编写一段基于Java的图像识别代码

以下是一个简单的基于Java的图像识别代码示例，用于识别一张图片中是否存在猫的面部特征：

import java.awt.image.BufferedImage;
import java.io.File;
import javax.imageio.ImageIO;
import org.tensorflow.Graph;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;

public class ImageRecognition {
  public static void main(String[] args) throws Exception {
    // Load the pre-trained model graph
    byte[] graphDef = readAllBytesOrExit(Paths.get("model.pb"));
    Graph graph = new Graph();
    graph.importGraphDef(graphDef);

    // Load the input image
    BufferedImage image = ImageIO.read(new File("input.jpg"));

    // Preprocess the image
    float[][][][] input = preprocess(image);

    // Create a TensorFlow session
    try (Session session = new Session(graph)) {
      // Feed the image into the graph
      Tensor<Float> inputTensor = Tensor.create(input);
      Tensor<Float> outputTensor = session
          .runner()
          .feed("input", inputTensor)
          .fetch("output")
          .run()
          .get(0)
          .expect(Float.class);

      // Get the prediction result
      float[] result = new float[2];
      outputTensor.copyTo(result);
      float catScore = result[0];
      float nonCatScore = result[1];

      // Print the prediction result
      if (catScore > nonCatScore) {
        System.out.println("The image contains a cat!");
      } else {
        System.out.println("The image does not contain a cat.");
      }
    }
  }

  private static byte[] readAllBytesOrExit(Path path) throws IOException {
    byte[] bytes = Files.readAllBytes(path);
    return bytes;
  }

  private static float[][][][] preprocess(BufferedImage image) {
    float[][][][] input = new float[1][224][224][3];
    for (int y = 0; y < 224; y++) {
      for (int x = 0; x < 224; x++) {
        int rgb = image.getRGB(x * image.getWidth() / 224, y * image.getHeight() / 224);
        input[0][y][x][0] = ((rgb >> 16) & 0xFF) / 255.0f;
        input[0][y][x][1] = ((rgb >> 8) & 0xFF) / 255.0f;
        input[0][y][x][2] = (rgb & 0xFF) / 255.0f;
      }
    }
    return input;
  }
}

该代码使用了 TensorFlow Java API，首先读取了一个预训练好的模型图（即一个.pb文件），然后加载输入图片并进行预处理，最后将其输入至模型中进行预测。在本例中，模型会返回一个包含两个元素的浮点数数组，分别表示输入图片中包含猫的概率和不包含猫的概率。最后，根据输出结果打印出相应的预测结果。