Mengelola set data

Sebuah project dapat memiliki beberapa set data, dan setiap data digunakan untuk melatih model terpisah. Anda dapat memperoleh daftar set data yang tersedia, memperoleh set data tertentu, mengekspor set data, dan dapat menghapus set data yang tidak lagi Anda butuhkan.

Mencantumkan set data

Project dapat mencakup banyak set data. Bagian ini menjelaskan cara mengambil daftar set data yang tersedia untuk project.

UI Web

Untuk melihat daftar set data yang tersedia menggunakan UI Deteksi Objek AutoML Vision, klik link Set Data di bagian atas menu navigasi sebelah kiri.

Halaman daftar set data

Untuk melihat set data project lain, pilih project dari menu drop-down di sisi kiri kolom judul.

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • project-id: Project ID GCP Anda.

Metode HTTP dan URL: 

GET https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Jalankan perintah berikut: 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets"

PowerShell

Jalankan perintah berikut: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets" | Select-Object -Expand Content

Anda akan menerima respons JSON yang mirip seperti berikut:

{
  "datasets": [
    {
      "name": "projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID",
      "displayName": "DISPLAY_NAME",
      "createTime": "2018-10-29T15:45:53.353442Z",
      "exampleCount": 227,
      "imageObjectDetectionDatasetMetadata": {}
    }
  ]
}

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
	"google.golang.org/api/iterator"
)

// listDatasets lists existing datasets.
func listDatasets(w io.Writer, projectID string, location string) error {
	// projectID := "my-project-id"
	// location := "us-central1"

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

	req := &automlpb.ListDatasetsRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
	}

	it := client.ListDatasets(ctx, req)

	// Iterate over all results
	for {
		dataset, err := it.Next()
		if err == iterator.Done {
			break
		}
		if err != nil {
			return fmt.Errorf("ListGlossaries.Next: %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 object detection
		if metadata := dataset.GetImageObjectDetectionDatasetMetadata(); metadata != nil {
			fmt.Fprintf(w, "Image object detection dataset metadata: %v\n", metadata)
		}

	}

	return nil
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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

class ListDatasets {

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

  // List the datasets
  static void listDatasets(String projectId) 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()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      ListDatasetsRequest request =
          ListDatasetsRequest.newBuilder().setParent(projectLocation.toString()).build();

      // List all the datasets available in the region by applying filter.
      System.out.println("List of datasets:");
      for (Dataset dataset : client.listDatasets(request).iterateAll()) {
        // Display the dataset information
        System.out.format("\nDataset 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 object detection dataset metadata: %s\n",
            dataset.getImageObjectDetectionDatasetMetadata());
      }
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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

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

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

async function listDatasets() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    filter: 'translation_dataset_metadata:*',
  };

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

  console.log('List of datasets:');
  for (const dataset of response) {
    console.log(`Dataset name: ${dataset.name}`);
    console.log(
      `Dataset id: ${
        dataset.name.split('/')[dataset.name.split('/').length - 1]
      }`
    );
    console.log(`Dataset display name: ${dataset.displayName}`);
    console.log('Dataset create time');
    console.log(`\tseconds ${dataset.createTime.seconds}`);
    console.log(`\tnanos ${dataset.createTime.nanos / 1e9}`);
    console.log(
      `Image object detection dataset metatdata: ${dataset.imageObjectDetectionDatasetMetatdata}`
    );
  }
}

listDatasets();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

from google.cloud import automl

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

client = automl.AutoMlClient()
# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"

# List all the datasets available in the region.
request = automl.ListDatasetsRequest(parent=project_location, filter="")
response = client.list_datasets(request=request)

print("List of datasets:")
for dataset in response:
    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 object detection dataset metadata: {}".format(
            dataset.image_object_detection_dataset_metadata
        )
    )

Bahasa tambahan

C# : Ikuti Petunjuk penyiapan C# di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek Vision AutoML untuk .NET.

PHP : Ikuti petunjuk penyiapan PHP di halaman library klien, lalu kunjungi dokumentasi referensi Deteksi Objek AutoML Vision untuk PHP.

Ruby : Ikuti Petunjuk penyiapan Ruby di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek AutoML Vision untuk Ruby.

Mendapatkan set data

Anda juga dapat memperoleh set data tertentu menggunakan ID set data.

UI Web

Untuk melihat daftar set data yang tersedia menggunakan UI Deteksi Objek AutoML Vision, klik link Set Data di bagian atas menu navigasi sebelah kiri.

Halaman daftar set data

Untuk melihat set data project lain, pilih project dari daftar drop-down di sisi kiri panel judul.

Mengakses set data tertentu dengan memilih namanya dari daftar.

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • project-id: project ID GCP Anda.
  • dataset-id: ID set data Anda. ID adalah elemen terakhir dari nama set data Anda. Contoh:
    • nama set data: projects/project-id/locations/location-id/datasets/3104518874390609379
    • id set data: 3104518874390609379

Metode HTTP dan URL: 

GET https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Jalankan perintah berikut: 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID"

PowerShell

Jalankan perintah berikut: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID" | Select-Object -Expand Content

Anda akan menerima respons JSON yang mirip seperti berikut:

{
  "name": "projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID",
  "displayName": "DISPLAY_NAME",
  "createTime": "2019-03-31T22:29:41.136184Z",
  "etag": "AB3BwFo-bssF99O7d4iI4_kwfnSi5pIK8FQ4D8h6Z_EaC4thAeZFbgbaIDvqXWuzjx9s",
  "exampleCount": 225,
  "imageObjectDetectionDatasetMetadata": {}
}

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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 object detection
	if metadata := dataset.GetImageObjectDetectionDatasetMetadata(); metadata != nil {
		fmt.Fprintf(w, "Image object detection dataset metadata: %v\n", metadata)
	}

	return nil
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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 object detection dataset metadata: %s\n",
          dataset.getImageObjectDetectionDatasetMetadata());
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

/**
 * 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 object detection dataset metatdata: ${response.imageObjectDetectionDatasetMetatdata}`
  );
}

getDataset();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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 object detection dataset metadata: {}".format(
        dataset.image_object_detection_dataset_metadata
    )
)

Bahasa tambahan

C# : Ikuti Petunjuk penyiapan C# di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek Vision AutoML untuk .NET.

PHP : Ikuti petunjuk penyiapan PHP di halaman library klien, lalu kunjungi dokumentasi referensi Deteksi Objek AutoML Vision untuk PHP.

Ruby : Ikuti Petunjuk penyiapan Ruby di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek AutoML Vision untuk Ruby.

Mengekspor set data

Anda dapat mengekspor file CSV yang berisi semua informasi set data ke bucket Google Cloud Storage. Hal ini sangat membantu untuk kasus ketika Anda menambahkan, menghapus, atau mengubah anotasi gambar latihan di UI.

UI web

Untuk mengekspor set data yang tidak kosong, selesaikan langkah-langkah berikut:

  1. Pilih set data yang tidak kosong dari halaman Set Data.

    Gambar set data listingan

    Memilih set data yang tidak kosong akan mengarahkan Anda ke halaman detail Set Data.

    Memberi label pada UI gambar pelatihan

  2. Memilih opsi Mengekspor data di bagian atas halaman detail Dataset.

    Tombol ekspor set data

    Tindakan ini akan membuka jendela tempat Anda dapat memilih lokasi bucket Google Cloud Storage, atau membuat bucket baru dan menetapkannya sebagai lokasi untuk menyimpan file CSV.

    Jendela pop-up statistik label

  3. Memilih Ekspor CSV setelah Anda memilih lokasi bucket Google Cloud Storage baru atau yang sudah ada.

    tombol ekspor CSV

  4. Anda akan menerima email saat proses ekspor data selesai.

    email ekspor selesai

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • project-id: project ID GCP Anda.
  • dataset-id: ID set data Anda. ID adalah elemen terakhir dari nama set data Anda. Contoh:
    • nama set data: projects/project-id/locations/location-id/datasets/3104518874390609379
    • id set data: 3104518874390609379
  • output-storage-bucket: bucket/direktori Google Cloud Storage tempat file output disimpan, yang dinyatakan dalam bentuk berikut: gs://bucket/directory/. Pengguna yang membuat permintaan harus memiliki izin tulis ke bucket.

Metode HTTP dan URL: 

POST https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID:exportData

Isi JSON permintaan: 

{
  "outputConfig": {
    "gcsDestination": {
      "outputUriPrefix": "CLOUD_STORAGE_BUCKET"
    }
  }
}

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID:exportData"

PowerShell

Simpan isi permintaan dalam file bernama request.json, dan jalankan perintah berikut: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID:exportData" | Select-Object -Expand Content

Anda akan melihat output yang serupa dengan berikut ini: Anda dapat menggunakan ID operasi untuk mendapatkan status tugas. Sebagai contoh, lihat Bekerja dengan operasi yang berjalan lama.

{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-08-12T18:52:31.637075Z",
    "updateTime": "2019-08-12T18:52:31.637075Z",
    "exportDataDetails": {
      "outputInfo": {
        "gcsOutputDirectory": "CLOUD_STORAGE_BUCKET/export_data-DATASET_NAME-TIMESTAMP_OF_EXPORT_CALL/"
      }
    }
  }
}

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import (
	"context"
	"fmt"
	"io"

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

// exportDataset exports a dataset.
func exportDataset(w io.Writer, projectID string, location string, datasetID string, outputURI string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "TRL123456789..."
	// outputURI := "gs://BUCKET_ID/path_to_export/"

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

	req := &automlpb.ExportDataRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/datasets/%s", projectID, location, datasetID),
		OutputConfig: &automlpb.OutputConfig{
			Destination: &automlpb.OutputConfig_GcsDestination{
				GcsDestination: &automlpb.GcsDestination{
					OutputUriPrefix: outputURI,
				},
			},
		},
	}

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

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

	fmt.Fprintf(w, "Dataset exported.\n")

	return nil
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.DatasetName;
import com.google.cloud.automl.v1.GcsDestination;
import com.google.cloud.automl.v1.OutputConfig;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class ExportDataset {

  static void exportDataset() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String gcsUri = "gs://BUCKET_ID/path_to_export/";
    exportDataset(projectId, datasetId, gcsUri);
  }

  // Export a dataset to a GCS bucket
  static void exportDataset(String projectId, String datasetId, String gcsUri)
      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()) {
      // Get the complete path of the dataset.
      DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(gcsUri).build();

      // Export the dataset to the output URI.
      OutputConfig outputConfig =
          OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();

      System.out.println("Processing export...");
      Empty response = client.exportDataAsync(datasetFullId, outputConfig).get();
      System.out.format("Dataset exported. %s\n", response);
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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

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

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

async function exportDataset() {
  // Construct request
  const request = {
    name: client.datasetPath(projectId, location, datasetId),
    outputConfig: {
      gcsDestination: {
        outputUriPrefix: gcsUri,
      },
    },
  };

  const [operation] = await client.exportData(request);
  // Wait for operation to complete.
  const [response] = await operation.promise();
  console.log(`Dataset exported: ${response}`);
}

exportDataset();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# gcs_uri = "gs://YOUR_BUCKET_ID/path/to/export/"

client = automl.AutoMlClient()

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

gcs_destination = automl.GcsDestination(output_uri_prefix=gcs_uri)
output_config = automl.OutputConfig(gcs_destination=gcs_destination)

response = client.export_data(name=dataset_full_id, output_config=output_config)
print(f"Dataset exported. {response.result()}")

Bahasa tambahan

C# : Ikuti Petunjuk penyiapan C# di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek Vision AutoML untuk .NET.

PHP : Ikuti petunjuk penyiapan PHP di halaman library klien, lalu kunjungi dokumentasi referensi Deteksi Objek AutoML Vision untuk PHP.

Ruby : Ikuti Petunjuk penyiapan Ruby di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek AutoML Vision untuk Ruby.

Format CSV yang diekspor

File CSV yang diekspor berisi format yang sama dengan CSV impor data pelatihan:

set,path,label,x_min,y_min,x_max,y_min,x_max,y_max,x_min,y_max

File CSV ini disimpan di folder ekspor yang telah dibuat yang dibedakan berdasarkan stempel waktu yang unik. Berikut adalah beberapa baris contoh dari file CSV yang diekspor:

/export_data-salad_dataset-2019-05-29T18:12:18.750Z/image_object_detection_1.csv

TRAIN,gs://my-storage-bucket/img/img009.jpg,Cheese,0.643239,0.362779,0.662498,0.362779,0.662498,0.416544,0.643239,0.416544
TRAIN,gs://my-storage-bucket/img/img009.jpg,Salad,0.205697,0.255249,0.459074,0.255249,0.459074,0.775244,0.205697,0.775244
TEST,gs://my-storage-bucket/img/img118.jpg,Cheese,0.320334,0.501238,0.726751,0.501238,0.726751,0.741431,0.320334,0.741431
TEST,gs://my-storage-bucket/img/img118.jpg,Salad,0.0,0.037361,1.0,0.037361,1.0,0.926321,0.0,0.926321
TEST,gs://my-storage-bucket/img/img118.jpg,Cheese,0.358745,0.29076,0.740381,0.29076,0.740381,0.497936,0.358745,0.497936
TRAIN,gs://my-storage-bucket/img/img375.jpg,Tomato,0.027274,0.41247,0.43122,0.41247,0.43122,0.702593,0.027274,0.702593
VALIDATION,gs://my-storage-bucket/img/img852.jpg,Tomato,0.716958,0.178534,0.805999,0.178534,0.805999,0.329861,0.716958,0.329861
VALIDATION,gs://my-storage-bucket/img/img852.jpg,Tomato,0.858044,0.297255,0.950847,0.297255,0.950847,0.39173,0.858044,0.39173
VALIDATION,gs://my-storage-bucket/img/img852.jpg,Tomato,0.199644,0.624155,0.321919,0.624155,0.321919,0.796384,0.199644,0.796384
VALIDATION,gs://my-storage-bucket/img/img852.jpg,Cheese,0.399672,0.277189,0.600955,0.277189,0.600955,0.47032,0.399672,0.47032

Perlu diperhatikan bahwa hanya ada satu representasi kotak pembatas dan labelnya per baris. Informasi ini memberi tahu Anda hal berikut:

  • img009.jpg - berada dalam set LATIHAN dan berisi dua kotak pembatas dengan label Cheese dan Salad
  • img118.jpg - berada dalam set PENGUJIAN dan memiliki tiga kotak pembatas dengan label Cheese, Salad, dan Cheese
  • img375.jpg - berada dalam set LATIHAN dan memiliki satu kotak pembatas dengan label Tomato
  • img852.jpg berada dalam set VALIDASI dan memiliki 4 kotak pembatas yang berbeda dengan label Tomato, Tomato, Tomato, dan Cheese

Menghapus set data

Anda dapat menghapus resource set data menggunakan ID set data.

UI web

  1. Di UI Deteksi Objek AutoML Vision, klik link Set Data di bagian atas menu navigasi sebelah kiri untuk menampilkan daftar set data yang tersedia.

    Menghapus UI set data

  2. Klik menu tiga titik di ujung kanan baris yang ingin Anda hapus, lalu pilih Hapus set data.

  3. Klik Hapus di kotak dialog konfirmasi.

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • project-id: project ID GCP Anda.
  • dataset-id: ID set data Anda. ID adalah elemen terakhir dari nama set data Anda. Contoh:
    • nama set data: projects/project-id/locations/location-id/datasets/3104518874390609379
    • id set data: 3104518874390609379

Metode HTTP dan URL: 

DELETE https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Jalankan perintah berikut: 

curl -X DELETE \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID"

PowerShell

Jalankan perintah berikut: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/datasets/DATASET_ID" | Select-Object -Expand Content

Anda akan melihat output yang mirip dengan berikut ini. Anda dapat menggunakan ID operasi untuk mendapatkan status tugas. Sebagai contoh, lihat Bekerja dengan operasi yang berjalan lama.

{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-11-08T22:37:19.822128Z",
    "updateTime": "2019-11-08T22:37:19.822128Z",
    "deleteDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import (
	"context"
	"fmt"
	"io"

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

// deleteDataset deletes a dataset.
func deleteDataset(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.DeleteDatasetRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/datasets/%s", projectID, location, datasetID),
	}

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

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

	fmt.Fprintf(w, "Dataset deleted.\n")

	return nil
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.DatasetName;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class DeleteDataset {

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

  // Delete a dataset
  static void deleteDataset(String projectId, String datasetId)
      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()) {
      // Get the full path of the dataset.
      DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
      Empty response = client.deleteDatasetAsync(datasetFullId).get();
      System.out.format("Dataset deleted. %s\n", response);
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

/**
 * 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 deleteDataset() {
  // Construct request
  const request = {
    name: client.datasetPath(projectId, location, datasetId),
  };

  const [operation] = await client.deleteDataset(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();
  console.log(`Dataset deleted: ${response}`);
}

deleteDataset();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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)
response = client.delete_dataset(name=dataset_full_id)

print(f"Dataset deleted. {response.result()}")

Bahasa tambahan

C# : Ikuti Petunjuk penyiapan C# di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek Vision AutoML untuk .NET.

PHP : Ikuti petunjuk penyiapan PHP di halaman library klien, lalu kunjungi dokumentasi referensi Deteksi Objek AutoML Vision untuk PHP.

Ruby: Ikuti Petunjuk penyiapan Ruby di halaman client libraries lalu kunjungi Dokumentasi referensi Deteksi Objek AutoML Vision untuk Ruby.