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/*//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"
#include "opencl_kernels_video.hpp"
namespace cv
{
/*
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)
*/
// default parameters of gaussian background detection algorithm
static const int defaultHistory2 = 500; // Learning rate; alpha = 1/defaultHistory2
static const float defaultVarThreshold2 = 4.0f*4.0f;
static const int defaultNMixtures2 = 5; // maximal number of Gaussians in mixture
static const float defaultBackgroundRatio2 = 0.9f; // threshold sum of weights for background test
static const float defaultVarThresholdGen2 = 3.0f*3.0f;
static const float defaultVarInit2 = 15.0f; // initial variance for new components
static const float defaultVarMax2 = 5*defaultVarInit2;
static const float defaultVarMin2 = 4.0f;
// additional parameters
static const float defaultfCT2 = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
static const unsigned char defaultnShadowDetection2 = (unsigned char)127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
static const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
class BackgroundSubtractorMOG2Impl : public BackgroundSubtractorMOG2
{
public:
//! the default constructor
BackgroundSubtractorMOG2Impl()
{
frameSize = Size(0,0);
frameType = 0;
nframes = 0;
history = defaultHistory2;
varThreshold = defaultVarThreshold2;
bShadowDetection = 1;
nmixtures = defaultNMixtures2;
backgroundRatio = defaultBackgroundRatio2;
fVarInit = defaultVarInit2;
fVarMax = defaultVarMax2;
fVarMin = defaultVarMin2;
varThresholdGen = defaultVarThresholdGen2;
fCT = defaultfCT2;
nShadowDetection = defaultnShadowDetection2;
fTau = defaultfTau;
opencl_ON = true;
}
//! the full constructor that takes the length of the history,
// the number of gaussian mixtures, the background ratio parameter and the noise strength
BackgroundSubtractorMOG2Impl(int _history, float _varThreshold, bool _bShadowDetection=true)
{
frameSize = Size(0,0);
frameType = 0;
nframes = 0;
history = _history > 0 ? _history : defaultHistory2;
varThreshold = (_varThreshold>0)? _varThreshold : defaultVarThreshold2;
bShadowDetection = _bShadowDetection;
nmixtures = defaultNMixtures2;
backgroundRatio = defaultBackgroundRatio2;
fVarInit = defaultVarInit2;
fVarMax = defaultVarMax2;
fVarMin = defaultVarMin2;
varThresholdGen = defaultVarThresholdGen2;
fCT = defaultfCT2;
nShadowDetection = defaultnShadowDetection2;
fTau = defaultfTau;
name_ = "BackgroundSubtractor.MOG2";
opencl_ON = true;
}
//! the destructor
~BackgroundSubtractorMOG2Impl() {}
//! the update operator
void apply(InputArray image, OutputArray fgmask, double learningRate=-1);
//! computes a background image which are the mean of all background gaussians
virtual void getBackgroundImage(OutputArray backgroundImage) const;
//! re-initiaization method
void initialize(Size _frameSize, int _frameType)
{
frameSize = _frameSize;
frameType = _frameType;
nframes = 0;
int nchannels = CV_MAT_CN(frameType);
CV_Assert( nchannels <= CV_CN_MAX );
CV_Assert( nmixtures <= 255);
if (ocl::useOpenCL() && opencl_ON)
{
create_ocl_apply_kernel();
kernel_getBg.create("getBackgroundImage2_kernel", ocl::video::bgfg_mog2_oclsrc, format( "-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
if (kernel_apply.empty() || kernel_getBg.empty())
opencl_ON = false;
}
else opencl_ON = false;
if (opencl_ON)
{
u_weight.create(frameSize.height * nmixtures, frameSize.width, CV_32FC1);
u_weight.setTo(Scalar::all(0));
u_variance.create(frameSize.height * nmixtures, frameSize.width, CV_32FC1);
u_variance.setTo(Scalar::all(0));
if (nchannels==3)
nchannels=4;
u_mean.create(frameSize.height * nmixtures, frameSize.width, CV_32FC(nchannels)); //4 channels
u_mean.setTo(Scalar::all(0));
//make the array for keeping track of the used modes per pixel - all zeros at start
u_bgmodelUsedModes.create(frameSize, CV_8UC1);
u_bgmodelUsedModes.setTo(cv::Scalar::all(0));
}
else
{
// for each gaussian mixture of each pixel bg model we store ...
// the mixture weight (w),
// the mean (nchannels values) and
// the covariance
bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + nchannels), CV_32F );
//make the array for keeping track of the used modes per pixel - all zeros at start
bgmodelUsedModes.create(frameSize,CV_8U);
bgmodelUsedModes = Scalar::all(0);
}
}
virtual int getHistory() const { return history; }
virtual void setHistory(int _nframes) { history = _nframes; }
virtual int getNMixtures() const { return nmixtures; }
virtual void setNMixtures(int nmix) { nmixtures = nmix; }
virtual double getBackgroundRatio() const { return backgroundRatio; }
virtual void setBackgroundRatio(double _backgroundRatio) { backgroundRatio = (float)_backgroundRatio; }
virtual double getVarThreshold() const { return varThreshold; }
virtual void setVarThreshold(double _varThreshold) { varThreshold = _varThreshold; }
virtual double getVarThresholdGen() const { return varThresholdGen; }
virtual void setVarThresholdGen(double _varThresholdGen) { varThresholdGen = (float)_varThresholdGen; }
virtual double getVarInit() const { return fVarInit; }
virtual void setVarInit(double varInit) { fVarInit = (float)varInit; }
virtual double getVarMin() const { return fVarMin; }
virtual void setVarMin(double varMin) { fVarMin = (float)varMin; }
virtual double getVarMax() const { return fVarMax; }
virtual void setVarMax(double varMax) { fVarMax = (float)varMax; }
virtual double getComplexityReductionThreshold() const { return fCT; }
virtual void setComplexityReductionThreshold(double ct) { fCT = (float)ct; }
virtual bool getDetectShadows() const { return bShadowDetection; }
virtual void setDetectShadows(bool detectshadows)
{
if ((bShadowDetection && detectshadows) || (!bShadowDetection && !detectshadows))
return;
bShadowDetection = detectshadows;
if (!kernel_apply.empty())
{
create_ocl_apply_kernel();
CV_Assert( !kernel_apply.empty() );
}
}
virtual int getShadowValue() const { return nShadowDetection; }
virtual void setShadowValue(int value) { nShadowDetection = (uchar)value; }
virtual double getShadowThreshold() const { return fTau; }
virtual void setShadowThreshold(double value) { fTau = (float)value; }
virtual void write(FileStorage& fs) const
{
fs << "name" << name_
<< "history" << history
<< "nmixtures" << nmixtures
<< "backgroundRatio" << backgroundRatio
<< "varThreshold" << varThreshold
<< "varThresholdGen" << varThresholdGen
<< "varInit" << fVarInit
<< "varMin" << fVarMin
<< "varMax" << fVarMax
<< "complexityReductionThreshold" << fCT
<< "detectShadows" << (int)bShadowDetection
<< "shadowValue" << (int)nShadowDetection
<< "shadowThreshold" << fTau;
}
virtual void read(const FileNode& fn)
{
CV_Assert( (String)fn["name"] == name_ );
history = (int)fn["history"];
nmixtures = (int)fn["nmixtures"];
backgroundRatio = (float)fn["backgroundRatio"];
varThreshold = (double)fn["varThreshold"];
varThresholdGen = (float)fn["varThresholdGen"];
fVarInit = (float)fn["varInit"];
fVarMin = (float)fn["varMin"];
fVarMax = (float)fn["varMax"];
fCT = (float)fn["complexityReductionThreshold"];
bShadowDetection = (int)fn["detectShadows"] != 0;
nShadowDetection = saturate_cast<uchar>((int)fn["shadowValue"]);
fTau = (float)fn["shadowThreshold"];
}
protected:
Size frameSize;
int frameType;
Mat bgmodel;
Mat bgmodelUsedModes;//keep track of number of modes per pixel
//for OCL
mutable bool opencl_ON;
UMat u_weight;
UMat u_variance;
UMat u_mean;
UMat u_bgmodelUsedModes;
mutable ocl::Kernel kernel_apply;
mutable ocl::Kernel kernel_getBg;
int nframes;
int history;
int nmixtures;
//! here it is the maximum allowed number of mixture components.
//! Actual number is determined dynamically per pixel
double varThreshold;
// threshold on the squared Mahalanobis distance 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 varThreshold=4*4=16; Corresponds to Tb in the paper.
/////////////////////////
// less important parameters - things you might change but be carefull
////////////////////////
float backgroundRatio;
// corresponds to 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 noiseSigma;
float varThresholdGen;
//correspondts to 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 fVarInit;
float fVarMin;
float fVarMax;
//initial variance 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
// min and max can be used to further control the variance
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)
//shadow detection parameters
bool bShadowDetection;//default 1 - do shadow detection
unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value
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.
String name_;
bool ocl_getBackgroundImage(OutputArray backgroundImage) const;
bool ocl_apply(InputArray _image, OutputArray _fgmask, double learningRate=-1);
void create_ocl_apply_kernel();
};
struct GaussBGStatModel2Params
{
//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.
};
struct GMM
{
float weight;
float variance;
};
// 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 bool
detectShadowGMM(const float* data, int nchannels, int nmodes,
const GMM* gmm, const float* mean,
float Tb, float TB, float tau)
{
float tWeight = 0;
// check all the components marked as background:
for( int mode = 0; mode < nmodes; mode++, mean += nchannels )
{
GMM g = gmm[mode];
float numerator = 0.0f;
float denominator = 0.0f;
for( int c = 0; c < nchannels; c++ )
{
numerator += data[c] * mean[c];
denominator += mean[c] * mean[c];
}
// no division by zero allowed
if( denominator == 0 )
return false;
// if tau < a < 1 then also check the color distortion
if( numerator <= denominator && numerator >= tau*denominator )
{
float a = numerator / denominator;
float dist2a = 0.0f;
for( int c = 0; c < nchannels; c++ )
{
float dD= a*mean[c] - data[c];
dist2a += dD*dD;
}
if (dist2a < Tb*g.variance*a*a)
return true;
};
tWeight += g.weight;
if( tWeight > TB )
return false;
};
return false;
}
//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
class MOG2Invoker : public ParallelLoopBody
{
public:
MOG2Invoker(const Mat& _src, Mat& _dst,
GMM* _gmm, float* _mean,
uchar* _modesUsed,
int _nmixtures, float _alphaT,
float _Tb, float _TB, float _Tg,
float _varInit, float _varMin, float _varMax,
float _prune, float _tau, bool _detectShadows,
uchar _shadowVal)
{
src = &_src;
dst = &_dst;
gmm0 = _gmm;
mean0 = _mean;
modesUsed0 = _modesUsed;
nmixtures = _nmixtures;
alphaT = _alphaT;
Tb = _Tb;
TB = _TB;
Tg = _Tg;
varInit = _varInit;
varMin = MIN(_varMin, _varMax);
varMax = MAX(_varMin, _varMax);
prune = _prune;
tau = _tau;
detectShadows = _detectShadows;
shadowVal = _shadowVal;
}
void operator()(const Range& range) const
{
int y0 = range.start, y1 = range.end;
int ncols = src->cols, nchannels = src->channels();
AutoBuffer<float> buf(src->cols*nchannels);
float alpha1 = 1.f - alphaT;
float dData[CV_CN_MAX];
for( int y = y0; y < y1; y++ )
{
const float* data = buf;
if( src->depth() != CV_32F )
src->row(y).convertTo(Mat(1, ncols, CV_32FC(nchannels), (void*)data), CV_32F);
else
data = src->ptr<float>(y);
float* mean = mean0 + ncols*nmixtures*nchannels*y;
GMM* gmm = gmm0 + ncols*nmixtures*y;
uchar* modesUsed = modesUsed0 + ncols*y;
uchar* mask = dst->ptr(y);
for( int x = 0; x < ncols; x++, data += nchannels, gmm += nmixtures, mean += nmixtures*nchannels )
{
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool background = false;//return value -> true - the pixel classified as background
//internal:
bool fitsPDF = false;//if it remains zero a new GMM mode will be added
int nmodes = modesUsed[x], nNewModes = nmodes;//current number of modes in GMM
float totalWeight = 0.f;
float* mean_m = mean;
//////
//go through all modes
for( int mode = 0; mode < nmodes; mode++, mean_m += nchannels )
{
float weight = alpha1*gmm[mode].weight + prune;//need only weight if fit is found
int swap_count = 0;
////
//fit not found yet
if( !fitsPDF )
{
//check if it belongs to some of the remaining modes
float var = gmm[mode].variance;
//calculate difference and distance
float dist2;
if( nchannels == 3 )
{
dData[0] = mean_m[0] - data[0];
dData[1] = mean_m[1] - data[1];
dData[2] = mean_m[2] - data[2];
dist2 = dData[0]*dData[0] + dData[1]*dData[1] + dData[2]*dData[2];
}
else
{
dist2 = 0.f;
for( int c = 0; c < nchannels; c++ )
{
dData[c] = mean_m[c] - data[c];
dist2 += dData[c]*dData[c];
}
}
//background? - Tb - usually larger than Tg
if( totalWeight < TB && dist2 < Tb*var )
background = true;
//check fit
if( dist2 < Tg*var )
{
/////
//belongs to the mode
fitsPDF = true;
//update distribution
//update weight
weight += alphaT;
float k = alphaT/weight;
//update mean
for( int c = 0; c < nchannels; c++ )
mean_m[c] -= k*dData[c];
//update variance
float varnew = var + k*(dist2-var);
//limit the variance
varnew = MAX(varnew, varMin);
varnew = MIN(varnew, varMax);
gmm[mode].variance = varnew;
//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 i = mode; i > 0; i-- )
{
//check one up
if( weight < gmm[i-1].weight )
break;
swap_count++;
//swap one up
std::swap(gmm[i], gmm[i-1]);
for( int c = 0; c < nchannels; c++ )
std::swap(mean[i*nchannels + c], mean[(i-1)*nchannels + c]);
}
//belongs to the mode - bFitsPDF becomes 1
/////
}
}//!bFitsPDF)
//check prune
if( weight < -prune )
{
weight = 0.0;
nmodes--;
}
gmm[mode-swap_count].weight = weight;//update weight by the calculated value
totalWeight += weight;
}
//go through all modes
//////
//renormalize weights
totalWeight = 1.f/totalWeight;
for( int mode = 0; mode < nmodes; mode++ )
{
gmm[mode].weight *= totalWeight;
}
nmodes = nNewModes;
//make new mode if needed and exit
if( !fitsPDF && alphaT > 0.f )
{
// replace the weakest or add a new one
int mode = nmodes == nmixtures ? nmixtures-1 : nmodes++;
if (nmodes==1)
gmm[mode].weight = 1.f;
else
{
gmm[mode].weight = alphaT;
// renormalize all other weights
for( int i = 0; i < nmodes-1; i++ )
gmm[i].weight *= alpha1;
}
// init
for( int c = 0; c < nchannels; c++ )
mean[mode*nchannels + c] = data[c];
gmm[mode].variance = varInit;
//sort
//find the new place for it
for( int i = nmodes - 1; i > 0; i-- )
{
// check one up
if( alphaT < gmm[i-1].weight )
break;
// swap one up
std::swap(gmm[i], gmm[i-1]);
for( int c = 0; c < nchannels; c++ )
std::swap(mean[i*nchannels + c], mean[(i-1)*nchannels + c]);
}
}
//set the number of modes
modesUsed[x] = uchar(nmodes);
mask[x] = background ? 0 :
detectShadows && detectShadowGMM(data, nchannels, nmodes, gmm, mean, Tb, TB, tau) ?
shadowVal : 255;
}
}
}
const Mat* src;
Mat* dst;
GMM* gmm0;
float* mean0;
uchar* modesUsed0;
int nmixtures;
float alphaT, Tb, TB, Tg;
float varInit, varMin, varMax, prune, tau;
bool detectShadows;
uchar shadowVal;
};
#ifdef HAVE_OPENCL
bool BackgroundSubtractorMOG2Impl::ocl_apply(InputArray _image, OutputArray _fgmask, double learningRate)
{
++nframes;
learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history );
CV_Assert(learningRate >= 0);
_fgmask.create(_image.size(), CV_8U);
UMat fgmask = _fgmask.getUMat();
const double alpha1 = 1.0f - learningRate;
UMat frame = _image.getUMat();
float varMax = MAX(fVarMin, fVarMax);
float varMin = MIN(fVarMin, fVarMax);
int idxArg = 0;
idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadOnly(frame));
idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_bgmodelUsedModes));
idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_weight));
idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_mean));
idxArg = kernel_apply.set(idxArg, ocl::KernelArg::PtrReadWrite(u_variance));
idxArg = kernel_apply.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(fgmask));
idxArg = kernel_apply.set(idxArg, (float)learningRate); //alphaT
idxArg = kernel_apply.set(idxArg, (float)alpha1);
idxArg = kernel_apply.set(idxArg, (float)(-learningRate*fCT)); //prune
idxArg = kernel_apply.set(idxArg, (float)varThreshold); //c_Tb
idxArg = kernel_apply.set(idxArg, backgroundRatio); //c_TB
idxArg = kernel_apply.set(idxArg, varThresholdGen); //c_Tg
idxArg = kernel_apply.set(idxArg, varMin);
idxArg = kernel_apply.set(idxArg, varMax);
idxArg = kernel_apply.set(idxArg, fVarInit);
idxArg = kernel_apply.set(idxArg, fTau);
if (bShadowDetection)
kernel_apply.set(idxArg, nShadowDetection);
size_t globalsize[] = {frame.cols, frame.rows, 1};
return kernel_apply.run(2, globalsize, NULL, true);
}
bool BackgroundSubtractorMOG2Impl::ocl_getBackgroundImage(OutputArray _backgroundImage) const
{
CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3);
_backgroundImage.create(frameSize, frameType);
UMat dst = _backgroundImage.getUMat();
int idxArg = 0;
idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::PtrReadOnly(u_bgmodelUsedModes));
idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::PtrReadOnly(u_weight));
idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::PtrReadOnly(u_mean));
idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::WriteOnly(dst));
kernel_getBg.set(idxArg, backgroundRatio);
size_t globalsize[2] = {u_bgmodelUsedModes.cols, u_bgmodelUsedModes.rows};
return kernel_getBg.run(2, globalsize, NULL, false);
}
#endif
void BackgroundSubtractorMOG2Impl::create_ocl_apply_kernel()
{
int nchannels = CV_MAT_CN(frameType);
String opts = format("-D CN=%d -D NMIXTURES=%d%s", nchannels, nmixtures, bShadowDetection ? " -D SHADOW_DETECT" : "");
kernel_apply.create("mog2_kernel", ocl::video::bgfg_mog2_oclsrc, opts);
}
void BackgroundSubtractorMOG2Impl::apply(InputArray _image, OutputArray _fgmask, double learningRate)
{
bool needToInitialize = nframes == 0 || learningRate >= 1 || _image.size() != frameSize || _image.type() != frameType;
if( needToInitialize )
initialize(_image.size(), _image.type());
if (opencl_ON)
{
CV_OCL_RUN(opencl_ON, ocl_apply(_image, _fgmask, learningRate))
opencl_ON = false;
initialize(_image.size(), _image.type());
}
Mat image = _image.getMat();
_fgmask.create( image.size(), CV_8U );
Mat fgmask = _fgmask.getMat();
++nframes;
learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history );
CV_Assert(learningRate >= 0);
parallel_for_(Range(0, image.rows),
MOG2Invoker(image, fgmask,
bgmodel.ptr<GMM>(),
(float*)(bgmodel.ptr() + sizeof(GMM)*nmixtures*image.rows*image.cols),
bgmodelUsedModes.ptr(), nmixtures, (float)learningRate,
(float)varThreshold,
backgroundRatio, varThresholdGen,
fVarInit, fVarMin, fVarMax, float(-learningRate*fCT), fTau,
bShadowDetection, nShadowDetection),
image.total()/(double)(1 << 16));
}
void BackgroundSubtractorMOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
{
if (opencl_ON)
{
CV_OCL_RUN(opencl_ON, ocl_getBackgroundImage(backgroundImage))
opencl_ON = false;
return;
}
int nchannels = CV_MAT_CN(frameType);
CV_Assert(nchannels == 1 || nchannels == 3);
Mat meanBackground(frameSize, CV_MAKETYPE(CV_8U, nchannels), Scalar::all(0));
int firstGaussianIdx = 0;
const GMM* gmm = bgmodel.ptr<GMM>();
const float* mean = reinterpret_cast<const float*>(gmm + frameSize.width*frameSize.height*nmixtures);
std::vector<float> meanVal(nchannels, 0.f);
for(int row=0; row<meanBackground.rows; row++)
{
for(int col=0; col<meanBackground.cols; col++)
{
int nmodes = bgmodelUsedModes.at<uchar>(row, col);
float totalWeight = 0.f;
for(int gaussianIdx = firstGaussianIdx; gaussianIdx < firstGaussianIdx + nmodes; gaussianIdx++)
{
GMM gaussian = gmm[gaussianIdx];
size_t meanPosition = gaussianIdx*nchannels;
for(int chn = 0; chn < nchannels; chn++)
{
meanVal[chn] += gaussian.weight * mean[meanPosition + chn];
}
totalWeight += gaussian.weight;
if(totalWeight > backgroundRatio)
break;
}
float invWeight = 1.f/totalWeight;
switch(nchannels)
{
case 1:
meanBackground.at<uchar>(row, col) = (uchar)(meanVal[0] * invWeight);
meanVal[0] = 0.f;
break;
case 3:
Vec3f& meanVec = *reinterpret_cast<Vec3f*>(&meanVal[0]);
meanBackground.at<Vec3b>(row, col) = Vec3b(meanVec * invWeight);
meanVec = 0.f;
break;
}
firstGaussianIdx += nmixtures;
}
}
meanBackground.copyTo(backgroundImage);
}
Ptr<BackgroundSubtractorMOG2> createBackgroundSubtractorMOG2(int _history, double _varThreshold,
bool _bShadowDetection)
{
return makePtr<BackgroundSubtractorMOG2Impl>(_history, (float)_varThreshold, _bShadowDetection);
}
}
/* End of file. */