java+opencv实现人脸识别功能

import org.apache.commons.lang.StringUtils;import org.opencv.core.*;import org.opencv.highgui.HighGui;import org.opencv.highgui.ImageWindow;import org.opencv.imgcodecs.Imgcodecs;import org.opencv.imgproc.Imgproc;import org.opencv.objdetect.CascadeClassifier;import org.opencv.videoio.VideoCapture;import org.opencv.videoio.VideoWriter;import org.opencv.videoio.Videoio;import org.slf4j.Logger;import org.slf4j.LoggerFactory;

import java.util.Arrays;

/*** 人脸比对工具类* @author Litluecat* @Title: Opencv 图片人脸识别、实时摄像头人脸识别**/ public class FaceVideo {

private static final Logger log = LoggerFactory.getLogger(FaceVideo.class);

private static final String endImgUrl = "C:\\Users\\lenovo\\Desktop\\";/*** opencv的人脸识别xml文件路径*/ private static final String faceDetectorXML2URL = "D:\\Sofeware\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";/*** opencv的人眼识别xml文件路径*/ private static final String eyeDetectorXML2URL = "D:\\Sofeware\\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;

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();// 1- 从摄像头实时人脸识别，识别成功保存图片到本地try{getVideoFromCamera(endImgUrl + "2.jpg");//仅用于强制抛异常，从而关闭GUI界面Thread.sleep(1000);int err = 1/0;

// 2- 比对本地2张图的人脸相似度 （越接近1越相似）

// double compareHist = FaceVideo.compare_image(endImgUrl + "test1.jpg" , endImgUrl + "face.jpg");// log.info("匹配度：{}",compareHist);// if (compareHist > 0.72) {// log.info("人脸匹配");// } else {// log.info("人脸不匹配");// }

}catch (Exception e){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);}}

/*** OpenCV-4.1.1 从摄像头实时读取* @param targetImgUrl 比对身份证图片* @return: void* @date: 2019年8月19日 17:20:13*/ public static void getVideoFromCamera(String targetImgUrl) {//1 如果要从摄像头获取视频 则要在 VideoCapture 的构造方法写 0 VideoCapture capture = new VideoCapture(0);Mat video = new Mat();int index = 0;if (capture.isOpened()) {while(i<3) {// 匹配成功3次退出capture.read(video);HighGui.imshow("实时人脸识别", getFace(video, targetImgUrl));//窗口延迟等待100ms，返回退出按键index = HighGui.waitKey(100);//当退出按键为Esc时，退出窗口if (index == 27) {break;}}}else{log.info("摄像头未开启");}//该窗口销毁不生效，该方法存在问题HighGui.destroyAllWindows();capture.release();return;}

/*** OpenCV-4.1.0 人脸识别* @param image 待处理Mat图片(视频中的某一帧)* @param targetImgUrl 匹配身份证照片地址* @return 处理后的图片*/ public static Mat getFace(Mat image, String targetImgUrl) {MatOfRect face = new MatOfRect();faceDetector.detectMultiScale(image, face);Rect[] rects=face.toArray();log.info("匹配到 "+rects.length+" 个人脸");if(rects != null && rects.length >= 1) {i++;if(i==3) {// 获取匹配成功第3次的照片Imgcodecs.imwrite(endImgUrl + "face.jpg", image);FaceVideoThread faceVideoThread = new FaceVideoThread(targetImgUrl , endImgUrl + "face.jpg");new Thread(faceVideoThread,"人脸比对线程").start();}}return image;}

/*** 人脸截图* @param img* @return*/ public static String face2Img(String img) {String faceImg = null;Mat image0 = Imgcodecs.imread(img);Mat image1 = new Mat();// 灰度化Imgproc.cvtColor(image0, image1, Imgproc.COLOR_BGR2GRAY);// 探测人脸MatOfRect faceDetections = new MatOfRect();faceDetector.detectMultiScale(image1, faceDetections);// rect中人脸图片的范围for (Rect rect : faceDetections.toArray()) {faceImg = img+"_.jpg";// 进行图片裁剪imageCut(img, faceImg, rect.x, rect.y, rect.width, rect.height);}if(null == faceImg){log.info("face2Img未识别出该图像中的人脸，img={}",img);}return faceImg;}

/*** 人脸比对* @param img_1* @param img_2* @return*/ public static double compare_image(String img_1, String img_2) {Mat mat_1 = conv_Mat(img_1);Mat mat_2 = conv_Mat(img_2);Mat hist_1 = new Mat();Mat hist_2 = new Mat();

//颜色范围MatOfFloat ranges = new MatOfFloat(0f, 256f);//直方图大小， 越大匹配越精确 (越慢) MatOfInt histSize = new MatOfInt(Matching_Accuracy);

Imgproc.calcHist(Arrays.asList(mat_1), new MatOfInt(0), new Mat(), hist_1, histSize, ranges);Imgproc.calcHist(Arrays.asList(mat_2), new MatOfInt(0), new Mat(), hist_2, histSize, ranges);

// CORREL 相关系数double res = Imgproc.compareHist(hist_1, hist_2, Imgproc.CV_COMP_CORREL);return res;}

/*** 灰度化人脸* @param img* @return*/ public static Mat conv_Mat(String img) {if(StringUtils.isBlank(img)){return null;}Mat image0 = Imgcodecs.imread(img);Mat image1 = new Mat();//Mat image2 = new Mat();// 灰度化Imgproc.cvtColor(image0, image1, Imgproc.COLOR_BGR2GRAY);//直方均匀//Imgproc.equalizeHist(image1, image2);

// 探测人脸MatOfRect faceDetections = new MatOfRect();faceDetector.detectMultiScale(image1, faceDetections);

//探测人眼// MatOfRect eyeDetections = new MatOfRect();// eyeDetector.detectMultiScale(image1, eyeDetections);

// rect中人脸图片的范围Mat face = null;for (Rect rect : faceDetections.toArray()) {

//给图片上画框框 参数1是图片 参数2是矩形 参数3是颜色 参数四是画出来的线条大小//Imgproc.rectangle(image0,rect,new Scalar(0,0,255),2);//输出图片//Imgcodecs.imwrite(img+"_.jpg",image0);

face = new Mat(image1, rect);}if(null == face){log.info("conv_Mat未识别出该图像中的人脸，img={}",img);}return face;}

import org.slf4j.Logger;import org.slf4j.LoggerFactory;

public class FaceVideoThread implements Runnable{private static final Logger log = LoggerFactory.getLogger(FaceVideoThread.class);