1014 lines
29 KiB
C++
1014 lines
29 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, Multicoreware, Inc., 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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// Fangfang Bai, fangfang@multicorewareinc.com
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// Jin Ma, jin@multicorewareinc.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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///////////// equalizeHist ////////////////////////
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PERFTEST(equalizeHist)
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{
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Mat src, dst, ocl_dst;
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int all_type[] = {CV_8UC1};
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std::string type_name[] = {"CV_8UC1"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] ;
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gen(src, size, size, all_type[j], 0, 256);
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equalizeHist(src, dst);
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CPU_ON;
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equalizeHist(src, dst);
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CPU_OFF;
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ocl::oclMat d_src(src);
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ocl::oclMat d_dst;
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ocl::oclMat d_hist;
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ocl::oclMat d_buf;
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WARMUP_ON;
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ocl::equalizeHist(d_src, d_dst);
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WARMUP_OFF;
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GPU_ON;
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ocl::equalizeHist(d_src, d_dst);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::equalizeHist(d_src, d_dst);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.1);
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}
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}
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}
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/////////// CopyMakeBorder //////////////////////
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PERFTEST(CopyMakeBorder)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_dst;
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int bordertype = BORDER_CONSTANT;
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int all_type[] = {CV_8UC1, CV_8UC4};
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std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] ;
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gen(src, size, size, all_type[j], 0, 256);
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copyMakeBorder(src, dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
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CPU_ON;
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copyMakeBorder(src, dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
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CPU_OFF;
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ocl::oclMat d_src(src);
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WARMUP_ON;
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ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
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WARMUP_OFF;
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GPU_ON;
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ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::copyMakeBorder(d_src, d_dst, 7, 5, 5, 7, bordertype, cv::Scalar(1.0));
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 0.0);
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}
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}
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}
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///////////// cornerMinEigenVal ////////////////////////
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PERFTEST(cornerMinEigenVal)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_dst;
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int blockSize = 7, apertureSize = 1 + 2 * (rand() % 4);
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int borderType = BORDER_REFLECT;
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int all_type[] = {CV_8UC1, CV_32FC1};
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std::string type_name[] = {"CV_8UC1", "CV_32FC1"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] ;
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gen(src, size, size, all_type[j], 0, 256);
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cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType);
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CPU_ON;
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cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType);
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CPU_OFF;
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ocl::oclMat d_src(src);
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WARMUP_ON;
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ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType);
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WARMUP_OFF;
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GPU_ON;
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ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::cornerMinEigenVal(d_src, d_dst, blockSize, apertureSize, borderType);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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}
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}
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///////////// cornerHarris ////////////////////////
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PERFTEST(cornerHarris)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_src, d_dst;
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int all_type[] = {CV_8UC1, CV_32FC1};
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std::string type_name[] = {"CV_8UC1", "CV_32FC1"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; BORDER_REFLECT";
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gen(src, size, size, all_type[j], 0, 1);
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cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT);
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CPU_ON;
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cornerHarris(src, dst, 5, 7, 0.1, BORDER_REFLECT);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT);
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WARMUP_OFF;
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GPU_ON;
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ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::cornerHarris(d_src, d_dst, 5, 7, 0.1, BORDER_REFLECT);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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}
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}
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///////////// integral ////////////////////////
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PERFTEST(integral)
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{
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Mat src, sum, ocl_sum;
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ocl::oclMat d_src, d_sum, d_buf;
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int all_type[] = {CV_8UC1};
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std::string type_name[] = {"CV_8UC1"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] ;
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gen(src, size, size, all_type[j], 0, 256);
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integral(src, sum);
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CPU_ON;
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integral(src, sum);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::integral(d_src, d_sum);
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WARMUP_OFF;
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GPU_ON;
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ocl::integral(d_src, d_sum);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::integral(d_src, d_sum);
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d_sum.download(ocl_sum);
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GPU_FULL_OFF;
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if(sum.type() == ocl_sum.type()) //we won't test accuracy when cpu function overlow
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TestSystem::instance().ExpectedMatNear(sum, ocl_sum, 0.0);
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}
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}
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}
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///////////// WarpAffine ////////////////////////
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PERFTEST(WarpAffine)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_src, d_dst;
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static const double coeffs[2][3] =
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{
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{cos(CV_PI / 6), -sin(CV_PI / 6), 100.0},
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{sin(CV_PI / 6), cos(CV_PI / 6), -100.0}
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};
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Mat M(2, 3, CV_64F, (void *)coeffs);
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int interpolation = INTER_NEAREST;
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int all_type[] = {CV_8UC1, CV_8UC4};
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std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] ;
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gen(src, size, size, all_type[j], 0, 256);
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gen(dst, size, size, all_type[j], 0, 256);
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Size size1 = Size(size, size);
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warpAffine(src, dst, M, size1, interpolation);
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CPU_ON;
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warpAffine(src, dst, M, size1, interpolation);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::warpAffine(d_src, d_dst, M, size1, interpolation);
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WARMUP_OFF;
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GPU_ON;
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ocl::warpAffine(d_src, d_dst, M, size1, interpolation);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::warpAffine(d_src, d_dst, M, size1, interpolation);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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}
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}
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///////////// WarpPerspective ////////////////////////
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PERFTEST(WarpPerspective)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_src, d_dst;
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static const double coeffs[3][3] =
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{
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{cos(CV_PI / 6), -sin(CV_PI / 6), 100.0},
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{sin(CV_PI / 6), cos(CV_PI / 6), -100.0},
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{0.0, 0.0, 1.0}
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};
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Mat M(3, 3, CV_64F, (void *)coeffs);
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int interpolation = INTER_LINEAR;
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int all_type[] = {CV_8UC1, CV_8UC4};
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std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] ;
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gen(src, size, size, all_type[j], 0, 256);
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gen(dst, size, size, all_type[j], 0, 256);
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Size size1 = Size(size, size);
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warpPerspective(src, dst, M, size1, interpolation);
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CPU_ON;
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warpPerspective(src, dst, M, size1, interpolation);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::warpPerspective(d_src, d_dst, M, size1, interpolation);
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WARMUP_OFF;
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GPU_ON;
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ocl::warpPerspective(d_src, d_dst, M, size1, interpolation);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::warpPerspective(d_src, d_dst, M, size1, interpolation);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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}
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}
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///////////// resize ////////////////////////
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PERFTEST(resize)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_src, d_dst;
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int all_type[] = {CV_8UC1, CV_8UC4};
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std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; up";
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gen(src, size, size, all_type[j], 0, 256);
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resize(src, dst, Size(), 2.0, 2.0);
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CPU_ON;
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resize(src, dst, Size(), 2.0, 2.0);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::resize(d_src, d_dst, Size(), 2.0, 2.0);
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WARMUP_OFF;
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GPU_ON;
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ocl::resize(d_src, d_dst, Size(), 2.0, 2.0);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::resize(d_src, d_dst, Size(), 2.0, 2.0);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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}
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
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{
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SUBTEST << size << 'x' << size << "; " << type_name[j] << " ; down";
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gen(src, size, size, all_type[j], 0, 256);
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resize(src, dst, Size(), 0.5, 0.5);
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CPU_ON;
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resize(src, dst, Size(), 0.5, 0.5);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::resize(d_src, d_dst, Size(), 0.5, 0.5);
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WARMUP_OFF;
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GPU_ON;
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ocl::resize(d_src, d_dst, Size(), 0.5, 0.5);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::resize(d_src, d_dst, Size(), 0.5, 0.5);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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}
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}
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///////////// threshold////////////////////////
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PERFTEST(threshold)
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{
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Mat src, dst, ocl_dst;
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ocl::oclMat d_src, d_dst;
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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SUBTEST << size << 'x' << size << "; 8UC1; THRESH_BINARY";
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gen(src, size, size, CV_8U, 0, 100);
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threshold(src, dst, 50.0, 0.0, THRESH_BINARY);
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CPU_ON;
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threshold(src, dst, 50.0, 0.0, THRESH_BINARY);
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CPU_OFF;
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d_src.upload(src);
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WARMUP_ON;
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ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY);
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WARMUP_OFF;
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GPU_ON;
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ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY);
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GPU_OFF;
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GPU_FULL_ON;
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d_src.upload(src);
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ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_BINARY);
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d_dst.download(ocl_dst);
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GPU_FULL_OFF;
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TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
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}
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for (int size = Min_Size; size <= Max_Size; size *= Multiple)
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{
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SUBTEST << size << 'x' << size << "; 32FC1; THRESH_TRUNC [NPP]";
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gen(src, size, size, CV_32FC1, 0, 100);
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threshold(src, dst, 50.0, 0.0, THRESH_TRUNC);
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CPU_ON;
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threshold(src, dst, 50.0, 0.0, THRESH_TRUNC);
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CPU_OFF;
|
|
|
|
d_src.upload(src);
|
|
|
|
WARMUP_ON;
|
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC);
|
|
WARMUP_OFF;
|
|
|
|
GPU_ON;
|
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC);
|
|
GPU_OFF;
|
|
|
|
GPU_FULL_ON;
|
|
d_src.upload(src);
|
|
ocl::threshold(d_src, d_dst, 50.0, 0.0, THRESH_TRUNC);
|
|
d_dst.download(ocl_dst);
|
|
GPU_FULL_OFF;
|
|
|
|
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
|
|
}
|
|
}
|
|
///////////// meanShiftFiltering////////////////////////
|
|
COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, cv::Size size, int sp, int sr, int maxIter, float eps, int *tab)
|
|
{
|
|
|
|
int isr2 = sr * sr;
|
|
int c0, c1, c2, c3;
|
|
int iter;
|
|
uchar *ptr = NULL;
|
|
uchar *pstart = NULL;
|
|
int revx = 0, revy = 0;
|
|
c0 = sptr[0];
|
|
c1 = sptr[1];
|
|
c2 = sptr[2];
|
|
c3 = sptr[3];
|
|
// iterate meanshift procedure
|
|
for(iter = 0; iter < maxIter; iter++ )
|
|
{
|
|
int count = 0;
|
|
int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0;
|
|
|
|
//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp)
|
|
int minx = x0 - sp;
|
|
int miny = y0 - sp;
|
|
int maxx = x0 + sp;
|
|
int maxy = y0 + sp;
|
|
|
|
//deal with the image boundary
|
|
if(minx < 0) minx = 0;
|
|
if(miny < 0) miny = 0;
|
|
if(maxx >= size.width) maxx = size.width - 1;
|
|
if(maxy >= size.height) maxy = size.height - 1;
|
|
if(iter == 0)
|
|
{
|
|
pstart = sptr;
|
|
}
|
|
else
|
|
{
|
|
pstart = pstart + revy * sstep + (revx << 2); //point to the new position
|
|
}
|
|
ptr = pstart;
|
|
ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2); //point to the start in the row
|
|
|
|
for( int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2))
|
|
{
|
|
int rowCount = 0;
|
|
int x = minx;
|
|
#if CV_ENABLE_UNROLLED
|
|
for( ; x + 4 <= maxx; x += 4, ptr += 16)
|
|
{
|
|
int t0, t1, t2;
|
|
t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
|
|
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
|
|
{
|
|
s0 += t0;
|
|
s1 += t1;
|
|
s2 += t2;
|
|
sx += x;
|
|
rowCount++;
|
|
}
|
|
t0 = ptr[4], t1 = ptr[5], t2 = ptr[6];
|
|
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
|
|
{
|
|
s0 += t0;
|
|
s1 += t1;
|
|
s2 += t2;
|
|
sx += x + 1;
|
|
rowCount++;
|
|
}
|
|
t0 = ptr[8], t1 = ptr[9], t2 = ptr[10];
|
|
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
|
|
{
|
|
s0 += t0;
|
|
s1 += t1;
|
|
s2 += t2;
|
|
sx += x + 2;
|
|
rowCount++;
|
|
}
|
|
t0 = ptr[12], t1 = ptr[13], t2 = ptr[14];
|
|
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
|
|
{
|
|
s0 += t0;
|
|
s1 += t1;
|
|
s2 += t2;
|
|
sx += x + 3;
|
|
rowCount++;
|
|
}
|
|
}
|
|
#endif
|
|
for(; x <= maxx; x++, ptr += 4)
|
|
{
|
|
int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2];
|
|
if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2)
|
|
{
|
|
s0 += t0;
|
|
s1 += t1;
|
|
s2 += t2;
|
|
sx += x;
|
|
rowCount++;
|
|
}
|
|
}
|
|
if(rowCount == 0)
|
|
continue;
|
|
count += rowCount;
|
|
sy += y * rowCount;
|
|
}
|
|
|
|
if( count == 0 )
|
|
break;
|
|
|
|
int x1 = sx / count;
|
|
int y1 = sy / count;
|
|
s0 = s0 / count;
|
|
s1 = s1 / count;
|
|
s2 = s2 / count;
|
|
|
|
bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) +
|
|
tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps);
|
|
|
|
//revise the pointer corresponding to the new (y0,x0)
|
|
revx = x1 - x0;
|
|
revy = y1 - y0;
|
|
|
|
x0 = x1;
|
|
y0 = y1;
|
|
c0 = s0;
|
|
c1 = s1;
|
|
c2 = s2;
|
|
|
|
if( stopFlag )
|
|
break;
|
|
} //for iter
|
|
|
|
dptr[0] = (uchar)c0;
|
|
dptr[1] = (uchar)c1;
|
|
dptr[2] = (uchar)c2;
|
|
dptr[3] = (uchar)c3;
|
|
|
|
COOR coor;
|
|
coor.x = static_cast<short>(x0);
|
|
coor.y = static_cast<short>(y0);
|
|
return coor;
|
|
}
|
|
|
|
static void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit)
|
|
{
|
|
if( src_roi.empty() )
|
|
CV_Error( CV_StsBadArg, "The input image is empty" );
|
|
|
|
if( src_roi.depth() != CV_8U || src_roi.channels() != 4 )
|
|
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" );
|
|
|
|
dst_roi.create(src_roi.size(), src_roi.type());
|
|
|
|
CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) );
|
|
CV_Assert( !(dst_roi.step & 0x3) );
|
|
|
|
if( !(crit.type & cv::TermCriteria::MAX_ITER) )
|
|
crit.maxCount = 5;
|
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
|
|
float eps;
|
|
if( !(crit.type & cv::TermCriteria::EPS) )
|
|
eps = 1.f;
|
|
eps = (float)std::max(crit.epsilon, 0.0);
|
|
|
|
int tab[512];
|
|
for(int i = 0; i < 512; i++)
|
|
tab[i] = (i - 255) * (i - 255);
|
|
uchar *sptr = src_roi.data;
|
|
uchar *dptr = dst_roi.data;
|
|
int sstep = (int)src_roi.step;
|
|
int dstep = (int)dst_roi.step;
|
|
cv::Size size = src_roi.size();
|
|
|
|
for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),
|
|
dptr += dstep - (size.width << 2))
|
|
{
|
|
for(int j = 0; j < size.width; j++, sptr += 4, dptr += 4)
|
|
{
|
|
do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab);
|
|
}
|
|
}
|
|
}
|
|
|
|
PERFTEST(meanShiftFiltering)
|
|
{
|
|
int sp = 5, sr = 6;
|
|
Mat src, dst, ocl_dst;
|
|
|
|
ocl::oclMat d_src, d_dst;
|
|
|
|
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
|
|
{
|
|
SUBTEST << size << 'x' << size << "; 8UC3 vs 8UC4";
|
|
|
|
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
|
|
|
|
cv::TermCriteria crit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1);
|
|
|
|
meanShiftFiltering_(src, dst, sp, sr, crit);
|
|
|
|
CPU_ON;
|
|
meanShiftFiltering_(src, dst, sp, sr, crit);
|
|
CPU_OFF;
|
|
|
|
d_src.upload(src);
|
|
|
|
WARMUP_ON;
|
|
ocl::meanShiftFiltering(d_src, d_dst, sp, sr, crit);
|
|
WARMUP_OFF;
|
|
|
|
GPU_ON;
|
|
ocl::meanShiftFiltering(d_src, d_dst, sp, sr, crit);
|
|
GPU_OFF;
|
|
|
|
GPU_FULL_ON;
|
|
d_src.upload(src);
|
|
ocl::meanShiftFiltering(d_src, d_dst, sp, sr, crit);
|
|
d_dst.download(ocl_dst);
|
|
GPU_FULL_OFF;
|
|
|
|
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 0.0);
|
|
}
|
|
}
|
|
|
|
void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, cv::TermCriteria crit)
|
|
{
|
|
if (src_roi.empty())
|
|
{
|
|
CV_Error(CV_StsBadArg, "The input image is empty");
|
|
}
|
|
if (src_roi.depth() != CV_8U || src_roi.channels() != 4)
|
|
{
|
|
CV_Error(CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported");
|
|
}
|
|
|
|
dst_roi.create(src_roi.size(), src_roi.type());
|
|
dstCoor_roi.create(src_roi.size(), CV_16SC2);
|
|
|
|
CV_Assert((src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) &&
|
|
(src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows));
|
|
CV_Assert(!(dstCoor_roi.step & 0x3));
|
|
|
|
if (!(crit.type & cv::TermCriteria::MAX_ITER))
|
|
{
|
|
crit.maxCount = 5;
|
|
}
|
|
|
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100);
|
|
float eps;
|
|
|
|
if (!(crit.type & cv::TermCriteria::EPS))
|
|
{
|
|
eps = 1.f;
|
|
}
|
|
|
|
eps = (float)std::max(crit.epsilon, 0.0);
|
|
|
|
int tab[512];
|
|
|
|
for (int i = 0; i < 512; i++)
|
|
{
|
|
tab[i] = (i - 255) * (i - 255);
|
|
}
|
|
|
|
uchar *sptr = src_roi.data;
|
|
uchar *dptr = dst_roi.data;
|
|
short *dCoorptr = (short *)dstCoor_roi.data;
|
|
int sstep = (int)src_roi.step;
|
|
int dstep = (int)dst_roi.step;
|
|
int dCoorstep = (int)dstCoor_roi.step >> 1;
|
|
cv::Size size = src_roi.size();
|
|
|
|
for (int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2),
|
|
dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1))
|
|
{
|
|
for (int j = 0; j < size.width; j++, sptr += 4, dptr += 4, dCoorptr += 2)
|
|
{
|
|
*((COOR *)dCoorptr) = do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab);
|
|
}
|
|
}
|
|
|
|
}
|
|
PERFTEST(meanShiftProc)
|
|
{
|
|
Mat src;
|
|
vector<Mat> dst(2), ocl_dst(2);
|
|
ocl::oclMat d_src, d_dst, d_dstCoor;
|
|
|
|
TermCriteria crit(TermCriteria::COUNT + TermCriteria::EPS, 5, 1);
|
|
|
|
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
|
|
{
|
|
SUBTEST << size << 'x' << size << "; 8UC4 and CV_16SC2 ";
|
|
|
|
gen(src, size, size, CV_8UC4, Scalar::all(0), Scalar::all(256));
|
|
|
|
meanShiftProc_(src, dst[0], dst[1], 5, 6, crit);
|
|
|
|
CPU_ON;
|
|
meanShiftProc_(src, dst[0], dst[1], 5, 6, crit);
|
|
CPU_OFF;
|
|
|
|
d_src.upload(src);
|
|
|
|
WARMUP_ON;
|
|
ocl::meanShiftProc(d_src, d_dst, d_dstCoor, 5, 6, crit);
|
|
WARMUP_OFF;
|
|
|
|
GPU_ON;
|
|
ocl::meanShiftProc(d_src, d_dst, d_dstCoor, 5, 6, crit);
|
|
GPU_OFF;
|
|
|
|
GPU_FULL_ON;
|
|
d_src.upload(src);
|
|
ocl::meanShiftProc(d_src, d_dst, d_dstCoor, 5, 6, crit);
|
|
d_dst.download(ocl_dst[0]);
|
|
d_dstCoor.download(ocl_dst[1]);
|
|
GPU_FULL_OFF;
|
|
|
|
vector<double> eps(2, 0.);
|
|
TestSystem::instance().ExpectMatsNear(dst, ocl_dst, eps);
|
|
}
|
|
}
|
|
|
|
///////////// remap////////////////////////
|
|
PERFTEST(remap)
|
|
{
|
|
Mat src, dst, xmap, ymap, ocl_dst;
|
|
ocl::oclMat d_src, d_dst, d_xmap, d_ymap;
|
|
|
|
int all_type[] = {CV_8UC1, CV_8UC4};
|
|
std::string type_name[] = {"CV_8UC1", "CV_8UC4"};
|
|
|
|
int interpolation = INTER_LINEAR;
|
|
int borderMode = BORDER_CONSTANT;
|
|
|
|
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
|
|
{
|
|
for (size_t t = 0; t < sizeof(all_type) / sizeof(int); t++)
|
|
{
|
|
SUBTEST << size << 'x' << size << "; src " << type_name[t] << "; map CV_32FC1";
|
|
|
|
gen(src, size, size, all_type[t], 0, 256);
|
|
|
|
xmap.create(size, size, CV_32FC1);
|
|
dst.create(size, size, CV_32FC1);
|
|
ymap.create(size, size, CV_32FC1);
|
|
|
|
for (int i = 0; i < size; ++i)
|
|
{
|
|
float *xmap_row = xmap.ptr<float>(i);
|
|
float *ymap_row = ymap.ptr<float>(i);
|
|
|
|
for (int j = 0; j < size; ++j)
|
|
{
|
|
xmap_row[j] = (j - size * 0.5f) * 0.75f + size * 0.5f;
|
|
ymap_row[j] = (i - size * 0.5f) * 0.75f + size * 0.5f;
|
|
}
|
|
}
|
|
|
|
remap(src, dst, xmap, ymap, interpolation, borderMode);
|
|
|
|
CPU_ON;
|
|
remap(src, dst, xmap, ymap, interpolation, borderMode);
|
|
CPU_OFF;
|
|
|
|
d_src.upload(src);
|
|
d_dst.upload(dst);
|
|
d_xmap.upload(xmap);
|
|
d_ymap.upload(ymap);
|
|
|
|
WARMUP_ON;
|
|
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
|
|
WARMUP_OFF;
|
|
|
|
GPU_ON;
|
|
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
|
|
GPU_OFF;
|
|
|
|
GPU_FULL_ON;
|
|
d_src.upload(src);
|
|
ocl::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
|
|
d_dst.download(ocl_dst);
|
|
GPU_FULL_OFF;
|
|
|
|
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 2.0);
|
|
}
|
|
|
|
}
|
|
}
|
|
///////////// CLAHE ////////////////////////
|
|
PERFTEST(CLAHE)
|
|
{
|
|
Mat src, dst, ocl_dst;
|
|
cv::ocl::oclMat d_src, d_dst;
|
|
int all_type[] = {CV_8UC1};
|
|
std::string type_name[] = {"CV_8UC1"};
|
|
|
|
double clipLimit = 40.0;
|
|
|
|
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(clipLimit);
|
|
cv::Ptr<cv::CLAHE> d_clahe = cv::ocl::createCLAHE(clipLimit);
|
|
|
|
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
|
|
{
|
|
for (size_t j = 0; j < sizeof(all_type) / sizeof(int); j++)
|
|
{
|
|
SUBTEST << size << 'x' << size << "; " << type_name[j] ;
|
|
|
|
gen(src, size, size, all_type[j], 0, 256);
|
|
|
|
CPU_ON;
|
|
clahe->apply(src, dst);
|
|
CPU_OFF;
|
|
|
|
d_src.upload(src);
|
|
|
|
WARMUP_ON;
|
|
d_clahe->apply(d_src, d_dst);
|
|
WARMUP_OFF;
|
|
|
|
ocl_dst = d_dst;
|
|
|
|
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 1.0);
|
|
|
|
GPU_ON;
|
|
d_clahe->apply(d_src, d_dst);
|
|
GPU_OFF;
|
|
|
|
GPU_FULL_ON;
|
|
d_src.upload(src);
|
|
d_clahe->apply(d_src, d_dst);
|
|
d_dst.download(dst);
|
|
GPU_FULL_OFF;
|
|
}
|
|
}
|
|
}
|
|
|
|
///////////// columnSum////////////////////////
|
|
PERFTEST(columnSum)
|
|
{
|
|
Mat src, dst, ocl_dst;
|
|
ocl::oclMat d_src, d_dst;
|
|
|
|
for (int size = Min_Size; size <= Max_Size; size *= Multiple)
|
|
{
|
|
SUBTEST << size << 'x' << size << "; CV_32FC1";
|
|
|
|
gen(src, size, size, CV_32FC1, 0, 256);
|
|
|
|
CPU_ON;
|
|
dst.create(src.size(), src.type());
|
|
for (int j = 0; j < src.cols; j++)
|
|
dst.at<float>(0, j) = src.at<float>(0, j);
|
|
|
|
for (int i = 1; i < src.rows; ++i)
|
|
for (int j = 0; j < src.cols; ++j)
|
|
dst.at<float>(i, j) = dst.at<float>(i - 1 , j) + src.at<float>(i , j);
|
|
CPU_OFF;
|
|
|
|
d_src.upload(src);
|
|
|
|
WARMUP_ON;
|
|
ocl::columnSum(d_src, d_dst);
|
|
WARMUP_OFF;
|
|
|
|
GPU_ON;
|
|
ocl::columnSum(d_src, d_dst);
|
|
GPU_OFF;
|
|
|
|
GPU_FULL_ON;
|
|
d_src.upload(src);
|
|
ocl::columnSum(d_src, d_dst);
|
|
d_dst.download(ocl_dst);
|
|
GPU_FULL_OFF;
|
|
|
|
TestSystem::instance().ExpectedMatNear(dst, ocl_dst, 5e-1);
|
|
}
|
|
}
|