OpenCV实现平均背景法

平均背景法的基本思想是计算每个像素的平均值和标准差作为它的背景模型。

平均背景法使用四个OpenCV函数：

代码：

/*
平均背景法
*/
 
#include "highgui.h"
#include "cv.h"
#include<stdlib.h>
#include<stdio.h>
 
//为不同的临时指针图像和统计属性创建指针
 
//Float, 3-channel images
IplImage* IavgF, * IdiffF, * IprevF, * IhiF, *IlowF;
 
IplImage* Iscratch, *Iscratch2;
 
//Float 1-channel images
 
IplImage* Igray1, * Igray2, * Igray3;
IplImage* Ilow1, * Ilow2, * Ilow3;
IplImage* Ihi1, *Ihi2, * Ihi3;
 
//Byte, 1-channel image
IplImage* Imask;
IplImage* Imaskt;
 
//Counts number of images learned for averaging later.
float Icount;
 
// 创建一个函数来给需要的所有临时图像分配内存
//为了方便，我们传递一幅图像(来自视频)作为大小参考来分配临时图像
 
void AllocateImages(IplImage* I)
{
 CvSize sz = cvGetSize(I);
 IavgF = cvCreateImage(sz, IPL_DEPTH_32F, 3);
 IdiffF = cvCreateImage(sz, IPL_DEPTH_32F,3);
 IprevF = cvCreateImage(sz, IPL_DEPTH_32F,3);
 IhiF = cvCreateImage(sz, IPL_DEPTH_32F, 3);
 IlowF = cvCreateImage(sz, IPL_DEPTH_32F,3);
 
 Ilow1 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Ilow2 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Ilow3 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Ihi1 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Ihi2 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Ihi3 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 cvZero(IavgF);
 cvZero(IdiffF);
 cvZero(IprevF);
 cvZero(IhiF);
 cvZero(IlowF);
 Icount = 0.00001;
 
 Iscratch = cvCreateImage(sz, IPL_DEPTH_32F,3);
 Iscratch2 = cvCreateImage(sz, IPL_DEPTH_32F,3);
 
 Igray1 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Igray2 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 Igray3 = cvCreateImage(sz, IPL_DEPTH_32F,1);
 
 Imask = cvCreateImage(sz, IPL_DEPTH_8U, 1);
 Imaskt = cvCreateImage(sz, IPL_DEPTH_8U,1);
 
 cvZero(Iscratch);
 cvZero(Iscratch2);
}
 
//学习累积背景图像和每一帧图像差值的绝对值
// Learn the background statistics for one more frame
// I is a color sample of the background, 3-channel, 8u
void accumulateBackground(IplImage *I)
{
 static int first = 1;
 cvCvtScale(I, Iscratch, 1, 0);
 if(!first)
 {
 cvAcc(Iscratch,IavgF);
 cvAbsDiff(Iscratch, IprevF, Iscratch2);
 cvAcc(Iscratch2,IdiffF);
 Icount += 1.0;
 }
 first = 0;
 cvCopy(Iscratch, IprevF);
}
 
//setHighThreshold和setLowThreshold都是基于每一帧图像平均绝对差设置阈值的有效函数
void setHighThreshold(float scale)
{
 cvConvertScale(IdiffF, Iscratch, scale);
 cvAdd(Iscratch, IavgF, IhiF);
 cvSplit(IhiF, Ihi1, Ihi2, Ihi3, 0);
}
void setLowThreshold(float scale)
{
 cvConvertScale(IdiffF, Iscratch, scale);
 cvSub(IavgF, Iscratch, IlowF);
 cvSplit(IlowF, Ilow1, Ilow2, Ilow3, 0);
}
 
//当积累了足够多的帧图像之后，就将其转化为一个背景的统计模型
//计算每一个像素的均值和方差观测
void createModelsfromStats()
{
 cvConvertScale(IavgF, IavgF, (double)(1.0/Icount));
 cvConvertScale(IdiffF, IdiffF, (double)(1.0/Icount));
 
 //Make sure diff is always something
 
 cvAddS(IdiffF, cvScalar(1.0, 1.0, 1.0), IdiffF);
 setHighThreshold(7.0);
 setLowThreshold(6.0);
}
 
//有了背景模型，同时给出了高，低阈值，就能用它将图像分割为前景和背景
// Create a binary: 0,255 mask where 255 means foregrond pixel
// I Input image, 3-channel, 8u
//Imask 
void backgroundDiff(IplImage* I)
{
 cvCvtScale(I, Iscratch, 1, 0);
 cvSplit(Iscratch, Igray1, Igray2, Igray3, 0);
 
 //Channel 1
 cvInRange(Igray1, Ilow1, Ihi1, Imask);
 
 //Channel 2
 cvInRange(Igray2, Ilow2, Ihi2, Imaskt);
 cvOr(Imask, Imaskt, Imask);
 
 //Channel 3
 cvInRange(Igray3, Ilow3, Ihi3, Imaskt);
 cvOr(Imask, Imaskt, Imask);
 
 //Finally, invert the result
 cvSubRS(Imask, cvScalar(255), Imask);
}
 
//完成背景建模后， 释放内存
void DeallocateImage()
{
 cvReleaseImage(&IavgF);
 cvReleaseImage(&IdiffF);
 cvReleaseImage(&IprevF);
 cvReleaseImage(&IhiF);
 cvReleaseImage(&IlowF);
 cvReleaseImage(&Ilow1);
 cvReleaseImage(&Ilow2);
 cvReleaseImage(&Ilow3);
 cvReleaseImage(&Iscratch);
 cvReleaseImage(&Iscratch2);
 cvReleaseImage(&Igray1);
 cvReleaseImage(&Igray2);
 cvReleaseImage(&Igray3);
 cvReleaseImage(&Imaskt);
}
 
//主函数
int main()
{
 CvCapture* capture = cvCreateFileCapture("tree.avi");
 if(!capture)
 {
 return -1;
 }
 cvNamedWindow("win1");
 cvNamedWindow("win2");
 
 IplImage* rawImage = cvQueryFrame(capture);
 cvShowImage("win1", rawImage);
 
 AllocateImages(rawImage);
 int i = 0;
 while(1)
 {
 if(i <= 30)
 {
  accumulateBackground(rawImage);
  if(i == 30)
  {
  createModelsfromStats();
  }
 }
 else
 {
  backgroundDiff(rawImage);
 }
 cvShowImage("win2", Imask);
 
 if(cvWaitKey(33) == 27)
 {
  break;
 }
 if(!(rawImage = cvQueryFrame(capture)))
 {
  break;
 }
 cvShowImage("win1", rawImage);
 if(i == 56 || i == 63)
  cvWaitKey();
 i = i+1;
 }
 DeallocateImage();
 return 0;
}

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