OpenCV混合高斯模型函数说明.doc

OpenCV混合高斯模型函数注释说明一、 cvaux.h#define CV_BGFG_MOG_MAX_NGAUSSIANS 500/高斯背景检测算法的默认参数设置#define CV_BGFG_MOG_BACKGROUND_THRESHOLD 0.7 /高斯分布权重之和阈值#define CV_BGFG_MOG_STD_THRESHOLD 2.5 /=2.5（99%）#define CV_BGFG_MOG_WINDOW_SIZE 200 /学习率=1/win_size#define CV_BGFG_MOG_NGAUSSIANS 5 /k=5个混合高斯模型#define CV_BGFG_MOG_WEIGHT_INIT 0.05 /初始权重#define CV_BGFG_MOG_SIGMA_INIT 30 /初始标准差#define CV_BGFG_MOG_MINAREA 15.f /？#define CV_BGFG_MOG_NCOLORS 3 /颜色通道数/* CV_BG_STAT_MODEL_FIELDS()的宏定义*/ #define CV_BG_STAT_MODEL_FIELDS() int type; /type of BG model CvReleaseBGStatModel release; / CvUpdateBGStatModel update; IplImage* background; /*8UC3 reference background image*/ IplImage* foreground; /*8UC1 foreground image*/ IplImage* layers; /*8UC3 reference background image, can be null */ 8UC3参考背景图片，可以是空的 int layer_count; /* can be zero */ CvMemStorage* storage; /*storage for foreground_regions?/ 储存前景区域 CvSeq* foreground_regions /*foreground object contours*/前景物体轮廓/*高斯背景模型参数结构体*/typedef struct CvGaussBGStatModelParams int win_size; /等于 1/alpha int n_gauss; /高斯模型的个数 double bg_threshold, std_threshold, minArea;/ bg_threshold：高斯分布权重之和阈值、std_threshold：2.5、minArea：？ double weight_init, variance_init;/权重和方差CvGaussBGStatModelParams;/*高斯分布模型结构体*/typedef struct CvGaussBGValues int match_sum; double weight; double varianceCV_BGFG_MOG_NCOLORS; double meanCV_BGFG_MOG_NCOLORS;CvGaussBGValues;typedef struct CvGaussBGPoint CvGaussBGValues* g_values;CvGaussBGPoint;/*高斯背景模型结构体*/typedef struct CvGaussBGModel CV_BG_STAT_MODEL_FIELDS(); CvGaussBGStatModelParams params; CvGaussBGPoint* g_point; int countFrames;CvGaussBGModel;二、 cvbgfg_gaussmix.cpp/ cvCreateGaussianBGModel/功能：高斯背景模型变量bg_model初始化赋值CV_IMPL CvBGStatModel* cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parameters) CvGaussBGModel* bg_model = 0;/高斯背景状态模型变量 CV_FUNCNAME( cvCreateGaussianBGModel ); _BEGIN_; double var_init; CvGaussBGStatModelParams params;/高斯背景状态模型参数变量 int i, j, k, n, m, p; /初始化参数，如果参数为空，则进行初始化赋值 if( parameters = NULL ) params.win_size = CV_BGFG_MOG_WINDOW_SIZE;/学习率=1/200=0.005 params.bg_threshold = CV_BGFG_MOG_BACKGROUND_THRESHOLD;/判断是否为背景点的阈值0.7 params.std_threshold = CV_BGFG_MOG_STD_THRESHOLD;/标准阈值2.5 params.weight_init = CV_BGFG_MOG_WEIGHT_INIT;/权重值0.05 params.variance_init = CV_BGFG_MOG_SIGMA_INIT*CV_BGFG_MOG_SIGMA_INIT; /方差30*30 params.minArea = CV_BGFG_MOG_MINAREA;/？ params.n_gauss = CV_BGFG_MOG_NGAUSSIANS;/高斯模型个数 else params = *parameters; if( !CV_IS_IMAGE(first_frame) )/如果第一帧不是图像，则报错 CV_ERROR( CV_StsBadArg, Invalid or NULL first_frame parameter ); CV_CALL( bg_model = (CvGaussBGModel*)cvAlloc( sizeof(*bg_model) );/申请内存空间 memset( bg_model, 0, sizeof(*bg_model) ); bg_model-type = CV_BG_MODEL_MOG;/ CV_BG_MODEL_MOG高斯背景模型 bg_model-release = (CvReleaseBGStatModel)icvReleaseGaussianBGModel;/释放内存的函数指针 bg_model-update = (CvUpdateBGStatModel)icvUpdateGaussianBGModel;/更新高斯模型的函数指针 bg_model-params = params; /申请内存空间 CV_CALL( bg_model-g_point = (CvGaussBGPoint*)cvAlloc(sizeof(CvGaussBGPoint)* (first_frame-width*first_frame-height) + 256);/256? CV_CALL( bg_model-background = cvCreateImage(cvSize(first_frame-width, first_frame-height), IPL_DEPTH_8U, first_frame-nChannels); CV_CALL( bg_model-foreground = cvCreateImage(cvSize(first_frame-width, first_frame-height), IPL_DEPTH_8U, 1); CV_CALL( bg_model-storage = cvCreateMemStorage(); /初始化 var_init = 2 * params.std_threshold * params.std_threshold;/初始化方差 CV_CALL( bg_model-g_point0.g_values = (CvGaussBGValues*)cvAlloc( sizeof(CvGaussBGValues)*params.n_gauss* (first_frame-width*first_frame-height + 128);/128?/n：表示像素点的索引值/p：表示当前像素对应颜色通道的首地址/ g_point：对应像素点、g_values：对应高斯模型、variance和 mean：对应颜色通道 for( i = 0, p = 0, n = 0; i height; i+ )/行 for( j = 0; j width; j+, n+ )/列 bg_model-g_pointn.g_values = bg_model-g_point0.g_values + n*params.n_gauss;/每个像素点的第一个高斯模型的地址（每个像素对应n_gauss个高斯分布模型） /初始化第一个高斯分布模型的参数 bg_model-g_pointn.g_values0.weight = 1; /取较大权重，此处设置为1 bg_model-g_pointn.g_values0.match_sum = 1;/高斯函数被匹配的次数（？） for( m = 0; m nChannels; m+) /对各颜色通道的方差和均值赋值 bg_model-g_pointn.g_values0.variancem = var_init;/初始化较大的方差 bg_model-g_pointn.g_values0.meanm = (unsigned char)first_frame-imageDatap + m;/赋值为当前像素值 /初始化剩下的高斯分布模型的参数 for( k = 1; k g_pointn.g_valuesk.weight = 0;/各高斯分布取相等且较小权重值，此处取0 bg_model-g_pointn.g_valuesk.match_sum = 0; for( m = 0; m nChannels; m+) bg_model-g_pointn.g_valuesk.variancem = var_init; /初始化较大的方差 bg_model-g_pointn.g_valuesk.meanm = 0; /赋值0 p += first_frame-nChannels; bg_model-countFrames = 0; _END_; if( cvGetErrStatus() release ) bg_model-release( &base_ptr ); else cvFree( &bg_model ); bg_model = 0; return (CvBGStatModel*)bg_model;/ icvUpdateGaussianBGModel /功能：对高斯背景模型变量bg_model进行更新static int CV_CDECL icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel* bg_model ) int i, j, k; int region_count = 0; CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL; bg_model-countFrames+; for( i = 0; i height; i+ )/行 for( j = 0; j width; j+ )/列 int matchCV_BGFG_MOG_MAX_NGAUSSIANS; double sort_keyCV_BGFG_MOG_MAX_NGAUSSIANS; const int nChannels = curr_frame-nChannels;/通道数目 const int n = i*curr_frame-width+j;/像素索引值 const int p = n*curr_frame-nChannels;/像素点颜色通道的首地址 / A few short cuts CvGaussBGPoint* g_point = &bg_model-g_pointn; const CvGaussBGStatModelParams bg_model_params = bg_model-params; double pixel4; int no_match; for( k = 0; k imageDatap+k; no_match = icvMatchTest( pixel, nChannels, match, g_point, &bg_model_params ); /判断高斯背景模型更新帧数是否达到设置值win_size（？）（初始更新阶段和一般更新阶段在更新处理过程中是不同的，其中定义初始更新阶段为帧数小于win_size） if( bg_model-countFrames = bg_model-params.win_size )/一般更新阶段 icvUpdateFullWindow( pixel, nChannels, match, g_point, &bg_model-params ); if( no_match = -1) icvUpdateFullNoMatch( curr_frame, p, match, g_point, &bg_model_params ); else icvUpdatePartialWindow( pixel, nChannels, match, g_point, &bg_model_params ); if( no_match = -1) icvUpdatePartialNoMatch( pixel, nChannels, match, g_point, &bg_model_params ); icvGetSortKey( nChannels, sort_key, g_point, &bg_model_params ); icvInsertionSortGaussians( g_point, sort_key, (CvGaussBGStatModelParams *)&bg_model_params ); icvBackgroundTest( nChannels, n, p, match, bg_model ); /foreground filtering /filter small regions cvClearMemStorage(bg_model-storage); /cvMorphologyEx( bg_model-foreground, bg_model-foreground, 0, 0, CV_MOP_OPEN, 1 ); /cvMorphologyEx( bg_model-foreground, bg_model-foreground, 0, 0, CV_MOP_CLOSE, 1 ); cvFindContours( bg_model-foreground, bg_model-storage, &first_seq, sizeof(CvContour), CV_RETR_LIST ); for( seq = first_seq; seq; seq = seq-h_next ) CvContour* cnt = (CvContour*)seq; if( cnt-rect.width * cnt-rect.height params.minArea ) /delete small contour prev_seq = seq-h_prev; if( prev_seq ) prev_seq-h_next = seq-h_next; if( seq-h_next ) seq-h_next-h_prev = prev_seq; else first_seq = seq-h_next; if( seq-h_next ) seq-h_next-h_prev = NULL; else region_count+; bg_model-foreground_regions = first_seq; cvZero(bg_model-foreground); cvDrawContours(bg_model-foreground, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1); return region_count;/ icvMatchTest /功能：将当前像素与个高斯分布进行匹配判断，如果匹配成功，则返回相应高斯分布的索引值static int icvMatchTest( double* src_pixel, int nChannels, int* match, const CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params ) int k; int matchPosition=-1; for ( k = 0; k n_gauss; k+) matchk=0;/高斯分布匹配标识数组初始化置0 for ( k = 0; k n_gauss; k+) double sum_d2 = 0.0; double var_threshold = 0.0; for(int m = 0; m g_valuesk.meanm- src_pixelm; sum_d2 += (d*d); var_threshold += g_point-g_valuesk.variancem; /difference STD_LIMIT*STD_LIMIT or difference*2 std_threshold*bg_model_params-std_threshold*var_threshold;/匹配方程为：或者 if(sum_d2 win_size);/学习率for(int k = 0; k n_gauss; k+) /若matchk=0，则权重的更新公式： /若matchk=0，则权重的更新公式： g_point-g_valuesk.weight = g_point-g_valuesk.weight + (learning_rate_weight*(double)matchk -g_point-g_valuesk.weight); if(matchk)/更新匹配的高斯分布的参数 /参数学习率double learning_rate_gaussian = (double)matchk/(g_point-g_valuesk.weight*(double)bg_model_params-win_size); for(int m = 0; m g_valuesk.meanm; /均值更新公式为： g_point-g_valuesk.meanm = g_point-g_valuesk.meanm +(learning_rate_gaussian * tmpDiff); /方差更新公式为： g_point-g_valuesk.variancem = g_point-g_valuesk.variancem+ (learning_rate_gaussian*(tmpDiff*tmpDiff) - g_point-g_valuesk.variancem); / icvUpdateFullNoMatch /功能：当前像素点与所有高斯分布都不匹配时，需要将比值最小的高斯分布替换为新的高斯分布（权值小、方差大），其余的高斯分布保持原来的均值和方差，但权值需要减小。static void icvUpdateFullNoMatch( IplImage* gm_image, int p, int* match, CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params) int k, m; double alpha; int match_sum_total = 0; /new value of last one g_point-g_valuesbg_model_params-n_gauss - 1.match_sum = 1;/将新的高斯分布的match_sum置为1 /get sum of all but last value of match_sum for( k = 0; k n_gauss ; k+ ) match_sum_total += g_point-g_valuesk.match_sum; /设置新的高斯分布的参数 g_point-g_valuesbg_model_params-n_gauss - 1.weight = 1./(double)match_sum_total; /给新的高斯分布设置一个较小的权值，即1.0/ match_sum_total for( m = 0; m nChannels ; m+ ) / first pass mean is image value g_point-g_valuesbg_model_params-n_gauss - 1.variancem = bg_model_params-variance_init;/初始化一个较大的方差 g_point-g_valuesbg_model_params-n_gauss - 1.meanm = (unsigned char)gm_image-imageDatap + m; /将当前像素值作为均值 /更新其余高斯分布的参数 alpha = 1.0 - (1.0/bg_model_params-win_size); for( k = 0; k n_gauss - 1; k+ ) /更新权值的公式为： g_point-g_valuesk.weight *= alpha; if( matchk )/对于匹配的高斯分布，权值更新公式为 g_point-g_valuesk