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Tetragramm
GitHub Repository: Tetragramm/opencv
Path: blob/master/modules/superres/src/cuda/btv_l1_gpu.cu
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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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#include "opencv2/opencv_modules.hpp"
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#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING) && defined(HAVE_OPENCV_CUDAFILTERS)
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#include "opencv2/core/cuda/common.hpp"
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#include "opencv2/core/cuda/transform.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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using namespace cv::cuda;
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using namespace cv::cuda::device;
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namespace btv_l1_cudev
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{
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void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY,
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PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY,
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PtrStepSzf forwardMapX, PtrStepSzf forwardMapY,
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PtrStepSzf backwardMapX, PtrStepSzf backwardMapY);
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template <int cn>
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void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
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void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream);
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void loadBtvWeights(const float* weights, size_t count);
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template <int cn> void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize);
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}
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namespace btv_l1_cudev
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{
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__global__ void buildMotionMapsKernel(const PtrStepSzf forwardMotionX, const PtrStepf forwardMotionY,
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PtrStepf backwardMotionX, PtrStepf backwardMotionY,
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PtrStepf forwardMapX, PtrStepf forwardMapY,
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PtrStepf backwardMapX, PtrStepf backwardMapY)
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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 >= forwardMotionX.cols || y >= forwardMotionX.rows)
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return;
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const float fx = forwardMotionX(y, x);
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const float fy = forwardMotionY(y, x);
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const float bx = backwardMotionX(y, x);
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const float by = backwardMotionY(y, x);
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forwardMapX(y, x) = x + bx;
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forwardMapY(y, x) = y + by;
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backwardMapX(y, x) = x + fx;
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backwardMapY(y, x) = y + fy;
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}
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void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY,
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PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY,
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PtrStepSzf forwardMapX, PtrStepSzf forwardMapY,
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PtrStepSzf backwardMapX, PtrStepSzf backwardMapY)
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{
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const dim3 block(32, 8);
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const dim3 grid(divUp(forwardMapX.cols, block.x), divUp(forwardMapX.rows, block.y));
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buildMotionMapsKernel<<<grid, block>>>(forwardMotionX, forwardMotionY,
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backwardMotionX, bacwardMotionY,
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forwardMapX, forwardMapY,
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backwardMapX, backwardMapY);
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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template <typename T>
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__global__ void upscaleKernel(const PtrStepSz<T> src, PtrStep<T> dst, const int scale)
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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 >= src.cols || y >= src.rows)
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return;
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dst(y * scale, x * scale) = src(y, x);
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}
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template <int cn>
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void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream)
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{
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typedef typename TypeVec<float, cn>::vec_type src_t;
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const dim3 block(32, 8);
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const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
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upscaleKernel<src_t><<<grid, block, 0, stream>>>((PtrStepSz<src_t>) src, (PtrStepSz<src_t>) dst, scale);
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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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template void upscale<1>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
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template void upscale<3>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
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template void upscale<4>(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
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__device__ __forceinline__ float diffSign(float a, float b)
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{
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return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
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}
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__device__ __forceinline__ float3 diffSign(const float3& a, const float3& b)
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{
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return make_float3(
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a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f,
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a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f,
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a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f
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);
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}
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__device__ __forceinline__ float4 diffSign(const float4& a, const float4& b)
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{
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return make_float4(
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a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f,
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a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f,
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a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f,
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0.0f
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);
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}
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struct DiffSign : binary_function<float, float, float>
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{
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__device__ __forceinline__ float operator ()(float a, float b) const
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{
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return diffSign(a, b);
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}
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};
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}
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namespace cv { namespace cuda { namespace device
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{
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template <> struct TransformFunctorTraits<btv_l1_cudev::DiffSign> : DefaultTransformFunctorTraits<btv_l1_cudev::DiffSign>
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{
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enum { smart_block_dim_y = 8 };
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enum { smart_shift = 4 };
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};
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}}}
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namespace btv_l1_cudev
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{
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void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream)
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{
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transform(src1, src2, dst, DiffSign(), WithOutMask(), stream);
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}
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__constant__ float c_btvRegWeights[16*16];
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template <typename T>
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__global__ void calcBtvRegularizationKernel(const PtrStepSz<T> src, PtrStep<T> dst, const int ksize)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x + ksize;
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const int y = blockIdx.y * blockDim.y + threadIdx.y + ksize;
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if (y >= src.rows - ksize || x >= src.cols - ksize)
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return;
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const T srcVal = src(y, x);
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T dstVal = VecTraits<T>::all(0);
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for (int m = 0, count = 0; m <= ksize; ++m)
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{
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for (int l = ksize; l + m >= 0; --l, ++count)
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dstVal = dstVal + c_btvRegWeights[count] * (diffSign(srcVal, src(y + m, x + l)) - diffSign(src(y - m, x - l), srcVal));
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}
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dst(y, x) = dstVal;
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}
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void loadBtvWeights(const float* weights, size_t count)
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{
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cudaSafeCall( cudaMemcpyToSymbol(c_btvRegWeights, weights, count * sizeof(float)) );
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}
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template <int cn>
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void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize)
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{
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typedef typename TypeVec<float, cn>::vec_type src_t;
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const dim3 block(32, 8);
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const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
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calcBtvRegularizationKernel<src_t><<<grid, block>>>((PtrStepSz<src_t>) src, (PtrStepSz<src_t>) dst, ksize);
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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template void calcBtvRegularization<1>(PtrStepSzb src, PtrStepSzb dst, int ksize);
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template void calcBtvRegularization<3>(PtrStepSzb src, PtrStepSzb dst, int ksize);
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template void calcBtvRegularization<4>(PtrStepSzb src, PtrStepSzb dst, int ksize);
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}
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#endif
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