前景检测算法_1(codebook和平均背景法)

原文链接

前景分割中一个非常重要的研究方向就是背景减图法，因为背景减图的方法简单，原理容易被想到，且在智能视频监控领域中，摄像机很多情况下是固定的，且背景也是基本不变或者是缓慢变换的，在这种场合背景减图法的应用驱使了其不少科研人员去研究它。

      但是背景减图获得前景图像的方法缺点也很多：比如说光照因素，遮挡因素，动态周期背景，且背景非周期背景，且一般情况下我们考虑的是每个像素点之间独立，这对实际应用留下了很大的隐患。

      这一小讲主要是讲简单背景减图法和codebook法。

 

一、简单背景减图法的工作原理。

      在视频对背景进行建模的过程中，每2帧图像之间对应像素点灰度值算出一个误差值，在背景建模时间内算出该像素点的平均值，误差平均值，然后在平均差值的基础上+-误差平均值的常数(这个系数需要手动调整)倍作为背景图像的阈值范围，所以当进行前景检测时，当相应点位置来了一个像素时，如果来的这个像素的每个通道的灰度值都在这个阈值范围内，则认为是背景用0表示，否则认为是前景用255表示。

      下面的一个工程是learning opencv一书中作者提供的源代码，关于简单背景减图的代码和注释如下：

     avg_background.h文件：

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Accumulate average and ~std (really absolute difference) image and use this to detect background and foreground
//
// Typical way of using this is to:
//     AllocateImages();
////loop for N images to accumulate background differences
//    accumulateBackground();
////When done, turn this into our avg and std model with high and low bounds
//    createModelsfromStats();
////Then use the function to return background in a mask (255 == foreground, 0 == background)
//    backgroundDiff(IplImage *I,IplImage *Imask, int num);
////Then tune the high and low difference from average image background acceptance thresholds
//    float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many average differences below average
//    scaleHigh(scalehigh);
//    scaleLow(scalelow);
////That is, change the scale high and low bounds for what should be background to make it work.
////Then continue detecting foreground in the mask image
//    backgroundDiff(IplImage *I,IplImage *Imask, int num);
//
//NOTES: num is camera number which varies from 0 ... NUM_CAMERAS - 1.  Typically you only have one camera, but this routine allows
//          you to index many.
//
#ifndef AVGSEG_
#define AVGSEG_


#include "cv.h"                // define all of the opencv classes etc.
#include "highgui.h"
#include "cxcore.h"

//IMPORTANT DEFINES:
#define NUM_CAMERAS   1              //This function can handle an array of cameras
#define HIGH_SCALE_NUM 7.0            //How many average differences from average image on the high side == background
#define LOW_SCALE_NUM 6.0        //How many average differences from average image on the low side == background

void AllocateImages(IplImage *I);
void DeallocateImages();
void accumulateBackground(IplImage *I, int number=0);
void scaleHigh(float scale = HIGH_SCALE_NUM, int num = 0);
void scaleLow(float scale = LOW_SCALE_NUM, int num = 0);
void createModelsfromStats();
void backgroundDiff(IplImage *I,IplImage *Imask, int num = 0);

#endif

   avg_background.cpp文件:

// avg_background.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "avg_background.h"


//GLOBALS

IplImage *IavgF[NUM_CAMERAS],*IdiffF[NUM_CAMERAS], *IprevF[NUM_CAMERAS], *IhiF[NUM_CAMERAS], *IlowF[NUM_CAMERAS];
IplImage *Iscratch,*Iscratch2,*Igray1,*Igray2,*Igray3,*Imaskt;
IplImage *Ilow1[NUM_CAMERAS],*Ilow2[NUM_CAMERAS],*Ilow3[NUM_CAMERAS],*Ihi1[NUM_CAMERAS],*Ihi2[NUM_CAMERAS],*Ihi3[NUM_CAMERAS];

float Icount[NUM_CAMERAS];

void AllocateImages(IplImage *I)  //I is just a sample for allocation purposes
{
    for(int i = 0; i<NUM_CAMERAS; i++){
        IavgF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 );
        IdiffF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 );
        IprevF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 );
        IhiF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 );
        IlowF[i] = cvCreateImage(cvGetSize(I), IPL_DEPTH_32F, 3 );
        Ilow1[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
        Ilow2[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
        Ilow3[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
        Ihi1[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
        Ihi2[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
        Ihi3[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
        cvZero(IavgF[i]  );
        cvZero(IdiffF[i]  );
        cvZero(IprevF[i]  );
        cvZero(IhiF[i] );
        cvZero(IlowF[i]  );        
        Icount[i] = 0.00001; //Protect against divide by zero
    }
    Iscratch = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 );
    Iscratch2 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 );
    Igray1 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
    Igray2 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
    Igray3 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 );
    Imaskt = cvCreateImage( cvGetSize(I), IPL_DEPTH_8U, 1 );

    cvZero(Iscratch);
    cvZero(Iscratch2 );
}

void DeallocateImages()
{
    for(int i=0; i<NUM_CAMERAS; i++){
        cvReleaseImage(&IavgF[i]);
        cvReleaseImage(&IdiffF[i] );
        cvReleaseImage(&IprevF[i] );
        cvReleaseImage(&IhiF[i] );
        cvReleaseImage(&IlowF[i] );
        cvReleaseImage(&Ilow1[i]  );
        cvReleaseImage(&Ilow2[i]  );
        cvReleaseImage(&Ilow3[i]  );
        cvReleaseImage(&Ihi1[i]   );
        cvReleaseImage(&Ihi2[i]   );
        cvReleaseImage(&Ihi3[i]  );
    }
    cvReleaseImage(&Iscratch);
    cvReleaseImage(&Iscratch2);

    cvReleaseImage(&Igray1  );
    cvReleaseImage(&Igray2 );
    cvReleaseImage(&Igray3 );

    cvReleaseImage(&Imaskt);
}

// Accumulate the background statistics for one more frame
// We accumulate the images, the image differences and the count of images for the 
//    the routine createModelsfromStats() to work on after we're done accumulating N frames.
// I        Background image, 3 channel, 8u
// number    Camera number
void accumulateBackground(IplImage *I, int number)
{
    static int first = 1;
    cvCvtScale(I,Iscratch,1,0); //To float;#define cvCvtScale cvConvertScale #define cvScale cvConvertScale
    if (!first){
        cvAcc(Iscratch,IavgF[number]);//将2幅图像相加：IavgF[number]=IavgF[number]+Iscratch，IavgF[]里面装的是时间序列图片的累加
        cvAbsDiff(Iscratch,IprevF[number],Iscratch2);//将2幅图像相减：Iscratch2=abs(Iscratch-IprevF[number]);
        cvAcc(Iscratch2,IdiffF[number]);//IdiffF[]里面装的是图像差的累积和
        Icount[number] += 1.0;//累积的图片帧数计数
    }
    first = 0;
    cvCopy(Iscratch,IprevF[number]);//执行完该函数后，将当前帧数据保存为前一帧数据
}

// Scale the average difference from the average image high acceptance threshold
void scaleHigh(float scale, int num)//设定背景建模时的高阈值函数
{
    cvConvertScale(IdiffF[num],Iscratch,scale); //Converts with rounding and saturation
    cvAdd(Iscratch,IavgF[num],IhiF[num]);//将平均累积图像与误差累积图像缩放scale倍然后再相加
    cvCvtPixToPlane( IhiF[num], Ihi1[num],Ihi2[num],Ihi3[num], 0 );//#define cvCvtPixToPlane cvSplit,且cvSplit是将一个多通道矩阵转换为几个单通道矩阵
}

// Scale the average difference from the average image low acceptance threshold
void scaleLow(float scale, int num)//设定背景建模时的低阈值函数
{
    cvConvertScale(IdiffF[num],Iscratch,scale); //Converts with rounding and saturation
    cvSub(IavgF[num],Iscratch,IlowF[num]);//将平均累积图像与误差累积图像缩放scale倍然后再相减
    cvCvtPixToPlane( IlowF[num], Ilow1[num],Ilow2[num],Ilow3[num], 0 );
}

//Once you've learned the background long enough, turn it into a background model
void createModelsfromStats()
{
    for(int i=0; i<NUM_CAMERAS; i++)
    {
        cvConvertScale(IavgF[i],IavgF[i],(double)(1.0/Icount[i]));//此处为求出累积求和图像的平均值
        cvConvertScale(IdiffF[i],IdiffF[i],(double)(1.0/Icount[i]));//此处为求出累计误差图像的平均值
        cvAddS(IdiffF[i],cvScalar(1.0,1.0,1.0),IdiffF[i]);  //Make sure diff is always something，cvAddS是用于一个数值和一个标量相加
        scaleHigh(HIGH_SCALE_NUM,i);//HIGH_SCALE_NUM初始定义为7，其实就是一个倍数
        scaleLow(LOW_SCALE_NUM,i);//LOW_SCALE_NUM初始定义为6
    }
}

// Create a binary: 0,255 mask where 255 means forground pixel
// I        Input image, 3 channel, 8u
// Imask    mask image to be created, 1 channel 8u
// num        camera number.
//
void backgroundDiff(IplImage *I,IplImage *Imask, int num)  //Mask should be grayscale
{
    cvCvtScale(I,Iscratch,1,0); //To float;
//Channel 1
    cvCvtPixToPlane( Iscratch, Igray1,Igray2,Igray3, 0 );
    cvInRange(Igray1,Ilow1[num],Ihi1[num],Imask);//Igray1[]中相应的点在Ilow1[]和Ihi1[]之间时，Imask中相应的点为255(背景符合)
//Channel 2
    cvInRange(Igray2,Ilow2[num],Ihi2[num],Imaskt);//也就是说对于每一幅图像的绝对值差小于绝对值差平均值的6倍或者大于绝对值差平均值的7倍被认为是前景图像
    cvOr(Imask,Imaskt,Imask);
    //Channel 3
    cvInRange(Igray3,Ilow3[num],Ihi3[num],Imaskt);//这里的固定阈值6和7太不合理了，还好工程后面可以根据实际情况手动调整！
    cvOr(Imask,Imaskt,Imask);
    //Finally, invert the results
    cvSubRS( Imask, cvScalar(255), Imask);//前景用255表示了，背景是用0表示
}

二、codebook算法工作原理

     考虑到简单背景减图法无法对动态的背景建模，有学者就提出了codebook算法。

     该算法为图像中每一个像素点建立一个码本，每个码本可以包括多个码元，每个码元有它的学习时最大最小阈值，检测时的最大最小阈值等成员。在背景建模期间，每当来了一幅新图片，对每个像素点进行码本匹配，也就是说如果该像素值在码本中某个码元的学习阈值内，则认为它离过去该对应点出现过的历史情况偏离不大，通过一定的像素值比较，如果满足条件，此时还可以更新对应点的学习阈值和检测阈值。如果新来的像素值对码本中每个码元都不匹配，则有可能是由于背景是动态的，所以我们需要为其建立一个新的码元，并且设置相应的码元成员变量。因此，在背景学习的过程中，每个像素点可以对应多个码元，这样就可以学到复杂的动态背景。

     关于codebook算法的代码和注释如下：

     cv_yuv_codebook.h文件：

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Accumulate average and ~std (really absolute difference) image and use this to detect background and foreground
//
// Typical way of using this is to:
//     AllocateImages();
////loop for N images to accumulate background differences
//    accumulateBackground();
////When done, turn this into our avg and std model with high and low bounds
//    createModelsfromStats();
////Then use the function to return background in a mask (255 == foreground, 0 == background)
//    backgroundDiff(IplImage *I,IplImage *Imask, int num);
////Then tune the high and low difference from average image background acceptance thresholds
//    float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many average differences below average
//    scaleHigh(scalehigh);
//    scaleLow(scalelow);
////That is, change the scale high and low bounds for what should be background to make it work.
////Then continue detecting foreground in the mask image
//    backgroundDiff(IplImage *I,IplImage *Imask, int num);
//
//NOTES: num is camera number which varies from 0 ... NUM_CAMERAS - 1.  Typically you only have one camera, but this routine allows
//          you to index many.
//
#ifndef AVGSEG_
#define AVGSEG_


#include "cv.h"                // define all of the opencv classes etc.
#include "highgui.h"
#include "cxcore.h"

//IMPORTANT DEFINES:
#define NUM_CAMERAS   1              //This function can handle an array of cameras
#define HIGH_SCALE_NUM 7.0            //How many average differences from average image on the high side == background
#define LOW_SCALE_NUM 6.0        //How many average differences from average image on the low side == background

void AllocateImages(IplImage *I);
void DeallocateImages();
void accumulateBackground(IplImage *I, int number=0);
void scaleHigh(float scale = HIGH_SCALE_NUM, int num = 0);
void scaleLow(float scale = LOW_SCALE_NUM, int num = 0);
void createModelsfromStats();
void backgroundDiff(IplImage *I,IplImage *Imask, int num = 0);

#endif

  cv_yuv_codebook.cpp文件：

////////YUV CODEBOOK
// Gary Bradski, July 14, 2005


#include "stdafx.h"
#include "cv_yuv_codebook.h"

//GLOBALS FOR ALL CAMERA MODELS

//For connected components:
int CVCONTOUR_APPROX_LEVEL = 2;   // Approx.threshold - the bigger it is, the simpler is the boundary
int CVCLOSE_ITR = 1;                // How many iterations of erosion and/or dialation there should be
//#define CVPERIMSCALE 4            // image (width+height)/PERIMSCALE.  If contour lenght < this, delete that contour

//For learning background

//Just some convienience macros
#define CV_CVX_WHITE    CV_RGB(0xff,0xff,0xff)
#define CV_CVX_BLACK    CV_RGB(0x00,0x00,0x00)


///////////////////////////////////////////////////////////////////////////////////
// int updateCodeBook(uchar *p, codeBook &c, unsigned cbBounds)
// Updates the codebook entry with a new data point
//
// p            Pointer to a YUV pixel
// c            Codebook for this pixel
// cbBounds        Learning bounds for codebook (Rule of thumb: 10)
// numChannels    Number of color channels we're learning
//
// NOTES:
//        cvBounds must be of size cvBounds[numChannels]
//
// RETURN
//    codebook index
int cvupdateCodeBook(uchar *p, codeBook &c, unsigned *cbBounds, int numChannels)
{

    if(c.numEntries == 0) c.t = 0;//说明每个像素如果遍历了的话至少对应一个码元
    c.t += 1;        //Record learning event，遍历该像素点的次数加1
//SET HIGH AND LOW BOUNDS
    int n;
    unsigned int high[3],low[3];
    for(n=0; n<numChannels; n++)//为该像素点的每个通道设置最大阈值和最小阈值，后面用来更新学习的高低阈值时有用
    {
        high[n] = *(p+n)+*(cbBounds+n);
        if(high[n] > 255) high[n] = 255;
        low[n] = *(p+n)-*(cbBounds+n);
        if(low[n] < 0) low[n] = 0;
    }
    int matchChannel;
    //SEE IF THIS FITS AN EXISTING CODEWORD
    int i;
    for(i=0; i<c.numEntries; i++)//需要对所有的码元进行扫描
    {
        matchChannel = 0;
        for(n=0; n<numChannels; n++)
        {
            //这个地方要非常小心，if条件不是下面表达的
//if((c.cb[i]->min[n]-c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->max[n]+c.cb[i]->learnHigh[n]))
//原因是因为在每次建立一个新码元的时候，learnHigh[n]和learnLow[n]的范围就在max[n]和min[n]上扩展了cbBounds[n]，所以说
//learnHigh[n]和learnLow[n]的变化范围实际上比max[n]和min[n]的大
            if((c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->learnHigh[n])) //Found an entry for this channel
            {
                matchChannel++;
            }
        }
        if(matchChannel == numChannels) //If an entry was found over all channels，找到了该元素此刻对应的码元
        {
            c.cb[i]->t_last_update = c.t;
            //adjust this codeword for the first channel
//更新每个码元的最大最小阈值，因为这2个阈值在后面的前景分离过程要用到
            for(n=0; n<numChannels; n++)
            {
                if(c.cb[i]->max[n] < *(p+n))//用该点的像素值更新该码元的最大值，所以max[n]保存的是实际上历史出现过的最大像素值
                {
                    c.cb[i]->max[n] = *(p+n);//因为这个for语句是在匹配成功了的条件阈值下的，所以一般来说改变后的max[n]和min[n]
//也不会过学习的高低阈值，并且学习的高低阈值也一直在缓慢变化  
                }
                else if(c.cb[i]->min[n] > *(p+n))//用该点的像素值更新该码元的最小值，所以min[n]保存的是实际上历史出现过的最小像素值
                {
                    c.cb[i]->min[n] = *(p+n);
                }
            }
            break;//一旦找到了该像素的一个码元后就不用继续往后找了，加快算法速度。因为最多只有一个码元与之对应
        }
    }

    //OVERHEAD TO TRACK POTENTIAL STALE ENTRIES
    for(int s=0; s<c.numEntries; s++)
    {
        //This garbage is to track which codebook entries are going stale
        int negRun = c.t - c.cb[s]->t_last_update;//negRun表示码元没有更新的时间间隔
        if(c.cb[s]->stale < negRun) c.cb[s]->stale = negRun;//更新每个码元的statle
    }


    //ENTER A NEW CODE WORD IF NEEDED
    if(i == c.numEntries)  //No existing code word found, make a new one，只有当该像素码本中的所有码元都不符合要求时才满足if条件
    {
        code_element **foo = new code_element* [c.numEntries+1];//创建一个新的码元序列
        for(int ii=0; ii<c.numEntries; ii++)
        {
            foo[ii] = c.cb[ii];//将码本前面所有的码元地址赋给foo
        }
        foo[c.numEntries] = new code_element;//创建一个新码元并赋给foo指针的下一个空位
        if(c.numEntries) delete [] c.cb;//？
        c.cb = foo;
        for(n=0; n<numChannels; n++)//给新建立的码元结构体元素赋值
        {
            c.cb[c.numEntries]->learnHigh[n] = high[n];//当建立一个新码元时，用当前值附近cbBounds范围作为码元box的学习阈值
            c.cb[c.numEntries]->learnLow[n] = low[n];
            c.cb[c.numEntries]->max[n] = *(p+n);//当建立一个新码元时，用当前值作为码元box的最大最小边界值
            c.cb[c.numEntries]->min[n] = *(p+n);
        }
        c.cb[c.numEntries]->t_last_update = c.t;
        c.cb[c.numEntries]->stale = 0;//因为刚建立，所有为0
        c.numEntries += 1;//码元的个数加1
    }

    //SLOWLY ADJUST LEARNING BOUNDS
    for(n=0; n<numChannels; n++)//每次遍历该像素点就将每个码元的学习最大阈值变大，最小阈值变小，但是都是缓慢变化的
    {                           //如果是新建立的码元，则if条件肯定不满足
        if(c.cb[i]->learnHigh[n] < high[n]) c.cb[i]->learnHigh[n] += 1;                
        if(c.cb[i]->learnLow[n] > low[n]) c.cb[i]->learnLow[n] -= 1;
    }

    return(i);//返回所找到码本中码元的索引
}

///////////////////////////////////////////////////////////////////////////////////
// uchar cvbackgroundDiff(uchar *p, codeBook &c, int minMod, int maxMod)
// Given a pixel and a code book, determine if the pixel is covered by the codebook
//
// p        pixel pointer (YUV interleaved)
// c        codebook reference
// numChannels  Number of channels we are testing
// maxMod    Add this (possibly negative) number onto max level when code_element determining if new pixel is foreground
// minMod    Subract this (possible negative) number from min level code_element when determining if pixel is foreground
//
// NOTES:
// minMod and maxMod must have length numChannels, e.g. 3 channels => minMod[3], maxMod[3].
//
// Return
// 0 => background, 255 => foreground
uchar cvbackgroundDiff(uchar *p, codeBook &c, int numChannels, int *minMod, int *maxMod)
{
    int matchChannel;
    //SEE IF THIS FITS AN EXISTING CODEWORD
    int i;
    for(i=0; i<c.numEntries; i++)
    {
        matchChannel = 0;
        for(int n=0; n<numChannels; n++)
        {
            if((c.cb[i]->min[n] - minMod[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->max[n] + maxMod[n]))
            {
                matchChannel++; //Found an entry for this channel
            }
            else
            {
                break;//加快速度，当一个通道不满足时提前结束
            }
        }
        if(matchChannel == numChannels)
        {
            break; //Found an entry that matched all channels，加快速度，当一个码元找到时，提前结束
        }
    }
    if(i >= c.numEntries) return(255);//255代表前景，因为所有的码元都不满足条件
    return(0);//0代表背景，因为至少有一个码元满足条件
}


//UTILITES/////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////
//int clearStaleEntries(codeBook &c)
// After you've learned for some period of time, periodically call this to clear out stale codebook entries
//
//c        Codebook to clean up
//
// Return
// number of entries cleared
int cvclearStaleEntries(codeBook &c)//对每一个码本进行检查
{
    int staleThresh = c.t>>1;//阈值设置为访问该码元的次数的一半，经验值
    int *keep = new int [c.numEntries];
    int keepCnt = 0;
    //SEE WHICH CODEBOOK ENTRIES ARE TOO STALE
    for(int i=0; i<c.numEntries; i++)
    {
        if(c.cb[i]->stale > staleThresh)//当在背景建模期间有一半的时间内，codebook的码元条目没有被访问，则该条目将被删除
            keep[i] = 0; //Mark for destruction
        else
        {
            keep[i] = 1; //Mark to keep，为1时，该码本的条目将被保留
            keepCnt += 1;//keepCnt记录了要保持的codebook的数目
        }
    }
    //KEEP ONLY THE GOOD
    c.t = 0;                        //Full reset on stale tracking
    code_element **foo = new code_element* [keepCnt];//重新建立一个码本的双指针
    int k=0;
    for(int ii=0; ii<c.numEntries; ii++)
    {
        if(keep[ii])
        {
            foo[k] = c.cb[ii];//要保持该码元的话就要把码元结构体复制到fook
            foo[k]->stale = 0;        //We have to refresh these entries for next clearStale，不被访问的累加器stale重新赋值0
            foo[k]->t_last_update = 0;//
            k++;
        }
    }
    //CLEAN UP
    delete [] keep;
    delete [] c.cb;
    c.cb = foo;
    int numCleared = c.numEntries - keepCnt;//numCleared中保存的是被删除码元的个数
    c.numEntries = keepCnt;//最后新的码元数为保存下来码元的个数
    return(numCleared);//返回被删除的码元个数
}

/////////////////////////////////////////////////////////////////////////////////
//int countSegmentation(codeBook *c, IplImage *I)
//
//Count how many pixels are detected as foreground
// c    Codebook
// I    Image (yuv, 24 bits)
// numChannels  Number of channels we are testing
// maxMod    Add this (possibly negative) number onto max level when code_element determining if new pixel is foreground
// minMod    Subract this (possible negative) number from min level code_element when determining if pixel is foreground
//
// NOTES:
// minMod and maxMod must have length numChannels, e.g. 3 channels => minMod[3], maxMod[3].
//
//Return
// Count of fg pixels
//
int cvcountSegmentation(codeBook *c, IplImage *I, int numChannels, int *minMod, int *maxMod)
{
    int count = 0,i;
    uchar *pColor;
    int imageLen = I->width * I->height;

    //GET BASELINE NUMBER OF FG PIXELS FOR Iraw
    pColor = (uchar *)((I)->imageData);
    for(i=0; i<imageLen; i++)
    {
        if(cvbackgroundDiff(pColor, c[i], numChannels, minMod, maxMod))//对每一个像素点都要检测其是否为前景，如果是的话，计数器count就加1
            count++;
        pColor += 3;
    }
    return(count);//返回图像I的前景像素点的个数
}


///////////////////////////////////////////////////////////////////////////////////////////
//void cvconnectedComponents(IplImage *mask, int poly1_hull0, float perimScale, int *num, CvRect *bbs, CvPoint *centers)
// This cleans up the forground segmentation mask derived from calls to cvbackgroundDiff
//
// mask            Is a grayscale (8 bit depth) "raw" mask image which will be cleaned up
//
// OPTIONAL PARAMETERS:
// poly1_hull0    If set, approximate connected component by (DEFAULT) polygon, or else convex hull (0)
// perimScale     Len = image (width+height)/perimScale.  If contour len < this, delete that contour (DEFAULT: 4)
// num            Maximum number of rectangles and/or centers to return, on return, will contain number filled (DEFAULT: NULL)
// bbs            Pointer to bounding box rectangle vector of length num.  (DEFAULT SETTING: NULL)
// centers        Pointer to contour centers vectore of length num (DEFULT: NULL)
//
void cvconnectedComponents(IplImage *mask, int poly1_hull0, float perimScale, int *num, CvRect *bbs, CvPoint *centers)
{
static CvMemStorage*    mem_storage    = NULL;
static CvSeq*            contours    = NULL;
//CLEAN UP RAW MASK
//开运算作用：平滑轮廓，去掉细节,断开缺口
    cvMorphologyEx( mask, mask, NULL, NULL, CV_MOP_OPEN, CVCLOSE_ITR );//对输入mask进行开操作，CVCLOSE_ITR为开操作的次数，输出为mask图像
//闭运算作用：平滑轮廓，连接缺口
    cvMorphologyEx( mask, mask, NULL, NULL, CV_MOP_CLOSE, CVCLOSE_ITR );//对输入mask进行闭操作，CVCLOSE_ITR为闭操作的次数，输出为mask图像

//FIND CONTOURS AROUND ONLY BIGGER REGIONS
    if( mem_storage==NULL ) mem_storage = cvCreateMemStorage(0);
    else cvClearMemStorage(mem_storage);

    //CV_RETR_EXTERNAL=0是在types_c.h中定义的，CV_CHAIN_APPROX_SIMPLE=2也是在该文件中定义的
    CvContourScanner scanner = cvStartFindContours(mask,mem_storage,sizeof(CvContour),CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);
    CvSeq* c;
    int numCont = 0;
    while( (c = cvFindNextContour( scanner )) != NULL )
    {
        double len = cvContourPerimeter( c );
        double q = (mask->height + mask->width) /perimScale;   //calculate perimeter len threshold
        if( len < q ) //Get rid of blob if it's perimeter is too small
        {
            cvSubstituteContour( scanner, NULL );
        }
        else //Smooth it's edges if it's large enough
        {
            CvSeq* c_new;
            if(poly1_hull0) //Polygonal approximation of the segmentation
                c_new = cvApproxPoly(c,sizeof(CvContour),mem_storage,CV_POLY_APPROX_DP, CVCONTOUR_APPROX_LEVEL,0);
            else //Convex Hull of the segmentation
                c_new = cvConvexHull2(c,mem_storage,CV_CLOCKWISE,1);
            cvSubstituteContour( scanner, c_new );
            numCont++;
        }
    }
    contours = cvEndFindContours( &scanner );

// PAINT THE FOUND REGIONS BACK INTO THE IMAGE
    cvZero( mask );
    IplImage *maskTemp;
    //CALC CENTER OF MASS AND OR BOUNDING RECTANGLES
    if(num != NULL)
    {
        int N = *num, numFilled = 0, i=0;
        CvMoments moments;
        double M00, M01, M10;
        maskTemp = cvCloneImage(mask);
        for(i=0, c=contours; c != NULL; c = c->h_next,i++ )
        {
            if(i < N) //Only process up to *num of them
            {
                cvDrawContours(maskTemp,c,CV_CVX_WHITE, CV_CVX_WHITE,-1,CV_FILLED,8);
                //Find the center of each contour
                if(centers != NULL)
                {
                    cvMoments(maskTemp,&moments,1);
                    M00 = cvGetSpatialMoment(&moments,0,0);
                    M10 = cvGetSpatialMoment(&moments,1,0);
                    M01 = cvGetSpatialMoment(&moments,0,1);
                    centers[i].x = (int)(M10/M00);
                    centers[i].y = (int)(M01/M00);
                }
                //Bounding rectangles around blobs
                if(bbs != NULL)
                {
                    bbs[i] = cvBoundingRect(c);
                }
                cvZero(maskTemp);
                numFilled++;
            }
            //Draw filled contours into mask
            cvDrawContours(mask,c,CV_CVX_WHITE,CV_CVX_WHITE,-1,CV_FILLED,8); //draw to central mask
        } //end looping over contours
        *num = numFilled;
        cvReleaseImage( &maskTemp);
    }
    //ELSE JUST DRAW PROCESSED CONTOURS INTO THE MASK
    else
    {
        for( c=contours; c != NULL; c = c->h_next )
        {
            cvDrawContours(mask,c,CV_CVX_WHITE, CV_CVX_BLACK,-1,CV_FILLED,8);
        }
    }
}

三、2种算法进行对比。

     Learning Opencv的作者将这两种算法做了下对比，用的视频是有风吹动树枝的动态背景，一段时间过后的前景是视频中移动的手。

     当然在这个工程中，作者除了体现上述简单背景差法和codobook算法的一些原理外，还引入了很多细节来优化前景分割效果。比如说误差计算时的方差和协方差计算加速方法，消除像素点内长时间没有被访问过的码元，对检测到的粗糙原始前景图用连通域分析法清楚噪声，其中引入了形态学中的几种操作，使用多边形拟合前景轮廓等细节处理。

     在看作者代码前，最好先看下下面几个变量的物理含义。

     maxMod[n]：用训练好的背景模型进行前景检测时用到，判断点是否小于max[n] + maxMod[n])。

     minMod[n]：用训练好的背景模型进行前景检测时用到，判断点是否小于min[n] -minMod[n])。

     cbBounds*：训练背景模型时用到，可以手动输入该参数，这个数主要是配合high[n]和low[n]来用的。

     learnHigh[n]：背景学习过程中当一个新像素来时用来判断是否在已有的码元中，是阈值的上界部分。

     learnLow[n]：背景学习过程中当一个新像素来时用来判断是否在已有的码元中，是阈值的下界部分。

     max[n]： 背景学习过程中每个码元学习到的最大值，在前景分割时配合maxMod[n]用的。

     min[n]： 背景学习过程中每个码元学习到的最小值，在前景分割时配合minMod[n]用的。

     high[n]：背景学习过程中用来调整learnHigh[n]的，如果learnHigh[n]<high[n],则learnHigh[n]缓慢加1

     low[n]： 背景学习过程中用来调整learnLow[n]的，如果learnLow[n]>Low[n],则learnLow[缓慢减1

  该工程带主函数部分代码和注释如下：

#include "stdafx.h"

#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include "avg_background.h"
#include "cv_yuv_codebook.h"

//VARIABLES for CODEBOOK METHOD:
codeBook *cB;   //This will be our linear model of the image, a vector 
                //of lengh = height*width
int maxMod[CHANNELS];    //Add these (possibly negative) number onto max 
                        // level when code_element determining if new pixel is foreground
int minMod[CHANNELS];     //Subract these (possible negative) number from min 
                        //level code_element when determining if pixel is foreground
unsigned cbBounds[CHANNELS]; //Code Book bounds for learning
bool ch[CHANNELS];        //This sets what channels should be adjusted for background bounds
int nChannels = CHANNELS;
int imageLen = 0;
uchar *pColor; //YUV pointer

void help() {
    printf("\nLearn background and find foreground using simple average and average difference learning method:\n"
        "\nUSAGE:\n  ch9_background startFrameCollection# endFrameCollection# [movie filename, else from camera]\n"
        "If from AVI, then optionally add HighAvg, LowAvg, HighCB_Y LowCB_Y HighCB_U LowCB_U HighCB_V LowCB_V\n\n"
        "***Keep the focus on the video windows, NOT the consol***\n\n"
        "INTERACTIVE PARAMETERS:\n"
        "\tESC,q,Q  - quit the program\n"
        "\th    - print this help\n"
        "\tp    - pause toggle\n"
        "\ts    - single step\n"
        "\tr    - run mode (single step off)\n"
        "=== AVG PARAMS ===\n"
        "\t-    - bump high threshold UP by 0.25\n"
        "\t=    - bump high threshold DOWN by 0.25\n"
        "\t[    - bump low threshold UP by 0.25\n"
        "\t]    - bump low threshold DOWN by 0.25\n"
        "=== CODEBOOK PARAMS ===\n"
        "\ty,u,v- only adjust channel 0(y) or 1(u) or 2(v) respectively\n"
        "\ta    - adjust all 3 channels at once\n"
        "\tb    - adjust both 2 and 3 at once\n"
        "\ti,o    - bump upper threshold up,down by 1\n"
        "\tk,l    - bump lower threshold up,down by 1\n"
        );
}

//
//USAGE:  ch9_background startFrameCollection# endFrameCollection# [movie filename, else from camera]
//If from AVI, then optionally add HighAvg, LowAvg, HighCB_Y LowCB_Y HighCB_U LowCB_U HighCB_V LowCB_V
//
int main(int argc, char** argv)
{
     IplImage* rawImage = 0, *yuvImage = 0; //yuvImage is for codebook method
    IplImage *ImaskAVG = 0,*ImaskAVGCC = 0;
    IplImage *ImaskCodeBook = 0,*ImaskCodeBookCC = 0;
    CvCapture* capture = 0;

    int startcapture = 1;
    int endcapture = 30;
    int c,n;

    maxMod[0] = 3;  //Set color thresholds to default values
    minMod[0] = 10;
    maxMod[1] = 1;
    minMod[1] = 1;
    maxMod[2] = 1;
    minMod[2] = 1;
    float scalehigh = HIGH_SCALE_NUM;//默认值为6
    float scalelow = LOW_SCALE_NUM;//默认值为7
    
    if(argc < 3) {//只有1个参数或者没有参数时，输出错误，并提示help信息，因为该程序本身就算进去了一个参数
        printf("ERROR: Too few parameters\n");
        help();
    }else{//至少有2个参数才算正确
        if(argc == 3){//输入为2个参数的情形是从摄像头输入数据
            printf("Capture from Camera\n");
            capture = cvCaptureFromCAM( 0 );
        }
        else {//输入大于2个参数时是从文件中读入视频数据
            printf("Capture from file %s\n",argv[3]);//第三个参数是读入视频文件的文件名
    //        capture = cvCaptureFromFile( argv[3] );
            capture = cvCreateFileCapture( argv[3] );
            if(!capture) { printf("Couldn't open %s\n",argv[3]); return -1;}//读入视频文件失败
        }
        if(isdigit(argv[1][0])) { //Start from of background capture
            startcapture = atoi(argv[1]);//第一个参数表示视频开始的背景训练时的帧，默认是1
            printf("startcapture = %d\n",startcapture);
        }
        if(isdigit(argv[2][0])) { //End frame of background capture
            endcapture = atoi(argv[2]);//第二个参数表示的结束背景训练时的，默认为30
            printf("endcapture = %d\n"); 
        }
        if(argc > 4){ //See if parameters are set from command line，输入多于4个参数表示后面的算法中用到的参数在这里直接输入
            //FOR AVG MODEL
            if(argc >= 5){
                if(isdigit(argv[4][0])){
                    scalehigh = (float)atoi(argv[4]);
                }
            }
            if(argc >= 6){
                if(isdigit(argv[5][0])){
                    scalelow = (float)atoi(argv[5]);
                }
            }
            //FOR CODEBOOK MODEL, CHANNEL 0
            if(argc >= 7){
                if(isdigit(argv[6][0])){
                    maxMod[0] = atoi(argv[6]);
                }
            }
            if(argc >= 8){
                if(isdigit(argv[7][0])){
                    minMod[0] = atoi(argv[7]);
                }
            }
            //Channel 1
            if(argc >= 9){
                if(isdigit(argv[8][0])){
                    maxMod[1] = atoi(argv[8]);
                }
            }
            if(argc >= 10){
                if(isdigit(argv[9][0])){
                    minMod[1] = atoi(argv[9]);
                }
            }
            //Channel 2
            if(argc >= 11){
                if(isdigit(argv[10][0])){
                    maxMod[2] = atoi(argv[10]);
                }
            }
            if(argc >= 12){
                if(isdigit(argv[11][0])){
                    minMod[2] = atoi(argv[11]);
                }
            }
        }
    }

    //MAIN PROCESSING LOOP:
    bool pause = false;
    bool singlestep = false;

    if( capture )
    {
      cvNamedWindow( "Raw", 1 );//原始视频图像
        cvNamedWindow( "AVG_ConnectComp",1);//平均法连通区域分析后的图像
        cvNamedWindow( "ForegroundCodeBook",1);//codebook法后图像
        cvNamedWindow( "CodeBook_ConnectComp",1);//codebook法连通区域分析后的图像
         cvNamedWindow( "ForegroundAVG",1);//平均法后图像
        int i = -1;
        
        for(;;)
        {
                if(!pause){
//                if( !cvGrabFrame( capture ))
//                    break;
//                rawImage = cvRetrieveFrame( capture );
                rawImage = cvQueryFrame( capture );
                ++i;//count it
//                printf("%d\n",i);
                if(!rawImage) 
                    break;
                //REMOVE THIS FOR GENERAL OPERATION, JUST A CONVIENIENCE WHEN RUNNING WITH THE SMALL tree.avi file
                if(i == 56){//程序开始运行几十帧后自动暂停，以便后面好手动调整参数
                    pause = 1;
                    printf("\n\nVideo paused for your convienience at frame 50 to work with demo\n"
                    "You may adjust parameters, single step or continue running\n\n");
                    help();
                }
            }
            if(singlestep){
                pause = true;
            }
            //First time:
            if(0 == i) {
                printf("\n . . . wait for it . . .\n"); //Just in case you wonder why the image is white at first
                //AVG METHOD ALLOCATION
                AllocateImages(rawImage);//为算法的使用分配内存
                scaleHigh(scalehigh);//设定背景建模时的高阈值函数
                scaleLow(scalelow);//设定背景建模时的低阈值函数
                ImaskAVG = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
                ImaskAVGCC = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
                cvSet(ImaskAVG,cvScalar(255));
                //CODEBOOK METHOD ALLOCATION:
                yuvImage = cvCloneImage(rawImage);
                ImaskCodeBook = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );//用来装前景背景图的，当然只要一个通道的图像即可
                ImaskCodeBookCC = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );
                cvSet(ImaskCodeBook,cvScalar(255));
                imageLen = rawImage->width*rawImage->height;
                cB = new codeBook [imageLen];//创建一个码本cB数组，每个像素对应一个码本
                for(int f = 0; f<imageLen; f++)
                {
                     cB[f].numEntries = 0;//每个码本的初始码元个数赋值为0
                }
                for(int nc=0; nc<nChannels;nc++)
                {
                    cbBounds[nc] = 10; //Learning bounds factor，初始值为10
                }
                ch[0] = true; //Allow threshold setting simultaneously for all channels
                ch[1] = true;
                ch[2] = true;
            }
            //If we've got an rawImage and are good to go:                
            if( rawImage )
            {
                cvCvtColor( rawImage, yuvImage, CV_BGR2YCrCb );//YUV For codebook method
                //This is where we build our background model
                if( !pause && i >= startcapture && i < endcapture  ){
                    //LEARNING THE AVERAGE AND AVG DIFF BACKGROUND
                    accumulateBackground(rawImage);//平均法累加过程
                    //LEARNING THE CODEBOOK BACKGROUND
                    pColor = (uchar *)((yuvImage)->imageData);//yuvImage矩阵的首位置
                    for(int c=0; c<imageLen; c++)
                    {
                        cvupdateCodeBook(pColor, cB[c], cbBounds, nChannels);//codebook算法建模过程
                        pColor += 3;
                    }
                }
                //When done, create the background model
                if(i == endcapture){
                    createModelsfromStats();//平均法建模过程
                }
                //Find the foreground if any
                if(i >= endcapture) {//endcapture帧后开始检测前景
                    //FIND FOREGROUND BY AVG METHOD:
                    backgroundDiff(rawImage,ImaskAVG);
                    cvCopy(ImaskAVG,ImaskAVGCC);
                    cvconnectedComponents(ImaskAVGCC);//平均法中的前景清除
                    //FIND FOREGROUND BY CODEBOOK METHOD
                    uchar maskPixelCodeBook;
                    pColor = (uchar *)((yuvImage)->imageData); //3 channel yuv image
                    uchar *pMask = (uchar *)((ImaskCodeBook)->imageData); //1 channel image
                    for(int c=0; c<imageLen; c++)
                    {
                         maskPixelCodeBook = cvbackgroundDiff(pColor, cB[c], nChannels, minMod, maxMod);//前景返回255，背景返回0
                        *pMask++ = maskPixelCodeBook;//将前景检测的结果返回到ImaskCodeBook中
                        pColor += 3;
                    }
                    //This part just to visualize bounding boxes and centers if desired
                    cvCopy(ImaskCodeBook,ImaskCodeBookCC);    
                    cvconnectedComponents(ImaskCodeBookCC);//codebook算法中的前景清除
                }
                //Display
                   cvShowImage( "Raw", rawImage );//除了这张是彩色图外，另外4张都是黑白图
                cvShowImage( "AVG_ConnectComp",ImaskAVGCC);
                   cvShowImage( "ForegroundAVG",ImaskAVG);
                 cvShowImage( "ForegroundCodeBook",ImaskCodeBook);
                 cvShowImage( "CodeBook_ConnectComp",ImaskCodeBookCC);

                //USER INPUT:
                 c = cvWaitKey(10)&0xFF;
                //End processing on ESC, q or Q
                if(c == 27 || c == 'q' | c == 'Q')
                    break;
                //Else check for user input
                switch(c)
                {
                    case 'h':
                        help();
                        break;
                    case 'p':
                        pause ^= 1;
                        break;
                    case 's':
                        singlestep = 1;
                        pause = false;
                        break;
                    case 'r':
                        pause = false;
                        singlestep = false;
                        break;
                    //AVG BACKROUND PARAMS
                    case '-'://调整scalehigh的参数，scalehigh的物理意义是误差累加的影响因子，其倒数为缩放倍数，加0.25实际上是减小其影响力
                        if(i > endcapture){
                            scalehigh += 0.25;
                            printf("AVG scalehigh=%f\n",scalehigh);
                            scaleHigh(scalehigh);
                        }
                        break;
                    case '='://scalehigh减少2.5是增加其影响力
                        if(i > endcapture){
                            scalehigh -= 0.25;
                            printf("AVG scalehigh=%f\n",scalehigh);
                            scaleHigh(scalehigh);
                        }
                        break;
                    case '[':
                        if(i > endcapture){//设置设定背景建模时的低阈值函数，同上
                            scalelow += 0.25;
                            printf("AVG scalelow=%f\n",scalelow);
                            scaleLow(scalelow);
                        }
                        break;
                    case ']':
                        if(i > endcapture){
                            scalelow -= 0.25;
                            printf("AVG scalelow=%f\n",scalelow);
                            scaleLow(scalelow);
                        }
                        break;
                //CODEBOOK PARAMS
                case 'y':
                case '0'://激活y通道
                        ch[0] = 1;
                        ch[1] = 0;
                        ch[2] = 0;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'u':
                case '1'://激活u通道
                        ch[0] = 0;
                        ch[1] = 1;
                        ch[2] = 0;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'v':
                case '2'://激活v通道
                        ch[0] = 0;
                        ch[1] = 0;
                        ch[2] = 1;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'a': //All
                case '3'://激活所有通道
                        ch[0] = 1;
                        ch[1] = 1;
                        ch[2] = 1;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'b':  //both u and v together
                        ch[0] = 0;
                        ch[1] = 1;
                        ch[2] = 1;
                        printf("CodeBook YUV Channels active: ");
                        for(n=0; n<nChannels; n++)
                                printf("%d, ",ch[n]);
                        printf("\n");
                        break;
                case 'i': //modify max classification bounds (max bound goes higher)
                    for(n=0; n<nChannels; n++){//maxMod和minMod是最大值和最小值跳动的阈值
                        if(ch[n])
                            maxMod[n] += 1;
                        printf("%.4d,",maxMod[n]);
                    }
                    printf(" CodeBook High Side\n");
                    break;
                case 'o': //modify max classification bounds (max bound goes lower)
                    for(n=0; n<nChannels; n++){
                        if(ch[n])
                            maxMod[n] -= 1;
                        printf("%.4d,",maxMod[n]);
                    }
                    printf(" CodeBook High Side\n");
                    break;
                case 'k': //modify min classification bounds (min bound goes lower)
                    for(n=0; n<nChannels; n++){
                        if(ch[n])
                            minMod[n] += 1;
                        printf("%.4d,",minMod[n]);
                    }
                    printf(" CodeBook Low Side\n");
                    break;
                case 'l': //modify min classification bounds (min bound goes higher)
                    for(n=0; n<nChannels; n++){
                        if(ch[n])
                            minMod[n] -= 1;
                        printf("%.4d,",minMod[n]);
                    }
                    printf(" CodeBook Low Side\n");
                    break;
                }
                
            }
        }        
      cvReleaseCapture( &capture );
      cvDestroyWindow( "Raw" );
        cvDestroyWindow( "ForegroundAVG" );
        cvDestroyWindow( "AVG_ConnectComp");
        cvDestroyWindow( "ForegroundCodeBook");
        cvDestroyWindow( "CodeBook_ConnectComp");
        DeallocateImages();//释放平均法背景建模过程中用到的内存
        if(yuvImage) cvReleaseImage(&yuvImage);
        if(ImaskAVG) cvReleaseImage(&ImaskAVG);
        if(ImaskAVGCC) cvReleaseImage(&ImaskAVGCC);
        if(ImaskCodeBook) cvReleaseImage(&ImaskCodeBook);
        if(ImaskCodeBookCC) cvReleaseImage(&ImaskCodeBookCC);
        delete [] cB;
    }
    else{ printf("\n\nDarn, Something wrong with the parameters\n\n"); help();
    }
    return 0;
}