小编所有的帖子都是基于unbuntu系统的，当然稍作修改同样试用于windows的，经过小编的绞尽脑汁，把刚刚发的那篇python 实现人脸和眼睛的检测的程序用C++ 实现了，当然，也参考了不少大神的博客，下面我们就一起来看看：
Linux系统下安装opencv我就再啰嗦一次，防止有些人没有安装没调试出来喷小编的程序是个坑，
sudo apt-get install libcv-dev
sudo apt-get install libopencv-dev
看看你的usr/share/opencv/haarcascades目录下有没有出现几个训练集.XML文件，接下来我拿人脸和眼睛检测作为实例玩一下，程序如下：
好多人不会编译opencv，我再多写几句解决一下好多菜鸟的困难吧
copy完代码之后，保存为xiaorun.cpp哦，记得编译试用个g++ -o xiaorun ./xiaorun.cpp -lopencv_highgui -lopenc_imgproc -lopencv_core -lopencv_objdetect
即可实现
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#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/core/core.hpp>#include <opencv2/objdetect/objdetect.hpp>#include <iostream>using namespace cv;using namespace std;void detectAndDraw( Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip );int main(){ CascadeClassifier cascade, nestedCascade; bool stop = false; cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml"); nestedCascade.load("/usr/share/opencv/haarcascades/haarcascade_eye.xml"); // frame = imread("renlian.jpg"); VideoCapture cap(0); //打开默认摄像头 if(!cap.isOpened()) { return -1; } Mat frame; Mat edges;while(!stop){cap>>frame; detectAndDraw( frame, cascade, nestedCascade,2,0 ); if(waitKey(30) >=0) stop = true; imshow("cam",frame);} //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("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");// nestedCascade.load("/usr/share/opencv/haarcascades/aarcascade_eye.xml");// frame = imread("renlian.jpg");// detectAndDraw( frame, cascade, nestedCascade,2,0 ); // waitKey(); //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 );} |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：http://blog.csdn.net/xiao__run/article/details/76833847
