使用OpenCV进行人脸识别的例子

#include "opencv/highgui.h"
#include "opencv/cv.h"
#include "opencv/cxcore.h"

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
using namespace std;
using namespace cv;
CascadeClassifier cascade, nestedCascade;

//训练好的文件名称路径，初始化的调用
cascade.load("D:\\OpenCV\\opencv\\sources\\data\\haarcascades_cuda\\haarcascade_frontalface_alt.xml");
nestedCascade.load("D:\\OpenCV\\opencv\\sources\\data\\haarcascades_cuda\\haarcascade_eye_tree_eyeglasses.xml");

IplImage* pFrame;
CvCapture* pCapture;

pCapture = cvCaptureFromCAM(0); //获取默认摄像头的画面
int retVal = cvGrabFrame(pCapture);
...

pFrame = cvQueryFrame(pCapture);
detectAndDraw( cv::cvarrToMat(pFrame), cascade, nestedCascade,2,0 );

//人脸识别
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 )
{
QDBG << r->x << r->y << r->width << r->height;
//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 );
//            QDBG << center.x << center.y << radius;
}
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);
QDBG;
}
if( nestedCascade.empty() )
continue;
smallImgROI = smallImg(*r);
#if 0
//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 );
}
#endif
}
//    cv::imshow( "result", img );
}