Analyser les thèmes des vidéos

L'analyse de thèmes permet de détecter les thèmes présents au sein d'une vidéo.

Cette section décrit plusieurs manières d'effectuer cette démarche.

Voici un exemple d'analyse de thèmes au sein d'un fichier vidéo local.

Vous souhaitez obtenir une analyse plus approfondie ? Consultez le tutoriel détaillé en Python.

Protocole

Reportez-vous au point de terminaison videos:annotate de l'API pour obtenir des informations complètes à ce sujet.

Pour lancer une détection de thèmes, utilisez une requête POST dont le corps approprié est :

POST https://videointelligence.googleapis.com/v1/videos:annotate?key=YOUR_API_KEY
{
  "inputContent": "/9j/7QBEUGhvdG9zaG9...base64-encoded-video-content...fXNWzvDEeYxxxzj/Coa6Bax//Z",
  "features": ["LABEL_DETECTION"]
}

Si une requête d'annotation Video Intelligence aboutit, elle renvoie une réponse qui ne contient qu'un champ de nom :

{
  "name": "us-west1.16680573"
}

Ce nom représente une opération de longue durée, qui peut être interrogée à l'aide de l'API v1.operations.

Pour récupérer la réponse de l'annotation vidéo, envoyez une requête GET au point de terminaison v1.operations, en transmettant la valeur de name dans l'URL. Si l'opération est terminée, elle renverra les résultats de l'annotation.

Les annotations de recherche de thèmes sont renvoyées dans le champ annotationResults. Exemple :

{
  "name": "us-east1.7397809392042093732",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.videointelligence.v1.AnnotateVideoProgress",
    "annotationProgress": [
      {
        "inputContent": "/9j/7QBEUGhvdG9zaG9...base64-encoded-video-content...fXNWzvDEeYxxxzj/Coa6Bax//Z",
        "progressPercent": 100,
        "startTime": "2017-05-18T21:14:35.235527Z",
        "updateTime": "2017-05-18T21:14:42.665369Z"
      }
    ]
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.videointelligence.v1.AnnotateVideoResponse",
    "annotationResults": [
          {
            "inputUri": "/demomaker/cat.mp4",
            "segmentLabelAnnotations": [
              {
                "entity": {
                  "entityId": "/m/01yrx",
                  "description": "cat",
                  "languageCode": "en-US"
                },
                "categoryEntities": [
                  {
                    "entityId": "/m/068hy",
                    "description": "pet",
                    "languageCode": "en-US"
                  }
                ],
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.98509187
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/0jbk",
                  "description": "animal",
                  "languageCode": "en-US"
                },
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.9809588
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/068hy",
                  "description": "pet",
                  "languageCode": "en-US"
                },
                "categoryEntities": [
                  {
                    "entityId": "/m/0jbk",
                    "description": "animal",
                    "languageCode": "en-US"
                  }
                ],
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.9382622
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/05h0n",
                  "description": "nature",
                  "languageCode": "en-US"
                },
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.8411303
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/07k6w8",
                  "description": "small to medium sized cats",
                  "languageCode": "en-US"
                },
                "categoryEntities": [
                  {
                    "entityId": "/m/04rky",
                    "description": "mammal",
                    "languageCode": "en-US"
                  }
                ],
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.8077077
                  }
                ]
              },
             <snip>
            ]
          }
        ]

C#

public static object AnalyzeLabels(string path)
{
    var client = VideoIntelligenceServiceClient.Create();
    var request = new AnnotateVideoRequest()
    {
        InputContent = Google.Protobuf.ByteString.CopyFrom(File.ReadAllBytes(path)),
        Features = { Feature.LabelDetection }
    };
    var op = client.AnnotateVideo(request).PollUntilCompleted();
    foreach (var result in op.Result.AnnotationResults)
    {
        PrintLabels("Video", result.SegmentLabelAnnotations);
        PrintLabels("Shot", result.ShotLabelAnnotations);
        PrintLabels("Frame", result.FrameLabelAnnotations);
    }
    return 0;
}

static void PrintLabels(string labelName,
    IEnumerable<LabelAnnotation> labelAnnotations)
{
    foreach (var annotation in labelAnnotations)
    {
        Console.WriteLine($"{labelName} label: {annotation.Entity.Description}");
        foreach (var entity in annotation.CategoryEntities)
        {
            Console.WriteLine($"{labelName} label category: {entity.Description}");
        }
        foreach (var segment in annotation.Segments)
        {
            Console.Write("Segment location: ");
            Console.Write(segment.Segment.StartTimeOffset);
            Console.Write(":");
            Console.WriteLine(segment.Segment.EndTimeOffset);
            System.Console.WriteLine($"Confidence: {segment.Confidence}");
        }
    }
}

Go

func label(w io.Writer, file string) error {
	ctx := context.Background()
	client, err := video.NewClient(ctx)
	if err != nil {
		return err
	}

	fileBytes, err := ioutil.ReadFile(file)
	if err != nil {
		return err
	}

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		Features: []videopb.Feature{
			videopb.Feature_LABEL_DETECTION,
		},
		InputContent: fileBytes,
	})
	if err != nil {
		return err
	}
	resp, err := op.Wait(ctx)
	if err != nil {
		return err
	}

	printLabels := func(labels []*videopb.LabelAnnotation) {
		for _, label := range labels {
			fmt.Fprintf(w, "\tDescription: %s\n", label.Entity.Description)
			for _, category := range label.CategoryEntities {
				fmt.Fprintf(w, "\t\tCategory: %s\n", category.Description)
			}
			for _, segment := range label.Segments {
				start, _ := ptypes.Duration(segment.Segment.StartTimeOffset)
				end, _ := ptypes.Duration(segment.Segment.EndTimeOffset)
				fmt.Fprintf(w, "\t\tSegment: %s to %s\n", start, end)
			}
		}
	}

	// A single video was processed. Get the first result.
	result := resp.AnnotationResults[0]

	fmt.Fprintln(w, "SegmentLabelAnnotations:")
	printLabels(result.SegmentLabelAnnotations)
	fmt.Fprintln(w, "ShotLabelAnnotations:")
	printLabels(result.ShotLabelAnnotations)
	fmt.Fprintln(w, "FrameLabelAnnotations:")
	printLabels(result.FrameLabelAnnotations)

	return nil
}

Java

// Instantiate a com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
  // Read file and encode into Base64
  Path path = Paths.get(filePath);
  byte[] data = Files.readAllBytes(path);

  AnnotateVideoRequest request = AnnotateVideoRequest.newBuilder()
      .setInputContent(ByteString.copyFrom(data))
      .addFeatures(Feature.LABEL_DETECTION)
      .build();
  // Create an operation that will contain the response when the operation completes.
  OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response =
      client.annotateVideoAsync(request);

  System.out.println("Waiting for operation to complete...");
  for (VideoAnnotationResults results : response.get().getAnnotationResultsList()) {
    // process video / segment level label annotations
    System.out.println("Locations: ");
    for (LabelAnnotation labelAnnotation : results.getSegmentLabelAnnotationsList()) {
      System.out
          .println("Video label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Video label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.getSegmentsList()) {
        double startTime = segment.getSegment().getStartTimeOffset().getSeconds()
            + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
        double endTime = segment.getSegment().getEndTimeOffset().getSeconds()
            + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
        System.out.printf("Segment location: %.3f:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }

    // process shot label annotations
    for (LabelAnnotation labelAnnotation : results.getShotLabelAnnotationsList()) {
      System.out
          .println("Shot label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Shot label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.getSegmentsList()) {
        double startTime = segment.getSegment().getStartTimeOffset().getSeconds()
            + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
        double endTime = segment.getSegment().getEndTimeOffset().getSeconds()
            + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
        System.out.printf("Segment location: %.3f:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }

    // process frame label annotations
    for (LabelAnnotation labelAnnotation : results.getFrameLabelAnnotationsList()) {
      System.out
          .println("Frame label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Frame label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.getSegmentsList()) {
        double startTime = segment.getSegment().getStartTimeOffset().getSeconds()
            + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
        double endTime = segment.getSegment().getEndTimeOffset().getSeconds()
            + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
        System.out.printf("Segment location: %.3f:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }
  }
}

Node.js

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

// Creates a client
const client = 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 readFile = util.promisify(fs.readFile);
const file = await readFile(path);
const inputContent = file.toString('base64');

// Constructs request
const request = {
  inputContent: inputContent,
  features: ['LABEL_DETECTION'],
};

// Detects labels in a video
const [operation] = await client.annotateVideo(request);
console.log('Waiting for operation to complete...');
const [operationResult] = await operation.promise();
// Gets annotations for video
const annotations = operationResult.annotationResults[0];

const labels = annotations.segmentLabelAnnotations;
labels.forEach(label => {
  console.log(`Label ${label.entity.description} occurs at:`);
  label.segments.forEach(segment => {
    const time = segment.segment;
    if (time.startTimeOffset.seconds === undefined) {
      time.startTimeOffset.seconds = 0;
    }
    if (time.startTimeOffset.nanos === undefined) {
      time.startTimeOffset.nanos = 0;
    }
    if (time.endTimeOffset.seconds === undefined) {
      time.endTimeOffset.seconds = 0;
    }
    if (time.endTimeOffset.nanos === undefined) {
      time.endTimeOffset.nanos = 0;
    }
    console.log(
      `\tStart: ${time.startTimeOffset.seconds}` +
        `.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(
      `\tEnd: ${time.endTimeOffset.seconds}.` +
        `${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(`\tConfidence: ${segment.confidence}`);
  });
});

Python

Pour en savoir plus sur l'installation et l'utilisation de la bibliothèque cliente de l'API Cloud Video Intelligence pour Python, reportez-vous aux bibliothèques clientes de l'API Cloud Video Intelligence.
"""Detect labels given a file path."""
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.enums.Feature.LABEL_DETECTION]

with io.open(path, 'rb') as movie:
    input_content = movie.read()

operation = video_client.annotate_video(
    features=features, input_content=input_content)
print('\nProcessing video for label annotations:')

result = operation.result(timeout=90)
print('\nFinished processing.')

# Process video/segment level label annotations
segment_labels = result.annotation_results[0].segment_label_annotations
for i, segment_label in enumerate(segment_labels):
    print('Video label description: {}'.format(
        segment_label.entity.description))
    for category_entity in segment_label.category_entities:
        print('\tLabel category description: {}'.format(
            category_entity.description))

    for i, segment in enumerate(segment_label.segments):
        start_time = (segment.segment.start_time_offset.seconds +
                      segment.segment.start_time_offset.nanos / 1e9)
        end_time = (segment.segment.end_time_offset.seconds +
                    segment.segment.end_time_offset.nanos / 1e9)
        positions = '{}s to {}s'.format(start_time, end_time)
        confidence = segment.confidence
        print('\tSegment {}: {}'.format(i, positions))
        print('\tConfidence: {}'.format(confidence))
    print('\n')

# Process shot level label annotations
shot_labels = result.annotation_results[0].shot_label_annotations
for i, shot_label in enumerate(shot_labels):
    print('Shot label description: {}'.format(
        shot_label.entity.description))
    for category_entity in shot_label.category_entities:
        print('\tLabel category description: {}'.format(
            category_entity.description))

    for i, shot in enumerate(shot_label.segments):
        start_time = (shot.segment.start_time_offset.seconds +
                      shot.segment.start_time_offset.nanos / 1e9)
        end_time = (shot.segment.end_time_offset.seconds +
                    shot.segment.end_time_offset.nanos / 1e9)
        positions = '{}s to {}s'.format(start_time, end_time)
        confidence = shot.confidence
        print('\tSegment {}: {}'.format(i, positions))
        print('\tConfidence: {}'.format(confidence))
    print('\n')

# Process frame level label annotations
frame_labels = result.annotation_results[0].frame_label_annotations
for i, frame_label in enumerate(frame_labels):
    print('Frame label description: {}'.format(
        frame_label.entity.description))
    for category_entity in frame_label.category_entities:
        print('\tLabel category description: {}'.format(
            category_entity.description))

    # Each frame_label_annotation has many frames,
    # here we print information only about the first frame.
    frame = frame_label.frames[0]
    time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9
    print('\tFirst frame time offset: {}s'.format(time_offset))
    print('\tFirst frame confidence: {}'.format(frame.confidence))
    print('\n')

PHP

use Google\Cloud\VideoIntelligence\V1\VideoIntelligenceServiceClient;
use Google\Cloud\VideoIntelligence\V1\Feature;

/**
 * Finds labels in the video.
 *
 * @param string $uri The cloud storage object to analyze. Must be formatted
 *                    like gs://bucketname/objectname.
 * @param array $options optional Array of options to pass to
 *                       OperationResponse::pollUntilComplete. This is useful
 *                       for increasing the "pollingIntervalSeconds" option.
 */
function analyze_labels($uri, array $options = [])
{
    # Instantiate a client.
    $video = new VideoIntelligenceServiceClient();

    # Execute a request.
    $operation = $video->annotateVideo([
        'inputUri' => $uri,
        'features' => [Feature::LABEL_DETECTION]
    ]);

    # Wait for the request to complete.
    $operation->pollUntilComplete($options);

    # Print the results.
    if ($operation->operationSucceeded()) {
        $results = $operation->getResult()->getAnnotationResults()[0];

        # Process video/segment level label annotations
        foreach ($results->getSegmentLabelAnnotations() as $label) {
            printf('Video label description: %s' . PHP_EOL, $label->getEntity()->getDescription());
            foreach ($label->getCategoryEntities() as $categoryEntity) {
                printf('  Category: %s' . PHP_EOL, $categoryEntity->getDescription());
            }
            foreach ($label->getSegments() as $segment) {
                $startTimeOffset = $segment->getSegment()->getStartTimeOffset();
                $startSeconds = $startTimeOffset->getSeconds();
                $startNanoseconds = floatval($startTimeOffset->getNanos())/1000000000.00;
                $startTime = $startSeconds + $startNanoseconds;
                $endTimeOffset = $segment->getSegment()->getEndTimeOffset();
                $endSeconds = $endTimeOffset->getSeconds();
                $endNanoseconds = floatval($endTimeOffset->getNanos())/1000000000.00;
                $endTime = $endSeconds + $endNanoseconds;
                printf('  Segment: %ss to %ss' . PHP_EOL, $startTime, $endTime);
                printf('  Confidence: %f' . PHP_EOL, $segment->getConfidence());
            }
        }
        print(PHP_EOL);

        # Process shot level label annotations
        foreach ($results->getShotLabelAnnotations() as $label) {
            printf('Shot label description: %s' . PHP_EOL, $label->getEntity()->getDescription());
            foreach ($label->getCategoryEntities() as $categoryEntity) {
                printf('  Category: %s' . PHP_EOL, $categoryEntity->getDescription());
            }
            foreach ($label->getSegments() as $shot) {
                $startTimeOffset = $shot->getSegment()->getStartTimeOffset();
                $startSeconds = $startTimeOffset->getSeconds();
                $startNanoseconds = floatval($startTimeOffset->getNanos())/1000000000.00;
                $startTime = $startSeconds + $startNanoseconds;
                $endTimeOffset = $shot->getSegment()->getEndTimeOffset();
                $endSecondseconds = $endTimeOffset->getSeconds();
                $endNanos = floatval($endTimeOffset->getNanos())/1000000000.00;
                $endTime = $endSeconds + $endNanoseconds;
                printf('  Shot: %ss to %ss' . PHP_EOL, $startTime, $endTime);
                printf('  Confidence: %f' . PHP_EOL, $shot->getConfidence());
            }
        }
        print(PHP_EOL);
    } else {
        print_r($operation->getError());
    }
}

Ruby

# path = "Path to a local video file: path/to/file.mp4"

require "google/cloud/video_intelligence"

video = Google::Cloud::VideoIntelligence.new

video_contents = File.binread path

# Register a callback during the method call
operation = video.annotate_video input_content: video_contents, features: [:LABEL_DETECTION] do |operation|
  raise operation.results.message? if operation.error?
  puts "Finished Processing."

  labels = operation.results.annotation_results.first.segment_label_annotations

  labels.each do |label|
    puts "Label description: #{label.entity.description}"

    label.category_entities.each do |category_entity|
      puts "Label category description: #{category_entity.description}"
    end

    label.segments.each do |segment|
      start_time = (segment.segment.start_time_offset.seconds +
                     segment.segment.start_time_offset.nanos / 1e9)
      end_time =   (segment.segment.end_time_offset.seconds +
                     segment.segment.end_time_offset.nanos / 1e9)

      puts "Segment: #{start_time} to #{end_time}"
      puts "Confidence: #{segment.confidence}"
    end
  end
end

puts "Processing video for label annotations:"
operation.wait_until_done!

Voici un exemple d'analyse de thèmes au sein d'un fichier vidéo se trouvant dans Google Cloud Storage.

Protocole

Reportez-vous au point de terminaison videos:annotate de l'API pour obtenir des informations complètes à ce sujet.

Pour lancer une détection de thèmes, utilisez une requête POST dont le corps approprié est :

POST https://videointelligence.googleapis.com/v1/videos:annotate?key=YOUR_API_KEY
{
  "inputUri": "gs://demomaker/cat.mp4",
  "features": ["LABEL_DETECTION"]
}

Si une requête d'annotation Video Intelligence aboutit, elle renvoie une réponse qui ne contient qu'un champ de nom :

{
  "name": "us-west1.16680573"
}

Ce nom représente une opération de longue durée, qui peut être interrogée à l'aide de l'API v1.operations.

Pour récupérer la réponse de l'annotation vidéo, envoyez une requête GET au point de terminaison v1.operations, en transmettant la valeur de name dans l'URL. Si l'opération est terminée, elle renverra les résultats de l'annotation.

Les annotations de recherche de thèmes sont renvoyées dans le champ annotationResults. Exemple :

{
  "name": "us-east1.7397809392042093732",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.videointelligence.v1.AnnotateVideoProgress",
    "annotationProgress": [
      {
        "inputUri": "/demomaker/cat.mp4",
        "progressPercent": 100,
        "startTime": "2017-05-18T21:14:35.235527Z",
        "updateTime": "2017-05-18T21:14:42.665369Z"
      }
    ]
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.videointelligence.v1.AnnotateVideoResponse",
    "annotationResults": [
          {
            "inputUri": "/demomaker/cat.mp4",
            "segmentLabelAnnotations": [
              {
                "entity": {
                  "entityId": "/m/01yrx",
                  "description": "cat",
                  "languageCode": "en-US"
                },
                "categoryEntities": [
                  {
                    "entityId": "/m/068hy",
                    "description": "pet",
                    "languageCode": "en-US"
                  }
                ],
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.98509187
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/0jbk",
                  "description": "animal",
                  "languageCode": "en-US"
                },
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.9809588
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/068hy",
                  "description": "pet",
                  "languageCode": "en-US"
                },
                "categoryEntities": [
                  {
                    "entityId": "/m/0jbk",
                    "description": "animal",
                    "languageCode": "en-US"
                  }
                ],
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.9382622
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/05h0n",
                  "description": "nature",
                  "languageCode": "en-US"
                },
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.8411303
                  }
                ]
              },
              {
                "entity": {
                  "entityId": "/m/07k6w8",
                  "description": "small to medium sized cats",
                  "languageCode": "en-US"
                },
                "categoryEntities": [
                  {
                    "entityId": "/m/04rky",
                    "description": "mammal",
                    "languageCode": "en-US"
                  }
                ],
                "segments": [
                  {
                    "segment": {
                      "startTimeOffset": "0s",
                      "endTimeOffset": "14.833664s"
                    },
                    "confidence": 0.8077077
                  }
                ]
              },
             <snip>
            ]
          }
        ]

C#

public static object AnalyzeLabelsGcs(string uri)
{
    var client = VideoIntelligenceServiceClient.Create();
    var request = new AnnotateVideoRequest()
    {
        InputUri = uri,
        Features = { Feature.LabelDetection }
    };
    var op = client.AnnotateVideo(request).PollUntilCompleted();
    foreach (var result in op.Result.AnnotationResults)
    {
        PrintLabels("Video", result.SegmentLabelAnnotations);
        PrintLabels("Shot", result.ShotLabelAnnotations);
        PrintLabels("Frame", result.FrameLabelAnnotations);
    }
    return 0;
}

static void PrintLabels(string labelName,
    IEnumerable<LabelAnnotation> labelAnnotations)
{
    foreach (var annotation in labelAnnotations)
    {
        Console.WriteLine($"{labelName} label: {annotation.Entity.Description}");
        foreach (var entity in annotation.CategoryEntities)
        {
            Console.WriteLine($"{labelName} label category: {entity.Description}");
        }
        foreach (var segment in annotation.Segments)
        {
            Console.Write("Segment location: ");
            Console.Write(segment.Segment.StartTimeOffset);
            Console.Write(":");
            Console.WriteLine(segment.Segment.EndTimeOffset);
            System.Console.WriteLine($"Confidence: {segment.Confidence}");
        }
    }
}

Go

func labelURI(w io.Writer, file string) error {
	ctx := context.Background()
	client, err := video.NewClient(ctx)
	if err != nil {
		return err
	}

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		Features: []videopb.Feature{
			videopb.Feature_LABEL_DETECTION,
		},
		InputUri: file,
	})
	if err != nil {
		return err
	}
	resp, err := op.Wait(ctx)
	if err != nil {
		return err
	}

	printLabels := func(labels []*videopb.LabelAnnotation) {
		for _, label := range labels {
			fmt.Fprintf(w, "\tDescription: %s\n", label.Entity.Description)
			for _, category := range label.CategoryEntities {
				fmt.Fprintf(w, "\t\tCategory: %s\n", category.Description)
			}
			for _, segment := range label.Segments {
				start, _ := ptypes.Duration(segment.Segment.StartTimeOffset)
				end, _ := ptypes.Duration(segment.Segment.EndTimeOffset)
				fmt.Fprintf(w, "\t\tSegment: %s to %s\n", start, end)
			}
		}
	}

	// A single video was processed. Get the first result.
	result := resp.AnnotationResults[0]

	fmt.Fprintln(w, "SegmentLabelAnnotations:")
	printLabels(result.SegmentLabelAnnotations)
	fmt.Fprintln(w, "ShotLabelAnnotations:")
	printLabels(result.ShotLabelAnnotations)
	fmt.Fprintln(w, "FrameLabelAnnotations:")
	printLabels(result.FrameLabelAnnotations)

	return nil
}

Java

// Instantiate a com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
  // Provide path to file hosted on GCS as "gs://bucket-name/..."
  AnnotateVideoRequest request = AnnotateVideoRequest.newBuilder()
      .setInputUri(gcsUri)
      .addFeatures(Feature.LABEL_DETECTION)
      .build();
  // Create an operation that will contain the response when the operation completes.
  OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response =
      client.annotateVideoAsync(request);

  System.out.println("Waiting for operation to complete...");
  for (VideoAnnotationResults results : response.get().getAnnotationResultsList()) {
    // process video / segment level label annotations
    System.out.println("Locations: ");
    for (LabelAnnotation labelAnnotation : results.getSegmentLabelAnnotationsList()) {
      System.out
          .println("Video label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Video label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.getSegmentsList()) {
        double startTime = segment.getSegment().getStartTimeOffset().getSeconds()
            + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
        double endTime = segment.getSegment().getEndTimeOffset().getSeconds()
            + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
        System.out.printf("Segment location: %.3f:%.3f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }

    // process shot label annotations
    for (LabelAnnotation labelAnnotation : results.getShotLabelAnnotationsList()) {
      System.out
          .println("Shot label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Shot label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.getSegmentsList()) {
        double startTime = segment.getSegment().getStartTimeOffset().getSeconds()
            + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
        double endTime = segment.getSegment().getEndTimeOffset().getSeconds()
            + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
        System.out.printf("Segment location: %.3f:%.3f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }

    // process frame label annotations
    for (LabelAnnotation labelAnnotation : results.getFrameLabelAnnotationsList()) {
      System.out
          .println("Frame label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Frame label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.getSegmentsList()) {
        double startTime = segment.getSegment().getStartTimeOffset().getSeconds()
            + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
        double endTime = segment.getSegment().getEndTimeOffset().getSeconds()
            + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
        System.out.printf("Segment location: %.3f:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }
  }
}

Node.js

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

// Creates a client
const client = 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: ['LABEL_DETECTION'],
};

// Detects labels in a video
const [operation] = await client.annotateVideo(request);
console.log('Waiting for operation to complete...');
const [operationResult] = await operation.promise();

// Gets annotations for video
const annotations = operationResult.annotationResults[0];

const labels = annotations.segmentLabelAnnotations;
labels.forEach(label => {
  console.log(`Label ${label.entity.description} occurs at:`);
  label.segments.forEach(segment => {
    const time = segment.segment;
    if (time.startTimeOffset.seconds === undefined) {
      time.startTimeOffset.seconds = 0;
    }
    if (time.startTimeOffset.nanos === undefined) {
      time.startTimeOffset.nanos = 0;
    }
    if (time.endTimeOffset.seconds === undefined) {
      time.endTimeOffset.seconds = 0;
    }
    if (time.endTimeOffset.nanos === undefined) {
      time.endTimeOffset.nanos = 0;
    }
    console.log(
      `\tStart: ${time.startTimeOffset.seconds}` +
        `.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(
      `\tEnd: ${time.endTimeOffset.seconds}.` +
        `${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(`\tConfidence: ${segment.confidence}`);
  });
});

Python

""" Detects labels given a GCS path. """
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.enums.Feature.LABEL_DETECTION]

mode = videointelligence.enums.LabelDetectionMode.SHOT_AND_FRAME_MODE
config = videointelligence.types.LabelDetectionConfig(
    label_detection_mode=mode)
context = videointelligence.types.VideoContext(
    label_detection_config=config)

operation = video_client.annotate_video(
    path, features=features, video_context=context)
print('\nProcessing video for label annotations:')

result = operation.result(timeout=180)
print('\nFinished processing.')

# Process video/segment level label annotations
segment_labels = result.annotation_results[0].segment_label_annotations
for i, segment_label in enumerate(segment_labels):
    print('Video label description: {}'.format(
        segment_label.entity.description))
    for category_entity in segment_label.category_entities:
        print('\tLabel category description: {}'.format(
            category_entity.description))

    for i, segment in enumerate(segment_label.segments):
        start_time = (segment.segment.start_time_offset.seconds +
                      segment.segment.start_time_offset.nanos / 1e9)
        end_time = (segment.segment.end_time_offset.seconds +
                    segment.segment.end_time_offset.nanos / 1e9)
        positions = '{}s to {}s'.format(start_time, end_time)
        confidence = segment.confidence
        print('\tSegment {}: {}'.format(i, positions))
        print('\tConfidence: {}'.format(confidence))
    print('\n')

# Process shot level label annotations
shot_labels = result.annotation_results[0].shot_label_annotations
for i, shot_label in enumerate(shot_labels):
    print('Shot label description: {}'.format(
        shot_label.entity.description))
    for category_entity in shot_label.category_entities:
        print('\tLabel category description: {}'.format(
            category_entity.description))

    for i, shot in enumerate(shot_label.segments):
        start_time = (shot.segment.start_time_offset.seconds +
                      shot.segment.start_time_offset.nanos / 1e9)
        end_time = (shot.segment.end_time_offset.seconds +
                    shot.segment.end_time_offset.nanos / 1e9)
        positions = '{}s to {}s'.format(start_time, end_time)
        confidence = shot.confidence
        print('\tSegment {}: {}'.format(i, positions))
        print('\tConfidence: {}'.format(confidence))
    print('\n')

# Process frame level label annotations
frame_labels = result.annotation_results[0].frame_label_annotations
for i, frame_label in enumerate(frame_labels):
    print('Frame label description: {}'.format(
        frame_label.entity.description))
    for category_entity in frame_label.category_entities:
        print('\tLabel category description: {}'.format(
            category_entity.description))

    # Each frame_label_annotation has many frames,
    # here we print information only about the first frame.
    frame = frame_label.frames[0]
    time_offset = (frame.time_offset.seconds +
                   frame.time_offset.nanos / 1e9)
    print('\tFirst frame time offset: {}s'.format(time_offset))
    print('\tFirst frame confidence: {}'.format(frame.confidence))
    print('\n')

PHP

use Google\Cloud\VideoIntelligence\V1\VideoIntelligenceServiceClient;
use Google\Cloud\VideoIntelligence\V1\Feature;

/**
 * Finds labels in the video.
 *
 * @param string $path File path to a video file to analyze.
 * @param array $options optional Array of options to pass to
 *                       OperationResponse::pollUntilComplete. This is useful
 *                       for increasing the "pollingIntervalSeconds" option.
 */
function analyze_labels_file($path, array $options = [])
{
    # Instantiate a client.
    $video = new VideoIntelligenceServiceClient();

    # Read the local video file
    $inputContent = file_get_contents($path);

    # Execute a request.
    $operation = $video->annotateVideo([
        'inputContent' => $inputContent,
        'features' => [Feature::LABEL_DETECTION]
    ]);

    # Wait for the request to complete.
    $operation->pollUntilComplete($options);

    # Print the results.
    if ($operation->operationSucceeded()) {
        $results = $operation->getResult()->getAnnotationResults()[0];

        # Process video/segment level label annotations
        foreach ($results->getSegmentLabelAnnotations() as $label) {
            printf('Video label description: %s' . PHP_EOL, $label->getEntity()->getDescription());
            foreach ($label->getCategoryEntities() as $categoryEntity) {
                printf('  Category: %s' . PHP_EOL, $categoryEntity->getDescription());
            }
            foreach ($label->getSegments() as $segment) {
                $startTimeOffset = $segment->getSegment()->getStartTimeOffset();
                $startSeconds = $startTimeOffset->getSeconds();
                $startNanoseconds = floatval($startTimeOffset->getNanos())/1000000000.00;
                $startTime = $startSeconds + $startNanoseconds;
                $endTimeOffset = $segment->getSegment()->getEndTimeOffset();
                $endSeconds = $endTimeOffset->getSeconds();
                $endNanoseconds = floatval($endTimeOffset->getNanos())/1000000000.00;
                $endTime = $endSeconds + $endNanoseconds;
                printf('  Segment: %ss to %ss' . PHP_EOL, $startTime, $endTime);
                printf('  Confidence: %f' . PHP_EOL, $segment->getConfidence());
            }
        }
        print(PHP_EOL);

        # Process shot level label annotations
        foreach ($results->getShotLabelAnnotations() as $label) {
            printf('Shot label description: %s' . PHP_EOL, $label->getEntity()->getDescription());
            foreach ($label->getCategoryEntities() as $categoryEntity) {
                printf('  Category: %s' . PHP_EOL, $categoryEntity->getDescription());
            }
            foreach ($label->getSegments() as $shot) {
                $startTimeOffset = $shot->getSegment()->getStartTimeOffset();
                $startSeconds = $startTimeOffset->getSeconds();
                $startNanoseconds = floatval($startTimeOffset->getNanos())/1000000000.00;
                $startTime = $startSeconds + $startNanoseconds;
                $endTimeOffset = $shot->getSegment()->getEndTimeOffset();
                $endSeconds = $endTimeOffset->getSeconds();
                $endNanoseconds = floatval($endTimeOffset->getNanos())/1000000000.00;
                $endTime = $endSeconds + $endNanoseconds;
                printf('  Shot: %ss to %ss' . PHP_EOL, $startTime, $endTime);
                printf('  Confidence: %f' . PHP_EOL, $shot->getConfidence());
            }
        }
        print(PHP_EOL);
    } else {
        print_r($operation->getError());
    }
}

Ruby

# path = "Path to a video file on Google Cloud Storage: gs://bucket/video.mp4"

require "google/cloud/video_intelligence"

video = Google::Cloud::VideoIntelligence.new

# Register a callback during the method call
operation = video.annotate_video input_uri: path, features: [:LABEL_DETECTION] do |operation|
  raise operation.results.message? if operation.error?
  puts "Finished Processing."

  labels = operation.results.annotation_results.first.segment_label_annotations

  labels.each do |label|
    puts "Label description: #{label.entity.description}"

    label.category_entities.each do |category_entity|
      puts "Label category description: #{category_entity.description}"
    end

    label.segments.each do |segment|
      start_time = (segment.segment.start_time_offset.seconds +
                     segment.segment.start_time_offset.nanos / 1e9)
      end_time =   (segment.segment.end_time_offset.seconds +
                     segment.segment.end_time_offset.nanos / 1e9)

      puts "Segment: #{start_time} to #{end_time}"
      puts "Confidence: #{segment.confidence}"
    end
  end
end

puts "Processing video for label annotations:"
operation.wait_until_done!