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Open Source Computer Vision Library
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453 lines
12 KiB
453 lines
12 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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// 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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// 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 "test_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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////////////////////////////////converto///////////////////////////////////////////////// |
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PARAM_TEST_CASE(ConvertToTestBase, MatType, MatType, int, bool) |
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{ |
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int src_depth, dst_depth; |
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int cn, dst_type; |
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bool use_roi; |
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// src mat |
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cv::Mat mat; |
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cv::Mat dst; |
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// set up roi |
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int roicols, roirows; |
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int srcx, srcy; |
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int dstx, dsty; |
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// src mat with roi |
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cv::Mat mat_roi; |
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cv::Mat dst_roi; |
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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 gsrc; |
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cv::ocl::oclMat gdst; |
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virtual void SetUp() |
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{ |
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src_depth = GET_PARAM(0); |
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dst_depth = GET_PARAM(1); |
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cn = GET_PARAM(2); |
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int src_type = CV_MAKE_TYPE(src_depth, cn); |
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dst_type = CV_MAKE_TYPE(dst_depth, cn); |
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use_roi = GET_PARAM(3); |
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mat = randomMat(randomSize(MIN_VALUE, MAX_VALUE), src_type, 5, 136, false); |
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dst = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : mat.size(), dst_type, 5, 136, false); |
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} |
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void random_roi() |
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{ |
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if (use_roi) |
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{ |
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// randomize ROI |
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roicols = rng.uniform(1, MIN_VALUE); |
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roirows = rng.uniform(1, MIN_VALUE); |
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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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dstx = rng.uniform(0, dst.cols - roicols); |
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dsty = rng.uniform(0, dst.rows - roirows); |
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} |
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else |
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{ |
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roicols = mat.cols; |
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roirows = mat.rows; |
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srcx = srcy = 0; |
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dstx = dsty = 0; |
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} |
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mat_roi = mat(Rect(srcx, srcy, 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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gsrc = mat_roi; |
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} |
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}; |
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typedef ConvertToTestBase ConvertTo; |
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OCL_TEST_P(ConvertTo, Accuracy) |
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{ |
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if((src_depth == CV_64F || dst_depth == CV_64F) && |
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!cv::ocl::Context::getContext()->supportsFeature(cv::ocl::FEATURE_CL_DOUBLE)) |
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{ |
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return; // returns silently |
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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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mat_roi.convertTo(dst_roi, dst_type); |
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gsrc.convertTo(gdst, dst_type); |
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EXPECT_MAT_NEAR(dst, Mat(gdst_whole), src_depth == CV_64F ? 1.0 : 0.0); |
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EXPECT_MAT_NEAR(dst_roi, Mat(gdst), src_depth == CV_64F ? 1.0 : 0.0); |
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} |
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} |
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///////////////////////////////////////////copyto///////////////////////////////////////////////////////////// |
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PARAM_TEST_CASE(CopyToTestBase, MatType, int, bool) |
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{ |
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bool use_roi; |
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cv::Mat src, mask, dst; |
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// set up roi |
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int roicols,roirows; |
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int srcx, srcy; |
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int dstx, dsty; |
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int maskx,masky; |
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// src mat with roi |
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cv::Mat src_roi; |
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cv::Mat mask_roi; |
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cv::Mat dst_roi; |
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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 gsrc, gdst, gmask; |
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virtual void SetUp() |
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{ |
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int type = CV_MAKETYPE(GET_PARAM(0), GET_PARAM(1)); |
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use_roi = GET_PARAM(2); |
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src = randomMat(randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false); |
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dst = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), type, 5, 16, false); |
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mask = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), CV_8UC1, 0, 2, false); |
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cv::threshold(mask, mask, 0.5, 255., CV_8UC1); |
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} |
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void random_roi() |
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{ |
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if (use_roi) |
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{ |
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// randomize ROI |
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roicols = rng.uniform(1, MIN_VALUE); |
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roirows = rng.uniform(1, MIN_VALUE); |
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srcx = rng.uniform(0, src.cols - roicols); |
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srcy = rng.uniform(0, src.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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maskx = rng.uniform(0, mask.cols - roicols); |
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masky = rng.uniform(0, mask.rows - roirows); |
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} |
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else |
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{ |
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roicols = src.cols; |
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roirows = src.rows; |
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srcx = srcy = 0; |
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dstx = dsty = 0; |
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maskx = masky = 0; |
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} |
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src_roi = src(Rect(srcx, srcy, roicols, roirows)); |
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mask_roi = mask(Rect(maskx, masky, 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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gsrc = src_roi; |
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gmask = mask_roi; |
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} |
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}; |
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typedef CopyToTestBase CopyTo; |
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OCL_TEST_P(CopyTo, Without_mask) |
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{ |
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if((src.depth() == CV_64F) && |
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!cv::ocl::Context::getContext()->supportsFeature(cv::ocl::FEATURE_CL_DOUBLE)) |
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{ |
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return; // returns silently |
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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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src_roi.copyTo(dst_roi); |
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gsrc.copyTo(gdst); |
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EXPECT_MAT_NEAR(dst, Mat(gdst_whole), 0.0); |
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} |
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} |
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OCL_TEST_P(CopyTo, With_mask) |
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{ |
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if(src.depth() == CV_64F && |
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!cv::ocl::Context::getContext()->supportsFeature(cv::ocl::FEATURE_CL_DOUBLE)) |
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{ |
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return; // returns silently |
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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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src_roi.copyTo(dst_roi, mask_roi); |
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gsrc.copyTo(gdst, gmask); |
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EXPECT_MAT_NEAR(dst, Mat(gdst_whole), 0.0); |
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} |
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} |
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/////////////////////////////////////////// setTo ///////////////////////////////////////////////////////////// |
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PARAM_TEST_CASE(SetToTestBase, MatType, int, bool) |
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{ |
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int depth, channels; |
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bool use_roi; |
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cv::Scalar val; |
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cv::Mat src; |
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cv::Mat mask; |
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// set up roi |
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int roicols, roirows; |
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int srcx, srcy; |
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int maskx, masky; |
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// src mat with roi |
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cv::Mat src_roi; |
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cv::Mat mask_roi; |
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// ocl dst mat for testing |
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cv::ocl::oclMat gsrc_whole; |
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// ocl mat with roi |
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cv::ocl::oclMat gsrc; |
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cv::ocl::oclMat gmask; |
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virtual void SetUp() |
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{ |
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depth = GET_PARAM(0); |
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channels = GET_PARAM(1); |
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use_roi = GET_PARAM(2); |
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int type = CV_MAKE_TYPE(depth, channels); |
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src = randomMat(randomSize(MIN_VALUE, MAX_VALUE), type, 5, 16, false); |
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mask = randomMat(use_roi ? randomSize(MIN_VALUE, MAX_VALUE) : src.size(), CV_8UC1, 0, 2, false); |
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cv::threshold(mask, mask, 0.5, 255., CV_8UC1); |
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val = cv::Scalar(rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0), |
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rng.uniform(-10.0, 10.0), rng.uniform(-10.0, 10.0)); |
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} |
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void random_roi() |
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{ |
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if (use_roi) |
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{ |
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// randomize ROI |
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roicols = rng.uniform(1, MIN_VALUE); |
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roirows = rng.uniform(1, MIN_VALUE); |
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srcx = rng.uniform(0, src.cols - roicols); |
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srcy = rng.uniform(0, src.rows - roirows); |
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maskx = rng.uniform(0, mask.cols - roicols); |
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masky = rng.uniform(0, mask.rows - roirows); |
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} |
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else |
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{ |
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roicols = src.cols; |
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roirows = src.rows; |
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srcx = srcy = 0; |
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maskx = masky = 0; |
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} |
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src_roi = src(Rect(srcx, srcy, roicols, roirows)); |
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mask_roi = mask(Rect(maskx, masky, roicols, roirows)); |
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gsrc_whole = src; |
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gsrc = gsrc_whole(Rect(srcx, srcy, roicols, roirows)); |
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gmask = mask_roi; |
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} |
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}; |
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typedef SetToTestBase SetTo; |
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OCL_TEST_P(SetTo, Without_mask) |
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{ |
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if(depth == CV_64F && |
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!cv::ocl::Context::getContext()->supportsFeature(cv::ocl::FEATURE_CL_DOUBLE)) |
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{ |
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return; // returns silently |
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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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src_roi.setTo(val); |
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gsrc.setTo(val); |
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EXPECT_MAT_NEAR(src, Mat(gsrc_whole), 1.); |
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} |
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} |
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OCL_TEST_P(SetTo, With_mask) |
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{ |
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if(depth == CV_64F && |
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!cv::ocl::Context::getContext()->supportsFeature(cv::ocl::FEATURE_CL_DOUBLE)) |
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{ |
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return; // returns silently |
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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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src_roi.setTo(val, mask_roi); |
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gsrc.setTo(val, gmask); |
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EXPECT_MAT_NEAR(src, Mat(gsrc_whole), 1.); |
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} |
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} |
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// convertC3C4 |
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PARAM_TEST_CASE(convertC3C4, MatType, bool) |
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{ |
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int depth; |
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bool use_roi; |
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//src mat |
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cv::Mat src; |
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// set up roi |
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int roicols, roirows; |
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int srcx, srcy; |
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//src mat with roi |
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cv::Mat src_roi; |
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//ocl mat with roi |
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cv::ocl::oclMat gsrc_roi; |
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virtual void SetUp() |
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{ |
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depth = GET_PARAM(0); |
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use_roi = GET_PARAM(1); |
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int type = CV_MAKE_TYPE(depth, 3); |
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src = randomMat(randomSize(1, MAX_VALUE), type, 0, 40, false); |
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} |
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void random_roi() |
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{ |
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if (use_roi) |
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{ |
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//randomize ROI |
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roicols = rng.uniform(1, src.cols); |
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roirows = rng.uniform(1, src.rows); |
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srcx = rng.uniform(0, src.cols - roicols); |
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srcy = rng.uniform(0, src.rows - roirows); |
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} |
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else |
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{ |
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roicols = src.cols; |
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roirows = src.rows; |
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srcx = srcy = 0; |
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} |
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src_roi = src(Rect(srcx, srcy, roicols, roirows)); |
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} |
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}; |
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OCL_TEST_P(convertC3C4, Accuracy) |
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{ |
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if(depth == CV_64F && |
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!cv::ocl::Context::getContext()->supportsFeature(cv::ocl::FEATURE_CL_DOUBLE)) |
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{ |
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return; // returns silently |
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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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gsrc_roi = src_roi; |
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EXPECT_MAT_NEAR(src_roi, Mat(gsrc_roi), 0.0); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(MatrixOperation, ConvertTo, Combine( |
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Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F), |
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Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F), |
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Range(1, 5), Bool())); |
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INSTANTIATE_TEST_CASE_P(MatrixOperation, CopyTo, Combine( |
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Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F), |
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testing::Range(1, 5), Bool())); |
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INSTANTIATE_TEST_CASE_P(MatrixOperation, SetTo, Combine( |
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Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F), |
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testing::Range(1, 5), Bool())); |
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INSTANTIATE_TEST_CASE_P(MatrixOperation, convertC3C4, Combine( |
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Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F), |
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Bool())); |
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#endif
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