java实现简单的图片类别识别

1.maven依赖

  <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>api</artifactId>
            <version>0.4.0</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>repository</artifactId>
            <version>0.4.0</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-model-zoo</artifactId>
            <version>0.4.0</version>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-native-auto</artifactId>
            <version>1.4.0</version>
            <scope>runtime</scope>
        </dependency>

2.代码

import ai.djl.Application;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.modality.cv.util.BufferedImageUtils;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;
import org.opencv.core.*;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;

import javax.imageio.ImageIO;
import javax.swing.*;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.*;

public class Main {
    

    public static void main(String[] args) throws IOException, ModelException, TranslateException {
    
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        String url = "d:/path/to/u=2304912836,1229708753&fm=193.jpg";

        VideoCapture camera = new VideoCapture(0); // 0 表示第一个摄像头设备，如果有多个摄像头可能需要调整参数
        if (!camera.isOpened()) {
    
            System.out.println("Error: Could not open camera");
            return;
        }
        // 创建窗口并设置关闭操作
        JFrame frame = new JFrame("Face Grid Live");
        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        frame.setSize(800, 600);
        frame.setResizable(false);

        // 创建用于显示图像的标签
        JLabel label = new JLabel();
        frame.getContentPane().add(label, BorderLayout.CENTER);
        Mat frameImage = new Mat();
        while(true){
    
            camera.read(frameImage);
            BufferedImage bufferedImage = matToBufferedImage(frameImage);



            Criteria<BufferedImage, DetectedObjects> criteria =
                    Criteria.builder()
                            .optApplication(Application.CV.OBJECT_DETECTION)
                            .setTypes(BufferedImage.class, DetectedObjects.class)
                            .optFilter("backbone", "resnet50")
                            .optProgress(new ProgressBar())
                            .build();

            try (ZooModel<BufferedImage, DetectedObjects> model = ModelZoo.loadModel(criteria)) {
    
                try (Predictor<BufferedImage, DetectedObjects> predictor = model.newPredictor()) {
    
                    DetectedObjects detection = predictor.predict(bufferedImage);
                    System.out.println(detection);
                }
            }
            // 将图像显示在标签中
            ImageIcon imageIcon = new ImageIcon(bufferedImage);
            label.setIcon(imageIcon);

            // 刷新窗口
            frame.pack();
            frame.setVisible(true);
        }

    }
    public static BufferedImage matToBufferedImage(Mat matrix) {
    
        int width = matrix.cols();
        int height = matrix.rows();
        int channels = matrix.channels();
        byte[] data = new byte[width * height * channels];
        matrix.get(0, 0, data);

        BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR);
        final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
        System.arraycopy(data, 0, targetPixels, 0, data.length);

        return image;
    }
}