Previsão para classificação de imagens

Obtém a previsão para a classificação de imagens através do método predict.

Exemplo de código

Java

Antes de experimentar este exemplo, siga as Javainstruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Java Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento 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 experimentar este exemplo, siga as Node.jsinstruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Node.js Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento 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 experimentar este exemplo, siga as Pythoninstruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Python Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento 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))

O que se segue?

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