Question

opencv人脸识别c++

自己用ORL数据集加上自己的照片进行训练，然后进行识别，最后在识别过程中的匹配的代码应该用什么样的，if（predictPCA==xxxxx），能否给出一点指导

Answer 1

#include <opencv2\core\core.hpp>#include <opencv2\imgproc\imgproc.hpp>#include <opencv2\highgui\highgui.hpp>#include <opencv2\video\background_segm.hpp>#include <opencv2/objdetect/objdetect.hpp>#include <iostream> using namespace cv;void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip );//Mat imageresize(Mat &image, Size size); /*int main(){ //VideoCapture cap(0); //打开默认摄像头 VideoCapture cap("F:/nihao.mp4"); if(!cap.isOpened()) { return -1; } Mat frame; Mat edges; CascadeClassifier cascade, nestedCascade; bool stop = false; //训练好的文件名称，放置在可执行文件同目录下 cascade.load("haarcascade_frontalface_alt.xml"); nestedCascade.load("haarcascade_eye_tree_eyeglasses.xml"); while(!stop) { cap>>frame; detectAndDraw( frame, cascade, nestedCascade,2,0 ); if(waitKey(30) >=0) stop = true; } return 0; } */int main(){ Mat image=imread("F:/quanjiafu.jpg"); CascadeClassifier cascade,nestedcascade; cascade.load("F:/Opencv2.4.9/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml"); nestedcascade.load("F:/Opencv2.4.9/opencv/sources/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml"); detectAndDraw(image,cascade,nestedcascade,2,0); waitKey(0); return 0;} void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip ) { int i = 0; double t = 0; //建立用于存放人脸的向量容器 vector<Rect> faces, faces2; //定义一些颜色，用来标示不同的人脸 const static Scalar colors[] = { CV_RGB(0,0,255), CV_RGB(0,128,255), CV_RGB(0,255,255), CV_RGB(0,255,0), CV_RGB(255,128,0), CV_RGB(255,255,0), CV_RGB(255,0,0), CV_RGB(255,0,255)} ; //建立缩小的图片，加快检测速度 //nt cvRound (double value) 对一个double型的数进行四舍五入，并返回一个整型数！ Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 ); //转成灰度图像，Harr特征基于灰度图 cvtColor( img, gray, CV_BGR2GRAY ); //改变图像大小，使用双线性差值 resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR ); //变换后的图像进行直方图均值化处理 equalizeHist( smallImg, smallImg ); //程序开始和结束插入此函数获取时间，经过计算求得算法执行时间 t = (double)cvGetTickCount(); //检测人脸 //detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg，faces表示检测到的人脸目标序列，1.1表示 //每次图像尺寸减小的比例为1.1，2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大 //小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测，而是缩放图像，Size(30, 30)为目标的 //最小最大尺寸 cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_SCALE_IMAGE , Size(30, 30)); //如果使能，翻转图像继续检测 if( tryflip ) { flip(smallImg, smallImg, 1); cascade.detectMultiScale( smallImg, faces2, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_SCALE_IMAGE , Size(30, 30) ); for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ ) { faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); } } t = (double)cvGetTickCount() - t; // qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) ); for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ ) { Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i%8]; int radius; double aspect_ratio = (double)r->width/r->height; if( 0.75 < aspect_ratio && aspect_ratio < 1.3 ) { //标示人脸时在缩小之前的图像上标示，所以这里根据缩放比例换算回去 center.x = cvRound((r->x + r->width*0.5)*scale); center.y = cvRound((r->y + r->height*0.5)*scale); //Size s=Size(cvRound((r->width + r->height)*0.25*scale)*2,cvRound((r->width + r->height)*0.25*scale)*2); //Mat image=imread("F:/yaoming1.jpg"); //Mat nimage=imageresize(image,s); //Mat imageROI=img(Rect(center.x-s.width/2,center.y-s.height/2,nimage.cols,nimage.rows)); //addWeighted(imageROI,0.1,nimage,3,0.,imageROI); radius = cvRound((r->width + r->height)*0.25*scale); circle( img, center, radius, color,2, 8, 0 ); } else rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)), cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)), color, 3, 8, 0); if( nestedCascade.empty() ) continue; smallImgROI = smallImg(*r); //同样方法检测人眼 nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING |CV_HAAR_SCALE_IMAGE , Size(30, 30) ); for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ ) { center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); radius = cvRound((nr->width + nr->height)*0.25*scale); circle( img, center, radius, color, 3, 8, 0 ); } } cv::imshow( "result", img );}//Mat imageresize(Mat& image,Size size){// Mat nimage=Mat(size,CV_32S);// resize(image,nimage,size);// return nimage;