Mehrere Bilder asynchron mit Anmerkungen versehen

Versehen Sie einen Batch von Bilddateien in Cloud Storage mit Annotationen, während Sie offline sind.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

Codebeispiel

Java

Bevor Sie dieses Beispiel ausprobieren, folgen Sie der Java-Einrichtungsanleitung in der Vision-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Java-Referenzdokumentation zur Vision API.

Richten Sie zur Authentifizierung bei Vision die Standardanmeldedaten für Anwendungen (ADC) ein. Weitere Informationen finden Sie unter ADC für eine lokale Entwicklungsumgebung einrichten. 

import com.google.cloud.vision.v1.AnnotateImageRequest;
import com.google.cloud.vision.v1.AsyncBatchAnnotateImagesRequest;
import com.google.cloud.vision.v1.AsyncBatchAnnotateImagesResponse;
import com.google.cloud.vision.v1.Feature;
import com.google.cloud.vision.v1.GcsDestination;
import com.google.cloud.vision.v1.Image;
import com.google.cloud.vision.v1.ImageAnnotatorClient;
import com.google.cloud.vision.v1.ImageSource;
import com.google.cloud.vision.v1.OutputConfig;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

public class AsyncBatchAnnotateImages {

  public static void asyncBatchAnnotateImages()
      throws InterruptedException, ExecutionException, IOException {
    String inputImageUri = "gs://cloud-samples-data/vision/label/wakeupcat.jpg";
    String outputUri = "gs://YOUR_BUCKET_ID/path/to/save/results/";
    asyncBatchAnnotateImages(inputImageUri, outputUri);
  }

  public static void asyncBatchAnnotateImages(String inputImageUri, String outputUri)
      throws IOException, ExecutionException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ImageAnnotatorClient imageAnnotatorClient = ImageAnnotatorClient.create()) {

      // You can send multiple images to be annotated, this sample demonstrates how to do this with
      // one image. If you want to use multiple images, you have to create a `AnnotateImageRequest`
      // object for each image that you want annotated.
      // First specify where the vision api can find the image
      ImageSource source = ImageSource.newBuilder().setImageUri(inputImageUri).build();
      Image image = Image.newBuilder().setSource(source).build();

      // Set the type of annotation you want to perform on the image
      // https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#google.cloud.vision.v1.Feature.Type
      Feature feature = Feature.newBuilder().setType(Feature.Type.LABEL_DETECTION).build();

      // Build the request object for that one image. Note: for additional images you have to create
      // additional `AnnotateImageRequest` objects and store them in a list to be used below.
      AnnotateImageRequest imageRequest =
          AnnotateImageRequest.newBuilder().setImage(image).addFeatures(feature).build();

      // Set where to store the results for the images that will be annotated.
      GcsDestination gcsDestination = GcsDestination.newBuilder().setUri(outputUri).build();
      OutputConfig outputConfig =
          OutputConfig.newBuilder()
              .setGcsDestination(gcsDestination)
              .setBatchSize(2) // The max number of responses to output in each JSON file
              .build();

      // Add each `AnnotateImageRequest` object to the batch request and add the output config.
      AsyncBatchAnnotateImagesRequest request =
          AsyncBatchAnnotateImagesRequest.newBuilder()
              .addRequests(imageRequest)
              .setOutputConfig(outputConfig)
              .build();

      // Make the asynchronous batch request.
      AsyncBatchAnnotateImagesResponse response =
          imageAnnotatorClient.asyncBatchAnnotateImagesAsync(request).get();

      // The output is written to GCS with the provided output_uri as prefix
      String gcsOutputUri = response.getOutputConfig().getGcsDestination().getUri();
      System.out.format("Output written to GCS with prefix: %s%n", gcsOutputUri);
    }
  }
}

Node.js

Bevor Sie dieses Beispiel ausprobieren, folgen Sie der Einrichtungsanleitung für Node.js in der Vision-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Node.js-Referenzdokumentation zur Vision API.

Richten Sie zur Authentifizierung bei Vision die Standardanmeldedaten für Anwendungen (ADC) ein. Weitere Informationen finden Sie unter ADC für eine lokale Entwicklungsumgebung einrichten. 

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const inputImageUri = 'gs://cloud-samples-data/vision/label/wakeupcat.jpg';
// const outputUri = 'gs://YOUR_BUCKET_ID/path/to/save/results/';

// Imports the Google Cloud client libraries
const {ImageAnnotatorClient} = require('@google-cloud/vision').v1;

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

// You can send multiple images to be annotated, this sample demonstrates how to do this with
// one image. If you want to use multiple images, you have to create a request object for each image that you want annotated.
async function asyncBatchAnnotateImages() {
  // Set the type of annotation you want to perform on the image
  // https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#google.cloud.vision.v1.Feature.Type
  const features = [{type: 'LABEL_DETECTION'}];

  // Build the image request object for that one image. Note: for additional images you have to create
  // additional image request objects and store them in a list to be used below.
  const imageRequest = {
    image: {
      source: {
        imageUri: inputImageUri,
      },
    },
    features: features,
  };

  // Set where to store the results for the images that will be annotated.
  const outputConfig = {
    gcsDestination: {
      uri: outputUri,
    },
    batchSize: 2, // The max number of responses to output in each JSON file
  };

  // Add each image request object to the batch request and add the output config.
  const request = {
    requests: [
      imageRequest, // add additional request objects here
    ],
    outputConfig,
  };

  // Make the asynchronous batch request.
  const [operation] = await client.asyncBatchAnnotateImages(request);

  // Wait for the operation to complete
  const [filesResponse] = await operation.promise();

  // The output is written to GCS with the provided output_uri as prefix
  const destinationUri = filesResponse.outputConfig.gcsDestination.uri;
  console.log(`Output written to GCS with prefix: ${destinationUri}`);
}

asyncBatchAnnotateImages();

Python

Bevor Sie dieses Beispiel ausprobieren, folgen Sie der Einrichtungsanleitung für Python in der Vision-Kurzanleitung zur Verwendung von Clientbibliotheken. Weitere Informationen finden Sie in der Python-Referenzdokumentation zur Vision API.

Richten Sie zur Authentifizierung bei Vision die Standardanmeldedaten für Anwendungen (ADC) ein. Weitere Informationen finden Sie unter ADC für eine lokale Entwicklungsumgebung einrichten. 


from google.cloud import vision_v1


def sample_async_batch_annotate_images(
    input_image_uri="gs://cloud-samples-data/vision/label/wakeupcat.jpg",
    output_uri="gs://your-bucket/prefix/",
):
    """Perform async batch image annotation."""
    client = vision_v1.ImageAnnotatorClient()

    source = {"image_uri": input_image_uri}
    image = {"source": source}
    features = [
        {"type_": vision_v1.Feature.Type.LABEL_DETECTION},
        {"type_": vision_v1.Feature.Type.IMAGE_PROPERTIES},
    ]

    # Each requests element corresponds to a single image.  To annotate more
    # images, create a request element for each image and add it to
    # the array of requests
    requests = [{"image": image, "features": features}]
    gcs_destination = {"uri": output_uri}

    # The max number of responses to output in each JSON file
    batch_size = 2
    output_config = {"gcs_destination": gcs_destination, "batch_size": batch_size}

    operation = client.async_batch_annotate_images(
        requests=requests, output_config=output_config
    )

    print("Waiting for operation to complete...")
    response = operation.result(90)

    # The output is written to GCS with the provided output_uri as prefix
    gcs_output_uri = response.output_config.gcs_destination.uri
    print(f"Output written to GCS with prefix: {gcs_output_uri}")

Nächste Schritte

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