#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 msn", 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 );
}