【OpenCV学习】基于轮廓寻找的视频流运动检测
作者：gnuhpc
出处：http://www.cnblogs.com/gnuhpc/
#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;
}
