露骨內容

煽情露骨內容偵測 偵測影片中的成人內容。 成人內容一般不適合未滿 18 歲的觀眾，包括但不限於裸露、性活動和色情內容。系統也會偵測卡通或動漫中的這類內容。

下列程式碼範例說明如何使用串流用戶端程式庫偵測是否存在煽情露骨內容。

Java

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


import com.google.api.gax.rpc.BidiStream;
import com.google.cloud.videointelligence.v1p3beta1.ExplicitContentFrame;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.StreamingFeature;
import com.google.cloud.videointelligence.v1p3beta1.StreamingLabelDetectionConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoAnnotationResults;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoIntelligenceServiceClient;
import com.google.protobuf.ByteString;
import io.grpc.StatusRuntimeException;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.concurrent.TimeoutException;

class StreamingExplicitContentDetection {

  // Perform streaming video detection for explicit content
  static void streamingExplicitContentDetection(String filePath)
      throws IOException, TimeoutException, StatusRuntimeException {
    // String filePath = "path_to_your_video_file";

    try (StreamingVideoIntelligenceServiceClient client =
        StreamingVideoIntelligenceServiceClient.create()) {

      Path path = Paths.get(filePath);
      byte[] data = Files.readAllBytes(path);
      // Set the chunk size to 5MB (recommended less than 10MB).
      int chunkSize = 5 * 1024 * 1024;
      int numChunks = (int) Math.ceil((double) data.length / chunkSize);

      StreamingLabelDetectionConfig labelConfig =
          StreamingLabelDetectionConfig.newBuilder().setStationaryCamera(false).build();

      StreamingVideoConfig streamingVideoConfig =
          StreamingVideoConfig.newBuilder()
              .setFeature(StreamingFeature.STREAMING_EXPLICIT_CONTENT_DETECTION)
              .setLabelDetectionConfig(labelConfig)
              .build();

      BidiStream<StreamingAnnotateVideoRequest, StreamingAnnotateVideoResponse> call =
          client.streamingAnnotateVideoCallable().call();

      // The first request must **only** contain the audio configuration:
      call.send(
          StreamingAnnotateVideoRequest.newBuilder().setVideoConfig(streamingVideoConfig).build());

      // Subsequent requests must **only** contain the audio data.
      // Send the requests in chunks
      for (int i = 0; i < numChunks; i++) {
        call.send(
            StreamingAnnotateVideoRequest.newBuilder()
                .setInputContent(
                    ByteString.copyFrom(
                        Arrays.copyOfRange(data, i * chunkSize, i * chunkSize + chunkSize)))
                .build());
      }

      // Tell the service you are done sending data
      call.closeSend();

      for (StreamingAnnotateVideoResponse response : call) {
        StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();

        for (ExplicitContentFrame frame :
            annotationResults.getExplicitAnnotation().getFramesList()) {

          double offset =
              frame.getTimeOffset().getSeconds() + frame.getTimeOffset().getNanos() / 1e9;

          System.out.format("Offset: %f\n", offset);
          System.out.format("\tPornography: %s", frame.getPornographyLikelihood());
        }
      }
    }
  }
}

Node.js

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const path = 'Local file to analyze, e.g. ./my-file.mp4';
const {StreamingVideoIntelligenceServiceClient} =
  require('@google-cloud/video-intelligence').v1p3beta1;
const fs = require('fs');

// Instantiates a client
const client = new StreamingVideoIntelligenceServiceClient();
// Streaming configuration
const configRequest = {
  videoConfig: {
    feature: 'STREAMING_EXPLICIT_CONTENT_DETECTION',
  },
};

const readStream = fs.createReadStream(path, {
  highWaterMark: 5 * 1024 * 1024, //chunk size set to 5MB (recommended less than 10MB)
  encoding: 'base64',
});
//Load file content
const chunks = [];
readStream
  .on('data', chunk => {
    const request = {
      inputContent: chunk.toString(),
    };
    chunks.push(request);
  })
  .on('close', () => {
    // configRequest should be the first in the stream of requests
    stream.write(configRequest);
    for (let i = 0; i < chunks.length; i++) {
      stream.write(chunks[i]);
    }
    stream.end();
  });

const stream = client.streamingAnnotateVideo().on('data', response => {
  //Gets annotations for video
  const annotations = response.annotationResults;
  const explicitContentResults = annotations.explicitAnnotation.frames;
  explicitContentResults.forEach(result => {
    console.log(
      `Time: ${result.timeOffset.seconds || 0}` +
        `.${(result.timeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(` Pornography likelihood: ${result.pornographyLikelihood}`);
  });
});

Python

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

from google.cloud import videointelligence_v1p3beta1 as videointelligence

# path = 'path_to_file'

client = videointelligence.StreamingVideoIntelligenceServiceClient()

# Set streaming config.
config = videointelligence.StreamingVideoConfig(
    feature=(
        videointelligence.StreamingFeature.STREAMING_EXPLICIT_CONTENT_DETECTION
    )
)

# config_request should be the first in the stream of requests.
config_request = videointelligence.StreamingAnnotateVideoRequest(
    video_config=config
)

# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024

# Load file content.
stream = []
with io.open(path, "rb") as video_file:
    while True:
        data = video_file.read(chunk_size)
        if not data:
            break
        stream.append(data)

def stream_generator():
    yield config_request
    for chunk in stream:
        yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)

requests = stream_generator()

# streaming_annotate_video returns a generator.
# The default timeout is about 300 seconds.
# To process longer videos it should be set to
# larger than the length (in seconds) of the stream.
responses = client.streaming_annotate_video(requests, timeout=900)

# Each response corresponds to about 1 second of video.
for response in responses:
    # Check for errors.
    if response.error.message:
        print(response.error.message)
        break

    for frame in response.annotation_results.explicit_annotation.frames:
        time_offset = (
            frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
        )
        pornography_likelihood = videointelligence.Likelihood(
            frame.pornography_likelihood
        )

        print("Time: {}s".format(time_offset))
        print("\tpornogaphy: {}".format(pornography_likelihood.name))