2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
387 lines
18 KiB
C++
387 lines
18 KiB
C++
/*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) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, 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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// @Authors
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// Jia Haipeng, jiahaipeng95@gmail.com
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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 oclMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include <vector>
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using namespace cv;
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using namespace cv::ocl;
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////////////////////////////////////////////////////////////////////////
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///////////////// oclMat merge and split ///////////////////////////////
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////////////////////////////////////////////////////////////////////////
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namespace cv
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{
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namespace ocl
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{
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///////////////////////////OpenCL kernel strings///////////////////////////
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extern const char *merge_mat;
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extern const char *split_mat;
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}
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}
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namespace cv
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{
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namespace ocl
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{
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namespace split_merge
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{
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///////////////////////////////////////////////////////////
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///////////////common/////////////////////////////////////
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/////////////////////////////////////////////////////////
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inline int divUp(int total, int grain)
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{
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return (total + grain - 1) / grain;
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}
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////////////////////////////////////////////////////////////////////////////
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////////////////////merge//////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////
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// static void merge_vector_run_no_roi(const oclMat *mat_src, size_t n, oclMat &mat_dst)
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// {
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// Context *clCxt = mat_dst.clCxt;
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// int channels = mat_dst.oclchannels();
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// int depth = mat_dst.depth();
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// std::string kernelName = "merge_vector";
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// int indexes[4][7] = {{0, 0, 0, 0, 0, 0, 0},
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// {4, 4, 2, 2, 1, 1, 1},
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// {4, 4, 2, 2 , 1, 1, 1},
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// {4, 4, 2, 2, 1, 1, 1}
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// };
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// size_t index = indexes[channels - 1][mat_dst.depth()];
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// int cols = divUp(mat_dst.cols, index);
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// size_t localThreads[3] = { 64, 4, 1 };
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// size_t globalThreads[3] = { divUp(cols, localThreads[0]) *localThreads[0],
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// divUp(mat_dst.rows, localThreads[1]) *localThreads[1],
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// 1
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// };
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// std::vector<std::pair<size_t , const void *> > args;
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst.rows));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&cols));
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst.data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst.step));
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[0].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[0].step));
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[1].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[1].step));
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// if(n >= 3)
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// {
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[2].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[2].step));
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// }
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// if(n >= 4)
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// {
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[3].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[3].step));
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// }
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// openCLExecuteKernel(clCxt, &merge_mat, kernelName, globalThreads, localThreads, args, channels, depth);
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// }
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static void merge_vector_run(const oclMat *mat_src, size_t n, oclMat &mat_dst)
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{
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if(mat_dst.clCxt -> impl -> double_support == 0 && mat_dst.type() == CV_64F)
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{
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CV_Error(CV_GpuNotSupported, "Selected device don't support double\r\n");
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return;
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}
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Context *clCxt = mat_dst.clCxt;
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int channels = mat_dst.oclchannels();
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int depth = mat_dst.depth();
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std::string kernelName = "merge_vector";
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int vector_lengths[4][7] = {{0, 0, 0, 0, 0, 0, 0},
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{2, 2, 1, 1, 1, 1, 1},
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{4, 4, 2, 2 , 1, 1, 1},
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{1, 1, 1, 1, 1, 1, 1}
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};
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size_t vector_length = vector_lengths[channels - 1][depth];
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int offset_cols = (mat_dst.offset / mat_dst.elemSize()) & (vector_length - 1);
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int cols = divUp(mat_dst.cols + offset_cols, vector_length);
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size_t localThreads[3] = { 64, 4, 1 };
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size_t globalThreads[3] = { divUp(cols, localThreads[0]) *localThreads[0],
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divUp(mat_dst.rows, localThreads[1]) *localThreads[1],
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1
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};
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int dst_step1 = mat_dst.cols * mat_dst.elemSize();
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std::vector<std::pair<size_t , const void *> > args;
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst.data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst.step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst.offset));
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[0].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[0].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[0].offset));
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[1].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[1].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[1].offset));
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if(channels == 4)
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{
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[2].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[2].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[2].offset));
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// if channel == 3, then the matrix will convert to channel =4
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//if(n == 3)
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&offset_cols));
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if(n == 3)
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{
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[2].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[2].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[2].offset));
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}
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else if( n == 4)
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{
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src[3].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[3].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src[3].offset));
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}
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}
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst.rows));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&cols));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst_step1));
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openCLExecuteKernel(clCxt, &merge_mat, kernelName, globalThreads, localThreads, args, channels, depth);
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}
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static void merge(const oclMat *mat_src, size_t n, oclMat &mat_dst)
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{
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CV_Assert(mat_src);
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CV_Assert(n > 0);
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int depth = mat_src[0].depth();
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Size size = mat_src[0].size();
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int total_channels = 0;
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for(size_t i = 0; i < n; ++i)
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{
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CV_Assert(depth == mat_src[i].depth());
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CV_Assert(size == mat_src[i].size());
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total_channels += mat_src[i].oclchannels();
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}
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CV_Assert(total_channels <= 4);
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if(total_channels == 1)
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{
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mat_src[0].copyTo(mat_dst);
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return;
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}
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mat_dst.create(size, CV_MAKETYPE(depth, total_channels));
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merge_vector_run(mat_src, n, mat_dst);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////split/////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////////
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// static void split_vector_run_no_roi(const oclMat &mat_src, oclMat *mat_dst)
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// {
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// Context *clCxt = mat_src.clCxt;
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// int channels = mat_src.oclchannels();
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// int depth = mat_src.depth();
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// std::string kernelName = "split_vector";
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// int indexes[4][7] = {{0, 0, 0, 0, 0, 0, 0},
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// {8, 8, 8, 8, 4, 4, 2},
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// {8, 8, 8, 8 , 4, 4, 4},
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// {4, 4, 2, 2, 1, 1, 1}
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// };
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// size_t index = indexes[channels - 1][mat_dst[0].depth()];
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// int cols = divUp(mat_src.cols, index);
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// size_t localThreads[3] = { 64, 4, 1 };
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// size_t globalThreads[3] = { divUp(cols, localThreads[0]) *localThreads[0],
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// divUp(mat_src.rows, localThreads[1]) *localThreads[1],
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// 1
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// };
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// std::vector<std::pair<size_t , const void *> > args;
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src.data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src.step));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src.rows));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&cols));
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[0].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[0].step));
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[1].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[1].step));
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// if(channels >= 3)
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// {
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[2].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[2].step));
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// }
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// if(channels >= 4)
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// {
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// args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[3].data));
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// args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[3].step));
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// }
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// openCLExecuteKernel(clCxt, &split_mat, kernelName, globalThreads, localThreads, args, channels, depth);
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// }
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static void split_vector_run(const oclMat &mat_src, oclMat *mat_dst)
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{
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if(mat_src.clCxt -> impl -> double_support == 0 && mat_src.type() == CV_64F)
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{
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CV_Error(CV_GpuNotSupported, "Selected device don't support double\r\n");
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return;
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}
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Context *clCxt = mat_src.clCxt;
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int channels = mat_src.oclchannels();
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int depth = mat_src.depth();
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std::string kernelName = "split_vector";
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int vector_lengths[4][7] = {{0, 0, 0, 0, 0, 0, 0},
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{4, 4, 2, 2, 1, 1, 1},
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{4, 4, 2, 2 , 1, 1, 1},
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{4, 4, 2, 2, 1, 1, 1}
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};
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size_t vector_length = vector_lengths[channels - 1][mat_dst[0].depth()];
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int max_offset_cols = 0;
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for(int i = 0; i < channels; i++)
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{
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int offset_cols = (mat_dst[i].offset / mat_dst[i].elemSize()) & (vector_length - 1);
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if(max_offset_cols < offset_cols)
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max_offset_cols = offset_cols;
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}
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int cols = vector_length == 1 ? divUp(mat_src.cols, vector_length)
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: divUp(mat_src.cols + max_offset_cols, vector_length);
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size_t localThreads[3] = { 64, 4, 1 };
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size_t globalThreads[3] = { divUp(cols, localThreads[0]) *localThreads[0],
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divUp(mat_src.rows, localThreads[1]) *localThreads[1], 1
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};
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int dst_step1 = mat_dst[0].cols * mat_dst[0].elemSize();
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std::vector<std::pair<size_t , const void *> > args;
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_src.data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src.step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src.offset));
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[0].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[0].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[0].offset));
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[1].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[1].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[1].offset));
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if(channels >= 3)
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{
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[2].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[2].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[2].offset));
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}
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if(channels >= 4)
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{
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args.push_back( std::make_pair( sizeof(cl_mem), (void *)&mat_dst[3].data));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[3].step));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_dst[3].offset));
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}
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&mat_src.rows));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&cols));
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args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst_step1));
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openCLExecuteKernel(clCxt, &split_mat, kernelName, globalThreads, localThreads, args, channels, depth);
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}
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static void split(const oclMat &mat_src, oclMat *mat_dst)
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{
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CV_Assert(mat_dst);
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int depth = mat_src.depth();
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int num_channels = mat_src.oclchannels();
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Size size = mat_src.size();
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if(num_channels == 1)
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{
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mat_src.copyTo(mat_dst[0]);
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return;
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}
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int i;
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for(i = 0; i < num_channels; i++)
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mat_dst[i].create(size, CV_MAKETYPE(depth, 1));
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split_vector_run(mat_src, mat_dst);
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}
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}
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}
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}
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void cv::ocl::merge(const oclMat *src, size_t n, oclMat &dst)
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{
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split_merge::merge(src, n, dst);
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}
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void cv::ocl::merge(const std::vector<oclMat> &src, oclMat &dst)
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{
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split_merge::merge(&src[0], src.size(), dst);
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}
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void cv::ocl::split(const oclMat &src, oclMat *dst)
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{
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split_merge::split(src, dst);
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}
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void cv::ocl::split(const oclMat &src, std::vector<oclMat> &dst)
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{
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dst.resize(src.oclchannels());
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if(src.oclchannels() > 0)
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split_merge::split(src, &dst[0]);
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}
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