背景：最近需要用到人脸识别，但又不花钱使用现有的第三方人脸识别接口，为此使用opencv结合java进行人脸识别（ps:opencv是开源的，使用它来做人脸识别存在一定的误差，效果一般）。
1.安装opencv官网地址：https://opencv.org/ , 由于官网下载速度是真的慢
百度网盘：
链接: https://pan.baidu.com/s/1RpsP-I7v8pP2dkqALDw7FQ
提取码: pq7v
如果是官网下载，就无脑安装就行了，安装完毕后。
将图一的两个文件复制到图二中。
从我网盘下载的，忽略这些。
2.在项目中引入pom依赖
<!-- opencv + javacv + ffmpeg--> <dependency> <groupId>org.bytedeco.javacpp-presets</groupId> <artifactId>ffmpeg</artifactId> <version>4.1-1.4.4</version> </dependency> <dependency> <groupId>org.bytedeco</groupId> <artifactId>javacv</artifactId> <version>1.4.4</version> </dependency> <!-- https://mvnrepository.com/artifact/org.bytedeco.javacpp-presets/ffmpeg-platform --> <dependency> <groupId>org.bytedeco.javacpp-presets</groupId> <artifactId>ffmpeg-platform</artifactId> <version>4.1-1.4.4</version> </dependency> <!-- 视频摄像头 --> <!-- https://mvnrepository.com/artifact/org.bytedeco/javacv-platform --> <dependency> <groupId>org.bytedeco</groupId> <artifactId>javacv-platform</artifactId> <version>1.4.4</version> </dependency> <!-- https://mvnrepository.com/artifact/org.bytedeco.javacpp-presets/opencv-platform --> <dependency> <groupId>org.bytedeco.javacpp-presets</groupId> <artifactId>opencv-platform</artifactId> <version>4.0.1-1.4.4</version> </dependency>
1.导入库依赖File –> Project Structure，点击Modules，选择需要使用opencv.jar的项目。
选择直接opencv安装路径
2.java代码demo
package org.Litluecat.utils;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; }}
这边的人脸识别是另外其线程进行比对，代码如下。
package org.Litluecat.utils;import org.slf4j.Logger;import org.slf4j.LoggerFactory;public class FaceVideoThread implements Runnable{ private static final Logger log = LoggerFactory.getLogger(FaceVideoThread.class); private String oneImgUrl = null; private String otherImgUrl = null; public FaceVideoThread(String oneImgUrl, String otherImgUrl){ this.oneImgUrl = oneImgUrl; this.otherImgUrl = otherImgUrl; } @Override public void run() { try { double compareHist = FaceVideo.compare_image(oneImgUrl , otherImgUrl); 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) { e.printStackTrace(); } }}
提醒：如果运行异常，请添加你opencv的安装地址-Djava.library.path=D:\Sofeware\opencv\build\java\x64;
总结：java+opencv做人脸识别的精度不够，我也是有待学习，如果大家有更好的方式，能将opencv更好的展现出来，并达到更精准的人脸识别，请分享给我，谢谢。
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你让我尝到了每时每刻想你的疼苦，
