split mog sources

modules/gpubgsegm/src/mog.cpp

@@ -50,12 +50,6 @@ void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, floa
 void cv::gpu::MOG_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_no_cuda(); }
 void cv::gpu::MOG_GPU::release() {}
 
-cv::gpu::MOG2_GPU::MOG2_GPU(int) { throw_no_cuda(); }
-void cv::gpu::MOG2_GPU::initialize(cv::Size, int) { throw_no_cuda(); }
-void cv::gpu::MOG2_GPU::operator()(const GpuMat&, GpuMat&, float, Stream&) { throw_no_cuda(); }
-void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_no_cuda(); }
-void cv::gpu::MOG2_GPU::release() {}
-
 #else
 
 namespace cv { namespace gpu { namespace cudev
@@ -66,10 +60,6 @@ namespace cv { namespace gpu { namespace cudev
                      int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma,
                      cudaStream_t stream);
         void getBackgroundImage_gpu(int cn, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, int nmixtures, float backgroundRatio, cudaStream_t stream);
-
-        void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
-        void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
-        void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
     }
 }}}
 
@@ -165,115 +155,4 @@ void cv::gpu::MOG_GPU::release()
     var_.release();
 }
 
-/////////////////////////////////////////////////////////////////
-// MOG2
-
-namespace mog2
-{
-    // default parameters of gaussian background detection algorithm
-    const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
-    const float defaultVarThreshold = 4.0f * 4.0f;
-    const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
-    const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
-    const float defaultVarThresholdGen = 3.0f * 3.0f;
-    const float defaultVarInit = 15.0f; // initial variance for new components
-    const float defaultVarMax = 5.0f * defaultVarInit;
-    const float defaultVarMin = 4.0f;
-
-    // additional parameters
-    const float defaultfCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
-    const unsigned char defaultnShadowDetection = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
-    const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
-}
-
-cv::gpu::MOG2_GPU::MOG2_GPU(int nmixtures) :
-    frameSize_(0, 0), frameType_(0), nframes_(0)
-{
-    nmixtures_ = nmixtures > 0 ? nmixtures : mog2::defaultNMixtures;
-
-    history = mog2::defaultHistory;
-    varThreshold = mog2::defaultVarThreshold;
-    bShadowDetection = true;
-
-    backgroundRatio = mog2::defaultBackgroundRatio;
-    fVarInit = mog2::defaultVarInit;
-    fVarMax  = mog2::defaultVarMax;
-    fVarMin = mog2::defaultVarMin;
-
-    varThresholdGen = mog2::defaultVarThresholdGen;
-    fCT = mog2::defaultfCT;
-    nShadowDetection =  mog2::defaultnShadowDetection;
-    fTau = mog2::defaultfTau;
-}
-
-void cv::gpu::MOG2_GPU::initialize(cv::Size frameSize, int frameType)
-{
-    using namespace cv::gpu::cudev::mog;
-
-    CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
-
-    frameSize_ = frameSize;
-    frameType_ = frameType;
-    nframes_ = 0;
-
-    int ch = CV_MAT_CN(frameType);
-    int work_ch = ch;
-
-    // for each gaussian mixture of each pixel bg model we store ...
-    // the mixture weight (w),
-    // the mean (nchannels values) and
-    // the covariance
-    weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
-    variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
-    mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
-
-    //make the array for keeping track of the used modes per pixel - all zeros at start
-    bgmodelUsedModes_.create(frameSize_, CV_8UC1);
-    bgmodelUsedModes_.setTo(cv::Scalar::all(0));
-
-    loadConstants(nmixtures_, varThreshold, backgroundRatio, varThresholdGen, fVarInit, fVarMin, fVarMax, fTau, nShadowDetection);
-}
-
-void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
-{
-    using namespace cv::gpu::cudev::mog;
-
-    int ch = frame.channels();
-    int work_ch = ch;
-
-    if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
-        initialize(frame.size(), frame.type());
-
-    fgmask.create(frameSize_, CV_8UC1);
-    fgmask.setTo(cv::Scalar::all(0));
-
-    ++nframes_;
-    learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
-    CV_Assert(learningRate >= 0.0f);
-
-    mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
-}
-
-void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
-{
-    using namespace cv::gpu::cudev::mog;
-
-    backgroundImage.create(frameSize_, frameType_);
-
-    getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
-}
-
-void cv::gpu::MOG2_GPU::release()
-{
-    frameSize_ = Size(0, 0);
-    frameType_ = 0;
-    nframes_ = 0;
-
-    weight_.release();
-    variance_.release();
-    mean_.release();
-
-    bgmodelUsedModes_.release();
-}
-
 #endif

modules/gpubgsegm/src/mog2.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
+
+cv::gpu::MOG2_GPU::MOG2_GPU(int) { throw_no_cuda(); }
+void cv::gpu::MOG2_GPU::initialize(cv::Size, int) { throw_no_cuda(); }
+void cv::gpu::MOG2_GPU::operator()(const GpuMat&, GpuMat&, float, Stream&) { throw_no_cuda(); }
+void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat&, Stream&) const { throw_no_cuda(); }
+void cv::gpu::MOG2_GPU::release() {}
+
+#else
+
+namespace cv { namespace gpu { namespace cudev
+{
+    namespace mog2
+    {
+        void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
+        void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
+        void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
+    }
+}}}
+
+namespace mog2
+{
+    // default parameters of gaussian background detection algorithm
+    const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
+    const float defaultVarThreshold = 4.0f * 4.0f;
+    const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
+    const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
+    const float defaultVarThresholdGen = 3.0f * 3.0f;
+    const float defaultVarInit = 15.0f; // initial variance for new components
+    const float defaultVarMax = 5.0f * defaultVarInit;
+    const float defaultVarMin = 4.0f;
+
+    // additional parameters
+    const float defaultfCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
+    const unsigned char defaultnShadowDetection = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
+    const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
+}
+
+cv::gpu::MOG2_GPU::MOG2_GPU(int nmixtures) :
+    frameSize_(0, 0), frameType_(0), nframes_(0)
+{
+    nmixtures_ = nmixtures > 0 ? nmixtures : mog2::defaultNMixtures;
+
+    history = mog2::defaultHistory;
+    varThreshold = mog2::defaultVarThreshold;
+    bShadowDetection = true;
+
+    backgroundRatio = mog2::defaultBackgroundRatio;
+    fVarInit = mog2::defaultVarInit;
+    fVarMax  = mog2::defaultVarMax;
+    fVarMin = mog2::defaultVarMin;
+
+    varThresholdGen = mog2::defaultVarThresholdGen;
+    fCT = mog2::defaultfCT;
+    nShadowDetection =  mog2::defaultnShadowDetection;
+    fTau = mog2::defaultfTau;
+}
+
+void cv::gpu::MOG2_GPU::initialize(cv::Size frameSize, int frameType)
+{
+    using namespace cv::gpu::cudev::mog2;
+
+    CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4);
+
+    frameSize_ = frameSize;
+    frameType_ = frameType;
+    nframes_ = 0;
+
+    int ch = CV_MAT_CN(frameType);
+    int work_ch = ch;
+
+    // for each gaussian mixture of each pixel bg model we store ...
+    // the mixture weight (w),
+    // the mean (nchannels values) and
+    // the covariance
+    weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
+    variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
+    mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
+
+    //make the array for keeping track of the used modes per pixel - all zeros at start
+    bgmodelUsedModes_.create(frameSize_, CV_8UC1);
+    bgmodelUsedModes_.setTo(cv::Scalar::all(0));
+
+    loadConstants(nmixtures_, varThreshold, backgroundRatio, varThresholdGen, fVarInit, fVarMin, fVarMax, fTau, nShadowDetection);
+}
+
+void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
+{
+    using namespace cv::gpu::cudev::mog2;
+
+    int ch = frame.channels();
+    int work_ch = ch;
+
+    if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
+        initialize(frame.size(), frame.type());
+
+    fgmask.create(frameSize_, CV_8UC1);
+    fgmask.setTo(cv::Scalar::all(0));
+
+    ++nframes_;
+    learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
+    CV_Assert(learningRate >= 0.0f);
+
+    mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
+}
+
+void cv::gpu::MOG2_GPU::getBackgroundImage(GpuMat& backgroundImage, Stream& stream) const
+{
+    using namespace cv::gpu::cudev::mog2;
+
+    backgroundImage.create(frameSize_, frameType_);
+
+    getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
+}
+
+void cv::gpu::MOG2_GPU::release()
+{
+    frameSize_ = Size(0, 0);
+    frameType_ = 0;
+    nframes_ = 0;
+
+    weight_.release();
+    variance_.release();
+    mean_.release();
+
+    bgmodelUsedModes_.release();
+}
+
+#endif