139 lines
5.4 KiB
C++
139 lines
5.4 KiB
C++
/*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) 2013, OpenCV Foundation, 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 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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// Authors:
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// * Peter Andreas Entschev, peter@entschev.com
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//
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//M*/
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#include "test_precomp.hpp"
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#ifdef HAVE_OPENCL
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////////////////////////////////////////////////////////
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// ORB
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namespace
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{
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IMPLEMENT_PARAM_CLASS(ORB_FeaturesCount, int)
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IMPLEMENT_PARAM_CLASS(ORB_ScaleFactor, float)
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IMPLEMENT_PARAM_CLASS(ORB_LevelsCount, int)
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IMPLEMENT_PARAM_CLASS(ORB_EdgeThreshold, int)
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IMPLEMENT_PARAM_CLASS(ORB_firstLevel, int)
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IMPLEMENT_PARAM_CLASS(ORB_WTA_K, int)
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IMPLEMENT_PARAM_CLASS(ORB_PatchSize, int)
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IMPLEMENT_PARAM_CLASS(ORB_BlurForDescriptor, bool)
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}
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CV_ENUM(ORB_ScoreType, ORB::HARRIS_SCORE, ORB::FAST_SCORE)
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PARAM_TEST_CASE(ORB, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold,
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ORB_firstLevel, ORB_WTA_K, ORB_ScoreType, ORB_PatchSize, ORB_BlurForDescriptor)
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{
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int nFeatures;
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float scaleFactor;
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int nLevels;
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int edgeThreshold;
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int firstLevel;
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int WTA_K;
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int scoreType;
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int patchSize;
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bool blurForDescriptor;
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virtual void SetUp()
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{
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nFeatures = GET_PARAM(0);
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scaleFactor = GET_PARAM(1);
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nLevels = GET_PARAM(2);
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edgeThreshold = GET_PARAM(3);
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firstLevel = GET_PARAM(4);
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WTA_K = GET_PARAM(5);
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scoreType = GET_PARAM(6);
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patchSize = GET_PARAM(7);
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blurForDescriptor = GET_PARAM(8);
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}
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};
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OCL_TEST_P(ORB, Accuracy)
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{
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cv::Mat image = readImage("gpu/perf/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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cv::Mat mask(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::ocl::oclMat ocl_image = cv::ocl::oclMat(image);
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cv::ocl::oclMat ocl_mask = cv::ocl::oclMat(mask);
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cv::ocl::ORB_OCL orb(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
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orb.blurForDescriptor = blurForDescriptor;
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std::vector<cv::KeyPoint> keypoints;
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cv::ocl::oclMat descriptors;
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orb(ocl_image, ocl_mask, keypoints, descriptors);
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cv::ORB orb_gold(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
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std::vector<cv::KeyPoint> keypoints_gold;
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cv::Mat descriptors_gold;
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orb_gold(image, mask, keypoints_gold, descriptors_gold);
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cv::BFMatcher matcher(cv::NORM_HAMMING);
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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_gold, 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, ORB, testing::Combine(
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testing::Values(ORB_FeaturesCount(1000)),
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testing::Values(ORB_ScaleFactor(1.2f)),
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testing::Values(ORB_LevelsCount(4), ORB_LevelsCount(8)),
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testing::Values(ORB_EdgeThreshold(31)),
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testing::Values(ORB_firstLevel(0), ORB_firstLevel(2)),
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testing::Values(ORB_WTA_K(2), ORB_WTA_K(3), ORB_WTA_K(4)),
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testing::Values(ORB_ScoreType(cv::ORB::HARRIS_SCORE)),
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testing::Values(ORB_PatchSize(31), ORB_PatchSize(29)),
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testing::Values(ORB_BlurForDescriptor(false), ORB_BlurForDescriptor(true))));
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#endif
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