人脸识别中的检测（在Opencv中加入了QT）

  1 #include <opencv2/highgui/highgui.hpp>
  2 #include <opencv2/imgproc/imgproc.hpp>
  3 #include <opencv2/core/core.hpp>
  4 #include <opencv2/objdetect/objdetect.hpp>
  5 #include <QDebug>
  6 
  7 using namespace cv;
  8 
  9 void detectAndDraw( Mat& img, CascadeClassifier& cascade,
 10                     CascadeClassifier& nestedCascade,
 11                     double scale, bool tryflip );
 12 
 13 int main()
 14 {
 15     VideoCapture cap(0);    //打开默认摄像头
 16     if(!cap.isOpened())
 17     {
 18         return -1;
 19     }
 20     Mat frame;
 21     Mat edges;
 22 
 23     CascadeClassifier cascade, nestedCascade;
 24     bool stop = false;
 25     //训练好的文件名称，放置在可执行文件同目录下
 26     cascade.load("haarcascade_frontalface_alt.xml");
 27     nestedCascade.load("haarcascade_eye_tree_eyeglasses.xml");
 28     while(!stop)
 29     {
 30         cap>>frame;
 31         detectAndDraw( frame, cascade, nestedCascade,2,0 );
 32         if(waitKey(30) >=0)
 33             stop = true;
 34     }
 35     return 0;
 36 }
 37 void detectAndDraw( Mat& img, CascadeClassifier& cascade,
 38                     CascadeClassifier& nestedCascade,
 39                     double scale, bool tryflip )
 40 {
 41     int i = 0;
 42     double t = 0;
 43     //建立用于存放人脸的向量容器
 44     vector<Rect> faces, faces2;
 45     //定义一些颜色，用来标示不同的人脸
 46     const static Scalar colors[] =  { CV_RGB(0,0,255),
 47         CV_RGB(0,128,255),
 48         CV_RGB(0,255,255),
 49         CV_RGB(0,255,0),
 50         CV_RGB(255,128,0),
 51         CV_RGB(255,255,0),
 52         CV_RGB(255,0,0),
 53         CV_RGB(255,0,255)} ;
 54     //建立缩小的图片，加快检测速度
 55     //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！
 56     Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
 57     //转成灰度图像，Harr特征基于灰度图
 58     cvtColor( img, gray, CV_BGR2GRAY );
 59     //改变图像大小，使用双线性差值
 60     resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
 61     //变换后的图像进行直方图均值化处理
 62     equalizeHist( smallImg, smallImg );
 63 
 64     //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
 65     t = (double)cvGetTickCount();
 66     //检测人脸
 67     //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
 68     //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
 69     //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
 70     //最小最大尺寸
 71     cascade.detectMultiScale( smallImg, faces,
 72         1.1, 2, 0
 73         //|CV_HAAR_FIND_BIGGEST_OBJECT
 74         //|CV_HAAR_DO_ROUGH_SEARCH
 75         |CV_HAAR_SCALE_IMAGE
 76         ,
 77         Size(30, 30));
 78     //如果使能，翻转图像继续检测
 79     if( tryflip )
 80     {
 81         flip(smallImg, smallImg, 1);
 82         cascade.detectMultiScale( smallImg, faces2,
 83                                  1.1, 2, 0
 84                                  //|CV_HAAR_FIND_BIGGEST_OBJECT
 85                                  //|CV_HAAR_DO_ROUGH_SEARCH
 86                                  |CV_HAAR_SCALE_IMAGE
 87                                  ,
 88                                  Size(30, 30) );
 89         for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
 90         {
 91             faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
 92         }
 93     }
 94     t = (double)cvGetTickCount() - t;
 95  //   qDebug( "detection time = %g ms
", t/((double)cvGetTickFrequency()*1000.) );
 96     for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
 97     {
 98         Mat smallImgROI;
 99         vector<Rect> nestedObjects;
100         Point center;
101         Scalar color = colors[i%8];
102         int radius;
103 
104         double aspect_ratio = (double)r->width/r->height;
105         if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
106         {
107             //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
108             center.x = cvRound((r->x + r->width*0.5)*scale);
109             center.y = cvRound((r->y + r->height*0.5)*scale);
110             radius = cvRound((r->width + r->height)*0.25*scale);
111             circle( img, center, radius, color, 3, 8, 0 );
112         }
113         else
114             rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
115                        cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)),
116                        color, 3, 8, 0);
117         if( nestedCascade.empty() )
118             continue;
119         smallImgROI = smallImg(*r);
120         //同样方法检测人眼
121         nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
122             1.1, 2, 0
123             //|CV_HAAR_FIND_BIGGEST_OBJECT
124             //|CV_HAAR_DO_ROUGH_SEARCH
125             //|CV_HAAR_DO_CANNY_PRUNING
126             |CV_HAAR_SCALE_IMAGE
127             ,
128             Size(30, 30) );
129         for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
130         {
131             center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
132             center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
133             radius = cvRound((nr->width + nr->height)*0.25*scale);
134             circle( img, center, radius, color, 3, 8, 0 );
135         }
136     }
137     cv::imshow( "result", img );
138 }