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코드 샘플
Go
자세한 내용은 AutoML Vision Go API 참조 문서를 참조하세요.
AutoML Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import (
"context"
"fmt"
"io"
automl "cloud.google.com/go/automl/apiv1"
"cloud.google.com/go/automl/apiv1/automlpb"
)
// 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: %w", 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: %w", err)
}
fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())
dataset, err := op.Wait(ctx)
if err != nil {
return fmt.Errorf("Wait: %w", err)
}
fmt.Fprintf(w, "Dataset name: %v\n", dataset.GetName())
return nil
}
Java
자세한 내용은 AutoML Vision Java API 참조 문서를 참조하세요.
AutoML Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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
자세한 내용은 AutoML Vision Node.js API 참조 문서를 참조하세요.
AutoML Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
/**
* 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
자세한 내용은 AutoML Vision Python API 참조 문서를 참조하세요.
AutoML Vision에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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, timeout=300
)
created_dataset = response.result()
# Display the dataset information
print(f"Dataset name: {created_dataset.name}")
print("Dataset id: {}".format(created_dataset.name.split("/")[-1]))다음 단계
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