Análisis de etiquetas

El análisis de etiquetas detecta las etiquetas en un video.

Usa un modelo estándar

En el siguiente ejemplo, se muestra cómo usar la detección de etiquetas de transmisión de la API de Video Intelligence para anotar un video.

Java

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.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 java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;

class StreamingLabelDetection {

  // Perform streaming video label detection
  static void streamingLabelDetection(String filePath) {
    // 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_LABEL_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 (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("%fs: %s (%f)\n", offset, entity, confidence);
        }
      }
    } catch (IOException e) {
      e.printStackTrace();
    }
  }
}

Node.js

/**
 * 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_LABEL_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 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}`);
  });
});

Python

from google.cloud import videointelligence_v1p3beta1 as videointelligence

# path = 'path_to_file'

client = videointelligence.StreamingVideoIntelligenceServiceClient()

# Set streaming config.
config = videointelligence.types.StreamingVideoConfig(
    feature=(videointelligence.enums.StreamingFeature.STREAMING_LABEL_DETECTION)
)

# config_request should be the first in the stream of requests.
config_request = videointelligence.types.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.types.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)

# 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

    label_annotations = response.annotation_results.label_annotations

    # label_annotations could be empty
    if not label_annotations:
        continue

    for annotation in label_annotations:
        # Each annotation has one frame, which has a timeoffset.
        frame = annotation.frames[0]
        time_offset = frame.time_offset.seconds + frame.time_offset.nanos / 1e9

        description = annotation.entity.description
        confidence = annotation.frames[0].confidence
        # description is in Unicode
        print(
            u"{}s: {} (confidence: {})".format(time_offset, description, confidence)
        )

Usa un modelo personalizado

En el siguiente ejemplo, se muestra cómo usar un modelo de AutoML personalizado con la detección de etiquetas de transmisión.

Java

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;

class StreamingAutoMlClassification {

  // Perform streaming video classification with an AutoML Model
  static void streamingAutoMlClassification(String filePath, String projectId, String modelId)
      throws 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
from google.cloud.videointelligence_v1p3beta1 import enums

# 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.types.StreamingAutomlClassificationConfig(
    model_name=model_path
)

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

# config_request should be the first in the stream of requests.
config_request = videointelligence.types.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.types.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,
                )
            )