AI:OPENCV实现人脸的自动识别

依赖jar包：

faceRecognition.java

 package opencv;

 import java.awt.Graphics;
 import java.awt.image.BufferedImage;

 import javax.swing.JFrame;
 import javax.swing.JPanel;
 import javax.swing.WindowConstants;

 import org.opencv.core.Core;
 import org.opencv.core.Mat;
 import org.opencv.core.MatOfDouble;
 import org.opencv.core.MatOfRect;
 import org.opencv.core.Point;
 import org.opencv.core.Rect;
 import org.opencv.core.Scalar;
 import org.opencv.imgproc.Imgproc;
 import org.opencv.objdetect.CascadeClassifier;
 import org.opencv.objdetect.HOGDescriptor;
 import org.opencv.videoio.VideoCapture;
 import org.opencv.videoio.Videoio;

 @SuppressWarnings("serial")
 public class faceRecognition extends JPanel {

     private BufferedImage mImg;

     private BufferedImage mat2BI(Mat mat) {
         int dataSize = mat.cols() * mat.rows() * (int) mat.elemSize();
         byte[] data = new byte[dataSize];
         mat.get(0, 0, data);
         int type = mat.channels() == 1 ? BufferedImage.TYPE_BYTE_GRAY : BufferedImage.TYPE_3BYTE_BGR;

         if (type == BufferedImage.TYPE_3BYTE_BGR) {
             for (int i = 0; i < dataSize; i += 3) {
                 byte blue = data[i + 0];
                 data[i + 0] = data[i + 2];
                 data[i + 2] = blue;
             }
         }
         BufferedImage image = new BufferedImage(mat.cols(), mat.rows(), type);
         image.getRaster().setDataElements(0, 0, mat.cols(), mat.rows(), data);

         return image;
     }

     public void paintComponent(Graphics g) {
         if (mImg != null) {
             g.drawImage(mImg, 0, 0, mImg.getWidth(), mImg.getHeight(), this);
         }
     }

     public static void main(String[] args) {
         try {
             System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

             Mat capImg = new Mat();
             VideoCapture capture = new VideoCapture(0);
             int height = (int) capture.get(Videoio.CAP_PROP_FRAME_HEIGHT);
             int width = (int) capture.get(Videoio.CAP_PROP_FRAME_WIDTH);
             if (height == 0 || width == 0) {
                 throw new Exception("未发现摄像头！");
             }

             // 创建jframe
             JFrame frame = new JFrame("摄像头");
             frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
             faceRecognition panel = new faceRecognition();
             // 添加鼠标监听器
             // panel.addMouseListener(new MouseAdapter() {
             // @Override
             // public void mouseClicked(MouseEvent arg0) {
             // System.out.println("鼠标点击");
             // }
             //
             // @Override
             // public void mouseMoved(MouseEvent arg0) {
             // System.out.println("鼠标移开");
             //
             // }
             //
             // @Override
             // public void mouseReleased(MouseEvent arg0) {
             // System.out.println("鼠标释放");
             // }
             //
             // @Override
             // public void mousePressed(MouseEvent arg0) {
             // System.out.println("鼠标按下");
             // }
             //
             // @Override
             // public void mouseExited(MouseEvent arg0) {
             // System.out.println("鼠标出界");
             // // System.out.println(arg0.toString());
             // }
             //
             // @Override
             // public void mouseDragged(MouseEvent arg0) {
             // System.out.println("mouseDragged");
             // // System.out.println(arg0.toString());
             // }
             // });
             frame.setContentPane(panel);
             frame.setVisible(true);
             frame.setSize(width + frame.getInsets().left + frame.getInsets().right,
                     height + frame.getInsets().top + frame.getInsets().bottom);
             int n = 1;
             Mat temp = new Mat();
             // while (frame.isShowing() && n <= 500) {
             while (frame.isShowing()) {
                 // System.out.println("第" + n + "张");
                 capture.read(capImg); // 载入图片
                 Imgproc.cvtColor(capImg, temp, Imgproc.COLOR_RGB2GRAY);
                 // Imgcodecs.imwrite("E:/opencv/wzg/picture_" + n + ".png", temp); // 存储图片到本地
                 panel.mImg = panel.mat2BI(detectFace(capImg)); // 调用人脸识别方法
                 panel.repaint();
                 // n++;
                 // break; // 识别500存入本地
             }
             capture.release();
             frame.dispose();
         } catch (Exception e) {
             System.out.println("例外：" + e);
         } finally {
             System.out.println("--done--");
         }

     }

     /**
      * opencv实现人脸识别
      *
      * @param img
      */
     public static Mat detectFace(Mat img) throws Exception {

         // System.out.println("开始人像识别 ... ");
         // 从配置文件lbpcascade_frontalface.xml中创建一个人脸识别器，该文件位于opencv安装目录中
         CascadeClassifier faceDetector = new CascadeClassifier("E:\\my_dev\\haarcascade_frontalface_alt.xml");

         // 在图片中检测人脸
         MatOfRect faceDetections = new MatOfRect();

         faceDetector.detectMultiScale(img, faceDetections);

         if (faceDetections.toArray().length == 0) {
 //            System.out.println("no face");
         } else {
             System.out.println(String.format("检测到  %s 个人像", faceDetections.toArray().length));
         }

         Rect[] rects = faceDetections.toArray();
         if (rects != null && rects.length >= 1) {
             for (Rect rect : rects) {
                 Imgproc.rectangle(img, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
                         new Scalar(0, 0, 255), 2);
             }
         }
         return img;
     }

     /**
      * opencv实现人型识别，hog默认的分类器。所以效果不好。
      *
      * @param img
      */
     public static Mat detectPeople(Mat img) {
         // System.out.println("detectPeople...");
         if (img.empty()) {
             System.out.println("image is exist");
         }
         HOGDescriptor hog = new HOGDescriptor();
         hog.setSVMDetector(HOGDescriptor.getDefaultPeopleDetector());
         System.out.println(HOGDescriptor.getDefaultPeopleDetector());
         hog.setSVMDetector(HOGDescriptor.getDaimlerPeopleDetector());
         MatOfRect regions = new MatOfRect();
         MatOfDouble foundWeights = new MatOfDouble();
         System.out.println(foundWeights.toString());
         hog.detectMultiScale(img, regions, foundWeights);
         for (Rect rect : regions.toArray()) {
             Imgproc.rectangle(img, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
                     new Scalar(0, 0, 255), 2);
         }
         return img;
     }

 }