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ローカル動画ファイル内の不適切なコンテンツを検出する

ローカルに保存された動画ファイル内の不適切なコンテンツを検出します。

このコードサンプルを含む詳細なドキュメントについては、以下をご覧ください。

動画内の不適切なコンテンツを検出する

コードサンプル

Go

Video Intelligence への認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。


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

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

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

	for _, frame := range result.Frames {
		offset, _ := ptypes.Duration(frame.TimeOffset)
		fmt.Fprintf(w, "%s - %s\n", offset, frame.PornographyLikelihood.String())
	}

	return nil
}

Java

// Instantiate a com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
  // Create an operation that will contain the response when the operation completes.
  AnnotateVideoRequest request =
      AnnotateVideoRequest.newBuilder()
          .setInputUri(gcsUri)
          .addFeatures(Feature.EXPLICIT_CONTENT_DETECTION)
          .build();

  OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response =
      client.annotateVideoAsync(request);

  System.out.println("Waiting for operation to complete...");
  // Print detected annotations and their positions in the analyzed video.
  for (VideoAnnotationResults result : response.get().getAnnotationResultsList()) {
    for (ExplicitContentFrame frame : result.getExplicitAnnotation().getFramesList()) {
      double frameTime =
          frame.getTimeOffset().getSeconds() + frame.getTimeOffset().getNanos() / 1e9;
      System.out.printf("Location: %.3fs\n", frameTime);
      System.out.println("Adult: " + frame.getPornographyLikelihood());
    }
  }

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 video to analyze, e.g. gs://my-bucket/my-video.mp4';

const request = {
  inputUri: gcsUri,
  features: ['EXPLICIT_CONTENT_DETECTION'],
};

// Human-readable likelihoods
const likelihoods = [
  'UNKNOWN',
  'VERY_UNLIKELY',
  'UNLIKELY',
  'POSSIBLE',
  'LIKELY',
  'VERY_LIKELY',
];

// Detects unsafe content
const [operation] = await client.annotateVideo(request);
console.log('Waiting for operation to complete...');
const [operationResult] = await operation.promise();
// Gets unsafe content
const explicitContentResults =
  operationResult.annotationResults[0].explicitAnnotation;
console.log('Explicit annotation results:');
explicitContentResults.frames.forEach(result => {
  if (result.timeOffset === undefined) {
    result.timeOffset = {};
  }
  if (result.timeOffset.seconds === undefined) {
    result.timeOffset.seconds = 0;
  }
  if (result.timeOffset.nanos === undefined) {
    result.timeOffset.nanos = 0;
  }
  console.log(
    `\tTime: ${result.timeOffset.seconds}` +
      `.${(result.timeOffset.nanos / 1e6).toFixed(0)}s`
  );
  console.log(
    `\t\tPornography likelihood: ${likelihoods[result.pornographyLikelihood]}`
  );
});

PHP

use Google\Cloud\VideoIntelligence\V1\AnnotateVideoRequest;
use Google\Cloud\VideoIntelligence\V1\Client\VideoIntelligenceServiceClient;
use Google\Cloud\VideoIntelligence\V1\Feature;
use Google\Cloud\VideoIntelligence\V1\Likelihood;

/**
 * @param string $uri The cloud storage object to analyze (gs://your-bucket-name/your-object-name)
 * @param int $pollingIntervalSeconds
 */
function analyze_explicit_content(string $uri, int $pollingIntervalSeconds = 0)
{
    $video = new VideoIntelligenceServiceClient();

    # Execute a request.
    $features = [Feature::EXPLICIT_CONTENT_DETECTION];
    $request = (new AnnotateVideoRequest())
        ->setInputUri($uri)
        ->setFeatures($features);
    $operation = $video->annotateVideo($request);

    # Wait for the request to complete.
    $operation->pollUntilComplete([
        'pollingIntervalSeconds' => $pollingIntervalSeconds
    ]);

    # Print the result.
    if ($operation->operationSucceeded()) {
        $results = $operation->getResult()->getAnnotationResults()[0];
        $explicitAnnotation = $results->getExplicitAnnotation();
        foreach ($explicitAnnotation->getFrames() as $frame) {
            $time = $frame->getTimeOffset();
            printf('At %ss:' . PHP_EOL, $time->getSeconds() + $time->getNanos() / 1000000000.0);
            printf('  pornography: ' . Likelihood::name($frame->getPornographyLikelihood()) . PHP_EOL);
        }
    } else {
        print_r($operation->getError());
    }
}

Python

"""Detects explicit content from the GCS path to a video."""
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.EXPLICIT_CONTENT_DETECTION]

operation = video_client.annotate_video(
    request={"features": features, "input_uri": path}
)
print("\nProcessing video for explicit content annotations:")

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

# Retrieve first result because a single video was processed
for frame in result.annotation_results[0].explicit_annotation.frames:
    likelihood = videointelligence.Likelihood(frame.pornography_likelihood)
    frame_time = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
    print("Time: {}s".format(frame_time))
    print("\tpornography: {}".format(likelihood.name))

次のステップ

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