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Open Source Computer Vision Library
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216 lines
7.2 KiB
216 lines
7.2 KiB
12 years ago
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/*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(cv::NORM_L2);
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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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