+++ /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
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-// 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 file implements the foreground/background pixel
-// discrimination algorithm described in
-//
-// Foreground Object Detection from Videos Containing Complex Background
-// Li, Huan, Gu, Tian 2003 9p
-// http://muq.org/~cynbe/bib/foreground-object-detection-from-videos-containing-complex-background.pdf
-
-
-#include "_cvaux.h"
-
-#include <math.h>
-#include <stdio.h>
-#include <stdlib.h>
-//#include <algorithm>
-
-static double* _cv_max_element( double* start, double* end )
-{
- double* p = start++;
-
- for( ; start != end; ++start) {
-
- if (*p < *start) p = start;
- }
-
- return p;
-}
-
-static void CV_CDECL icvReleaseFGDStatModel( CvFGDStatModel** model );
-static int CV_CDECL icvUpdateFGDStatModel( IplImage* curr_frame,
- CvFGDStatModel* model );
-
-// Function cvCreateFGDStatModel initializes foreground detection process
-// parameters:
-// first_frame - frame from video sequence
-// parameters - (optional) if NULL default parameters of the algorithm will be used
-// p_model - pointer to CvFGDStatModel structure
-CV_IMPL CvBGStatModel*
-cvCreateFGDStatModel( IplImage* first_frame, CvFGDStatModelParams* parameters )
-{
- CvFGDStatModel* p_model = 0;
-
- CV_FUNCNAME( "cvCreateFGDStatModel" );
-
- __BEGIN__;
-
- int i, j, k, pixel_count, buf_size;
- CvFGDStatModelParams params;
-
- if( !CV_IS_IMAGE(first_frame) )
- CV_ERROR( CV_StsBadArg, "Invalid or NULL first_frame parameter" );
-
- if (first_frame->nChannels != 3)
- CV_ERROR( CV_StsBadArg, "first_frame must have 3 color channels" );
-
- // Initialize parameters:
- if( parameters == NULL )
- {
- params.Lc = CV_BGFG_FGD_LC;
- params.N1c = CV_BGFG_FGD_N1C;
- params.N2c = CV_BGFG_FGD_N2C;
-
- params.Lcc = CV_BGFG_FGD_LCC;
- params.N1cc = CV_BGFG_FGD_N1CC;
- params.N2cc = CV_BGFG_FGD_N2CC;
-
- params.delta = CV_BGFG_FGD_DELTA;
-
- params.alpha1 = CV_BGFG_FGD_ALPHA_1;
- params.alpha2 = CV_BGFG_FGD_ALPHA_2;
- params.alpha3 = CV_BGFG_FGD_ALPHA_3;
-
- params.T = CV_BGFG_FGD_T;
- params.minArea = CV_BGFG_FGD_MINAREA;
-
- params.is_obj_without_holes = 1;
- params.perform_morphing = 1;
- }
- else
- {
- params = *parameters;
- }
-
- CV_CALL( p_model = (CvFGDStatModel*)cvAlloc( sizeof(*p_model) ));
- memset( p_model, 0, sizeof(*p_model) );
- p_model->type = CV_BG_MODEL_FGD;
- p_model->release = (CvReleaseBGStatModel)icvReleaseFGDStatModel;
- p_model->update = (CvUpdateBGStatModel)icvUpdateFGDStatModel;;
- p_model->params = params;
-
- // Initialize storage pools:
- pixel_count = first_frame->width * first_frame->height;
-
- buf_size = pixel_count*sizeof(p_model->pixel_stat[0]);
- CV_CALL( p_model->pixel_stat = (CvBGPixelStat*)cvAlloc(buf_size) );
- memset( p_model->pixel_stat, 0, buf_size );
-
- buf_size = pixel_count*params.N2c*sizeof(p_model->pixel_stat[0].ctable[0]);
- CV_CALL( p_model->pixel_stat[0].ctable = (CvBGPixelCStatTable*)cvAlloc(buf_size) );
- memset( p_model->pixel_stat[0].ctable, 0, buf_size );
-
- buf_size = pixel_count*params.N2cc*sizeof(p_model->pixel_stat[0].cctable[0]);
- CV_CALL( p_model->pixel_stat[0].cctable = (CvBGPixelCCStatTable*)cvAlloc(buf_size) );
- memset( p_model->pixel_stat[0].cctable, 0, buf_size );
-
- for( i = 0, k = 0; i < first_frame->height; i++ ) {
- for( j = 0; j < first_frame->width; j++, k++ )
- {
- p_model->pixel_stat[k].ctable = p_model->pixel_stat[0].ctable + k*params.N2c;
- p_model->pixel_stat[k].cctable = p_model->pixel_stat[0].cctable + k*params.N2cc;
- }
- }
-
- // Init temporary images:
- CV_CALL( p_model->Ftd = cvCreateImage(cvSize(first_frame->width, first_frame->height), IPL_DEPTH_8U, 1));
- CV_CALL( p_model->Fbd = cvCreateImage(cvSize(first_frame->width, first_frame->height), IPL_DEPTH_8U, 1));
- CV_CALL( p_model->foreground = cvCreateImage(cvSize(first_frame->width, first_frame->height), IPL_DEPTH_8U, 1));
-
- CV_CALL( p_model->background = cvCloneImage(first_frame));
- CV_CALL( p_model->prev_frame = cvCloneImage(first_frame));
- CV_CALL( p_model->storage = cvCreateMemStorage());
-
- __END__;
-
- if( cvGetErrStatus() < 0 )
- {
- CvBGStatModel* base_ptr = (CvBGStatModel*)p_model;
-
- if( p_model && p_model->release )
- p_model->release( &base_ptr );
- else
- cvFree( &p_model );
- p_model = 0;
- }
-
- return (CvBGStatModel*)p_model;
-}
-
-
-static void CV_CDECL
-icvReleaseFGDStatModel( CvFGDStatModel** _model )
-{
- CV_FUNCNAME( "icvReleaseFGDStatModel" );
-
- __BEGIN__;
-
- if( !_model )
- CV_ERROR( CV_StsNullPtr, "" );
-
- if( *_model )
- {
- CvFGDStatModel* model = *_model;
- if( model->pixel_stat )
- {
- cvFree( &model->pixel_stat[0].ctable );
- cvFree( &model->pixel_stat[0].cctable );
- cvFree( &model->pixel_stat );
- }
-
- cvReleaseImage( &model->Ftd );
- cvReleaseImage( &model->Fbd );
- cvReleaseImage( &model->foreground );
- cvReleaseImage( &model->background );
- cvReleaseImage( &model->prev_frame );
- cvReleaseMemStorage(&model->storage);
-
- cvFree( _model );
- }
-
- __END__;
-}
-
-// Function cvChangeDetection performs change detection for Foreground detection algorithm
-// parameters:
-// prev_frame -
-// curr_frame -
-// change_mask -
-CV_IMPL int
-cvChangeDetection( IplImage* prev_frame,
- IplImage* curr_frame,
- IplImage* change_mask )
-{
- int i, j, b, x, y, thres;
- const int PIXELRANGE=256;
-
- if( !prev_frame
- || !curr_frame
- || !change_mask
- || prev_frame->nChannels != 3
- || curr_frame->nChannels != 3
- || change_mask->nChannels != 1
- || prev_frame->depth != IPL_DEPTH_8U
- || curr_frame->depth != IPL_DEPTH_8U
- || change_mask->depth != IPL_DEPTH_8U
- || prev_frame->width != curr_frame->width
- || prev_frame->height != curr_frame->height
- || prev_frame->width != change_mask->width
- || prev_frame->height != change_mask->height
- ){
- return 0;
- }
-
- cvZero ( change_mask );
-
- // All operations per colour
- for (b=0 ; b<prev_frame->nChannels ; b++) {
-
- // Create histogram:
-
- long HISTOGRAM[PIXELRANGE];
- for (i=0 ; i<PIXELRANGE; i++) HISTOGRAM[i]=0;
-
- for (y=0 ; y<curr_frame->height ; y++)
- {
- uchar* rowStart1 = (uchar*)curr_frame->imageData + y * curr_frame->widthStep + b;
- uchar* rowStart2 = (uchar*)prev_frame->imageData + y * prev_frame->widthStep + b;
- for (x=0 ; x<curr_frame->width ; x++, rowStart1+=curr_frame->nChannels, rowStart2+=prev_frame->nChannels) {
- int diff = abs( int(*rowStart1) - int(*rowStart2) );
- HISTOGRAM[diff]++;
- }
- }
-
- double relativeVariance[PIXELRANGE];
- for (i=0 ; i<PIXELRANGE; i++) relativeVariance[i]=0;
-
- for (thres=PIXELRANGE-2; thres>=0 ; thres--)
- {
- // fprintf(stderr, "Iter %d\n", thres);
- double sum=0;
- double sqsum=0;
- int count=0;
- // fprintf(stderr, "Iter %d entering loop\n", thres);
- for (j=thres ; j<PIXELRANGE ; j++) {
- sum += double(j)*double(HISTOGRAM[j]);
- sqsum += double(j*j)*double(HISTOGRAM[j]);
- count += HISTOGRAM[j];
- }
- count = count == 0 ? 1 : count;
- // fprintf(stderr, "Iter %d finishing loop\n", thres);
- double my = sum / count;
- double sigma = sqrt( sqsum/count - my*my);
- // fprintf(stderr, "Iter %d sum=%g sqsum=%g count=%d sigma = %g\n", thres, sum, sqsum, count, sigma);
- // fprintf(stderr, "Writing to %x\n", &(relativeVariance[thres]));
- relativeVariance[thres] = sigma;
- // fprintf(stderr, "Iter %d finished\n", thres);
- }
-
- // Find maximum:
- uchar bestThres = 0;
-
- double* pBestThres = _cv_max_element(relativeVariance, relativeVariance+PIXELRANGE);
- bestThres = (uchar)(*pBestThres); if (bestThres <10) bestThres=10;
-
- for (y=0 ; y<prev_frame->height ; y++)
- {
- uchar* rowStart1 = (uchar*)(curr_frame->imageData) + y * curr_frame->widthStep + b;
- uchar* rowStart2 = (uchar*)(prev_frame->imageData) + y * prev_frame->widthStep + b;
- uchar* rowStart3 = (uchar*)(change_mask->imageData) + y * change_mask->widthStep;
- for (x = 0; x < curr_frame->width; x++, rowStart1+=curr_frame->nChannels,
- rowStart2+=prev_frame->nChannels, rowStart3+=change_mask->nChannels) {
- // OR between different color channels
- int diff = abs( int(*rowStart1) - int(*rowStart2) );
- if ( diff > bestThres)
- *rowStart3 |=255;
- }
- }
- }
-
- return 1;
-}
-
-
-#define MIN_PV 1E-10
-
-
-#define V_C(k,l) ctable[k].v[l]
-#define PV_C(k) ctable[k].Pv
-#define PVB_C(k) ctable[k].Pvb
-#define V_CC(k,l) cctable[k].v[l]
-#define PV_CC(k) cctable[k].Pv
-#define PVB_CC(k) cctable[k].Pvb
-
-
-// Function cvUpdateFGDStatModel updates statistical model and returns number of foreground regions
-// parameters:
-// curr_frame - current frame from video sequence
-// p_model - pointer to CvFGDStatModel structure
-static int CV_CDECL
-icvUpdateFGDStatModel( IplImage* curr_frame, CvFGDStatModel* model )
-{
- int mask_step = model->Ftd->widthStep;
- CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
- IplImage* prev_frame = model->prev_frame;
- int region_count = 0;
- int FG_pixels_count = 0;
- int deltaC = cvRound(model->params.delta * 256 / model->params.Lc);
- int deltaCC = cvRound(model->params.delta * 256 / model->params.Lcc);
- int i, j, k, l;
-
- //clear storages
- cvClearMemStorage(model->storage);
- cvZero(model->foreground);
-
- // From foreground pixel candidates using image differencing
- // with adaptive thresholding. The algorithm is from:
- //
- // Thresholding for Change Detection
- // Paul L. Rosin 1998 6p
- // http://www.cis.temple.edu/~latecki/Courses/CIS750-03/Papers/thresh-iccv.pdf
- //
- cvChangeDetection( prev_frame, curr_frame, model->Ftd );
- cvChangeDetection( model->background, curr_frame, model->Fbd );
-
- for( i = 0; i < model->Ftd->height; i++ )
- {
- for( j = 0; j < model->Ftd->width; j++ )
- {
- if( ((uchar*)model->Fbd->imageData)[i*mask_step+j] || ((uchar*)model->Ftd->imageData)[i*mask_step+j] )
- {
- float Pb = 0;
- float Pv = 0;
- float Pvb = 0;
-
- CvBGPixelStat* stat = model->pixel_stat + i * model->Ftd->width + j;
-
- CvBGPixelCStatTable* ctable = stat->ctable;
- CvBGPixelCCStatTable* cctable = stat->cctable;
-
- uchar* curr_data = (uchar*)(curr_frame->imageData) + i*curr_frame->widthStep + j*3;
- uchar* prev_data = (uchar*)(prev_frame->imageData) + i*prev_frame->widthStep + j*3;
-
- int val = 0;
-
- // Is it a motion pixel?
- if( ((uchar*)model->Ftd->imageData)[i*mask_step+j] )
- {
- if( !stat->is_trained_dyn_model ) {
-
- val = 1;
-
- } else {
-
- // Compare with stored CCt vectors:
- for( k = 0; PV_CC(k) > model->params.alpha2 && k < model->params.N1cc; k++ )
- {
- if ( abs( V_CC(k,0) - prev_data[0]) <= deltaCC &&
- abs( V_CC(k,1) - prev_data[1]) <= deltaCC &&
- abs( V_CC(k,2) - prev_data[2]) <= deltaCC &&
- abs( V_CC(k,3) - curr_data[0]) <= deltaCC &&
- abs( V_CC(k,4) - curr_data[1]) <= deltaCC &&
- abs( V_CC(k,5) - curr_data[2]) <= deltaCC)
- {
- Pv += PV_CC(k);
- Pvb += PVB_CC(k);
- }
- }
- Pb = stat->Pbcc;
- if( 2 * Pvb * Pb <= Pv ) val = 1;
- }
- }
- else if( stat->is_trained_st_model )
- {
- // Compare with stored Ct vectors:
- for( k = 0; PV_C(k) > model->params.alpha2 && k < model->params.N1c; k++ )
- {
- if ( abs( V_C(k,0) - curr_data[0]) <= deltaC &&
- abs( V_C(k,1) - curr_data[1]) <= deltaC &&
- abs( V_C(k,2) - curr_data[2]) <= deltaC )
- {
- Pv += PV_C(k);
- Pvb += PVB_C(k);
- }
- }
- Pb = stat->Pbc;
- if( 2 * Pvb * Pb <= Pv ) val = 1;
- }
-
- // Update foreground:
- ((uchar*)model->foreground->imageData)[i*mask_step+j] = (uchar)(val*255);
- FG_pixels_count += val;
-
- } // end if( change detection...
- } // for j...
- } // for i...
- //end BG/FG classification
-
- // Foreground segmentation.
- // Smooth foreground map:
- if( model->params.perform_morphing ){
- cvMorphologyEx( model->foreground, model->foreground, 0, 0, CV_MOP_OPEN, model->params.perform_morphing );
- cvMorphologyEx( model->foreground, model->foreground, 0, 0, CV_MOP_CLOSE, model->params.perform_morphing );
- }
-
-
- if( model->params.minArea > 0 || model->params.is_obj_without_holes ){
-
- // Discard under-size foreground regions:
- //
- cvFindContours( model->foreground, 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 < model->params.minArea ||
- (model->params.is_obj_without_holes && CV_IS_SEQ_HOLE(seq)) )
- {
- // Delete under-size 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++;
- }
- }
- model->foreground_regions = first_seq;
- cvZero(model->foreground);
- cvDrawContours(model->foreground, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1);
-
- } else {
-
- model->foreground_regions = NULL;
- }
-
- // Check ALL BG update condition:
- if( ((float)FG_pixels_count/(model->Ftd->width*model->Ftd->height)) > CV_BGFG_FGD_BG_UPDATE_TRESH )
- {
- for( i = 0; i < model->Ftd->height; i++ )
- for( j = 0; j < model->Ftd->width; j++ )
- {
- CvBGPixelStat* stat = model->pixel_stat + i * model->Ftd->width + j;
- stat->is_trained_st_model = stat->is_trained_dyn_model = 1;
- }
- }
-
-
- // Update background model:
- for( i = 0; i < model->Ftd->height; i++ )
- {
- for( j = 0; j < model->Ftd->width; j++ )
- {
- CvBGPixelStat* stat = model->pixel_stat + i * model->Ftd->width + j;
- CvBGPixelCStatTable* ctable = stat->ctable;
- CvBGPixelCCStatTable* cctable = stat->cctable;
-
- uchar *curr_data = (uchar*)(curr_frame->imageData)+i*curr_frame->widthStep+j*3;
- uchar *prev_data = (uchar*)(prev_frame->imageData)+i*prev_frame->widthStep+j*3;
-
- if( ((uchar*)model->Ftd->imageData)[i*mask_step+j] || !stat->is_trained_dyn_model )
- {
- float alpha = stat->is_trained_dyn_model ? model->params.alpha2 : model->params.alpha3;
- float diff = 0;
- int dist, min_dist = 2147483647, indx = -1;
-
- //update Pb
- stat->Pbcc *= (1.f-alpha);
- if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
- {
- stat->Pbcc += alpha;
- }
-
- // Find best Vi match:
- for(k = 0; PV_CC(k) && k < model->params.N2cc; k++ )
- {
- // Exponential decay of memory
- PV_CC(k) *= (1-alpha);
- PVB_CC(k) *= (1-alpha);
- if( PV_CC(k) < MIN_PV )
- {
- PV_CC(k) = 0;
- PVB_CC(k) = 0;
- continue;
- }
-
- dist = 0;
- for( l = 0; l < 3; l++ )
- {
- int val = abs( V_CC(k,l) - prev_data[l] );
- if( val > deltaCC ) break;
- dist += val;
- val = abs( V_CC(k,l+3) - curr_data[l] );
- if( val > deltaCC) break;
- dist += val;
- }
- if( l == 3 && dist < min_dist )
- {
- min_dist = dist;
- indx = k;
- }
- }
-
-
- if( indx < 0 )
- { // Replace N2th elem in the table by new feature:
- indx = model->params.N2cc - 1;
- PV_CC(indx) = alpha;
- PVB_CC(indx) = alpha;
- //udate Vt
- for( l = 0; l < 3; l++ )
- {
- V_CC(indx,l) = prev_data[l];
- V_CC(indx,l+3) = curr_data[l];
- }
- }
- else
- { // Update:
- PV_CC(indx) += alpha;
- if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
- {
- PVB_CC(indx) += alpha;
- }
- }
-
- //re-sort CCt table by Pv
- for( k = 0; k < indx; k++ )
- {
- if( PV_CC(k) <= PV_CC(indx) )
- {
- //shift elements
- CvBGPixelCCStatTable tmp1, tmp2 = cctable[indx];
- for( l = k; l <= indx; l++ )
- {
- tmp1 = cctable[l];
- cctable[l] = tmp2;
- tmp2 = tmp1;
- }
- break;
- }
- }
-
-
- float sum1=0, sum2=0;
- //check "once-off" changes
- for(k = 0; PV_CC(k) && k < model->params.N1cc; k++ )
- {
- sum1 += PV_CC(k);
- sum2 += PVB_CC(k);
- }
- if( sum1 > model->params.T ) stat->is_trained_dyn_model = 1;
-
- diff = sum1 - stat->Pbcc * sum2;
- // Update stat table:
- if( diff > model->params.T )
- {
- //printf("once off change at motion mode\n");
- //new BG features are discovered
- for( k = 0; PV_CC(k) && k < model->params.N1cc; k++ )
- {
- PVB_CC(k) =
- (PV_CC(k)-stat->Pbcc*PVB_CC(k))/(1-stat->Pbcc);
- }
- assert(stat->Pbcc<=1 && stat->Pbcc>=0);
- }
- }
-
- // Handle "stationary" pixel:
- if( !((uchar*)model->Ftd->imageData)[i*mask_step+j] )
- {
- float alpha = stat->is_trained_st_model ? model->params.alpha2 : model->params.alpha3;
- float diff = 0;
- int dist, min_dist = 2147483647, indx = -1;
-
- //update Pb
- stat->Pbc *= (1.f-alpha);
- if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
- {
- stat->Pbc += alpha;
- }
-
- //find best Vi match
- for( k = 0; k < model->params.N2c; k++ )
- {
- // Exponential decay of memory
- PV_C(k) *= (1-alpha);
- PVB_C(k) *= (1-alpha);
- if( PV_C(k) < MIN_PV )
- {
- PV_C(k) = 0;
- PVB_C(k) = 0;
- continue;
- }
-
- dist = 0;
- for( l = 0; l < 3; l++ )
- {
- int val = abs( V_C(k,l) - curr_data[l] );
- if( val > deltaC ) break;
- dist += val;
- }
- if( l == 3 && dist < min_dist )
- {
- min_dist = dist;
- indx = k;
- }
- }
-
- if( indx < 0 )
- {//N2th elem in the table is replaced by a new features
- indx = model->params.N2c - 1;
- PV_C(indx) = alpha;
- PVB_C(indx) = alpha;
- //udate Vt
- for( l = 0; l < 3; l++ )
- {
- V_C(indx,l) = curr_data[l];
- }
- } else
- {//update
- PV_C(indx) += alpha;
- if( !((uchar*)model->foreground->imageData)[i*mask_step+j] )
- {
- PVB_C(indx) += alpha;
- }
- }
-
- //re-sort Ct table by Pv
- for( k = 0; k < indx; k++ )
- {
- if( PV_C(k) <= PV_C(indx) )
- {
- //shift elements
- CvBGPixelCStatTable tmp1, tmp2 = ctable[indx];
- for( l = k; l <= indx; l++ )
- {
- tmp1 = ctable[l];
- ctable[l] = tmp2;
- tmp2 = tmp1;
- }
- break;
- }
- }
-
- // Check "once-off" changes:
- float sum1=0, sum2=0;
- for( k = 0; PV_C(k) && k < model->params.N1c; k++ )
- {
- sum1 += PV_C(k);
- sum2 += PVB_C(k);
- }
- diff = sum1 - stat->Pbc * sum2;
- if( sum1 > model->params.T ) stat->is_trained_st_model = 1;
-
- // Update stat table:
- if( diff > model->params.T )
- {
- //printf("once off change at stat mode\n");
- //new BG features are discovered
- for( k = 0; PV_C(k) && k < model->params.N1c; k++ )
- {
- PVB_C(k) = (PV_C(k)-stat->Pbc*PVB_C(k))/(1-stat->Pbc);
- }
- stat->Pbc = 1 - stat->Pbc;
- }
- } // if !(change detection) at pixel (i,j)
-
- // Update the reference BG image:
- if( !((uchar*)model->foreground->imageData)[i*mask_step+j])
- {
- uchar* ptr = ((uchar*)model->background->imageData) + i*model->background->widthStep+j*3;
-
- if( !((uchar*)model->Ftd->imageData)[i*mask_step+j] &&
- !((uchar*)model->Fbd->imageData)[i*mask_step+j] )
- {
- // Apply IIR filter:
- for( l = 0; l < 3; l++ )
- {
- int a = cvRound(ptr[l]*(1 - model->params.alpha1) + model->params.alpha1*curr_data[l]);
- ptr[l] = (uchar)a;
- //((uchar*)model->background->imageData)[i*model->background->widthStep+j*3+l]*=(1 - model->params.alpha1);
- //((uchar*)model->background->imageData)[i*model->background->widthStep+j*3+l] += model->params.alpha1*curr_data[l];
- }
- }
- else
- {
- // Background change detected:
- for( l = 0; l < 3; l++ )
- {
- //((uchar*)model->background->imageData)[i*model->background->widthStep+j*3+l] = curr_data[l];
- ptr[l] = curr_data[l];
- }
- }
- }
- } // j
- } // i
-
- // Keep previous frame:
- cvCopy( curr_frame, model->prev_frame );
-
- return region_count;
-}
-
-/* End of file. */
projects / opencv / blobdiff