基于开源OPENCV人脸识别（java版）

import java.util.List; import java.util.Vector; import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.core.MatOfRect; import org.opencv.core.Point; import org.opencv.core.Rect; import org.opencv.core.Scalar; import org.opencv.core.Size; import org.opencv.imgcodecs.Imgcodecs; import org.opencv.imgproc.Imgproc; import org.opencv.objdetect.CascadeClassifier; import org.opencv.videoio.VideoCapture; import org.opencv.videoio.Videoio; class SimpleSample { final static int CV_HAAR_DO_CANNY_PRUNING = 1; final static int CV_HAAR_SCALE_IMAGE = 2; final static int CV_HAAR_FIND_BIGGEST_OBJECT = 4; final static int CV_HAAR_DO_ROUGH_SEARCH = 8; static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); } public void FaceDetection(String fileinname) { double scale = 1.3; CascadeClassifier faceDetector = new CascadeClassifier("E://workspace//facetest//bin//haarcascade_frontalface_alt2.xml"); CascadeClassifier eyeDetector = new CascadeClassifier("E://workspace//facetest//bin//haarcascade_eye.xml"); Mat image = Imgcodecs.imread(fileinname); // Detect faces in the image. // MatOfRect is a special container class for Rect. //MatOfRect faceDetections = new MatOfRect(); //faceDetector.detectMultiScale(image, faceDetections); if( !image.empty() )//读取图片数据不能为空 { int i = 0; double t = 0; MatOfRect faceDetections = new MatOfRect(); Scalar colors[] = { new Scalar(0,0,255),new Scalar(0,128,255),new Scalar(0,255,255), new Scalar(0,255,0),new Scalar(255,128,0),new Scalar(255,255,0), new Scalar(255,0,0),new Scalar(255,0,255)} ; // Imgproc. Mat gray = new Mat(); Mat smallImg = new Mat((int)(image.rows()/scale), (int)(image.cols()/scale), CvType.CV_8UC1 ); Imgproc.cvtColor( image, gray, Imgproc.COLOR_BGR2GRAY );//因为用的是类haar特征，所以都是基于灰度图像的，这里要转换成灰度图像 Imgproc.resize( gray, smallImg, smallImg.size(), 0, 0, Imgproc.INTER_LINEAR );//将尺寸缩小到1/scale,用线性插值 Imgproc.equalizeHist( smallImg, smallImg );//直方图均衡 t = (double)Core.getTickCount();//用来计算算法执行时间 faceDetector.detectMultiScale( smallImg, faceDetections,1.1, 2, 0|CV_HAAR_SCALE_IMAGE,new Size(100, 100),new Size(200, 200) ); t = (double)Core.getTickCount() - t;//相减为算法执行的时间 //System.out.println("detection time = "+t/((double)Core.getTickCount()*1000.)+" ms\n"); List<Rect> rects = faceDetections.toList(); for(int m=0;m<rects.size();m++){ Rect rect = rects.get(m); Mat smallImgROI = new Mat(); MatOfRect nestedObjects = new MatOfRect(); Point center = new Point(); Scalar color = colors[i%8]; int radius; center.x = (int)((rect.x + rect.width*0.5)*scale); center.y = (int)((rect.y + rect.height*0.5)*scale); radius = (int)((rect.width + rect.height)*0.25*scale); Imgproc.circle( image, center, radius, color, 3, 8, 0 ); if(eyeDetector.empty() ) continue; smallImgROI = new Mat(smallImg,rect); //和上面的函数功能一样 eyeDetector.detectMultiScale( smallImgROI, nestedObjects,1.1, 2, 0|CV_HAAR_SCALE_IMAGE,new Size(30, 30),new Size(100, 100) ); List<Rect> rectEyes = nestedObjects.toList(); for(int n=0;n<rectEyes.size();n++){ Rect rectEye = rectEyes.get(n); center.x = (int)((rect.x + rectEye.x + rectEye.width*0.5)*scale); center.y = (int)((rect.y + rectEye.y + rectEye.height*0.5)*scale); radius = (int)((rectEye.width + rectEye.height)*0.25*scale); Imgproc.circle( image, center, radius, color, 3, 8, 0 );//将眼睛也画出来，和对应人脸的图形是一样的 } } String filename = fileinname.toUpperCase().replace("JPG", "PNG"); System.out.println(String.format("Writing %s", filename)); Imgcodecs.imwrite(filename, image); } } public void FaceDetection(Mat imageSrc,String fileinname) { double scale = 1.3; CascadeClassifier faceDetector = new CascadeClassifier("E://workspace//facetest//bin//haarcascade_frontalface_alt2.xml"); CascadeClassifier eyeDetector = new CascadeClassifier("E://workspace//facetest//bin//haarcascade_eye.xml"); Mat image = imageSrc; // Detect faces in the image. // MatOfRect is a special container class for Rect. //MatOfRect faceDetections = new MatOfRect(); //faceDetector.detectMultiScale(image, faceDetections); if( !image.empty() )//读取图片数据不能为空 { int i = 0; double t = 0; MatOfRect faceDetections = new MatOfRect(); Scalar colors[] = { new Scalar(0,0,255),new Scalar(0,128,255),new Scalar(0,255,255), new Scalar(0,255,0),new Scalar(255,128,0),new Scalar(255,255,0), new Scalar(255,0,0),new Scalar(255,0,255)} ; Mat gray = new Mat(); Mat smallImg = new Mat((int)(image.rows()/scale), (int)(image.cols()/scale), CvType.CV_8UC1 ); Imgproc.cvtColor( image, gray, Imgproc.COLOR_BGR2GRAY );//因为用的是类haar特征，所以都是基于灰度图像的，这里要转换成灰度图像 Imgproc.resize( gray, smallImg, smallImg.size(), 0, 0, Imgproc.INTER_LINEAR );//将尺寸缩小到1/scale,用线性插值 Imgproc.equalizeHist( smallImg, smallImg );//直方图均衡 t = (double)Core.getTickCount();//用来计算算法执行时间 faceDetector.detectMultiScale( smallImg, faceDetections,1.1, 2, 0|CV_HAAR_SCALE_IMAGE,new Size(100, 100),new Size(200, 200) ); t = (double)Core.getTickCount() - t;//相减为算法执行的时间 //System.out.println("detection time = "+t/((double)Core.getTickCount()*1000.)+" ms\n"); List<Rect> rects = faceDetections.toList(); for(int m=0;m<rects.size();m++){ Rect rect = rects.get(m); Mat smallImgROI = new Mat(); MatOfRect nestedObjects = new MatOfRect(); Point center = new Point(); Scalar color = colors[i%8]; int radius; center.x = (int)((rect.x + rect.width*0.5)*scale); center.y = (int)((rect.y + rect.height*0.5)*scale); radius = (int)((rect.width + rect.height)*0.25*scale); Imgproc.circle( image, center, radius, color, 3, 8, 0 ); if(eyeDetector.empty() ) continue; smallImgROI = new Mat(smallImg,rect); //和上面的函数功能一样 eyeDetector.detectMultiScale( smallImgROI, nestedObjects,1.1, 2, 0|CV_HAAR_SCALE_IMAGE,new Size(30, 30),new Size(100, 100) ); List<Rect> rectEyes = nestedObjects.toList(); for(int n=0;n<rectEyes.size();n++){ Rect rectEye = rectEyes.get(n); center.x = (int)((rect.x + rectEye.x + rectEye.width*0.5)*scale); center.y = (int)((rect.y + rectEye.y + rectEye.height*0.5)*scale); radius = (int)((rectEye.width + rectEye.height)*0.25*scale); Imgproc.circle( image, center, radius, color, 3, 8, 0 );//将眼睛也画出来，和对应人脸的图形是一样的 } } String filename = fileinname.toUpperCase().replace("JPG", "PNG"); System.out.println(String.format("Writing %s", filename)); Imgcodecs.imwrite(filename, image); } } public void run(String fileinname) { System.out.println("\nRunning DetectFaceDemo"); // Create a face detector from the cascade file in the resources // directory. CascadeClassifier faceDetector = new CascadeClassifier("E://workspace//facetest//bin//lbpcascade_frontalface.xml"); //getClass().getResource("