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Commit 62edeeed authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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refactored FGD algorithm

parent 69779309
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Showing with 420 additions and 493 deletions
modules/gpubgsegm/include/opencv2/gpubgsegm.hpp
View file @ 62edeeed
......@@ -50,9 +50,6 @@
#include "opencv2/core/gpu.hpp"
#include "opencv2/video/background_segm.hpp"
#include <memory>
#include "opencv2/gpufilters.hpp"
namespace cv { namespace gpu {
////////////////////////////////////////////////////
......@@ -105,76 +102,51 @@ public:
CV_EXPORTS Ptr<gpu::BackgroundSubtractorGMG>
createBackgroundSubtractorGMG(int initializationFrames = 120, double decisionThreshold = 0.8);
// Foreground Object Detection from Videos Containing Complex Background.
// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
// ACM MM2003 9p
class CV_EXPORTS FGDStatModel
////////////////////////////////////////////////////
// FGD
/**
* Foreground Object Detection from Videos Containing Complex Background.
* Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
* ACM MM2003 9p
*/
class CV_EXPORTS BackgroundSubtractorFGD : public cv::BackgroundSubtractor
{
public:
struct CV_EXPORTS Params
{
int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
// Used to allow the first N1c vectors to adapt over time to changing background.
int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
// Used to allow the first N1cc vectors to adapt over time to changing background.
bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
// These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
float delta; // Affects color and color co-occurrence quantization, typically set to 2.
float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
// default Params
Params();
};
// out_cn - channels count in output result (can be 3 or 4)
// 4-channels require more memory, but a bit faster
explicit FGDStatModel(int out_cn = 3);
explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
~FGDStatModel();
void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
void release();
int update(const cv::gpu::GpuMat& curFrame);
//8UC3 or 8UC4 reference background image
cv::gpu::GpuMat background;
//8UC1 foreground image
cv::gpu::GpuMat foreground;
std::vector< std::vector<cv::Point> > foreground_regions;
private:
FGDStatModel(const FGDStatModel&);
FGDStatModel& operator=(const FGDStatModel&);
virtual void getForegroundRegions(OutputArrayOfArrays foreground_regions) = 0;
};
class Impl;
std::auto_ptr<Impl> impl_;
struct CV_EXPORTS FGDParams
{
int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
// Used to allow the first N1c vectors to adapt over time to changing background.
int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
// Used to allow the first N1cc vectors to adapt over time to changing background.
bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
// These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
float delta; // Affects color and color co-occurrence quantization, typically set to 2.
float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
// default Params
FGDParams();
};
CV_EXPORTS Ptr<gpu::BackgroundSubtractorFGD>
createBackgroundSubtractorFGD(const FGDParams& params = FGDParams());
}} // namespace cv { namespace gpu {
#endif /* __OPENCV_GPUBGSEGM_HPP__ */
modules/gpubgsegm/perf/perf_bgsegm.cpp
View file @ 62edeeed
......@@ -42,6 +42,7 @@
#include "perf_precomp.hpp"
#include "opencv2/legacy.hpp"
#include "opencv2/gpuimgproc.hpp"
using namespace std;
using namespace testing;
......@@ -90,10 +91,10 @@ PERF_TEST_P(Video, FGDStatModel,
if (PERF_RUN_GPU())
{
cv::gpu::GpuMat d_frame(frame);
cv::gpu::GpuMat d_frame(frame), foreground, background3, background;
cv::gpu::FGDStatModel d_model(4);
d_model.create(d_frame);
cv::Ptr<cv::gpu::BackgroundSubtractorFGD> d_fgd = cv::gpu::createBackgroundSubtractorFGD();
d_fgd->apply(d_frame, foreground);
for (int i = 0; i < 10; ++i)
{
......@@ -103,12 +104,12 @@ PERF_TEST_P(Video, FGDStatModel,
d_frame.upload(frame);
startTimer(); next();
d_model.update(d_frame);
d_fgd->apply(d_frame, foreground);
stopTimer();
}
const cv::gpu::GpuMat background = d_model.background;
const cv::gpu::GpuMat foreground = d_model.foreground;
d_fgd->getBackgroundImage(background3);
cv::gpu::cvtColor(background3, background, cv::COLOR_BGR2BGRA);
GPU_SANITY_CHECK(background, 1e-2, ERROR_RELATIVE);
GPU_SANITY_CHECK(foreground, 1e-2, ERROR_RELATIVE);
......
modules/gpubgsegm/src/cuda/fgd.cu
View file @ 62edeeed
......@@ -53,7 +53,7 @@
using namespace cv::gpu;
using namespace cv::gpu::cudev;
namespace bgfg
namespace fgd
{
////////////////////////////////////////////////////////////////////////////
// calcDiffHistogram
......
modules/gpubgsegm/src/cuda/fgd.hpp
View file @ 62edeeed
......@@ -45,7 +45,7 @@
#include "opencv2/core/gpu_types.hpp"
namespace bgfg
namespace fgd
{
struct BGPixelStat
{
......
modules/gpubgsegm/src/fgd.cpp
View file @ 62edeeed
......@@ -42,329 +42,150 @@
#include "precomp.hpp"
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
using namespace cv;
using namespace cv::gpu;
class cv::gpu::FGDStatModel::Impl
{
};
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
cv::gpu::FGDStatModel::Params::Params() { throw_no_cuda(); }
cv::gpu::FGDParams::FGDParams() { 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; }
Ptr<gpu::BackgroundSubtractorFGD> cv::gpu::createBackgroundSubtractorFGD(const FGDParams&) { throw_no_cuda(); return Ptr<gpu::BackgroundSubtractorFGD>(); }
#else
#include "cuda/fgd.hpp"
#include "opencv2/imgproc/imgproc_c.h"
/////////////////////////////////////////////////////////////////////////
// FGDParams
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;
// Default parameters of foreground detection algorithm:
const int BGFG_FGD_LC = 128;
const int BGFG_FGD_N1C = 15;
const int BGFG_FGD_N2C = 25;
stat.ctable_Pvb_data_ = ctable_Pvb_.data;
stat.ctable_Pvb_step_ = ctable_Pvb_.step;
const int BGFG_FGD_LCC = 64;
const int BGFG_FGD_N1CC = 25;
const int BGFG_FGD_N2CC = 40;
stat.ctable_v_data_ = ctable_v_.data;
stat.ctable_v_step_ = ctable_v_.step;
// Background reference image update parameter:
const float BGFG_FGD_ALPHA_1 = 0.1f;
stat.cctable_Pv_data_ = cctable_Pv_.data;
stat.cctable_Pv_step_ = cctable_Pv_.step;
// 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;
stat.cctable_Pvb_data_ = cctable_Pvb_.data;
stat.cctable_Pvb_step_ = cctable_Pvb_.step;
// start value for alpha parameter (to fast initiate statistic model)
const float BGFG_FGD_ALPHA_3 = 0.1f;
stat.cctable_v1_data_ = cctable_v1_.data;
stat.cctable_v1_step_ = cctable_v1_.step;
const float BGFG_FGD_DELTA = 2.0f;
stat.cctable_v2_data_ = cctable_v2_.data;
stat.cctable_v2_step_ = cctable_v2_.step;
const float BGFG_FGD_T = 0.9f;
return stat;
}
const float BGFG_FGD_MINAREA= 15.0f;
}
class cv::gpu::FGDStatModel::Impl
cv::gpu::FGDParams::FGDParams()
{
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_;
Lc = BGFG_FGD_LC;
N1c = BGFG_FGD_N1C;
N2c = BGFG_FGD_N2C;
cv::gpu::GpuMat buf_;
cv::gpu::GpuMat filterBrd_;
Lcc = BGFG_FGD_LCC;
N1cc = BGFG_FGD_N1CC;
N2cc = BGFG_FGD_N2CC;
cv::Ptr<cv::gpu::Filter> dilateFilter_;
cv::Ptr<cv::gpu::Filter> erodeFilter_;
delta = BGFG_FGD_DELTA;
CvMemStorage* storage_;
};
alpha1 = BGFG_FGD_ALPHA_1;
alpha2 = BGFG_FGD_ALPHA_2;
alpha3 = BGFG_FGD_ALPHA_3;
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 );
T = BGFG_FGD_T;
minArea = BGFG_FGD_MINAREA;
storage_ = cvCreateMemStorage();
CV_Assert( storage_ != 0 );
is_obj_without_holes = true;
perform_morphing = 1;
}
cv::gpu::FGDStatModel::Impl::~Impl()
{
cvReleaseMemStorage(&storage_);
}
/////////////////////////////////////////////////////////////////////////
// copyChannels
namespace
{
void copyChannels(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, int dst_cn = -1)
void copyChannels(const GpuMat& src, 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);
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, COLOR_GRAY2BGR, COLOR_GRAY2BGRA},
{-1, -1, -1, -1},
{cv::COLOR_BGR2GRAY, -1, -1, cv::COLOR_BGR2BGRA},
{cv::COLOR_BGRA2GRAY, -1, cv::COLOR_BGRA2BGR, -1}
{COLOR_BGR2GRAY, -1, -1, COLOR_BGR2BGRA},
{COLOR_BGRA2GRAY, -1, 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);
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(cv::MORPH_DILATE, CV_8UC1, kernel, anchor);
erodeFilter_ = cv::gpu::createMorphologyFilter(cv::MORPH_ERODE, CV_8UC1, kernel, 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();
filterBrd_.release();
}
/////////////////////////////////////////////////////////////////////////
// changeDetection
namespace
{
void calcDiffHistogram(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, cv::gpu::GpuMat& hist, cv::gpu::GpuMat& histBuf)
void calcDiffHistogram(const GpuMat& prevFrame, const GpuMat& curFrame, GpuMat& hist, 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);
typedef void (*func_t)(PtrStepSzb prevFrame, 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>}
{0,0,fgd::calcDiffHistogram_gpu<uchar3, uchar3>,fgd::calcDiffHistogram_gpu<uchar3, uchar4>},
{0,0,fgd::calcDiffHistogram_gpu<uchar4, uchar3>,fgd::calcDiffHistogram_gpu<uchar4, uchar4>}
};
hist.create(3, 256, CV_32SC1);
histBuf.create(3, bgfg::PARTIAL_HISTOGRAM_COUNT * bgfg::HISTOGRAM_BIN_COUNT, CV_32SC1);
histBuf.create(3, fgd::PARTIAL_HISTOGRAM_COUNT * fgd::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);
deviceSupports(FEATURE_SET_COMPUTE_20), 0);
}
void calcRelativeVariance(unsigned int hist[3 * 256], double relativeVariance[3][bgfg::HISTOGRAM_BIN_COUNT])
void calcRelativeVariance(unsigned int hist[3 * 256], double relativeVariance[3][fgd::HISTOGRAM_BIN_COUNT])
{
std::memset(relativeVariance, 0, 3 * bgfg::HISTOGRAM_BIN_COUNT * sizeof(double));
std::memset(relativeVariance, 0, 3 * fgd::HISTOGRAM_BIN_COUNT * sizeof(double));
for (int thres = bgfg::HISTOGRAM_BIN_COUNT - 2; thres >= 0; --thres)
for (int thres = fgd::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);
Vec3d sum(0.0, 0.0, 0.0);
Vec3d sqsum(0.0, 0.0, 0.0);
Vec3i count(0, 0, 0);
for (int j = thres; j < bgfg::HISTOGRAM_BIN_COUNT; ++j)
for (int j = thres; j < fgd::HISTOGRAM_BIN_COUNT; ++j)
{
sum[0] += static_cast<double>(j) * hist[j];
sqsum[0] += static_cast<double>(j * j) * hist[j];
......@@ -383,7 +204,7 @@ namespace
count[1] = std::max(count[1], 1);
count[2] = std::max(count[2], 1);
cv::Vec3d my(
Vec3d my(
sum[0] / count[0],
sum[1] / count[1],
sum[2] / count[2]
......@@ -395,37 +216,39 @@ namespace
}
}
void calcDiffThreshMask(const cv::gpu::GpuMat& prevFrame, const cv::gpu::GpuMat& curFrame, cv::Vec3d bestThres, cv::gpu::GpuMat& changeMask)
void calcDiffThreshMask(const GpuMat& prevFrame, const GpuMat& curFrame, Vec3d bestThres, GpuMat& changeMask)
{
typedef void (*func_t)(cv::gpu::PtrStepSzb prevFrame, cv::gpu::PtrStepSzb curFrame, uchar3 bestThres, cv::gpu::PtrStepSzb changeMask, cudaStream_t stream);
typedef void (*func_t)(PtrStepSzb prevFrame, PtrStepSzb curFrame, uchar3 bestThres, 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>}
{0,0,fgd::calcDiffThreshMask_gpu<uchar3, uchar3>,fgd::calcDiffThreshMask_gpu<uchar3, uchar4>},
{0,0,fgd::calcDiffThreshMask_gpu<uchar4, uchar3>,fgd::calcDiffThreshMask_gpu<uchar4, uchar4>}
};
changeMask.setTo(cv::Scalar::all(0));
changeMask.setTo(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);
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)
void changeDetection(const GpuMat& prevFrame, const GpuMat& curFrame, GpuMat& changeMask, GpuMat& hist, GpuMat& histBuf)
{
calcDiffHistogram(prevFrame, curFrame, hist, histBuf);
unsigned int histData[3 * 256];
cv::Mat h_hist(3, 256, CV_32SC1, histData);
Mat h_hist(3, 256, CV_32SC1, histData);
hist.download(h_hist);
double relativeVariance[3][bgfg::HISTOGRAM_BIN_COUNT];
double relativeVariance[3][fgd::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)
Vec3d bestThres(10.0, 10.0, 10.0);
for (int i = 0; i < fgd::HISTOGRAM_BIN_COUNT; ++i)
{
bestThres[0] = std::max(bestThres[0], relativeVariance[0][i]);
bestThres[1] = std::max(bestThres[1], relativeVariance[1][i]);
......@@ -441,12 +264,12 @@ namespace
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)
int bgfgClassification(const GpuMat& prevFrame, const GpuMat& curFrame,
const GpuMat& Ftd, const GpuMat& Fbd,
GpuMat& foreground, GpuMat& countBuf,
const FGDParams& 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,
typedef void (*func_t)(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd, PtrStepSzb foreground,
int deltaC, int deltaCC, float alpha2, int N1c, int N1cc, cudaStream_t stream);
static const func_t funcs[4][4][4] =
{
......@@ -458,24 +281,26 @@ namespace
},
{
{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,fgd::bgfgClassification_gpu<uchar3, uchar3, uchar3>,fgd::bgfgClassification_gpu<uchar3, uchar3, uchar4>},
{0,0,fgd::bgfgClassification_gpu<uchar3, uchar4, uchar3>,fgd::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>}
{0,0,fgd::bgfgClassification_gpu<uchar4, uchar3, uchar3>,fgd::bgfgClassification_gpu<uchar4, uchar3, uchar4>},
{0,0,fgd::bgfgClassification_gpu<uchar4, uchar4, uchar3>,fgd::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);
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);
int count = gpu::countNonZero(foreground, countBuf);
cv::gpu::multiply(foreground, cv::Scalar::all(255), foreground);
gpu::multiply(foreground, Scalar::all(255), foreground);
return count;
}
......@@ -486,20 +311,20 @@ namespace
namespace
{
void morphology(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, cv::gpu::GpuMat& filterBrd, int brd, cv::Ptr<cv::gpu::Filter>& filter, cv::Scalar brdVal)
void morphology(const GpuMat& src, GpuMat& dst, GpuMat& filterBrd, int brd, Ptr<gpu::Filter>& filter, 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);
gpu::copyMakeBorder(src, filterBrd, brd, brd, brd, brd, BORDER_CONSTANT, brdVal);
filter->apply(filterBrd(Rect(brd, brd, src.cols, src.rows)), dst);
}
void smoothForeground(cv::gpu::GpuMat& foreground, cv::gpu::GpuMat& filterBrd, cv::gpu::GpuMat& buf,
cv::Ptr<cv::gpu::Filter>& erodeFilter, cv::Ptr<cv::gpu::Filter>& dilateFilter,
const cv::gpu::FGDStatModel::Params& params)
void smoothForeground(GpuMat& foreground, GpuMat& filterBrd, GpuMat& buf,
Ptr<gpu::Filter>& erodeFilter, Ptr<gpu::Filter>& dilateFilter,
const FGDParams& params)
{
const int brd = params.perform_morphing;
const cv::Scalar erodeBrdVal = cv::Scalar::all(UCHAR_MAX);
const cv::Scalar dilateBrdVal = cv::Scalar::all(0);
const Scalar erodeBrdVal = Scalar::all(UCHAR_MAX);
const Scalar dilateBrdVal = Scalar::all(0);
// MORPH_OPEN
morphology(foreground, buf, filterBrd, brd, erodeFilter, erodeBrdVal);
......@@ -516,28 +341,28 @@ namespace
namespace
{
void seqToContours(CvSeq* _ccontours, CvMemStorage* storage, cv::OutputArrayOfArrays _contours)
void seqToContours(CvSeq* _ccontours, CvMemStorage* storage, OutputArrayOfArrays _contours)
{
cv::Seq<CvSeq*> all_contours(cvTreeToNodeSeq(_ccontours, sizeof(CvSeq), storage));
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();
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);
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 findForegroundRegions(GpuMat& d_foreground, Mat& h_foreground, std::vector< std::vector<Point> >& foreground_regions,
CvMemStorage* storage, const FGDParams& params)
{
int region_count = 0;
......@@ -581,7 +406,7 @@ namespace
seqToContours(first_seq, storage, foreground_regions);
h_foreground.setTo(0);
cv::drawContours(h_foreground, foreground_regions, -1, cv::Scalar::all(255), -1);
drawContours(h_foreground, foreground_regions, -1, Scalar::all(255), -1);
d_foreground.upload(h_foreground);
......@@ -594,12 +419,12 @@ namespace
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)
void updateBackgroundModel(const GpuMat& prevFrame, const GpuMat& curFrame, const GpuMat& Ftd, const GpuMat& Fbd,
const GpuMat& foreground, GpuMat& background,
const FGDParams& 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,
typedef void (*func_t)(PtrStepSzb prevFrame, PtrStepSzb curFrame, PtrStepSzb Ftd, PtrStepSzb Fbd,
PtrStepSzb foreground, 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] =
{
......@@ -611,13 +436,13 @@ namespace
},
{
{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,fgd::updateBackgroundModel_gpu<uchar3, uchar3, uchar3>,fgd::updateBackgroundModel_gpu<uchar3, uchar3, uchar4>},
{0,0,fgd::updateBackgroundModel_gpu<uchar3, uchar4, uchar3>,fgd::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>}
{0,0,fgd::updateBackgroundModel_gpu<uchar4, uchar3, uchar3>,fgd::updateBackgroundModel_gpu<uchar4, uchar3, uchar4>},
{0,0,fgd::updateBackgroundModel_gpu<uchar4, uchar4, uchar3>,fgd::updateBackgroundModel_gpu<uchar4, uchar4, uchar4>}
}
};
......@@ -626,126 +451,271 @@ namespace
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,
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)
namespace
{
CV_Assert(curFrame.type() == CV_8UC3 || curFrame.type() == CV_8UC4);
CV_Assert(curFrame.size() == prevFrame_.size());
class BGPixelStat
{
public:
void create(Size size, const FGDParams& params);
void setTrained();
operator fgd::BGPixelStat();
cvClearMemStorage(storage_);
foreground_regions_.clear();
foreground_.setTo(cv::Scalar::all(0));
private:
GpuMat Pbc_;
GpuMat Pbcc_;
GpuMat is_trained_st_model_;
GpuMat is_trained_dyn_model_;
GpuMat ctable_Pv_;
GpuMat ctable_Pvb_;
GpuMat ctable_v_;
GpuMat cctable_Pv_;
GpuMat cctable_Pvb_;
GpuMat cctable_v1_;
GpuMat cctable_v2_;
};
changeDetection(prevFrame_, curFrame, Ftd_, hist_, histBuf_);
changeDetection(background_, curFrame, Fbd_, hist_, histBuf_);
void BGPixelStat::create(Size size, const FGDParams& params)
{
gpu::ensureSizeIsEnough(size, CV_32FC1, Pbc_);
Pbc_.setTo(Scalar::all(0));
int FG_pixels_count = bgfgClassification(prevFrame_, curFrame, Ftd_, Fbd_, foreground_, countBuf_, params_, out_cn_);
gpu::ensureSizeIsEnough(size, CV_32FC1, Pbcc_);
Pbcc_.setTo(Scalar::all(0));
if (params_.perform_morphing > 0)
smoothForeground(foreground_, filterBrd_, buf_, erodeFilter_, dilateFilter_, params_);
gpu::ensureSizeIsEnough(size, CV_8UC1, is_trained_st_model_);
is_trained_st_model_.setTo(Scalar::all(0));
int region_count = 0;
if (params_.minArea > 0 || params_.is_obj_without_holes)
region_count = findForegroundRegions(foreground_, h_foreground_, foreground_regions_, storage_, params_);
gpu::ensureSizeIsEnough(size, CV_8UC1, is_trained_dyn_model_);
is_trained_dyn_model_.setTo(Scalar::all(0));
// 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();
gpu::ensureSizeIsEnough(params.N2c * size.height, size.width, CV_32FC1, ctable_Pv_);
ctable_Pv_.setTo(Scalar::all(0));
updateBackgroundModel(prevFrame_, curFrame, Ftd_, Fbd_, foreground_, background_, params_);
gpu::ensureSizeIsEnough(params.N2c * size.height, size.width, CV_32FC1, ctable_Pvb_);
ctable_Pvb_.setTo(Scalar::all(0));
copyChannels(curFrame, prevFrame_);
gpu::ensureSizeIsEnough(params.N2c * size.height, size.width, CV_8UC4, ctable_v_);
ctable_v_.setTo(Scalar::all(0));
return region_count;
}
gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_32FC1, cctable_Pv_);
cctable_Pv_.setTo(Scalar::all(0));
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;
gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_32FC1, cctable_Pvb_);
cctable_Pvb_.setTo(Scalar::all(0));
const int BGFG_FGD_LCC = 64;
const int BGFG_FGD_N1CC = 25;
const int BGFG_FGD_N2CC = 40;
gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_8UC4, cctable_v1_);
cctable_v1_.setTo(Scalar::all(0));
// Background reference image update parameter:
const float BGFG_FGD_ALPHA_1 = 0.1f;
gpu::ensureSizeIsEnough(params.N2cc * size.height, size.width, CV_8UC4, cctable_v2_);
cctable_v2_.setTo(Scalar::all(0));
}
// 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;
void BGPixelStat::setTrained()
{
is_trained_st_model_.setTo(Scalar::all(1));
is_trained_dyn_model_.setTo(Scalar::all(1));
}
// start value for alpha parameter (to fast initiate statistic model)
const float BGFG_FGD_ALPHA_3 = 0.1f;
BGPixelStat::operator fgd::BGPixelStat()
{
fgd::BGPixelStat stat;
const float BGFG_FGD_DELTA = 2.0f;
stat.rows_ = Pbc_.rows;
const float BGFG_FGD_T = 0.9f;
stat.Pbc_data_ = Pbc_.data;
stat.Pbc_step_ = Pbc_.step;
const float BGFG_FGD_MINAREA= 15.0f;
}
stat.Pbcc_data_ = Pbcc_.data;
stat.Pbcc_step_ = Pbcc_.step;
cv::gpu::FGDStatModel::Params::Params()
{
Lc = BGFG_FGD_LC;
N1c = BGFG_FGD_N1C;
N2c = BGFG_FGD_N2C;
stat.is_trained_st_model_data_ = is_trained_st_model_.data;
stat.is_trained_st_model_step_ = is_trained_st_model_.step;
Lcc = BGFG_FGD_LCC;
N1cc = BGFG_FGD_N1CC;
N2cc = BGFG_FGD_N2CC;
stat.is_trained_dyn_model_data_ = is_trained_dyn_model_.data;
stat.is_trained_dyn_model_step_ = is_trained_dyn_model_.step;
delta = BGFG_FGD_DELTA;
stat.ctable_Pv_data_ = ctable_Pv_.data;
stat.ctable_Pv_step_ = ctable_Pv_.step;
alpha1 = BGFG_FGD_ALPHA_1;
alpha2 = BGFG_FGD_ALPHA_2;
alpha3 = BGFG_FGD_ALPHA_3;
stat.ctable_Pvb_data_ = ctable_Pvb_.data;
stat.ctable_Pvb_step_ = ctable_Pvb_.step;
T = BGFG_FGD_T;
minArea = BGFG_FGD_MINAREA;
stat.ctable_v_data_ = ctable_v_.data;
stat.ctable_v_step_ = ctable_v_.step;
is_obj_without_holes = true;
perform_morphing = 1;
}
stat.cctable_Pv_data_ = cctable_Pv_.data;
stat.cctable_Pv_step_ = cctable_Pv_.step;
cv::gpu::FGDStatModel::FGDStatModel(int out_cn)
{
impl_.reset(new Impl(background, foreground, foreground_regions, out_cn));
}
stat.cctable_Pvb_data_ = cctable_Pvb_.data;
stat.cctable_Pvb_step_ = cctable_Pvb_.step;
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);
}
stat.cctable_v1_data_ = cctable_v1_.data;
stat.cctable_v1_step_ = cctable_v1_.step;
cv::gpu::FGDStatModel::~FGDStatModel()
{
}
stat.cctable_v2_data_ = cctable_v2_.data;
stat.cctable_v2_step_ = cctable_v2_.step;
void cv::gpu::FGDStatModel::create(const cv::gpu::GpuMat& firstFrame, const Params& params)
{
impl_->create(firstFrame, params);
}
return stat;
}
void cv::gpu::FGDStatModel::release()
{
impl_->release();
class FGDImpl : public gpu::BackgroundSubtractorFGD
{
public:
explicit FGDImpl(const FGDParams& params);
~FGDImpl();
void apply(InputArray image, OutputArray fgmask, double learningRate=-1);
void getBackgroundImage(OutputArray backgroundImage) const;
void getForegroundRegions(OutputArrayOfArrays foreground_regions);
private:
void initialize(const GpuMat& firstFrame);
FGDParams params_;
Size frameSize_;
GpuMat background_;
GpuMat foreground_;
std::vector< std::vector<Point> > foreground_regions_;
Mat h_foreground_;
GpuMat prevFrame_;
GpuMat Ftd_;
GpuMat Fbd_;
BGPixelStat stat_;
GpuMat hist_;
GpuMat histBuf_;
GpuMat countBuf_;
GpuMat buf_;
GpuMat filterBrd_;
Ptr<gpu::Filter> dilateFilter_;
Ptr<gpu::Filter> erodeFilter_;
CvMemStorage* storage_;
};
FGDImpl::FGDImpl(const FGDParams& params) : params_(params), frameSize_(0, 0)
{
storage_ = cvCreateMemStorage();
CV_Assert( storage_ != 0 );
}
FGDImpl::~FGDImpl()
{
cvReleaseMemStorage(&storage_);
}
void FGDImpl::apply(InputArray _frame, OutputArray fgmask, double)
{
GpuMat curFrame = _frame.getGpuMat();
if (curFrame.size() != frameSize_)
{
initialize(curFrame);
return;
}
CV_Assert( curFrame.type() == CV_8UC3 || curFrame.type() == CV_8UC4 );
CV_Assert( curFrame.size() == prevFrame_.size() );
cvClearMemStorage(storage_);
foreground_regions_.clear();
foreground_.setTo(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_, 4);
if (params_.perform_morphing > 0)
smoothForeground(foreground_, filterBrd_, buf_, erodeFilter_, dilateFilter_, params_);
if (params_.minArea > 0 || params_.is_obj_without_holes)
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_, 4);
foreground_.copyTo(fgmask);
}
void FGDImpl::getBackgroundImage(OutputArray backgroundImage) const
{
gpu::cvtColor(background_, backgroundImage, COLOR_BGRA2BGR);
}
void FGDImpl::getForegroundRegions(OutputArrayOfArrays dst)
{
size_t total = foreground_regions_.size();
dst.create((int) total, 1, 0, -1, true);
for (size_t i = 0; i < total; ++i)
{
std::vector<Point>& c = foreground_regions_[i];
dst.create((int) c.size(), 1, CV_32SC2, (int) i, true);
Mat ci = dst.getMat((int) i);
Mat(ci.size(), ci.type(), &c[0]).copyTo(ci);
}
}
void FGDImpl::initialize(const GpuMat& firstFrame)
{
CV_Assert( firstFrame.type() == CV_8UC3 || firstFrame.type() == CV_8UC4 );
frameSize_ = firstFrame.size();
gpu::ensureSizeIsEnough(firstFrame.size(), CV_8UC1, foreground_);
copyChannels(firstFrame, background_, 4);
copyChannels(firstFrame, prevFrame_, 4);
gpu::ensureSizeIsEnough(firstFrame.size(), CV_8UC1, Ftd_);
gpu::ensureSizeIsEnough(firstFrame.size(), CV_8UC1, Fbd_);
stat_.create(firstFrame.size(), params_);
fgd::setBGPixelStat(stat_);
if (params_.perform_morphing > 0)
{
Mat kernel = getStructuringElement(MORPH_RECT, Size(1 + params_.perform_morphing * 2, 1 + params_.perform_morphing * 2));
Point anchor(params_.perform_morphing, params_.perform_morphing);
dilateFilter_ = gpu::createMorphologyFilter(MORPH_DILATE, CV_8UC1, kernel, anchor);
erodeFilter_ = gpu::createMorphologyFilter(MORPH_ERODE, CV_8UC1, kernel, anchor);
}
}
}
int cv::gpu::FGDStatModel::update(const cv::gpu::GpuMat& curFrame)
Ptr<gpu::BackgroundSubtractorFGD> cv::gpu::createBackgroundSubtractorFGD(const FGDParams& params)
{
return impl_->update(curFrame);
return new FGDImpl(params);
}
#endif // HAVE_CUDA
modules/gpubgsegm/test/test_bgsegm.cpp
View file @ 62edeeed
......@@ -72,11 +72,10 @@ namespace cv
}
}
PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels)
PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string)
{
cv::gpu::DeviceInfo devInfo;
std::string inputFile;
int out_cn;
virtual void SetUp()
{
......@@ -84,8 +83,6 @@ PARAM_TEST_CASE(FGDStatModel, cv::gpu::DeviceInfo, std::string, Channels)
cv::gpu::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
out_cn = GET_PARAM(2);
}
};
......@@ -102,15 +99,10 @@ GPU_TEST_P(FGDStatModel, Update)
cv::Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame));
cv::gpu::GpuMat d_frame(frame);
cv::gpu::FGDStatModel d_model(out_cn);
d_model.create(d_frame);
cv::Mat h_background;
cv::Mat h_foreground;
cv::Mat h_background3;
cv::Mat backgroundDiff;
cv::Mat foregroundDiff;
cv::Ptr<cv::gpu::BackgroundSubtractorFGD> d_fgd = cv::gpu::createBackgroundSubtractorFGD();
cv::gpu::GpuMat d_foreground, d_background;
std::vector< std::vector<cv::Point> > foreground_regions;
d_fgd->apply(d_frame, d_foreground);
for (int i = 0; i < 5; ++i)
{
......@@ -121,32 +113,23 @@ GPU_TEST_P(FGDStatModel, Update)
int gold_count = cvUpdateBGStatModel(&ipl_frame, model);
d_frame.upload(frame);
int count = d_model.update(d_frame);
ASSERT_EQ(gold_count, count);
d_fgd->apply(d_frame, d_foreground);
d_fgd->getBackgroundImage(d_background);
d_fgd->getForegroundRegions(foreground_regions);
int count = (int) foreground_regions.size();
cv::Mat gold_background = cv::cvarrToMat(model->background);
cv::Mat gold_foreground = cv::cvarrToMat(model->foreground);
if (out_cn == 3)
d_model.background.download(h_background3);
else
{
d_model.background.download(h_background);
cv::cvtColor(h_background, h_background3, cv::COLOR_BGRA2BGR);
}
d_model.foreground.download(h_foreground);
ASSERT_MAT_NEAR(gold_background, h_background3, 1.0);
ASSERT_MAT_NEAR(gold_foreground, h_foreground, 0.0);
ASSERT_MAT_NEAR(gold_background, d_background, 1.0);
ASSERT_MAT_NEAR(gold_foreground, d_foreground, 0.0);
ASSERT_EQ(gold_count, count);
}
}
INSTANTIATE_TEST_CASE_P(GPU_BgSegm, FGDStatModel, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi")),
testing::Values(Channels(3), Channels(4))));
testing::Values(std::string("768x576.avi"))));
#endif
......
samples/gpu/bgfg_segm.cpp
View file @ 62edeeed
......@@ -78,7 +78,7 @@ int main(int argc, const char** argv)
Ptr<BackgroundSubtractor> mog = gpu::createBackgroundSubtractorMOG();
Ptr<BackgroundSubtractor> mog2 = gpu::createBackgroundSubtractorMOG2();
Ptr<BackgroundSubtractor> gmg = gpu::createBackgroundSubtractorGMG(40);
FGDStatModel fgd_stat;
Ptr<BackgroundSubtractor> fgd = gpu::createBackgroundSubtractorFGD();
GpuMat d_fgmask;
GpuMat d_fgimg;
......@@ -103,7 +103,7 @@ int main(int argc, const char** argv)
break;
case FGD_STAT:
fgd_stat.create(d_frame);
fgd->apply(d_frame, d_fgmask);
break;
}
......@@ -142,9 +142,8 @@ int main(int argc, const char** argv)
break;
case FGD_STAT:
fgd_stat.update(d_frame);
d_fgmask = fgd_stat.foreground;
d_bgimg = fgd_stat.background;
fgd->apply(d_frame, d_fgmask);
fgd->getBackgroundImage(d_bgimg);
break;
}
......
samples/gpu/performance/tests.cpp
View file @ 62edeeed
......@@ -1271,14 +1271,14 @@ TEST(FGDStatModel)
{
const std::string inputFile = abspath("768x576.avi");
cv::VideoCapture cap(inputFile);
VideoCapture cap(inputFile);
if (!cap.isOpened()) throw runtime_error("can't open 768x576.avi");
cv::Mat frame;
Mat frame;
cap >> frame;
IplImage ipl_frame = frame;
cv::Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame));
Ptr<CvBGStatModel> model(cvCreateFGDStatModel(&ipl_frame));
while (!TestSystem::instance().stop())
{
......@@ -1297,8 +1297,10 @@ TEST(FGDStatModel)
cap >> frame;
cv::gpu::GpuMat d_frame(frame);
cv::gpu::FGDStatModel d_model(d_frame);
gpu::GpuMat d_frame(frame), d_fgmask;
Ptr<BackgroundSubtractor> d_fgd = gpu::createBackgroundSubtractorFGD();
d_fgd->apply(d_frame, d_fgmask);
while (!TestSystem::instance().stop())
{
......@@ -1307,7 +1309,7 @@ TEST(FGDStatModel)
TestSystem::instance().gpuOn();
d_model.update(d_frame);
d_fgd->apply(d_frame, d_fgmask);
TestSystem::instance().gpuOff();
}
......
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