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Objekt-Tracking

Mit Objekt-Tracking können in einem Eingabevideo mehrere Objekte erkannt werden.

AutoML Video verwenden

Hinweis

Hintergrundinformationen zum Erstellen eines AutoML-Modells finden Sie in der Anleitung für Einsteiger der Vertex AI. Eine Anleitung zum Erstellen eines AutoML-Modells finden Sie unter „Dataset erstellen” mithilfe der Konsole oder der API.

Ihr AutoML-Modell verwenden

Im folgenden Codebeispiel wird veranschaulicht, wie Sie Ihr AutoML-Modell mithilfe der Streaming-Clientbibliothek für das Objekt-Tracking verwenden.

Java

Richten Sie zur Authentifizierung bei Video Intelligence die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.


import com.google.api.gax.rpc.BidiStream;
import com.google.cloud.videointelligence.v1p3beta1.ObjectTrackingAnnotation;
import com.google.cloud.videointelligence.v1p3beta1.ObjectTrackingFrame;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAutomlObjectTrackingConfig;
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;

class StreamingAutoMlObjectTracking {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String filePath = "YOUR_VIDEO_FILE";
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_AUTOML_OBJECT_TRACKING_MODEL_ID";
    streamingAutoMlObjectTracking(filePath, projectId, modelId);
  }

  // Perform streaming video object tracking with an AutoML Model
  static void streamingAutoMlObjectTracking(String filePath, String projectId, String modelId)
      throws StatusRuntimeException, IOException {

    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);

      StreamingAutomlObjectTrackingConfig streamingAutomlObjectTrackingConfig =
          StreamingAutomlObjectTrackingConfig.newBuilder().setModelName(modelPath).build();

      StreamingVideoConfig streamingVideoConfig =
          StreamingVideoConfig.newBuilder()
              .setFeature(StreamingFeature.STREAMING_AUTOML_OBJECT_TRACKING)
              .setAutomlObjectTrackingConfig(streamingAutomlObjectTrackingConfig)
              .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 (ObjectTrackingAnnotation objectAnnotations :
            annotationResults.getObjectAnnotationsList()) {

          String entity = objectAnnotations.getEntity().getDescription();
          float confidence = objectAnnotations.getConfidence();
          long trackId = objectAnnotations.getTrackId();
          System.out.format("%s: %f (ID: %d)\n", entity, confidence, trackId);

          // In streaming, there is always one frame.
          ObjectTrackingFrame frame = objectAnnotations.getFrames(0);
          double offset =
              frame.getTimeOffset().getSeconds() + frame.getTimeOffset().getNanos() / 1e9;
          System.out.format("Offset: %f\n", offset);

          System.out.println("Bounding Box:");
          System.out.format("\tLeft: %f\n", frame.getNormalizedBoundingBox().getLeft());
          System.out.format("\tTop: %f\n", frame.getNormalizedBoundingBox().getTop());
          System.out.format("\tRight: %f\n", frame.getNormalizedBoundingBox().getRight());
          System.out.format("\tBottom: %f\n", frame.getNormalizedBoundingBox().getBottom());
        }
      }
      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 modelName = `projects/${projectId}/locations/us-central1/models/${modelId}`;
const configRequest = {
  videoConfig: {
    feature: 'STREAMING_AUTOML_OBJECT_TRACKING',
    automlObjectTrackingConfig: {
      modelName: modelName,
    },
  },
};

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 objects = annotations.objectAnnotations;
  objects.forEach(object => {
    console.log(`Entity description: ${object.entity.description}`);
    console.log(`Entity id: ${object.entity.entityId}`);
    console.log(`Track id: ${object.trackId}`);
    console.log(`Confidence: ${object.confidence}`);
    console.log(
      `Time offset for the frame: ${
        object.frames[0].timeOffset.seconds || 0
      }` + `.${(object.frames[0].timeOffset.nanos / 1e6).toFixed(0)}s`
    );
    //Every annotation has only one frame.
    const box = object.frames[0].normalizedBoundingBox;
    console.log('Bounding box position:');
    console.log(`\tleft: ${box.left}`);
    console.log(`\ttop: ${box.top}`);
    console.log(`\tright: ${box.right}`);
    console.log(`\tbottom: ${box.bottom}`);
  });
});

Python

import io

from google.cloud import videointelligence_v1p3beta1 as videointelligence

# path = 'path_to_file'
# project_id = 'project_id'
# model_id = 'automl_object_tracking_model_id'

client = videointelligence.StreamingVideoIntelligenceServiceClient()

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

automl_config = videointelligence.StreamingAutomlObjectTrackingConfig(
    model_name=model_path
)

video_config = videointelligence.StreamingVideoConfig(
    feature=videointelligence.StreamingFeature.STREAMING_AUTOML_OBJECT_TRACKING,
    automl_object_tracking_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=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

    object_annotations = response.annotation_results.object_annotations

    # object_annotations could be empty
    if not object_annotations:
        continue

    for annotation in object_annotations:
        # Each annotation has one frame, which has a timeoffset.
        frame = annotation.frames[0]
        time_offset = (
            frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
        )

        description = annotation.entity.description
        confidence = annotation.confidence

        # track_id tracks the same object in the video.
        track_id = annotation.track_id

        # description is in Unicode
        print("{}s".format(time_offset))
        print("\tEntity description: {}".format(description))
        print("\tTrack Id: {}".format(track_id))
        if annotation.entity.entity_id:
            print("\tEntity id: {}".format(annotation.entity.entity_id))

        print("\tConfidence: {}".format(confidence))

        # Every annotation has only one frame
        frame = annotation.frames[0]
        box = frame.normalized_bounding_box
        print("\tBounding box position:")
        print("\tleft  : {}".format(box.left))
        print("\ttop   : {}".format(box.top))
        print("\tright : {}".format(box.right))
        print("\tbottom: {}\n".format(box.bottom))