// 初始化人脸探测器

static CascadeClassifier faceDetector;

static int i=0;

static {

System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

faceDetector = new CascadeClassifier("D:\\Projects\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml");

}

public static void main(String[] args) {

// 1- 从摄像头实时人脸识别，识别成功保存图片到本地

getVideoFromCamera();

// 2- 从本地视频文件中识别人脸

// 3- 本地图片人脸识别，识别成功并保存人脸图片到本地

// face();

// 4- 比对本地2张图的人脸相似度 （越接近1越相似）

String basePicPath = "D:\\www\\img\\";

double compareHist = compare_image(basePicPath + "fc.jpg", basePicPath + "fc_1.png");

System.out.println(compareHist);

if (compareHist > 0.72) {

System.out.println("人脸匹配");

} else {

System.out.println("人脸不匹配");

}

}

/**

public static void getVideoFromCamera() {

//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));

index=HighGui.waitKey(100);

if (index==27) {

capture.release();

break;

}

}

}else{

System.out.println("摄像头未开启");

}

try {

capture.release();

Thread.sleep(1000);

System.exit(0);

} catch (InterruptedException e) {

e.printStackTrace();

}

return;

}

/**

public static void getVideoFromFile() {

VideoCapture capture=new VideoCapture();

capture.open("C:\\Users\\Administrator\\Desktop\\1.avi");//1 读取视频文件的路径

if(!capture.isOpened()){

System.out.println("读取视频文件失败！");

return;

}

Mat video=new Mat();

int index=0;

while(capture.isOpened()) {

capture.read(video);//2 视频文件的视频写入 Mat video 中

HighGui.imshow("本地视频识别人脸", getFace(video));//3 显示图像

index=HighGui.waitKey(100);//4 获取键盘输入

if(index==27) {//5 如果是 Esc 则退出

capture.release();

return;

}

}

}

/**

public static Mat getFace(Mat image) {

// 1 读取OpenCV自带的人脸识别特征XML文件(faceDetector)

// 2 特征匹配类

MatOfRect face = new MatOfRect();

// 3 特征匹配

faceDetector.detectMultiScale(image, face);

Rect[] rects=face.toArray();

System.out.println("匹配到 "+rects.length+" 个人脸");

if(rects != null && rects.length >= 1) {

// 4 为每张识别到的人脸画一个圈

for (int i = 0; i < rects.length; i++) {

Imgproc.rectangle(image, new Point(rects[i].x, rects[i].y), new Point(rects[i].x + rects[i].width, rects[i].y + rects[i].height), new Scalar(0, 255, 0));

Imgproc.putText(image, "Human", new Point(rects[i].x, rects[i].y), Imgproc.FONT_HERSHEY_SCRIPT_SIMPLEX, 1.0, new Scalar(0, 255, 0), 1, Imgproc.LINE_AA, false);

//Mat dst=image.clone();

//Imgproc.resize(image, image, new Size(300,300));

}

i++;

if(i==3) {// 获取匹配成功第10次的照片

Imgcodecs.imwrite("D:\\Documents\\Pictures\\" + "face.png", image);

}

}

return image;

}

/**

public static void face() {

// 1 读取OpenCV自带的人脸识别特征XML文件

//OpenCV 图像识别库一般位于 opencv\sources\data 下面

// 2 读取测试图片

String imgPath = "D:\\www\\img\\fc_1.png";

Mat image=Imgcodecs.imread(imgPath);

if(image.empty()){

System.out.println("image 内容不存在！");

return;

}

// 3 特征匹配

MatOfRect face = new MatOfRect();

faceDetector.detectMultiScale(image, face);

// 4 匹配 Rect 矩阵 数组

Rect[] rects=face.toArray();

System.out.println("匹配到 "+rects.length+" 个人脸");

// 5 为每张识别到的人脸画一个圈

int i =1 ;

for (Rect rect : face.toArray()) {

Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),

new Scalar(0, 255, 0), 3);

imageCut(imgPath, "D:\\Documents\\Pictures\\"+i+".jpg", rect.x, rect.y, rect.width, rect.height);// 进行图片裁剪

i++;

}

// 6 展示图片

HighGui.imshow("人脸识别", image);

HighGui.waitKey(0);

}

/**

public static void imageCut(String imagePath, String outFile, int posX, int posY, int width, int height) {

// 原始图像

Mat image = Imgcodecs.imread(imagePath);

// 截取的区域：参数,坐标X,坐标Y,截图宽度,截图长度

Rect rect = new Rect(posX, posY, width, height);

// 两句效果一样

Mat sub = image.submat(rect); // Mat sub = new Mat(image,rect);

Mat mat = new Mat();

Size size = new Size(width, height);

Imgproc.resize(sub, mat, size);// 将人脸进行截图并保存

Imgcodecs.imwrite(outFile, mat);

System.out.println(String.format("图片裁切成功，裁切后图片文件为： %s", outFile));

}

/**

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(100);

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);

//Imgproc. resize(src, dstImg, new Size(80, 80));

// CORREL 相关系数

double res = Imgproc.compareHist(hist_1, hist_2, Imgproc.CV_COMP_CORREL);

return res;

}

/**

public static Mat conv_Mat(String img) {

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()) {

Mat face = new Mat(image1, rect);

return face;

}

return null;

}

/**

public static void writeVideo() {

//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;

Size size=new Size(capture.get(Videoio.CAP_PROP_FRAME_WIDTH),capture.get(Videoio.CAP_PROP_FRAME_HEIGHT));

VideoWriter writer=new VideoWriter("D:/a.mp4",VideoWriter.fourcc('D', 'I', 'V', 'X'), 15.0,size, true);

while(capture.isOpened()) {

capture.read(video);//2 将摄像头的视频写入 Mat video 中

writer.write(video);

HighGui.imshow("像头获取视频", video);//3 显示图像

index=HighGui.waitKey(100);//4 获取键盘输入

if(index==27) {//5 如果是 Esc 则退出

capture.release();

writer.release();

return;

}

}

}