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Open Source Computer Vision Library
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374 lines
11 KiB
374 lines
11 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Jia Haipeng, jiahaipeng95@gmail.com |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other oclMaterials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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#ifdef HAVE_OPENCL |
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using namespace cvtest; |
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using namespace testing; |
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using namespace std; |
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PARAM_TEST_CASE(MergeTestBase, MatType, int) |
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{ |
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int type; |
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int channels; |
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//src mat |
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cv::Mat mat1; |
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cv::Mat mat2; |
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cv::Mat mat3; |
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cv::Mat mat4; |
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//dst mat |
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cv::Mat dst; |
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// set up roi |
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int roicols; |
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int roirows; |
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int src1x; |
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int src1y; |
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int src2x; |
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int src2y; |
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int src3x; |
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int src3y; |
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int src4x; |
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int src4y; |
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int dstx; |
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int dsty; |
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//src mat with roi |
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cv::Mat mat1_roi; |
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cv::Mat mat2_roi; |
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cv::Mat mat3_roi; |
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cv::Mat mat4_roi; |
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//dst mat with roi |
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cv::Mat dst_roi; |
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//std::vector<cv::ocl::Info> oclinfo; |
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//ocl dst mat for testing |
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cv::ocl::oclMat gdst_whole; |
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//ocl mat with roi |
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cv::ocl::oclMat gmat1; |
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cv::ocl::oclMat gmat2; |
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cv::ocl::oclMat gmat3; |
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cv::ocl::oclMat gmat4; |
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cv::ocl::oclMat gdst; |
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virtual void SetUp() |
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{ |
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type = GET_PARAM(0); |
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channels = GET_PARAM(1); |
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cv::RNG &rng = TS::ptr()->get_rng(); |
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cv::Size size(MWIDTH, MHEIGHT); |
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mat1 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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mat2 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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mat3 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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mat4 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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dst = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false); |
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//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE); |
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//CV_Assert(devnums > 0); |
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////if you want to use undefault device, set it here |
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////setDevice(oclinfo[0]); |
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} |
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void random_roi() |
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{ |
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#ifdef RANDOMROI |
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//randomize ROI |
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cv::RNG &rng = TS::ptr()->get_rng(); |
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roicols = rng.uniform(1, mat1.cols); |
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roirows = rng.uniform(1, mat1.rows); |
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src1x = rng.uniform(0, mat1.cols - roicols); |
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src1y = rng.uniform(0, mat1.rows - roirows); |
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src2x = rng.uniform(0, mat2.cols - roicols); |
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src2y = rng.uniform(0, mat2.rows - roirows); |
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src3x = rng.uniform(0, mat3.cols - roicols); |
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src3y = rng.uniform(0, mat3.cols - roirows); |
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src4x = rng.uniform(0, mat4.rows - roicols); |
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src4y = rng.uniform(0, mat4.rows - roirows); |
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dstx = rng.uniform(0, dst.cols - roicols); |
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dsty = rng.uniform(0, dst.rows - roirows); |
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#else |
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roicols = mat1.cols; |
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roirows = mat1.rows; |
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src1x = 0; |
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src1y = 0; |
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src2x = 0; |
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src2y = 0; |
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src3x = 0; |
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src3y = 0; |
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src4x = 0; |
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src4y = 0; |
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dstx = 0; |
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dsty = 0; |
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#endif |
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mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows)); |
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mat2_roi = mat2(Rect(src2x, src2y, roicols, roirows)); |
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mat3_roi = mat3(Rect(src3x, src3y, roicols, roirows)); |
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mat4_roi = mat4(Rect(src4x, src4y, roicols, roirows)); |
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dst_roi = dst(Rect(dstx, dsty, roicols, roirows)); |
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gdst_whole = dst; |
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gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows)); |
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gmat1 = mat1_roi; |
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gmat2 = mat2_roi; |
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gmat3 = mat3_roi; |
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gmat4 = mat4_roi; |
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} |
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}; |
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struct Merge : MergeTestBase {}; |
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TEST_P(Merge, Accuracy) |
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{ |
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for(int j = 0; j < LOOP_TIMES; j++) |
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{ |
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random_roi(); |
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std::vector<cv::Mat> dev_src; |
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dev_src.push_back(mat1_roi); |
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dev_src.push_back(mat2_roi); |
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dev_src.push_back(mat3_roi); |
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dev_src.push_back(mat4_roi); |
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std::vector<cv::ocl::oclMat> dev_gsrc; |
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dev_gsrc.push_back(gmat1); |
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dev_gsrc.push_back(gmat2); |
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dev_gsrc.push_back(gmat3); |
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dev_gsrc.push_back(gmat4); |
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cv::merge(dev_src, dst_roi); |
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cv::ocl::merge(dev_gsrc, gdst); |
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cv::Mat cpu_dst; |
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gdst_whole.download(cpu_dst); |
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char sss[1024]; |
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sprintf(sss, "roicols=%d,roirows=%d,src1x =%d,src1y=%d,src2x =%d,src2y=%d,src3x =%d,src3y=%d,src4x =%d,src4y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, src2x , src2y, src3x , src3y, src4x , src4y, dstx, dsty); |
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EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss); |
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} |
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} |
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PARAM_TEST_CASE(SplitTestBase, MatType, int) |
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{ |
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int type; |
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int channels; |
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//src mat |
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cv::Mat mat; |
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//dstmat |
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cv::Mat dst1; |
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cv::Mat dst2; |
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cv::Mat dst3; |
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cv::Mat dst4; |
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// set up roi |
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int roicols; |
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int roirows; |
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int srcx; |
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int srcy; |
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int dst1x; |
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int dst1y; |
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int dst2x; |
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int dst2y; |
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int dst3x; |
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int dst3y; |
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int dst4x; |
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int dst4y; |
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//src mat with roi |
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cv::Mat mat_roi; |
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//dst mat with roi |
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cv::Mat dst1_roi; |
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cv::Mat dst2_roi; |
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cv::Mat dst3_roi; |
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cv::Mat dst4_roi; |
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//std::vector<cv::ocl::Info> oclinfo; |
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//ocl dst mat for testing |
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cv::ocl::oclMat gdst1_whole; |
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cv::ocl::oclMat gdst2_whole; |
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cv::ocl::oclMat gdst3_whole; |
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cv::ocl::oclMat gdst4_whole; |
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//ocl mat with roi |
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cv::ocl::oclMat gmat; |
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cv::ocl::oclMat gdst1; |
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cv::ocl::oclMat gdst2; |
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cv::ocl::oclMat gdst3; |
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cv::ocl::oclMat gdst4; |
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virtual void SetUp() |
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{ |
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type = GET_PARAM(0); |
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channels = GET_PARAM(1); |
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cv::RNG &rng = TS::ptr()->get_rng(); |
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cv::Size size(MWIDTH, MHEIGHT); |
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mat = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false); |
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dst1 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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dst2 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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dst3 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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dst4 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false); |
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//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE); |
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//CV_Assert(devnums > 0); |
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////if you want to use undefault device, set it here |
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////setDevice(oclinfo[0]); |
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} |
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void random_roi() |
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{ |
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#ifdef RANDOMROI |
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//randomize ROI |
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cv::RNG &rng = TS::ptr()->get_rng(); |
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roicols = rng.uniform(1, mat.cols); |
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roirows = rng.uniform(1, mat.rows); |
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srcx = rng.uniform(0, mat.cols - roicols); |
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srcy = rng.uniform(0, mat.rows - roirows); |
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dst1x = rng.uniform(0, dst1.cols - roicols); |
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dst1y = rng.uniform(0, dst1.rows - roirows); |
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dst2x = rng.uniform(0, dst2.cols - roicols); |
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dst2y = rng.uniform(0, dst2.rows - roirows); |
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dst3x = rng.uniform(0, dst3.cols - roicols); |
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dst3y = rng.uniform(0, dst3.rows - roirows); |
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dst4x = rng.uniform(0, dst4.cols - roicols); |
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dst4y = rng.uniform(0, dst4.rows - roirows); |
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#else |
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roicols = mat.cols; |
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roirows = mat.rows; |
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srcx = 0; |
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srcy = 0; |
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dst1x = 0; |
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dst1y = 0; |
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dst2x = 0; |
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dst2y = 0; |
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dst3x = 0; |
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dst3y = 0; |
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dst4x = 0; |
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dst4y = 0; |
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#endif |
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mat_roi = mat(Rect(srcx, srcy, roicols, roirows)); |
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dst1_roi = dst1(Rect(dst1x, dst1y, roicols, roirows)); |
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dst2_roi = dst2(Rect(dst2x, dst2y, roicols, roirows)); |
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dst3_roi = dst3(Rect(dst3x, dst3y, roicols, roirows)); |
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dst4_roi = dst4(Rect(dst4x, dst4y, roicols, roirows)); |
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gdst1_whole = dst1; |
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gdst1 = gdst1_whole(Rect(dst1x, dst1y, roicols, roirows)); |
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gdst2_whole = dst2; |
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gdst2 = gdst2_whole(Rect(dst2x, dst2y, roicols, roirows)); |
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gdst3_whole = dst3; |
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gdst3 = gdst3_whole(Rect(dst3x, dst3y, roicols, roirows)); |
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gdst4_whole = dst4; |
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gdst4 = gdst4_whole(Rect(dst4x, dst4y, roicols, roirows)); |
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gmat = mat_roi; |
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} |
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}; |
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struct Split : SplitTestBase {}; |
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TEST_P(Split, Accuracy) |
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{ |
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for(int j = 0; j < LOOP_TIMES; j++) |
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{ |
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random_roi(); |
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cv::Mat dev_dst[4] = {dst1_roi, dst2_roi, dst3_roi, dst4_roi}; |
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cv::ocl::oclMat dev_gdst[4] = {gdst1, gdst2, gdst3, gdst4}; |
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cv::split(mat_roi, dev_dst); |
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cv::ocl::split(gmat, dev_gdst); |
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cv::Mat cpu_dst1; |
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cv::Mat cpu_dst2; |
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cv::Mat cpu_dst3; |
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cv::Mat cpu_dst4; |
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gdst1_whole.download(cpu_dst1); |
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gdst2_whole.download(cpu_dst2); |
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gdst3_whole.download(cpu_dst3); |
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gdst4_whole.download(cpu_dst4); |
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char sss[1024]; |
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sprintf(sss, "roicols=%d,roirows=%d,dst1x =%d,dsty=%d,dst2x =%d,dst2y=%d,dst3x =%d,dst3y=%d,dst4x =%d,dst4y=%d,srcx=%d,srcy=%d", roicols, roirows, dst1x , dst1y, dst2x , dst2y, dst3x , dst3y, dst4x , dst4y, srcx, srcy); |
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EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.0, sss); |
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EXPECT_MAT_NEAR(dst2, cpu_dst2, 0.0, sss); |
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EXPECT_MAT_NEAR(dst3, cpu_dst3, 0.0, sss); |
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EXPECT_MAT_NEAR(dst4, cpu_dst4, 0.0, sss); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(SplitMerge, Merge, Combine( |
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Values(CV_8U, CV_32S, CV_32F), Values(1, 3,4))); |
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INSTANTIATE_TEST_CASE_P(SplitMerge, Split , Combine( |
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Values(CV_8U, CV_32S, CV_32F), Values(1, 3,4))); |
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#endif // HAVE_OPENCL
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