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cce2d9927e
The bug caused crash of GPU version of feature matcher in stitcher when we use ORB features.
656 lines
23 KiB
C++
656 lines
23 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) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage 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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// 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 "precomp.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::detail;
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using namespace cv::gpu;
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#ifdef HAVE_OPENCV_NONFREE
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#include "opencv2/nonfree/nonfree.hpp"
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static bool makeUseOfNonfree = initModule_nonfree();
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#endif
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namespace {
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struct DistIdxPair
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{
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bool operator<(const DistIdxPair &other) const { return dist < other.dist; }
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double dist;
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int idx;
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};
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struct MatchPairsBody : ParallelLoopBody
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{
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MatchPairsBody(FeaturesMatcher &_matcher, const vector<ImageFeatures> &_features,
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vector<MatchesInfo> &_pairwise_matches, vector<pair<int,int> > &_near_pairs)
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: matcher(_matcher), features(_features),
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pairwise_matches(_pairwise_matches), near_pairs(_near_pairs) {}
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void operator ()(const Range &r) const
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{
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const int num_images = static_cast<int>(features.size());
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for (int i = r.start; i < r.end; ++i)
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{
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int from = near_pairs[i].first;
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int to = near_pairs[i].second;
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int pair_idx = from*num_images + to;
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matcher(features[from], features[to], pairwise_matches[pair_idx]);
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pairwise_matches[pair_idx].src_img_idx = from;
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pairwise_matches[pair_idx].dst_img_idx = to;
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size_t dual_pair_idx = to*num_images + from;
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pairwise_matches[dual_pair_idx] = pairwise_matches[pair_idx];
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pairwise_matches[dual_pair_idx].src_img_idx = to;
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pairwise_matches[dual_pair_idx].dst_img_idx = from;
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if (!pairwise_matches[pair_idx].H.empty())
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pairwise_matches[dual_pair_idx].H = pairwise_matches[pair_idx].H.inv();
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for (size_t j = 0; j < pairwise_matches[dual_pair_idx].matches.size(); ++j)
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std::swap(pairwise_matches[dual_pair_idx].matches[j].queryIdx,
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pairwise_matches[dual_pair_idx].matches[j].trainIdx);
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LOG(".");
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}
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}
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FeaturesMatcher &matcher;
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const vector<ImageFeatures> &features;
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vector<MatchesInfo> &pairwise_matches;
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vector<pair<int,int> > &near_pairs;
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private:
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void operator =(const MatchPairsBody&);
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};
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//////////////////////////////////////////////////////////////////////////////
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typedef set<pair<int,int> > MatchesSet;
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// These two classes are aimed to find features matches only, not to
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// estimate homography
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class CpuMatcher : public FeaturesMatcher
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{
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public:
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CpuMatcher(float match_conf) : FeaturesMatcher(true), match_conf_(match_conf) {}
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void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);
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private:
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float match_conf_;
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};
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#if defined(HAVE_OPENCV_GPU) && !defined(DYNAMIC_CUDA_SUPPORT)
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class GpuMatcher : public FeaturesMatcher
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{
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public:
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GpuMatcher(float match_conf) : match_conf_(match_conf) {}
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void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);
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void collectGarbage();
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private:
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float match_conf_;
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GpuMat descriptors1_, descriptors2_;
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GpuMat train_idx_, distance_, all_dist_;
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vector< vector<DMatch> > pair_matches;
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};
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#endif
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void CpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
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{
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CV_Assert(features1.descriptors.type() == features2.descriptors.type());
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CV_Assert(features2.descriptors.depth() == CV_8U || features2.descriptors.depth() == CV_32F);
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#ifdef HAVE_TEGRA_OPTIMIZATION
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if (tegra::match2nearest(features1, features2, matches_info, match_conf_))
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return;
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#endif
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matches_info.matches.clear();
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Ptr<flann::IndexParams> indexParams = new flann::KDTreeIndexParams();
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Ptr<flann::SearchParams> searchParams = new flann::SearchParams();
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if (features2.descriptors.depth() == CV_8U)
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{
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indexParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
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searchParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
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}
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FlannBasedMatcher matcher(indexParams, searchParams);
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vector< vector<DMatch> > pair_matches;
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MatchesSet matches;
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// Find 1->2 matches
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matcher.knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
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for (size_t i = 0; i < pair_matches.size(); ++i)
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{
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if (pair_matches[i].size() < 2)
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continue;
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const DMatch& m0 = pair_matches[i][0];
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const DMatch& m1 = pair_matches[i][1];
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if (m0.distance < (1.f - match_conf_) * m1.distance)
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{
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matches_info.matches.push_back(m0);
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matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
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}
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}
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LOG("\n1->2 matches: " << matches_info.matches.size() << endl);
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// Find 2->1 matches
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pair_matches.clear();
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matcher.knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
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for (size_t i = 0; i < pair_matches.size(); ++i)
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{
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if (pair_matches[i].size() < 2)
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continue;
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const DMatch& m0 = pair_matches[i][0];
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const DMatch& m1 = pair_matches[i][1];
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if (m0.distance < (1.f - match_conf_) * m1.distance)
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if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
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matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
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}
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LOG("1->2 & 2->1 matches: " << matches_info.matches.size() << endl);
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}
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#if defined(HAVE_OPENCV_GPU) && !defined(DYNAMIC_CUDA_SUPPORT)
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void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
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{
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matches_info.matches.clear();
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ensureSizeIsEnough(features1.descriptors.size(), features1.descriptors.type(), descriptors1_);
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ensureSizeIsEnough(features2.descriptors.size(), features2.descriptors.type(), descriptors2_);
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descriptors1_.upload(features1.descriptors);
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descriptors2_.upload(features2.descriptors);
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BFMatcher_GPU matcher(NORM_L1);
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MatchesSet matches;
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// Find 1->2 matches
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pair_matches.clear();
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matcher.knnMatchSingle(descriptors1_, descriptors2_, train_idx_, distance_, all_dist_, 2);
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matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
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for (size_t i = 0; i < pair_matches.size(); ++i)
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{
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if (pair_matches[i].size() < 2)
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continue;
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const DMatch& m0 = pair_matches[i][0];
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const DMatch& m1 = pair_matches[i][1];
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if (m0.distance < (1.f - match_conf_) * m1.distance)
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{
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matches_info.matches.push_back(m0);
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matches.insert(make_pair(m0.queryIdx, m0.trainIdx));
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}
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}
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// Find 2->1 matches
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pair_matches.clear();
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matcher.knnMatchSingle(descriptors2_, descriptors1_, train_idx_, distance_, all_dist_, 2);
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matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
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for (size_t i = 0; i < pair_matches.size(); ++i)
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{
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if (pair_matches[i].size() < 2)
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continue;
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const DMatch& m0 = pair_matches[i][0];
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const DMatch& m1 = pair_matches[i][1];
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if (m0.distance < (1.f - match_conf_) * m1.distance)
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if (matches.find(make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
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matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
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}
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}
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void GpuMatcher::collectGarbage()
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{
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descriptors1_.release();
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descriptors2_.release();
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train_idx_.release();
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distance_.release();
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all_dist_.release();
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vector< vector<DMatch> >().swap(pair_matches);
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}
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#endif
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} // namespace
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namespace cv {
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namespace detail {
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void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features)
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{
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find(image, features);
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features.img_size = image.size();
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}
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void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features, const vector<Rect> &rois)
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{
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vector<ImageFeatures> roi_features(rois.size());
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size_t total_kps_count = 0;
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int total_descriptors_height = 0;
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for (size_t i = 0; i < rois.size(); ++i)
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{
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find(image(rois[i]), roi_features[i]);
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total_kps_count += roi_features[i].keypoints.size();
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total_descriptors_height += roi_features[i].descriptors.rows;
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}
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features.img_size = image.size();
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features.keypoints.resize(total_kps_count);
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features.descriptors.create(total_descriptors_height,
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roi_features[0].descriptors.cols,
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roi_features[0].descriptors.type());
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int kp_idx = 0;
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int descr_offset = 0;
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for (size_t i = 0; i < rois.size(); ++i)
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{
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for (size_t j = 0; j < roi_features[i].keypoints.size(); ++j, ++kp_idx)
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{
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features.keypoints[kp_idx] = roi_features[i].keypoints[j];
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features.keypoints[kp_idx].pt.x += (float)rois[i].x;
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features.keypoints[kp_idx].pt.y += (float)rois[i].y;
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}
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Mat subdescr = features.descriptors.rowRange(
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descr_offset, descr_offset + roi_features[i].descriptors.rows);
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roi_features[i].descriptors.copyTo(subdescr);
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descr_offset += roi_features[i].descriptors.rows;
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}
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}
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SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
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int num_octaves_descr, int num_layers_descr)
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{
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if (num_octaves_descr == num_octaves && num_layers_descr == num_layers)
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{
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surf = Algorithm::create<Feature2D>("Feature2D.SURF");
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if( surf.empty() )
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CV_Error( CV_StsNotImplemented, "OpenCV was built without SURF support" );
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surf->set("hessianThreshold", hess_thresh);
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surf->set("nOctaves", num_octaves);
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surf->set("nOctaveLayers", num_layers);
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}
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else
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{
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detector_ = Algorithm::create<FeatureDetector>("Feature2D.SURF");
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extractor_ = Algorithm::create<DescriptorExtractor>("Feature2D.SURF");
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if( detector_.empty() || extractor_.empty() )
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CV_Error( CV_StsNotImplemented, "OpenCV was built without SURF support" );
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detector_->set("hessianThreshold", hess_thresh);
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detector_->set("nOctaves", num_octaves);
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detector_->set("nOctaveLayers", num_layers);
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extractor_->set("nOctaves", num_octaves_descr);
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extractor_->set("nOctaveLayers", num_layers_descr);
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}
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}
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void SurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
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{
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Mat gray_image;
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CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC1));
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if(image.type() == CV_8UC3)
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{
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cvtColor(image, gray_image, CV_BGR2GRAY);
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}
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else
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{
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gray_image = image;
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}
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if (surf == 0)
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{
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detector_->detect(gray_image, features.keypoints);
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extractor_->compute(gray_image, features.keypoints, features.descriptors);
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}
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else
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{
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Mat descriptors;
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(*surf)(gray_image, Mat(), features.keypoints, descriptors);
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features.descriptors = descriptors.reshape(1, (int)features.keypoints.size());
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}
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}
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OrbFeaturesFinder::OrbFeaturesFinder(Size _grid_size, int n_features, float scaleFactor, int nlevels)
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{
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grid_size = _grid_size;
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orb = new ORB(n_features * (99 + grid_size.area())/100/grid_size.area(), scaleFactor, nlevels);
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}
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void OrbFeaturesFinder::find(const Mat &image, ImageFeatures &features)
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{
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Mat gray_image;
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CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC4) || (image.type() == CV_8UC1));
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if (image.type() == CV_8UC3) {
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cvtColor(image, gray_image, CV_BGR2GRAY);
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} else if (image.type() == CV_8UC4) {
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cvtColor(image, gray_image, CV_BGRA2GRAY);
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} else if (image.type() == CV_8UC1) {
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gray_image=image;
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} else {
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CV_Error(CV_StsUnsupportedFormat, "");
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}
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if (grid_size.area() == 1)
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(*orb)(gray_image, Mat(), features.keypoints, features.descriptors);
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else
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{
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features.keypoints.clear();
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features.descriptors.release();
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std::vector<KeyPoint> points;
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Mat descriptors;
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for (int r = 0; r < grid_size.height; ++r)
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for (int c = 0; c < grid_size.width; ++c)
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{
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int xl = c * gray_image.cols / grid_size.width;
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int yl = r * gray_image.rows / grid_size.height;
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int xr = (c+1) * gray_image.cols / grid_size.width;
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int yr = (r+1) * gray_image.rows / grid_size.height;
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// LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
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// << " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
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// << " yl=" << yl << ", yr=" << yr << ", "
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// << " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
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// << "gray_image.dims=" << gray_image.dims << "\n");
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Mat gray_image_part=gray_image(Range(yl, yr), Range(xl, xr));
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// LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
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// << " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
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// << " gray_image_part.dims=" << gray_image_part.dims << ", "
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// << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
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(*orb)(gray_image_part, Mat(), points, descriptors);
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features.keypoints.reserve(features.keypoints.size() + points.size());
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for (std::vector<KeyPoint>::iterator kp = points.begin(); kp != points.end(); ++kp)
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{
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kp->pt.x += xl;
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kp->pt.y += yl;
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features.keypoints.push_back(*kp);
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}
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features.descriptors.push_back(descriptors);
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}
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}
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}
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#if defined(HAVE_OPENCV_NONFREE) && defined(HAVE_OPENCV_GPU) && !defined(DYNAMIC_CUDA_SUPPORT)
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SurfFeaturesFinderGpu::SurfFeaturesFinderGpu(double hess_thresh, int num_octaves, int num_layers,
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int num_octaves_descr, int num_layers_descr)
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{
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surf_.keypointsRatio = 0.1f;
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surf_.hessianThreshold = hess_thresh;
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surf_.extended = false;
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num_octaves_ = num_octaves;
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num_layers_ = num_layers;
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num_octaves_descr_ = num_octaves_descr;
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num_layers_descr_ = num_layers_descr;
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}
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void SurfFeaturesFinderGpu::find(const Mat &image, ImageFeatures &features)
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{
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CV_Assert(image.depth() == CV_8U);
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ensureSizeIsEnough(image.size(), image.type(), image_);
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image_.upload(image);
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ensureSizeIsEnough(image.size(), CV_8UC1, gray_image_);
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cvtColor(image_, gray_image_, CV_BGR2GRAY);
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surf_.nOctaves = num_octaves_;
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surf_.nOctaveLayers = num_layers_;
|
|
surf_.upright = false;
|
|
surf_(gray_image_, GpuMat(), keypoints_);
|
|
|
|
surf_.nOctaves = num_octaves_descr_;
|
|
surf_.nOctaveLayers = num_layers_descr_;
|
|
surf_.upright = true;
|
|
surf_(gray_image_, GpuMat(), keypoints_, descriptors_, true);
|
|
surf_.downloadKeypoints(keypoints_, features.keypoints);
|
|
|
|
descriptors_.download(features.descriptors);
|
|
}
|
|
|
|
void SurfFeaturesFinderGpu::collectGarbage()
|
|
{
|
|
surf_.releaseMemory();
|
|
image_.release();
|
|
gray_image_.release();
|
|
keypoints_.release();
|
|
descriptors_.release();
|
|
}
|
|
#elif defined(HAVE_OPENCV_NONFREE)
|
|
SurfFeaturesFinderGpu::SurfFeaturesFinderGpu(double hess_thresh, int num_octaves, int num_layers,
|
|
int num_octaves_descr, int num_layers_descr)
|
|
{
|
|
(void)hess_thresh;
|
|
(void)num_octaves;
|
|
(void)num_layers;
|
|
(void)num_octaves_descr;
|
|
(void)num_layers_descr;
|
|
CV_Error(CV_StsNotImplemented, "CUDA optimization is unavailable");
|
|
}
|
|
|
|
|
|
void SurfFeaturesFinderGpu::find(const Mat &image, ImageFeatures &features)
|
|
{
|
|
(void)image;
|
|
(void)features;
|
|
CV_Error(CV_StsNotImplemented, "CUDA optimization is unavailable");
|
|
}
|
|
|
|
void SurfFeaturesFinderGpu::collectGarbage()
|
|
{
|
|
}
|
|
#endif
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
|
|
MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0), confidence(0) {}
|
|
|
|
MatchesInfo::MatchesInfo(const MatchesInfo &other) { *this = other; }
|
|
|
|
const MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
|
|
{
|
|
src_img_idx = other.src_img_idx;
|
|
dst_img_idx = other.dst_img_idx;
|
|
matches = other.matches;
|
|
inliers_mask = other.inliers_mask;
|
|
num_inliers = other.num_inliers;
|
|
H = other.H.clone();
|
|
confidence = other.confidence;
|
|
return *this;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
|
|
void FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches,
|
|
const Mat &mask)
|
|
{
|
|
const int num_images = static_cast<int>(features.size());
|
|
|
|
CV_Assert(mask.empty() || (mask.type() == CV_8U && mask.cols == num_images && mask.rows));
|
|
Mat_<uchar> mask_(mask);
|
|
if (mask_.empty())
|
|
mask_ = Mat::ones(num_images, num_images, CV_8U);
|
|
|
|
vector<pair<int,int> > near_pairs;
|
|
for (int i = 0; i < num_images - 1; ++i)
|
|
for (int j = i + 1; j < num_images; ++j)
|
|
if (features[i].keypoints.size() > 0 && features[j].keypoints.size() > 0 && mask_(i, j))
|
|
near_pairs.push_back(make_pair(i, j));
|
|
|
|
pairwise_matches.resize(num_images * num_images);
|
|
MatchPairsBody body(*this, features, pairwise_matches, near_pairs);
|
|
|
|
if (is_thread_safe_)
|
|
parallel_for_(Range(0, static_cast<int>(near_pairs.size())), body);
|
|
else
|
|
body(Range(0, static_cast<int>(near_pairs.size())));
|
|
LOGLN_CHAT("");
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
|
|
BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
|
|
{
|
|
#if defined(HAVE_OPENCV_GPU) && !defined(DYNAMIC_CUDA_SUPPORT)
|
|
if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
|
|
impl_ = new GpuMatcher(match_conf);
|
|
else
|
|
#else
|
|
(void)try_use_gpu;
|
|
#endif
|
|
impl_ = new CpuMatcher(match_conf);
|
|
|
|
is_thread_safe_ = impl_->isThreadSafe();
|
|
num_matches_thresh1_ = num_matches_thresh1;
|
|
num_matches_thresh2_ = num_matches_thresh2;
|
|
}
|
|
|
|
|
|
void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
|
|
MatchesInfo &matches_info)
|
|
{
|
|
(*impl_)(features1, features2, matches_info);
|
|
|
|
// Check if it makes sense to find homography
|
|
if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
|
|
return;
|
|
|
|
// Construct point-point correspondences for homography estimation
|
|
Mat src_points(1, static_cast<int>(matches_info.matches.size()), CV_32FC2);
|
|
Mat dst_points(1, static_cast<int>(matches_info.matches.size()), CV_32FC2);
|
|
for (size_t i = 0; i < matches_info.matches.size(); ++i)
|
|
{
|
|
const DMatch& m = matches_info.matches[i];
|
|
|
|
Point2f p = features1.keypoints[m.queryIdx].pt;
|
|
p.x -= features1.img_size.width * 0.5f;
|
|
p.y -= features1.img_size.height * 0.5f;
|
|
src_points.at<Point2f>(0, static_cast<int>(i)) = p;
|
|
|
|
p = features2.keypoints[m.trainIdx].pt;
|
|
p.x -= features2.img_size.width * 0.5f;
|
|
p.y -= features2.img_size.height * 0.5f;
|
|
dst_points.at<Point2f>(0, static_cast<int>(i)) = p;
|
|
}
|
|
|
|
// Find pair-wise motion
|
|
matches_info.H = findHomography(src_points, dst_points, matches_info.inliers_mask, CV_RANSAC);
|
|
if (std::abs(determinant(matches_info.H)) < numeric_limits<double>::epsilon())
|
|
return;
|
|
|
|
// Find number of inliers
|
|
matches_info.num_inliers = 0;
|
|
for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
|
|
if (matches_info.inliers_mask[i])
|
|
matches_info.num_inliers++;
|
|
|
|
// These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic Image Stitching
|
|
// using Invariant Features"
|
|
matches_info.confidence = matches_info.num_inliers / (8 + 0.3 * matches_info.matches.size());
|
|
|
|
// Set zero confidence to remove matches between too close images, as they don't provide
|
|
// additional information anyway. The threshold was set experimentally.
|
|
matches_info.confidence = matches_info.confidence > 3. ? 0. : matches_info.confidence;
|
|
|
|
// Check if we should try to refine motion
|
|
if (matches_info.num_inliers < num_matches_thresh2_)
|
|
return;
|
|
|
|
// Construct point-point correspondences for inliers only
|
|
src_points.create(1, matches_info.num_inliers, CV_32FC2);
|
|
dst_points.create(1, matches_info.num_inliers, CV_32FC2);
|
|
int inlier_idx = 0;
|
|
for (size_t i = 0; i < matches_info.matches.size(); ++i)
|
|
{
|
|
if (!matches_info.inliers_mask[i])
|
|
continue;
|
|
|
|
const DMatch& m = matches_info.matches[i];
|
|
|
|
Point2f p = features1.keypoints[m.queryIdx].pt;
|
|
p.x -= features1.img_size.width * 0.5f;
|
|
p.y -= features1.img_size.height * 0.5f;
|
|
src_points.at<Point2f>(0, inlier_idx) = p;
|
|
|
|
p = features2.keypoints[m.trainIdx].pt;
|
|
p.x -= features2.img_size.width * 0.5f;
|
|
p.y -= features2.img_size.height * 0.5f;
|
|
dst_points.at<Point2f>(0, inlier_idx) = p;
|
|
|
|
inlier_idx++;
|
|
}
|
|
|
|
// Rerun motion estimation on inliers only
|
|
matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
|
|
}
|
|
|
|
void BestOf2NearestMatcher::collectGarbage()
|
|
{
|
|
impl_->collectGarbage();
|
|
}
|
|
|
|
} // namespace detail
|
|
} // namespace cv
|