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1002 lines
32 KiB
C++
1002 lines
32 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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// 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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namespace
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{
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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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testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char* actual_expr, std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
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{
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if (gold.size() != actual.size())
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{
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return testing::AssertionFailure() << "KeyPoints size mistmach\n"
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<< "\"" << gold_expr << "\" : " << gold.size() << "\n"
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<< "\"" << actual_expr << "\" : " << actual.size();
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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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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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{
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return testing::AssertionFailure() << "KeyPoints differ at " << i << "\n"
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<< "\"" << gold_expr << "\" vs \"" << actual_expr << "\" : \n"
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<< "pt : " << testing::PrintToString(p1.pt) << " vs " << testing::PrintToString(p2.pt) << "\n"
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<< "size : " << p1.size << " vs " << p2.size << "\n"
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<< "angle : " << p1.angle << " vs " << p2.angle << "\n"
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<< "response : " << p1.response << " vs " << p2.response << "\n"
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<< "octave : " << p1.octave << " vs " << p2.octave << "\n"
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<< "class_id : " << p1.class_id << " vs " << p2.class_id;
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}
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}
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return ::testing::AssertionSuccess();
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}
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#define ASSERT_KEYPOINTS_EQ(gold, actual) EXPECT_PRED_FORMAT2(assertKeyPointsEquals, gold, actual);
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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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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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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// SURF
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namespace
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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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}
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PARAM_TEST_CASE(SURF, cv::gpu::DeviceInfo, SURF_HessianThreshold, SURF_Octaves, SURF_OctaveLayers, SURF_Extended, SURF_Upright)
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{
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cv::gpu::DeviceInfo devInfo;
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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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devInfo = GET_PARAM(0);
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hessianThreshold = GET_PARAM(1);
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nOctaves = GET_PARAM(2);
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nOctaveLayers = GET_PARAM(3);
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extended = GET_PARAM(4);
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upright = GET_PARAM(5);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(SURF, Detector)
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{
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cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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cv::gpu::SURF_GPU surf;
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surf.hessianThreshold = 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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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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{
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try
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{
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std::vector<cv::KeyPoint> keypoints;
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surf(loadMat(image), cv::gpu::GpuMat(), keypoints);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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surf(loadMat(image), cv::gpu::GpuMat(), 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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}
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GPU_TEST_P(SURF, Detector_Masked)
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{
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cv::Mat image = readImage("features2d/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::gpu::SURF_GPU surf;
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surf.hessianThreshold = 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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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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{
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try
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{
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std::vector<cv::KeyPoint> keypoints;
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surf(loadMat(image), loadMat(mask), keypoints);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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surf(loadMat(image), loadMat(mask), 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, mask, 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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}
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GPU_TEST_P(SURF, Descriptor)
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{
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cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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cv::gpu::SURF_GPU surf;
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surf.hessianThreshold = 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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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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{
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try
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{
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std::vector<cv::KeyPoint> keypoints;
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cv::gpu::GpuMat descriptors;
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surf(loadMat(image), cv::gpu::GpuMat(), keypoints, descriptors);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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surf_gold(image, cv::noArray(), keypoints);
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cv::gpu::GpuMat descriptors;
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surf(loadMat(image), cv::gpu::GpuMat(), 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, 0.6);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_Features2D, SURF, testing::Combine(
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ALL_DEVICES,
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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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/////////////////////////////////////////////////////////////////////////////////////////////////
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// FAST
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namespace
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{
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IMPLEMENT_PARAM_CLASS(FAST_Threshold, int)
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IMPLEMENT_PARAM_CLASS(FAST_NonmaxSupression, bool)
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}
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PARAM_TEST_CASE(FAST, cv::gpu::DeviceInfo, FAST_Threshold, FAST_NonmaxSupression)
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{
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cv::gpu::DeviceInfo devInfo;
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int threshold;
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bool nonmaxSupression;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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threshold = GET_PARAM(1);
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nonmaxSupression = GET_PARAM(2);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(FAST, Accuracy)
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{
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cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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cv::gpu::FAST_GPU fast(threshold);
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fast.nonmaxSupression = nonmaxSupression;
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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{
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try
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{
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std::vector<cv::KeyPoint> keypoints;
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fast(loadMat(image), cv::gpu::GpuMat(), keypoints);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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fast(loadMat(image), cv::gpu::GpuMat(), keypoints);
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std::vector<cv::KeyPoint> keypoints_gold;
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cv::FAST(image, keypoints_gold, threshold, nonmaxSupression);
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ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints);
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}
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}
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INSTANTIATE_TEST_CASE_P(GPU_Features2D, FAST, testing::Combine(
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ALL_DEVICES,
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testing::Values(FAST_Threshold(25), FAST_Threshold(50)),
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testing::Values(FAST_NonmaxSupression(false), FAST_NonmaxSupression(true))));
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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, cv::ORB::HARRIS_SCORE, cv::ORB::FAST_SCORE)
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PARAM_TEST_CASE(ORB, cv::gpu::DeviceInfo, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold, ORB_firstLevel, ORB_WTA_K, ORB_ScoreType, ORB_PatchSize, ORB_BlurForDescriptor)
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{
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cv::gpu::DeviceInfo devInfo;
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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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devInfo = GET_PARAM(0);
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nFeatures = GET_PARAM(1);
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scaleFactor = GET_PARAM(2);
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nLevels = GET_PARAM(3);
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edgeThreshold = GET_PARAM(4);
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firstLevel = GET_PARAM(5);
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WTA_K = GET_PARAM(6);
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scoreType = GET_PARAM(7);
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patchSize = GET_PARAM(8);
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blurForDescriptor = GET_PARAM(9);
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cv::gpu::setDevice(devInfo.deviceID());
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}
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};
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GPU_TEST_P(ORB, Accuracy)
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{
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cv::Mat image = readImage("features2d/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::gpu::ORB_GPU orb(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
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orb.blurForDescriptor = blurForDescriptor;
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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{
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try
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{
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std::vector<cv::KeyPoint> keypoints;
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cv::gpu::GpuMat descriptors;
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orb(loadMat(image), loadMat(mask), keypoints, descriptors);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(CV_StsNotImplemented, e.code);
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}
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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cv::gpu::GpuMat descriptors;
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orb(loadMat(image), loadMat(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;
|
|
cv::Mat descriptors_gold;
|
|
orb_gold(image, mask, keypoints_gold, descriptors_gold);
|
|
|
|
cv::BFMatcher matcher(cv::NORM_HAMMING);
|
|
std::vector<cv::DMatch> matches;
|
|
matcher.match(descriptors_gold, cv::Mat(descriptors), matches);
|
|
|
|
int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints, matches);
|
|
double matchedRatio = static_cast<double>(matchedCount) / keypoints.size();
|
|
|
|
EXPECT_GT(matchedRatio, 0.35);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_Features2D, ORB, testing::Combine(
|
|
ALL_DEVICES,
|
|
testing::Values(ORB_FeaturesCount(1000)),
|
|
testing::Values(ORB_ScaleFactor(1.2f)),
|
|
testing::Values(ORB_LevelsCount(4), ORB_LevelsCount(8)),
|
|
testing::Values(ORB_EdgeThreshold(31)),
|
|
testing::Values(ORB_firstLevel(0), ORB_firstLevel(2)),
|
|
testing::Values(ORB_WTA_K(2), ORB_WTA_K(3), ORB_WTA_K(4)),
|
|
testing::Values(ORB_ScoreType(cv::ORB::HARRIS_SCORE)),
|
|
testing::Values(ORB_PatchSize(31), ORB_PatchSize(29)),
|
|
testing::Values(ORB_BlurForDescriptor(false), ORB_BlurForDescriptor(true))));
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// BruteForceMatcher
|
|
|
|
namespace
|
|
{
|
|
IMPLEMENT_PARAM_CLASS(DescriptorSize, int)
|
|
IMPLEMENT_PARAM_CLASS(UseMask, bool)
|
|
}
|
|
|
|
PARAM_TEST_CASE(BruteForceMatcher, cv::gpu::DeviceInfo, NormCode, DescriptorSize, UseMask)
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
int normCode;
|
|
int dim;
|
|
bool useMask;
|
|
|
|
int queryDescCount;
|
|
int countFactor;
|
|
|
|
cv::Mat query, train;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GET_PARAM(0);
|
|
normCode = GET_PARAM(1);
|
|
dim = GET_PARAM(2);
|
|
useMask = GET_PARAM(3);
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
|
|
queryDescCount = 300; // must be even number because we split train data in some cases in two
|
|
countFactor = 4; // do not change it
|
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
|
|
|
|
cv::Mat queryBuf, trainBuf;
|
|
|
|
// Generate query descriptors randomly.
|
|
// Descriptor vector elements are integer values.
|
|
queryBuf.create(queryDescCount, dim, CV_32SC1);
|
|
rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
|
|
queryBuf.convertTo(queryBuf, CV_32FC1);
|
|
|
|
// Generate train decriptors as follows:
|
|
// copy each query descriptor to train set countFactor times
|
|
// and perturb some one element of the copied descriptors in
|
|
// in ascending order. General boundaries of the perturbation
|
|
// are (0.f, 1.f).
|
|
trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
|
|
float step = 1.f / countFactor;
|
|
for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
|
|
{
|
|
cv::Mat queryDescriptor = queryBuf.row(qIdx);
|
|
for (int c = 0; c < countFactor; c++)
|
|
{
|
|
int tIdx = qIdx * countFactor + c;
|
|
cv::Mat trainDescriptor = trainBuf.row(tIdx);
|
|
queryDescriptor.copyTo(trainDescriptor);
|
|
int elem = rng(dim);
|
|
float diff = rng.uniform(step * c, step * (c + 1));
|
|
trainDescriptor.at<float>(0, elem) += diff;
|
|
}
|
|
}
|
|
|
|
queryBuf.convertTo(query, CV_32F);
|
|
trainBuf.convertTo(train, CV_32F);
|
|
}
|
|
};
|
|
|
|
GPU_TEST_P(BruteForceMatcher, Match_Single)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
cv::gpu::GpuMat mask;
|
|
if (useMask)
|
|
{
|
|
mask.create(query.rows, train.rows, CV_8UC1);
|
|
mask.setTo(cv::Scalar::all(1));
|
|
}
|
|
|
|
std::vector<cv::DMatch> matches;
|
|
matcher.match(loadMat(query), loadMat(train), matches, mask);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
cv::DMatch match = matches[i];
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
|
|
badCount++;
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, Match_Collection)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++)
|
|
{
|
|
masks[mi] = cv::gpu::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount/2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
std::vector<cv::DMatch> matches;
|
|
if (useMask)
|
|
matcher.match(cv::gpu::GpuMat(query), matches, masks);
|
|
else
|
|
matcher.match(cv::gpu::GpuMat(query), matches);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 1 : 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
cv::DMatch match = matches[i];
|
|
|
|
if ((int)i < queryDescCount / 2)
|
|
{
|
|
bool validQueryIdx = (match.queryIdx == (int)i);
|
|
bool validTrainIdx = (match.trainIdx == (int)i * countFactor + shift);
|
|
bool validImgIdx = (match.imgIdx == 0);
|
|
if (!validQueryIdx || !validTrainIdx || !validImgIdx)
|
|
badCount++;
|
|
}
|
|
else
|
|
{
|
|
bool validQueryIdx = (match.queryIdx == (int)i);
|
|
bool validTrainIdx = (match.trainIdx == ((int)i - queryDescCount / 2) * countFactor + shift);
|
|
bool validImgIdx = (match.imgIdx == 1);
|
|
if (!validQueryIdx || !validTrainIdx || !validImgIdx)
|
|
badCount++;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
const int knn = 2;
|
|
|
|
cv::gpu::GpuMat mask;
|
|
if (useMask)
|
|
{
|
|
mask.create(query.rows, train.rows, CV_8UC1);
|
|
mask.setTo(cv::Scalar::all(1));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 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++)
|
|
{
|
|
cv::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;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, KnnMatch_3_Single)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
const int knn = 3;
|
|
|
|
cv::gpu::GpuMat mask;
|
|
if (useMask)
|
|
{
|
|
mask.create(query.rows, train.rows, CV_8UC1);
|
|
mask.setTo(cv::Scalar::all(1));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 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++)
|
|
{
|
|
cv::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;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, KnnMatch_2_Collection)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
const int knn = 2;
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++ )
|
|
{
|
|
masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount / 2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
if (useMask)
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
|
|
else
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 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++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
{
|
|
if ((int)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;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, KnnMatch_3_Collection)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
const int knn = 3;
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++ )
|
|
{
|
|
masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount / 2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
if (useMask)
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
|
|
else
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 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++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
{
|
|
if ((int)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;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, RadiusMatch_Single)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
const float radius = 1.f / countFactor;
|
|
|
|
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
|
|
{
|
|
try
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius);
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
ASSERT_EQ(CV_StsNotImplemented, e.code);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::gpu::GpuMat mask;
|
|
if (useMask)
|
|
{
|
|
mask.create(query.rows, train.rows, CV_8UC1);
|
|
mask.setTo(cv::Scalar::all(1));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius, mask);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
if ((int)matches[i].size() != 1)
|
|
badCount++;
|
|
else
|
|
{
|
|
cv::DMatch match = matches[i][0];
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
|
|
badCount++;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
}
|
|
|
|
GPU_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
|
|
{
|
|
cv::gpu::BFMatcher_GPU matcher(normCode);
|
|
|
|
const int n = 3;
|
|
const float radius = 1.f / countFactor * n;
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++)
|
|
{
|
|
masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount / 2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
|
|
{
|
|
try
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius, masks);
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
ASSERT_EQ(CV_StsNotImplemented, e.code);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
if (useMask)
|
|
matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius, masks);
|
|
else
|
|
matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 1 : 0;
|
|
int needMatchCount = useMask ? 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++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
{
|
|
if ((int)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;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_Features2D, BruteForceMatcher, testing::Combine(
|
|
ALL_DEVICES,
|
|
testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2)),
|
|
testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)),
|
|
testing::Values(UseMask(false), UseMask(true))));
|
|
|
|
#endif // HAVE_CUDA
|