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 );
}
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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