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: http://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SFZ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SFZ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。
