Mengimpor data untuk pelacakan objek video

Mengimpor data untuk pelacakan objek video menggunakan metode import_data.

Jelajahi lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat referensi berikut:

Contoh kode

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DatasetName;
import com.google.cloud.aiplatform.v1.DatasetServiceClient;
import com.google.cloud.aiplatform.v1.DatasetServiceSettings;
import com.google.cloud.aiplatform.v1.GcsSource;
import com.google.cloud.aiplatform.v1.ImportDataConfig;
import com.google.cloud.aiplatform.v1.ImportDataOperationMetadata;
import com.google.cloud.aiplatform.v1.ImportDataResponse;
import java.io.IOException;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class ImportDataVideoObjectTrackingSample {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    String gcsSourceUri =
        "gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_video_source/[file.csv/file.jsonl]";
    String project = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    importDataVideObjectTracking(gcsSourceUri, project, datasetId);
  }

  static void importDataVideObjectTracking(String gcsSourceUri, String project, String datasetId)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    DatasetServiceSettings datasetServiceSettings =
        DatasetServiceSettings.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 (DatasetServiceClient datasetServiceClient =
        DatasetServiceClient.create(datasetServiceSettings)) {
      String location = "us-central1";
      String importSchemaUri =
          "gs://google-cloud-aiplatform/schema/dataset/ioformat/"
              + "video_object_tracking_io_format_1.0.0.yaml";

      GcsSource.Builder gcsSource = GcsSource.newBuilder();
      gcsSource.addUris(gcsSourceUri);
      DatasetName datasetName = DatasetName.of(project, location, datasetId);
      List<ImportDataConfig> importDataConfigs =
          Collections.singletonList(
              ImportDataConfig.newBuilder()
                  .setGcsSource(gcsSource)
                  .setImportSchemaUri(importSchemaUri)
                  .build());

      OperationFuture<ImportDataResponse, ImportDataOperationMetadata> importDataResponseFuture =
          datasetServiceClient.importDataAsync(datasetName, importDataConfigs);
      System.out.format(
          "Operation name: %s\n", importDataResponseFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      ImportDataResponse importDataResponse = importDataResponseFuture.get(300, TimeUnit.SECONDS);

      System.out.format(
          "Import Data Video Object Tracking Response: %s\n", importDataResponse.toString());
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Node.js di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Node.js Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 */

// const datasetId = 'YOUR_DATASET_ID';
// const gcsSourceUri = 'YOUR_GCS_SOURCE_URI';
// eg. 'gs://<your-gcs-bucket>/<import_source_path>/[file.csv/file.jsonl]'
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Dataset Service Client library
const {DatasetServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
const datasetServiceClient = new DatasetServiceClient(clientOptions);

async function importDataVideoObjectTracking() {
  const name = datasetServiceClient.datasetPath(project, location, datasetId);
  // Here we use only one import config with one source
  const importConfigs = [
    {
      gcsSource: {uris: [gcsSourceUri]},
      importSchemaUri:
        'gs://google-cloud-aiplatform/schema/dataset/ioformat/video_object_tracking_io_format_1.0.0.yaml',
    },
  ];
  const request = {
    name,
    importConfigs,
  };

  // Create Import Data Request
  const [response] = await datasetServiceClient.importData(request);
  console.log(`Long running operation: ${JSON.stringify(response.name)}`);

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

  console.log(
    `Import data video object tracking response : \
      ${JSON.stringify(importDataResponse)}`
  );
}
importDataVideoObjectTracking();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

from google.cloud import aiplatform

def import_data_video_object_tracking_sample(
    project: str,
    dataset_id: str,
    gcs_source_uri: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 1800,
):
    # 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.DatasetServiceClient(client_options=client_options)
    import_configs = [
        {
            "gcs_source": {"uris": [gcs_source_uri]},
            "import_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/ioformat/video_object_tracking_io_format_1.0.0.yaml",
        }
    ]
    name = client.dataset_path(project=project, location=location, dataset=dataset_id)
    response = client.import_data(name=name, import_configs=import_configs)
    print("Long running operation:", response.operation.name)
    import_data_response = response.result(timeout=timeout)
    print("import_data_response:", import_data_response)

Langkah selanjutnya

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