利用opencv实现人脸检测（C++版）

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

#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 );}收藏