前景检测

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：

 1 /////////////////////////////////////////////////////////////////////////////////////////////////////////////////// 2 // Accumulate average and ~std (really absolute difference) image and use this to detect background and foreground 3 // 4 // Typical way of using this is to: 5 //     AllocateImages(); 6 ////loop for N images to accumulate background differences 7 //    accumulateBackground(); 8 ////When done, turn this into our avg and std model with high and low bounds 9 //    createModelsfromStats();10 ////Then use the function to return background in a mask (255 == foreground, 0 == background)11 //    backgroundDiff(IplImage *I,IplImage *Imask, int num);12 ////Then tune the high and low difference from average image background acceptance thresholds13 //    float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many average differences below average14 //    scaleHigh(scalehigh);15 //    scaleLow(scalelow);16 ////That is, change the scale high and low bounds for what should be background to make it work.17 ////Then continue detecting foreground in the mask image18 //    backgroundDiff(IplImage *I,IplImage *Imask, int num);19 //20 //NOTES: num is camera number which varies from 0 ... NUM_CAMERAS - 1.  Typically you only have one camera, but this routine allows21 //          you to index many.22 //23 #ifndef AVGSEG_24 #define AVGSEG_25 26 27 #include "cv.h"                // define all of the opencv classes etc.28 #include "highgui.h"29 #include "cxcore.h"30 31 //IMPORTANT DEFINES:32 #define NUM_CAMERAS   1              //This function can handle an array of cameras33 #define HIGH_SCALE_NUM 7.0            //How many average differences from average image on the high side == background34 #define LOW_SCALE_NUM 6.0        //How many average differences from average image on the low side == background35 36 void AllocateImages(IplImage *I);37 void DeallocateImages();38 void accumulateBackground(IplImage *I, int number=0);39 void scaleHigh(float scale = HIGH_SCALE_NUM, int num = 0);40 void scaleLow(float scale = LOW_SCALE_NUM, int num = 0);41 void createModelsfromStats();42 void backgroundDiff(IplImage *I,IplImage *Imask, int num = 0);43 44 #endif

 

     avg_background.cpp文件:

  1 // avg_background.cpp : 定义控制台应用程序的入口点。  2 //  3   4 #include "stdafx.h"  5 #include "avg_background.h"  6   7   8 //GLOBALS  9  10 IplImage *IavgF[NUM_CAMERAS],*IdiffF[NUM_CAMERAS], *IprevF[NUM_CAMERAS], *IhiF[NUM_CAMERAS], *IlowF[NUM_CAMERAS]; 11 IplImage *Iscratch,*Iscratch2,*Igray1,*Igray2,*Igray3,*Imaskt; 12 IplImage *Ilow1[NUM_CAMERAS],*Ilow2[NUM_CAMERAS],*Ilow3[NUM_CAMERAS],*Ihi1[NUM_CAMERAS],*Ihi2[NUM_CAMERAS],*Ihi3[NUM_CAMERAS]; 13  14 float Icount[NUM_CAMERAS]; 15  16 void AllocateImages(IplImage *I)  //I is just a sample for allocation purposes 17 { 18     for(int i = 0; i<NUM_CAMERAS; i++){ 19         IavgF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); 20         IdiffF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); 21         IprevF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); 22         IhiF[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); 23         IlowF[i] = cvCreateImage(cvGetSize(I), IPL_DEPTH_32F, 3 ); 24         Ilow1[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 25         Ilow2[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 26         Ilow3[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 27         Ihi1[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 28         Ihi2[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 29         Ihi3[i] = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 30         cvZero(IavgF[i]  ); 31         cvZero(IdiffF[i]  ); 32         cvZero(IprevF[i]  ); 33         cvZero(IhiF[i] ); 34         cvZero(IlowF[i]  );         35         Icount[i] = 0.00001; //Protect against divide by zero 36     } 37     Iscratch = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); 38     Iscratch2 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 3 ); 39     Igray1 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 40     Igray2 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 41     Igray3 = cvCreateImage( cvGetSize(I), IPL_DEPTH_32F, 1 ); 42     Imaskt = cvCreateImage( cvGetSize(I), IPL_DEPTH_8U, 1 ); 43  44     cvZero(Iscratch); 45     cvZero(Iscratch2 ); 46 } 47  48 void DeallocateImages() 49 { 50     for(int i=0; i<NUM_CAMERAS; i++){ 51         cvReleaseImage(&IavgF[i]); 52         cvReleaseImage(&IdiffF[i] ); 53         cvReleaseImage(&IprevF[i] ); 54         cvReleaseImage(&IhiF[i] ); 55         cvReleaseImage(&IlowF[i] ); 56         cvReleaseImage(&Ilow1[i]  ); 57         cvReleaseImage(&Ilow2[i]  ); 58         cvReleaseImage(&Ilow3[i]  ); 59         cvReleaseImage(&Ihi1[i]   ); 60         cvReleaseImage(&Ihi2[i]   ); 61         cvReleaseImage(&Ihi3[i]  ); 62     } 63     cvReleaseImage(&Iscratch); 64     cvReleaseImage(&Iscratch2); 65  66     cvReleaseImage(&Igray1  ); 67     cvReleaseImage(&Igray2 ); 68     cvReleaseImage(&Igray3 ); 69  70     cvReleaseImage(&Imaskt); 71 } 72  73 // Accumulate the background statistics for one more frame 74 // We accumulate the images, the image differences and the count of images for the  75 //    the routine createModelsfromStats() to work on after we're done accumulating N frames. 76 // I        Background image, 3 channel, 8u 77 // number    Camera number 78 void accumulateBackground(IplImage *I, int number) 79 { 80     static int first = 1; 81     cvCvtScale(I,Iscratch,1,0); //To float;#define cvCvtScale cvConvertScale #define cvScale cvConvertScale 82     if (!first){ 83         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 84         cvAbsDiff(Iscratch,IprevF[number],Iscratch2);//将2幅图像相减：Iscratch2=abs(Iscratch-IprevF[number]); 85         cvAcc(Iscratch2,IdiffF[number]);//IdiffF[]里面装的是图像差的累积和 86         Icount[number] += 1.0;//累积的图片帧数计数 87     } 88     first = 0; 89     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 90 } 91  92 // Scale the average difference from the average image high acceptance threshold 93 void scaleHigh(float scale, int num)//设定背景建模时的高阈值函数 94 { 95     cvConvertScale(IdiffF[num],Iscratch,scale); //Converts with rounding and saturation 96     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 97     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 98 } 99 100 // Scale the average difference from the average image low acceptance threshold101 void scaleLow(float scale, int num)//设定背景建模时的低阈值函数102 {103     cvConvertScale(IdiffF[num],Iscratch,scale); //Converts with rounding and saturation104     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105     cvCvtPixToPlane( IlowF[num], Ilow1[num],Ilow2[num],Ilow3[num], 0 );106 }107 108 //Once you've learned the background long enough, turn it into a background model109 void createModelsfromStats()110 {111     for(int i=0; i<NUM_CAMERAS; i++)112     {113         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114         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115         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116         scaleHigh(HIGH_SCALE_NUM,i);//HIGH_SCALE_NUM初始定义为7，其实就是一个倍数117         scaleLow(LOW_SCALE_NUM,i);//LOW_SCALE_NUM初始定义为6118     }119 }120 121 // Create a binary: 0,255 mask where 255 means forground pixel122 // I        Input image, 3 channel, 8u123 // Imask    mask image to be created, 1 channel 8u124 // num        camera number.125 //126 void backgroundDiff(IplImage *I,IplImage *Imask, int num)  //Mask should be grayscale127 {128     cvCvtScale(I,Iscratch,1,0); //To float;129 //Channel 1130     cvCvtPixToPlane( Iscratch, Igray1,Igray2,Igray3, 0 );131     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)132 //Channel 2133     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134     cvOr(Imask,Imaskt,Imask);135     //Channel 3136     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！137     cvOr(Imask,Imaskt,Imask);138     //Finally, invert the results139     cvSubRS( Imask, cvScalar(255), Imask);//前景用255表示了，背景是用0表示140 }

 

 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：

 1 /////////////////////////////////////////////////////////////////////////////////////////////////////////////////// 2 // Accumulate average and ~std (really absolute difference) image and use this to detect background and foreground 3 // 4 // Typical way of using this is to: 5 //     AllocateImages(); 6 ////loop for N images to accumulate background differences 7 //    accumulateBackground(); 8 ////When done, turn this into our avg and std model with high and low bounds 9 //    createModelsfromStats();10 ////Then use the function to return background in a mask (255 == foreground, 0 == background)11 //    backgroundDiff(IplImage *I,IplImage *Imask, int num);12 ////Then tune the high and low difference from average image background acceptance thresholds13 //    float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many average differences below average14 //    scaleHigh(scalehigh);15 //    scaleLow(scalelow);16 ////That is, change the scale high and low bounds for what should be background to make it work.17 ////Then continue detecting foreground in the mask image18 //    backgroundDiff(IplImage *I,IplImage *Imask, int num);19 //20 //NOTES: num is camera number which varies from 0 ... NUM_CAMERAS - 1.  Typically you only have one camera, but this routine allows21 //          you to index many.22 //23 #ifndef AVGSEG_24 #define AVGSEG_25 26 27 #include "cv.h"                // define all of the opencv classes etc.28 #include "highgui.h"29 #include "cxcore.h"30 31 //IMPORTANT DEFINES:32 #define NUM_CAMERAS   1              //This function can handle an array of cameras33 #define HIGH_SCALE_NUM 7.0            //How many average differences from average image on the high side == background34 #define LOW_SCALE_NUM 6.0        //How many average differences from average image on the low side == background35 36 void AllocateImages(IplImage *I);37 void DeallocateImages();38 void accumulateBackground(IplImage *I, int number=0);39 void scaleHigh(float scale = HIGH_SCALE_NUM, int num = 0);40 void scaleLow(float scale = LOW_SCALE_NUM, int num = 0);41 void createModelsfromStats();42 void backgroundDiff(IplImage *I,IplImage *Imask, int num = 0);43 44 #endif

 

     cv_yuv_codebook.cpp文件：

  1 ////////YUV CODEBOOK  2 // Gary Bradski, July 14, 2005  3   4   5 #include "stdafx.h"  6 #include "cv_yuv_codebook.h"  7   8 //GLOBALS FOR ALL CAMERA MODELS  9  10 //For connected components: 11 int CVCONTOUR_APPROX_LEVEL = 2;   // Approx.threshold - the bigger it is, the simpler is the boundary 12 int CVCLOSE_ITR = 1;                // How many iterations of erosion and/or dialation there should be 13 //#define CVPERIMSCALE 4            // image (width+height)/PERIMSCALE.  If contour lenght < this, delete that contour 14  15 //For learning background 16  17 //Just some convienience macros 18 #define CV_CVX_WHITE    CV_RGB(0xff,0xff,0xff) 19 #define CV_CVX_BLACK    CV_RGB(0x00,0x00,0x00) 20  21  22 /////////////////////////////////////////////////////////////////////////////////// 23 // int updateCodeBook(uchar *p, codeBook &c, unsigned cbBounds) 24 // Updates the codebook entry with a new data point 25 // 26 // p            Pointer to a YUV pixel 27 // c            Codebook for this pixel 28 // cbBounds        Learning bounds for codebook (Rule of thumb: 10) 29 // numChannels    Number of color channels we're learning 30 // 31 // NOTES: 32 //        cvBounds must be of size cvBounds[numChannels] 33 // 34 // RETURN 35 //    codebook index 36 int cvupdateCodeBook(uchar *p, codeBook &c, unsigned *cbBounds, int numChannels) 37 { 38  39     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 40     c.t += 1;        //Record learning event，遍历该像素点的次数加1 41 //SET HIGH AND LOW BOUNDS 42     int n; 43     unsigned int high[3],low[3]; 44     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 45     { 46         high[n] = *(p+n)+*(cbBounds+n); 47         if(high[n] > 255) high[n] = 255; 48         low[n] = *(p+n)-*(cbBounds+n); 49         if(low[n] < 0) low[n] = 0; 50     } 51     int matchChannel; 52     //SEE IF THIS FITS AN EXISTING CODEWORD 53     int i; 54     for(i=0; i<c.numEntries; i++)//需要对所有的码元进行扫描 55     { 56         matchChannel = 0; 57         for(n=0; n<numChannels; n++) 58         { 59             //这个地方要非常小心，if条件不是下面表达的 60 //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])) 61 //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 62 //learnHigh[n]和learnLow[n]的变化范围实际上比max[n]和min[n]的大 63             if((c.cb[i]->learnLow[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->learnHigh[n])) //Found an entry for this channel 64             { 65                 matchChannel++; 66             } 67         } 68         if(matchChannel == numChannels) //If an entry was found over all channels，找到了该元素此刻对应的码元 69         { 70             c.cb[i]->t_last_update = c.t; 71             //adjust this codeword for the first channel 72 //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 73             for(n=0; n<numChannels; n++) 74             { 75                 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 76                 { 77                     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] 78 //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   79                 } 80                 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 81                 { 82                     c.cb[i]->min[n] = *(p+n); 83                 } 84             } 85             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 86         } 87     } 88  89     //OVERHEAD TO TRACK POTENTIAL STALE ENTRIES 90     for(int s=0; s<c.numEntries; s++) 91     { 92         //This garbage is to track which codebook entries are going stale 93         int negRun = c.t - c.cb[s]->t_last_update;//negRun表示码元没有更新的时间间隔 94         if(c.cb[s]->stale < negRun) c.cb[s]->stale = negRun;//更新每个码元的statle 95     } 96  97  98     //ENTER A NEW CODE WORD IF NEEDED 99     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100     {101         code_element **foo = new code_element* [c.numEntries+1];//创建一个新的码元序列102         for(int ii=0; ii<c.numEntries; ii++)103         {104             foo[ii] = c.cb[ii];//将码本前面所有的码元地址赋给foo105         }106         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107         if(c.numEntries) delete [] c.cb;//？108         c.cb = foo;109         for(n=0; n<numChannels; n++)//给新建立的码元结构体元素赋值110         {111             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112             c.cb[c.numEntries]->learnLow[n] = low[n];113             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114             c.cb[c.numEntries]->min[n] = *(p+n);115         }116         c.cb[c.numEntries]->t_last_update = c.t;117         c.cb[c.numEntries]->stale = 0;//因为刚建立，所有为0118         c.numEntries += 1;//码元的个数加1119     }120 121     //SLOWLY ADJUST LEARNING BOUNDS122     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123     {                           //如果是新建立的码元，则if条件肯定不满足124         if(c.cb[i]->learnHigh[n] < high[n]) c.cb[i]->learnHigh[n] += 1;                125         if(c.cb[i]->learnLow[n] > low[n]) c.cb[i]->learnLow[n] -= 1;126     }127 128     return(i);//返回所找到码本中码元的索引129 }130 131 ///////////////////////////////////////////////////////////////////////////////////132 // uchar cvbackgroundDiff(uchar *p, codeBook &c, int minMod, int maxMod)133 // Given a pixel and a code book, determine if the pixel is covered by the codebook134 //135 // p        pixel pointer (YUV interleaved)136 // c        codebook reference137 // numChannels  Number of channels we are testing138 // maxMod    Add this (possibly negative) number onto max level when code_element determining if new pixel is foreground139 // minMod    Subract this (possible negative) number from min level code_element when determining if pixel is foreground140 //141 // NOTES:142 // minMod and maxMod must have length numChannels, e.g. 3 channels => minMod[3], maxMod[3].143 //144 // Return145 // 0 => background, 255 => foreground146 uchar cvbackgroundDiff(uchar *p, codeBook &c, int numChannels, int *minMod, int *maxMod)147 {148     int matchChannel;149     //SEE IF THIS FITS AN EXISTING CODEWORD150     int i;151     for(i=0; i<c.numEntries; i++)152     {153         matchChannel = 0;154         for(int n=0; n<numChannels; n++)155         {156             if((c.cb[i]->min[n] - minMod[n] <= *(p+n)) && (*(p+n) <= c.cb[i]->max[n] + maxMod[n]))157             {158                 matchChannel++; //Found an entry for this channel159             }160             else161             {162                 break;//加快速度，当一个通道不满足时提前结束163             }164         }165         if(matchChannel == numChannels)166         {167             break; //Found an entry that matched all channels，加快速度，当一个码元找到时，提前结束168         }169     }170     if(i >= c.numEntries) return(255);//255代表前景，因为所有的码元都不满足条件171     return(0);//0代表背景，因为至少有一个码元满足条件172 }173 174 175 //UTILITES/////////////////////////////////////////////////////////////////////////////////////176 /////////////////////////////////////////////////////////////////////////////////177 //int clearStaleEntries(codeBook &c)178 // After you've learned for some period of time, periodically call this to clear out stale codebook entries179 //180 //c        Codebook to clean up181 //182 // Return183 // number of entries cleared184 int cvclearStaleEntries(codeBook &c)//对每一个码本进行检查185 {186     int staleThresh = c.t>>1;//阈值设置为访问该码元的次数的一半，经验值187     int *keep = new int [c.numEntries];188     int keepCnt = 0;189     //SEE WHICH CODEBOOK ENTRIES ARE TOO STALE190     for(int i=0; i<c.numEntries; i++)191     {192         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193             keep[i] = 0; //Mark for destruction194         else195         {196             keep[i] = 1; //Mark to keep，为1时，该码本的条目将被保留197             keepCnt += 1;//keepCnt记录了要保持的codebook的数目198         }199     }200     //KEEP ONLY THE GOOD201     c.t = 0;                        //Full reset on stale tracking202     code_element **foo = new code_element* [keepCnt];//重新建立一个码本的双指针203     int k=0;204     for(int ii=0; ii<c.numEntries; ii++)205     {206         if(keep[ii])207         {208             foo[k] = c.cb[ii];//要保持该码元的话就要把码元结构体复制到fook209             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0210             foo[k]->t_last_update = 0;//211             k++;212         }213     }214     //CLEAN UP215     delete [] keep;216     delete [] c.cb;217     c.cb = foo;218     int numCleared = c.numEntries - keepCnt;//numCleared中保存的是被删除码元的个数219     c.numEntries = keepCnt;//最后新的码元数为保存下来码元的个数220     return(numCleared);//返回被删除的码元个数221 }222 223 /////////////////////////////////////////////////////////////////////////////////224 //int countSegmentation(codeBook *c, IplImage *I)225 //226 //Count how many pixels are detected as foreground227 // c    Codebook228 // I    Image (yuv, 24 bits)229 // numChannels  Number of channels we are testing230 // maxMod    Add this (possibly negative) number onto max level when code_element determining if new pixel is foreground231 // minMod    Subract this (possible negative) number from min level code_element when determining if pixel is foreground232 //233 // NOTES:234 // minMod and maxMod must have length numChannels, e.g. 3 channels => minMod[3], maxMod[3].235 //236 //Return237 // Count of fg pixels238 //239 int cvcountSegmentation(codeBook *c, IplImage *I, int numChannels, int *minMod, int *maxMod)240 {241     int count = 0,i;242     uchar *pColor;243     int imageLen = I->width * I->height;244 245     //GET BASELINE NUMBER OF FG PIXELS FOR Iraw246     pColor = (uchar *)((I)->imageData);247     for(i=0; i<imageLen; i++)248     {249         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1250             count++;251         pColor += 3;252     }253     return(count);//返回图像I的前景像素点的个数254 }255 256 257 ///////////////////////////////////////////////////////////////////////////////////////////258 //void cvconnectedComponents(IplImage *mask, int poly1_hull0, float perimScale, int *num, CvRect *bbs, CvPoint *centers)259 // This cleans up the forground segmentation mask derived from calls to cvbackgroundDiff260 //261 // mask            Is a grayscale (8 bit depth) "raw" mask image which will be cleaned up262 //263 // OPTIONAL PARAMETERS:264 // poly1_hull0    If set, approximate connected component by (DEFAULT) polygon, or else convex hull (0)265 // perimScale     Len = image (width+height)/perimScale.  If contour len < this, delete that contour (DEFAULT: 4)266 // num            Maximum number of rectangles and/or centers to return, on return, will contain number filled (DEFAULT: NULL)267 // bbs            Pointer to bounding box rectangle vector of length num.  (DEFAULT SETTING: NULL)268 // centers        Pointer to contour centers vectore of length num (DEFULT: NULL)269 //270 void cvconnectedComponents(IplImage *mask, int poly1_hull0, float perimScale, int *num, CvRect *bbs, CvPoint *centers)271 {272 static CvMemStorage*    mem_storage    = NULL;273 static CvSeq*            contours    = NULL;274 //CLEAN UP RAW MASK275 //开运算作用：平滑轮廓，去掉细节,断开缺口276     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277 //闭运算作用：平滑轮廓，连接缺口278     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279 280 //FIND CONTOURS AROUND ONLY BIGGER REGIONS281     if( mem_storage==NULL ) mem_storage = cvCreateMemStorage(0);282     else cvClearMemStorage(mem_storage);283 284     //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285     CvContourScanner scanner = cvStartFindContours(mask,mem_storage,sizeof(CvContour),CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE);286     CvSeq* c;287     int numCont = 0;288     while( (c = cvFindNextContour( scanner )) != NULL )289     {290         double len = cvContourPerimeter( c );291         double q = (mask->height + mask->width) /perimScale;   //calculate perimeter len threshold292         if( len < q ) //Get rid of blob if it's perimeter is too small293         {294             cvSubstituteContour( scanner, NULL );295         }296         else //Smooth it's edges if it's large enough297         {298             CvSeq* c_new;299             if(poly1_hull0) //Polygonal approximation of the segmentation300                 c_new = cvApproxPoly(c,sizeof(CvContour),mem_storage,CV_POLY_APPROX_DP, CVCONTOUR_APPROX_LEVEL,0);301             else //Convex Hull of the segmentation302                 c_new = cvConvexHull2(c,mem_storage,CV_CLOCKWISE,1);303             cvSubstituteContour( scanner, c_new );304             numCont++;305         }306     }307     contours = cvEndFindContours( &scanner );308 309 // PAINT THE FOUND REGIONS BACK INTO THE IMAGE310     cvZero( mask );311     IplImage *maskTemp;312     //CALC CENTER OF MASS AND OR BOUNDING RECTANGLES313     if(num != NULL)314     {315         int N = *num, numFilled = 0, i=0;316         CvMoments moments;317         double M00, M01, M10;318         maskTemp = cvCloneImage(mask);319         for(i=0, c=contours; c != NULL; c = c->h_next,i++ )320         {321             if(i < N) //Only process up to *num of them322             {323                 cvDrawContours(maskTemp,c,CV_CVX_WHITE, CV_CVX_WHITE,-1,CV_FILLED,8);324                 //Find the center of each contour325                 if(centers != NULL)326                 {327                     cvMoments(maskTemp,&moments,1);328                     M00 = cvGetSpatialMoment(&moments,0,0);329                     M10 = cvGetSpatialMoment(&moments,1,0);330                     M01 = cvGetSpatialMoment(&moments,0,1);331                     centers[i].x = (int)(M10/M00);332                     centers[i].y = (int)(M01/M00);333                 }334                 //Bounding rectangles around blobs335                 if(bbs != NULL)336                 {337                     bbs[i] = cvBoundingRect(c);338                 }339                 cvZero(maskTemp);340                 numFilled++;341             }342             //Draw filled contours into mask343             cvDrawContours(mask,c,CV_CVX_WHITE,CV_CVX_WHITE,-1,CV_FILLED,8); //draw to central mask344         } //end looping over contours345         *num = numFilled;346         cvReleaseImage( &maskTemp);347     }348     //ELSE JUST DRAW PROCESSED CONTOURS INTO THE MASK349     else350     {351         for( c=contours; c != NULL; c = c->h_next )352         {353             cvDrawContours(mask,c,CV_CVX_WHITE, CV_CVX_BLACK,-1,CV_FILLED,8);354         }355     }356 }

 

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*widthint maxMod[CHANNELS];    //Add these (possibly negative) number onto max                         // level when code_element determining if new pixel is foregroundint minMod[CHANNELS];     //Subract these (possible negative) number from min                         //level code_element when determining if pixel is foregroundunsigned cbBounds[CHANNELS]; //Code Book bounds for learningbool ch[CHANNELS];        //This sets what channels should be adjusted for background boundsint nChannels = CHANNELS;int imageLen = 0;uchar *pColor; //YUV pointervoid 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;}

 

     运行结果截图如下：

     训练过程视频原图截图：

     

 

     测试过程视频原图截图：

     

 

     前景检测过程截图：

     

 

     可以看到左边2幅截图的对比，codebook算法的效果明显比简单减图法要好，手型比较清晰些。