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932 lines
27 KiB
932 lines
27 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, Multicoreware, Inc., all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Fangfang Bai, fangfang@multicorewareinc.com |
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// Jin Ma, jin@multicorewareinc.com |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other 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 "perf_precomp.hpp" |
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using namespace perf; |
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using std::tr1::tuple; |
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using std::tr1::get; |
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///////////// equalizeHist //////////////////////// |
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typedef TestBaseWithParam<Size> equalizeHistFixture; |
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PERF_TEST_P(equalizeHistFixture, equalizeHist, OCL_TYPICAL_MAT_SIZES) |
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{ |
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const Size srcSize = GetParam(); |
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const double eps = 1 + DBL_EPSILON; |
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Mat src(srcSize, CV_8UC1), dst(srcSize, CV_8UC1); |
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declare.in(src, WARMUP_RNG).out(dst); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(srcSize, src.type()); |
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OCL_TEST_CYCLE() cv::ocl::equalizeHist(oclSrc, oclDst); |
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oclDst.download(dst); |
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SANITY_CHECK(dst, eps); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::equalizeHist(src, dst); |
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SANITY_CHECK(dst, eps); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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/////////// CopyMakeBorder ////////////////////// |
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CV_ENUM(Border, BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT, |
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BORDER_WRAP, BORDER_REFLECT_101) |
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typedef tuple<Size, MatType, Border> CopyMakeBorderParamType; |
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typedef TestBaseWithParam<CopyMakeBorderParamType> CopyMakeBorderFixture; |
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PERF_TEST_P(CopyMakeBorderFixture, CopyMakeBorder, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_8UC4), |
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Border::all())) |
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{ |
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const CopyMakeBorderParamType params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int type = get<1>(params), borderType = get<2>(params); |
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Mat src(srcSize, type), dst; |
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const Size dstSize = srcSize + Size(12, 12); |
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dst.create(dstSize, type); |
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declare.in(src, WARMUP_RNG).out(dst); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(dstSize, type); |
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OCL_TEST_CYCLE() cv::ocl::copyMakeBorder(oclSrc, oclDst, 7, 5, 5, 7, borderType, cv::Scalar(1.0)); |
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oclDst.download(dst); |
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SANITY_CHECK(dst); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::copyMakeBorder(src, dst, 7, 5, 5, 7, borderType, cv::Scalar(1.0)); |
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SANITY_CHECK(dst); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// cornerMinEigenVal //////////////////////// |
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typedef Size_MatType cornerMinEigenValFixture; |
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PERF_TEST_P(cornerMinEigenValFixture, cornerMinEigenVal, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_32FC1))) |
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{ |
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const Size_MatType_t params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int type = get<1>(params), borderType = BORDER_REFLECT; |
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const int blockSize = 7, apertureSize = 1 + 2 * 3; |
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Mat src(srcSize, type), dst(srcSize, CV_32FC1); |
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declare.in(src, WARMUP_RNG).out(dst) |
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.time(srcSize == OCL_SIZE_4000 ? 20 : srcSize == OCL_SIZE_2000 ? 5 : 3); |
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const int depth = CV_MAT_DEPTH(type); |
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const ERROR_TYPE errorType = depth == CV_8U ? ERROR_ABSOLUTE : ERROR_RELATIVE; |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1); |
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OCL_TEST_CYCLE() cv::ocl::cornerMinEigenVal(oclSrc, oclDst, blockSize, apertureSize, borderType); |
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oclDst.download(dst); |
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SANITY_CHECK(dst, 1e-6, errorType); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::cornerMinEigenVal(src, dst, blockSize, apertureSize, borderType); |
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SANITY_CHECK(dst, 1e-6, errorType); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// cornerHarris //////////////////////// |
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typedef Size_MatType cornerHarrisFixture; |
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PERF_TEST_P(cornerHarrisFixture, cornerHarris, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_32FC1))) |
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{ |
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const Size_MatType_t params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int type = get<1>(params), borderType = BORDER_REFLECT; |
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Mat src(srcSize, type), dst(srcSize, CV_32FC1); |
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randu(src, 0, 1); |
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declare.in(src).out(dst) |
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.time(srcSize == OCL_SIZE_4000 ? 20 : srcSize == OCL_SIZE_2000 ? 5 : 3); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1); |
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OCL_TEST_CYCLE() cv::ocl::cornerHarris(oclSrc, oclDst, 5, 7, 0.1, borderType); |
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oclDst.download(dst); |
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SANITY_CHECK(dst, 3e-5); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::cornerHarris(src, dst, 5, 7, 0.1, borderType); |
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SANITY_CHECK(dst, 3e-5); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// integral //////////////////////// |
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typedef TestBaseWithParam<Size> integralFixture; |
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PERF_TEST_P(integralFixture, integral, OCL_TYPICAL_MAT_SIZES) |
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{ |
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const Size srcSize = GetParam(); |
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Mat src(srcSize, CV_8UC1), dst; |
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declare.in(src, WARMUP_RNG); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst; |
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OCL_TEST_CYCLE() cv::ocl::integral(oclSrc, oclDst); |
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oclDst.download(dst); |
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SANITY_CHECK(dst); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::integral(src, dst); |
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SANITY_CHECK(dst); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// WarpAffine //////////////////////// |
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typedef Size_MatType WarpAffineFixture; |
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PERF_TEST_P(WarpAffineFixture, WarpAffine, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_8UC4))) |
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{ |
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static const double coeffs[2][3] = |
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{ |
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{ cos(CV_PI / 6), -sin(CV_PI / 6), 100.0 }, |
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{ sin(CV_PI / 6), cos(CV_PI / 6), -100.0 } |
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}; |
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Mat M(2, 3, CV_64F, (void *)coeffs); |
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const int interpolation = INTER_NEAREST; |
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const Size_MatType_t params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int type = get<1>(params); |
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Mat src(srcSize, type), dst(srcSize, type); |
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declare.in(src, WARMUP_RNG).out(dst); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(srcSize, type); |
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OCL_TEST_CYCLE() cv::ocl::warpAffine(oclSrc, oclDst, M, srcSize, interpolation); |
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oclDst.download(dst); |
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SANITY_CHECK(dst); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::warpAffine(src, dst, M, srcSize, interpolation); |
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SANITY_CHECK(dst); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// WarpPerspective //////////////////////// |
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typedef Size_MatType WarpPerspectiveFixture; |
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PERF_TEST_P(WarpPerspectiveFixture, WarpPerspective, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_8UC4))) |
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{ |
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static const double coeffs[3][3] = |
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{ |
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{cos(CV_PI / 6), -sin(CV_PI / 6), 100.0}, |
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{sin(CV_PI / 6), cos(CV_PI / 6), -100.0}, |
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{0.0, 0.0, 1.0} |
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}; |
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Mat M(3, 3, CV_64F, (void *)coeffs); |
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const int interpolation = INTER_LINEAR; |
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const Size_MatType_t params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int type = get<1>(params); |
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Mat src(srcSize, type), dst(srcSize, type); |
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declare.in(src, WARMUP_RNG).out(dst) |
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.time(srcSize == OCL_SIZE_4000 ? 18 : srcSize == OCL_SIZE_2000 ? 5 : 2); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(srcSize, type); |
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OCL_TEST_CYCLE() cv::ocl::warpPerspective(oclSrc, oclDst, M, srcSize, interpolation); |
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oclDst.download(dst); |
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SANITY_CHECK(dst); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::warpPerspective(src, dst, M, srcSize, interpolation); |
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SANITY_CHECK(dst); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// resize //////////////////////// |
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CV_ENUM(resizeInterType, INTER_NEAREST, INTER_LINEAR) |
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typedef tuple<Size, MatType, resizeInterType, double> resizeParams; |
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typedef TestBaseWithParam<resizeParams> resizeFixture; |
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PERF_TEST_P(resizeFixture, resize, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_8UC4), |
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resizeInterType::all(), |
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::testing::Values(0.5, 2.0))) |
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{ |
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const resizeParams params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int type = get<1>(params), interType = get<2>(params); |
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double scale = get<3>(params); |
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Mat src(srcSize, type), dst; |
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const Size dstSize(cvRound(srcSize.width * scale), cvRound(srcSize.height * scale)); |
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dst.create(dstSize, type); |
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declare.in(src, WARMUP_RNG).out(dst); |
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if (interType == INTER_LINEAR && type == CV_8UC4 && OCL_SIZE_4000 == srcSize) |
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declare.time(11); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(dstSize, type); |
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OCL_TEST_CYCLE() cv::ocl::resize(oclSrc, oclDst, Size(), scale, scale, interType); |
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oclDst.download(dst); |
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SANITY_CHECK(dst, 1 + DBL_EPSILON); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::resize(src, dst, Size(), scale, scale, interType); |
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SANITY_CHECK(dst, 1 + DBL_EPSILON); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// threshold//////////////////////// |
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CV_ENUM(ThreshType, THRESH_BINARY, THRESH_TOZERO_INV) |
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typedef tuple<Size, MatType, ThreshType> ThreshParams; |
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typedef TestBaseWithParam<ThreshParams> ThreshFixture; |
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PERF_TEST_P(ThreshFixture, threshold, |
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::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
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OCL_PERF_ENUM(CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC4, CV_32FC1), |
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ThreshType::all())) |
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{ |
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const ThreshParams params = GetParam(); |
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const Size srcSize = get<0>(params); |
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const int srcType = get<1>(params); |
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const int threshType = get<2>(params); |
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const double maxValue = 220.0, threshold = 50; |
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Mat src(srcSize, srcType), dst(srcSize, srcType); |
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randu(src, 0, 100); |
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declare.in(src).out(dst); |
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if (RUN_OCL_IMPL) |
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{ |
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ocl::oclMat oclSrc(src), oclDst(srcSize, CV_8U); |
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OCL_TEST_CYCLE() cv::ocl::threshold(oclSrc, oclDst, threshold, maxValue, threshType); |
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oclDst.download(dst); |
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SANITY_CHECK(dst); |
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} |
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else if (RUN_PLAIN_IMPL) |
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{ |
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TEST_CYCLE() cv::threshold(src, dst, threshold, maxValue, threshType); |
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SANITY_CHECK(dst); |
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} |
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else |
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OCL_PERF_ELSE |
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} |
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///////////// meanShiftFiltering//////////////////////// |
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typedef struct _COOR |
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{ |
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short x; |
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short y; |
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} COOR; |
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static COOR do_meanShift(int x0, int y0, uchar *sptr, uchar *dptr, int sstep, cv::Size size, int sp, int sr, int maxIter, float eps, int *tab) |
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{ |
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int isr2 = sr * sr; |
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int c0, c1, c2, c3; |
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int iter; |
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uchar *ptr = NULL; |
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uchar *pstart = NULL; |
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int revx = 0, revy = 0; |
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c0 = sptr[0]; |
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c1 = sptr[1]; |
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c2 = sptr[2]; |
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c3 = sptr[3]; |
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// iterate meanshift procedure |
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for(iter = 0; iter < maxIter; iter++ ) |
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{ |
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int count = 0; |
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int s0 = 0, s1 = 0, s2 = 0, sx = 0, sy = 0; |
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//mean shift: process pixels in window (p-sigmaSp)x(p+sigmaSp) |
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int minx = x0 - sp; |
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int miny = y0 - sp; |
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int maxx = x0 + sp; |
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int maxy = y0 + sp; |
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//deal with the image boundary |
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if(minx < 0) minx = 0; |
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if(miny < 0) miny = 0; |
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if(maxx >= size.width) maxx = size.width - 1; |
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if(maxy >= size.height) maxy = size.height - 1; |
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if(iter == 0) |
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{ |
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pstart = sptr; |
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} |
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else |
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{ |
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pstart = pstart + revy * sstep + (revx << 2); //point to the new position |
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} |
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ptr = pstart; |
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ptr = ptr + (miny - y0) * sstep + ((minx - x0) << 2); //point to the start in the row |
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for( int y = miny; y <= maxy; y++, ptr += sstep - ((maxx - minx + 1) << 2)) |
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{ |
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int rowCount = 0; |
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int x = minx; |
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#if CV_ENABLE_UNROLLED |
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for( ; x + 4 <= maxx; x += 4, ptr += 16) |
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{ |
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int t0, t1, t2; |
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t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x; |
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rowCount++; |
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} |
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t0 = ptr[4], t1 = ptr[5], t2 = ptr[6]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x + 1; |
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rowCount++; |
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} |
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t0 = ptr[8], t1 = ptr[9], t2 = ptr[10]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x + 2; |
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rowCount++; |
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} |
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t0 = ptr[12], t1 = ptr[13], t2 = ptr[14]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x + 3; |
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rowCount++; |
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} |
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} |
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#endif |
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for(; x <= maxx; x++, ptr += 4) |
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{ |
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int t0 = ptr[0], t1 = ptr[1], t2 = ptr[2]; |
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if(tab[t0 - c0 + 255] + tab[t1 - c1 + 255] + tab[t2 - c2 + 255] <= isr2) |
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{ |
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s0 += t0; |
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s1 += t1; |
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s2 += t2; |
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sx += x; |
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rowCount++; |
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} |
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} |
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if(rowCount == 0) |
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continue; |
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count += rowCount; |
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sy += y * rowCount; |
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} |
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|
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if( count == 0 ) |
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break; |
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|
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int x1 = sx / count; |
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int y1 = sy / count; |
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s0 = s0 / count; |
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s1 = s1 / count; |
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s2 = s2 / count; |
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|
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bool stopFlag = (x0 == x1 && y0 == y1) || (abs(x1 - x0) + abs(y1 - y0) + |
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tab[s0 - c0 + 255] + tab[s1 - c1 + 255] + tab[s2 - c2 + 255] <= eps); |
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|
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//revise the pointer corresponding to the new (y0,x0) |
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revx = x1 - x0; |
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revy = y1 - y0; |
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x0 = x1; |
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y0 = y1; |
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c0 = s0; |
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c1 = s1; |
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c2 = s2; |
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|
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if( stopFlag ) |
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break; |
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} //for iter |
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|
|
dptr[0] = (uchar)c0; |
|
dptr[1] = (uchar)c1; |
|
dptr[2] = (uchar)c2; |
|
dptr[3] = (uchar)c3; |
|
|
|
COOR coor; |
|
coor.x = static_cast<short>(x0); |
|
coor.y = static_cast<short>(y0); |
|
return coor; |
|
} |
|
|
|
static void meanShiftFiltering_(const Mat &src_roi, Mat &dst_roi, int sp, int sr, cv::TermCriteria crit) |
|
{ |
|
if( src_roi.empty() ) |
|
CV_Error( CV_StsBadArg, "The input image is empty" ); |
|
|
|
if( src_roi.depth() != CV_8U || src_roi.channels() != 4 ) |
|
CV_Error( CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported" ); |
|
|
|
dst_roi.create(src_roi.size(), src_roi.type()); |
|
|
|
CV_Assert( (src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) ); |
|
CV_Assert( !(dst_roi.step & 0x3) ); |
|
|
|
if( !(crit.type & cv::TermCriteria::MAX_ITER) ) |
|
crit.maxCount = 5; |
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100); |
|
float eps; |
|
if( !(crit.type & cv::TermCriteria::EPS) ) |
|
eps = 1.f; |
|
eps = (float)std::max(crit.epsilon, 0.0); |
|
|
|
int tab[512]; |
|
for(int i = 0; i < 512; i++) |
|
tab[i] = (i - 255) * (i - 255); |
|
uchar *sptr = src_roi.data; |
|
uchar *dptr = dst_roi.data; |
|
int sstep = (int)src_roi.step; |
|
int dstep = (int)dst_roi.step; |
|
cv::Size size = src_roi.size(); |
|
|
|
for(int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), |
|
dptr += dstep - (size.width << 2)) |
|
{ |
|
for(int j = 0; j < size.width; j++, sptr += 4, dptr += 4) |
|
{ |
|
do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); |
|
} |
|
} |
|
} |
|
|
|
typedef TestBaseWithParam<Size> meanShiftFilteringFixture; |
|
|
|
PERF_TEST_P(meanShiftFilteringFixture, meanShiftFiltering, |
|
OCL_TYPICAL_MAT_SIZES) |
|
{ |
|
const Size srcSize = GetParam(); |
|
const int sp = 5, sr = 6; |
|
cv::TermCriteria crit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 5, 1); |
|
|
|
Mat src(srcSize, CV_8UC4), dst(srcSize, CV_8UC4); |
|
declare.in(src, WARMUP_RNG).out(dst) |
|
.time(srcSize == OCL_SIZE_4000 ? |
|
56 : srcSize == OCL_SIZE_2000 ? 15 : 3.8); |
|
|
|
if (RUN_PLAIN_IMPL) |
|
{ |
|
TEST_CYCLE() meanShiftFiltering_(src, dst, sp, sr, crit); |
|
|
|
SANITY_CHECK(dst); |
|
} |
|
else if (RUN_OCL_IMPL) |
|
{ |
|
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_8UC4); |
|
|
|
OCL_TEST_CYCLE() ocl::meanShiftFiltering(oclSrc, oclDst, sp, sr, crit); |
|
|
|
oclDst.download(dst); |
|
|
|
SANITY_CHECK(dst); |
|
} |
|
else |
|
OCL_PERF_ELSE |
|
} |
|
|
|
static void meanShiftProc_(const Mat &src_roi, Mat &dst_roi, Mat &dstCoor_roi, int sp, int sr, cv::TermCriteria crit) |
|
{ |
|
if (src_roi.empty()) |
|
{ |
|
CV_Error(CV_StsBadArg, "The input image is empty"); |
|
} |
|
if (src_roi.depth() != CV_8U || src_roi.channels() != 4) |
|
{ |
|
CV_Error(CV_StsUnsupportedFormat, "Only 8-bit, 4-channel images are supported"); |
|
} |
|
|
|
dst_roi.create(src_roi.size(), src_roi.type()); |
|
dstCoor_roi.create(src_roi.size(), CV_16SC2); |
|
|
|
CV_Assert((src_roi.cols == dst_roi.cols) && (src_roi.rows == dst_roi.rows) && |
|
(src_roi.cols == dstCoor_roi.cols) && (src_roi.rows == dstCoor_roi.rows)); |
|
CV_Assert(!(dstCoor_roi.step & 0x3)); |
|
|
|
if (!(crit.type & cv::TermCriteria::MAX_ITER)) |
|
{ |
|
crit.maxCount = 5; |
|
} |
|
|
|
int maxIter = std::min(std::max(crit.maxCount, 1), 100); |
|
float eps; |
|
|
|
if (!(crit.type & cv::TermCriteria::EPS)) |
|
{ |
|
eps = 1.f; |
|
} |
|
|
|
eps = (float)std::max(crit.epsilon, 0.0); |
|
|
|
int tab[512]; |
|
|
|
for (int i = 0; i < 512; i++) |
|
{ |
|
tab[i] = (i - 255) * (i - 255); |
|
} |
|
|
|
uchar *sptr = src_roi.data; |
|
uchar *dptr = dst_roi.data; |
|
short *dCoorptr = (short *)dstCoor_roi.data; |
|
int sstep = (int)src_roi.step; |
|
int dstep = (int)dst_roi.step; |
|
int dCoorstep = (int)dstCoor_roi.step >> 1; |
|
cv::Size size = src_roi.size(); |
|
|
|
for (int i = 0; i < size.height; i++, sptr += sstep - (size.width << 2), |
|
dptr += dstep - (size.width << 2), dCoorptr += dCoorstep - (size.width << 1)) |
|
{ |
|
for (int j = 0; j < size.width; j++, sptr += 4, dptr += 4, dCoorptr += 2) |
|
{ |
|
*((COOR *)dCoorptr) = do_meanShift(j, i, sptr, dptr, sstep, size, sp, sr, maxIter, eps, tab); |
|
} |
|
} |
|
|
|
} |
|
|
|
typedef TestBaseWithParam<Size> meanShiftProcFixture; |
|
|
|
PERF_TEST_P(meanShiftProcFixture, meanShiftProc, |
|
OCL_TYPICAL_MAT_SIZES) |
|
{ |
|
const Size srcSize = GetParam(); |
|
TermCriteria crit(TermCriteria::COUNT + TermCriteria::EPS, 5, 1); |
|
|
|
Mat src(srcSize, CV_8UC4), dst1(srcSize, CV_8UC4), |
|
dst2(srcSize, CV_16SC2); |
|
declare.in(src, WARMUP_RNG).out(dst1, dst2) |
|
.time(srcSize == OCL_SIZE_4000 ? |
|
56 : srcSize == OCL_SIZE_2000 ? 15 : 3.8);; |
|
|
|
if (RUN_PLAIN_IMPL) |
|
{ |
|
TEST_CYCLE() meanShiftProc_(src, dst1, dst2, 5, 6, crit); |
|
|
|
SANITY_CHECK(dst1); |
|
SANITY_CHECK(dst2); |
|
} |
|
else if (RUN_OCL_IMPL) |
|
{ |
|
ocl::oclMat oclSrc(src), oclDst1(srcSize, CV_8UC4), |
|
oclDst2(srcSize, CV_16SC2); |
|
|
|
OCL_TEST_CYCLE() ocl::meanShiftProc(oclSrc, oclDst1, oclDst2, 5, 6, crit); |
|
|
|
oclDst1.download(dst1); |
|
oclDst2.download(dst2); |
|
|
|
SANITY_CHECK(dst1); |
|
SANITY_CHECK(dst2); |
|
} |
|
else |
|
OCL_PERF_ELSE |
|
} |
|
|
|
///////////// remap//////////////////////// |
|
|
|
CV_ENUM(RemapInterType, INTER_NEAREST, INTER_LINEAR) |
|
|
|
typedef tuple<Size, MatType, RemapInterType> remapParams; |
|
typedef TestBaseWithParam<remapParams> remapFixture; |
|
|
|
PERF_TEST_P(remapFixture, remap, |
|
::testing::Combine(OCL_TYPICAL_MAT_SIZES, |
|
OCL_PERF_ENUM(CV_8UC1, CV_8UC4), |
|
RemapInterType::all())) |
|
{ |
|
const remapParams params = GetParam(); |
|
const Size srcSize = get<0>(params); |
|
const int type = get<1>(params), interpolation = get<2>(params); |
|
|
|
Mat src(srcSize, type), dst(srcSize, type); |
|
declare.in(src, WARMUP_RNG).out(dst); |
|
|
|
if (srcSize == OCL_SIZE_4000 && interpolation == INTER_LINEAR) |
|
declare.time(9); |
|
|
|
Mat xmap, ymap; |
|
xmap.create(srcSize, CV_32FC1); |
|
ymap.create(srcSize, CV_32FC1); |
|
|
|
for (int i = 0; i < srcSize.height; ++i) |
|
{ |
|
float * const xmap_row = xmap.ptr<float>(i); |
|
float * const ymap_row = ymap.ptr<float>(i); |
|
|
|
for (int j = 0; j < srcSize.width; ++j) |
|
{ |
|
xmap_row[j] = (j - srcSize.width * 0.5f) * 0.75f + srcSize.width * 0.5f; |
|
ymap_row[j] = (i - srcSize.height * 0.5f) * 0.75f + srcSize.height * 0.5f; |
|
} |
|
} |
|
|
|
const int borderMode = BORDER_CONSTANT; |
|
|
|
if (RUN_OCL_IMPL) |
|
{ |
|
ocl::oclMat oclSrc(src), oclDst(srcSize, type); |
|
ocl::oclMat oclXMap(xmap), oclYMap(ymap); |
|
|
|
OCL_TEST_CYCLE() cv::ocl::remap(oclSrc, oclDst, oclXMap, oclYMap, interpolation, borderMode); |
|
|
|
oclDst.download(dst); |
|
|
|
SANITY_CHECK(dst, 1 + DBL_EPSILON); |
|
} |
|
else if (RUN_PLAIN_IMPL) |
|
{ |
|
TEST_CYCLE() cv::remap(src, dst, xmap, ymap, interpolation, borderMode); |
|
|
|
SANITY_CHECK(dst, 1 + DBL_EPSILON); |
|
} |
|
else |
|
OCL_PERF_ELSE |
|
} |
|
|
|
///////////// CLAHE //////////////////////// |
|
|
|
typedef TestBaseWithParam<Size> CLAHEFixture; |
|
|
|
PERF_TEST_P(CLAHEFixture, CLAHE, OCL_TYPICAL_MAT_SIZES) |
|
{ |
|
const Size srcSize = GetParam(); |
|
const string impl = getSelectedImpl(); |
|
|
|
Mat src(srcSize, CV_8UC1), dst; |
|
const double clipLimit = 40.0; |
|
declare.in(src, WARMUP_RNG); |
|
|
|
if (srcSize == OCL_SIZE_4000) |
|
declare.time(11); |
|
|
|
if (RUN_OCL_IMPL) |
|
{ |
|
ocl::oclMat oclSrc(src), oclDst; |
|
cv::Ptr<cv::CLAHE> oclClahe = cv::ocl::createCLAHE(clipLimit); |
|
|
|
OCL_TEST_CYCLE() oclClahe->apply(oclSrc, oclDst); |
|
|
|
oclDst.download(dst); |
|
|
|
SANITY_CHECK(dst); |
|
} |
|
else if (RUN_PLAIN_IMPL) |
|
{ |
|
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(clipLimit); |
|
TEST_CYCLE() clahe->apply(src, dst); |
|
|
|
SANITY_CHECK(dst); |
|
} |
|
else |
|
OCL_PERF_ELSE |
|
} |
|
|
|
///////////// columnSum//////////////////////// |
|
|
|
typedef TestBaseWithParam<Size> columnSumFixture; |
|
|
|
static void columnSumPerfTest(const Mat & src, Mat & dst) |
|
{ |
|
for (int j = 0; j < src.cols; j++) |
|
dst.at<float>(0, j) = src.at<float>(0, j); |
|
|
|
for (int i = 1; i < src.rows; ++i) |
|
for (int j = 0; j < src.cols; ++j) |
|
dst.at<float>(i, j) = dst.at<float>(i - 1 , j) + src.at<float>(i , j); |
|
} |
|
|
|
PERF_TEST_P(columnSumFixture, columnSum, OCL_TYPICAL_MAT_SIZES) |
|
{ |
|
const Size srcSize = GetParam(); |
|
|
|
Mat src(srcSize, CV_32FC1), dst(srcSize, CV_32FC1); |
|
declare.in(src, WARMUP_RNG).out(dst); |
|
|
|
if (srcSize == OCL_SIZE_4000) |
|
declare.time(5); |
|
|
|
if (RUN_OCL_IMPL) |
|
{ |
|
ocl::oclMat oclSrc(src), oclDst(srcSize, CV_32FC1); |
|
|
|
OCL_TEST_CYCLE() cv::ocl::columnSum(oclSrc, oclDst); |
|
|
|
oclDst.download(dst); |
|
|
|
SANITY_CHECK(dst); |
|
} |
|
else if (RUN_PLAIN_IMPL) |
|
{ |
|
TEST_CYCLE() columnSumPerfTest(src, dst); |
|
|
|
SANITY_CHECK(dst); |
|
} |
|
else |
|
OCL_PERF_ELSE |
|
} |
|
|
|
//////////////////////////////distanceToCenters//////////////////////////////////////////////// |
|
|
|
CV_ENUM(DistType, NORM_L1, NORM_L2SQR); |
|
typedef tuple<Size, DistType> distanceToCentersParameters; |
|
typedef TestBaseWithParam<distanceToCentersParameters> distanceToCentersFixture; |
|
|
|
static void distanceToCentersPerfTest(Mat& src, Mat& centers, Mat& dists, Mat& labels, int distType) |
|
{ |
|
Mat batch_dists; |
|
cv::batchDistance(src,centers,batch_dists, CV_32FC1, noArray(), distType); |
|
std::vector<float> dists_v; |
|
std::vector<int> labels_v; |
|
for(int i = 0; i<batch_dists.rows; i++) |
|
{ |
|
Mat r = batch_dists.row(i); |
|
double mVal; |
|
Point mLoc; |
|
minMaxLoc(r, &mVal, NULL, &mLoc, NULL); |
|
dists_v.push_back((float)mVal); |
|
labels_v.push_back(mLoc.x); |
|
} |
|
Mat temp_dists(dists_v); |
|
Mat temp_labels(labels_v); |
|
temp_dists.reshape(1,1).copyTo(dists); |
|
temp_labels.reshape(1,1).copyTo(labels); |
|
} |
|
|
|
PERF_TEST_P(distanceToCentersFixture, distanceToCenters, ::testing::Combine(::testing::Values(cv::Size(256,256), cv::Size(512,512)), DistType::all()) ) |
|
{ |
|
Size size = get<0>(GetParam()); |
|
int distType = get<1>(GetParam()); |
|
Mat src(size, CV_32FC1); |
|
Mat centers(size, CV_32FC1); |
|
Mat dists(cv::Size(src.rows,1), CV_32FC1); |
|
Mat labels(cv::Size(src.rows,1), CV_32SC1); |
|
declare.in(src, centers, WARMUP_RNG).out(dists, labels); |
|
if (RUN_OCL_IMPL) |
|
{ |
|
ocl::oclMat ocl_src(src); |
|
ocl::oclMat ocl_centers(centers); |
|
ocl::oclMat ocl_dists(dists); |
|
ocl::oclMat ocl_labels(labels); |
|
|
|
OCL_TEST_CYCLE() ocl::distanceToCenters(ocl_dists,ocl_labels,ocl_src, ocl_centers, distType); |
|
|
|
ocl_dists.download(dists); |
|
ocl_labels.download(labels); |
|
|
|
SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE); |
|
SANITY_CHECK(labels); |
|
} |
|
else if (RUN_PLAIN_IMPL) |
|
{ |
|
TEST_CYCLE() distanceToCentersPerfTest(src,centers,dists,labels,distType); |
|
SANITY_CHECK(dists, 1e-6, ERROR_RELATIVE); |
|
SANITY_CHECK(labels); |
|
} |
|
else |
|
OCL_PERF_ELSE |
|
}
|
|
|