Creazione di un job di etichettatura dati per video

Crea un job di etichettatura dei dati per i video utilizzando il metodo create_data_labeling_job.

Esempio di codice

Java

Prima di provare questo esempio, segui le istruzioni di configurazione di Java riportate nella guida rapida di Vertex AI sull'utilizzo delle librerie client. Per maggiori informazioni, consulta la documentazione di riferimento dell'API Java Vertex AI.

Per eseguire l'autenticazione in Vertex AI, configura le Credenziali predefinite dell'applicazione. Per maggiori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

import com.google.cloud.aiplatform.v1.DataLabelingJob;
import com.google.cloud.aiplatform.v1.DatasetName;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import com.google.type.Money;
import java.io.IOException;
import java.util.Map;

public class CreateDataLabelingJobVideoSample {
  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String displayName = "YOUR_DATA_LABELING_DISPLAY_NAME";
    String datasetId = "YOUR_DATASET_ID";
    String instructionUri =
        "gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_data_labeling_source/file.pdf";
    String annotationSpec = "YOUR_ANNOTATION_SPEC";
    createDataLabelingJobVideo(project, displayName, datasetId, instructionUri, annotationSpec);
  }

  static void createDataLabelingJobVideo(
      String project,
      String displayName,
      String datasetId,
      String instructionUri,
      String annotationSpec)
      throws IOException {
    JobServiceSettings jobServiceSettings =
        JobServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // 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 (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
      String location = "us-central1";
      LocationName locationName = LocationName.of(project, location);

      String jsonString = "{\"annotation_specs\": [ " + annotationSpec + "]}";
      Value.Builder annotationSpecValue = Value.newBuilder();
      JsonFormat.parser().merge(jsonString, annotationSpecValue);

      DatasetName datasetName = DatasetName.of(project, location, datasetId);
      DataLabelingJob dataLabelingJob =
          DataLabelingJob.newBuilder()
              .setDisplayName(displayName)
              .setLabelerCount(1)
              .setInstructionUri(instructionUri)
              .setInputsSchemaUri(
                  "gs://google-cloud-aiplatform/schema/datalabelingjob/inputs/"
                      + "video_classification.yaml")
              .addDatasets(datasetName.toString())
              .setInputs(annotationSpecValue)
              .putAnnotationLabels(
                  "aiplatform.googleapis.com/annotation_set_name", "my_test_saved_query")
              .build();

      DataLabelingJob dataLabelingJobResponse =
          jobServiceClient.createDataLabelingJob(locationName, dataLabelingJob);

      System.out.println("Create Data Labeling Job Video Response");
      System.out.format("\tName: %s\n", dataLabelingJobResponse.getName());
      System.out.format("\tDisplay Name: %s\n", dataLabelingJobResponse.getDisplayName());
      System.out.format("\tDatasets: %s\n", dataLabelingJobResponse.getDatasetsList());
      System.out.format("\tLabeler Count: %s\n", dataLabelingJobResponse.getLabelerCount());
      System.out.format("\tInstruction Uri: %s\n", dataLabelingJobResponse.getInstructionUri());
      System.out.format("\tInputs Schema Uri: %s\n", dataLabelingJobResponse.getInputsSchemaUri());
      System.out.format("\tInputs: %s\n", dataLabelingJobResponse.getInputs());
      System.out.format("\tState: %s\n", dataLabelingJobResponse.getState());
      System.out.format("\tLabeling Progress: %s\n", dataLabelingJobResponse.getLabelingProgress());
      System.out.format("\tCreate Time: %s\n", dataLabelingJobResponse.getCreateTime());
      System.out.format("\tUpdate Time: %s\n", dataLabelingJobResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", dataLabelingJobResponse.getLabelsMap());
      System.out.format(
          "\tSpecialist Pools: %s\n", dataLabelingJobResponse.getSpecialistPoolsList());
      for (Map.Entry<String, String> annotationLabelMap :
          dataLabelingJobResponse.getAnnotationLabelsMap().entrySet()) {
        System.out.println("\tAnnotation Level");
        System.out.format("\t\tkey: %s\n", annotationLabelMap.getKey());
        System.out.format("\t\tvalue: %s\n", annotationLabelMap.getValue());
      }

      Money money = dataLabelingJobResponse.getCurrentSpend();
      System.out.println("\tCurrent Spend");
      System.out.format("\t\tCurrency Code: %s\n", money.getCurrencyCode());
      System.out.format("\t\tUnits: %s\n", money.getUnits());
      System.out.format("\t\tNanos: %s\n", money.getNanos());
    }
  }
}

Python

Prima di provare questo esempio, segui le istruzioni di configurazione di Python riportate nella guida rapida di Vertex AI sull'utilizzo delle librerie client. Per maggiori informazioni, consulta la documentazione di riferimento dell'API Python Vertex AI.

Per eseguire l'autenticazione in Vertex AI, configura le Credenziali predefinite dell'applicazione. Per maggiori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

def create_data_labeling_job_video_sample(
    project: str,
    display_name: str,
    dataset: str,
    instruction_uri: str,
    annotation_spec: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.JobServiceClient(client_options=client_options)
    inputs_dict = {"annotation_specs": [annotation_spec]}
    inputs = json_format.ParseDict(inputs_dict, Value())

    data_labeling_job = {
        "display_name": display_name,
        # Full resource name: projects/{project_id}/locations/{location}/datasets/{dataset_id}
        "datasets": [dataset],
        # labeler_count must be 1, 3, or 5
        "labeler_count": 1,
        "instruction_uri": instruction_uri,
        "inputs_schema_uri": "gs://google-cloud-aiplatform/schema/datalabelingjob/inputs/video_classification_1.0.0.yaml",
        "inputs": inputs,
        "annotation_labels": {
            "aiplatform.googleapis.com/annotation_set_name": "my_test_saved_query"
        },
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_data_labeling_job(
        parent=parent, data_labeling_job=data_labeling_job
    )
    print("response:", response)

Passaggi successivi

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