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opencv/modules/gpu/src/fgd_bgfg.cpp

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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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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"
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
class cv::gpu::FGDStatModel::Impl
{
};
cv::gpu::FGDStatModel::Params::Params() { throw_no_cuda(); }
cv::gpu::FGDStatModel::FGDStatModel(int) { throw_no_cuda(); }
cv::gpu::FGDStatModel::FGDStatModel(const cv::gpu::GpuMat&, const Params&, int) { throw_no_cuda(); }
cv::gpu::FGDStatModel::~FGDStatModel() {}
void cv::gpu::FGDStatModel::create(const cv::gpu::GpuMat&, const Params&) { throw_no_cuda(); }
void cv::gpu::FGDStatModel::release() {}
int cv::gpu::FGDStatModel::update(const cv::gpu::GpuMat&) { throw_no_cuda(); return 0; }
#else
#include "fgd_bgfg_common.hpp"
namespace
{
class BGPixelStat
{
public:
void create(cv::Size size, const cv::gpu::FGDStatModel::Params& params, int out_cn);
void release();
void setTrained();
operator bgfg::BGPixelStat();
private:
cv::gpu::GpuMat Pbc_;
cv::gpu::GpuMat Pbcc_;
cv::gpu::GpuMat is_trained_st_model_;
cv::gpu::GpuMat is_trained_dyn_model_;
cv::gpu::GpuMat ctable_Pv_;
cv::gpu::GpuMat ctable_Pvb_;
cv::gpu::GpuMat ctable_v_;
cv::gpu::GpuMat cctable_Pv_;
cv::gpu::GpuMat cctable_Pvb_;
cv::gpu::GpuMat cctable_v1_;
cv::gpu::GpuMat cctable_v2_;
};
void BGPixelStat::create(cv::Size size, const cv::gpu::FGDStatModel::Params& params, int out_cn)
{
cv::gpu::ensureSizeIsEnough(size, CV_32FC1, Pbc_);
Pbc_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(size, CV_32FC1, Pbcc_);
Pbcc_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(size, CV_8UC1, is_trained_st_model_);
is_trained_st_model_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(size, CV_8UC1, is_trained_dyn_model_);
is_trained_dyn_model_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2c * size.height, size.width, CV_32FC1, ctable_Pv_);
ctable_Pv_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2c * size.height, size.width, CV_32FC1, ctable_Pvb_);
ctable_Pvb_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2c * size.height, size.width, CV_8UC(out_cn), ctable_v_);
ctable_v_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_32FC1, cctable_Pv_);
cctable_Pv_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_32FC1, cctable_Pvb_);
cctable_Pvb_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_8UC(out_cn), cctable_v1_);
cctable_v1_.setTo(cv::Scalar::all(0));
cv::gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_8UC(out_cn), cctable_v2_);
cctable_v2_.setTo(cv::Scalar::all(0));
}
void BGPixelStat::release()
{
Pbc_.release();
Pbcc_.release();
is_trained_st_model_.release();
is_trained_dyn_model_.release();
ctable_Pv_.release();
ctable_Pvb_.release();
ctable_v_.release();
cctable_Pv_.release();
cctable_Pvb_.release();
cctable_v1_.release();
cctable_v2_.release();
}
void BGPixelStat::setTrained()
{
is_trained_st_model_.setTo(cv::Scalar::all(1));
is_trained_dyn_model_.setTo(cv::Scalar::all(1));
}
BGPixelStat::operator bgfg::BGPixelStat()
{
bgfg::BGPixelStat stat;
stat.rows_ = Pbc_.rows;
stat.Pbc_data_ = Pbc_.data;
stat.Pbc_step_ = Pbc_.step;
stat.Pbcc_data_ = Pbcc_.data;
stat.Pbcc_step_ = Pbcc_.step;
stat.is_trained_st_model_data_ = is_trained_st_model_.data;
stat.is_trained_st_model_step_ = is_trained_st_model_.step;
stat.is_trained_dyn_model_data_ = is_trained_dyn_model_.data;
stat.is_trained_dyn_model_step_ = is_trained_dyn_model_.step;
stat.ctable_Pv_data_ = ctable_Pv_.data;
stat.ctable_Pv_step_ = ctable_Pv_.step;
stat.ctable_Pvb_data_ = ctable_Pvb_.data;
stat.ctable_Pvb_step_ = ctable_Pvb_.step;
stat.ctable_v_data_ = ctable_v_.data;
stat.ctable_v_step_ = ctable_v_.step;
stat.cctable_Pv_data_ = cctable_Pv_.data;
stat.cctable_Pv_step_ = cctable_Pv_.step;
stat.cctable_Pvb_data_ = cctable_Pvb_.data;
stat.cctable_Pvb_step_ = cctable_Pvb_.step;
stat.cctable_v1_data_ = cctable_v1_.data;
stat.cctable_v1_step_ = cctable_v1_.step;
stat.cctable_v2_data_ = cctable_v2_.data;
stat.cctable_v2_step_ = cctable_v2_.step;
return stat;
}
}
class cv::gpu::FGDStatModel::Impl
{
public:
Impl(cv::gpu::GpuMat& background, cv::gpu::GpuMat& foreground, std::vector< std::vector<cv::Point> >& foreground_regions, int out_cn);
~Impl();
void create(const cv::gpu::GpuMat& firstFrame, const cv::gpu::FGDStatModel::Params& params);
void release();
int update(const cv::gpu::GpuMat& curFrame);
private:
Impl(const Impl&);
Impl& operator=(const Impl&);
int out_cn_;
cv::gpu::FGDStatModel::Params params_;
cv::gpu::GpuMat& background_;
cv::gpu::GpuMat& foreground_;
std::vector< std::vector<cv::Point> >& foreground_regions_;
cv::Mat h_foreground_;
cv::gpu::GpuMat prevFrame_;
cv::gpu::GpuMat Ftd_;
cv::gpu::GpuMat Fbd_;
BGPixelStat stat_;
cv::gpu::GpuMat hist_;
cv::gpu::GpuMat histBuf_;
cv::gpu::GpuMat countBuf_;
cv::gpu::GpuMat buf_;
cv::gpu::GpuMat filterBuf_;
cv::gpu::GpuMat filterBrd_;
cv::Ptr<cv::gpu::FilterEngine_GPU> dilateFilter_;
cv::Ptr<cv::gpu::FilterEngine_GPU> erodeFilter_;
CvMemStorage* storage_;
};
cv::gpu::FGDStatModel::Impl::Impl(cv::gpu::GpuMat& background, cv::gpu::GpuMat& foreground, std::vector< std::vector<cv::Point> >& foreground_regions, int out_cn) :
out_cn_(out_cn), background_(background), foreground_(foreground), foreground_regions_(foreground_regions)
{
CV_Assert( out_cn_ == 3 || out_cn_ == 4 );
storage_ = cvCreateMemStorage();
CV_Assert( storage_ != 0 );
}
cv::gpu::FGDStatModel::Impl::~Impl()
{
cvReleaseMemStorage(&storage_);
}
namespace
{
void copyChannels(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, int dst_cn = -1)
{
const int src_cn = src.channels();
if (dst_cn < 0)
dst_cn = src_cn;
cv::gpu::ensureSizeIsEnough(src.size(), CV_MAKE_TYPE(src.depth(), dst_cn), dst);
if (src_cn == dst_cn)
src.copyTo(dst);
else
{
static const int cvt_codes[4][4] =
{
{-1, -1, cv::COLOR_GRAY2BGR, cv::COLOR_GRAY2BGRA},
{-1, -1, -1, -1},
{cv::COLOR_BGR2GRAY, -1, -1, cv::COLOR_BGR2BGRA},
{cv::COLOR_BGRA2GRAY, -1, cv::COLOR_BGRA2BGR, -1}
};
const int cvt_code = cvt_codes[src_cn - 1][dst_cn - 1];
CV_DbgAssert( cvt_code >= 0 );
cv::gpu::cvtColor(src, dst, cvt_code, dst_cn);
}
}
}
void cv::gpu::FGDStatModel::Impl::create(const cv::gpu::GpuMat& firstFrame, const cv::gpu::FGDStatModel::Params& params)
{
CV_Assert(firstFrame.type() == CV_8UC3 || firstFrame.type() == CV_8UC4);
params_ = params;
cv::gpu::ensureSizeIsEnough(firstFrame.size(), CV_8UC1, foreground_);
copyChannels(firstFrame, background_, out_cn_);
copyChannels(firstFrame, prevFrame_);
cv::gpu::ensureSizeIsEnough(firstFrame.size(), CV_8UC1, Ftd_);
cv::gpu::ensureSizeIsEnough(firstFrame.size(), CV_8UC1, Fbd_);
stat_.create(firstFrame.size(), params_, out_cn_);
bgfg::setBGPixelStat(stat_);
if (params_.perform_morphing > 0)
{
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(1 + params_.perform_morphing * 2, 1 + params_.perform_morphing * 2));
cv::Point anchor(params_.perform_morphing, params_.perform_morphing);
dilateFilter_ = cv::gpu::createMorphologyFilter_GPU(cv::MORPH_DILATE, CV_8UC1, kernel, filterBuf_, anchor);
erodeFilter_ = cv::gpu::createMorphologyFilter_GPU(cv::MORPH_ERODE, CV_8UC1, kernel, filterBuf_, anchor);
}
}
void cv::gpu::FGDStatModel::Impl::release()
{
background_.release();
foreground_.release();
prevFrame_.release();
Ftd_.release();
Fbd_.release();
stat_.release();
hist_.release();
histBuf_.release();
countBuf_.release();
buf_.release();
filterBuf_.release();
filterBrd_.release();
}
/////////////////////////////////////////////////////////////////////////
// changeDetection
namespace
{
void calcDiffHistogram(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, cv::gpu::GpuMat& hist, cv::gpu::GpuMat& histBuf)
{
typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, unsigned int* hist0, unsigned int* hist1, unsigned int* hist2, unsigned int* partialBuf0, unsigned int* partialBuf1, unsigned int* partialBuf2, bool cc20, cudaStream_t stream);
static const func_t funcs[4][4] =
{
{0,0,0,0},
{0,0,0,0},
{0,0,bgfg::calcDiffHistogram_gpu<uchar3, uchar3>,bgfg::calcDiffHistogram_gpu<uchar3, uchar4>},
{0,0,bgfg::calcDiffHistogram_gpu<uchar4, uchar3>,bgfg::calcDiffHistogram_gpu<uchar4, uchar4>}
};
hist.create(3, 256, CV_32SC1);
histBuf.create(3, bgfg::PARTIAL_HISTOGRAM_COUNT * bgfg::HISTOGRAM_BIN_COUNT, CV_32SC1);
funcs[prevFrame.channels() - 1][curFrame.channels() - 1](
prevFrame, curFrame,
hist.ptr<unsigned int>(0), hist.ptr<unsigned int>(1), hist.ptr<unsigned int>(2),
histBuf.ptr<unsigned int>(0), histBuf.ptr<unsigned int>(1), histBuf.ptr<unsigned int>(2),
cv::gpu::deviceSupports(cv::gpu::FEATURE_SET_COMPUTE_20), 0);
}
void calcRelativeVariance(unsigned int hist[3 * 256], double relativeVariance[3][bgfg::HISTOGRAM_BIN_COUNT])
{
std::memset(relativeVariance, 0, 3 * bgfg::HISTOGRAM_BIN_COUNT * sizeof(double));
for (int thres = bgfg::HISTOGRAM_BIN_COUNT - 2; thres >= 0; --thres)
{
cv::Vec3d sum(0.0, 0.0, 0.0);
cv::Vec3d sqsum(0.0, 0.0, 0.0);
cv::Vec3i count(0, 0, 0);
for (int j = thres; j < bgfg::HISTOGRAM_BIN_COUNT; ++j)
{
sum[0] += static_cast<double>(j) * hist[j];
sqsum[0] += static_cast<double>(j * j) * hist[j];
count[0] += hist[j];
sum[1] += static_cast<double>(j) * hist[j + 256];
sqsum[1] += static_cast<double>(j * j) * hist[j + 256];
count[1] += hist[j + 256];
sum[2] += static_cast<double>(j) * hist[j + 512];
sqsum[2] += static_cast<double>(j * j) * hist[j + 512];
count[2] += hist[j + 512];
}
count[0] = std::max(count[0], 1);
count[1] = std::max(count[1], 1);
count[2] = std::max(count[2], 1);
cv::Vec3d my(
sum[0] / count[0],
sum[1] / count[1],
sum[2] / count[2]
);
relativeVariance[0][thres] = std::sqrt(sqsum[0] / count[0] - my[0] * my[0]);
relativeVariance[1][thres] = std::sqrt(sqsum[1] / count[1] - my[1] * my[1]);
relativeVariance[2][thres] = std::sqrt(sqsum[2] / count[2] - my[2] * my[2]);
}
}
void calcDiffThreshMask(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, cv::Vec3d bestThres, cv::gpu::GpuMat& changeMask)
{
typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, uchar3 bestThres, cv::gpu::PtrStepSzb changeMask, cudaStream_t stream);
static const func_t funcs[4][4] =
{
{0,0,0,0},
{0,0,0,0},
{0,0,bgfg::calcDiffThreshMask_gpu<uchar3, uchar3>,bgfg::calcDiffThreshMask_gpu<uchar3, uchar4>},
{0,0,bgfg::calcDiffThreshMask_gpu<uchar4, uchar3>,bgfg::calcDiffThreshMask_gpu<uchar4, uchar4>}
};
changeMask.setTo(cv::Scalar::all(0));
funcs[prevFrame.channels() - 1][curFrame.channels() - 1](prevFrame, curFrame, make_uchar3((uchar)bestThres[0], (uchar)bestThres[1], (uchar)bestThres[2]), changeMask, 0);
}
// performs change detection for Foreground detection algorithm
void changeDetection(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, cv::gpu::GpuMat& changeMask, cv::gpu::GpuMat& hist, cv::gpu::GpuMat& histBuf)
{
calcDiffHistogram(prevFrame, curFrame, hist, histBuf);
unsigned int histData[3 * 256];
cv::Mat h_hist(3, 256, CV_32SC1, histData);
hist.download(h_hist);
double relativeVariance[3][bgfg::HISTOGRAM_BIN_COUNT];
calcRelativeVariance(histData, relativeVariance);
// Find maximum:
cv::Vec3d bestThres(10.0, 10.0, 10.0);
for (int i = 0; i < bgfg::HISTOGRAM_BIN_COUNT; ++i)
{
bestThres[0] = std::max(bestThres[0], relativeVariance[0][i]);
bestThres[1] = std::max(bestThres[1], relativeVariance[1][i]);
bestThres[2] = std::max(bestThres[2], relativeVariance[2][i]);
}
calcDiffThreshMask(prevFrame, curFrame, bestThres, changeMask);
}
}
/////////////////////////////////////////////////////////////////////////
// bgfgClassification
namespace
{
int bgfgClassification(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame,
const cv::gpu::GpuMat& Ftd, const cv::gpu::GpuMat& Fbd,
cv::gpu::GpuMat& foreground, cv::gpu::GpuMat& countBuf,
const cv::gpu::FGDStatModel::Params& params, int out_cn)
{
typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, cv::gpu::PtrStepSzb Ftd, cv::gpu::PtrStepSzb Fbd, cv::gpu::PtrStepSzb foreground,
int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
static const func_t funcs[4][4][4] =
{
{
{0,0,0,0}, {0,0,0,0}, {0,0,0,0}, {0,0,0,0}
},
{
{0,0,0,0}, {0,0,0,0}, {0,0,0,0}, {0,0,0,0}
},
{
{0,0,0,0}, {0,0,0,0},
{0,0,bgfg::bgfgClassification_gpu<uchar3, uchar3, uchar3>,bgfg::bgfgClassification_gpu<uchar3, uchar3, uchar4>},
{0,0,bgfg::bgfgClassification_gpu<uchar3, uchar4, uchar3>,bgfg::bgfgClassification_gpu<uchar3, uchar4, uchar4>}
},
{
{0,0,0,0}, {0,0,0,0},
{0,0,bgfg::bgfgClassification_gpu<uchar4, uchar3, uchar3>,bgfg::bgfgClassification_gpu<uchar4, uchar3, uchar4>},
{0,0,bgfg::bgfgClassification_gpu<uchar4, uchar4, uchar3>,bgfg::bgfgClassification_gpu<uchar4, uchar4, uchar4>}
}
};
const int deltaC = cvRound(params.delta * 256 / params.Lc);
const int deltaCC = cvRound(params.delta * 256 / params.Lcc);
funcs[prevFrame.channels() - 1][curFrame.channels() - 1][out_cn - 1](prevFrame, curFrame, Ftd, Fbd, foreground, deltaC, deltaCC, params.alpha2, params.N1c, params.N1cc, 0);
int count = cv::gpu::countNonZero(foreground, countBuf);
cv::gpu::multiply(foreground, cv::Scalar::all(255), foreground);
return count;
}
}
/////////////////////////////////////////////////////////////////////////
// smoothForeground
namespace
{
void morphology(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, cv::gpu::GpuMat& filterBrd, int brd, cv::Ptr<cv::gpu::FilterEngine_GPU>& filter, cv::Scalar brdVal)
{
cv::gpu::copyMakeBorder(src, filterBrd, brd, brd, brd, brd, cv::BORDER_CONSTANT, brdVal);
filter->apply(filterBrd(cv::Rect(brd, brd, src.cols, src.rows)), dst, cv::Rect(0, 0, src.cols, src.rows));
}
void smoothForeground(cv::gpu::GpuMat& foreground, cv::gpu::GpuMat& filterBrd, cv::gpu::GpuMat& buf,
cv::Ptr<cv::gpu::FilterEngine_GPU>& erodeFilter, cv::Ptr<cv::gpu::FilterEngine_GPU>& dilateFilter,
const cv::gpu::FGDStatModel::Params& params)
{
const int brd = params.perform_morphing;
const cv::Scalar erodeBrdVal = cv::Scalar::all(UCHAR_MAX);
const cv::Scalar dilateBrdVal = cv::Scalar::all(0);
// MORPH_OPEN
morphology(foreground, buf, filterBrd, brd, erodeFilter, erodeBrdVal);
morphology(buf, foreground, filterBrd, brd, dilateFilter, dilateBrdVal);
// MORPH_CLOSE
morphology(foreground, buf, filterBrd, brd, dilateFilter, dilateBrdVal);
morphology(buf, foreground, filterBrd, brd, erodeFilter, erodeBrdVal);
}
}
/////////////////////////////////////////////////////////////////////////
// findForegroundRegions
namespace
{
void seqToContours(CvSeq* _ccontours, CvMemStorage* storage, cv::OutputArrayOfArrays _contours)
{
cv::Seq<CvSeq*> all_contours(cvTreeToNodeSeq(_ccontours, sizeof(CvSeq), storage));
size_t total = all_contours.size();
_contours.create((int) total, 1, 0, -1, true);
cv::SeqIterator<CvSeq*> it = all_contours.begin();
for (size_t i = 0; i < total; ++i, ++it)
{
CvSeq* c = *it;
((CvContour*)c)->color = (int)i;
_contours.create((int)c->total, 1, CV_32SC2, (int)i, true);
cv::Mat ci = _contours.getMat((int)i);
CV_Assert( ci.isContinuous() );
cvCvtSeqToArray(c, ci.data);
}
}
int findForegroundRegions(cv::gpu::GpuMat& d_foreground, cv::Mat& h_foreground, std::vector< std::vector<cv::Point> >& foreground_regions,
CvMemStorage* storage, const cv::gpu::FGDStatModel::Params& params)
{
int region_count = 0;
// Discard under-size foreground regions:
d_foreground.download(h_foreground);
IplImage ipl_foreground = h_foreground;
CvSeq* first_seq = 0;
cvFindContours(&ipl_foreground, storage, &first_seq, sizeof(CvContour), CV_RETR_LIST);
for (CvSeq* seq = first_seq; seq; seq = seq->h_next)
{
CvContour* cnt = reinterpret_cast<CvContour*>(seq);
if (cnt->rect.width * cnt->rect.height < params.minArea || (params.is_obj_without_holes && CV_IS_SEQ_HOLE(seq)))
{
// Delete under-size contour:
CvSeq* 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++;
}
}
seqToContours(first_seq, storage, foreground_regions);
h_foreground.setTo(0);
cv::drawContours(h_foreground, foreground_regions, -1, cv::Scalar::all(255), -1);
d_foreground.upload(h_foreground);
return region_count;
}
}
/////////////////////////////////////////////////////////////////////////
// updateBackgroundModel
namespace
{
void updateBackgroundModel(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, const cv::gpu::GpuMat& Ftd, const cv::gpu::GpuMat& Fbd,
const cv::gpu::GpuMat& foreground, cv::gpu::GpuMat& background,
const cv::gpu::FGDStatModel::Params& params)
{
typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, cv::gpu::PtrStepSzb Ftd, cv::gpu::PtrStepSzb Fbd,
cv::gpu::PtrStepSzb foreground, cv::gpu::PtrStepSzb background,
int deltaC, int deltaCC, float alpha1, float alpha2, float alpha3, int N1c, int N1cc, int N2c, int N2cc, float T, cudaStream_t stream);
static const func_t funcs[4][4][4] =
{
{
{0,0,0,0}, {0,0,0,0}, {0,0,0,0}, {0,0,0,0}
},
{
{0,0,0,0}, {0,0,0,0}, {0,0,0,0}, {0,0,0,0}
},
{
{0,0,0,0}, {0,0,0,0},
{0,0,bgfg::updateBackgroundModel_gpu<uchar3, uchar3, uchar3>,bgfg::updateBackgroundModel_gpu<uchar3, uchar3, uchar4>},
{0,0,bgfg::updateBackgroundModel_gpu<uchar3, uchar4, uchar3>,bgfg::updateBackgroundModel_gpu<uchar3, uchar4, uchar4>}
},
{
{0,0,0,0}, {0,0,0,0},
{0,0,bgfg::updateBackgroundModel_gpu<uchar4, uchar3, uchar3>,bgfg::updateBackgroundModel_gpu<uchar4, uchar3, uchar4>},
{0,0,bgfg::updateBackgroundModel_gpu<uchar4, uchar4, uchar3>,bgfg::updateBackgroundModel_gpu<uchar4, uchar4, uchar4>}
}
};
const int deltaC = cvRound(params.delta * 256 / params.Lc);
const int deltaCC = cvRound(params.delta * 256 / params.Lcc);
funcs[prevFrame.channels() - 1][curFrame.channels() - 1][background.channels() - 1](
prevFrame, curFrame, Ftd, Fbd, foreground, background,
deltaC, deltaCC, params.alpha1, params.alpha2, params.alpha3, params.N1c, params.N1cc, params.N2c, params.N2cc, params.T,
0);
}
}
/////////////////////////////////////////////////////////////////////////
// Impl::update
int cv::gpu::FGDStatModel::Impl::update(const cv::gpu::GpuMat& curFrame)
{
CV_Assert(curFrame.type() == CV_8UC3 || curFrame.type() == CV_8UC4);
CV_Assert(curFrame.size() == prevFrame_.size());
cvClearMemStorage(storage_);
foreground_regions_.clear();
foreground_.setTo(cv::Scalar::all(0));
changeDetection(prevFrame_, curFrame, Ftd_, hist_, histBuf_);
changeDetection(background_, curFrame, Fbd_, hist_, histBuf_);
int FG_pixels_count = bgfgClassification(prevFrame_, curFrame, Ftd_, Fbd_, foreground_, countBuf_, params_, out_cn_);
if (params_.perform_morphing > 0)
smoothForeground(foreground_, filterBrd_, buf_, erodeFilter_, dilateFilter_, params_);
int region_count = 0;
if (params_.minArea > 0 || params_.is_obj_without_holes)
region_count = findForegroundRegions(foreground_, h_foreground_, foreground_regions_, storage_, params_);
// Check ALL BG update condition:
const double BGFG_FGD_BG_UPDATE_TRESH = 0.5;
if (static_cast<double>(FG_pixels_count) / Ftd_.size().area() > BGFG_FGD_BG_UPDATE_TRESH)
stat_.setTrained();
updateBackgroundModel(prevFrame_, curFrame, Ftd_, Fbd_, foreground_, background_, params_);
copyChannels(curFrame, prevFrame_);
return region_count;
}
namespace
{
// Default parameters of foreground detection algorithm:
const int BGFG_FGD_LC = 128;
const int BGFG_FGD_N1C = 15;
const int BGFG_FGD_N2C = 25;
const int BGFG_FGD_LCC = 64;
const int BGFG_FGD_N1CC = 25;
const int BGFG_FGD_N2CC = 40;
// Background reference image update parameter:
const float BGFG_FGD_ALPHA_1 = 0.1f;
// stat model update parameter
// 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG)
const float BGFG_FGD_ALPHA_2 = 0.005f;
// start value for alpha parameter (to fast initiate statistic model)
const float BGFG_FGD_ALPHA_3 = 0.1f;
const float BGFG_FGD_DELTA = 2.0f;
const float BGFG_FGD_T = 0.9f;
const float BGFG_FGD_MINAREA= 15.0f;
}
cv::gpu::FGDStatModel::Params::Params()
{
Lc = BGFG_FGD_LC;
N1c = BGFG_FGD_N1C;
N2c = BGFG_FGD_N2C;
Lcc = BGFG_FGD_LCC;
N1cc = BGFG_FGD_N1CC;
N2cc = BGFG_FGD_N2CC;
delta = BGFG_FGD_DELTA;
alpha1 = BGFG_FGD_ALPHA_1;
alpha2 = BGFG_FGD_ALPHA_2;
alpha3 = BGFG_FGD_ALPHA_3;
T = BGFG_FGD_T;
minArea = BGFG_FGD_MINAREA;
is_obj_without_holes = true;
perform_morphing = 1;
}
cv::gpu::FGDStatModel::FGDStatModel(int out_cn)
{
impl_.reset(new Impl(background, foreground, foreground_regions, out_cn));
}
cv::gpu::FGDStatModel::FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params, int out_cn)
{
impl_.reset(new Impl(background, foreground, foreground_regions, out_cn));
create(firstFrame, params);
}
cv::gpu::FGDStatModel::~FGDStatModel()
{
}
void cv::gpu::FGDStatModel::create(const cv::gpu::GpuMat& firstFrame, const Params& params)
{
impl_->create(firstFrame, params);
}
void cv::gpu::FGDStatModel::release()
{
impl_->release();
}
int cv::gpu::FGDStatModel::update(const cv::gpu::GpuMat& curFrame)
{
return impl_->update(curFrame);
}
#endif // HAVE_CUDA