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//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. */