mirror of https://github.com/opencv/opencv.git
Merge pull request #632 from pengx17:2.4
Andrey Kamaev
12 years ago
committed by
OpenCV Buildbot
commit
00d8ad9e7e
5 changed files with 247 additions and 11 deletions
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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 oclMaterials 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 "precomp.hpp" |
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#ifdef HAVE_OPENCL |
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extern std::string workdir; |
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using namespace std; |
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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 = 1.0; |
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const double maxSizeDif = 1.0; |
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const double maxAngleDif = 2.0; |
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const double maxResponseDif = 0.1; |
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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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struct KeyPointLess : std::binary_function<cv::KeyPoint, cv::KeyPoint, bool> |
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{ |
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bool operator()(const cv::KeyPoint& kp1, const cv::KeyPoint& kp2) const |
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{ |
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return kp1.pt.y < kp2.pt.y || (kp1.pt.y == kp2.pt.y && kp1.pt.x < kp2.pt.x); |
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} |
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}; |
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#define ASSERT_KEYPOINTS_EQ(gold, actual) EXPECT_PRED_FORMAT2(assertKeyPointsEquals, gold, actual); |
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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(), KeyPointLess()); |
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std::sort(gold.begin(), gold.end(), KeyPointLess()); |
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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(SURF_HessianThreshold, double) |
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IMPLEMENT_PARAM_CLASS(SURF_Octaves, int) |
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IMPLEMENT_PARAM_CLASS(SURF_OctaveLayers, int) |
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IMPLEMENT_PARAM_CLASS(SURF_Extended, bool) |
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IMPLEMENT_PARAM_CLASS(SURF_Upright, bool) |
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PARAM_TEST_CASE(SURF, SURF_HessianThreshold, SURF_Octaves, SURF_OctaveLayers, SURF_Extended, SURF_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_PARAM(0); |
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nOctaves = GET_PARAM(1); |
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nOctaveLayers = GET_PARAM(2); |
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extended = GET_PARAM(3); |
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upright = GET_PARAM(4); |
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} |
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}; |
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TEST_P(SURF, Detector) |
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{ |
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cv::Mat image = readImage(workdir + "fruits.jpg", 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, 0.95); |
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} |
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TEST_P(SURF, Descriptor) |
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{ |
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cv::Mat image = readImage(workdir + "fruits.jpg", 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(/*SURF_HessianThreshold(100.0), */SURF_HessianThreshold(500.0), SURF_HessianThreshold(1000.0)), |
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testing::Values(SURF_Octaves(3), SURF_Octaves(4)), |
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testing::Values(SURF_OctaveLayers(2), SURF_OctaveLayers(3)), |
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testing::Values(SURF_Extended(false), SURF_Extended(true)), |
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testing::Values(SURF_Upright(false), SURF_Upright(true)))); |
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#endif |
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