Get a dataset for image classification

Retrieves a dataset for image classification.

Explore further

For detailed documentation that includes this code sample, see the following:

• Managing datasets

Code sample

Go

For more information, see the AutoML Vision Go API reference documentation.

To authenticate to AutoML Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// getDataset gets a dataset.
func getDataset(w io.Writer, projectID string, location string, datasetID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "TRL123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &automlpb.GetDatasetRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/datasets/%s", projectID, location, datasetID),
	}

	dataset, err := client.GetDataset(ctx, req)
	if err != nil {
		return fmt.Errorf("DeleteDataset: %w", err)
	}

	fmt.Fprintf(w, "Dataset name: %v\n", dataset.GetName())
	fmt.Fprintf(w, "Dataset display name: %v\n", dataset.GetDisplayName())
	fmt.Fprintf(w, "Dataset create time:\n")
	fmt.Fprintf(w, "\tseconds: %v\n", dataset.GetCreateTime().GetSeconds())
	fmt.Fprintf(w, "\tnanos: %v\n", dataset.GetCreateTime().GetNanos())

	// Vision classification
	if metadata := dataset.GetImageClassificationDatasetMetadata(); metadata != nil {
		fmt.Fprintf(w, "Image classification dataset metadata: %v\n", metadata)
	}

	return nil
}

Java

For more information, see the AutoML Vision Java API reference documentation.

To authenticate to AutoML Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.Dataset;
import com.google.cloud.automl.v1.DatasetName;
import java.io.IOException;

class GetDataset {

  static void getDataset() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    getDataset(projectId, datasetId);
  }

  // Get a dataset
  static void getDataset(String projectId, String datasetId) throws IOException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the complete path of the dataset.
      DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
      Dataset dataset = client.getDataset(datasetFullId);

      // Display the dataset information
      System.out.format("Dataset name: %s\n", dataset.getName());
      // To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
      // required for other methods.
      // Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
      String[] names = dataset.getName().split("/");
      String retrievedDatasetId = names[names.length - 1];
      System.out.format("Dataset id: %s\n", retrievedDatasetId);
      System.out.format("Dataset display name: %s\n", dataset.getDisplayName());
      System.out.println("Dataset create time:");
      System.out.format("\tseconds: %s\n", dataset.getCreateTime().getSeconds());
      System.out.format("\tnanos: %s\n", dataset.getCreateTime().getNanos());
      System.out.format(
          "Image classification dataset metadata: %s\n",
          dataset.getImageClassificationDatasetMetadata());
    }
  }
}

Node.js

For more information, see the AutoML Vision Node.js API reference documentation.

To authenticate to AutoML Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const datasetId = 'YOUR_DATASET_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

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

async function getDataset() {
  // Construct request
  const request = {
    name: client.datasetPath(projectId, location, datasetId),
  };

  const [response] = await client.getDataset(request);

  console.log(`Dataset name: ${response.name}`);
  console.log(
    `Dataset id: ${
      response.name.split('/')[response.name.split('/').length - 1]
    }`
  );
  console.log(`Dataset display name: ${response.displayName}`);
  console.log('Dataset create time');
  console.log(`\tseconds ${response.createTime.seconds}`);
  console.log(`\tnanos ${response.createTime.nanos / 1e9}`);
  console.log(
    `Image classification dataset metadata: ${response.imageClassificationDatasetMetadata}`
  );
}

getDataset();

Python

For more information, see the AutoML Vision Python API reference documentation.

To authenticate to AutoML Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"

client = automl.AutoMlClient()
# Get the full path of the dataset
dataset_full_id = client.dataset_path(project_id, "us-central1", dataset_id)
dataset = client.get_dataset(name=dataset_full_id)

# Display the dataset information
print(f"Dataset name: {dataset.name}")
print("Dataset id: {}".format(dataset.name.split("/")[-1]))
print(f"Dataset display name: {dataset.display_name}")
print(f"Dataset create time: {dataset.create_time}")
print(
    "Image classification dataset metadata: {}".format(
        dataset.image_classification_dataset_metadata
    )
)

What's next

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