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258 lines
9.8 KiB
Plaintext
258 lines
9.8 KiB
Plaintext
/*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 bpied warranties, including, but not limited to, the bpied
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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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#if !defined CUDA_DISABLER
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#include "opencv2/gpu/device/common.hpp"
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namespace cv { namespace gpu { namespace device
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{
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namespace vibe
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{
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__constant__ int c_nbSamples;
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__constant__ int c_reqMatches;
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__constant__ int c_radius;
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__constant__ int c_subsamplingFactor;
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void loadConstants(int nbSamples, int reqMatches, int radius, int subsamplingFactor)
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{
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cudaSafeCall( cudaMemcpyToSymbol(c_nbSamples, &nbSamples, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_reqMatches, &reqMatches, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_radius, &radius, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_subsamplingFactor, &subsamplingFactor, sizeof(int)) );
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}
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__device__ __forceinline__ uint nextRand(uint& state)
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{
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const unsigned int CV_RNG_COEFF = 4164903690U;
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state = state * CV_RNG_COEFF + (state >> 16);
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return state;
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}
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__constant__ int c_xoff[9] = {-1, 0, 1, -1, 1, -1, 0, 1, 0};
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__constant__ int c_yoff[9] = {-1, -1, -1, 0, 0, 1, 1, 1, 0};
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__device__ __forceinline__ int2 chooseRandomNeighbor(int x, int y, uint& randState, int count = 8)
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{
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int idx = nextRand(randState) % count;
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return make_int2(x + c_xoff[idx], y + c_yoff[idx]);
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}
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__device__ __forceinline__ uchar cvt(uchar val)
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{
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return val;
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}
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__device__ __forceinline__ uchar4 cvt(const uchar3& val)
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{
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return make_uchar4(val.x, val.y, val.z, 0);
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}
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__device__ __forceinline__ uchar4 cvt(const uchar4& val)
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{
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return val;
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}
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template <typename SrcT, typename SampleT>
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__global__ void init(const PtrStepSz<SrcT> frame, PtrStep<SampleT> samples, PtrStep<uint> randStates)
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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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uint localState = randStates(y, x);
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for (int k = 0; k < c_nbSamples; ++k)
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{
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int2 np = chooseRandomNeighbor(x, y, localState, 9);
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np.x = ::max(0, ::min(np.x, frame.cols - 1));
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np.y = ::max(0, ::min(np.y, frame.rows - 1));
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SrcT pix = frame(np.y, np.x);
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samples(k * frame.rows + y, x) = cvt(pix);
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}
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randStates(y, x) = localState;
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}
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template <typename SrcT, typename SampleT>
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void init_caller(PtrStepSzb frame, PtrStepSzb samples, PtrStepSz<uint> randStates, 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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cudaSafeCall( cudaFuncSetCacheConfig(init<SrcT, SampleT>, cudaFuncCachePreferL1) );
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init<SrcT, SampleT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, (PtrStepSz<SampleT>) samples, randStates);
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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 init_gpu(PtrStepSzb frame, int cn, PtrStepSzb samples, PtrStepSz<uint> randStates, cudaStream_t stream)
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{
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typedef void (*func_t)(PtrStepSzb frame, PtrStepSzb samples, PtrStepSz<uint> randStates, cudaStream_t stream);
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static const func_t funcs[] =
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{
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0, init_caller<uchar, uchar>, 0, init_caller<uchar3, uchar4>, init_caller<uchar4, uchar4>
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};
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funcs[cn](frame, samples, randStates, stream);
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}
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__device__ __forceinline__ int calcDist(uchar a, uchar b)
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{
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return ::abs(a - b);
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}
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__device__ __forceinline__ int calcDist(const uchar3& a, const uchar4& b)
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{
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return (::abs(a.x - b.x) + ::abs(a.y - b.y) + ::abs(a.z - b.z)) / 3;
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}
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__device__ __forceinline__ int calcDist(const uchar4& a, const uchar4& b)
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{
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return (::abs(a.x - b.x) + ::abs(a.y - b.y) + ::abs(a.z - b.z)) / 3;
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}
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template <typename SrcT, typename SampleT>
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__global__ void update(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStep<SampleT> samples, PtrStep<uint> randStates)
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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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uint localState = randStates(y, x);
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SrcT imgPix = frame(y, x);
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// comparison with the model
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int count = 0;
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for (int k = 0; (count < c_reqMatches) && (k < c_nbSamples); ++k)
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{
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SampleT samplePix = samples(k * frame.rows + y, x);
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int distance = calcDist(imgPix, samplePix);
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if (distance < c_radius)
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++count;
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}
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// pixel classification according to reqMatches
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fgmask(y, x) = (uchar) (-(count < c_reqMatches));
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if (count >= c_reqMatches)
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{
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// the pixel belongs to the background
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// gets a random number between 0 and subsamplingFactor-1
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int randomNumber = nextRand(localState) % c_subsamplingFactor;
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// update of the current pixel model
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if (randomNumber == 0)
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{
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// random subsampling
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int k = nextRand(localState) % c_nbSamples;
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samples(k * frame.rows + y, x) = cvt(imgPix);
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}
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// update of a neighboring pixel model
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randomNumber = nextRand(localState) % c_subsamplingFactor;
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if (randomNumber == 0)
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{
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// random subsampling
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// chooses a neighboring pixel randomly
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int2 np = chooseRandomNeighbor(x, y, localState);
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np.x = ::max(0, ::min(np.x, frame.cols - 1));
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np.y = ::max(0, ::min(np.y, frame.rows - 1));
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// chooses the value to be replaced randomly
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int k = nextRand(localState) % c_nbSamples;
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samples(k * frame.rows + np.y, np.x) = cvt(imgPix);
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}
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}
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randStates(y, x) = localState;
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}
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template <typename SrcT, typename SampleT>
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void update_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb samples, PtrStepSz<uint> randStates, 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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cudaSafeCall( cudaFuncSetCacheConfig(update<SrcT, SampleT>, cudaFuncCachePreferL1) );
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update<SrcT, SampleT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, (PtrStepSz<SampleT>) samples, randStates);
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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 update_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb samples, PtrStepSz<uint> randStates, cudaStream_t stream)
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{
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typedef void (*func_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb samples, PtrStepSz<uint> randStates, cudaStream_t stream);
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static const func_t funcs[] =
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{
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0, update_caller<uchar, uchar>, 0, update_caller<uchar3, uchar4>, update_caller<uchar4, uchar4>
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};
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funcs[cn](frame, fgmask, samples, randStates, stream);
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}
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}
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}}}
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#endif /* CUDA_DISABLER */ |