opencv/modules/video/src/bgfg_gaussmix.cpp

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#include "precomp.hpp"
#include <float.h>
// to make sure we can use these short names
#undef K
#undef L
#undef T
// 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.
//
namespace cv
{
BackgroundSubtractor::~BackgroundSubtractor() {}
void BackgroundSubtractor::operator()(const InputArray&, OutputArray, double)
{
}
static const int defaultNMixtures = CV_BGFG_MOG_NGAUSSIANS;
static const int defaultHistory = CV_BGFG_MOG_WINDOW_SIZE;
static const double defaultBackgroundRatio = CV_BGFG_MOG_BACKGROUND_THRESHOLD;
static const double defaultVarThreshold = CV_BGFG_MOG_STD_THRESHOLD*CV_BGFG_MOG_STD_THRESHOLD;
static const double defaultNoiseSigma = CV_BGFG_MOG_SIGMA_INIT*0.5;
BackgroundSubtractorMOG::BackgroundSubtractorMOG()
{
frameSize = Size(0,0);
frameType = 0;
nframes = 0;
nmixtures = defaultNMixtures;
history = defaultHistory;
varThreshold = defaultVarThreshold;
backgroundRatio = defaultBackgroundRatio;
noiseSigma = defaultNoiseSigma;
}
BackgroundSubtractorMOG::BackgroundSubtractorMOG(int _history, int _nmixtures,
double _backgroundRatio,
double _noiseSigma)
{
frameSize = Size(0,0);
frameType = 0;
nframes = 0;
nmixtures = min(_nmixtures > 0 ? _nmixtures : defaultNMixtures, 8);
history = _history > 0 ? _history : defaultHistory;
varThreshold = defaultVarThreshold;
backgroundRatio = min(_backgroundRatio > 0 ? _backgroundRatio : 0.95, 1.);
noiseSigma = _noiseSigma <= 0 ? defaultNoiseSigma : _noiseSigma;
}
BackgroundSubtractorMOG::~BackgroundSubtractorMOG()
{
}
void BackgroundSubtractorMOG::initialize(Size _frameSize, int _frameType)
{
frameSize = _frameSize;
frameType = _frameType;
nframes = 0;
int nchannels = CV_MAT_CN(frameType);
CV_Assert( CV_MAT_DEPTH(frameType) == CV_8U );
// for each gaussian mixture of each pixel bg model we store ...
// the mixture sort key (w/sum_of_variances), the mixture weight (w),
// the mean (nchannels values) and
// the diagonal covariance matrix (another nchannels values)
bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + 2*nchannels), CV_32F );
bgmodel = Scalar::all(0);
}
template<typename VT> struct MixData
{
float sortKey;
float weight;
VT mean;
VT var;
};
static void process8uC1( BackgroundSubtractorMOG& obj, const Mat& image, Mat& fgmask, double learningRate )
{
int x, y, k, k1, rows = image.rows, cols = image.cols;
float alpha = (float)learningRate, T = (float)obj.backgroundRatio, vT = (float)obj.varThreshold;
int K = obj.nmixtures;
MixData<float>* mptr = (MixData<float>*)obj.bgmodel.data;
const float w0 = (float)CV_BGFG_MOG_WEIGHT_INIT;
const float sk0 = (float)(w0/CV_BGFG_MOG_SIGMA_INIT);
const float var0 = (float)(CV_BGFG_MOG_SIGMA_INIT*CV_BGFG_MOG_SIGMA_INIT);
const float minVar = (float)(obj.noiseSigma*obj.noiseSigma);
for( y = 0; y < rows; y++ )
{
const uchar* src = image.ptr<uchar>(y);
uchar* dst = fgmask.ptr<uchar>(y);
if( alpha > 0 )
{
for( x = 0; x < cols; x++, mptr += K )
{
float wsum = 0;
float pix = src[x];
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
float w = mptr[k].weight;
wsum += w;
if( w < FLT_EPSILON )
break;
float mu = mptr[k].mean;
float var = mptr[k].var;
float diff = pix - mu;
float d2 = diff*diff;
if( d2 < vT*var )
{
wsum -= w;
float dw = alpha*(1.f - w);
mptr[k].weight = w + dw;
mptr[k].mean = mu + alpha*diff;
var = max(var + alpha*(d2 - var), minVar);
mptr[k].var = var;
mptr[k].sortKey = w/sqrt(var);
for( k1 = k-1; k1 >= 0; k1-- )
{
if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
break;
std::swap( mptr[k1], mptr[k1+1] );
}
kHit = k1+1;
break;
}
}
if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
{
kHit = k = min(k, K-1);
wsum += w0 - mptr[k].weight;
mptr[k].weight = w0;
mptr[k].mean = pix;
mptr[k].var = var0;
mptr[k].sortKey = sk0;
}
else
for( ; k < K; k++ )
wsum += mptr[k].weight;
float wscale = 1.f/wsum;
wsum = 0;
for( k = 0; k < K; k++ )
{
wsum += mptr[k].weight *= wscale;
mptr[k].sortKey *= wscale;
if( wsum > T && kForeground < 0 )
kForeground = k+1;
}
dst[x] = (uchar)(-(kHit >= kForeground));
}
}
else
{
for( x = 0; x < cols; x++, mptr += K )
{
float pix = src[x];
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
if( mptr[k].weight < FLT_EPSILON )
break;
float mu = mptr[k].mean;
float var = mptr[k].var;
float diff = pix - mu;
float d2 = diff*diff;
if( d2 < vT*var )
{
kHit = k;
break;
}
}
if( kHit >= 0 )
{
float wsum = 0;
for( k = 0; k < K; k++ )
{
wsum += mptr[k].weight;
if( wsum > T )
{
kForeground = k+1;
break;
}
}
}
dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
}
}
}
}
static void process8uC3( BackgroundSubtractorMOG& obj, const Mat& image, Mat& fgmask, double learningRate )
{
int x, y, k, k1, rows = image.rows, cols = image.cols;
float alpha = (float)learningRate, T = (float)obj.backgroundRatio, vT = (float)obj.varThreshold;
int K = obj.nmixtures;
const float w0 = (float)CV_BGFG_MOG_WEIGHT_INIT;
const float sk0 = (float)(w0/CV_BGFG_MOG_SIGMA_INIT*sqrt(3.));
const float var0 = (float)(CV_BGFG_MOG_SIGMA_INIT*CV_BGFG_MOG_SIGMA_INIT);
const float minVar = (float)(obj.noiseSigma*obj.noiseSigma);
MixData<Vec3f>* mptr = (MixData<Vec3f>*)obj.bgmodel.data;
for( y = 0; y < rows; y++ )
{
const uchar* src = image.ptr<uchar>(y);
uchar* dst = fgmask.ptr<uchar>(y);
if( alpha > 0 )
{
for( x = 0; x < cols; x++, mptr += K )
{
float wsum = 0;
Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
float w = mptr[k].weight;
wsum += w;
if( w < FLT_EPSILON )
break;
Vec3f mu = mptr[k].mean;
Vec3f var = mptr[k].var;
Vec3f diff = pix - mu;
float d2 = diff.dot(diff);
if( d2 < vT*(var[0] + var[1] + var[2]) )
{
wsum -= w;
float dw = alpha*(1.f - w);
mptr[k].weight = w + dw;
mptr[k].mean = mu + alpha*diff;
var = Vec3f(max(var[0] + alpha*(diff[0]*diff[0] - var[0]), minVar),
max(var[1] + alpha*(diff[1]*diff[1] - var[1]), minVar),
max(var[2] + alpha*(diff[2]*diff[2] - var[2]), minVar));
mptr[k].var = var;
mptr[k].sortKey = w/sqrt(var[0] + var[1] + var[2]);
for( k1 = k-1; k1 >= 0; k1-- )
{
if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
break;
std::swap( mptr[k1], mptr[k1+1] );
}
kHit = k1+1;
break;
}
}
if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
{
kHit = k = min(k, K-1);
wsum += w0 - mptr[k].weight;
mptr[k].weight = w0;
mptr[k].mean = pix;
mptr[k].var = Vec3f(var0, var0, var0);
mptr[k].sortKey = sk0;
}
else
for( ; k < K; k++ )
wsum += mptr[k].weight;
float wscale = 1.f/wsum;
wsum = 0;
for( k = 0; k < K; k++ )
{
wsum += mptr[k].weight *= wscale;
mptr[k].sortKey *= wscale;
if( wsum > T && kForeground < 0 )
kForeground = k+1;
}
dst[x] = (uchar)(-(kHit >= kForeground));
}
}
else
{
for( x = 0; x < cols; x++, mptr += K )
{
Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
int kHit = -1, kForeground = -1;
for( k = 0; k < K; k++ )
{
if( mptr[k].weight < FLT_EPSILON )
break;
Vec3f mu = mptr[k].mean;
Vec3f var = mptr[k].var;
Vec3f diff = pix - mu;
float d2 = diff.dot(diff);
if( d2 < vT*(var[0] + var[1] + var[2]) )
{
kHit = k;
break;
}
}
if( kHit >= 0 )
{
float wsum = 0;
for( k = 0; k < K; k++ )
{
wsum += mptr[k].weight;
if( wsum > T )
{
kForeground = k+1;
break;
}
}
}
dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
}
}
}
}
void BackgroundSubtractorMOG::operator()(const InputArray& _image, OutputArray _fgmask, double learningRate)
{
Mat image = _image.getMat();
bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;
if( needToInitialize )
initialize(image.size(), image.type());
CV_Assert( image.depth() == CV_8U );
_fgmask.create( image.size(), CV_8U );
Mat fgmask = _fgmask.getMat();
++nframes;
learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./min( nframes, history );
CV_Assert(learningRate >= 0);
if( image.type() == CV_8UC1 )
process8uC1( *this, image, fgmask, learningRate );
else if( image.type() == CV_8UC3 )
process8uC3( *this, image, fgmask, learningRate );
else
CV_Error( CV_StsUnsupportedFormat, "Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOG" );
}
}
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;
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 );
/*
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);*/
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;
}
/* End of file. */