基于opencv249+vs2010的人眼跟踪检测

#include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/ml/ml.hpp" #include "cv.h" #include <iostream> #include <stdio.h> //#include "stdlib.h " using namespace std; using namespace cv; static CvMemStorage* storage = 0; void detectAndDraw(IplImage* img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale); //String cascadeName = "./lbpcascade_frontalface.xml";//人脸检测器（快速的LBP） //String cascadeName = "./haarcascade_frontalface_default.xml";//人脸检测器（默认） String cascadeName = "./haarcascade_frontalface_alt2.xml";//人脸检测器（快速的Haar） //String nestedCascadeName = "./haarcascade_eye_tree_eyeglasses.xml";//戴眼镜人眼的训练数据 String nestedCascadeName = "./haarcascade_mcs_lefteye.xml"; //String nestedCascadeName = "./haarcascade_eye.xml";//人眼的训练数据 //一、检测区域 //可用来检测睁开或闭着的眼睛： //haarcascade_mcs_lefteye.xml //haarcascade_lefteye_2splits.xml //仅可以检测睁开的眼睛： //haarcascade_eye.xml //haarcascade_eye_tree_eyeglasses.xml [仅在带被检测者戴眼镜时方可检测] //二、使用区别 //1)、在训练检测器中，眼睛睁开、闭合或者左眼还是右眼的数据都是单独训练的。所以我们在使用的时候要使用不同的检测器。 //2)、上述四个检测器的可靠性也是从上往下逐渐降低的，所以我们在不考虑戴眼镜的情况下检测眼镜的时候，第一个检测器是最好的。 //3)、如果对于眼部周围很窄的区域搜索，则haarcascade_eye.xml 的检测效果最好 //4)、如果对于眼部周围较大区域，用haarcascade_mcs_lefteye.xml 、haarcascade_lefteye_2splits.xml 会更好。 int eyesopen = 0; int eyesclose = 0; int main(int argc, const char** argv) { CvCapture *capture = 0; IplImage *frame, *frame_copy = 0; int optlen = strlen("--cascade="); //const char* input_name; Mat image; CascadeClassifier cascade, nestedCascade;//创建级联分类器对象 double scale = 4; if (!cascade.load(cascadeName))//从指定的文件目录中加载级联分类器 { cerr << "ERROR: Could not load classifier cascade唉唉出错了" << endl; return 0; } if (!nestedCascade.load(nestedCascadeName)) { cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; return 0; } storage = cvCreateMemStorage(0); cvNamedWindow("result", 1); //检测视频 capture = cvCaptureFromAVI("1.mp4"); if (capture) { for (;;) { if (!cvGrabFrame(capture)) break; frame = cvRetrieveFrame(capture); if (!frame) break; if (!frame_copy) frame_copy = cvCreateImage(cvSize(frame->width, frame->height), IPL_DEPTH_8U, frame->nChannels); if (frame->origin == IPL_ORIGIN_TL) cvCopy(frame, frame_copy, 0); else cvFlip(frame, frame_copy, 0); IplImage *equ = cvCreateImage(cvGetSize(frame_copy), 8, 1); IplImage *gray = cvCreateImage(cvGetSize(frame_copy), 8, 1); cvCvtColor(frame_copy, gray, CV_BGR2GRAY); cvEqualizeHist(gray,equ); detectAndDraw(frame_copy, cascade, nestedCascade, scale); if (cvWaitKey(10) >= 0) break; } cvReleaseImage(&frame_copy); cvReleaseCapture(&capture); } cvWaitKey(-1); //system（"pause"）; return 0; } void detectAndDraw(IplImage* img1, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale) { cv::Mat img(img1, 0); int i = 0;//框图颜色 double t = 0; vector<Rect> faces; 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) };//用不同的颜色表示不同的人脸 Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);//将图片缩小，加快检测速度 cvtColor(img, gray, CV_BGR2GRAY);//因为用的是类haar特征，所以都是基于灰度图像的，这里要转换成灰度图像 resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);//将尺寸缩小到1/scale,用线性插值 equalizeHist(smallImg, smallImg);//直方图均衡 t = (double)cvGetTickCount();//用来计算算法执行时间 //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)); t = (double)cvGetTickCount() - t;//相减为算法执行的时间 printf("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];//i%8 int radius; center.x = cvRound((r->x + r->width*0.5)*scale);//还原成原来的大小 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); //检测人眼，在每幅人脸图上画出人眼 if (nestedCascade.empty()) continue; smallImgROI = smallImg(*r); //和上面的函数功能一样 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)); if (nestedObjects.empty()) eyesclose++; else eyesopen++; 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);//将眼睛也画出来，和对应人脸的图形是一样的 radius //eyesopen++; } } cv::imshow("result", img); }