OpenCV混合高斯模型函数注释说明
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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 //λ=2.5（99%）#define CV_BGFG_MOG_WINDOW_SIZE 200 //学习率α=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 */ \ int layer_count; /* can be zero */ \ CvMemStorage* storage; /*storage for foreground_regions?/ \ CvSeq* foreground_regions /*foreground object contours*//*************************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 variance[CV_BGFG_MOG_NCOLORS]; double mean[CV_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α=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_point[0].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 < first_frame->height; i++ )//行 { for( j = 0; j < first_frame->width; j++, n++ )//列 { bg_model->g_point[n].g_values = bg_model->g_point[0].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_point[n].g_values[0].weight = 1; //取较大权重，此处设置为1 bg_model->g_point[n].g_values[0].match_sum = 1;//高斯函数被匹配的次数（？？？） for( m = 0; m < first_frame->nChannels; m++) //对各颜色通道的方差和均值赋值 { bg_model->g_point[n].g_values[0].variance[m] = var_init;//初始化较大的方差 bg_model->g_point[n].g_values[0].mean[m] = (unsigned char)first_frame->imageData[p + 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 < params.n_gauss; k++) { bg_model->g_point[n].g_values[k].weight = 0;//各高斯分布取相等且较小权重值，此处取0 bg_model->g_point[n].g_values[k].match_sum = 0; for( m = 0; m < first_frame->nChannels; m++){ bg_model->g_point[n].g_values[k].variance[m] = var_init; //初始化较大的方差 bg_model->g_point[n].g_values[k].mean[m] = 0; //赋值0 } } p += first_frame->nChannels; } } bg_model->countFrames = 0; __END__; if( cvGetErrStatus() < 0 ) { CvBGStatModel* base_ptr = (CvBGStatModel*)bg_model; if( bg_model && bg_model->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 < curr_frame->height; i++ )//行 { for( j = 0; j < curr_frame->width; j++ )//列 { int match[CV_BGFG_MOG_MAX_NGAUSSIANS]; double sort_key[CV_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_point[n]; const CvGaussBGStatModelParams bg_model_params = bg_model->params; double pixel[4]; int no_match; for( k = 0; k < nChannels; k++ )//拷贝各通道颜色分量值 pixel[k] = (uchar)curr_frame->imageData[p+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 < bg_model->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 < bg_model_params->n_gauss; k++) match[k]=0;//高斯分布匹配标识数组初始化置0 for ( k = 0; k < bg_model_params->n_gauss; k++){ double sum_d2 = 0.0; double var_threshold = 0.0; for(int m = 0; m < nChannels; m++)//计算当前高斯分布各通道均值与像素点各通道值相减{ double d = g_point->g_values[k].mean[m]- src_pixel[m]; sum_d2 += (d*d); var_threshold += g_point->g_values[k].variance[m]; } //difference < STD_LIMIT*STD_LIMIT or difference**2 < STD_LIMIT*STD_LIMIT*VAR var_threshold = _model_params->std_threshold*bg_model_params->std_threshold*var_threshold;//匹配方程为：或者 if(sum_d2 < var_threshold){ match[k] = 1;//匹配时标识置1 matchPosition = k;//存储匹配的高斯分布索引值 break;//一旦匹配，就终止与后续高斯分布的匹配 } } return matchPosition;//返回匹配上的高斯分布索引值}//////////////////////////////////////////////////// icvUpdateFullWindow ////////////////////////////////////////////////////////////功能：更新各高斯分布的权重值（对于匹配上的高斯分布要增大权值，其余的减小权值），如果存在匹配上的高斯分布，还要更新其均值和方差。static void icvUpdateFullWindow( double* src_pixel, int nChannels, int* match, CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params ){ const double learning_rate_weight = (1.0/(double)bg_model_params->win_size);//学习率αfor(int k = 0; k < bg_model_params->n_gauss; k++){ //若match[k]=0，则权重ω的更新公式： //若match[k]=0，则权重ω的更新公式： g_point->g_values[k].weight = g_point->g_values[k].weight + (learning_rate_weight*((double)match[k] -g_point->g_values[k].weight)); if(match[k])//更新匹配的高斯分布的参数{ //参数学习率double learning_rate_gaussian = (double)match[k]/(g_point->g_values[k].weight*(double)bg_model_params->win_size); for(int m = 0; m < nChannels; m++){ const double tmpDiff = src_pixel[m] - g_point->g_values[k].mean[m]; //均值μ更新公式为： g_point->g_values[k].mean[m] = g_point->g_values[k].mean[m] +(learning_rate_gaussian * tmpDiff); //方差更新公式为： g_point->g_values[k].variance[m] = g_point->g_values[k].variance[m]+ (learning_rate_gaussian*((tmpDiff*tmpDiff) - g_point->g_values[k].variance[m])); } } }}//////////////////////////////////////////////////// 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_values[bg_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 < bg_model_params->n_gauss ; k++ ) match_sum_total += g_point->g_values[k].match_sum; //设置新的高斯分布的参数 g_point->g_values[bg_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 < gm_image->nChannels ; m++ ) { // first pass mean is image value g_point->g_values[bg_model_params->n_gauss - 1].variance[m] = bg_model_params->variance_init;//初始化一个较大的方差 g_point->g_values[bg_model_params->n_gauss - 1].mean[m] = (unsigned char)gm_image->imageData[p + 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 < bg_model_params->n_gauss - 1; k++ ){ //更新权值的公式为： g_point->g_values[k].weight *= alpha; if( match[k] )//对于匹配的高斯分布，权值更新公式为 g_point->g_values[k].weight += alpha; }}//////////////////////////////////////////////////// icvUpdatePartialWindow ////////////////////////////////////////////////////////////功能：更新各高斯分布的权重值（对于匹配上的高斯分布要增大权值，其余的减小权值），如果存在匹配上的高斯分布，还要更新其均值和方差。static void icvUpdatePartialWindow( double* src_pixel, int nChannels, int* match, CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params ){ int k, m; int window_current = 0; for( k = 0; k < bg_model_params->n_gauss; k++ ) window_current += g_point->g_values[k].match_sum; for( k = 0; k < bg_model_params->n_gauss; k++ ) { g_point->g_values[k].match_sum += match[k]; double learning_rate_weight = (1.0/((double)window_current + 1.0)); //increased by one since sum g_point->g_values[k].weight = g_point->g_values[k].weight + (learning_rate_weight*((double)match[k] - g_point->g_values[k].weight)); if( g_point->g_values[k].match_sum > 0 && match[k] ) { double learning_rate_gaussian = (double)match[k]/((double)g_point->g_values[k].match_sum); for( m = 0; m < nChannels; m++ ) { const double tmpDiff = src_pixel[m] - g_point->g_values[k].mean[m]; g_point->g_values[k].mean[m] = g_point->g_values[k].mean[m] + (learning_rate_gaussian*tmpDiff); g_point->g_values[k].variance[m] = g_point->g_values[k].variance[m]+ (learning_rate_gaussian*((tmpDiff*tmpDiff) - g_point->g_values[k].variance[m])); } } }} 1 0
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