動画のラベル分析

ラベル分析では、動画内のラベルを検出します。

このセクションでは、動画のラベルを分析する方法について説明します。

この例では、ローカル ファイルに対して動画分析を実行し、ラベルを検出します。

詳細については、Python のチュートリアルをご覧ください。

プロトコル

詳細については、videos:annotate API エンドポイントをご覧ください。

ラベル検出を行うには、POST リクエストを作成して適切なリクエスト本文を提供します。

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"]
}

Video Intelligence のアノテーション リクエストに成功すると、次のような name フィールドを含むレスポンスが返されます。

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

この名前は長時間実行オペレーションを表しています。v1.operations API を使用すると、長時間実行オペレーションをクエリできます。

動画のアノテーション レスポンスを取得するには、v1.operations エンドポイントに GET リクエストを送信し、URL で name の値を渡します。オペレーションが完了すると、アノテーションの結果が返されます。

ラベル検索のアノテーションが annotationResults に返されます。次に例を示します。

{
  "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

Python 用の Cloud Video Intelligence API クライアント ライブラリのインストールと使用方法については、Cloud Video Intelligence API クライアント ライブラリをご覧ください。
"""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;

/** Uncomment and populate these variables in your code */
// $uri = 'The cloud storage object to analyze (gs://your-bucket-name/your-object-name)';
// $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) {
            $start = $segment->getSegment()->getStartTimeOffset();
            $end = $segment->getSegment()->getEndTimeOffset();
            printf('  Segment: %ss to %ss' . PHP_EOL,
                $start->getSeconds() + $start->getNanos()/1000000000.0,
                $end->getSeconds() + $end->getNanos()/1000000000.0);
            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) {
            $start = $shot->getSegment()->getStartTimeOffset();
            $end = $shot->getSegment()->getEndTimeOffset();
            printf('  Shot: %ss to %ss' . PHP_EOL,
                $start->getSeconds() + $start->getNanos()/1000000000.0,
                $end->getSeconds() + $end->getNanos()/1000000000.0);
            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!

この例では、Google Cloud Storage にあるファイルに対して動画分析を実行し、ラベルを検出します。

プロトコル

詳細については、videos:annotate API エンドポイントをご覧ください。

ラベル検出を行うには、POST リクエストを作成して適切なリクエスト本文を提供します。

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

Video Intelligence のアノテーション リクエストに成功すると、次のような name フィールドを含むレスポンスが返されます。

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

この名前は長時間実行オペレーションを表しています。v1.operations API を使用すると、長時間実行オペレーションをクエリできます。

動画のアノテーション レスポンスを取得するには、v1.operations エンドポイントに GET リクエストを送信し、URL で name の値を渡します。オペレーションが完了すると、アノテーションの結果が返されます。

ラベル検索のアノテーションが annotationResults に返されます。次に例を示します。

{
  "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;

/** Uncomment and populate these variables in your code */
// $path = 'File path to a video file to analyze';
// $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) {
            $start = $segment->getSegment()->getStartTimeOffset();
            $end = $segment->getSegment()->getEndTimeOffset();
            printf('  Segment: %ss to %ss' . PHP_EOL,
                $start->getSeconds() + $start->getNanos()/1000000000.0,
                $end->getSeconds() + $end->getNanos()/1000000000.0);
            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) {
            $start = $shot->getSegment()->getStartTimeOffset();
            $end = $shot->getSegment()->getEndTimeOffset();
            printf('  Shot: %ss to %ss' . PHP_EOL,
                $start->getSeconds() + $start->getNanos()/1000000000.0,
                $end->getSeconds() + $end->getNanos()/1000000000.0);
            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!

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