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opencv/modules/legacy/src/bgfg_gaussmix.cpp
Andrey Kamaev a4f5216d52 Fixed gcc build after r7751
2012-03-30 14:03:18 +00:00

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/*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*/
#include "precomp.hpp"
/////////////////////////////////////// MOG model //////////////////////////////////////////
static void CV_CDECL
icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
{
if( !bg_model )
CV_Error( CV_StsNullPtr, "" );
if( *bg_model )
{
delete (cv::Mat*)((*bg_model)->g_point);
cvReleaseImage( &(*bg_model)->background );
cvReleaseImage( &(*bg_model)->foreground );
cvReleaseMemStorage(&(*bg_model)->storage);
memset( *bg_model, 0, sizeof(**bg_model) );
delete *bg_model;
*bg_model = 0;
}
}
static int CV_CDECL
icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel* bg_model, double learningRate )
{
int region_count = 0;
cv::Mat image = cv::cvarrToMat(curr_frame), mask = cv::cvarrToMat(bg_model->foreground);
cv::BackgroundSubtractorMOG mog;
mog.bgmodel = *(cv::Mat*)bg_model->g_point;
mog.frameSize = mog.bgmodel.data ? cv::Size(cvGetSize(curr_frame)) : cv::Size();
mog.frameType = image.type();
mog.nframes = bg_model->countFrames;
mog.history = bg_model->params.win_size;
mog.nmixtures = bg_model->params.n_gauss;
mog.varThreshold = bg_model->params.std_threshold*bg_model->params.std_threshold;
mog.backgroundRatio = bg_model->params.bg_threshold;
mog(image, mask, learningRate);
bg_model->countFrames = mog.nframes;
if( ((cv::Mat*)bg_model->g_point)->data != mog.bgmodel.data )
*((cv::Mat*)bg_model->g_point) = mog.bgmodel;
//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 );
#if 0
CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
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);
#endif
CvMat _mask = mask;
cvCopy(&_mask, bg_model->foreground);
return region_count;
}
CV_IMPL CvBGStatModel*
cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parameters )
{
CvGaussBGStatModelParams params;
CV_Assert( CV_IS_IMAGE(first_frame) );
//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;
CvGaussBGModel* bg_model = new CvGaussBGModel;
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
bg_model->g_point = (CvGaussBGPoint*)new cv::Mat();
bg_model->background = cvCreateImage(cvSize(first_frame->width,
first_frame->height), IPL_DEPTH_8U, first_frame->nChannels);
bg_model->foreground = cvCreateImage(cvSize(first_frame->width,
first_frame->height), IPL_DEPTH_8U, 1);
bg_model->storage = cvCreateMemStorage();
bg_model->countFrames = 0;
icvUpdateGaussianBGModel( first_frame, bg_model, 1 );
return (CvBGStatModel*)bg_model;
}
//////////////////////////////////////////// MOG2 //////////////////////////////////////////////
/*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*/
/*//Implementation of the Gaussian mixture model background subtraction from:
//
//"Improved adaptive Gausian mixture model for background subtraction"
//Z.Zivkovic
//International Conference Pattern Recognition, UK, August, 2004
//http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
//The code is very fast and performs also shadow detection.
//Number of Gausssian components is adapted per pixel.
//
// and
//
//"Efficient Adaptive Density Estimapion per Image Pixel for the Task of Background Subtraction"
//Z.Zivkovic, F. van der Heijden
//Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.
//
//The algorithm similar to the standard Stauffer&Grimson algorithm with
//additional selection of the number of the Gaussian components based on:
//
//"Recursive unsupervised learning of finite mixture models "
//Z.Zivkovic, F.van der Heijden
//IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, no.5, pages 651-656, 2004
//http://www.zoranz.net/Publications/zivkovic2004PAMI.pdf
//
//
//Example usage with as cpp class
// BackgroundSubtractorMOG2 bg_model;
//For each new image the model is updates using:
// bg_model(img, fgmask);
//
//Example usage as part of the CvBGStatModel:
// CvBGStatModel* bg_model = cvCreateGaussianBGModel2( first_frame );
//
// //update for each frame
// cvUpdateBGStatModel( tmp_frame, bg_model );//segmentation result is in bg_model->foreground
//
// //release at the program termination
// cvReleaseBGStatModel( &bg_model );
//
//Author: Z.Zivkovic, www.zoranz.net
//Date: 7-April-2011, Version:1.0
///////////*/
#include "precomp.hpp"
/*
Interface of Gaussian mixture algorithm from:
"Improved adaptive Gausian mixture model for background subtraction"
Z.Zivkovic
International Conference Pattern Recognition, UK, August, 2004
http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
Advantages:
-fast - number of Gausssian components is constantly adapted per pixel.
-performs also shadow detection (see bgfg_segm_test.cpp example)
*/
#define CV_BG_MODEL_MOG2 3 /* "Mixture of Gaussians 2". */
/* default parameters of gaussian background detection algorithm */
#define CV_BGFG_MOG2_STD_THRESHOLD 4.0f /* lambda=2.5 is 99% */
#define CV_BGFG_MOG2_WINDOW_SIZE 500 /* Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */
#define CV_BGFG_MOG2_BACKGROUND_THRESHOLD 0.9f /* threshold sum of weights for background test */
#define CV_BGFG_MOG2_STD_THRESHOLD_GENERATE 3.0f /* lambda=2.5 is 99% */
#define CV_BGFG_MOG2_NGAUSSIANS 5 /* = K = number of Gaussians in mixture */
#define CV_BGFG_MOG2_VAR_INIT 15.0f /* initial variance for new components*/
#define CV_BGFG_MOG2_VAR_MIN 4.0f
#define CV_BGFG_MOG2_VAR_MAX 5*CV_BGFG_MOG2_VAR_INIT
#define CV_BGFG_MOG2_MINAREA 15.0f /* for postfiltering */
/* additional parameters */
#define CV_BGFG_MOG2_CT 0.05f /* complexity reduction prior constant 0 - no reduction of number of components*/
#define CV_BGFG_MOG2_SHADOW_VALUE 127 /* value to use in the segmentation mask for shadows, sot 0 not to do shadow detection*/
#define CV_BGFG_MOG2_SHADOW_TAU 0.5f /* Tau - shadow threshold, see the paper for explanation*/
typedef struct CvGaussBGStatModel2Params
{
//image info
int nWidth;
int nHeight;
int nND;//number of data dimensions (image channels)
bool bPostFiltering;//defult 1 - do postfiltering - will make shadow detection results also give value 255
double minArea; // for postfiltering
bool bInit;//default 1, faster updates at start
/////////////////////////
//very important parameters - things you will change
////////////////////////
float fAlphaT;
//alpha - speed of update - if the time interval you want to average over is T
//set alpha=1/T. It is also usefull at start to make T slowly increase
//from 1 until the desired T
float fTb;
//Tb - threshold on the squared Mahalan. dist. to decide if it is well described
//by the background model or not. Related to Cthr from the paper.
//This does not influence the update of the background. A typical value could be 4 sigma
//and that is Tb=4*4=16;
/////////////////////////
//less important parameters - things you might change but be carefull
////////////////////////
float fTg;
//Tg - threshold on the squared Mahalan. dist. to decide
//when a sample is close to the existing components. If it is not close
//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
//Smaller Tg leads to more generated components and higher Tg might make
//lead to small number of components but they can grow too large
float fTB;//1-cf from the paper
//TB - threshold when the component becomes significant enough to be included into
//the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
//For alpha=0.001 it means that the mode should exist for approximately 105 frames before
//it is considered foreground
float fVarInit;
float fVarMax;
float fVarMin;
//initial standard deviation for the newly generated components.
//It will will influence the speed of adaptation. A good guess should be made.
//A simple way is to estimate the typical standard deviation from the images.
//I used here 10 as a reasonable value
float fCT;//CT - complexity reduction prior
//this is related to the number of samples needed to accept that a component
//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
//even less important parameters
int nM;//max number of modes - const - 4 is usually enough
//shadow detection parameters
bool bShadowDetection;//default 1 - do shadow detection
unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result
float fTau;
// Tau - shadow threshold. The shadow is detected if the pixel is darker
//version of the background. Tau is a threshold on how much darker the shadow can be.
//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
} CvGaussBGStatModel2Params;
#define CV_BGFG_MOG2_NDMAX 3
typedef struct CvPBGMMGaussian
{
float weight;
float mean[CV_BGFG_MOG2_NDMAX];
float variance;
}CvPBGMMGaussian;
typedef struct CvGaussBGStatModel2Data
{
CvPBGMMGaussian* rGMM; //array for the mixture of Gaussians
unsigned char* rnUsedModes;//number of Gaussian components per pixel (maximum 255)
} CvGaussBGStatModel2Data;
/*
//only foreground image is updated
//no filtering included
typedef struct CvGaussBGModel2
{
CV_BG_STAT_MODEL_FIELDS();
CvGaussBGStatModel2Params params;
CvGaussBGStatModel2Data data;
int countFrames;
} CvGaussBGModel2;
CVAPI(CvBGStatModel*) cvCreateGaussianBGModel2( IplImage* first_frame,
CvGaussBGStatModel2Params* params CV_DEFAULT(NULL) );
*/
//shadow detection performed per pixel
// should work for rgb data, could be usefull for gray scale and depth data as well
// See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
CV_INLINE int _icvRemoveShadowGMM(float* data, int nD,
unsigned char nModes,
CvPBGMMGaussian* pGMM,
float m_fTb,
float m_fTB,
float m_fTau)
{
float tWeight = 0;
float numerator, denominator;
// check all the components marked as background:
for (int iModes=0;iModes<nModes;iModes++)
{
CvPBGMMGaussian g=pGMM[iModes];
numerator = 0.0f;
denominator = 0.0f;
for (int iD=0;iD<nD;iD++)
{
numerator += data[iD] * g.mean[iD];
denominator += g.mean[iD]* g.mean[iD];
}
// no division by zero allowed
if (denominator == 0)
{
return 0;
};
float a = numerator / denominator;
// if tau < a < 1 then also check the color distortion
if ((a <= 1) && (a >= m_fTau))
{
float dist2a=0.0f;
for (int iD=0;iD<nD;iD++)
{
float dD= a*g.mean[iD] - data[iD];
dist2a += (dD*dD);
}
if (dist2a<m_fTb*g.variance*a*a)
{
return 2;
}
};
tWeight += g.weight;
if (tWeight > m_fTB)
{
return 0;
};
};
return 0;
}
//update GMM - the base update function performed per pixel
//
//"Efficient Adaptive Density Estimapion per Image Pixel for the Task of Background Subtraction"
//Z.Zivkovic, F. van der Heijden
//Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.
//
//The algorithm similar to the standard Stauffer&Grimson algorithm with
//additional selection of the number of the Gaussian components based on:
//
//"Recursive unsupervised learning of finite mixture models "
//Z.Zivkovic, F.van der Heijden
//IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, no.5, pages 651-656, 2004
//http://www.zoranz.net/Publications/zivkovic2004PAMI.pdf
CV_INLINE int _icvUpdateGMM(float* data, int nD,
unsigned char* pModesUsed,
CvPBGMMGaussian* pGMM,
int m_nM,
float m_fAlphaT,
float m_fTb,
float m_fTB,
float m_fTg,
float m_fVarInit,
float m_fVarMax,
float m_fVarMin,
float m_fPrune)
{
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool bBackground=0;//return value -> true - the pixel classified as background
//internal:
bool bFitsPDF=0;//if it remains zero a new GMM mode will be added
float m_fOneMinAlpha=1-m_fAlphaT;
unsigned char nModes=*pModesUsed;//current number of modes in GMM
float totalWeight=0.0f;
//////
//go through all modes
int iMode=0;
CvPBGMMGaussian* pGauss=pGMM;
for (;iMode<nModes;iMode++,pGauss++)
{
float weight = pGauss->weight;//need only weight if fit is found
weight=m_fOneMinAlpha*weight+m_fPrune;
////
//fit not found yet
if (!bFitsPDF)
{
//check if it belongs to some of the remaining modes
float var=pGauss->variance;
//calculate difference and distance
float dist2=0.0f;
#if (CV_BGFG_MOG2_NDMAX==1)
float dData=pGauss->mean[0]-data[0];
dist2=dData*dData;
#else
float dData[CV_BGFG_MOG2_NDMAX];
for (int iD=0;iD<nD;iD++)
{
dData[iD]=pGauss->mean[iD]-data[iD];
dist2+=dData[iD]*dData[iD];
}
#endif
//background? - m_fTb - usually larger than m_fTg
if ((totalWeight<m_fTB)&&(dist2<m_fTb*var))
bBackground=1;
//check fit
if (dist2<m_fTg*var)
{
/////
//belongs to the mode - bFitsPDF becomes 1
bFitsPDF=1;
//update distribution
//update weight
weight+=m_fAlphaT;
float k = m_fAlphaT/weight;
//update mean
#if (CV_BGFG_MOG2_NDMAX==1)
pGauss->mean[0]-=k*dData;
#else
for (int iD=0;iD<nD;iD++)
{
pGauss->mean[iD]-=k*dData[iD];
}
#endif
//update variance
float varnew = var + k*(dist2-var);
//limit the variance
pGauss->variance = MIN(m_fVarMax,MAX(varnew,m_fVarMin));
//sort
//all other weights are at the same place and
//only the matched (iModes) is higher -> just find the new place for it
for (int iLocal = iMode;iLocal>0;iLocal--)
{
//check one up
if (weight < (pGMM[iLocal-1].weight))
{
break;
}
else
{
//swap one up
CvPBGMMGaussian temp = pGMM[iLocal];
pGMM[iLocal] = pGMM[iLocal-1];
pGMM[iLocal-1] = temp;
pGauss--;
}
}
//belongs to the mode - bFitsPDF becomes 1
/////
}
}//!bFitsPDF)
//check prune
if (weight<-m_fPrune)
{
weight=0.0;
nModes--;
}
pGauss->weight=weight;//update weight by the calculated value
totalWeight+=weight;
}
//go through all modes
//////
//renormalize weights
for (iMode = 0; iMode < nModes; iMode++)
{
pGMM[iMode].weight = pGMM[iMode].weight/totalWeight;
}
//make new mode if needed and exit
if (!bFitsPDF)
{
if (nModes==m_nM)
{
//replace the weakest
pGauss=pGMM+m_nM-1;
}
else
{
//add a new one
pGauss=pGMM+nModes;
nModes++;
}
if (nModes==1)
{
pGauss->weight=1;
}
else
{
pGauss->weight=m_fAlphaT;
//renormalize all weights
for (iMode = 0; iMode < nModes-1; iMode++)
{
pGMM[iMode].weight *=m_fOneMinAlpha;
}
}
//init
memcpy(pGauss->mean,data,nD*sizeof(float));
pGauss->variance=m_fVarInit;
//sort
//find the new place for it
for (int iLocal = nModes-1;iLocal>0;iLocal--)
{
//check one up
if (m_fAlphaT < (pGMM[iLocal-1].weight))
{
break;
}
else
{
//swap one up
CvPBGMMGaussian temp = pGMM[iLocal];
pGMM[iLocal] = pGMM[iLocal-1];
pGMM[iLocal-1] = temp;
}
}
}
//set the number of modes
*pModesUsed=nModes;
return bBackground;
}
// a bit more efficient implementation for common case of 3 channel (rgb) images
CV_INLINE int _icvUpdateGMM_C3(float r,float g, float b,
unsigned char* pModesUsed,
CvPBGMMGaussian* pGMM,
int m_nM,
float m_fAlphaT,
float m_fTb,
float m_fTB,
float m_fTg,
float m_fVarInit,
float m_fVarMax,
float m_fVarMin,
float m_fPrune)
{
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool bBackground=0;//return value -> true - the pixel classified as background
//internal:
bool bFitsPDF=0;//if it remains zero a new GMM mode will be added
float m_fOneMinAlpha=1-m_fAlphaT;
unsigned char nModes=*pModesUsed;//current number of modes in GMM
float totalWeight=0.0f;
//////
//go through all modes
int iMode=0;
CvPBGMMGaussian* pGauss=pGMM;
for (;iMode<nModes;iMode++,pGauss++)
{
float weight = pGauss->weight;//need only weight if fit is found
weight=m_fOneMinAlpha*weight+m_fPrune;
////
//fit not found yet
if (!bFitsPDF)
{
//check if it belongs to some of the remaining modes
float var=pGauss->variance;
//calculate difference and distance
float muR = pGauss->mean[0];
float muG = pGauss->mean[1];
float muB = pGauss->mean[2];
float dR=muR - r;
float dG=muG - g;
float dB=muB - b;
float dist2=(dR*dR+dG*dG+dB*dB);
//background? - m_fTb - usually larger than m_fTg
if ((totalWeight<m_fTB)&&(dist2<m_fTb*var))
bBackground=1;
//check fit
if (dist2<m_fTg*var)
{
/////
//belongs to the mode - bFitsPDF becomes 1
bFitsPDF=1;
//update distribution
//update weight
weight+=m_fAlphaT;
float k = m_fAlphaT/weight;
//update mean
pGauss->mean[0] = muR - k*(dR);
pGauss->mean[1] = muG - k*(dG);
pGauss->mean[2] = muB - k*(dB);
//update variance
float varnew = var + k*(dist2-var);
//limit the variance
pGauss->variance = MIN(m_fVarMax,MAX(varnew,m_fVarMin));
//sort
//all other weights are at the same place and
//only the matched (iModes) is higher -> just find the new place for it
for (int iLocal = iMode;iLocal>0;iLocal--)
{
//check one up
if (weight < (pGMM[iLocal-1].weight))
{
break;
}
else
{
//swap one up
CvPBGMMGaussian temp = pGMM[iLocal];
pGMM[iLocal] = pGMM[iLocal-1];
pGMM[iLocal-1] = temp;
pGauss--;
}
}
//belongs to the mode - bFitsPDF becomes 1
/////
}
}//!bFitsPDF)
//check prunning
if (weight<-m_fPrune)
{
weight=0.0;
nModes--;
}
pGauss->weight=weight;
totalWeight+=weight;
}
//go through all modes
//////
//renormalize weights
for (iMode = 0; iMode < nModes; iMode++)
{
pGMM[iMode].weight = pGMM[iMode].weight/totalWeight;
}
//make new mode if needed and exit
if (!bFitsPDF)
{
if (nModes==m_nM)
{
//replace the weakest
pGauss=pGMM+m_nM-1;
}
else
{
//add a new one
pGauss=pGMM+nModes;
nModes++;
}
if (nModes==1)
{
pGauss->weight=1;
}
else
{
pGauss->weight=m_fAlphaT;
//renormalize all weights
for (iMode = 0; iMode < nModes-1; iMode++)
{
pGMM[iMode].weight *=m_fOneMinAlpha;
}
}
//init
pGauss->mean[0]=r;
pGauss->mean[1]=g;
pGauss->mean[2]=b;
pGauss->variance=m_fVarInit;
//sort
//find the new place for it
for (int iLocal = nModes-1;iLocal>0;iLocal--)
{
//check one up
if (m_fAlphaT < (pGMM[iLocal-1].weight))
{
break;
}
else
{
//swap one up
CvPBGMMGaussian temp = pGMM[iLocal];
pGMM[iLocal] = pGMM[iLocal-1];
pGMM[iLocal-1] = temp;
}
}
}
//set the number of modes
*pModesUsed=nModes;
return bBackground;
}
//the main function to update the background model
static void icvUpdatePixelBackgroundGMM2( const CvArr* srcarr, CvArr* dstarr ,
CvPBGMMGaussian *pGMM,
unsigned char *pUsedModes,
//CvGaussBGStatModel2Params* pGMMPar,
int nM,
float fTb,
float fTB,
float fTg,
float fVarInit,
float fVarMax,
float fVarMin,
float fCT,
float fTau,
bool bShadowDetection,
unsigned char nShadowDetection,
float alpha)
{
CvMat sstub, *src = cvGetMat(srcarr, &sstub);
CvMat dstub, *dst = cvGetMat(dstarr, &dstub);
CvSize size = cvGetMatSize(src);
int nD=CV_MAT_CN(src->type);
//reshape if possible
if( CV_IS_MAT_CONT(src->type & dst->type) )
{
size.width *= size.height;
size.height = 1;
}
int x, y;
float data[CV_BGFG_MOG2_NDMAX];
float prune=-alpha*fCT;
//general nD
if (nD!=3)
{
switch (CV_MAT_DEPTH(src->type))
{
case CV_8U:
for( y = 0; y < size.height; y++ )
{
uchar* sptr = src->data.ptr + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
for (int iD=0;iD<nD;iD++) data[iD]=float(sptr[iD]);
//update GMM model
int result = _icvUpdateGMM(data,nD,pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_16S:
for( y = 0; y < size.height; y++ )
{
short* sptr = src->data.s + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
for (int iD=0;iD<nD;iD++) data[iD]=float(sptr[iD]);
//update GMM model
int result = _icvUpdateGMM(data,nD,pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_16U:
for( y = 0; y < size.height; y++ )
{
unsigned short* sptr = (unsigned short*) (src->data.s + src->step*y);
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
for (int iD=0;iD<nD;iD++) data[iD]=float(sptr[iD]);
//update GMM model
int result = _icvUpdateGMM(data,nD,pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_32S:
for( y = 0; y < size.height; y++ )
{
int* sptr = src->data.i + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
for (int iD=0;iD<nD;iD++) data[iD]=float(sptr[iD]);
//update GMM model
int result = _icvUpdateGMM(data,nD,pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_32F:
for( y = 0; y < size.height; y++ )
{
float* sptr = src->data.fl + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//update GMM model
int result = _icvUpdateGMM(sptr,nD,pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_64F:
for( y = 0; y < size.height; y++ )
{
double* sptr = src->data.db + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
for (int iD=0;iD<nD;iD++) data[iD]=float(sptr[iD]);
//update GMM model
int result = _icvUpdateGMM(data,nD,pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
}
}else ///if (nD==3) - a bit faster
{
switch (CV_MAT_DEPTH(src->type))
{
case CV_8U:
for( y = 0; y < size.height; y++ )
{
uchar* sptr = src->data.ptr + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
data[0]=float(sptr[0]),data[1]=float(sptr[1]),data[2]=float(sptr[2]);
//update GMM model
int result = _icvUpdateGMM_C3(data[0],data[1],data[2],pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_16S:
for( y = 0; y < size.height; y++ )
{
short* sptr = src->data.s + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
data[0]=float(sptr[0]),data[1]=float(sptr[1]),data[2]=float(sptr[2]);
//update GMM model
int result = _icvUpdateGMM_C3(data[0],data[1],data[2],pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_16U:
for( y = 0; y < size.height; y++ )
{
unsigned short* sptr = (unsigned short*) src->data.s + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
data[0]=float(sptr[0]),data[1]=float(sptr[1]),data[2]=float(sptr[2]);
//update GMM model
int result = _icvUpdateGMM_C3(data[0],data[1],data[2],pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_32S:
for( y = 0; y < size.height; y++ )
{
int* sptr = src->data.i + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
data[0]=float(sptr[0]),data[1]=float(sptr[1]),data[2]=float(sptr[2]);
//update GMM model
int result = _icvUpdateGMM_C3(data[0],data[1],data[2],pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_32F:
for( y = 0; y < size.height; y++ )
{
float* sptr = src->data.fl + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//update GMM model
int result = _icvUpdateGMM_C3(sptr[0],sptr[1],sptr[2],pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
case CV_64F:
for( y = 0; y < size.height; y++ )
{
double* sptr = src->data.db + src->step*y;
uchar* pDataOutput = dst->data.ptr + dst->step*y;
for( x = 0; x < size.width; x++,
pGMM+=nM,pUsedModes++,pDataOutput++,sptr+=nD)
{
//convert data
data[0]=float(sptr[0]),data[1]=float(sptr[1]),data[2]=float(sptr[2]);
//update GMM model
int result = _icvUpdateGMM_C3(data[0],data[1],data[2],pUsedModes,pGMM,nM,alpha, fTb, fTB, fTg, fVarInit, fVarMax, fVarMin,prune);
//detect shadows in the foreground
if (bShadowDetection)
if (result==0) result= _icvRemoveShadowGMM(data,nD,(*pUsedModes),pGMM,fTb,fTB,fTau);
//generate output
(* pDataOutput)= (result==1) ? 0 : (result==2) ? (nShadowDetection) : 255;
}
}
break;
}
}//a bit faster for nD=3;
}
//only foreground image is updated
//no filtering included
typedef struct CvGaussBGModel2
{
CV_BG_STAT_MODEL_FIELDS();
CvGaussBGStatModel2Params params;
CvGaussBGStatModel2Data data;
int countFrames;
} CvGaussBGModel2;
CVAPI(CvBGStatModel*) cvCreateGaussianBGModel2( IplImage* first_frame,
CvGaussBGStatModel2Params* params CV_DEFAULT(NULL) );
//////////////////////////////////////////////
//implementation as part of the CvBGStatModel
static void CV_CDECL icvReleaseGaussianBGModel2( CvGaussBGModel2** bg_model );
static int CV_CDECL icvUpdateGaussianBGModel2( IplImage* curr_frame, CvGaussBGModel2* bg_model );
CV_IMPL CvBGStatModel*
cvCreateGaussianBGModel2( IplImage* first_frame, CvGaussBGStatModel2Params* parameters )
{
CvGaussBGModel2* bg_model = 0;
int w,h;
CV_FUNCNAME( "cvCreateGaussianBGModel2" );
__BEGIN__;
CvGaussBGStatModel2Params params;
if( !CV_IS_IMAGE(first_frame) )
CV_ERROR( CV_StsBadArg, "Invalid or NULL first_frame parameter" );
if( first_frame->nChannels>CV_BGFG_MOG2_NDMAX )
CV_ERROR( CV_StsBadArg, "Maxumum number of channels in the image is excedded (change CV_BGFG_MOG2_MAXBANDS constant)!" );
CV_CALL( bg_model = (CvGaussBGModel2*)cvAlloc( sizeof(*bg_model) ));
memset( bg_model, 0, sizeof(*bg_model) );
bg_model->type = CV_BG_MODEL_MOG2;
bg_model->release = (CvReleaseBGStatModel) icvReleaseGaussianBGModel2;
bg_model->update = (CvUpdateBGStatModel) icvUpdateGaussianBGModel2;
//init parameters
if( parameters == NULL )
{
memset(&params, 0, sizeof(params));
// These constants are defined in cvaux/include/cvaux.h
params.bShadowDetection = 1;
params.bPostFiltering=0;
params.minArea=CV_BGFG_MOG2_MINAREA;
//set parameters
// K - max number of Gaussians per pixel
params.nM = CV_BGFG_MOG2_NGAUSSIANS;//4;
// Tb - the threshold - n var
//pGMM->fTb = 4*4;
params.fTb = CV_BGFG_MOG2_STD_THRESHOLD*CV_BGFG_MOG2_STD_THRESHOLD;
// Tbf - the threshold
//pGMM->fTB = 0.9f;//1-cf from the paper
params.fTB = CV_BGFG_MOG2_BACKGROUND_THRESHOLD;
// Tgenerate - the threshold
params.fTg = CV_BGFG_MOG2_STD_THRESHOLD_GENERATE*CV_BGFG_MOG2_STD_THRESHOLD_GENERATE;//update the mode or generate new
//pGMM->fSigma= 11.0f;//sigma for the new mode
params.fVarInit = CV_BGFG_MOG2_VAR_INIT;
params.fVarMax = CV_BGFG_MOG2_VAR_MAX;
params.fVarMin = CV_BGFG_MOG2_VAR_MIN;
// alpha - the learning factor
params.fAlphaT = 1.0f/CV_BGFG_MOG2_WINDOW_SIZE;//0.003f;
// complexity reduction prior constant
params.fCT = CV_BGFG_MOG2_CT;//0.05f;
//shadow
// Shadow detection
params.nShadowDetection = (unsigned char)CV_BGFG_MOG2_SHADOW_VALUE;//value 0 to turn off
params.fTau = CV_BGFG_MOG2_SHADOW_TAU;//0.5f;// Tau - shadow threshold
}
else
{
params = *parameters;
}
bg_model->params = params;
//image data
w = first_frame->width;
h = first_frame->height;
bg_model->params.nWidth = w;
bg_model->params.nHeight = h;
bg_model->params.nND = first_frame->nChannels;
//allocate GMM data
//GMM for each pixel
bg_model->data.rGMM = (CvPBGMMGaussian*) malloc(w*h * params.nM * sizeof(CvPBGMMGaussian));
//used modes per pixel
bg_model->data.rnUsedModes = (unsigned char* ) malloc(w*h);
memset(bg_model->data.rnUsedModes,0,w*h);//no modes used
//prepare storages
CV_CALL( bg_model->background = cvCreateImage(cvSize(w,h), IPL_DEPTH_8U, first_frame->nChannels));
CV_CALL( bg_model->foreground = cvCreateImage(cvSize(w,h), IPL_DEPTH_8U, 1));
//for eventual filtering
CV_CALL( bg_model->storage = cvCreateMemStorage());
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
icvReleaseGaussianBGModel2( CvGaussBGModel2** _bg_model )
{
CV_FUNCNAME( "icvReleaseGaussianBGModel2" );
__BEGIN__;
if( !_bg_model )
CV_ERROR( CV_StsNullPtr, "" );
if( *_bg_model )
{
CvGaussBGModel2* bg_model = *_bg_model;
free (bg_model->data.rGMM);
free (bg_model->data.rnUsedModes);
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
icvUpdateGaussianBGModel2( IplImage* curr_frame, CvGaussBGModel2* bg_model )
{
//checks
if ((curr_frame->height!=bg_model->params.nHeight)||(curr_frame->width!=bg_model->params.nWidth)||(curr_frame->nChannels!=bg_model->params.nND))
CV_Error( CV_StsBadSize, "the image not the same size as the reserved GMM background model");
float alpha=bg_model->params.fAlphaT;
bg_model->countFrames++;
//faster initial updates - increase value of alpha
if (bg_model->params.bInit){
float alphaInit=(1.0f/(2*bg_model->countFrames+1));
if (alphaInit>alpha)
{
alpha = alphaInit;
}
else
{
bg_model->params.bInit = 0;
}
}
//update background
//icvUpdatePixelBackgroundGMM2( curr_frame, bg_model->foreground, bg_model->data.rGMM,bg_model->data.rnUsedModes,&(bg_model->params),alpha);
icvUpdatePixelBackgroundGMM2( curr_frame, bg_model->foreground, bg_model->data.rGMM,bg_model->data.rnUsedModes,
bg_model->params.nM,
bg_model->params.fTb,
bg_model->params.fTB,
bg_model->params.fTg,
bg_model->params.fVarInit,
bg_model->params.fVarMax,
bg_model->params.fVarMin,
bg_model->params.fCT,
bg_model->params.fTau,
bg_model->params.bShadowDetection,
bg_model->params.nShadowDetection,
alpha);
//foreground filtering
if (bg_model->params.bPostFiltering==1)
{
int region_count = 0;
CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
//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;
}
return 1;
}
/* End of file. */
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