Recognize logos in a Cloud Storage file

Detect, track, and recognize the presence of logos in video content from file in Cloud Storage.

Documentation pages that include this code sample

To view the code sample used in context, see the following documentation:

Code sample

Go

import (
	"context"
	"fmt"
	"io"
	"time"

	video "cloud.google.com/go/videointelligence/apiv1"
	"github.com/golang/protobuf/ptypes"
	videopb "google.golang.org/genproto/googleapis/cloud/videointelligence/v1"
)

// logoDetectionGCS analyzes a video and extracts logos with their bounding boxes.
func logoDetectionGCS(w io.Writer, gcsURI string) error {
	// gcsURI := "gs://cloud-samples-data/video/googlework_tiny.mp4"

	ctx := context.Background()

	// Creates a client.
	client, err := video.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("video.NewClient: %v", err)
	}
	defer client.Close()

	ctx, cancel := context.WithTimeout(ctx, time.Second*180)
	defer cancel()

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		InputUri: gcsURI,
		Features: []videopb.Feature{
			videopb.Feature_LOGO_RECOGNITION,
		},
	})
	if err != nil {
		return fmt.Errorf("AnnotateVideo: %v", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %v", err)
	}

	// Only one video was processed, so get the first result.
	result := resp.GetAnnotationResults()[0]

	// Annotations for list of logos detected, tracked and recognized in video.
	for _, annotation := range result.LogoRecognitionAnnotations {
		fmt.Fprintf(w, "Description: %q\n", annotation.Entity.GetDescription())
		// Opaque entity ID. Some IDs may be available in Google Knowledge
		// Graph Search API (https://developers.google.com/knowledge-graph/).
		if len(annotation.Entity.EntityId) > 0 {
			fmt.Fprintf(w, "\tEntity ID: %q\n", annotation.Entity.GetEntityId())
		}

		// All logo tracks where the recognized logo appears. Each track
		// corresponds to one logo instance appearing in consecutive frames.
		for _, track := range annotation.Tracks {
			// Video segment of a track.
			segment := track.GetSegment()
			start, _ := ptypes.Duration(segment.GetStartTimeOffset())
			end, _ := ptypes.Duration(segment.GetEndTimeOffset())
			fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
			fmt.Fprintf(w, "\tConfidence: %f\n", track.GetConfidence())

			// The object with timestamp and attributes per frame in the track.
			for _, timestampedObject := range track.TimestampedObjects {
				// Normalized Bounding box in a frame, where the object is
				// located.
				box := timestampedObject.GetNormalizedBoundingBox()
				fmt.Fprintf(w, "\tBounding box position:\n")
				fmt.Fprintf(w, "\t\tleft  : %f\n", box.GetLeft())
				fmt.Fprintf(w, "\t\ttop   : %f\n", box.GetTop())
				fmt.Fprintf(w, "\t\tright : %f\n", box.GetRight())
				fmt.Fprintf(w, "\t\tbottom: %f\n", box.GetBottom())

				// Optional. The attributes of the object in the bounding box.
				for _, attribute := range timestampedObject.Attributes {
					fmt.Fprintf(w, "\t\t\tName: %q\n", attribute.GetName())
					fmt.Fprintf(w, "\t\t\tConfidence: %f\n", attribute.GetConfidence())
					fmt.Fprintf(w, "\t\t\tValue: %q\n", attribute.GetValue())
				}
			}

			// Optional. Attributes in the track level.
			for _, trackAttribute := range track.Attributes {
				fmt.Fprintf(w, "\t\tName: %q\n", trackAttribute.GetName())
				fmt.Fprintf(w, "\t\tConfidence: %f\n", trackAttribute.GetConfidence())
				fmt.Fprintf(w, "\t\tValue: %q\n", trackAttribute.GetValue())
			}
		}

		// All video segments where the recognized logo appears. There might be
		// multiple instances of the same logo class appearing in one VideoSegment.
		for _, segment := range annotation.Segments {
			start, _ := ptypes.Duration(segment.GetStartTimeOffset())
			end, _ := ptypes.Duration(segment.GetEndTimeOffset())
			fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
		}
	}

	return nil
}

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.Entity;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.LogoRecognitionAnnotation;
import com.google.cloud.videointelligence.v1.NormalizedBoundingBox;
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.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.VideoSegment;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class LogoDetectionGcs {

  public static void detectLogoGcs() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String gcsUri = "gs://YOUR_BUCKET_ID/path/to/your/video.mp4";
    detectLogoGcs(gcsUri);
  }

  public static void detectLogoGcs(String inputUri)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
      // Create the request
      AnnotateVideoRequest request =
          AnnotateVideoRequest.newBuilder()
              .setInputUri(inputUri)
              .addFeatures(Feature.LOGO_RECOGNITION)
              .build();

      // asynchronously perform object tracking on videos
      OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> future =
          client.annotateVideoAsync(request);

      System.out.println("Waiting for operation to complete...");
      // The first result is retrieved because a single video was processed.
      AnnotateVideoResponse response = future.get(300, TimeUnit.SECONDS);
      VideoAnnotationResults annotationResult = response.getAnnotationResults(0);

      // Annotations for list of logos detected, tracked and recognized in video.
      for (LogoRecognitionAnnotation logoRecognitionAnnotation :
          annotationResult.getLogoRecognitionAnnotationsList()) {
        Entity entity = logoRecognitionAnnotation.getEntity();
        // Opaque entity ID. Some IDs may be available in
        // [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
        System.out.printf("Entity Id : %s\n", entity.getEntityId());
        System.out.printf("Description : %s\n", entity.getDescription());
        // All logo tracks where the recognized logo appears. Each track corresponds to one logo
        // instance appearing in consecutive frames.
        for (Track track : logoRecognitionAnnotation.getTracksList()) {

          // Video segment of a track.
          Duration startTimeOffset = track.getSegment().getStartTimeOffset();
          System.out.printf(
              "\n\tStart Time Offset: %s.%s\n",
              startTimeOffset.getSeconds(), startTimeOffset.getNanos());
          Duration endTimeOffset = track.getSegment().getEndTimeOffset();
          System.out.printf(
              "\tEnd Time Offset: %s.%s\n", endTimeOffset.getSeconds(), endTimeOffset.getNanos());
          System.out.printf("\tConfidence: %s\n", track.getConfidence());

          // The object with timestamp and attributes per frame in the track.
          for (TimestampedObject timestampedObject : track.getTimestampedObjectsList()) {

            // Normalized Bounding box in a frame, where the object is located.
            NormalizedBoundingBox normalizedBoundingBox =
                timestampedObject.getNormalizedBoundingBox();
            System.out.printf("\n\t\tLeft: %s\n", normalizedBoundingBox.getLeft());
            System.out.printf("\t\tTop: %s\n", normalizedBoundingBox.getTop());
            System.out.printf("\t\tRight: %s\n", normalizedBoundingBox.getRight());
            System.out.printf("\t\tBottom: %s\n", normalizedBoundingBox.getBottom());

            // Optional. The attributes of the object in the bounding box.
            for (DetectedAttribute attribute : timestampedObject.getAttributesList()) {
              System.out.printf("\n\t\t\tName: %s\n", attribute.getName());
              System.out.printf("\t\t\tConfidence: %s\n", attribute.getConfidence());
              System.out.printf("\t\t\tValue: %s\n", attribute.getValue());
            }
          }

          // Optional. Attributes in the track level.
          for (DetectedAttribute trackAttribute : track.getAttributesList()) {
            System.out.printf("\n\t\tName : %s\n", trackAttribute.getName());
            System.out.printf("\t\tConfidence : %s\n", trackAttribute.getConfidence());
            System.out.printf("\t\tValue : %s\n", trackAttribute.getValue());
          }
        }

        // All video segments where the recognized logo appears. There might be multiple instances
        // of the same logo class appearing in one VideoSegment.
        for (VideoSegment segment : logoRecognitionAnnotation.getSegmentsList()) {
          System.out.printf(
              "\n\tStart Time Offset : %s.%s\n",
              segment.getStartTimeOffset().getSeconds(), segment.getStartTimeOffset().getNanos());
          System.out.printf(
              "\tEnd Time Offset : %s.%s\n",
              segment.getEndTimeOffset().getSeconds(), segment.getEndTimeOffset().getNanos());
        }
      }
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const inputUri = 'gs://cloud-samples-data/video/googlework_short.mp4';

// Imports the Google Cloud client libraries
const Video = require('@google-cloud/video-intelligence');

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

// Performs asynchronous video annotation for logo recognition on a file hosted in GCS.
async function detectLogoGcs() {
  // Build the request with the input uri and logo recognition feature.
  const request = {
    inputUri: inputUri,
    features: ['LOGO_RECOGNITION'],
  };

  // Make the asynchronous request
  const [operation] = await client.annotateVideo(request);

  // Wait for the results
  const [response] = await operation.promise();

  // Get the first response, since we sent only one video.
  const annotationResult = response.annotationResults[0];
  for (const logoRecognitionAnnotation of annotationResult.logoRecognitionAnnotations) {
    const entity = logoRecognitionAnnotation.entity;
    // Opaque entity ID. Some IDs may be available in
    // [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
    console.log(`Entity Id: ${entity.entityId}`);
    console.log(`Description: ${entity.description}`);

    // All logo tracks where the recognized logo appears.
    // Each track corresponds to one logo instance appearing in consecutive frames.
    for (const track of logoRecognitionAnnotation.tracks) {
      console.log(
        `\n\tStart Time Offset: ${track.segment.startTimeOffset.seconds}.${track.segment.startTimeOffset.nanos}`
      );
      console.log(
        `\tEnd Time Offset: ${track.segment.endTimeOffset.seconds}.${track.segment.endTimeOffset.nanos}`
      );
      console.log(`\tConfidence: ${track.confidence}`);

      // The object with timestamp and attributes per frame in the track.
      for (const timestampedObject of track.timestampedObjects) {
        // Normalized Bounding box in a frame, where the object is located.
        const normalizedBoundingBox = timestampedObject.normalizedBoundingBox;
        console.log(`\n\t\tLeft: ${normalizedBoundingBox.left}`);
        console.log(`\t\tTop: ${normalizedBoundingBox.top}`);
        console.log(`\t\tRight: ${normalizedBoundingBox.right}`);
        console.log(`\t\tBottom: ${normalizedBoundingBox.bottom}`);
        // Optional. The attributes of the object in the bounding box.
        for (const attribute of timestampedObject.attributes) {
          console.log(`\n\t\t\tName: ${attribute.name}`);
          console.log(`\t\t\tConfidence: ${attribute.confidence}`);
          console.log(`\t\t\tValue: ${attribute.value}`);
        }
      }

      // Optional. Attributes in the track level.
      for (const trackAttribute of track.attributes) {
        console.log(`\n\t\tName: ${trackAttribute.name}`);
        console.log(`\t\tConfidence: ${trackAttribute.confidence}`);
        console.log(`\t\tValue: ${trackAttribute.value}`);
      }
    }

    // All video segments where the recognized logo appears.
    // There might be multiple instances of the same logo class appearing in one VideoSegment.
    for (const segment of logoRecognitionAnnotation.segments) {
      console.log(
        `\n\tStart Time Offset: ${segment.startTimeOffset.seconds}.${segment.startTimeOffset.nanos}`
      );
      console.log(
        `\tEnd Time Offset: ${segment.endTimeOffset.seconds}.${segment.endTimeOffset.nanos}`
      );
    }
  }
}

detectLogoGcs();

Python


from google.cloud import videointelligence


def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):

    client = videointelligence.VideoIntelligenceServiceClient()

    features = [videointelligence.Feature.LOGO_RECOGNITION]

    operation = client.annotate_video(
        request={"features": features, "input_uri": input_uri}
    )

    print(u"Waiting for operation to complete...")
    response = operation.result()

    # Get the first response, since we sent only one video.
    annotation_result = response.annotation_results[0]

    # Annotations for list of logos detected, tracked and recognized in video.
    for logo_recognition_annotation in annotation_result.logo_recognition_annotations:
        entity = logo_recognition_annotation.entity

        # Opaque entity ID. Some IDs may be available in [Google Knowledge Graph
        # Search API](https://developers.google.com/knowledge-graph/).
        print(u"Entity Id : {}".format(entity.entity_id))

        print(u"Description : {}".format(entity.description))

        # All logo tracks where the recognized logo appears. Each track corresponds
        # to one logo instance appearing in consecutive frames.
        for track in logo_recognition_annotation.tracks:

            # Video segment of a track.
            print(
                u"\n\tStart Time Offset : {}.{}".format(
                    track.segment.start_time_offset.seconds,
                    track.segment.start_time_offset.microseconds * 1000,
                )
            )
            print(
                u"\tEnd Time Offset : {}.{}".format(
                    track.segment.end_time_offset.seconds,
                    track.segment.end_time_offset.microseconds * 1000,
                )
            )
            print(u"\tConfidence : {}".format(track.confidence))

            # The object with timestamp and attributes per frame in the track.
            for timestamped_object in track.timestamped_objects:
                # Normalized Bounding box in a frame, where the object is located.
                normalized_bounding_box = timestamped_object.normalized_bounding_box
                print(u"\n\t\tLeft : {}".format(normalized_bounding_box.left))
                print(u"\t\tTop : {}".format(normalized_bounding_box.top))
                print(u"\t\tRight : {}".format(normalized_bounding_box.right))
                print(u"\t\tBottom : {}".format(normalized_bounding_box.bottom))

                # Optional. The attributes of the object in the bounding box.
                for attribute in timestamped_object.attributes:
                    print(u"\n\t\t\tName : {}".format(attribute.name))
                    print(u"\t\t\tConfidence : {}".format(attribute.confidence))
                    print(u"\t\t\tValue : {}".format(attribute.value))

            # Optional. Attributes in the track level.
            for track_attribute in track.attributes:
                print(u"\n\t\tName : {}".format(track_attribute.name))
                print(u"\t\tConfidence : {}".format(track_attribute.confidence))
                print(u"\t\tValue : {}".format(track_attribute.value))

        # All video segments where the recognized logo appears. There might be
        # multiple instances of the same logo class appearing in one VideoSegment.
        for segment in logo_recognition_annotation.segments:
            print(
                u"\n\tStart Time Offset : {}.{}".format(
                    segment.start_time_offset.seconds,
                    segment.start_time_offset.microseconds * 1000,
                )
            )
            print(
                u"\tEnd Time Offset : {}.{}".format(
                    segment.end_time_offset.seconds,
                    segment.end_time_offset.microseconds * 1000,
                )
            )

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