async function detectPerson()

in video-intelligence/analyze-person-detection.js [39:92]


  async function detectPerson() {
    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 faces in a video
    // We get the first result because we only process 1 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) {
        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 (i.e., body
        // landmarks) of the person detected.
        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}`);
        }
      }
    }
  }