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220 lines
8.4 KiB
220 lines
8.4 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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// Intel 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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// Third party copyrights are property of their respective owners. |
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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 Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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#ifdef HAVE_OPENCL |
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namespace |
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{ |
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///////////////////////////////////////////////////////////////////////////////////////////////// |
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// BruteForceMatcher |
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CV_ENUM(DistType, cv::ocl::BruteForceMatcher_OCL_base::L1Dist, cv::ocl::BruteForceMatcher_OCL_base::L2Dist, cv::ocl::BruteForceMatcher_OCL_base::HammingDist) |
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IMPLEMENT_PARAM_CLASS(DescriptorSize, int) |
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PARAM_TEST_CASE(BruteForceMatcher/*, NormCode*/, DistType, DescriptorSize) |
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{ |
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//std::vector<cv::ocl::Info> oclinfo; |
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cv::ocl::BruteForceMatcher_OCL_base::DistType distType; |
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int normCode; |
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int dim; |
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int queryDescCount; |
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int countFactor; |
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cv::Mat query, train; |
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virtual void SetUp() |
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{ |
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//normCode = GET_PARAM(0); |
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distType = (cv::ocl::BruteForceMatcher_OCL_base::DistType)(int)GET_PARAM(0); |
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dim = GET_PARAM(1); |
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//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE); |
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//CV_Assert(devnums > 0); |
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queryDescCount = 300; // must be even number because we split train data in some cases in two |
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countFactor = 4; // do not change it |
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cv::RNG &rng = cvtest::TS::ptr()->get_rng(); |
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cv::Mat queryBuf, trainBuf; |
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// Generate query descriptors randomly. |
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// Descriptor vector elements are integer values. |
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queryBuf.create(queryDescCount, dim, CV_32SC1); |
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rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3)); |
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queryBuf.convertTo(queryBuf, CV_32FC1); |
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// Generate train decriptors as follows: |
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// copy each query descriptor to train set countFactor times |
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// and perturb some one element of the copied descriptors in |
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// in ascending order. General boundaries of the perturbation |
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// are (0.f, 1.f). |
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trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1); |
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float step = 1.f / countFactor; |
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for (int qIdx = 0; qIdx < queryDescCount; qIdx++) |
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{ |
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cv::Mat queryDescriptor = queryBuf.row(qIdx); |
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for (int c = 0; c < countFactor; c++) |
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{ |
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int tIdx = qIdx * countFactor + c; |
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cv::Mat trainDescriptor = trainBuf.row(tIdx); |
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queryDescriptor.copyTo(trainDescriptor); |
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int elem = rng(dim); |
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float diff = rng.uniform(step * c, step * (c + 1)); |
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trainDescriptor.at<float>(0, elem) += diff; |
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} |
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} |
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queryBuf.convertTo(query, CV_32F); |
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trainBuf.convertTo(train, CV_32F); |
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} |
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}; |
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TEST_P(BruteForceMatcher, Match_Single) |
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{ |
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cv::ocl::BruteForceMatcher_OCL_base matcher(distType); |
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std::vector<cv::DMatch> matches; |
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matcher.match(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches); |
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); |
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int badCount = 0; |
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for (size_t i = 0; i < matches.size(); i++) |
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{ |
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cv::DMatch match = matches[i]; |
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0)) |
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badCount++; |
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} |
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ASSERT_EQ(0, badCount); |
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} |
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TEST_P(BruteForceMatcher, KnnMatch_2_Single) |
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{ |
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const int knn = 2; |
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cv::ocl::BruteForceMatcher_OCL_base matcher(distType); |
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std::vector< std::vector<cv::DMatch> > matches; |
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matcher.knnMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, knn); |
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); |
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int badCount = 0; |
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for (size_t i = 0; i < matches.size(); i++) |
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{ |
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if ((int)matches[i].size() != knn) |
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badCount++; |
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else |
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{ |
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int localBadCount = 0; |
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for (int k = 0; k < knn; k++) |
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{ |
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cv::DMatch match = matches[i][k]; |
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0)) |
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localBadCount++; |
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} |
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badCount += localBadCount > 0 ? 1 : 0; |
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} |
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} |
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ASSERT_EQ(0, badCount); |
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} |
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TEST_P(BruteForceMatcher, RadiusMatch_Single) |
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{ |
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float radius; |
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if(distType == cv::ocl::BruteForceMatcher_OCL_base::L2Dist) |
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radius = 1.f / countFactor / countFactor; |
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else |
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radius = 1.f / countFactor; |
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cv::ocl::BruteForceMatcher_OCL_base matcher(distType); |
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// assume support atomic. |
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//if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) |
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//{ |
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// try |
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// { |
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// std::vector< std::vector<cv::DMatch> > matches; |
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// matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius); |
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// } |
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// catch (const cv::Exception& e) |
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// { |
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// ASSERT_EQ(CV_StsNotImplemented, e.code); |
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// } |
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//} |
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//else |
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{ |
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std::vector< std::vector<cv::DMatch> > matches; |
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matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius); |
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); |
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int badCount = 0; |
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for (size_t i = 0; i < matches.size(); i++) |
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{ |
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if ((int)matches[i].size() != 1) |
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{ |
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badCount++; |
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} |
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else |
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{ |
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cv::DMatch match = matches[i][0]; |
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0)) |
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badCount++; |
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} |
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} |
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ASSERT_EQ(0, badCount); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(GPU_Features2D, BruteForceMatcher, testing::Combine( |
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//ALL_DEVICES, |
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testing::Values(DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist), DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)), |
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testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)))); |
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} // namespace |
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#endif
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