+++ /dev/null
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-//
-//
-// Intel License Agreement
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-// * Redistribution's of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// * Redistribution's in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-//
-// * The name of Intel Corporation may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-
-// This is based on the "An Improved Adaptive Background Mixture Model for
-// Real-time Tracking with Shadow Detection" by P. KaewTraKulPong and R. Bowden
-// http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
-//
-// The windowing method is used, but not the shadow detection. I make some of my
-// own modifications which make more sense. There are some errors in some of their
-// equations.
-//
-//IplImage values of image that are useful
-//int nSize; /* sizeof(IplImage) */
-//int depth; /* pixel depth in bits: IPL_DEPTH_8U ...*/
-//int nChannels; /* OpenCV functions support 1,2,3 or 4 channels */
-//int width; /* image width in pixels */
-//int height; /* image height in pixels */
-//int imageSize; /* image data size in bytes in case of interleaved data)*/
-//char *imageData; /* pointer to aligned image data */
-//char *imageDataOrigin; /* pointer to very origin of image -deallocation */
-//Values useful for gaussian integral
-//0.5 - 0.19146 - 0.38292
-//1.0 - 0.34134 - 0.68268
-//1.5 - 0.43319 - 0.86638
-//2.0 - 0.47725 - 0.95450
-//2.5 - 0.49379 - 0.98758
-//3.0 - 0.49865 - 0.99730
-//3.5 - 0.4997674 - 0.9995348
-//4.0 - 0.4999683 - 0.9999366
-
-#include "_cvaux.h"
-
-
-//internal functions for gaussian background detection
-static void icvInsertionSortGaussians( CvGaussBGPoint* g_point, double* sort_key, CvGaussBGStatModelParams *bg_model_params );
-
-/*
- Test whether pixel can be explained by background model;
- Return -1 if no match was found; otherwise the index in match[] is returned
-
- icvMatchTest(...) assumes what all color channels component exhibit the same variance
- icvMatchTest2(...) accounts for different variances per color channel
- */
-static int icvMatchTest( double* src_pixel, int nChannels, int* match,
- const CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params );
-/*static int icvMatchTest2( double* src_pixel, int nChannels, int* match,
- const CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params );*/
-
-
-/*
- The update procedure differs between
- * the initialization phase (named *Partial* ) and
- * the normal phase (named *Full* )
- The initalization phase is defined as not having processed <win_size> frames yet
- */
-static void icvUpdateFullWindow( double* src_pixel, int nChannels,
- int* match,
- CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params );
-static void icvUpdateFullNoMatch( IplImage* gm_image, int p,
- int* match,
- CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params);
-static void icvUpdatePartialWindow( double* src_pixel, int nChannels, int* match,
- CvGaussBGPoint* g_point, const CvGaussBGStatModelParams *bg_model_params );
-static void icvUpdatePartialNoMatch( double* src_pixel, int nChannels,
- int* match,
- CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params);
-
-
-static void icvGetSortKey( const int nChannels, double* sort_key, const CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params );
-static void icvBackgroundTest( const int nChannels, int n, int i, int j, int *match, CvGaussBGModel* bg_model );
-
-static void CV_CDECL icvReleaseGaussianBGModel( CvGaussBGModel** bg_model );
-static int CV_CDECL icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel* bg_model );
-
-//#define for if(0);else for
-
-//g = 1 for first gaussian in list that matches else g = 0
-//Rw is the learning rate for weight and Rg is leaning rate for mean and variance
-//Ms is the match_sum which is the sum of matches for a particular gaussian
-//Ms values are incremented until the sum of Ms values in the list equals window size L
-//SMs is the sum of match_sums for gaussians in the list
-//Rw = 1/SMs note the smallest Rw gets is 1/L
-//Rg = g/Ms for SMs < L and Rg = g/(w*L) for SMs = L
-//The list is maintained in sorted order using w/sqrt(variance) as a key
-//If there is no match the last gaussian in the list is replaced by the new gaussian
-//This will result in changes to SMs which results in changes in Rw and Rg.
-//If a gaussian is replaced and SMs previously equaled L values of Ms are computed from w
-//w[n+1] = w[n] + Rw*(g - w[n]) weight
-//u[n+1] = u[n] + Rg*(x[n+1] - u[n]) mean value Sg is sum n values of g
-//v[n+1] = v[n] + Rg*((x[n+1] - u[n])*(x[n+1] - u[n])) - v[n]) variance
-//
-
-CV_IMPL CvBGStatModel*
-cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parameters )
-{
- CvGaussBGModel* bg_model = 0;
-
- CV_FUNCNAME( "cvCreateGaussianBGModel" );
-
- __BEGIN__;
-
- double var_init;
- CvGaussBGStatModelParams params;
- int i, j, k, m, n;
-
- //init parameters
- if( parameters == NULL )
- { /* These constants are defined in cvaux/include/cvaux.h: */
- params.win_size = CV_BGFG_MOG_WINDOW_SIZE;
- params.bg_threshold = CV_BGFG_MOG_BACKGROUND_THRESHOLD;
-
- params.std_threshold = CV_BGFG_MOG_STD_THRESHOLD;
- params.weight_init = CV_BGFG_MOG_WEIGHT_INIT;
-
- params.variance_init = CV_BGFG_MOG_SIGMA_INIT*CV_BGFG_MOG_SIGMA_INIT;
- params.minArea = CV_BGFG_MOG_MINAREA;
- params.n_gauss = CV_BGFG_MOG_NGAUSSIANS;
- }
- else
- {
- params = *parameters;
- }
-
- if( !CV_IS_IMAGE(first_frame) )
- CV_ERROR( CV_StsBadArg, "Invalid or NULL first_frame parameter" );
-
- CV_CALL( bg_model = (CvGaussBGModel*)cvAlloc( sizeof(*bg_model) ));
- memset( bg_model, 0, sizeof(*bg_model) );
- bg_model->type = CV_BG_MODEL_MOG;
- bg_model->release = (CvReleaseBGStatModel)icvReleaseGaussianBGModel;
- bg_model->update = (CvUpdateBGStatModel)icvUpdateGaussianBGModel;
-
- bg_model->params = params;
-
- //prepare storages
- CV_CALL( bg_model->g_point = (CvGaussBGPoint*)cvAlloc(sizeof(CvGaussBGPoint)*
- ((first_frame->width*first_frame->height) + 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());
-
- //initializing
- var_init = 2 * params.std_threshold * params.std_threshold;
- CV_CALL( bg_model->g_point[0].g_values =
- (CvGaussBGValues*)cvAlloc( sizeof(CvGaussBGValues)*params.n_gauss*
- (first_frame->width*first_frame->height + 128)));
-
- for( i = 0, n = 0; i < first_frame->height; i++ )
- {
- for( j = 0; j < first_frame->width; j++, n++ )
- {
- const int p = i*first_frame->widthStep+j*first_frame->nChannels;
-
- bg_model->g_point[n].g_values =
- bg_model->g_point[0].g_values + n*params.n_gauss;
- bg_model->g_point[n].g_values[0].weight = 1; //the first value seen has weight one
- bg_model->g_point[n].g_values[0].match_sum = 1;
- for( m = 0; m < first_frame->nChannels; m++)
- {
- bg_model->g_point[n].g_values[0].variance[m] = var_init;
- bg_model->g_point[n].g_values[0].mean[m] = (unsigned char)first_frame->imageData[p + m];
- }
- for( k = 1; k < params.n_gauss; k++)
- {
- bg_model->g_point[n].g_values[k].weight = 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;
- bg_model->g_point[n].g_values[k].mean[m] = 0;
- }
- }
- }
- }
-
- 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;
-}
-
-
-static void CV_CDECL
-icvReleaseGaussianBGModel( CvGaussBGModel** _bg_model )
-{
- CV_FUNCNAME( "icvReleaseGaussianBGModel" );
-
- __BEGIN__;
-
- if( !_bg_model )
- CV_ERROR( CV_StsNullPtr, "" );
-
- if( *_bg_model )
- {
- CvGaussBGModel* bg_model = *_bg_model;
- if( bg_model->g_point )
- {
- cvFree( &bg_model->g_point[0].g_values );
- cvFree( &bg_model->g_point );
- }
-
- cvReleaseImage( &bg_model->background );
- cvReleaseImage( &bg_model->foreground );
- cvReleaseMemStorage(&bg_model->storage);
- memset( bg_model, 0, sizeof(*bg_model) );
- cvFree( _bg_model );
- }
-
- __END__;
-}
-
-
-static int CV_CDECL
-icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel* bg_model )
-{
- int i, j, k, n;
- int region_count = 0;
- CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
-
- bg_model->countFrames++;
-
- for( i = 0, n = 0; i < curr_frame->height; i++ )
- {
- for( j = 0; j < curr_frame->width; j++, n++ )
- {
- int match[CV_BGFG_MOG_MAX_NGAUSSIANS];
- double sort_key[CV_BGFG_MOG_MAX_NGAUSSIANS];
- const int nChannels = curr_frame->nChannels;
- const int p = curr_frame->widthStep*i+j*nChannels;
-
- // 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++ )
- pixel[k] = (uchar)curr_frame->imageData[p+k];
-
- no_match = icvMatchTest( pixel, nChannels, match, g_point, &bg_model_params );
- if( bg_model->countFrames >= bg_model->params.win_size )
- {
- 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, i, j, 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;
-}
-
-static void icvInsertionSortGaussians( CvGaussBGPoint* g_point, double* sort_key, CvGaussBGStatModelParams *bg_model_params )
-{
- int i, j;
- for( i = 1; i < bg_model_params->n_gauss; i++ )
- {
- double index = sort_key[i];
- for( j = i; j > 0 && sort_key[j-1] < index; j-- ) //sort decending order
- {
- double temp_sort_key = sort_key[j];
- sort_key[j] = sort_key[j-1];
- sort_key[j-1] = temp_sort_key;
-
- CvGaussBGValues temp_gauss_values = g_point->g_values[j];
- g_point->g_values[j] = g_point->g_values[j-1];
- g_point->g_values[j-1] = temp_gauss_values;
- }
-// sort_key[j] = index;
- }
-}
-
-
-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;
-
- 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++){
- 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 = bg_model_params->std_threshold*bg_model_params->std_threshold*var_threshold;
- if(sum_d2 < var_threshold){
- match[k] = 1;
- matchPosition = k;
- break;
- }
- }
-
- return matchPosition;
-}
-
-/*
-static int icvMatchTest2( double* src_pixel, int nChannels, int* match,
- const CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params )
-{
- int k, m;
- int matchPosition=-1;
-
- for( k = 0; k < bg_model_params->n_gauss; k++ )
- match[k] = 0;
-
- for( k = 0; k < bg_model_params->n_gauss; k++ )
- {
- double sum_d2 = 0.0, var_threshold;
- for( m = 0; m < nChannels; m++ )
- {
- double d = g_point->g_values[k].mean[m]- src_pixel[m];
- sum_d2 += (d*d) / (g_point->g_values[k].variance[m] * g_point->g_values[k].variance[m]);
- } //difference < STD_LIMIT*STD_LIMIT or difference**2 < STD_LIMIT*STD_LIMIT*VAR
-
- var_threshold = bg_model_params->std_threshold*bg_model_params->std_threshold;
- if( sum_d2 < var_threshold )
- {
- match[k] = 1;
- matchPosition = k;
- break;
- }
- }
-
- return matchPosition;
-}
-*/
-
-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);
- for(int k = 0; k < bg_model_params->n_gauss; k++){
- 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]){
- 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];
- 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]));
- }
- }
- }
-}
-
-
-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]));
- }
- }
- }
-}
-
-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;
-
- //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;
-
- g_point->g_values[bg_model_params->n_gauss - 1].weight = 1./(double)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;
- g_point->g_values[bg_model_params->n_gauss - 1].mean[m] = (unsigned char)gm_image->imageData[p + m];
- }
-
- alpha = 1.0 - (1.0/bg_model_params->win_size);
- for( k = 0; k < bg_model_params->n_gauss - 1; k++ )
- {
- g_point->g_values[k].weight *= alpha;
- if( match[k] )
- g_point->g_values[k].weight += alpha;
- }
-}
-
-
-static void
-icvUpdatePartialNoMatch(double *pixel,
- int nChannels,
- int* /*match*/,
- CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params)
-{
- int k, m;
- //new value of last one
- g_point->g_values[bg_model_params->n_gauss - 1].match_sum = 1;
-
- //get sum of all but last value of match_sum
- int match_sum_total = 0;
- for(k = 0; k < bg_model_params->n_gauss ; k++)
- match_sum_total += g_point->g_values[k].match_sum;
-
- for(m = 0; m < 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;
- g_point->g_values[bg_model_params->n_gauss - 1].mean[m] = pixel[m];
- }
- for(k = 0; k < bg_model_params->n_gauss; k++)
- {
- g_point->g_values[k].weight = (double)g_point->g_values[k].match_sum /
- (double)match_sum_total;
- }
-}
-
-static void icvGetSortKey( const int nChannels, double* sort_key, const CvGaussBGPoint* g_point,
- const CvGaussBGStatModelParams *bg_model_params )
-{
- int k, m;
- for( k = 0; k < bg_model_params->n_gauss; k++ )
- {
- // Avoid division by zero
- if( g_point->g_values[k].match_sum > 0 )
- {
- // Independence assumption between components
- double variance_sum = 0.0;
- for( m = 0; m < nChannels; m++ )
- variance_sum += g_point->g_values[k].variance[m];
-
- sort_key[k] = g_point->g_values[k].weight/sqrt(variance_sum);
- }
- else
- sort_key[k]= 0.0;
- }
-}
-
-
-static void icvBackgroundTest( const int nChannels, int n, int i, int j, int *match, CvGaussBGModel* bg_model )
-{
- int m, b;
- uchar pixelValue = (uchar)255; // will switch to 0 if match found
- double weight_sum = 0.0;
- CvGaussBGPoint* g_point = bg_model->g_point;
-
- for( m = 0; m < nChannels; m++)
- bg_model->background->imageData[ bg_model->background->widthStep*i + j*nChannels + m] = (unsigned char)(g_point[n].g_values[0].mean[m]+0.5);
-
- for( b = 0; b < bg_model->params.n_gauss; b++)
- {
- weight_sum += g_point[n].g_values[b].weight;
- if( match[b] )
- pixelValue = 0;
- if( weight_sum > bg_model->params.bg_threshold )
- break;
- }
-
- bg_model->foreground->imageData[ bg_model->foreground->widthStep*i + j] = pixelValue;
-}
-
-/* End of file. */