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569 lines
20 KiB
Plaintext
569 lines
20 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 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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// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
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//
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// The original code was written by Paul Furgale and Chi Hay Tong
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// and later optimized and prepared for integration into OpenCV by Itseez.
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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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#include "opencv2/gpu/device/utility.hpp"
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#include "opencv2/gpu/device/functional.hpp"
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#include "opencv2/gpu/device/limits.hpp"
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#include "opencv2/gpu/device/vec_math.hpp"
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#include "opencv2/gpu/device/reduce.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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namespace
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{
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__constant__ int c_winSize_x;
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__constant__ int c_winSize_y;
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__constant__ int c_halfWin_x;
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__constant__ int c_halfWin_y;
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__constant__ int c_iters;
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texture<float, cudaTextureType2D, cudaReadModeElementType> tex_If(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_If4(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_Ib(false, cudaFilterModePoint, cudaAddressModeClamp);
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texture<float, cudaTextureType2D, cudaReadModeElementType> tex_Jf(false, cudaFilterModeLinear, cudaAddressModeClamp);
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texture<float4, cudaTextureType2D, cudaReadModeElementType> tex_Jf4(false, cudaFilterModeLinear, cudaAddressModeClamp);
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template <int cn> struct Tex_I;
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template <> struct Tex_I<1>
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{
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static __device__ __forceinline__ float read(float x, float y)
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{
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return tex2D(tex_If, x, y);
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}
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};
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template <> struct Tex_I<4>
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{
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static __device__ __forceinline__ float4 read(float x, float y)
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{
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return tex2D(tex_If4, x, y);
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}
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};
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template <int cn> struct Tex_J;
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template <> struct Tex_J<1>
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{
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static __device__ __forceinline__ float read(float x, float y)
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{
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return tex2D(tex_Jf, x, y);
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}
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};
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template <> struct Tex_J<4>
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{
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static __device__ __forceinline__ float4 read(float x, float y)
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{
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return tex2D(tex_Jf4, x, y);
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}
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};
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__device__ __forceinline__ void accum(float& dst, float val)
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{
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dst += val;
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}
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__device__ __forceinline__ void accum(float& dst, const float4& val)
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{
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dst += val.x + val.y + val.z;
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}
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__device__ __forceinline__ float abs_(float a)
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{
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return ::fabsf(a);
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}
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__device__ __forceinline__ float4 abs_(const float4& a)
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{
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return abs(a);
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}
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template <int cn, int PATCH_X, int PATCH_Y, bool calcErr>
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__global__ void sparse(const float2* prevPts, float2* nextPts, uchar* status, float* err, const int level, const int rows, const int cols)
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{
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#if __CUDA_ARCH__ <= 110
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const int BLOCK_SIZE = 128;
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#else
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const int BLOCK_SIZE = 256;
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#endif
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__shared__ float smem1[BLOCK_SIZE];
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__shared__ float smem2[BLOCK_SIZE];
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__shared__ float smem3[BLOCK_SIZE];
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const unsigned int tid = threadIdx.y * blockDim.x + threadIdx.x;
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float2 prevPt = prevPts[blockIdx.x];
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prevPt.x *= (1.0f / (1 << level));
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prevPt.y *= (1.0f / (1 << level));
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if (prevPt.x < 0 || prevPt.x >= cols || prevPt.y < 0 || prevPt.y >= rows)
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{
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if (tid == 0 && level == 0)
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status[blockIdx.x] = 0;
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return;
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}
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prevPt.x -= c_halfWin_x;
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prevPt.y -= c_halfWin_y;
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// extract the patch from the first image, compute covariation matrix of derivatives
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float A11 = 0;
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float A12 = 0;
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float A22 = 0;
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typedef typename TypeVec<float, cn>::vec_type work_type;
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work_type I_patch [PATCH_Y][PATCH_X];
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work_type dIdx_patch[PATCH_Y][PATCH_X];
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work_type dIdy_patch[PATCH_Y][PATCH_X];
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for (int yBase = threadIdx.y, i = 0; yBase < c_winSize_y; yBase += blockDim.y, ++i)
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{
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for (int xBase = threadIdx.x, j = 0; xBase < c_winSize_x; xBase += blockDim.x, ++j)
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{
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float x = prevPt.x + xBase + 0.5f;
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float y = prevPt.y + yBase + 0.5f;
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I_patch[i][j] = Tex_I<cn>::read(x, y);
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// Sharr Deriv
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work_type dIdx = 3.0f * Tex_I<cn>::read(x+1, y-1) + 10.0f * Tex_I<cn>::read(x+1, y) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
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(3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x-1, y) + 3.0f * Tex_I<cn>::read(x-1, y+1));
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work_type dIdy = 3.0f * Tex_I<cn>::read(x-1, y+1) + 10.0f * Tex_I<cn>::read(x, y+1) + 3.0f * Tex_I<cn>::read(x+1, y+1) -
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(3.0f * Tex_I<cn>::read(x-1, y-1) + 10.0f * Tex_I<cn>::read(x, y-1) + 3.0f * Tex_I<cn>::read(x+1, y-1));
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dIdx_patch[i][j] = dIdx;
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dIdy_patch[i][j] = dIdy;
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accum(A11, dIdx * dIdx);
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accum(A12, dIdx * dIdy);
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accum(A22, dIdy * dIdy);
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}
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}
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reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2, smem3), thrust::tie(A11, A12, A22), tid, thrust::make_tuple(plus<float>(), plus<float>(), plus<float>()));
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#if __CUDA_ARCH__ >= 300
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if (tid == 0)
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{
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smem1[0] = A11;
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smem2[0] = A12;
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smem3[0] = A22;
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}
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#endif
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__syncthreads();
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A11 = smem1[0];
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A12 = smem2[0];
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A22 = smem3[0];
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float D = A11 * A22 - A12 * A12;
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if (D < numeric_limits<float>::epsilon())
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{
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if (tid == 0 && level == 0)
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status[blockIdx.x] = 0;
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return;
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}
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D = 1.f / D;
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A11 *= D;
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A12 *= D;
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A22 *= D;
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float2 nextPt = nextPts[blockIdx.x];
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nextPt.x *= 2.f;
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nextPt.y *= 2.f;
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nextPt.x -= c_halfWin_x;
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nextPt.y -= c_halfWin_y;
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for (int k = 0; k < c_iters; ++k)
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{
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if (nextPt.x < -c_halfWin_x || nextPt.x >= cols || nextPt.y < -c_halfWin_y || nextPt.y >= rows)
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{
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if (tid == 0 && level == 0)
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status[blockIdx.x] = 0;
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return;
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}
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float b1 = 0;
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float b2 = 0;
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for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
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{
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for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
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{
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work_type I_val = I_patch[i][j];
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work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
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work_type diff = (J_val - I_val) * 32.0f;
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accum(b1, diff * dIdx_patch[i][j]);
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accum(b2, diff * dIdy_patch[i][j]);
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}
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}
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reduce<BLOCK_SIZE>(smem_tuple(smem1, smem2), thrust::tie(b1, b2), tid, thrust::make_tuple(plus<float>(), plus<float>()));
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#if __CUDA_ARCH__ >= 300
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if (tid == 0)
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{
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smem1[0] = b1;
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smem2[0] = b2;
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}
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#endif
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__syncthreads();
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b1 = smem1[0];
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b2 = smem2[0];
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float2 delta;
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delta.x = A12 * b2 - A22 * b1;
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delta.y = A12 * b1 - A11 * b2;
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nextPt.x += delta.x;
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nextPt.y += delta.y;
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if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
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break;
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}
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float errval = 0;
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if (calcErr)
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{
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for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
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{
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for (int x = threadIdx.x, j = 0; x < c_winSize_x; x += blockDim.x, ++j)
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{
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work_type I_val = I_patch[i][j];
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work_type J_val = Tex_J<cn>::read(nextPt.x + x + 0.5f, nextPt.y + y + 0.5f);
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work_type diff = J_val - I_val;
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accum(errval, abs_(diff));
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}
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}
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reduce<BLOCK_SIZE>(smem1, errval, tid, plus<float>());
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}
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if (tid == 0)
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{
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nextPt.x += c_halfWin_x;
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nextPt.y += c_halfWin_y;
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nextPts[blockIdx.x] = nextPt;
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if (calcErr)
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err[blockIdx.x] = static_cast<float>(errval) / (cn * c_winSize_x * c_winSize_y);
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}
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}
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template <int cn, int PATCH_X, int PATCH_Y>
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void sparse_caller(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
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int level, dim3 block, cudaStream_t stream)
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{
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dim3 grid(ptcount);
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if (level == 0 && err)
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sparse<cn, PATCH_X, PATCH_Y, true><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
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else
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sparse<cn, PATCH_X, PATCH_Y, false><<<grid, block>>>(prevPts, nextPts, status, err, level, rows, cols);
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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 <bool calcErr>
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__global__ void dense(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
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{
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extern __shared__ int smem[];
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const int patchWidth = blockDim.x + 2 * c_halfWin_x;
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const int patchHeight = blockDim.y + 2 * c_halfWin_y;
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int* I_patch = smem;
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int* dIdx_patch = I_patch + patchWidth * patchHeight;
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int* dIdy_patch = dIdx_patch + patchWidth * patchHeight;
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const int xBase = blockIdx.x * blockDim.x;
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const int yBase = blockIdx.y * blockDim.y;
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for (int i = threadIdx.y; i < patchHeight; i += blockDim.y)
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{
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for (int j = threadIdx.x; j < patchWidth; j += blockDim.x)
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{
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float x = xBase - c_halfWin_x + j + 0.5f;
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float y = yBase - c_halfWin_y + i + 0.5f;
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I_patch[i * patchWidth + j] = tex2D(tex_Ib, x, y);
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// Sharr Deriv
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dIdx_patch[i * patchWidth + j] = 3 * tex2D(tex_Ib, x+1, y-1) + 10 * tex2D(tex_Ib, x+1, y) + 3 * tex2D(tex_Ib, x+1, y+1) -
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(3 * tex2D(tex_Ib, x-1, y-1) + 10 * tex2D(tex_Ib, x-1, y) + 3 * tex2D(tex_Ib, x-1, y+1));
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dIdy_patch[i * patchWidth + j] = 3 * tex2D(tex_Ib, x-1, y+1) + 10 * tex2D(tex_Ib, x, y+1) + 3 * tex2D(tex_Ib, x+1, y+1) -
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(3 * tex2D(tex_Ib, x-1, y-1) + 10 * tex2D(tex_Ib, x, y-1) + 3 * tex2D(tex_Ib, x+1, y-1));
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}
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}
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__syncthreads();
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const int x = xBase + threadIdx.x;
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const int y = yBase + threadIdx.y;
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if (x >= cols || y >= rows)
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return;
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int A11i = 0;
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int A12i = 0;
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int A22i = 0;
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for (int i = 0; i < c_winSize_y; ++i)
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{
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for (int j = 0; j < c_winSize_x; ++j)
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{
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int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
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int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
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A11i += dIdx * dIdx;
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A12i += dIdx * dIdy;
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A22i += dIdy * dIdy;
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}
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}
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float A11 = A11i;
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float A12 = A12i;
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float A22 = A22i;
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float D = A11 * A22 - A12 * A12;
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if (D < numeric_limits<float>::epsilon())
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{
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if (calcErr)
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err(y, x) = numeric_limits<float>::max();
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return;
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}
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D = 1.f / D;
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A11 *= D;
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A12 *= D;
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A22 *= D;
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float2 nextPt;
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nextPt.x = x + prevU(y/2, x/2) * 2.0f;
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nextPt.y = y + prevV(y/2, x/2) * 2.0f;
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for (int k = 0; k < c_iters; ++k)
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{
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if (nextPt.x < 0 || nextPt.x >= cols || nextPt.y < 0 || nextPt.y >= rows)
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{
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if (calcErr)
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err(y, x) = numeric_limits<float>::max();
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return;
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}
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int b1 = 0;
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int b2 = 0;
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for (int i = 0; i < c_winSize_y; ++i)
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{
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for (int j = 0; j < c_winSize_x; ++j)
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{
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int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
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int J = tex2D(tex_Jf, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
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int diff = (J - I) * 32;
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int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
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int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
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b1 += diff * dIdx;
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b2 += diff * dIdy;
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}
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}
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float2 delta;
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delta.x = A12 * b2 - A22 * b1;
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delta.y = A12 * b1 - A11 * b2;
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nextPt.x += delta.x;
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nextPt.y += delta.y;
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if (::fabs(delta.x) < 0.01f && ::fabs(delta.y) < 0.01f)
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break;
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}
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u(y, x) = nextPt.x - x;
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v(y, x) = nextPt.y - y;
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if (calcErr)
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{
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int errval = 0;
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for (int i = 0; i < c_winSize_y; ++i)
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{
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for (int j = 0; j < c_winSize_x; ++j)
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{
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int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
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int J = tex2D(tex_Jf, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
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|
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errval += ::abs(J - I);
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}
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}
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|
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err(y, x) = static_cast<float>(errval) / (c_winSize_x * c_winSize_y);
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}
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|
}
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}
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|
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namespace pyrlk
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|
{
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void loadConstants(int2 winSize, int iters)
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|
{
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cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
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|
cudaSafeCall( cudaMemcpyToSymbol(c_winSize_y, &winSize.y, sizeof(int)) );
|
|
|
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int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
|
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cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_x, &halfWin.x, sizeof(int)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(c_halfWin_y, &halfWin.y, sizeof(int)) );
|
|
|
|
cudaSafeCall( cudaMemcpyToSymbol(c_iters, &iters, sizeof(int)) );
|
|
}
|
|
|
|
void sparse1(PtrStepSzf I, PtrStepSzf J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
|
|
int level, dim3 block, dim3 patch, cudaStream_t stream)
|
|
{
|
|
typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
|
|
int level, dim3 block, cudaStream_t stream);
|
|
|
|
static const func_t funcs[5][5] =
|
|
{
|
|
{::sparse_caller<1, 1, 1>, ::sparse_caller<1, 2, 1>, ::sparse_caller<1, 3, 1>, ::sparse_caller<1, 4, 1>, ::sparse_caller<1, 5, 1>},
|
|
{::sparse_caller<1, 1, 2>, ::sparse_caller<1, 2, 2>, ::sparse_caller<1, 3, 2>, ::sparse_caller<1, 4, 2>, ::sparse_caller<1, 5, 2>},
|
|
{::sparse_caller<1, 1, 3>, ::sparse_caller<1, 2, 3>, ::sparse_caller<1, 3, 3>, ::sparse_caller<1, 4, 3>, ::sparse_caller<1, 5, 3>},
|
|
{::sparse_caller<1, 1, 4>, ::sparse_caller<1, 2, 4>, ::sparse_caller<1, 3, 4>, ::sparse_caller<1, 4, 4>, ::sparse_caller<1, 5, 4>},
|
|
{::sparse_caller<1, 1, 5>, ::sparse_caller<1, 2, 5>, ::sparse_caller<1, 3, 5>, ::sparse_caller<1, 4, 5>, ::sparse_caller<1, 5, 5>}
|
|
};
|
|
|
|
bindTexture(&tex_If, I);
|
|
bindTexture(&tex_Jf, J);
|
|
|
|
funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
|
|
level, block, stream);
|
|
}
|
|
|
|
void sparse4(PtrStepSz<float4> I, PtrStepSz<float4> J, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
|
|
int level, dim3 block, dim3 patch, cudaStream_t stream)
|
|
{
|
|
typedef void (*func_t)(int rows, int cols, const float2* prevPts, float2* nextPts, uchar* status, float* err, int ptcount,
|
|
int level, dim3 block, cudaStream_t stream);
|
|
|
|
static const func_t funcs[5][5] =
|
|
{
|
|
{::sparse_caller<4, 1, 1>, ::sparse_caller<4, 2, 1>, ::sparse_caller<4, 3, 1>, ::sparse_caller<4, 4, 1>, ::sparse_caller<4, 5, 1>},
|
|
{::sparse_caller<4, 1, 2>, ::sparse_caller<4, 2, 2>, ::sparse_caller<4, 3, 2>, ::sparse_caller<4, 4, 2>, ::sparse_caller<4, 5, 2>},
|
|
{::sparse_caller<4, 1, 3>, ::sparse_caller<4, 2, 3>, ::sparse_caller<4, 3, 3>, ::sparse_caller<4, 4, 3>, ::sparse_caller<4, 5, 3>},
|
|
{::sparse_caller<4, 1, 4>, ::sparse_caller<4, 2, 4>, ::sparse_caller<4, 3, 4>, ::sparse_caller<4, 4, 4>, ::sparse_caller<4, 5, 4>},
|
|
{::sparse_caller<4, 1, 5>, ::sparse_caller<4, 2, 5>, ::sparse_caller<4, 3, 5>, ::sparse_caller<4, 4, 5>, ::sparse_caller<4, 5, 5>}
|
|
};
|
|
|
|
bindTexture(&tex_If4, I);
|
|
bindTexture(&tex_Jf4, J);
|
|
|
|
funcs[patch.y - 1][patch.x - 1](I.rows, I.cols, prevPts, nextPts, status, err, ptcount,
|
|
level, block, stream);
|
|
}
|
|
|
|
void dense(PtrStepSzb I, PtrStepSzf J, PtrStepSzf u, PtrStepSzf v, PtrStepSzf prevU, PtrStepSzf prevV, PtrStepSzf err, int2 winSize, cudaStream_t stream)
|
|
{
|
|
dim3 block(16, 16);
|
|
dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
|
|
|
|
bindTexture(&tex_Ib, I);
|
|
bindTexture(&tex_Jf, J);
|
|
|
|
int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
|
|
const int patchWidth = block.x + 2 * halfWin.x;
|
|
const int patchHeight = block.y + 2 * halfWin.y;
|
|
size_t smem_size = 3 * patchWidth * patchHeight * sizeof(int);
|
|
|
|
if (err.data)
|
|
{
|
|
::dense<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
|
|
cudaSafeCall( cudaGetLastError() );
|
|
}
|
|
else
|
|
{
|
|
::dense<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
|
|
cudaSafeCall( cudaGetLastError() );
|
|
}
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
}
|
|
|
|
#endif /* CUDA_DISABLER */
|