feat: face and person detection samples

.gitignore

@@ -10,3 +10,4 @@ system-test/*key.json
 package-lock.json
 .vscode
 __pycache__
+*.code-workspace

samples/analyze.v1p3beta1.js

@@ -0,0 +1,362 @@
+// Copyright 2020 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+'use strict';
+
+async function detectPerson(path) {
+  //[START video_detect_person_beta]
+  // Imports the Google Cloud Video Intelligence library + Node's fs library
+  const Video = require('@google-cloud/video-intelligence').v1p3beta1;
+  const fs = require('fs');
+  // Creates a client
+  const video = new Video.VideoIntelligenceServiceClient();
+
+  /**
+   * TODO(developer): Uncomment the following line before running the sample.
+   */
+  // const path = 'Local file to analyze, e.g. ./my-file.mp4';
+
+  // Reads a local video file and converts it to base64
+  const file = fs.readFileSync(path);
+  const inputContent = file.toString('base64');
+
+  const request = {
+    inputContent: inputContent,
+    features: ['PERSON_DETECTION'],
+    videoContext: {
+      personDetectionConfig: {
+        // Must set includeBoundingBoxes to true to get poses and attributes.
+        includeBoundingBoxes: true,
+        includePoseLandmarks: true,
+        includeAttributes: true,
+      },
+    },
+  };
+  // Detects people in a video
+  const [operation] = await video.annotateVideo(request);
+  const results = await operation.promise();
+  console.log('Waiting for operation to complete...');
+
+  // Gets annotations for video
+  const personAnnotations =
+    results[0].annotationResults[0].personDetectionAnnotations;
+
+  for (const {tracks} of personAnnotations) {
+    console.log('Person detected:');
+    for (const {segment, timestampedObjects} of tracks) {
+      if (segment.startTimeOffset.seconds === undefined) {
+        segment.startTimeOffset.seconds = 0;
+      }
+      if (segment.startTimeOffset.nanos === undefined) {
+        segment.startTimeOffset.nanos = 0;
+      }
+      if (segment.endTimeOffset.seconds === undefined) {
+        segment.endTimeOffset.seconds = 0;
+      }
+      if (segment.endTimeOffset.nanos === undefined) {
+        segment.endTimeOffset.nanos = 0;
+      }
+      console.log(
+        `\tStart: ${segment.startTimeOffset.seconds}.` +
+          `${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+      console.log(
+        `\tEnd: ${segment.endTimeOffset.seconds}.` +
+          `${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+
+      // Each segment includes timestamped objects that
+      // include characteristic--e.g. clothes, posture
+      // of the person detected.
+      const [firstTimestampedObject] = timestampedObjects;
+
+      // Attributes include unique pieces of clothing,
+      // poses, or hair color.
+      for (const {name, value} of firstTimestampedObject.attributes) {
+        console.log(`\tAttribute: ${name}; ` + `Value: ${value}`);
+      }
+
+      // Landmarks in person detection include body parts.
+      for (const {name, point} of firstTimestampedObject.landmarks) {
+        console.log(`\tLandmark: ${name}; Vertex: ${point.x}, ${point.y}`);
+      }
+    }
+  }
+  // [END video_detect_person_beta]
+}
+async function detectPersonGCS(gcsUri) {
+  //[START video_detect_person_gcs_beta]
+  // Imports the Google Cloud Video Intelligence library
+  const Video = require('@google-cloud/video-intelligence').v1p3beta1;
+  // Creates a client
+  const video = new Video.VideoIntelligenceServiceClient();
+
+  /**
+   * TODO(developer): Uncomment the following line before running the sample.
+   */
+  // const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';
+
+  const request = {
+    inputUri: gcsUri,
+    features: ['PERSON_DETECTION'],
+    videoContext: {
+      personDetectionConfig: {
+        // Must set includeBoundingBoxes to true to get poses and attributes.
+        includeBoundingBoxes: true,
+        includePoseLandmarks: true,
+        includeAttributes: true,
+      },
+    },
+  };
+  // Detects people in a video
+  const [operation] = await video.annotateVideo(request);
+  const results = await operation.promise();
+  console.log('Waiting for operation to complete...');
+
+  // Gets annotations for video
+  const personAnnotations =
+    results[0].annotationResults[0].personDetectionAnnotations;
+
+  for (const {tracks} of personAnnotations) {
+    console.log('Person detected:');
+
+    for (const {segment, timestampedObjects} of tracks) {
+      if (segment.startTimeOffset.seconds === undefined) {
+        segment.startTimeOffset.seconds = 0;
+      }
+      if (segment.startTimeOffset.nanos === undefined) {
+        segment.startTimeOffset.nanos = 0;
+      }
+      if (segment.endTimeOffset.seconds === undefined) {
+        segment.endTimeOffset.seconds = 0;
+      }
+      if (segment.endTimeOffset.nanos === undefined) {
+        segment.endTimeOffset.nanos = 0;
+      }
+      console.log(
+        `\tStart: ${segment.startTimeOffset.seconds}` +
+          `.${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+      console.log(
+        `\tEnd: ${segment.endTimeOffset.seconds}.` +
+          `${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+
+      // Each segment includes timestamped objects that
+      // include characteristic--e.g. clothes, posture
+      // of the person detected.
+      const [firstTimestampedObject] = timestampedObjects;
+
+      // Attributes include unique pieces of clothing,
+      // poses, or hair color.
+      for (const {name, value} of firstTimestampedObject.attributes) {
+        console.log(`\tAttribute: ${name}; ` + `Value: ${value}`);
+      }
+
+      // Landmarks in person detection include body parts.
+      for (const {name, point} of firstTimestampedObject.landmarks) {
+        console.log(`\tLandmark: ${name}; Vertex: ${point.x}, ${point.y}`);
+      }
+    }
+  }
+  // [END video_detect_person_beta]
+}
+async function detectFaces(path) {
+  //[START video_detect_faces_beta]
+  // Imports the Google Cloud Video Intelligence library + Node's fs library
+  const Video = require('@google-cloud/video-intelligence').v1p3beta1;
+  const fs = require('fs');
+  // Creates a client
+  const video = new Video.VideoIntelligenceServiceClient();
+
+  /**
+   * TODO(developer): Uncomment the following line before running the sample.
+   */
+  // const path = 'Local file to analyze, e.g. ./my-file.mp4';
+
+  // Reads a local video file and converts it to base64
+  const file = fs.readFileSync(path);
+  const inputContent = file.toString('base64');
+
+  const request = {
+    inputContent: inputContent,
+    features: ['FACE_DETECTION'],
+    videoContext: {
+      faceDetectionConfig: {
+        // Must set includeBoundingBoxes to true to get facial attributes.
+        includeBoundingBoxes: true,
+        includeAttributes: true,
+      },
+    },
+  };
+  // Detects faces in a video
+  const [operation] = await video.annotateVideo(request);
+  const results = await operation.promise();
+  console.log('Waiting for operation to complete...');
+
+  // Gets annotations for video
+  const faceAnnotations =
+    results[0].annotationResults[0].faceDetectionAnnotations;
+
+  for (const {tracks} of faceAnnotations) {
+    console.log('Face detected:');
+    for (const {segment, timestampedObjects} of tracks) {
+      if (segment.startTimeOffset.seconds === undefined) {
+        segment.startTimeOffset.seconds = 0;
+      }
+      if (segment.startTimeOffset.nanos === undefined) {
+        segment.startTimeOffset.nanos = 0;
+      }
+      if (segment.endTimeOffset.seconds === undefined) {
+        segment.endTimeOffset.seconds = 0;
+      }
+      if (segment.endTimeOffset.nanos === undefined) {
+        segment.endTimeOffset.nanos = 0;
+      }
+      console.log(
+        `\tStart: ${segment.startTimeOffset.seconds}` +
+          `.${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+      console.log(
+        `\tEnd: ${segment.endTimeOffset.seconds}.` +
+          `${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+
+      // Each segment includes timestamped objects that
+      // include characteristics of the face detected.
+      const [firstTimestapedObject] = timestampedObjects;
+
+      for (const {name} of firstTimestapedObject.attributes) {
+        // Attributes include unique pieces of clothing, like glasses,
+        // poses, or hair color.
+        console.log(`\tAttribute: ${name}; `);
+      }
+    }
+  }
+}
+async function detectFacesGCS(gcsUri) {
+  //[START video_detect_faces_gcs_beta]
+  // Imports the Google Cloud Video Intelligence library
+  const Video = require('@google-cloud/video-intelligence').v1p3beta1;
+  // Creates a client
+  const video = new Video.VideoIntelligenceServiceClient();
+
+  /**
+   * TODO(developer): Uncomment the following line before running the sample.
+   */
+  // const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';
+
+  const request = {
+    inputUri: gcsUri,
+    features: ['FACE_DETECTION'],
+    videoContext: {
+      faceDetectionConfig: {
+        // Must set includeBoundingBoxes to true to get facial attributes.
+        includeBoundingBoxes: true,
+        includeAttributes: true,
+      },
+    },
+  };
+  // Detects faces in a video
+  const [operation] = await video.annotateVideo(request);
+  const results = await operation.promise();
+  console.log('Waiting for operation to complete...');
+
+  // Gets annotations for video
+  const faceAnnotations =
+    results[0].annotationResults[0].faceDetectionAnnotations;
+
+  for (const {tracks} of faceAnnotations) {
+    console.log('Face detected:');
+
+    for (const {segment, timestampedObjects} of tracks) {
+      if (segment.startTimeOffset.seconds === undefined) {
+        segment.startTimeOffset.seconds = 0;
+      }
+      if (segment.startTimeOffset.nanos === undefined) {
+        segment.startTimeOffset.nanos = 0;
+      }
+      if (segment.endTimeOffset.seconds === undefined) {
+        segment.endTimeOffset.seconds = 0;
+      }
+      if (segment.endTimeOffset.nanos === undefined) {
+        segment.endTimeOffset.nanos = 0;
+      }
+      console.log(
+        `\tStart: ${segment.startTimeOffset.seconds}.` +
+          `${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+      console.log(
+        `\tEnd: ${segment.endTimeOffset.seconds}.` +
+          `${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
+      );
+
+      // Each segment includes timestamped objects that
+      // include characteristics of the face detected.
+      const [firstTimestapedObject] = timestampedObjects;
+
+      for (const {name} of firstTimestapedObject.attributes) {
+        // Attributes include unique pieces of clothing, like glasses,
+        // poses, or hair color.
+        console.log(`\tAttribute: ${name}; `);
+      }
+    }
+  }
+}
+
+async function main() {
+  require(`yargs`)
+    .demand(1)
+    .command(
+      `video-person-gcs <gcsUri>`,
+      `Detects people in a video stored in Google Cloud Storage using the Cloud Video Intelligence API.`,
+      {},
+      opts => detectPersonGCS(opts.gcsUri)
+    )
+    .command(
+      `video-person <path>`,
+      `Detects people in a video stored in a local file using the Cloud Video Intelligence API.`,
+      {},
+      opts => detectPerson(opts.path)
+    )
+    .command(
+      `video-faces-gcs <gcsUri>`,
+      `Detects faces in a video stored in Google Cloud Storage using the Cloud Video Intelligence API.`,
+      {},
+      opts => detectFacesGCS(opts.gcsUri)
+    )
+    .command(
+      `video-faces <path>`,
+      `Detects faces in a video stored in a local file using the Cloud Video Intelligence API.`,
+      {},
+      opts => detectFaces(opts.path)
+    )
+    .example(`node $0 video-person ./resources/googlework_short.mp4`)
+    .example(
+      `node $0 video-person-gcs gs://cloud-samples-data/video/googlework_short.mp4`
+    )
+    .example(`node $0 video-faces ./resources/googlework_short.mp4`)
+    .example(
+      `node $0 video-faces-gcs gs://cloud-samples-data/video/googlework_short.mp4`
+    )
+    .wrap(120)
+    .recommendCommands()
+    .epilogue(
+      `For more information, see https://cloud.google.com/video-intelligence/docs`
+    )
+    .help()
+    .strict().argv;
+}
+
+main().catch(console.error);