233 lines
7.7 KiB
C++
233 lines
7.7 KiB
C++
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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_CUDA_VEC_DISTANCE_HPP
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#define OPENCV_CUDA_VEC_DISTANCE_HPP
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#include "reduce.hpp"
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#include "functional.hpp"
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#include "detail/vec_distance_detail.hpp"
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/** @file
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* @deprecated Use @ref cudev instead.
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*/
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//! @cond IGNORED
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namespace cv { namespace cuda { namespace device
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{
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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<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<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<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<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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vec_distance_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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{
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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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}}} // namespace cv { namespace cuda { namespace cudev
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//! @endcond
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#endif // OPENCV_CUDA_VEC_DISTANCE_HPP
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