创建maven项目 导入openCV图像包处理 开始人脸识别

file
new project


点击finish

导入完毕

代码 ：

package test;

import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;


public class t1 {
    private static final Logger log= (Logger) LoggerFactory.getLogger(FaceVideoTest.class);
    private static int startFrom = 0;
    private static int sample=0;
    private static String id="";
    public static long startTime;
    private static final String endImgUrl = "D:\\ImgageLocal";
	//下面的三行是openCV的安装路径 以及配置文件的位置
	private static final String faceDetectorXML2URL ="D:\\opencv\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
	
	
	private static final String eyeDetectorXML2URL= "D:\\opencv\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml";
	
    /*
    直方图大小，越大精度越高，运行越慢
     */
    private static int Matching_Accuracy=100000;
    /*
    初始化人脸探测器
     */
    private static CascadeClassifier faceDetector;
    /*
    初始化人眼探测器
     */
    private static CascadeClassifier eyeDetector;
    private static int i=0;
    /*
    初始化分类器
     */
    static {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        faceDetector=new CascadeClassifier(faceDetectorXML2URL);
        eyeDetector=new CascadeClassifier(eyeDetectorXML2URL);
    }

    public static void main(String[] args)
    {
        log.info("开始人脸匹配");
        long begin=System.currentTimeMillis();
        //从摄像头实时人脸识别，识别成功保存图片到本地
        try{
            //从摄像头获取图片，比较本地图片，进行人脸识别
            //getVideoFromCamera(endImgUrl+"2.png");
            //人眼识别
        //    getVideoFromCamera(true);
            //人脸识别
          //  getVideoFromCamera(false);
            //搜集人脸图片
            CollectImageData("2");
            //仅用于强制抛异常，从而关闭GUI界面
            Thread.sleep(1000);
            int err=1/0;
        } catch (Exception e) {
            log.info("开始强制关闭"+e.getMessage());
            log.info("人脸匹配结束，总耗时：{}ms",(System.currentTimeMillis()-begin));
            System.exit(0);
        }
    }
    //搜集需要的人脸，并设置工号
    private static void CollectImageData(String id)
    {
        VideoCapture capture=new VideoCapture(0);
        Mat video=new Mat();
        int index=0;
        if(capture.isOpened()){
            while(true){
                //匹配成功3次退出
                capture.read(video);
                detectAndCollect(video,faceDetector,id);
                HighGui.imshow("实时人脸识别",video);
                index=HighGui.waitKey(100);
                if(index==27){
                    break;
                }
            }
        }else{
            log.info("摄像头未开启");
        }
        HighGui.destroyAllWindows();
        capture.release();
        return;
    }
    //采集人脸并保存到本地
    private static void detectAndCollect(Mat frame, CascadeClassifier faceCascade, String id)
    {
        MatOfRect faces=new MatOfRect();
        Mat grayFrame=new Mat();
        Imgproc.cvtColor(frame,grayFrame,Imgproc.COLOR_BGR2GRAY);
        faceCascade.detectMultiScale(grayFrame,faces);
        Rect[] facesArray=faces.toArray();
        //连续采集50张照片
        if(facesArray.length>=1){
            if ((sample==0 && System.currentTimeMillis()-startTime>10000 || (sample>0 && sample<50 && System.currentTimeMillis()-startTime>300))){
                startTime=System.currentTimeMillis();
                sample++;
                System.out.println("image:"+sample);
                Imgcodecs.imwrite(endImgUrl+"\\face"+"/img."+id+"."+(startFrom+sample)+".jpg",frame.submat(facesArray[0]));
            }
        }
        for (int i=0;i<facesArray.length;i++){
            Imgproc.rectangle(frame,facesArray[i].tl(),facesArray[i].br(),new Scalar(0,255,0),2);

        }
    }
    //人脸识别，人眼识别
   private static void getVideoFromCamera(boolean iseyes)
    {
        //如果要从摄像头获取视频，则要在VideoCapture的构造方法颉0
        VideoCapture capture=new VideoCapture(0);
        Mat video=new Mat();
        int index=0;
        if(capture.isOpened()){
            while (true){
                //从摄像头取出图片
                capture.read(video);
                if(iseyes){
                    MatOfRect eyeDeteCtions=new MatOfRect();
                    eyeDetector.detectMultiScale(video,eyeDeteCtions);
                    for (Rect re:eyeDeteCtions.toArray()){
                        Imgproc.rectangle(video,new Point(re.x,re.y),new Point(re.x+re.width,re.y+re.height),new Scalar(255,0,0));
                    }
                    HighGui.imshow("实时人眼识别",video);
                }else {
                    MatOfRect faceDetections=new MatOfRect();
                    faceDetector.detectMultiScale(video,faceDetections);
                    //rect中人脸的范围
                    Mat face=null;
                    for(Rect rect : faceDetections.toArray()){
                        Imgproc.rectangle(video,rect,new Scalar(0,0,255),2);
                    }
                    HighGui.imshow("实时人脸识别",video);
                }
                index= HighGui.waitKey(100);
                if(index==27){
                    break;
                }
            }
        }else{
            log.info("摄像头未开启");
        }
        HighGui.destroyAllWindows();
        capture.release();
        return;
    }
}

第二个程序会识别采集人脸的可信度

package test;

import org.opencv.core.*;
import org.opencv.face.LBPHFaceRecognizer;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;
import org.opencv.videoio.VideoCapture;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.io.IOException;
import java.text.DecimalFormat;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;


public class t2 {
    private static final Logger Log= (Logger) LoggerFactory.getLogger(TrainImageCV.class);
    //opencv的人脸识别xml文件路径
private static final String faceDetectorXML2URL ="D:\\opencv\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
	
	
	private static final String eyeDetectorXML2URL= "D:\\opencv\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml";
	static{
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
	}

	public static void main(String[] args)throws IOException {
		
		long begin = System.currentTimeMillis();
		try{
			System.out.println("Welcom to OpenCV" +Core.VERSION);
			train("D:\\ImgageLocal\\face","D:/ImgageLocal/model");
			Label("D:\\ImgageLocal\\test","D:/ImgageLocal/model");
			getVideoFromCamera("D:/ImgageLocal/model");
            Thread.sleep(1000);
//            int err=1/0;
        } catch (Exception e) {
           e.printStackTrace();
        }
        Log.info("开始强制关闭");
        Log.info("人脸匹配结束，总耗时：{}ms",(System.currentTimeMillis()-begin));
        System.exit(0);

    }
//识别摄像头里的人脸标签，并显示
    private static void getVideoFromCamera(String saveFolder)
    {
        LBPHFaceRecognizer faceRecognizer=LBPHFaceRecognizer.create();
        faceRecognizer.read(saveFolder+"/face_model.yml");
        CascadeClassifier faceCascade=new CascadeClassifier();
        faceCascade.load(faceDetectorXML2URL);
        //从摄像头获取视频，则要在VideoCapture的构造方法写0
        VideoCapture capture=new VideoCapture(0);
        //初始化要处理的OpenCV图片
        Mat video=new Mat();
        int index=0;
        if (capture.isOpened()){
            while(true){
                //从摄像头取出图片
                capture.read(video);
                //探测人脸
                MatOfRect faceDetections=new MatOfRect();
                faceCascade.detectMultiScale(video,faceDetections);
                Mat grayFrame=new Mat();
                //图像灰度化
                Imgproc.cvtColor(video,grayFrame,Imgproc.COLOR_BGR2GRAY);
                Imgproc.equalizeHist(grayFrame,grayFrame);
                //rect中人脸图片的范围
                Mat face=null;
                int i=0;
                for (Rect rect : faceDetections.toArray()) {
                    int[ ] label = new int[1];
                    double[ ] confidence =new double[1];
                    //根据模型预测人脸识别结果
                    faceRecognizer.predict(grayFrame. submat(rect),label,confidence);
                    String name = faceRecognizer.getLabelInfo( label[0]);
                    Scalar color;
                    if ( confidence[0] < 70) {
                        color = new Scalar( 153,204,51);
                        name = name + " " + new DecimalFormat( "#.0").format(confidence[0]);
                    }else {
                        name="face";
                        color=new Scalar(0,0,255);
                    }
                    //识别结果打标签
                    Imgproc.rectangle(video,rect.tl(),rect.br(),color,2);
                    setLabel(video,name,rect.tl(),color);
                }
                HighGui.imshow("实时人脸识别",video);
                //窗口延迟等待100ms,返回按退出键
                index=HighGui.waitKey(100);
                //
                if (index==27){
                    HighGui.destroyAllWindows();
                    capture.release();
                    break;
                }

            }
        }else {
            Log.info("摄像头未开启");
        }
        return;
    }
    //打标签
    private static void Label(String testFolder, String saveFolder)throws IOException
    {
        LBPHFaceRecognizer faceRecognizer=LBPHFaceRecognizer.create();
//        LBPHFaceRecognizer faceRecognizer=LBPHFaceRecognizer.create();
        faceRecognizer.read(saveFolder+"/face_model.yml");
        //读取文件于数组中
        File[] files =new File(testFolder).listFiles();

        CascadeClassifier faceCascade=new CascadeClassifier();
        //opencv的模型
        faceCascade.load(faceDetectorXML2URL);
        for (int index=0;index<files.length;index++){
            MatOfRect faces=new MatOfRect();
            Mat grayFrame=new Mat();
            Mat mat=Imgcodecs.imread(files[index].getCanonicalPath());
            Mat gray=new Mat();
            //图片预处理
            Imgproc.cvtColor(mat,grayFrame,Imgproc.COLOR_BGR2GRAY);
            Imgproc.equalizeHist(grayFrame,grayFrame);
            //detect faces
            faceCascade.detectMultiScale(grayFrame,faces);
            Imgproc.equalizeHist(grayFrame,grayFrame);
            Rect[] facesArray=faces.toArray();
            System.out.println("facesaArray.length======="+facesArray.length);
            for (int i=0;i<facesArray.length;i++){
                int[] label=new int[1];
                double[ ] confidence = new double[1];
                faceRecognizer.predict(grayFrame.submat(facesArray[i]),label,confidence);
                String name = faceRecognizer.getLabelInfo(label[0]);
                System.out.print("测试图片: " + files[index].getCanonicalPath());
                System.out.print( ",标签( label) : "+label[0]);
                System.out.println( ",置信度（confidence) : " + confidence[0]);
                System.out.println( ",name: "+name ) ;
                Scalar color;
                if(confidence[0]<70){
                    if (label[0]==0){
                        color=new Scalar(255,0,0);
                    }else if (label[0]==1){
                        color=new Scalar(153,204,51);
                    }else{
                        color=new Scalar(0,255,255);
                    }
                    name=name+" "+new DecimalFormat("#.0").format(confidence[0]);
                }else{
                    name="face";
                    color=new Scalar(0,0,255);
                }
                Imgproc.rectangle(mat,facesArray[i].tl(),facesArray[i].br(),color,2);
                setLabel(mat,name,facesArray[i].tl(),color);
                Imgcodecs.imwrite(testFolder+"\\"+files[index].getName()+".jpg",mat);

            }

        }
    }
/*
显示识别的人脸标签
 */
    private static void setLabel(Mat im, String label, Point or, Scalar color)
    {
        int fontface=Imgproc.FONT_HERSHEY_SIMPLEX;
        double scale=0.8;
        int thickness=2;
        int[] baseline=new int[1];
        Size text = Imgproc.getTextSize(label,fontface,scale,thickness, baseline);
        Imgproc.rectangle(im,new Point(or.x, or.y),new Point(or.x + text.width,or.y-text.height-baseline[0]-baseline[0]), color,Core.FILLED);
        if (!label.equals("face")){
            String[] sp=label.split(" ");
            Map<String,Object> map=new HashMap<String,Object>();
        }
        Imgproc.putText(im,label,new Point(or.x,or.y-baseline[0]),fontface,scale,new Scalar(255,255,255),thickness);
    }
    /*
     *训练模型的方法，传入人脸图片所在的文件夹路径，
     * *和模型输出的路径训练结束后模型文件会在模型输出路径里边
     */
    private static void train(String imageFolder, String saveFolder) throws IOException
    {
        LBPHFaceRecognizer faceRecognizer=LBPHFaceRecognizer.create();
        CascadeClassifier faceCascade=new CascadeClassifier();
        //opencv模型
        faceCascade.load(faceDetectorXML2URL);
        //读取文件于数组中
        File[] files =new File(imageFolder).listFiles();
        Map<String,Integer> nameMapId=new HashMap<String,Integer>(10);
        //图片集合
        List<Mat> images=new ArrayList<Mat>(files.length);
        //名称集合
        List<String> names=new ArrayList<String>(files.length);
        List<Integer> ids=new ArrayList<Integer>(files.length);
        for(int index=0;index<files.length;index++){
            //解析文件名获取名称
            File file = files [index];
            String name = file.getName( ).split("\\.")[1];
            Integer id = nameMapId.get(name);
            if (id==null){
                id=names.size();
                names.add(name);
                nameMapId.put(name,id);
                faceRecognizer.setLabelInfo(id,name);
            }
            Mat mat= Imgcodecs.imread(file.getCanonicalPath());
            Mat gray=new Mat();
            Imgproc.cvtColor(mat,gray,Imgproc.COLOR_BGR2GRAY);
            images.add(gray);
            System.out.println("add total"+ images.size());
            ids.add(id);
        }
        int[] idsInt=new int[ids.size()];
        for (int i=0;i<idsInt.length;i++){
            idsInt[i]=ids.get(i).intValue();
        }
        //显示标签
        MatOfInt labels=new MatOfInt(idsInt);
        //调用训练方法
        faceRecognizer.train(images,labels);
        //输出持久化模型文件。训练一次后就可以一直调用
        faceRecognizer.save(saveFolder+"/face_model.yml");
    }

}

简单的人脸识别程序结束