在进行人脸识别时候，为了达到效果，我们使用OpenCv的分类器。进行对图片进行识别。
#include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/core/core.hpp> #include <opencv2/objdetect/objdetect.hpp> using namespace cv; using namespace std; void detectAndDraw(Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip); int main() { //VideoCapture cap(0); //打开默认摄像头 //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("E:\\OpenFile\\OpenCv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt.xml"); nestedCascade.load("E:\\OpenFile\\OpenCv\\opencv\\build\\etc\\haarcascades\\haarcascade_eye.xml"); frame = imread("E:\\GitHubSample\\101.jpg"); detectAndDraw(frame, cascade, nestedCascade, 2, 0); waitKey(6000); //while(!stop) //{ // cap>>frame; // detectAndDraw( frame, cascade, nestedCascade,2,0 ); // if(waitKey(30) >=0) // stop = true; //} 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); imshow("灰度", gray); //改变图像大小，使用双线性差值 resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR); imshow("缩小尺寸", smallImg); //变换后的图像进行直方图均值化处理 equalizeHist(smallImg, smallImg); imshow("直方图均值处理", 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); imshow("反转图像", smallImg); 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); radius = cvRound((r->width + r->height)*0.25*scale); circle(img, center, radius, color, 3, 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); } } imshow("识别结果", img); }效果图：
