opencv学习记录【6】（codebook

9.2-9.16&＃xff1a;codebook
【一】 基本原理
CodeBook算法的基本思想是为每一个像素点建立一个codebook&＃xff0c;每个codebook包含一个或者多个码元code_elements&＃xff0c;并且随着背景像素值波动情况的不同&＃xff0c;不同像素点所包含的码元数目不一定相同。
CodeBook算法为当前图像的每一个像素建立一个码本CodeBook(CB)结构,每个CodeBook由多个码元code_element(ce)组成。CB和ce的形式如下&＃xff1a;
CB&＃61;{**cb,numEntries,t }
ce&＃61;{learnHigh, learnLow,max,min,t_last_update,stale}
其中numEntries为一个CB中所包含的ce的数目&＃xff0c;当n太小时&＃xff0c;退化为简单背景&＃xff0c;当n较大时可以对复杂背景进行建模;t为CB更新的次数。ce中learnHigh和learnLow作为更新时的学习上下界&＃xff0c;max和min记录当前像素的最大值和最小值。t_last_update表示此码元最后一次更新的时间&＃xff0c;stale表示此码元最长不更新时间(即该ce多久未被访问)&＃xff0c;用来删除很少使用的码元&＃xff0c;精简码本。
【二】 步骤
1 模型训练
假设当前训练图像I中某一像素 (x,y)&＃xff0c;该像素的CB的更新算法如下&＃xff0c;另外记背景阈值的增长判定阈值为Bounds&＃xff1a;
(1) CB的访问次数加1&＃xff1b;
(2) 遍历CB中的每个ce&＃xff0c;如果存在一个ce中的learnHigh&＃xff0c;learnLow满足learnLow≤I(x,y)≤learnHigh&＃xff0c;则转(4)&＃xff1b;
(3) 创建一个新的码字new ce加入到CB中, new ce的max与min赋值为I(x,y)&＃xff0c;learnHigh <- I(x,y) &＃43; Bounds&＃xff0c;learnLow <- I(x,y) – Bounds&＃xff0c;并且转(6)&＃xff1b;
(4) 更新该码字的t_last_update&＃xff0c;若当前像素值I(x,y)大于该码字的max&＃xff0c;则max <- I(x,y)&＃xff0c;若I(x,y)小于该码字的min&＃xff0c;则min <- I(x,y)&＃xff1b;
(5) 更新该码字的学习上下界&＃xff0c;以增加背景模型对于复杂背景的适应能力&＃xff0c;具体做法是&＃xff1a;若learnHigh I(x,y) – Bounds&＃xff0c;则learnLow减少1&＃xff1b;
(6) 更新CB中每个ce的stale。设定刷新时间&＃xff0c;遍历每个码元&＃xff0c;若码元中的不更新时间大于设定的刷新时间&＃xff0c;则删除&＃xff1b;在刷新时间内则保持。
2 背景检测
使用已建立好的CB进行运动目标检测&＃xff0c;记判断前景的范围上下界为minMod和maxMod&＃xff0c;对于当前待检测图像上的某一像素I(x,y)&＃xff0c;遍历它对应像素背景模型CB中的每一个码字ce&＃xff0c;若存在一个ce&＃xff0c;使得I(x,y) min – minMod&＃xff0c;则I(x,y)被判断为背景&＃xff0c;否则被判断为前景。
【三】 实验程序
个人认为《Learning opencv3》中codebook示例程序可读性差&＃xff0c;网上的大多数示例程序是基于较低版本opencv的&＃xff0c;找了很久找到适合的示例程序&＃xff0c;做了少许修改。

#include
#include
using namespace cv;
using namespace std;
#define CHANNELS 3//码元
typedef struct ce {uchar learnHigh[CHANNELS];uchar learnLow[CHANNELS];uchar max[CHANNELS];uchar min[CHANNELS];int t_last_update;int stale;
}code_element;//码本
typedef struct code_book {code_element **cb;int numEntries;int t;
}codeBook;//每一帧每个像素调用以更新码本
int updateCodeBook(uchar *p, codeBook&c, unsigned*cbBounds, int numChannels)
{if (c.numEntries &＃61;&＃61; 0)c.t &＃61; 0;//初始化时间c.t &＃43;&＃61; 1;int n;unsigned int high[3], low[3];for (n &＃61; 0; n 255){high[n] &＃61; 255;}low[n]&＃61; *(p &＃43; n) - *(cbBounds &＃43; n);if (low[n]<0){low[n] &＃61; 0;}}//该像素是否在码本范围之内&＃xff0c;是的话更新选中码元int matchChannel;int i;for ( i &＃61; 0; i learnLow[n]<&＃61;*(p&＃43;n)&&(*(p &＃43; n)<&＃61;c.cb[i]->learnHigh[n])){matchChannel&＃43;&＃43;;}}if (matchChannel&＃61;&＃61;numChannels){c.cb[i]->t_last_update &＃61; c.t;for (n &＃61; 0; n max[n] <*(p &＃43; n)){c.cb[i]->max[n] &＃61; *(p &＃43; n);}else if (c.cb[i]->min[n] > *(p &＃43; n)){c.cb[i]->min[n] &＃61; *(p &＃43; n);}}break;}}//该像素不满足任何一码元&＃xff0c;创建一个新码元if (i&＃61;&＃61;c.numEntries){code_element **foo &＃61; new code_element*[c.numEntries &＃43; 1];for (int j &＃61; 0; j learnHigh[n] &＃61; high[n];c.cb[c.numEntries]->learnLow[n] &＃61; low[n]; c.cb[c.numEntries]->max[n] &＃61; *(p &＃43; n);c.cb[c.numEntries]->min[n] &＃61; *(p &＃43; n);}c.cb[c.numEntries]->t_last_update &＃61; c.t;c.cb[c.numEntries]->stale &＃61; 0;c.numEntries &＃43;&＃61; 1;}//更新码元中不更新时间for (int j &＃61; 0; j t_last_update;if (tmp>c.cb[j]->stale){c.cb[j]->stale &＃61; tmp;}}//调整学习域for ( n &＃61; 0; n learnHigh[n] learnHigh[n] &＃43;&＃61; 1;if (c.cb[i]->learnLow[n] > low[n])c.cb[i]->learnLow[n] -&＃61; 1;}return (i);
}//区分背景
uchar backgroundDiff(uchar *p, codeBook&c, int numChannels, int *minMod, int *maxMod)
{int matchChannel;int i;for ( i &＃61; 0; i min[n]-minMod[n]<&＃61;*(p&＃43;n))&&(*(p&＃43;n)<&＃61;c.cb[i]->max[n]&＃43;maxMod[n])){matchChannel&＃43;&＃43;;}else break;}if (matchChannel &＃61;&＃61; numChannels)break;}//无码元匹配&＃xff0c;则判断为前景&＃xff0c;返回255白色&＃xff0c;否则返回0黑色if (i &＃61;&＃61; c.numEntries)return(255);return (0);
}//清除长时间未更新码元
int clearStaleEntries(codeBook&c)
{int staleThresh &＃61; c.t >> 1;int *keep &＃61; new int[c.numEntries];int keepCnt &＃61; 0;for (int i &＃61; 0; i stale>staleThresh){keep[i] &＃61; 0;}else{keep[i] &＃61; 1;keepCnt&＃43;&＃43;;}}c.t &＃61; 0;code_element **foo &＃61; new code_element*[keepCnt];int k &＃61; 0;for (int j &＃61; 0; j stale &＃61; 0;foo[k]->t_last_update &＃61; 0;k&＃43;&＃43;;}}delete[]keep;delete[]c.cb;c.cb &＃61; foo;int numCleared &＃61; c.numEntries - keepCnt;c.numEntries &＃61; keepCnt;return(numCleared);
}int main()
{VideoCapture capture;capture.open("tree.avi");Mat rawImage;capture >> rawImage;Mat yuvImage(rawImage.rows, rawImage.cols, CV_8UC3);Mat ImaskCodeBook(rawImage.rows, rawImage.cols, CV_8UC1, Scalar(255));uchar *pColor;int imageLen&＃61;rawImage.rows*rawImage.cols;codeBook*cB&＃61;new codeBook[imageLen];int nChannel &＃61; CHANNELS;unsigned cbBound[CHANNELS];int minMod[CHANNELS];int maxMod[CHANNELS];//定义像素个码本for (int i &＃61; 0; i > rawImage;if (rawImage.empty()){break;}cvtColor(rawImage, yuvImage, COLOR_BGR2YCrCb);if (i <&＃61; 30){pColor &＃61; (uchar *)(yuvImage.ptr(0));for (int c &＃61; 0; c (0));uchar *pMask &＃61; (uchar*)(ImaskCodeBook.ptr(0));for (int c &＃61; 0; c }

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