Merge pull request #1151 from jet47:gpubgsegm-refactoring
Andrey Pavlenko
12 years ago
committed by
OpenCV Buildbot
commit
487ff4f3aa
19 changed files with 1607 additions and 1676 deletions
@ -0,0 +1,438 @@ |
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/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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|
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#if !defined CUDA_DISABLER |
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|
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#include "opencv2/core/cuda/common.hpp" |
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#include "opencv2/core/cuda/vec_traits.hpp" |
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#include "opencv2/core/cuda/vec_math.hpp" |
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#include "opencv2/core/cuda/limits.hpp" |
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|
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namespace cv { namespace gpu { namespace cudev |
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{ |
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namespace mog2 |
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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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|
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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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|
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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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|
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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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/////////////////////////////////////////////////////////////// |
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// MOG2 |
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__constant__ int c_nmixtures; |
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__constant__ float c_Tb; |
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__constant__ float c_TB; |
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__constant__ float c_Tg; |
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__constant__ float c_varInit; |
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__constant__ float c_varMin; |
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__constant__ float c_varMax; |
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__constant__ float c_tau; |
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__constant__ unsigned char c_shadowVal; |
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|
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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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{ |
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varMin = ::fminf(varMin, varMax); |
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varMax = ::fmaxf(varMin, varMax); |
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cudaSafeCall( cudaMemcpyToSymbol(c_nmixtures, &nmixtures, sizeof(int)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_Tb, &Tb, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_TB, &TB, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_Tg, &Tg, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_varInit, &varInit, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_varMin, &varMin, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_varMax, &varMax, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_tau, &tau, sizeof(float)) ); |
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cudaSafeCall( cudaMemcpyToSymbol(c_shadowVal, &shadowVal, sizeof(unsigned char)) ); |
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} |
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template <bool detectShadows, typename SrcT, typename WorkT> |
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__global__ void mog2(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed, |
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PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep<WorkT> gmm_mean, |
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const float alphaT, const float alpha1, const float prune) |
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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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//calculate distances to the modes (+ sort) |
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//here we need to go in descending order!!! |
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bool background = false; // true - the pixel classified as background |
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//internal: |
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bool fitsPDF = false; //if it remains zero a new GMM mode will be added |
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int nmodes = modesUsed(y, x); |
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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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//go through all modes |
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for (int mode = 0; mode < nmodes; ++mode) |
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{ |
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//need only weight if fit is found |
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float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune; |
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//fit not found yet |
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if (!fitsPDF) |
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{ |
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//check if it belongs to some of the remaining modes |
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float var = gmm_variance(mode * frame.rows + y, x); |
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WorkT mean = gmm_mean(mode * frame.rows + y, x); |
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//calculate difference and distance |
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WorkT diff = mean - pix; |
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float dist2 = sqr(diff); |
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//background? - Tb - usually larger than Tg |
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if (totalWeight < c_TB && dist2 < c_Tb * var) |
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background = true; |
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//check fit |
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if (dist2 < c_Tg * var) |
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{ |
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//belongs to the mode |
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fitsPDF = true; |
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//update distribution |
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//update weight |
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weight += alphaT; |
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float k = alphaT / weight; |
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//update mean |
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gmm_mean(mode * frame.rows + y, x) = mean - k * diff; |
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//update variance |
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float varnew = var + k * (dist2 - var); |
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//limit the variance |
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varnew = ::fmaxf(varnew, c_varMin); |
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varnew = ::fminf(varnew, c_varMax); |
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gmm_variance(mode * frame.rows + y, x) = varnew; |
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//sort |
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//all other weights are at the same place and |
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//only the matched (iModes) is higher -> just find the new place for it |
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for (int i = mode; i > 0; --i) |
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{ |
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//check one up |
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if (weight < gmm_weight((i - 1) * frame.rows + y, x)) |
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break; |
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//swap one up |
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swap(gmm_weight, x, y, i - 1, frame.rows); |
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swap(gmm_variance, x, y, i - 1, frame.rows); |
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swap(gmm_mean, x, y, i - 1, frame.rows); |
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} |
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//belongs to the mode - bFitsPDF becomes 1 |
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} |
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} // !fitsPDF |
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//check prune |
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if (weight < -prune) |
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{ |
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weight = 0.0; |
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nmodes--; |
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} |
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gmm_weight(mode * frame.rows + y, x) = weight; //update weight by the calculated value |
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totalWeight += weight; |
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} |
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//renormalize weights |
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totalWeight = 1.f / totalWeight; |
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for (int mode = 0; mode < nmodes; ++mode) |
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gmm_weight(mode * frame.rows + y, x) *= totalWeight; |
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nmodes = nNewModes; |
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//make new mode if needed and exit |
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if (!fitsPDF) |
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{ |
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// replace the weakest or add a new one |
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int mode = nmodes == c_nmixtures ? c_nmixtures - 1 : nmodes++; |
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if (nmodes == 1) |
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gmm_weight(mode * frame.rows + y, x) = 1.f; |
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else |
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{ |
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gmm_weight(mode * frame.rows + y, x) = alphaT; |
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// renormalize all other weights |
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for (int i = 0; i < nmodes - 1; ++i) |
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gmm_weight(i * frame.rows + y, x) *= alpha1; |
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} |
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// init |
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gmm_mean(mode * frame.rows + y, x) = pix; |
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gmm_variance(mode * frame.rows + y, x) = c_varInit; |
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//sort |
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//find the new place for it |
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for (int i = nmodes - 1; i > 0; --i) |
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{ |
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// check one up |
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if (alphaT < gmm_weight((i - 1) * frame.rows + y, x)) |
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break; |
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//swap one up |
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swap(gmm_weight, x, y, i - 1, frame.rows); |
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swap(gmm_variance, x, y, i - 1, frame.rows); |
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swap(gmm_mean, x, y, i - 1, frame.rows); |
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} |
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} |
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//set the number of modes |
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modesUsed(y, x) = nmodes; |
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bool isShadow = false; |
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if (detectShadows && !background) |
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{ |
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float tWeight = 0.0f; |
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// check all the components marked as background: |
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for (int mode = 0; mode < nmodes; ++mode) |
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{ |
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WorkT mean = gmm_mean(mode * frame.rows + y, x); |
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WorkT pix_mean = pix * mean; |
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float numerator = sum(pix_mean); |
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float denominator = sqr(mean); |
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// no division by zero allowed |
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if (denominator == 0) |
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break; |
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// if tau < a < 1 then also check the color distortion |
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if (numerator <= denominator && numerator >= c_tau * denominator) |
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{ |
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float a = numerator / denominator; |
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WorkT dD = a * mean - pix; |
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if (sqr(dD) < c_Tb * gmm_variance(mode * frame.rows + y, x) * a * a) |
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{ |
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isShadow = true; |
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break; |
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} |
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}; |
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tWeight += gmm_weight(mode * frame.rows + y, x); |
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if (tWeight > c_TB) |
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break; |
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} |
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} |
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fgmask(y, x) = background ? 0 : isShadow ? c_shadowVal : 255; |
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} |
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template <typename SrcT, typename WorkT> |
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void mog2_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, |
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float alphaT, float prune, bool detectShadows, cudaStream_t stream) |
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{ |
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dim3 block(32, 8); |
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dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y)); |
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const float alpha1 = 1.0f - alphaT; |
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if (detectShadows) |
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{ |
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cudaSafeCall( cudaFuncSetCacheConfig(mog2<true, SrcT, WorkT>, cudaFuncCachePreferL1) ); |
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mog2<true, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed, |
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weight, variance, (PtrStepSz<WorkT>) mean, |
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alphaT, alpha1, prune); |
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} |
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else |
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{ |
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cudaSafeCall( cudaFuncSetCacheConfig(mog2<false, SrcT, WorkT>, cudaFuncCachePreferL1) ); |
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mog2<false, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed, |
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weight, variance, (PtrStepSz<WorkT>) mean, |
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alphaT, alpha1, prune); |
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} |
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cudaSafeCall( cudaGetLastError() ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, |
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float alphaT, float prune, bool detectShadows, cudaStream_t stream) |
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{ |
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typedef void (*func_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream); |
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|
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static const func_t funcs[] = |
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{ |
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0, mog2_caller<uchar, float>, 0, mog2_caller<uchar3, float3>, mog2_caller<uchar4, float4> |
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}; |
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funcs[cn](frame, fgmask, modesUsed, weight, variance, mean, alphaT, prune, detectShadows, stream); |
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} |
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template <typename WorkT, typename OutT> |
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__global__ void getBackgroundImage2(const PtrStepSzb modesUsed, const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, PtrStep<OutT> dst) |
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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 >= modesUsed.cols || y >= modesUsed.rows) |
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return; |
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int nmodes = modesUsed(y, x); |
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WorkT meanVal = VecTraits<WorkT>::all(0.0f); |
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float totalWeight = 0.0f; |
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for (int mode = 0; mode < nmodes; ++mode) |
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{ |
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float weight = gmm_weight(mode * modesUsed.rows + y, x); |
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WorkT mean = gmm_mean(mode * modesUsed.rows + y, x); |
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meanVal = meanVal + weight * mean; |
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totalWeight += weight; |
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if(totalWeight > c_TB) |
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break; |
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} |
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meanVal = meanVal * (1.f / totalWeight); |
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dst(y, x) = saturate_cast<OutT>(meanVal); |
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} |
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template <typename WorkT, typename OutT> |
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void getBackgroundImage2_caller(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream) |
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{ |
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dim3 block(32, 8); |
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dim3 grid(divUp(modesUsed.cols, block.x), divUp(modesUsed.rows, block.y)); |
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cudaSafeCall( cudaFuncSetCacheConfig(getBackgroundImage2<WorkT, OutT>, cudaFuncCachePreferL1) ); |
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getBackgroundImage2<WorkT, OutT><<<grid, block, 0, stream>>>(modesUsed, weight, (PtrStepSz<WorkT>) mean, (PtrStepSz<OutT>) dst); |
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cudaSafeCall( cudaGetLastError() ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream) |
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{ |
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typedef void (*func_t)(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream); |
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|
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static const func_t funcs[] = |
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{ |
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0, getBackgroundImage2_caller<float, uchar>, 0, getBackgroundImage2_caller<float3, uchar3>, getBackgroundImage2_caller<float4, uchar4> |
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}; |
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funcs[cn](modesUsed, weight, mean, dst, stream); |
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} |
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} |
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}}} |
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#endif /* CUDA_DISABLER */ |
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@ -0,0 +1,253 @@ |
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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 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*/
|
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|
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#include "precomp.hpp" |
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|
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using namespace cv; |
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using namespace cv::gpu; |
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|
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#if !defined HAVE_CUDA || defined(CUDA_DISABLER) |
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|
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Ptr<gpu::BackgroundSubtractorMOG2> cv::gpu::createBackgroundSubtractorMOG2(int, double, bool) { throw_no_cuda(); return Ptr<gpu::BackgroundSubtractorMOG2>(); } |
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|
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#else |
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|
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namespace cv { namespace gpu { namespace cudev |
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{ |
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namespace mog2 |
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{ |
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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(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream); |
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void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream); |
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} |
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}}} |
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|
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namespace |
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{ |
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// default parameters of gaussian background detection algorithm
|
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const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
|
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const float defaultVarThreshold = 4.0f * 4.0f; |
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const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
|
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const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
|
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const float defaultVarThresholdGen = 3.0f * 3.0f; |
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const float defaultVarInit = 15.0f; // initial variance for new components
|
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const float defaultVarMax = 5.0f * defaultVarInit; |
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const float defaultVarMin = 4.0f; |
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|
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// additional parameters
|
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const float defaultCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
|
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const unsigned char defaultShadowValue = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
|
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const float defaultShadowThreshold = 0.5f; // Tau - shadow threshold, see the paper for explanation
|
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|
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class MOG2Impl : public gpu::BackgroundSubtractorMOG2 |
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{ |
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public: |
||||
MOG2Impl(int history, double varThreshold, bool detectShadows); |
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|
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void apply(InputArray image, OutputArray fgmask, double learningRate=-1); |
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void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream); |
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|
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void getBackgroundImage(OutputArray backgroundImage) const; |
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void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const; |
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|
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int getHistory() const { return history_; } |
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void setHistory(int history) { history_ = history; } |
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|
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int getNMixtures() const { return nmixtures_; } |
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void setNMixtures(int nmixtures) { nmixtures_ = nmixtures; } |
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|
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double getBackgroundRatio() const { return backgroundRatio_; } |
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void setBackgroundRatio(double ratio) { backgroundRatio_ = (float) ratio; } |
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|
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double getVarThreshold() const { return varThreshold_; } |
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void setVarThreshold(double varThreshold) { varThreshold_ = (float) varThreshold; } |
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|
||||
double getVarThresholdGen() const { return varThresholdGen_; } |
||||
void setVarThresholdGen(double varThresholdGen) { varThresholdGen_ = (float) varThresholdGen; } |
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|
||||
double getVarInit() const { return varInit_; } |
||||
void setVarInit(double varInit) { varInit_ = (float) varInit; } |
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|
||||
double getVarMin() const { return varMin_; } |
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void setVarMin(double varMin) { varMin_ = (float) varMin; } |
||||
|
||||
double getVarMax() const { return varMax_; } |
||||
void setVarMax(double varMax) { varMax_ = (float) varMax; } |
||||
|
||||
double getComplexityReductionThreshold() const { return ct_; } |
||||
void setComplexityReductionThreshold(double ct) { ct_ = (float) ct; } |
||||
|
||||
bool getDetectShadows() const { return detectShadows_; } |
||||
void setDetectShadows(bool detectShadows) { detectShadows_ = detectShadows; } |
||||
|
||||
int getShadowValue() const { return shadowValue_; } |
||||
void setShadowValue(int value) { shadowValue_ = (uchar) value; } |
||||
|
||||
double getShadowThreshold() const { return shadowThreshold_; } |
||||
void setShadowThreshold(double threshold) { shadowThreshold_ = (float) threshold; } |
||||
|
||||
private: |
||||
void initialize(Size frameSize, int frameType); |
||||
|
||||
int history_; |
||||
int nmixtures_; |
||||
float backgroundRatio_; |
||||
float varThreshold_; |
||||
float varThresholdGen_; |
||||
float varInit_; |
||||
float varMin_; |
||||
float varMax_; |
||||
float ct_; |
||||
bool detectShadows_; |
||||
uchar shadowValue_; |
||||
float shadowThreshold_; |
||||
|
||||
Size frameSize_; |
||||
int frameType_; |
||||
int nframes_; |
||||
|
||||
GpuMat weight_; |
||||
GpuMat variance_; |
||||
GpuMat mean_; |
||||
|
||||
//keep track of number of modes per pixel
|
||||
GpuMat bgmodelUsedModes_; |
||||
}; |
||||
|
||||
MOG2Impl::MOG2Impl(int history, double varThreshold, bool detectShadows) : |
||||
frameSize_(0, 0), frameType_(0), nframes_(0) |
||||
{ |
||||
history_ = history > 0 ? history : defaultHistory; |
||||
varThreshold_ = varThreshold > 0 ? (float) varThreshold : defaultVarThreshold; |
||||
detectShadows_ = detectShadows; |
||||
|
||||
nmixtures_ = defaultNMixtures; |
||||
backgroundRatio_ = defaultBackgroundRatio; |
||||
varInit_ = defaultVarInit; |
||||
varMax_ = defaultVarMax; |
||||
varMin_ = defaultVarMin; |
||||
varThresholdGen_ = defaultVarThresholdGen; |
||||
ct_ = defaultCT; |
||||
shadowValue_ = defaultShadowValue; |
||||
shadowThreshold_ = defaultShadowThreshold; |
||||
} |
||||
|
||||
void MOG2Impl::apply(InputArray image, OutputArray fgmask, double learningRate) |
||||
{ |
||||
apply(image, fgmask, learningRate, Stream::Null()); |
||||
} |
||||
|
||||
void MOG2Impl::apply(InputArray _frame, OutputArray _fgmask, double learningRate, Stream& stream) |
||||
{ |
||||
using namespace cv::gpu::cudev::mog2; |
||||
|
||||
GpuMat frame = _frame.getGpuMat(); |
||||
|
||||
int ch = frame.channels(); |
||||
int work_ch = ch; |
||||
|
||||
if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels()) |
||||
initialize(frame.size(), frame.type()); |
||||
|
||||
_fgmask.create(frameSize_, CV_8UC1); |
||||
GpuMat fgmask = _fgmask.getGpuMat(); |
||||
|
||||
fgmask.setTo(Scalar::all(0), stream); |
||||
|
||||
++nframes_; |
||||
learningRate = learningRate >= 0 && nframes_ > 1 ? learningRate : 1.0 / std::min(2 * nframes_, history_); |
||||
CV_Assert( learningRate >= 0 ); |
||||
|
||||
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, |
||||
(float) learningRate, static_cast<float>(-learningRate * ct_), detectShadows_, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
void MOG2Impl::getBackgroundImage(OutputArray backgroundImage) const |
||||
{ |
||||
getBackgroundImage(backgroundImage, Stream::Null()); |
||||
} |
||||
|
||||
void MOG2Impl::getBackgroundImage(OutputArray _backgroundImage, Stream& stream) const |
||||
{ |
||||
using namespace cv::gpu::cudev::mog2; |
||||
|
||||
_backgroundImage.create(frameSize_, frameType_); |
||||
GpuMat backgroundImage = _backgroundImage.getGpuMat(); |
||||
|
||||
getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
void MOG2Impl::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(Scalar::all(0)); |
||||
|
||||
loadConstants(nmixtures_, varThreshold_, backgroundRatio_, varThresholdGen_, varInit_, varMin_, varMax_, shadowThreshold_, shadowValue_); |
||||
} |
||||
} |
||||
|
||||
Ptr<gpu::BackgroundSubtractorMOG2> cv::gpu::createBackgroundSubtractorMOG2(int history, double varThreshold, bool detectShadows) |
||||
{ |
||||
return new MOG2Impl(history, varThreshold, detectShadows); |
||||
} |
||||
|
||||
#endif |
||||
Loading…
Reference in new issue