465 lines
22 KiB
Plaintext
465 lines
22 KiB
Plaintext
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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 bpied warranties, including, but not limited to, the bpied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "internal_shared.hpp"
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#include "opencv2/gpu/device/limits.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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namespace cv { namespace gpu { namespace bfmatcher
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{
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template <typename VecDiff, typename Dist, typename T, typename Mask>
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__device__ void distanceCalcLoop(const PtrStep_<T>& query, const DevMem2D_<T>& train, const Mask& m, int queryIdx,
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typename Dist::result_type& distMin1, typename Dist::result_type& distMin2, int& bestTrainIdx1, int& bestTrainIdx2,
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typename Dist::result_type* smem)
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{
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const VecDiff vecDiff(query.ptr(queryIdx), train.cols, (typename Dist::value_type*)smem, threadIdx.y * blockDim.x + threadIdx.x, threadIdx.x);
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typename Dist::result_type* sdiffRow = smem + blockDim.x * threadIdx.y;
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distMin1 = numeric_limits<typename Dist::result_type>::max();
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distMin2 = numeric_limits<typename Dist::result_type>::max();
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bestTrainIdx1 = -1;
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bestTrainIdx2 = -1;
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for (int trainIdx = threadIdx.y; trainIdx < train.rows; trainIdx += blockDim.y)
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{
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if (m(queryIdx, trainIdx))
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{
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Dist dist;
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const T* trainRow = train.ptr(trainIdx);
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vecDiff.calc(trainRow, train.cols, dist, sdiffRow, threadIdx.x);
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const typename Dist::result_type val = dist;
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if (val < distMin1)
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{
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distMin1 = val;
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bestTrainIdx1 = trainIdx;
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}
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else if (val < distMin2)
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{
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distMin2 = val;
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bestTrainIdx2 = trainIdx;
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}
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}
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}
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}
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template <int BLOCK_DIM_X, int BLOCK_DIM_Y, typename VecDiff, typename Dist, typename T, typename Mask>
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__global__ void knnMatch2(const PtrStep_<T> query, const DevMem2D_<T> train, const Mask m, int2* trainIdx, float2* distance)
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{
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typedef typename Dist::result_type result_type;
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typedef typename Dist::value_type value_type;
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__shared__ result_type smem[BLOCK_DIM_X * BLOCK_DIM_Y];
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const int queryIdx = blockIdx.x;
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result_type distMin1;
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result_type distMin2;
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int bestTrainIdx1;
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int bestTrainIdx2;
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distanceCalcLoop<VecDiff, Dist>(query, train, m, queryIdx, distMin1, distMin2, bestTrainIdx1, bestTrainIdx2, smem);
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__syncthreads();
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volatile result_type* sdistMinRow = smem;
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volatile int* sbestTrainIdxRow = (int*)(sdistMinRow + 2 * BLOCK_DIM_Y);
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if (threadIdx.x == 0)
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{
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sdistMinRow[threadIdx.y] = distMin1;
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sdistMinRow[threadIdx.y + BLOCK_DIM_Y] = distMin2;
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sbestTrainIdxRow[threadIdx.y] = bestTrainIdx1;
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sbestTrainIdxRow[threadIdx.y + BLOCK_DIM_Y] = bestTrainIdx2;
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}
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__syncthreads();
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if (threadIdx.x == 0 && threadIdx.y == 0)
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{
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distMin1 = numeric_limits<result_type>::max();
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distMin2 = numeric_limits<result_type>::max();
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bestTrainIdx1 = -1;
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bestTrainIdx2 = -1;
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#pragma unroll
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for (int i = 0; i < BLOCK_DIM_Y; ++i)
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{
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result_type val = sdistMinRow[i];
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if (val < distMin1)
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{
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distMin1 = val;
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bestTrainIdx1 = sbestTrainIdxRow[i];
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}
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else if (val < distMin2)
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{
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distMin2 = val;
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bestTrainIdx2 = sbestTrainIdxRow[i];
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}
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}
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#pragma unroll
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for (int i = BLOCK_DIM_Y; i < 2 * BLOCK_DIM_Y; ++i)
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{
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result_type val = sdistMinRow[i];
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if (val < distMin2)
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{
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distMin2 = val;
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bestTrainIdx2 = sbestTrainIdxRow[i];
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}
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}
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trainIdx[queryIdx] = make_int2(bestTrainIdx1, bestTrainIdx2);
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distance[queryIdx] = make_float2(distMin1, distMin2);
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}
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}
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///////////////////////////////////////////////////////////////////////////////
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// Knn 2 Match kernel caller
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template <int BLOCK_DIM_X, int BLOCK_DIM_Y, typename Dist, typename T, typename Mask>
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void knnMatch2Simple_caller(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
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const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
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cudaStream_t stream)
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{
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const dim3 grid(query.rows, 1, 1);
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const dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y, 1);
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knnMatch2<BLOCK_DIM_X, BLOCK_DIM_Y, VecDiffGlobal<BLOCK_DIM_X, T>, Dist, T>
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<<<grid, threads, 0, stream>>>(query, train, mask, trainIdx, distance);
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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 <int BLOCK_DIM_X, int BLOCK_DIM_Y, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T, typename Mask>
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void knnMatch2Cached_caller(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
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const DevMem2D_<int2>& trainIdx, const DevMem2D_<float2>& distance,
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cudaStream_t stream)
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{
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StaticAssert<BLOCK_DIM_X * BLOCK_DIM_Y >= MAX_LEN>::check(); // block size must be greter than descriptors length
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StaticAssert<MAX_LEN % BLOCK_DIM_X == 0>::check(); // max descriptors length must divide to blockDimX
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const dim3 grid(query.rows, 1, 1);
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const dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y, 1);
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knnMatch2<BLOCK_DIM_X, BLOCK_DIM_Y, VecDiffCachedRegister<BLOCK_DIM_X, MAX_LEN, LEN_EQ_MAX_LEN, typename Dist::value_type>, Dist, T>
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<<<grid, threads, 0, stream>>>(query, train, mask, trainIdx.data, distance.data);
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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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///////////////////////////////////////////////////////////////////////////////
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// Knn 2 Match Dispatcher
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template <typename Dist, typename T, typename Mask>
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void knnMatch2Dispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask,
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const DevMem2D& trainIdx, const DevMem2D& distance,
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int cc, cudaStream_t stream)
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{
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if (query.cols < 64)
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{
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knnMatch2Cached_caller<16, 16, 64, false, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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else if (query.cols == 64)
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{
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knnMatch2Cached_caller<16, 16, 64, true, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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else if (query.cols < 128)
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{
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knnMatch2Cached_caller<16, 16, 128, false, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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else if (query.cols == 128 && cc >= 12)
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{
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knnMatch2Cached_caller<16, 16, 128, true, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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else if (query.cols < 256 && cc >= 12)
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{
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knnMatch2Cached_caller<16, 16, 256, false, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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else if (query.cols == 256 && cc >= 12)
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{
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knnMatch2Cached_caller<16, 16, 256, true, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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else
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{
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knnMatch2Simple_caller<16, 16, Dist>(
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query, train, mask,
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static_cast< DevMem2D_<int2> >(trainIdx), static_cast< DevMem2D_<float2> >(distance),
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stream);
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}
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}
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///////////////////////////////////////////////////////////////////////////////
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// Calc distance kernel
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template <int BLOCK_DIM_X, int BLOCK_DIM_Y, typename Dist, typename T, typename Mask>
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__global__ void calcDistance(const PtrStep_<T> query, const DevMem2D_<T> train, const Mask mask, PtrStepf distance)
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{
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__shared__ typename Dist::result_type sdiff[BLOCK_DIM_X * BLOCK_DIM_Y];
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typename Dist::result_type* sdiff_row = sdiff + BLOCK_DIM_X * threadIdx.y;
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const int queryIdx = blockIdx.x;
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const T* queryDescs = query.ptr(queryIdx);
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const int trainIdx = blockIdx.y * BLOCK_DIM_Y + threadIdx.y;
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if (trainIdx < train.rows)
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{
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const T* trainDescs = train.ptr(trainIdx);
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typename Dist::result_type myDist = numeric_limits<typename Dist::result_type>::max();
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if (mask(queryIdx, trainIdx))
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{
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Dist dist;
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calcVecDiffGlobal<BLOCK_DIM_X>(queryDescs, trainDescs, train.cols, dist, sdiff_row, threadIdx.x);
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myDist = dist;
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}
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if (threadIdx.x == 0)
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distance.ptr(queryIdx)[trainIdx] = myDist;
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}
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}
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///////////////////////////////////////////////////////////////////////////////
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// Calc distance kernel caller
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template <int BLOCK_DIM_X, int BLOCK_DIM_Y, typename Dist, typename T, typename Mask>
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void calcDistance_caller(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask, const DevMem2Df& distance, cudaStream_t stream)
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{
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const dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y, 1);
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const dim3 grid(query.rows, divUp(train.rows, BLOCK_DIM_Y), 1);
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calcDistance<BLOCK_DIM_X, BLOCK_DIM_Y, Dist, T><<<grid, threads, 0, stream>>>(query, train, mask, distance);
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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 <typename Dist, typename T, typename Mask>
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void calcDistanceDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, const Mask& mask, const DevMem2D& allDist, cudaStream_t stream)
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{
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calcDistance_caller<16, 16, Dist>(query, train, mask, static_cast<DevMem2Df>(allDist), stream);
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}
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///////////////////////////////////////////////////////////////////////////////
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// find knn match kernel
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template <int BLOCK_SIZE> __global__ void findBestMatch(DevMem2Df allDist_, int i, PtrStepi trainIdx_, PtrStepf distance_)
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{
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const int SMEM_SIZE = BLOCK_SIZE > 64 ? BLOCK_SIZE : 64;
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__shared__ float sdist[SMEM_SIZE];
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__shared__ int strainIdx[SMEM_SIZE];
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const int queryIdx = blockIdx.x;
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float* allDist = allDist_.ptr(queryIdx);
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int* trainIdx = trainIdx_.ptr(queryIdx);
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float* distance = distance_.ptr(queryIdx);
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float dist = numeric_limits<float>::max();
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int bestIdx = -1;
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for (int i = threadIdx.x; i < allDist_.cols; i += BLOCK_SIZE)
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{
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float reg = allDist[i];
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if (reg < dist)
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{
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dist = reg;
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bestIdx = i;
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}
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}
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|
|
|
||
|
|
sdist[threadIdx.x] = dist;
|
||
|
|
strainIdx[threadIdx.x] = bestIdx;
|
||
|
|
__syncthreads();
|
||
|
|
|
||
|
|
reducePredVal<BLOCK_SIZE>(sdist, dist, strainIdx, bestIdx, threadIdx.x, less<volatile float>());
|
||
|
|
|
||
|
|
if (threadIdx.x == 0)
|
||
|
|
{
|
||
|
|
if (dist < numeric_limits<float>::max())
|
||
|
|
{
|
||
|
|
allDist[bestIdx] = numeric_limits<float>::max();
|
||
|
|
trainIdx[i] = bestIdx;
|
||
|
|
distance[i] = dist;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
///////////////////////////////////////////////////////////////////////////////
|
||
|
|
// find knn match kernel caller
|
||
|
|
|
||
|
|
template <int BLOCK_SIZE> void findKnnMatch_caller(int k, const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2Df& allDist, cudaStream_t stream)
|
||
|
|
{
|
||
|
|
const dim3 threads(BLOCK_SIZE, 1, 1);
|
||
|
|
const dim3 grid(trainIdx.rows, 1, 1);
|
||
|
|
|
||
|
|
for (int i = 0; i < k; ++i)
|
||
|
|
{
|
||
|
|
findBestMatch<BLOCK_SIZE><<<grid, threads, 0, stream>>>(allDist, i, trainIdx, distance);
|
||
|
|
cudaSafeCall( cudaGetLastError() );
|
||
|
|
}
|
||
|
|
|
||
|
|
if (stream == 0)
|
||
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
||
|
|
}
|
||
|
|
|
||
|
|
void findKnnMatchDispatcher(int k, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, cudaStream_t stream)
|
||
|
|
{
|
||
|
|
findKnnMatch_caller<256>(k, static_cast<DevMem2Di>(trainIdx), static_cast<DevMem2Df>(distance), static_cast<DevMem2Df>(allDist), stream);
|
||
|
|
}
|
||
|
|
|
||
|
|
///////////////////////////////////////////////////////////////////////////////
|
||
|
|
// knn match Dispatcher
|
||
|
|
|
||
|
|
template <typename Dist, typename T>
|
||
|
|
void knnMatchDispatcher(const DevMem2D_<T>& query, const DevMem2D_<T>& train, int k, const DevMem2D& mask,
|
||
|
|
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist,
|
||
|
|
int cc, cudaStream_t stream)
|
||
|
|
{
|
||
|
|
if (mask.data)
|
||
|
|
{
|
||
|
|
if (k == 2)
|
||
|
|
{
|
||
|
|
knnMatch2Dispatcher<Dist>(query, train, SingleMask(mask), trainIdx, distance, cc, stream);
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
calcDistanceDispatcher<Dist>(query, train, SingleMask(mask), allDist, stream);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
if (k == 2)
|
||
|
|
{
|
||
|
|
knnMatch2Dispatcher<Dist>(query, train, WithOutMask(), trainIdx, distance, cc, stream);
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
calcDistanceDispatcher<Dist>(query, train, WithOutMask(), allDist, stream);
|
||
|
|
}
|
||
|
|
|
||
|
|
findKnnMatchDispatcher(k, trainIdx, distance, allDist, stream);
|
||
|
|
}
|
||
|
|
|
||
|
|
///////////////////////////////////////////////////////////////////////////////
|
||
|
|
// knn match caller
|
||
|
|
|
||
|
|
template <typename T> void knnMatchL1_gpu(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
|
||
|
|
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist,
|
||
|
|
int cc, cudaStream_t stream)
|
||
|
|
{
|
||
|
|
knnMatchDispatcher< L1Dist<T> >(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), k, mask, trainIdx, distance, allDist, cc, stream);
|
||
|
|
}
|
||
|
|
|
||
|
|
template void knnMatchL1_gpu<uchar >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchL1_gpu<schar >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchL1_gpu<ushort>(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchL1_gpu<short >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchL1_gpu<int >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchL1_gpu<float >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
|
||
|
|
template <typename T> void knnMatchL2_gpu(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
|
||
|
|
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist,
|
||
|
|
int cc, cudaStream_t stream)
|
||
|
|
{
|
||
|
|
knnMatchDispatcher<L2Dist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), k, mask, trainIdx, distance, allDist, cc, stream);
|
||
|
|
}
|
||
|
|
|
||
|
|
//template void knnMatchL2_gpu<uchar >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchL2_gpu<schar >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchL2_gpu<ushort>(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchL2_gpu<short >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchL2_gpu<int >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchL2_gpu<float >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
|
||
|
|
template <typename T> void knnMatchHamming_gpu(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
|
||
|
|
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist,
|
||
|
|
int cc, cudaStream_t stream)
|
||
|
|
{
|
||
|
|
knnMatchDispatcher<HammingDist>(static_cast< DevMem2D_<T> >(query), static_cast< DevMem2D_<T> >(train), k, mask, trainIdx, distance, allDist, cc, stream);
|
||
|
|
}
|
||
|
|
|
||
|
|
template void knnMatchHamming_gpu<uchar >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchHamming_gpu<schar >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchHamming_gpu<ushort>(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
//template void knnMatchHamming_gpu<short >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
template void knnMatchHamming_gpu<int >(const DevMem2D& queryDescs, const DevMem2D& trainDescs, int k, const DevMem2D& mask, const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2D& allDist, int cc, cudaStream_t stream);
|
||
|
|
}}}
|