前景檢測算法_1(codebook和平均背景法)

????? 前景分割中一個非常重要的研究方向就是背景減圖法，因為背景減圖的方法簡單，原理容易被想到，且在智能視頻監控領域中，攝像機很多情況下是固定的，且背景也是基本不變或者是緩慢變換的，在這種場合背景減圖法的應用驅使了其不少科研人員去研究它。

????? 但是背景減圖獲得前景圖像的方法缺點也很多：比如說光照因素，遮擋因素，動態周期背景，且背景非周期背景，且一般情況下我們考慮的是每個像素點之間獨立，這對實際應用留下了很大的隱患。

????? 這一小講主要是講簡單背景減圖法和codebook法。

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一、簡單背景減圖法的工作原理。

????? 在視頻對背景進行建模的過程中，每2幀圖像之間對應像素點灰度值算出一個誤差值，在背景建模時間內算出該像素點的平均值，誤差平均值，然后在平均差值的基礎上+-誤差平均值的常數(這個系數需要手動調整)倍作為背景圖像的閾值范圍，所以當進行前景檢測時，當相應點位置來了一個像素時，如果來的這個像素的每個通道的灰度值都在這個閾值范圍內，則認為是背景用0表示，否則認為是前景用255表示。

????? 下面的一個工程是learning opencv一書中作者提供的源代碼，關于簡單背景減圖的代碼和注釋如下：

?????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 thresholds 13 // float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many average differences below average 14 // 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 image 18 // 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 allows 21 // 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 cameras 33 #define HIGH_SCALE_NUM 7.0 //How many average differences from average image on the high side == background 34 #define LOW_SCALE_NUM 6.0 //How many average differences from average image on the low side == background 35 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

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?????avg_background.cpp文件:

1 // avg_background.cpp : 定義控制臺應用程序的入口點。 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]);//將2幅圖像相加：IavgF[number]=IavgF[number]+Iscratch，IavgF[]里面裝的是時間序列圖片的累加 84 cvAbsDiff(Iscratch,IprevF[number],Iscratch2);//將2幅圖像相減：Iscratch2=abs(Iscratch-IprevF[number]); 85 cvAcc(Iscratch2,IdiffF[number]);//IdiffF[]里面裝的是圖像差的累積和 86 Icount[number] += 1.0;//累積的圖片幀數計數 87 } 88 first = 0; 89 cvCopy(Iscratch,IprevF[number]);//執行完該函數后，將當前幀數據保存為前一幀數據 90 } 91 92 // Scale the average difference from the average image high acceptance threshold 93 void scaleHigh(float scale, int num)//設定背景建模時的高閾值函數 94 { 95 cvConvertScale(IdiffF[num],Iscratch,scale); //Converts with rounding and saturation 96 cvAdd(Iscratch,IavgF[num],IhiF[num]);//將平均累積圖像與誤差累積圖像縮放scale倍然后再相加 97 cvCvtPixToPlane( IhiF[num], Ihi1[num],Ihi2[num],Ihi3[num], 0 );//#define cvCvtPixToPlane cvSplit,且cvSplit是將一個多通道矩陣轉換為幾個單通道矩陣 98 } 99 100 // Scale the average difference from the average image low acceptance threshold 101 void scaleLow(float scale, int num)//設定背景建模時的低閾值函數 102 { 103 cvConvertScale(IdiffF[num],Iscratch,scale); //Converts with rounding and saturation 104 cvSub(IavgF[num],Iscratch,IlowF[num]);//將平均累積圖像與誤差累積圖像縮放scale倍然后再相減 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 model 109 void createModelsfromStats() 110 { 111 for(int i=0; i<NUM_CAMERAS; i++) 112 { 113 cvConvertScale(IavgF[i],IavgF[i],(double)(1.0/Icount[i]));//此處為求出累積求和圖像的平均值 114 cvConvertScale(IdiffF[i],IdiffF[i],(double)(1.0/Icount[i]));//此處為求出累計誤差圖像的平均值 115 cvAddS(IdiffF[i],cvScalar(1.0,1.0,1.0),IdiffF[i]); //Make sure diff is always something，cvAddS是用于一個數值和一個標量相加 116 scaleHigh(HIGH_SCALE_NUM,i);//HIGH_SCALE_NUM初始定義為7，其實就是一個倍數 117 scaleLow(LOW_SCALE_NUM,i);//LOW_SCALE_NUM初始定義為6 118 } 119 } 120 121 // Create a binary: 0,255 mask where 255 means forground pixel 122 // I Input image, 3 channel, 8u 123 // Imask mask image to be created, 1 channel 8u 124 // num camera number. 125 // 126 void backgroundDiff(IplImage *I,IplImage *Imask, int num) //Mask should be grayscale 127 { 128 cvCvtScale(I,Iscratch,1,0); //To float; 129 //Channel 1 130 cvCvtPixToPlane( Iscratch, Igray1,Igray2,Igray3, 0 ); 131 cvInRange(Igray1,Ilow1[num],Ihi1[num],Imask);//Igray1[]中相應的點在Ilow1[]和Ihi1[]之間時，Imask中相應的點為255(背景符合) 132 //Channel 2 133 cvInRange(Igray2,Ilow2[num],Ihi2[num],Imaskt);//也就是說對于每一幅圖像的絕對值差小于絕對值差平均值的6倍或者大于絕對值差平均值的7倍被認為是前景圖像 134 cvOr(Imask,Imaskt,Imask); 135 //Channel 3 136 cvInRange(Igray3,Ilow3[num],Ihi3[num],Imaskt);//這里的固定閾值6和7太不合理了，還好工程后面可以根據實際情況手動調整！ 137 cvOr(Imask,Imaskt,Imask); 138 //Finally, invert the results 139 cvSubRS( Imask, cvScalar(255), Imask);//前景用255表示了，背景是用0表示 140 }

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?二、codebook算法工作原理

???? 考慮到簡單背景減圖法無法對動態的背景建模，有學者就提出了codebook算法。

???? 該算法為圖像中每一個像素點建立一個碼本，每個碼本可以包括多個碼元，每個碼元有它的學習時最大最小閾值，檢測時的最大最小閾值等成員。在背景建模期間，每當來了一幅新圖片，對每個像素點進行碼本匹配，也就是說如果該像素值在碼本中某個碼元的學習閾值內，則認為它離過去該對應點出現過的歷史情況偏離不大，通過一定的像素值比較，如果滿足條件，此時還可以更新對應點的學習閾值和檢測閾值。如果新來的像素值對碼本中每個碼元都不匹配，則有可能是由于背景是動態的，所以我們需要為其建立一個新的碼元，并且設置相應的碼元成員變量。因此，在背景學習的過程中，每個像素點可以對應多個碼元，這樣就可以學到復雜的動態背景。

???? 關于codebook算法的代碼和注釋如下：

?????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 thresholds 13 // float scalehigh,scalelow; //Set these, defaults are 7 and 6. Note: scalelow is how many average differences below average 14 // 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 image 18 // 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 allows 21 // 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 cameras 33 #define HIGH_SCALE_NUM 7.0 //How many average differences from average image on the high side == background 34 #define LOW_SCALE_NUM 6.0 //How many average differences from average image on the low side == background 35 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

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?????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;//說明每個像素如果遍歷了的話至少對應一個碼元 40 c.t += 1; //Record learning event，遍歷該像素點的次數加1 41 //SET HIGH AND LOW BOUNDS 42 int n; 43 unsigned int high[3],low[3]; 44 for(n=0; n<numChannels; n++)//為該像素點的每個通道設置最大閾值和最小閾值，后面用來更新學習的高低閾值時有用 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++)//需要對所有的碼元進行掃描 55 { 56 matchChannel = 0; 57 for(n=0; n<numChannels; n++) 58 { 59 //這個地方要非常小心，if條件不是下面表達的 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 //原因是因為在每次建立一個新碼元的時候，learnHigh[n]和learnLow[n]的范圍就在max[n]和min[n]上擴展了cbBounds[n]，所以說 62 //learnHigh[n]和learnLow[n]的變化范圍實際上比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，找到了該元素此刻對應的碼元 69 { 70 c.cb[i]->t_last_update = c.t; 71 //adjust this codeword for the first channel 72 //更新每個碼元的最大最小閾值，因為這2個閾值在后面的前景分離過程要用到 73 for(n=0; n<numChannels; n++) 74 { 75 if(c.cb[i]->max[n] < *(p+n))//用該點的像素值更新該碼元的最大值，所以max[n]保存的是實際上歷史出現過的最大像素值 76 { 77 c.cb[i]->max[n] = *(p+n);//因為這個for語句是在匹配成功了的條件閾值下的，所以一般來說改變后的max[n]和min[n] 78 //也不會過學習的高低閾值，并且學習的高低閾值也一直在緩慢變化 79 } 80 else if(c.cb[i]->min[n] > *(p+n))//用該點的像素值更新該碼元的最小值，所以min[n]保存的是實際上歷史出現過的最小像素值 81 { 82 c.cb[i]->min[n] = *(p+n); 83 } 84 } 85 break;//一旦找到了該像素的一個碼元后就不用繼續往后找了，加快算法速度。因為最多只有一個碼元與之對應 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表示碼元沒有更新的時間間隔 94 if(c.cb[s]->stale < negRun) c.cb[s]->stale = negRun;//更新每個碼元的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，只有當該像素碼本中的所有碼元都不符合要求時才滿足if條件 100 { 101 code_element **foo = new code_element* [c.numEntries+1];//創建一個新的碼元序列 102 for(int ii=0; ii<c.numEntries; ii++) 103 { 104 foo[ii] = c.cb[ii];//將碼本前面所有的碼元地址賦給foo 105 } 106 foo[c.numEntries] = new code_element;//創建一個新碼元并賦給foo指針的下一個空位 107 if(c.numEntries) delete [] c.cb;//？ 108 c.cb = foo; 109 for(n=0; n<numChannels; n++)//給新建立的碼元結構體元素賦值 110 { 111 c.cb[c.numEntries]->learnHigh[n] = high[n];//當建立一個新碼元時，用當前值附近cbBounds范圍作為碼元box的學習閾值 112 c.cb[c.numEntries]->learnLow[n] = low[n]; 113 c.cb[c.numEntries]->max[n] = *(p+n);//當建立一個新碼元時，用當前值作為碼元box的最大最小邊界值 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;//因為剛建立，所有為0 118 c.numEntries += 1;//碼元的個數加1 119 } 120 121 //SLOWLY ADJUST LEARNING BOUNDS 122 for(n=0; n<numChannels; n++)//每次遍歷該像素點就將每個碼元的學習最大閾值變大，最小閾值變小，但是都是緩慢變化的 123 { //如果是新建立的碼元，則if條件肯定不滿足 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);//返回所找到碼本中碼元的索引 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 codebook 134 // 135 // p pixel pointer (YUV interleaved) 136 // c codebook reference 137 // numChannels Number of channels we are testing 138 // maxMod Add this (possibly negative) number onto max level when code_element determining if new pixel is foreground 139 // minMod Subract this (possible negative) number from min level code_element when determining if pixel is foreground 140 // 141 // NOTES: 142 // minMod and maxMod must have length numChannels, e.g. 3 channels => minMod[3], maxMod[3]. 143 // 144 // Return 145 // 0 => background, 255 => foreground 146 uchar cvbackgroundDiff(uchar *p, codeBook &c, int numChannels, int *minMod, int *maxMod) 147 { 148 int matchChannel; 149 //SEE IF THIS FITS AN EXISTING CODEWORD 150 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 channel 159 } 160 else 161 { 162 break;//加快速度，當一個通道不滿足時提前結束 163 } 164 } 165 if(matchChannel == numChannels) 166 { 167 break; //Found an entry that matched all channels，加快速度，當一個碼元找到時，提前結束 168 } 169 } 170 if(i >= c.numEntries) return(255);//255代表前景，因為所有的碼元都不滿足條件 171 return(0);//0代表背景，因為至少有一個碼元滿足條件 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 entries 179 // 180 //c Codebook to clean up 181 // 182 // Return 183 // number of entries cleared 184 int cvclearStaleEntries(codeBook &c)//對每一個碼本進行檢查 185 { 186 int staleThresh = c.t>>1;//閾值設置為訪問該碼元的次數的一半，經驗值 187 int *keep = new int [c.numEntries]; 188 int keepCnt = 0; 189 //SEE WHICH CODEBOOK ENTRIES ARE TOO STALE 190 for(int i=0; i<c.numEntries; i++) 191 { 192 if(c.cb[i]->stale > staleThresh)//當在背景建模期間有一半的時間內，codebook的碼元條目沒有被訪問，則該條目將被刪除 193 keep[i] = 0; //Mark for destruction 194 else 195 { 196 keep[i] = 1; //Mark to keep，為1時，該碼本的條目將被保留 197 keepCnt += 1;//keepCnt記錄了要保持的codebook的數目 198 } 199 } 200 //KEEP ONLY THE GOOD 201 c.t = 0; //Full reset on stale tracking 202 code_element **foo = new code_element* [keepCnt];//重新建立一個碼本的雙指針 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];//要保持該碼元的話就要把碼元結構體復制到fook 209 foo[k]->stale = 0; //We have to refresh these entries for next clearStale，不被訪問的累加器stale重新賦值0 210 foo[k]->t_last_update = 0;// 211 k++; 212 } 213 } 214 //CLEAN UP 215 delete [] keep; 216 delete [] c.cb; 217 c.cb = foo; 218 int numCleared = c.numEntries - keepCnt;//numCleared中保存的是被刪除碼元的個數 219 c.numEntries = keepCnt;//最后新的碼元數為保存下來碼元的個數 220 return(numCleared);//返回被刪除的碼元個數 221 } 222 223 / 224 //int countSegmentation(codeBook *c, IplImage *I) 225 // 226 //Count how many pixels are detected as foreground 227 // c Codebook 228 // I Image (yuv, 24 bits) 229 // numChannels Number of channels we are testing 230 // maxMod Add this (possibly negative) number onto max level when code_element determining if new pixel is foreground 231 // minMod Subract this (possible negative) number from min level code_element when determining if pixel is foreground 232 // 233 // NOTES: 234 // minMod and maxMod must have length numChannels, e.g. 3 channels => minMod[3], maxMod[3]. 235 // 236 //Return 237 // Count of fg pixels 238 // 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 Iraw 246 pColor = (uchar *)((I)->imageData); 247 for(i=0; i<imageLen; i++) 248 { 249 if(cvbackgroundDiff(pColor, c[i], numChannels, minMod, maxMod))//對每一個像素點都要檢測其是否為前景，如果是的話，計數器count就加1 250 count++; 251 pColor += 3; 252 } 253 return(count);//返回圖像I的前景像素點的個數 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 cvbackgroundDiff 260 // 261 // mask Is a grayscale (8 bit depth) "raw" mask image which will be cleaned up 262 // 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 MASK 275 //開運算作用：平滑輪廓，去掉細節,斷開缺口 276 cvMorphologyEx( mask, mask, NULL, NULL, CV_MOP_OPEN, CVCLOSE_ITR );//對輸入mask進行開操作，CVCLOSE_ITR為開操作的次數，輸出為mask圖像 277 //閉運算作用：平滑輪廓，連接缺口 278 cvMorphologyEx( mask, mask, NULL, NULL, CV_MOP_CLOSE, CVCLOSE_ITR );//對輸入mask進行閉操作，CVCLOSE_ITR為閉操作的次數，輸出為mask圖像 279 280 //FIND CONTOURS AROUND ONLY BIGGER REGIONS 281 if( mem_storage==NULL ) mem_storage = cvCreateMemStorage(0); 282 else cvClearMemStorage(mem_storage); 283 284 //CV_RETR_EXTERNAL=0是在types_c.h中定義的，CV_CHAIN_APPROX_SIMPLE=2也是在該文件中定義的 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 threshold 292 if( len < q ) //Get rid of blob if it's perimeter is too small 293 { 294 cvSubstituteContour( scanner, NULL ); 295 } 296 else //Smooth it's edges if it's large enough 297 { 298 CvSeq* c_new; 299 if(poly1_hull0) //Polygonal approximation of the segmentation 300 c_new = cvApproxPoly(c,sizeof(CvContour),mem_storage,CV_POLY_APPROX_DP, CVCONTOUR_APPROX_LEVEL,0); 301 else //Convex Hull of the segmentation 302 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 IMAGE 310 cvZero( mask ); 311 IplImage *maskTemp; 312 //CALC CENTER OF MASS AND OR BOUNDING RECTANGLES 313 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 them 322 { 323 cvDrawContours(maskTemp,c,CV_CVX_WHITE, CV_CVX_WHITE,-1,CV_FILLED,8); 324 //Find the center of each contour 325 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 blobs 335 if(bbs != NULL) 336 { 337 bbs[i] = cvBoundingRect(c); 338 } 339 cvZero(maskTemp); 340 numFilled++; 341 } 342 //Draw filled contours into mask 343 cvDrawContours(mask,c,CV_CVX_WHITE,CV_CVX_WHITE,-1,CV_FILLED,8); //draw to central mask 344 } //end looping over contours 345 *num = numFilled; 346 cvReleaseImage( &maskTemp); 347 } 348 //ELSE JUST DRAW PROCESSED CONTOURS INTO THE MASK 349 else 350 { 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種算法進行對比。

???? Learning Opencv的作者將這兩種算法做了下對比，用的視頻是有風吹動樹枝的動態背景，一段時間過后的前景是視頻中移動的手。

???? 當然在這個工程中，作者除了體現上述簡單背景差法和codobook算法的一些原理外，還引入了很多細節來優化前景分割效果。比如說誤差計算時的方差和協方差計算加速方法，消除像素點內長時間沒有被訪問過的碼元，對檢測到的粗糙原始前景圖用連通域分析法清楚噪聲，其中引入了形態學中的幾種操作，使用多邊形擬合前景輪廓等細節處理。

???? 在看作者代碼前，最好先看下下面幾個變量的物理含義。

???? maxMod[n]：用訓練好的背景模型進行前景檢測時用到，判斷點是否小于max[n] + maxMod[n])。

???? minMod[n]：用訓練好的背景模型進行前景檢測時用到，判斷點是否小于min[n] -minMod[n])。

???? cbBounds*：訓練背景模型時用到，可以手動輸入該參數，這個數主要是配合high[n]和low[n]來用的。

???? learnHigh[n]：背景學習過程中當一個新像素來時用來判斷是否在已有的碼元中，是閾值的上界部分。

???? learnLow[n]：背景學習過程中當一個新像素來時用來判斷是否在已有的碼元中，是閾值的下界部分。

???? max[n]： 背景學習過程中每個碼元學習到的最大值，在前景分割時配合maxMod[n]用的。

???? min[n]： 背景學習過程中每個碼元學習到的最小值，在前景分割時配合minMod[n]用的。

???? high[n]：背景學習過程中用來調整learnHigh[n]的，如果learnHigh[n]<high[n],則learnHigh[n]緩慢加1

???? low[n]： 背景學習過程中用來調整learnLow[n]的，如果learnLow[n]>Low[n],則learnLow[緩慢減1

?????該工程帶主函數部分代碼和注釋如下：

#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;//默認值為6 float scalelow = LOW_SCALE_NUM;//默認值為7 if(argc < 3) {//只有1個參數或者沒有參數時，輸出錯誤，并提示help信息，因為該程序本身就算進去了一個參數 printf("ERROR: Too few parameters\n");help();}else{//至少有2個參數才算正確 if(argc == 3){//輸入為2個參數的情形是從攝像頭輸入數據 printf("Capture from Camera\n");capture = cvCaptureFromCAM( 0 );}else {//輸入大于2個參數時是從文件中讀入視頻數據 printf("Capture from file %s\n",argv[3]);//第三個參數是讀入視頻文件的文件名// capture = cvCaptureFromFile( argv[3] ); capture = cvCreateFileCapture( argv[3] );if(!capture) { printf("Couldn't open %s\n",argv[3]); return -1;}//讀入視頻文件失敗 }if(isdigit(argv[1][0])) { //Start from of background capture startcapture = atoi(argv[1]);//第一個參數表示視頻開始的背景訓練時的幀，默認是1 printf("startcapture = %d\n",startcapture);}if(isdigit(argv[2][0])) { //End frame of background capture endcapture = atoi(argv[2]);//第二個參數表示的結束背景訓練時的，默認為30 printf("endcapture = %d\n"); }if(argc > 4){ //See if parameters are set from command line，輸入多于4個參數表示后面的算法中用到的參數在這里直接輸入//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 );//原始視頻圖像 cvNamedWindow( "AVG_ConnectComp",1);//平均法連通區域分析后的圖像 cvNamedWindow( "ForegroundCodeBook",1);//codebook法后圖像 cvNamedWindow( "CodeBook_ConnectComp",1);//codebook法連通區域分析后的圖像 cvNamedWindow( "ForegroundAVG",1);//平均法后圖像 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){//程序開始運行幾十幀后自動暫停，以便后面好手動調整參數 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);//為算法的使用分配內存 scaleHigh(scalehigh);//設定背景建模時的高閾值函數 scaleLow(scalelow);//設定背景建模時的低閾值函數 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 );//用來裝前景背景圖的，當然只要一個通道的圖像即可 ImaskCodeBookCC = cvCreateImage( cvGetSize(rawImage), IPL_DEPTH_8U, 1 );cvSet(ImaskCodeBook,cvScalar(255));imageLen = rawImage->width*rawImage->height;cB = new codeBook [imageLen];//創建一個碼本cB數組，每個像素對應一個碼本 for(int f = 0; f<imageLen; f++){cB[f].numEntries = 0;//每個碼本的初始碼元個數賦值為0 }for(int nc=0; nc<nChannels;nc++){cbBounds[nc] = 10; //Learning bounds factor，初始值為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);//平均法累加過程//LEARNING THE CODEBOOK BACKGROUND pColor = (uchar *)((yuvImage)->imageData);//yuvImage矩陣的首位置 for(int c=0; c<imageLen; c++){cvupdateCodeBook(pColor, cB[c], cbBounds, nChannels);//codebook算法建模過程 pColor += 3;}}//When done, create the background model if(i == endcapture){createModelsfromStats();//平均法建模過程 }//Find the foreground if any if(i >= endcapture) {//endcapture幀后開始檢測前景//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;//將前景檢測的結果返回到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 );//除了這張是彩色圖外，另外4張都是黑白圖 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 '-'://調整scalehigh的參數，scalehigh的物理意義是誤差累加的影響因子，其倒數為縮放倍數，加0.25實際上是減小其影響力 if(i > endcapture){scalehigh += 0.25;printf("AVG scalehigh=%f\n",scalehigh);scaleHigh(scalehigh);}break;case '='://scalehigh減少2.5是增加其影響力 if(i > endcapture){scalehigh -= 0.25;printf("AVG scalehigh=%f\n",scalehigh);scaleHigh(scalehigh);}break;case '[':if(i > endcapture){//設置設定背景建模時的低閾值函數，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動的閾值 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();//釋放平均法背景建模過程中用到的內存 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; }

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?? ? 運行結果截圖如下：

???? 訓練過程視頻原圖截圖：

?????

?

???? 測試過程視頻原圖截圖：

?????

?

???? 前景檢測過程截圖：

?????

?

???? 可以看到左邊2幅截圖的對比，codebook算法的效果明顯比簡單減圖法要好，手型比較清晰些。

?

?四、參考文獻

????? Bradski, G. and A. Kaehler (2008). Learning OpenCV: Computer vision with the OpenCV library, O'Reilly Media.

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作者：tornadomeet 出處：http://www.cnblogs.com/tornadomeet 歡迎轉載或分享，但請務必聲明文章出處。 （新浪微博：tornadomeet,歡迎交流！）

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