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Open Source Computer Vision Library
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610 lines
18 KiB
610 lines
18 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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// Niko Li, newlife20080214@gmail.com |
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// Jia Haipeng, jiahaipeng95@gmail.com |
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// Shengen Yan, yanshengen@gmail.com |
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// Jiang Liyuan, lyuan001.good@163.com |
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// Rock Li, Rock.Li@amd.com |
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// Wu Zailong, bullet@yeah.net |
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// Xu Pang, pangxu010@163.com |
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// Sen Liu, swjtuls1987@126.com |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials 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 testing; |
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using namespace std; |
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using namespace cv; |
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/////////////////////////////////////////////////////////////////////////////// |
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PARAM_TEST_CASE(ImgprocTestBase, MatType, |
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int, // blockSize |
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int, // border type |
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bool) // roi or not |
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{ |
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int type, borderType, blockSize; |
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bool useRoi; |
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Mat src, dst_whole, src_roi, dst_roi; |
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ocl::oclMat gsrc_whole, gsrc_roi, gdst_whole, gdst_roi; |
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virtual void SetUp() |
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{ |
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type = GET_PARAM(0); |
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blockSize = GET_PARAM(1); |
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borderType = GET_PARAM(2); |
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useRoi = GET_PARAM(3); |
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} |
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virtual void random_roi() |
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{ |
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Size roiSize = randomSize(1, MAX_VALUE); |
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Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(src, src_roi, roiSize, srcBorder, type, 5, 256); |
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Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(dst_whole, dst_roi, roiSize, dstBorder, type, 5, 16); |
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generateOclMat(gsrc_whole, gsrc_roi, src, roiSize, srcBorder); |
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generateOclMat(gdst_whole, gdst_roi, dst_whole, roiSize, dstBorder); |
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} |
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void Near(double threshold = 0.0, bool relative = false) |
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{ |
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Mat roi, whole; |
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gdst_whole.download(whole); |
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gdst_roi.download(roi); |
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if (relative) |
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{ |
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EXPECT_MAT_NEAR_RELATIVE(dst_whole, whole, threshold); |
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EXPECT_MAT_NEAR_RELATIVE(dst_roi, roi, threshold); |
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} |
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else |
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{ |
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EXPECT_MAT_NEAR(dst_whole, whole, threshold); |
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EXPECT_MAT_NEAR(dst_roi, roi, threshold); |
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} |
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} |
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}; |
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////////////////////////////////copyMakeBorder//////////////////////////////////////////// |
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PARAM_TEST_CASE(CopyMakeBorder, MatDepth, // depth |
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Channels, // channels |
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bool, // isolated or not |
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Border, // border type |
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bool) // roi or not |
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{ |
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int type, borderType; |
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bool useRoi; |
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Border border; |
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Scalar val; |
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Mat src, dst_whole, src_roi, dst_roi; |
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ocl::oclMat gsrc_whole, gsrc_roi, gdst_whole, gdst_roi; |
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virtual void SetUp() |
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{ |
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type = CV_MAKE_TYPE(GET_PARAM(0), GET_PARAM(1)); |
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borderType = GET_PARAM(3); |
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if (GET_PARAM(2)) |
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borderType |= BORDER_ISOLATED; |
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useRoi = GET_PARAM(4); |
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} |
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void random_roi() |
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{ |
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border = randomBorder(0, MAX_VALUE << 2); |
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val = randomScalar(-MAX_VALUE, MAX_VALUE); |
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Size roiSize = randomSize(1, MAX_VALUE); |
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Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(src, src_roi, roiSize, srcBorder, type, -MAX_VALUE, MAX_VALUE); |
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Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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dstBorder.top += border.top; |
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dstBorder.lef += border.lef; |
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dstBorder.rig += border.rig; |
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dstBorder.bot += border.bot; |
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randomSubMat(dst_whole, dst_roi, roiSize, dstBorder, type, -MAX_VALUE, MAX_VALUE); |
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generateOclMat(gsrc_whole, gsrc_roi, src, roiSize, srcBorder); |
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generateOclMat(gdst_whole, gdst_roi, dst_whole, roiSize, dstBorder); |
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} |
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void Near(double threshold = 0.0) |
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{ |
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Mat whole, roi; |
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gdst_whole.download(whole); |
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gdst_roi.download(roi); |
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EXPECT_MAT_NEAR(dst_whole, whole, threshold); |
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EXPECT_MAT_NEAR(dst_roi, roi, threshold); |
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} |
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}; |
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OCL_TEST_P(CopyMakeBorder, Mat) |
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{ |
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for (int i = 0; i < LOOP_TIMES; ++i) |
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{ |
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random_roi(); |
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cv::copyMakeBorder(src_roi, dst_roi, border.top, border.bot, border.lef, border.rig, borderType, val); |
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ocl::copyMakeBorder(gsrc_roi, gdst_roi, border.top, border.bot, border.lef, border.rig, borderType, val); |
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Near(); |
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} |
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} |
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////////////////////////////////equalizeHist////////////////////////////////////////////// |
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typedef ImgprocTestBase EqualizeHist; |
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OCL_TEST_P(EqualizeHist, Mat) |
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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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equalizeHist(src_roi, dst_roi); |
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ocl::equalizeHist(gsrc_roi, gdst_roi); |
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Near(1.1); |
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} |
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} |
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////////////////////////////////cornerMinEigenVal////////////////////////////////////////// |
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struct CornerTestBase : |
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public ImgprocTestBase |
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{ |
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virtual void random_roi() |
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{ |
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Mat image = readImageType("gpu/stereobm/aloe-L.png", type); |
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ASSERT_FALSE(image.empty()); |
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bool isFP = CV_MAT_DEPTH(type) >= CV_32F; |
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float val = 255.0f; |
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if (isFP) |
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{ |
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image.convertTo(image, -1, 1.0 / 255); |
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val /= 255.0f; |
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} |
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Size roiSize = image.size(); |
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Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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Size wholeSize = Size(roiSize.width + srcBorder.lef + srcBorder.rig, roiSize.height + srcBorder.top + srcBorder.bot); |
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src = randomMat(wholeSize, type, -val, val, false); |
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src_roi = src(Rect(srcBorder.lef, srcBorder.top, roiSize.width, roiSize.height)); |
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image.copyTo(src_roi); |
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Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(dst_whole, dst_roi, roiSize, dstBorder, CV_32FC1, 5, 16); |
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generateOclMat(gsrc_whole, gsrc_roi, src, roiSize, srcBorder); |
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generateOclMat(gdst_whole, gdst_roi, dst_whole, roiSize, dstBorder); |
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} |
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}; |
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typedef CornerTestBase CornerMinEigenVal; |
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OCL_TEST_P(CornerMinEigenVal, Mat) |
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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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int apertureSize = 3; |
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cornerMinEigenVal(src_roi, dst_roi, blockSize, apertureSize, borderType); |
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ocl::cornerMinEigenVal(gsrc_roi, gdst_roi, blockSize, apertureSize, borderType); |
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Near(1e-5, true); |
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} |
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} |
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////////////////////////////////cornerHarris////////////////////////////////////////// |
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struct CornerHarris : |
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public ImgprocTestBase |
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{ |
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void Near(double threshold = 0.0) |
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{ |
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Mat whole, roi; |
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gdst_whole.download(whole); |
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gdst_roi.download(roi); |
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absdiff(whole, dst_whole, whole); |
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absdiff(roi, dst_roi, roi); |
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divide(whole, dst_whole, whole); |
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divide(roi, dst_roi, roi); |
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absdiff(dst_whole, dst_whole, dst_whole); |
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absdiff(dst_roi, dst_roi, dst_roi); |
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EXPECT_MAT_NEAR(dst_whole, whole, threshold); |
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EXPECT_MAT_NEAR(dst_roi, roi, threshold); |
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} |
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}; |
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OCL_TEST_P(CornerHarris, Mat) |
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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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int apertureSize = 3; |
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double k = randomDouble(0.01, 0.9); |
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cornerHarris(src_roi, dst_roi, blockSize, apertureSize, k, borderType); |
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ocl::cornerHarris(gsrc_roi, gdst_roi, blockSize, apertureSize, k, borderType); |
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Near(1e-5); |
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} |
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} |
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//////////////////////////////////integral///////////////////////////////////////////////// |
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typedef ImgprocTestBase Integral; |
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OCL_TEST_P(Integral, Mat1) |
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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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ocl::integral(gsrc_roi, gdst_roi); |
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integral(src_roi, dst_roi); |
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Near(); |
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} |
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} |
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// TODO wrong output type |
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OCL_TEST_P(Integral, DISABLED_Mat2) |
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{ |
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Mat dst1; |
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ocl::oclMat gdst1; |
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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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integral(src_roi, dst1, dst_roi); |
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ocl::integral(gsrc_roi, gdst1, gdst_roi); |
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Near(); |
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} |
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} |
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/////////////////////////////////////////////////////////////////////////////////////////////////// |
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//// threshold |
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struct Threshold : |
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public ImgprocTestBase |
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{ |
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int thresholdType; |
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virtual void SetUp() |
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{ |
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type = GET_PARAM(0); |
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blockSize = GET_PARAM(1); |
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thresholdType = GET_PARAM(2); |
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useRoi = GET_PARAM(3); |
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} |
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}; |
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OCL_TEST_P(Threshold, Mat) |
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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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double maxVal = randomDouble(20.0, 127.0); |
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double thresh = randomDouble(0.0, maxVal); |
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threshold(src_roi, dst_roi, thresh, maxVal, thresholdType); |
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ocl::threshold(gsrc_roi, gdst_roi, thresh, maxVal, thresholdType); |
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Near(1); |
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} |
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} |
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///////////////////////////////////////////////////////////////////////////////////////// |
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// calcHist |
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static void calcHistGold(const Mat &src, Mat &hist) |
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{ |
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hist = Mat(1, 256, CV_32SC1, Scalar::all(0)); |
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int * const hist_row = hist.ptr<int>(); |
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for (int y = 0; y < src.rows; ++y) |
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{ |
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const uchar * const src_row = src.ptr(y); |
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for (int x = 0; x < src.cols; ++x) |
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++hist_row[src_row[x]]; |
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} |
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} |
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typedef ImgprocTestBase CalcHist; |
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OCL_TEST_P(CalcHist, Mat) |
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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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calcHistGold(src_roi, dst_roi); |
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ocl::calcHist(gsrc_roi, gdst_roi); |
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Near(); |
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} |
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} |
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///////////////////////////////////////////////////////////////////////////////////////////////////////// |
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//// CLAHE |
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PARAM_TEST_CASE(CLAHETest, Size, double, bool) |
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{ |
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Size gridSize; |
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double clipLimit; |
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bool useRoi; |
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Mat src, dst_whole, src_roi, dst_roi; |
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ocl::oclMat gsrc_whole, gsrc_roi, gdst_whole, gdst_roi; |
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virtual void SetUp() |
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{ |
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gridSize = GET_PARAM(0); |
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clipLimit = GET_PARAM(1); |
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useRoi = GET_PARAM(2); |
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} |
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void random_roi() |
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{ |
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Size roiSize = randomSize(std::max(gridSize.height, gridSize.width), MAX_VALUE); |
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Border srcBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(src, src_roi, roiSize, srcBorder, CV_8UC1, 5, 256); |
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Border dstBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(dst_whole, dst_roi, roiSize, dstBorder, CV_8UC1, 5, 16); |
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generateOclMat(gsrc_whole, gsrc_roi, src, roiSize, srcBorder); |
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generateOclMat(gdst_whole, gdst_roi, dst_whole, roiSize, dstBorder); |
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} |
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void Near(double threshold = 0.0) |
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{ |
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Mat whole, roi; |
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gdst_whole.download(whole); |
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gdst_roi.download(roi); |
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EXPECT_MAT_NEAR(dst_whole, whole, threshold); |
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EXPECT_MAT_NEAR(dst_roi, roi, threshold); |
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} |
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}; |
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OCL_TEST_P(CLAHETest, Accuracy) |
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{ |
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for (int i = 0; i < LOOP_TIMES; ++i) |
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{ |
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random_roi(); |
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Ptr<CLAHE> clahe = ocl::createCLAHE(clipLimit, gridSize); |
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clahe->apply(gsrc_roi, gdst_roi); |
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Ptr<CLAHE> clahe_gold = createCLAHE(clipLimit, gridSize); |
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clahe_gold->apply(src_roi, dst_roi); |
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Near(1.0); |
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} |
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} |
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/////////////////////////////Convolve////////////////////////////////// |
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static void convolve_gold(const Mat & src, const Mat & kernel, Mat & dst) |
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{ |
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for (int i = 0; i < src.rows; i++) |
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{ |
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float * const dstptr = dst.ptr<float>(i); |
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for (int j = 0; j < src.cols; j++) |
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{ |
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float temp = 0; |
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for (int m = 0; m < kernel.rows; m++) |
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{ |
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const float * const kptr = kernel.ptr<float>(m); |
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for (int n = 0; n < kernel.cols; n++) |
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{ |
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int r = clipInt(i - kernel.rows / 2 + m, 0, src.rows - 1); |
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int c = clipInt(j - kernel.cols / 2 + n, 0, src.cols - 1); |
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temp += src.ptr<float>(r)[c] * kptr[n]; |
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} |
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} |
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dstptr[j] = temp; |
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} |
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} |
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} |
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typedef ImgprocTestBase Convolve; |
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OCL_TEST_P(Convolve, Mat) |
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{ |
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Mat kernel, kernel_roi; |
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ocl::oclMat gkernel, gkernel_roi; |
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const Size roiSize(7, 7); |
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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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Border kernelBorder = randomBorder(0, useRoi ? MAX_VALUE : 0); |
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randomSubMat(kernel, kernel_roi, roiSize, kernelBorder, type, 5, 16); |
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generateOclMat(gkernel, gkernel_roi, kernel, roiSize, kernelBorder); |
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convolve_gold(src_roi, kernel_roi, dst_roi); |
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ocl::convolve(gsrc_roi, gkernel_roi, gdst_roi); |
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Near(1); |
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} |
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} |
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////////////////////////////////// ColumnSum ////////////////////////////////////// |
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static void columnSum_gold(const Mat & src, Mat & dst) |
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{ |
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float * prevdptr = dst.ptr<float>(0); |
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const float * sptr = src.ptr<float>(0); |
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for (int x = 0; x < src.cols; ++x) |
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prevdptr[x] = sptr[x]; |
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for (int y = 1; y < src.rows; ++y) |
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{ |
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sptr = src.ptr<float>(y); |
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float * const dptr = dst.ptr<float>(y); |
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for (int x = 0; x < src.cols; ++x) |
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dptr[x] = prevdptr[x] + sptr[x]; |
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prevdptr = dptr; |
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} |
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} |
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typedef ImgprocTestBase ColumnSum; |
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OCL_TEST_P(ColumnSum, Accuracy) |
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{ |
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for (int i = 0; i < LOOP_TIMES; ++i) |
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{ |
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random_roi(); |
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columnSum_gold(src_roi, dst_roi); |
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ocl::columnSum(gsrc_roi, gdst_roi); |
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Near(1e-5); |
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} |
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} |
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///////////////////////////////////////////////////////////////////////////////////// |
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INSTANTIATE_TEST_CASE_P(Imgproc, EqualizeHist, Combine( |
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Values((MatType)CV_8UC1), |
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Values(0), // not used |
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Values(0), // not used |
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Bool())); |
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INSTANTIATE_TEST_CASE_P(Imgproc, CornerMinEigenVal, Combine( |
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Values((MatType)CV_8UC1, (MatType)CV_32FC1), |
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Values(3, 5), |
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Values((int)BORDER_CONSTANT, (int)BORDER_REPLICATE, (int)BORDER_REFLECT, (int)BORDER_REFLECT101), |
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Bool())); |
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INSTANTIATE_TEST_CASE_P(Imgproc, CornerHarris, Combine( |
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Values((MatType)CV_8UC1, CV_32FC1), |
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Values(3, 5), |
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Values( (int)BORDER_CONSTANT, (int)BORDER_REPLICATE, (int)BORDER_REFLECT, (int)BORDER_REFLECT_101), |
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Bool())); |
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INSTANTIATE_TEST_CASE_P(Imgproc, Integral, Combine( |
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Values((MatType)CV_8UC1), // TODO does not work with CV_32F, CV_64F |
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Values(0), // not used |
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Values(0), // not used |
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Bool())); |
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INSTANTIATE_TEST_CASE_P(Imgproc, Threshold, Combine( |
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Values(CV_8UC1, CV_8UC2, CV_8UC3, CV_8UC4, |
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CV_16SC1, CV_16SC2, CV_16SC3, CV_16SC4, |
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CV_32FC1, CV_32FC2, CV_32FC3, CV_32FC4), |
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Values(0), |
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Values(ThreshOp(THRESH_BINARY), |
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ThreshOp(THRESH_BINARY_INV), ThreshOp(THRESH_TRUNC), |
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ThreshOp(THRESH_TOZERO), ThreshOp(THRESH_TOZERO_INV)), |
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Bool())); |
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INSTANTIATE_TEST_CASE_P(Imgproc, CalcHist, Combine( |
|
Values((MatType)CV_8UC1), |
|
Values(0), // not used |
|
Values(0), // not used |
|
Bool())); |
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, CLAHETest, Combine( |
|
Values(Size(4, 4), Size(32, 8), Size(8, 64)), |
|
Values(0.0, 10.0, 62.0, 300.0), |
|
Bool())); |
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, Convolve, Combine( |
|
Values((MatType)CV_32FC1), |
|
Values(0), // not used |
|
Values(0), // not used |
|
Bool())); |
|
|
|
INSTANTIATE_TEST_CASE_P(Imgproc, ColumnSum, Combine( |
|
Values(MatType(CV_32FC1)), |
|
Values(0), // not used |
|
Values(0), // not used |
|
Bool())); |
|
|
|
INSTANTIATE_TEST_CASE_P(ImgprocTestBase, CopyMakeBorder, Combine( |
|
testing::Values((MatDepth)CV_8U, (MatDepth)CV_16S, (MatDepth)CV_32S, (MatDepth)CV_32F), |
|
testing::Values(Channels(1), Channels(3), (Channels)4), |
|
Bool(), // border isolated or not |
|
Values((Border)BORDER_REPLICATE, (Border)BORDER_REFLECT, |
|
(Border)BORDER_WRAP, (Border)BORDER_REFLECT_101), |
|
Bool())); |
|
|
|
#endif // HAVE_OPENCL
|
|
|