mirror of
https://github.com/opencv/opencv.git
synced 2026-07-30 15:53:03 +04:00
added bicubic interpolation to gpu::remap
This commit is contained in:
Vladislav Vinogradov
@@ -42,7 +42,7 @@
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#include "internal_shared.hpp"
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#include "opencv2/gpu/device/limits.hpp"
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#include "opencv2/gpu/device/utility.hpp"
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#include "opencv2/gpu/device/vec_distance.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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@@ -45,7 +45,7 @@
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#include "opencv2/gpu/device/vec_traits.hpp"
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#include "opencv2/gpu/device/vec_math.hpp"
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#include "opencv2/gpu/device/saturate_cast.hpp"
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#include "opencv2/gpu/device/utility.hpp"
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#include "opencv2/gpu/device/filters.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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@@ -186,7 +186,7 @@ namespace cv { namespace gpu { namespace imgproc
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{
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typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2Df& xmap, const DevMem2Df& ymap, const DevMem2D_<T>& dst, const float* borderValue, cudaStream_t stream);
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static const caller_t callers[2][5] =
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static const caller_t callers[3][5] =
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{
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{
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RemapDispatcher<PointFilter, BrdReflect101, T>::call,
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@@ -201,6 +201,13 @@ namespace cv { namespace gpu { namespace imgproc
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RemapDispatcher<LinearFilter, BrdConstant, T>::call,
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RemapDispatcher<LinearFilter, BrdReflect, T>::call,
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RemapDispatcher<LinearFilter, BrdWrap, T>::call
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},
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{
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RemapDispatcher<CubicFilter, BrdReflect101, T>::call,
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RemapDispatcher<CubicFilter, BrdReplicate, T>::call,
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RemapDispatcher<CubicFilter, BrdConstant, T>::call,
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RemapDispatcher<CubicFilter, BrdReflect, T>::call,
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RemapDispatcher<CubicFilter, BrdWrap, T>::call
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}
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};
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@@ -50,6 +50,7 @@
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#include "opencv2/gpu/device/saturate_cast.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/filters.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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@@ -131,7 +131,7 @@ void cv::gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const Gp
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CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
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CV_Assert(xmap.type() == CV_32F && ymap.type() == CV_32F && xmap.size() == ymap.size());
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CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR);
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CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
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CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
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int gpuBorderType;
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@@ -0,0 +1,135 @@
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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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#ifndef __OPENCV_GPU_FILTERS_HPP__
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#define __OPENCV_GPU_FILTERS_HPP__
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#include "saturate_cast.hpp"
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#include "vec_traits.hpp"
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#include "vec_math.hpp"
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namespace cv { namespace gpu { namespace device
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{
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template <typename Ptr2D> struct PointFilter
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{
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typedef typename Ptr2D::elem_type elem_type;
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typedef float index_type;
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explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_) : src(src_) {}
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__device__ __forceinline__ elem_type operator ()(float y, float x) const
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{
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return src(__float2int_rn(y), __float2int_rn(x));
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}
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const Ptr2D src;
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};
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template <typename Ptr2D> struct LinearFilter
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{
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typedef typename Ptr2D::elem_type elem_type;
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typedef float index_type;
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explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_) : src(src_) {}
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__device__ __forceinline__ elem_type operator ()(float y, float x) const
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{
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typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
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work_type out = VecTraits<work_type>::all(0);
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const int x1 = __float2int_rd(x);
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const int y1 = __float2int_rd(y);
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const int x2 = x1 + 1;
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const int y2 = y1 + 1;
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elem_type src_reg = src(y1, x1);
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out = out + src_reg * ((x2 - x) * (y2 - y));
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src_reg = src(y1, x2);
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out = out + src_reg * ((x - x1) * (y2 - y));
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src_reg = src(y2, x1);
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out = out + src_reg * ((x2 - x) * (y - y1));
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src_reg = src(y2, x2);
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out = out + src_reg * ((x - x1) * (y - y1));
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return saturate_cast<elem_type>(out);
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}
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const Ptr2D src;
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};
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template <typename Ptr2D> struct CubicFilter
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{
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typedef typename Ptr2D::elem_type elem_type;
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typedef float index_type;
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typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
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explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_) : src(src_) {}
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static __device__ __forceinline__ work_type cubicInterpolate(const work_type& p0, const work_type& p1, const work_type& p2, const work_type& p3, float x)
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{
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return p1 + 0.5f * x * (p2 - p0 + x * (2.0f * p0 - 5.0f * p1 + 4.0f * p2 - p3 + x * (3.0f * (p1 - p2) + p3 - p0)));
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}
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__device__ elem_type operator ()(float y, float x) const
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{
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const int xi = __float2int_rn(x);
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const int yi = __float2int_rn(y);
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work_type arr[4];
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arr[0] = cubicInterpolate(saturate_cast<work_type>(src(yi - 1, xi - 1)), saturate_cast<work_type>(src(yi - 1, xi)), saturate_cast<work_type>(src(yi - 1, xi + 1)), saturate_cast<work_type>(src(yi - 1, xi + 2)), x - xi);
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arr[1] = cubicInterpolate(saturate_cast<work_type>(src(yi , xi - 1)), saturate_cast<work_type>(src(yi , xi)), saturate_cast<work_type>(src(yi , xi + 1)), saturate_cast<work_type>(src(yi , xi + 2)), x - xi);
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arr[2] = cubicInterpolate(saturate_cast<work_type>(src(yi + 1, xi - 1)), saturate_cast<work_type>(src(yi + 1, xi)), saturate_cast<work_type>(src(yi + 1, xi + 1)), saturate_cast<work_type>(src(yi + 1, xi + 2)), x - xi);
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arr[3] = cubicInterpolate(saturate_cast<work_type>(src(yi + 2, xi - 1)), saturate_cast<work_type>(src(yi + 2, xi)), saturate_cast<work_type>(src(yi + 2, xi + 1)), saturate_cast<work_type>(src(yi + 2, xi + 2)), x - xi);
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return saturate_cast<elem_type>(cubicInterpolate(arr[0], arr[1], arr[2], arr[3], y - yi));
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}
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const Ptr2D src;
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};
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}}}
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#endif // __OPENCV_GPU_FILTERS_HPP__
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@@ -135,180 +135,6 @@ namespace cv { namespace gpu { namespace device
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StaticAssert<n >= 8 && n <= 512>::check();
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detail::PredValReductionDispatcher<n <= 64>::reduce<n>(myData, myVal, sdata, sval, tid, pred);
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}
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///////////////////////////////////////////////////////////////////////////////
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// Vector Distance
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template <typename T> struct L1Dist
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{
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typedef int value_type;
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typedef int result_type;
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__device__ __forceinline__ L1Dist() : mySum(0) {}
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__device__ __forceinline__ void reduceIter(int val1, int val2)
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{
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mySum = __sad(val1, val2, mySum);
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}
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template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
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{
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reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
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}
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__device__ __forceinline__ operator int() const
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{
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return mySum;
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}
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int mySum;
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};
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template <> struct L1Dist<float>
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{
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typedef float value_type;
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typedef float result_type;
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__device__ __forceinline__ L1Dist() : mySum(0.0f) {}
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__device__ __forceinline__ void reduceIter(float val1, float val2)
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{
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mySum += ::fabs(val1 - val2);
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}
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template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
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{
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reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
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}
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__device__ __forceinline__ operator float() const
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{
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return mySum;
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}
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float mySum;
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};
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struct L2Dist
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{
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typedef float value_type;
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typedef float result_type;
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__device__ __forceinline__ L2Dist() : mySum(0.0f) {}
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__device__ __forceinline__ void reduceIter(float val1, float val2)
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{
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float reg = val1 - val2;
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mySum += reg * reg;
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}
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template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
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{
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reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
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}
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__device__ __forceinline__ operator float() const
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{
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return sqrtf(mySum);
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}
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float mySum;
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};
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struct HammingDist
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{
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typedef int value_type;
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typedef int result_type;
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__device__ __forceinline__ HammingDist() : mySum(0) {}
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__device__ __forceinline__ void reduceIter(int val1, int val2)
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{
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mySum += __popc(val1 ^ val2);
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}
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template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
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{
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reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
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}
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__device__ __forceinline__ operator int() const
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{
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return mySum;
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}
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int mySum;
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};
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// calc distance between two vectors in global memory
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template <int THREAD_DIM, typename Dist, typename T1, typename T2>
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__device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
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{
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for (int i = tid; i < len; i += THREAD_DIM)
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{
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T1 val1;
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ForceGlob<T1>::Load(vec1, i, val1);
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T2 val2;
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ForceGlob<T2>::Load(vec2, i, val2);
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dist.reduceIter(val1, val2);
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}
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dist.reduceAll<THREAD_DIM>(smem, tid);
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}
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// calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
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template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
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__device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
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{
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detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
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dist.reduceAll<THREAD_DIM>(smem, tid);
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}
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// calc distance between two vectors in global memory
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template <int THREAD_DIM, typename T1> struct VecDiffGlobal
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{
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explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
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{
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vec1 = vec1_;
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}
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template <typename T2, typename Dist>
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__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
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{
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calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
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}
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const T1* vec1;
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};
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// calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
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template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
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{
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template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
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{
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if (glob_tid < len)
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smem[glob_tid] = vec1[glob_tid];
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__syncthreads();
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U* vec1ValsPtr = vec1Vals;
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#pragma unroll
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for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
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*vec1ValsPtr++ = smem[i];
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__syncthreads();
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}
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template <typename T2, typename Dist>
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__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
|
||||
{
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calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
|
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}
|
||||
|
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U vec1Vals[MAX_LEN / THREAD_DIM];
|
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};
|
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///////////////////////////////////////////////////////////////////////////////
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// Solve linear system
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@@ -363,60 +189,6 @@ namespace cv { namespace gpu { namespace device
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return false;
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}
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///////////////////////////////////////////////////////////////////////////////
|
||||
// Filters
|
||||
|
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template <typename Ptr2D> struct PointFilter
|
||||
{
|
||||
typedef typename Ptr2D::elem_type elem_type;
|
||||
typedef float index_type;
|
||||
|
||||
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_) : src(src_) {}
|
||||
|
||||
__device__ __forceinline__ elem_type operator ()(float y, float x) const
|
||||
{
|
||||
return src(__float2int_rn(y), __float2int_rn(x));
|
||||
}
|
||||
|
||||
const Ptr2D src;
|
||||
};
|
||||
|
||||
template <typename Ptr2D> struct LinearFilter
|
||||
{
|
||||
typedef typename Ptr2D::elem_type elem_type;
|
||||
typedef float index_type;
|
||||
|
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explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_) : src(src_) {}
|
||||
|
||||
__device__ __forceinline__ elem_type operator ()(float y, float x) const
|
||||
{
|
||||
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
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||||
|
||||
work_type out = VecTraits<work_type>::all(0);
|
||||
|
||||
const int x1 = __float2int_rd(x);
|
||||
const int y1 = __float2int_rd(y);
|
||||
const int x2 = x1 + 1;
|
||||
const int y2 = y1 + 1;
|
||||
|
||||
elem_type src_reg = src(y1, x1);
|
||||
out = out + src_reg * ((x2 - x) * (y2 - y));
|
||||
|
||||
src_reg = src(y1, x2);
|
||||
out = out + src_reg * ((x - x1) * (y2 - y));
|
||||
|
||||
src_reg = src(y2, x1);
|
||||
out = out + src_reg * ((x2 - x) * (y - y1));
|
||||
|
||||
src_reg = src(y2, x2);
|
||||
out = out + src_reg * ((x - x1) * (y - y1));
|
||||
|
||||
return saturate_cast<elem_type>(out);
|
||||
}
|
||||
|
||||
const Ptr2D src;
|
||||
};
|
||||
}}}
|
||||
|
||||
#endif // __OPENCV_GPU_UTILITY_HPP__
|
||||
|
||||
@@ -0,0 +1,223 @@
|
||||
/*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*/
|
||||
|
||||
#ifndef __OPENCV_GPU_VEC_DISTANCE_HPP__
|
||||
#define __OPENCV_GPU_VEC_DISTANCE_HPP__
|
||||
|
||||
#include "utility.hpp"
|
||||
|
||||
namespace cv { namespace gpu { namespace device
|
||||
{
|
||||
|
||||
template <typename T> struct L1Dist
|
||||
{
|
||||
typedef int value_type;
|
||||
typedef int result_type;
|
||||
|
||||
__device__ __forceinline__ L1Dist() : mySum(0) {}
|
||||
|
||||
__device__ __forceinline__ void reduceIter(int val1, int val2)
|
||||
{
|
||||
mySum = __sad(val1, val2, mySum);
|
||||
}
|
||||
|
||||
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
|
||||
{
|
||||
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
|
||||
}
|
||||
|
||||
__device__ __forceinline__ operator int() const
|
||||
{
|
||||
return mySum;
|
||||
}
|
||||
|
||||
int mySum;
|
||||
};
|
||||
template <> struct L1Dist<float>
|
||||
{
|
||||
typedef float value_type;
|
||||
typedef float result_type;
|
||||
|
||||
__device__ __forceinline__ L1Dist() : mySum(0.0f) {}
|
||||
|
||||
__device__ __forceinline__ void reduceIter(float val1, float val2)
|
||||
{
|
||||
mySum += ::fabs(val1 - val2);
|
||||
}
|
||||
|
||||
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
|
||||
{
|
||||
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
|
||||
}
|
||||
|
||||
__device__ __forceinline__ operator float() const
|
||||
{
|
||||
return mySum;
|
||||
}
|
||||
|
||||
float mySum;
|
||||
};
|
||||
|
||||
struct L2Dist
|
||||
{
|
||||
typedef float value_type;
|
||||
typedef float result_type;
|
||||
|
||||
__device__ __forceinline__ L2Dist() : mySum(0.0f) {}
|
||||
|
||||
__device__ __forceinline__ void reduceIter(float val1, float val2)
|
||||
{
|
||||
float reg = val1 - val2;
|
||||
mySum += reg * reg;
|
||||
}
|
||||
|
||||
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
|
||||
{
|
||||
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile float>());
|
||||
}
|
||||
|
||||
__device__ __forceinline__ operator float() const
|
||||
{
|
||||
return sqrtf(mySum);
|
||||
}
|
||||
|
||||
float mySum;
|
||||
};
|
||||
|
||||
struct HammingDist
|
||||
{
|
||||
typedef int value_type;
|
||||
typedef int result_type;
|
||||
|
||||
__device__ __forceinline__ HammingDist() : mySum(0) {}
|
||||
|
||||
__device__ __forceinline__ void reduceIter(int val1, int val2)
|
||||
{
|
||||
mySum += __popc(val1 ^ val2);
|
||||
}
|
||||
|
||||
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
|
||||
{
|
||||
reduce<THREAD_DIM>(smem, mySum, tid, plus<volatile int>());
|
||||
}
|
||||
|
||||
__device__ __forceinline__ operator int() const
|
||||
{
|
||||
return mySum;
|
||||
}
|
||||
|
||||
int mySum;
|
||||
};
|
||||
|
||||
// calc distance between two vectors in global memory
|
||||
template <int THREAD_DIM, typename Dist, typename T1, typename T2>
|
||||
__device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
|
||||
{
|
||||
for (int i = tid; i < len; i += THREAD_DIM)
|
||||
{
|
||||
T1 val1;
|
||||
ForceGlob<T1>::Load(vec1, i, val1);
|
||||
|
||||
T2 val2;
|
||||
ForceGlob<T2>::Load(vec2, i, val2);
|
||||
|
||||
dist.reduceIter(val1, val2);
|
||||
}
|
||||
|
||||
dist.reduceAll<THREAD_DIM>(smem, tid);
|
||||
}
|
||||
|
||||
// calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
|
||||
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
|
||||
__device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
|
||||
{
|
||||
detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
|
||||
|
||||
dist.reduceAll<THREAD_DIM>(smem, tid);
|
||||
}
|
||||
|
||||
// calc distance between two vectors in global memory
|
||||
template <int THREAD_DIM, typename T1> struct VecDiffGlobal
|
||||
{
|
||||
explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
|
||||
{
|
||||
vec1 = vec1_;
|
||||
}
|
||||
|
||||
template <typename T2, typename Dist>
|
||||
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
|
||||
{
|
||||
calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
|
||||
}
|
||||
|
||||
const T1* vec1;
|
||||
};
|
||||
|
||||
// calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
|
||||
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
|
||||
{
|
||||
template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
|
||||
{
|
||||
if (glob_tid < len)
|
||||
smem[glob_tid] = vec1[glob_tid];
|
||||
__syncthreads();
|
||||
|
||||
U* vec1ValsPtr = vec1Vals;
|
||||
|
||||
#pragma unroll
|
||||
for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
|
||||
*vec1ValsPtr++ = smem[i];
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
template <typename T2, typename Dist>
|
||||
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
|
||||
{
|
||||
calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
|
||||
}
|
||||
|
||||
U vec1Vals[MAX_LEN / THREAD_DIM];
|
||||
};
|
||||
}}}
|
||||
|
||||
#endif // __OPENCV_GPU_VEC_DISTANCE_HPP__
|
||||
Reference in New Issue
Block a user