背景：最近需要用到人臉識別，但又不花錢使用現有的第三方人臉識別接口，為此使用opencv結合java進行人臉識別（ps:opencv是開源的，使用它來做人臉識別存在一定的誤差，效果一般）。

1.安裝opencv官網地址：https://opencv.org/ , 由于官網下載速度是真的慢

百度網盤：

鏈接: https://pan.baidu.com/s/1RpsP-I7v8pP2dkqALDw7FQ

提取碼: pq7v

如果是官網下載，就無腦安裝就行了，安裝完畢后。

將圖一的兩個文件復制到圖二中。

從我網盤下載的，忽略這些。

2.在項目中引入pom依賴

<!-- 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><!-- 視頻攝像頭 --><!-- 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.導入庫依賴File --> Project Structure，點擊Modules，選擇需要使用opencv.jar的項目。

選擇直接opencv安裝路徑

2.java代碼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;/** * 人臉比對工具類 * @author Litluecat * @Title: Opencv 圖片人臉識別、實時攝像頭人臉識別**/public class FaceVideo { private static final Logger log = LoggerFactory.getLogger(FaceVideo.class); private static final String endImgUrl = 'C:UserslenovoDesktop'; /** * opencv的人臉識別xml文件路徑 */ private static final String faceDetectorXML2URL = 'D:Sofewareopencvsourcesdatahaarcascadeshaarcascade_frontalface_alt.xml'; /** * opencv的人眼識別xml文件路徑 */ private static final String eyeDetectorXML2URL = 'D:Sofewareopencvsourcesdatahaarcascadeshaarcascade_eye.xml'; /** * 直方圖大小，越大精度越高，運行越慢 */ private static int Matching_Accuracy = 100000; /** * 初始化人臉探測器 */ private static CascadeClassifier faceDetector; /** * 初始化人眼探測器 */ 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('開始人臉匹配');long begin = System.currentTimeMillis();// 1- 從攝像頭實時人臉識別，識別成功保存圖片到本地try{ getVideoFromCamera(endImgUrl + '2.jpg'); //僅用于強制拋異常，從而關閉GUI界面 Thread.sleep(1000); int err = 1/0; // 2- 比對本地2張圖的人臉相似度 （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('人臉匹配');// } else {//log.info('人臉不匹配');// }}catch (Exception e){ log.info('開始強制關閉'); log.info('人臉匹配結束，總耗時：{}ms',(System.currentTimeMillis()-begin)); System.exit(0);} } /** * OpenCV-4.1.1 從攝像頭實時讀取 * @param targetImgUrl 比對身份證圖片 * @return: void * @date: 2019年8月19日 17:20:13 */ public static void getVideoFromCamera(String targetImgUrl) {//1 如果要從攝像頭獲取視頻 則要在 VideoCapture 的構造方法寫 0VideoCapture 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('實時人臉識別', getFace(video, targetImgUrl));//窗口延遲等待100ms，返回退出按鍵index = HighGui.waitKey(100);//當退出按鍵為Esc時，退出窗口if (index == 27) { break;} }}else{ log.info('攝像頭未開啟');}//該窗口銷毀不生效，該方法存在問題HighGui.destroyAllWindows();capture.release();return; } /** * OpenCV-4.1.0 人臉識別 * @param image 待處理Mat圖片(視頻中的某一幀) * @param targetImgUrl 匹配身份證照片地址 * @return 處理后的圖片 */ 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+' 個人臉');if(rects != null && rects.length >= 1) { i++; if(i==3) {// 獲取匹配成功第3次的照片Imgcodecs.imwrite(endImgUrl + 'face.jpg', image);FaceVideoThread faceVideoThread = new FaceVideoThread(targetImgUrl , endImgUrl + 'face.jpg');new Thread(faceVideoThread,'人臉比對線程').start(); }}return image; } /** * 人臉截圖 * @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);// 探測人臉MatOfRect faceDetections = new MatOfRect();faceDetector.detectMultiScale(image1, faceDetections);// rect中人臉圖片的范圍for (Rect rect : faceDetections.toArray()) { faceImg = img+'_.jpg'; // 進行圖片裁剪 imageCut(img, faceImg, rect.x, rect.y, rect.width, rect.height);}if(null == faceImg){ log.info('face2Img未識別出該圖像中的人臉，img={}',img);}return faceImg; } /** * 人臉比對 * @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();//顏色范圍MatOfFloat ranges = new MatOfFloat(0f, 256f);//直方圖大小， 越大匹配越精確 (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 相關系數double res = Imgproc.compareHist(hist_1, hist_2, Imgproc.CV_COMP_CORREL);return res; } /** * 灰度化人臉 * @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);//直方均勻//Imgproc.equalizeHist(image1, image2);// 探測人臉MatOfRect faceDetections = new MatOfRect();faceDetector.detectMultiScale(image1, faceDetections);//探測人眼//MatOfRect eyeDetections = new MatOfRect();//eyeDetector.detectMultiScale(image1, eyeDetections);// rect中人臉圖片的范圍Mat face = null;for (Rect rect : faceDetections.toArray()) { //給圖片上畫框框 參數1是圖片 參數2是矩形 參數3是顏色 參數四是畫出來的線條大小 //Imgproc.rectangle(image0,rect,new Scalar(0,0,255),2); //輸出圖片 //Imgcodecs.imwrite(img+'_.jpg',image0); face = new Mat(image1, rect);}if(null == face){ log.info('conv_Mat未識別出該圖像中的人臉，img={}',img);}return face; }}

這邊的人臉識別是另外其線程進行比對，代碼如下。

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('人臉匹配'); } else {log.info('人臉不匹配'); }} catch (Exception e) { e.printStackTrace();} }}

提醒：如果運行異常，請添加你opencv的安裝地址-Djava.library.path=D:Sofewareopencvbuildjavax64;

總結：java+opencv做人臉識別的精度不夠，我也是有待學習，如果大家有更好的方式，能將opencv更好的展現出來，并達到更精準的人臉識別，請分享給我，謝謝。

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