added MOG_GPU and MOG2_GPU (Gaussian Mixture background subtraction)
This commit is contained in:
Vladislav Vinogradov
241
modules/gpu/src/bgfg_mog.cpp
Normal file
241
modules/gpu/src/bgfg_mog.cpp
Normal file
@@ -0,0 +1,241 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
|
||||
// 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.
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||||
//
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||||
//M*/
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#include "precomp.hpp"
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#ifndef HAVE_CUDA
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cv::gpu::MOG_GPU::MOG_GPU(int) { throw_nogpu(); }
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void cv::gpu::MOG_GPU::initialize(cv::Size, int) { throw_nogpu(); }
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void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, float, Stream&) { throw_nogpu(); }
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cv::gpu::MOG2_GPU::MOG2_GPU(int) { throw_nogpu(); }
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void cv::gpu::MOG2_GPU::initialize(cv::Size, int) { throw_nogpu(); }
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void cv::gpu::MOG2_GPU::operator()(const GpuMat&, GpuMat&, float, Stream&) { throw_nogpu(); }
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void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_nogpu(); }
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#else
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namespace cv { namespace gpu { namespace device
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{
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namespace mog
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{
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void mog_gpu(DevMem2Db frame, int cn, DevMem2Db fgmask, DevMem2Df weight, DevMem2Df sortKey, DevMem2Db mean, DevMem2Db var,
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int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma,
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cudaStream_t stream);
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void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
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void mog2_gpu(DevMem2Db frame, int cn, DevMem2Db fgmask, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Df variance, DevMem2Db mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
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void getBackgroundImage_gpu(int cn, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Db mean, DevMem2Db dst, cudaStream_t stream);
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}
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}}}
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namespace mog
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{
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const int defaultNMixtures = 5;
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const int defaultHistory = 200;
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const float defaultBackgroundRatio = 0.7f;
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const float defaultVarThreshold = 2.5f * 2.5f;
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const float defaultNoiseSigma = 30.0f * 0.5f;
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const float defaultInitialWeight = 0.05f;
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}
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cv::gpu::MOG_GPU::MOG_GPU(int nmixtures) :
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frameSize_(0, 0), nframes_(0)
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{
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nmixtures_ = std::min(nmixtures > 0 ? nmixtures : mog::defaultNMixtures, 8);
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history = mog::defaultHistory;
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varThreshold = mog::defaultVarThreshold;
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backgroundRatio = mog::defaultBackgroundRatio;
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noiseSigma = mog::defaultNoiseSigma;
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}
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void cv::gpu::MOG_GPU::initialize(cv::Size frameSize, int frameType)
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{
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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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int ch = CV_MAT_CN(frameType);
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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 sort key (w/sum_of_variances), the mixture weight (w),
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// the mean (nchannels values) and
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// the diagonal covariance matrix (another nchannels values)
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weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
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sortKey_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
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mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
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var_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
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weight_.setTo(cv::Scalar::all(0));
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sortKey_.setTo(cv::Scalar::all(0));
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mean_.setTo(cv::Scalar::all(0));
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var_.setTo(cv::Scalar::all(0));
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nframes_ = 0;
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}
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void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat& frame, cv::gpu::GpuMat& fgmask, float learningRate, Stream& stream)
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{
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using namespace cv::gpu::device::mog;
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CV_Assert(frame.depth() == CV_8U);
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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());
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fgmask.create(frameSize_, CV_8UC1);
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++nframes_;
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learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(nframes_, history);
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CV_Assert(learningRate >= 0.0f);
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mog_gpu(frame, ch, fgmask, weight_, sortKey_, mean_, var_, nmixtures_,
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varThreshold, learningRate, backgroundRatio, noiseSigma,
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StreamAccessor::getStream(stream));
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}
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/////////////////////////////////////////////////////////////////
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// MOG2
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namespace mog2
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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 defaultfCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
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const unsigned char defaultnShadowDetection = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
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const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
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}
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cv::gpu::MOG2_GPU::MOG2_GPU(int nmixtures) :
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frameSize_(0, 0), frameType_(0), nframes_(0)
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{
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nmixtures_ = nmixtures > 0 ? nmixtures : mog2::defaultNMixtures;
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history = mog2::defaultHistory;
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varThreshold = mog2::defaultVarThreshold;
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bShadowDetection = true;
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backgroundRatio = mog2::defaultBackgroundRatio;
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fVarInit = mog2::defaultVarInit;
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fVarMax = mog2::defaultVarMax;
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fVarMin = mog2::defaultVarMin;
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varThresholdGen = mog2::defaultVarThresholdGen;
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fCT = mog2::defaultfCT;
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nShadowDetection = mog2::defaultnShadowDetection;
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fTau = mog2::defaultfTau;
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}
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void cv::gpu::MOG2_GPU::initialize(cv::Size frameSize, int frameType)
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{
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using namespace cv::gpu::device::mog;
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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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int ch = CV_MAT_CN(frameType);
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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 * nmixtures_, frameSize_.width, CV_32FC1);
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variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
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mean_.create(frameSize.height * nmixtures_, 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(cv::Scalar::all(0));
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loadConstants(nmixtures_, varThreshold, backgroundRatio, varThresholdGen, fVarInit, fVarMin, fVarMax, fTau, nShadowDetection);
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}
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void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
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{
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using namespace cv::gpu::device::mog;
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int ch = frame.channels();
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int work_ch = ch;
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if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
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initialize(frame.size(), frame.type());
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fgmask.create(frameSize_, CV_8UC1);
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fgmask.setTo(cv::Scalar::all(0));
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++nframes_;
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learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
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CV_Assert(learningRate >= 0.0f);
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if (learningRate > 0.0f)
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mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
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}
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void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
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{
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using namespace cv::gpu::device::mog;
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backgroundImage.create(frameSize_, frameType_);
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getBackgroundImage_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
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}
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#endif
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703
modules/gpu/src/cuda/bgfg_mog.cu
Normal file
703
modules/gpu/src/cuda/bgfg_mog.cu
Normal file
@@ -0,0 +1,703 @@
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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 bpied warranties, including, but not limited to, the bpied
|
||||
// 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*/
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#include <stdio.h>
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#include "opencv2/gpu/device/common.hpp"
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#include "opencv2/gpu/device/vec_traits.hpp"
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#include "opencv2/gpu/device/vec_math.hpp"
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#include "opencv2/gpu/device/limits.hpp"
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namespace cv { namespace gpu { namespace device
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{
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namespace mog
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{
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///////////////////////////////////////////////////////////////
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// Utility
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__device__ __forceinline__ float cvt(uchar val)
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{
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return val;
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}
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__device__ __forceinline__ float3 cvt(const uchar3& val)
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{
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return make_float3(val.x, val.y, val.z);
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}
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__device__ __forceinline__ float4 cvt(const uchar4& val)
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{
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return make_float4(val.x, val.y, val.z, val.w);
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}
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__device__ __forceinline__ float sqr(float val)
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{
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return val * val;
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}
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__device__ __forceinline__ float sqr(const float3& val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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__device__ __forceinline__ float sqr(const float4& val)
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{
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return val.x * val.x + val.y * val.y + val.z * val.z;
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}
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__device__ __forceinline__ float sum(float val)
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{
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return val;
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}
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__device__ __forceinline__ float sum(const float3& val)
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{
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return val.x + val.y + val.z;
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}
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__device__ __forceinline__ float sum(const float4& val)
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{
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return val.x + val.y + val.z;
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}
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__device__ __forceinline__ float clamp(float var, float learningRate, float diff, float minVar)
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{
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return ::fmaxf(var + learningRate * (diff * diff - var), minVar);
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}
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__device__ __forceinline__ float3 clamp(const float3& var, float learningRate, const float3& diff, float minVar)
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{
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return make_float3(::fmaxf(var.x + learningRate * (diff.x * diff.x - var.x), minVar),
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::fmaxf(var.y + learningRate * (diff.y * diff.y - var.y), minVar),
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::fmaxf(var.z + learningRate * (diff.z * diff.z - var.z), minVar));
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}
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__device__ __forceinline__ float4 clamp(const float4& var, float learningRate, const float4& diff, float minVar)
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{
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return make_float4(::fmaxf(var.x + learningRate * (diff.x * diff.x - var.x), minVar),
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::fmaxf(var.y + learningRate * (diff.y * diff.y - var.y), minVar),
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::fmaxf(var.z + learningRate * (diff.z * diff.z - var.z), minVar),
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0.0f);
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}
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template <class Ptr2D>
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__device__ __forceinline__ void swap(Ptr2D& ptr, int x, int y, int k, int rows)
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{
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typename Ptr2D::elem_type val = ptr(k * rows + y, x);
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ptr(k * rows + y, x) = ptr((k + 1) * rows + y, x);
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ptr((k + 1) * rows + y, x) = val;
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}
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///////////////////////////////////////////////////////////////
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// MOG without learning
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template <typename SrcT, typename WorkT>
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__global__ void mog_withoutLearning(const DevMem2D_<SrcT> frame, PtrStepb fgmask,
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const PtrStepf gmm_weight, const PtrStep_<WorkT> gmm_mean, const PtrStep_<WorkT> gmm_var,
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const int nmixtures, const float varThreshold, const float backgroundRatio)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x >= frame.cols || y >= frame.rows)
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return;
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WorkT pix = cvt(frame(y, x));
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int kHit = -1;
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int kForeground = -1;
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for (int k = 0; k < nmixtures; ++k)
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{
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if (gmm_weight(k * frame.rows + y, x) < numeric_limits<float>::epsilon())
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break;
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WorkT mu = gmm_mean(k * frame.rows + y, x);
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WorkT var = gmm_var(k * frame.rows + y, x);
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||||
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WorkT diff = pix - mu;
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if (sqr(diff) < varThreshold * sum(var))
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||||
{
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||||
kHit = k;
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break;
|
||||
}
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||||
}
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||||
|
||||
if (kHit >= 0)
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||||
{
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||||
float wsum = 0.0f;
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||||
for (int k = 0; k < nmixtures; ++k)
|
||||
{
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||||
wsum += gmm_weight(k * frame.rows + y, x);
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||||
|
||||
if (wsum > backgroundRatio)
|
||||
{
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||||
kForeground = k + 1;
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||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fgmask(y, x) = (uchar) (-(kHit < 0 || kHit >= kForeground));
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||||
}
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||||
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template <typename SrcT, typename WorkT>
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||||
void mog_withoutLearning_caller(DevMem2Db frame, DevMem2Db fgmask, DevMem2Df weight, DevMem2Db mean, DevMem2Db var,
|
||||
int nmixtures, float varThreshold, float backgroundRatio, cudaStream_t stream)
|
||||
{
|
||||
dim3 block(32, 8);
|
||||
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
|
||||
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(mog_withoutLearning<SrcT, WorkT>, cudaFuncCachePreferL1) );
|
||||
|
||||
mog_withoutLearning<SrcT, WorkT><<<grid, block, 0, stream>>>((DevMem2D_<SrcT>) frame, fgmask,
|
||||
weight, (DevMem2D_<WorkT>) mean, (DevMem2D_<WorkT>) var,
|
||||
nmixtures, varThreshold, backgroundRatio);
|
||||
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////
|
||||
// MOG with learning
|
||||
|
||||
template <typename SrcT, typename WorkT>
|
||||
__global__ void mog_withLearning(const DevMem2D_<SrcT> frame, PtrStepb fgmask,
|
||||
PtrStepf gmm_weight, PtrStepf gmm_sortKey, PtrStep_<WorkT> gmm_mean, PtrStep_<WorkT> gmm_var,
|
||||
const int nmixtures, const float varThreshold, const float backgroundRatio, const float learningRate, const float minVar)
|
||||
{
|
||||
const float w0 = 0.05f;
|
||||
const float sk0 = w0 / (30.0f * 0.5f * 2.0f);
|
||||
const float var0 = 30.0f * 0.5f * 30.0f * 0.5f * 4.0f;
|
||||
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
if (x >= frame.cols || y >= frame.rows)
|
||||
return;
|
||||
|
||||
WorkT pix = cvt(frame(y, x));
|
||||
|
||||
float wsum = 0.0f;
|
||||
int kHit = -1;
|
||||
int kForeground = -1;
|
||||
|
||||
int k = 0;
|
||||
for (; k < nmixtures; ++k)
|
||||
{
|
||||
float w = gmm_weight(k * frame.rows + y, x);
|
||||
wsum += w;
|
||||
|
||||
if (w < numeric_limits<float>::epsilon())
|
||||
break;
|
||||
|
||||
WorkT mu = gmm_mean(k * frame.rows + y, x);
|
||||
WorkT var = gmm_var(k * frame.rows + y, x);
|
||||
|
||||
WorkT diff = pix - mu;
|
||||
|
||||
if (sqr(diff) < varThreshold * sum(var))
|
||||
{
|
||||
wsum -= w;
|
||||
float dw = learningRate * (1.0f - w);
|
||||
|
||||
var = clamp(var, learningRate, diff, minVar);
|
||||
|
||||
float sortKey_prev = w / ::sqrtf(sum(var));
|
||||
gmm_sortKey(k * frame.rows + y, x) = sortKey_prev;
|
||||
|
||||
float weight_prev = w + dw;
|
||||
gmm_weight(k * frame.rows + y, x) = weight_prev;
|
||||
|
||||
WorkT mean_prev = mu + learningRate * diff;
|
||||
gmm_mean(k * frame.rows + y, x) = mean_prev;
|
||||
|
||||
WorkT var_prev = var;
|
||||
gmm_var(k * frame.rows + y, x) = var_prev;
|
||||
|
||||
int k1 = k - 1;
|
||||
|
||||
if (k1 >= 0)
|
||||
{
|
||||
float sortKey_next = gmm_sortKey(k1 * frame.rows + y, x);
|
||||
float weight_next = gmm_weight(k1 * frame.rows + y, x);
|
||||
WorkT mean_next = gmm_mean(k1 * frame.rows + y, x);
|
||||
WorkT var_next = gmm_var(k1 * frame.rows + y, x);
|
||||
|
||||
for (; sortKey_next < sortKey_prev && k1 >= 0; --k1)
|
||||
{
|
||||
gmm_sortKey(k1 * frame.rows + y, x) = sortKey_prev;
|
||||
gmm_sortKey((k1 + 1) * frame.rows + y, x) = sortKey_next;
|
||||
|
||||
gmm_weight(k1 * frame.rows + y, x) = weight_prev;
|
||||
gmm_weight((k1 + 1) * frame.rows + y, x) = weight_next;
|
||||
|
||||
gmm_mean(k1 * frame.rows + y, x) = mean_prev;
|
||||
gmm_mean((k1 + 1) * frame.rows + y, x) = mean_next;
|
||||
|
||||
gmm_var(k1 * frame.rows + y, x) = var_prev;
|
||||
gmm_var((k1 + 1) * frame.rows + y, x) = var_next;
|
||||
|
||||
sortKey_prev = sortKey_next;
|
||||
sortKey_next = k1 > 0 ? gmm_sortKey((k1 - 1) * frame.rows + y, x) : 0.0f;
|
||||
|
||||
weight_prev = weight_next;
|
||||
weight_next = k1 > 0 ? gmm_weight((k1 - 1) * frame.rows + y, x) : 0.0f;
|
||||
|
||||
mean_prev = mean_next;
|
||||
mean_next = k1 > 0 ? gmm_mean((k1 - 1) * frame.rows + y, x) : VecTraits<WorkT>::all(0.0f);
|
||||
|
||||
var_prev = var_next;
|
||||
var_next = k1 > 0 ? gmm_var((k1 - 1) * frame.rows + y, x) : VecTraits<WorkT>::all(0.0f);
|
||||
}
|
||||
}
|
||||
|
||||
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, nmixtures - 1);
|
||||
wsum += w0 - gmm_weight(k * frame.rows + y, x);
|
||||
|
||||
gmm_weight(k * frame.rows + y, x) = w0;
|
||||
gmm_mean(k * frame.rows + y, x) = pix;
|
||||
gmm_var(k * frame.rows + y, x) = VecTraits<WorkT>::all(var0);
|
||||
gmm_sortKey(k * frame.rows + y, x) = sk0;
|
||||
}
|
||||
else
|
||||
{
|
||||
for( ; k < nmixtures; k++)
|
||||
wsum += gmm_weight(k * frame.rows + y, x);
|
||||
}
|
||||
|
||||
float wscale = 1.0f / wsum;
|
||||
wsum = 0;
|
||||
for (k = 0; k < nmixtures; ++k)
|
||||
{
|
||||
float w = gmm_weight(k * frame.rows + y, x);
|
||||
wsum += w *= wscale;
|
||||
|
||||
gmm_weight(k * frame.rows + y, x) = w;
|
||||
gmm_sortKey(k * frame.rows + y, x) *= wscale;
|
||||
|
||||
if (wsum > backgroundRatio && kForeground < 0)
|
||||
kForeground = k + 1;
|
||||
}
|
||||
|
||||
fgmask(y, x) = (uchar)(-(kHit >= kForeground));
|
||||
}
|
||||
|
||||
template <typename SrcT, typename WorkT>
|
||||
void mog_withLearning_caller(DevMem2Db frame, DevMem2Db fgmask, DevMem2Df weight, DevMem2Df sortKey, DevMem2Db mean, DevMem2Db var,
|
||||
int nmixtures, float varThreshold, float backgroundRatio, float learningRate, float minVar,
|
||||
cudaStream_t stream)
|
||||
{
|
||||
dim3 block(32, 8);
|
||||
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
|
||||
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(mog_withLearning<SrcT, WorkT>, cudaFuncCachePreferL1) );
|
||||
|
||||
mog_withLearning<SrcT, WorkT><<<grid, block, 0, stream>>>((DevMem2D_<SrcT>) frame, fgmask,
|
||||
weight, sortKey, (DevMem2D_<WorkT>) mean, (DevMem2D_<WorkT>) var,
|
||||
nmixtures, varThreshold, backgroundRatio, learningRate, minVar);
|
||||
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////
|
||||
// MOG
|
||||
|
||||
void mog_gpu(DevMem2Db frame, int cn, DevMem2Db fgmask, DevMem2Df weight, DevMem2Df sortKey, DevMem2Db mean, DevMem2Db var, int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*withoutLearning_t)(DevMem2Db frame, DevMem2Db fgmask, DevMem2Df weight, DevMem2Db mean, DevMem2Db var, int nmixtures, float varThreshold, float backgroundRatio, cudaStream_t stream);
|
||||
typedef void (*withLearning_t)(DevMem2Db frame, DevMem2Db fgmask, DevMem2Df weight, DevMem2Df sortKey, DevMem2Db mean, DevMem2Db var, int nmixtures, float varThreshold, float backgroundRatio, float learningRate, float minVar, cudaStream_t stream);
|
||||
|
||||
static const withoutLearning_t withoutLearning[] =
|
||||
{
|
||||
0, mog_withoutLearning_caller<uchar, float>, 0, mog_withoutLearning_caller<uchar3, float3>, mog_withoutLearning_caller<uchar4, float4>
|
||||
};
|
||||
static const withLearning_t withLearning[] =
|
||||
{
|
||||
0, mog_withLearning_caller<uchar, float>, 0, mog_withLearning_caller<uchar3, float3>, mog_withLearning_caller<uchar4, float4>
|
||||
};
|
||||
|
||||
const float minVar = noiseSigma * noiseSigma;
|
||||
|
||||
if (learningRate > 0.0f)
|
||||
withLearning[cn](frame, fgmask, weight, sortKey, mean, var, nmixtures, varThreshold, backgroundRatio, learningRate, minVar, stream);
|
||||
else
|
||||
withoutLearning[cn](frame, fgmask, weight, mean, var, nmixtures, varThreshold, backgroundRatio, stream);
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////
|
||||
// MOG2
|
||||
|
||||
__constant__ int c_nmixtures;
|
||||
__constant__ float c_Tb;
|
||||
__constant__ float c_TB;
|
||||
__constant__ float c_Tg;
|
||||
__constant__ float c_varInit;
|
||||
__constant__ float c_varMin;
|
||||
__constant__ float c_varMax;
|
||||
__constant__ float c_tau;
|
||||
__constant__ unsigned char c_shadowVal;
|
||||
|
||||
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal)
|
||||
{
|
||||
varMin = ::fminf(varMin, varMax);
|
||||
varMax = ::fmaxf(varMin, varMax);
|
||||
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_nmixtures, &nmixtures, sizeof(int)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_Tb, &Tb, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_TB, &TB, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_Tg, &Tg, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_varInit, &varInit, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_varMin, &varMin, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_varMax, &varMax, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_tau, &tau, sizeof(float)) );
|
||||
cudaSafeCall( cudaMemcpyToSymbol(c_shadowVal, &shadowVal, sizeof(unsigned char)) );
|
||||
}
|
||||
|
||||
template <bool detectShadows, typename SrcT, typename WorkT>
|
||||
__global__ void mog2(const DevMem2D_<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed,
|
||||
PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep_<WorkT> gmm_mean,
|
||||
const float alphaT, const float alpha1, const float prune)
|
||||
{
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
if (x >= frame.cols || y >= frame.rows)
|
||||
return;
|
||||
|
||||
WorkT pix = cvt(frame(y, x));
|
||||
|
||||
//calculate distances to the modes (+ sort)
|
||||
//here we need to go in descending order!!!
|
||||
|
||||
bool background = false; // true - the pixel classified as background
|
||||
|
||||
//internal:
|
||||
|
||||
bool fitsPDF = false; //if it remains zero a new GMM mode will be added
|
||||
|
||||
int nmodes = modesUsed(y, x);
|
||||
int nNewModes = nmodes; //current number of modes in GMM
|
||||
|
||||
float totalWeight = 0.0f;
|
||||
|
||||
//go through all modes
|
||||
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
//need only weight if fit is found
|
||||
float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune;
|
||||
|
||||
//fit not found yet
|
||||
if (!fitsPDF)
|
||||
{
|
||||
//check if it belongs to some of the remaining modes
|
||||
float var = gmm_variance(mode * frame.rows + y, x);
|
||||
|
||||
WorkT mean = gmm_mean(mode * frame.rows + y, x);
|
||||
|
||||
//calculate difference and distance
|
||||
WorkT diff = mean - pix;
|
||||
float dist2 = sqr(diff);
|
||||
|
||||
//background? - Tb - usually larger than Tg
|
||||
if (totalWeight < c_TB && dist2 < c_Tb * var)
|
||||
background = true;
|
||||
|
||||
//check fit
|
||||
if (dist2 < c_Tg * var)
|
||||
{
|
||||
//belongs to the mode
|
||||
fitsPDF = true;
|
||||
|
||||
//update distribution
|
||||
|
||||
//update weight
|
||||
weight += alphaT;
|
||||
float k = alphaT / weight;
|
||||
|
||||
//update mean
|
||||
gmm_mean(mode * frame.rows + y, x) = mean - k * diff;
|
||||
|
||||
//update variance
|
||||
float varnew = var + k * (dist2 - var);
|
||||
|
||||
//limit the variance
|
||||
varnew = ::fmaxf(varnew, c_varMin);
|
||||
varnew = ::fminf(varnew, c_varMax);
|
||||
|
||||
gmm_variance(mode * frame.rows + y, x) = varnew;
|
||||
|
||||
//sort
|
||||
//all other weights are at the same place and
|
||||
//only the matched (iModes) is higher -> just find the new place for it
|
||||
|
||||
for (int i = mode; i > 0; --i)
|
||||
{
|
||||
//check one up
|
||||
if (weight < gmm_weight((i - 1) * frame.rows + y, x))
|
||||
break;
|
||||
|
||||
//swap one up
|
||||
swap(gmm_weight, x, y, i - 1, frame.rows);
|
||||
swap(gmm_variance, x, y, i - 1, frame.rows);
|
||||
swap(gmm_mean, x, y, i - 1, frame.rows);
|
||||
}
|
||||
|
||||
//belongs to the mode - bFitsPDF becomes 1
|
||||
}
|
||||
} // !fitsPDF
|
||||
|
||||
//check prune
|
||||
if (weight < -prune)
|
||||
{
|
||||
weight = 0.0;
|
||||
nmodes--;
|
||||
}
|
||||
|
||||
gmm_weight(mode * frame.rows + y, x) = weight; //update weight by the calculated value
|
||||
totalWeight += weight;
|
||||
}
|
||||
|
||||
//renormalize weights
|
||||
|
||||
totalWeight = 1.f / totalWeight;
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
gmm_weight(mode * frame.rows + y, x) *= totalWeight;
|
||||
|
||||
nmodes = nNewModes;
|
||||
|
||||
//make new mode if needed and exit
|
||||
|
||||
if (!fitsPDF)
|
||||
{
|
||||
// replace the weakest or add a new one
|
||||
int mode = nmodes == c_nmixtures ? c_nmixtures - 1 : nmodes++;
|
||||
|
||||
if (nmodes == 1)
|
||||
gmm_weight(mode * frame.rows + y, x) = 1.f;
|
||||
else
|
||||
{
|
||||
gmm_weight(mode * frame.rows + y, x) = alphaT;
|
||||
|
||||
// renormalize all other weights
|
||||
|
||||
for (int i = 0; i < nmodes - 1; ++i)
|
||||
gmm_weight(i * frame.rows + y, x) *= alpha1;
|
||||
}
|
||||
|
||||
// init
|
||||
|
||||
gmm_mean(mode * frame.rows + y, x) = pix;
|
||||
gmm_variance(mode * frame.rows + y, x) = c_varInit;
|
||||
|
||||
//sort
|
||||
//find the new place for it
|
||||
|
||||
for (int i = nmodes - 1; i > 0; --i)
|
||||
{
|
||||
// check one up
|
||||
if (alphaT < gmm_weight((i - 1) * frame.rows + y, x))
|
||||
break;
|
||||
|
||||
//swap one up
|
||||
swap(gmm_weight, x, y, i - 1, frame.rows);
|
||||
swap(gmm_variance, x, y, i - 1, frame.rows);
|
||||
swap(gmm_mean, x, y, i - 1, frame.rows);
|
||||
}
|
||||
}
|
||||
|
||||
//set the number of modes
|
||||
modesUsed(y, x) = nmodes;
|
||||
|
||||
bool isShadow = false;
|
||||
if (detectShadows && !background)
|
||||
{
|
||||
float tWeight = 0.0f;
|
||||
|
||||
// check all the components marked as background:
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
WorkT mean = gmm_mean(mode * frame.rows + y, x);
|
||||
|
||||
WorkT pix_mean = pix * mean;
|
||||
|
||||
float numerator = sum(pix_mean);
|
||||
float denominator = sqr(mean);
|
||||
|
||||
// no division by zero allowed
|
||||
if (denominator == 0)
|
||||
break;
|
||||
|
||||
// if tau < a < 1 then also check the color distortion
|
||||
if (numerator <= denominator && numerator >= c_tau * denominator)
|
||||
{
|
||||
float a = numerator / denominator;
|
||||
|
||||
WorkT dD = a * mean - pix;
|
||||
|
||||
if (sqr(dD) < c_Tb * gmm_variance(mode * frame.rows + y, x) * a * a)
|
||||
{
|
||||
isShadow = true;
|
||||
break;
|
||||
}
|
||||
};
|
||||
|
||||
tWeight += gmm_weight(mode * frame.rows + y, x);
|
||||
if (tWeight > c_TB)
|
||||
break;
|
||||
};
|
||||
}
|
||||
|
||||
fgmask(y, x) = background ? 0 : isShadow ? c_shadowVal : 255;
|
||||
}
|
||||
|
||||
template <typename SrcT, typename WorkT>
|
||||
void mog2_caller(DevMem2Db frame, DevMem2Db fgmask, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Df variance, DevMem2Db mean,
|
||||
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
|
||||
{
|
||||
dim3 block(32, 8);
|
||||
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
|
||||
|
||||
const float alpha1 = 1.0f - alphaT;
|
||||
|
||||
if (detectShadows)
|
||||
{
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(mog2<true, SrcT, WorkT>, cudaFuncCachePreferL1) );
|
||||
|
||||
mog2<true, SrcT, WorkT><<<grid, block, 0, stream>>>((DevMem2D_<SrcT>) frame, fgmask, modesUsed,
|
||||
weight, variance, (DevMem2D_<WorkT>) mean,
|
||||
alphaT, alpha1, prune);
|
||||
}
|
||||
else
|
||||
{
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(mog2<false, SrcT, WorkT>, cudaFuncCachePreferL1) );
|
||||
|
||||
mog2<false, SrcT, WorkT><<<grid, block, 0, stream>>>((DevMem2D_<SrcT>) frame, fgmask, modesUsed,
|
||||
weight, variance, (DevMem2D_<WorkT>) mean,
|
||||
alphaT, alpha1, prune);
|
||||
}
|
||||
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
void mog2_gpu(DevMem2Db frame, int cn, DevMem2Db fgmask, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Df variance, DevMem2Db mean,
|
||||
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(DevMem2Db frame, DevMem2Db fgmask, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Df variance, DevMem2Db mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
|
||||
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
0, mog2_caller<uchar, float>, 0, mog2_caller<uchar3, float3>, mog2_caller<uchar4, float4>
|
||||
};
|
||||
|
||||
funcs[cn](frame, fgmask, modesUsed, weight, variance, mean, alphaT, prune, detectShadows, stream);
|
||||
}
|
||||
|
||||
template <typename WorkT, typename OutT>
|
||||
__global__ void getBackgroundImage(const DevMem2Db modesUsed, const PtrStepf gmm_weight, const PtrStep_<WorkT> gmm_mean, PtrStep_<OutT> dst)
|
||||
{
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
if (x >= modesUsed.cols || y >= modesUsed.rows)
|
||||
return;
|
||||
|
||||
int nmodes = modesUsed(y, x);
|
||||
|
||||
WorkT meanVal = VecTraits<WorkT>::all(0.0f);
|
||||
float totalWeight = 0.0f;
|
||||
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
float weight = gmm_weight(mode * modesUsed.rows + y, x);
|
||||
|
||||
WorkT mean = gmm_mean(mode * modesUsed.rows + y, x);
|
||||
meanVal = meanVal + weight * mean;
|
||||
|
||||
totalWeight += weight;
|
||||
|
||||
if(totalWeight > c_TB)
|
||||
break;
|
||||
}
|
||||
|
||||
meanVal = meanVal * (1.f / totalWeight);
|
||||
|
||||
dst(y, x) = saturate_cast<OutT>(meanVal);
|
||||
}
|
||||
|
||||
template <typename WorkT, typename OutT>
|
||||
void getBackgroundImage_caller(DevMem2Db modesUsed, DevMem2Df weight, DevMem2Db mean, DevMem2Db dst, cudaStream_t stream)
|
||||
{
|
||||
dim3 block(32, 8);
|
||||
dim3 grid(divUp(modesUsed.cols, block.x), divUp(modesUsed.rows, block.y));
|
||||
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(getBackgroundImage<WorkT, OutT>, cudaFuncCachePreferL1) );
|
||||
|
||||
getBackgroundImage<WorkT, OutT><<<grid, block, 0, stream>>>(modesUsed, weight, (DevMem2D_<WorkT>) mean, (DevMem2D_<OutT>) dst);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
void getBackgroundImage_gpu(int cn, DevMem2Db modesUsed, DevMem2Df weight, DevMem2Db mean, DevMem2Db dst, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(DevMem2Db modesUsed, DevMem2Df weight, DevMem2Db mean, DevMem2Db dst, cudaStream_t stream);
|
||||
|
||||
static const func_t funcs[] =
|
||||
{
|
||||
0, getBackgroundImage_caller<float, uchar>, 0, getBackgroundImage_caller<float3, uchar3>, getBackgroundImage_caller<float4, uchar4>
|
||||
};
|
||||
|
||||
funcs[cn](modesUsed, weight, mean, dst, stream);
|
||||
}
|
||||
}
|
||||
}}}
|
||||
@@ -1,3 +1,45 @@
|
||||
/*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 bpied warranties, including, but not limited to, the bpied
|
||||
// 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 "opencv2/gpu/device/common.hpp"
|
||||
#include "opencv2/gpu/device/vec_math.hpp"
|
||||
#include "opencv2/gpu/device/limits.hpp"
|
||||
|
||||
Reference in New Issue
Block a user