使用预测方法获取图片对象检测的预测。
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代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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.ImageObjectDetectionPredictionInstance;
import com.google.cloud.aiplatform.v1.schema.predict.params.ImageObjectDetectionPredictionParams;
import com.google.cloud.aiplatform.v1.schema.predict.prediction.ImageObjectDetectionPredictionResult;
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 PredictImageObjectDetectionSample {
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";
predictImageObjectDetection(project, fileName, endpointId);
}
static void predictImageObjectDetection(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);
ImageObjectDetectionPredictionParams params =
ImageObjectDetectionPredictionParams.newBuilder()
.setConfidenceThreshold((float) (0.5))
.setMaxPredictions(5)
.build();
ImageObjectDetectionPredictionInstance instance =
ImageObjectDetectionPredictionInstance.newBuilder().setContent(content).build();
List<Value> instances = new ArrayList<>();
instances.add(ValueConverter.toValue(instance));
PredictResponse predictResponse =
predictionServiceClient.predict(endpointName, instances, ValueConverter.toValue(params));
System.out.println("Predict Image Object Detection Response");
System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());
System.out.println("Predictions");
for (Value prediction : predictResponse.getPredictionsList()) {
ImageObjectDetectionPredictionResult.Builder resultBuilder =
ImageObjectDetectionPredictionResult.newBuilder();
ImageObjectDetectionPredictionResult result =
(ImageObjectDetectionPredictionResult)
ValueConverter.fromValue(resultBuilder, prediction);
for (int i = 0; i < result.getIdsCount(); i++) {
System.out.printf("\tDisplay name: %s\n", result.getDisplayNames(i));
System.out.printf("\tConfidences: %f\n", result.getConfidences(i));
System.out.printf("\tIDs: %d\n", result.getIds(i));
System.out.printf("\tBounding boxes: %s\n", result.getBboxes(i));
}
}
}
}
}Node.js
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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 predictImageObjectDetection() {
// Configure the endpoint resource
const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
const parametersObj = new params.ImageObjectDetectionPredictionParams({
confidenceThreshold: 0.5,
maxPredictions: 5,
});
const parameters = parametersObj.toValue();
const fs = require('fs');
const image = fs.readFileSync(filename, 'base64');
const instanceObj = new instance.ImageObjectDetectionPredictionInstance({
content: image,
});
const instanceVal = instanceObj.toValue();
const instances = [instanceVal];
const request = {
endpoint,
instances,
parameters,
};
// Predict request
const [response] = await predictionServiceClient.predict(request);
console.log('Predict image object detection response');
console.log(`\tDeployed model id : ${response.deployedModelId}`);
const predictions = response.predictions;
console.log('Predictions :');
for (const predictionResultVal of predictions) {
const predictionResultObj =
prediction.ImageObjectDetectionPredictionResult.fromValue(
predictionResultVal
);
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]}`);
console.log(`\tBounding boxes: ${predictionResultObj.bboxes[i]}\n\n`);
}
}
}
predictImageObjectDetection();Python
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import base64
from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict
def predict_image_object_detection_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.ImageObjectDetectionPredictionInstance(
content=encoded_content,
).to_value()
instances = [instance]
# See gs://google-cloud-aiplatform/schema/predict/params/image_object_detection_1.0.0.yaml for the format of the parameters.
parameters = predict.params.ImageObjectDetectionPredictionParams(
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_object_detection_1.0.0.yaml for the format of the predictions.
predictions = response.predictions
for prediction in predictions:
print(" prediction:", dict(prediction))
后续步骤
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