diff --git a/.gitignore b/.gitignore index df08f80..6c9606a 100644 --- a/.gitignore +++ b/.gitignore @@ -41,6 +41,10 @@ MANIFEST *.manifest *.spec +# pictures +*.png +*.jpg + # Installer logs pip-log.txt pip-delete-this-directory.txt diff --git a/data/datasets/source/bear.zarr/.zgroup b/data/datasets/source/demo_ori/bear.zarr/.zgroup similarity index 100% rename from data/datasets/source/bear.zarr/.zgroup rename to data/datasets/source/demo_ori/bear.zarr/.zgroup diff --git a/data/datasets/source/bear.zarr/data/.zgroup b/data/datasets/source/demo_ori/bear.zarr/data/.zgroup similarity index 100% rename from data/datasets/source/bear.zarr/data/.zgroup rename to data/datasets/source/demo_ori/bear.zarr/data/.zgroup diff --git a/data/datasets/source/bear.zarr/data/action/.zarray b/data/datasets/source/demo_ori/bear.zarr/data/action/.zarray similarity index 100% rename from data/datasets/source/bear.zarr/data/action/.zarray rename to data/datasets/source/demo_ori/bear.zarr/data/action/.zarray 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data/datasets/source/sauce.zarr/meta/episode_ends/.zarray rename to data/datasets/source/demo_ori/sauce.zarr/meta/episode_ends/.zarray diff --git a/data/datasets/source/sauce.zarr/meta/episode_ends/0 b/data/datasets/source/demo_ori/sauce.zarr/meta/episode_ends/0 similarity index 100% rename from data/datasets/source/sauce.zarr/meta/episode_ends/0 rename to data/datasets/source/demo_ori/sauce.zarr/meta/episode_ends/0 diff --git a/data/datasets/source/process_data_demogen.py b/data/datasets/source/process_data_demogen.py new file mode 100644 index 0000000..44f2600 --- /dev/null +++ b/data/datasets/source/process_data_demogen.py @@ -0,0 +1,161 @@ +import pickle, os +import numpy as np +import pdb +from copy import deepcopy +import zarr +import shutil +import argparse +import yaml +import cv2 +import h5py + + +def load_hdf5(dataset_path): + if not os.path.isfile(dataset_path): + print(f"Dataset does not exist at \n{dataset_path}\n") + exit() + + with h5py.File(dataset_path, "r") as root: + left_gripper, left_arm = ( + root["/joint_action/left_gripper"][()], + root["/joint_action/left_arm"][()], + ) + right_gripper, right_arm = ( + root["/joint_action/right_gripper"][()], + root["/joint_action/right_arm"][()], + ) + vector = root["/joint_action/vector"][()] + pointcloud = root["/pointcloud"][()] + + return left_gripper, left_arm, right_gripper, right_arm, vector, pointcloud + + +def main(): + + parser = argparse.ArgumentParser(description="Process some episodes.") + parser.add_argument( + "task_name", + type=str, + help="The name of the task (e.g., beat_block_hammer)", + ) + parser.add_argument("task_config", type=str) + parser.add_argument( + "expert_data_num", + type=int, + help="Number of episodes to process (e.g., 50)", + ) + parser.add_argument( + "episode_indices", + type=str, + help="Python list of episode indices to process, e.g. [0,2,5]", + ) + args = parser.parse_args() + + task_name = args.task_name + num = args.expert_data_num + task_config = args.task_config + # 解析 episode_indices 参数 + try: + episode_indices = eval(args.episode_indices) + assert isinstance(episode_indices, list) + assert len(episode_indices) == num + except Exception as e: + print("Error parsing episode_indices, should be Python list like [0,2,5]") + raise + + load_dir = "../../data/" + str(task_name) + "/" + str(task_config) + + total_count = 0 + + idx=episode_indices[0] # Use the first episode index for naming + save_dir = f"./data/{task_name}-{task_config}-{num}-demogen-{idx}.zarr" + + if os.path.exists(save_dir): + shutil.rmtree(save_dir) + + zarr_root = zarr.group(save_dir) + zarr_data = zarr_root.create_group("data") + zarr_meta = zarr_root.create_group("meta") + + point_cloud_arrays = [] + episode_ends_arrays, action_arrays, state_arrays, joint_action_arrays = ( + [], + [], + [], + [], + ) + + for idx, ep_num in enumerate(episode_indices): + print(f"processing episode: {idx + 1} / {num} (episode{ep_num})", end="\r") + load_path = os.path.join(load_dir, f"data/episode{ep_num}.hdf5") + ( + left_gripper_all, + left_arm_all, + right_gripper_all, + right_arm_all, + vector_all, + pointcloud_all, + ) = load_hdf5(load_path) + + for j in range(0, left_gripper_all.shape[0]): + pointcloud = pointcloud_all[j] + joint_state = vector_all[j] + if j != left_gripper_all.shape[0] - 1: + point_cloud_arrays.append(pointcloud) + state_arrays.append(joint_state) + if j != 0: + joint_action_arrays.append(joint_state) + + total_count += left_gripper_all.shape[0] - 1 + episode_ends_arrays.append(total_count) + + print() + try: + episode_ends_arrays = np.array(episode_ends_arrays) + state_arrays = np.array(state_arrays) + point_cloud_arrays = np.array(point_cloud_arrays) + joint_action_arrays = np.array(joint_action_arrays) + + compressor = zarr.Blosc(cname="zstd", clevel=3, shuffle=1) + state_chunk_size = (100, state_arrays.shape[1]) + joint_chunk_size = (100, joint_action_arrays.shape[1]) + point_cloud_chunk_size = (100, point_cloud_arrays.shape[1]) + zarr_data.create_dataset( + "point_cloud", + data=point_cloud_arrays, + chunks=point_cloud_chunk_size, + overwrite=True, + compressor=compressor, + ) + zarr_data.create_dataset( + "state", + data=state_arrays, + chunks=state_chunk_size, + dtype="float32", + overwrite=True, + compressor=compressor, + ) + zarr_data.create_dataset( + "action", + data=joint_action_arrays, + chunks=joint_chunk_size, + dtype="float32", + overwrite=True, + compressor=compressor, + ) + zarr_meta.create_dataset( + "episode_ends", + data=episode_ends_arrays, + dtype="int64", + overwrite=True, + compressor=compressor, + ) + except ZeroDivisionError as e: + print("If you get a `ZeroDivisionError: division by zero`, check that `data/pointcloud` in the task config is set to true.") + raise + except Exception as e: + print(f"An unexpected error occurred ({type(e).__name__}): {e}") + raise + +if __name__ == "__main__": + main() diff --git a/data/sam_mask/banana/0/banana.png b/data/sam_mask/banana/0/banana.png deleted 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--git a/data/sam_mask/get_mask.py b/data/sam_mask/get_mask.py index 785eb7b..069df87 100644 --- a/data/sam_mask/get_mask.py +++ b/data/sam_mask/get_mask.py @@ -53,12 +53,12 @@ def save_binary_mask(mask, output_path): model = LangSAM() -image_pil = Image.open("./assets/source_0.jpg").convert("RGB") -text_prompt = "green basket in the middle." +image_pil = Image.open("./assets/source_0.jpg").convert("RGB") +text_prompt = "an iron hammer with a black handle" # 此处需修改 results = model.predict([image_pil], [text_prompt]) masks = results[0]["masks"] print(masks.shape) assert masks.shape[0] == 1 -save_binary_mask(masks[0], f"assets/outputs/basket_0.png") \ No newline at end of file +save_binary_mask(masks[0], f"assets/outputs/hammer_0.png") # 此处需修改 \ No newline at end of file diff --git a/data/sam_mask/get_mask_bk.py b/data/sam_mask/get_mask_bk.py new file mode 100644 index 0000000..785eb7b --- /dev/null +++ b/data/sam_mask/get_mask_bk.py @@ -0,0 +1,64 @@ +# https://github.com/luca-medeiros/lang-segment-anything + +from PIL import Image +from lang_sam import LangSAM +import numpy as np + + +# Function to apply transparent masks on the image +def save_mask_results(image, masks, output_path): + # Ensure the image is in RGBA mode (with transparency support) + image = image_pil.convert("RGBA") + + # Iterate over the binary masks + for mask in masks: + # Convert mask to the same size as the image + mask_resized = Image.fromarray(mask).resize(image.size, Image.NEAREST).convert("L") + + # Create an RGBA mask with the same size + mask_rgba = mask_resized.convert("RGBA") + + # Get the alpha channel from the mask to apply transparency + mask_rgba = np.array(mask_rgba) + alpha_channel = mask_rgba[:, :, 0] # Assuming the mask is single-channel + + # Create a new transparent image with the same size as the input image + transparent_overlay = np.zeros((image.height, image.width, 4), dtype=np.uint8) + + # Set the red, green, and blue channels to some color (e.g., red) + transparent_overlay[:, :, 0] = 255 # Red channel + transparent_overlay[:, :, 1] = 0 # Green channel + transparent_overlay[:, :, 2] = 0 # Blue channel + + # Set the alpha channel to apply transparency from the mask + transparent_overlay[:, :, 3] = alpha_channel + + # Convert the overlay to an Image object + overlay_image = Image.fromarray(transparent_overlay, "RGBA") + + # Composite the overlay with the original image + image = Image.alpha_composite(image, overlay_image) + + # Save the resulting image + image.save(output_path, "PNG") + +# Function to save a binary mask as an image +def save_binary_mask(mask, output_path): + # Convert the binary mask to a PIL Image object + # Convert the mask to uint8 type (0 or 255) + mask_image = Image.fromarray((mask * 255).astype(np.uint8)) # Multiply by 255 to make 1 white and 0 black + + # Save the mask as an image (you can choose PNG, JPEG, etc.) + mask_image.save(output_path) + + +model = LangSAM() +image_pil = Image.open("./assets/source_0.jpg").convert("RGB") +text_prompt = "green basket in the middle." +results = model.predict([image_pil], [text_prompt]) + +masks = results[0]["masks"] +print(masks.shape) +assert masks.shape[0] == 1 + +save_binary_mask(masks[0], f"assets/outputs/basket_0.png") \ No newline at end of file diff --git a/data/sam_mask/lang-segment-anything/.dockerignore b/data/sam_mask/lang-segment-anything/.dockerignore new file mode 100644 index 0000000..1d17dae --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.dockerignore @@ -0,0 +1 @@ +.venv diff --git a/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/bugs.yaml b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/bugs.yaml new file mode 100644 index 0000000..21b736a --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/bugs.yaml @@ -0,0 +1,54 @@ +name: 🐛 Bugs +description: Report bugs +labels: ["bug"] +title: "Please read & provide the following" +body: + - type: markdown + attributes: + value: | + ## Instructions To Reproduce the 🐛 Bug: + + 1. Background explanation + + - type: textarea + attributes: + label: "Full runnable code or full changes you made:" + description: Please provide the code or changes that led to the bug. + placeholder: | + ``` + ``` + validations: + required: true + + - type: textarea + attributes: + label: "What exact command you ran:" + description: Describe the exact command you ran that triggered the bug. + validations: + required: true + + - type: markdown + attributes: + value: | + 1. Please simplify the steps as much as possible so they do not require additional resources to + run, such as a private dataset. + + ## Expected behavior: + + If there are no obvious errors in "full logs" provided above, + please tell us the expected behavior. + + - type: textarea + attributes: + label: Expected behavior + description: Describe the expected behavior if the bug had not occurred. + validations: + required: true + + - type: checkboxes + attributes: + label: Environment + description: Indicate your environment details. + options: + - label: "I'm using the latest version!" + - label: "It's not a user-side mistake!" diff --git a/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/config.yml b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..c19e249 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,9 @@ +# require an issue template to be chosen +blank_issues_enabled: false + +# Unexpected behaviors & bugs are split to two templates. +# When they are one template, users think "it's not a bug" and don't choose the template. +# +# But the file name is still "unexpected-problems-bugs.md" so that old references +# to this issue template still works. +# It's ok since this template should be a superset of "bugs.md" (unexpected behaviors is a superset of bugs) diff --git a/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/documentation.yaml b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/documentation.yaml new file mode 100644 index 0000000..ea40997 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/documentation.yaml @@ -0,0 +1,26 @@ +name: 📚 Documentation Issue +description: Report a problem about existing documentation or comments. +labels: ["documentation"] +body: + - type: markdown + attributes: + value: | + ## 📚 Documentation Issue + + This issue category is for problems about existing documentation, not for asking how-to questions. + + - type: input + attributes: + label: Provide a link to existing documentation/comment + description: Paste the URL or path to the documentation or comment that has an issue. + placeholder: "https://example.com/docs/section" + validations: + required: true + + - type: textarea + attributes: + label: How should the above documentation/comment improve? + description: Describe the changes or improvements that should be made to the documentation or comment. + placeholder: "Please describe the suggested improvements here." + validations: + required: true diff --git a/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/feature-request.yaml b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/feature-request.yaml new file mode 100644 index 0000000..a4d5dfe --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/feature-request.yaml @@ -0,0 +1,30 @@ +name: 🚀 Feature Request +description: Suggest an improvement or new feature +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + ## 🚀 Feature + + A clear and concise description of the feature proposal. + + - type: textarea + attributes: + label: Motivation & Examples + description: | + Tell us why the feature is useful. + + Describe what the feature would look like if it is implemented. Best demonstrated using **code examples** in addition to words. + placeholder: | + + validations: + required: true + + - type: markdown + attributes: + value: | + ## Note + + We only consider adding new features if they are relevant to this library. + Consider if this new feature deserves to be here or should be a new library. diff --git a/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/refactoring.yaml b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/refactoring.yaml new file mode 100644 index 0000000..cee323e --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/ISSUE_TEMPLATE/refactoring.yaml @@ -0,0 +1,27 @@ +name: Refactor +description: Suggest a code refactor or deprecation +labels: ["needs triage"] +body: + - type: textarea + attributes: + label: Outline & Motivation + description: A clear and concise description of the refactor... + placeholder: | + Please outline the motivation for the proposal. + If this is related to another GitHub issue, please link it here + validations: + required: true + + - type: textarea + attributes: + label: Pitch + description: A clear and concise description of what you want to happen. + validations: + required: false + + - type: textarea + attributes: + label: Additional context + description: Add any other context or screenshots here. + validations: + required: false diff --git a/data/sam_mask/lang-segment-anything/.github/pull_request_template.md b/data/sam_mask/lang-segment-anything/.github/pull_request_template.md new file mode 100644 index 0000000..05699d1 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/pull_request_template.md @@ -0,0 +1,11 @@ +## Describe your changes and approach used + +Fixes #ISSUE-NUMBER + +## Checklist before requesting a review + +- [ ] I have performed a self-review of my code. +- [ ] I have performed linting on my code. +- [ ] I have linked the related issue. +- [ ] The code is documented accordingly. +- [ ] If it is a core feature, I have added thorough tests. diff --git a/data/sam_mask/lang-segment-anything/.github/workflows/pre-commit.yml b/data/sam_mask/lang-segment-anything/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..306a690 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.github/workflows/pre-commit.yml @@ -0,0 +1,29 @@ +name: pre-commit +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - name: Checkout branch + uses: actions/checkout@v3 + with: + fetch-depth: 0 + + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@v35 + with: + files: '**/*.py' + + - name: Set up Python [3.11] + uses: actions/setup-python@v1 + with: + python-version: "3.11" + + - name: Install pre-commit + run: pip install pre-commit + + - name: Run pre-commit on changed files + run: | + pre-commit run --files ${{ steps.changed-files.outputs.all_changed_files }} diff --git a/diffusion_policies/.gitignore b/data/sam_mask/lang-segment-anything/.gitignore old mode 100755 new mode 100644 similarity index 93% rename from diffusion_policies/.gitignore rename to data/sam_mask/lang-segment-anything/.gitignore index 23dbd4e..1e7dd58 --- a/diffusion_policies/.gitignore +++ b/data/sam_mask/lang-segment-anything/.gitignore @@ -1,23 +1,3 @@ -bin -logs -wandb -outputs -data -data_local -.vscode -_wandb - -**/.DS_Store - -fuse.cfg - -*.ai - -# Generation results -results/ - -ray/auth.json - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -25,6 +5,7 @@ __pycache__/ # C extensions *.so +*.pth # Distribution / packaging .Python @@ -121,6 +102,15 @@ celerybeat.pid # SageMath parsed files *.sage.py +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + # Spyder project settings .spyderproject .spyproject diff --git a/data/sam_mask/lang-segment-anything/.pre-commit-config.yaml b/data/sam_mask/lang-segment-anything/.pre-commit-config.yaml new file mode 100644 index 0000000..0d2cdbb --- /dev/null +++ b/data/sam_mask/lang-segment-anything/.pre-commit-config.yaml @@ -0,0 +1,66 @@ + +default_language_version: + python: python3.11 + + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.1.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-yaml + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: check-case-conflict + - id: check-added-large-files + args: ['--maxkb=500', '--enforce-all'] + - id: detect-private-key + + - repo: https://github.com/commitizen-tools/commitizen + rev: v2.27.0 + hooks: + - id: commitizen + stages: [commit-msg] + name: Format commit msg + + - repo: https://github.com/asottile/pyupgrade + rev: v2.31.0 + hooks: + - id: pyupgrade + args: [--py311-plus] + name: Upgrade code + + - repo: https://github.com/myint/docformatter + rev: 06907d0267368b49b9180eed423fae5697c1e909 # todo: fix for docformatter after last 1.7.5 + hooks: + - id: docformatter + args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120] + + - repo: https://github.com/asottile/yesqa + rev: v1.3.0 + hooks: + - id: yesqa + name: Unused noqa + + - repo: https://github.com/charliermarsh/ruff-pre-commit + # Ruff version. + rev: 'v0.0.235' + hooks: + - id: ruff + + - repo: https://github.com/asottile/blacken-docs + rev: v1.12.0 + hooks: + - id: blacken-docs + args: [--line-length=120] + additional_dependencies: [black==21.12b0] + + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.11 + hooks: + - id: mdformat + additional_dependencies: + - mdformat-gfm + - mdformat_frontmatter + exclude: CHANGELOG.md diff --git a/data/sam_mask/lang-segment-anything/Dockerfile b/data/sam_mask/lang-segment-anything/Dockerfile new file mode 100644 index 0000000..dc65317 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/Dockerfile @@ -0,0 +1,27 @@ +FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 + +# Set environment variables +ENV DEBIAN_FRONTEND=noninteractive +ENV GRADIO_SERVER_NAME="0.0.0.0" + +# Install necessary packages +RUN apt-get update && apt-get install -y \ + python3 \ + python3-pip \ + openssh-client \ + build-essential \ + git \ + libgl1-mesa-glx + +COPY . /lang-segment-anything + +# Install dependencies +WORKDIR /lang-segment-anything +RUN pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt + +EXPOSE 8000 + +# Entry point +CMD ["python3", "app.py"] + +RUN apt-get update && apt-get install -y libglib2.0-0 diff --git a/data/sam_mask/lang-segment-anything/LICENSE b/data/sam_mask/lang-segment-anything/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Built on the recently released Meta model, Segment Anything Model 2, and the GroundingDINO detection model, it's an easy-to-use and effective tool for object detection and image segmentation. + +![person.png](/assets/outputs/person.png) + +## Features + +- Zero-shot text-to-bbox approach for object detection. +- GroundingDINO detection model integration. +- SAM 2.1 +- Batch inference support. +- Easy endpoint deployment using the Lightning AI litserve platform. +- Customizable text prompts for precise object segmentation. + +## Getting Started + +### Prerequisites + +- Python 3.10 or higher + +### Installation + +#### Installing PyTorch Dependencies + +Before installing `lang-sam`, please install PyTorch using the following command: + +```bash + +pip install torch==2.4.1 torchvision==0.19.1 --extra-index-url https://download.pytorch.org/whl/cu124 + +``` + +```bash + +pip install -U git+https://github.com/luca-medeiros/lang-segment-anything.git + +``` + +Or +Clone the repository and install the required packages: + +```bash + +git clone https://github.com/luca-medeiros/lang-segment-anything && cd lang-segment-anything +pip install -e . + +``` + +#### Docker Installation + +Build and run the image. + +```bash + +git clone https://github.com/luca-medeiros/lang-segment-anything && cd lang-segment-anything +docker build --tag lang-segment-anything:latest . +docker run --gpus all -p 8000:8000 lang-segment-anything:latest + +``` + +### Usage + +To run the gradio APP: + +`python app.py` +And open `http://0.0.0.0:8000/gradio` + +Use as a library: + +```python +from PIL import Image +from lang_sam import LangSAM + +model = LangSAM() +image_pil = Image.open("./assets/car.jpeg").convert("RGB") +text_prompt = "wheel." +results = model.predict([image_pil], [text_prompt]) +``` + +If desired, arguments below can be passed for offline operations: +```python +LangSAM(sam_ckpt_path, # path for segment anything model + gdino_model_ckpt_path, # path for grounding dino's model checkpoint + gdino_processor_ckpt_path # path for grounding dino's processor checkpoint +) +``` + +## Examples + +![car.png](/assets/outputs/car.png) + +![fruits.png](/assets/outputs/fruits.png) + +## Acknowledgments + +This project is based on/used the following repositories: + +- [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) +- [Segment-Anything](https://github.com/facebookresearch/segment-anything-2) +- [LitServe](https://github.com/Lightning-AI/LitServe/) +- [Supervision](https://github.com/roboflow/supervision) + +## License + +This project is licensed under the Apache 2.0 License diff --git a/diffusion_policies/diffusion_policies/dataset/__init__.py b/data/sam_mask/lang-segment-anything/[2600:1f18:2148:bc01:9e79:39e7:91ec:665c]:443: old mode 100755 new mode 100644 similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/__init__.py rename to data/sam_mask/lang-segment-anything/[2600:1f18:2148:bc01:9e79:39e7:91ec:665c]:443: diff --git a/data/sam_mask/lang-segment-anything/app.py b/data/sam_mask/lang-segment-anything/app.py new file mode 100644 index 0000000..6f10a74 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/app.py @@ -0,0 +1,98 @@ +import os +from io import BytesIO + +import gradio as gr +import requests +from PIL import Image + +from lang_sam import SAM_MODELS +from lang_sam.server import PORT, server + + +def inference(sam_type, box_threshold, text_threshold, image, text_prompt): + """Gradio function that makes a request to the /predict LitServe endpoint.""" + url = f"http://localhost:{PORT}/predict" # Adjust port if needed + + # Prepare the multipart form data + with open(image, "rb") as img_file: + files = { + "image": img_file, + } + data = { + "sam_type": sam_type, + "box_threshold": str(box_threshold), + "text_threshold": str(text_threshold), + "text_prompt": text_prompt, + } + + try: + response = requests.post(url, files=files, data=data) + except Exception as e: + print(f"Request failed: {e}") + return None + + if response.status_code == 200: + try: + output_image = Image.open(BytesIO(response.content)).convert("RGB") + return output_image + except Exception as e: + print(f"Failed to process response image: {e}") + return None + else: + print(f"Request failed with status code {response.status_code}: {response.text}") + return None + + +with gr.Blocks(title="lang-sam") as blocks: + with gr.Row(): + sam_model_choices = gr.Dropdown(choices=list(SAM_MODELS.keys()), label="SAM Model", value="sam2.1_hiera_small") + box_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.3, label="Box Threshold") + text_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.25, label="Text Threshold") + with gr.Row(): + image_input = gr.Image(type="filepath", label="Input Image") + output_image = gr.Image(type="pil", label="Output Image") + text_prompt = gr.Textbox(lines=1, label="Text Prompt") + + submit_btn = gr.Button("Run Prediction") + + submit_btn.click( + fn=inference, + inputs=[sam_model_choices, box_threshold, text_threshold, image_input, text_prompt], + outputs=output_image, + ) + + examples = [ + [ + "sam2.1_hiera_small", + 0.32, + 0.25, + os.path.join(os.path.dirname(__file__), "assets", "fruits.jpg"), + "kiwi. watermelon. blueberry.", + ], + [ + "sam2.1_hiera_small", + 0.3, + 0.25, + os.path.join(os.path.dirname(__file__), "assets", "car.jpeg"), + "wheel.", + ], + [ + "sam2.1_hiera_small", + 0.3, + 0.25, + os.path.join(os.path.dirname(__file__), "assets", "food.jpg"), + "food.", + ], + ] + + gr.Examples( + examples=examples, + inputs=[sam_model_choices, box_threshold, text_threshold, image_input, text_prompt], + outputs=output_image, + ) + +server.app = gr.mount_gradio_app(server.app, blocks, path="/gradio") + +if __name__ == "__main__": + print(f"Starting LitServe and Gradio server on port {PORT}...") + server.run(port=PORT) diff --git a/data/sam_mask/lang-segment-anything/assets/car.jpeg b/data/sam_mask/lang-segment-anything/assets/car.jpeg new file mode 100644 index 0000000..65a1c0a Binary files /dev/null and b/data/sam_mask/lang-segment-anything/assets/car.jpeg differ diff --git a/data/sam_mask/lang-segment-anything/client.py b/data/sam_mask/lang-segment-anything/client.py new file mode 100644 index 0000000..5edcbe9 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/client.py @@ -0,0 +1,7 @@ +# This file is auto-generated by LitServe. +# Disable auto-generation by setting `generate_client_file=False` in `LitServer.run()`. + +import requests + +response = requests.post("http://127.0.0.1:8000/predict", json={"input": 4.0}) +print(f"Status: {response.status_code}\nResponse:\n {response.text}") diff --git a/data/sam_mask/lang-segment-anything/get_mask_bbhl.py b/data/sam_mask/lang-segment-anything/get_mask_bbhl.py new file mode 100644 index 0000000..9595f94 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/get_mask_bbhl.py @@ -0,0 +1,73 @@ +# https://github.com/luca-medeiros/lang-segment-anything + +from PIL import Image +from lang_sam import LangSAM +import numpy as np +import os + + +# Function to apply transparent masks on the image +def save_mask_results(image, masks, output_path): + # Ensure the image is in RGBA mode (with transparency support) + image = image_pil.convert("RGBA") + + # Iterate over the binary masks + for mask in masks: + # Convert mask to the same size as the image + mask_resized = Image.fromarray(mask).resize(image.size, Image.NEAREST).convert("L") + + # Create an RGBA mask with the same size + mask_rgba = mask_resized.convert("RGBA") + + # Get the alpha channel from the mask to apply transparency + mask_rgba = np.array(mask_rgba) + alpha_channel = mask_rgba[:, :, 0] # Assuming the mask is single-channel + + # Create a new transparent image with the same size as the input image + transparent_overlay = np.zeros((image.height, image.width, 4), dtype=np.uint8) + + # Set the red, green, and blue channels to some color (e.g., red) + transparent_overlay[:, :, 0] = 255 # Red channel + transparent_overlay[:, :, 1] = 0 # Green channel + transparent_overlay[:, :, 2] = 0 # Blue channel + + # Set the alpha channel to apply transparency from the mask + transparent_overlay[:, :, 3] = alpha_channel + + # Convert the overlay to an Image object + overlay_image = Image.fromarray(transparent_overlay, "RGBA") + + # Composite the overlay with the original image + image = Image.alpha_composite(image, overlay_image) + + # Save the resulting image + image.save(output_path, "PNG") + +# Function to save a binary mask as an image +def save_binary_mask(mask, output_path): + # Ensure the output directory exists + os.makedirs(os.path.dirname(output_path), exist_ok=True) + # Convert the binary mask to a PIL Image object + mask_image = Image.fromarray((mask * 255).astype(np.uint8)) # Multiply by 255 to make 1 white and 0 black + # Save the mask as an image + mask_image.save(output_path) + + +model = LangSAM() +image_paths = [ + f"/home/lemonhdl/workspace/DemoGen/data/sam_mask/beat_block_hammer_loop5/000000.jpg" + for i in range(1) +] +# text_prompt = "an iron hammer with black handle" +text_prompt = "a red cubic building block" + +for idx, image_path in enumerate(image_paths): + image_pil = Image.open(image_path).convert("RGB") + results = model.predict([image_pil], [text_prompt]) + masks = results[0]["masks"] + print(masks.shape) + # assert masks.shape[0] == 1 + save_binary_mask( + masks[0], + f"/home/lemonhdl/workspace/DemoGen/data/sam_mask/beat_block_hammer_loop5/0/block.jpg" + ) \ No newline at end of file diff --git a/data/sam_mask/lang-segment-anything/lang_sam/__init__.py b/data/sam_mask/lang-segment-anything/lang_sam/__init__.py new file mode 100644 index 0000000..9130847 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/__init__.py @@ -0,0 +1,2 @@ +from lang_sam.lang_sam import LangSAM +from lang_sam.models.sam import SAM_MODELS diff --git a/data/sam_mask/lang-segment-anything/lang_sam/lang_sam.py b/data/sam_mask/lang-segment-anything/lang_sam/lang_sam.py new file mode 100644 index 0000000..eea0606 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/lang_sam.py @@ -0,0 +1,83 @@ +import numpy as np +from PIL import Image + +from lang_sam.models.gdino import GDINO +from lang_sam.models.sam import SAM +from lang_sam.models.utils import DEVICE + + +class LangSAM: + def __init__(self, sam_type="sam2.1_hiera_small", sam_ckpt_path: str | None = None, gdino_model_ckpt_path: str | None = None, gdino_processor_ckpt_path: str | None = None, device=DEVICE): + self.sam_type = sam_type + + self.sam = SAM() + self.sam.build_model(sam_type, sam_ckpt_path, device=device) + self.gdino = GDINO() + self.gdino.build_model(model_ckpt_path=gdino_model_ckpt_path, processor_ckpt_path=gdino_processor_ckpt_path, device=device) + + def predict( + self, + images_pil: list[Image.Image], + texts_prompt: list[str], + box_threshold: float = 0.3, + text_threshold: float = 0.25, + ): + """Predicts masks for given images and text prompts using GDINO and SAM models. + + Parameters: + images_pil (list[Image.Image]): List of input images. + texts_prompt (list[str]): List of text prompts corresponding to the images. + box_threshold (float): Threshold for box predictions. + text_threshold (float): Threshold for text predictions. + + Returns: + list[dict]: List of results containing masks and other outputs for each image. + Output format: + [{ + "boxes": np.ndarray, + "scores": np.ndarray, + "masks": np.ndarray, + "mask_scores": np.ndarray, + }, ...] + """ + + gdino_results = self.gdino.predict(images_pil, texts_prompt, box_threshold, text_threshold) + all_results = [] + sam_images = [] + sam_boxes = [] + sam_indices = [] + for idx, result in enumerate(gdino_results): + result = {k: (v.cpu().numpy() if hasattr(v, "numpy") else v) for k, v in result.items()} + processed_result = { + **result, + "masks": [], + "mask_scores": [], + } + + if result["labels"]: + sam_images.append(np.asarray(images_pil[idx])) + sam_boxes.append(processed_result["boxes"]) + sam_indices.append(idx) + + all_results.append(processed_result) + if sam_images: + print(f"Predicting {len(sam_boxes)} masks") + masks, mask_scores, _ = self.sam.predict_batch(sam_images, xyxy=sam_boxes) + for idx, mask, score in zip(sam_indices, masks, mask_scores): + all_results[idx].update( + { + "masks": mask, + "mask_scores": score, + } + ) + print(f"Predicted {len(all_results)} masks") + return all_results + + +if __name__ == "__main__": + model = LangSAM() + out = model.predict( + [Image.open("./assets/food.jpg"), Image.open("./assets/car.jpeg")], + ["food", "car"], + ) + print(out) diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/bet/libraries/mingpt/__init__.py b/data/sam_mask/lang-segment-anything/lang_sam/models/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/bet/libraries/mingpt/__init__.py rename to data/sam_mask/lang-segment-anything/lang_sam/models/__init__.py diff --git a/data/sam_mask/lang-segment-anything/lang_sam/models/gdino.py b/data/sam_mask/lang-segment-anything/lang_sam/models/gdino.py new file mode 100644 index 0000000..81d6a6b --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/models/gdino.py @@ -0,0 +1,64 @@ +import torch +from PIL import Image +from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor + +from lang_sam.models.utils import DEVICE + + +class GDINO: + def build_model(self, model_ckpt_path: str | None = None, processor_ckpt_path: str | None = None, device=DEVICE): + if not model_ckpt_path or not processor_ckpt_path: # indicates that we somehow able to load the model from internet + model_id = "IDEA-Research/grounding-dino-base" + print(f"One or both local paths not provided. Loading from Hugging Face Hub: {model_id}") + self.processor = AutoProcessor.from_pretrained(model_id) + self.model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) + + else: + print(f"Attempting to load processor from local path: {processor_ckpt_path}") + self.processor = AutoProcessor.from_pretrained( + processor_ckpt_path, + local_files_only=True, # never goes online + trust_remote_code=True, # Grounding-DINO uses custom code + ) + print(f"Attempting to load model from local path: {model_ckpt_path}") + self.model = AutoModelForZeroShotObjectDetection.from_pretrained( + model_ckpt_path, + local_files_only=True, + trust_remote_code=True, + use_safetensors=True, + ).to(device) + + def predict( + self, + images_pil: list[Image.Image], + texts_prompt: list[str], + box_threshold: float, + text_threshold: float, + ) -> list[dict]: + texts_prompt = [prompt if prompt[-1] == "." else prompt + "." for prompt in texts_prompt] + inputs = self.processor( + images=images_pil, text=texts_prompt, padding=True, return_tensors="pt" + ).to(self.model.device) + with torch.no_grad(): + outputs = self.model(**inputs) + + results = self.processor.post_process_grounded_object_detection( + outputs, + inputs.input_ids, + box_threshold, + text_threshold=text_threshold, + target_sizes=[k.size[::-1] for k in images_pil], + ) + return results + + +if __name__ == "__main__": + gdino = GDINO() + gdino.build_model() + out = gdino.predict( + [Image.open("./assets/car.jpeg"), Image.open("./assets/car.jpeg")], + ["wheel", "wheel"], + 0.3, + 0.25, + ) + print(out) diff --git a/data/sam_mask/lang-segment-anything/lang_sam/models/sam.py b/data/sam_mask/lang-segment-anything/lang_sam/models/sam.py new file mode 100644 index 0000000..6cc8b30 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/models/sam.py @@ -0,0 +1,97 @@ +import numpy as np +import torch +from hydra import compose +from hydra.utils import instantiate +from omegaconf import OmegaConf +from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator +from sam2.sam2_image_predictor import SAM2ImagePredictor + +from lang_sam.models.utils import DEVICE + +SAM_MODELS = { + "sam2.1_hiera_tiny": { + "url": "https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt", + "config": "configs/sam2.1/sam2.1_hiera_t.yaml", + }, + "sam2.1_hiera_small": { + "url": "https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt", + "config": "configs/sam2.1/sam2.1_hiera_s.yaml", + }, + "sam2.1_hiera_base_plus": { + "url": "https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt", + "config": "configs/sam2.1/sam2.1_hiera_b+.yaml", + }, + "sam2.1_hiera_large": { + "url": "https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt", + "config": "configs/sam2.1/sam2.1_hiera_l.yaml", + }, +} + + +class SAM: + def build_model(self, sam_type: str, ckpt_path: str | None = None, device=DEVICE): + self.sam_type = sam_type + self.ckpt_path = ckpt_path + cfg = compose(config_name=SAM_MODELS[self.sam_type]["config"], overrides=[]) + OmegaConf.resolve(cfg) + self.model = instantiate(cfg.model, _recursive_=True) + self._load_checkpoint(self.model) + self.model = self.model.to(device) + self.model.eval() + self.mask_generator = SAM2AutomaticMaskGenerator(self.model) + self.predictor = SAM2ImagePredictor(self.model) + + def _load_checkpoint(self, model: torch.nn.Module): + if self.ckpt_path is None: + checkpoint_url = SAM_MODELS[self.sam_type]["url"] + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"] + else: + checkpoint_url = self.ckpt_path # Ensure checkpoint_url is defined + state_dict = torch.load(self.ckpt_path, map_location="cpu", weights_only=True)["model"] + try: + model.load_state_dict(state_dict, strict=True) + except Exception as e: + raise ValueError( + f"Problem loading SAM please make sure you have the right model type: {self.sam_type} \ + and a working checkpoint: {checkpoint_url}. Recommend deleting the checkpoint and \ + re-downloading it. Error: {e}" + ) + + def generate(self, image_rgb: np.ndarray) -> list[dict]: + """ + Output format + SAM2AutomaticMaskGenerator returns a list of masks, where each mask is a dict containing various information + about the mask: + + segmentation - [np.ndarray] - the mask with (W, H) shape, and bool type + area - [int] - the area of the mask in pixels + bbox - [List[int]] - the boundary box of the mask in xywh format + predicted_iou - [float] - the model's own prediction for the quality of the mask + point_coords - [List[List[float]]] - the sampled input point that generated this mask + stability_score - [float] - an additional measure of mask quality + crop_box - List[int] - the crop of the image used to generate this mask in xywh format + """ + + sam2_result = self.mask_generator.generate(image_rgb) + return sam2_result + + def predict(self, image_rgb: np.ndarray, xyxy: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + self.predictor.set_image(image_rgb) + masks, scores, logits = self.predictor.predict(box=xyxy, multimask_output=False) + if len(masks.shape) > 3: + masks = np.squeeze(masks, axis=1) + return masks, scores, logits + + def predict_batch( + self, + images_rgb: list[np.ndarray], + xyxy: list[np.ndarray], + ) -> tuple[list[np.ndarray], list[np.ndarray], list[np.ndarray]]: + self.predictor.set_image_batch(images_rgb) + + masks, scores, logits = self.predictor.predict_batch(box_batch=xyxy, multimask_output=False) + + masks = [np.squeeze(mask, axis=1) if len(mask.shape) > 3 else mask for mask in masks] + scores = [np.squeeze(score) for score in scores] + logits = [np.squeeze(logit, axis=1) if len(logit.shape) > 3 else logit for logit in logits] + return masks, scores, logits diff --git a/data/sam_mask/lang-segment-anything/lang_sam/models/utils.py b/data/sam_mask/lang-segment-anything/lang_sam/models/utils.py new file mode 100644 index 0000000..895b53d --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/models/utils.py @@ -0,0 +1,23 @@ +import logging + +import torch + + +def get_device_type() -> str: + if torch.backends.mps.is_available(): + return "mps" + elif torch.cuda.is_available(): + return "cuda" + else: + logging.warning("No GPU found, using CPU instead") + return "cpu" + + +device_type = get_device_type() +DEVICE = torch.device(device_type) + +if torch.cuda.is_available(): + torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__() + if torch.cuda.get_device_properties(0).major >= 8: + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True diff --git a/data/sam_mask/lang-segment-anything/lang_sam/server.py b/data/sam_mask/lang-segment-anything/lang_sam/server.py new file mode 100644 index 0000000..1acba82 --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/server.py @@ -0,0 +1,121 @@ +from io import BytesIO + +import litserve as ls +import numpy as np +from fastapi import Response, UploadFile +from PIL import Image + +from lang_sam import LangSAM +from lang_sam.utils import draw_image + +PORT = 8000 + + +class LangSAMAPI(ls.LitAPI): + def setup(self, device: str) -> None: + """Initialize or load the LangSAM model.""" + self.model = LangSAM(sam_type="sam2.1_hiera_small", device=device) + print("LangSAM model initialized.") + + def decode_request(self, request) -> dict: + """Decode the incoming request to extract parameters and image bytes. + + Assumes the request is sent as multipart/form-data with fields: + - sam_type: str + - box_threshold: float + - text_threshold: float + - text_prompt: str + - image: UploadFile + """ + # Extract form data + sam_type = request.get("sam_type") + box_threshold = float(request.get("box_threshold", 0.3)) + text_threshold = float(request.get("text_threshold", 0.25)) + text_prompt = request.get("text_prompt", "") + + # Extract image file + image_file: UploadFile = request.get("image") + if image_file is None: + raise ValueError("No image file provided in the request.") + + image_bytes = image_file.file.read() + + return { + "sam_type": sam_type, + "box_threshold": box_threshold, + "text_threshold": text_threshold, + "image_bytes": image_bytes, + "text_prompt": text_prompt, + } + + def predict(self, inputs: dict) -> dict: + """Perform prediction using the LangSAM model. + + Yields: + dict: Contains the processed output image. + """ + print("Starting prediction with parameters:") + print( + f"sam_type: {inputs['sam_type']}, \ + box_threshold: {inputs['box_threshold']}, \ + text_threshold: {inputs['text_threshold']}, \ + text_prompt: {inputs['text_prompt']}" + ) + + if inputs["sam_type"] != self.model.sam_type: + print(f"Updating SAM model type to {inputs['sam_type']}") + self.model.sam.build_model(inputs["sam_type"]) + + try: + image_pil = Image.open(BytesIO(inputs["image_bytes"])).convert("RGB") + except Exception as e: + raise ValueError(f"Invalid image data: {e}") + + results = self.model.predict( + images_pil=[image_pil], + texts_prompt=[inputs["text_prompt"]], + box_threshold=inputs["box_threshold"], + text_threshold=inputs["text_threshold"], + ) + results = results[0] + + if not len(results["masks"]): + print("No masks detected. Returning original image.") + return {"output_image": image_pil} + + # Draw results on the image + image_array = np.asarray(image_pil) + output_image = draw_image( + image_array, + results["masks"], + results["boxes"], + results["scores"], + results["labels"], + ) + output_image = Image.fromarray(np.uint8(output_image)).convert("RGB") + + return {"output_image": output_image} + + def encode_response(self, output: dict) -> Response: + """Encode the prediction result into an HTTP response. + + Returns: + Response: Contains the processed image in PNG format. + """ + try: + image = output["output_image"] + buffer = BytesIO() + image.save(buffer, format="PNG") + buffer.seek(0) + return Response(content=buffer.getvalue(), media_type="image/png") + except StopIteration: + raise ValueError("No output generated by the prediction.") + + +lit_api = LangSAMAPI() +server = ls.LitServer(lit_api) + + +if __name__ == "__main__": + print(f"Starting LitServe and Gradio server on port {PORT}...") + server.run(port=PORT) diff --git a/data/sam_mask/lang-segment-anything/lang_sam/utils.py b/data/sam_mask/lang-segment-anything/lang_sam/utils.py new file mode 100644 index 0000000..fef18ed --- /dev/null +++ b/data/sam_mask/lang-segment-anything/lang_sam/utils.py @@ -0,0 +1,99 @@ +import cv2 +import numpy as np +import supervision as sv +from PIL import Image + +MIN_AREA = 100 + + +def load_image(image_path: str): + return Image.open(image_path).convert("RGB") + + +def draw_image(image_rgb, masks, xyxy, probs, labels): + box_annotator = sv.BoxCornerAnnotator() + label_annotator = sv.LabelAnnotator() + mask_annotator = sv.MaskAnnotator() + # Create class_id for each unique label + unique_labels = list(set(labels)) + class_id_map = {label: idx for idx, label in enumerate(unique_labels)} + class_id = [class_id_map[label] for label in labels] + + # Add class_id to the Detections object + detections = sv.Detections( + xyxy=xyxy, + mask=masks.astype(bool), + confidence=probs, + class_id=np.array(class_id), + ) + annotated_image = box_annotator.annotate(scene=image_rgb.copy(), detections=detections) + annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections, labels=labels) + annotated_image = mask_annotator.annotate(scene=annotated_image, detections=detections) + return annotated_image + + +def get_contours(mask): + if len(mask.shape) > 2: + mask = np.squeeze(mask, 0) + mask = mask.astype(np.uint8) + mask *= 255 + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + effContours = [] + for c in contours: + area = cv2.contourArea(c) + if area > MIN_AREA: + effContours.append(c) + return effContours + + +def contour_to_points(contour): + pointsNum = len(contour) + contour = contour.reshape(pointsNum, -1).astype(np.float32) + points = [point.tolist() for point in contour] + return points + + +def generate_labelme_json(binary_masks, labels, image_size, image_path=None): + """Generate a LabelMe format JSON file from binary mask tensor. + + Args: + binary_masks: Binary mask tensor of shape [N, H, W]. + labels: List of labels for each mask. + image_size: Tuple of (height, width) for the image size. + image_path: Path to the image file (optional). + + Returns: + A dictionary representing the LabelMe JSON file. + """ + num_masks = binary_masks.shape[0] + binary_masks = binary_masks.numpy() + + json_dict = { + "version": "4.5.6", + "imageHeight": image_size[0], + "imageWidth": image_size[1], + "imagePath": image_path, + "flags": {}, + "shapes": [], + "imageData": None, + } + + # Loop through the masks and add them to the JSON dictionary + for i in range(num_masks): + mask = binary_masks[i] + label = labels[i] + effContours = get_contours(mask) + + for effContour in effContours: + points = contour_to_points(effContour) + shape_dict = { + "label": label, + "line_color": None, + "fill_color": None, + "points": points, + "shape_type": "polygon", + } + + json_dict["shapes"].append(shape_dict) + + return json_dict diff --git a/data/sam_mask/lang-segment-anything/pyproject.toml b/data/sam_mask/lang-segment-anything/pyproject.toml new file mode 100644 index 0000000..8a83ebb --- /dev/null +++ b/data/sam_mask/lang-segment-anything/pyproject.toml @@ -0,0 +1,59 @@ +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "lang-sam" +version = "0.2.1" +description = "Language segment-anything" +readme = "README.md" +authors = [ + { name = "Luca Medeiros", email = "lucamedeiros@outlook.com" }, +] +requires-python = ">=3.10" +dynamic = ["dependencies"] + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements.txt"]} + +[tool.setuptools.packages.find] +where = ["."] +include = ["lang_sam", "lang_sam.*"] + +[tool.ruff] +target-version = "py311" +line-length = 120 +fix = true +select = [ + # https://github.com/charliermarsh/ruff#pyflakes-f + "F", # Pyflakes + # https://github.com/charliermarsh/ruff#pycodestyle-e-w + "E", # pycodestyle + "W", # Warning + # https://github.com/charliermarsh/ruff#flake8-comprehensions-c4 + # https://github.com/charliermarsh/ruff#mccabe-c90 + "C", # Complexity (mccabe+) & comprehensions + # https://github.com/charliermarsh/ruff#pyupgrade-up + "UP", # pyupgrade + # https://github.com/charliermarsh/ruff#isort-i + "I", # isort +] +ignore = [ + # https://github.com/charliermarsh/ruff#pycodestyle-e-w + "E402", # module level import not at top of file + # https://github.com/charliermarsh/ruff#pyupgrade-up + "UP006", # use-pep585-annotation + "UP007", # use-pep604-annotation + "E741", # Ambiguous variable name +] +[tool.ruff.per-file-ignores] +"__init__.py" = [ + "F401", # unused import + "F403", # star imports + ] + +[tool.ruff.mccabe] +max-complexity = 24 + +[tool.ruff.pydocstyle] +convention = "numpy" 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e375fb9..0000000 Binary files a/data/sam_mask/sauce/check/plate_0.jpg and /dev/null differ diff --git a/data/sam_mask/sauce/check/plate_1.jpg b/data/sam_mask/sauce/check/plate_1.jpg deleted file mode 100644 index 1eb7615..0000000 Binary files a/data/sam_mask/sauce/check/plate_1.jpg and /dev/null differ diff --git a/data/sam_mask/sauce/check/plate_2.jpg b/data/sam_mask/sauce/check/plate_2.jpg deleted file mode 100644 index f1bfecf..0000000 Binary files a/data/sam_mask/sauce/check/plate_2.jpg and /dev/null differ diff --git a/demo_generation/USAGE.md b/demo_generation/USAGE.md new file mode 100644 index 0000000..85097d5 --- /dev/null +++ b/demo_generation/USAGE.md @@ -0,0 +1,71 @@ +auto_pipeline 使用说明 +===================== + +概述 +---- +`auto_pipeline.py` 是一个交互式小工具，用来把单个 HDF5 源轨迹导出为可视化帧、把首帧复制到 `data/sam_mask//0/` 供手动制作 SAM 掩码、等待人工准备掩码后再继续进行 gen_demo 等后续步骤。 + +快速上手（推荐） +---------------- +1. 进入脚本目录： + +```bash +cd /home/liaohaoran/code/DemoGen/demo_generation +``` + +2. 激活 `demogen` 环境（已在 `~/.bashrc` 中添加 `doge` 快捷）： + +```bash +# 推荐：使用别名（已添加到 ~/.bashrc），在一个新的交互 shell 中运行： +doge +# 或者手动激活： +# conda activate demogen +``` + +3. 运行交互脚本： + +```bash +python3 auto_pipeline.py +``` + +脚本流程（简要） +---------------- +- 脚本会询问一个源 HDF5 的绝对路径（例如 `/path/to/episode0.hdf5`）。 +- 导出该 HDF5 中的 RGB 帧到 HDF5 同目录（文件名 `000000.jpg`, `000001.jpg`, ...）。 +- 将首帧复制到 `DemoGen/data/sam_mask//0/`，等待你在该目录下手动放置/编辑掩码文件（PNG/JPG）。脚本会在检测到掩码后继续。 +- 脚本会暂停，等待你准备好 `demogen` 的配置（config）并按回车继续。 +- 可在交互中提供用于运行 `gen_demo` 的命令，脚本会在该目录内执行该命令（注意：脚本不会自动修改你的 config）。 +- 运行完成后，脚本会尝试根据生成目录写 `loop_times.txt`，并可选择调用描述生成脚本。 + +注意事项 +-------- +- 掩码步骤为手动交互：当前脚本不再尝试自动运行 `get_mask.py`（用户可手动运行或在准备目录置入掩码文件）。 +- 若想恢复自动化 mask（需安装额外依赖，如 `lang_sam`），请确保 `demogen` 环境中已正确安装所有依赖。 +- 我已修复 `~/.bashrc` 中的语法错误；如遇到启动时的报错请先重新打开一个终端以加载修复后的配置。 + +示例（测试） +----------- +下面是在本仓库中用作快速测试的命令（示例文件 `test_dummy.h5` 已被本地用于验证）： + +```bash +# 在仓库的 demo_generation 目录下运行（会交互） +python3 auto_pipeline.py +# 当脚本询问 HDF5 路径时，输入： +# /home/liaohaoran/code/DemoGen/demo_generation/test_dummy.h5 +# 然后按多次回车以接受默认值并跳过 gen_demo 步骤 +``` + +常见问题 +-------- +- Q: 为什么脚本检测到的掩码是首帧（jpg）？ + A: 脚本只是检测目标目录内的图片文件以便给出提示。你可以覆盖该目录中的图片（用真实掩码文件替换）或手动放置掩码文件。 + +- Q: 我想脚本自动运行 `get_mask.py`，该怎么做？ + A: 我可以把 `get_mask.py` 改为接收输入/输出路径参数，并把 `auto_pipeline.py` 加回自动化选项；但这要求目标环境已正确安装 `lang_sam` 及其依赖（可能包括大型依赖，如 PyTorch）。如果需要，我可以帮你实现并执行安装（安装可能较耗时）。 + +文件位置 +-------- +- 脚本：`demo_generation/auto_pipeline.py` +- 使用说明：`demo_generation/USAGE.md` + +如需我把这段说明合并回仓库根 README 或创建 PR，请告诉我。 \ No newline at end of file diff --git a/demo_generation/auto_pipeline.py b/demo_generation/auto_pipeline.py new file mode 100644 index 0000000..9277d18 --- /dev/null +++ b/demo_generation/auto_pipeline.py @@ -0,0 +1,458 @@ +#!/usr/bin/env python3 +""" +auto_pipeline.py +----------------- +这个脚本是一个交互式的管道（pipeline）框架，用来把单个 HDF5 轨迹文件： + +- 导出 RGB 帧（命名为 6 位零填充的 jpg，例如 `000000.jpg`） +- 将首帧复制到 `data/sam_mask//0/`（与仓库中 `get_mask.py` 的约定路径一致） +- 调用 SAM mask 生成脚本（`get_mask.py`），并在此处暂停以便人工编辑/放置配置 +- 运行 `gen_demo.py`（由用户提供具体命令）来生成增强后的 episode +- 根据生成结果写入 `loop_times.txt`，并可选调用描述/指令生成脚本 + +注意：该脚本较为保守，不会自动修改你的 config 或强制安装依赖；对于 SAM 部分若缺少 `lang_sam`，请先安装或手动准备 mask 文件。 + +使用方式： + cd DemoGen/demo_generation + python3 auto_pipeline.py + +脚本采用交互方式询问 HDF5 路径、get_mask.py 路径以及 gen_demo 的运行命令。 +""" + +import os +import shutil +import subprocess +import h5py +import io +import numpy as np +from PIL import Image +import cv2 +import time +import shutil + + +def save_rgb_frames_from_h5(h5_path, out_dir, camera_preference=('head_camera', 'left_camera', 'right_camera')): + """ + 从 HDF5 中寻找 RGB 帧并保存为 6 位零填充的 JPG 文件。 + + 针对常见的数据布局做了几种查找策略： + 1. 优先查找 `observation/['rgb']`（按传入的 camera_preference 顺序）。 + 2. 若未命中，则在 `observation` 下查找任何包含 `rgb` 的子 group。 + 3. 最后回退到顶层的 `rgb` / `images` / `frames` 等候选名称。 + + 参数： + h5_path: HDF5 路径 + out_dir: 输出目录（会被创建） + camera_preference: 优先使用的相机组名顺序 + + 返回：保存的文件路径列表（按帧序） + """ + os.makedirs(out_dir, exist_ok=True) + saved = [] + + with h5py.File(h5_path, 'r') as f: + rgb_ds = None + + # 优先在 observation 下查找 camera.rgb + if 'observation' in f: + obs = f['observation'] + for cam in camera_preference: + if cam in obs and 'rgb' in obs[cam]: + rgb_ds = obs[cam]['rgb'] + break + # 回退：找任何包含 rgb 的 group + if rgb_ds is None: + for name, item in obs.items(): + if isinstance(item, h5py.Group) and 'rgb' in item: + rgb_ds = item['rgb'] + break + + # 更后备的顶层候选项 + if rgb_ds is None: + for candidate in ('rgb', 'images', 'frames'): + if candidate in f: + rgb_ds = f[candidate] + break + + if rgb_ds is None: + raise RuntimeError('在 HDF5 中未找到 rgb 数据。期望路径示例：observation//rgb 或 顶层 rgb。') + + # 逐帧读取并保存为 JPG + n = rgb_ds.shape[0] + for i in range(n): + v = rgb_ds[i] + out_path = os.path.join(out_dir, f"{i:06d}.jpg") + + # 有些 HDF5 存储的是已经编码的 bytes（例如 jpeg bytes），有些则是 HxWxC 的 numpy 数组 + if isinstance(v, (bytes, bytearray, np.bytes_)) or (hasattr(v, 'tobytes') and getattr(v, 'dtype', None) is not None and v.dtype.kind == 'S'): + # 尝试作为二进制图像解码（参考 hdf52rgb_all.py） + try: + # 将可能的 bytes-like 对象转换为 bytes + b = bytes(v) + # 用 OpenCV 解码（得到 BGR），然后转换为 RGB 保存，确保通道顺序正确 + arr = np.frombuffer(b, dtype=np.uint8) + cv_img = cv2.imdecode(arr, cv2.IMREAD_COLOR) + if cv_img is None: + # fallback: 使用 PIL 直接解码 + img = Image.open(io.BytesIO(b)).convert('RGB') + img.save(out_path, quality=95) + else: + # 严格遵循 hdf52rgb_all.py：直接保存 OpenCV 解码结果（不做通道重排） + Image.fromarray(cv_img).save(out_path, quality=95) + except Exception: + # 退回到把 v 当作数组处理 + arr = np.asarray(v) + Image.fromarray(arr).convert('RGB').save(out_path, quality=95) + else: + arr = np.asarray(v) + # 如果元素是 bytes 字符串数组 + if arr.dtype.kind == 'S': + try: + b = arr.tobytes() + arr2 = np.frombuffer(b, dtype=np.uint8) + cv_img = cv2.imdecode(arr2, cv2.IMREAD_COLOR) + if cv_img is None: + Image.open(io.BytesIO(b)).convert('RGB').save(out_path, quality=95) + else: + # 严格遵循 hdf52rgb_all.py：直接保存 OpenCV 解码结果（不做通道重排） + Image.fromarray(cv_img).save(out_path, quality=95) + except Exception: + Image.fromarray(np.frombuffer(arr, dtype=np.uint8)).convert('RGB').save(out_path, quality=95) + else: + # 正常数组 HxWxC + if arr.ndim == 3: + Image.fromarray(arr.astype('uint8')).convert('RGB').save(out_path, quality=95) + else: + raise RuntimeError('无法识别的 rgb 帧格式（既不是编码 bytes 也不是 HxWxC 数组）') + + saved.append(out_path) + + return saved + + +def copy_first_frame_to_sam(saved_frames, h5_path): + """ + 将导出的首帧复制到仓库约定的 sam_mask 路径： + DemoGen/demo_generation/data/sam_mask//0/ + + 这样 `get_mask.py` 在默认行为下就能找到要处理的首帧。 + """ + base = os.path.splitext(os.path.basename(h5_path))[0] + dest_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data', 'sam_mask', base, '0') + os.makedirs(dest_dir, exist_ok=True) + + if not saved_frames: + raise RuntimeError('没有可复制的帧，saved_frames 为空') + + shutil.copy2(saved_frames[0], os.path.join(dest_dir, os.path.basename(saved_frames[0]))) + return dest_dir + + +def run_get_mask_script(get_mask_py): + """ + 以子 shell 调用 `get_mask.py`。子 shell 会 `source ~/.bashrc`，以便可用 `doge` 等别名。 + + 返回值：脚本的返回码（0 表示成功） + """ + cmd = f"bash -lc 'source ~/.bashrc >/dev/null 2>&1; python3 {get_mask_py}'" + print('Running mask script:', cmd) + rc = subprocess.call(cmd, shell=True) + return rc + + +def write_looptimes_for_generated(gen_output_dir, out_loop_times_path): + """ + 遍历生成目录中的 `episode*.hdf5`，尝试读取 `loop_times` 并把它们写入一个单行的 `loop_times.txt` 文件（以空格分隔）。 + + 这是为了与上游使用 `loop_times.txt` 的流程兼容。 + """ + eps = sorted([p for p in os.listdir(gen_output_dir) if p.startswith('episode') and p.endswith('.hdf5')]) + loop_times = [] + + for ep in eps: + p = os.path.join(gen_output_dir, ep) + try: + with h5py.File(p, 'r') as f: + if 'loop_times' in f: + lt = f['loop_times'][:] + if np.isscalar(lt) or getattr(lt, 'shape', ()) == (): + loop_times.append(int(lt)) + else: + loop_times.append(int(lt[0])) + elif 'loop_times' in f.attrs: + loop_times.append(int(f.attrs['loop_times'])) + else: + loop_times.append(1) + except Exception: + # 任何读取失败都退回为 1，保证文件可写 + loop_times.append(1) + + with open(out_loop_times_path, 'w') as fh: + for lt in loop_times: + fh.write(f"{lt} ") + + return out_loop_times_path + + +def main(): + print('交互式 DemoGen 开始...') + + # 安全输入封装：捕获 Ctrl+C (KeyboardInterrupt) 并重新提示输入 + def safe_input(prompt=''): + while True: + try: + return input(prompt) + except KeyboardInterrupt: + # 首次 Ctrl+C：提示并等待 1 秒，在此期间若再次 Ctrl+C 则退出程序 + print('\n输入被中断（Ctrl+C）。若在 1 秒内再次按 Ctrl+C，则退出程序；或等待继续并重新输入...') + try: + time.sleep(1) + except KeyboardInterrupt: + print('\n检测到二次 Ctrl+C，退出程序') + raise + continue + except EOFError: + # 将 EOF (Ctrl+D) 视为空输入并返回空字符串 + print('\n检测到 EOF（Ctrl+D），视为空输入。') + return '' + + # 1) 询问 HDF5 路径 + h5_input = safe_input('请复制粘贴一条源 HDF5 文件的绝对路径（输入 skip 表示已完成此步并跳过；留空则退出）：').strip() + if not h5_input: + print('已中止') + return + + frames = [] + frames_dir = None + if h5_input.lower() == 'skip': + # 跳过 HDF5 导出，询问用户是否已有导出帧目录 + frames_dir = safe_input('你选择跳过 HDF5 导出。如果你已有导出的帧，请输入帧目录绝对路径（输入 skip 则继续跳过帧相关步骤）：').strip() + if frames_dir and frames_dir.lower() != 'skip': + if not os.path.exists(frames_dir): + print('指定的帧目录不存在：', frames_dir) + return + frames = sorted([os.path.join(frames_dir, f) for f in os.listdir(frames_dir) if f.lower().endswith(('.jpg', '.png', '.jpeg'))]) + if not frames: + print('在指定目录未找到图片文件，继续但没有可用帧') + else: + # frames_dir 为 skip 或为空，表示完全跳过帧相关的步骤 + frames = [] + # 询问用于后续写 loop_times 的 src_dir（默认取帧目录的父目录或当前目录） + if frames_dir: + default_src_dir = os.path.dirname(frames_dir) + else: + default_src_dir = os.getcwd() + src_dir = safe_input(f'请输入源数据根目录（用于写 loop_times，默认: {default_src_dir}）：').strip() or default_src_dir + h5_path = None + else: + h5_path = h5_input + if not os.path.exists(h5_path): + print('未找到文件：', h5_path) + return + src_dir = os.path.dirname(h5_path) + + # 根据 h5 路径推断 task_name 与 idx + # 例如: /.../beat_block_hammer_loop/loop1-8-all/data/episode0.hdf5 + # task_name = basename(third ancestor) = beat_block_hammer_loop + # idx = episode number extracted from filename (episode0 -> 0) + def _infer_task_and_idx(path): + p0 = os.path.dirname(path) # parent (e.g., .../data) + p1 = os.path.dirname(p0) # e.g., .../loop1-8-all + p2 = os.path.dirname(p1) # e.g., .../beat_block_hammer_loop + task_name = os.path.basename(p2) or os.path.basename(p1) or 'unknown' + base = os.path.splitext(os.path.basename(path))[0] + import re + m = re.search(r'episode(\d+)', base) + if m: + idx = int(m.group(1)) + else: + # fallback: try to find any digits in the basename + m2 = re.search(r'(\d+)', base) + idx = int(m2.group(1)) if m2 else 0 + return task_name, idx + + task_name, idx = _infer_task_and_idx(h5_path) + # 可视化输出目录：DemoGen/data/sam_mask///vis_all + vis_out_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data', 'sam_mask', task_name, str(idx), 'vis_all') + + # 2) 导出帧到 HDF5 同目录（避免额外路径管理） + # 2) 导出帧或使用已有帧 + print('正在将帧保存到可视化目录 (vis_all) 或使用已有帧...') + if h5_path: + try: + frames = save_rgb_frames_from_h5(h5_path, vis_out_dir) + print('逐帧rgb可视化已保存到：', vis_out_dir) + except Exception as e: + print('提取帧失败：', e) + return + else: + # h5 跳过的情况下，若 frames_dir 已设置则 frames 已被填充 + if not frames: + print('未使用 HDF5 也未提供帧目录，跳过与帧相关的步骤。') + + if frames: + print(f'已保存/找到帧数量: {len(frames)}，首帧: {frames[0]}') + + # 3) 把首帧复制到 sam_mask 的约定目录，供 get_mask.py 使用 + # 3) 把首帧复制到 sam_mask 的约定目录，供 get_mask.py 使用 + sam_dest = None + copy_prompt = safe_input('准备复制首帧到 sam_mask 文件夹以便 get_mask.py 使用（按 Enter 执行；输入 skip 表示已完成并跳过此步）：').strip() + if copy_prompt.lower() == 'skip': + print('已跳过复制首帧步骤。') + else: + if frames: + try: + sam_dest = copy_first_frame_to_sam(frames, h5_path if h5_path else frames[0]) + print('首帧已复制到', sam_dest) + except Exception as e: + print('复制首帧失败：', e) + # 继续，但 sam_dest 可能为 None + else: + # 没有可用帧 + provided = safe_input('当前没有可用帧，若你已手动复制首帧请输入该文件所在目录（或输入 skip 继续跳过）：').strip() + if provided and provided.lower() != 'skip': + if os.path.exists(provided): + sam_dest = provided + else: + print('指定路径不存在：', provided) + sam_dest = None + else: + print('跳过复制首帧。') + + # 4) SAM mask 步骤：纯交互式，让用户在指定目录准备好掩码 + # 说明：脚本会把首帧复制到 sam_dest（由 copy_first_frame_to_sam 返回）。 + # 用户需在该目录中手动放置掩码文件（PNG/JPG），准备好后按回车继续。 + print('\n下一步：准备 SAM 掩码（mask）') + print('首帧已复制到：', sam_dest) + default_mask_dir = sam_dest + mask_input = safe_input(f'请输入准备掩码的目录路径（默认: {default_mask_dir}；或输入 skip 表示掩码已准备好/跳过）：').strip() + if mask_input.lower() == 'skip': + mask_dir = None + print('跳过掩码准备步骤。') + else: + mask_dir = mask_input or default_mask_dir + if mask_dir is None: + print('未指定掩码目录，继续但不会检测掩码文件。') + else: + if not os.path.exists(mask_dir): + print('指定的掩码目录不存在，已创建：', mask_dir) + os.makedirs(mask_dir, exist_ok=True) + print('请在该目录放置掩码文件（例如 PNG/JPG），准备好后按回车继续。目录：', mask_dir) + safe_input() + + # 检查掩码目录中是否存在掩码文件（png/jpg）以便提示用户 + mask_files = [] + try: + for fname in os.listdir(mask_dir): + if fname.lower().endswith(('.png', '.jpg', '.jpeg')): + mask_files.append(os.path.join(mask_dir, fname)) + except Exception: + mask_files = [] + + if mask_files: + print(f'检测到 {len(mask_files)} 个掩码文件（示例: {mask_files[0]}）。') + else: + print('未检测到掩码文件。请确认你已将掩码放在目录中。') + + # 5) 暂停：让用户创建/调整 demogen 的 config 或其它手动必要文件 + cfg_input = safe_input('现在请为该源创建 demogen 配置并放置好，准备好后按回车继续（或输入 skip 表示已完成）：').strip() + if cfg_input.lower() == 'skip': + print('跳过 demogen 配置准备步骤。') + + # 6) 运行 gen_demo：使用仓库自带的 `gen_demo.sh`，并引导用户输入参数以减少拼写错误 + print('\n现在将使用仓库中的 `gen_demo.sh` 脚本来运行生成。') + print('如果你更倾向于直接提供完整命令，也可以直接输入（留空则使用交互式参数）') + direct_cmd = safe_input('若要直接提供完整命令请粘贴到此（输入 skip 跳过 gen_demo；留空使用交互式参数）：').strip() + demo_dir = os.path.dirname(__file__) + if direct_cmd.lower() == 'skip': + print('跳过 gen_demo 执行。') + elif direct_cmd: + full = f"bash -lc 'cd {demo_dir} && source ~/.bashrc >/dev/null 2>&1; {direct_cmd}'" + print('正在运行（直接命令）：', full) + subprocess.call(full, shell=True) + else: + # 基于文件结构提供合理默认值 + default_task = task_name + default_range = 'test' + default_mode = 'random' + default_n = '40' + default_render = 'True' + + task = safe_input(f'任务名 `task`（对应仓库中 config 名称，默认: {default_task}）：').strip() or default_task + gen_range = safe_input(f'生成范围 `gen_range`（例如: test，默认: {default_range}）：').strip() or default_range + gen_mode = safe_input(f'生成模式 `gen_mode`（例如: random 或 grid，默认: {default_mode}）：').strip() or default_mode + n_gen_per_source = safe_input(f'每源生成数量 `n_gen_per_source`（整数，默认: {default_n}）：').strip() or default_n + render_video = safe_input(f'是否渲染视频 `render_video`（True/False，默认: {default_render}）：').strip() or default_render + + # 最终命令使用仓库里的 `gen_demo.sh` wrapper + sh_cmd = f"sh gen_demo.sh {task} {gen_range} {gen_mode} {n_gen_per_source} {render_video}" + + # 优先使用 `conda run -n demogen` 来在指定 conda 环境中执行，避免找不到包 + # 如果系统 PATH 中找不到 conda，则回退到 eval/activate 的方式（会尝试 source ~/.bashrc） + if shutil.which('conda'): + print('检测到 `conda` 可用，使用 `conda run -n demogen` 执行命令。') + # 使用列表形式避免 shell 转义问题 + rc = subprocess.call(['conda', 'run', '-n', 'demogen', '--no-capture-output', 'bash', '-lc', sh_cmd]) + else: + full = f"bash -lc 'cd {demo_dir} && eval \"$(conda shell.bash hook)\" >/dev/null 2>&1 || true; source ~/.bashrc >/dev/null 2>&1; conda activate demogen >/dev/null 2>&1 && {sh_cmd}'" + print('未在 PATH 中找到 `conda`，尝试通过 source ~/.bashrc + conda activate 来激活环境。将要执行：', full) + rc = subprocess.call(full, shell=True) + + if rc != 0: + print(f'警告：gen_demo 执行返回非零退出码：{rc}。请在终端手动运行下面的命令以调试：') + if shutil.which('conda'): + print(f"conda run -n demogen bash -lc '{sh_cmd}'") + else: + print(f"cd {demo_dir} && eval \"$(conda shell.bash hook)\"; conda activate demogen; {sh_cmd}") + if rc != 0: + print(f'警告：gen_demo 脚本返回非零退出码：{rc}。如果是在未激活 conda 环境下运行，考虑手动运行：\n') + print(f' cd {demo_dir} && conda activate demogen && sh gen_demo.sh {task} {gen_range} {gen_mode} {n_gen_per_source} {render_video}') + + # 7) 后处理：尝试根据生成的 episode 写 loop_times.txt + gen_root = os.path.join(src_dir, 'datasets', 'generated') + gen_dirs = [] + if os.path.exists(gen_root): + gen_dirs = sorted([os.path.join(gen_root, d) for d in os.listdir(gen_root)], key=os.path.getmtime) + + if gen_dirs: + last = gen_dirs[-1] + if last.endswith('_episodes'): + ep_dir = last + else: + cand = [os.path.join(last, f) for f in os.listdir(last) if f.endswith('_episodes')] + ep_dir = cand[0] if cand else last + else: + ep_dir = os.path.join(src_dir, 'datasets', 'generated') + + # 7) 后处理：写 loop_times，可由用户跳过 + do_loops = safe_input(f'是否尝试写入 loop_times.txt（会在 {src_dir} 下写入；输入 skip 跳过，按回车执行）：').strip() + if do_loops.lower() == 'skip': + print('跳过写入 loop_times.txt。') + else: + print('尝试根据以下生成的 episode 写入 loop_times.txt：', ep_dir) + loop_times_path = os.path.join(src_dir, 'loop_times.txt') + try: + write_looptimes_for_generated(ep_dir, loop_times_path) + print('已写入 loop_times 到', loop_times_path) + except Exception as e: + print('写入 loop_times 失败：', e) + + # 8) 可选：调用描述/指令生成脚本 + do_desc = safe_input('是否运行指令生成（会调用 description/gen_episode_instructions.sh）（输入 skip 跳过；输入 y 运行）？[y/N]：').strip().lower() + if do_desc == 'skip' or do_desc == 'n' or do_desc == '': + print('跳过指令生成步骤。') + elif do_desc == 'y': + task_name = safe_input('输入 task_name（用于描述脚本）：').strip() + task_config = safe_input('输入 task_config（用于描述脚本）：').strip() + language_num = safe_input('输入 language_num（例如：1）：').strip() or '1' + # 说明：描述脚本位置相对于仓库结构，若你的实际路径不同，请手动调整 + desc_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'RoboTwin', 'description')) + cmd = f"bash -lc 'cd {desc_dir} && bash gen_episode_instructions.sh {task_name} {task_config} {language_num}'" + print('正在运行：', cmd) + subprocess.call(cmd, shell=True) + + print('管道执行完成。') + + +if __name__ == '__main__': + main() diff --git a/demo_generation/demo_generation/config/beat_block_hammer_loop1.yaml b/demo_generation/demo_generation/config/beat_block_hammer_loop1.yaml new file mode 100644 index 0000000..0671076 --- /dev/null +++ b/demo_generation/demo_generation/config/beat_block_hammer_loop1.yaml @@ -0,0 +1,48 @@ +_target_: demo_generation.demogen.DemoGen + +data_root: data + +source_name: beat_block_hammer_loop +task_n_object: 2 # 根据任务实际设置,这里是一个锤子和一个积木块 + +use_linear_interpolation: false +interpolate_step_size: 1 + +use_manual_parsing_frames: true +parsing_frames: + motion-1: 0 + skill-1: 8 + motion-2: 33 + skill-2: 37 + +mask_names: + object: hammer + target: block + +trans_range: + src: + object: [[0, 0, 0], [0, 0, 0]] + target: [[0, 0, 0], [0, 0, 0]] + test: + object: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + target: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + +generation: + range_name: test + n_gen_per_source: 16 + render_video: True + mode: grid + +# range_name 除了设置为 src，还可以设置为你自定义的标签，比如： + +# train：用于训练集的空间分布 +# test：用于测试集的空间分布 +# generalize、extrapolate：用于泛化或外推分布 +# custom1、custom2：任意自定义标签 +# 只要你在 trans_range 下定义了对应标签（如 trans_range.train），就可以在 range_name 里切换，实现不同空间分布的数据合成。例如： +# range_name: test +# trans_range: +# src: ... +# test: +# object: [[0.2, 0.2, 0], [0.3, 0.3, 0]] +# target: [[0.2, 0.2, 0], [0.3, 0.3, 0]] \ No newline at end of file diff --git a/demo_generation/demo_generation/config/beat_block_hammer_loop2.yaml b/demo_generation/demo_generation/config/beat_block_hammer_loop2.yaml new file mode 100644 index 0000000..b7cbb1d --- /dev/null +++ b/demo_generation/demo_generation/config/beat_block_hammer_loop2.yaml @@ -0,0 +1,49 @@ +_target_: demo_generation.demogen.DemoGen + +data_root: data + +source_name: beat_block_hammer_loop2 + +task_n_object: 2 # 根据任务实际设置,这里是一个锤子和一个积木块 + +use_linear_interpolation: false +interpolate_step_size: 1 + +use_manual_parsing_frames: true +parsing_frames: + motion-1: 0 + skill-1: 8 + motion-2: 29 + skill-2: 37 + +mask_names: + object: hammer + target: block + +trans_range: + src: + object: [[0, 0, 0], [0, 0, 0]] + target: [[0, 0, 0], [0, 0, 0]] + test: + object: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + target: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + +generation: + range_name: test + n_gen_per_source: 16 + render_video: True + mode: grid + +# range_name 除了设置为 src，还可以设置为你自定义的标签，比如： + +# train：用于训练集的空间分布 +# test：用于测试集的空间分布 +# generalize、extrapolate：用于泛化或外推分布 +# custom1、custom2：任意自定义标签 +# 只要你在 trans_range 下定义了对应标签（如 trans_range.train），就可以在 range_name 里切换，实现不同空间分布的数据合成。例如： +# range_name: test +# trans_range: +# src: ... +# test: +# object: [[0.2, 0.2, 0], [0.3, 0.3, 0]] +# target: [[0.2, 0.2, 0], [0.3, 0.3, 0]] \ No newline at end of file diff --git a/demo_generation/demo_generation/config/beat_block_hammer_loop3.yaml b/demo_generation/demo_generation/config/beat_block_hammer_loop3.yaml new file mode 100644 index 0000000..2a37f2f --- /dev/null +++ b/demo_generation/demo_generation/config/beat_block_hammer_loop3.yaml @@ -0,0 +1,49 @@ +_target_: demo_generation.demogen.DemoGen + +data_root: data + +source_name: beat_block_hammer_loop3 + +task_n_object: 2 # 根据任务实际设置,这里是一个锤子和一个积木块 + +use_linear_interpolation: false +interpolate_step_size: 1 + +use_manual_parsing_frames: true +parsing_frames: + motion-1: 0 + skill-1: 8 + motion-2: 25 + skill-2: 37 + +mask_names: + object: hammer + target: block + +trans_range: + src: + object: [[0, 0, 0], [0, 0, 0]] + target: [[0, 0, 0], [0, 0, 0]] + test: + object: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + target: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + +generation: + range_name: test + n_gen_per_source: 16 + render_video: True + mode: grid + +# range_name 除了设置为 src，还可以设置为你自定义的标签，比如： + +# train：用于训练集的空间分布 +# test：用于测试集的空间分布 +# generalize、extrapolate：用于泛化或外推分布 +# custom1、custom2：任意自定义标签 +# 只要你在 trans_range 下定义了对应标签（如 trans_range.train），就可以在 range_name 里切换，实现不同空间分布的数据合成。例如： +# range_name: test +# trans_range: +# src: ... +# test: +# object: [[0.2, 0.2, 0], [0.3, 0.3, 0]] +# target: [[0.2, 0.2, 0], [0.3, 0.3, 0]] \ No newline at end of file diff --git a/demo_generation/demo_generation/config/beat_block_hammer_loop4.yaml b/demo_generation/demo_generation/config/beat_block_hammer_loop4.yaml new file mode 100644 index 0000000..5916aa1 --- /dev/null +++ b/demo_generation/demo_generation/config/beat_block_hammer_loop4.yaml @@ -0,0 +1,49 @@ +_target_: demo_generation.demogen.DemoGen + +data_root: data + +source_name: beat_block_hammer_loop4 + +task_n_object: 2 # 根据任务实际设置,这里是一个锤子和一个积木块 + +use_linear_interpolation: false +interpolate_step_size: 1 + +use_manual_parsing_frames: true +parsing_frames: + motion-1: 0 + skill-1: 8 + motion-2: 30 + skill-2: 38 + +mask_names: + object: hammer + target: block + +trans_range: + src: + object: [[0, 0, 0], [0, 0, 0]] + target: [[0, 0, 0], [0, 0, 0]] + test: + object: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + target: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + +generation: + range_name: test + n_gen_per_source: 16 + render_video: True + mode: grid + +# range_name 除了设置为 src，还可以设置为你自定义的标签，比如： + +# train：用于训练集的空间分布 +# test：用于测试集的空间分布 +# generalize、extrapolate：用于泛化或外推分布 +# custom1、custom2：任意自定义标签 +# 只要你在 trans_range 下定义了对应标签（如 trans_range.train），就可以在 range_name 里切换，实现不同空间分布的数据合成。例如： +# range_name: test +# trans_range: +# src: ... +# test: +# object: [[0.2, 0.2, 0], [0.3, 0.3, 0]] +# target: [[0.2, 0.2, 0], [0.3, 0.3, 0]] \ No newline at end of file diff --git a/demo_generation/demo_generation/config/beat_block_hammer_loop5.yaml b/demo_generation/demo_generation/config/beat_block_hammer_loop5.yaml new file mode 100644 index 0000000..06435cf --- /dev/null +++ b/demo_generation/demo_generation/config/beat_block_hammer_loop5.yaml @@ -0,0 +1,49 @@ +_target_: demo_generation.demogen.DemoGen + +data_root: data + +source_name: beat_block_hammer_loop5 + +task_n_object: 2 # 根据任务实际设置,这里是一个锤子和一个积木块 + +use_linear_interpolation: false +interpolate_step_size: 1 + +use_manual_parsing_frames: true +parsing_frames: + motion-1: 0 + skill-1: 9 + motion-2: 30 + skill-2: 40 + +mask_names: + object: hammer + target: block + +trans_range: + src: + object: [[0, 0, 0], [0, 0, 0]] + target: [[0, 0, 0], [0, 0, 0]] + test: + object: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + target: [[-0.10, -0.10, 0], [0.10, 0.10, 0]] + +generation: + range_name: test + n_gen_per_source: 16 + render_video: True + mode: grid + +# range_name 除了设置为 src，还可以设置为你自定义的标签，比如： + +# train：用于训练集的空间分布 +# test：用于测试集的空间分布 +# generalize、extrapolate：用于泛化或外推分布 +# custom1、custom2：任意自定义标签 +# 只要你在 trans_range 下定义了对应标签（如 trans_range.train），就可以在 range_name 里切换，实现不同空间分布的数据合成。例如： +# range_name: test +# trans_range: +# src: ... +# test: +# object: [[0.2, 0.2, 0], [0.3, 0.3, 0]] +# target: [[0.2, 0.2, 0], [0.3, 0.3, 0]] \ No newline at end of file diff --git a/demo_generation/demo_generation/demogen.py b/demo_generation/demo_generation/demogen.py index 5f1097a..194d95d 100644 --- a/demo_generation/demo_generation/demogen.py +++ b/demo_generation/demo_generation/demogen.py @@ -1,5 +1,8 @@ # from re import T # from turtle import st +# 强制使用 Agg 后端，解决 FigureCanvasTkAgg 无 tstring_rgb 报错 +import matplotlib +matplotlib.use('Agg') from diffusion_policies.common.replay_buffer import ReplayBuffer # from regex import I # import pcd_visualizer @@ -40,15 +43,113 @@ def __init__(self, cfg): cprint("[NOTE] Rendering video is enabled. It takes ~10s to render a single generated trajectory.", "yellow") self.gen_mode = cfg.generation.mode - source_zarr = os.path.join(self.data_root, "datasets", "source", self.source_name + ".zarr") + source_dir = os.path.join(self.data_root, "datasets", "source") + source_zarr = os.path.join(source_dir, self.source_name + ".zarr") + # If exact path does not exist, try to fuzzy-match available zarr directories + if not os.path.exists(source_zarr): + try: + candidates = [d for d in os.listdir(source_dir) if self.source_name in d] + except Exception: + candidates = [] + if len(candidates) == 1: + matched = candidates[0] + source_zarr = os.path.join(source_dir, matched) + cprint(f"[INFO] source zarr not found at expected path. Using fuzzy match: {matched}", 'yellow') + elif len(candidates) > 1: + cprint(f"[ERROR] Multiple candidate zarrs found for source_name '{self.source_name}': {candidates}", 'red') + raise FileNotFoundError(f"Multiple candidate zarrs found for source_name '{self.source_name}': {candidates}") + else: + cprint(f"[ERROR] Source zarr not found: {source_zarr}", 'red') + # 如果是交互式 shell，让用户输入一个 HDF5 或 zarr 的绝对路径 + try: + import sys + if sys.stdin.isatty(): + user_path = input('未找到 source zarr。请输入 HDF5 (.h5/.hdf5) 或 zarr 目录的绝对路径，或直接回车放弃：').strip() + if not user_path: + raise FileNotFoundError(f"Source zarr not found: {source_zarr}") + if not os.path.exists(user_path): + cprint(f"[ERROR] 指定路径不存在: {user_path}", 'red') + raise FileNotFoundError(f"Specified path does not exist: {user_path}") + source_zarr = user_path + else: + raise FileNotFoundError(f"Source zarr not found: {source_zarr}") + except KeyboardInterrupt: + raise FileNotFoundError(f"Source zarr not found: {source_zarr}") + self._load_from_zarr(source_zarr) def _load_from_zarr(self, zarr_path): cprint(f"Loading data from {zarr_path}", "blue") - self.replay_buffer = ReplayBuffer.copy_from_path( - zarr_path, keys=['state', 'action', 'point_cloud']) + # Support both zarr and hdf5 files. If path ends with .hdf5 or .h5, + # read HDF5 and construct an in-memory ReplayBuffer-compatible dict. + if str(zarr_path).endswith('.hdf5') or str(zarr_path).endswith('.h5'): + try: + import h5py + except Exception: + raise RuntimeError('h5py is required to load .hdf5 files. Please install h5py in your environment.') + with h5py.File(os.path.expanduser(zarr_path), 'r') as f: + # Try multiple common names used across datasets/collectors + def _read_first(keys): + for k in keys: + if k in f: + try: + return f[k][:] + except Exception: + return None + return None + + # state can be 'agent_pos', 'state', or 'endpose' + state = _read_first(['agent_pos', 'state', 'endpose']) + if state is None: + raise KeyError('HDF5 file missing "agent_pos" or "state" or "endpose" dataset') + + # action can be 'action' or 'joint_action/vector' + action = _read_first(['action', 'joint_action/vector', 'joint_action/vector']) + if action is None: + # try to reconstruct an action if left/right arms exist + if 'joint_action' in f and isinstance(f['joint_action'], h5py.Group): + # try to concatenate available sub-datasets + parts = [] + for name in ['left_arm', 'left_gripper', 'right_arm', 'right_gripper', 'vector']: + if f['joint_action'].get(name) is not None: + parts.append(f['joint_action'][name][:]) + if parts: + try: + action = np.concatenate(parts, axis=-1) + except Exception: + action = None + if action is None: + raise KeyError('HDF5 file missing "action" or "joint_action/vector" dataset') + + # point cloud dataset names + point_cloud = _read_first(['point_cloud', 'pointcloud', 'pointCloud']) + if point_cloud is None: + raise KeyError('HDF5 file missing "point_cloud" or "pointcloud" dataset') + + if 'episode_ends' in f: + episode_ends = f['episode_ends'][:] + else: + # fallback: single episode covering entire length + episode_ends = np.array([state.shape[0]], dtype=np.int64) + + root = { + 'meta': { + 'episode_ends': episode_ends + }, + 'data': { + 'state': state, + 'action': action, + 'point_cloud': point_cloud + } + } + self.replay_buffer = ReplayBuffer(root=root) + else: + # assume zarr + self.replay_buffer = ReplayBuffer.copy_from_path( + zarr_path, keys=['state', 'action', 'point_cloud']) + self.n_source_episodes = self.replay_buffer.n_episodes - self.demo_name = zarr_path.split("/")[-1].split(".")[0] + self.demo_name = os.path.basename(zarr_path).split(".")[0] def generate_trans_vectors(self, trans_range, n_demos, mode="random"): """ @@ -103,6 +204,18 @@ def get_objects_pcd_from_sam_mask(self, pcd, demo_idx, object_or_target="object" mask = imageio.imread(os.path.join(self.data_root, f"sam_mask/{self.source_name}/{demo_idx}/{self.mask_names[object_or_target]}.jpg")) mask = mask > 128 filtered_pcd = restore_and_filter_pcd(pcd, mask) + # If filter removed all points, dump debug info for inspection and warn + if filtered_pcd.size == 0: + debug_dir = os.path.join(self.data_root, 'debug_masks', self.source_name, str(demo_idx)) + os.makedirs(debug_dir, exist_ok=True) + try: + # save original pcd and mask for inspection + np.save(os.path.join(debug_dir, f"{object_or_target}_pcd_original.npy"), pcd) + # save mask image (as uint8) if available + imageio.imwrite(os.path.join(debug_dir, f"{object_or_target}_mask.png"), (mask.astype(np.uint8) * 255)) + except Exception: + pass + cprint(f"[WARN] Filtered {object_or_target} point cloud is empty for demo {demo_idx}. Saved debug files to {debug_dir}", 'yellow') return filtered_pcd def generate_demo(self): @@ -113,7 +226,7 @@ def generate_demo(self): else: raise NotImplementedError - def parse_frames_two_stage(self, pcds, demo_idx, ee_poses, distance_mode="ee2pcd", threshold_1=0.15, threshold_2=0.235, threshold_3=0.275,): + def parse_frames_two_stage(self, pcds, demo_idx, ee_poses, distance_mode="ee2pcd", threshold_1=0.8375, threshold_2=0.3, threshold_3=0.8376): """ There are two ways to parse the frames of whole trajectory into object-centric segments: (1) Either by comparing the distance between the end-effector and the object point cloud, (2) Or by manually specifying the frames when `self.use_manual_parsing_frames = True`. @@ -123,10 +236,15 @@ def parse_frames_two_stage(self, pcds, demo_idx, ee_poses, distance_mode="ee2pcd frame_idx on the left top of the video. """ assert distance_mode in ["ee2pcd", "pcd2pcd"] - stage = 1 + skill_1_frame = 0 + motion_2_frame = 0 + skill_2_frame = 0 + stage = 1 # 1: to skill-1, 2: to motion-2, 3: to skill-2 for i in range(pcds.shape[0]): object_pcd = self.get_objects_pcd_from_sam_mask(pcds[i], demo_idx, "object") target_pcd = self.get_objects_pcd_from_sam_mask(pcds[i], demo_idx, "target") + # [DEBUG] 输出已去除 + # ...existing code... if stage == 1: if distance_mode == "ee2pcd": if self.average_distance_to_point_cloud(ee_poses[i], object_pcd) <= threshold_1: @@ -171,7 +289,6 @@ def parse_frames_two_stage(self, pcds, demo_idx, ee_poses, distance_mode="ee2pcd if self.chamfer_distance(ee_obj_pcd, target_pcd) <= threshold_3: skill_2_frame = i break - print(f"Stage 1: {skill_1_frame}, Pre-2: {motion_2_frame}, Stage 2: {skill_2_frame}") return skill_1_frame, motion_2_frame, skill_2_frame @@ -216,10 +333,15 @@ def check_fourth_power(arr): else: ee_poses = source_demo["state"][:, :3] skill_1_frame, motion_2_frame, skill_2_frame = self.parse_frames_two_stage(pcds, i, ee_poses) - # print(f"Skill-1: {skill_1_frame}, Motion-2: {motion_2_frame}, Skill-2: {skill_2_frame}") + print(f"Skill-1: {skill_1_frame}, Motion-2: {motion_2_frame}, Skill-2: {skill_2_frame}") pcd_obj = self.get_objects_pcd_from_sam_mask(pcds[0], i, "object") pcd_tar = self.get_objects_pcd_from_sam_mask(pcds[0], i, "target") + # If either object or target point cloud is empty after masking, skip this source demo + if pcd_obj is None or pcd_obj.shape[0] == 0 or pcd_tar is None or pcd_tar.shape[0] == 0: + cprint(f"[WARN] Skipping source demo {i} because object/target point cloud is empty after SAM mask.", 'yellow') + continue + obj_bbox = self.pcd_bbox(pcd_obj) tar_bbox = self.pcd_bbox(pcd_tar) @@ -558,52 +680,91 @@ def _examine_actions(self, demo_trajectory): print(f"inensity: {vfunc(intensity)}") def save_episodes(self, generated_episodes, save_dir): - os.makedirs(save_dir, exist_ok=True) - cprint(f"Saving data to {save_dir}", "green") - # self.replay_buffer.save_to_store(save_dir) - - zarr_root = zarr.group(save_dir) - zarr_data = zarr_root.create_group('data') - zarr_meta = zarr_root.create_group('meta') - state_arrays = np.concatenate([ep["state"] for ep in generated_episodes], axis=0) - point_cloud_arrays = np.concatenate([ep["point_cloud"] for ep in generated_episodes], axis=0) - action_arrays = np.concatenate([ep["action"] for ep in generated_episodes], axis=0) - episode_ends = [] - count = 0 - for ep in generated_episodes: - count += len(ep["state"]) - episode_ends.append(count) - episode_ends_arrays = np.array(episode_ends) - print(episode_ends_arrays) - - compressor = zarr.Blosc(cname='zstd', clevel=3, shuffle=1) - state_chunk_size = (100, state_arrays.shape[1]) - point_cloud_chunk_size = (100, point_cloud_arrays.shape[1], point_cloud_arrays.shape[2]) - action_chunk_size = (100, action_arrays.shape[1]) - zarr_data.create_dataset('agent_pos', data=state_arrays, chunks=state_chunk_size, dtype='float32', overwrite=True, compressor=compressor) - zarr_data.create_dataset('point_cloud', data=point_cloud_arrays, chunks=point_cloud_chunk_size, dtype='float32', overwrite=True, compressor=compressor) - zarr_data.create_dataset('action', data=action_arrays, chunks=action_chunk_size, dtype='float32', overwrite=True, compressor=compressor) - zarr_meta.create_dataset('episode_ends', data=episode_ends_arrays, dtype='int64', overwrite=True, compressor=compressor) - - cprint(f'-'*50, 'cyan') - # print shape - cprint(f'point_cloud shape: {point_cloud_arrays.shape}, range: [{np.min(point_cloud_arrays)}, {np.max(point_cloud_arrays)}]', 'green') - cprint(f'state shape: {state_arrays.shape}, range: [{np.min(state_arrays)}, {np.max(state_arrays)}]', 'green') - cprint(f'action shape: {action_arrays.shape}, range: [{np.min(action_arrays)}, {np.max(action_arrays)}]', 'green') - cprint(f'Saved zarr file to {save_dir}', 'green') - - # save to hdf5 + # Save each generated episode using HDF5 layout compatible with source data import h5py - save_dir = save_dir.replace('.zarr', '.hdf5') - with h5py.File(save_dir, 'w') as f: - f.create_dataset('agent_pos', data=state_arrays, compression='gzip') - f.create_dataset('point_cloud', data=point_cloud_arrays, compression='gzip') - f.create_dataset('action', data=action_arrays, compression='gzip') - f.create_dataset('episode_ends', data=episode_ends_arrays, compression='gzip') - cprint(f'Saved hdf5 file to {save_dir}', 'green') + base_dir = save_dir.replace('.zarr', '_episodes') + os.makedirs(base_dir, exist_ok=True) + cprint(f"Saving each episode to {base_dir}", "green") + # determine starting index by scanning existing files (so numbering is continuous) + import glob + existing = glob.glob(os.path.join(base_dir, 'episode*.hdf5')) + start_idx = 0 + if existing: + # parse existing indices like 'episode4.hdf5' or 'episode_4.hdf5' and continue after the max + try: + import re + nums = [] + for p in existing: + m = re.search(r'episode(?:_)?(\d+)\.hdf5$', os.path.basename(p)) + if m: + nums.append(int(m.group(1))) + if nums: + start_idx = max(nums) + 1 + else: + start_idx = len(existing) + except Exception: + start_idx = len(existing) + + for rel_idx, ep in enumerate(generated_episodes): + idx = start_idx + rel_idx + ep_agent_pos = np.array(ep["state"]) # shape: (T, state_dim) + ep_point_cloud = np.array(ep["point_cloud"]) # shape: (T, N, 6) + ep_action = np.array(ep["action"]) # shape: (T, action_dim) + T = ep_agent_pos.shape[0] + ep_episode_ends = np.array([T]) + ep_path = os.path.join(base_dir, f'episode{idx}.hdf5') + + # Create HDF5 with keys similar to source collector + with h5py.File(ep_path, 'w') as f: + # endpose / state + f.create_dataset('endpose', data=ep_agent_pos, compression='gzip') + + # joint_action group: prefer storing 'vector' to match source + ja_grp = f.create_group('joint_action') + try: + ja_grp.create_dataset('vector', data=ep_action, compression='gzip') + except Exception: + # fallback: store as plain dataset under joint_action + f.create_dataset('joint_action', data=ep_action, compression='gzip') + + # loop_counter and loop_times + loop_counter = np.arange(T, dtype=np.int64) + f.create_dataset('loop_counter', data=loop_counter, compression='gzip') + f.create_dataset('loop_times', data=np.array(T, dtype=np.int32)) + + # object_pos: placeholder (shape T x 7) + obj_grp = f.create_group('object_pos') + obj_grp.create_dataset('obj0', data=np.zeros((T, 7), dtype=np.float32), compression='gzip') + + # observation group placeholders for head/left/right cameras + obs_grp = f.create_group('observation') + for cam in ['head_camera', 'left_camera', 'right_camera']: + cg = obs_grp.create_group(cam) + # cam2world_gl: (T,4,4) + cg.create_dataset('cam2world_gl', data=np.zeros((T, 4, 4), dtype=np.float32), compression='gzip') + # depth: placeholder (T, H, W) use (240,320) as in source if available + try: + cg.create_dataset('depth', data=np.zeros((T, 240, 320), dtype=np.float64), compression='gzip') + except Exception: + # in case too large, create a tiny placeholder + cg.create_dataset('depth', data=np.zeros((T, 4, 4), dtype=np.float64), compression='gzip') + cg.create_dataset('extrinsic_cv', data=np.zeros((T, 3, 4), dtype=np.float32), compression='gzip') + cg.create_dataset('intrinsic_cv', data=np.zeros((T, 3, 3), dtype=np.float32), compression='gzip') + # rgb: store empty bytes per-frame as placeholder + dt = h5py.string_dtype(encoding='utf-8') + rgb_ds = cg.create_dataset('rgb', shape=(T,), dtype=dt) + rgb_ds[:] = ['' for _ in range(T)] + + # pointcloud: name matches source ('pointcloud') and shape (T, N, 6) + f.create_dataset('pointcloud', data=ep_point_cloud.astype(np.float32), compression='gzip') + + # episode_ends + f.create_dataset('episode_ends', data=ep_episode_ends, compression='gzip') + + cprint(f'Saved: {ep_path}', 'cyan') @staticmethod - def point_cloud_to_video(point_clouds, output_file, fps=15, elev=30, azim=45): + def point_cloud_to_video(point_clouds, output_file, fps=15, *args, **kwargs): """ Converts a sequence of point cloud frames into a video. @@ -611,8 +772,6 @@ def point_cloud_to_video(point_clouds, output_file, fps=15, elev=30, azim=45): point_clouds (list): A list of (N, 6) numpy arrays representing the point clouds. output_file (str): The path to the output video file. fps (int, optional): The frames per second of the output video. Defaults to 15. - elev (float, optional): The elevation angle (in degrees) for the 3D view. Defaults to 30. - azim (float, optional): The azimuth angle (in degrees) for the 3D view. Defaults to 45. """ fig = plt.figure(figsize=(8, 6), dpi=300) ax = fig.add_subplot(111, projection='3d') @@ -627,38 +786,30 @@ def point_cloud_to_video(point_clouds, output_file, fps=15, elev=30, azim=45): for frame, points in enumerate(point_clouds): ax.clear() - color = points[:, 3:] / 255 + color = points[:, 3:] ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=color, marker='.') ax.set_box_aspect([1.6, 2.2, 1]) - # ax.set_box_aspect([1.6, 1.6, 1]) - # ax.set_aspect('equal') - ax.set_xlim(0.1, 0.8) - ax.set_ylim(-0.4, 0.6) - # ax.set_ylim(-0.3, 0.4) - ax.set_zlim(0.1, 0.4) - - x_ticks = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] - y_ticks = [-0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6] - # y_ticks = [-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4] - z_ticks = [0.1, 0.2, 0.3, 0.4] + # 坐标轴范围与zarr可视化一致，直接用所有点的实际范围 + ax.set_xlim(np.min(all_points[:, 0]), np.max(all_points[:, 0])) + ax.set_ylim(np.min(all_points[:, 1]), np.max(all_points[:, 1])) + ax.set_zlim(np.min(all_points[:, 2]), np.max(all_points[:, 2])) + ax.tick_params(axis='both', which='major', labelsize=8) - ax.xaxis.set_major_locator(FixedLocator(x_ticks)) - ax.yaxis.set_major_locator(FixedLocator(y_ticks)) - ax.zaxis.set_major_locator(FixedLocator(z_ticks)) - - formatter = FormatStrFormatter('%.1f') + formatter = FormatStrFormatter('%.2f') ax.xaxis.set_major_formatter(formatter) ax.yaxis.set_major_formatter(formatter) ax.zaxis.set_major_formatter(formatter) - ax.view_init(elev=elev, azim=azim) + # 使用matplotlib默认视角，不设置elev和azim + # ax.view_init() # 可选：如需强制默认视角可加此行 ax.text2D(0.05, 0.95, f'Frame: {frame}', transform=ax.transAxes, fontsize=14, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.7)) - + fig.canvas.draw() - img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) - img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + img = np.frombuffer(fig.canvas.buffer_rgba(), dtype=np.uint8) + img = img.reshape(fig.canvas.get_width_height()[::-1] + (4,)) + img = img[..., :3] # 只保留RGB通道 writer.append_data(img) writer.close() @@ -680,7 +831,23 @@ def pcd_divide(pcd, bbox_list): masks.append(np.logical_not(np.any(masks, axis=0))) for mask in masks: selected_pcds.append(pcd[mask]) - assert np.sum([len(p) for p in selected_pcds]) == pcd.shape[0] + total_selected = np.sum([len(p) for p in selected_pcds]) + if total_selected != pcd.shape[0]: + # Fallback: ensure no points are lost due to boundary conditions or boolean casting + cprint(f"[WARN] pcd_divide selected {total_selected} points but input has {pcd.shape[0]}. Adjusting masks to include remainder.", 'yellow') + # recompute masks but make them mutually exclusive: assign points to first matching bbox + masks = [] + assigned = np.zeros(pcd.shape[0], dtype=bool) + for bbox in bbox_list: + mask = np.all(pcd[:, :3] > bbox[0], axis=1) & np.all(pcd[:, :3] < bbox[1], axis=1) + # remove already assigned points to avoid double counting + mask = mask & (~assigned) + masks.append(mask) + assigned |= mask + # remainder (points not assigned to any bbox) + remainder = ~assigned + masks.append(remainder) + selected_pcds = [pcd[mask] for mask in masks] return selected_pcds @staticmethod @@ -742,7 +909,7 @@ def average_distance_to_point_cloud(target_point, point_cloud): return average_distance @staticmethod - def pcd_bbox(pcd, relax=True): + def pcd_bbox(pcd, relax=False): min_vals = np.min(pcd[:, :3], axis=0) max_vals = np.max(pcd[:, :3], axis=0) if relax: @@ -751,4 +918,3 @@ def pcd_bbox(pcd, relax=True): min_vals[2] = 0.0 return np.array([min_vals, max_vals]) - \ No newline at end of file diff --git a/demo_generation/demo_generation/mask_util.py b/demo_generation/demo_generation/mask_util.py index ee39042..80c0484 100644 --- a/demo_generation/demo_generation/mask_util.py +++ b/demo_generation/demo_generation/mask_util.py @@ -19,29 +19,44 @@ ori = R.from_quat(ROBOT2CAM_QUAT_INITIAL) * OFFSET_ORI_X * OFFSET_ORI_Y * OFFSET_ORI_Z ROBOT2CAM_QUAT = ori.as_quat() -robot2cam_mat = np.eye(4) -robot2cam_mat[:3, :3] = R.from_quat(ROBOT2CAM_QUAT).as_matrix() -robot2cam_mat[:3, 3] = ROBOT2CAM_POS +# 仿真环境 cam2world_gl 外参 +cam2world_gl = np.array([ + [1.0, 0.0, 0.0, -0.032], + [0.0, 0.8, -0.6, -0.45], + [0.0, 0.6, 0.8, 1.35], + [0.0, 0.0, 0.0, 1.0] +]) + +# 取逆得到 robot2cam_mat +def inverse_extrinsic_matrix(matrix): + R = matrix[:3, :3] + t = matrix[:3, 3] + R_inv = R.T + t_inv = -R_inv @ t + inv_matrix = np.eye(4) + inv_matrix[:3, :3] = R_inv + inv_matrix[:3, 3] = t_inv + return inv_matrix + +robot2cam_mat = inverse_extrinsic_matrix(cam2world_gl) -REALSENSE_SCALE = 0.0002500000118743628 -fx = 1354.796875 -fy = 1354.6197509765625 -cx = 986.0130004882812 -cy = 548.7333374023438 +REALSENSE_SCALE = 0.001 +# 新的 320x240 分辨率下的内参 +fx = 358.64218 +fy = 358.64218 +cx = 160.0 +cy = 120.0 intrinsic_matrix = np.array([ [fx, 0, cx], [ 0, fy, cy], [ 0, 0, 1] ]) - -T_link2viz = np.array([[0, 0, 1, 0], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 0, 1]]) +T_link2viz = np.eye(4) transform_realsense_util = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) +# 默认 image_size 改为 mask 分辨率 image_size = (1920, 1080) ############################################## @@ -61,26 +76,14 @@ def restore_original_pcd(transformed_points): xyz = transformed_points[:, :3] rgb = transformed_points[:, 3:] - robot2cam_extrinsic_matrix = np.eye(4) - robot2cam_extrinsic_matrix[:3, :3] = R.from_quat(ROBOT2CAM_QUAT).as_matrix() - robot2cam_extrinsic_matrix[:3, 3] = ROBOT2CAM_POS - - cam_T_robot = inverse_extrinsic_matrix(robot2cam_extrinsic_matrix) - viz_T_link = inverse_extrinsic_matrix(T_link2viz) - inverse_trans = inverse_extrinsic_matrix(transform_realsense_util) - - homogeneous_points = np.hstack((xyz, np.ones((xyz.shape[0], 1)))) - - restored_points = homogeneous_points.T - restored_points = cam_T_robot @ restored_points - restored_points = viz_T_link @ restored_points - restored_points = inverse_trans @ restored_points - restored_points = restored_points.T - - restored_xyz = restored_points[:, :3] - restored_xyz /= REALSENSE_SCALE - restored_points = np.hstack((restored_xyz, rgb)) - + # 先做相机外参逆变换 + points_hom = np.hstack((xyz, np.ones((xyz.shape[0], 1)))) + xyz_trans = (robot2cam_mat @ points_hom.T).T[:, :3] + # 再将 x 轴取反 + xyz_trans[:, 0] *= -1 + restored_points = np.hstack((xyz_trans, rgb)) + np.save('pcd_restore_debug.npy', restored_points) + print(f"[DEBUG] restore_original_pcd: saved to pcd_restore_debug.npy, shape: {restored_points.shape}, xyz min: {xyz_trans.min(axis=0)}, max: {xyz_trans.max(axis=0)}") return restored_points def project_points_to_image(point_cloud, K, R=np.eye(3), T=np.zeros(3)): @@ -96,30 +99,32 @@ def project_points_to_image(point_cloud, K, R=np.eye(3), T=np.zeros(3)): def filter_points_by_mask(points, mask, intrinsic_matrix, image_size): projected_points = project_points_to_image(points, intrinsic_matrix, R=np.eye(3), T=np.zeros(3)) - pixel_coords = np.floor(projected_points).astype(int) - + pixel_coords = np.floor(projected_points).astype(int) + # 调试：输出投影坐标和有效点统计 + print(f"[DEBUG] pixel_coords min: {pixel_coords.min(axis=0)}, max: {pixel_coords.max(axis=0)}") + print(f"[DEBUG] mask shape: {mask.shape}, image_size: {image_size}") valid_points = (pixel_coords[:, 0] >= 0) & (pixel_coords[:, 0] < image_size[0]) & \ (pixel_coords[:, 1] >= 0) & (pixel_coords[:, 1] < image_size[1]) - - mask_values = mask[pixel_coords[valid_points, 1], pixel_coords[valid_points, 0]] - + print(f"[DEBUG] valid_points count: {np.sum(valid_points)} / {len(points)}") + if np.sum(valid_points) > 0: + mask_values = mask[pixel_coords[valid_points, 1], pixel_coords[valid_points, 0]] + print(f"[DEBUG] mask_values True count: {np.sum(mask_values)} / {len(mask_values)}") + else: + mask_values = np.array([]) + print("[DEBUG] No valid points in mask range!") final_mask = np.zeros(len(points), dtype=bool) final_mask[valid_points] = mask_values - filtered_points = points[final_mask] - + print(f"[DEBUG] filtered_points shape: {filtered_points.shape}") return filtered_points def trans_pcd(points): - robot2cam_extrinsic_matrix = np.eye(4) - robot2cam_extrinsic_matrix[:3, :3] = R.from_quat(ROBOT2CAM_QUAT).as_matrix() - robot2cam_extrinsic_matrix[:3, 3] = ROBOT2CAM_POS - # scale + # 直接使用 robot2cam_mat 作为外参 points_xyz = points[..., :3] * REALSENSE_SCALE point_homogeneous = np.hstack((points_xyz, np.ones((points_xyz.shape[0], 1)))) point_homogeneous = transform_realsense_util @ point_homogeneous.T point_homogeneous = T_link2viz @ point_homogeneous - point_homogeneous = robot2cam_extrinsic_matrix @ point_homogeneous + point_homogeneous = robot2cam_mat @ point_homogeneous point_homogeneous = point_homogeneous.T point_xyz = point_homogeneous[..., :-1] @@ -128,7 +133,17 @@ def trans_pcd(points): return points def restore_and_filter_pcd(pcd_robot, mask, intrinsic_matrix=intrinsic_matrix, image_size=image_size): - pcd_cam = restore_original_pcd(pcd_robot) - filtered_points = filter_points_by_mask(pcd_cam, mask, intrinsic_matrix, image_size) - filtered_points = trans_pcd(filtered_points) - return filtered_points + # 只筛选首帧，其余帧直接返回原始点云 + if hasattr(restore_and_filter_pcd, 'frame_idx'): + restore_and_filter_pcd.frame_idx += 1 + else: + restore_and_filter_pcd.frame_idx = 0 + + if restore_and_filter_pcd.frame_idx == 0: + pcd_cam = restore_original_pcd(pcd_robot) + filtered_points = filter_points_by_mask(pcd_cam, mask, intrinsic_matrix, image_size) + filtered_points = trans_pcd(filtered_points) + return filtered_points + else: + # 非首帧直接返回原始点云（可根据实际需求调整） + return pcd_robot diff --git a/diffusion_policies/diffusion_policies/codecs/imagecodecs_numcodecs.py b/demo_generation/diffusion_policies/codecs/imagecodecs_numcodecs.py similarity index 100% rename from diffusion_policies/diffusion_policies/codecs/imagecodecs_numcodecs.py rename to demo_generation/diffusion_policies/codecs/imagecodecs_numcodecs.py diff --git a/diffusion_policies/diffusion_policies/common/checkpoint_util.py b/demo_generation/diffusion_policies/common/checkpoint_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/checkpoint_util.py rename to demo_generation/diffusion_policies/common/checkpoint_util.py diff --git a/diffusion_policies/diffusion_policies/common/gpu_sampler.py b/demo_generation/diffusion_policies/common/gpu_sampler.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/gpu_sampler.py rename to demo_generation/diffusion_policies/common/gpu_sampler.py diff --git a/diffusion_policies/diffusion_policies/common/json_logger.py b/demo_generation/diffusion_policies/common/json_logger.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/json_logger.py rename to demo_generation/diffusion_policies/common/json_logger.py diff --git a/diffusion_policies/diffusion_policies/common/logger_util.py b/demo_generation/diffusion_policies/common/logger_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/logger_util.py rename to demo_generation/diffusion_policies/common/logger_util.py diff --git a/diffusion_policies/diffusion_policies/common/model_util.py b/demo_generation/diffusion_policies/common/model_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/model_util.py rename to demo_generation/diffusion_policies/common/model_util.py diff --git a/diffusion_policies/diffusion_policies/common/normalize_util.py b/demo_generation/diffusion_policies/common/normalize_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/normalize_util.py rename to demo_generation/diffusion_policies/common/normalize_util.py diff --git a/diffusion_policies/diffusion_policies/common/pytorch_util.py b/demo_generation/diffusion_policies/common/pytorch_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/pytorch_util.py rename to demo_generation/diffusion_policies/common/pytorch_util.py diff --git a/diffusion_policies/diffusion_policies/common/replay_buffer.py b/demo_generation/diffusion_policies/common/replay_buffer.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/replay_buffer.py rename to demo_generation/diffusion_policies/common/replay_buffer.py diff --git a/diffusion_policies/diffusion_policies/common/robomimic_config_util.py b/demo_generation/diffusion_policies/common/robomimic_config_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/robomimic_config_util.py rename to demo_generation/diffusion_policies/common/robomimic_config_util.py diff --git a/diffusion_policies/diffusion_policies/common/robomimic_util.py b/demo_generation/diffusion_policies/common/robomimic_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/robomimic_util.py rename to demo_generation/diffusion_policies/common/robomimic_util.py diff --git a/diffusion_policies/diffusion_policies/common/sampler.py b/demo_generation/diffusion_policies/common/sampler.py similarity index 100% rename from diffusion_policies/diffusion_policies/common/sampler.py rename to demo_generation/diffusion_policies/common/sampler.py diff --git a/diffusion_policies/diffusion_policies/config/dp3.yaml b/demo_generation/diffusion_policies/config/dp3.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp3.yaml rename to demo_generation/diffusion_policies/config/dp3.yaml diff --git a/diffusion_policies/diffusion_policies/config/dp_clip.yaml b/demo_generation/diffusion_policies/config/dp_clip.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp_clip.yaml rename to demo_generation/diffusion_policies/config/dp_clip.yaml diff --git a/diffusion_policies/diffusion_policies/config/dp_dinov2.yaml b/demo_generation/diffusion_policies/config/dp_dinov2.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp_dinov2.yaml rename to demo_generation/diffusion_policies/config/dp_dinov2.yaml diff --git a/diffusion_policies/diffusion_policies/config/dp_r3m.yaml b/demo_generation/diffusion_policies/config/dp_r3m.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp_r3m.yaml rename to demo_generation/diffusion_policies/config/dp_r3m.yaml diff --git a/diffusion_policies/diffusion_policies/config/dp_resnet-in1k.yaml b/demo_generation/diffusion_policies/config/dp_resnet-in1k.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp_resnet-in1k.yaml rename to demo_generation/diffusion_policies/config/dp_resnet-in1k.yaml diff --git a/diffusion_policies/diffusion_policies/config/dp_resnet-scratch.yaml b/demo_generation/diffusion_policies/config/dp_resnet-scratch.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp_resnet-scratch.yaml rename to demo_generation/diffusion_policies/config/dp_resnet-scratch.yaml diff --git a/diffusion_policies/diffusion_policies/config/dp_vit-scratch.yaml b/demo_generation/diffusion_policies/config/dp_vit-scratch.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/dp_vit-scratch.yaml rename to demo_generation/diffusion_policies/config/dp_vit-scratch.yaml diff --git a/diffusion_policies/diffusion_policies/config/idp3.yaml b/demo_generation/diffusion_policies/config/idp3.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/idp3.yaml rename to demo_generation/diffusion_policies/config/idp3.yaml diff --git a/diffusion_policies/diffusion_policies/config/simple_dp3.yaml b/demo_generation/diffusion_policies/config/simple_dp3.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/simple_dp3.yaml rename to demo_generation/diffusion_policies/config/simple_dp3.yaml diff --git a/diffusion_policies/diffusion_policies/config/task/galaxea.yaml b/demo_generation/diffusion_policies/config/task/galaxea.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/task/galaxea.yaml rename to demo_generation/diffusion_policies/config/task/galaxea.yaml diff --git a/diffusion_policies/diffusion_policies/config/task/oyhand.yaml b/demo_generation/diffusion_policies/config/task/oyhand.yaml similarity index 100% rename from diffusion_policies/diffusion_policies/config/task/oyhand.yaml rename to demo_generation/diffusion_policies/config/task/oyhand.yaml diff --git a/demo_generation/diffusion_policies/dataset/__init__.py b/demo_generation/diffusion_policies/dataset/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/diffusion_policies/diffusion_policies/dataset/base_dataset.py b/demo_generation/diffusion_policies/dataset/base_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/base_dataset.py rename to demo_generation/diffusion_policies/dataset/base_dataset.py diff --git a/diffusion_policies/diffusion_policies/dataset/galaxea_dataset.py b/demo_generation/diffusion_policies/dataset/galaxea_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/galaxea_dataset.py rename to demo_generation/diffusion_policies/dataset/galaxea_dataset.py diff --git a/diffusion_policies/diffusion_policies/dataset/metaworld_image_dataset.py b/demo_generation/diffusion_policies/dataset/metaworld_image_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/metaworld_image_dataset.py rename to demo_generation/diffusion_policies/dataset/metaworld_image_dataset.py diff --git a/diffusion_policies/diffusion_policies/dataset/metaworld_pointcloud_dataset.py b/demo_generation/diffusion_policies/dataset/metaworld_pointcloud_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/metaworld_pointcloud_dataset.py rename to demo_generation/diffusion_policies/dataset/metaworld_pointcloud_dataset.py diff --git a/diffusion_policies/diffusion_policies/dataset/metaworld_state_dataset.py b/demo_generation/diffusion_policies/dataset/metaworld_state_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/metaworld_state_dataset.py rename to demo_generation/diffusion_policies/dataset/metaworld_state_dataset.py diff --git a/diffusion_policies/diffusion_policies/dataset/panda_dataset.py b/demo_generation/diffusion_policies/dataset/panda_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/panda_dataset.py rename to demo_generation/diffusion_policies/dataset/panda_dataset.py diff --git a/diffusion_policies/diffusion_policies/dataset/robosuite_pointcloud_dataset.py b/demo_generation/diffusion_policies/dataset/robosuite_pointcloud_dataset.py similarity index 100% rename from diffusion_policies/diffusion_policies/dataset/robosuite_pointcloud_dataset.py rename to demo_generation/diffusion_policies/dataset/robosuite_pointcloud_dataset.py diff --git a/diffusion_policies/diffusion_policies/env_runner/base_runner.py b/demo_generation/diffusion_policies/env_runner/base_runner.py similarity index 100% rename from diffusion_policies/diffusion_policies/env_runner/base_runner.py rename to 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rename from diffusion_policies/diffusion_policies/model_dp_umi/diffusion/positional_embedding.py rename to demo_generation/diffusion_policies/model_dp_umi/diffusion/positional_embedding.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/diffusion/transformer_for_action_diffusion.py b/demo_generation/diffusion_policies/model_dp_umi/diffusion/transformer_for_action_diffusion.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/diffusion/transformer_for_action_diffusion.py rename to demo_generation/diffusion_policies/model_dp_umi/diffusion/transformer_for_action_diffusion.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/diffusion/transformer_for_diffusion.py b/demo_generation/diffusion_policies/model_dp_umi/diffusion/transformer_for_diffusion.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/diffusion/transformer_for_diffusion.py rename to demo_generation/diffusion_policies/model_dp_umi/diffusion/transformer_for_diffusion.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/vision/crop_randomizer.py b/demo_generation/diffusion_policies/model_dp_umi/vision/crop_randomizer.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/vision/crop_randomizer.py rename to demo_generation/diffusion_policies/model_dp_umi/vision/crop_randomizer.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/vision/model_getter.py b/demo_generation/diffusion_policies/model_dp_umi/vision/model_getter.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/vision/model_getter.py rename to demo_generation/diffusion_policies/model_dp_umi/vision/model_getter.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/vision/multi_image_obs_encoder.py b/demo_generation/diffusion_policies/model_dp_umi/vision/multi_image_obs_encoder.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/vision/multi_image_obs_encoder.py rename to demo_generation/diffusion_policies/model_dp_umi/vision/multi_image_obs_encoder.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/vision/timm_obs_encoder.py b/demo_generation/diffusion_policies/model_dp_umi/vision/timm_obs_encoder.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/vision/timm_obs_encoder.py rename to demo_generation/diffusion_policies/model_dp_umi/vision/timm_obs_encoder.py diff --git a/diffusion_policies/diffusion_policies/model_dp_umi/vision/transformer_obs_encoder.py b/demo_generation/diffusion_policies/model_dp_umi/vision/transformer_obs_encoder.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_dp_umi/vision/transformer_obs_encoder.py rename to demo_generation/diffusion_policies/model_dp_umi/vision/transformer_obs_encoder.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/common/dict_of_tensor_mixin.py b/demo_generation/diffusion_policies/model_idp3/common/dict_of_tensor_mixin.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/common/dict_of_tensor_mixin.py rename to demo_generation/diffusion_policies/model_idp3/common/dict_of_tensor_mixin.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/common/lr_scheduler.py b/demo_generation/diffusion_policies/model_idp3/common/lr_scheduler.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/common/lr_scheduler.py rename to demo_generation/diffusion_policies/model_idp3/common/lr_scheduler.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/common/module_attr_mixin.py b/demo_generation/diffusion_policies/model_idp3/common/module_attr_mixin.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/common/module_attr_mixin.py rename to demo_generation/diffusion_policies/model_idp3/common/module_attr_mixin.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/common/normalizer.py b/demo_generation/diffusion_policies/model_idp3/common/normalizer.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/common/normalizer.py rename to demo_generation/diffusion_policies/model_idp3/common/normalizer.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/common/shape_util.py b/demo_generation/diffusion_policies/model_idp3/common/shape_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/common/shape_util.py rename to demo_generation/diffusion_policies/model_idp3/common/shape_util.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/common/tensor_util.py b/demo_generation/diffusion_policies/model_idp3/common/tensor_util.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/common/tensor_util.py rename to demo_generation/diffusion_policies/model_idp3/common/tensor_util.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/diffusion/conditional_unet1d.py b/demo_generation/diffusion_policies/model_idp3/diffusion/conditional_unet1d.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/diffusion/conditional_unet1d.py rename to demo_generation/diffusion_policies/model_idp3/diffusion/conditional_unet1d.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/diffusion/conv1d_components.py b/demo_generation/diffusion_policies/model_idp3/diffusion/conv1d_components.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/diffusion/conv1d_components.py rename to demo_generation/diffusion_policies/model_idp3/diffusion/conv1d_components.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/diffusion/ema_model.py b/demo_generation/diffusion_policies/model_idp3/diffusion/ema_model.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/diffusion/ema_model.py rename to demo_generation/diffusion_policies/model_idp3/diffusion/ema_model.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/diffusion/mask_generator.py b/demo_generation/diffusion_policies/model_idp3/diffusion/mask_generator.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/diffusion/mask_generator.py rename to demo_generation/diffusion_policies/model_idp3/diffusion/mask_generator.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/diffusion/positional_embedding.py b/demo_generation/diffusion_policies/model_idp3/diffusion/positional_embedding.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/diffusion/positional_embedding.py rename to demo_generation/diffusion_policies/model_idp3/diffusion/positional_embedding.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/vision/crop_randomizer.py b/demo_generation/diffusion_policies/model_idp3/vision/crop_randomizer.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/vision/crop_randomizer.py rename to demo_generation/diffusion_policies/model_idp3/vision/crop_randomizer.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/vision/model_getter.py b/demo_generation/diffusion_policies/model_idp3/vision/model_getter.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/vision/model_getter.py rename to demo_generation/diffusion_policies/model_idp3/vision/model_getter.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/vision/timm_obs_encoder.py b/demo_generation/diffusion_policies/model_idp3/vision/timm_obs_encoder.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/vision/timm_obs_encoder.py rename to demo_generation/diffusion_policies/model_idp3/vision/timm_obs_encoder.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/vision_3d/multi_stage_pointnet.py b/demo_generation/diffusion_policies/model_idp3/vision_3d/multi_stage_pointnet.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/vision_3d/multi_stage_pointnet.py rename to demo_generation/diffusion_policies/model_idp3/vision_3d/multi_stage_pointnet.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/vision_3d/point_process.py b/demo_generation/diffusion_policies/model_idp3/vision_3d/point_process.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/vision_3d/point_process.py rename to demo_generation/diffusion_policies/model_idp3/vision_3d/point_process.py diff --git a/diffusion_policies/diffusion_policies/model_idp3/vision_3d/pointnet_extractor.py b/demo_generation/diffusion_policies/model_idp3/vision_3d/pointnet_extractor.py similarity index 100% rename from diffusion_policies/diffusion_policies/model_idp3/vision_3d/pointnet_extractor.py rename to demo_generation/diffusion_policies/model_idp3/vision_3d/pointnet_extractor.py diff --git a/diffusion_policies/diffusion_policies/policy/base_image_policy.py b/demo_generation/diffusion_policies/policy/base_image_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/base_image_policy.py rename to demo_generation/diffusion_policies/policy/base_image_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/base_pointcloud_policy.py b/demo_generation/diffusion_policies/policy/base_pointcloud_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/base_pointcloud_policy.py rename to demo_generation/diffusion_policies/policy/base_pointcloud_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/diffusion_unet_hybrid_image_policy.py b/demo_generation/diffusion_policies/policy/diffusion_unet_hybrid_image_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/diffusion_unet_hybrid_image_policy.py rename to demo_generation/diffusion_policies/policy/diffusion_unet_hybrid_image_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/diffusion_unet_hybrid_pointcloud_policy.py b/demo_generation/diffusion_policies/policy/diffusion_unet_hybrid_pointcloud_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/diffusion_unet_hybrid_pointcloud_policy.py rename to demo_generation/diffusion_policies/policy/diffusion_unet_hybrid_pointcloud_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/diffusion_unet_hybrid_state_policy.py b/demo_generation/diffusion_policies/policy/diffusion_unet_hybrid_state_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/diffusion_unet_hybrid_state_policy.py rename to demo_generation/diffusion_policies/policy/diffusion_unet_hybrid_state_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/diffusion_unet_image_policy.py b/demo_generation/diffusion_policies/policy/diffusion_unet_image_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/diffusion_unet_image_policy.py rename to demo_generation/diffusion_policies/policy/diffusion_unet_image_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/diffusion_unet_timm_policy.py b/demo_generation/diffusion_policies/policy/diffusion_unet_timm_policy.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/diffusion_unet_timm_policy.py rename to demo_generation/diffusion_policies/policy/diffusion_unet_timm_policy.py diff --git a/diffusion_policies/diffusion_policies/policy/idp3.py b/demo_generation/diffusion_policies/policy/idp3.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/idp3.py rename to demo_generation/diffusion_policies/policy/idp3.py diff --git a/diffusion_policies/diffusion_policies/policy/simple_dp3.py b/demo_generation/diffusion_policies/policy/simple_dp3.py similarity index 100% rename from diffusion_policies/diffusion_policies/policy/simple_dp3.py rename to demo_generation/diffusion_policies/policy/simple_dp3.py diff --git a/diffusion_policies/diffusion_policies/workspace/base_workspace.py b/demo_generation/diffusion_policies/workspace/base_workspace.py similarity index 100% rename from diffusion_policies/diffusion_policies/workspace/base_workspace.py rename to demo_generation/diffusion_policies/workspace/base_workspace.py diff --git a/diffusion_policies/diffusion_policies/workspace/train_diffusion_unet_hybrid_pointcloud_workspace.py b/demo_generation/diffusion_policies/workspace/train_diffusion_unet_hybrid_pointcloud_workspace.py similarity index 100% rename from diffusion_policies/diffusion_policies/workspace/train_diffusion_unet_hybrid_pointcloud_workspace.py rename to demo_generation/diffusion_policies/workspace/train_diffusion_unet_hybrid_pointcloud_workspace.py diff --git a/diffusion_policies/diffusion_policies/workspace/train_diffusion_unet_image_workspace.py b/demo_generation/diffusion_policies/workspace/train_diffusion_unet_image_workspace.py similarity index 100% rename from diffusion_policies/diffusion_policies/workspace/train_diffusion_unet_image_workspace.py rename to demo_generation/diffusion_policies/workspace/train_diffusion_unet_image_workspace.py diff --git a/demo_generation/gen_demo.sh b/demo_generation/gen_demo.sh index fcfe301..f4588ed 100644 --- a/demo_generation/gen_demo.sh +++ b/demo_generation/gen_demo.sh @@ -6,10 +6,16 @@ render_video=${5} data_root=../data +# If user accidentally passes grid with 1 sample, fallback to random mode +if [ "${gen_mode}" = "grid" ] && [ "${n_gen_per_source}" = "1" ]; then + echo "Warning: grid mode requires n_gen_per_source to be a square (and >1). Switching to 'random' mode." + gen_mode="random" +fi + python -W ignore gen_demo.py --config-name=${task}.yaml \ - data_root=${data_root} \ - generation.range_name=${gen_range} \ - generation.mode=${gen_mode} \ - generation.n_gen_per_source=${n_gen_per_source} \ - generation.render_video=${render_video} + data_root=${data_root} \ + generation.range_name=${gen_range} \ + generation.mode=${gen_mode} \ + generation.n_gen_per_source=${n_gen_per_source} \ + generation.render_video=${render_video} \ No newline at end of file diff --git a/demo_generation/hdf52rgb.py b/demo_generation/hdf52rgb.py new file mode 100644 index 0000000..138179d --- /dev/null +++ b/demo_generation/hdf52rgb.py @@ -0,0 +1,37 @@ +import numpy as np +import h5py +import os +import cv2 + +def _decode_rgb_image(rgb_dataset): + """解码首帧RGB图像数据集""" + camera_bits = rgb_dataset[0] + camera_img = cv2.imdecode(np.frombuffer(camera_bits, np.uint8), cv2.IMREAD_COLOR) + return camera_img + +def save_first_rgb_frame_from_h5(h5_file_path, output_dir): + # 创建输出目录 + rgb_output_dir = os.path.join(output_dir, 'rgb') + os.makedirs(rgb_output_dir, exist_ok=True) + + # 打开h5文件 + with h5py.File(h5_file_path, 'r') as h5_file: + rgb_data = h5_file['observation/head_camera/rgb'] + # 解码首帧RGB图像 + rgb_frame = _decode_rgb_image(rgb_data) + # 保存首帧，文件名为000000.png + rgb_frame_path = os.path.join(rgb_output_dir, 'mask.jpg') + save_image(rgb_frame, rgb_frame_path) + +def save_image(image_array, file_path): + from PIL import Image + image = Image.fromarray(image_array) + image.save(file_path) + +def new_func(i): + h5_file_path = f'/home/lemonhdl/workspace/DemoGen/data/datasets/robotwin_ori/beat_block_hammer/loop1-8-all/data/episode{i}.hdf5' + output_dir = f'/home/lemonhdl/workspace/DemoGen/data/sam_mask/beat_block_hammer/block' + os.makedirs(output_dir, exist_ok=True) + save_first_rgb_frame_from_h5(h5_file_path, output_dir) + +new_func(i=0) # 示例调用，处理episode0.hdf5 diff --git a/demo_generation/hdf52rgb_all.py b/demo_generation/hdf52rgb_all.py new file mode 100644 index 0000000..026817e --- /dev/null +++ b/demo_generation/hdf52rgb_all.py @@ -0,0 +1,37 @@ +import numpy as np +import h5py +import os +import cv2 + +def _decode_rgb_image(camera_bits): + """解码单帧RGB图像数据""" + camera_img = cv2.imdecode(np.frombuffer(camera_bits, np.uint8), cv2.IMREAD_COLOR) + return camera_img + +def save_all_rgb_frames_from_h5(h5_file_path, output_dir): + # 创建输出目录 + rgb_output_dir = os.path.join(output_dir, 'rgb') + os.makedirs(rgb_output_dir, exist_ok=True) + + # 打开h5文件 + with h5py.File(h5_file_path, 'r') as h5_file: + rgb_data = h5_file['observation/head_camera/rgb'] + # 遍历所有帧 + for idx in range(len(rgb_data)): + camera_bits = rgb_data[idx] + rgb_frame = _decode_rgb_image(camera_bits) + rgb_frame_path = os.path.join(rgb_output_dir, f'{idx:06d}.jpg') + save_image(rgb_frame, rgb_frame_path) + +def save_image(image_array, file_path): + from PIL import Image + image = Image.fromarray(image_array) + image.save(file_path) + +def new_func(i): + h5_file_path = f'/home/lemonhdl/workspace/DemoGen/data/datasets/robotwin_ori/beat_block_hammer/loop1-8-all/data/episode0.hdf5' + output_dir = f'/home/lemonhdl/workspace/DemoGen/data/sam_mask/beat_block_hammer-loop1-single/vis_all' + os.makedirs(output_dir, exist_ok=True) + save_all_rgb_frames_from_h5(h5_file_path, output_dir) + +new_func(i=0) # 示例调用，处理episode0.hdf5 diff --git a/demo_generation/pcd_restore_debug.npy b/demo_generation/pcd_restore_debug.npy new file mode 100644 index 0000000..72ad2b6 Binary files /dev/null and b/demo_generation/pcd_restore_debug.npy differ diff --git a/demo_generation/run_gen_demo_bbhl.sh b/demo_generation/run_gen_demo_bbhl.sh new file mode 100644 index 0000000..c6473cb --- /dev/null +++ b/demo_generation/run_gen_demo_bbhl.sh @@ -0,0 +1,10 @@ +bash gen_demo.sh beat_block_hammer-loop1 test random 40 True +# bash gen_demo.sh beat_block_hammer-loop2 test random 40 True +# bash gen_demo.sh beat_block_hammer-loop3 test random 40 True +# bash gen_demo.sh beat_block_hammer-loop4 test random 40 True +# bash gen_demo.sh beat_block_hammer-loop5 test random 40 True +# bash gen_demo.sh beat_block_hammer-loop1 src random 1 True +# bash gen_demo.sh beat_block_hammer-loop2 src random 1 True +# bash gen_demo.sh beat_block_hammer-loop3 src random 1 True +# bash gen_demo.sh beat_block_hammer-loop4 src random 1 True +# bash gen_demo.sh beat_block_hammer-loop5 src random 1 True diff --git a/demo_generation/test_dummy.h5 b/demo_generation/test_dummy.h5 new file mode 100644 index 0000000..63ba1f3 Binary files /dev/null and b/demo_generation/test_dummy.h5 differ diff --git a/diffusion_policies/LICENSE b/diffusion_policies/LICENSE deleted file mode 100755 index 1e94ccd..0000000 --- a/diffusion_policies/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 Columbia Artificial Intelligence and Robotics Lab - -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/diffusion_policies/MUJOCO_LOG.TXT b/diffusion_policies/MUJOCO_LOG.TXT deleted file mode 100755 index 8ed41cd..0000000 --- a/diffusion_policies/MUJOCO_LOG.TXT +++ /dev/null @@ -1,30 +0,0 @@ -Mon Sep 16 23:03:39 2024 -ERROR: GLEW initalization error: Missing GL version - -Mon Sep 16 23:06:58 2024 -ERROR: GLEW initalization error: Missing GL version - -Mon Sep 16 23:20:16 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 13:34:07 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 14:02:52 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 14:03:33 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 14:06:24 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 14:07:33 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 14:14:39 2024 -ERROR: GLEW initalization error: Missing GL version - -Tue Sep 17 14:39:19 2024 -ERROR: GLEW initalization error: Missing GL version - diff --git a/diffusion_policies/diffusion_policies/env/__init__.py b/diffusion_policies/diffusion_policies/env/__init__.py deleted file mode 100755 index 9823d0f..0000000 --- a/diffusion_policies/diffusion_policies/env/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ - -# from .adroit import AdroitEnv -# from .metaworld import MetaWorldEnv -# from .dexart import DexArtEnv - -# from .robosuite import Robosuite3DEnv - diff --git a/diffusion_policies/diffusion_policies/env/metaworld/__init__.py b/diffusion_policies/diffusion_policies/env/metaworld/__init__.py deleted file mode 100755 index 0976f55..0000000 --- a/diffusion_policies/diffusion_policies/env/metaworld/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .metaworld_wrapper import MetaWorldEnv \ No newline at end of file diff --git a/diffusion_policies/diffusion_policies/env/metaworld/metaworld_wrapper.py b/diffusion_policies/diffusion_policies/env/metaworld/metaworld_wrapper.py deleted file mode 100755 index 589127f..0000000 --- a/diffusion_policies/diffusion_policies/env/metaworld/metaworld_wrapper.py +++ /dev/null @@ -1,534 +0,0 @@ -import torch -import gym -import numpy as np -import matplotlib.pyplot as plt -import os -import metaworld -import cv2 - -from natsort import natsorted -from termcolor import cprint -from gym import spaces -from diffusion_policies.gym_util.mujoco_point_cloud import PointCloudGenerator -from diffusion_policies.gym_util.mjpc_wrapper import point_cloud_sampling - - -TASK_BOUDNS = { - # x_min, y_min, z_min, x_max, y_max, z_max (0.4) - 'default': [-1, 0.25, -0.001, 1, 1.3, 0.37], -} - -# TASK_BOUDNS = { -# # x_min, y_min, z_min, x_max, y_max, z_max (0.4) -# 'default': [-1, 0.35, -0.001, 1, 1.3, 0.3], -# } - - -# cam names: ('topview', 'corner', 'corner2', 'corner3', 'behindGripper', 'gripperPOV') -# WRIST_CAMERA_NAME = 'gripperPOV50' # wrist camera -WRIST_CAMERA_NAME = 'gripper435' # wrist camera -WRIST_2_CAMERA_NAME = 'gripper120' # wrist camera, other side -MAIN_CAMERA_NAME = 'front30' # third-person view, front435 -SIDE_CAMERA_NAME = 'corner' -IMG_SIZE = 224 -NUM_POINTS = 512 # 512 -DEPTH_CAMERA_NAME = 'depth' -DEPTH_IMG_SIZE = 224 -DEPTH_WIDTH = 120 -DEPTH_HEIGHT = 100 -N_SIM_STEPS = 5 # make EE movement more accurate -MAX_EP_LENGTH = 200 -COLLISION_THRESHOLD = 0.01 - - -class MetaWorldEnv(gym.Env): - metadata = {"render.modes": ["rgb_array"], "video.frames_per_second": 10} - - def __init__(self, task_name, device="cuda:0", - use_point_crop=True, - reset_mode="default", - image_obs_only=False, - state_obs_only=False - ): - # print("MetaWorldEnv init", reset_mode) - super(MetaWorldEnv, self).__init__() - - if '-v2' not in task_name: - task_name = task_name + '-v2-goal-observable' - - self.env = metaworld.envs.ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE[task_name]() - self.env._freeze_rand_vec = False # this is important for domain randomization - - self.cam_id = self.env.sim.model.camera_name2id(MAIN_CAMERA_NAME) - self.pc_offset = self.env.sim.model.cam_pos[self.cam_id] - self.pc_transform = self.env.sim.model.cam_mat0[self.cam_id].reshape(3, 3) - self.pc_scale = np.array([1, 1, 1]) - - # print("after super", reset_mode) - - self.reset_mode = reset_mode - self.env.reset_mode = reset_mode - self.image_obs_only = image_obs_only - self.state_obs_only = state_obs_only - - #################################################################### - # BUG: Delete this. Otherwise wrist camera observation will crash. - # !!! this is very important, otherwise the point cloud will be wrong. - # self.env.sim.model.vis.map.znear = 0.1 - # self.env.sim.model.vis.map.zfar = 1.5 - ##################################################################### - - self.device = device - self.device_id = int(device.split(":")[-1]) - - # self.device_id = 0 - # cprint("Using device 0 for env render", 'light_red') - - # set the device of mujoco simulation - - - self.image_size = IMG_SIZE - self.depth_image_size = DEPTH_IMG_SIZE - - self.pc_generator = PointCloudGenerator(sim=self.env.sim, cam_names=[DEPTH_CAMERA_NAME], img_width=DEPTH_WIDTH, img_height=DEPTH_HEIGHT) - self.use_point_crop = use_point_crop - # cprint("[MetaWorldEnv] use_point_crop: {}".format(self.use_point_crop), "cyan") - # self.num_points = num_points # 512 - self.num_points = NUM_POINTS - - x_min, y_min, z_min, x_max, y_max, z_max = TASK_BOUDNS['default'] - self.min_bound = [x_min, y_min, z_min] - self.max_bound = [x_max, y_max, z_max] - - - # self.episode_length = self.env.max_path_length # this is 500 - self.episode_length = self._max_episode_steps = MAX_EP_LENGTH - - - self.action_space = self.env.action_space - - # cprint("[MetaWorldEnv] action_space: {}".format(self.env.action_space.shape), "yellow") - - self.obs_sensor_dim = self.get_robot_state().shape[0] - - - - self.observation_space = spaces.Dict({ - 'main_img': spaces.Box( - low=0, - high=255, - shape=(3, self.image_size, self.image_size), - dtype=np.float32 - ), - 'wrist_img': spaces.Box( - low=0, - high=255, - shape=(3, self.image_size, self.image_size), - dtype=np.float32 - ), - 'wrist2_img': spaces.Box( - low=0, - high=255, - shape=(3, self.image_size, self.image_size), - dtype=np.float32 - ), - 'side_img': spaces.Box( - low=0, - high=255, - shape=(3, self.image_size, self.image_size), - dtype=np.float32 - ), - 'depth': spaces.Box( - low=0, - high=255, - shape=(self.depth_image_size, self.depth_image_size), - dtype=np.float32 - ), - 'agent_pos': spaces.Box( - low=-np.inf, - high=np.inf, - shape=(self.obs_sensor_dim,), - dtype=np.float32 - ), - 'point_cloud': spaces.Box( - low=-np.inf, - high=np.inf, - shape=(self.num_points, 3), - dtype=np.float32 - ), - 'full_state': spaces.Box( - low=-np.inf, - high=np.inf, - shape=(20, ), - dtype=np.float32 - ), - 'cheat_state': spaces.Box( - low=-np.inf, - high=np.inf, - shape=(10, ), - dtype=np.float32 - ), - }) - - @staticmethod - def cheat_state_extractor(raw_state): - ee = raw_state[:4] - object_pos = raw_state[4:7] - target_pos = raw_state[-3:] - return np.concatenate([ee, object_pos, target_pos]) - - def get_robot_state(self): - eef_pos = self.env.get_endeff_pos() - finger_right, finger_left = ( - self.env._get_site_pos('rightEndEffector'), - self.env._get_site_pos('leftEndEffector') - ) - # finger_gap = finger_right - finger_left - finger_gap = [finger_left[1] - finger_right[1]] - # print("finger_gap:", finger_gap) - # return np.concatenate([eef_pos, finger_right, finger_left]) - return np.concatenate([eef_pos, finger_gap]) - - def get_rgb(self): - # cam names: ('topview', 'corner', 'corner2', 'corner3', 'behindGripper', 'gripperPOV') - # os.system("unset LD_PRELOAD") - main_img = self.env.sim.render(width=self.image_size, height=self.image_size, camera_name=MAIN_CAMERA_NAME, device_id=self.device_id) - - - # main_img = self.env.sim.render(width=112, height=224, camera_name=MAIN_CAMERA_NAME, device_id=self.device_id) - # main_img = cv2.resize(main_img, (self.image_size, self.image_size)) - - wrist_img = self.env.sim.render(width=320, height=640, camera_name=WRIST_CAMERA_NAME, device_id=self.device_id) - wrist_img = cv2.resize(wrist_img, (self.image_size, self.image_size)) - # print(wrist_img.shape) - - - wrist2_img = self.env.sim.render(width=self.image_size, height=self.image_size, camera_name=WRIST_2_CAMERA_NAME, device_id=self.device_id) - # side_img = self.env.sim.render(width=self.image_size, height=self.image_size, camera_name=SIDE_CAMERA_NAME, device_id=self.device_id) - - side_img = self.env.sim.render(width=DEPTH_WIDTH, height=DEPTH_HEIGHT, camera_name=DEPTH_CAMERA_NAME, device_id=self.device_id) - side_img = cv2.resize(side_img, (self.image_size, self.image_size)) - - return main_img, wrist_img, wrist2_img, side_img - - def render_high_res(self, resolution=1024): - main_img = self.env.sim.render(width=resolution, height=resolution, camera_name=MAIN_CAMERA_NAME, device_id=self.device_id) - wrist_img = self.env.sim.render(width=resolution, height=resolution, camera_name=WRIST_CAMERA_NAME, device_id=self.device_id) - wrist2_img = self.env.sim.render(width=resolution, height=resolution, camera_name=WRIST_2_CAMERA_NAME, device_id=self.device_id) - side_img = self.env.sim.render(width=resolution, height=resolution, camera_name=SIDE_CAMERA_NAME, device_id=self.device_id) - return main_img, wrist_img, wrist2_img, side_img - - - def get_point_cloud(self, use_rgb=True): - point_cloud, depth = self.pc_generator.generateCroppedPointCloud(device_id=self.device_id) # raw point cloud, Nx3 - - - if not use_rgb: - point_cloud = point_cloud[..., :3] - - - # do transform, scale, offset, and crop - if self.pc_transform is not None: - point_cloud[:, :3] = point_cloud[:, :3] @ self.pc_transform.T - if self.pc_scale is not None: - point_cloud[:, :3] = point_cloud[:, :3] * self.pc_scale - - if self.pc_offset is not None: - point_cloud[:, :3] = point_cloud[:, :3] + self.pc_offset - # import pcd_visualizer; pcd_visualizer.visualize_pointcloud(point_cloud) - # import ipdb; ipdb.set_trace() - - # self.use_point_crop = False - if self.use_point_crop: - # if self.min_bound is not None: - # mask = np.all(point_cloud[:, :3] > self.min_bound, axis=1) - # point_cloud = point_cloud[mask] - # if self.max_bound is not None: - # mask = np.all(point_cloud[:, :3] < self.max_bound, axis=1) - # point_cloud = point_cloud[mask] - - point_cloud = point_cloud[ - (point_cloud[:, 0] >= self.min_bound[0]) & (point_cloud[:, 0] <= self.max_bound[0]) & - (point_cloud[:, 1] >= self.min_bound[1]) & (point_cloud[:, 1] <= self.max_bound[1]) & - (point_cloud[:, 2] >= self.min_bound[2]) & (point_cloud[:, 2] <= self.max_bound[2])] - - - # print("y_min before fps:", min(point_cloud[:, 1])) - - # import pcd_visualizer; pcd_visualizer.visualize_pointcloud(point_cloud) - # import ipdb; ipdb.set_trace() - - # fps_start = time.time() - point_cloud = point_cloud_sampling(point_cloud, self.num_points, 'fps', self.device) - - # print("pcd after fps:", point_cloud[:3]) - # print("y_min after fps:", min(point_cloud[:, 1])) - - - # print("FPS sampling time: ", time.time() - fps_start) - - # import pcd_visualizer; pcd_visualizer.visualize_pointcloud(point_cloud) - # import ipdb; ipdb.set_trace() - - depth = depth[::-1] # flip vertically - - return point_cloud, depth - - - def get_visual_obs(self): - robot_state = self.get_robot_state() - if not self.state_obs_only: - main_img, wrist_img, wrist2_img, side_img = self.get_rgb() - else: - main_img = np.zeros((3, self.image_size, self.image_size)) - wrist_img = np.zeros((3, self.image_size, self.image_size)) - wrist2_img = np.zeros((3, self.image_size, self.image_size)) - side_img = np.zeros((3, self.image_size, self.image_size)) - - if main_img.shape[0] != 3: # make channel first - main_img = main_img.transpose(2, 0, 1) - if wrist_img.shape[0] != 3: # make channel first - wrist_img = wrist_img.transpose(2, 0, 1) - if wrist2_img.shape[0] != 3: # make channel first - wrist2_img = wrist2_img.transpose(2, 0, 1) - if side_img.shape[0] != 3: # make channel first - side_img = side_img.transpose(2, 0, 1) - - - # print("finger gap", robot_state[7] - robot_state[4]) - - if not self.image_obs_only and not self.state_obs_only: - point_cloud, depth = self.get_point_cloud() - else: - point_cloud = np.zeros((self.num_points, 3)) - depth = np.zeros((self.depth_image_size, self.depth_image_size)) - - obs_dict = { - 'main_img': main_img, - 'wrist_img': wrist_img, - 'wrist2_img': wrist2_img, - 'side_img': side_img, - 'depth': depth, - 'agent_pos': robot_state, - 'point_cloud': point_cloud, - } - return obs_dict - - - def step(self, action: np.array): - - action = np.clip(action, -1, 1) - - # import ipdb; ipdb.set_trace() - - ee_pos_before = self.get_robot_state()[:3] - ee_pos_desire = ee_pos_before + action[:3] / 100 - vfunc = np.vectorize("{:.2e}".format) - # print("action scaled: ", vfunc(action[:3] / 100)) - # cprint(f"ee_pos_desire: {ee_pos_desire}", "light_blue") - - full_state, reward, done, env_info = self.env.step(action) - self.cur_step += 1 - # print("self.cur_step: ", self.cur_step) - - blank_action = action.copy() - blank_action[:3] = 0 - # print("blank_action: ", blank_action) - for _ in range(N_SIM_STEPS): - self.env.step(blank_action) - - ee_pos_after = self.get_robot_state()[:3] - mocap_target = self.env.data.mocap_pos - - mocap_pos_diff = mocap_target - ee_pos_after - # print(mocap_pos_diff) - - if mocap_pos_diff[0][-1] < - COLLISION_THRESHOLD: - # cprint(f"Collision occurs? mocap_pos_diff: {vfunc(mocap_pos_diff)}", 'red') - # cprint(f"mocap_target: {vfunc(mocap_target)}", 'blue') - env_info['collision'] = True - done = True - else: - env_info['collision'] = False - - - # print("ee_pos_after: ", ee_pos_after) - # ee_pos_diff = ee_pos_desire - ee_pos_after - - delta_action = ee_pos_after - ee_pos_desire - # cprint(f"mocap_pos_diff: {vfunc(mocap_pos_diff)}", 'red') - # cprint(f"delta_action: {vfunc(delta_action)}", "blue") - - obs_dict = self.get_visual_obs() - obs_dict['full_state'] = full_state - obs_dict['cheat_state'] = self.cheat_state_extractor(full_state) - - - done = done or self.cur_step >= self.episode_length - - return obs_dict, reward, done, env_info - - def reset(self, config_idx=None): - self.env.reset() - full_state = self.env.reset_model(config_idx) - # if config_idx is not None: - # # print("reset with config index: ", config_idx) - # full_state = self.env.reset_model_index(config_idx) # deterministic reset model with config index - # # print("env._target_pos before env.reset:", self.env.get_target_pos()) - # else: - # full_state = self.env.reset_model() - - if full_state is None: # handling skipped demo - return None - - # raw_obs = self.env.reset() - # print("env._target_pos after env.reset:", self.env.get_target_pos()) - self.cur_step = 0 - - # make the EE movement more accurate - for _ in range(N_SIM_STEPS * 5): - self.env.step(np.zeros(4)) - - obs_dict = self.get_visual_obs() - obs_dict['full_state'] = full_state - obs_dict['cheat_state'] = self.cheat_state_extractor(full_state) - - return obs_dict - - def reset_to_state(self, object_states): - return NotImplementedError - - def seed(self, seed=None): - # self.env.seed(seed) - pass - - def set_seed(self, seed=None): - # self.env.seed(seed) - pass - - def render(self, mode='rgb_array'): - main_img, wrist_img, wrist2_img, side_img = self.get_rgb() - return main_img, wrist_img, wrist2_img, side_img - - def close(self): - pass - - def get_target_pos(self): - return self.env._target_pos - - def get_object_pos(self): - return self.env.obj_init_pos - - def get_robot_bbox(self): - eef_pos = self.env.get_endeff_pos() - bbox = np.array([[eef_pos[0] - 0.068, eef_pos[1] - 0.065, eef_pos[2] - 0.06], - [eef_pos[0] + 0.068, eef_pos[1] + 0.065, eef_pos[2] + 10]]) - - return bbox - - @staticmethod - def pcd_clip(pcd, bbox): - """ - pcd: (n, 6) - bbox: (2, 3), [[x_min, y_min, z_min], [x_max, y_max, z_max]] - """ - points_inside = np.all(pcd[:, :3] > bbox[0], axis=1) & np.all(pcd[:, :3] < bbox[1], axis=1) - points_outside = np.logical_not(points_inside) - pcd_inside = pcd[points_inside] - pcd_outside = pcd[points_outside] - assert pcd_inside.shape[0] + pcd_outside.shape[0] == pcd.shape[0] - return pcd_inside, pcd_outside - - @staticmethod - def pcd_translate_inside_bbox(pcd, bbox, trans_vec): - """ - Translate the points inside the bbox with trans_vec, leaving other points unchanged - pcd: (n, 6) - bbox: (2, 3), [[x_min, y_min, z_min], [x_max, y_max, z_max]] - trans_vec (3,) - """ - pcd_inside, pcd_outside = MetaWorldEnv.pcd_clip(pcd, bbox) - pcd_inside[:, :3] += trans_vec - return np.concatenate([pcd_inside, pcd_outside], axis=0) - - @staticmethod - def pcd_translate_outside_bbox(pcd, bbox, trans_vec): - """ - Translate the points outside the bbox with trans_vec - pcd: (n, 6) - bbox: (2, 3), [[x_min, y_min, z_min], [x_max, y_max, z_max]] - trans_vec (3,) - """ - pcd_inside, pcd_outside = MetaWorldEnv.pcd_clip(pcd, bbox) - pcd_outside[:, :3] += trans_vec - return np.concatenate([pcd_inside, pcd_outside], axis=0) - - - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--env', type=str, default='assembly') - args = parser.parse_args() - env_name = args.env - # env_name = args.env + '-v2' - - # env = MetaWorldEnv(env_name) - # from metaworld.policies import SawyerAssemblyV2Policy - # policy = SawyerAssemblyV2Policy() - - env = MetaWorldEnv("handle-press-side-spatial") - from metaworld.policies import SawyerHandlePressSideSpatialV2Policy - policy = SawyerHandlePressSideSpatialV2Policy() - - import pcd_visualizer - vis = pcd_visualizer.pcd_visualizer() - # def visualize_env(env, reset_mode): - # env.env.reset_mode = reset_mode - # obs = env.reset() - # rgb = env.get_rgb() - # plt.imsave(f"debug/{reset_mode}.png", rgb) - - # pcd, _ = env.get_point_cloud() - # vis.save_visualization_to_file(pcd, f"debug/{reset_mode}.html") - - # reset_modes = ["low", "high", "single"] - # for reset_mode in reset_modes: - # visualize_env(env, reset_mode) - - - target_pos = env.get_target_pos() - print("target_pos: ", target_pos) - - ee_pos = env.get_robot_state()[:3] - print("ee_pos: ", ee_pos) - - pcd, _ = env.get_point_cloud() - bbox_robot = env.get_robot_bbox() - - # trans_vec = np.array([0.1, 0.1, 0]) - - trans_vec = np.array([0., 0., 0]) - - # pcd_visualizer.visualize_pointcloud(pcd) - pcd_visualizer.visualize_pointcloud(env.pcd_translate_outside_bbox(pcd, bbox_robot, trans_vec)) - - - # print(pcd[0].shape) - - - - for i in range(10): - action = env.action_space.sample() - # action = policy.get_action(obs) - obs, reward, done, info = env.step(action) - env.get_point_cloud() - main_img, wrist_img = env.get_rgb() - plt.imsave("main_camera.png", main_img) - plt.imsave("wrist_camera.png", wrist_img) - # time.sleep(0.5) - print(i, reward, done) - if done: - break - env.close() - cprint("MetaWorld env successfully closed", "green") diff --git a/diffusion_policies/diffusion_policies/env/robosuite/__init__.py b/diffusion_policies/diffusion_policies/env/robosuite/__init__.py deleted file mode 100755 index 9be4a1f..0000000 --- a/diffusion_policies/diffusion_policies/env/robosuite/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .robosuite_wrapper import Robosuite3DEnv \ No newline at end of file diff --git a/diffusion_policies/diffusion_policies/env/robosuite/robosuite_wrapper.py b/diffusion_policies/diffusion_policies/env/robosuite/robosuite_wrapper.py deleted file mode 100755 index d9e1375..0000000 --- a/diffusion_policies/diffusion_policies/env/robosuite/robosuite_wrapper.py +++ /dev/null @@ -1,199 +0,0 @@ -import gym -from gym import spaces -import numpy as np -import matplotlib.pyplot as plt -import open3d as o3d -from scipy.spatial.transform import Rotation as R -from diffusion_policies.gym_util.mjpc_wrapper import point_cloud_sampling - -from robomimic.envs.env_robosuite import EnvRobosuite -import robomimic.utils.env_utils as EnvUtils -import robomimic.utils.obs_utils as ObsUtils -import robomimic.utils.file_utils as FileUtils -from robosuite.controllers import load_composite_controller_config -from pcd_visualizer import visualize_pointcloud - - -TASK_BOUDNS = { - # x_min, y_min, z_min, x_max, y_max, z_max - 'Coffee': [-0.4, -0.5, 0.810, 0.19, 0.5, 1.15], - 'Kitchen': [-0.5, -0.5, 0.912, 0.2, 0.5, 1.3], - 'HammerCleanup': [-0.4, -0.5, 0.91, 0.19, 0.5, 1.3], - # 'MugCleanup': [-0.4, -0.5, 0.91, 0.19, 0.5, 1.3], - 'ThreePieceAssembly': [-0.4, -0.5, 0.81, 0.36, 0.5, 1.3], -} - -SINGLE_CEMERA_NAME = { - 'Coffee': 'myfrontlow', - 'Kitchen': 'myfronthigh', - 'HammerCleanup': 'myfronthigh', - 'ThreePieceAssembly': 'myfrontlow', -} - -# MULTI_VIEW_CAMS = ['birdview', 'agentview', 'left', 'right', 'robot0_eye_in_hand'] -MULTI_VIEW_CAMS = ['left', 'right', 'agentview'] -SINGLE_VIEW_CAMS = ['myfront'] -MULTI_VIEW_NUM_POINTS = 1024 -SINGLE_VIEW_NUM_POINTS = 512 -USE_CROP = True -N_CONTROL_STEPS = 5 # 2 - - -class Robosuite3DEnv(gym.Env): - def __init__(self, source_demo_path, - multi_view=False, - cam_width=128, - cam_height=128, - render=False, - render_cam="myfront", - support_osc_control=False, - ): - if multi_view: - raise NotImplementedError("Multi-view not supported.") - else: - self.n_points = SINGLE_VIEW_NUM_POINTS - - - dummy_spec = dict(obs=dict(low_dim=["robot0_eef_pos"], rgb=[],),) - ObsUtils.initialize_obs_utils_with_obs_specs(obs_modality_specs=dummy_spec) - env_meta = FileUtils.get_env_metadata_from_dataset(source_demo_path) - print("EnvUtils.is_robosuite_env(env_meta)", EnvUtils.is_robosuite_env(env_meta)) - if not support_osc_control: - controller_config = load_composite_controller_config( - controller='WHOLE_BODY_MINK_IK', - robot="Panda", - ) - env_meta["env_kwargs"]["controller_configs"] = controller_config - - self.task_name = env_meta["env_name"].split("_")[0] - if self.task_name not in TASK_BOUDNS.keys(): - raise ValueError(f"Task {self.task_name} not supported.") - env_meta["env_name"] = self.task_name + "_D0" - - self.cam_width = cam_width - self.cam_height = cam_height - env_meta["env_kwargs"]["camera_heights"] = cam_height - env_meta["env_kwargs"]["camera_widths"] = cam_width - env_meta["env_kwargs"]["render_camera"] = render_cam - - - if multi_view: - self.cam_names = MULTI_VIEW_CAMS - self.n_points = MULTI_VIEW_NUM_POINTS - else: - self.cam_names = [SINGLE_CEMERA_NAME[self.task_name]] - env_meta["env_kwargs"]["camera_names"] = self.cam_names - - self.env = EnvUtils.create_env_from_metadata(env_meta=env_meta, render=render, - use_image_obs=True, use_depth_obs=True) - - self.action_space = spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float32) - self.observation_space = spaces.Dict({ - "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float32), - "point_cloud": spaces.Box(low=-np.inf, high=np.inf, shape=(self.n_points, 6), dtype=np.float32), - }) - - @staticmethod - def _convert_state_quat_to_action_rotvec(state_quat): - """ - Handle confusing rotation transform. - - Args: - state_quat (np.array): 4-element quaternion representing the state - - Returns: - action_rotvec (np.array): 3-element rotation vector representing the action - """ - state_mat = R.from_quat(state_quat).as_matrix() - T = R.from_rotvec([0, 0, -1.5707]).as_matrix() - action_mat = state_mat @ T - return R.from_matrix(action_mat).as_rotvec() - - @staticmethod - def _pcd_crop(pcd, bounds): - mask = np.all(pcd[:, :3] > bounds[:3], axis=1) & np.all(pcd[:, :3] < bounds[3:], axis=1) - return pcd[mask] - - def process_obs_dict(self, obs_dict): - keys = obs_dict.keys() - # print("keys:", keys) - processed_obs = { - "agent_pos": np.zeros(7), - "point_cloud": np.zeros((self.n_points, 6)), - } - - # process robot state - robot_keys = ["robot0_eef_pos", "robot0_eef_quat", "robot0_gripper_qpos"] - if all([k in keys for k in robot_keys]): - pos = obs_dict["robot0_eef_pos"] - rot = self._convert_state_quat_to_action_rotvec(obs_dict["robot0_eef_quat"]) - gripper = obs_dict["robot0_gripper_qpos"][0] - obs_dict["robot0_gripper_qpos"][1] - robot_state = np.concatenate([pos, rot, [gripper]]) - processed_obs["agent_pos"] = robot_state - - # process point cloud - pcd_list = [] - for cam_name in self.cam_names: - rgb_name = cam_name + "_image" - depth_name = cam_name + "_depth" - if all([k in keys for k in [rgb_name, depth_name]]): - # print("cam_name:", cam_name) - rgb = obs_dict[rgb_name] - depth = obs_dict[depth_name] - pcd = self.get_pcd_from_rgbd(cam_name, rgb, depth) - pcd_list.append(pcd) - else: - break - if len(pcd_list) > 0: - pcd = np.concatenate(pcd_list, axis=0) - if USE_CROP: - pcd = self._pcd_crop(pcd, TASK_BOUDNS[self.task_name]) - pcd = point_cloud_sampling(pcd, self.n_points) - processed_obs["point_cloud"] = pcd - - # import pcd_visualizer - # pcd_visualizer.visualize_pointcloud(pcd) - - return processed_obs - - def get_pcd_from_rgbd(self, cam_name, rgb, depth): - def verticalFlip(img): - return np.flip(img, axis=0) - - def get_o3d_cammat(): - cam_mat = self.env.get_camera_intrinsic_matrix(cam_name, self.cam_width, self.cam_height) - cx = cam_mat[0,2] - fx = cam_mat[0,0] - cy = cam_mat[1,2] - fy = cam_mat[1,1] - return o3d.camera.PinholeCameraIntrinsic(self.cam_width, self.cam_height, fx, fy, cx, cy) - - rgb = verticalFlip(rgb) - depth = self.env.get_real_depth_map(verticalFlip(depth)) - o3d_cammat = get_o3d_cammat() - o3d_depth = o3d.geometry.Image(depth) - o3d_pcd = o3d.geometry.PointCloud.create_from_depth_image(o3d_depth, o3d_cammat) - world_T_cam = self.env.get_camera_extrinsic_matrix(cam_name) - o3d_pcd.transform(world_T_cam) - points = np.asarray(o3d_pcd.points) - colors = rgb.reshape(-1, 3) - pcd = np.concatenate([points, colors], axis=1) - return pcd - - def reset(self): - obs_dict = self.env.reset() - return self.process_obs_dict(obs_dict) - - def reset_to(self, state): - return self.env.reset_to(state) - - def step(self, action): - for _ in range(N_CONTROL_STEPS): - obs_dict, r, done, info = self.env.step(action) - return self.process_obs_dict(obs_dict), r, done, info - - def render(self, **kwargs): - return self.env.render(**kwargs) - - def check_success(self): - return self.env.env._check_success() \ No newline at end of file diff --git a/diffusion_policies/eval.py b/diffusion_policies/eval.py deleted file mode 100755 index d0a7762..0000000 --- a/diffusion_policies/eval.py +++ /dev/null @@ -1,37 +0,0 @@ -import sys -# use line-buffering for both stdout and stderr -sys.stdout = open(sys.stdout.fileno(), mode='w', buffering=1) -sys.stderr = open(sys.stderr.fileno(), mode='w', buffering=1) - -import hydra -from omegaconf import OmegaConf -import pathlib -from diffusion_policies.workspace.base_workspace import BaseWorkspace -import os -os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' - -os.environ['WANDB_SILENT'] = "True" - -# allows arbitrary python code execution in configs using the ${eval:''} resolver -OmegaConf.register_new_resolver("eval", eval, replace=True) - - -@hydra.main( - version_base=None, - config_path=str(pathlib.Path(__file__).parent.joinpath( - 'diffusion_policies','config')) -) -def main(cfg: OmegaConf): - # resolve immediately so all the ${now:} resolvers - # will use the same time. - OmegaConf.resolve(cfg) - - # import ipdb; ipdb.set_trace() - # cfg.task.env_runner.eval_episodes = 100 - - cls = hydra.utils.get_class(cfg._target_) - workspace: BaseWorkspace = cls(cfg) - workspace.eval() - -if __name__ == "__main__": - main() diff --git a/diffusion_policies/setup.py b/diffusion_policies/setup.py deleted file mode 100755 index 2573cfa..0000000 --- a/diffusion_policies/setup.py +++ /dev/null @@ -1,6 +0,0 @@ -from setuptools import setup, find_packages - -setup( - name='diffusion_policies', - packages=find_packages(), -) diff --git a/diffusion_policies/train.py b/diffusion_policies/train.py deleted file mode 100755 index e91c3e8..0000000 --- a/diffusion_policies/train.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Usage: -Training: -python train.py --config-name=train_diffusion_lowdim_workspace -""" -import os -os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' -from diffusion_policies.workspace.base_workspace import BaseWorkspace -import pathlib -from omegaconf import OmegaConf -import hydra -from termcolor import cprint - -import sys -# use line-buffering for both stdout and stderr -sys.stdout = open(sys.stdout.fileno(), mode='w', buffering=1) -sys.stderr = open(sys.stderr.fileno(), mode='w', buffering=1) - -os.environ['WANDB_SILENT'] = "True" - -# allow for detecting segmentation fault -# import faulthandler -# faulthandler.enable() -# cprint("[fault handler enabled]", "cyan") - -# allows arbitrary python code execution in configs using the ${eval:''} resolver -OmegaConf.register_new_resolver("eval", eval, replace=True) - - -@hydra.main( - version_base=None, - config_path=str(pathlib.Path(__file__).parent.joinpath( - 'diffusion_policies', 'config')) -) -def main(cfg: OmegaConf): - # resolve immediately so all the ${now:} resolvers - # will use the same time. - OmegaConf.resolve(cfg) - - cls = hydra.utils.get_class(cfg._target_) - workspace: BaseWorkspace = cls(cfg) - workspace.run() - - -if __name__ == "__main__": - main() diff --git a/diffusion_policies/train.sh b/diffusion_policies/train.sh deleted file mode 100755 index ce8d11e..0000000 --- a/diffusion_policies/train.sh +++ /dev/null @@ -1,22 +0,0 @@ -demo=${1} -algo=${2} -task=${3} -seed=${4} - -data_root=../data -exp_name=${demo}-${algo}-seed${seed} -run_dir=${data_root}/ckpts/${exp_name} - -zarr_path=${data_root}/datasets/demogen/${demo}.zarr - -export HYDRA_FULL_ERROR=1 -python -W ignore train.py --config-name=${algo}.yaml \ - task=${task} \ - hydra.run.dir=${run_dir} \ - training.debug=False \ - training.seed=${seed} \ - training.device="cuda:0" \ - exp_name=${exp_name} \ - logging.mode=offline \ - training.num_epochs=1 \ - task.dataset.zarr_path=${zarr_path} \ No newline at end of file diff --git a/pics/method.png b/pics/method.png deleted file mode 100644 index 8c78930..0000000 Binary files a/pics/method.png and /dev/null differ diff --git a/pics/teaser.png b/pics/teaser.png deleted file mode 100644 index d0bc25b..0000000 Binary files a/pics/teaser.png and /dev/null differ