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Open Source Computer Vision Library
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215 lines
7.2 KiB
215 lines
7.2 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved. |
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// @Authors |
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// Peng Xiao, pengxiao@multicorewareinc.com |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors as is and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#ifdef HAVE_OPENCV_OCL |
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using namespace std; |
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using std::tr1::get; |
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static bool keyPointsEquals(const cv::KeyPoint& p1, const cv::KeyPoint& p2) |
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{ |
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const double maxPtDif = 0.1; |
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const double maxSizeDif = 0.1; |
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const double maxAngleDif = 0.1; |
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const double maxResponseDif = 0.01; |
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double dist = 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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return true; |
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} |
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return false; |
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} |
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static int getMatchedPointsCount(std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual) |
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{ |
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std::sort(actual.begin(), actual.end(), perf::comparators::KeypointGreater()); |
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std::sort(gold.begin(), gold.end(), perf::comparators::KeypointGreater()); |
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int validCount = 0; |
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for (size_t i = 0; i < gold.size(); ++i) |
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{ |
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const cv::KeyPoint& p1 = gold[i]; |
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const cv::KeyPoint& p2 = actual[i]; |
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if (keyPointsEquals(p1, p2)) |
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++validCount; |
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} |
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return validCount; |
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} |
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static int getMatchedPointsCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches) |
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{ |
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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 = keypoints1[m.queryIdx]; |
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const cv::KeyPoint& p2 = keypoints2[m.trainIdx]; |
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if (keyPointsEquals(p1, p2)) |
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++validCount; |
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} |
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return validCount; |
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} |
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IMPLEMENT_PARAM_CLASS(HessianThreshold, double) |
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IMPLEMENT_PARAM_CLASS(Octaves, int) |
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IMPLEMENT_PARAM_CLASS(OctaveLayers, int) |
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IMPLEMENT_PARAM_CLASS(Extended, bool) |
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IMPLEMENT_PARAM_CLASS(Upright, bool) |
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PARAM_TEST_CASE(SURF, HessianThreshold, Octaves, OctaveLayers, Extended, Upright) |
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{ |
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double hessianThreshold; |
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int nOctaves; |
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int nOctaveLayers; |
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bool extended; |
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bool upright; |
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virtual void SetUp() |
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{ |
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hessianThreshold = get<0>(GetParam()); |
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nOctaves = get<1>(GetParam()); |
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nOctaveLayers = get<2>(GetParam()); |
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extended = get<3>(GetParam()); |
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upright = get<4>(GetParam()); |
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} |
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}; |
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TEST_P(SURF, DISABLED_Detector) |
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{ |
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cv::Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/fruits.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(image.empty()); |
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cv::ocl::SURF_OCL surf; |
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surf.hessianThreshold = static_cast<float>(hessianThreshold); |
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surf.nOctaves = nOctaves; |
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surf.nOctaveLayers = nOctaveLayers; |
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surf.extended = extended; |
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surf.upright = upright; |
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surf.keypointsRatio = 0.05f; |
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std::vector<cv::KeyPoint> keypoints; |
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surf(cv::ocl::oclMat(image), cv::ocl::oclMat(), keypoints); |
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cv::SURF surf_gold; |
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surf_gold.hessianThreshold = hessianThreshold; |
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surf_gold.nOctaves = nOctaves; |
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surf_gold.nOctaveLayers = nOctaveLayers; |
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surf_gold.extended = extended; |
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surf_gold.upright = upright; |
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std::vector<cv::KeyPoint> keypoints_gold; |
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surf_gold(image, cv::noArray(), keypoints_gold); |
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ASSERT_EQ(keypoints_gold.size(), keypoints.size()); |
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int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints); |
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double matchedRatio = static_cast<double>(matchedCount) / keypoints_gold.size(); |
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EXPECT_GT(matchedRatio, 0.99); |
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} |
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TEST_P(SURF, DISABLED_Descriptor) |
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{ |
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cv::Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/fruits.png", cv::IMREAD_GRAYSCALE); |
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ASSERT_FALSE(image.empty()); |
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cv::ocl::SURF_OCL surf; |
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surf.hessianThreshold = static_cast<float>(hessianThreshold); |
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surf.nOctaves = nOctaves; |
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surf.nOctaveLayers = nOctaveLayers; |
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surf.extended = extended; |
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surf.upright = upright; |
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surf.keypointsRatio = 0.05f; |
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cv::SURF surf_gold; |
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surf_gold.hessianThreshold = hessianThreshold; |
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surf_gold.nOctaves = nOctaves; |
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surf_gold.nOctaveLayers = nOctaveLayers; |
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surf_gold.extended = extended; |
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surf_gold.upright = upright; |
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std::vector<cv::KeyPoint> keypoints; |
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surf_gold(image, cv::noArray(), keypoints); |
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cv::ocl::oclMat descriptors; |
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surf(cv::ocl::oclMat(image), cv::ocl::oclMat(), keypoints, descriptors, true); |
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cv::Mat descriptors_gold; |
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surf_gold(image, cv::noArray(), keypoints, descriptors_gold, true); |
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cv::BFMatcher matcher(surf.defaultNorm()); |
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std::vector<cv::DMatch> matches; |
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matcher.match(descriptors_gold, cv::Mat(descriptors), matches); |
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int matchedCount = getMatchedPointsCount(keypoints, keypoints, matches); |
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double matchedRatio = static_cast<double>(matchedCount) / keypoints.size(); |
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EXPECT_GT(matchedRatio, 0.35); |
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} |
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INSTANTIATE_TEST_CASE_P(OCL_Features2D, SURF, testing::Combine( |
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testing::Values(HessianThreshold(500.0), HessianThreshold(1000.0)), |
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testing::Values(Octaves(3), Octaves(4)), |
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testing::Values(OctaveLayers(2), OctaveLayers(3)), |
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testing::Values(Extended(false), Extended(true)), |
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testing::Values(Upright(false), Upright(true)))); |
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#endif // HAVE_OPENCV_OCL
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