opencv实现三帧差法解析

#include<iostream>
#include<opencv2\core\core.hpp>
#include<opencv2\highgui\highgui.hpp>
#include<opencv2\imgproc\imgproc.hpp>
using namespace cv;
using namespace std;
const unsigned char FORE_GROUD = 255;int thresh = 10;
int main(int argc,char*argv[])
{   
 VideoCapture video(argv[1]); //判断如果video是否可以打开 
 if(!video.isOpened())  
 return -1;  

//用于保存当前帧的图片 

 Mat currentBGRFrame;  
//用来保存上一帧和当前帧的灰度图片 
 Mat previousSecondGrayFrame; Mat previousFirstGrayFrame; Mat currentGaryFrame; 
 //保存两次的帧差 
Mat previousFrameDifference; //previousFrameFirst - previousFrameSecond的差分 
Mat currentFrameDifference;  //currentFrame - previousFrameFirst; 
 //用来保存帧差的绝对值 
Mat absFrameDifferece; 
//用来显示前景 
 Mat previousSegmentation; Mat currentSegmentation; Mat segmentation; 
 //显示前景 
 namedWindow("segmentation",1); 
 createTrackbar("阈值:","segmentation",&thresh,FORE_GROUD,NULL); 
 //帧数 
 int numberFrame = 0; 
//形态学处理用到的算子 
 Mat morphologyKernel = getStructuringElement(MORPH_RECT,Size(3,3),Point(-1,-1));  
 for(;;) 
 {  //读取当前帧  
 video >> currentBGRFrame;  
 //判断当前帧是否存在  
 if(!currentBGRFrame.data)   
 break;   
  numberFrame++;  
 //颜色空间的转换  
 cvtColor(currentBGRFrame,currentGaryFrame,COLOR_BGR2GRAY);   
 if( numberFrame == 1)  {  
 //保存当前帧的灰度图   
 previousSecondGrayFrame = currentGaryFrame.clone();    
  //显示视频  
 imshow("video",currentBGRFrame);   continue;  }  
 else if( numberFrame == 2)  
 {   
 //保存当前帧的灰度图  
  previousFirstGrayFrame = currentGaryFrame.clone();   
 //previousFirst - previousSecond   
 subtract(previousFirstGrayFrame,previousSecondGrayFrame,previousFrameDifference,Mat(),CV_16SC1);   
 //取绝对值   
 absFrameDifferece = abs(previousFrameDifference);      
 //位深的改变   
 absFrameDifferece.convertTo(absFrameDifferece,CV_8UC1,1,0);   
 //阈值处理  
 threshold(absFrameDifferece,previousSegmentation,double(thresh),double(FORE_GROUD),THRESH_BINARY);    
  //显示视频  
 imshow("video",currentBGRFrame);   
 continue;  }  
 else  
 {   
 //src1-src2   
subtract(currentGaryFrame,previousFirstGrayFrame,currentFrameDifference,Mat(),CV_16SC1);    
   //取绝对值  
  absFrameDifferece = abs(currentFrameDifference);      
 //位深的改变   absFrameDifferece.convertTo(absFrameDifferece,CV_8UC1,1,0); 
   //阈值处理   
 threshold(absFrameDifferece,currentSegmentation,double(thresh),double(FORE_GROUD),THRESH_BINARY);  //与运算   
 bitwise_and(previousSegmentation,currentSegmentation,segmentation);    //中值滤波   
medianBlur(segmentation,segmentation,3); //形态学处理(开闭运算)   
 //morphologyEx(segmentation,segmentation,MORPH_OPEN,morphologyKernel,Point(-1,-1),1,BORDER_REPLICATE);   
 morphologyEx(segmentation,segmentation,MORPH_CLOSE,morphologyKernel,Point(-1,-1),2,BORDER_REPLICATE);      
      //找边界   
 vector< vector<oint> > contours;   
 vector<Vec4i> hierarchy;   
 //复制segmentation  
  Mat tempSegmentation = segmentation.clone();  
  findContours( segmentation, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );//CV_RETR_TREE  
  vector< vector<oint> > contours_poly( contours.size() );     
  /*存储运动物体*/   
  vector<Rect> boundRect;   
  boundRect.clear();      
 //画出运动物体   
 for(int index = 0;index < contours.size() ;index++)   
 {   
 approxPolyDP( Mat(contours[index]), contours_poly[index], 3, true );   
  Rect rect = boundingRect( Mat(contours_poly[index]) );    
 rectangle(currentBGRFrame,rect,Scalar(0,255,255),2);   }  
 //显示视频  
 imshow("video",currentBGRFrame);      
 //前景检测   
imshow("segmentation",segmentation);   
 //保存当前帧的灰度图   
 previousFirstGrayFrame = currentGaryFrame.clone();      
 //保存当前的前景检测   
 previousSegmentation = currentSegmentation.clone();  
 }    
 if(waitKey(33) == 'q')   
 break; 
 } 
 return 0;
}