Pv)w0k3MKUApG-)DowwGnFc|vmz0CvOrk|*w)lWV+tQmfnJmCM(`kAx$ zCvW!yZa>)fICnPMF5bs<5jJqnoK>rxIvxiNR!@T3Ymt-{Rc0QoT`^$!2ezhq(<81!a3+|9 zFb7RS&?z;b>xe^KfH9#p2aY2l289^YF@PbsFw_kNd7i#_w4{WkbHJWlFhnraOJ=;d z^*9zoaG?O4k6#grA%u{2!>Uy3CjXQA{6j6AmEgc z*80FS84eh-ifg~V_4dcxP5%J5Htyy(7SZpbV-@^SG_k;9D?=)%ECD3v;f&k2Q(Ik} zNc~zbwKrb_HNV|%!|o{KlW6Ws8`);fZQz+%&Q&x@>ik9-Ut*7kBCVqJJ2*3)|iii0xzzOg}id5 ztKJ`TK!pZ$`H3D_se;2Wy1FddSbNGH1Bx0Em3&7G5=97I)x2NM^X&V12?= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + + # Teardown + for sid in recorded_device_correlation_ids_dict.values(): + client.delete_liveness_session(sid) + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_get_session_result(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + + session = client.get_liveness_session_result(recorded_session_id) + assert session.id == recorded_session_id + assert session.created_date_time is not None + assert session.session_start_date_time is not None + assert isinstance(session.session_expired, bool) + _assert_is_string_and_not_empty(session.device_correlation_id) + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + assert isinstance(session.status, FaceSessionStatus) + _assert_liveness_session_audit_entry_is_valid( + session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_get_session_audit_entries(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + + entries = client.get_liveness_session_audit_entries(recorded_session_id) + assert len(entries) == 2 + for entry in entries: + _assert_liveness_session_audit_entry_is_valid( + entry, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + + return variables + + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_delete_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + created_session = client.create_liveness_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id)) + session_id = created_session.session_id + _assert_is_string_and_not_empty(session_id) + + # Test `delete session` operation + client.delete_liveness_session(session_id) + + with pytest.raises(ResourceNotFoundError) as exception: + client.get_liveness_session_result(session_id) + assert exception.value.status_code == 404 + assert exception.value.error.code == "SessionNotFound" + + return variables diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py new file mode 100644 index 000000000000..e79f5281e6ae --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py @@ -0,0 +1,158 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +import pytest +import uuid + +from devtools_testutils import AzureRecordedTestCase +from devtools_testutils.aio import recorded_by_proxy_async +from azure.core.exceptions import ResourceNotFoundError +from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + FaceSessionStatus, + LivenessOperationMode, +) + +from preparers import AsyncFaceSessionClientPreparer, FacePreparer +from _shared.asserter import ( + _assert_is_string_and_not_empty, + _assert_liveness_session_audit_entry_is_valid, +) + + +class TestLivenessSessionAsync(AzureRecordedTestCase): + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_create_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + # Test `create session` operation + created_session = await client.create_liveness_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id)) + + _assert_is_string_and_not_empty(created_session.session_id) + _assert_is_string_and_not_empty(created_session.auth_token) + session_id = created_session.session_id + + session = await client.get_liveness_session_result(session_id) + assert session.device_correlation_id == recorded_device_correlation_id + + # Teardown + await client.delete_liveness_session(session_id) + await client.close() + + return variables + + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_list_sessions(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + + # key-value pairs = {device_correlation_id, created_session_id} + recorded_device_correlation_ids_dict = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + } + + # Create 2 sessions with different deivce_correlation_id + for dcid in recorded_device_correlation_ids_dict.keys(): + created_session = await client.create_liveness_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=dcid)) + + _assert_is_string_and_not_empty(created_session.session_id) + recorded_device_correlation_ids_dict[dcid] = created_session.session_id + + # Test `list sessions` operation + result = await client.get_liveness_sessions() + + assert len(result) == 2 + assert result[0].id < result[1].id + for session in result: + assert session.device_correlation_id in recorded_device_correlation_ids_dict + assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.created_date_time is not None + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + + # Teardown + for sid in recorded_device_correlation_ids_dict.values(): + await client.delete_liveness_session(sid) + await client.close() + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_get_session_result(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + + session = await client.get_liveness_session_result(recorded_session_id) + assert session.id == recorded_session_id + assert session.created_date_time is not None + assert session.session_start_date_time is not None + assert isinstance(session.session_expired, bool) + _assert_is_string_and_not_empty(session.device_correlation_id) + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + assert isinstance(session.status, FaceSessionStatus) + _assert_liveness_session_audit_entry_is_valid( + session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + await client.close() + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_get_session_audit_entries(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + + entries = await client.get_liveness_session_audit_entries(recorded_session_id) + assert len(entries) == 2 + for entry in entries: + _assert_liveness_session_audit_entry_is_valid( + entry, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + await client.close() + + return variables + + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_delete_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + created_session = await client.create_liveness_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id)) + session_id = created_session.session_id + _assert_is_string_and_not_empty(session_id) + + # Test `delete session` operation + await client.delete_liveness_session(session_id) + + with pytest.raises(ResourceNotFoundError) as exception: + await client.get_liveness_session_result(session_id) + assert exception.value.status_code == 404 + assert exception.value.error.code == "SessionNotFound" + await client.close() + + return variables diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py new file mode 100644 index 000000000000..2170c826dadf --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py @@ -0,0 +1,192 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +from pathlib import Path +import pytest +import uuid + +from devtools_testutils import AzureRecordedTestCase, recorded_by_proxy +from azure.core.exceptions import ResourceNotFoundError +from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + FaceSessionStatus, + LivenessOperationMode, +) + +from preparers import FaceSessionClientPreparer, FacePreparer +from _shared.asserter import ( + _assert_is_string_and_not_empty, + _assert_liveness_session_audit_entry_is_valid, + _assert_liveness_with_verify_image_not_empty, +) +from _shared.constants import IMAGE_DETECTION_1 + + +class TestLivenessWithVerifySession(AzureRecordedTestCase): + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_create_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + # Test `create session` operation + created_session = client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id), + None) # verify_image = None + + _assert_is_string_and_not_empty(created_session.session_id) + _assert_is_string_and_not_empty(created_session.auth_token) + session_id = created_session.session_id + + session = client.get_liveness_with_verify_session_result(session_id) + assert session.device_correlation_id == recorded_device_correlation_id + + # Teardown + client.delete_liveness_with_verify_session(session_id) + + return variables + + @pytest.mark.live_test_only # It's hard to sanitize the multipart form-data request. + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_create_session_with_verify_image(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + # verify_image + sample_file_path = Path(__file__).resolve().parent / IMAGE_DETECTION_1 + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + # Test `create session` operation + created_session = client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id), + verify_image=file_content) + + _assert_is_string_and_not_empty(created_session.session_id) + _assert_is_string_and_not_empty(created_session.auth_token) + _assert_liveness_with_verify_image_not_empty(created_session.verify_image) + + session_id = created_session.session_id + + session = client.get_liveness_with_verify_session_result(session_id) + assert session.device_correlation_id == recorded_device_correlation_id + + # Teardown + client.delete_liveness_with_verify_session(session_id) + + return variables + + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_list_sessions(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + + # key-value pairs = {device_correlation_id, created_session_id} + recorded_device_correlation_ids_dict = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + } + + # Create 2 sessions with different deivce_correlation_id + for dcid in recorded_device_correlation_ids_dict.keys(): + created_session = client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=dcid), + None) # verify_image = None + + _assert_is_string_and_not_empty(created_session.session_id) + recorded_device_correlation_ids_dict[dcid] = created_session.session_id + + # Test `list sessions` operation + result = client.get_liveness_with_verify_sessions() + + assert len(result) == 2 + assert result[0].id < result[1].id + for session in result: + assert session.device_correlation_id in recorded_device_correlation_ids_dict + assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.created_date_time is not None + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + + # Teardown + for sid in recorded_device_correlation_ids_dict.values(): + client.delete_liveness_with_verify_session(sid) + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_get_session_result(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + + session = client.get_liveness_with_verify_session_result(recorded_session_id) + assert session.id == recorded_session_id + assert session.created_date_time is not None + assert session.session_start_date_time is not None + assert isinstance(session.session_expired, bool) + _assert_is_string_and_not_empty(session.device_correlation_id) + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + assert isinstance(session.status, FaceSessionStatus) + _assert_liveness_session_audit_entry_is_valid( + session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_get_session_audit_entries(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + + entries = client.get_liveness_with_verify_session_audit_entries(recorded_session_id) + assert len(entries) == 2 + for entry in entries: + _assert_liveness_session_audit_entry_is_valid( + entry, expected_session_id=recorded_session_id, is_liveness_with_verify=True) + + return variables + + @FacePreparer() + @FaceSessionClientPreparer() + @recorded_by_proxy + def test_delete_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + created_session = client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id), + None) # verify_image = None + session_id = created_session.session_id + _assert_is_string_and_not_empty(session_id) + + # Test `delete session` operation + client.delete_liveness_with_verify_session(session_id) + + with pytest.raises(ResourceNotFoundError) as exception: + client.get_liveness_with_verify_session_result(session_id) + assert exception.value.status_code == 404 + assert exception.value.error.code == "SessionNotFound" + + return variables diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py new file mode 100644 index 000000000000..2ba9381fe013 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py @@ -0,0 +1,199 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +from pathlib import Path +import pytest +import uuid + +from devtools_testutils import AzureRecordedTestCase +from devtools_testutils.aio import recorded_by_proxy_async +from azure.core.exceptions import ResourceNotFoundError +from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + FaceSessionStatus, + LivenessOperationMode, +) + +from preparers import AsyncFaceSessionClientPreparer, FacePreparer +from _shared.asserter import ( + _assert_is_string_and_not_empty, + _assert_liveness_session_audit_entry_is_valid, + _assert_liveness_with_verify_image_not_empty, +) +from _shared.constants import IMAGE_DETECTION_1 + + +class TestLivenessWithVerifySessionAsync(AzureRecordedTestCase): + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_create_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + # Test `create session` operation + created_session = await client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id), + None) # verify_image = None + + _assert_is_string_and_not_empty(created_session.session_id) + _assert_is_string_and_not_empty(created_session.auth_token) + session_id = created_session.session_id + + session = await client.get_liveness_with_verify_session_result(session_id) + assert session.device_correlation_id == recorded_device_correlation_id + + # Teardown + await client.delete_liveness_with_verify_session(session_id) + await client.close() + + return variables + + @pytest.mark.live_test_only # It's hard to sanitize the multipart form-data request. + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_create_session_with_verify_image(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + # verify_image + sample_file_path = Path(__file__).resolve().parent / IMAGE_DETECTION_1 + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + # Test `create session` operation + created_session = await client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id), + verify_image=file_content) + + _assert_is_string_and_not_empty(created_session.session_id) + _assert_is_string_and_not_empty(created_session.auth_token) + _assert_liveness_with_verify_image_not_empty(created_session.verify_image) + + session_id = created_session.session_id + + session = await client.get_liveness_with_verify_session_result(session_id) + assert session.device_correlation_id == recorded_device_correlation_id + + # Teardown + await client.delete_liveness_with_verify_session(session_id) + await client.close() + + return variables + + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_list_sessions(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + + # key-value pairs = {device_correlation_id, created_session_id} + recorded_device_correlation_ids_dict = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + } + + # Create 2 sessions with different deivce_correlation_id + for dcid in recorded_device_correlation_ids_dict.keys(): + created_session = await client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=dcid), + None) # verify_image = None + + _assert_is_string_and_not_empty(created_session.session_id) + recorded_device_correlation_ids_dict[dcid] = created_session.session_id + + # Test `list sessions` operation + result = await client.get_liveness_with_verify_sessions() + + assert len(result) == 2 + assert result[0].id < result[1].id + for session in result: + assert session.device_correlation_id in recorded_device_correlation_ids_dict + assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.created_date_time is not None + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + + # Teardown + for sid in recorded_device_correlation_ids_dict.values(): + await client.delete_liveness_with_verify_session(sid) + await client.close() + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_get_session_result(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + + session = await client.get_liveness_with_verify_session_result(recorded_session_id) + assert session.id == recorded_session_id + assert session.created_date_time is not None + assert session.session_start_date_time is not None + assert isinstance(session.session_expired, bool) + _assert_is_string_and_not_empty(session.device_correlation_id) + assert session.auth_token_time_to_live_in_seconds >= 60 + assert session.auth_token_time_to_live_in_seconds <= 86400 + assert isinstance(session.status, FaceSessionStatus) + _assert_liveness_session_audit_entry_is_valid( + session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + await client.close() + + return variables + + @pytest.mark.playback_test_only + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_get_session_audit_entries(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + + entries = await client.get_liveness_with_verify_session_audit_entries(recorded_session_id) + assert len(entries) == 2 + for entry in entries: + _assert_liveness_session_audit_entry_is_valid( + entry, expected_session_id=recorded_session_id, is_liveness_with_verify=True) + await client.close() + + return variables + + @FacePreparer() + @AsyncFaceSessionClientPreparer() + @recorded_by_proxy_async + async def test_delete_session(self, client, **kwargs): + variables = kwargs.pop("variables", {}) + recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + + created_session = await client.create_liveness_with_verify_session( + CreateLivenessSessionContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=recorded_device_correlation_id), + None) # verify_image = None + session_id = created_session.session_id + _assert_is_string_and_not_empty(session_id) + + # Test `delete session` operation + await client.delete_liveness_with_verify_session(session_id) + + with pytest.raises(ResourceNotFoundError) as exception: + await client.get_liveness_with_verify_session_result(session_id) + assert exception.value.status_code == 404 + assert exception.value.error.code == "SessionNotFound" + await client.close() + + return variables From 931b3a6d39b944df75b51853117ca3dcdadbd943 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Tue, 30 Apr 2024 22:32:36 +0800 Subject: [PATCH 07/41] Add SDK README.md --- sdk/face/azure-ai-vision-face/README.md | 441 +++++++++++++++++++++++- 1 file changed, 433 insertions(+), 8 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index 7ba6d76e8240..a51f3ede1384 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -1,21 +1,417 @@ +# Azure Ai Vision Face client library for Python +The Azure AI Face service provides AI algorithms that detect, recognize, and analyze human faces in images. It includes the following main features: -# Azure Ai Vision Face client library for Python - +- Face detection and analyzsis +- Liveness detection +- Face recognition + - Face verification ("one-to-one" matching) + - Face identification ("one-to-many" matching) +- Find similar faces +- Group faces + +[Source code][source_code] +| [Package (PyPI)][face_pypi] +| [Package (Conda)](https://anaconda.org/microsoft/azure-ai-vision-face/) +| [API reference documentation][face_ref_docs] +| [Product documentation][face_product_docs] +| [Samples][face_samples] ## Getting started +### Prerequisites + +- Python 3.8 or later is required to use this package. +- You need an [Azure subscription][azure_sub] to use this package and either + * an [Azure Face account][azure_portal_list_face_account] or + * an [Azure Cognitive Service account][azure_portal_list_cognitive_service_account] + +### Create a Face or a Cognitive Services resource + +Azure AI Face supports both [multi-service][azure_cognitive_service_account] and single-service access. Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Face access only, create a Face resource. Please note that you will need a single-service resource if you intend to use [Azure Active Directory authentication](#create-the-client-with-an-azure-active-directory-credential). + +* To create a new Face or Cognitive Services account, you can use [Azure Portal][azure_portal_create_face_account], [Azure PowerShell][quick_start_create_account_via_azure_powershell], or [Azure CLI][quick_start_create_account_via_azure_cli]. + ### Install the package ```bash python -m pip install azure-ai-vision-face ``` -#### Prequisites +### Authenticate the client -- Python 3.8 or later is required to use this package. -- You need an [Azure subscription][azure_sub] to use this package. -- An existing Azure Ai Vision Face instance. +In order to interact with the Face service, you will need to create an instance of a client. +An **endpoint** and **credential** are necessary to instantiate the client object. + +#### Get the endpoint and API keys + +You can find the endpoint and keys for your Face resource using the [Azure Portal][get_endpoint_via_azure_portal] or [Azure CLI][get_endpoint_via_azure_cli]: + +```bash +# Get the endpoint for the Face resource +az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint" +``` + +Either a regional endpoint or a custom subdomain can be used for authentication. They are formatted as follows: + +``` +Regional endpoint: https://.api.cognitive.microsoft.com/ +Custom subdomain: https://.cognitiveservices.azure.com/ +``` + +A regional endpoint is the same for every resource in a region. A complete list of supported regional endpoints can be consulted [here][regional_endpoints]. Please note that regional endpoints do not support AAD authentication. + +A custom subdomain, on the other hand, is a name that is unique to the Face resource. They can only be used by [single-service resources][azure_portal_create_face_account]. + +```bash +# Get the API keys for the Face resource +az cognitiveservices account keys list --name "" --resource-group "" +``` + +#### Create the client with AzureKeyCredential + +To use an API key as the `credential` parameter, pass the key as a string into an instance of [AzureKeyCredential][azure_sdk_python_azure_key_credential]. + +```python +from azure.core.credentials import AzureKeyCredential +from azure.ai.vision.face import FaceClient + +endpoint = "https://.cognitiveservices.azure.com/" +credential = AzureKeyCredential("") +face_client = FaceClient(endpoint, credential) +``` + +#### Create the client with an Azure Active Directory credential + +`AzureKeyCredential` authentication is used in the examples in this getting started guide, but you can also authenticate with Azure Active Directory using the [azure-identity][azure_sdk_python_identity] library. +Note that regional endpoints do not support AAD authentication. Create a [custom subdomain][custom_subdomain] name for your resource in order to use this type of authentication. + +To use the [DefaultAzureCredential][azure_sdk_python_default_azure_credential] type shown below, or other credential types provided with the Azure SDK, please install the `azure-identity` package: + +``` +pip install azure-identity +``` + +You will also need to [register a new AAD application and grant access][register_aad_app] to Face by assigning the `"Cognitive Services User"` role to your service principal. + +Once completed, set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: +`AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, `AZURE_CLIENT_SECRET`. + +```python +"""DefaultAzureCredential will use the values from these environment +variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET +""" +from azure.ai.vision.face import FaceClient +from azure.identity import DefaultAzureCredential + +endpoint = "https://.cognitiveservices.azure.com/" +credential = DefaultAzureCredential() + +face_client = FaceClient(endpoint, credential) +``` + +## Key concepts + +### FaceClient + +`FaceClient` provides operations for: + + - Face detection and analysis: Detect human faces in an image and return the rectangle coordinates of their locations, + and optinally with landmarks, and face-related attributes. This operation is required as a first step in all the + other face recognition scenarios. + - Face recognition: Confirm that a user is who they claim to be based on how closely their face data matches the target face. + It includes Face verification ("one-to-one" matching) and Face identification ("one-to-many" matching). + - Finding similar faces from a smaller set of faces that look similar to the target face. + - Grouping faces into several smaller groups based on similarity. + +### FaceAdministrationClient + +`FaceAdministrationClient` is provided to interact with the following data structures that hold data on faces and +persons for Face recognition: + + - PersonDirectory + - FaceList + - LargeFaceList + - PersonGroup + - LargePersonGroup + +### FaceSessionClient + +`FaceSessionClient` is provided to interact with sessions which is used for Liveness detection. + + - Create, query, and delete the session. + - Query the liveness and verification result. + - Query the audit result. + +### Long-running operations + +Long-running operations are operations which consist of an initial request sent to the service to start an operation, +followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has +succeeded, to get the result. + +Methods that train a group (LargeFaceList, PersonGroup or LargePersonGroup), create/delete a Person/DynamicPersonGroup, +add a face or delete a face from a Person are modeled as long-running operations. +The client exposes a `begin_` method that returns an `LROPoller` or `AsyncLROPoller`. Callers should wait +for the operation to complete by calling `result()` on the poller object returned from the `begin_` method. +Sample code snippets are provided to illustrate using long-running operations [below](#examples "Examples"). + +## Examples + +The following section provides several code snippets covering some of the most common Face tasks, including: + +* [Detecting faces in an image](#face-detection "Face Detection") +* [Identifing the specific face from a LargePersonGroup](#face-recognition-from-largepersongroup "Face Recognition from LargePersonGroup") +* [Determining if a face in an video is real (live) or fake (spoof)](#liveness-detection "Liveness Detection") + +### Face Detection +Detect faces and analyze them from an binary data. + +```python +from azure.core.credentials import AzureKeyCredential +from azure.ai.vision.face import FaceClient +from azure.ai.vision.face.models import ( + FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) + +endpoint = "" +key = "" + +with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_client: + sample_file_path = "" + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + result = face_client.detect( + file_content, + FaceDetectionModel.DETECTION_03, + FaceRecognitionModel.RECOGNITION_04, + True, # return_face_id + return_face_attributes=[ + FaceAttributeTypeDetection03.HEAD_POSE, + FaceAttributeTypeDetection03.MASK, + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], + return_face_landmarks=True, + return_recognition_model=True, + face_id_time_to_live=120) + + print(f"Detect faces from the file: {sample_file_path}") + for idx, face in enumerate(result): + print(f"----- Detection result: #{idx+1} -----") + print(f"Face: {face.as_dict()}") +``` + +### Face Recognition from LargePersonGroup +Identify a face against a defined LargePersonGroup. + +First, we have to use `FaceAdministrationClient` to create a LargePersonGroup, add a few Person to it, +and then register faces with these Person. + +```python +from azure.core.credentials import AzureKeyCredential +from azure.ai.vision.face import FaceAdministrationClient, FaceClient +from azure.ai.vision.face.models import (FaceDetectionModel, FaceRecognitionModel) + + +def _read_file_content(file_path: str): + with open(file_path, "rb") as fd: + file_content = fd.read() + + return file_content + + +endpoint = "" +key = "" + +large_person_group_id = "lpg_family" + +with FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_admin_client: + print(f"Create a large person group with id: {large_person_group_id}") + face_admin_client.create_large_person_group( + large_person_group_id, name="My Family", recognition_model=FaceRecognitionModel.RECOGNITION_04) + + print("Create a Person Bill and add a face to him.") + bill_person_id = face_admin_client.create_large_person_group_person( + large_person_group_id, name="Bill", user_data="Dad").person_id + bill_image_file_path = "./samples/images/Family1-Dad1.jpg" + face_admin_client.add_large_person_group_person_face( + large_person_group_id, + bill_person_id, + _read_file_content(bill_image_file_path), + detection_model=FaceDetectionModel.DETECTION_03, + user_data="Dad-0001") + + print("Create a Person Clare and add a face to her.") + clare_person_id = face_admin_client.create_large_person_group_person( + large_person_group_id, name="Clare", user_data="Mom").person_id + clare_image_file_path = "./samples/images/Family1-Mom1.jpg" + face_admin_client.add_large_person_group_person_face( + large_person_group_id, + clare_person_id, + _read_file_content(clare_image_file_path), + detection_model=FaceDetectionModel.DETECTION_03, + user_data="Mom-0001") +``` + +Before doing the identification, we need to train the LargePersonGroup first. +```python + print(f"Start to train the large person group: {large_person_group_id}.") + poller = face_admin_client.begin_train_large_person_group(large_person_group_id) + + # Wait for the train operation to be completed. + # If the training status isn't succeed, an exception will be thrown from the poller. + training_result = poller.result() +``` + +When the training operation is completed successfully, we can identify the faces in this LargePersonGroup throught +`FaceClient`. +```python +with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_client: + # Detect the face from the target image. + target_image_file_path = "./samples/images/identification1.jpg" + detect_result = face_client.detect( + _read_file_content(target_image_file_path), + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True) + target_face_ids = list(f.face_id for f in detect_result) + + # Identify the faces in the large person group. + result = face_client.identify_from_large_person_group( + face_ids=target_face_ids, large_person_group_id=large_person_group_id) + for idx, r in enumerate(result): + print(f"----- Identification result: #{idx+1} -----") + print(f"{r.as_dict()}") +``` + +Finally, use `FaceAdministrationClient` to remove the large person group if you don't need it anymore. +```python +with FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_admin_client: + print(f"Delete the large person group: {large_person_group_id}") + face_admin_client.delete_large_person_group(large_person_group_id) +``` + +### Liveness detection +Face Liveness detection can be used to determine if a face in an input video stream is real (live) or fake (spoof). +The goal of liveness detection is to ensure that the system is interacting with a physically present live person at +the time of authentication. The whole process of authentication is called a session. + +There're two different components in the authentication: a mobile application and an app server/orchestrator. +Before uploading the video stream, the app server has to create a session, and then the mobile client could upload +the payload with a `session authorization token` to call the liveness detection. The app server can query for the +liveness detection result and audit logs anytime untill the session is deleted. + +The Liveness detection operation can not only confirm if the input is live or spoof, but also verify whether the input +belongs to the expected person's face, which is called **liveness detection with face verification**. For the detail +information, please refer to the [tutorial][liveness_tutorial]. + +We'll only demonstrates how to create, query, delete a session and get the audit logs here. For how to perform a +liveness detection, please see the sample of [mobile applications][integrate_liveness_into_mobile_application]. + +Here is an example to create and get the liveness detection result of a session. +```python +from azure.core.credentials import AzureKeyCredential +from azure.ai.vision.face import FaceSessionClient +from azure.ai.vision.face.models import LivenessSessionCreationContent, LivenessOperationMode + +endpoint = "" +key = "" + +with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_session_client: + # Create a session. + print("Create a new liveness session.") + created_session = face_session_client.create_liveness_session( + LivenessSessionCreationContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=str(uuid.uuid4()), + send_results_to_client=False)) + print(f"Result: {created_session}") + + # Get the liveness detection result. + print("Get the liveness detection result.") + liveness_result = face_session_client.get_liveness_session_result(created_session.session_id) + print(f"Result: {liveness_result}") +``` + +Here is another example for the liveness detection with face verification. +```python +from azure.core.credentials import AzureKeyCredential +from azure.ai.vision.face import FaceSessionClient +from azure.ai.vision.face.models import LivenessOperationMode, LivenessSessionCreationContent + +endpoint = "" +key = "" + +with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_session_client: + sample_file_path = "" + with open(sample_file_path, "rb") as fd: + file_content = fd.read() + + # Create a session. + print("Create a new liveness with verify session with verify image.") + + created_session = face_session_client.create_liveness_with_verify_session( + LivenessSessionCreationContent( + liveness_operation_mode=LivenessOperationMode.PASSIVE, + device_correlation_id=str(uuid.uuid4()), + send_results_to_client=False), + verify_image=file_content) + print(f"Result: {created_session}") + + # Get the liveness detection and verification result. + print("Get the liveness detection and verification result.") + liveness_result = face_session_client.get_liveness_with_verify_session_result(created_session.session_id) + print(f"Result: {liveness_result}") +``` + +## Troubleshooting + +### General + +Face client library will raise exceptions defined in [Azure Core][python_azure_core_exceptions]. +Error codes and messages raised by the Face service can be found in the [service documentation][face_errors]. + +### Logging + +This library uses the standard [logging][python_logging] library for logging. + +Basic information about HTTP sessions (URLs, headers, etc.) is logged at `INFO` level. + +Detailed `DEBUG` level logging, including request/response bodies and **unredacted** +headers, can be enabled on the client or per-operation with the `logging_enable` keyword argument. + +See full SDK logging documentation with examples [here][sdk_logging_docs]. + +```python +import sys +import logging + +from azure.ai.vision.face import FaceClient +from azure.core.credentials import AzureKeyCredential + +logging.basicConfig(level=logging.DEBUG, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + stream=sys.stdout) + +endpoint = "https://.cognitiveservices.azure.com/" +credential = AzureKeyCredential("") +face_client = FaceClient(endpoint, credential) + +face.detect(..., logging_enable=True) +``` + +### Optional Configuration + +Optional keyword arguments can be passed in at the client and per-operation level. +The azure-core [reference documentation][azure_core_ref_docs] describes available configurations for retries, logging, transport protocols, and more. + +## Next steps + +### More sample code + +See the [Sample README][face_samples] for several code snippets illustrating common patterns used in the Face Python API. + +### Additional documentation + +For more extensive documentation on Azure AI Face, see the [Face documentation][face_product_docs] on learn.microsoft.com. ## Contributing @@ -35,11 +431,40 @@ see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments. +[source_code]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/azure/ai/vision/face +[face_pypi]: https://pypi.org/project/azure-ai-vision-face/ +[face_ref_docs]: https://aka.ms/azsdk-python-face-ref +[face_product_docs]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity +[face_samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples + +[azure_sub]: https://azure.microsoft.com/free/ +[azure_portal_list_face_account]: https://portal.azure.com/#blade/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/Face +[azure_portal_list_cognitive_service_account]: https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/AllInOne +[azure_portal_create_face_account]: https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFace +[quick_start_create_account_via_azure_cli]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli +[quick_start_create_account_via_azure_powershell]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azpowershell + +[get_endpoint_via_azure_portal]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#get-the-keys-for-your-resource +[get_endpoint_via_azure_cli]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli#get-the-keys-for-your-resource +[regional_endpoints]: https://azure.microsoft.com/global-infrastructure/services/?products=cognitive-services +[azure_sdk_python_azure_key_credential]: https://aka.ms/azsdk/python/core/azurekeycredential +[azure_sdk_python_identity]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity +[custom_subdomain]: https://docs.microsoft.com/azure/cognitive-services/authentication#create-a-resource-with-a-custom-subdomain +[azure_sdk_python_default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential +[register_aad_app]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal + +[liveness_tutorial]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness +[integrate_liveness_into_mobile_application]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application + +[python_azure_core_exceptions]: https://aka.ms/azsdk/python/core/docs#module-azure.core.exceptions +[face_errors]: https://aka.ms/face-error-codes-and-messages +[python_logging]: https://docs.python.org/3/library/logging.html +[sdk_logging_docs]: https://docs.microsoft.com/azure/developer/python/sdk/azure-sdk-logging +[azure_core_ref_docs]: https://aka.ms/azsdk/python/core/docs + [code_of_conduct]: https://opensource.microsoft.com/codeofconduct/ [authenticate_with_token]: https://docs.microsoft.com/azure/cognitive-services/authentication?tabs=powershell#authenticate-with-an-authentication-token [azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials [azure_identity_pip]: https://pypi.org/project/azure-identity/ [default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential [pip]: https://pypi.org/project/pip/ -[azure_sub]: https://azure.microsoft.com/free/ - From d591f328d47d05b4fef7c3bcef79be796e8114fc Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 3 May 2024 18:31:30 +0800 Subject: [PATCH 08/41] Rename the folder from samples/_shared to samples/shared --- .../samples/async_samples/samples_authentication_async.py | 6 +++--- .../samples/async_samples/samples_face_detection_async.py | 6 +++--- .../samples/async_samples/samples_face_grouping_async.py | 6 +++--- .../async_samples/samples_face_liveness_detection_async.py | 6 +++--- ...mples_face_liveness_detection_with_verification_async.py | 6 +++--- .../async_samples/samples_find_similar_faces_async.py | 6 +++--- .../azure-ai-vision-face/samples/samples_authentication.py | 4 ++-- .../azure-ai-vision-face/samples/samples_face_detection.py | 4 ++-- .../azure-ai-vision-face/samples/samples_face_grouping.py | 4 ++-- .../samples/samples_face_liveness_detection.py | 4 ++-- .../samples_face_liveness_detection_with_verification.py | 4 ++-- .../samples/samples_find_similar_faces.py | 4 ++-- .../samples/{_shared => shared}/__init__.py | 0 .../samples/{_shared => shared}/constants.py | 0 .../samples/{_shared => shared}/helpers.py | 0 15 files changed, 30 insertions(+), 30 deletions(-) rename sdk/face/azure-ai-vision-face/samples/{_shared => shared}/__init__.py (100%) rename sdk/face/azure-ai-vision-face/samples/{_shared => shared}/constants.py (100%) rename sdk/face/azure-ai-vision-face/samples/{_shared => shared}/helpers.py (100%) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py index a0ccee1c7601..bf7ed053d954 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py @@ -32,17 +32,17 @@ from dotenv import find_dotenv, load_dotenv -# To import utility files in ../_shared folder +# To import utility files in ../shared folder sys.path.insert(1, os.path.join(sys.path[0], '..')) -from _shared.constants import ( # noqa: E402 +from shared.constants import ( # noqa: E402 CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, DEFAULT_IMAGE_FILE, ) -from _shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger # noqa: E402 class FaceAuthentication(): diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py index e33d6c25b3c6..7ff8789c4cbe 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py @@ -24,10 +24,10 @@ from dotenv import find_dotenv, load_dotenv -# To import utility files in ../_shared folder +# To import utility files in ../shared folder sys.path.insert(1, os.path.join(sys.path[0], '..')) -from _shared.constants import ( # noqa: E402 +from shared.constants import ( # noqa: E402 CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, @@ -35,7 +35,7 @@ DEFAULT_IMAGE_URL, IMAGE_DETECTION_5, ) -from _shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger # noqa: E402 class DetectFaces(): diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py index fc674a9f4ab1..3aa6942f0e05 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py @@ -24,17 +24,17 @@ from dotenv import find_dotenv, load_dotenv -# To import utility files in ../_shared folder +# To import utility files in ../shared folder sys.path.insert(1, os.path.join(sys.path[0], '..')) -from _shared.constants import ( # noqa: E402 +from shared.constants import ( # noqa: E402 CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, IMAGE_NINE_FACES, ) -from _shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger # noqa: E402 class GroupFaces(): diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py index c28fd46cab86..49d9c96426d4 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py @@ -28,16 +28,16 @@ from dotenv import find_dotenv, load_dotenv -# To import utility files in ../_shared folder +# To import utility files in ../shared folder sys.path.insert(1, os.path.join(sys.path[0], '..')) -from _shared.constants import ( # noqa: E402 +from shared.constants import ( # noqa: E402 CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, ) -from _shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger # noqa: E402 class DetectLiveness(): diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py index 2005cec903a0..0589721a4671 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py @@ -28,17 +28,17 @@ from dotenv import find_dotenv, load_dotenv -# To import utility files in ../_shared folder +# To import utility files in ../shared folder sys.path.insert(1, os.path.join(sys.path[0], '..')) -from _shared.constants import ( # noqa: E402 +from shared.constants import ( # noqa: E402 CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, DEFAULT_IMAGE_FILE, ) -from _shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger # noqa: E402 class DetectLivenessWithVerify(): diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py index 1148e6130f44..30f3c496545f 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py @@ -24,10 +24,10 @@ from dotenv import find_dotenv, load_dotenv -# To import utility files in ../_shared folder +# To import utility files in ../shared folder sys.path.insert(1, os.path.join(sys.path[0], '..')) -from _shared.constants import ( # noqa: E402 +from shared.constants import ( # noqa: E402 CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, @@ -40,7 +40,7 @@ IMAGE_FINDSIMILAR, IMAGE_NINE_FACES, ) -from _shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger # noqa: E402 def _read_file_content(file_path: str): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index a75b754edc1a..2ea9390d452f 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -30,14 +30,14 @@ from dotenv import find_dotenv, load_dotenv -from _shared.constants import ( +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, DEFAULT_IMAGE_FILE, ) -from _shared.helpers import beautify_json, get_logger +from shared.helpers import beautify_json, get_logger class FaceAuthentication(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 7ac84cf0f62f..a8a2b08416e8 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -22,7 +22,7 @@ from dotenv import find_dotenv, load_dotenv -from _shared.constants import ( +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, @@ -30,7 +30,7 @@ DEFAULT_IMAGE_URL, IMAGE_DETECTION_5, ) -from _shared.helpers import beautify_json, get_logger +from shared.helpers import beautify_json, get_logger class DetectFaces(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index 9bc81bcd9853..81c322b1f991 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -22,14 +22,14 @@ from dotenv import find_dotenv, load_dotenv -from _shared.constants import ( +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, IMAGE_NINE_FACES, ) -from _shared.helpers import beautify_json, get_logger +from shared.helpers import beautify_json, get_logger class GroupFaces(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py index 6643d8575154..2e3846021580 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py @@ -25,13 +25,13 @@ from dotenv import find_dotenv, load_dotenv -from _shared.constants import ( +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, ) -from _shared.helpers import beautify_json, get_logger +from shared.helpers import beautify_json, get_logger class DetectLiveness(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index 1ab053e762af..26b3ca636c02 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -26,14 +26,14 @@ from dotenv import find_dotenv, load_dotenv -from _shared.constants import ( +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, DEFAULT_IMAGE_FILE, ) -from _shared.helpers import beautify_json, get_logger +from shared.helpers import beautify_json, get_logger class DetectLivenessWithVerify(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index 65e1c7210d59..9de24307ce2d 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -22,7 +22,7 @@ from dotenv import find_dotenv, load_dotenv -from _shared.constants import ( +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, @@ -35,7 +35,7 @@ IMAGE_FINDSIMILAR, IMAGE_NINE_FACES, ) -from _shared.helpers import beautify_json, get_logger +from shared.helpers import beautify_json, get_logger def _read_file_content(file_path: str): diff --git a/sdk/face/azure-ai-vision-face/samples/_shared/__init__.py b/sdk/face/azure-ai-vision-face/samples/shared/__init__.py similarity index 100% rename from sdk/face/azure-ai-vision-face/samples/_shared/__init__.py rename to sdk/face/azure-ai-vision-face/samples/shared/__init__.py diff --git a/sdk/face/azure-ai-vision-face/samples/_shared/constants.py b/sdk/face/azure-ai-vision-face/samples/shared/constants.py similarity index 100% rename from sdk/face/azure-ai-vision-face/samples/_shared/constants.py rename to sdk/face/azure-ai-vision-face/samples/shared/constants.py diff --git a/sdk/face/azure-ai-vision-face/samples/_shared/helpers.py b/sdk/face/azure-ai-vision-face/samples/shared/helpers.py similarity index 100% rename from sdk/face/azure-ai-vision-face/samples/_shared/helpers.py rename to sdk/face/azure-ai-vision-face/samples/shared/helpers.py From 5ee2b8bb14f205039cb4092d3a9730b9e401b3ab Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 3 May 2024 18:43:43 +0800 Subject: [PATCH 09/41] Update the detection interface --- .../azure/ai/vision/face/_patch.py | 76 +++++++++---------- .../azure/ai/vision/face/aio/_patch.py | 74 ++++++++---------- .../samples_authentication_async.py | 12 +-- .../samples_face_detection_async.py | 14 ++-- .../samples_face_grouping_async.py | 2 +- .../samples_find_similar_faces_async.py | 6 +- .../samples/samples_authentication.py | 12 +-- .../samples/samples_face_detection.py | 14 ++-- .../samples/samples_face_grouping.py | 2 +- .../samples/samples_find_similar_faces.py | 6 +- .../tests/test_authentication.py | 6 +- .../tests/test_authentication_async.py | 6 +- 12 files changed, 107 insertions(+), 123 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 513f42282beb..05de82533208 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -7,23 +7,13 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ from typing import Any, IO, List, Optional, Union, cast -import sys from azure.core.tracing.decorator import distributed_trace from . import models as _models from ._client import FaceClient as FaceClientGenerated from ._client import FaceSessionClient as FaceSessionClientGenerated -from ._operations._operations import ( - FaceClientOperationsMixin, - FaceSessionClientOperationsMixin, -) - -if sys.version_info >= (3, 9): - from collections.abc import MutableMapping -else: - from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +from ._operations._operations import JSON, _Unset class FaceClient(FaceClientGenerated): @@ -44,11 +34,12 @@ class FaceClient(FaceClientGenerated): @distributed_trace def detect_from_url( self, + body: Union[JSON, IO[bytes]] = _Unset, + *, url: str, detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], return_face_id: bool, - *, return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, return_face_landmarks: Optional[bool] = None, return_recognition_model: Optional[bool] = None, @@ -105,23 +96,25 @@ def detect_from_url( refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. - :param url: URL of input image. Required. - :type url: str - :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required when body is not set. + :paramtype url: str + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". Required. - :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel - :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. - :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel - :param return_face_id: Return faceIds of the detected faces or not. Required. - :type return_face_id: bool + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. Required. + :paramtype return_face_id: bool :keyword return_face_attributes: Analyze and return the one or more specified face attributes in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute analysis has additional computational and time cost. Default value is None. @@ -430,15 +423,15 @@ def detect_from_url( if return_face_id is not True: return_face_id = False - return FaceClientOperationsMixin._detect_from_url( - self, + return super()._detect_from_url( + body, url=url, + detection_model=detection_model, + recognition_model=recognition_model, return_face_id=return_face_id, - return_face_landmarks=return_face_landmarks, return_face_attributes=return_face_attributes, - recognition_model=recognition_model, + return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, - detection_model=detection_model, face_id_time_to_live=face_id_time_to_live, **kwargs) @@ -446,10 +439,10 @@ def detect_from_url( def detect( self, image_content: bytes, + *, detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], return_face_id: bool, - *, return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, return_face_landmarks: Optional[bool] = None, return_recognition_model: Optional[bool] = None, @@ -508,21 +501,21 @@ def detect( :param image_content: The input image binary. Required. :type image_content: bytes - :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". Required. - :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel - :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. - :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel - :param return_face_id: Return faceIds of the detected faces or not. Required. - :type return_face_id: bool + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. Required. + :paramtype return_face_id: bool :keyword return_face_attributes: Analyze and return the one or more specified face attributes in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute analysis has additional computational and time cost. Default value is None. @@ -826,15 +819,14 @@ def detect( if return_face_id is not True: return_face_id = False - return FaceClientOperationsMixin._detect( - self, - image_content=image_content, + return super()._detect( + image_content, + detection_model=detection_model, + recognition_model=recognition_model, return_face_id=return_face_id, - return_face_landmarks=return_face_landmarks, return_face_attributes=return_face_attributes, - recognition_model=recognition_model, + return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, - detection_model=detection_model, face_id_time_to_live=face_id_time_to_live, **kwargs) @@ -953,11 +945,11 @@ def create_liveness_with_verify_session( parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), verify_image=("verify-image", verify_image) ) - return FaceSessionClientOperationsMixin._create_liveness_with_verify_session_with_verify_image( - self, request_body, **kwargs) + return super()._create_liveness_with_verify_session_with_verify_image( + request_body, **kwargs) - return FaceSessionClientOperationsMixin._create_liveness_with_verify_session( - self, body, **kwargs) + return super()._create_liveness_with_verify_session( + body, **kwargs) __all__: List[str] = [ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 4b87274a1f87..33a574b9efc9 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -7,23 +7,13 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ from typing import Any, IO, List, Optional, Union, cast -import sys from azure.core.tracing.decorator_async import distributed_trace_async from .. import models as _models from ._client import FaceClient as FaceClientGenerated from ._client import FaceSessionClient as FaceSessionClientGenerated -from ._operations._operations import ( - FaceClientOperationsMixin, - FaceSessionClientOperationsMixin, -) - -if sys.version_info >= (3, 9): - from collections.abc import MutableMapping -else: - from typing import MutableMapping # type: ignore # pylint: disable=ungrouped-imports -JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object +from ._operations._operations import JSON, _Unset class FaceClient(FaceClientGenerated): @@ -44,11 +34,12 @@ class FaceClient(FaceClientGenerated): @distributed_trace_async async def detect_from_url( self, + body: Union[JSON, IO[bytes]] = _Unset, + *, url: str, detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], return_face_id: bool, - *, return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, return_face_landmarks: Optional[bool] = None, return_recognition_model: Optional[bool] = None, @@ -105,23 +96,25 @@ async def detect_from_url( refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. - :param url: URL of input image. Required. - :type url: str - :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword url: URL of input image. Required when body is not set. + :paramtype url: str + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". Required. - :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel - :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. - :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel - :param return_face_id: Return faceIds of the detected faces or not. Required. - :type return_face_id: bool + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. Required. + :paramtype return_face_id: bool :keyword return_face_attributes: Analyze and return the one or more specified face attributes in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute analysis has additional computational and time cost. Default value is None. @@ -430,15 +423,15 @@ async def detect_from_url( if return_face_id is not True: return_face_id = False - return await FaceClientOperationsMixin._detect_from_url( - self, + return await super()._detect_from_url( + body, url=url, + detection_model=detection_model, + recognition_model=recognition_model, return_face_id=return_face_id, - return_face_landmarks=return_face_landmarks, return_face_attributes=return_face_attributes, - recognition_model=recognition_model, + return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, - detection_model=detection_model, face_id_time_to_live=face_id_time_to_live, **kwargs) @@ -508,21 +501,21 @@ async def detect( :param image_content: The input image binary. Required. :type image_content: bytes - :param detection_model: The 'detectionModel' associated with the detected faceIds. Supported + :keyword detection_model: The 'detectionModel' associated with the detected faceIds. Supported 'detectionModel' values include 'detection_01', 'detection_02' and 'detection_03'. The default value is 'detection_01'. Known values are: "detection_01", "detection_02", and "detection_03". Required. - :type detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel - :param recognition_model: The 'recognitionModel' associated with the detected faceIds. + :paramtype detection_model: str or ~azure.ai.vision.face.models.FaceDetectionModel + :keyword recognition_model: The 'recognitionModel' associated with the detected faceIds. Supported 'recognitionModel' values include 'recognition_01', 'recognition_02', 'recognition_03' or 'recognition_04'. The default value is 'recognition_01'. 'recognition_04' is recommended since its accuracy is improved on faces wearing masks compared with 'recognition_03', and its overall accuracy is improved compared with 'recognition_01' and 'recognition_02'. Known values are: "recognition_01", "recognition_02", "recognition_03", and "recognition_04". Required. - :type recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel - :param return_face_id: Return faceIds of the detected faces or not. Required. - :type return_face_id: bool + :paramtype recognition_model: str or ~azure.ai.vision.face.models.FaceRecognitionModel + :keyword return_face_id: Return faceIds of the detected faces or not. Required. + :paramtype return_face_id: bool :keyword return_face_attributes: Analyze and return the one or more specified face attributes in the comma-separated string like 'returnFaceAttributes=headPose,glasses'. Face attribute analysis has additional computational and time cost. Default value is None. @@ -826,15 +819,14 @@ async def detect( if return_face_id is not True: return_face_id = False - return await FaceClientOperationsMixin._detect( - self, - image_content=image_content, + return await super()._detect( + image_content, + detection_model=detection_model, + recognition_model=recognition_model, return_face_id=return_face_id, - return_face_landmarks=return_face_landmarks, return_face_attributes=return_face_attributes, - recognition_model=recognition_model, + return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, - detection_model=detection_model, face_id_time_to_live=face_id_time_to_live, **kwargs) @@ -953,11 +945,11 @@ async def create_liveness_with_verify_session( parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), verify_image=("verify-image", verify_image) ) - return await FaceSessionClientOperationsMixin._create_liveness_with_verify_session_with_verify_image( - self, request_body, **kwargs) + return await super()._create_liveness_with_verify_session_with_verify_image( + request_body, **kwargs) - return await FaceSessionClientOperationsMixin._create_liveness_with_verify_session( - self, body, **kwargs) + return await super()._create_liveness_with_verify_session( + body, **kwargs) __all__: List[str] = [ diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py index bf7ed053d954..c8565953a0a5 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py @@ -67,9 +67,9 @@ async def authentication_by_api_key(self): result = await face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - False) # return_face_id = False + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -93,9 +93,9 @@ async def authentication_by_aad_credential(self): result = await face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - False) # return_face_id = False + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py index 7ff8789c4cbe..84a6101c3a2d 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py @@ -60,9 +60,9 @@ async def detect(self): result = await face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - True, # return_face_id + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True, return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, @@ -85,10 +85,10 @@ async def detect_from_url(self): async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_url = DEFAULT_IMAGE_URL result = await face_client.detect_from_url( - sample_url, - FaceDetectionModel.DETECTION_01, - FaceRecognitionModel.RECOGNITION_04, - False, # return_face_id + url=sample_url, + detection_model=FaceDetectionModel.DETECTION_01, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False, return_face_attributes=[ FaceAttributeTypeDetection01.ACCESSORIES, FaceAttributeTypeDetection01.EXPOSURE, diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py index 3aa6942f0e05..2b637fd4fbc3 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py @@ -57,7 +57,7 @@ async def group(self): file_content = fd.read() detect_result = await face_client.detect( - image_content=file_content, + file_content, detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py index 30f3c496545f..40524a1a09ad 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py @@ -71,7 +71,7 @@ async def find_similar_from_face_ids(self): # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = _get_full_file_path(IMAGE_NINE_FACES) detect_result1 = await face_client.detect( - image_content=_read_file_content(nine_faces_file_path), + _read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -82,7 +82,7 @@ async def find_similar_from_face_ids(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) detect_result2 = await face_client.detect( - image_content=_read_file_content(find_similar_file_path), + _read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -140,7 +140,7 @@ async def find_similar_from_large_face_list(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) detect_result = await face_client.detect( - image_content=_read_file_content(find_similar_file_path), + _read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index 2ea9390d452f..2bb58767c089 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -62,9 +62,9 @@ def authentication_by_api_key(self): result = face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - False) + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -86,9 +86,9 @@ def authentication_by_aad_credential(self): result = face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - False) + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index a8a2b08416e8..d75d02c38b15 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -55,9 +55,9 @@ def detect(self): result = face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - True, # return_face_id + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True, return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, @@ -80,10 +80,10 @@ def detect_from_url(self): with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_url = DEFAULT_IMAGE_URL result = face_client.detect_from_url( - sample_url, - FaceDetectionModel.DETECTION_01, - FaceRecognitionModel.RECOGNITION_04, - False, # return_face_id + url=sample_url, + detection_model=FaceDetectionModel.DETECTION_01, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False, return_face_attributes=[ FaceAttributeTypeDetection01.ACCESSORIES, FaceAttributeTypeDetection01.EXPOSURE, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index 81c322b1f991..6a62053b1757 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -52,7 +52,7 @@ def group(self): file_content = fd.read() detect_result = face_client.detect( - image_content=file_content, + file_content, detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index 9de24307ce2d..c355f56d98e2 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -66,7 +66,7 @@ def find_similar_from_face_ids(self): # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = _get_full_file_path(IMAGE_NINE_FACES) detect_result1 = face_client.detect( - image_content=_read_file_content(nine_faces_file_path), + _read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -77,7 +77,7 @@ def find_similar_from_face_ids(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) detect_result2 = face_client.detect( - image_content=_read_file_content(find_similar_file_path), + _read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -135,7 +135,7 @@ def find_similar_from_large_face_list(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) detect_result = face_client.detect( - image_content=_read_file_content(find_similar_file_path), + _read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/tests/test_authentication.py b/sdk/face/azure-ai-vision-face/tests/test_authentication.py index e1a69b3096fd..79d3b24641e3 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_authentication.py +++ b/sdk/face/azure-ai-vision-face/tests/test_authentication.py @@ -28,9 +28,9 @@ def test_face_client_api_key_authentication(self, client, **kwargs): result = client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - False, # return_face_id + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False, return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, diff --git a/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py b/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py index d1aeee2ca464..bf3c49c34da8 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py @@ -29,9 +29,9 @@ async def test_face_client_api_key_authentication(self, client, **kwargs): result = await client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - False, # return_face_id + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=False, return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, From d71585f1a2862a428c8effc2471ad5d49e653f76 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 3 May 2024 18:49:02 +0800 Subject: [PATCH 10/41] Correct the comments in sample codes --- .../samples/async_samples/samples_authentication_async.py | 4 ++-- .../samples/async_samples/samples_face_detection_async.py | 4 ++-- .../samples/async_samples/samples_face_grouping_async.py | 4 ++-- .../samples_face_liveness_detection_async.py | 4 ++-- ...les_face_liveness_detection_with_verification_async.py | 4 ++-- .../async_samples/samples_find_similar_faces_async.py | 8 ++++---- .../samples/samples_authentication.py | 4 ++-- .../samples/samples_face_detection.py | 4 ++-- .../azure-ai-vision-face/samples/samples_face_grouping.py | 4 ++-- .../samples/samples_face_liveness_detection.py | 4 ++-- .../samples_face_liveness_detection_with_verification.py | 4 ++-- .../samples/samples_find_similar_faces.py | 8 ++++---- 12 files changed, 28 insertions(+), 28 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py index c8565953a0a5..e4bb3eb5bccc 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py @@ -17,9 +17,9 @@ python sample_authentication_async.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. The environment variable below is used for api key authentication. - 2) FACE_KEY - your Face API key. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. The following environment variables are required for using azure-identity's DefaultAzureCredential. For more information, refer to https://aka.ms/azsdk/python/identity/docs#azure.identity.DefaultAzureCredential 3) AZURE_TENANT_ID - the tenant ID in Azure Active Directory diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py index 84a6101c3a2d..df5a51c05423 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py @@ -15,8 +15,8 @@ python sample_face_detection_async.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import asyncio import os diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py index 2b637fd4fbc3..4a30fb6a8b64 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py @@ -15,8 +15,8 @@ python sample_face_grouping_async.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import asyncio import os diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py index 49d9c96426d4..7e072add6b53 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py @@ -18,8 +18,8 @@ python sample_face_liveness_detection_async.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import asyncio import os diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py index 0589721a4671..dd7d39f4d42b 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py @@ -18,8 +18,8 @@ python sample_face_liveness_detection_with_verification_async.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import asyncio import os diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py index 40524a1a09ad..3a2ce7c12eb4 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py @@ -6,17 +6,17 @@ # license information. # -------------------------------------------------------------------------- """ -FILE: sample_findsimilar_faces_async.py +FILE: sample_find_similar_faces_async.py DESCRIPTION: This sample demonstrates how to find similar faces from a specified list of face ids or a largeFaceList. USAGE: - python sample_findsimilar_faces_async.py + python sample_find_similar_faces_async.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import asyncio import os diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index 2bb58767c089..adf3101e9521 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -17,9 +17,9 @@ python sample_authentication.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. The environment variable below is used for api key authentication. - 2) FACE_KEY - your Face API key. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. The following environment variables are required for using azure-identity's DefaultAzureCredential. For more information, refer to https://aka.ms/azsdk/python/identity/docs#azure.identity.DefaultAzureCredential 3) AZURE_TENANT_ID - the tenant ID in Azure Active Directory diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index d75d02c38b15..99b4f70f1e24 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -15,8 +15,8 @@ python sample_face_detection.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import os diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index 6a62053b1757..0e5f49cff452 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -15,8 +15,8 @@ python sample_face_grouping.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import os diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py index 2e3846021580..56498008acd7 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py @@ -17,8 +17,8 @@ python sample_face_liveness_detection.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import os import uuid diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index 26b3ca636c02..e6adfa072cf2 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -18,8 +18,8 @@ python sample_face_liveness_detection_with_verification.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import os import uuid diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index c355f56d98e2..d9c1fe59b291 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -6,17 +6,17 @@ # license information. # -------------------------------------------------------------------------- """ -FILE: sample_findsimilar_faces.py +FILE: sample_find_similar_faces.py DESCRIPTION: This sample demonstrates how to find similar faces from a specified list of face ids or a largeFaceList. USAGE: - python sample_findsimilar_faces.py + python sample_find_similar_faces.py Set the environment variables with your own values before running this sample: - 1) FACE_ENDPOINT - the endpoint to your Face resource. - 2) FACE_KEY - your Face API key. + 1) AZURE_FACE_API_ENDPOINT - the endpoint to your Face resource. + 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. """ import os From 92a2fbf92d70e279303dd2e3db39e165b17839d4 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 3 May 2024 18:49:57 +0800 Subject: [PATCH 11/41] Update the README.md --- sdk/face/azure-ai-vision-face/README.md | 46 ++++++++++++++----------- 1 file changed, 26 insertions(+), 20 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index a51f3ede1384..efe31bf65c09 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -2,7 +2,7 @@ The Azure AI Face service provides AI algorithms that detect, recognize, and analyze human faces in images. It includes the following main features: -- Face detection and analyzsis +- Face detection and analysis - Liveness detection - Face recognition - Face verification ("one-to-one" matching) @@ -12,7 +12,6 @@ The Azure AI Face service provides AI algorithms that detect, recognize, and ana [Source code][source_code] | [Package (PyPI)][face_pypi] -| [Package (Conda)](https://anaconda.org/microsoft/azure-ai-vision-face/) | [API reference documentation][face_ref_docs] | [Product documentation][face_product_docs] | [Samples][face_samples] @@ -117,7 +116,7 @@ face_client = FaceClient(endpoint, credential) `FaceClient` provides operations for: - Face detection and analysis: Detect human faces in an image and return the rectangle coordinates of their locations, - and optinally with landmarks, and face-related attributes. This operation is required as a first step in all the + and optionally with landmarks, and face-related attributes. This operation is required as a first step in all the other face recognition scenarios. - Face recognition: Confirm that a user is who they claim to be based on how closely their face data matches the target face. It includes Face verification ("one-to-one" matching) and Face identification ("one-to-many" matching). @@ -160,7 +159,7 @@ Sample code snippets are provided to illustrate using long-running operations [b The following section provides several code snippets covering some of the most common Face tasks, including: * [Detecting faces in an image](#face-detection "Face Detection") -* [Identifing the specific face from a LargePersonGroup](#face-recognition-from-largepersongroup "Face Recognition from LargePersonGroup") +* [Identifying the specific face from a LargePersonGroup](#face-recognition-from-largepersongroup "Face Recognition from LargePersonGroup") * [Determining if a face in an video is real (live) or fake (spoof)](#liveness-detection "Liveness Detection") ### Face Detection @@ -182,9 +181,9 @@ with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_c result = face_client.detect( file_content, - FaceDetectionModel.DETECTION_03, - FaceRecognitionModel.RECOGNITION_04, - True, # return_face_id + detection_model=FaceDetectionModel.DETECTION_03, + recognition_model=FaceRecognitionModel.RECOGNITION_04, + return_face_id=True, return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, @@ -202,8 +201,8 @@ with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_c ### Face Recognition from LargePersonGroup Identify a face against a defined LargePersonGroup. -First, we have to use `FaceAdministrationClient` to create a LargePersonGroup, add a few Person to it, -and then register faces with these Person. +First, we have to use `FaceAdministrationClient` to create a `LargePersonGroup`, add a few `Person` to it, +and then register faces with these `Person`. ```python from azure.core.credentials import AzureKeyCredential @@ -211,7 +210,7 @@ from azure.ai.vision.face import FaceAdministrationClient, FaceClient from azure.ai.vision.face.models import (FaceDetectionModel, FaceRecognitionModel) -def _read_file_content(file_path: str): +def read_file_content(file_path: str): with open(file_path, "rb") as fd: file_content = fd.read() @@ -235,7 +234,7 @@ with FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(k face_admin_client.add_large_person_group_person_face( large_person_group_id, bill_person_id, - _read_file_content(bill_image_file_path), + read_file_content(bill_image_file_path), detection_model=FaceDetectionModel.DETECTION_03, user_data="Dad-0001") @@ -246,7 +245,7 @@ with FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(k face_admin_client.add_large_person_group_person_face( large_person_group_id, clare_person_id, - _read_file_content(clare_image_file_path), + read_file_content(clare_image_file_path), detection_model=FaceDetectionModel.DETECTION_03, user_data="Mom-0001") ``` @@ -268,7 +267,7 @@ with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_c # Detect the face from the target image. target_image_file_path = "./samples/images/identification1.jpg" detect_result = face_client.detect( - _read_file_content(target_image_file_path), + read_file_content(target_image_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -297,7 +296,7 @@ the time of authentication. The whole process of authentication is called a sess There're two different components in the authentication: a mobile application and an app server/orchestrator. Before uploading the video stream, the app server has to create a session, and then the mobile client could upload the payload with a `session authorization token` to call the liveness detection. The app server can query for the -liveness detection result and audit logs anytime untill the session is deleted. +liveness detection result and audit logs anytime until the session is deleted. The Liveness detection operation can not only confirm if the input is live or spoof, but also verify whether the input belongs to the expected person's face, which is called **liveness detection with face verification**. For the detail @@ -308,9 +307,11 @@ liveness detection, please see the sample of [mobile applications][integrate_liv Here is an example to create and get the liveness detection result of a session. ```python +import uuid + from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient -from azure.ai.vision.face.models import LivenessSessionCreationContent, LivenessOperationMode +from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode endpoint = "" key = "" @@ -319,10 +320,11 @@ with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as # Create a session. print("Create a new liveness session.") created_session = face_session_client.create_liveness_session( - LivenessSessionCreationContent( + CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), - send_results_to_client=False)) + send_results_to_client=False, + auth_token_time_to_live_in_seconds=60)) print(f"Result: {created_session}") # Get the liveness detection result. @@ -333,9 +335,11 @@ with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as Here is another example for the liveness detection with face verification. ```python +import uuid + from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient -from azure.ai.vision.face.models import LivenessOperationMode, LivenessSessionCreationContent +from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode endpoint = "" key = "" @@ -349,10 +353,11 @@ with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as print("Create a new liveness with verify session with verify image.") created_session = face_session_client.create_liveness_with_verify_session( - LivenessSessionCreationContent( + CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), - send_results_to_client=False), + send_results_to_client=False, + auth_token_time_to_live_in_seconds=60), verify_image=file_content) print(f"Result: {created_session}") @@ -440,6 +445,7 @@ additional questions or comments. [azure_sub]: https://azure.microsoft.com/free/ [azure_portal_list_face_account]: https://portal.azure.com/#blade/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/Face [azure_portal_list_cognitive_service_account]: https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/AllInOne +[azure_cognitive_service_account]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#supported-services-with-a-multi-service-resource [azure_portal_create_face_account]: https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFace [quick_start_create_account_via_azure_cli]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli [quick_start_create_account_via_azure_powershell]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azpowershell From 0623a38f98c92ba7e7133b57333e273cf8be7e95 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 3 May 2024 22:25:52 +0800 Subject: [PATCH 12/41] Update the interface of detect_from_url() --- sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py | 2 +- .../azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 05de82533208..c5a7deb9a2ec 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -36,7 +36,7 @@ def detect_from_url( self, body: Union[JSON, IO[bytes]] = _Unset, *, - url: str, + url: str = _Unset, detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], return_face_id: bool, diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 33a574b9efc9..44fd4b100658 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -36,7 +36,7 @@ async def detect_from_url( self, body: Union[JSON, IO[bytes]] = _Unset, *, - url: str, + url: str = _Unset, detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], return_face_id: bool, From 9dc2109e1fb4ec58cb79e8b936566235f3068f04 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 3 May 2024 23:02:49 +0800 Subject: [PATCH 13/41] Removed TODO tag --- sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py | 2 -- .../azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py | 2 -- 2 files changed, 4 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index c5a7deb9a2ec..2eadf9f803ba 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -846,7 +846,6 @@ class FaceSessionClient(FaceSessionClientGenerated): :paramtype api_version: str or ~azure.ai.vision.face.models.Versions """ - # TODO @distributed_trace def create_liveness_with_verify_session( self, @@ -939,7 +938,6 @@ def create_liveness_with_verify_session( corresponding Session DELETE operation. Required. } """ - # TODO if verify_image is not None: request_body = _models._models.CreateLivenessWithVerifySessionContent( parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 44fd4b100658..6faccb77f37d 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -846,7 +846,6 @@ class FaceSessionClient(FaceSessionClientGenerated): :paramtype api_version: str or ~azure.ai.vision.face.models.Versions """ - # TODO @distributed_trace_async async def create_liveness_with_verify_session( self, @@ -939,7 +938,6 @@ async def create_liveness_with_verify_session( corresponding Session DELETE operation. Required. } """ - # TODO if verify_image is not None: request_body = _models._models.CreateLivenessWithVerifySessionContent( parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), From 8bd89ff990b0a0f922a7930311f10437d514948e Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 6 May 2024 14:30:32 +0800 Subject: [PATCH 14/41] Update README.md --- sdk/face/azure-ai-vision-face/README.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index efe31bf65c09..ce1f6c7ff544 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -21,7 +21,9 @@ The Azure AI Face service provides AI algorithms that detect, recognize, and ana ### Prerequisites - Python 3.8 or later is required to use this package. -- You need an [Azure subscription][azure_sub] to use this package and either +- You need an [Azure subscription][azure_sub] to use this package. +- Your Azure account must have a `Cognitive Services Contributor` role assigned in order for you to agree to the responsible AI terms and create a resource. To get this role assigned to your account, follow the steps in the [Assign roles][azure_role_assignment] documentation, or contact your administrator. +- Once you have sufficient permissions to control your Azure subscription, you need either * an [Azure Face account][azure_portal_list_face_account] or * an [Azure Cognitive Service account][azure_portal_list_cognitive_service_account] @@ -163,7 +165,10 @@ The following section provides several code snippets covering some of the most c * [Determining if a face in an video is real (live) or fake (spoof)](#liveness-detection "Liveness Detection") ### Face Detection -Detect faces and analyze them from an binary data. +Detect faces and analyze them from an binary data. The latest model is the most accurate and recommended to be used. +For the detailed differences between different versions of **Detection** and **Recognition** model, please refer to the following links. +* [Detection model](evaluate_different_detection_models) +* [Recognition model](recommended_recognition_model) ```python from azure.core.credentials import AzureKeyCredential @@ -181,8 +186,8 @@ with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_c result = face_client.detect( file_content, - detection_model=FaceDetectionModel.DETECTION_03, - recognition_model=FaceRecognitionModel.RECOGNITION_04, + detection_model=FaceDetectionModel.DETECTION_03, # The latest detection model. + recognition_model=FaceRecognitionModel.RECOGNITION_04, # The latest recognition model. return_face_id=True, return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, @@ -302,7 +307,7 @@ The Liveness detection operation can not only confirm if the input is live or sp belongs to the expected person's face, which is called **liveness detection with face verification**. For the detail information, please refer to the [tutorial][liveness_tutorial]. -We'll only demonstrates how to create, query, delete a session and get the audit logs here. For how to perform a +This package is only responsible for creating, quering, deleting a session and getting audit logs. For how to perform a liveness detection, please see the sample of [mobile applications][integrate_liveness_into_mobile_application]. Here is an example to create and get the liveness detection result of a session. @@ -443,6 +448,7 @@ additional questions or comments. [face_samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples [azure_sub]: https://azure.microsoft.com/free/ +[azure_role_assignment]: https://learn.microsoft.com/en-us/azure/role-based-access-control/role-assignments-steps [azure_portal_list_face_account]: https://portal.azure.com/#blade/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/Face [azure_portal_list_cognitive_service_account]: https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/AllInOne [azure_cognitive_service_account]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#supported-services-with-a-multi-service-resource @@ -459,6 +465,8 @@ additional questions or comments. [azure_sdk_python_default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential [register_aad_app]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal +[evaluate_different_detection_models]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model#evaluate-different-models +[recommended_recognition_model]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model#recommended-model [liveness_tutorial]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness [integrate_liveness_into_mobile_application]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application From 91699a764bbfe5a9df9f7956a5fa9c2b33bd3152 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 6 May 2024 14:32:10 +0800 Subject: [PATCH 15/41] Correct the path of the model object --- sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py | 4 ++-- .../azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 2eadf9f803ba..d6f096ebb1a7 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -45,7 +45,7 @@ def detect_from_url( return_recognition_model: Optional[bool] = None, face_id_time_to_live: Optional[int] = None, **kwargs: Any, - ) -> List[_models._models.FaceDetectionResult]: + ) -> List[_models.FaceDetectionResult]: # pylint: disable=line-too-long """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes. @@ -448,7 +448,7 @@ def detect( return_recognition_model: Optional[bool] = None, face_id_time_to_live: Optional[int] = None, **kwargs: Any, - ) -> List[_models._models.FaceDetectionResult]: + ) -> List[_models.FaceDetectionResult]: # pylint: disable=line-too-long """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes. diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 6faccb77f37d..e7ab3d28b79f 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -45,7 +45,7 @@ async def detect_from_url( return_recognition_model: Optional[bool] = None, face_id_time_to_live: Optional[int] = None, **kwargs: Any, - ) -> List[_models._models.FaceDetectionResult]: + ) -> List[_models.FaceDetectionResult]: # pylint: disable=line-too-long """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes. @@ -448,7 +448,7 @@ async def detect( return_recognition_model: Optional[bool] = None, face_id_time_to_live: Optional[int] = None, **kwargs: Any, - ) -> List[_models._models.FaceDetectionResult]: + ) -> List[_models.FaceDetectionResult]: # pylint: disable=line-too-long """Detect human faces in an image, return face rectangles, and optionally with faceIds, landmarks, and attributes. From 66492d13b6166542ecfc1dee43f5f483e56cb675 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 6 May 2024 16:14:37 +0800 Subject: [PATCH 16/41] Refine liveness samples --- .../samples_face_liveness_detection_async.py | 65 +++++++++++++----- ...eness_detection_with_verification_async.py | 66 ++++++++++++++----- .../samples_face_liveness_detection.py | 64 +++++++++++++----- ...ce_liveness_detection_with_verification.py | 65 +++++++++++++----- 4 files changed, 196 insertions(+), 64 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py index 7e072add6b53..2efd9e26a40c 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py @@ -5,14 +5,17 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- - """ FILE: sample_face_liveness_detection_async.py DESCRIPTION: This sample demonstrates how to determine if a face in an input video stream is real (live) or fake (spoof). - There is a step during the execution of the program that requires the user to switch to mobile SDK for performing - the liveness detection. + + The liveness solution integration involves two different components: a mobile application and + an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. + + For more information about liveness detection, see + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection_async.py @@ -47,14 +50,36 @@ def __init__(self): self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) self.logger = get_logger("sample_face_liveness_detection_async") + async def wait_for_liveness_check_request(self): + # The logic to wait for liveness check request from mobile application. + pass + + async def send_auth_token_to_client(self, token): + # The logic to provide the session-authorization-token to the mobile application. + pass + + async def wait_for_liveness_session_complete(self): + # The logic to wait the notification from mobile application. + self.logger.info( + "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + " and use the mobile client SDK to perform liveness detection on your mobile application.") + input("Press any key to continue when you complete these steps to run sample to get session results ...") + pass + async def livenessSession(self): + """This example demonstrates the liveness detection from a app server-side perspective. + To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#orchestrate-the-liveness-solution. # noqa: E501 + """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceSessionClient from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode async with FaceSessionClient( endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: - # Create a session. + # 1. Wait for liveness check request + await self.wait_for_liveness_check_request() + + # 2. Create a session. self.logger.info("Create a new liveness session.") created_session = await face_session_client.create_liveness_session( CreateLivenessSessionContent( @@ -64,29 +89,37 @@ async def livenessSession(self): auth_token_time_to_live_in_seconds=60)) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") - # Get the liveness detection result. - self.logger.info("Get the liveness detection result.") - liveness_result = await face_session_client.get_liveness_session_result(created_session.session_id) - self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + # 3. Provide session authorization token to mobile application. + token = created_session.auth_token + await self.send_auth_token_to_client(token) - self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" - " to download client SDK to run session starts and detcet liveness call.") - input("Press any key to continue when you complete these steps to run sample to get session results.") + # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. + # To learn how to integrate the UI and the code into your native mobile application, see + # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 - # Get the liveness detection result again. - self.logger.info("Get the liveness detection result again.") + # 7. Wait for session completion notification from client. + await self.wait_for_liveness_session_complete() + + # 8. Query for the liveness detection result as the session is completed. + self.logger.info("Get the liveness detection result.") liveness_result = await face_session_client.get_liveness_session_result(created_session.session_id) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") - # Get audit entries + # Furthermore, you can query all request and response for this sessions, and list all sessions you have by + # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") audit_entries = await face_session_client.get_liveness_session_audit_entries(created_session.session_id) for idx, entry in enumerate(audit_entries): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") - # Delete the session + self.logger.info("List all liveness sessions.") + sessions = await face_session_client.get_liveness_sessions() + for idx, session in enumerate(sessions): + self.logger.info(f"----- Sessions: #{idx+1}-----") + self.logger.info(f"Session: {beautify_json(session.as_dict())}") + + # Clean up: delete the session self.logger.info("Delete the session.") await face_session_client.delete_liveness_session(created_session.session_id) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py index dd7d39f4d42b..cbc254c3089b 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py @@ -11,8 +11,12 @@ DESCRIPTION: This sample demonstrates how to determine if a face in an input video stream is real (live) or fake (spoof), and verify if the face belongs to the particular person. - There is a step during the execution of the program that requires the user to switch to mobile SDK for performing - the liveness detection. + + The liveness solution integration involves two different components: a mobile application and + an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. + + For more information about liveness detection, see + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection_with_verification_async.py @@ -48,22 +52,43 @@ def __init__(self): self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) self.logger = get_logger("sample_face_liveness_detection_with_verification_async") + async def wait_for_liveness_check_request(self): + # The logic to wait for liveness check request from mobile application. + pass + + async def send_auth_token_to_client(self, token): + # The logic to provide the session-authorization-token to the mobile application. + pass + + async def wait_for_liveness_session_complete(self): + # The logic to wait the notification from mobile application. + self.logger.info( + "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + " and use the mobile client SDK to perform liveness detection on your mobile application.") + input("Press any key to continue when you complete these steps to run sample to get session results ...") + pass + async def livenessSessionWithVerify(self): + """This example demonstrates the liveness detection with face verification from a app server-side perspective. + To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#perform-liveness-detection-with-face-verification # noqa: E501 + """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceSessionClient from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode async with FaceSessionClient( endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + # 1. Wait for liveness check request + await self.wait_for_liveness_check_request() + + # 2. Create a session with verify image. from pathlib import Path verify_image_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) with open(verify_image_file_path, "rb") as fd: file_content = fd.read() - # Create a session. self.logger.info("Create a new liveness with verify session with verify image.") - created_session = await face_session_client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, @@ -73,24 +98,25 @@ async def livenessSessionWithVerify(self): verify_image=file_content) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") - # Get the liveness detection and verification result. - self.logger.info("Get the liveness detection and verification result.") - liveness_result = await face_session_client.get_liveness_with_verify_session_result( - created_session.session_id) - self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + # 3. Provide session authorization token to mobile application. + token = created_session.auth_token + await self.send_auth_token_to_client(token) + + # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. + # To learn how to integrate the UI and the code into your native mobile application, see + # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 - self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" - " to download client SDK to run session starts and detcet liveness call.") - input("Press any key to continue when you complete these steps to run sample to get session results.") + # 7. Wait for session completion notification from client. + await self.wait_for_liveness_session_complete() - # Get the liveness detection result again. - self.logger.info("Get the liveness detection result again.") + # 8. Query for the liveness detection result as the session is completed. + self.logger.info("Get the liveness detection result.") liveness_result = await face_session_client.get_liveness_with_verify_session_result( created_session.session_id) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") - # Get audit entries + # Furthermore, you can query all request and response for this sessions, and list all sessions you have by + # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") audit_entries = await face_session_client.get_liveness_with_verify_session_audit_entries( created_session.session_id) @@ -98,7 +124,13 @@ async def livenessSessionWithVerify(self): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") - # Delete the session + self.logger.info("List all liveness sessions.") + sessions = await face_session_client.get_liveness_sessions() + for idx, session in enumerate(sessions): + self.logger.info(f"----- Sessions: #{idx+1}-----") + self.logger.info(f"Session: {beautify_json(session.as_dict())}") + + # Clean up: Delete the session self.logger.info("Delete the session.") await face_session_client.delete_liveness_with_verify_session(created_session.session_id) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py index 56498008acd7..3402b803d522 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py @@ -10,8 +10,12 @@ DESCRIPTION: This sample demonstrates how to determine if a face in an input video stream is real (live) or fake (spoof). - There is a step during the execution of the program that requires the user to switch to mobile SDK for performing - the liveness detection. + + The liveness solution integration involves two different components: a mobile application and + an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. + + For more information about liveness detection, see + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection.py @@ -41,13 +45,35 @@ def __init__(self): self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) self.logger = get_logger("sample_face_liveness_detection") + def wait_for_liveness_check_request(self): + # The logic to wait for liveness check request from mobile application. + pass + + def send_auth_token_to_client(self, token): + # The logic to provide the session-authorization-token to the mobile application. + pass + + def wait_for_liveness_session_complete(self): + # The logic to wait the notification from mobile application. + self.logger.info( + "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + " and use the mobile client SDK to perform liveness detection on your mobile application.") + input("Press any key to continue when you complete these steps to run sample to get session results ...") + pass + def livenessSession(self): + """This example demonstrates the liveness detection from a app server-side perspective. + To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#orchestrate-the-liveness-solution. # noqa: E501 + """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode with FaceSessionClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: - # Create a session. + # 1. Wait for liveness check request + self.wait_for_liveness_check_request() + + # 2. Create a session. self.logger.info("Create a new liveness session.") created_session = face_session_client.create_liveness_session( CreateLivenessSessionContent( @@ -57,29 +83,37 @@ def livenessSession(self): auth_token_time_to_live_in_seconds=60)) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") - # Get the liveness detection result. - self.logger.info("Get the liveness detection result.") - liveness_result = face_session_client.get_liveness_session_result(created_session.session_id) - self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + # 3. Provide session authorization token to mobile application. + token = created_session.auth_token + self.send_auth_token_to_client(token) - self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" - " to download client SDK to run session starts and detcet liveness call.") - input("Press any key to continue when you complete these steps to run sample to get session results.") + # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. + # To learn how to integrate the UI and the code into your native mobile application, see + # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 - # Get the liveness detection result again. - self.logger.info("Get the liveness detection result again.") + # 7. Wait for session completion notification from client. + self.wait_for_liveness_session_complete() + + # 8. Query for the liveness detection result as the session is completed. + self.logger.info("Get the liveness detection result.") liveness_result = face_session_client.get_liveness_session_result(created_session.session_id) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") - # Get audit entries + # Furthermore, you can query all request and response for this sessions, and list all sessions you have by + # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") audit_entries = face_session_client.get_liveness_session_audit_entries(created_session.session_id) for idx, entry in enumerate(audit_entries): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") - # Delete the session + self.logger.info("List all liveness sessions.") + sessions = face_session_client.get_liveness_sessions() + for idx, session in enumerate(sessions): + self.logger.info(f"----- Sessions: #{idx+1}-----") + self.logger.info(f"Session: {beautify_json(session.as_dict())}") + + # Clean up: delete the session self.logger.info("Delete the session.") face_session_client.delete_liveness_session(created_session.session_id) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index e6adfa072cf2..426f5e915901 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -11,8 +11,12 @@ DESCRIPTION: This sample demonstrates how to determine if a face in an input video stream is real (live) or fake (spoof), and verify if the face belongs to the particular person. - There is a step during the execution of the program that requires the user to switch to mobile SDK for performing - the liveness detection. + + The liveness solution integration involves two different components: a mobile application and + an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. + + For more information about liveness detection, see + https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection_with_verification.py @@ -43,21 +47,42 @@ def __init__(self): self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) self.logger = get_logger("sample_face_liveness_detection_with_verification") + def wait_for_liveness_check_request(self): + # The logic to wait for liveness check request from mobile application. + pass + + def send_auth_token_to_client(self, token): + # The logic to provide the session-authorization-token to the mobile application. + pass + + def wait_for_liveness_session_complete(self): + # The logic to wait the notification from mobile application. + self.logger.info( + "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + " and use the mobile client SDK to perform liveness detection on your mobile application.") + input("Press any key to continue when you complete these steps to run sample to get session results ...") + pass + def livenessSessionWithVerify(self): + """This example demonstrates the liveness detection with face verification from a app server-side perspective. + To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#perform-liveness-detection-with-face-verification # noqa: E501 + """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode with FaceSessionClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + # 1. Wait for liveness check request + self.wait_for_liveness_check_request() + + # 2. Create a session with verify image. from pathlib import Path verify_image_file_path = Path(__file__).resolve().parent / DEFAULT_IMAGE_FILE with open(verify_image_file_path, "rb") as fd: file_content = fd.read() - # Create a session. self.logger.info("Create a new liveness with verify session with verify image.") - created_session = face_session_client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, @@ -67,22 +92,24 @@ def livenessSessionWithVerify(self): verify_image=file_content) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") - # Get the liveness detection and verification result. - self.logger.info("Get the liveness detection and verification result.") - liveness_result = face_session_client.get_liveness_with_verify_session_result(created_session.session_id) - self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") + # 3. Provide session authorization token to mobile application. + token = created_session.auth_token + self.send_auth_token_to_client(token) - self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" - " to download client SDK to run session starts and detcet liveness call.") - input("Press any key to continue when you complete these steps to run sample to get session results.") + # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. + # To learn how to integrate the UI and the code into your native mobile application, see + # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 - # Get the liveness detection result again. - self.logger.info("Get the liveness detection result again.") + # 7. Wait for session completion notification from client. + self.wait_for_liveness_session_complete() + + # 8. Query for the liveness detection result as the session is completed. + self.logger.info("Get the liveness detection result.") liveness_result = face_session_client.get_liveness_with_verify_session_result(created_session.session_id) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") - # Get audit entries + # Furthermore, you can query all request and response for this sessions, and list all sessions you have by + # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") audit_entries = face_session_client.get_liveness_with_verify_session_audit_entries( created_session.session_id) @@ -90,7 +117,13 @@ def livenessSessionWithVerify(self): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") - # Delete the session + self.logger.info("List all liveness sessions.") + sessions = face_session_client.get_liveness_sessions() + for idx, session in enumerate(sessions): + self.logger.info(f"----- Sessions: #{idx+1}-----") + self.logger.info(f"Session: {beautify_json(session.as_dict())}") + + # Clean up: Delete the session self.logger.info("Delete the session.") face_session_client.delete_liveness_with_verify_session(created_session.session_id) From 8d869d1e4e02453f09d30e7af8022f11293f13cd Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 6 May 2024 18:47:13 +0800 Subject: [PATCH 17/41] Reorganize samples and tests folder - Only use images under samples/images for test. - Move all async samples under samples folder to address relative import failure - Delete the folder 'tests/images' - Add two utility methods in helpers class. --- .../samples/images/nine-faces.png | Bin .../samples/samples_authentication.py | 21 ++--- .../samples_authentication_async.py | 30 ++----- .../samples/samples_face_detection.py | 15 ++-- .../samples_face_detection_async.py | 23 ++--- .../samples/samples_face_grouping.py | 12 +-- .../samples_face_grouping_async.py | 20 ++--- .../samples_face_liveness_detection_async.py | 8 +- ...ce_liveness_detection_with_verification.py | 11 +-- ...eness_detection_with_verification_async.py | 19 ++-- .../samples/samples_find_similar_faces.py | 50 ++++------- .../samples_find_similar_faces_async.py | 56 +++++------- .../samples/shared/constants.py | 81 +++++++++--------- .../samples/shared/helpers.py | 15 +++- .../tests/_shared/constants.py | 79 +++++++++-------- .../tests/_shared/helpers.py | 16 +++- .../tests/images/Family1-Dad1.jpg | Bin 44796 -> 0 bytes .../tests/images/Family1-Dad2.jpg | Bin 46585 -> 0 bytes .../tests/images/Family1-Dad3.jpg | Bin 34562 -> 0 bytes .../tests/images/Family1-Daughter1.jpg | Bin 26921 -> 0 bytes .../tests/images/Family1-Daughter2.jpg | Bin 27307 -> 0 bytes .../tests/images/Family1-Daughter3.jpg | Bin 18559 -> 0 bytes .../tests/images/Family1-Mom1.jpg | Bin 34200 -> 0 bytes .../tests/images/Family1-Mom2.jpg | Bin 26212 -> 0 bytes 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.../tests/images/extra-woman-image.jpg | Bin 62275 -> 0 bytes .../tests/images/findsimilar.jpg | Bin 46585 -> 0 bytes .../tests/images/identification1.jpg | Bin 94734 -> 0 bytes .../tests/images/man1-person-group.jpg | Bin 52928 -> 0 bytes .../tests/images/man2-person-group.jpg | Bin 56200 -> 0 bytes .../tests/images/man3-person-group.jpg | Bin 40339 -> 0 bytes .../tests/images/test-image-person-group.jpg | Bin 81678 -> 0 bytes .../tests/images/woman1-person-group.jpg | Bin 40877 -> 0 bytes .../tests/images/woman2-person-group.jpg | Bin 40873 -> 0 bytes .../tests/images/woman3-person-group.jpg | Bin 62275 -> 0 bytes .../tests/test_authentication.py | 13 +-- .../tests/test_authentication_async.py | 13 +-- .../test_liveness_with_verify_session.py | 10 +-- ...test_liveness_with_verify_session_async.py | 10 +-- 55 files changed, 215 insertions(+), 287 deletions(-) mode change 100755 => 100644 sdk/face/azure-ai-vision-face/samples/images/nine-faces.png rename sdk/face/azure-ai-vision-face/samples/{async_samples => }/samples_authentication_async.py (83%) rename sdk/face/azure-ai-vision-face/samples/{async_samples => }/samples_face_detection_async.py (87%) rename sdk/face/azure-ai-vision-face/samples/{async_samples => }/samples_face_grouping_async.py (82%) rename sdk/face/azure-ai-vision-face/samples/{async_samples => }/samples_face_liveness_detection_async.py (96%) rename sdk/face/azure-ai-vision-face/samples/{async_samples => }/samples_face_liveness_detection_with_verification_async.py (92%) rename sdk/face/azure-ai-vision-face/samples/{async_samples => }/samples_find_similar_faces_async.py (80%) delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Dad1.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Dad2.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Dad3.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Daughter1.jpg delete mode 100644 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sdk/face/azure-ai-vision-face/tests/images/man1-person-group.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/man2-person-group.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/man3-person-group.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/test-image-person-group.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/woman1-person-group.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/woman2-person-group.jpg delete mode 100644 sdk/face/azure-ai-vision-face/tests/images/woman3-person-group.jpg diff --git a/sdk/face/azure-ai-vision-face/samples/images/nine-faces.png b/sdk/face/azure-ai-vision-face/samples/images/nine-faces.png old mode 100755 new mode 100644 diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index adf3101e9521..9872a78f1b58 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -35,8 +35,9 @@ CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - DEFAULT_IMAGE_FILE, + TestImages, ) +from shared import helpers from shared.helpers import beautify_json, get_logger @@ -54,14 +55,9 @@ def authentication_by_api_key(self): self.logger.info("Instantiate a FaceClient using an api key") with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / DEFAULT_IMAGE_FILE - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False) @@ -78,14 +74,9 @@ def authentication_by_aad_credential(self): self.logger.info("Instantiate a FaceClient using a TokenCredential") with FaceClient(endpoint=self.endpoint, credential=DefaultAzureCredential()) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / DEFAULT_IMAGE_FILE - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py similarity index 83% rename from sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py rename to sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py index e4bb3eb5bccc..56e06f0e1cbe 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py @@ -28,21 +28,18 @@ """ import asyncio import os -import sys from dotenv import find_dotenv, load_dotenv -# To import utility files in ../shared folder -sys.path.insert(1, os.path.join(sys.path[0], '..')) - -from shared.constants import ( # noqa: E402 +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - DEFAULT_IMAGE_FILE, + TestImages, ) -from shared.helpers import beautify_json, get_logger # noqa: E402 +from shared import helpers +from shared.helpers import beautify_json, get_logger class FaceAuthentication(): @@ -59,14 +56,9 @@ async def authentication_by_api_key(self): self.logger.info("Instantiate a FaceClient using an api key") async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = await face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False) @@ -84,15 +76,9 @@ async def authentication_by_aad_credential(self): self.logger.info("Instantiate a FaceClient using a TokenCredential") async with DefaultAzureCredential() as credential, \ FaceClient(endpoint=self.endpoint, credential=credential) as face_client: - - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = await face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 99b4f70f1e24..92813091c270 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -27,9 +27,9 @@ CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - DEFAULT_IMAGE_URL, - IMAGE_DETECTION_5, + TestImages, ) +from shared import helpers from shared.helpers import beautify_json, get_logger @@ -47,14 +47,9 @@ def detect(self): FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / IMAGE_DETECTION_5 - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) result = face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True, @@ -78,7 +73,7 @@ def detect_from_url(self): FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection01) with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - sample_url = DEFAULT_IMAGE_URL + sample_url = TestImages.DEFAULT_IMAGE_URL result = face_client.detect_from_url( url=sample_url, detection_model=FaceDetectionModel.DETECTION_01, diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py similarity index 87% rename from sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py rename to sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py index df5a51c05423..6c4e699b8f5f 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py @@ -20,22 +20,18 @@ """ import asyncio import os -import sys from dotenv import find_dotenv, load_dotenv -# To import utility files in ../shared folder -sys.path.insert(1, os.path.join(sys.path[0], '..')) - -from shared.constants import ( # noqa: E402 +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - DEFAULT_IMAGE_URL, - IMAGE_DETECTION_5, + TestImages, ) -from shared.helpers import beautify_json, get_logger # noqa: E402 +from shared import helpers +from shared.helpers import beautify_json, get_logger class DetectFaces(): @@ -52,14 +48,9 @@ async def detect(self): FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / ("../" + IMAGE_DETECTION_5) - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) result = await face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True, @@ -83,7 +74,7 @@ async def detect_from_url(self): FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection01) async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - sample_url = DEFAULT_IMAGE_URL + sample_url = TestImages.DEFAULT_IMAGE_URL result = await face_client.detect_from_url( url=sample_url, detection_model=FaceDetectionModel.DETECTION_01, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index 0e5f49cff452..c16d29be16bd 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -27,8 +27,9 @@ CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - IMAGE_NINE_FACES, + TestImages, ) +from shared import helpers from shared.helpers import beautify_json, get_logger @@ -45,14 +46,9 @@ def group(self): from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / IMAGE_NINE_FACES - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result = face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py similarity index 82% rename from sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py rename to sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py index 4a30fb6a8b64..90e29c9e88c9 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py @@ -20,21 +20,18 @@ """ import asyncio import os -import sys from dotenv import find_dotenv, load_dotenv -# To import utility files in ../shared folder -sys.path.insert(1, os.path.join(sys.path[0], '..')) - -from shared.constants import ( # noqa: E402 +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - IMAGE_NINE_FACES, + TestImages, ) -from shared.helpers import beautify_json, get_logger # noqa: E402 +from shared import helpers +from shared.helpers import beautify_json, get_logger class GroupFaces(): @@ -50,14 +47,9 @@ async def group(self): from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: - from pathlib import Path - - sample_file_path = Path(__file__).resolve().parent / ("../" + IMAGE_NINE_FACES) - with open(sample_file_path, "rb") as fd: - file_content = fd.read() - + sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result = await face_client.detect( - file_content, + helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py similarity index 96% rename from sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py rename to sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py index 2efd9e26a40c..fa482ad861fa 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py @@ -26,21 +26,17 @@ """ import asyncio import os -import sys import uuid from dotenv import find_dotenv, load_dotenv -# To import utility files in ../shared folder -sys.path.insert(1, os.path.join(sys.path[0], '..')) - -from shared.constants import ( # noqa: E402 +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, ) -from shared.helpers import beautify_json, get_logger # noqa: E402 +from shared.helpers import beautify_json, get_logger class DetectLiveness(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index 426f5e915901..0ede0dbdb38e 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -35,8 +35,9 @@ CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - DEFAULT_IMAGE_FILE, + TestImages, ) +from shared import helpers from shared.helpers import beautify_json, get_logger @@ -76,11 +77,7 @@ def livenessSessionWithVerify(self): self.wait_for_liveness_check_request() # 2. Create a session with verify image. - from pathlib import Path - - verify_image_file_path = Path(__file__).resolve().parent / DEFAULT_IMAGE_FILE - with open(verify_image_file_path, "rb") as fd: - file_content = fd.read() + verify_image_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) self.logger.info("Create a new liveness with verify session with verify image.") created_session = face_session_client.create_liveness_with_verify_session( @@ -89,7 +86,7 @@ def livenessSessionWithVerify(self): device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, auth_token_time_to_live_in_seconds=60), - verify_image=file_content) + verify_image=helpers.read_file_content(verify_image_file_path)) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") # 3. Provide session authorization token to mobile application. diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py similarity index 92% rename from sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py rename to sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py index cbc254c3089b..a280167f7d75 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py @@ -27,22 +27,19 @@ """ import asyncio import os -import sys import uuid from dotenv import find_dotenv, load_dotenv -# To import utility files in ../shared folder -sys.path.insert(1, os.path.join(sys.path[0], '..')) - -from shared.constants import ( # noqa: E402 +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - DEFAULT_IMAGE_FILE, + TestImages, ) -from shared.helpers import beautify_json, get_logger # noqa: E402 +from shared import helpers +from shared.helpers import beautify_json, get_logger class DetectLivenessWithVerify(): @@ -82,11 +79,7 @@ async def livenessSessionWithVerify(self): await self.wait_for_liveness_check_request() # 2. Create a session with verify image. - from pathlib import Path - - verify_image_file_path = Path(__file__).resolve().parent / ("../" + DEFAULT_IMAGE_FILE) - with open(verify_image_file_path, "rb") as fd: - file_content = fd.read() + verify_image_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) self.logger.info("Create a new liveness with verify session with verify image.") created_session = await face_session_client.create_liveness_with_verify_session( @@ -95,7 +88,7 @@ async def livenessSessionWithVerify(self): device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, auth_token_time_to_live_in_seconds=60), - verify_image=file_content) + verify_image=helpers.read_file_content(verify_image_file_path)) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") # 3. Provide session authorization token to mobile application. diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index d9c1fe59b291..7dc536825829 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -27,29 +27,12 @@ CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - IMAGE_FAMILY_1_MOM_1, - IMAGE_FAMILY_1_MOM_2, - IMAGE_FAMILY_2_LADY_1, - IMAGE_FAMILY_2_LADY_2, - IMAGE_FAMILY_3_LADY_1, - IMAGE_FINDSIMILAR, - IMAGE_NINE_FACES, + TestImages, ) +from shared import helpers from shared.helpers import beautify_json, get_logger -def _read_file_content(file_path: str): - with open(file_path, "rb") as fd: - file_content = fd.read() - - return file_content - - -def _get_full_file_path(image_file_name: str): - from pathlib import Path - return Path(__file__).resolve().parent / image_file_name - - class FindSimilarFaces(): def __init__(self): load_dotenv(find_dotenv()) @@ -64,9 +47,9 @@ def find_similar_from_face_ids(self): with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: # Detect faces from 'IMAGE_NINE_FACES' - nine_faces_file_path = _get_full_file_path(IMAGE_NINE_FACES) + nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result1 = face_client.detect( - _read_file_content(nine_faces_file_path), + helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -75,9 +58,9 @@ def find_similar_from_face_ids(self): self.logger.info(f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}") # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) detect_result2 = face_client.detect( - _read_file_content(find_similar_file_path), + helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -91,7 +74,7 @@ def find_similar_from_face_ids(self): find_similar_result1 = face_client.find_similar(face_id=face_id, face_ids=face_ids) self.logger.info("Find Similar with matchPerson mode:") for r in find_similar_result1: - self.logger.info(f"{beautify_json(r.as_dict())}") + self.logger.info(f"{helpers.beautify_json(r.as_dict())}") def find_similar_from_large_face_list(self): from azure.core.credentials import AzureKeyCredential @@ -112,19 +95,24 @@ def find_similar_from_large_face_list(self): # Add faces into the largeFaceList self.logger.info(f"Add faces into the LargeFaceList {large_face_list_id}") face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_1_MOM_1)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_1)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-1") face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_1_MOM_2)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_2)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-2") face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_2_LADY_1)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_1)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-1") face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_2_LADY_2)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_2)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-2") face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_3_LADY_1)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_3_LADY_1)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady3-1") # The LargeFaceList should be trained to make it ready for find similar operation. @@ -133,9 +121,9 @@ def find_similar_from_large_face_list(self): poller.wait() # Keep polling until the "Train" operation completes. # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) detect_result = face_client.detect( - _read_file_content(find_similar_file_path), + helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py similarity index 80% rename from sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py rename to sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py index 3a2ce7c12eb4..76f319bda1eb 100644 --- a/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py @@ -20,39 +20,18 @@ """ import asyncio import os -import sys from dotenv import find_dotenv, load_dotenv -# To import utility files in ../shared folder -sys.path.insert(1, os.path.join(sys.path[0], '..')) - -from shared.constants import ( # noqa: E402 +from shared.constants import ( CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ENDPOINT, - IMAGE_FAMILY_1_MOM_1, - IMAGE_FAMILY_1_MOM_2, - IMAGE_FAMILY_2_LADY_1, - IMAGE_FAMILY_2_LADY_2, - IMAGE_FAMILY_3_LADY_1, - IMAGE_FINDSIMILAR, - IMAGE_NINE_FACES, + TestImages, ) -from shared.helpers import beautify_json, get_logger # noqa: E402 - - -def _read_file_content(file_path: str): - with open(file_path, "rb") as fd: - file_content = fd.read() - - return file_content - - -def _get_full_file_path(image_file_name: str): - from pathlib import Path - return Path(__file__).resolve().parent / ("../" + image_file_name) +from shared import helpers +from shared.helpers import beautify_json, get_logger class FindSimilarFaces(): @@ -69,9 +48,9 @@ async def find_similar_from_face_ids(self): async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: # Detect faces from 'IMAGE_NINE_FACES' - nine_faces_file_path = _get_full_file_path(IMAGE_NINE_FACES) + nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result1 = await face_client.detect( - _read_file_content(nine_faces_file_path), + helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -80,9 +59,9 @@ async def find_similar_from_face_ids(self): self.logger.info(f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}") # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) detect_result2 = await face_client.detect( - _read_file_content(find_similar_file_path), + helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) @@ -117,19 +96,24 @@ async def find_similar_from_large_face_list(self): # Add faces into the largeFaceList self.logger.info(f"Add faces into the LargeFaceList {large_face_list_id}") await face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_1_MOM_1)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_1)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-1") await face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_1_MOM_2)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_2)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-2") await face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_2_LADY_1)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_1)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-1") await face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_2_LADY_2)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_2)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-2") await face_admin_client.add_large_face_list_face( - large_face_list_id, _read_file_content(_get_full_file_path(IMAGE_FAMILY_3_LADY_1)), + large_face_list_id, + helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_3_LADY_1)), detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady3-1") # The LargeFaceList should be trained to make it ready for find similar operation. @@ -138,9 +122,9 @@ async def find_similar_from_large_face_list(self): await poller.wait() # Keep polling until the "Train" operation completes. # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = _get_full_file_path(IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) detect_result = await face_client.detect( - _read_file_content(find_similar_file_path), + helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True) diff --git a/sdk/face/azure-ai-vision-face/samples/shared/constants.py b/sdk/face/azure-ai-vision-face/samples/shared/constants.py index c4f49c6f325f..23d7627ae384 100644 --- a/sdk/face/azure-ai-vision-face/samples/shared/constants.py +++ b/sdk/face/azure-ai-vision-face/samples/shared/constants.py @@ -19,43 +19,46 @@ DEFAULT_FACE_API_ENDPOINT = "" DEFAULT_FACE_API_ACCOUNT_KEY = "" + # Test images -IMAGE_FAMILY_1_DAD_1 = "images/Family1-Dad1.jpg" -IMAGE_FAMILY_1_DAD_2 = "images/Family1-Dad2.jpg" -IMAGE_FAMILY_1_DAD_3 = "images/Family1-Dad3.jpg" -IMAGE_FAMILY_1_DAUGHTER_1 = "images/Family1-Daughter1.jpg" -IMAGE_FAMILY_1_DAUGHTER_2 = "images/Family1-Daughter2.jpg" -IMAGE_FAMILY_1_DAUGHTER_3 = "images/Family1-Daughter3.jpg" -IMAGE_FAMILY_1_MOM_1 = "images/Family1-Mom1.jpg" -IMAGE_FAMILY_1_MOM_2 = "images/Family1-Mom2.jpg" -IMAGE_FAMILY_1_SON_1 = "images/Family1-Son1.jpg" -IMAGE_FAMILY_1_SON_2 = "images/Family1-Son2.jpg" -IMAGE_FAMILY_2_LADY_1 = "images/Family2-Lady1.jpg" -IMAGE_FAMILY_2_LADY_2 = "images/Family2-Lady2.jpg" -IMAGE_FAMILY_2_Man_1 = "images/Family2-Man1.jpg" -IMAGE_FAMILY_2_Man_2 = "images/Family2-Man2.jpg" -IMAGE_FAMILY_3_LADY_1 = "images/Family3-Lady1.jpg" -IMAGE_FAMILY_3_Man_1 = "images/Family3-Man1.jpg" -IMAGE_CHILD_1_PERSON_GROUP = "images/child1-person-group.jpg" -IMAGE_CHILD_2_PERSON_GROUP = "images/child2-person-group.jpg" -IMAGE_CHILD_3_PERSON_GROUP = "images/child3-person-group.jpg" -IMAGE_DETECTION_1 = "images/detection1.jpg" -IMAGE_DETECTION_2 = "images/detection2.jpg" -IMAGE_DETECTION_3 = "images/detection3.jpg" -IMAGE_DETECTION_4 = "images/detection4.jpg" -IMAGE_DETECTION_5 = "images/detection5.jpg" -IMAGE_DETECTION_6 = "images/detection6.jpg" -IMAGE_EXTRA_WOMAN_IMAGE = "images/extra-woman-image.jpg" -IMAGE_FINDSIMILAR = "images/findsimilar.jpg" -IMAGE_IDENTIFICATION1 = "images/indentification1.jpg" -IMAGE_MAN1_PERSON_GROUP = "images/man1-person-group.jpg" -IMAGE_MAN2_PERSON_GROUP = "images/man2-person-group.jpg" -IMAGE_MAN3_PERSON_GROUP = "images/man3-person-group.jpg" -TEST_IMAGE_PERSON_GROUP = "images/test-image-person-group.jpg" -IMAGE_WOMAN1_PERSON_GROUP = "images/woman1-person-group.jpg" -IMAGE_WOMAN2_PERSON_GROUP = "images/woman2-person-group.jpg" -IMAGE_WOMAN3_PERSON_GROUP = "images/woman3-person-group.jpg" -IMAGE_NINE_FACES = "images/nine-faces.png" - -DEFAULT_IMAGE_URL = "https://aka.ms/facesampleurl" -DEFAULT_IMAGE_FILE = IMAGE_DETECTION_1 +class TestImages(object): + IMAGE_PARENT_FOLDER = "../images/" + IMAGE_FAMILY_1_DAD_1 = "Family1-Dad1.jpg" + IMAGE_FAMILY_1_DAD_2 = "Family1-Dad2.jpg" + IMAGE_FAMILY_1_DAD_3 = "Family1-Dad3.jpg" + IMAGE_FAMILY_1_DAUGHTER_1 = "Family1-Daughter1.jpg" + IMAGE_FAMILY_1_DAUGHTER_2 = "Family1-Daughter2.jpg" + IMAGE_FAMILY_1_DAUGHTER_3 = "Family1-Daughter3.jpg" + IMAGE_FAMILY_1_MOM_1 = "Family1-Mom1.jpg" + IMAGE_FAMILY_1_MOM_2 = "Family1-Mom2.jpg" + IMAGE_FAMILY_1_SON_1 = "Family1-Son1.jpg" + IMAGE_FAMILY_1_SON_2 = "Family1-Son2.jpg" + IMAGE_FAMILY_2_LADY_1 = "Family2-Lady1.jpg" + IMAGE_FAMILY_2_LADY_2 = "Family2-Lady2.jpg" + IMAGE_FAMILY_2_Man_1 = "Family2-Man1.jpg" + IMAGE_FAMILY_2_Man_2 = "Family2-Man2.jpg" + IMAGE_FAMILY_3_LADY_1 = "Family3-Lady1.jpg" + IMAGE_FAMILY_3_Man_1 = "Family3-Man1.jpg" + IMAGE_CHILD_1_PERSON_GROUP = "child1-person-group.jpg" + IMAGE_CHILD_2_PERSON_GROUP = "child2-person-group.jpg" + IMAGE_CHILD_3_PERSON_GROUP = "child3-person-group.jpg" + IMAGE_DETECTION_1 = "detection1.jpg" + IMAGE_DETECTION_2 = "detection2.jpg" + IMAGE_DETECTION_3 = "detection3.jpg" + IMAGE_DETECTION_4 = "detection4.jpg" + IMAGE_DETECTION_5 = "detection5.jpg" + IMAGE_DETECTION_6 = "detection6.jpg" + IMAGE_EXTRA_WOMAN_IMAGE = "extra-woman-image.jpg" + IMAGE_FINDSIMILAR = "findsimilar.jpg" + IMAGE_IDENTIFICATION1 = "indentification1.jpg" + IMAGE_MAN1_PERSON_GROUP = "man1-person-group.jpg" + IMAGE_MAN2_PERSON_GROUP = "man2-person-group.jpg" + IMAGE_MAN3_PERSON_GROUP = "man3-person-group.jpg" + TEST_IMAGE_PERSON_GROUP = "test-image-person-group.jpg" + IMAGE_WOMAN1_PERSON_GROUP = "woman1-person-group.jpg" + IMAGE_WOMAN2_PERSON_GROUP = "woman2-person-group.jpg" + IMAGE_WOMAN3_PERSON_GROUP = "woman3-person-group.jpg" + IMAGE_NINE_FACES = "nine-faces.png" + + DEFAULT_IMAGE_URL = "https://aka.ms/facesampleurl" + DEFAULT_IMAGE_FILE = IMAGE_DETECTION_1 diff --git a/sdk/face/azure-ai-vision-face/samples/shared/helpers.py b/sdk/face/azure-ai-vision-face/samples/shared/helpers.py index 4423317ee5a7..f2a57dcb09fc 100644 --- a/sdk/face/azure-ai-vision-face/samples/shared/helpers.py +++ b/sdk/face/azure-ai-vision-face/samples/shared/helpers.py @@ -5,13 +5,13 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- - """ FILE: helpers.py DESCRIPTION: This module loads logger and some utility methods required to run the sample codes. """ +from pathlib import Path import json import logging @@ -33,3 +33,16 @@ def get_logger(name): def beautify_json(obj: typing.Dict[str, typing.Any]): return json.dumps(obj, indent=4) + + +def get_image_path(image_file_name: str): + from .constants import TestImages + + return Path(__file__).resolve().parent / (TestImages.IMAGE_PARENT_FOLDER + "/" + image_file_name) + + +def read_file_content(file_path: Path): + with open(file_path, "rb") as fd: + file_content = fd.read() + + return file_content diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/constants.py b/sdk/face/azure-ai-vision-face/tests/_shared/constants.py index 4132663508cc..d786c787e607 100644 --- a/sdk/face/azure-ai-vision-face/tests/_shared/constants.py +++ b/sdk/face/azure-ai-vision-face/tests/_shared/constants.py @@ -5,52 +5,55 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- - """ FILE: constants.py DESCRIPTION: This module defines constants required to run the test cases. """ - CONFIGURATION_NAME_FACE_API_ENDPOINT = "azure_face_api_endpoint" CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY = "azure_face_api_account_key" + # Test images -IMAGE_FAMILY_1_DAD_1 = "images/Family1-Dad1.jpg" -IMAGE_FAMILY_1_DAD_2 = "images/Family1-Dad2.jpg" -IMAGE_FAMILY_1_DAD_3 = "images/Family1-Dad3.jpg" -IMAGE_FAMILY_1_DAUGHTER_1 = "images/Family1-Daughter1.jpg" -IMAGE_FAMILY_1_DAUGHTER_2 = "images/Family1-Daughter2.jpg" -IMAGE_FAMILY_1_DAUGHTER_3 = "images/Family1-Daughter3.jpg" -IMAGE_FAMILY_1_MOM_1 = "images/Family1-Mom1.jpg" -IMAGE_FAMILY_1_MOM_2 = "images/Family1-Mom2.jpg" -IMAGE_FAMILY_1_SON_1 = "images/Family1-Son1.jpg" -IMAGE_FAMILY_1_SON_2 = "images/Family1-Son2.jpg" -IMAGE_FAMILY_2_LADY_1 = "images/Family2-Lady1.jpg" -IMAGE_FAMILY_2_LADY_2 = "images/Family2-Lady2.jpg" -IMAGE_FAMILY_2_Man_1 = "images/Family2-Man1.jpg" -IMAGE_FAMILY_2_Man_2 = "images/Family2-Man2.jpg" -IMAGE_FAMILY_3_LADY_1 = "images/Family3-Lady1.jpg" -IMAGE_FAMILY_3_Man_1 = "images/Family3-Man1.jpg" -IMAGE_CHILD_1_PERSON_GROUP = "images/child1-person-group.jpg" -IMAGE_CHILD_2_PERSON_GROUP = "images/child2-person-group.jpg" -IMAGE_CHILD_3_PERSON_GROUP = "images/child3-person-group.jpg" -IMAGE_DETECTION_1 = "images/detection1.jpg" -IMAGE_DETECTION_2 = "images/detection2.jpg" -IMAGE_DETECTION_3 = "images/detection3.jpg" -IMAGE_DETECTION_4 = "images/detection4.jpg" -IMAGE_DETECTION_5 = "images/detection5.jpg" -IMAGE_DETECTION_6 = "images/detection6.jpg" -IMAGE_EXTRA_WOMAN_IMAGE = "images/extra-woman-image.jpg" -IMAGE_FINDSIMILAR = "images/findsimilar.jpg" -IMAGE_IDENTIFICATION1 = "images/indentification1.jpg" -IMAGE_MAN1_PERSON_GROUP = "images/man1-person-group.jpg" -IMAGE_MAN2_PERSON_GROUP = "images/man2-person-group.jpg" -IMAGE_MAN3_PERSON_GROUP = "images/man3-person-group.jpg" -TEST_IMAGE_PERSON_GROUP = "images/test-image-person-group.jpg" -IMAGE_WOMAN1_PERSON_GROUP = "images/woman1-person-group.jpg" -IMAGE_WOMAN2_PERSON_GROUP = "images/woman2-person-group.jpg" -IMAGE_WOMAN3_PERSON_GROUP = "images/woman3-person-group.jpg" +class TestImages(object): + IMAGE_PARENT_FOLDER = "../../samples/images/" + + IMAGE_FAMILY_1_DAD_1 = "Family1-Dad1.jpg" + IMAGE_FAMILY_1_DAD_2 = "Family1-Dad2.jpg" + IMAGE_FAMILY_1_DAD_3 = "Family1-Dad3.jpg" + IMAGE_FAMILY_1_DAUGHTER_1 = "Family1-Daughter1.jpg" + IMAGE_FAMILY_1_DAUGHTER_2 = "Family1-Daughter2.jpg" + IMAGE_FAMILY_1_DAUGHTER_3 = "Family1-Daughter3.jpg" + IMAGE_FAMILY_1_MOM_1 = "Family1-Mom1.jpg" + IMAGE_FAMILY_1_MOM_2 = "Family1-Mom2.jpg" + IMAGE_FAMILY_1_SON_1 = "Family1-Son1.jpg" + IMAGE_FAMILY_1_SON_2 = "Family1-Son2.jpg" + IMAGE_FAMILY_2_LADY_1 = "Family2-Lady1.jpg" + IMAGE_FAMILY_2_LADY_2 = "Family2-Lady2.jpg" + IMAGE_FAMILY_2_Man_1 = "Family2-Man1.jpg" + IMAGE_FAMILY_2_Man_2 = "Family2-Man2.jpg" + IMAGE_FAMILY_3_LADY_1 = "Family3-Lady1.jpg" + IMAGE_FAMILY_3_Man_1 = "Family3-Man1.jpg" + IMAGE_CHILD_1_PERSON_GROUP = "child1-person-group.jpg" + IMAGE_CHILD_2_PERSON_GROUP = "child2-person-group.jpg" + IMAGE_CHILD_3_PERSON_GROUP = "child3-person-group.jpg" + IMAGE_DETECTION_1 = "detection1.jpg" + IMAGE_DETECTION_2 = "detection2.jpg" + IMAGE_DETECTION_3 = "detection3.jpg" + IMAGE_DETECTION_4 = "detection4.jpg" + IMAGE_DETECTION_5 = "detection5.jpg" + IMAGE_DETECTION_6 = "detection6.jpg" + IMAGE_EXTRA_WOMAN_IMAGE = "extra-woman-image.jpg" + IMAGE_FINDSIMILAR = "findsimilar.jpg" + IMAGE_IDENTIFICATION1 = "identification1.jpg" + IMAGE_MAN1_PERSON_GROUP = "man1-person-group.jpg" + IMAGE_MAN2_PERSON_GROUP = "man2-person-group.jpg" + IMAGE_MAN3_PERSON_GROUP = "man3-person-group.jpg" + TEST_IMAGE_PERSON_GROUP = "test-image-person-group.jpg" + IMAGE_WOMAN1_PERSON_GROUP = "woman1-person-group.jpg" + IMAGE_WOMAN2_PERSON_GROUP = "woman2-person-group.jpg" + IMAGE_WOMAN3_PERSON_GROUP = "woman3-person-group.jpg" + IMAGE_NINE_FACES = "nine-faces.png" -IMAGE_URL = "https://aka.ms/facesampleurl" + IMAGE_URL = "https://aka.ms/facesampleurl" diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py b/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py index cd66969f894a..f9665f11b2be 100644 --- a/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py +++ b/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py @@ -5,10 +5,11 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +from pathlib import Path from .constants import ( CONFIGURATION_NAME_FACE_API_ENDPOINT, - CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, ) @@ -18,3 +19,16 @@ def get_face_endpoint(**kwargs): def get_account_key(**kwargs): return kwargs.pop(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY) + + +def get_image_path(image_file_name: str): + from .constants import TestImages + + return Path(__file__).resolve().parent / (TestImages.IMAGE_PARENT_FOLDER + "/" + image_file_name) + + +def read_file_content(file_path: Path): + with open(file_path, "rb") as fd: + file_content = fd.read() + + return file_content diff --git a/sdk/face/azure-ai-vision-face/tests/images/Family1-Dad1.jpg b/sdk/face/azure-ai-vision-face/tests/images/Family1-Dad1.jpg deleted file mode 100644 index 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Date: Tue, 7 May 2024 10:30:38 +0800 Subject: [PATCH 18/41] Add assets.json --- sdk/face/azure-ai-vision-face/assets.json | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 sdk/face/azure-ai-vision-face/assets.json diff --git a/sdk/face/azure-ai-vision-face/assets.json b/sdk/face/azure-ai-vision-face/assets.json new file mode 100644 index 000000000000..708b6e58d26c --- /dev/null +++ b/sdk/face/azure-ai-vision-face/assets.json @@ -0,0 +1,6 @@ +{ + "AssetsRepo": "Azure/azure-sdk-assets", + "AssetsRepoPrefixPath": "python", + "TagPrefix": "python/face/azure-ai-vision-face", + "Tag": "python/face/azure-ai-vision-face_6a3095279a" +} From 0309e20e50e376e577972c0ca56e0a645a7ec42a Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Tue, 7 May 2024 11:33:11 +0800 Subject: [PATCH 19/41] Update CHANGELOG and README --- sdk/face/azure-ai-vision-face/CHANGELOG.md | 23 +++++++++++++++++-- sdk/face/azure-ai-vision-face/README.md | 16 ++++++------- .../azure-ai-vision-face/samples/README.md | 2 +- 3 files changed, 30 insertions(+), 11 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/CHANGELOG.md b/sdk/face/azure-ai-vision-face/CHANGELOG.md index 628743d283a9..1562f0af2215 100644 --- a/sdk/face/azure-ai-vision-face/CHANGELOG.md +++ b/sdk/face/azure-ai-vision-face/CHANGELOG.md @@ -1,5 +1,24 @@ # Release History -## 1.0.0b1 (1970-01-01) +## 1.0.0b1 (2024-05-21) -- Initial version +This is the first preview of the `azure-ai-vsion-face` client library that follows the [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html). +This library replaces the package [azure-cognitiveservices-vision-face](https://pypi.org/project/azure-cognitiveservices-vision-face/). + +This package's [documentation](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/) and [samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples) demonstrate the new API. + +**Breaking changes: New API design** + +- This library supports only the Azure AI Face v1.1-preview.1 API. +- The namespace/package name for Azure AI Face has changed from `azure.cognitiveservices.vision.face` to `azure.ai.vision.face`. +- Three client design: + - `FaceClient` to perform core Face functions such as face detection, recognition(identification and verification), + finding similar faces and grouping faces. + - `FaceAdministrationClient` to managed the following data structures that hold data on faces and persons for + Face recognition, like `face_list`, `large_face_list`, `person_group`, `large_person_group`, `person` and + `dynamic_person_group`. + - `FaceSessionClient` to interact with sessions which is used for Liveness detection. +- Asynchronous APIs added under `azure.ai.vision.face.aio` namespace +- New function names that comply with [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html): + - For example, the method `person_group_person.create()` is changed to `create_person_group_person()` +- Authentication with API key supported using `AzureKeyCredential("")` from `azure.core.credentials` diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index ce1f6c7ff544..35655ff5c8fb 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -130,11 +130,11 @@ face_client = FaceClient(endpoint, credential) `FaceAdministrationClient` is provided to interact with the following data structures that hold data on faces and persons for Face recognition: - - PersonDirectory - - FaceList - - LargeFaceList - - PersonGroup - - LargePersonGroup + - `person` and `dynamic_person_group` in [PersonDirectory](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/use-persondirectory) + - `face_list` + - `large_face_list` + - `person_group` + - `large_person_group` ### FaceSessionClient @@ -167,8 +167,8 @@ The following section provides several code snippets covering some of the most c ### Face Detection Detect faces and analyze them from an binary data. The latest model is the most accurate and recommended to be used. For the detailed differences between different versions of **Detection** and **Recognition** model, please refer to the following links. -* [Detection model](evaluate_different_detection_models) -* [Recognition model](recommended_recognition_model) +* [Detection model][evaluate_different_detection_models] +* [Recognition model][recommended_recognition_model] ```python from azure.core.credentials import AzureKeyCredential @@ -265,7 +265,7 @@ Before doing the identification, we need to train the LargePersonGroup first. training_result = poller.result() ``` -When the training operation is completed successfully, we can identify the faces in this LargePersonGroup throught +When the training operation is completed successfully, we can identify the faces in this LargePersonGroup through `FaceClient`. ```python with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_client: diff --git a/sdk/face/azure-ai-vision-face/samples/README.md b/sdk/face/azure-ai-vision-face/samples/README.md index cfaea0419f25..f3f62a97d2eb 100644 --- a/sdk/face/azure-ai-vision-face/samples/README.md +++ b/sdk/face/azure-ai-vision-face/samples/README.md @@ -3,7 +3,7 @@ page_type: sample languages: - python products: - - azure-ai-vision-face + - azure-face urlFragment: face-samples --- From 05291d819d2dd2d4c1517caf79c84f4b6895fe76 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Tue, 7 May 2024 15:35:06 +0800 Subject: [PATCH 20/41] Fix errors reported by CI pipeline --- .vscode/cspell.json | 23 +++++++++++++++++++ .../azure/ai/vision/face/_patch.py | 4 ++-- .../azure/ai/vision/face/aio/_patch.py | 6 ++--- .../azure/ai/vision/face/aio/_vendor.py | 3 +-- .../azure/ai/vision/face/models/_models.py | 2 +- .../azure/ai/vision/face/models/_patch.py | 16 ++++++------- .../samples/samples_authentication.py | 4 ++-- .../samples/samples_authentication_async.py | 4 ++-- .../samples/samples_face_detection.py | 18 +++++++-------- .../samples/samples_face_detection_async.py | 18 +++++++-------- .../samples/samples_face_grouping.py | 2 +- .../samples/samples_face_grouping_async.py | 2 +- ...ce_liveness_detection_with_verification.py | 2 +- ...eness_detection_with_verification_async.py | 2 +- .../samples/samples_find_similar_faces.py | 8 +++---- .../samples_find_similar_faces_async.py | 8 +++---- .../samples/shared/constants.py | 2 +- .../tests/test_liveness_session.py | 2 +- .../tests/test_liveness_session_async.py | 2 +- .../test_liveness_with_verify_session.py | 2 +- ...test_liveness_with_verify_session_async.py | 2 +- 21 files changed, 77 insertions(+), 55 deletions(-) diff --git a/.vscode/cspell.json b/.vscode/cspell.json index 686980f86256..4dcc4aec6460 100644 --- a/.vscode/cspell.json +++ b/.vscode/cspell.json @@ -1771,6 +1771,29 @@ "wday", "SNOMED" ] + }, + { + "filename": "sdk/face/azure-ai-vision-face/**", + "words": [ + "mros", + "Nify", + "ctxt", + "wday", + "faceid", + "facelistid", + "largefacelistid", + "persongroupid", + "largepersongroupid", + "dynamicpersongroupid", + "personid", + "maxnumofcandidatesreturned" + ] + }, + { + "filename": "sdk/face/azure-ai-vision-face/tests/**", + "words": [ + "dcid" + ] } ], "allowCompoundWords": true diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index d6f096ebb1a7..45aea9216849 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -939,8 +939,8 @@ def create_liveness_with_verify_session( } """ if verify_image is not None: - request_body = _models._models.CreateLivenessWithVerifySessionContent( - parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), + request_body = _models._models.CreateLivenessWithVerifySessionContent( # pylint: disable=protected-access + parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), # pylint: disable=protected-access # noqa: E501 verify_image=("verify-image", verify_image) ) return super()._create_liveness_with_verify_session_with_verify_image( diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index e7ab3d28b79f..147220d360f4 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -439,10 +439,10 @@ async def detect_from_url( async def detect( self, image_content: bytes, + *, detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], return_face_id: bool, - *, return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, return_face_landmarks: Optional[bool] = None, return_recognition_model: Optional[bool] = None, @@ -939,8 +939,8 @@ async def create_liveness_with_verify_session( } """ if verify_image is not None: - request_body = _models._models.CreateLivenessWithVerifySessionContent( - parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), + request_body = _models._models.CreateLivenessWithVerifySessionContent( # pylint: disable=protected-access + parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), # pylint: disable=protected-access # noqa: E501 verify_image=("verify-image", verify_image) ) return await super()._create_liveness_with_verify_session_with_verify_image( diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py index 459a9e05c2b4..9821d9faba8f 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_vendor.py @@ -6,8 +6,7 @@ # -------------------------------------------------------------------------- from abc import ABC -import json -from typing import Any, Dict, IO, List, Mapping, Optional, Sequence, TYPE_CHECKING, Tuple, Union +from typing import TYPE_CHECKING from ._configuration import ( FaceAdministrationClientConfiguration, diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py index c917c9209d29..d847bbfcbc5b 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py @@ -1115,7 +1115,7 @@ class FaceLandmarks(_model_base.Model): # pylint: disable=too-many-instance-att """The coordinates of the under lip bottom. Required.""" @overload - def __init__( + def __init__( # pylint: disable=too-many-locals self, *, pupil_left: "_models.LandmarkCoordinate", diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py index f240c057460a..549cde822927 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py @@ -11,8 +11,8 @@ from ._enums import FaceAttributeType -FaceAttributeTypeDetection01 = Enum("FaceAttributeTypeDetection01", [ - (a.name, a.value) for a in FaceAttributeType if a in [ +FaceAttributeTypeDetection01 = Enum("FaceAttributeTypeDetection01", [ # type: ignore[misc] + (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] FaceAttributeType.ACCESSORIES, FaceAttributeType.BLUR, FaceAttributeType.EXPOSURE, @@ -22,19 +22,19 @@ FaceAttributeType.OCCLUSION]]) """Available attribute options for detection_01 model.""" -FaceAttributeTypeDetection03 = Enum("FaceAttributeTypeDetection03", [ - (a.name, a.value) for a in FaceAttributeType if a in [ +FaceAttributeTypeDetection03 = Enum("FaceAttributeTypeDetection03", [ # type: ignore[misc] + (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] FaceAttributeType.HEAD_POSE, FaceAttributeType.MASK]]) """Available attribute options for detection_03 model.""" -FaceAttributeTypeRecognition03 = Enum("FaceAttributeTypeRecognition03", [ - (a.name, a.value) for a in FaceAttributeType if a in [ +FaceAttributeTypeRecognition03 = Enum("FaceAttributeTypeRecognition03", [ # type: ignore[misc] + (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] FaceAttributeType.QUALITY_FOR_RECOGNITION]]) """Available attribute options for recognition_03 model.""" -FaceAttributeTypeRecognition04 = Enum("FaceAttributeTypeRecognition04", [ - (a.name, a.value) for a in FaceAttributeType if a in [ +FaceAttributeTypeRecognition04 = Enum("FaceAttributeTypeRecognition04", [ # type: ignore[misc] + (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] FaceAttributeType.QUALITY_FOR_RECOGNITION]]) """Available attribute options for recognition_04 model.""" diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index 9872a78f1b58..04a8d4b992b5 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -56,7 +56,7 @@ def authentication_by_api_key(self): self.logger.info("Instantiate a FaceClient using an api key") with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = face_client.detect( + result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -75,7 +75,7 @@ def authentication_by_aad_credential(self): self.logger.info("Instantiate a FaceClient using a TokenCredential") with FaceClient(endpoint=self.endpoint, credential=DefaultAzureCredential()) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = face_client.detect( + result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py index 56e06f0e1cbe..c044995429a1 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py @@ -57,7 +57,7 @@ async def authentication_by_api_key(self): self.logger.info("Instantiate a FaceClient using an api key") async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = await face_client.detect( + result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -77,7 +77,7 @@ async def authentication_by_aad_credential(self): async with DefaultAzureCredential() as credential, \ FaceClient(endpoint=self.endpoint, credential=credential) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = await face_client.detect( + result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 92813091c270..3edc5aae5c6b 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -48,15 +48,15 @@ def detect(self): with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) - result = face_client.detect( + result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True, return_face_attributes=[ - FaceAttributeTypeDetection03.HEAD_POSE, - FaceAttributeTypeDetection03.MASK, - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], + FaceAttributeTypeDetection03.HEAD_POSE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection03.MASK, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 return_face_landmarks=True, return_recognition_model=True, face_id_time_to_live=120) @@ -74,16 +74,16 @@ def detect_from_url(self): with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_url = TestImages.DEFAULT_IMAGE_URL - result = face_client.detect_from_url( + result = face_client.detect_from_url( # type: ignore url=sample_url, detection_model=FaceDetectionModel.DETECTION_01, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False, return_face_attributes=[ - FaceAttributeTypeDetection01.ACCESSORIES, - FaceAttributeTypeDetection01.EXPOSURE, - FaceAttributeTypeDetection01.GLASSES, - FaceAttributeTypeDetection01.NOISE]) + FaceAttributeTypeDetection01.ACCESSORIES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.EXPOSURE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.GLASSES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.NOISE]) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 self.logger.info(f"Detect faces from the url: {sample_url}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py index 6c4e699b8f5f..4722c3f4d733 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py @@ -49,15 +49,15 @@ async def detect(self): async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) - result = await face_client.detect( + result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True, return_face_attributes=[ - FaceAttributeTypeDetection03.HEAD_POSE, - FaceAttributeTypeDetection03.MASK, - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], + FaceAttributeTypeDetection03.HEAD_POSE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection03.MASK, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 return_face_landmarks=True, return_recognition_model=True, face_id_time_to_live=120) @@ -75,16 +75,16 @@ async def detect_from_url(self): async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_url = TestImages.DEFAULT_IMAGE_URL - result = await face_client.detect_from_url( + result = await face_client.detect_from_url( # type: ignore url=sample_url, detection_model=FaceDetectionModel.DETECTION_01, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False, return_face_attributes=[ - FaceAttributeTypeDetection01.ACCESSORIES, - FaceAttributeTypeDetection01.EXPOSURE, - FaceAttributeTypeDetection01.GLASSES, - FaceAttributeTypeDetection01.NOISE]) + FaceAttributeTypeDetection01.ACCESSORIES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.EXPOSURE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.GLASSES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.NOISE]) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 self.logger.info(f"Detect faces from the url: {sample_url}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index c16d29be16bd..ee0419e512d3 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -47,7 +47,7 @@ def group(self): with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result = face_client.detect( + detect_result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py index 90e29c9e88c9..eb21f7cb6711 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py @@ -48,7 +48,7 @@ async def group(self): async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result = await face_client.detect( + detect_result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index 0ede0dbdb38e..76cc769680ca 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -80,7 +80,7 @@ def livenessSessionWithVerify(self): verify_image_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) self.logger.info("Create a new liveness with verify session with verify image.") - created_session = face_session_client.create_liveness_with_verify_session( + created_session = face_session_client.create_liveness_with_verify_session( # type: ignore CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py index a280167f7d75..3dab074d5e92 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py @@ -82,7 +82,7 @@ async def livenessSessionWithVerify(self): verify_image_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) self.logger.info("Create a new liveness with verify session with verify image.") - created_session = await face_session_client.create_liveness_with_verify_session( + created_session = await face_session_client.create_liveness_with_verify_session( # type: ignore CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index 7dc536825829..90b9085dc420 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -48,7 +48,7 @@ def find_similar_from_face_ids(self): with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result1 = face_client.detect( + detect_result1 = face_client.detect( # type: ignore helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -59,7 +59,7 @@ def find_similar_from_face_ids(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) - detect_result2 = face_client.detect( + detect_result2 = face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -86,7 +86,7 @@ def find_similar_from_large_face_list(self): FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: large_face_list_id = "lfl01" - # Prepare a LargeFaceList which contains serveral faces. + # Prepare a LargeFaceList which contains several faces. self.logger.info(f"Create a LargeFaceList, id = {large_face_list_id}") face_admin_client.create_large_face_list( large_face_list_id, name="List of Face", user_data="Large Face List for Test", @@ -122,7 +122,7 @@ def find_similar_from_large_face_list(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) - detect_result = face_client.detect( + detect_result = face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py index 76f319bda1eb..616ca847b2df 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py @@ -49,7 +49,7 @@ async def find_similar_from_face_ids(self): async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result1 = await face_client.detect( + detect_result1 = await face_client.detect( # type: ignore helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -60,7 +60,7 @@ async def find_similar_from_face_ids(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) - detect_result2 = await face_client.detect( + detect_result2 = await face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -87,7 +87,7 @@ async def find_similar_from_large_face_list(self): FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: large_face_list_id = "lfl01" - # Prepare a LargeFaceList which contains serveral faces. + # Prepare a LargeFaceList which contains several faces. self.logger.info(f"Create a LargeFaceList, id = {large_face_list_id}") await face_admin_client.create_large_face_list( large_face_list_id, name="List of Face", user_data="Large Face List for Test", @@ -123,7 +123,7 @@ async def find_similar_from_large_face_list(self): # Detect face from 'IMAGE_FINDSIMILAR' find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) - detect_result = await face_client.detect( + detect_result = await face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/shared/constants.py b/sdk/face/azure-ai-vision-face/samples/shared/constants.py index 23d7627ae384..7f2c8156d13f 100644 --- a/sdk/face/azure-ai-vision-face/samples/shared/constants.py +++ b/sdk/face/azure-ai-vision-face/samples/shared/constants.py @@ -50,7 +50,7 @@ class TestImages(object): IMAGE_DETECTION_6 = "detection6.jpg" IMAGE_EXTRA_WOMAN_IMAGE = "extra-woman-image.jpg" IMAGE_FINDSIMILAR = "findsimilar.jpg" - IMAGE_IDENTIFICATION1 = "indentification1.jpg" + IMAGE_IDENTIFICATION1 = "identification1.jpg" IMAGE_MAN1_PERSON_GROUP = "man1-person-group.jpg" IMAGE_MAN2_PERSON_GROUP = "man2-person-group.jpg" IMAGE_MAN3_PERSON_GROUP = "man3-person-group.jpg" diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py index 9b1b9709fc5e..5f815ad546f6 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py @@ -61,7 +61,7 @@ def test_list_sessions(self, client, **kwargs): variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" } - # Create 2 sessions with different deivce_correlation_id + # Create 2 sessions with different device_correlation_id for dcid in recorded_device_correlation_ids_dict.keys(): created_session = client.create_liveness_session( CreateLivenessSessionContent( diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py index e79f5281e6ae..79d16d814d43 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py @@ -63,7 +63,7 @@ async def test_list_sessions(self, client, **kwargs): variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" } - # Create 2 sessions with different deivce_correlation_id + # Create 2 sessions with different device_correlation_id for dcid in recorded_device_correlation_ids_dict.keys(): created_session = await client.create_liveness_session( CreateLivenessSessionContent( diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py index 20d242314776..ca224ff34468 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py @@ -97,7 +97,7 @@ def test_list_sessions(self, client, **kwargs): variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" } - # Create 2 sessions with different deivce_correlation_id + # Create 2 sessions with different device_correlation_id for dcid in recorded_device_correlation_ids_dict.keys(): created_session = client.create_liveness_with_verify_session( CreateLivenessSessionContent( diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py index 50261846c29c..2ed3b2f63834 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py @@ -100,7 +100,7 @@ async def test_list_sessions(self, client, **kwargs): variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" } - # Create 2 sessions with different deivce_correlation_id + # Create 2 sessions with different device_correlation_id for dcid in recorded_device_correlation_ids_dict.keys(): created_session = await client.create_liveness_with_verify_session( CreateLivenessSessionContent( From b27ccd276ff414e9729adf009ab90632b6a59efb Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Wed, 8 May 2024 09:59:35 +0800 Subject: [PATCH 21/41] Fix typo --- sdk/face/azure-ai-vision-face/CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/face/azure-ai-vision-face/CHANGELOG.md b/sdk/face/azure-ai-vision-face/CHANGELOG.md index 1562f0af2215..fc29b0c4f429 100644 --- a/sdk/face/azure-ai-vision-face/CHANGELOG.md +++ b/sdk/face/azure-ai-vision-face/CHANGELOG.md @@ -2,7 +2,7 @@ ## 1.0.0b1 (2024-05-21) -This is the first preview of the `azure-ai-vsion-face` client library that follows the [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html). +This is the first preview of the `azure-ai-vision-face` client library that follows the [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html). This library replaces the package [azure-cognitiveservices-vision-face](https://pypi.org/project/azure-cognitiveservices-vision-face/). This package's [documentation](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/) and [samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples) demonstrate the new API. From 559c065a2781d3c313f21ca7bcf55804b77e4a45 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Wed, 8 May 2024 18:28:20 +0800 Subject: [PATCH 22/41] Fix tests error --- sdk/face/azure-ai-vision-face/tests/_shared/asserter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py b/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py index d0ee74c99fbf..a3ef7e23aee8 100644 --- a/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py +++ b/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py @@ -63,7 +63,7 @@ def _assert_session_audit_entry_response_info_not_empty( def _assert_liveness_session_audit_entry_is_valid( audit_entry: models.LivenessSessionAuditEntry, expected_session_id="", is_liveness_with_verify: bool = True): - assert audit_entry.id > 0 + assert bool(audit_entry.id) assert bool(expected_session_id) or audit_entry.session_id == expected_session_id assert bool(audit_entry.request_id) assert audit_entry.received_date_time is not None From c2d6fa7a1be6686d308c8ea08d0b5c642c96039a Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Thu, 9 May 2024 18:15:49 +0800 Subject: [PATCH 23/41] Update CHANGELOG and README --- sdk/face/azure-ai-vision-face/CHANGELOG.md | 17 ++-- sdk/face/azure-ai-vision-face/README.md | 83 ++++++++++++------- .../azure/ai/vision/face/_patch.py | 12 +-- .../azure/ai/vision/face/aio/_patch.py | 12 +-- .../azure-ai-vision-face/samples/README.md | 14 ++-- .../samples_face_liveness_detection.py | 8 +- .../samples_face_liveness_detection_async.py | 8 +- ...ce_liveness_detection_with_verification.py | 8 +- ...eness_detection_with_verification_async.py | 8 +- 9 files changed, 102 insertions(+), 68 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/CHANGELOG.md b/sdk/face/azure-ai-vision-face/CHANGELOG.md index fc29b0c4f429..7a80063eb2d0 100644 --- a/sdk/face/azure-ai-vision-face/CHANGELOG.md +++ b/sdk/face/azure-ai-vision-face/CHANGELOG.md @@ -1,13 +1,20 @@ # Release History -## 1.0.0b1 (2024-05-21) +## 1.0.0b1 (Unreleased) This is the first preview of the `azure-ai-vision-face` client library that follows the [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html). This library replaces the package [azure-cognitiveservices-vision-face](https://pypi.org/project/azure-cognitiveservices-vision-face/). This package's [documentation](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/) and [samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples) demonstrate the new API. -**Breaking changes: New API design** +### Features Added + +- Added support for Liveness detection. +- Added support for `person` and `dynamic_person_group` operations. +- Added support for face recognition with `PersonDirectory` (`identify_from_person_directory()` and `identify_from_dynamic_person_group()`). +- Asynchronous APIs are added under `azure.ai.vision.face.aio` namespace. + +### Breaking Changes - This library supports only the Azure AI Face v1.1-preview.1 API. - The namespace/package name for Azure AI Face has changed from `azure.cognitiveservices.vision.face` to `azure.ai.vision.face`. @@ -18,7 +25,7 @@ This package's [documentation](https://github.com/Azure/azure-sdk-for-python/tre Face recognition, like `face_list`, `large_face_list`, `person_group`, `large_person_group`, `person` and `dynamic_person_group`. - `FaceSessionClient` to interact with sessions which is used for Liveness detection. -- Asynchronous APIs added under `azure.ai.vision.face.aio` namespace - New function names that comply with [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html): - - For example, the method `person_group_person.create()` is changed to `create_person_group_person()` -- Authentication with API key supported using `AzureKeyCredential("")` from `azure.core.credentials` + - For example, the method `person_group_person.create()` is changed to `create_person_group_person()`. +- Authentication with Microsoft Entra ID is supported using `DefaultAzureCredential()` from `azure.identity`. +- The Snapshot operations are all removed as [the Snapshot API is no longer supported](https://azure.microsoft.com/updates/facelimitedaccess/). diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index 35655ff5c8fb..1a17d47ed575 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -44,9 +44,12 @@ python -m pip install azure-ai-vision-face In order to interact with the Face service, you will need to create an instance of a client. An **endpoint** and **credential** are necessary to instantiate the client object. -#### Get the endpoint and API keys +Both key credential and Microsoft Entra ID credential are supported to authenticate the client. +For enhanced security, we strongly recommend utilizing Microsoft Entra ID credential for authentication in the production environment, while AzureKeyCredential should be reserved exclusively for the testing environment. -You can find the endpoint and keys for your Face resource using the [Azure Portal][get_endpoint_via_azure_portal] or [Azure CLI][get_endpoint_via_azure_cli]: +#### Get the endpoint + +You can find the endpoint for your Face resource using the Azure Portal or Azure CLI: ```bash # Get the endpoint for the Face resource @@ -60,18 +63,20 @@ Regional endpoint: https://.api.cognitive.microsoft.com/ Custom subdomain: https://.cognitiveservices.azure.com/ ``` -A regional endpoint is the same for every resource in a region. A complete list of supported regional endpoints can be consulted [here][regional_endpoints]. Please note that regional endpoints do not support AAD authentication. +A regional endpoint is the same for every resource in a region. A complete list of supported regional endpoints can be consulted [here][regional_endpoints]. Please note that regional endpoints do not support Microsoft Entra ID authentication. A custom subdomain, on the other hand, is a name that is unique to the Face resource. They can only be used by [single-service resources][azure_portal_create_face_account]. -```bash -# Get the API keys for the Face resource -az cognitiveservices account keys list --name "" --resource-group "" -``` #### Create the client with AzureKeyCredential To use an API key as the `credential` parameter, pass the key as a string into an instance of [AzureKeyCredential][azure_sdk_python_azure_key_credential]. +You can get the API key for your Face resource using the [Azure Portal][get_key_via_azure_portal] or [Azure CLI][get_key_via_azure_cli]: + +```bash +# Get the API keys for the Face resource +az cognitiveservices account keys list --name "" --resource-group "" +``` ```python from azure.core.credentials import AzureKeyCredential @@ -82,10 +87,10 @@ credential = AzureKeyCredential("") face_client = FaceClient(endpoint, credential) ``` -#### Create the client with an Azure Active Directory credential +#### Create the client with an Microsoft Entra ID credential `AzureKeyCredential` authentication is used in the examples in this getting started guide, but you can also authenticate with Azure Active Directory using the [azure-identity][azure_sdk_python_identity] library. -Note that regional endpoints do not support AAD authentication. Create a [custom subdomain][custom_subdomain] name for your resource in order to use this type of authentication. +Note that regional endpoints do not support Microsoft Entra ID authentication. Create a [custom subdomain][custom_subdomain] name for your resource in order to use this type of authentication. To use the [DefaultAzureCredential][azure_sdk_python_default_azure_credential] type shown below, or other credential types provided with the Azure SDK, please install the `azure-identity` package: @@ -93,9 +98,9 @@ To use the [DefaultAzureCredential][azure_sdk_python_default_azure_credential] t pip install azure-identity ``` -You will also need to [register a new AAD application and grant access][register_aad_app] to Face by assigning the `"Cognitive Services User"` role to your service principal. +You will also need to [register a new Microsoft Entra ID application and grant access][register_aad_app] to Face by assigning the `"Cognitive Services User"` role to your service principal. -Once completed, set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: +Once completed, set the values of the client ID, tenant ID, and client secret of the Microsoft Entra ID application as environment variables: `AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, `AZURE_CLIENT_SECRET`. ```python @@ -130,11 +135,28 @@ face_client = FaceClient(endpoint, credential) `FaceAdministrationClient` is provided to interact with the following data structures that hold data on faces and persons for Face recognition: - - `person` and `dynamic_person_group` in [PersonDirectory](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/use-persondirectory) - - `face_list` - - `large_face_list` - - `person_group` - - `large_person_group` + - `person`: It is a container that holds faces and is used by face recognition. It can either not belong to any group or be a member of multiple `dynamic_person_group` simultaneously. + - Each person can have up to 248 faces, and the total number of persons can reach 75 million. + - For [face verification][face_verification], call `verify_from_person_directory()`. + - For [face identification][face_identification], training is not needed before calling `identify_from_person_directory()`. + - See [Use the PersonDirectory structure][use_person_directory_structure] to get more information. + - `dynamic_person_group`: It is a group that can hold several `person`, and is used by face identification. + - The total `dynamic_person_group` is unlimited. + - Training is not needed before calling `identify_from_dynamic_person_group()`. + - See [Use the PersonDirectory structure][use_person_directory_structure] to get more information. + - `face_list`: It is a list of faces and used by [find similar faces][find_similar] (call `find_similar_from_face_list()`). + - It can up to 1,000 faces. + - `large_face_list`: It is a list of faces which can hold more faces and used by [find similar faces][find_similar]. + - It can up to 1,000,000 faces. + - Training (`begin_train_large_face_list()`) is required before calling `find_similar_from_large_face_list()`. + - `person_group`: It is a container that store the uploaded person data, including their face recognition features, and is used by face recognition. + - It can up to 10,000 persons, with each person capable of holding up to 248 faces. + - For [face verification][face_verification], call `verify_from_person_group()`. + - For [face identification][face_identification], training (`begin_train_person_group()`) is required before calling `identify_from_person_group()`. + - `large_person_group`: It is a container which can hold more persons, and is used by face recognition. + - It can up to 1,000,000 persons, with each person capable of holding up to 248 faces. The total persons in all `large_person_group` should not exceed 1,000,000,000. + - For [face verification][face_verification], call `verify_from_large_person_group()`. + - For [face identification][face_identification], training (`begin_train_large_person_group()`) is required before calling `identify_from_large_person_group()`. ### FaceSessionClient @@ -298,7 +320,7 @@ Face Liveness detection can be used to determine if a face in an input video str The goal of liveness detection is to ensure that the system is interacting with a physically present live person at the time of authentication. The whole process of authentication is called a session. -There're two different components in the authentication: a mobile application and an app server/orchestrator. +There are two different components in the authentication: a mobile application and an app server/orchestrator. Before uploading the video stream, the app server has to create a session, and then the mobile client could upload the payload with a `session authorization token` to call the liveness detection. The app server can query for the liveness detection result and audit logs anytime until the session is deleted. @@ -444,20 +466,20 @@ additional questions or comments. [source_code]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/azure/ai/vision/face [face_pypi]: https://pypi.org/project/azure-ai-vision-face/ [face_ref_docs]: https://aka.ms/azsdk-python-face-ref -[face_product_docs]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity +[face_product_docs]: https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity [face_samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples [azure_sub]: https://azure.microsoft.com/free/ -[azure_role_assignment]: https://learn.microsoft.com/en-us/azure/role-based-access-control/role-assignments-steps +[azure_role_assignment]: https://learn.microsoft.com/azure/role-based-access-control/role-assignments-steps [azure_portal_list_face_account]: https://portal.azure.com/#blade/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/Face [azure_portal_list_cognitive_service_account]: https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/AllInOne -[azure_cognitive_service_account]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#supported-services-with-a-multi-service-resource +[azure_cognitive_service_account]: https://learn.microsoft.com/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#supported-services-with-a-multi-service-resource [azure_portal_create_face_account]: https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesFace -[quick_start_create_account_via_azure_cli]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli -[quick_start_create_account_via_azure_powershell]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azpowershell +[quick_start_create_account_via_azure_cli]: https://learn.microsoft.com/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli +[quick_start_create_account_via_azure_powershell]: https://learn.microsoft.com/azure/ai-services/multi-service-resource?tabs=windows&pivots=azpowershell -[get_endpoint_via_azure_portal]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#get-the-keys-for-your-resource -[get_endpoint_via_azure_cli]: https://learn.microsoft.com/en-us/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli#get-the-keys-for-your-resource +[get_key_via_azure_portal]: https://learn.microsoft.com/azure/ai-services/multi-service-resource?tabs=windows&pivots=azportal#get-the-keys-for-your-resource +[get_key_via_azure_cli]: https://learn.microsoft.com/azure/ai-services/multi-service-resource?tabs=windows&pivots=azcli#get-the-keys-for-your-resource [regional_endpoints]: https://azure.microsoft.com/global-infrastructure/services/?products=cognitive-services [azure_sdk_python_azure_key_credential]: https://aka.ms/azsdk/python/core/azurekeycredential [azure_sdk_python_identity]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity @@ -465,10 +487,15 @@ additional questions or comments. [azure_sdk_python_default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential [register_aad_app]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal -[evaluate_different_detection_models]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model#evaluate-different-models -[recommended_recognition_model]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model#recommended-model -[liveness_tutorial]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness -[integrate_liveness_into_mobile_application]: https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application +[face_verification]: https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity#verification +[face_identification]: https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity#identification +[find_similar]: https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity#find-similar-faces +[use_person_directory_structure]: https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/use-persondirectory + +[evaluate_different_detection_models]: https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model#evaluate-different-models +[recommended_recognition_model]: https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model#recommended-model +[liveness_tutorial]: https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness +[integrate_liveness_into_mobile_application]: https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application [python_azure_core_exceptions]: https://aka.ms/azsdk/python/core/docs#module-azure.core.exceptions [face_errors]: https://aka.ms/face-error-codes-and-messages diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 45aea9216849..5ea7be6cf820 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -56,7 +56,7 @@ def detect_from_url( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -81,7 +81,7 @@ def detect_from_url( 200x200 pixels (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -94,7 +94,7 @@ def detect_from_url( needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param body: Is either a JSON type or a IO[bytes] type. Required. :type body: JSON or IO[bytes] @@ -459,7 +459,7 @@ def detect( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -484,7 +484,7 @@ def detect( 200x200 pixels (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -497,7 +497,7 @@ def detect( needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param image_content: The input image binary. Required. :type image_content: bytes diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 147220d360f4..5aacb966875c 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -56,7 +56,7 @@ async def detect_from_url( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -81,7 +81,7 @@ async def detect_from_url( 200x200 pixels (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -94,7 +94,7 @@ async def detect_from_url( needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param body: Is either a JSON type or a IO[bytes] type. Required. :type body: JSON or IO[bytes] @@ -459,7 +459,7 @@ async def detect( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -484,7 +484,7 @@ async def detect( 200x200 pixels (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -497,7 +497,7 @@ async def detect( needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param image_content: The input image binary. Required. :type image_content: bytes diff --git a/sdk/face/azure-ai-vision-face/samples/README.md b/sdk/face/azure-ai-vision-face/samples/README.md index f3f62a97d2eb..3e88712534b2 100644 --- a/sdk/face/azure-ai-vision-face/samples/README.md +++ b/sdk/face/azure-ai-vision-face/samples/README.md @@ -14,27 +14,27 @@ The async versions of the samples (the python sample files appended with `_async Several Azure Face Python SDK samples are available to you in the SDK's GitHub repository. These samples provide example code for additional scenarios commonly encountered while working with Face: -* [samples_authentication.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_authentication.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_authentication_async.py)) - Examples for authenticating and creating the client: +* [samples_authentication.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_authentication.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py)) - Examples for authenticating and creating the client: * From a key * From Microsoft Entra ID -* [samples_face_liveness_detection.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_async.py)) - Examples for performing liveness detection +* [samples_face_liveness_detection.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py)) - Examples for performing liveness detection -* [samples_face_liveness_detection_with_verification.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_liveness_detection_with_verification_async.py)) - Examples for performing liveness detection with face verification +* [samples_face_liveness_detection_with_verification.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py)) - Examples for performing liveness detection with face verification -* [samples_face_detection.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_detection_async.py)) - Examples for detecting human faces in an image: +* [samples_face_detection.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py)) - Examples for detecting human faces in an image: * From a binary data * From a URL -* [samples_face_grouping.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_face_grouping_async.py)) - Examples for dividing candidate faces into groups based on face similarity. +* [samples_face_grouping.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py)) - Examples for dividing candidate faces into groups based on face similarity. -* [samples_find_similar_faces.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/async_samples/samples_find_similar_faces_async.py)) - Examples for searching the similar-looking faces from a set of candidate faces: +* [samples_find_similar_faces.py](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py)) - Examples for searching the similar-looking faces from a set of candidate faces: * From a faceId array * From a large face list ## Prerequisites * Python 3.8 or later is required to use this package -* You must have an [Azure subscription](https://azure.microsoft.com/free/) and an [Face APIs account](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity) +* You must have an [Azure subscription](https://azure.microsoft.com/free/) and an [Face APIs account](https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity) to run these samples. ## Setup diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py index 3402b803d522..f7a39b13703d 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py @@ -15,7 +15,7 @@ an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. For more information about liveness detection, see - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. + https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection.py @@ -56,14 +56,14 @@ def send_auth_token_to_client(self, token): def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" " and use the mobile client SDK to perform liveness detection on your mobile application.") input("Press any key to continue when you complete these steps to run sample to get session results ...") pass def livenessSession(self): """This example demonstrates the liveness detection from a app server-side perspective. - To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#orchestrate-the-liveness-solution. # noqa: E501 + To get the full picture of the entire steps, see https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#orchestrate-the-liveness-solution. # noqa: E501 """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient @@ -89,7 +89,7 @@ def livenessSession(self): # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. # To learn how to integrate the UI and the code into your native mobile application, see - # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 + # https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 # 7. Wait for session completion notification from client. self.wait_for_liveness_session_complete() diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py index fa482ad861fa..3a3c823298f9 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py @@ -15,7 +15,7 @@ an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. For more information about liveness detection, see - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. + https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection_async.py @@ -57,14 +57,14 @@ async def send_auth_token_to_client(self, token): async def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" " and use the mobile client SDK to perform liveness detection on your mobile application.") input("Press any key to continue when you complete these steps to run sample to get session results ...") pass async def livenessSession(self): """This example demonstrates the liveness detection from a app server-side perspective. - To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#orchestrate-the-liveness-solution. # noqa: E501 + To get the full picture of the entire steps, see https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#orchestrate-the-liveness-solution. # noqa: E501 """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceSessionClient @@ -91,7 +91,7 @@ async def livenessSession(self): # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. # To learn how to integrate the UI and the code into your native mobile application, see - # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 + # https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 # 7. Wait for session completion notification from client. await self.wait_for_liveness_session_complete() diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index 76cc769680ca..57a4153a37d1 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -16,7 +16,7 @@ an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. For more information about liveness detection, see - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. + https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection_with_verification.py @@ -59,14 +59,14 @@ def send_auth_token_to_client(self, token): def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" " and use the mobile client SDK to perform liveness detection on your mobile application.") input("Press any key to continue when you complete these steps to run sample to get session results ...") pass def livenessSessionWithVerify(self): """This example demonstrates the liveness detection with face verification from a app server-side perspective. - To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#perform-liveness-detection-with-face-verification # noqa: E501 + To get the full picture of the entire steps, see https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#perform-liveness-detection-with-face-verification # noqa: E501 """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient @@ -95,7 +95,7 @@ def livenessSessionWithVerify(self): # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. # To learn how to integrate the UI and the code into your native mobile application, see - # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 + # https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 # 7. Wait for session completion notification from client. self.wait_for_liveness_session_complete() diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py index 3dab074d5e92..143eb0315665 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py @@ -16,7 +16,7 @@ an app server/orchestrator, and here we demonstrate the entire process from the perspective of the server side. For more information about liveness detection, see - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness. + https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness. USAGE: python sample_face_liveness_detection_with_verification_async.py @@ -60,14 +60,14 @@ async def send_auth_token_to_client(self, token): async def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( - "Please refer to https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness" + "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" " and use the mobile client SDK to perform liveness detection on your mobile application.") input("Press any key to continue when you complete these steps to run sample to get session results ...") pass async def livenessSessionWithVerify(self): """This example demonstrates the liveness detection with face verification from a app server-side perspective. - To get the full picture of the entire steps, see https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#perform-liveness-detection-with-face-verification # noqa: E501 + To get the full picture of the entire steps, see https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#perform-liveness-detection-with-face-verification # noqa: E501 """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceSessionClient @@ -97,7 +97,7 @@ async def livenessSessionWithVerify(self): # 4 ~ 6. The mobile application uses the session-authorization-token to perform the liveness detection. # To learn how to integrate the UI and the code into your native mobile application, see - # https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 + # https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness#integrate-liveness-into-mobile-application # noqa: E501 # 7. Wait for session completion notification from client. await self.wait_for_liveness_session_complete() From 6895791466ac02b5a38e0cfa2b724ed78590eb33 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Thu, 9 May 2024 18:57:36 +0800 Subject: [PATCH 24/41] Update customized operations --- .../azure/ai/vision/face/_patch.py | 91 +++++++++++++++++-- .../azure/ai/vision/face/aio/_patch.py | 91 +++++++++++++++++-- .../test_liveness_with_verify_session.py | 6 +- ...test_liveness_with_verify_session_async.py | 6 +- 4 files changed, 176 insertions(+), 18 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 5ea7be6cf820..e816ad694aed 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -6,7 +6,7 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from typing import Any, IO, List, Optional, Union, cast +from typing import Any, IO, List, Optional, Union, cast, overload from azure.core.tracing.decorator import distributed_trace @@ -31,6 +31,54 @@ class FaceClient(FaceClientGenerated): :paramtype api_version: str or ~azure.ai.vision.face.models.Versions """ + @overload + def detect_from_url( + self, + body: JSON, + *, + content_type: str = "application/json", + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + + @overload + def detect_from_url( + self, + *, + url: str, + content_type: str = "application/json", + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + + @overload + def detect_from_url( + self, + body: IO[bytes], + *, + content_type: str = "application/json", + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + @distributed_trace def detect_from_url( self, @@ -846,12 +894,43 @@ class FaceSessionClient(FaceSessionClientGenerated): :paramtype api_version: str or ~azure.ai.vision.face.models.Versions """ + @overload + def create_liveness_with_verify_session( + self, + body: _models.CreateLivenessSessionContent, + *, + verify_image: Union[bytes, None], + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @overload + def create_liveness_with_verify_session( + self, + body: JSON, + *, + verify_image: Union[bytes, None], + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @overload + def create_liveness_with_verify_session( + self, + body: IO[bytes], + *, + verify_image: Union[bytes, None], + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @distributed_trace def create_liveness_with_verify_session( self, body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], - verify_image: bytes, - **kwargs: Any + *, + verify_image: Union[bytes, None], + **kwargs: Any, ) -> _models.CreateLivenessWithVerifySessionResult: # pylint: disable=line-too-long """Create a new liveness session with verify. Client device submits VerifyImage during the @@ -896,9 +975,9 @@ def create_liveness_with_verify_session( :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] Required. :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] - :param verify_image: The image for verify. If you don't have any images to use for verification, set it to None. - Required. - :type verify_image: bytes + :keyword verify_image: The image for verify. If you don't have any images to use for verification, + set it to None. Required. + :paramtype verify_image: bytes or None :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is compatible with MutableMapping :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 5aacb966875c..47b7219f26f2 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -6,7 +6,7 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from typing import Any, IO, List, Optional, Union, cast +from typing import Any, IO, List, Optional, Union, cast, overload from azure.core.tracing.decorator_async import distributed_trace_async @@ -31,6 +31,54 @@ class FaceClient(FaceClientGenerated): :paramtype api_version: str or ~azure.ai.vision.face.models.Versions """ + @overload + async def detect_from_url( + self, + body: JSON, + *, + content_type: str = "application/json", + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + + @overload + async def detect_from_url( + self, + *, + url: str, + content_type: str = "application/json", + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + + @overload + async def detect_from_url( + self, + body: IO[bytes], + *, + content_type: str = "application/json", + detection_model: Union[str, _models.FaceDetectionModel], + recognition_model: Union[str, _models.FaceRecognitionModel], + return_face_id: bool, + return_face_attributes: Optional[List[Union[str, _models.FaceAttributeType]]] = None, + return_face_landmarks: Optional[bool] = None, + return_recognition_model: Optional[bool] = None, + face_id_time_to_live: Optional[int] = None, + **kwargs: Any, + ) -> List[_models.FaceDetectionResult]: ... + @distributed_trace_async async def detect_from_url( self, @@ -846,12 +894,43 @@ class FaceSessionClient(FaceSessionClientGenerated): :paramtype api_version: str or ~azure.ai.vision.face.models.Versions """ + @overload + async def create_liveness_with_verify_session( + self, + body: _models.CreateLivenessSessionContent, + *, + verify_image: Union[bytes, None], + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @overload + async def create_liveness_with_verify_session( + self, + body: JSON, + *, + verify_image: Union[bytes, None], + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.CreateLivenessWithVerifySessionResult: ... + + @overload + async def create_liveness_with_verify_session( + self, + body: IO[bytes], + *, + verify_image: Union[bytes, None], + content_type: str = "application/json", + **kwargs: Any, + ) -> _models.CreateLivenessWithVerifySessionResult: ... + @distributed_trace_async async def create_liveness_with_verify_session( self, body: Union[_models.CreateLivenessSessionContent, JSON, IO[bytes]], - verify_image: bytes, - **kwargs: Any + *, + verify_image: Union[bytes, None], + **kwargs: Any, ) -> _models.CreateLivenessWithVerifySessionResult: # pylint: disable=line-too-long """Create a new liveness session with verify. Client device submits VerifyImage during the @@ -896,9 +975,9 @@ async def create_liveness_with_verify_session( :param body: Is one of the following types: CreateLivenessSessionContent, JSON, IO[bytes] Required. :type body: ~azure.ai.vision.face.models.CreateLivenessSessionContent or JSON or IO[bytes] - :param verify_image: The image for verify. If you don't have any images to use for verification, set it to None. - Required. - :type verify_image: bytes + :keyword verify_image: The image for verify. If you don't have any images to use for verification, + set it to None. Required. + :paramtype verify_image: bytes or None :return: CreateLivenessWithVerifySessionResult. The CreateLivenessWithVerifySessionResult is compatible with MutableMapping :rtype: ~azure.ai.vision.face.models.CreateLivenessWithVerifySessionResult diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py index ca224ff34468..b80618e612a8 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py @@ -39,7 +39,7 @@ def test_create_session(self, client, **kwargs): CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=recorded_device_correlation_id), - None) # verify_image = None + verify_image=None) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -103,7 +103,7 @@ def test_list_sessions(self, client, **kwargs): CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=dcid), - None) # verify_image = None + verify_image=None) _assert_is_string_and_not_empty(created_session.session_id) recorded_device_correlation_ids_dict[dcid] = created_session.session_id @@ -175,7 +175,7 @@ def test_delete_session(self, client, **kwargs): CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=recorded_device_correlation_id), - None) # verify_image = None + verify_image=None) session_id = created_session.session_id _assert_is_string_and_not_empty(session_id) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py index 2ed3b2f63834..b63f61ad79f9 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py @@ -40,7 +40,7 @@ async def test_create_session(self, client, **kwargs): CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=recorded_device_correlation_id), - None) # verify_image = None + verify_image=None) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -106,7 +106,7 @@ async def test_list_sessions(self, client, **kwargs): CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=dcid), - None) # verify_image = None + verify_image=None) _assert_is_string_and_not_empty(created_session.session_id) recorded_device_correlation_ids_dict[dcid] = created_session.session_id @@ -181,7 +181,7 @@ async def test_delete_session(self, client, **kwargs): CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=recorded_device_correlation_id), - None) # verify_image = None + verify_image=None) session_id = created_session.session_id _assert_is_string_and_not_empty(session_id) From 8106b401d8ed0b2deabdaab7a55d573d9158353a Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Thu, 9 May 2024 19:41:12 +0800 Subject: [PATCH 25/41] Update tests to fix test errors --- .../tests/test_liveness_session.py | 26 ++++++++++--------- .../tests/test_liveness_session_async.py | 26 ++++++++++--------- .../test_liveness_with_verify_session.py | 26 ++++++++++--------- ...test_liveness_with_verify_session_async.py | 26 ++++++++++--------- 4 files changed, 56 insertions(+), 48 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py index 5f815ad546f6..35e2df03957b 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py @@ -5,6 +5,7 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +from collections import deque import pytest import uuid @@ -54,37 +55,39 @@ def test_create_session(self, client, **kwargs): @recorded_by_proxy def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) - - # key-value pairs = {device_correlation_id, created_session_id} - recorded_device_correlation_ids_dict = { - variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + recorded_device_correlation_ids = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) } + # key = session_id, value = device_correlation_id + created_session_dict = dict() + # Create 2 sessions with different device_correlation_id - for dcid in recorded_device_correlation_ids_dict.keys(): + for dcid in recorded_device_correlation_ids: created_session = client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=dcid)) _assert_is_string_and_not_empty(created_session.session_id) - recorded_device_correlation_ids_dict[dcid] = created_session.session_id + created_session_dict[created_session.session_id] = dcid + + # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. + expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) # Test `list sessions` operation result = client.get_liveness_sessions() assert len(result) == 2 - assert result[0].id < result[1].id for session in result: - assert session.device_correlation_id in recorded_device_correlation_ids_dict - assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.device_correlation_id == expected_dcid_queue.popleft() assert session.created_date_time is not None assert session.auth_token_time_to_live_in_seconds >= 60 assert session.auth_token_time_to_live_in_seconds <= 86400 # Teardown - for sid in recorded_device_correlation_ids_dict.values(): + for sid in created_session_dict.keys(): client.delete_liveness_session(sid) return variables @@ -98,7 +101,6 @@ def test_get_session_result(self, client, **kwargs): recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") session = client.get_liveness_session_result(recorded_session_id) - assert session.id == recorded_session_id assert session.created_date_time is not None assert session.session_start_date_time is not None assert isinstance(session.session_expired, bool) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py index 79d16d814d43..7318d51f04e1 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py @@ -5,6 +5,7 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +from collections import deque import pytest import uuid @@ -56,37 +57,39 @@ async def test_create_session(self, client, **kwargs): @recorded_by_proxy_async async def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) - - # key-value pairs = {device_correlation_id, created_session_id} - recorded_device_correlation_ids_dict = { - variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + recorded_device_correlation_ids = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) } + # key = session_id, value = device_correlation_id + created_session_dict = dict() + # Create 2 sessions with different device_correlation_id - for dcid in recorded_device_correlation_ids_dict.keys(): + for dcid in recorded_device_correlation_ids: created_session = await client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=dcid)) _assert_is_string_and_not_empty(created_session.session_id) - recorded_device_correlation_ids_dict[dcid] = created_session.session_id + created_session_dict[created_session.session_id] = dcid + + # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. + expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) # Test `list sessions` operation result = await client.get_liveness_sessions() assert len(result) == 2 - assert result[0].id < result[1].id for session in result: - assert session.device_correlation_id in recorded_device_correlation_ids_dict - assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.device_correlation_id == expected_dcid_queue.popleft() assert session.created_date_time is not None assert session.auth_token_time_to_live_in_seconds >= 60 assert session.auth_token_time_to_live_in_seconds <= 86400 # Teardown - for sid in recorded_device_correlation_ids_dict.values(): + for sid in created_session_dict.keys(): await client.delete_liveness_session(sid) await client.close() @@ -101,7 +104,6 @@ async def test_get_session_result(self, client, **kwargs): recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") session = await client.get_liveness_session_result(recorded_session_id) - assert session.id == recorded_session_id assert session.created_date_time is not None assert session.session_start_date_time is not None assert isinstance(session.session_expired, bool) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py index b80618e612a8..218631623f95 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py @@ -5,6 +5,7 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +from collections import deque import pytest import uuid @@ -90,15 +91,16 @@ def test_create_session_with_verify_image(self, client, **kwargs): @recorded_by_proxy def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) - - # key-value pairs = {device_correlation_id, created_session_id} - recorded_device_correlation_ids_dict = { - variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + recorded_device_correlation_ids = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) } + # key = session_id, value = device_correlation_id + created_session_dict = dict() + # Create 2 sessions with different device_correlation_id - for dcid in recorded_device_correlation_ids_dict.keys(): + for dcid in recorded_device_correlation_ids: created_session = client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, @@ -106,22 +108,23 @@ def test_list_sessions(self, client, **kwargs): verify_image=None) _assert_is_string_and_not_empty(created_session.session_id) - recorded_device_correlation_ids_dict[dcid] = created_session.session_id + created_session_dict[created_session.session_id] = dcid + + # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. + expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) # Test `list sessions` operation result = client.get_liveness_with_verify_sessions() assert len(result) == 2 - assert result[0].id < result[1].id for session in result: - assert session.device_correlation_id in recorded_device_correlation_ids_dict - assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.device_correlation_id == expected_dcid_queue.popleft() assert session.created_date_time is not None assert session.auth_token_time_to_live_in_seconds >= 60 assert session.auth_token_time_to_live_in_seconds <= 86400 # Teardown - for sid in recorded_device_correlation_ids_dict.values(): + for sid in created_session_dict.keys(): client.delete_liveness_with_verify_session(sid) return variables @@ -135,7 +138,6 @@ def test_get_session_result(self, client, **kwargs): recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") session = client.get_liveness_with_verify_session_result(recorded_session_id) - assert session.id == recorded_session_id assert session.created_date_time is not None assert session.session_start_date_time is not None assert isinstance(session.session_expired, bool) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py index b63f61ad79f9..13e2aeb53166 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py @@ -5,6 +5,7 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +from collections import deque import pytest import uuid @@ -93,15 +94,16 @@ async def test_create_session_with_verify_image(self, client, **kwargs): @recorded_by_proxy_async async def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) - - # key-value pairs = {device_correlation_id, created_session_id} - recorded_device_correlation_ids_dict = { - variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())): "", # '{id1}': "" - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())): "", # '{id2}': "" + recorded_device_correlation_ids = { + variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) } + # key = session_id, value = device_correlation_id + created_session_dict = dict() + # Create 2 sessions with different device_correlation_id - for dcid in recorded_device_correlation_ids_dict.keys(): + for dcid in recorded_device_correlation_ids: created_session = await client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, @@ -109,22 +111,23 @@ async def test_list_sessions(self, client, **kwargs): verify_image=None) _assert_is_string_and_not_empty(created_session.session_id) - recorded_device_correlation_ids_dict[dcid] = created_session.session_id + created_session_dict[created_session.session_id] = dcid + + # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. + expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) # Test `list sessions` operation result = await client.get_liveness_with_verify_sessions() assert len(result) == 2 - assert result[0].id < result[1].id for session in result: - assert session.device_correlation_id in recorded_device_correlation_ids_dict - assert session.id == recorded_device_correlation_ids_dict[session.device_correlation_id] + assert session.device_correlation_id == expected_dcid_queue.popleft() assert session.created_date_time is not None assert session.auth_token_time_to_live_in_seconds >= 60 assert session.auth_token_time_to_live_in_seconds <= 86400 # Teardown - for sid in recorded_device_correlation_ids_dict.values(): + for sid in created_session_dict.keys(): await client.delete_liveness_with_verify_session(sid) await client.close() @@ -139,7 +142,6 @@ async def test_get_session_result(self, client, **kwargs): recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") session = await client.get_liveness_with_verify_session_result(recorded_session_id) - assert session.id == recorded_session_id assert session.created_date_time is not None assert session.session_start_date_time is not None assert isinstance(session.session_expired, bool) From e7c81b741d93304cda6c40de04f86ae25f84ed13 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Thu, 9 May 2024 20:17:02 +0800 Subject: [PATCH 26/41] Not support live tests for this preview release --- sdk/face/test-resources.json | 105 ----------------------------------- 1 file changed, 105 deletions(-) delete mode 100644 sdk/face/test-resources.json diff --git a/sdk/face/test-resources.json b/sdk/face/test-resources.json deleted file mode 100644 index 00457548d98b..000000000000 --- a/sdk/face/test-resources.json +++ /dev/null @@ -1,105 +0,0 @@ -{ - "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", - "contentVersion": "1.0.0.0", - "parameters": { - "baseName": { - "type": "string", - "defaultValue": "[resourceGroup().name]" - }, - "location": { - "type": "string", - "defaultValue": "[resourceGroup().location]" - }, - "cognitiveServicesEndpointSuffix": { - "type": "string", - "defaultValue": ".cognitiveservices.azure.com" - }, - "testApplicationOid": { - "type": "string", - "metadata": { - "description": "The principal to assign the role to. This is application object id." - } - }, - "tenantId": { - "type": "String", - "metadata": { - "description": "The tenant id to which the application and resources belong." - } - }, - "testApplicationId": { - "type": "String", - "metadata": { - "description": "The application client id used to run tests." - } - }, - "testApplicationSecret": { - "type": "String", - "metadata": { - "description": "The application client secret used to run tests." - } - } - }, - "variables": { - "authorizationApiVersion": "2018-09-01-preview", - "faceApiVersion": "2022-03-01", - "faceAccountName": "[concat('face', parameters('baseName'))]", - "azureFaceUrl": "[concat('https://', variables('faceAccountName'), parameters('cognitiveServicesEndpointSuffix'))]", - "cognitiveServiceUserRoleId": "[concat('/subscriptions/', subscription().subscriptionId, '/providers/Microsoft.Authorization/roleDefinitions/a97b65f3-24c7-4388-baec-2e87135dc908')]" - }, - "resources": [ - { - "type": "Microsoft.Authorization/roleAssignments", - "apiVersion": "[variables('authorizationApiVersion')]", - "name": "[guid(concat(variables('cognitiveServiceUserRoleId'), variables('faceAccountName')))]", - "dependsOn": [ - "[variables('faceAccountName')]" - ], - "properties": { - "principalId": "[parameters('testApplicationOid')]", - "roleDefinitionId": "[variables('cognitiveServiceUserRoleId')]" - } - }, - { - "type": "Microsoft.CognitiveServices/accounts", - "apiVersion": "[variables('faceApiVersion')]", - "name": "[variables('faceAccountName')]", - "location": "[parameters('location')]", - "kind": "Face", - "tags": { - "Owner": "[resourceGroup().name]" - }, - "sku": { - "name": "S0" - }, - "properties": { - "customSubDomainName": "[toLower(variables('faceAccountName'))]" - } - } - ], - "outputs": { - "AZURE_TENANT_ID": { - "type": "String", - "value": "[parameters('tenantId')]" - }, - "AZURE_CLIENT_ID": { - "type": "String", - "value": "[parameters('testApplicationId')]" - }, - "AZURE_CLIENT_SECRET": { - "type": "String", - "value": "[parameters('testApplicationSecret')]" - }, - "AZURE_FACE_API_NAME": { - "type": "string", - "value": "[variables('faceAccountName')]" - }, - "AZURE_FACE_API_ENDPOINT": { - "type": "string", - "value": "[variables('azureFaceUrl')]" - }, - "AZURE_FACE_API_KEY": { - "type": "string", - "value": "[listKeys(resourceId('Microsoft.CognitiveServices/accounts', variables('faceAccountName')), variables('faceApiVersion')).key1]" - } - } -} From 85554abeffb93d5a5cab0e419e45a581082d6fcb Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Thu, 9 May 2024 20:38:01 +0800 Subject: [PATCH 27/41] Use `black` to reformat all samples and tests --- .../samples/samples_authentication.py | 24 +++- .../samples/samples_authentication_async.py | 25 ++-- .../samples/samples_face_detection.py | 43 +++++-- .../samples/samples_face_detection_async.py | 43 +++++-- .../samples/samples_face_grouping.py | 21 ++- .../samples/samples_face_grouping_async.py | 22 +++- .../samples_face_liveness_detection.py | 40 ++++-- .../samples_face_liveness_detection_async.py | 43 +++++-- ...ce_liveness_detection_with_verification.py | 59 ++++++--- ...eness_detection_with_verification_async.py | 57 ++++++--- .../samples/samples_find_similar_faces.py | 121 +++++++++++++----- .../samples_find_similar_faces_async.py | 121 +++++++++++++----- .../samples/shared/helpers.py | 10 +- .../tests/_shared/asserter.py | 35 +++-- .../tests/_shared/helpers.py | 4 +- .../tests/_shared/testcase.py | 18 ++- .../azure-ai-vision-face/tests/conftest.py | 2 +- .../azure-ai-vision-face/tests/preparers.py | 27 ++-- .../tests/test_authentication.py | 12 +- .../tests/test_authentication_async.py | 12 +- .../tests/test_liveness_session.py | 45 +++++-- .../tests/test_liveness_session_async.py | 45 +++++-- .../test_liveness_with_verify_session.py | 65 +++++++--- ...test_liveness_with_verify_session_async.py | 69 +++++++--- 24 files changed, 700 insertions(+), 263 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index 04a8d4b992b5..ad765288239a 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -41,11 +41,15 @@ from shared.helpers import beautify_json, get_logger -class FaceAuthentication(): +class FaceAuthentication: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_authentication") def authentication_by_api_key(self): @@ -54,13 +58,16 @@ def authentication_by_api_key(self): from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel self.logger.info("Instantiate a FaceClient using an api key") - with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=False) + return_face_id=False, + ) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -73,13 +80,16 @@ def authentication_by_aad_credential(self): from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel self.logger.info("Instantiate a FaceClient using a TokenCredential") - with FaceClient(endpoint=self.endpoint, credential=DefaultAzureCredential()) as face_client: + with FaceClient( + endpoint=self.endpoint, credential=DefaultAzureCredential() + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=False) + return_face_id=False, + ) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py index c044995429a1..6886e1515730 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py @@ -42,11 +42,15 @@ from shared.helpers import beautify_json, get_logger -class FaceAuthentication(): +class FaceAuthentication: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_authentication_async") async def authentication_by_api_key(self): @@ -55,13 +59,16 @@ async def authentication_by_api_key(self): from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel self.logger.info("Instantiate a FaceClient using an api key") - async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + async with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=False) + return_face_id=False, + ) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -74,14 +81,16 @@ async def authentication_by_aad_credential(self): from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel self.logger.info("Instantiate a FaceClient using a TokenCredential") - async with DefaultAzureCredential() as credential, \ - FaceClient(endpoint=self.endpoint, credential=credential) as face_client: + async with DefaultAzureCredential() as credential, FaceClient( + endpoint=self.endpoint, credential=credential + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=False) + return_face_id=False, + ) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 3edc5aae5c6b..5e2ca798ff10 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -33,20 +33,30 @@ from shared.helpers import beautify_json, get_logger -class DetectFaces(): +class DetectFaces: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_detection") def detect(self): from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceClient from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) - - with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection03, + FaceAttributeTypeRecognition04, + ) + + with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), @@ -56,10 +66,12 @@ def detect(self): return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 FaceAttributeTypeDetection03.MASK, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, + ], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 return_face_landmarks=True, return_recognition_model=True, - face_id_time_to_live=120) + face_id_time_to_live=120, + ) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -70,9 +82,14 @@ def detect_from_url(self): from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceClient from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection01) - - with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection01, + ) + + with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_url = TestImages.DEFAULT_IMAGE_URL result = face_client.detect_from_url( # type: ignore url=sample_url, @@ -83,7 +100,9 @@ def detect_from_url(self): FaceAttributeTypeDetection01.ACCESSORIES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 FaceAttributeTypeDetection01.EXPOSURE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 FaceAttributeTypeDetection01.GLASSES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection01.NOISE]) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.NOISE, + ], + ) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 self.logger.info(f"Detect faces from the url: {sample_url}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py index 4722c3f4d733..58bceb6b80d0 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py @@ -34,20 +34,30 @@ from shared.helpers import beautify_json, get_logger -class DetectFaces(): +class DetectFaces: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_detection_async") async def detect(self): from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceClient from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) - - async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection03, + FaceAttributeTypeRecognition04, + ) + + async with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), @@ -57,10 +67,12 @@ async def detect(self): return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 FaceAttributeTypeDetection03.MASK, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, + ], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 return_face_landmarks=True, return_recognition_model=True, - face_id_time_to_live=120) + face_id_time_to_live=120, + ) self.logger.info(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -71,9 +83,14 @@ async def detect_from_url(self): from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceClient from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection01) - - async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection01, + ) + + async with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_url = TestImages.DEFAULT_IMAGE_URL result = await face_client.detect_from_url( # type: ignore url=sample_url, @@ -84,7 +101,9 @@ async def detect_from_url(self): FaceAttributeTypeDetection01.ACCESSORIES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 FaceAttributeTypeDetection01.EXPOSURE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 FaceAttributeTypeDetection01.GLASSES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection01.NOISE]) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.NOISE, + ], + ) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 self.logger.info(f"Detect faces from the url: {sample_url}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index ee0419e512d3..9d01d806a74a 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -33,11 +33,15 @@ from shared.helpers import beautify_json, get_logger -class GroupFaces(): +class GroupFaces: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_grouping") def group(self): @@ -45,16 +49,21 @@ def group(self): from azure.ai.vision.face import FaceClient from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel - with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result = face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) face_ids = [face.face_id for face in detect_result] - self.logger.info(f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}") + self.logger.info( + f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}" + ) group_result = face_client.group(face_ids=face_ids) self.logger.info(f"Group result: {beautify_json(group_result.as_dict())}") diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py index eb21f7cb6711..c5b2c74b5474 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py @@ -34,11 +34,15 @@ from shared.helpers import beautify_json, get_logger -class GroupFaces(): +class GroupFaces: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_grouping_async") async def group(self): @@ -46,16 +50,21 @@ async def group(self): from azure.ai.vision.face.aio import FaceClient from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel - async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + async with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result = await face_client.detect( # type: ignore helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) face_ids = [face.face_id for face in detect_result] - self.logger.info(f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}") + self.logger.info( + f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}" + ) group_result = await face_client.group(face_ids=face_ids) self.logger.info(f"Group result: {beautify_json(group_result.as_dict())}") @@ -65,5 +74,6 @@ async def main(): sample = GroupFaces() await sample.group() + if __name__ == "__main__": asyncio.run(main()) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py index f7a39b13703d..5901c6f8c11e 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection.py @@ -38,11 +38,15 @@ from shared.helpers import beautify_json, get_logger -class DetectLiveness(): +class DetectLiveness: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_liveness_detection") def wait_for_liveness_check_request(self): @@ -57,8 +61,11 @@ def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" - " and use the mobile client SDK to perform liveness detection on your mobile application.") - input("Press any key to continue when you complete these steps to run sample to get session results ...") + " and use the mobile client SDK to perform liveness detection on your mobile application." + ) + input( + "Press any key to continue when you complete these steps to run sample to get session results ..." + ) pass def livenessSession(self): @@ -67,9 +74,14 @@ def livenessSession(self): """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient - from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode - - with FaceSessionClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + LivenessOperationMode, + ) + + with FaceSessionClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_session_client: # 1. Wait for liveness check request self.wait_for_liveness_check_request() @@ -80,7 +92,9 @@ def livenessSession(self): liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, - auth_token_time_to_live_in_seconds=60)) + auth_token_time_to_live_in_seconds=60, + ) + ) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") # 3. Provide session authorization token to mobile application. @@ -96,13 +110,17 @@ def livenessSession(self): # 8. Query for the liveness detection result as the session is completed. self.logger.info("Get the liveness detection result.") - liveness_result = face_session_client.get_liveness_session_result(created_session.session_id) + liveness_result = face_session_client.get_liveness_session_result( + created_session.session_id + ) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") # Furthermore, you can query all request and response for this sessions, and list all sessions you have by # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") - audit_entries = face_session_client.get_liveness_session_audit_entries(created_session.session_id) + audit_entries = face_session_client.get_liveness_session_audit_entries( + created_session.session_id + ) for idx, entry in enumerate(audit_entries): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py index 3a3c823298f9..375d74ae20e8 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_async.py @@ -39,11 +39,15 @@ from shared.helpers import beautify_json, get_logger -class DetectLiveness(): +class DetectLiveness: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_liveness_detection_async") async def wait_for_liveness_check_request(self): @@ -58,8 +62,11 @@ async def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" - " and use the mobile client SDK to perform liveness detection on your mobile application.") - input("Press any key to continue when you complete these steps to run sample to get session results ...") + " and use the mobile client SDK to perform liveness detection on your mobile application." + ) + input( + "Press any key to continue when you complete these steps to run sample to get session results ..." + ) pass async def livenessSession(self): @@ -68,10 +75,14 @@ async def livenessSession(self): """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceSessionClient - from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode + from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + LivenessOperationMode, + ) async with FaceSessionClient( - endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_session_client: # 1. Wait for liveness check request await self.wait_for_liveness_check_request() @@ -82,7 +93,9 @@ async def livenessSession(self): liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, - auth_token_time_to_live_in_seconds=60)) + auth_token_time_to_live_in_seconds=60, + ) + ) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") # 3. Provide session authorization token to mobile application. @@ -98,13 +111,19 @@ async def livenessSession(self): # 8. Query for the liveness detection result as the session is completed. self.logger.info("Get the liveness detection result.") - liveness_result = await face_session_client.get_liveness_session_result(created_session.session_id) + liveness_result = await face_session_client.get_liveness_session_result( + created_session.session_id + ) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") # Furthermore, you can query all request and response for this sessions, and list all sessions you have by # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") - audit_entries = await face_session_client.get_liveness_session_audit_entries(created_session.session_id) + audit_entries = ( + await face_session_client.get_liveness_session_audit_entries( + created_session.session_id + ) + ) for idx, entry in enumerate(audit_entries): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") @@ -117,7 +136,9 @@ async def livenessSession(self): # Clean up: delete the session self.logger.info("Delete the session.") - await face_session_client.delete_liveness_session(created_session.session_id) + await face_session_client.delete_liveness_session( + created_session.session_id + ) async def main(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index 57a4153a37d1..d833bf788ea8 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -41,11 +41,15 @@ from shared.helpers import beautify_json, get_logger -class DetectLivenessWithVerify(): +class DetectLivenessWithVerify: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_face_liveness_detection_with_verification") def wait_for_liveness_check_request(self): @@ -60,8 +64,11 @@ def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" - " and use the mobile client SDK to perform liveness detection on your mobile application.") - input("Press any key to continue when you complete these steps to run sample to get session results ...") + " and use the mobile client SDK to perform liveness detection on your mobile application." + ) + input( + "Press any key to continue when you complete these steps to run sample to get session results ..." + ) pass def livenessSessionWithVerify(self): @@ -70,23 +77,34 @@ def livenessSessionWithVerify(self): """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceSessionClient - from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode - - with FaceSessionClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + LivenessOperationMode, + ) + + with FaceSessionClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_session_client: # 1. Wait for liveness check request self.wait_for_liveness_check_request() # 2. Create a session with verify image. - verify_image_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) + verify_image_file_path = helpers.get_image_path( + TestImages.DEFAULT_IMAGE_FILE + ) - self.logger.info("Create a new liveness with verify session with verify image.") + self.logger.info( + "Create a new liveness with verify session with verify image." + ) created_session = face_session_client.create_liveness_with_verify_session( # type: ignore CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, - auth_token_time_to_live_in_seconds=60), - verify_image=helpers.read_file_content(verify_image_file_path)) + auth_token_time_to_live_in_seconds=60, + ), + verify_image=helpers.read_file_content(verify_image_file_path), + ) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") # 3. Provide session authorization token to mobile application. @@ -102,14 +120,21 @@ def livenessSessionWithVerify(self): # 8. Query for the liveness detection result as the session is completed. self.logger.info("Get the liveness detection result.") - liveness_result = face_session_client.get_liveness_with_verify_session_result(created_session.session_id) + liveness_result = ( + face_session_client.get_liveness_with_verify_session_result( + created_session.session_id + ) + ) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") # Furthermore, you can query all request and response for this sessions, and list all sessions you have by # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") - audit_entries = face_session_client.get_liveness_with_verify_session_audit_entries( - created_session.session_id) + audit_entries = ( + face_session_client.get_liveness_with_verify_session_audit_entries( + created_session.session_id + ) + ) for idx, entry in enumerate(audit_entries): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") @@ -122,7 +147,9 @@ def livenessSessionWithVerify(self): # Clean up: Delete the session self.logger.info("Delete the session.") - face_session_client.delete_liveness_with_verify_session(created_session.session_id) + face_session_client.delete_liveness_with_verify_session( + created_session.session_id + ) if __name__ == "__main__": diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py index 143eb0315665..6c4d1952a0c6 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py @@ -42,12 +42,18 @@ from shared.helpers import beautify_json, get_logger -class DetectLivenessWithVerify(): +class DetectLivenessWithVerify: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) - self.logger = get_logger("sample_face_liveness_detection_with_verification_async") + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) + self.logger = get_logger( + "sample_face_liveness_detection_with_verification_async" + ) async def wait_for_liveness_check_request(self): # The logic to wait for liveness check request from mobile application. @@ -61,8 +67,11 @@ async def wait_for_liveness_session_complete(self): # The logic to wait the notification from mobile application. self.logger.info( "Please refer to https://learn.microsoft.com/azure/ai-services/computer-vision/tutorials/liveness" - " and use the mobile client SDK to perform liveness detection on your mobile application.") - input("Press any key to continue when you complete these steps to run sample to get session results ...") + " and use the mobile client SDK to perform liveness detection on your mobile application." + ) + input( + "Press any key to continue when you complete these steps to run sample to get session results ..." + ) pass async def livenessSessionWithVerify(self): @@ -71,24 +80,34 @@ async def livenessSessionWithVerify(self): """ from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceSessionClient - from azure.ai.vision.face.models import CreateLivenessSessionContent, LivenessOperationMode + from azure.ai.vision.face.models import ( + CreateLivenessSessionContent, + LivenessOperationMode, + ) async with FaceSessionClient( - endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_session_client: + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_session_client: # 1. Wait for liveness check request await self.wait_for_liveness_check_request() # 2. Create a session with verify image. - verify_image_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) + verify_image_file_path = helpers.get_image_path( + TestImages.DEFAULT_IMAGE_FILE + ) - self.logger.info("Create a new liveness with verify session with verify image.") + self.logger.info( + "Create a new liveness with verify session with verify image." + ) created_session = await face_session_client.create_liveness_with_verify_session( # type: ignore CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, - auth_token_time_to_live_in_seconds=60), - verify_image=helpers.read_file_content(verify_image_file_path)) + auth_token_time_to_live_in_seconds=60, + ), + verify_image=helpers.read_file_content(verify_image_file_path), + ) self.logger.info(f"Result: {beautify_json(created_session.as_dict())}") # 3. Provide session authorization token to mobile application. @@ -104,15 +123,19 @@ async def livenessSessionWithVerify(self): # 8. Query for the liveness detection result as the session is completed. self.logger.info("Get the liveness detection result.") - liveness_result = await face_session_client.get_liveness_with_verify_session_result( - created_session.session_id) + liveness_result = ( + await face_session_client.get_liveness_with_verify_session_result( + created_session.session_id + ) + ) self.logger.info(f"Result: {beautify_json(liveness_result.as_dict())}") # Furthermore, you can query all request and response for this sessions, and list all sessions you have by # calling `get_liveness_session_audit_entries` and `get_liveness_sessions`. self.logger.info("Get the audit entries of this session.") audit_entries = await face_session_client.get_liveness_with_verify_session_audit_entries( - created_session.session_id) + created_session.session_id + ) for idx, entry in enumerate(audit_entries): self.logger.info(f"----- Audit entries: #{idx+1}-----") self.logger.info(f"Entry: {beautify_json(entry.as_dict())}") @@ -125,7 +148,9 @@ async def livenessSessionWithVerify(self): # Clean up: Delete the session self.logger.info("Delete the session.") - await face_session_client.delete_liveness_with_verify_session(created_session.session_id) + await face_session_client.delete_liveness_with_verify_session( + created_session.session_id + ) async def main(): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index 90b9085dc420..3775847dc6d2 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -33,11 +33,15 @@ from shared.helpers import beautify_json, get_logger -class FindSimilarFaces(): +class FindSimilarFaces: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_findsimilar_faces") def find_similar_from_face_ids(self): @@ -45,33 +49,45 @@ def find_similar_from_face_ids(self): from azure.ai.vision.face import FaceClient from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel - with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result1 = face_client.detect( # type: ignore helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) face_ids = [face.face_id for face in detect_result1] - self.logger.info(f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}") + self.logger.info( + f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}" + ) # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path( + TestImages.IMAGE_FINDSIMILAR + ) detect_result2 = face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) assert len(detect_result2) == 1 face_id = detect_result2[0].face_id - self.logger.info(f"Detect 1 face from the file '{find_similar_file_path}': {face_id}") + self.logger.info( + f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" + ) # Call Find Similar # The default find similar mode is MATCH_PERSON - find_similar_result1 = face_client.find_similar(face_id=face_id, face_ids=face_ids) + find_similar_result1 = face_client.find_similar( + face_id=face_id, face_ids=face_ids + ) self.logger.info("Find Similar with matchPerson mode:") for r in find_similar_result1: self.logger.info(f"{helpers.beautify_json(r.as_dict())}") @@ -79,63 +95,104 @@ def find_similar_from_face_ids(self): def find_similar_from_large_face_list(self): from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceAdministrationClient, FaceClient - from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel, FindSimilarMatchMode + from azure.ai.vision.face.models import ( + FaceDetectionModel, + FaceRecognitionModel, + FindSimilarMatchMode, + ) with FaceAdministrationClient( - endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_admin_client, \ - FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_admin_client, FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: large_face_list_id = "lfl01" # Prepare a LargeFaceList which contains several faces. self.logger.info(f"Create a LargeFaceList, id = {large_face_list_id}") face_admin_client.create_large_face_list( - large_face_list_id, name="List of Face", user_data="Large Face List for Test", - recognition_model=FaceRecognitionModel.RECOGNITION_04) + large_face_list_id, + name="List of Face", + user_data="Large Face List for Test", + recognition_model=FaceRecognitionModel.RECOGNITION_04, + ) # Add faces into the largeFaceList self.logger.info(f"Add faces into the LargeFaceList {large_face_list_id}") face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_1)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-1") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_1) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady1-1", + ) face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_2)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-2") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_2) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady1-2", + ) face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_1)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-1") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_1) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady2-1", + ) face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_2)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-2") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_2) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady2-2", + ) face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_3_LADY_1)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady3-1") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_3_LADY_1) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady3-1", + ) # The LargeFaceList should be trained to make it ready for find similar operation. - self.logger.info(f"Train the LargeFaceList {large_face_list_id}, and wait until the operation completes.") - poller = face_admin_client.begin_train_large_face_list(large_face_list_id, polling_interval=30) + self.logger.info( + f"Train the LargeFaceList {large_face_list_id}, and wait until the operation completes." + ) + poller = face_admin_client.begin_train_large_face_list( + large_face_list_id, polling_interval=30 + ) poller.wait() # Keep polling until the "Train" operation completes. # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path( + TestImages.IMAGE_FINDSIMILAR + ) detect_result = face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) assert len(detect_result) == 1 face_id = detect_result[0].face_id - self.logger.info(f"Detect 1 face from the file '{find_similar_file_path}': {face_id}") + self.logger.info( + f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" + ) # Call Find Similar find_similar_result = face_client.find_similar_from_large_face_list( - face_id=face_id, large_face_list_id=large_face_list_id, - max_num_of_candidates_returned=3, mode=FindSimilarMatchMode.MATCH_FACE) + face_id=face_id, + large_face_list_id=large_face_list_id, + max_num_of_candidates_returned=3, + mode=FindSimilarMatchMode.MATCH_FACE, + ) self.logger.info("Find Similar with matchFace mode:") for r in find_similar_result: self.logger.info(f"{beautify_json(r.as_dict())}") diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py index 616ca847b2df..2c0e8e26b477 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py @@ -34,11 +34,15 @@ from shared.helpers import beautify_json, get_logger -class FindSimilarFaces(): +class FindSimilarFaces: def __init__(self): load_dotenv(find_dotenv()) - self.endpoint = os.getenv(CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT) - self.key = os.getenv(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY) + self.endpoint = os.getenv( + CONFIGURATION_NAME_FACE_API_ENDPOINT, DEFAULT_FACE_API_ENDPOINT + ) + self.key = os.getenv( + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY, DEFAULT_FACE_API_ACCOUNT_KEY + ) self.logger = get_logger("sample_findsimilar_faces_async") async def find_similar_from_face_ids(self): @@ -46,33 +50,45 @@ async def find_similar_from_face_ids(self): from azure.ai.vision.face.aio import FaceClient from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel - async with FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + async with FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) detect_result1 = await face_client.detect( # type: ignore helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) face_ids = [face.face_id for face in detect_result1] - self.logger.info(f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}") + self.logger.info( + f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}" + ) # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path( + TestImages.IMAGE_FINDSIMILAR + ) detect_result2 = await face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) assert len(detect_result2) == 1 face_id = detect_result2[0].face_id - self.logger.info(f"Detect 1 face from the file '{find_similar_file_path}': {face_id}") + self.logger.info( + f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" + ) # Call Find Similar # The default find similar mode is MATCH_PERSON - find_similar_result1 = await face_client.find_similar(face_id=face_id, face_ids=face_ids) + find_similar_result1 = await face_client.find_similar( + face_id=face_id, face_ids=face_ids + ) self.logger.info("Find Similar with matchPerson mode:") for r in find_similar_result1: self.logger.info(f"{beautify_json(r.as_dict())}") @@ -80,63 +96,104 @@ async def find_similar_from_face_ids(self): async def find_similar_from_large_face_list(self): from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face.aio import FaceAdministrationClient, FaceClient - from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel, FindSimilarMatchMode + from azure.ai.vision.face.models import ( + FaceDetectionModel, + FaceRecognitionModel, + FindSimilarMatchMode, + ) async with FaceAdministrationClient( - endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_admin_client, \ - FaceClient(endpoint=self.endpoint, credential=AzureKeyCredential(self.key)) as face_client: + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_admin_client, FaceClient( + endpoint=self.endpoint, credential=AzureKeyCredential(self.key) + ) as face_client: large_face_list_id = "lfl01" # Prepare a LargeFaceList which contains several faces. self.logger.info(f"Create a LargeFaceList, id = {large_face_list_id}") await face_admin_client.create_large_face_list( - large_face_list_id, name="List of Face", user_data="Large Face List for Test", - recognition_model=FaceRecognitionModel.RECOGNITION_04) + large_face_list_id, + name="List of Face", + user_data="Large Face List for Test", + recognition_model=FaceRecognitionModel.RECOGNITION_04, + ) # Add faces into the largeFaceList self.logger.info(f"Add faces into the LargeFaceList {large_face_list_id}") await face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_1)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-1") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_1) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady1-1", + ) await face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_2)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady1-2") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_1_MOM_2) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady1-2", + ) await face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_1)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-1") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_1) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady2-1", + ) await face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_2)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady2-2") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_2_LADY_2) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady2-2", + ) await face_admin_client.add_large_face_list_face( large_face_list_id, - helpers.read_file_content(helpers.get_image_path(TestImages.IMAGE_FAMILY_3_LADY_1)), - detection_model=FaceDetectionModel.DETECTION_02, user_data="Lady3-1") + helpers.read_file_content( + helpers.get_image_path(TestImages.IMAGE_FAMILY_3_LADY_1) + ), + detection_model=FaceDetectionModel.DETECTION_02, + user_data="Lady3-1", + ) # The LargeFaceList should be trained to make it ready for find similar operation. - self.logger.info(f"Train the LargeFaceList {large_face_list_id}, and wait until the operation completes.") - poller = await face_admin_client.begin_train_large_face_list(large_face_list_id, polling_interval=30) + self.logger.info( + f"Train the LargeFaceList {large_face_list_id}, and wait until the operation completes." + ) + poller = await face_admin_client.begin_train_large_face_list( + large_face_list_id, polling_interval=30 + ) await poller.wait() # Keep polling until the "Train" operation completes. # Detect face from 'IMAGE_FINDSIMILAR' - find_similar_file_path = helpers.get_image_path(TestImages.IMAGE_FINDSIMILAR) + find_similar_file_path = helpers.get_image_path( + TestImages.IMAGE_FINDSIMILAR + ) detect_result = await face_client.detect( # type: ignore helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) assert len(detect_result) == 1 face_id = detect_result[0].face_id - self.logger.info(f"Detect 1 face from the file '{find_similar_file_path}': {face_id}") + self.logger.info( + f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" + ) # Call Find Similar find_similar_result = await face_client.find_similar_from_large_face_list( - face_id=face_id, large_face_list_id=large_face_list_id, - max_num_of_candidates_returned=3, mode=FindSimilarMatchMode.MATCH_FACE) + face_id=face_id, + large_face_list_id=large_face_list_id, + max_num_of_candidates_returned=3, + mode=FindSimilarMatchMode.MATCH_FACE, + ) self.logger.info("Find Similar with matchFace mode:") for r in find_similar_result: self.logger.info(f"{beautify_json(r.as_dict())}") diff --git a/sdk/face/azure-ai-vision-face/samples/shared/helpers.py b/sdk/face/azure-ai-vision-face/samples/shared/helpers.py index f2a57dcb09fc..40def24e7284 100644 --- a/sdk/face/azure-ai-vision-face/samples/shared/helpers.py +++ b/sdk/face/azure-ai-vision-face/samples/shared/helpers.py @@ -21,7 +21,11 @@ def get_logger(name): # create console handler handler = logging.StreamHandler() - handler.setFormatter(logging.Formatter(fmt='%(asctime)s %(levelname)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')) + handler.setFormatter( + logging.Formatter( + fmt="%(asctime)s %(levelname)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S" + ) + ) # create Logger logger = logging.getLogger(name) @@ -38,7 +42,9 @@ def beautify_json(obj: typing.Dict[str, typing.Any]): def get_image_path(image_file_name: str): from .constants import TestImages - return Path(__file__).resolve().parent / (TestImages.IMAGE_PARENT_FOLDER + "/" + image_file_name) + return Path(__file__).resolve().parent / ( + TestImages.IMAGE_PARENT_FOLDER + "/" + image_file_name + ) def read_file_content(file_path: Path): diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py b/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py index a3ef7e23aee8..40a4d87f02bf 100644 --- a/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py +++ b/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py @@ -28,18 +28,26 @@ def _assert_liveness_outputs_target_not_empty(target: models.LivenessOutputsTarg assert isinstance(target.image_type, models.FaceImageType) -def _assert_liveness_with_verify_image_not_empty(verify_image: models.LivenessWithVerifyImage): +def _assert_liveness_with_verify_image_not_empty( + verify_image: models.LivenessWithVerifyImage, +): _assert_face_rectangle_not_empty(verify_image.face_rectangle) - assert isinstance(verify_image.quality_for_recognition, models.QualityForRecognition) + assert isinstance( + verify_image.quality_for_recognition, models.QualityForRecognition + ) -def _assert_liveness_with_verify_outputs_not_empty(output: models.LivenessWithVerifyOutputs): +def _assert_liveness_with_verify_outputs_not_empty( + output: models.LivenessWithVerifyOutputs, +): _assert_liveness_with_verify_image_not_empty(output.verify_image) assert output.match_confidence > 0 assert output.is_identical is not None -def _assert_liveness_response_body_not_empty(body: models.LivenessResponseBody, is_liveness_with_verify: bool = True): +def _assert_liveness_response_body_not_empty( + body: models.LivenessResponseBody, is_liveness_with_verify: bool = True +): assert body.liveness_decision in models.FaceLivenessDecision _assert_liveness_outputs_target_not_empty(body.target) assert body.model_version_used in models.LivenessModel @@ -47,7 +55,9 @@ def _assert_liveness_response_body_not_empty(body: models.LivenessResponseBody, _assert_liveness_with_verify_outputs_not_empty(body.verify_result) -def _assert_session_audit_entry_request_info_not_empty(request: models.AuditRequestInfo): +def _assert_session_audit_entry_request_info_not_empty( + request: models.AuditRequestInfo, +): assert bool(request.url) assert bool(request.method) assert request.content_length > 0 @@ -55,19 +65,26 @@ def _assert_session_audit_entry_request_info_not_empty(request: models.AuditRequ def _assert_session_audit_entry_response_info_not_empty( - response: models.AuditLivenessResponseInfo, is_liveness_with_verify: bool = True): - _assert_liveness_response_body_not_empty(response.body, is_liveness_with_verify=is_liveness_with_verify) + response: models.AuditLivenessResponseInfo, is_liveness_with_verify: bool = True +): + _assert_liveness_response_body_not_empty( + response.body, is_liveness_with_verify=is_liveness_with_verify + ) assert response.status_code > 0 assert response.latency_in_milliseconds > 0 def _assert_liveness_session_audit_entry_is_valid( - audit_entry: models.LivenessSessionAuditEntry, expected_session_id="", is_liveness_with_verify: bool = True): + audit_entry: models.LivenessSessionAuditEntry, + expected_session_id="", + is_liveness_with_verify: bool = True, +): assert bool(audit_entry.id) assert bool(expected_session_id) or audit_entry.session_id == expected_session_id assert bool(audit_entry.request_id) assert audit_entry.received_date_time is not None _assert_session_audit_entry_request_info_not_empty(audit_entry.request) _assert_session_audit_entry_response_info_not_empty( - audit_entry.response, is_liveness_with_verify=is_liveness_with_verify) + audit_entry.response, is_liveness_with_verify=is_liveness_with_verify + ) assert bool(audit_entry.digest) diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py b/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py index f9665f11b2be..81da59c9513d 100644 --- a/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py +++ b/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py @@ -24,7 +24,9 @@ def get_account_key(**kwargs): def get_image_path(image_file_name: str): from .constants import TestImages - return Path(__file__).resolve().parent / (TestImages.IMAGE_PARENT_FOLDER + "/" + image_file_name) + return Path(__file__).resolve().parent / ( + TestImages.IMAGE_PARENT_FOLDER + "/" + image_file_name + ) def read_file_content(file_path: Path): diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py b/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py index 12b4e2322096..ddd0987ab87b 100644 --- a/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py +++ b/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py @@ -13,25 +13,31 @@ from azure.core.credentials import AzureKeyCredential -class FaceAdminClientTestCase(): +class FaceAdminClientTestCase: def _set_up(self, endpoint, account_key) -> None: - self._client = FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(account_key)) + self._client = FaceAdministrationClient( + endpoint=endpoint, credential=AzureKeyCredential(account_key) + ) def _tear_down(self) -> None: self._client.close() -class FaceClientTestCase(): +class FaceClientTestCase: def _set_up(self, endpoint, account_key) -> None: - self._client = FaceClient(endpoint=endpoint, credential=AzureKeyCredential(account_key)) + self._client = FaceClient( + endpoint=endpoint, credential=AzureKeyCredential(account_key) + ) def _tear_down(self) -> None: self._client.close() -class FaceSessionClientTestCase(): +class FaceSessionClientTestCase: def _set_up(self, endpoint, account_key) -> None: - self._client = FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(account_key)) + self._client = FaceSessionClient( + endpoint=endpoint, credential=AzureKeyCredential(account_key) + ) def _tear_down(self) -> None: self._client.close() diff --git a/sdk/face/azure-ai-vision-face/tests/conftest.py b/sdk/face/azure-ai-vision-face/tests/conftest.py index ab0ac54b2ff5..12cba1646c23 100644 --- a/sdk/face/azure-ai-vision-face/tests/conftest.py +++ b/sdk/face/azure-ai-vision-face/tests/conftest.py @@ -19,6 +19,6 @@ def add_sanitizers(test_proxy): add_remove_header_sanitizer(headers="Ocp-Apim-Subscription-Key") add_general_regex_sanitizer( value="fakeendpoint", - regex="(?<=\\/\\/)[a-z-]+(?=\\.cognitiveservices\\.azure\\.com)" + regex="(?<=\\/\\/)[a-z-]+(?=\\.cognitiveservices\\.azure\\.com)", ) add_body_key_sanitizer(value="fakeauthtoken", json_path="authToken") diff --git a/sdk/face/azure-ai-vision-face/tests/preparers.py b/sdk/face/azure-ai-vision-face/tests/preparers.py index a90afc3d3f0a..05aca21cf24e 100644 --- a/sdk/face/azure-ai-vision-face/tests/preparers.py +++ b/sdk/face/azure-ai-vision-face/tests/preparers.py @@ -19,10 +19,7 @@ class ClientPreparer(AzureMgmtPreparer): def __init__(self, client_cls, client_kwargs={}, **kwargs): - super(ClientPreparer, self).__init__( - name_prefix="", - random_name_length=24 - ) + super(ClientPreparer, self).__init__(name_prefix="", random_name_length=24) self._client_kwargs = client_kwargs self._client_cls = client_cls self._client = None @@ -32,8 +29,12 @@ def create_resource(self, name, **kwargs): account_key = helpers.get_account_key(**kwargs) self._client = self._client_cls(endpoint, AzureKeyCredential(account_key)) - env_name = self._client_kwargs["client_env_name"] \ - if self._client_kwargs is not None and "client_env_name" in self._client_kwargs else "client" + env_name = ( + self._client_kwargs["client_env_name"] + if self._client_kwargs is not None + and "client_env_name" in self._client_kwargs + else "client" + ) kwargs.update({env_name: self._client}) return kwargs @@ -49,14 +50,20 @@ def remove_resource(self, name, **kwargs): "face", azure_face_api_endpoint="https://fakeendpoint.cognitiveservices.azure.com/", azure_face_api_name="fakeaccountname", - azure_face_api_account_key="fakeaccountkey" + azure_face_api_account_key="fakeaccountkey", ) -FaceAdministrationClientPreparer = functools.partial(ClientPreparer, Client.FaceAdministrationClient) +FaceAdministrationClientPreparer = functools.partial( + ClientPreparer, Client.FaceAdministrationClient +) FaceClientPreparer = functools.partial(ClientPreparer, Client.FaceClient) FaceSessionClientPreparer = functools.partial(ClientPreparer, Client.FaceSessionClient) # Async client -AsyncFaceAdministrationClientPreparer = functools.partial(ClientPreparer, AsyncClient.FaceAdministrationClient) +AsyncFaceAdministrationClientPreparer = functools.partial( + ClientPreparer, AsyncClient.FaceAdministrationClient +) AsyncFaceClientPreparer = functools.partial(ClientPreparer, AsyncClient.FaceClient) -AsyncFaceSessionClientPreparer = functools.partial(ClientPreparer, AsyncClient.FaceSessionClient) +AsyncFaceSessionClientPreparer = functools.partial( + ClientPreparer, AsyncClient.FaceSessionClient +) diff --git a/sdk/face/azure-ai-vision-face/tests/test_authentication.py b/sdk/face/azure-ai-vision-face/tests/test_authentication.py index b0f66a059ed9..041dc3d4fc73 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_authentication.py +++ b/sdk/face/azure-ai-vision-face/tests/test_authentication.py @@ -8,7 +8,11 @@ from devtools_testutils import AzureRecordedTestCase, recorded_by_proxy from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection03, + FaceAttributeTypeRecognition04, +) from preparers import FaceClientPreparer, FacePreparer from _shared.constants import TestImages @@ -29,7 +33,9 @@ def test_face_client_api_key_authentication(self, client, **kwargs): return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], - return_face_landmarks=True) + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, + ], + return_face_landmarks=True, + ) assert result is not None diff --git a/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py b/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py index 3f74f646a563..b71182b1f286 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_authentication_async.py @@ -9,7 +9,11 @@ from devtools_testutils.aio import recorded_by_proxy_async from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection03, + FaceAttributeTypeRecognition04, +) from preparers import AsyncFaceClientPreparer, FacePreparer from _shared.constants import TestImages @@ -30,8 +34,10 @@ async def test_face_client_api_key_authentication(self, client, **kwargs): return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], - return_face_landmarks=True) + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, + ], + return_face_landmarks=True, + ) assert result is not None diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py index 35e2df03957b..04d18fe37071 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session.py @@ -30,13 +30,17 @@ class TestLivenessSession(AzureRecordedTestCase): @recorded_by_proxy def test_create_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) # Test `create session` operation created_session = client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id)) + device_correlation_id=recorded_device_correlation_id, + ) + ) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -57,7 +61,7 @@ def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) recorded_device_correlation_ids = { variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())), } # key = session_id, value = device_correlation_id @@ -68,13 +72,18 @@ def test_list_sessions(self, client, **kwargs): created_session = client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=dcid)) + device_correlation_id=dcid, + ) + ) _assert_is_string_and_not_empty(created_session.session_id) created_session_dict[created_session.session_id] = dcid # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. - expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) + expected_dcid_queue = deque( + value + for _, value in sorted(created_session_dict.items(), key=lambda t: t[0]) + ) # Test `list sessions` operation result = client.get_liveness_sessions() @@ -98,7 +107,9 @@ def test_list_sessions(self, client, **kwargs): @recorded_by_proxy def test_get_session_result(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + recorded_session_id = variables.setdefault( + "sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e" + ) session = client.get_liveness_session_result(recorded_session_id) assert session.created_date_time is not None @@ -109,7 +120,10 @@ def test_get_session_result(self, client, **kwargs): assert session.auth_token_time_to_live_in_seconds <= 86400 assert isinstance(session.status, FaceSessionStatus) _assert_liveness_session_audit_entry_is_valid( - session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + session.result, + expected_session_id=recorded_session_id, + is_liveness_with_verify=False, + ) return variables @@ -119,13 +133,18 @@ def test_get_session_result(self, client, **kwargs): @recorded_by_proxy def test_get_session_audit_entries(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + recorded_session_id = variables.setdefault( + "sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e" + ) entries = client.get_liveness_session_audit_entries(recorded_session_id) assert len(entries) == 2 for entry in entries: _assert_liveness_session_audit_entry_is_valid( - entry, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + entry, + expected_session_id=recorded_session_id, + is_liveness_with_verify=False, + ) return variables @@ -134,12 +153,16 @@ def test_get_session_audit_entries(self, client, **kwargs): @recorded_by_proxy def test_delete_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) created_session = client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id)) + device_correlation_id=recorded_device_correlation_id, + ) + ) session_id = created_session.session_id _assert_is_string_and_not_empty(session_id) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py index 7318d51f04e1..ac9846ff152b 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py @@ -31,13 +31,17 @@ class TestLivenessSessionAsync(AzureRecordedTestCase): @recorded_by_proxy_async async def test_create_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) # Test `create session` operation created_session = await client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id)) + device_correlation_id=recorded_device_correlation_id, + ) + ) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -59,7 +63,7 @@ async def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) recorded_device_correlation_ids = { variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())), } # key = session_id, value = device_correlation_id @@ -70,13 +74,18 @@ async def test_list_sessions(self, client, **kwargs): created_session = await client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=dcid)) + device_correlation_id=dcid, + ) + ) _assert_is_string_and_not_empty(created_session.session_id) created_session_dict[created_session.session_id] = dcid # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. - expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) + expected_dcid_queue = deque( + value + for _, value in sorted(created_session_dict.items(), key=lambda t: t[0]) + ) # Test `list sessions` operation result = await client.get_liveness_sessions() @@ -101,7 +110,9 @@ async def test_list_sessions(self, client, **kwargs): @recorded_by_proxy_async async def test_get_session_result(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + recorded_session_id = variables.setdefault( + "sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e" + ) session = await client.get_liveness_session_result(recorded_session_id) assert session.created_date_time is not None @@ -112,7 +123,10 @@ async def test_get_session_result(self, client, **kwargs): assert session.auth_token_time_to_live_in_seconds <= 86400 assert isinstance(session.status, FaceSessionStatus) _assert_liveness_session_audit_entry_is_valid( - session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + session.result, + expected_session_id=recorded_session_id, + is_liveness_with_verify=False, + ) await client.close() return variables @@ -123,13 +137,18 @@ async def test_get_session_result(self, client, **kwargs): @recorded_by_proxy_async async def test_get_session_audit_entries(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e") + recorded_session_id = variables.setdefault( + "sessionId", "5f8e0996-4ef0-4142-9b5d-e42fa5748a7e" + ) entries = await client.get_liveness_session_audit_entries(recorded_session_id) assert len(entries) == 2 for entry in entries: _assert_liveness_session_audit_entry_is_valid( - entry, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + entry, + expected_session_id=recorded_session_id, + is_liveness_with_verify=False, + ) await client.close() return variables @@ -139,12 +158,16 @@ async def test_get_session_audit_entries(self, client, **kwargs): @recorded_by_proxy_async async def test_delete_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) created_session = await client.create_liveness_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id)) + device_correlation_id=recorded_device_correlation_id, + ) + ) session_id = created_session.session_id _assert_is_string_and_not_empty(session_id) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py index 218631623f95..1d23311e4c86 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py @@ -33,14 +33,18 @@ class TestLivenessWithVerifySession(AzureRecordedTestCase): @recorded_by_proxy def test_create_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) # Test `create session` operation created_session = client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id), - verify_image=None) + device_correlation_id=recorded_device_correlation_id, + ), + verify_image=None, + ) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -60,7 +64,9 @@ def test_create_session(self, client, **kwargs): @recorded_by_proxy def test_create_session_with_verify_image(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) # verify_image sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_1) @@ -69,8 +75,10 @@ def test_create_session_with_verify_image(self, client, **kwargs): created_session = client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id), - verify_image=helpers.read_file_content(sample_file_path)) + device_correlation_id=recorded_device_correlation_id, + ), + verify_image=helpers.read_file_content(sample_file_path), + ) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -93,7 +101,7 @@ def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) recorded_device_correlation_ids = { variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())), } # key = session_id, value = device_correlation_id @@ -104,14 +112,19 @@ def test_list_sessions(self, client, **kwargs): created_session = client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=dcid), - verify_image=None) + device_correlation_id=dcid, + ), + verify_image=None, + ) _assert_is_string_and_not_empty(created_session.session_id) created_session_dict[created_session.session_id] = dcid # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. - expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) + expected_dcid_queue = deque( + value + for _, value in sorted(created_session_dict.items(), key=lambda t: t[0]) + ) # Test `list sessions` operation result = client.get_liveness_with_verify_sessions() @@ -135,7 +148,9 @@ def test_list_sessions(self, client, **kwargs): @recorded_by_proxy def test_get_session_result(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + recorded_session_id = variables.setdefault( + "sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854" + ) session = client.get_liveness_with_verify_session_result(recorded_session_id) assert session.created_date_time is not None @@ -146,7 +161,10 @@ def test_get_session_result(self, client, **kwargs): assert session.auth_token_time_to_live_in_seconds <= 86400 assert isinstance(session.status, FaceSessionStatus) _assert_liveness_session_audit_entry_is_valid( - session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + session.result, + expected_session_id=recorded_session_id, + is_liveness_with_verify=False, + ) return variables @@ -156,13 +174,20 @@ def test_get_session_result(self, client, **kwargs): @recorded_by_proxy def test_get_session_audit_entries(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + recorded_session_id = variables.setdefault( + "sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854" + ) - entries = client.get_liveness_with_verify_session_audit_entries(recorded_session_id) + entries = client.get_liveness_with_verify_session_audit_entries( + recorded_session_id + ) assert len(entries) == 2 for entry in entries: _assert_liveness_session_audit_entry_is_valid( - entry, expected_session_id=recorded_session_id, is_liveness_with_verify=True) + entry, + expected_session_id=recorded_session_id, + is_liveness_with_verify=True, + ) return variables @@ -171,13 +196,17 @@ def test_get_session_audit_entries(self, client, **kwargs): @recorded_by_proxy def test_delete_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) created_session = client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id), - verify_image=None) + device_correlation_id=recorded_device_correlation_id, + ), + verify_image=None, + ) session_id = created_session.session_id _assert_is_string_and_not_empty(session_id) diff --git a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py index 13e2aeb53166..4e05b2a7a4cb 100644 --- a/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py +++ b/sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py @@ -34,14 +34,18 @@ class TestLivenessWithVerifySessionAsync(AzureRecordedTestCase): @recorded_by_proxy_async async def test_create_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) # Test `create session` operation created_session = await client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id), - verify_image=None) + device_correlation_id=recorded_device_correlation_id, + ), + verify_image=None, + ) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -62,7 +66,9 @@ async def test_create_session(self, client, **kwargs): @recorded_by_proxy_async async def test_create_session_with_verify_image(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) # verify_image sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_1) @@ -71,8 +77,10 @@ async def test_create_session_with_verify_image(self, client, **kwargs): created_session = await client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id), - verify_image=helpers.read_file_content(sample_file_path)) + device_correlation_id=recorded_device_correlation_id, + ), + verify_image=helpers.read_file_content(sample_file_path), + ) _assert_is_string_and_not_empty(created_session.session_id) _assert_is_string_and_not_empty(created_session.auth_token) @@ -96,7 +104,7 @@ async def test_list_sessions(self, client, **kwargs): variables = kwargs.pop("variables", {}) recorded_device_correlation_ids = { variables.setdefault("deviceCorrelationId1", str(uuid.uuid4())), - variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())) + variables.setdefault("deviceCorrelationId2", str(uuid.uuid4())), } # key = session_id, value = device_correlation_id @@ -107,14 +115,19 @@ async def test_list_sessions(self, client, **kwargs): created_session = await client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=dcid), - verify_image=None) + device_correlation_id=dcid, + ), + verify_image=None, + ) _assert_is_string_and_not_empty(created_session.session_id) created_session_dict[created_session.session_id] = dcid # Sort the dict by key because the `list sessions` operation returns sessions in ascending alphabetical order. - expected_dcid_queue = deque(value for _, value in sorted(created_session_dict.items(), key=lambda t: t[0])) + expected_dcid_queue = deque( + value + for _, value in sorted(created_session_dict.items(), key=lambda t: t[0]) + ) # Test `list sessions` operation result = await client.get_liveness_with_verify_sessions() @@ -139,9 +152,13 @@ async def test_list_sessions(self, client, **kwargs): @recorded_by_proxy_async async def test_get_session_result(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + recorded_session_id = variables.setdefault( + "sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854" + ) - session = await client.get_liveness_with_verify_session_result(recorded_session_id) + session = await client.get_liveness_with_verify_session_result( + recorded_session_id + ) assert session.created_date_time is not None assert session.session_start_date_time is not None assert isinstance(session.session_expired, bool) @@ -150,7 +167,10 @@ async def test_get_session_result(self, client, **kwargs): assert session.auth_token_time_to_live_in_seconds <= 86400 assert isinstance(session.status, FaceSessionStatus) _assert_liveness_session_audit_entry_is_valid( - session.result, expected_session_id=recorded_session_id, is_liveness_with_verify=False) + session.result, + expected_session_id=recorded_session_id, + is_liveness_with_verify=False, + ) await client.close() return variables @@ -161,13 +181,20 @@ async def test_get_session_result(self, client, **kwargs): @recorded_by_proxy_async async def test_get_session_audit_entries(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_session_id = variables.setdefault("sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854") + recorded_session_id = variables.setdefault( + "sessionId", "1b79f44d-d8e0-4652-8f2d-637c4205d854" + ) - entries = await client.get_liveness_with_verify_session_audit_entries(recorded_session_id) + entries = await client.get_liveness_with_verify_session_audit_entries( + recorded_session_id + ) assert len(entries) == 2 for entry in entries: _assert_liveness_session_audit_entry_is_valid( - entry, expected_session_id=recorded_session_id, is_liveness_with_verify=True) + entry, + expected_session_id=recorded_session_id, + is_liveness_with_verify=True, + ) await client.close() return variables @@ -177,13 +204,17 @@ async def test_get_session_audit_entries(self, client, **kwargs): @recorded_by_proxy_async async def test_delete_session(self, client, **kwargs): variables = kwargs.pop("variables", {}) - recorded_device_correlation_id = variables.setdefault("deviceCorrelationId", str(uuid.uuid4())) + recorded_device_correlation_id = variables.setdefault( + "deviceCorrelationId", str(uuid.uuid4()) + ) created_session = await client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, - device_correlation_id=recorded_device_correlation_id), - verify_image=None) + device_correlation_id=recorded_device_correlation_id, + ), + verify_image=None, + ) session_id = created_session.session_id _assert_is_string_and_not_empty(session_id) From 94c082f667c55869195e82e0779973230b47778d Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 10 May 2024 11:12:13 +0800 Subject: [PATCH 28/41] Remove all ignore flag from samples --- .../samples/samples_authentication.py | 4 ++-- .../samples/samples_authentication_async.py | 4 ++-- .../samples/samples_face_detection.py | 18 +++++++++--------- .../samples/samples_face_detection_async.py | 18 +++++++++--------- .../samples/samples_face_grouping.py | 2 +- .../samples/samples_face_grouping_async.py | 2 +- ...ace_liveness_detection_with_verification.py | 2 +- ...veness_detection_with_verification_async.py | 2 +- .../samples/samples_find_similar_faces.py | 6 +++--- .../samples_find_similar_faces_async.py | 6 +++--- 10 files changed, 32 insertions(+), 32 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index ad765288239a..923f1b68438e 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -62,7 +62,7 @@ def authentication_by_api_key(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = face_client.detect( # type: ignore + result = face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -84,7 +84,7 @@ def authentication_by_aad_credential(self): endpoint=self.endpoint, credential=DefaultAzureCredential() ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = face_client.detect( # type: ignore + result = face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py index 6886e1515730..77de1da07e42 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py @@ -63,7 +63,7 @@ async def authentication_by_api_key(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = await face_client.detect( # type: ignore + result = await face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -85,7 +85,7 @@ async def authentication_by_aad_credential(self): endpoint=self.endpoint, credential=credential ) as face_client: sample_file_path = helpers.get_image_path(TestImages.DEFAULT_IMAGE_FILE) - result = await face_client.detect( # type: ignore + result = await face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 5e2ca798ff10..56a4c57f9b8a 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -58,16 +58,16 @@ def detect(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) - result = face_client.detect( # type: ignore + result = face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True, return_face_attributes=[ - FaceAttributeTypeDetection03.HEAD_POSE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection03.MASK, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection03.HEAD_POSE, + FaceAttributeTypeDetection03.MASK, FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, - ], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + ], return_face_landmarks=True, return_recognition_model=True, face_id_time_to_live=120, @@ -91,18 +91,18 @@ def detect_from_url(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_url = TestImages.DEFAULT_IMAGE_URL - result = face_client.detect_from_url( # type: ignore + result = face_client.detect_from_url( url=sample_url, detection_model=FaceDetectionModel.DETECTION_01, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False, return_face_attributes=[ - FaceAttributeTypeDetection01.ACCESSORIES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection01.EXPOSURE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection01.GLASSES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.ACCESSORIES, + FaceAttributeTypeDetection01.EXPOSURE, + FaceAttributeTypeDetection01.GLASSES, FaceAttributeTypeDetection01.NOISE, ], - ) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + ) self.logger.info(f"Detect faces from the url: {sample_url}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py index 58bceb6b80d0..578dedb7467f 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py @@ -59,16 +59,16 @@ async def detect(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_DETECTION_5) - result = await face_client.detect( # type: ignore + result = await face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=True, return_face_attributes=[ - FaceAttributeTypeDetection03.HEAD_POSE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection03.MASK, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection03.HEAD_POSE, + FaceAttributeTypeDetection03.MASK, FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, - ], # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + ], return_face_landmarks=True, return_recognition_model=True, face_id_time_to_live=120, @@ -92,18 +92,18 @@ async def detect_from_url(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_url = TestImages.DEFAULT_IMAGE_URL - result = await face_client.detect_from_url( # type: ignore + result = await face_client.detect_from_url( url=sample_url, detection_model=FaceDetectionModel.DETECTION_01, recognition_model=FaceRecognitionModel.RECOGNITION_04, return_face_id=False, return_face_attributes=[ - FaceAttributeTypeDetection01.ACCESSORIES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection01.EXPOSURE, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 - FaceAttributeTypeDetection01.GLASSES, # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + FaceAttributeTypeDetection01.ACCESSORIES, + FaceAttributeTypeDetection01.EXPOSURE, + FaceAttributeTypeDetection01.GLASSES, FaceAttributeTypeDetection01.NOISE, ], - ) # type: ignore # pyright: ignore[reportAttributeAccessIssue] # noqa: E501 + ) self.logger.info(f"Detect faces from the url: {sample_url}") for idx, face in enumerate(result): diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index 9d01d806a74a..e4525a034452 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -53,7 +53,7 @@ def group(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result = face_client.detect( # type: ignore + detect_result = face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py index c5b2c74b5474..6dcb7bd1d4d1 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py @@ -54,7 +54,7 @@ async def group(self): endpoint=self.endpoint, credential=AzureKeyCredential(self.key) ) as face_client: sample_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result = await face_client.detect( # type: ignore + detect_result = await face_client.detect( helpers.read_file_content(sample_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py index d833bf788ea8..c9634f61bf00 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification.py @@ -96,7 +96,7 @@ def livenessSessionWithVerify(self): self.logger.info( "Create a new liveness with verify session with verify image." ) - created_session = face_session_client.create_liveness_with_verify_session( # type: ignore + created_session = face_session_client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py index 6c4d1952a0c6..14376aca0fc4 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_liveness_detection_with_verification_async.py @@ -99,7 +99,7 @@ async def livenessSessionWithVerify(self): self.logger.info( "Create a new liveness with verify session with verify image." ) - created_session = await face_session_client.create_liveness_with_verify_session( # type: ignore + created_session = await face_session_client.create_liveness_with_verify_session( CreateLivenessSessionContent( liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index 3775847dc6d2..19354d3f7770 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -54,7 +54,7 @@ def find_similar_from_face_ids(self): ) as face_client: # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result1 = face_client.detect( # type: ignore + detect_result1 = face_client.detect( helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -70,7 +70,7 @@ def find_similar_from_face_ids(self): find_similar_file_path = helpers.get_image_path( TestImages.IMAGE_FINDSIMILAR ) - detect_result2 = face_client.detect( # type: ignore + detect_result2 = face_client.detect( helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -173,7 +173,7 @@ def find_similar_from_large_face_list(self): find_similar_file_path = helpers.get_image_path( TestImages.IMAGE_FINDSIMILAR ) - detect_result = face_client.detect( # type: ignore + detect_result = face_client.detect( helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py index 2c0e8e26b477..f42a45cb8301 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py @@ -55,7 +55,7 @@ async def find_similar_from_face_ids(self): ) as face_client: # Detect faces from 'IMAGE_NINE_FACES' nine_faces_file_path = helpers.get_image_path(TestImages.IMAGE_NINE_FACES) - detect_result1 = await face_client.detect( # type: ignore + detect_result1 = await face_client.detect( helpers.read_file_content(nine_faces_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -71,7 +71,7 @@ async def find_similar_from_face_ids(self): find_similar_file_path = helpers.get_image_path( TestImages.IMAGE_FINDSIMILAR ) - detect_result2 = await face_client.detect( # type: ignore + detect_result2 = await face_client.detect( helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, @@ -174,7 +174,7 @@ async def find_similar_from_large_face_list(self): find_similar_file_path = helpers.get_image_path( TestImages.IMAGE_FINDSIMILAR ) - detect_result = await face_client.detect( # type: ignore + detect_result = await face_client.detect( helpers.read_file_content(find_similar_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, From d305e5b14f1f07597940fab5aaa3869f323c19c3 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 10 May 2024 11:13:34 +0800 Subject: [PATCH 29/41] Add pyproject.toml --- sdk/face/azure-ai-vision-face/pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 sdk/face/azure-ai-vision-face/pyproject.toml diff --git a/sdk/face/azure-ai-vision-face/pyproject.toml b/sdk/face/azure-ai-vision-face/pyproject.toml new file mode 100644 index 000000000000..0817f7c7a6c2 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/pyproject.toml @@ -0,0 +1,2 @@ +[tool.generate] +autorest-post-process = true From 0bfae977545e691aa6a74229b0d403a54b05879d Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 10 May 2024 21:38:24 +0800 Subject: [PATCH 30/41] Regenerated code --- .../azure/ai/vision/face/__init__.py | 12 ++++-------- .../azure/ai/vision/face/aio/__init__.py | 12 ++++-------- .../azure/ai/vision/face/models/__init__.py | 13 ++++++++++--- 3 files changed, 18 insertions(+), 19 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py index 4cc609842e93..aba5d9a9e382 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/__init__.py @@ -6,18 +6,14 @@ # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- -from ._client import FaceClient +from ._patch import FaceClient from ._client import FaceAdministrationClient -from ._client import FaceSessionClient +from ._patch import FaceSessionClient from ._version import VERSION __version__ = VERSION -try: - from ._patch import __all__ as _patch_all - from ._patch import * # pylint: disable=unused-wildcard-import -except ImportError: - _patch_all = [] + from ._patch import patch_sdk as _patch_sdk __all__ = [ @@ -25,6 +21,6 @@ "FaceAdministrationClient", "FaceSessionClient", ] -__all__.extend([p for p in _patch_all if p not in __all__]) + _patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py index 696348801174..a6b1cdec0b00 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/__init__.py @@ -6,15 +6,11 @@ # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- -from ._client import FaceClient +from ._patch import FaceClient from ._client import FaceAdministrationClient -from ._client import FaceSessionClient +from ._patch import FaceSessionClient + -try: - from ._patch import __all__ as _patch_all - from ._patch import * # pylint: disable=unused-wildcard-import -except ImportError: - _patch_all = [] from ._patch import patch_sdk as _patch_sdk __all__ = [ @@ -22,6 +18,6 @@ "FaceAdministrationClient", "FaceSessionClient", ] -__all__.extend([p for p in _patch_all if p not in __all__]) + _patch_sdk() diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py index dcbd91525083..03a8a31cd092 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/__init__.py @@ -82,11 +82,18 @@ from ._enums import NoiseLevel from ._enums import QualityForRecognition from ._enums import Versions -from ._patch import __all__ as _patch_all -from ._patch import * # pylint: disable=unused-wildcard-import + +from ._patch import FaceAttributeTypeDetection01 +from ._patch import FaceAttributeTypeDetection03 +from ._patch import FaceAttributeTypeRecognition03 +from ._patch import FaceAttributeTypeRecognition04 from ._patch import patch_sdk as _patch_sdk __all__ = [ + "FaceAttributeTypeDetection01", + "FaceAttributeTypeDetection03", + "FaceAttributeTypeRecognition03", + "FaceAttributeTypeRecognition04", "AccessoryItem", "AddFaceResult", "AuditLivenessResponseInfo", @@ -163,5 +170,5 @@ "QualityForRecognition", "Versions", ] -__all__.extend([p for p in _patch_all if p not in __all__]) + _patch_sdk() From bd1f697aa4a2ae984b972e9bb448d0f384d4e575 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 10 May 2024 21:45:01 +0800 Subject: [PATCH 31/41] update patches --- .../azure/ai/vision/face/_patch.py | 35 ++++++++++--------- .../azure/ai/vision/face/aio/_patch.py | 35 ++++++++++--------- .../azure/ai/vision/face/models/_patch.py | 18 +++++----- 3 files changed, 48 insertions(+), 40 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index e816ad694aed..4d901cf3babf 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -467,10 +468,6 @@ def detect_from_url( } ] """ - # By default, return_face_id is False. - if return_face_id is not True: - return_face_id = False - return super()._detect_from_url( body, url=url, @@ -481,7 +478,8 @@ def detect_from_url( return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, face_id_time_to_live=face_id_time_to_live, - **kwargs) + **kwargs, + ) @distributed_trace def detect( @@ -863,10 +861,6 @@ def detect( } ] """ - # By default, return_face_id is False. - if return_face_id is not True: - return_face_id = False - return super()._detect( image_content, detection_model=detection_model, @@ -876,7 +870,12 @@ def detect( return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, face_id_time_to_live=face_id_time_to_live, - **kwargs) + **kwargs, + ) + + def __enter__(self) -> "FaceClient": + super().__enter__() + return self class FaceSessionClient(FaceSessionClientGenerated): @@ -1019,14 +1018,18 @@ def create_liveness_with_verify_session( """ if verify_image is not None: request_body = _models._models.CreateLivenessWithVerifySessionContent( # pylint: disable=protected-access - parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), # pylint: disable=protected-access # noqa: E501 - verify_image=("verify-image", verify_image) + parameters=cast( + _models._models.CreateLivenessSessionContentForMultipart, body # pylint: disable=protected-access + ), + verify_image=("verify-image", verify_image), ) - return super()._create_liveness_with_verify_session_with_verify_image( - request_body, **kwargs) + return super()._create_liveness_with_verify_session_with_verify_image(request_body, **kwargs) + + return super()._create_liveness_with_verify_session(body, **kwargs) - return super()._create_liveness_with_verify_session( - body, **kwargs) + def __enter__(self) -> "FaceSessionClient": + super().__enter__() + return self __all__: List[str] = [ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 47b7219f26f2..3f29427d7e1e 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -467,10 +468,6 @@ async def detect_from_url( } ] """ - # By default, return_face_id is False. - if return_face_id is not True: - return_face_id = False - return await super()._detect_from_url( body, url=url, @@ -481,7 +478,8 @@ async def detect_from_url( return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, face_id_time_to_live=face_id_time_to_live, - **kwargs) + **kwargs, + ) @distributed_trace_async async def detect( @@ -863,10 +861,6 @@ async def detect( } ] """ - # By default, return_face_id is False. - if return_face_id is not True: - return_face_id = False - return await super()._detect( image_content, detection_model=detection_model, @@ -876,7 +870,12 @@ async def detect( return_face_landmarks=return_face_landmarks, return_recognition_model=return_recognition_model, face_id_time_to_live=face_id_time_to_live, - **kwargs) + **kwargs, + ) + + async def __aenter__(self) -> "FaceClient": + await super().__aenter__() + return self class FaceSessionClient(FaceSessionClientGenerated): @@ -1019,14 +1018,18 @@ async def create_liveness_with_verify_session( """ if verify_image is not None: request_body = _models._models.CreateLivenessWithVerifySessionContent( # pylint: disable=protected-access - parameters=cast(_models._models.CreateLivenessSessionContentForMultipart, body), # pylint: disable=protected-access # noqa: E501 - verify_image=("verify-image", verify_image) + parameters=cast( + _models._models.CreateLivenessSessionContentForMultipart, body # pylint: disable=protected-access + ), + verify_image=("verify-image", verify_image), ) - return await super()._create_liveness_with_verify_session_with_verify_image( - request_body, **kwargs) + return await super()._create_liveness_with_verify_session_with_verify_image(request_body, **kwargs) + + return await super()._create_liveness_with_verify_session(body, **kwargs) - return await super()._create_liveness_with_verify_session( - body, **kwargs) + async def __aenter__(self) -> "FaceSessionClient": + await super().__aenter__() + return self __all__: List[str] = [ diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py index 549cde822927..c9522e83a0ed 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py @@ -1,3 +1,5 @@ +# mypy: disable_error_code=misc +# pyright: reportGeneralTypeIssues=false # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. @@ -11,8 +13,8 @@ from ._enums import FaceAttributeType -FaceAttributeTypeDetection01 = Enum("FaceAttributeTypeDetection01", [ # type: ignore[misc] - (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] +FaceAttributeTypeDetection01 = Enum("FaceAttributeTypeDetection01", [ + (a.name, a.value) for a in FaceAttributeType if a in [ FaceAttributeType.ACCESSORIES, FaceAttributeType.BLUR, FaceAttributeType.EXPOSURE, @@ -22,19 +24,19 @@ FaceAttributeType.OCCLUSION]]) """Available attribute options for detection_01 model.""" -FaceAttributeTypeDetection03 = Enum("FaceAttributeTypeDetection03", [ # type: ignore[misc] - (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] +FaceAttributeTypeDetection03 = Enum("FaceAttributeTypeDetection03", [ + (a.name, a.value) for a in FaceAttributeType if a in [ FaceAttributeType.HEAD_POSE, FaceAttributeType.MASK]]) """Available attribute options for detection_03 model.""" -FaceAttributeTypeRecognition03 = Enum("FaceAttributeTypeRecognition03", [ # type: ignore[misc] - (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] +FaceAttributeTypeRecognition03 = Enum("FaceAttributeTypeRecognition03", [ + (a.name, a.value) for a in FaceAttributeType if a in [ FaceAttributeType.QUALITY_FOR_RECOGNITION]]) """Available attribute options for recognition_03 model.""" -FaceAttributeTypeRecognition04 = Enum("FaceAttributeTypeRecognition04", [ # type: ignore[misc] - (a.name, a.value) for a in FaceAttributeType if a in [ # pyright: ignore[reportGeneralTypeIssues] +FaceAttributeTypeRecognition04 = Enum("FaceAttributeTypeRecognition04", [ + (a.name, a.value) for a in FaceAttributeType if a in [ FaceAttributeType.QUALITY_FOR_RECOGNITION]]) """Available attribute options for recognition_04 model.""" From 22dbb02a01dd85ec3ee0f17e1b1271f48545af67 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 10 May 2024 21:57:56 +0800 Subject: [PATCH 32/41] fix samples --- .../azure-ai-vision-face/samples/samples_face_detection.py | 2 ++ .../samples/samples_face_detection_async.py | 2 ++ .../azure-ai-vision-face/samples/samples_face_grouping.py | 2 +- .../samples/samples_face_grouping_async.py | 2 +- .../samples/samples_find_similar_faces.py | 6 +++--- .../samples/samples_find_similar_faces_async.py | 6 +++--- 6 files changed, 12 insertions(+), 8 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 56a4c57f9b8a..15b2ff269af7 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -5,6 +5,8 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +# mypy: disable_error_code=attr-defined +# pyright: reportAttributeAccessIssue=false """ FILE: sample_face_detection.py diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py index 578dedb7467f..b0894f0d6e6a 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py @@ -5,6 +5,8 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- +# mypy: disable_error_code=attr-defined +# pyright: reportAttributeAccessIssue=false """ FILE: sample_face_detection_async.py diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py index e4525a034452..83a88f0242e8 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping.py @@ -60,7 +60,7 @@ def group(self): return_face_id=True, ) - face_ids = [face.face_id for face in detect_result] + face_ids = [str(face.face_id) for face in detect_result] self.logger.info( f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}" ) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py index 6dcb7bd1d4d1..b7909a60aa78 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_grouping_async.py @@ -61,7 +61,7 @@ async def group(self): return_face_id=True, ) - face_ids = [face.face_id for face in detect_result] + face_ids = [str(face.face_id) for face in detect_result] self.logger.info( f"Detect {len(face_ids)} faces from the file '{sample_file_path}': {face_ids}" ) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py index 19354d3f7770..7d9b37a8a5e7 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces.py @@ -61,7 +61,7 @@ def find_similar_from_face_ids(self): return_face_id=True, ) - face_ids = [face.face_id for face in detect_result1] + face_ids = [str(face.face_id) for face in detect_result1] self.logger.info( f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}" ) @@ -78,7 +78,7 @@ def find_similar_from_face_ids(self): ) assert len(detect_result2) == 1 - face_id = detect_result2[0].face_id + face_id = str(detect_result2[0].face_id) self.logger.info( f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" ) @@ -181,7 +181,7 @@ def find_similar_from_large_face_list(self): ) assert len(detect_result) == 1 - face_id = detect_result[0].face_id + face_id = str(detect_result[0].face_id) self.logger.info( f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" ) diff --git a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py index f42a45cb8301..7bd8e0ff5698 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_find_similar_faces_async.py @@ -62,7 +62,7 @@ async def find_similar_from_face_ids(self): return_face_id=True, ) - face_ids = [face.face_id for face in detect_result1] + face_ids = [str(face.face_id) for face in detect_result1] self.logger.info( f"Detect {len(face_ids)} faces from the file '{nine_faces_file_path}': {face_ids}" ) @@ -79,7 +79,7 @@ async def find_similar_from_face_ids(self): ) assert len(detect_result2) == 1 - face_id = detect_result2[0].face_id + face_id = str(detect_result2[0].face_id) self.logger.info( f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" ) @@ -182,7 +182,7 @@ async def find_similar_from_large_face_list(self): ) assert len(detect_result) == 1 - face_id = detect_result[0].face_id + face_id = str(detect_result[0].face_id) self.logger.info( f"Detect 1 face from the file '{find_similar_file_path}': {face_id}" ) From f02131f3bfde3267a97f5b48d5c1d142cea664ea Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Fri, 10 May 2024 22:03:27 +0800 Subject: [PATCH 33/41] Update CHANGELOG --- sdk/face/azure-ai-vision-face/CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/face/azure-ai-vision-face/CHANGELOG.md b/sdk/face/azure-ai-vision-face/CHANGELOG.md index 7a80063eb2d0..53e703ad0cdf 100644 --- a/sdk/face/azure-ai-vision-face/CHANGELOG.md +++ b/sdk/face/azure-ai-vision-face/CHANGELOG.md @@ -13,6 +13,7 @@ This package's [documentation](https://github.com/Azure/azure-sdk-for-python/tre - Added support for `person` and `dynamic_person_group` operations. - Added support for face recognition with `PersonDirectory` (`identify_from_person_directory()` and `identify_from_dynamic_person_group()`). - Asynchronous APIs are added under `azure.ai.vision.face.aio` namespace. +- Authentication with Microsoft Entra ID is supported using `DefaultAzureCredential()` from `azure.identity`. ### Breaking Changes @@ -27,5 +28,4 @@ This package's [documentation](https://github.com/Azure/azure-sdk-for-python/tre - `FaceSessionClient` to interact with sessions which is used for Liveness detection. - New function names that comply with [Azure Python SDK Design Guidelines](https://azure.github.io/azure-sdk/python_design.html): - For example, the method `person_group_person.create()` is changed to `create_person_group_person()`. -- Authentication with Microsoft Entra ID is supported using `DefaultAzureCredential()` from `azure.identity`. - The Snapshot operations are all removed as [the Snapshot API is no longer supported](https://azure.microsoft.com/updates/facelimitedaccess/). From 29ab198d103dc8dccc894d108cdbca0a7f3142c0 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 13 May 2024 12:12:06 +0800 Subject: [PATCH 34/41] Regenerate SDK based on the latest TypeSpec --- .../ai/vision/face/_operations/_operations.py | 70 +++++++++++-------- .../face/aio/_operations/_operations.py | 70 +++++++++++-------- .../azure/ai/vision/face/models/_enums.py | 15 ++-- .../azure/ai/vision/face/models/_models.py | 49 +------------ .../azure-ai-vision-face/tsp-location.yaml | 2 +- 5 files changed, 90 insertions(+), 116 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py index 1a5deace3dbf..bd19badabe3a 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_operations/_operations.py @@ -2600,7 +2600,7 @@ def _detect_from_url( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -2625,7 +2625,7 @@ def _detect_from_url( (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -2638,7 +2638,7 @@ def _detect_from_url( latest model needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param body: Is either a JSON type or a IO[bytes] type. Required. :type body: JSON or IO[bytes] @@ -3057,7 +3057,7 @@ def _detect( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -3082,7 +3082,7 @@ def _detect( (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -3095,7 +3095,7 @@ def _detect( latest model needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param image_content: The input image binary. Required. :type image_content: bytes @@ -5263,6 +5263,8 @@ def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -5350,6 +5352,8 @@ def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -5422,6 +5426,8 @@ def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -5489,6 +5495,8 @@ def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -8078,7 +8086,7 @@ def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -8159,7 +8167,7 @@ def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -8235,7 +8243,7 @@ def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -8311,7 +8319,7 @@ def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -8455,7 +8463,7 @@ def add_face_list_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -9550,7 +9558,7 @@ def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -9641,7 +9649,7 @@ def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -9727,7 +9735,7 @@ def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -9813,7 +9821,7 @@ def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -9967,7 +9975,7 @@ def add_large_face_list_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -12130,7 +12138,7 @@ def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -12213,7 +12221,7 @@ def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -12291,7 +12299,7 @@ def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -12369,7 +12377,7 @@ def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -12516,7 +12524,7 @@ def add_person_group_person_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -14599,7 +14607,7 @@ def add_large_person_group_person_face_from_url( # pylint: disable=name-too-lon face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -14683,7 +14691,7 @@ def add_large_person_group_person_face_from_url( # pylint: disable=name-too-lon face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -14762,7 +14770,7 @@ def add_large_person_group_person_face_from_url( # pylint: disable=name-too-lon face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -14841,7 +14849,7 @@ def add_large_person_group_person_face_from_url( # pylint: disable=name-too-lon face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -14989,7 +14997,7 @@ def add_large_person_group_person_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -16321,7 +16329,7 @@ def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -16413,7 +16421,7 @@ def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -16500,7 +16508,7 @@ def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -16587,7 +16595,7 @@ def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -16807,7 +16815,7 @@ def begin_add_person_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py index 2cd629abda47..a7a4f1d0c6c3 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_operations/_operations.py @@ -216,7 +216,7 @@ async def _detect_from_url( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -241,7 +241,7 @@ async def _detect_from_url( (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -254,7 +254,7 @@ async def _detect_from_url( latest model needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param body: Is either a JSON type or a IO[bytes] type. Required. :type body: JSON or IO[bytes] @@ -673,7 +673,7 @@ async def _detect( To mitigate potential misuse that can subject people to stereotyping, discrimination, or unfair denial of services, we are retiring Face API attributes that predict emotion, gender, age, smile, facial hair, hair, and makeup. Read more about this decision - https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. + https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/. * @@ -698,7 +698,7 @@ async def _detect( (100 pixels between eyes). * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * 'detection_02': Face attributes and landmarks are disabled if you choose this detection model. @@ -711,7 +711,7 @@ async def _detect( latest model needed, please explicitly specify the model you need in this parameter. Once specified, the detected faceIds will be associated with the specified recognition model. More details, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-recognition-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-recognition-model. :param image_content: The input image binary. Required. :type image_content: bytes @@ -2879,6 +2879,8 @@ async def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -2966,6 +2968,8 @@ async def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -3038,6 +3042,8 @@ async def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -3105,6 +3111,8 @@ async def identify_from_person_directory( For each face in the faceIds array, Face Identify will compute similarities between the query face and all the faces in the Person Directory Persons (given by personIds), and return candidate person(s) for that face ranked by similarity confidence. + Passing personIds with an array with one element "*" can perform the operation over entire + person directory. .. @@ -5696,7 +5704,7 @@ async def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -5777,7 +5785,7 @@ async def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -5853,7 +5861,7 @@ async def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -5929,7 +5937,7 @@ async def add_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -6073,7 +6081,7 @@ async def add_face_list_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param face_list_id: Valid character is letter in lower case or digit or '-' or '_', maximum length is 64. Required. @@ -7169,7 +7177,7 @@ async def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -7260,7 +7268,7 @@ async def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -7346,7 +7354,7 @@ async def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -7432,7 +7440,7 @@ async def add_large_face_list_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -7586,7 +7594,7 @@ async def add_large_face_list_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model .. @@ -9752,7 +9760,7 @@ async def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -9835,7 +9843,7 @@ async def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -9913,7 +9921,7 @@ async def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -9991,7 +9999,7 @@ async def add_person_group_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -10138,7 +10146,7 @@ async def add_person_group_person_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param person_group_id: ID of the container. Required. :type person_group_id: str @@ -12222,7 +12230,7 @@ async def add_large_person_group_person_face_from_url( # pylint: disable=name-t face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -12306,7 +12314,7 @@ async def add_large_person_group_person_face_from_url( # pylint: disable=name-t face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -12385,7 +12393,7 @@ async def add_large_person_group_person_face_from_url( # pylint: disable=name-t face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -12464,7 +12472,7 @@ async def add_large_person_group_person_face_from_url( # pylint: disable=name-t face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -12612,7 +12620,7 @@ async def add_large_person_group_person_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model. + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model. :param large_person_group_id: ID of the container. Required. :type large_person_group_id: str @@ -13946,7 +13954,7 @@ async def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -14038,7 +14046,7 @@ async def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -14125,7 +14133,7 @@ async def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -14212,7 +14220,7 @@ async def begin_add_person_face_from_url( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. @@ -14433,7 +14441,7 @@ async def begin_add_person_face( face size. * Different 'detectionModel' values can be provided. To use and compare different detection models, please refer to - https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/specify-detection-model + https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/specify-detection-model * * Adding/deleting faces to/from a same person will be processed sequentially. Adding/deleting faces to/from different persons are processed in parallel. diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py index 4ebb4c54d967..d29512713bb2 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_enums.py @@ -103,8 +103,11 @@ class FaceImageType(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The type of image.""" COLOR = "Color" + """Color image.""" INFRARED = "Infrared" + """Infrared image.""" DEPTH = "Depth" + """Depth image.""" class FaceLivenessDecision(str, Enum, metaclass=CaseInsensitiveEnumMeta): @@ -112,9 +115,9 @@ class FaceLivenessDecision(str, Enum, metaclass=CaseInsensitiveEnumMeta): UNCERTAIN = "uncertain" """The algorithm could not classify the target face as either real or spoof.""" - REALFACE = "realface" + REAL_FACE = "realface" """The algorithm has classified the target face as real.""" - SPOOFFACE = "spoofface" + SPOOF_FACE = "spoofface" """The algorithm has classified the target face as a spoof.""" @@ -203,10 +206,10 @@ class HairColorType(str, Enum, metaclass=CaseInsensitiveEnumMeta): class LivenessModel(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The model version used for liveness classification.""" - ENUM_2020_02_15_PREVIEW_01 = "2020-02-15-preview.01" - ENUM_2021_11_12_PREVIEW_03 = "2021-11-12-preview.03" - ENUM_2022_10_15_PREVIEW_04 = "2022-10-15-preview.04" - ENUM_2023_03_02_PREVIEW_05 = "2023-03-02-preview.05" + V2020_02_15_PREVIEW_01 = "2020-02-15-preview.01" + V2021_11_12_PREVIEW_03 = "2021-11-12-preview.03" + V2022_10_15_PREVIEW_04 = "2022-10-15-preview.04" + V2023_03_02_PREVIEW_05 = "2023-03-02-preview.05" class LivenessOperationMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py index d847bbfcbc5b..6467daa94a02 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_models.py @@ -291,50 +291,6 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: # pylint: disable=useles super().__init__(*args, **kwargs) -class CreateLivenessSessionContentForMultipart(_model_base.Model): - """Dedicated parameter model for multipart/form-data. - - All required parameters must be populated in order to send to server. - - :ivar liveness_operation_mode: Type of liveness mode the client should follow. Required. - "Passive" - :vartype liveness_operation_mode: str or ~azure.ai.vision.face.models.LivenessOperationMode - :ivar send_results_to_client: Whether or not to allow a '200 - Success' response body to be - sent to the client, which may be undesirable for security reasons. Default is false, clients - will receive a '204 - NoContent' empty body response. Regardless of selection, calling Session - GetResult will always contain a response body enabling business logic to be implemented. - :vartype send_results_to_client: bool - :ivar device_correlation_id_set_in_client: Whether or not to allow client to set their own - 'deviceCorrelationId' via the Vision SDK. Default is false, and 'deviceCorrelationId' must be - set in this request body. - :vartype device_correlation_id_set_in_client: bool - :ivar device_correlation_id: Unique Guid per each end-user device. This is to provide rate - limiting and anti-hammering. If 'deviceCorrelationIdSetInClient' is true in this request, this - 'deviceCorrelationId' must be null. - :vartype device_correlation_id: str - :ivar auth_token_time_to_live_in_seconds: Seconds the session should last for. Range is 60 to - 86400 seconds. Default value is 600. - :vartype auth_token_time_to_live_in_seconds: int - """ - - liveness_operation_mode: Union[str, "_models.LivenessOperationMode"] = rest_field(name="livenessOperationMode") - """Type of liveness mode the client should follow. Required. \"Passive\"""" - send_results_to_client: Optional[bool] = rest_field(name="sendResultsToClient") - """Whether or not to allow a '200 - Success' response body to be sent to the client, which may be - undesirable for security reasons. Default is false, clients will receive a '204 - NoContent' - empty body response. Regardless of selection, calling Session GetResult will always contain a - response body enabling business logic to be implemented.""" - device_correlation_id_set_in_client: Optional[bool] = rest_field(name="deviceCorrelationIdSetInClient") - """Whether or not to allow client to set their own 'deviceCorrelationId' via the Vision SDK. - Default is false, and 'deviceCorrelationId' must be set in this request body.""" - device_correlation_id: Optional[str] = rest_field(name="deviceCorrelationId") - """Unique Guid per each end-user device. This is to provide rate limiting and anti-hammering. If - 'deviceCorrelationIdSetInClient' is true in this request, this 'deviceCorrelationId' must be - null.""" - auth_token_time_to_live_in_seconds: Optional[int] = rest_field(name="authTokenTimeToLiveInSeconds") - """Seconds the session should last for. Range is 60 to 86400 seconds. Default value is 600.""" - - class CreateLivenessSessionResult(_model_base.Model): """Response of liveness session creation. @@ -383,14 +339,13 @@ class CreateLivenessWithVerifySessionContent(_model_base.Model): All required parameters must be populated in order to send to server. :ivar parameters: The parameters for creating session. Required. - :vartype parameters: - ~azure.ai.vision.face.models._models.CreateLivenessSessionContentForMultipart + :vartype parameters: ~azure.ai.vision.face.models.CreateLivenessSessionContent :ivar verify_image: The image stream for verify. Content-Disposition header field for this part must have filename. Required. :vartype verify_image: bytes """ - parameters: "_models._models.CreateLivenessSessionContentForMultipart" = rest_field(name="Parameters") + parameters: "_models.CreateLivenessSessionContent" = rest_field(name="Parameters") """The parameters for creating session. Required.""" verify_image: FileType = rest_field(name="VerifyImage", is_multipart_file_input=True) """The image stream for verify. Content-Disposition header field for this part must have filename. diff --git a/sdk/face/azure-ai-vision-face/tsp-location.yaml b/sdk/face/azure-ai-vision-face/tsp-location.yaml index e64b5978f87a..94d47ba080a4 100644 --- a/sdk/face/azure-ai-vision-face/tsp-location.yaml +++ b/sdk/face/azure-ai-vision-face/tsp-location.yaml @@ -1,4 +1,4 @@ directory: specification/ai/Face -commit: 37acfe2967e5e1be1169146ac461eb1875c9476e +commit: 969c68562c5c4850257c9242a7a64b73c15f110d repo: Azure/azure-rest-api-specs additionalDirectories: From 8aae3af71191f8c19269527dc4517e467ac12e98 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 13 May 2024 14:00:10 +0800 Subject: [PATCH 35/41] Update customized method to address the TypeSpec changes --- .../azure-ai-vision-face/azure/ai/vision/face/_patch.py | 6 ++---- .../azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py | 6 ++---- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 4d901cf3babf..85578d72f01e 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -7,7 +7,7 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from typing import Any, IO, List, Optional, Union, cast, overload +from typing import Any, IO, List, Optional, Union, overload from azure.core.tracing.decorator import distributed_trace @@ -1018,9 +1018,7 @@ def create_liveness_with_verify_session( """ if verify_image is not None: request_body = _models._models.CreateLivenessWithVerifySessionContent( # pylint: disable=protected-access - parameters=cast( - _models._models.CreateLivenessSessionContentForMultipart, body # pylint: disable=protected-access - ), + parameters=body, verify_image=("verify-image", verify_image), ) return super()._create_liveness_with_verify_session_with_verify_image(request_body, **kwargs) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index 3f29427d7e1e..c6ba4969842e 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -7,7 +7,7 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from typing import Any, IO, List, Optional, Union, cast, overload +from typing import Any, IO, List, Optional, Union, overload from azure.core.tracing.decorator_async import distributed_trace_async @@ -1018,9 +1018,7 @@ async def create_liveness_with_verify_session( """ if verify_image is not None: request_body = _models._models.CreateLivenessWithVerifySessionContent( # pylint: disable=protected-access - parameters=cast( - _models._models.CreateLivenessSessionContentForMultipart, body # pylint: disable=protected-access - ), + parameters=body, verify_image=("verify-image", verify_image), ) return await super()._create_liveness_with_verify_session_with_verify_image(request_body, **kwargs) From d49f74081f29bc088b73390af7043fa857528cdc Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 13 May 2024 13:56:30 +0800 Subject: [PATCH 36/41] Update customized Enum classes definition and remove ignore flags from samples --- .../azure/ai/vision/face/models/_patch.py | 57 ++++++++++--------- .../samples/samples_face_detection.py | 2 - .../samples/samples_face_detection_async.py | 2 - 3 files changed, 31 insertions(+), 30 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py index c9522e83a0ed..de4bc248f36f 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py @@ -8,37 +8,42 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ +from azure.core import CaseInsensitiveEnumMeta from enum import Enum from typing import List from ._enums import FaceAttributeType -FaceAttributeTypeDetection01 = Enum("FaceAttributeTypeDetection01", [ - (a.name, a.value) for a in FaceAttributeType if a in [ - FaceAttributeType.ACCESSORIES, - FaceAttributeType.BLUR, - FaceAttributeType.EXPOSURE, - FaceAttributeType.GLASSES, - FaceAttributeType.HEAD_POSE, - FaceAttributeType.NOISE, - FaceAttributeType.OCCLUSION]]) -"""Available attribute options for detection_01 model.""" - -FaceAttributeTypeDetection03 = Enum("FaceAttributeTypeDetection03", [ - (a.name, a.value) for a in FaceAttributeType if a in [ - FaceAttributeType.HEAD_POSE, - FaceAttributeType.MASK]]) -"""Available attribute options for detection_03 model.""" - -FaceAttributeTypeRecognition03 = Enum("FaceAttributeTypeRecognition03", [ - (a.name, a.value) for a in FaceAttributeType if a in [ - FaceAttributeType.QUALITY_FOR_RECOGNITION]]) -"""Available attribute options for recognition_03 model.""" - -FaceAttributeTypeRecognition04 = Enum("FaceAttributeTypeRecognition04", [ - (a.name, a.value) for a in FaceAttributeType if a in [ - FaceAttributeType.QUALITY_FOR_RECOGNITION]]) -"""Available attribute options for recognition_04 model.""" + +class FaceAttributeTypeDetection01(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available attribute options for detection_01 model.""" + + ACCESSORIES = FaceAttributeType.ACCESSORIES.value + BLUR = FaceAttributeType.BLUR.value + EXPOSURE = FaceAttributeType.EXPOSURE.value + GLASSES = FaceAttributeType.GLASSES.value + HEAD_POSE = FaceAttributeType.HEAD_POSE.value + NOISE = FaceAttributeType.NOISE.value + OCCLUSION = FaceAttributeType.OCCLUSION.value + + +class FaceAttributeTypeDetection03(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available attribute options for detection_03 model.""" + + HEAD_POSE = FaceAttributeType.HEAD_POSE.value + MASK = FaceAttributeType.MASK.value + + +class FaceAttributeTypeRecognition03(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available attribute options for recognition_03 model.""" + + QUALITY_FOR_RECOGNITION = FaceAttributeType.QUALITY_FOR_RECOGNITION.value + + +class FaceAttributeTypeRecognition04(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Available attribute options for recognition_04 model.""" + + QUALITY_FOR_RECOGNITION = FaceAttributeType.QUALITY_FOR_RECOGNITION.value __all__: List[str] = [ diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py index 15b2ff269af7..56a4c57f9b8a 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection.py @@ -5,8 +5,6 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- -# mypy: disable_error_code=attr-defined -# pyright: reportAttributeAccessIssue=false """ FILE: sample_face_detection.py diff --git a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py index b0894f0d6e6a..578dedb7467f 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_face_detection_async.py @@ -5,8 +5,6 @@ # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- -# mypy: disable_error_code=attr-defined -# pyright: reportAttributeAccessIssue=false """ FILE: sample_face_detection_async.py From aeb6ee5a576276f02448358a8a3704e854ef0463 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 13 May 2024 14:13:55 +0800 Subject: [PATCH 37/41] Update README.md --- sdk/face/azure-ai-vision-face/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index 1a17d47ed575..3b6902943c20 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -464,7 +464,7 @@ additional questions or comments. [source_code]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/azure/ai/vision/face -[face_pypi]: https://pypi.org/project/azure-ai-vision-face/ +[face_pypi]: https://aka.ms/azsdk-python-face-pkg [face_ref_docs]: https://aka.ms/azsdk-python-face-ref [face_product_docs]: https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity [face_samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/face/azure-ai-vision-face/samples From 26264e7123cd5eadcd243938a4cebd3f512d9829 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Mon, 13 May 2024 14:41:10 +0800 Subject: [PATCH 38/41] Fix pylint error --- .../azure-ai-vision-face/azure/ai/vision/face/models/_patch.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py index de4bc248f36f..cc2815b630cc 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/models/_patch.py @@ -8,10 +8,11 @@ Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ -from azure.core import CaseInsensitiveEnumMeta from enum import Enum from typing import List +from azure.core import CaseInsensitiveEnumMeta + from ._enums import FaceAttributeType From 6d4edec898bbf82e26e8b785a084de3a56c141e3 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Wed, 15 May 2024 10:56:46 +0800 Subject: [PATCH 39/41] Use the new name of AAD: 'Microsoft Entra ID' --- sdk/face/azure-ai-vision-face/README.md | 4 ++-- .../azure-ai-vision-face/samples/samples_authentication.py | 4 ++-- .../samples/samples_authentication_async.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index 3b6902943c20..4675dbf33264 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -29,7 +29,7 @@ The Azure AI Face service provides AI algorithms that detect, recognize, and ana ### Create a Face or a Cognitive Services resource -Azure AI Face supports both [multi-service][azure_cognitive_service_account] and single-service access. Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Face access only, create a Face resource. Please note that you will need a single-service resource if you intend to use [Azure Active Directory authentication](#create-the-client-with-an-azure-active-directory-credential). +Azure AI Face supports both [multi-service][azure_cognitive_service_account] and single-service access. Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Face access only, create a Face resource. Please note that you will need a single-service resource if you intend to use [Microsoft Entra ID authentication](#create-the-client-with-an-azure-active-directory-credential). * To create a new Face or Cognitive Services account, you can use [Azure Portal][azure_portal_create_face_account], [Azure PowerShell][quick_start_create_account_via_azure_powershell], or [Azure CLI][quick_start_create_account_via_azure_cli]. @@ -89,7 +89,7 @@ face_client = FaceClient(endpoint, credential) #### Create the client with an Microsoft Entra ID credential -`AzureKeyCredential` authentication is used in the examples in this getting started guide, but you can also authenticate with Azure Active Directory using the [azure-identity][azure_sdk_python_identity] library. +`AzureKeyCredential` authentication is used in the examples in this getting started guide, but you can also authenticate with Microsoft Entra ID using the [azure-identity][azure_sdk_python_identity] library. Note that regional endpoints do not support Microsoft Entra ID authentication. Create a [custom subdomain][custom_subdomain] name for your resource in order to use this type of authentication. To use the [DefaultAzureCredential][azure_sdk_python_default_azure_credential] type shown below, or other credential types provided with the Azure SDK, please install the `azure-identity` package: diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py index 923f1b68438e..54562a238a39 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication.py @@ -11,7 +11,7 @@ DESCRIPTION: This sample demonstrates authenticating a client via: * api key - * Azure Active Directory(AAD) + * Microsoft Entra ID USAGE: python sample_authentication.py @@ -22,7 +22,7 @@ 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. The following environment variables are required for using azure-identity's DefaultAzureCredential. For more information, refer to https://aka.ms/azsdk/python/identity/docs#azure.identity.DefaultAzureCredential - 3) AZURE_TENANT_ID - the tenant ID in Azure Active Directory + 3) AZURE_TENANT_ID - the tenant ID in Microsoft Entra ID 4) AZURE_CLIENT_ID - the application (client) ID registered in the AAD tenant 5) AZURE_CLIENT_SECRET - the client secret for the registered application """ diff --git a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py index 77de1da07e42..2f9763fe21e9 100644 --- a/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py +++ b/sdk/face/azure-ai-vision-face/samples/samples_authentication_async.py @@ -11,7 +11,7 @@ DESCRIPTION: This sample demonstrates authenticating a client via: * api key - * Azure Active Directory(AAD) + * Microsoft Entra ID USAGE: python sample_authentication_async.py @@ -22,7 +22,7 @@ 2) AZURE_FACE_API_ACCOUNT_KEY - your Face API key. The following environment variables are required for using azure-identity's DefaultAzureCredential. For more information, refer to https://aka.ms/azsdk/python/identity/docs#azure.identity.DefaultAzureCredential - 3) AZURE_TENANT_ID - the tenant ID in Azure Active Directory + 3) AZURE_TENANT_ID - the tenant ID in Microsoft Entra ID 4) AZURE_CLIENT_ID - the application (client) ID registered in the AAD tenant 5) AZURE_CLIENT_SECRET - the client secret for the registered application """ From 0f6d75e494da72ef3c05dd208d808482f6f95a51 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Wed, 15 May 2024 11:00:42 +0800 Subject: [PATCH 40/41] Change the order of overload functions of --- sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py | 4 ++-- .../azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py index 85578d72f01e..54c9b916747b 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/_patch.py @@ -35,8 +35,8 @@ class FaceClient(FaceClientGenerated): @overload def detect_from_url( self, - body: JSON, *, + url: str, content_type: str = "application/json", detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], @@ -51,8 +51,8 @@ def detect_from_url( @overload def detect_from_url( self, + body: JSON, *, - url: str, content_type: str = "application/json", detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], diff --git a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py index c6ba4969842e..61fb93215f3e 100644 --- a/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py +++ b/sdk/face/azure-ai-vision-face/azure/ai/vision/face/aio/_patch.py @@ -35,8 +35,8 @@ class FaceClient(FaceClientGenerated): @overload async def detect_from_url( self, - body: JSON, *, + url: str, content_type: str = "application/json", detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], @@ -51,8 +51,8 @@ async def detect_from_url( @overload async def detect_from_url( self, + body: JSON, *, - url: str, content_type: str = "application/json", detection_model: Union[str, _models.FaceDetectionModel], recognition_model: Union[str, _models.FaceRecognitionModel], From 168c21aa2e14f7d5c3a57286a0c3efdc02631763 Mon Sep 17 00:00:00 2001 From: Judith Ting Date: Wed, 15 May 2024 13:56:30 +0800 Subject: [PATCH 41/41] Re-format the code snippet in README.md --- sdk/face/azure-ai-vision-face/README.md | 45 +++++++++++++++++-------- 1 file changed, 31 insertions(+), 14 deletions(-) diff --git a/sdk/face/azure-ai-vision-face/README.md b/sdk/face/azure-ai-vision-face/README.md index 4675dbf33264..05e998e6517f 100644 --- a/sdk/face/azure-ai-vision-face/README.md +++ b/sdk/face/azure-ai-vision-face/README.md @@ -196,7 +196,11 @@ For the detailed differences between different versions of **Detection** and **R from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceClient from azure.ai.vision.face.models import ( - FaceDetectionModel, FaceRecognitionModel, FaceAttributeTypeDetection03, FaceAttributeTypeRecognition04) + FaceDetectionModel, + FaceRecognitionModel, + FaceAttributeTypeDetection03, + FaceAttributeTypeRecognition04, +) endpoint = "" key = "" @@ -214,10 +218,12 @@ with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_c return_face_attributes=[ FaceAttributeTypeDetection03.HEAD_POSE, FaceAttributeTypeDetection03.MASK, - FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION], + FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION, + ], return_face_landmarks=True, return_recognition_model=True, - face_id_time_to_live=120) + face_id_time_to_live=120, + ) print(f"Detect faces from the file: {sample_file_path}") for idx, face in enumerate(result): @@ -234,7 +240,7 @@ and then register faces with these `Person`. ```python from azure.core.credentials import AzureKeyCredential from azure.ai.vision.face import FaceAdministrationClient, FaceClient -from azure.ai.vision.face.models import (FaceDetectionModel, FaceRecognitionModel) +from azure.ai.vision.face.models import FaceDetectionModel, FaceRecognitionModel def read_file_content(file_path: str): @@ -252,29 +258,34 @@ large_person_group_id = "lpg_family" with FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_admin_client: print(f"Create a large person group with id: {large_person_group_id}") face_admin_client.create_large_person_group( - large_person_group_id, name="My Family", recognition_model=FaceRecognitionModel.RECOGNITION_04) + large_person_group_id, name="My Family", recognition_model=FaceRecognitionModel.RECOGNITION_04 + ) print("Create a Person Bill and add a face to him.") bill_person_id = face_admin_client.create_large_person_group_person( - large_person_group_id, name="Bill", user_data="Dad").person_id + large_person_group_id, name="Bill", user_data="Dad" + ).person_id bill_image_file_path = "./samples/images/Family1-Dad1.jpg" face_admin_client.add_large_person_group_person_face( large_person_group_id, bill_person_id, read_file_content(bill_image_file_path), detection_model=FaceDetectionModel.DETECTION_03, - user_data="Dad-0001") + user_data="Dad-0001", + ) print("Create a Person Clare and add a face to her.") clare_person_id = face_admin_client.create_large_person_group_person( - large_person_group_id, name="Clare", user_data="Mom").person_id + large_person_group_id, name="Clare", user_data="Mom" + ).person_id clare_image_file_path = "./samples/images/Family1-Mom1.jpg" face_admin_client.add_large_person_group_person_face( large_person_group_id, clare_person_id, read_file_content(clare_image_file_path), detection_model=FaceDetectionModel.DETECTION_03, - user_data="Mom-0001") + user_data="Mom-0001", + ) ``` Before doing the identification, we need to train the LargePersonGroup first. @@ -297,12 +308,14 @@ with FaceClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as face_c read_file_content(target_image_file_path), detection_model=FaceDetectionModel.DETECTION_03, recognition_model=FaceRecognitionModel.RECOGNITION_04, - return_face_id=True) + return_face_id=True, + ) target_face_ids = list(f.face_id for f in detect_result) # Identify the faces in the large person group. result = face_client.identify_from_large_person_group( - face_ids=target_face_ids, large_person_group_id=large_person_group_id) + face_ids=target_face_ids, large_person_group_id=large_person_group_id + ) for idx, r in enumerate(result): print(f"----- Identification result: #{idx+1} -----") print(f"{r.as_dict()}") @@ -351,7 +364,9 @@ with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, - auth_token_time_to_live_in_seconds=60)) + auth_token_time_to_live_in_seconds=60, + ) + ) print(f"Result: {created_session}") # Get the liveness detection result. @@ -384,8 +399,10 @@ with FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(key)) as liveness_operation_mode=LivenessOperationMode.PASSIVE, device_correlation_id=str(uuid.uuid4()), send_results_to_client=False, - auth_token_time_to_live_in_seconds=60), - verify_image=file_content) + auth_token_time_to_live_in_seconds=60, + ), + verify_image=file_content, + ) print(f"Result: {created_session}") # Get the liveness detection and verification result.

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Date: Wed, 3 Apr 2024 22:41:43 +0800 Subject: [PATCH 06/41] Testcases --- .../tests/_shared/asserter.py | 73 +++++++ .../tests/_shared/constants.py | 56 +++++ .../tests/_shared/helpers.py | 20 ++ .../tests/_shared/testcase.py | 37 ++++ .../azure-ai-vision-face/tests/conftest.py | 24 +++ .../tests/images/Family1-Dad1.jpg | Bin 0 -> 44796 bytes .../tests/images/Family1-Dad2.jpg | Bin 0 -> 46585 bytes .../tests/images/Family1-Dad3.jpg | Bin 0 -> 34562 bytes .../tests/images/Family1-Daughter1.jpg | Bin 0 -> 26921 bytes .../tests/images/Family1-Daughter2.jpg | Bin 0 -> 27307 bytes .../tests/images/Family1-Daughter3.jpg | Bin 0 -> 18559 bytes .../tests/images/Family1-Mom1.jpg | Bin 0 -> 34200 bytes .../tests/images/Family1-Mom2.jpg | Bin 0 -> 26212 bytes .../tests/images/Family1-Son1.jpg | Bin 0 -> 38031 bytes .../tests/images/Family1-Son2.jpg | Bin 0 -> 36035 bytes .../tests/images/Family2-Lady1.jpg | Bin 0 -> 62275 bytes .../tests/images/Family2-Lady2.jpg | Bin 0 -> 40873 bytes .../tests/images/Family2-Man1.jpg | Bin 0 -> 40339 bytes .../tests/images/Family2-Man2.jpg | Bin 0 -> 52928 bytes .../tests/images/Family3-Lady1.jpg | Bin 0 -> 54874 bytes .../tests/images/Family3-Man1.jpg | Bin 0 -> 53816 bytes .../tests/images/child1-person-group.jpg | Bin 0 -> 18559 bytes .../tests/images/child2-person-group.jpg | Bin 0 -> 26921 bytes .../tests/images/child3-person-group.jpg | Bin 0 -> 27307 bytes .../tests/images/detection1.jpg | Bin 0 -> 491315 bytes .../tests/images/detection2.jpg | Bin 0 -> 365448 bytes .../tests/images/detection3.jpg | Bin 0 -> 172788 bytes .../tests/images/detection4.jpg | Bin 0 -> 132540 bytes .../tests/images/detection5.jpg | Bin 0 -> 580312 bytes .../tests/images/detection6.jpg | Bin 0 -> 586647 bytes .../tests/images/extra-woman-image.jpg | Bin 0 -> 62275 bytes .../tests/images/findsimilar.jpg | Bin 0 -> 46585 bytes .../tests/images/identification1.jpg | Bin 0 -> 94734 bytes .../tests/images/man1-person-group.jpg | Bin 0 -> 52928 bytes .../tests/images/man2-person-group.jpg | Bin 0 -> 56200 bytes .../tests/images/man3-person-group.jpg | Bin 0 -> 40339 bytes .../tests/images/test-image-person-group.jpg | Bin 0 -> 81678 bytes .../tests/images/woman1-person-group.jpg | Bin 0 -> 40877 bytes .../tests/images/woman2-person-group.jpg | Bin 0 -> 40873 bytes .../tests/images/woman3-person-group.jpg | Bin 0 -> 62275 bytes .../azure-ai-vision-face/tests/preparers.py | 62 ++++++ .../tests/test_authentication.py | 40 ++++ .../tests/test_authentication_async.py | 43 ++++ .../tests/test_liveness_session.py | 152 +++++++++++++ .../tests/test_liveness_session_async.py | 158 ++++++++++++++ .../test_liveness_with_verify_session.py | 192 +++++++++++++++++ ...test_liveness_with_verify_session_async.py | 199 ++++++++++++++++++ 47 files changed, 1056 insertions(+) create mode 100644 sdk/face/azure-ai-vision-face/tests/_shared/asserter.py create mode 100644 sdk/face/azure-ai-vision-face/tests/_shared/constants.py create mode 100644 sdk/face/azure-ai-vision-face/tests/_shared/helpers.py create mode 100644 sdk/face/azure-ai-vision-face/tests/_shared/testcase.py create mode 100644 sdk/face/azure-ai-vision-face/tests/conftest.py create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Dad1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Dad2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Dad3.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Daughter1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Daughter2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Daughter3.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Mom1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Mom2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Son1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family1-Son2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family2-Lady1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family2-Lady2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family2-Man1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family2-Man2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family3-Lady1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/Family3-Man1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/child1-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/child2-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/child3-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/detection1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/detection2.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/detection3.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/detection4.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/detection5.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/detection6.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/extra-woman-image.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/findsimilar.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/identification1.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/man1-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/man2-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/man3-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/test-image-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/woman1-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/woman2-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/images/woman3-person-group.jpg create mode 100644 sdk/face/azure-ai-vision-face/tests/preparers.py create mode 100644 sdk/face/azure-ai-vision-face/tests/test_authentication.py create mode 100644 sdk/face/azure-ai-vision-face/tests/test_authentication_async.py create mode 100644 sdk/face/azure-ai-vision-face/tests/test_liveness_session.py create mode 100644 sdk/face/azure-ai-vision-face/tests/test_liveness_session_async.py create mode 100644 sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session.py create mode 100644 sdk/face/azure-ai-vision-face/tests/test_liveness_with_verify_session_async.py diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py b/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py new file mode 100644 index 000000000000..d0ee74c99fbf --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/_shared/asserter.py @@ -0,0 +1,73 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +import azure.ai.vision.face.models as models + + +def _assert_is_string_and_not_empty(val: str): + assert isinstance(val, str) and bool(val) + + +def _assert_face_rectangle_not_empty(face_rectangle: models.FaceRectangle): + assert face_rectangle is not None + assert face_rectangle.top > 0 + assert face_rectangle.left > 0 + assert face_rectangle.width > 0 + assert face_rectangle.height > 0 + + +def _assert_liveness_outputs_target_not_empty(target: models.LivenessOutputsTarget): + _assert_face_rectangle_not_empty(target.face_rectangle) + assert bool(target.file_name) + assert target.time_offset_within_file >= 0 + assert isinstance(target.image_type, models.FaceImageType) + + +def _assert_liveness_with_verify_image_not_empty(verify_image: models.LivenessWithVerifyImage): + _assert_face_rectangle_not_empty(verify_image.face_rectangle) + assert isinstance(verify_image.quality_for_recognition, models.QualityForRecognition) + + +def _assert_liveness_with_verify_outputs_not_empty(output: models.LivenessWithVerifyOutputs): + _assert_liveness_with_verify_image_not_empty(output.verify_image) + assert output.match_confidence > 0 + assert output.is_identical is not None + + +def _assert_liveness_response_body_not_empty(body: models.LivenessResponseBody, is_liveness_with_verify: bool = True): + assert body.liveness_decision in models.FaceLivenessDecision + _assert_liveness_outputs_target_not_empty(body.target) + assert body.model_version_used in models.LivenessModel + if is_liveness_with_verify: + _assert_liveness_with_verify_outputs_not_empty(body.verify_result) + + +def _assert_session_audit_entry_request_info_not_empty(request: models.AuditRequestInfo): + assert bool(request.url) + assert bool(request.method) + assert request.content_length > 0 + assert bool(request.content_type) + + +def _assert_session_audit_entry_response_info_not_empty( + response: models.AuditLivenessResponseInfo, is_liveness_with_verify: bool = True): + _assert_liveness_response_body_not_empty(response.body, is_liveness_with_verify=is_liveness_with_verify) + assert response.status_code > 0 + assert response.latency_in_milliseconds > 0 + + +def _assert_liveness_session_audit_entry_is_valid( + audit_entry: models.LivenessSessionAuditEntry, expected_session_id="", is_liveness_with_verify: bool = True): + assert audit_entry.id > 0 + assert bool(expected_session_id) or audit_entry.session_id == expected_session_id + assert bool(audit_entry.request_id) + assert audit_entry.received_date_time is not None + _assert_session_audit_entry_request_info_not_empty(audit_entry.request) + _assert_session_audit_entry_response_info_not_empty( + audit_entry.response, is_liveness_with_verify=is_liveness_with_verify) + assert bool(audit_entry.digest) diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/constants.py b/sdk/face/azure-ai-vision-face/tests/_shared/constants.py new file mode 100644 index 000000000000..4132663508cc --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/_shared/constants.py @@ -0,0 +1,56 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +""" +FILE: constants.py + +DESCRIPTION: + This module defines constants required to run the test cases. +""" + +CONFIGURATION_NAME_FACE_API_ENDPOINT = "azure_face_api_endpoint" +CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY = "azure_face_api_account_key" + +# Test images +IMAGE_FAMILY_1_DAD_1 = "images/Family1-Dad1.jpg" +IMAGE_FAMILY_1_DAD_2 = "images/Family1-Dad2.jpg" +IMAGE_FAMILY_1_DAD_3 = "images/Family1-Dad3.jpg" +IMAGE_FAMILY_1_DAUGHTER_1 = "images/Family1-Daughter1.jpg" +IMAGE_FAMILY_1_DAUGHTER_2 = "images/Family1-Daughter2.jpg" +IMAGE_FAMILY_1_DAUGHTER_3 = "images/Family1-Daughter3.jpg" +IMAGE_FAMILY_1_MOM_1 = "images/Family1-Mom1.jpg" +IMAGE_FAMILY_1_MOM_2 = "images/Family1-Mom2.jpg" +IMAGE_FAMILY_1_SON_1 = "images/Family1-Son1.jpg" +IMAGE_FAMILY_1_SON_2 = "images/Family1-Son2.jpg" +IMAGE_FAMILY_2_LADY_1 = "images/Family2-Lady1.jpg" +IMAGE_FAMILY_2_LADY_2 = "images/Family2-Lady2.jpg" +IMAGE_FAMILY_2_Man_1 = "images/Family2-Man1.jpg" +IMAGE_FAMILY_2_Man_2 = "images/Family2-Man2.jpg" +IMAGE_FAMILY_3_LADY_1 = "images/Family3-Lady1.jpg" +IMAGE_FAMILY_3_Man_1 = "images/Family3-Man1.jpg" +IMAGE_CHILD_1_PERSON_GROUP = "images/child1-person-group.jpg" +IMAGE_CHILD_2_PERSON_GROUP = "images/child2-person-group.jpg" +IMAGE_CHILD_3_PERSON_GROUP = "images/child3-person-group.jpg" +IMAGE_DETECTION_1 = "images/detection1.jpg" +IMAGE_DETECTION_2 = "images/detection2.jpg" +IMAGE_DETECTION_3 = "images/detection3.jpg" +IMAGE_DETECTION_4 = "images/detection4.jpg" +IMAGE_DETECTION_5 = "images/detection5.jpg" +IMAGE_DETECTION_6 = "images/detection6.jpg" +IMAGE_EXTRA_WOMAN_IMAGE = "images/extra-woman-image.jpg" +IMAGE_FINDSIMILAR = "images/findsimilar.jpg" +IMAGE_IDENTIFICATION1 = "images/indentification1.jpg" +IMAGE_MAN1_PERSON_GROUP = "images/man1-person-group.jpg" +IMAGE_MAN2_PERSON_GROUP = "images/man2-person-group.jpg" +IMAGE_MAN3_PERSON_GROUP = "images/man3-person-group.jpg" +TEST_IMAGE_PERSON_GROUP = "images/test-image-person-group.jpg" +IMAGE_WOMAN1_PERSON_GROUP = "images/woman1-person-group.jpg" +IMAGE_WOMAN2_PERSON_GROUP = "images/woman2-person-group.jpg" +IMAGE_WOMAN3_PERSON_GROUP = "images/woman3-person-group.jpg" + +IMAGE_URL = "https://aka.ms/facesampleurl" diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py b/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py new file mode 100644 index 000000000000..cd66969f894a --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/_shared/helpers.py @@ -0,0 +1,20 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +from .constants import ( + CONFIGURATION_NAME_FACE_API_ENDPOINT, + CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY +) + + +def get_face_endpoint(**kwargs): + return kwargs.pop(CONFIGURATION_NAME_FACE_API_ENDPOINT) + + +def get_account_key(**kwargs): + return kwargs.pop(CONFIGURATION_NAME_FACE_API_ACCOUNT_KEY) diff --git a/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py b/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py new file mode 100644 index 000000000000..12b4e2322096 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/_shared/testcase.py @@ -0,0 +1,37 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- +from azure.ai.vision.face import ( + FaceAdministrationClient, + FaceClient, + FaceSessionClient, +) +from azure.core.credentials import AzureKeyCredential + + +class FaceAdminClientTestCase(): + def _set_up(self, endpoint, account_key) -> None: + self._client = FaceAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(account_key)) + + def _tear_down(self) -> None: + self._client.close() + + +class FaceClientTestCase(): + def _set_up(self, endpoint, account_key) -> None: + self._client = FaceClient(endpoint=endpoint, credential=AzureKeyCredential(account_key)) + + def _tear_down(self) -> None: + self._client.close() + + +class FaceSessionClientTestCase(): + def _set_up(self, endpoint, account_key) -> None: + self._client = FaceSessionClient(endpoint=endpoint, credential=AzureKeyCredential(account_key)) + + def _tear_down(self) -> None: + self._client.close() diff --git a/sdk/face/azure-ai-vision-face/tests/conftest.py b/sdk/face/azure-ai-vision-face/tests/conftest.py new file mode 100644 index 000000000000..ab0ac54b2ff5 --- /dev/null +++ b/sdk/face/azure-ai-vision-face/tests/conftest.py @@ -0,0 +1,24 @@ +# coding: utf-8 + +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +import pytest +from devtools_testutils import ( + add_body_key_sanitizer, + add_general_regex_sanitizer, + add_remove_header_sanitizer, +) + + +@pytest.fixture(scope="session", autouse=True) +def add_sanitizers(test_proxy): + add_remove_header_sanitizer(headers="Ocp-Apim-Subscription-Key") + add_general_regex_sanitizer( + value="fakeendpoint", + regex="(?<=\\/\\/)[a-z-]+(?=\\.cognitiveservices\\.azure\\.com)" + ) + add_body_key_sanitizer(value="fakeauthtoken", json_path="authToken") diff --git a/sdk/face/azure-ai-vision-face/tests/images/Family1-Dad1.jpg b/sdk/face/azure-ai-vision-face/tests/images/Family1-Dad1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a9baaa7c58a6b574dd8eea1b48a37598f8e1876d GIT binary patch literal 44796 zcmeFZbzD_V*D!qOZV_oDMY_ABK|;Ev4&i}wfOBX8MU(~sK}6~9?owJQ>25^2JHG?Q 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