예측 메서드를 사용하여 이미지 분류를 위한 예측을 가져옵니다.
코드 샘플
Java
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vertex AI Java API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.cloud.aiplatform.v1.schema.predict.instance.ImageClassificationPredictionInstance;
import com.google.cloud.aiplatform.v1.schema.predict.params.ImageClassificationPredictionParams;
import com.google.cloud.aiplatform.v1.schema.predict.prediction.ClassificationPredictionResult;
import com.google.protobuf.Value;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.Base64;
import java.util.List;
public class PredictImageClassificationSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String fileName = "YOUR_IMAGE_FILE_PATH";
String endpointId = "YOUR_ENDPOINT_ID";
predictImageClassification(project, fileName, endpointId);
}
static void predictImageClassification(String project, String fileName, String endpointId)
throws IOException {
PredictionServiceSettings settings =
PredictionServiceSettings.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 (PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(settings)) {
String location = "us-central1";
EndpointName endpointName = EndpointName.of(project, location, endpointId);
byte[] contents = Base64.getEncoder().encode(Files.readAllBytes(Paths.get(fileName)));
String content = new String(contents, StandardCharsets.UTF_8);
ImageClassificationPredictionInstance predictionInstance =
ImageClassificationPredictionInstance.newBuilder().setContent(content).build();
List<Value> instances = new ArrayList<>();
instances.add(ValueConverter.toValue(predictionInstance));
ImageClassificationPredictionParams predictionParams =
ImageClassificationPredictionParams.newBuilder()
.setConfidenceThreshold((float) 0.5)
.setMaxPredictions(5)
.build();
PredictResponse predictResponse =
predictionServiceClient.predict(
endpointName, instances, ValueConverter.toValue(predictionParams));
System.out.println("Predict Image Classification Response");
System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());
System.out.println("Predictions");
for (Value prediction : predictResponse.getPredictionsList()) {
ClassificationPredictionResult.Builder resultBuilder =
ClassificationPredictionResult.newBuilder();
// Display names and confidences values correspond to
// IDs in the ID list.
ClassificationPredictionResult result =
(ClassificationPredictionResult) ValueConverter.fromValue(resultBuilder, prediction);
int counter = 0;
for (Long id : result.getIdsList()) {
System.out.printf("Label ID: %d\n", id);
System.out.printf("Label: %s\n", result.getDisplayNames(counter));
System.out.printf("Confidence: %.4f\n", result.getConfidences(counter));
counter++;
}
}
}
}
}Node.js
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Vertex AI Node.js API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const filename = "YOUR_PREDICTION_FILE_NAME";
// const endpointId = "YOUR_ENDPOINT_ID";
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {instance, params, prediction} =
aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;
// Imports the Google Cloud Prediction Service Client library
const {PredictionServiceClient} = aiplatform.v1;
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);
async function predictImageClassification() {
// Configure the endpoint resource
const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
const parametersObj = new params.ImageClassificationPredictionParams({
confidenceThreshold: 0.5,
maxPredictions: 5,
});
const parameters = parametersObj.toValue();
const fs = require('fs');
const image = fs.readFileSync(filename, 'base64');
const instanceObj = new instance.ImageClassificationPredictionInstance({
content: image,
});
const instanceValue = instanceObj.toValue();
const instances = [instanceValue];
const request = {
endpoint,
instances,
parameters,
};
// Predict request
const [response] = await predictionServiceClient.predict(request);
console.log('Predict image classification response');
console.log(`\tDeployed model id : ${response.deployedModelId}`);
const predictions = response.predictions;
console.log('\tPredictions :');
for (const predictionValue of predictions) {
const predictionResultObj =
prediction.ClassificationPredictionResult.fromValue(predictionValue);
for (const [i, label] of predictionResultObj.displayNames.entries()) {
console.log(`\tDisplay name: ${label}`);
console.log(`\tConfidences: ${predictionResultObj.confidences[i]}`);
console.log(`\tIDs: ${predictionResultObj.ids[i]}\n\n`);
}
}
}
predictImageClassification();Python
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import base64
from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict
def predict_image_classification_sample(
project: str,
endpoint_id: str,
filename: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
# 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.PredictionServiceClient(client_options=client_options)
with open(filename, "rb") as f:
file_content = f.read()
# The format of each instance should conform to the deployed model's prediction input schema.
encoded_content = base64.b64encode(file_content).decode("utf-8")
instance = predict.instance.ImageClassificationPredictionInstance(
content=encoded_content,
).to_value()
instances = [instance]
# See gs://google-cloud-aiplatform/schema/predict/params/image_classification_1.0.0.yaml for the format of the parameters.
parameters = predict.params.ImageClassificationPredictionParams(
confidence_threshold=0.5,
max_predictions=5,
).to_value()
endpoint = client.endpoint_path(
project=project, location=location, endpoint=endpoint_id
)
response = client.predict(
endpoint=endpoint, instances=instances, parameters=parameters
)
print("response")
print(" deployed_model_id:", response.deployed_model_id)
# See gs://google-cloud-aiplatform/schema/predict/prediction/image_classification_1.0.0.yaml for the format of the predictions.
predictions = response.predictions
for prediction in predictions:
print(" prediction:", dict(prediction))
다음 단계
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