동영상 분류

동영상 분류는 객체, 위치, 활동, 동물 종, 제품 등을 식별합니다.

AutoML 동영상 사용

시작하기 전에

AutoML 모델 생성에 대한 배경 정보는 Vertex AI 초보자 가이드를 참조하세요. AutoML 모델을 만드는 방법에 대한 자세한 내용은 Console 또는 api를 사용하여 '데이터 세트 만들기'로 시작하세요.

AutoML 모델 사용

다음 코드 샘플은 스트리밍 클라이언트 라이브러리를 사용하는 동영상 분류를 위해 AutoML 모델을 사용하는 방법을 보여줍니다.

자바


import com.google.api.gax.rpc.BidiStream;
import com.google.cloud.videointelligence.v1p3beta1.LabelAnnotation;
import com.google.cloud.videointelligence.v1p3beta1.LabelFrame;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAutomlClassificationConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingFeature;
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 StreamingAutoMlClassification {

  // Perform streaming video classification with an AutoML Model
  static void streamingAutoMlClassification(String filePath, String projectId, String modelId)
      throws TimeoutException, StatusRuntimeException, IOException {
    // String filePath = "path_to_your_video_file";
    // String projectId = "YOUR_GCP_PROJECT_ID";
    // String modelId = "YOUR_AUTO_ML_CLASSIFICATION_MODEL_ID";

    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);

      String modelPath =
          String.format("projects/%s/locations/us-central1/models/%s", projectId, modelId);

      System.out.println(modelPath);

      StreamingAutomlClassificationConfig streamingAutomlClassificationConfig =
          StreamingAutomlClassificationConfig.newBuilder().setModelName(modelPath).build();

      StreamingVideoConfig streamingVideoConfig =
          StreamingVideoConfig.newBuilder()
              .setFeature(StreamingFeature.STREAMING_AUTOML_CLASSIFICATION)
              .setAutomlClassificationConfig(streamingAutomlClassificationConfig)
              .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) {
        if (response.hasError()) {
          System.out.println(response.getError().getMessage());
          break;
        }

        StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();

        for (LabelAnnotation annotation : annotationResults.getLabelAnnotationsList()) {
          String entity = annotation.getEntity().getDescription();

          // There is only one frame per annotation
          LabelFrame labelFrame = annotation.getFrames(0);
          double offset =
              labelFrame.getTimeOffset().getSeconds() + labelFrame.getTimeOffset().getNanos() / 1e9;
          float confidence = labelFrame.getConfidence();

          System.out.format("At %fs segment: %s (%f)\n", offset, entity, confidence);
        }
      }
      System.out.println("Video streamed successfully.");
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const path = 'Local file to analyze, e.g. ./my-file.mp4';
// const modelId = 'autoMl model'
// const projectId = 'Your GCP Project'

const {StreamingVideoIntelligenceServiceClient} =
  require('@google-cloud/video-intelligence').v1p3beta1;
const fs = require('fs');

// Instantiates a client
const client = new StreamingVideoIntelligenceServiceClient();

// Streaming configuration
const modelPath = `projects/${projectId}/locations/us-central1/models/${modelId}`;
const configRequest = {
  videoConfig: {
    feature: 'STREAMING_AUTOML_CLASSIFICATION',
    automlClassificationConfig: {
      modelName: modelPath,
    },
  },
};

const readStream = fs.createReadStream(path, {
  highWaterMark: 5 * 1024 * 1024, //chunk size set to 5MB (recommended less than 10MB)
  encoding: 'base64',
});
//Load file content
// Note: Input videos must have supported video codecs. See
// https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
// for more details.
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 labels = annotations.labelAnnotations;
    labels.forEach(label => {
      console.log(
        `Label ${label.entity.description} occurs at: ${
          label.frames[0].timeOffset.seconds || 0
        }` + `.${(label.frames[0].timeOffset.nanos / 1e6).toFixed(0)}s`
      );
      console.log(` Confidence: ${label.frames[0].confidence}`);
    });
  })
  .on('error', response => {
    console.error(response);
  });

Python

import io

from google.cloud import videointelligence_v1p3beta1 as videointelligence

# path = 'path_to_file'
# project_id = 'gcp_project_id'
# model_id = 'automl_classification_model_id'

client = videointelligence.StreamingVideoIntelligenceServiceClient()

model_path = "projects/{}/locations/us-central1/models/{}".format(
    project_id, model_id
)

# Here we use classification as an example.
automl_config = videointelligence.StreamingAutomlClassificationConfig(
    model_name=model_path
)

video_config = videointelligence.StreamingVideoConfig(
    feature=videointelligence.StreamingFeature.STREAMING_AUTOML_CLASSIFICATION,
    automl_classification_config=automl_config,
)

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

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

# Load file content.
# Note: Input videos must have supported video codecs. See
# https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
# for more details.
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=600)

for response in responses:
    # Check for errors.
    if response.error.message:
        print(response.error.message)
        break

    for label in response.annotation_results.label_annotations:
        for frame in label.frames:
            print(
                "At {:3d}s segment, {:5.1%} {}".format(
                    frame.time_offset.seconds,
                    frame.confidence,
                    label.entity.entity_id,
                )
            )