{

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

//4表示每一个目标至少要被检测到4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)

//#define CV_HAAR_DO_CANNY_PRUNING 1 //这个值告诉分类器跳过平滑（无边缘）区域

//#define CV_HAAR_SCALE_IMAGE 2 //这个值告诉分类器不要缩放分类器，而是缩放图像

//#define CV_HAAR_FIND_BIGGEST_OBJECT 4 //告诉分类器只返回最大的目标

//#define CV_HAAR_DO_ROUGH_SEARCH 8 //它只能和上面一个参数一起使用，告诉分类器在任何窗口，只要第一个候选者被发现则结束搜寻

//Size(35, 35)为目标的最小最大尺寸

cascade.detectMultiScale( smallImg, faces,

1.1, neighbor, 0

//|CV_HAAR_FIND_BIGGEST_OBJECT

//|CV_HAAR_DO_ROUGH_SEARCH

|CV_HAAR_SCALE_IMAGE

|CV_HAAR_DO_CANNY_PRUNING

,Size(35, 35));

//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, neighbor, 0

//|CV_HAAR_FIND_BIGGEST_OBJECT

//|CV_HAAR_DO_ROUGH_SEARCH

|CV_HAAR_SCALE_IMAGE

|CV_HAAR_DO_CANNY_PRUNING

,Size(35, 35) );

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;

printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );

//用于在图片上标记出人脸位置

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;

//cout<<"ratio=" << aspect_ratio << endl;

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);

//cout <<"print cirle"<<endl;

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);

}

//imshow( "识别结果", img );

//调试使用，输出检测到的人脸数量

cout << "The number of detected faces is " << faces.size()<<endl;

return faces.size();