mirror of
https://github.com/opencv/opencv.git
synced 2026-07-30 07:43:03 +04:00
Added ocl_Mog2
This commit is contained in:
Elena Gvozdeva
@@ -83,6 +83,7 @@
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///////////*/
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#include "precomp.hpp"
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#include "opencl_kernels.hpp"
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namespace cv
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{
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@@ -141,6 +142,8 @@ public:
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fCT = defaultfCT2;
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nShadowDetection = defaultnShadowDetection2;
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fTau = defaultfTau;
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opencl_ON = true;
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}
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//! the full constructor that takes the length of the history,
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// the number of gaussian mixtures, the background ratio parameter and the noise strength
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@@ -165,6 +168,8 @@ public:
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nShadowDetection = defaultnShadowDetection2;
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fTau = defaultfTau;
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name_ = "BackgroundSubtractor.MOG2";
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opencl_ON = true;
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}
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//! the destructor
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~BackgroundSubtractorMOG2Impl() {}
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@@ -184,14 +189,44 @@ public:
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int nchannels = CV_MAT_CN(frameType);
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CV_Assert( nchannels <= CV_CN_MAX );
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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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bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + nchannels), CV_32F );
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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_8U);
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bgmodelUsedModes = Scalar::all(0);
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if (ocl::useOpenCL() && opencl_ON)
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{
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kernel_apply.create("mog2_kernel", ocl::video::bgfg_mog2_oclsrc, format("-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
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kernel_getBg.create("getBackgroundImage2_kernel", ocl::video::bgfg_mog2_oclsrc, format( "-D CN=%d -D NMIXTURES=%d", nchannels, nmixtures));
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if (kernel_apply.empty() || kernel_getBg.empty())
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opencl_ON = false;
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}
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else opencl_ON = false;
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if (opencl_ON)
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{
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u_weight.create(frameSize.height * nmixtures, frameSize.width, CV_32FC1);
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u_weight.setTo(Scalar::all(0));
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u_variance.create(frameSize.height * nmixtures, frameSize.width, CV_32FC1);
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u_variance.setTo(Scalar::all(0));
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if (nchannels==3)
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nchannels=4;
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u_mean.create(frameSize.height * nmixtures, frameSize.width, CV_32FC(nchannels)); //4 channels
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u_mean.setTo(Scalar::all(0));
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//make the array for keeping track of the used modes per pixel - all zeros at start
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u_bgmodelUsedModes.create(frameSize, CV_32FC1);
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u_bgmodelUsedModes.setTo(cv::Scalar::all(0));
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}
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else
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{
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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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bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + nchannels), CV_32F );
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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_8U);
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bgmodelUsedModes = Scalar::all(0);
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}
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}
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virtual AlgorithmInfo* info() const { return 0; }
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@@ -271,6 +306,19 @@ protected:
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int frameType;
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Mat bgmodel;
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Mat bgmodelUsedModes;//keep track of number of modes per pixel
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//for OCL
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mutable bool opencl_ON;
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UMat u_weight;
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UMat u_variance;
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UMat u_mean;
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UMat u_bgmodelUsedModes;
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mutable ocl::Kernel kernel_apply;
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mutable ocl::Kernel kernel_getBg;
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int nframes;
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int history;
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int nmixtures;
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@@ -321,6 +369,9 @@ protected:
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//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
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String name_;
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bool ocl_getBackgroundImage(OutputArray backgroundImage) const;
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bool ocl_apply(InputArray _image, OutputArray _fgmask, bool needToInitialize, double learningRate=-1);
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};
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struct GaussBGStatModel2Params
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@@ -685,14 +736,78 @@ public:
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uchar shadowVal;
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};
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bool BackgroundSubtractorMOG2Impl::ocl_apply(InputArray _image, OutputArray _fgmask, bool needToInitialize, double learningRate)
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{
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++nframes;
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learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history );
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CV_Assert(learningRate >= 0);
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UMat fgmask(_image.size(), CV_32SC1);
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fgmask.setTo(cv::Scalar::all(1));
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const float alpha1 = 1.0f - learningRate;
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int detectShadows_flag = 0;
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if(bShadowDetection)
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detectShadows_flag = 1;
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UMat frame = _image.getUMat();
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float varMax = MAX(fVarMin, fVarMax);
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float varMin = MIN(fVarMin, fVarMax);
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int idxArg = 0;
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadOnly(frame));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_bgmodelUsedModes));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_weight));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_mean));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::ReadWriteNoSize(u_variance));
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idxArg = kernel_apply.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(fgmask));
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idxArg = kernel_apply.set(idxArg, (float)learningRate); //alphaT
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idxArg = kernel_apply.set(idxArg, (float)alpha1);
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idxArg = kernel_apply.set(idxArg, (float)(-learningRate*fCT)); //prune
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idxArg = kernel_apply.set(idxArg, detectShadows_flag);
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idxArg = kernel_apply.set(idxArg, (float)varThreshold); //c_Tb
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idxArg = kernel_apply.set(idxArg, backgroundRatio); //c_TB
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idxArg = kernel_apply.set(idxArg, varThresholdGen); //c_Tg
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idxArg = kernel_apply.set(idxArg, varMin);
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idxArg = kernel_apply.set(idxArg, varMax);
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idxArg = kernel_apply.set(idxArg, fVarInit);
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idxArg = kernel_apply.set(idxArg, fTau);
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idxArg = kernel_apply.set(idxArg, nShadowDetection);
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size_t globalsize[] = {frame.cols, frame.rows, 1};
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if (!(kernel_apply.run(2, globalsize, NULL, true)))
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return false;
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_fgmask.create(_image.size(),CV_8U);
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UMat temp = _fgmask.getUMat();
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fgmask.convertTo(temp, CV_8U);
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return true;
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}
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void BackgroundSubtractorMOG2Impl::apply(InputArray _image, OutputArray _fgmask, double learningRate)
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{
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Mat image = _image.getMat();
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bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;
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bool needToInitialize = nframes == 0 || learningRate >= 1 || _image.size() != frameSize || _image.type() != frameType;
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if( needToInitialize )
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initialize(image.size(), image.type());
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initialize(_image.size(), _image.type());
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if (opencl_ON)
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{
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if (ocl_apply(_image,_fgmask, needToInitialize, learningRate))
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return;
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else
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initialize(_image.size(), _image.type());
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}
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opencl_ON = false;
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Mat image = _image.getMat();
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_fgmask.create( image.size(), CV_8U );
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Mat fgmask = _fgmask.getMat();
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@@ -712,8 +827,36 @@ void BackgroundSubtractorMOG2Impl::apply(InputArray _image, OutputArray _fgmask,
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image.total()/(double)(1 << 16));
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}
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bool BackgroundSubtractorMOG2Impl::ocl_getBackgroundImage(OutputArray _backgroundImage) const
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{
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CV_Assert(frameType == CV_8UC1 || frameType == CV_8UC3);
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_backgroundImage.create(frameSize, frameType);
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UMat dst = _backgroundImage.getUMat();
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int idxArg = 0;
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnly(u_bgmodelUsedModes));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnlyNoSize(u_weight));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::ReadOnlyNoSize(u_mean));
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idxArg = kernel_getBg.set(idxArg, ocl::KernelArg::WriteOnlyNoSize(dst));
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idxArg = kernel_getBg.set(idxArg, backgroundRatio);
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size_t globalsize[2] = {u_bgmodelUsedModes.cols, u_bgmodelUsedModes.rows};
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return kernel_getBg.run(2, globalsize, NULL, false);
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}
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void BackgroundSubtractorMOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
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{
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if (opencl_ON)
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{
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if (ocl_getBackgroundImage(backgroundImage));
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return;
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opencl_ON = false;
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return;
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}
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int nchannels = CV_MAT_CN(frameType);
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CV_Assert( nchannels == 3 );
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Mat meanBackground(frameSize, CV_8UC3, Scalar::all(0));
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@@ -765,7 +908,6 @@ void BackgroundSubtractorMOG2Impl::getBackgroundImage(OutputArray backgroundImag
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}
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}
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Ptr<BackgroundSubtractorMOG2> createBackgroundSubtractorMOG2(int _history, double _varThreshold,
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bool _bShadowDetection)
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{
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@@ -774,4 +916,4 @@ Ptr<BackgroundSubtractorMOG2> createBackgroundSubtractorMOG2(int _history, doubl
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}
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/* End of file. */
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/* End of file. */
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@@ -0,0 +1,272 @@
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#if CN==1
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#define T_MEAN float
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#define F_ZERO (0.0f)
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#define cnMode 1
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#define frameToMean(a, b) (b) = *(a);
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#define meanToFrame(a, b) *b = convert_uchar_sat(a);
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inline float sqr(float val)
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{
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return val * val;
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}
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inline float sum(float val)
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{
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return val;
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}
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#else
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#define T_MEAN float4
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#define F_ZERO (0.0f, 0.0f, 0.0f, 0.0f)
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#define cnMode 4
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#define meanToFrame(a, b)\
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b[0] = convert_uchar_sat(a.x); \
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b[1] = convert_uchar_sat(a.y); \
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b[2] = convert_uchar_sat(a.z);
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#define frameToMean(a, b)\
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b.x = a[0]; \
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b.y = a[1]; \
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b.z = a[2]; \
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b.w = 0.0f;
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inline 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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inline 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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inline void swap4(__global float4* ptr, int x, int y, int k, int rows, int ptr_step)
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{
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float4 val = ptr[(k * rows + y) * ptr_step + x];
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ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
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ptr[((k + 1) * rows + y) * ptr_step + x] = val;
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}
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#endif
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inline void swap(__global float* ptr, int x, int y, int k, int rows, int ptr_step)
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{
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float val = ptr[(k * rows + y) * ptr_step + x];
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ptr[(k * rows + y) * ptr_step + x] = ptr[((k + 1) * rows + y) * ptr_step + x];
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ptr[((k + 1) * rows + y) * ptr_step + x] = val;
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}
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__kernel void mog2_kernel(__global const uchar* frame, int frame_step, int frame_offset, int frame_row, int frame_col, //uchar || uchar3
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__global uchar* modesUsed, int modesUsed_step, int modesUsed_offset, //int
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__global uchar* weight, int weight_step, int weight_offset, //float
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__global uchar* mean, int mean_step, int mean_offset, //T_MEAN=float || float4
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__global uchar* variance, int var_step, int var_offset, //float
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__global uchar* fgmask, int fgmask_step, int fgmask_offset, //int
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float alphaT, float alpha1, float prune,
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int detectShadows_flag,
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float c_Tb, float c_TB, float c_Tg, float c_varMin, //constants
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float c_varMax, float c_varInit, float c_tau, uchar c_shadowVal)
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{
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int x = get_global_id(0);
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int y = get_global_id(1);
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weight_step/= sizeof(float);
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var_step /= sizeof(float);
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mean_step /= (sizeof(float)*cnMode);
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if( x < frame_col && y < frame_row)
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{
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__global const uchar* _frame = (frame + mad24( y, frame_step, x*CN + frame_offset));
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T_MEAN pix;
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frameToMean(_frame, pix);
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bool background = false; // true - the pixel classified as background
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bool fitsPDF = false; //if it remains zero a new GMM mode will be added
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__global int* _modesUsed = (__global int*)(modesUsed + mad24( y, modesUsed_step, x*(int)(sizeof(int))));
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int nmodes = _modesUsed[0];
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int nNewModes = nmodes; //current number of modes in GMM
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float totalWeight = 0.0f;
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__global float* _weight = (__global float*)(weight);
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__global float* _variance = (__global float*)(variance);
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__global T_MEAN* _mean = (__global T_MEAN*)(mean);
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for (int mode = 0; mode < nmodes; ++mode)
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{
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float c_weight = alpha1 * _weight[(mode * frame_row + y) * weight_step + x] + prune;
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if (!fitsPDF)
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{
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float c_var = _variance[(mode * frame_row + y) * var_step + x];
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T_MEAN c_mean = _mean[(mode * frame_row + y) * mean_step + x];
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T_MEAN diff = c_mean - pix;
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float dist2 = sqr(diff);
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if (totalWeight < c_TB && dist2 < c_Tb * c_var)
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background = true;
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if (dist2 < c_Tg * c_var)
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{
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fitsPDF = true;
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c_weight += alphaT;
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float k = alphaT / c_weight;
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_mean[(mode * frame_row + y) * mean_step + x] = c_mean - k * diff;
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float varnew = c_var + k * (dist2 - c_var);
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varnew = fmax(varnew, c_varMin);
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varnew = fmin(varnew, c_varMax);
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_variance[(mode * frame_row + y) * var_step + x] = varnew;
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for (int i = mode; i > 0; --i)
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{
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if (c_weight < _weight[((i - 1) * frame_row + y) * weight_step + x])
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break;
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swap(_weight, x, y, i - 1, frame_row, weight_step);
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swap(_variance, x, y, i - 1, frame_row, var_step);
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#if (CN==1)
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swap(_mean, x, y, i - 1, frame_row, mean_step);
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#else
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swap4(_mean, x, y, i - 1, frame_row, mean_step);
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#endif
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}
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}
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} // !fitsPDF
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if (c_weight < -prune)
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{
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c_weight = 0.0f;
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nmodes--;
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}
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_weight[(mode * frame_row + y) * weight_step + x] = c_weight; //update weight by the calculated value
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totalWeight += c_weight;
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}
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totalWeight = 1.f / totalWeight;
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for (int mode = 0; mode < nmodes; ++mode)
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_weight[(mode * frame_row + y) * weight_step + x] *= totalWeight;
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nmodes = nNewModes;
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if (!fitsPDF)
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{
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int mode = nmodes == (NMIXTURES) ? (NMIXTURES) - 1 : nmodes++;
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if (nmodes == 1)
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_weight[(mode * frame_row + y) * weight_step + x] = 1.f;
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else
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{
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_weight[(mode * frame_row + y) * weight_step + x] = alphaT;
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for (int i = 0; i < nmodes - 1; ++i)
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_weight[(i * frame_row + y) * weight_step + x] *= alpha1;
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}
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_mean[(mode * frame_row + y) * mean_step + x] = pix;
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_variance[(mode * frame_row + y) * var_step + x] = c_varInit;
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for (int i = nmodes - 1; i > 0; --i)
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{
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if (alphaT < _weight[((i - 1) * frame_row + y) * weight_step + x])
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break;
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swap(_weight, x, y, i - 1, frame_row, weight_step);
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swap(_variance, x, y, i - 1, frame_row, var_step);
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#if (CN==1)
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swap(_mean, x, y, i - 1, frame_row, mean_step);
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#else
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swap4(_mean, x, y, i - 1, frame_row, mean_step);
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#endif
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}
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}
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_modesUsed[0] = nmodes;
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bool isShadow = false;
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if (detectShadows_flag && !background)
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{
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float tWeight = 0.0f;
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|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
T_MEAN c_mean = _mean[(mode * frame_row + y) * mean_step + x];
|
||||
|
||||
T_MEAN pix_mean = pix * c_mean;
|
||||
|
||||
float numerator = sum(pix_mean);
|
||||
float denominator = sqr(c_mean);
|
||||
|
||||
if (denominator == 0)
|
||||
break;
|
||||
|
||||
if (numerator <= denominator && numerator >= c_tau * denominator)
|
||||
{
|
||||
float a = numerator / denominator;
|
||||
|
||||
T_MEAN dD = a * c_mean - pix;
|
||||
|
||||
if (sqr(dD) < c_Tb * _variance[(mode * frame_row + y) * var_step + x] * a * a)
|
||||
{
|
||||
isShadow = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
tWeight += _weight[(mode * frame_row + y) * weight_step + x];
|
||||
if (tWeight > c_TB)
|
||||
break;
|
||||
}
|
||||
}
|
||||
__global int* _fgmask = (__global int*)(fgmask + mad24(y, fgmask_step, x*(int)(sizeof(int)) + fgmask_offset));
|
||||
*_fgmask = background ? 0 : isShadow ? c_shadowVal : 255;
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void getBackgroundImage2_kernel(__global const uchar* modesUsed, int modesUsed_step, int modesUsed_offset, int modesUsed_row, int modesUsed_col,
|
||||
__global const uchar* weight, int weight_step, int weight_offset,
|
||||
__global const uchar* mean, int mean_step, int mean_offset,
|
||||
__global uchar* dst, int dst_step, int dst_offset,
|
||||
float c_TB)
|
||||
{
|
||||
int x = get_global_id(0);
|
||||
int y = get_global_id(1);
|
||||
|
||||
if(x < modesUsed_col && y < modesUsed_row)
|
||||
{
|
||||
__global int* _modesUsed = (__global int*)(modesUsed + mad24( y, modesUsed_step, x*(int)(sizeof(int))));
|
||||
int nmodes = _modesUsed[0];
|
||||
|
||||
T_MEAN meanVal = (T_MEAN)F_ZERO;
|
||||
|
||||
float totalWeight = 0.0f;
|
||||
|
||||
for (int mode = 0; mode < nmodes; ++mode)
|
||||
{
|
||||
__global const float* _weight = (__global const float*)(weight + mad24(mode * modesUsed_row + y, weight_step, x*(int)(sizeof(float))));
|
||||
float c_weight = _weight[0];
|
||||
|
||||
__global const T_MEAN* _mean = (__global const T_MEAN*)(mean + mad24(mode * modesUsed_row + y, mean_step, x*(int)(sizeof(float))*cnMode));
|
||||
T_MEAN c_mean = _mean[0];
|
||||
meanVal = meanVal + c_weight * c_mean;
|
||||
|
||||
totalWeight += c_weight;
|
||||
|
||||
if(totalWeight > c_TB)
|
||||
break;
|
||||
}
|
||||
|
||||
meanVal = meanVal * (1.f / totalWeight);
|
||||
__global uchar* _dst = dst + y * dst_step + x*CN + dst_offset;
|
||||
meanToFrame(meanVal, _dst);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user