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Créer un ensemble de données pour la classification d'images

Crée un ensemble de données pour la classification d'images.

Pages de documentation incluant cet exemple de code

Pour afficher l'exemple de code utilisé en contexte, consultez la documentation suivante :

Exemple de code

Go

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)

// visionClassificationCreateDataset creates a dataset for image classification.
func visionClassificationCreateDataset(w io.Writer, projectID string, location string, datasetName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetName := "dataset_display_name"

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

	req := &automlpb.CreateDatasetRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Dataset: &automlpb.Dataset{
			DisplayName: datasetName,
			DatasetMetadata: &automlpb.Dataset_ImageClassificationDatasetMetadata{
				ImageClassificationDatasetMetadata: &automlpb.ImageClassificationDatasetMetadata{
					// Specify the classification type:
					// - MULTILABEL: Multiple labels are allowed for one example.
					// - MULTICLASS: At most one label is allowed per example.
					ClassificationType: automlpb.ClassificationType_MULTILABEL,
				},
			},
		},
	}

	op, err := client.CreateDataset(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDataset: %v", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	dataset, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %v", err)
	}

	fmt.Fprintf(w, "Dataset name: %v\n", dataset.GetName())

	return nil
}

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ClassificationType;
import com.google.cloud.automl.v1.Dataset;
import com.google.cloud.automl.v1.ImageClassificationDatasetMetadata;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.OperationMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class VisionClassificationCreateDataset {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String displayName = "YOUR_DATASET_NAME";
    createDataset(projectId, displayName);
  }

  // Create a dataset
  static void createDataset(String projectId, String displayName)
      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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");

      // Specify the classification type
      // Types:
      // MultiLabel: Multiple labels are allowed for one example.
      // MultiClass: At most one label is allowed per example.
      ClassificationType classificationType = ClassificationType.MULTILABEL;
      ImageClassificationDatasetMetadata metadata =
          ImageClassificationDatasetMetadata.newBuilder()
              .setClassificationType(classificationType)
              .build();
      Dataset dataset =
          Dataset.newBuilder()
              .setDisplayName(displayName)
              .setImageClassificationDatasetMetadata(metadata)
              .build();
      OperationFuture<Dataset, OperationMetadata> future =
          client.createDatasetAsync(projectLocation, dataset);

      Dataset createdDataset = future.get();

      // Display the dataset information.
      System.out.format("Dataset name: %s\n", createdDataset.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 = createdDataset.getName().split("/");
      String datasetId = names[names.length - 1];
      System.out.format("Dataset id: %s\n", datasetId);
    }
  }
}

Node.js

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

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

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

async function createDataset() {
  // Construct request
  // Specify the classification type
  // Types:
  // MultiLabel: Multiple labels are allowed for one example.
  // MultiClass: At most one label is allowed per example.
  const request = {
    parent: client.locationPath(projectId, location),
    dataset: {
      displayName: displayName,
      imageClassificationDatasetMetadata: {
        classificationType: 'MULTILABEL',
      },
    },
  };

  // Create dataset
  const [operation] = await client.createDataset(request);

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

  console.log(`Dataset name: ${response.name}`);
  console.log(`
    Dataset id: ${
      response.name
        .split('/')
        [response.name.split('/').length - 1].split('\n')[0]
    }`);
}

createDataset();

Python

from google.cloud import automl

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

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# Specify the classification type
# Types:
# MultiLabel: Multiple labels are allowed for one example.
# MultiClass: At most one label is allowed per example.
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#classificationtype
metadata = automl.ImageClassificationDatasetMetadata(
    classification_type=automl.ClassificationType.MULTILABEL
)
dataset = automl.Dataset(
    display_name=display_name,
    image_classification_dataset_metadata=metadata,
)

# Create a dataset with the dataset metadata in the region.
response = client.create_dataset(parent=project_location, dataset=dataset)

created_dataset = response.result()

# Display the dataset information
print("Dataset name: {}".format(created_dataset.name))
print("Dataset id: {}".format(created_dataset.name.split("/")[-1]))

Étape suivante

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