Label image

Start an image labeling task.

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To view the code sample used in context, see the following documentation:

Code sample

Java

To learn how to install and use the client library for Data Labeling Service, see Data Labeling Service client libraries. For more information, see the Data Labeling Service Java API reference documentation.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.datalabeling.v1beta1.AnnotatedDataset;
import com.google.cloud.datalabeling.v1beta1.DataLabelingServiceClient;
import com.google.cloud.datalabeling.v1beta1.DataLabelingServiceSettings;
import com.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig;
import com.google.cloud.datalabeling.v1beta1.ImageClassificationConfig;
import com.google.cloud.datalabeling.v1beta1.LabelImageRequest;
import com.google.cloud.datalabeling.v1beta1.LabelImageRequest.Feature;
import com.google.cloud.datalabeling.v1beta1.LabelOperationMetadata;
import com.google.cloud.datalabeling.v1beta1.StringAggregationType;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class LabelImage {

  // Start an Image Labeling Task
  static void labelImage(
      String formattedInstructionName,
      String formattedAnnotationSpecSetName,
      String formattedDatasetName)
      throws IOException {
    // String formattedInstructionName = DataLabelingServiceClient.formatInstructionName(
    //      "YOUR_PROJECT_ID", "YOUR_INSTRUCTION_UUID");
    // String formattedAnnotationSpecSetName =
    //     DataLabelingServiceClient.formatAnnotationSpecSetName(
    //         "YOUR_PROJECT_ID", "YOUR_ANNOTATION_SPEC_SET_UUID");
    // String formattedDatasetName = DataLabelingServiceClient.formatDatasetName(
    //      "YOUR_PROJECT_ID", "YOUR_DATASET_UUID");


    DataLabelingServiceSettings settings =
        DataLabelingServiceSettings.newBuilder()
            .build();
    try (DataLabelingServiceClient dataLabelingServiceClient =
        DataLabelingServiceClient.create(settings)) {
      HumanAnnotationConfig humanAnnotationConfig =
          HumanAnnotationConfig.newBuilder()
              .setAnnotatedDatasetDisplayName("annotated_displayname")
              .setAnnotatedDatasetDescription("annotated_description")
              .setInstruction(formattedInstructionName)
              .build();

      ImageClassificationConfig imageClassificationConfig =
          ImageClassificationConfig.newBuilder()
              .setAllowMultiLabel(true)
              .setAnswerAggregationType(StringAggregationType.MAJORITY_VOTE)
              .setAnnotationSpecSet(formattedAnnotationSpecSetName)
              .build();

      LabelImageRequest labelImageRequest =
          LabelImageRequest.newBuilder()
              .setParent(formattedDatasetName)
              .setBasicConfig(humanAnnotationConfig)
              .setImageClassificationConfig(imageClassificationConfig)
              .setFeature(Feature.CLASSIFICATION)
              .build();

      OperationFuture<AnnotatedDataset, LabelOperationMetadata> operation =
          dataLabelingServiceClient.labelImageAsync(labelImageRequest);

      // You'll want to save this for later to retrieve your completed operation.
      System.out.format("Operation Name: %s\n", operation.getName());

      // Cancel the operation to avoid charges when testing.
      dataLabelingServiceClient.getOperationsClient().cancelOperation(operation.getName());

    } catch (IOException | InterruptedException | ExecutionException e) {
      e.printStackTrace();
    }
  }
}

Python

To learn how to install and use the client library for Data Labeling Service, see Data Labeling Service client libraries. For more information, see the Data Labeling Service Python API reference documentation.

def label_image(
    dataset_resource_name, instruction_resource_name, annotation_spec_set_resource_name
):
    """Labels an image dataset."""
    from google.cloud import datalabeling_v1beta1 as datalabeling

    client = datalabeling.DataLabelingServiceClient()

    basic_config = datalabeling.HumanAnnotationConfig(
        instruction=instruction_resource_name,
        annotated_dataset_display_name="YOUR_ANNOTATED_DATASET_DISPLAY_NAME",
        label_group="YOUR_LABEL_GROUP",
        replica_count=1,
    )

    feature = datalabeling.LabelImageRequest.Feature.CLASSIFICATION

    # annotation_spec_set_resource_name needs to be created beforehand.
    # See the examples in the following:
    # https://cloud.google.com/ai-platform/data-labeling/docs/label-sets
    config = datalabeling.ImageClassificationConfig(
        annotation_spec_set=annotation_spec_set_resource_name,
        allow_multi_label=False,
        answer_aggregation_type=datalabeling.StringAggregationType.MAJORITY_VOTE,
    )

    response = client.label_image(
        request={
            "parent": dataset_resource_name,
            "basic_config": basic_config,
            "feature": feature,
            "image_classification_config": config,
        }
    )

    print("Label_image operation name: {}".format(response.operation.name))
    return response

What's next

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