1601 lines
75 KiB
C++
1601 lines
75 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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// Copyright (C) 2010-2012, Multicoreware, 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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// Niko Li, newlife20080214@gmail.com
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// Jia Haipeng, jiahaipeng95@gmail.com
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// Shengen Yan, yanshengen@gmail.com
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// Rock Li, Rock.Li@amd.com
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// Zero Lin, Zero.Lin@amd.com
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// Zhang Ying, zhangying913@gmail.com
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// Xu Pang, pangxu010@163.com
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// Wu Zailong, bullet@yeah.net
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// Wenju He, wenju@multicorewareinc.com
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// Sen Liu, swjtuls1987@126.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 "opencl_kernels.hpp"
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using namespace cv;
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using namespace cv::ocl;
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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 call wrappers////////////////////////////
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template <typename T> struct index_and_sizeof;
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template <> struct index_and_sizeof<char>
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{
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enum { index = 1 };
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};
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template <> struct index_and_sizeof<unsigned char>
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{
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enum { index = 2 };
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};
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template <> struct index_and_sizeof<short>
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{
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enum { index = 3 };
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};
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template <> struct index_and_sizeof<unsigned short>
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{
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enum { index = 4 };
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};
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template <> struct index_and_sizeof<int>
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{
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enum { index = 5 };
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};
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template <> struct index_and_sizeof<float>
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{
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enum { index = 6 };
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};
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template <> struct index_and_sizeof<double>
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{
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enum { index = 7 };
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};
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/////////////////////////////////////////////////////////////////////////////////////
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// threshold
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typedef void (*gpuThresh_t)(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type);
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static void threshold_8u(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type)
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{
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uchar thresh_uchar = cvFloor(thresh);
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uchar max_val = cvRound(maxVal);
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size_t cols = (dst.cols + (dst.offset % 16) + 15) / 16;
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size_t bSizeX = 16, bSizeY = 16;
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size_t gSizeX = cols % bSizeX == 0 ? cols : (cols + bSizeX - 1) / bSizeX * bSizeX;
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size_t gSizeY = dst.rows;
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size_t globalThreads[3] = {gSizeX, gSizeY, 1};
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size_t localThreads[3] = {bSizeX, bSizeY, 1};
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair(sizeof(cl_mem), &src.data));
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args.push_back( make_pair(sizeof(cl_mem), &dst.data));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.step));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.step));
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args.push_back( make_pair(sizeof(cl_uchar), (void *)&thresh_uchar));
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args.push_back( make_pair(sizeof(cl_uchar), (void *)&max_val));
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args.push_back( make_pair(sizeof(cl_int), (void *)&type));
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openCLExecuteKernel(src.clCxt, &imgproc_threshold, "threshold", globalThreads, localThreads, args, src.oclchannels(), src.depth());
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}
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static void threshold_32f(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type)
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{
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float thresh_f = thresh;
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float max_val = maxVal;
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int dst_offset = (dst.offset >> 2);
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int dst_step = (dst.step >> 2);
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int src_offset = (src.offset >> 2);
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int src_step = (src.step >> 2);
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size_t cols = (dst.cols + (dst_offset & 3) + 3) / 4;
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size_t bSizeX = 16, bSizeY = 16;
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size_t gSizeX = cols % bSizeX == 0 ? cols : (cols + bSizeX - 1) / bSizeX * bSizeX;
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size_t gSizeY = dst.rows;
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size_t globalThreads[3] = {gSizeX, gSizeY, 1};
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size_t localThreads[3] = {bSizeX, bSizeY, 1};
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair(sizeof(cl_mem), &src.data));
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args.push_back( make_pair(sizeof(cl_mem), &dst.data));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src_offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src_step));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst_offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst_step));
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args.push_back( make_pair(sizeof(cl_float), (void *)&thresh_f));
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args.push_back( make_pair(sizeof(cl_float), (void *)&max_val));
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args.push_back( make_pair(sizeof(cl_int), (void *)&type));
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openCLExecuteKernel(src.clCxt, &imgproc_threshold, "threshold", globalThreads, localThreads, args, src.oclchannels(), src.depth());
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}
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// threshold: support 8UC1 and 32FC1 data type and five threshold type
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double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type)
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{
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//TODO: These limitations shall be removed later.
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CV_Assert(src.type() == CV_8UC1 || src.type() == CV_32FC1);
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CV_Assert(type == THRESH_BINARY || type == THRESH_BINARY_INV || type == THRESH_TRUNC
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|| type == THRESH_TOZERO || type == THRESH_TOZERO_INV );
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static const gpuThresh_t gpuThresh_callers[2] = {threshold_8u, threshold_32f};
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dst.create( src.size(), src.type() );
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gpuThresh_callers[(src.type() == CV_32FC1)](src, dst, thresh, maxVal, type);
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return thresh;
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}
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////////////////////////////////////////////////////////////////////////////////////////////
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/////////////////////////////// remap //////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////
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void remap( const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int borderType, const Scalar &borderValue )
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{
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Context *clCxt = src.clCxt;
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bool supportsDouble = clCxt->supportsFeature(FEATURE_CL_DOUBLE);
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if (!supportsDouble && src.depth() == CV_64F)
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{
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CV_Error(CV_OpenCLDoubleNotSupported, "Selected device does not support double");
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return;
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}
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CV_Assert(interpolation == INTER_LINEAR || interpolation == INTER_NEAREST
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|| interpolation == INTER_CUBIC || interpolation == INTER_LANCZOS4);
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CV_Assert((map1.type() == CV_16SC2 && !map2.data) || (map1.type() == CV_32FC2 && !map2.data) ||
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(map1.type() == CV_32FC1 && map2.type() == CV_32FC1));
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CV_Assert(!map2.data || map2.size() == map1.size());
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CV_Assert(borderType == BORDER_CONSTANT || borderType == BORDER_REPLICATE || borderType == BORDER_WRAP
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|| borderType == BORDER_REFLECT_101 || borderType == BORDER_REFLECT);
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dst.create(map1.size(), src.type());
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const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
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const char * const channelMap[] = { "", "", "2", "4", "4" };
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const char * const interMap[] = { "INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_LINEAR", "INTER_LANCZOS" };
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const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP",
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"BORDER_REFLECT_101", "BORDER_TRANSPARENT" };
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string kernelName = "remap";
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if ( map1.type() == CV_32FC2 && !map2.data )
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kernelName += "_32FC2";
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else if (map1.type() == CV_16SC2 && !map2.data)
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kernelName += "_16SC2";
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else if (map1.type() == CV_32FC1 && map2.type() == CV_32FC1)
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kernelName += "_2_32FC1";
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else
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CV_Error(CV_StsBadArg, "Unsupported map types");
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int ocn = dst.oclchannels();
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size_t localThreads[3] = { 16, 16, 1};
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size_t globalThreads[3] = { dst.cols, dst.rows, 1};
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Mat scalar(1, 1, CV_MAKE_TYPE(dst.depth(), ocn), borderValue);
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std::string buildOptions = format("-D %s -D %s -D T=%s%s", interMap[interpolation],
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borderMap[borderType], typeMap[src.depth()], channelMap[ocn]);
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if (interpolation != INTER_NEAREST)
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{
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int wdepth = std::max(CV_32F, dst.depth());
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if (!supportsDouble)
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wdepth = std::min(CV_32F, wdepth);
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buildOptions += format(" -D WT=%s%s -D convertToT=convert_%s%s%s -D convertToWT=convert_%s%s"
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" -D convertToWT2=convert_%s2 -D WT2=%s2",
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typeMap[wdepth], channelMap[ocn],
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typeMap[src.depth()], channelMap[ocn], src.depth() < CV_32F ? "_sat_rte" : "",
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typeMap[wdepth], channelMap[ocn],
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typeMap[wdepth], typeMap[wdepth]);
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}
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int src_step = src.step / src.elemSize(), src_offset = src.offset / src.elemSize();
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int map1_step = map1.step / map1.elemSize(), map1_offset = map1.offset / map1.elemSize();
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int map2_step = map2.step / map2.elemSize(), map2_offset = map2.offset / map2.elemSize();
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int dst_step = dst.step / dst.elemSize(), dst_offset = dst.offset / dst.elemSize();
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vector< pair<size_t, const void *> > args;
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args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data));
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args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data));
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args.push_back( make_pair(sizeof(cl_mem), (void *)&map1.data));
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if (!map2.empty())
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args.push_back( make_pair(sizeof(cl_mem), (void *)&map2.data));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src_offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst_offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&map1_offset));
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if (!map2.empty())
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args.push_back( make_pair(sizeof(cl_int), (void *)&map2_offset));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src_step));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst_step));
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args.push_back( make_pair(sizeof(cl_int), (void *)&map1_step));
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if (!map2.empty())
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args.push_back( make_pair(sizeof(cl_int), (void *)&map2_step));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows));
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args.push_back( make_pair(scalar.elemSize(), (void *)scalar.data));
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openCLExecuteKernel(clCxt, &imgproc_remap, kernelName, globalThreads, localThreads, args, -1, -1, buildOptions.c_str());
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}
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////////////////////////////////////////////////////////////////////////////////////////////
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// resize
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static void resize_gpu( const oclMat &src, oclMat &dst, double fx, double fy, int interpolation)
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{
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CV_Assert( (src.channels() == dst.channels()) );
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Context *clCxt = src.clCxt;
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float ifx = 1. / fx;
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float ify = 1. / fy;
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double ifx_d = 1. / fx;
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double ify_d = 1. / fy;
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int srcStep_in_pixel = src.step1() / src.oclchannels();
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int srcoffset_in_pixel = src.offset / src.elemSize();
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int dstStep_in_pixel = dst.step1() / dst.oclchannels();
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int dstoffset_in_pixel = dst.offset / dst.elemSize();
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string kernelName;
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if (interpolation == INTER_LINEAR)
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kernelName = "resizeLN";
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else if (interpolation == INTER_NEAREST)
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kernelName = "resizeNN";
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//TODO: improve this kernel
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size_t blkSizeX = 16, blkSizeY = 16;
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size_t glbSizeX;
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if (src.type() == CV_8UC1)
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{
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size_t cols = (dst.cols + dst.offset % 4 + 3) / 4;
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glbSizeX = cols % blkSizeX == 0 && cols != 0 ? cols : (cols / blkSizeX + 1) * blkSizeX;
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}
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else
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glbSizeX = dst.cols % blkSizeX == 0 && dst.cols != 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX;
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size_t glbSizeY = dst.rows % blkSizeY == 0 && dst.rows != 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY;
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size_t globalThreads[3] = {glbSizeX, glbSizeY, 1};
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size_t localThreads[3] = {blkSizeX, blkSizeY, 1};
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vector< pair<size_t, const void *> > args;
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if (interpolation == INTER_NEAREST)
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{
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args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data));
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args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dstoffset_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&srcoffset_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dstStep_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&srcStep_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows));
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if (src.clCxt->supportsFeature(FEATURE_CL_DOUBLE))
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{
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args.push_back( make_pair(sizeof(cl_double), (void *)&ifx_d));
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args.push_back( make_pair(sizeof(cl_double), (void *)&ify_d));
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}
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else
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{
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args.push_back( make_pair(sizeof(cl_float), (void *)&ifx));
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args.push_back( make_pair(sizeof(cl_float), (void *)&ify));
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}
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}
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else
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{
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args.push_back( make_pair(sizeof(cl_mem), (void *)&dst.data));
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args.push_back( make_pair(sizeof(cl_mem), (void *)&src.data));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dstoffset_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&srcoffset_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dstStep_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&srcStep_in_pixel));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&src.rows));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols));
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args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows));
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args.push_back( make_pair(sizeof(cl_float), (void *)&ifx));
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args.push_back( make_pair(sizeof(cl_float), (void *)&ify));
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}
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openCLExecuteKernel(clCxt, &imgproc_resize, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth());
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}
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void resize(const oclMat &src, oclMat &dst, Size dsize,
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double fx, double fy, int interpolation)
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{
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CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4
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|| src.type() == CV_32FC1 || src.type() == CV_32FC3 || src.type() == CV_32FC4);
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CV_Assert(interpolation == INTER_LINEAR || interpolation == INTER_NEAREST);
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CV_Assert( src.size().area() > 0 );
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CV_Assert( !(dsize == Size()) || (fx > 0 && fy > 0) );
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if (!(dsize == Size()) && (fx > 0 && fy > 0))
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if (dsize.width != (int)(src.cols * fx) || dsize.height != (int)(src.rows * fy))
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CV_Error(CV_StsUnmatchedSizes, "invalid dsize and fx, fy!");
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if ( dsize == Size() )
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dsize = Size(saturate_cast<int>(src.cols * fx), saturate_cast<int>(src.rows * fy));
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else
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{
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fx = (double)dsize.width / src.cols;
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fy = (double)dsize.height / src.rows;
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}
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dst.create(dsize, src.type());
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if ( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR )
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{
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resize_gpu( src, dst, fx, fy, interpolation);
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return;
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}
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CV_Error(CV_StsUnsupportedFormat, "Non-supported interpolation method");
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}
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////////////////////////////////////////////////////////////////////////
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// medianFilter
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|
|
void medianFilter(const oclMat &src, oclMat &dst, int m)
|
|
{
|
|
CV_Assert( m % 2 == 1 && m > 1 );
|
|
CV_Assert( (src.depth() == CV_8U || src.depth() == CV_32F) && (src.channels() == 1 || src.channels() == 4));
|
|
dst.create(src.size(), src.type());
|
|
|
|
int srcStep = src.step / src.elemSize(), dstStep = dst.step / dst.elemSize();
|
|
int srcOffset = src.offset / src.elemSize(), dstOffset = dst.offset / dst.elemSize();
|
|
|
|
Context *clCxt = src.clCxt;
|
|
|
|
vector< pair<size_t, const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&srcOffset));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dstOffset));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&src.cols));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&src.rows));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&srcStep));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dstStep));
|
|
|
|
size_t globalThreads[3] = {(src.cols + 18) / 16 * 16, (src.rows + 15) / 16 * 16, 1};
|
|
size_t localThreads[3] = {16, 16, 1};
|
|
|
|
if (m == 3)
|
|
{
|
|
string kernelName = "medianFilter3";
|
|
openCLExecuteKernel(clCxt, &imgproc_median, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth());
|
|
}
|
|
else if (m == 5)
|
|
{
|
|
string kernelName = "medianFilter5";
|
|
openCLExecuteKernel(clCxt, &imgproc_median, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth());
|
|
}
|
|
else
|
|
CV_Error(CV_StsBadArg, "Non-supported filter length");
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// copyMakeBorder
|
|
|
|
void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int bordertype, const Scalar &scalar)
|
|
{
|
|
if (!src.clCxt->supportsFeature(FEATURE_CL_DOUBLE) && src.depth() == CV_64F)
|
|
{
|
|
CV_Error(CV_OpenCLDoubleNotSupported, "Selected device does not support double");
|
|
return;
|
|
}
|
|
|
|
oclMat _src = src;
|
|
|
|
CV_Assert(top >= 0 && bottom >= 0 && left >= 0 && right >= 0);
|
|
|
|
if( (_src.wholecols != _src.cols || _src.wholerows != _src.rows) && (bordertype & BORDER_ISOLATED) == 0 )
|
|
{
|
|
Size wholeSize;
|
|
Point ofs;
|
|
_src.locateROI(wholeSize, ofs);
|
|
int dtop = std::min(ofs.y, top);
|
|
int dbottom = std::min(wholeSize.height - _src.rows - ofs.y, bottom);
|
|
int dleft = std::min(ofs.x, left);
|
|
int dright = std::min(wholeSize.width - _src.cols - ofs.x, right);
|
|
_src.adjustROI(dtop, dbottom, dleft, dright);
|
|
top -= dtop;
|
|
left -= dleft;
|
|
bottom -= dbottom;
|
|
right -= dright;
|
|
}
|
|
bordertype &= ~cv::BORDER_ISOLATED;
|
|
|
|
dst.create(_src.rows + top + bottom, _src.cols + left + right, _src.type());
|
|
int srcStep = _src.step / _src.elemSize(), dstStep = dst.step / dst.elemSize();
|
|
int srcOffset = _src.offset / _src.elemSize(), dstOffset = dst.offset / dst.elemSize();
|
|
int depth = _src.depth(), ochannels = _src.oclchannels();
|
|
|
|
int __bordertype[] = { BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, BORDER_WRAP, BORDER_REFLECT_101 };
|
|
const char *borderstr[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT_101" };
|
|
|
|
int bordertype_index = -1;
|
|
for (int i = 0, end = sizeof(__bordertype) / sizeof(int); i < end; i++)
|
|
if (__bordertype[i] == bordertype)
|
|
{
|
|
bordertype_index = i;
|
|
break;
|
|
}
|
|
if (bordertype_index < 0)
|
|
CV_Error(CV_StsBadArg, "Unsupported border type");
|
|
|
|
size_t localThreads[3] = { 16, 16, 1 };
|
|
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
|
|
|
vector< pair<size_t, const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&_src.data));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&_src.cols));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&_src.rows));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&srcStep));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&srcOffset));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dstStep));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dstOffset));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&top));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&left));
|
|
|
|
const char * const typeMap[] = { "uchar", "char", "ushort", "short", "int", "float", "double" };
|
|
const char * const channelMap[] = { "", "", "2", "4", "4" };
|
|
std::string buildOptions = format("-D GENTYPE=%s%s -D %s",
|
|
typeMap[depth], channelMap[ochannels],
|
|
borderstr[bordertype_index]);
|
|
|
|
int cn = src.channels(), ocn = src.oclchannels();
|
|
int bufSize = src.elemSize1() * ocn;
|
|
AutoBuffer<uchar> _buf(bufSize);
|
|
uchar * buf = (uchar *)_buf;
|
|
scalarToRawData(scalar, buf, dst.type());
|
|
memset(buf + src.elemSize1() * cn, 0, (ocn - cn) * src.elemSize1());
|
|
|
|
args.push_back( make_pair( bufSize , (void *)buf ));
|
|
|
|
openCLExecuteKernel(src.clCxt, &imgproc_copymakeboder, "copymakeborder", globalThreads,
|
|
localThreads, args, -1, -1, buildOptions.c_str());
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// warp
|
|
|
|
namespace
|
|
{
|
|
#define F double
|
|
|
|
void convert_coeffs(F *M)
|
|
{
|
|
double D = M[0] * M[4] - M[1] * M[3];
|
|
D = D != 0 ? 1. / D : 0;
|
|
double A11 = M[4] * D, A22 = M[0] * D;
|
|
M[0] = A11;
|
|
M[1] *= -D;
|
|
M[3] *= -D;
|
|
M[4] = A22;
|
|
double b1 = -M[0] * M[2] - M[1] * M[5];
|
|
double b2 = -M[3] * M[2] - M[4] * M[5];
|
|
M[2] = b1;
|
|
M[5] = b2;
|
|
}
|
|
|
|
double invert(double *M)
|
|
{
|
|
#define Sd(y,x) (Sd[y*3+x])
|
|
#define Dd(y,x) (Dd[y*3+x])
|
|
#define det3(m) (m(0,0)*(m(1,1)*m(2,2) - m(1,2)*m(2,1)) - \
|
|
m(0,1)*(m(1,0)*m(2,2) - m(1,2)*m(2,0)) + \
|
|
m(0,2)*(m(1,0)*m(2,1) - m(1,1)*m(2,0)))
|
|
double *Sd = M;
|
|
double *Dd = M;
|
|
double d = det3(Sd);
|
|
double result = 0;
|
|
if ( d != 0)
|
|
{
|
|
double t[9];
|
|
result = d;
|
|
d = 1. / d;
|
|
|
|
t[0] = (Sd(1, 1) * Sd(2, 2) - Sd(1, 2) * Sd(2, 1)) * d;
|
|
t[1] = (Sd(0, 2) * Sd(2, 1) - Sd(0, 1) * Sd(2, 2)) * d;
|
|
t[2] = (Sd(0, 1) * Sd(1, 2) - Sd(0, 2) * Sd(1, 1)) * d;
|
|
|
|
t[3] = (Sd(1, 2) * Sd(2, 0) - Sd(1, 0) * Sd(2, 2)) * d;
|
|
t[4] = (Sd(0, 0) * Sd(2, 2) - Sd(0, 2) * Sd(2, 0)) * d;
|
|
t[5] = (Sd(0, 2) * Sd(1, 0) - Sd(0, 0) * Sd(1, 2)) * d;
|
|
|
|
t[6] = (Sd(1, 0) * Sd(2, 1) - Sd(1, 1) * Sd(2, 0)) * d;
|
|
t[7] = (Sd(0, 1) * Sd(2, 0) - Sd(0, 0) * Sd(2, 1)) * d;
|
|
t[8] = (Sd(0, 0) * Sd(1, 1) - Sd(0, 1) * Sd(1, 0)) * d;
|
|
|
|
Dd(0, 0) = t[0];
|
|
Dd(0, 1) = t[1];
|
|
Dd(0, 2) = t[2];
|
|
Dd(1, 0) = t[3];
|
|
Dd(1, 1) = t[4];
|
|
Dd(1, 2) = t[5];
|
|
Dd(2, 0) = t[6];
|
|
Dd(2, 1) = t[7];
|
|
Dd(2, 2) = t[8];
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void warpAffine_gpu(const oclMat &src, oclMat &dst, F coeffs[2][3], int interpolation)
|
|
{
|
|
CV_Assert( (src.oclchannels() == dst.oclchannels()) );
|
|
int srcStep = src.step1();
|
|
int dstStep = dst.step1();
|
|
float float_coeffs[2][3];
|
|
cl_mem coeffs_cm;
|
|
|
|
Context *clCxt = src.clCxt;
|
|
string s[3] = {"NN", "Linear", "Cubic"};
|
|
string kernelName = "warpAffine" + s[interpolation];
|
|
|
|
if (src.clCxt->supportsFeature(FEATURE_CL_DOUBLE))
|
|
{
|
|
cl_int st;
|
|
coeffs_cm = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(), CL_MEM_READ_WRITE, sizeof(F) * 2 * 3, NULL, &st );
|
|
openCLVerifyCall(st);
|
|
openCLSafeCall(clEnqueueWriteBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(), (cl_mem)coeffs_cm, 1, 0,
|
|
sizeof(F) * 2 * 3, coeffs, 0, 0, 0));
|
|
}
|
|
else
|
|
{
|
|
cl_int st;
|
|
for(int m = 0; m < 2; m++)
|
|
for(int n = 0; n < 3; n++)
|
|
float_coeffs[m][n] = coeffs[m][n];
|
|
|
|
coeffs_cm = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(), CL_MEM_READ_WRITE, sizeof(float) * 2 * 3, NULL, &st );
|
|
openCLSafeCall(clEnqueueWriteBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(), (cl_mem)coeffs_cm,
|
|
1, 0, sizeof(float) * 2 * 3, float_coeffs, 0, 0, 0));
|
|
|
|
}
|
|
//TODO: improve this kernel
|
|
size_t blkSizeX = 16, blkSizeY = 16;
|
|
size_t glbSizeX;
|
|
size_t cols;
|
|
|
|
if (src.type() == CV_8UC1 && interpolation != 2)
|
|
{
|
|
cols = (dst.cols + dst.offset % 4 + 3) / 4;
|
|
glbSizeX = cols % blkSizeX == 0 ? cols : (cols / blkSizeX + 1) * blkSizeX;
|
|
}
|
|
else
|
|
{
|
|
cols = dst.cols;
|
|
glbSizeX = dst.cols % blkSizeX == 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX;
|
|
}
|
|
|
|
size_t glbSizeY = dst.rows % blkSizeY == 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY;
|
|
size_t globalThreads[3] = {glbSizeX, glbSizeY, 1};
|
|
size_t localThreads[3] = {blkSizeX, blkSizeY, 1};
|
|
|
|
vector< pair<size_t, const void *> > args;
|
|
|
|
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
|
|
args.push_back(make_pair(sizeof(cl_mem), (void *)&dst.data));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&src.cols));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dst.cols));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dst.rows));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&srcStep));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dstStep));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&src.offset));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dst.offset));
|
|
args.push_back(make_pair(sizeof(cl_mem), (void *)&coeffs_cm));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&cols));
|
|
|
|
openCLExecuteKernel(clCxt, &imgproc_warpAffine, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth());
|
|
openCLSafeCall(clReleaseMemObject(coeffs_cm));
|
|
}
|
|
|
|
void warpPerspective_gpu(const oclMat &src, oclMat &dst, double coeffs[3][3], int interpolation)
|
|
{
|
|
CV_Assert( (src.oclchannels() == dst.oclchannels()) );
|
|
int srcStep = src.step1();
|
|
int dstStep = dst.step1();
|
|
float float_coeffs[3][3];
|
|
cl_mem coeffs_cm;
|
|
|
|
Context *clCxt = src.clCxt;
|
|
string s[3] = {"NN", "Linear", "Cubic"};
|
|
string kernelName = "warpPerspective" + s[interpolation];
|
|
|
|
if (src.clCxt->supportsFeature(FEATURE_CL_DOUBLE))
|
|
{
|
|
cl_int st;
|
|
coeffs_cm = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(), CL_MEM_READ_WRITE, sizeof(double) * 3 * 3, NULL, &st );
|
|
openCLVerifyCall(st);
|
|
openCLSafeCall(clEnqueueWriteBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(), (cl_mem)coeffs_cm, 1, 0,
|
|
sizeof(double) * 3 * 3, coeffs, 0, 0, 0));
|
|
}
|
|
else
|
|
{
|
|
cl_int st;
|
|
for(int m = 0; m < 3; m++)
|
|
for(int n = 0; n < 3; n++)
|
|
float_coeffs[m][n] = coeffs[m][n];
|
|
|
|
coeffs_cm = clCreateBuffer(*(cl_context*)clCxt->getOpenCLContextPtr(), CL_MEM_READ_WRITE, sizeof(float) * 3 * 3, NULL, &st );
|
|
openCLVerifyCall(st);
|
|
openCLSafeCall(clEnqueueWriteBuffer(*(cl_command_queue*)clCxt->getOpenCLCommandQueuePtr(), (cl_mem)coeffs_cm, 1, 0,
|
|
sizeof(float) * 3 * 3, float_coeffs, 0, 0, 0));
|
|
}
|
|
|
|
//TODO: improve this kernel
|
|
size_t blkSizeX = 16, blkSizeY = 16;
|
|
size_t glbSizeX;
|
|
size_t cols;
|
|
if (src.type() == CV_8UC1 && interpolation == 0)
|
|
{
|
|
cols = (dst.cols + dst.offset % 4 + 3) / 4;
|
|
glbSizeX = cols % blkSizeX == 0 ? cols : (cols / blkSizeX + 1) * blkSizeX;
|
|
}
|
|
else
|
|
{
|
|
cols = dst.cols;
|
|
glbSizeX = dst.cols % blkSizeX == 0 ? dst.cols : (dst.cols / blkSizeX + 1) * blkSizeX;
|
|
}
|
|
|
|
size_t glbSizeY = dst.rows % blkSizeY == 0 ? dst.rows : (dst.rows / blkSizeY + 1) * blkSizeY;
|
|
size_t globalThreads[3] = {glbSizeX, glbSizeY, 1};
|
|
size_t localThreads[3] = {blkSizeX, blkSizeY, 1};
|
|
|
|
vector< pair<size_t, const void *> > args;
|
|
|
|
args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
|
|
args.push_back(make_pair(sizeof(cl_mem), (void *)&dst.data));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&src.cols));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&src.rows));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dst.cols));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dst.rows));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&srcStep));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dstStep));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&src.offset));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&dst.offset));
|
|
args.push_back(make_pair(sizeof(cl_mem), (void *)&coeffs_cm));
|
|
args.push_back(make_pair(sizeof(cl_int), (void *)&cols));
|
|
|
|
openCLExecuteKernel(clCxt, &imgproc_warpPerspective, kernelName, globalThreads, localThreads, args, src.oclchannels(), src.depth());
|
|
openCLSafeCall(clReleaseMemObject(coeffs_cm));
|
|
}
|
|
}
|
|
|
|
void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags)
|
|
{
|
|
int interpolation = flags & INTER_MAX;
|
|
|
|
CV_Assert((src.depth() == CV_8U || src.depth() == CV_32F) && src.oclchannels() != 2 && src.oclchannels() != 3);
|
|
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
|
|
|
|
dst.create(dsize, src.type());
|
|
|
|
CV_Assert(M.rows == 2 && M.cols == 3);
|
|
|
|
int warpInd = (flags & WARP_INVERSE_MAP) >> 4;
|
|
F coeffs[2][3];
|
|
|
|
double coeffsM[2*3];
|
|
Mat coeffsMat(2, 3, CV_64F, (void *)coeffsM);
|
|
M.convertTo(coeffsMat, coeffsMat.type());
|
|
if (!warpInd)
|
|
convert_coeffs(coeffsM);
|
|
|
|
for(int i = 0; i < 2; ++i)
|
|
for(int j = 0; j < 3; ++j)
|
|
coeffs[i][j] = coeffsM[i*3+j];
|
|
|
|
warpAffine_gpu(src, dst, coeffs, interpolation);
|
|
}
|
|
|
|
void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags)
|
|
{
|
|
int interpolation = flags & INTER_MAX;
|
|
|
|
CV_Assert((src.depth() == CV_8U || src.depth() == CV_32F) && src.oclchannels() != 2 && src.oclchannels() != 3);
|
|
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
|
|
|
|
dst.create(dsize, src.type());
|
|
|
|
|
|
CV_Assert(M.rows == 3 && M.cols == 3);
|
|
|
|
int warpInd = (flags & WARP_INVERSE_MAP) >> 4;
|
|
double coeffs[3][3];
|
|
|
|
double coeffsM[3*3];
|
|
Mat coeffsMat(3, 3, CV_64F, (void *)coeffsM);
|
|
M.convertTo(coeffsMat, coeffsMat.type());
|
|
if (!warpInd)
|
|
invert(coeffsM);
|
|
|
|
for(int i = 0; i < 3; ++i)
|
|
for(int j = 0; j < 3; ++j)
|
|
coeffs[i][j] = coeffsM[i*3+j];
|
|
|
|
warpPerspective_gpu(src, dst, coeffs, interpolation);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// integral
|
|
|
|
void integral(const oclMat &src, oclMat &sum, oclMat &sqsum)
|
|
{
|
|
CV_Assert(src.type() == CV_8UC1);
|
|
if (!src.clCxt->supportsFeature(ocl::FEATURE_CL_DOUBLE) && src.depth() == CV_64F)
|
|
{
|
|
CV_Error(CV_OpenCLDoubleNotSupported, "Select device doesn't support double");
|
|
return;
|
|
}
|
|
|
|
int vlen = 4;
|
|
int offset = src.offset / vlen;
|
|
int pre_invalid = src.offset % vlen;
|
|
int vcols = (pre_invalid + src.cols + vlen - 1) / vlen;
|
|
|
|
oclMat t_sum , t_sqsum;
|
|
int w = src.cols + 1, h = src.rows + 1;
|
|
int depth = src.depth() == CV_8U ? CV_32S : CV_64F;
|
|
int type = CV_MAKE_TYPE(depth, 1);
|
|
|
|
t_sum.create(src.cols, src.rows, type);
|
|
sum.create(h, w, type);
|
|
|
|
t_sqsum.create(src.cols, src.rows, CV_32FC1);
|
|
sqsum.create(h, w, CV_32FC1);
|
|
|
|
int sum_offset = sum.offset / vlen;
|
|
int sqsum_offset = sqsum.offset / vlen;
|
|
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sqsum.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&pre_invalid ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step));
|
|
size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
|
|
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_cols", gt, lt, args, -1, depth);
|
|
|
|
args.clear();
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sqsum.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&sum.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&sqsum.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum.step));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sqsum_offset));
|
|
size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
|
|
openCLExecuteKernel(src.clCxt, &imgproc_integral, "integral_rows", gt2, lt2, args, -1, depth);
|
|
}
|
|
|
|
void integral(const oclMat &src, oclMat &sum)
|
|
{
|
|
CV_Assert(src.type() == CV_8UC1);
|
|
int vlen = 4;
|
|
int offset = src.offset / vlen;
|
|
int pre_invalid = src.offset % vlen;
|
|
int vcols = (pre_invalid + src.cols + vlen - 1) / vlen;
|
|
|
|
oclMat t_sum;
|
|
int w = src.cols + 1, h = src.rows + 1;
|
|
int depth = src.depth() == CV_8U ? CV_32S : CV_32F;
|
|
int type = CV_MAKE_TYPE(depth, 1);
|
|
|
|
t_sum.create(src.cols, src.rows, type);
|
|
sum.create(h, w, type);
|
|
|
|
int sum_offset = sum.offset / vlen;
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&pre_invalid ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step));
|
|
size_t gt[3] = {((vcols + 1) / 2) * 256, 1, 1}, lt[3] = {256, 1, 1};
|
|
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_cols", gt, lt, args, -1, depth);
|
|
|
|
args.clear();
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&t_sum.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&sum.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&t_sum.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum.step));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sum_offset));
|
|
size_t gt2[3] = {t_sum.cols * 32, 1, 1}, lt2[3] = {256, 1, 1};
|
|
openCLExecuteKernel(src.clCxt, &imgproc_integral_sum, "integral_sum_rows", gt2, lt2, args, -1, depth);
|
|
}
|
|
|
|
/////////////////////// corner //////////////////////////////
|
|
|
|
static void extractCovData(const oclMat &src, oclMat &Dx, oclMat &Dy,
|
|
int blockSize, int ksize, int borderType)
|
|
{
|
|
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_32FC1);
|
|
double scale = static_cast<double>(1 << ((ksize > 0 ? ksize : 3) - 1)) * blockSize;
|
|
if (ksize < 0)
|
|
scale *= 2.;
|
|
|
|
if (src.depth() == CV_8U)
|
|
{
|
|
scale *= 255.;
|
|
scale = 1. / scale;
|
|
}
|
|
else
|
|
scale = 1. / scale;
|
|
|
|
if (ksize > 0)
|
|
{
|
|
Sobel(src, Dx, CV_32F, 1, 0, ksize, scale, 0, borderType);
|
|
Sobel(src, Dy, CV_32F, 0, 1, ksize, scale, 0, borderType);
|
|
}
|
|
else
|
|
{
|
|
Scharr(src, Dx, CV_32F, 1, 0, scale, 0, borderType);
|
|
Scharr(src, Dy, CV_32F, 0, 1, scale, 0, borderType);
|
|
}
|
|
CV_Assert(Dx.offset == 0 && Dy.offset == 0);
|
|
}
|
|
|
|
static void corner_ocl(const cv::ocl::ProgramEntry* source, string kernelName, int block_size, float k, oclMat &Dx, oclMat &Dy,
|
|
oclMat &dst, int border_type)
|
|
{
|
|
char borderType[30];
|
|
switch (border_type)
|
|
{
|
|
case cv::BORDER_CONSTANT:
|
|
sprintf(borderType, "BORDER_CONSTANT");
|
|
break;
|
|
case cv::BORDER_REFLECT101:
|
|
sprintf(borderType, "BORDER_REFLECT101");
|
|
break;
|
|
case cv::BORDER_REFLECT:
|
|
sprintf(borderType, "BORDER_REFLECT");
|
|
break;
|
|
case cv::BORDER_REPLICATE:
|
|
sprintf(borderType, "BORDER_REPLICATE");
|
|
break;
|
|
default:
|
|
CV_Error(CV_StsBadFlag, "BORDER type is not supported!");
|
|
}
|
|
|
|
std::string buildOptions = format("-D anX=%d -D anY=%d -D ksX=%d -D ksY=%d -D %s",
|
|
block_size / 2, block_size / 2, block_size, block_size, borderType);
|
|
|
|
size_t blockSizeX = 256, blockSizeY = 1;
|
|
size_t gSize = blockSizeX - block_size / 2 * 2;
|
|
size_t globalSizeX = (Dx.cols) % gSize == 0 ? Dx.cols / gSize * blockSizeX : (Dx.cols / gSize + 1) * blockSizeX;
|
|
size_t rows_per_thread = 2;
|
|
size_t globalSizeY = ((Dx.rows + rows_per_thread - 1) / rows_per_thread) % blockSizeY == 0 ?
|
|
((Dx.rows + rows_per_thread - 1) / rows_per_thread) :
|
|
(((Dx.rows + rows_per_thread - 1) / rows_per_thread) / blockSizeY + 1) * blockSizeY;
|
|
|
|
size_t gt[3] = { globalSizeX, globalSizeY, 1 };
|
|
size_t lt[3] = { blockSizeX, blockSizeY, 1 };
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&Dx.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&Dy.data));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&Dx.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&Dx.wholerows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&Dx.wholecols ));
|
|
args.push_back( make_pair(sizeof(cl_int), (void *)&Dx.step));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&Dy.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&Dy.wholerows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&Dy.wholecols ));
|
|
args.push_back( make_pair(sizeof(cl_int), (void *)&Dy.step));
|
|
args.push_back( make_pair(sizeof(cl_int), (void *)&dst.offset));
|
|
args.push_back( make_pair(sizeof(cl_int), (void *)&dst.rows));
|
|
args.push_back( make_pair(sizeof(cl_int), (void *)&dst.cols));
|
|
args.push_back( make_pair(sizeof(cl_int), (void *)&dst.step));
|
|
args.push_back( make_pair( sizeof(cl_float) , (void *)&k));
|
|
openCLExecuteKernel(dst.clCxt, source, kernelName, gt, lt, args, -1, -1, buildOptions.c_str());
|
|
}
|
|
|
|
void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize,
|
|
double k, int borderType)
|
|
{
|
|
oclMat dx, dy;
|
|
cornerHarris_dxdy(src, dst, dx, dy, blockSize, ksize, k, borderType);
|
|
}
|
|
|
|
void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &dx, oclMat &dy, int blockSize, int ksize,
|
|
double k, int borderType)
|
|
{
|
|
if (!src.clCxt->supportsFeature(FEATURE_CL_DOUBLE) && src.depth() == CV_64F)
|
|
{
|
|
CV_Error(CV_OpenCLDoubleNotSupported, "Select device doesn't support double");
|
|
return;
|
|
}
|
|
|
|
CV_Assert(src.cols >= blockSize / 2 && src.rows >= blockSize / 2);
|
|
CV_Assert(borderType == cv::BORDER_CONSTANT || borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE
|
|
|| borderType == cv::BORDER_REFLECT);
|
|
extractCovData(src, dx, dy, blockSize, ksize, borderType);
|
|
dst.create(src.size(), CV_32F);
|
|
corner_ocl(&imgproc_calcHarris, "calcHarris", blockSize, static_cast<float>(k), dx, dy, dst, borderType);
|
|
}
|
|
|
|
void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int borderType)
|
|
{
|
|
oclMat dx, dy;
|
|
cornerMinEigenVal_dxdy(src, dst, dx, dy, blockSize, ksize, borderType);
|
|
}
|
|
|
|
void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &dx, oclMat &dy, int blockSize, int ksize, int borderType)
|
|
{
|
|
if (!src.clCxt->supportsFeature(FEATURE_CL_DOUBLE) && src.depth() == CV_64F)
|
|
{
|
|
CV_Error(CV_OpenCLDoubleNotSupported, "select device don't support double");
|
|
return;
|
|
}
|
|
|
|
CV_Assert(src.cols >= blockSize / 2 && src.rows >= blockSize / 2);
|
|
CV_Assert(borderType == cv::BORDER_CONSTANT || borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECT);
|
|
extractCovData(src, dx, dy, blockSize, ksize, borderType);
|
|
dst.create(src.size(), CV_32F);
|
|
|
|
corner_ocl(&imgproc_calcMinEigenVal, "calcMinEigenVal", blockSize, 0, dx, dy, dst, borderType);
|
|
}
|
|
|
|
/////////////////////////////////// MeanShiftfiltering ///////////////////////////////////////////////
|
|
|
|
static void meanShiftFiltering_gpu(const oclMat &src, oclMat dst, int sp, int sr, int maxIter, float eps)
|
|
{
|
|
CV_Assert( (src.cols == dst.cols) && (src.rows == dst.rows) );
|
|
CV_Assert( !(dst.step & 0x3) );
|
|
|
|
//Arrange the NDRange
|
|
int col = src.cols, row = src.rows;
|
|
int ltx = 16, lty = 8;
|
|
if (src.cols % ltx != 0)
|
|
col = (col / ltx + 1) * ltx;
|
|
if (src.rows % lty != 0)
|
|
row = (row / lty + 1) * lty;
|
|
|
|
size_t globalThreads[3] = {col, row, 1};
|
|
size_t localThreads[3] = {ltx, lty, 1};
|
|
|
|
//set args
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dst.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.step ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dst.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sp ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sr ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&maxIter ));
|
|
args.push_back( make_pair( sizeof(cl_float) , (void *)&eps ));
|
|
|
|
openCLExecuteKernel(src.clCxt, &meanShift, "meanshift_kernel", globalThreads, localThreads, args, -1, -1);
|
|
}
|
|
|
|
void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr, TermCriteria criteria)
|
|
{
|
|
if ( src.empty() )
|
|
CV_Error( CV_StsBadArg, "The input image is empty" );
|
|
|
|
if ( src.depth() != CV_8U || src.oclchannels() != 4 )
|
|
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
|
|
|
|
dst.create( src.size(), CV_8UC4 );
|
|
|
|
if ( !(criteria.type & TermCriteria::MAX_ITER) )
|
|
criteria.maxCount = 5;
|
|
|
|
int maxIter = std::min(std::max(criteria.maxCount, 1), 100);
|
|
|
|
float eps;
|
|
if ( !(criteria.type & TermCriteria::EPS) )
|
|
eps = 1.f;
|
|
eps = (float)std::max(criteria.epsilon, 0.0);
|
|
|
|
meanShiftFiltering_gpu(src, dst, sp, sr, maxIter, eps);
|
|
}
|
|
|
|
static void meanShiftProc_gpu(const oclMat &src, oclMat dstr, oclMat dstsp, int sp, int sr, int maxIter, float eps)
|
|
{
|
|
//sanity checks
|
|
CV_Assert( (src.cols == dstr.cols) && (src.rows == dstr.rows) &&
|
|
(src.rows == dstsp.rows) && (src.cols == dstsp.cols));
|
|
CV_Assert( !(dstsp.step & 0x3) );
|
|
|
|
//Arrange the NDRange
|
|
int col = src.cols, row = src.rows;
|
|
int ltx = 16, lty = 8;
|
|
if (src.cols % ltx != 0)
|
|
col = (col / ltx + 1) * ltx;
|
|
if (src.rows % lty != 0)
|
|
row = (row / lty + 1) * lty;
|
|
|
|
size_t globalThreads[3] = {col, row, 1};
|
|
size_t localThreads[3] = {ltx, lty, 1};
|
|
|
|
//set args
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&src.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dstr.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem) , (void *)&dstsp.data ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstsp.step ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&src.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstsp.offset ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&dstr.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sp ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&sr ));
|
|
args.push_back( make_pair( sizeof(cl_int) , (void *)&maxIter ));
|
|
args.push_back( make_pair( sizeof(cl_float) , (void *)&eps ));
|
|
|
|
openCLExecuteKernel(src.clCxt, &meanShift, "meanshiftproc_kernel", globalThreads, localThreads, args, -1, -1);
|
|
}
|
|
|
|
void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr, TermCriteria criteria)
|
|
{
|
|
if ( src.empty() )
|
|
CV_Error( CV_StsBadArg, "The input image is empty" );
|
|
|
|
if ( src.depth() != CV_8U || src.oclchannels() != 4 )
|
|
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
|
|
|
|
// if (!src.clCxt->supportsFeature(FEATURE_CL_DOUBLE))
|
|
// {
|
|
// CV_Error( CV_OpenCLDoubleNotSupportedNotSupported, "Selected device doesn't support double, so a deviation exists.\nIf the accuracy is acceptable, the error can be ignored.\n");
|
|
// return;
|
|
// }
|
|
|
|
dstr.create( src.size(), CV_8UC4 );
|
|
dstsp.create( src.size(), CV_16SC2 );
|
|
|
|
if ( !(criteria.type & TermCriteria::MAX_ITER) )
|
|
criteria.maxCount = 5;
|
|
|
|
int maxIter = std::min(std::max(criteria.maxCount, 1), 100);
|
|
|
|
float eps;
|
|
if ( !(criteria.type & TermCriteria::EPS) )
|
|
eps = 1.f;
|
|
eps = (float)std::max(criteria.epsilon, 0.0);
|
|
|
|
meanShiftProc_gpu(src, dstr, dstsp, sp, sr, maxIter, eps);
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////////////////////////////
|
|
////////////////////////////////////////////////////hist///////////////////////////////////////////////
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace histograms
|
|
{
|
|
const int PARTIAL_HISTOGRAM256_COUNT = 256;
|
|
const int HISTOGRAM256_BIN_COUNT = 256;
|
|
}
|
|
///////////////////////////////calcHist/////////////////////////////////////////////////////////////////
|
|
static void calc_sub_hist(const oclMat &mat_src, const oclMat &mat_sub_hist)
|
|
{
|
|
using namespace histograms;
|
|
|
|
int depth = mat_src.depth();
|
|
|
|
size_t localThreads[3] = { HISTOGRAM256_BIN_COUNT, 1, 1 };
|
|
size_t globalThreads[3] = { PARTIAL_HISTOGRAM256_COUNT *localThreads[0], 1, 1};
|
|
|
|
int dataWidth = 16;
|
|
int dataWidth_bits = 4;
|
|
int mask = dataWidth - 1;
|
|
|
|
int cols = mat_src.cols * mat_src.oclchannels();
|
|
int src_offset = mat_src.offset;
|
|
int hist_step = mat_sub_hist.step >> 2;
|
|
int left_col = 0, right_col = 0;
|
|
|
|
if (cols >= dataWidth * 2 - 1)
|
|
{
|
|
left_col = dataWidth - (src_offset & mask);
|
|
left_col &= mask;
|
|
src_offset += left_col;
|
|
cols -= left_col;
|
|
right_col = cols & mask;
|
|
cols -= right_col;
|
|
}
|
|
else
|
|
{
|
|
left_col = cols;
|
|
right_col = 0;
|
|
cols = 0;
|
|
globalThreads[0] = 0;
|
|
}
|
|
|
|
vector<pair<size_t , const void *> > args;
|
|
if (globalThreads[0] != 0)
|
|
{
|
|
int tempcols = cols >> dataWidth_bits;
|
|
int inc_x = globalThreads[0] % tempcols;
|
|
int inc_y = globalThreads[0] / tempcols;
|
|
src_offset >>= dataWidth_bits;
|
|
int src_step = mat_src.step >> dataWidth_bits;
|
|
int datacount = tempcols * mat_src.rows;
|
|
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&src_step));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&src_offset));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_sub_hist.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&datacount));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&tempcols));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&inc_x));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&inc_y));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&hist_step));
|
|
|
|
openCLExecuteKernel(mat_src.clCxt, &imgproc_histogram, "calc_sub_hist", globalThreads, localThreads, args, -1, depth);
|
|
}
|
|
|
|
if (left_col != 0 || right_col != 0)
|
|
{
|
|
src_offset = mat_src.offset;
|
|
localThreads[0] = 1;
|
|
localThreads[1] = 256;
|
|
globalThreads[0] = left_col + right_col;
|
|
globalThreads[1] = mat_src.rows;
|
|
|
|
args.clear();
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_src.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src.step));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&src_offset));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_sub_hist.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&left_col));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&cols));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&mat_src.rows));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&hist_step));
|
|
|
|
openCLExecuteKernel(mat_src.clCxt, &imgproc_histogram, "calc_sub_hist_border", globalThreads, localThreads, args, -1, depth);
|
|
}
|
|
}
|
|
|
|
static void merge_sub_hist(const oclMat &sub_hist, oclMat &mat_hist)
|
|
{
|
|
using namespace histograms;
|
|
|
|
size_t localThreads[3] = { 256, 1, 1 };
|
|
size_t globalThreads[3] = { HISTOGRAM256_BIN_COUNT *localThreads[0], 1, 1};
|
|
int src_step = sub_hist.step >> 2;
|
|
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&sub_hist.data));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_hist.data));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&src_step));
|
|
|
|
openCLExecuteKernel(sub_hist.clCxt, &imgproc_histogram, "merge_hist", globalThreads, localThreads, args, -1, -1);
|
|
}
|
|
|
|
void calcHist(const oclMat &mat_src, oclMat &mat_hist)
|
|
{
|
|
using namespace histograms;
|
|
CV_Assert(mat_src.type() == CV_8UC1);
|
|
mat_hist.create(1, 256, CV_32SC1);
|
|
|
|
oclMat buf(PARTIAL_HISTOGRAM256_COUNT, HISTOGRAM256_BIN_COUNT, CV_32SC1);
|
|
buf.setTo(0);
|
|
|
|
calc_sub_hist(mat_src, buf);
|
|
merge_sub_hist(buf, mat_hist);
|
|
}
|
|
|
|
///////////////////////////////////equalizeHist/////////////////////////////////////////////////////
|
|
void equalizeHist(const oclMat &mat_src, oclMat &mat_dst)
|
|
{
|
|
mat_dst.create(mat_src.rows, mat_src.cols, CV_8UC1);
|
|
|
|
oclMat mat_hist(1, 256, CV_32SC1);
|
|
|
|
calcHist(mat_src, mat_hist);
|
|
|
|
size_t localThreads[3] = { 256, 1, 1};
|
|
size_t globalThreads[3] = { 256, 1, 1};
|
|
oclMat lut(1, 256, CV_8UC1);
|
|
int total = mat_src.rows * mat_src.cols;
|
|
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&lut.data));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&mat_hist.data));
|
|
args.push_back( make_pair( sizeof(int), (void *)&total));
|
|
|
|
openCLExecuteKernel(mat_src.clCxt, &imgproc_histogram, "calLUT", globalThreads, localThreads, args, -1, -1);
|
|
LUT(mat_src, lut, mat_dst);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// CLAHE
|
|
namespace clahe
|
|
{
|
|
static void calcLut(const oclMat &src, oclMat &dst,
|
|
const int tilesX, const int tilesY, const cv::Size tileSize,
|
|
const int clipLimit, const float lutScale)
|
|
{
|
|
cl_int2 tile_size;
|
|
tile_size.s[0] = tileSize.width;
|
|
tile_size.s[1] = tileSize.height;
|
|
|
|
std::vector<pair<size_t , const void *> > args;
|
|
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&src.data ));
|
|
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&dst.data ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.step ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.step ));
|
|
args.push_back( std::make_pair( sizeof(cl_int2), (void *)&tile_size ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&tilesX ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&clipLimit ));
|
|
args.push_back( std::make_pair( sizeof(cl_float), (void *)&lutScale ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.offset ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.offset ));
|
|
|
|
String kernelName = "calcLut";
|
|
size_t localThreads[3] = { 32, 8, 1 };
|
|
size_t globalThreads[3] = { tilesX * localThreads[0], tilesY * localThreads[1], 1 };
|
|
bool is_cpu = isCpuDevice();
|
|
if (is_cpu)
|
|
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, kernelName, globalThreads, localThreads, args, -1, -1, (char*)"-D CPU");
|
|
else
|
|
{
|
|
cl_kernel kernel = openCLGetKernelFromSource(Context::getContext(), &imgproc_clahe, kernelName);
|
|
int wave_size = (int)queryWaveFrontSize(kernel);
|
|
openCLSafeCall(clReleaseKernel(kernel));
|
|
|
|
std::string opt = format("-D WAVE_SIZE=%d", wave_size);
|
|
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, kernelName, globalThreads, localThreads, args, -1, -1, opt.c_str());
|
|
}
|
|
}
|
|
|
|
static void transform(const oclMat &src, oclMat &dst, const oclMat &lut,
|
|
const int tilesX, const int tilesY, const Size & tileSize)
|
|
{
|
|
cl_int2 tile_size;
|
|
tile_size.s[0] = tileSize.width;
|
|
tile_size.s[1] = tileSize.height;
|
|
|
|
std::vector<pair<size_t , const void *> > args;
|
|
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&src.data ));
|
|
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&dst.data ));
|
|
args.push_back( std::make_pair( sizeof(cl_mem), (void *)&lut.data ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.step ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.step ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&lut.step ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.cols ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.rows ));
|
|
args.push_back( std::make_pair( sizeof(cl_int2), (void *)&tile_size ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&tilesX ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&tilesY ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&src.offset ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&dst.offset ));
|
|
args.push_back( std::make_pair( sizeof(cl_int), (void *)&lut.offset ));
|
|
|
|
size_t localThreads[3] = { 32, 8, 1 };
|
|
size_t globalThreads[3] = { src.cols, src.rows, 1 };
|
|
|
|
openCLExecuteKernel(Context::getContext(), &imgproc_clahe, "transform", globalThreads, localThreads, args, -1, -1);
|
|
}
|
|
}
|
|
|
|
namespace
|
|
{
|
|
class CLAHE_Impl : public cv::CLAHE
|
|
{
|
|
public:
|
|
CLAHE_Impl(double clipLimit = 40.0, int tilesX = 8, int tilesY = 8);
|
|
|
|
cv::AlgorithmInfo* info() const;
|
|
|
|
void apply(cv::InputArray src, cv::OutputArray dst);
|
|
|
|
void setClipLimit(double clipLimit);
|
|
double getClipLimit() const;
|
|
|
|
void setTilesGridSize(cv::Size tileGridSize);
|
|
cv::Size getTilesGridSize() const;
|
|
|
|
void collectGarbage();
|
|
|
|
private:
|
|
double clipLimit_;
|
|
int tilesX_;
|
|
int tilesY_;
|
|
|
|
oclMat srcExt_;
|
|
oclMat lut_;
|
|
};
|
|
|
|
CLAHE_Impl::CLAHE_Impl(double clipLimit, int tilesX, int tilesY) :
|
|
clipLimit_(clipLimit), tilesX_(tilesX), tilesY_(tilesY)
|
|
{
|
|
}
|
|
|
|
CV_INIT_ALGORITHM(CLAHE_Impl, "CLAHE_OCL",
|
|
obj.info()->addParam(obj, "clipLimit", obj.clipLimit_);
|
|
obj.info()->addParam(obj, "tilesX", obj.tilesX_);
|
|
obj.info()->addParam(obj, "tilesY", obj.tilesY_))
|
|
|
|
void CLAHE_Impl::apply(cv::InputArray src_raw, cv::OutputArray dst_raw)
|
|
{
|
|
oclMat& src = getOclMatRef(src_raw);
|
|
oclMat& dst = getOclMatRef(dst_raw);
|
|
CV_Assert( src.type() == CV_8UC1 );
|
|
|
|
dst.create( src.size(), src.type() );
|
|
|
|
const int histSize = 256;
|
|
|
|
ensureSizeIsEnough(tilesX_ * tilesY_, histSize, CV_8UC1, lut_);
|
|
|
|
cv::Size tileSize;
|
|
oclMat srcForLut;
|
|
|
|
if (src.cols % tilesX_ == 0 && src.rows % tilesY_ == 0)
|
|
{
|
|
tileSize = cv::Size(src.cols / tilesX_, src.rows / tilesY_);
|
|
srcForLut = src;
|
|
}
|
|
else
|
|
{
|
|
ocl::copyMakeBorder(src, srcExt_, 0, tilesY_ - (src.rows % tilesY_), 0,
|
|
tilesX_ - (src.cols % tilesX_), BORDER_REFLECT_101, Scalar::all(0));
|
|
|
|
tileSize = Size(srcExt_.cols / tilesX_, srcExt_.rows / tilesY_);
|
|
srcForLut = srcExt_;
|
|
}
|
|
|
|
const int tileSizeTotal = tileSize.area();
|
|
const float lutScale = static_cast<float>(histSize - 1) / tileSizeTotal;
|
|
|
|
int clipLimit = 0;
|
|
if (clipLimit_ > 0.0)
|
|
{
|
|
clipLimit = static_cast<int>(clipLimit_ * tileSizeTotal / histSize);
|
|
clipLimit = std::max(clipLimit, 1);
|
|
}
|
|
|
|
clahe::calcLut(srcForLut, lut_, tilesX_, tilesY_, tileSize, clipLimit, lutScale);
|
|
clahe::transform(src, dst, lut_, tilesX_, tilesY_, tileSize);
|
|
}
|
|
|
|
void CLAHE_Impl::setClipLimit(double clipLimit)
|
|
{
|
|
clipLimit_ = clipLimit;
|
|
}
|
|
|
|
double CLAHE_Impl::getClipLimit() const
|
|
{
|
|
return clipLimit_;
|
|
}
|
|
|
|
void CLAHE_Impl::setTilesGridSize(cv::Size tileGridSize)
|
|
{
|
|
tilesX_ = tileGridSize.width;
|
|
tilesY_ = tileGridSize.height;
|
|
}
|
|
|
|
cv::Size CLAHE_Impl::getTilesGridSize() const
|
|
{
|
|
return cv::Size(tilesX_, tilesY_);
|
|
}
|
|
|
|
void CLAHE_Impl::collectGarbage()
|
|
{
|
|
srcExt_.release();
|
|
lut_.release();
|
|
}
|
|
}
|
|
|
|
cv::Ptr<cv::CLAHE> createCLAHE(double clipLimit, cv::Size tileGridSize)
|
|
{
|
|
return new CLAHE_Impl(clipLimit, tileGridSize.width, tileGridSize.height);
|
|
}
|
|
|
|
//////////////////////////////////bilateralFilter////////////////////////////////////////////////////
|
|
|
|
static void oclbilateralFilter_8u( const oclMat &src, oclMat &dst, int d,
|
|
double sigma_color, double sigma_space,
|
|
int borderType )
|
|
{
|
|
int cn = src.channels();
|
|
int i, j, maxk, radius;
|
|
|
|
CV_Assert( (src.channels() == 1 || src.channels() == 3) &&
|
|
src.type() == dst.type() && src.size() == dst.size() &&
|
|
src.data != dst.data );
|
|
|
|
if ( sigma_color <= 0 )
|
|
sigma_color = 1;
|
|
if ( sigma_space <= 0 )
|
|
sigma_space = 1;
|
|
|
|
double gauss_color_coeff = -0.5 / (sigma_color * sigma_color);
|
|
double gauss_space_coeff = -0.5 / (sigma_space * sigma_space);
|
|
|
|
if ( d <= 0 )
|
|
radius = cvRound(sigma_space * 1.5);
|
|
else
|
|
radius = d / 2;
|
|
radius = MAX(radius, 1);
|
|
d = radius * 2 + 1;
|
|
|
|
oclMat temp;
|
|
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
|
|
|
|
vector<float> _color_weight(cn * 256);
|
|
vector<float> _space_weight(d * d);
|
|
vector<int> _space_ofs(d * d);
|
|
float *color_weight = &_color_weight[0];
|
|
float *space_weight = &_space_weight[0];
|
|
int *space_ofs = &_space_ofs[0];
|
|
|
|
int dst_step_in_pixel = dst.step / dst.elemSize();
|
|
int dst_offset_in_pixel = dst.offset / dst.elemSize();
|
|
int temp_step_in_pixel = temp.step / temp.elemSize();
|
|
|
|
// initialize color-related bilateral filter coefficients
|
|
for( i = 0; i < 256 * cn; i++ )
|
|
color_weight[i] = (float)std::exp(i * i * gauss_color_coeff);
|
|
|
|
// initialize space-related bilateral filter coefficients
|
|
for( i = -radius, maxk = 0; i <= radius; i++ )
|
|
for( j = -radius; j <= radius; j++ )
|
|
{
|
|
double r = std::sqrt((double)i * i + (double)j * j);
|
|
if ( r > radius )
|
|
continue;
|
|
space_weight[maxk] = (float)std::exp(r * r * gauss_space_coeff);
|
|
space_ofs[maxk++] = (int)(i * temp_step_in_pixel + j);
|
|
}
|
|
|
|
oclMat oclcolor_weight(1, cn * 256, CV_32FC1, color_weight);
|
|
oclMat oclspace_weight(1, d * d, CV_32FC1, space_weight);
|
|
oclMat oclspace_ofs(1, d * d, CV_32SC1, space_ofs);
|
|
|
|
string kernelName = "bilateral";
|
|
size_t localThreads[3] = { 16, 16, 1 };
|
|
size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
|
|
|
|
if ((dst.type() == CV_8UC1) && ((dst.offset & 3) == 0) && ((dst.cols & 3) == 0))
|
|
{
|
|
kernelName = "bilateral2";
|
|
globalThreads[0] = dst.cols >> 2;
|
|
}
|
|
|
|
vector<pair<size_t , const void *> > args;
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&temp.data ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dst.cols ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&maxk ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&radius ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_step_in_pixel ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset_in_pixel ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&temp_step_in_pixel ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&temp.rows ));
|
|
args.push_back( make_pair( sizeof(cl_int), (void *)&temp.cols ));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&oclcolor_weight.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&oclspace_weight.data ));
|
|
args.push_back( make_pair( sizeof(cl_mem), (void *)&oclspace_ofs.data ));
|
|
|
|
openCLExecuteKernel(src.clCxt, &imgproc_bilateral, kernelName, globalThreads, localThreads, args, dst.oclchannels(), dst.depth());
|
|
}
|
|
|
|
void bilateralFilter(const oclMat &src, oclMat &dst, int radius, double sigmaclr, double sigmaspc, int borderType)
|
|
{
|
|
dst.create( src.size(), src.type() );
|
|
if ( src.depth() == CV_8U )
|
|
oclbilateralFilter_8u( src, dst, radius, sigmaclr, sigmaspc, borderType );
|
|
else
|
|
CV_Error( CV_StsUnsupportedFormat, "Bilateral filtering is only implemented for CV_8U images" );
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}
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}
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}
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//////////////////////////////////convolve////////////////////////////////////////////////////
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static void convolve_run(const oclMat &src, const oclMat &temp1, oclMat &dst, string kernelName, const cv::ocl::ProgramEntry* source)
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{
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dst.create(src.size(), src.type());
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size_t localThreads[3] = { 16, 16, 1 };
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size_t globalThreads[3] = { dst.cols, dst.rows, 1 };
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|
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int src_step = src.step / src.elemSize(), src_offset = src.offset / src.elemSize();
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int dst_step = dst.step / dst.elemSize(), dst_offset = dst.offset / dst.elemSize();
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int temp1_step = temp1.step / temp1.elemSize(), temp1_offset = temp1.offset / temp1.elemSize();
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|
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vector<pair<size_t , const void *> > args;
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args.push_back( make_pair( sizeof(cl_mem), (void *)&src.data ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&temp1.data ));
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args.push_back( make_pair( sizeof(cl_mem), (void *)&dst.data ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src.rows ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src.cols ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src_step ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&dst_step ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&temp1_step ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&temp1.rows ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&temp1.cols ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&src_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&dst_offset ));
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args.push_back( make_pair( sizeof(cl_int), (void *)&temp1_offset ));
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|
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openCLExecuteKernel(src.clCxt, source, kernelName, globalThreads, localThreads, args, -1, dst.depth());
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}
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void cv::ocl::convolve(const oclMat &x, const oclMat &t, oclMat &y)
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{
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CV_Assert(x.depth() == CV_32F && t.depth() == CV_32F);
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CV_Assert(t.cols <= 17 && t.rows <= 17);
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|
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y.create(x.size(), x.type());
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convolve_run(x, t, y, "convolve", &imgproc_convolve);
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}
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