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Detecta rostros en un archivo de Cloud Storage

Detecta rostros en un video almacenado en Cloud Storage.

Páginas de documentación que incluyen esta muestra de código

Para ver la muestra de código usada en contexto, consulta la siguiente documentación:

Muestra de código

Java


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.videointelligence.v1p3beta1.AnnotateVideoProgress;
import com.google.cloud.videointelligence.v1p3beta1.AnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.AnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.DetectedAttribute;
import com.google.cloud.videointelligence.v1p3beta1.FaceDetectionAnnotation;
import com.google.cloud.videointelligence.v1p3beta1.FaceDetectionConfig;
import com.google.cloud.videointelligence.v1p3beta1.Feature;
import com.google.cloud.videointelligence.v1p3beta1.TimestampedObject;
import com.google.cloud.videointelligence.v1p3beta1.Track;
import com.google.cloud.videointelligence.v1p3beta1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1p3beta1.VideoContext;
import com.google.cloud.videointelligence.v1p3beta1.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1p3beta1.VideoSegment;

public class DetectFacesGcs {

  public static void detectFacesGcs() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String gcsUri = "gs://cloud-samples-data/video/googlework_short.mp4";
    detectFacesGcs(gcsUri);
  }

  // Detects faces in a video stored in Google Cloud Storage using the Cloud Video Intelligence API.
  public static void detectFacesGcs(String gcsUri) throws Exception {
    try (VideoIntelligenceServiceClient videoIntelligenceServiceClient =
        VideoIntelligenceServiceClient.create()) {

      FaceDetectionConfig faceDetectionConfig =
          FaceDetectionConfig.newBuilder()
              // Must set includeBoundingBoxes to true to get facial attributes.
              .setIncludeBoundingBoxes(true)
              .setIncludeAttributes(true)
              .build();
      VideoContext videoContext =
          VideoContext.newBuilder().setFaceDetectionConfig(faceDetectionConfig).build();

      AnnotateVideoRequest request =
          AnnotateVideoRequest.newBuilder()
              .setInputUri(gcsUri)
              .addFeatures(Feature.FACE_DETECTION)
              .setVideoContext(videoContext)
              .build();

      // Detects faces in a video
      OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> future =
          videoIntelligenceServiceClient.annotateVideoAsync(request);

      System.out.println("Waiting for operation to complete...");
      AnnotateVideoResponse response = future.get();

      // Gets annotations for video
      VideoAnnotationResults annotationResult = response.getAnnotationResultsList().get(0);

      // Annotations for list of people detected, tracked and recognized in video.
      for (FaceDetectionAnnotation faceDetectionAnnotation :
          annotationResult.getFaceDetectionAnnotationsList()) {
        System.out.print("Face detected:\n");
        for (Track track : faceDetectionAnnotation.getTracksList()) {
          VideoSegment segment = track.getSegment();
          System.out.printf(
              "\tStart: %d.%.0fs\n",
              segment.getStartTimeOffset().getSeconds(),
              segment.getStartTimeOffset().getNanos() / 1e6);
          System.out.printf(
              "\tEnd: %d.%.0fs\n",
              segment.getEndTimeOffset().getSeconds(), segment.getEndTimeOffset().getNanos() / 1e6);

          // Each segment includes timestamped objects that
          // include characteristics of the face detected.
          TimestampedObject firstTimestampedObject = track.getTimestampedObjects(0);

          for (DetectedAttribute attribute : firstTimestampedObject.getAttributesList()) {
            // Attributes include unique pieces of clothing, like glasses,
            // poses, or hair color.
            System.out.printf("\tAttribute: %s;\n", attribute.getName());
          }
        }
      }
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';

// Imports the Google Cloud Video Intelligence library + Node's fs library
const Video = require('@google-cloud/video-intelligence').v1p3beta1;

// Creates a client
const video = new Video.VideoIntelligenceServiceClient();

async function detectFacesGCS() {
  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
  // 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 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 'glasses', 'headwear', 'smiling'.
        console.log(`\tAttribute: ${name}; `);
      }
    }
  }
}

detectFacesGCS();

Python

from google.cloud import videointelligence_v1p3beta1 as videointelligence

def detect_faces(gcs_uri="gs://YOUR_BUCKET_ID/path/to/your/video.mp4"):
    """Detects faces in a video."""

    client = videointelligence.VideoIntelligenceServiceClient()

    # Configure the request
    config = videointelligence.FaceDetectionConfig(
        include_bounding_boxes=True, include_attributes=True
    )
    context = videointelligence.VideoContext(face_detection_config=config)

    # Start the asynchronous request
    operation = client.annotate_video(
        request={
            "features": [videointelligence.Feature.FACE_DETECTION],
            "input_uri": gcs_uri,
            "video_context": context,
        }
    )

    print("\nProcessing video for face detection annotations.")
    result = operation.result(timeout=300)

    print("\nFinished processing.\n")

    # Retrieve the first result, because a single video was processed.
    annotation_result = result.annotation_results[0]

    for annotation in annotation_result.face_detection_annotations:
        print("Face detected:")
        for track in annotation.tracks:
            print(
                "Segment: {}s to {}s".format(
                    track.segment.start_time_offset.seconds
                    + track.segment.start_time_offset.microseconds / 1e6,
                    track.segment.end_time_offset.seconds
                    + track.segment.end_time_offset.microseconds / 1e6,
                )
            )

            # Each segment includes timestamped faces that include
            # characteristics of the face detected.
            # Grab the first timestamped face
            timestamped_object = track.timestamped_objects[0]
            box = timestamped_object.normalized_bounding_box
            print("Bounding box:")
            print("\tleft  : {}".format(box.left))
            print("\ttop   : {}".format(box.top))
            print("\tright : {}".format(box.right))
            print("\tbottom: {}".format(box.bottom))

            # Attributes include glasses, headwear, smiling, direction of gaze
            print("Attributes:")
            for attribute in timestamped_object.attributes:
                print(
                    "\t{}:{} {}".format(
                        attribute.name, attribute.value, attribute.confidence
                    )
                )