Detect faces

The Video Intelligence API Face detection feature looks for faces in a video.

Face detection from a file in Cloud Storage

The following samples demonstrate face detection on a file located in Cloud Storage.

REST & CMD LINE

Send video annotation request

The following shows how to send a POST request to the videos:annotate method. The example uses the access token for a service account set up for the project using the Cloud SDK. For instructions on installing the Cloud SDK, setting up a project with a service account, and obtaining an access token, see the Video Intelligence API Quickstart.

Before using any of the request data, make the following replacements:

  • INPUT_URI: a Cloud Storage bucket that contains the file you want to annotate, including the file name. Must start with gs://.
    For example: `"inputUri": "gs://cloud-samples-data/video/googlework_short.mp4"`

HTTP method and URL: 

POST https://videointelligence.googleapis.com/v1/videos:annotate

Request JSON body: 

{
    "inputUri": "INPUT_URI",
    "features": ["FACE_DETECTION"]
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

If the response is successful, the Video Intelligence API returns the name for your operation. The above shows an example of such a response, where:

  • PROJECT_NUMBER: the number of your project
  • LOCATION_ID: the Cloud region where annotation should take place. Supported cloud regions are: us-east1, us-west1, europe-west1, asia-east1. If no region is specified, a region is selected based on video file location.
  • OPERATION_ID: the ID of the long running operation created for the request and provided in the response when you started the operation, for example 12345...

Get annotation results

To retrieve the result of the operation, make a GET request, using the operation name returned from the call to videos:annotate, as shown in the following example.

Before using any of the request data, make the following replacements:

  • OPERATION_NAME: the name of the operation as returned by Video Intelligence API. The operation name has the format projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID

HTTP method and URL: 

GET https://videointelligence.googleapis.com/v1/OPERATION_NAME

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

Face detection annotations are returned as a faceAnnotations list. Note: The done field is only returned when its value is True. It's not included in responses for which the operation has not completed.

Java


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.videointelligence.v1.AnnotateVideoProgress;
import com.google.cloud.videointelligence.v1.AnnotateVideoRequest;
import com.google.cloud.videointelligence.v1.AnnotateVideoResponse;
import com.google.cloud.videointelligence.v1.DetectedAttribute;
import com.google.cloud.videointelligence.v1.FaceDetectionAnnotation;
import com.google.cloud.videointelligence.v1.FaceDetectionConfig;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.TimestampedObject;
import com.google.cloud.videointelligence.v1.Track;
import com.google.cloud.videointelligence.v1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1.VideoContext;
import com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.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 glasses, headwear, smiling, direction of gaze
            System.out.printf(
                "\tAttribute %s: %s %s\n",
                attribute.getName(), attribute.getValue(), attribute.getConfidence());
          }
        }
      }
    }
  }
}

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').v1;

// 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_v1 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
                    )
                )

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the Video Intelligence reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Video Intelligence reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Video Intelligence reference documentation for Ruby.

Face detection from a local file

The following example uses face detection to find entities in a video from a video file uploaded from your local machine.

REST & CMD LINE

Send the process request

To perform face detection on a local video file, base64-encode the contents of the video file. For information on how to base64-encode the contents of a video file, see Base64 Encoding. Then, make a POST request to the videos:annotate method. Include the base64-encoded contents in the inputContent field of the request and specify the FACE_DETECTION feature.

The following is an example of a POST request using curl. This example uses the access token for a service account set up for the project using the Cloud SDK. For instructions on installing the Cloud SDK, setting up a project with a service account, and obtaining an access token, see the Video Intelligence API Quickstart

Before using any of the request data, make the following replacements:

  • inputContent: Local video file in binary format
    For example: 'AAAAGGZ0eXBtcDQyAAAAAGlzb21tcDQyAAGVYW1vb3YAAABsbXZoZAAAAADWvhlR1r4ZUQABX5ABCOxo AAEAAAEAAAAAAA4...'

HTTP method and URL: 

POST https://videointelligence.googleapis.com/v1/videos:annotate

Request JSON body: 

{
    inputContent: "Local video file in binary format",
    "features": ["FACE_DETECTION"]
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

If the request is successful, Video Intelligence the name for your operation. The above shows an example of such a response, where project-number is the number of your project and operation-id is the ID of the long-running operation created for the request.

{ "name": "us-west1.17122464255125931980" }

Get the results

To retrieve the result of the operation, make a GET request to the operations endpoint and specify the name of your operation.

Before using any of the request data, make the following replacements:

  • OPERATION_NAME: the name of the operation as returned by Video Intelligence API. The operation name has the format projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID

HTTP method and URL: 

GET https://videointelligence.googleapis.com/v1/OPERATION_NAME

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

Java


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.videointelligence.v1.AnnotateVideoProgress;
import com.google.cloud.videointelligence.v1.AnnotateVideoRequest;
import com.google.cloud.videointelligence.v1.AnnotateVideoResponse;
import com.google.cloud.videointelligence.v1.DetectedAttribute;
import com.google.cloud.videointelligence.v1.FaceDetectionAnnotation;
import com.google.cloud.videointelligence.v1.FaceDetectionConfig;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.TimestampedObject;
import com.google.cloud.videointelligence.v1.Track;
import com.google.cloud.videointelligence.v1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1.VideoContext;
import com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.VideoSegment;
import com.google.protobuf.ByteString;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

public class DetectFaces {

  public static void detectFaces() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String localFilePath = "resources/googlework_short.mp4";
    detectFaces(localFilePath);
  }

  // Detects faces in a video stored in a local file using the Cloud Video Intelligence API.
  public static void detectFaces(String localFilePath) throws Exception {
    try (VideoIntelligenceServiceClient videoIntelligenceServiceClient =
        VideoIntelligenceServiceClient.create()) {
      // Reads a local video file and converts it to base64.
      Path path = Paths.get(localFilePath);
      byte[] data = Files.readAllBytes(path);
      ByteString inputContent = ByteString.copyFrom(data);

      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()
              .setInputContent(inputContent)
              .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 faces 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 glasses, headwear, smiling, direction of gaze
            System.out.printf(
                "\tAttribute %s: %s %s\n",
                attribute.getName(), attribute.getValue(), attribute.getConfidence());
          }
        }
      }
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const path = 'Local file to analyze, e.g. ./my-file.mp4';

// Imports the Google Cloud Video Intelligence library + Node's fs library
const Video = require('@google-cloud/video-intelligence').v1;
const fs = require('fs');

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

// Reads a local video file and converts it to base64
const file = fs.readFileSync(path);
const inputContent = file.toString('base64');

async function detectFaces() {
  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
  // 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}; `);
      }
    }
  }
}

detectFaces();

Python

import io

from google.cloud import videointelligence_v1 as videointelligence


def detect_faces(local_file_path="path/to/your/video-file.mp4"):
    """Detects faces in a video from a local file."""

    client = videointelligence.VideoIntelligenceServiceClient()

    with io.open(local_file_path, "rb") as f:
        input_content = f.read()

    # 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_content": input_content,
            "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
                    )
                )

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the Video Intelligence reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Video Intelligence reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Video Intelligence reference documentation for Ruby.