Predice para la clasificación de imágenes

Obtiene la predicción para la clasificación de imágenes mediante el método de predicción.

Muestra de código

Java

Antes de probar este ejemplo, sigue las instrucciones de configuración para Java incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Java.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local. 


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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Node.js incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Node.js.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local. 

/**
 * 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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Python incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Python.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local. 

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))

¿Qué sigue?

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