OpenCV学习——基于外廓寻找的视频流运动检测

OpenCV学习——基于轮廓寻找的视频流运动检测

#include "cv.h" #include "highgui.h" #include <time.h> #include <math.h> #include <ctype.h> #include <stdio.h> #include <string.h> // various tracking parameters (in seconds) //跟踪的参数（单位为秒） const double MHI_DURATION = 0.5;//0.5s为运动跟踪的最大持续时间 const double MAX_TIME_DELTA = 0.5; const double MIN_TIME_DELTA = 0.05; const int N = 3; // const int CONTOUR_MAX_AERA = 1000; // ring image buffer 圈出图像缓冲 IplImage **buf = 0;//指针的指针 int last = 0; // temporary images临时图像 IplImage *mhi = 0; // MHI: motion history image CvFilter filter = CV_GAUSSIAN_5x5; CvConnectedComp *cur_comp, min_comp; CvConnectedComp comp; CvMemStorage *storage; CvPoint pt[4]; // 参数： // img – 输入视频帧 // dst – 检测结果 void update_mhi( IplImage* img, IplImage* dst, int diff_threshold ) { double timestamp = clock()/100.; // get current time in seconds 时间戳 CvSize size = cvSize(img->width,img->height); // get current frame size，得到当前帧的尺寸 int i, idx1, idx2; IplImage* silh; IplImage* pyr = cvCreateImage( cvSize((size.width & -2)/2, (size.height & -2)/2), 8, 1 ); CvMemStorage *stor; CvSeq *cont; /*先进行数据的初始化*/ if( !mhi || mhi->width != size.width || mhi->height != size.height ) { if( buf == 0 ) //若尚没有初始化则分配内存给他 { buf = (IplImage**)malloc(N*sizeof(buf[0])); memset( buf, 0, N*sizeof(buf[0])); } for( i = 0; i < N; i++ ) { cvReleaseImage( &buf[i] ); buf[i] = cvCreateImage( size, IPL_DEPTH_8U, 1 ); cvZero( buf[i] );// clear Buffer Frame at the beginning } cvReleaseImage( &mhi ); mhi = cvCreateImage( size, IPL_DEPTH_32F, 1 ); cvZero( mhi ); // clear MHI at the beginning } // end of if(mhi) /*将当前要处理的帧转化为灰度放到buffer的最后一帧中*/ cvCvtColor( img, buf[last], CV_BGR2GRAY ); // convert frame to grayscale /*设定帧的序号*/ /* last---->idx1 ^ | | | idx2<-----(last+1)%3 */ idx1 = last; idx2 = (last + 1) % N; // index of (last - (N-1))th frame last = idx2; // 做帧差 silh = buf[idx2];//差值的指向idx2 |idx2-idx1|-->idx2(<-silh) cvAbsDiff( buf[idx1], buf[idx2], silh ); // get difference between frames // 对差图像做二值化 cvThreshold( silh, silh, 30, 255, CV_THRESH_BINARY ); //threshold it,二值化 cvUpdateMotionHistory( silh, mhi, timestamp, MHI_DURATION ); // update MHI cvConvert( mhi, dst );//将mhi转化为dst,dst=mhi // 中值滤波，消除小的噪声 cvSmooth( dst, dst, CV_MEDIAN, 3, 0, 0, 0 ); cvPyrDown( dst, pyr, CV_GAUSSIAN_5x5 );// 向下采样，去掉噪声，图像是原图像的四分之一 cvDilate( pyr, pyr, 0, 1 ); // 做膨胀操作，消除目标的不连续空洞 cvPyrUp( pyr, dst, CV_GAUSSIAN_5x5 );// 向上采样，恢复图像，图像是原图像的四倍 // // 下面的程序段用来找到轮廓 // // Create dynamic structure and sequence. stor = cvCreateMemStorage(0); cont = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint) , stor); // 找到所有轮廓 cvFindContours( dst, stor, &cont, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0)); // 直接使用CONTOUR中的矩形来画轮廓 for(;cont;cont = cont->h_next) { CvRect r = ((CvContour*)cont)->rect; 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); } } // free memory cvReleaseMemStorage(&stor); cvReleaseImage( &pyr ); } int main(int argc, char** argv) { IplImage* motion = 0; CvCapture* capture = 0; if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 );//摄像头为视频来源 else if( argc == 2 ) capture = cvCaptureFromAVI( argv[1] );//AVI为视频来源 if( capture ) { cvNamedWindow( "Motion", 1 );//建立窗口 for(;;) { IplImage* image; if( !cvGrabFrame( capture ))//捕捉一桢 break; image = cvRetrieveFrame( capture );//取出这个帧 if( image )//若取到则判断motion是否为空 { if( !motion ) { motion = cvCreateImage( cvSize(image->width,image->height), 8, 1 ); //创建motion帧，八位，一通道 cvZero( motion ); //零填充motion motion->origin = image->origin; //内存存储的顺序和取出的帧相同 } } update_mhi( image, motion, 60 );//更新历史图像 cvShowImage( "Motion", image );//显示处理过的图像 if( cvWaitKey(10) >= 0 )//10ms中按任意键退出 break; } cvReleaseCapture( &capture );//释放设备 cvDestroyWindow( "Motion" );//销毁窗口 } return 0; }

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