OpenCV实现帧差法检测运动目标
时间:2021-03-29 09:42:27|栏目:C代码|点击: 次
今天的目标是用OpenCV实现对运动目标的检测,这里选用三帧帧差法。代码如下:
#include <opencv2/opencv.hpp> #include <cv.h> #include <highgui.h> #include <stdio.h> #include <ctype.h> double Threshold_index=0; const int CONTOUR_MAX_AERA = 200; void trackbar(int pos) { Threshold_index=(double)pos; } int main(int argc, char* argv[]) { CvCapture *capture=cvCaptureFromCAM(0); int n_cnt=0; IplImage *img=NULL, *img_gray1=NULL, *img_gray2=NULL, *img_gray3=NULL, *img_diff1=NULL, *img_diff2=NULL, *img_diff_and=NULL, *img_binary=NULL, *img_dilate=NULL; CvMemStorage *stor; CvSeq *cont; stor=cvCreateMemStorage(0); cont=cvCreateSeq(CV_SEQ_ELTYPE_POINT,sizeof(CvSeq),sizeof(CvPoint),stor); cvNamedWindow("test",CV_WINDOW_AUTOSIZE); cvNamedWindow("dilate",CV_WINDOW_AUTOSIZE); img=cvQueryFrame(capture); img_gray1=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_gray2=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_gray3=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_diff1=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_diff2=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_diff_and=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_binary=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); img_dilate=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1); int index=1; cvCreateTrackbar("Threshold","test",&index,255,trackbar); while(img=cvQueryFrame(capture)) { if(n_cnt%3==0) cvCvtColor(img,img_gray1,CV_BGR2GRAY); else if(n_cnt%3==1) cvCvtColor(img,img_gray2,CV_BGR2GRAY); else if(n_cnt%3==2) cvCvtColor(img,img_gray3,CV_BGR2GRAY); char c=(char)cvWaitKey(25); if(c==27) break; if(n_cnt>3) { cvAbsDiff(img_gray1,img_gray2,img_diff1); cvAbsDiff(img_gray2,img_gray3,img_diff2); cvAnd(img_diff1,img_diff2,img_diff_and); cvThreshold(img_diff_and,img_binary,Threshold_index,255,CV_THRESH_BINARY); cvShowImage("test",img_binary); cvDilate(img_binary,img_dilate); //cvShowImage("dilate",img_dilate); cvFindContours(img_dilate,stor,&cont,sizeof(CvContour),CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE,cvPoint(0,0)); for(;cont;cont = cont->h_next) { CvRect r = ((CvContour*)cont)->rect;//子类转换为父类例子 if(r.height * r.width > CONTOUR_MAX_AERA) // 面积小的方形抛弃掉 { cvRectangle(img, cvPoint(r.x,r.y), cvPoint(r.x + r.width, r.y + r.height), CV_RGB(255,0,0), 1, CV_AA,0); } } cvShowImage("dilate",img); } if(c=='s') { cvSaveImage("d:/img.bmp",img); cvSaveImage("d:/img_binary.bmp",img_dilate); } n_cnt++; } cvDestroyAllWindows(); cvReleaseCapture(&capture); cvReleaseImage(&img_gray1); cvReleaseImage(&img_gray2); cvReleaseImage(&img_gray3); cvReleaseImage(&img_diff1); cvReleaseImage(&img_diff2); cvReleaseImage(&img_diff_and); cvReleaseImage(&img_binary); cvReleaseImage(&img_dilate); cvReleaseMemStorage(&stor); return 0; }
下图是检测的运动目标二值化图像以及在实际图像中叠加的矩形框效果图。