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Open Source Computer Vision Library
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1334 lines
44 KiB
1334 lines
44 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) 2000, Intel Corporation, 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 "test_precomp.hpp" |
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#ifdef HAVE_CUDA |
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///////////////////////////////////////////////////////////////////////////////////////////////// |
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// SURF |
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struct SURF : testing::TestWithParam<cv::gpu::DeviceInfo> |
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{ |
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static cv::Mat image; |
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static cv::Mat mask; |
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static std::vector<cv::KeyPoint> keypoints_gold; |
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static std::vector<float> descriptors_gold; |
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static void SetUpTestCase() |
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{ |
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image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE); |
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mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1)); |
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mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0)); |
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cv::SURF fdetector_gold; fdetector_gold.extended = false; |
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fdetector_gold(image, mask, keypoints_gold, descriptors_gold); |
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} |
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static void TearDownTestCase() |
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{ |
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image.release(); |
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mask.release(); |
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keypoints_gold.clear(); |
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descriptors_gold.clear(); |
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} |
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cv::gpu::DeviceInfo devInfo; |
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virtual void SetUp() |
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{ |
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devInfo = GetParam(); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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} |
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bool isSimilarKeypoints(const cv::KeyPoint& p1, const cv::KeyPoint& p2) |
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{ |
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const float maxPtDif = 1.f; |
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const float maxSizeDif = 1.f; |
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const float maxAngleDif = 2.f; |
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const float maxResponseDif = 0.1f; |
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float dist = (float)cv::norm(p1.pt - p2.pt); |
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return (dist < maxPtDif && |
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fabs(p1.size - p2.size) < maxSizeDif && |
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abs(p1.angle - p2.angle) < maxAngleDif && |
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abs(p1.response - p2.response) < maxResponseDif && |
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p1.octave == p2.octave && |
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p1.class_id == p2.class_id ); |
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} |
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}; |
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cv::Mat SURF::image; |
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cv::Mat SURF::mask; |
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std::vector<cv::KeyPoint> SURF::keypoints_gold; |
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std::vector<float> SURF::descriptors_gold; |
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TEST_P(SURF, EmptyDataTest) |
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{ |
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PRINT_PARAM(devInfo); |
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cv::gpu::SURF_GPU fdetector; |
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cv::gpu::GpuMat image; |
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std::vector<cv::KeyPoint> keypoints; |
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std::vector<float> descriptors; |
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ASSERT_NO_THROW( |
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fdetector(image, cv::gpu::GpuMat(), keypoints, descriptors); |
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); |
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EXPECT_TRUE(keypoints.empty()); |
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EXPECT_TRUE(descriptors.empty()); |
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} |
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TEST_P(SURF, Accuracy) |
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{ |
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ASSERT_TRUE(!image.empty()); |
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PRINT_PARAM(devInfo); |
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// Compute keypoints. |
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std::vector<cv::KeyPoint> keypoints; |
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cv::Mat descriptors; |
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ASSERT_NO_THROW( |
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cv::gpu::GpuMat dev_descriptors; |
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cv::gpu::SURF_GPU fdetector; fdetector.extended = false; |
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fdetector(cv::gpu::GpuMat(image), cv::gpu::GpuMat(mask), keypoints, dev_descriptors); |
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dev_descriptors.download(descriptors); |
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); |
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cv::BruteForceMatcher< cv::L2<float> > matcher; |
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std::vector<cv::DMatch> matches; |
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matcher.match(cv::Mat(keypoints_gold.size(), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches); |
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int validCount = 0; |
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for (size_t i = 0; i < matches.size(); ++i) |
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{ |
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const cv::DMatch& m = matches[i]; |
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const cv::KeyPoint& p1 = keypoints_gold[m.queryIdx]; |
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const cv::KeyPoint& p2 = keypoints[m.trainIdx]; |
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const float maxPtDif = 1.f; |
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const float maxSizeDif = 1.f; |
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const float maxAngleDif = 2.f; |
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const float maxResponseDif = 0.1f; |
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float dist = (float)cv::norm(p1.pt - p2.pt); |
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if (dist < maxPtDif && |
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fabs(p1.size - p2.size) < maxSizeDif && |
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abs(p1.angle - p2.angle) < maxAngleDif && |
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abs(p1.response - p2.response) < maxResponseDif && |
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p1.octave == p2.octave && |
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p1.class_id == p2.class_id ) |
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{ |
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++validCount; |
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} |
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} |
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double validRatio = (double)validCount / matches.size(); |
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EXPECT_GT(validRatio, 0.5); |
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} |
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INSTANTIATE_TEST_CASE_P(Features2D, SURF, testing::ValuesIn(devices(cv::gpu::GLOBAL_ATOMICS))); |
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///////////////////////////////////////////////////////////////////////////////////////////////// |
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// BruteForceMatcher |
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static const char* dists[] = {"L1Dist", "L2Dist", "HammingDist"}; |
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struct BruteForceMatcher : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, cv::gpu::BruteForceMatcher_GPU_base::DistType, int> > |
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{ |
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static const int queryDescCount = 300; // must be even number because we split train data in some cases in two |
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static const int countFactor = 4; // do not change it |
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cv::gpu::DeviceInfo devInfo; |
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cv::gpu::BruteForceMatcher_GPU_base::DistType distType; |
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int dim; |
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cv::Mat query, train; |
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virtual void SetUp() |
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{ |
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devInfo = std::tr1::get<0>(GetParam()); |
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distType = std::tr1::get<1>(GetParam()); |
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dim = std::tr1::get<2>(GetParam()); |
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cv::gpu::setDevice(devInfo.deviceID()); |
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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) |
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{ |
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const char* distStr = dists[distType]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(distStr); |
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PRINT_PARAM(dim); |
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std::vector<cv::DMatch> matches; |
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ASSERT_NO_THROW( |
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType); |
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matcher.match(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches); |
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); |
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ASSERT_EQ(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, MatchAdd) |
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{ |
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const char* distStr = dists[distType]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(distStr); |
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PRINT_PARAM(dim); |
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std::vector<cv::DMatch> matches; |
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bool isMaskSupported; |
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ASSERT_NO_THROW( |
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType); |
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cv::gpu::GpuMat d_train(train); |
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// make add() twice to test such case |
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows/2))); |
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows/2, train.rows))); |
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// prepare masks (make first nearest match illegal) |
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std::vector<cv::gpu::GpuMat> masks(2); |
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for (int mi = 0; mi < 2; mi++) |
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{ |
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masks[mi] = cv::gpu::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1)); |
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for (int di = 0; di < queryDescCount/2; di++) |
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); |
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} |
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matcher.match(cv::gpu::GpuMat(query), matches, masks); |
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isMaskSupported = matcher.isMaskSupported(); |
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); |
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ASSERT_EQ(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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int shift = isMaskSupported ? 1 : 0; |
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{ |
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if (i < queryDescCount / 2) |
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{ |
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0)) |
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badCount++; |
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} |
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else |
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{ |
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if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1)) |
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badCount++; |
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} |
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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, KnnMatch) |
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{ |
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const char* distStr = dists[distType]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(distStr); |
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PRINT_PARAM(dim); |
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const int knn = 3; |
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std::vector< std::vector<cv::DMatch> > matches; |
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ASSERT_NO_THROW( |
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType); |
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matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn); |
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); |
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ASSERT_EQ(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, KnnMatchAdd) |
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{ |
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const char* distStr = dists[distType]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(distStr); |
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PRINT_PARAM(dim); |
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const int knn = 2; |
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std::vector< std::vector<cv::DMatch> > matches; |
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bool isMaskSupported; |
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ASSERT_NO_THROW( |
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType); |
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cv::gpu::GpuMat d_train(train); |
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// make add() twice to test such case |
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2))); |
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows))); |
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// prepare masks (make first nearest match illegal) |
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std::vector<cv::gpu::GpuMat> masks(2); |
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for (int mi = 0; mi < 2; mi++ ) |
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{ |
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masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1)); |
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for (int di = 0; di < queryDescCount / 2; di++) |
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); |
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} |
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matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks); |
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isMaskSupported = matcher.isMaskSupported(); |
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); |
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ASSERT_EQ(queryDescCount, matches.size()); |
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int badCount = 0; |
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int shift = isMaskSupported ? 1 : 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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{ |
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if (i < queryDescCount / 2) |
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{ |
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) |
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localBadCount++; |
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} |
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else |
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{ |
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if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) |
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localBadCount++; |
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} |
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} |
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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) |
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{ |
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) |
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return; |
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const char* distStr = dists[distType]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(distStr); |
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PRINT_PARAM(dim); |
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const float radius = 1.f / countFactor; |
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std::vector< std::vector<cv::DMatch> > matches; |
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ASSERT_NO_THROW( |
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType); |
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matcher.radiusMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, radius); |
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); |
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ASSERT_EQ(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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badCount++; |
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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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TEST_P(BruteForceMatcher, RadiusMatchAdd) |
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{ |
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) |
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return; |
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const char* distStr = dists[distType]; |
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PRINT_PARAM(devInfo); |
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PRINT_PARAM(distStr); |
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PRINT_PARAM(dim); |
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int n = 3; |
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const float radius = 1.f / countFactor * n; |
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std::vector< std::vector<cv::DMatch> > matches; |
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bool isMaskSupported; |
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ASSERT_NO_THROW( |
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType); |
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cv::gpu::GpuMat d_train(train); |
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// make add() twice to test such case |
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2))); |
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows))); |
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// prepare masks (make first nearest match illegal) |
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std::vector<cv::gpu::GpuMat> masks(2); |
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for (int mi = 0; mi < 2; mi++) |
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{ |
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masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1)); |
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for (int di = 0; di < queryDescCount / 2; di++) |
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); |
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} |
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matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius, masks); |
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isMaskSupported = matcher.isMaskSupported(); |
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); |
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ASSERT_EQ(queryDescCount, matches.size()); |
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int badCount = 0; |
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int shift = isMaskSupported ? 1 : 0; |
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int needMatchCount = isMaskSupported ? n-1 : n; |
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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() != needMatchCount) |
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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 < needMatchCount; k++) |
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{ |
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cv::DMatch match = matches[i][k]; |
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{ |
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if (i < queryDescCount / 2) |
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{ |
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) |
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localBadCount++; |
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} |
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else |
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{ |
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if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) |
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localBadCount++; |
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} |
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} |
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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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INSTANTIATE_TEST_CASE_P(Features2D, BruteForceMatcher, testing::Combine( |
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testing::ValuesIn(devices()), |
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testing::Values(cv::gpu::BruteForceMatcher_GPU_base::L1Dist, cv::gpu::BruteForceMatcher_GPU_base::L2Dist), |
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testing::Values(57, 64, 83, 128, 179, 256, 304))); |
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#endif // HAVE_CUDA |
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//struct CV_GpuBFMTest : CV_GpuTestBase |
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//{ |
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// void run_gpu_test(); |
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// |
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// void generateData(GpuMat& query, GpuMat& train, int dim, int depth); |
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// |
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// virtual void test(const GpuMat& query, const GpuMat& train, BruteForceMatcher_GPU_base& matcher) = 0; |
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// |
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// static const int queryDescCount = 300; // must be even number because we split train data in some cases in two |
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// static const int countFactor = 4; // do not change it |
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//}; |
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// |
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//void CV_GpuBFMTest::run_gpu_test() |
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//{ |
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// BruteForceMatcher_GPU_base::DistType dists[] = {BruteForceMatcher_GPU_base::L1Dist, BruteForceMatcher_GPU_base::L2Dist, BruteForceMatcher_GPU_base::HammingDist}; |
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// const char* dists_str[] = {"L1Dist", "L2Dist", "HammingDist"}; |
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// int dists_count = sizeof(dists) / sizeof(dists[0]); |
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// |
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// RNG rng = ts->get_rng(); |
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// |
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// int dims[] = {rng.uniform(30, 60), 64, rng.uniform(70, 110), 128, rng.uniform(130, 250), 256, rng.uniform(260, 350)}; |
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// int dims_count = sizeof(dims) / sizeof(dims[0]); |
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// |
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// for (int dist = 0; dist < dists_count; ++dist) |
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// { |
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// int depth_end = dists[dist] == BruteForceMatcher_GPU_base::HammingDist ? CV_32S : CV_32F; |
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// |
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// for (int depth = CV_8U; depth <= depth_end; ++depth) |
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// { |
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// for (int dim = 0; dim < dims_count; ++dim) |
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// { |
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// PRINT_ARGS("dist=%s depth=%s dim=%d", dists_str[dist], getTypeName(depth), dims[dim]); |
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// |
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// BruteForceMatcher_GPU_base matcher(dists[dist]); |
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// |
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// GpuMat query, train; |
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// generateData(query, train, dim, depth); |
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// |
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// test(query, train, matcher); |
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// } |
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// } |
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// } |
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//} |
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// |
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//void CV_GpuBFMTest::generateData(GpuMat& queryGPU, GpuMat& trainGPU, int dim, int depth) |
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//{ |
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// RNG& rng = ts->get_rng(); |
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// |
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// Mat queryBuf, trainBuf; |
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// |
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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, RNG::UNIFORM, Scalar::all(0), Scalar(3)); |
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// queryBuf.convertTo(queryBuf, CV_32FC1); |
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// |
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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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// 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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// 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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// |
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// Mat query, train; |
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// queryBuf.convertTo(query, depth); |
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// trainBuf.convertTo(train, depth); |
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// |
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// queryGPU.upload(query); |
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// trainGPU.upload(train); |
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//} |
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// |
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//#define GPU_BFM_TEST(test_name) \ |
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// struct CV_GpuBFM_ ##test_name ## _Test : CV_GpuBFMTest \ |
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// { \ |
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// void test(const GpuMat& query, const GpuMat& train, BruteForceMatcher_GPU_base& matcher); \ |
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// }; \ |
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// TEST(BruteForceMatcher, test_name) { CV_GpuBFM_ ##test_name ## _Test test; test.safe_run(); } \ |
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// void CV_GpuBFM_ ##test_name ## _Test::test(const GpuMat& query, const GpuMat& train, BruteForceMatcher_GPU_base& matcher) |
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// |
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///////////////////////////////////////////////////////////////////////////////////////////////////////// |
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//// match |
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// |
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//GPU_BFM_TEST(match) |
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//{ |
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// vector<DMatch> matches; |
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// |
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// matcher.match(query, train, matches); |
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// |
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// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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// |
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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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// 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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// |
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// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//} |
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// |
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//GPU_BFM_TEST(match_add) |
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//{ |
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// vector<DMatch> matches; |
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// |
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// // make add() twice to test such case |
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// matcher.add(vector<GpuMat>(1, train.rowRange(0, train.rows/2))); |
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// matcher.add(vector<GpuMat>(1, train.rowRange(train.rows/2, train.rows))); |
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// |
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// // prepare masks (make first nearest match illegal) |
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// vector<GpuMat> masks(2); |
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// for (int mi = 0; mi < 2; mi++) |
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// { |
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// masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
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// for (int di = 0; di < queryDescCount/2; di++) |
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// masks[mi].col(di * countFactor).setTo(Scalar::all(0)); |
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// } |
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// |
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// matcher.match(query, matches, masks); |
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// |
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// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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// |
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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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// DMatch match = matches[i]; |
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// int shift = matcher.isMaskSupported() ? 1 : 0; |
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// { |
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// if (i < queryDescCount / 2) |
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// { |
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// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0)) |
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// badCount++; |
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// } |
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// else |
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// { |
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// if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1)) |
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// badCount++; |
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// } |
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// } |
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// } |
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// |
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// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//} |
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// |
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///////////////////////////////////////////////////////////////////////////////////////////////////////// |
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//// knnMatch |
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// |
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//GPU_BFM_TEST(knnMatch) |
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//{ |
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// const int knn = 3; |
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// |
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// vector< vector<DMatch> > matches; |
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// |
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// matcher.knnMatch(query, train, matches, knn); |
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// |
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// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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// |
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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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// 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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// |
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// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//} |
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// |
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//GPU_BFM_TEST(knnMatch_add) |
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//{ |
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// const int knn = 2; |
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// vector<vector<DMatch> > matches; |
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// |
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// // make add() twice to test such case |
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// matcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2))); |
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// matcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows))); |
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// |
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// // prepare masks (make first nearest match illegal) |
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// vector<GpuMat> masks(2); |
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// for (int mi = 0; mi < 2; mi++ ) |
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// { |
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// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1)); |
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// for (int di = 0; di < queryDescCount / 2; di++) |
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// masks[mi].col(di * countFactor).setTo(Scalar::all(0)); |
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// } |
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// |
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// matcher.knnMatch(query, matches, knn, masks); |
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// |
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// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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// |
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// int badCount = 0; |
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// int shift = matcher.isMaskSupported() ? 1 : 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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// DMatch match = matches[i][k]; |
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// { |
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// if (i < queryDescCount / 2) |
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// { |
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// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) |
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// localBadCount++; |
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// } |
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// else |
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// { |
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// if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) |
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// localBadCount++; |
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// } |
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// } |
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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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// |
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// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//} |
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// |
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///////////////////////////////////////////////////////////////////////////////////////////////////////// |
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//// radiusMatch |
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// |
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//GPU_BFM_TEST(radiusMatch) |
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//{ |
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// CHECK_RETURN(support(GLOBAL_ATOMICS), TS::SKIPPED); |
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// |
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// const float radius = 1.f / countFactor; |
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// |
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// vector< vector<DMatch> > matches; |
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// |
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// matcher.radiusMatch(query, train, matches, radius); |
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// |
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// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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// |
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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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// badCount++; |
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// else |
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// { |
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// 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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// |
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// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//} |
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// |
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//GPU_BFM_TEST(radiusMatch_add) |
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//{ |
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// CHECK_RETURN(support(GLOBAL_ATOMICS), TS::SKIPPED); |
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// |
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// int n = 3; |
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// const float radius = 1.f / countFactor * n; |
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// vector< vector<DMatch> > matches; |
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// |
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// // make add() twice to test such case |
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// matcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2))); |
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// matcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows))); |
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// |
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// // prepare masks (make first nearest match illegal) |
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// vector<GpuMat> masks(2); |
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// for (int mi = 0; mi < 2; mi++) |
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// { |
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// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1)); |
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// for (int di = 0; di < queryDescCount / 2; di++) |
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// masks[mi].col(di * countFactor).setTo(Scalar::all(0)); |
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// } |
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// |
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// matcher.radiusMatch(query, matches, radius, masks); |
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// |
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// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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// |
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// int badCount = 0; |
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// int shift = matcher.isMaskSupported() ? 1 : 0; |
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// int needMatchCount = matcher.isMaskSupported() ? n-1 : n; |
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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() != needMatchCount) |
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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 < needMatchCount; k++) |
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// { |
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// DMatch match = matches[i][k]; |
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// { |
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// if (i < queryDescCount / 2) |
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// { |
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// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) |
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// localBadCount++; |
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// } |
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// else |
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// { |
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// if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) |
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// localBadCount++; |
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// } |
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// } |
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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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// |
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// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//} |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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// |
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////struct CV_GpuBruteForceMatcherTest : CV_GpuTestBase |
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////{ |
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//// void run_gpu_test(); |
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//// |
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//// void emptyDataTest(); |
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//// void dataTest(int dim); |
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//// |
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//// void generateData(GpuMat& query, GpuMat& train, int dim); |
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//// |
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//// void matchTest(const GpuMat& query, const GpuMat& train); |
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//// void knnMatchTest(const GpuMat& query, const GpuMat& train); |
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//// void radiusMatchTest(const GpuMat& query, const GpuMat& train); |
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//// |
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//// BruteForceMatcher_GPU< L2<float> > dmatcher; |
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//// |
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//// static const int queryDescCount = 300; // must be even number because we split train data in some cases in two |
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//// static const int countFactor = 4; // do not change it |
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////}; |
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//// |
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////void CV_GpuBruteForceMatcherTest::emptyDataTest() |
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////{ |
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//// GpuMat queryDescriptors, trainDescriptors, mask; |
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//// vector<GpuMat> trainDescriptorCollection, masks; |
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//// vector<DMatch> matches; |
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//// vector< vector<DMatch> > vmatches; |
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//// |
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//// try |
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//// { |
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//// dmatcher.match(queryDescriptors, trainDescriptors, matches, mask); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("match() on empty descriptors must not generate exception (1)"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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//// try |
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//// { |
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//// dmatcher.knnMatch(queryDescriptors, trainDescriptors, vmatches, 2, mask); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("knnMatch() on empty descriptors must not generate exception (1)"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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//// try |
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//// { |
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//// dmatcher.radiusMatch(queryDescriptors, trainDescriptors, vmatches, 10.f, mask); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("radiusMatch() on empty descriptors must not generate exception (1)"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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//// try |
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//// { |
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//// dmatcher.add(trainDescriptorCollection); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("add() on empty descriptors must not generate exception"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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//// try |
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//// { |
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//// dmatcher.match(queryDescriptors, matches, masks); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("match() on empty descriptors must not generate exception (2)"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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//// try |
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//// { |
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//// dmatcher.knnMatch(queryDescriptors, vmatches, 2, masks); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("knnMatch() on empty descriptors must not generate exception (2)"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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//// try |
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//// { |
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//// dmatcher.radiusMatch( queryDescriptors, vmatches, 10.f, masks ); |
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//// } |
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//// catch(...) |
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//// { |
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//// PRINTLN("radiusMatch() on empty descriptors must not generate exception (2)"); |
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//// ts->set_failed_test_info(TS::FAIL_EXCEPTION); |
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//// } |
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//// |
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////} |
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//// |
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////void CV_GpuBruteForceMatcherTest::generateData(GpuMat& queryGPU, GpuMat& trainGPU, int dim) |
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////{ |
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//// Mat query, train; |
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//// RNG& rng = ts->get_rng(); |
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//// |
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//// // Generate query descriptors randomly. |
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//// // Descriptor vector elements are integer values. |
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//// Mat buf(queryDescCount, dim, CV_32SC1); |
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//// rng.fill(buf, RNG::UNIFORM, Scalar::all(0), Scalar(3)); |
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//// buf.convertTo(query, CV_32FC1); |
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//// |
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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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//// train.create( query.rows*countFactor, query.cols, CV_32FC1 ); |
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//// float step = 1.f / countFactor; |
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//// for (int qIdx = 0; qIdx < query.rows; qIdx++) |
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//// { |
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//// Mat queryDescriptor = query.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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//// Mat trainDescriptor = train.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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//// |
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//// queryGPU.upload(query); |
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//// trainGPU.upload(train); |
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////} |
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//// |
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////void CV_GpuBruteForceMatcherTest::matchTest(const GpuMat& query, const GpuMat& train) |
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////{ |
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//// dmatcher.clear(); |
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//// |
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//// // test const version of match() |
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//// { |
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//// vector<DMatch> matches; |
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//// dmatcher.match(query, train, matches); |
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//// |
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//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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//// |
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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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//// 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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//// |
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//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
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//// } |
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//// |
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//// // test version of match() with add() |
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//// { |
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//// vector<DMatch> matches; |
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//// |
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//// // make add() twice to test such case |
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//// dmatcher.add(vector<GpuMat>(1, train.rowRange(0, train.rows/2))); |
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//// dmatcher.add(vector<GpuMat>(1, train.rowRange(train.rows/2, train.rows))); |
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//// |
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//// // prepare masks (make first nearest match illegal) |
|
//// vector<GpuMat> masks(2); |
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//// for (int mi = 0; mi < 2; mi++) |
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//// { |
|
//// masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1)); |
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//// for (int di = 0; di < queryDescCount/2; di++) |
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//// masks[mi].col(di * countFactor).setTo(Scalar::all(0)); |
|
//// } |
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//// |
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//// dmatcher.match(query, matches, masks); |
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//// |
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
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//// |
|
//// int badCount = 0; |
|
//// for (size_t i = 0; i < matches.size(); i++) |
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//// { |
|
//// DMatch match = matches[i]; |
|
//// int shift = dmatcher.isMaskSupported() ? 1 : 0; |
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//// { |
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//// if (i < queryDescCount / 2) |
|
//// { |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0)) |
|
//// badCount++; |
|
//// } |
|
//// else |
|
//// { |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1)) |
|
//// badCount++; |
|
//// } |
|
//// } |
|
//// } |
|
//// |
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
|
//// } |
|
////} |
|
//// |
|
////void CV_GpuBruteForceMatcherTest::knnMatchTest(const GpuMat& query, const GpuMat& train) |
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////{ |
|
//// dmatcher.clear(); |
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//// |
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//// // test const version of knnMatch() |
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//// { |
|
//// const int knn = 3; |
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//// |
|
//// vector< vector<DMatch> > matches; |
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//// dmatcher.knnMatch(query, train, matches, knn); |
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//// |
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//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
|
//// |
|
//// int badCount = 0; |
|
//// for (size_t i = 0; i < matches.size(); i++) |
|
//// { |
|
//// if ((int)matches[i].size() != knn) |
|
//// badCount++; |
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//// else |
|
//// { |
|
//// int localBadCount = 0; |
|
//// for (int k = 0; k < knn; k++) |
|
//// { |
|
//// DMatch match = matches[i][k]; |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0)) |
|
//// localBadCount++; |
|
//// } |
|
//// badCount += localBadCount > 0 ? 1 : 0; |
|
//// } |
|
//// } |
|
//// |
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
|
//// } |
|
//// |
|
//// // test version of knnMatch() with add() |
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//// { |
|
//// const int knn = 2; |
|
//// vector<vector<DMatch> > matches; |
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//// |
|
//// // make add() twice to test such case |
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2))); |
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows))); |
|
//// |
|
//// // prepare masks (make first nearest match illegal) |
|
//// vector<GpuMat> masks(2); |
|
//// for (int mi = 0; mi < 2; mi++ ) |
|
//// { |
|
//// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1)); |
|
//// for (int di = 0; di < queryDescCount / 2; di++) |
|
//// masks[mi].col(di * countFactor).setTo(Scalar::all(0)); |
|
//// } |
|
//// |
|
//// dmatcher.knnMatch(query, matches, knn, masks); |
|
//// |
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
|
//// |
|
//// int badCount = 0; |
|
//// int shift = dmatcher.isMaskSupported() ? 1 : 0; |
|
//// for (size_t i = 0; i < matches.size(); i++) |
|
//// { |
|
//// if ((int)matches[i].size() != knn) |
|
//// badCount++; |
|
//// else |
|
//// { |
|
//// int localBadCount = 0; |
|
//// for (int k = 0; k < knn; k++) |
|
//// { |
|
//// DMatch match = matches[i][k]; |
|
//// { |
|
//// if (i < queryDescCount / 2) |
|
//// { |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) |
|
//// localBadCount++; |
|
//// } |
|
//// else |
|
//// { |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) |
|
//// localBadCount++; |
|
//// } |
|
//// } |
|
//// } |
|
//// badCount += localBadCount > 0 ? 1 : 0; |
|
//// } |
|
//// } |
|
//// |
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
|
//// } |
|
////} |
|
//// |
|
////void CV_GpuBruteForceMatcherTest::radiusMatchTest(const GpuMat& query, const GpuMat& train) |
|
////{ |
|
//// CHECK_RETURN(support(GLOBAL_ATOMICS), TS::SKIPPED); |
|
//// |
|
//// dmatcher.clear(); |
|
//// |
|
//// // test const version of match() |
|
//// { |
|
//// const float radius = 1.f / countFactor; |
|
//// |
|
//// vector< vector<DMatch> > matches; |
|
//// dmatcher.radiusMatch(query, train, matches, radius); |
|
//// |
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
|
//// |
|
//// int badCount = 0; |
|
//// for (size_t i = 0; i < matches.size(); i++) |
|
//// { |
|
//// if ((int)matches[i].size() != 1) |
|
//// badCount++; |
|
//// else |
|
//// { |
|
//// DMatch match = matches[i][0]; |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0)) |
|
//// badCount++; |
|
//// } |
|
//// } |
|
//// |
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
|
//// } |
|
//// |
|
//// // test version of match() with add() |
|
//// { |
|
//// int n = 3; |
|
//// const float radius = 1.f / countFactor * n; |
|
//// vector< vector<DMatch> > matches; |
|
//// |
|
//// // make add() twice to test such case |
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2))); |
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows))); |
|
//// |
|
//// // prepare masks (make first nearest match illegal) |
|
//// vector<GpuMat> masks(2); |
|
//// for (int mi = 0; mi < 2; mi++) |
|
//// { |
|
//// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1)); |
|
//// for (int di = 0; di < queryDescCount / 2; di++) |
|
//// masks[mi].col(di * countFactor).setTo(Scalar::all(0)); |
|
//// } |
|
//// |
|
//// dmatcher.radiusMatch(query, matches, radius, masks); |
|
//// |
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT); |
|
//// |
|
//// int badCount = 0; |
|
//// int shift = dmatcher.isMaskSupported() ? 1 : 0; |
|
//// int needMatchCount = dmatcher.isMaskSupported() ? n-1 : n; |
|
//// for (size_t i = 0; i < matches.size(); i++) |
|
//// { |
|
//// if ((int)matches[i].size() != needMatchCount) |
|
//// badCount++; |
|
//// else |
|
//// { |
|
//// int localBadCount = 0; |
|
//// for (int k = 0; k < needMatchCount; k++) |
|
//// { |
|
//// DMatch match = matches[i][k]; |
|
//// { |
|
//// if (i < queryDescCount / 2) |
|
//// { |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) |
|
//// localBadCount++; |
|
//// } |
|
//// else |
|
//// { |
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) |
|
//// localBadCount++; |
|
//// } |
|
//// } |
|
//// } |
|
//// badCount += localBadCount > 0 ? 1 : 0; |
|
//// } |
|
//// } |
|
//// |
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT); |
|
//// } |
|
////} |
|
//// |
|
////void CV_GpuBruteForceMatcherTest::dataTest(int dim) |
|
////{ |
|
//// GpuMat query, train; |
|
//// generateData(query, train, dim); |
|
//// |
|
//// matchTest(query, train); |
|
//// knnMatchTest(query, train); |
|
//// radiusMatchTest(query, train); |
|
//// |
|
//// dmatcher.clear(); |
|
////} |
|
//// |
|
////void CV_GpuBruteForceMatcherTest::run_gpu_test() |
|
////{ |
|
//// emptyDataTest(); |
|
//// |
|
//// dataTest(50); |
|
//// dataTest(64); |
|
//// dataTest(100); |
|
//// dataTest(128); |
|
//// dataTest(200); |
|
//// dataTest(256); |
|
//// dataTest(300); |
|
////} |
|
//// |
|
////TEST(BruteForceMatcher, accuracy) { CV_GpuBruteForceMatcherTest test; test.safe_run(); }
|
|
|