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272 lines
11 KiB
C++
272 lines
11 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "cuda/mog2.hpp"
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#include "opencv2/core/utils/logger.hpp"
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using namespace cv;
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using namespace cv::cuda;
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using namespace cv::cuda::device::mog2;
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#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
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Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int, double, bool)
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{
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throw_no_cuda();
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return Ptr<cuda::BackgroundSubtractorMOG2>();
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}
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#else
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namespace
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{
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// default parameters of gaussian background detection algorithm
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const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
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const float defaultVarThreshold = 4.0f * 4.0f;
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const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
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const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
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const float defaultVarThresholdGen = 3.0f * 3.0f;
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const float defaultVarInit = 15.0f; // initial variance for new components
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const float defaultVarMax = 5.0f * defaultVarInit;
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const float defaultVarMin = 4.0f;
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// additional parameters
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const float defaultCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
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const unsigned char defaultShadowValue = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
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const float defaultShadowThreshold = 0.5f; // Tau - shadow threshold, see the paper for explanation
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class MOG2Impl CV_FINAL : public cuda::BackgroundSubtractorMOG2
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{
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public:
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MOG2Impl(int history, double varThreshold, bool detectShadows);
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~MOG2Impl();
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void apply(InputArray image, OutputArray fgmask, double learningRate = -1) CV_OVERRIDE;
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void apply(InputArray image, OutputArray fgmask, double learningRate, Stream &stream) CV_OVERRIDE;
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void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate = -1) CV_OVERRIDE;
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void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate, Stream& stream) CV_OVERRIDE;
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void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
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void getBackgroundImage(OutputArray backgroundImage, Stream &stream) const CV_OVERRIDE;
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int getHistory() const CV_OVERRIDE { return history_; }
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void setHistory(int history) CV_OVERRIDE { history_ = history; }
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int getNMixtures() const CV_OVERRIDE { return constantsHost_.nmixtures_; }
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void setNMixtures(int nmixtures) CV_OVERRIDE { constantsHost_.nmixtures_ = nmixtures; }
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double getBackgroundRatio() const CV_OVERRIDE { return constantsHost_.TB_; }
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void setBackgroundRatio(double ratio) CV_OVERRIDE { constantsHost_.TB_ = (float)ratio; }
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double getVarThreshold() const CV_OVERRIDE { return constantsHost_.Tb_; }
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void setVarThreshold(double varThreshold) CV_OVERRIDE { constantsHost_.Tb_ = (float)varThreshold; }
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double getVarThresholdGen() const CV_OVERRIDE { return constantsHost_.Tg_; }
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void setVarThresholdGen(double varThresholdGen) CV_OVERRIDE { constantsHost_.Tg_ = (float)varThresholdGen; }
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double getVarInit() const CV_OVERRIDE { return constantsHost_.varInit_; }
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void setVarInit(double varInit) CV_OVERRIDE { constantsHost_.varInit_ = (float)varInit; }
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double getVarMin() const CV_OVERRIDE { return constantsHost_.varMin_; }
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void setVarMin(double varMin) CV_OVERRIDE { constantsHost_.varMin_ = ::fminf((float)varMin, constantsHost_.varMax_); }
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double getVarMax() const CV_OVERRIDE { return constantsHost_.varMax_; }
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void setVarMax(double varMax) CV_OVERRIDE { constantsHost_.varMax_ = ::fmaxf(constantsHost_.varMin_, (float)varMax); }
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double getComplexityReductionThreshold() const CV_OVERRIDE { return ct_; }
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void setComplexityReductionThreshold(double ct) CV_OVERRIDE { ct_ = (float)ct; }
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bool getDetectShadows() const CV_OVERRIDE { return detectShadows_; }
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void setDetectShadows(bool detectShadows) CV_OVERRIDE { detectShadows_ = detectShadows; }
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int getShadowValue() const CV_OVERRIDE { return constantsHost_.shadowVal_; }
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void setShadowValue(int value) CV_OVERRIDE { constantsHost_.shadowVal_ = (uchar)value; }
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double getShadowThreshold() const CV_OVERRIDE { return constantsHost_.tau_; }
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void setShadowThreshold(double threshold) CV_OVERRIDE { constantsHost_.tau_ = (float)threshold; }
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private:
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void initialize(Size frameSize, int frameType, Stream &stream);
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Constants constantsHost_;
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Constants *constantsDevice_;
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int history_;
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float ct_;
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bool detectShadows_;
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Size frameSize_;
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int frameType_;
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int nframes_;
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GpuMat weight_;
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GpuMat variance_;
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GpuMat mean_;
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//keep track of number of modes per pixel
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GpuMat bgmodelUsedModes_;
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};
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MOG2Impl::MOG2Impl(int history, double varThreshold, bool detectShadows) : frameSize_(0, 0), frameType_(0), nframes_(0)
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{
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history_ = history > 0 ? history : defaultHistory;
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detectShadows_ = detectShadows;
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ct_ = defaultCT;
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setNMixtures(defaultNMixtures);
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setBackgroundRatio(defaultBackgroundRatio);
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setVarInit(defaultVarInit);
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setVarMin(defaultVarMin);
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setVarMax(defaultVarMax);
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setVarThreshold(varThreshold > 0 ? (float)varThreshold : defaultVarThreshold);
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setVarThresholdGen(defaultVarThresholdGen);
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setShadowValue(defaultShadowValue);
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setShadowThreshold(defaultShadowThreshold);
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cudaSafeCall(cudaMalloc((void **)&constantsDevice_, sizeof(Constants)));
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}
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MOG2Impl::~MOG2Impl()
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{
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cudaFree(constantsDevice_);
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}
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void MOG2Impl::apply(InputArray image, OutputArray fgmask, double learningRate)
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{
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apply(image, fgmask, learningRate, Stream::Null());
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}
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void MOG2Impl::apply(InputArray _image, InputArray _knownForegroundMask, OutputArray _fgmask, double learningRate){
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if(!_knownForegroundMask.empty())
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{
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CV_Error( Error::StsNotImplemented, "Known Foreground Masking has not been implemented for this specific background subtractor, falling back to subtraction without known foreground");
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}
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apply(_image, _fgmask, learningRate, Stream::Null());
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}
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void MOG2Impl::apply(InputArray _image, InputArray _knownForegroundMask, OutputArray _fgmask, double learningRate, Stream &stream){
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if(!_knownForegroundMask.empty())
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{
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CV_Error( Error::StsNotImplemented, "Known Foreground Masking has not been implemented for this specific background subtractor, falling back to subtraction without known foreground");
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}
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apply(_image, _fgmask, learningRate, stream);
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}
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void MOG2Impl::apply(InputArray _frame, OutputArray _fgmask, double learningRate, Stream &stream)
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{
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using namespace cv::cuda::device::mog2;
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GpuMat frame = _frame.getGpuMat();
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int ch = frame.channels();
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int work_ch = ch;
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if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
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initialize(frame.size(), frame.type(), stream);
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_fgmask.create(frameSize_, CV_8UC1);
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GpuMat fgmask = _fgmask.getGpuMat();
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fgmask.setTo(Scalar::all(0), stream);
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++nframes_;
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learningRate = learningRate >= 0 && nframes_ > 1 ? learningRate : 1.0 / std::min(2 * nframes_, history_);
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CV_Assert(learningRate >= 0);
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mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_,
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(float)learningRate, static_cast<float>(-learningRate * ct_), detectShadows_, constantsDevice_, StreamAccessor::getStream(stream));
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}
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void MOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
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{
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getBackgroundImage(backgroundImage, Stream::Null());
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}
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void MOG2Impl::getBackgroundImage(OutputArray _backgroundImage, Stream &stream) const
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{
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using namespace cv::cuda::device::mog2;
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_backgroundImage.create(frameSize_, frameType_);
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GpuMat backgroundImage = _backgroundImage.getGpuMat();
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getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, constantsDevice_, StreamAccessor::getStream(stream));
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}
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void MOG2Impl::initialize(cv::Size frameSize, int frameType, Stream &stream)
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{
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using namespace cv::cuda::device::mog2;
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CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
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frameSize_ = frameSize;
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frameType_ = frameType;
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nframes_ = 0;
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const int ch = CV_MAT_CN(frameType);
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const int work_ch = ch;
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// for each gaussian mixture of each pixel bg model we store ...
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// the mixture weight (w),
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// the mean (nchannels values) and
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// the covariance
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weight_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC1);
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variance_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC1);
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mean_.create(frameSize.height * getNMixtures(), frameSize_.width, CV_32FC(work_ch));
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//make the array for keeping track of the used modes per pixel - all zeros at start
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bgmodelUsedModes_.create(frameSize_, CV_8UC1);
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bgmodelUsedModes_.setTo(Scalar::all(0));
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cudaSafeCall(cudaMemcpyAsync(constantsDevice_, &constantsHost_, sizeof(Constants), cudaMemcpyHostToDevice, StreamAccessor::getStream(stream)));
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}
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} // namespace
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Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int history, double varThreshold, bool detectShadows)
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{
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return makePtr<MOG2Impl>(history, varThreshold, detectShadows);
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}
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#endif
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