459 lines
17 KiB
C++
459 lines
17 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 <algorithm>
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#include <functional>
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#include "matchers.hpp"
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#include "util.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::gpu;
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//////////////////////////////////////////////////////////////////////////////
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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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//features.img = image.clone();
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}
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//////////////////////////////////////////////////////////////////////////////
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namespace
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{
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class CpuSurfFeaturesFinder : public FeaturesFinder
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{
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public:
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CpuSurfFeaturesFinder(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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detector_ = new SurfFeatureDetector(hess_thresh, num_octaves, num_layers);
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extractor_ = new SurfDescriptorExtractor(num_octaves_descr, num_layers_descr);
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}
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protected:
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void find(const Mat &image, ImageFeatures &features);
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private:
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Ptr<FeatureDetector> detector_;
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Ptr<DescriptorExtractor> extractor_;
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};
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class GpuSurfFeaturesFinder : public FeaturesFinder
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{
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public:
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GpuSurfFeaturesFinder(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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protected:
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void find(const Mat &image, ImageFeatures &features);
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private:
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SURF_GPU surf_;
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int num_octaves_, num_layers_;
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int num_octaves_descr_, num_layers_descr_;
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};
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void CpuSurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
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{
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Mat gray_image;
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CV_Assert(image.depth() == CV_8U);
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cvtColor(image, gray_image, CV_BGR2GRAY);
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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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void GpuSurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
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{
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GpuMat gray_image;
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CV_Assert(image.depth() == CV_8U);
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cvtColor(GpuMat(image), gray_image, CV_BGR2GRAY);
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GpuMat d_keypoints;
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GpuMat d_descriptors;
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surf_.nOctaves = num_octaves_;
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surf_.nOctaveLayers = num_layers_;
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surf_(gray_image, GpuMat(), d_keypoints);
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surf_.nOctaves = num_octaves_descr_;
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surf_.nOctaveLayers = num_layers_descr_;
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surf_(gray_image, GpuMat(), d_keypoints, d_descriptors, true);
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surf_.downloadKeypoints(d_keypoints, features.keypoints);
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d_descriptors.download(features.descriptors);
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}
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} // anonymous namespace
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SurfFeaturesFinder::SurfFeaturesFinder(bool try_use_gpu, 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 (try_use_gpu && getCudaEnabledDeviceCount() > 0)
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impl_ = new GpuSurfFeaturesFinder(hess_thresh, num_octaves, num_layers, num_octaves_descr, num_layers_descr);
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else
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impl_ = new CpuSurfFeaturesFinder(hess_thresh, num_octaves, num_layers, num_octaves_descr, num_layers_descr);
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}
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void SurfFeaturesFinder::find(const Mat &image, ImageFeatures &features)
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{
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(*impl_)(image, features);
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}
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//////////////////////////////////////////////////////////////////////////////
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MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0), confidence(0) {}
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MatchesInfo::MatchesInfo(const MatchesInfo &other) { *this = other; }
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const MatchesInfo& MatchesInfo::operator =(const MatchesInfo &other)
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{
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src_img_idx = other.src_img_idx;
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dst_img_idx = other.dst_img_idx;
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matches = other.matches;
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inliers_mask = other.inliers_mask;
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num_inliers = other.num_inliers;
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H = other.H.clone();
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confidence = other.confidence;
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return *this;
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}
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//////////////////////////////////////////////////////////////////////////////
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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
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{
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MatchPairsBody(const MatchPairsBody& other)
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: matcher(other.matcher), features(other.features),
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pairwise_matches(other.pairwise_matches), near_pairs(other.near_pairs) {}
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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 BlockedRange &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.begin(); 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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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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void FeaturesMatcher::operator ()(const vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches)
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{
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const int num_images = static_cast<int>(features.size());
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vector<pair<int,int> > near_pairs;
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for (int i = 0; i < num_images - 1; ++i)
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for (int j = i + 1; j < num_images; ++j)
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near_pairs.push_back(make_pair(i, j));
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pairwise_matches.resize(num_images * num_images);
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MatchPairsBody body(*this, features, pairwise_matches, near_pairs);
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if (is_thread_safe_)
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parallel_for(BlockedRange(0, static_cast<int>(near_pairs.size())), body);
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else
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body(BlockedRange(0, static_cast<int>(near_pairs.size())));
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LOGLN("");
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}
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//////////////////////////////////////////////////////////////////////////////
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namespace
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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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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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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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};
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void CpuMatcher::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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FlannBasedMatcher matcher;
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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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// 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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}
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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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descriptors1_.upload(features1.descriptors);
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descriptors2_.upload(features2.descriptors);
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BruteForceMatcher_GPU< L2<float> > matcher;
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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(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.knnMatch(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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} // anonymous namespace
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BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
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{
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if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
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impl_ = new GpuMatcher(match_conf);
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else
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impl_ = new CpuMatcher(match_conf);
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is_thread_safe_ = impl_->isThreadSafe();
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num_matches_thresh1_ = num_matches_thresh1;
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num_matches_thresh2_ = num_matches_thresh2;
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}
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void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
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MatchesInfo &matches_info)
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{
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(*impl_)(features1, features2, matches_info);
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//Mat out;
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//drawMatches(features1.img, features1.keypoints, features2.img, features2.keypoints, matches_info.matches, out);
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//stringstream ss;
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//ss << features1.img_idx << features2.img_idx << ".png";
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//imwrite(ss.str(), out);
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// Check if it makes sense to find homography
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if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
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return;
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// Construct point-point correspondences for homography estimation
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Mat src_points(1, matches_info.matches.size(), CV_32FC2);
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Mat dst_points(1, matches_info.matches.size(), CV_32FC2);
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for (size_t i = 0; i < matches_info.matches.size(); ++i)
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{
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const DMatch& m = matches_info.matches[i];
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Point2f p = features1.keypoints[m.queryIdx].pt;
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p.x -= features1.img_size.width * 0.5f;
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p.y -= features1.img_size.height * 0.5f;
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src_points.at<Point2f>(0, i) = p;
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p = features2.keypoints[m.trainIdx].pt;
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p.x -= features2.img_size.width * 0.5f;
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p.y -= features2.img_size.height * 0.5f;
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dst_points.at<Point2f>(0, i) = p;
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}
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// Find pair-wise motion
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matches_info.H = findHomography(src_points, dst_points, matches_info.inliers_mask, CV_RANSAC);
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// Find number of inliers
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matches_info.num_inliers = 0;
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for (size_t i = 0; i < matches_info.inliers_mask.size(); ++i)
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if (matches_info.inliers_mask[i])
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matches_info.num_inliers++;
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matches_info.confidence = matches_info.num_inliers / (8 + 0.3*matches_info.matches.size());
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// Check if we should try to refine motion
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if (matches_info.num_inliers < num_matches_thresh2_)
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return;
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// Construct point-point correspondences for inliers only
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src_points.create(1, matches_info.num_inliers, CV_32FC2);
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dst_points.create(1, matches_info.num_inliers, CV_32FC2);
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int inlier_idx = 0;
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for (size_t i = 0; i < matches_info.matches.size(); ++i)
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{
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if (!matches_info.inliers_mask[i])
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continue;
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const DMatch& m = matches_info.matches[i];
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Point2f p = features1.keypoints[m.queryIdx].pt;
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p.x -= features1.img_size.width * 0.5f;
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p.y -= features1.img_size.height * 0.5f;
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src_points.at<Point2f>(0, inlier_idx) = p;
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p = features2.keypoints[m.trainIdx].pt;
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p.x -= features2.img_size.width * 0.5f;
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p.y -= features2.img_size.height * 0.5f;
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dst_points.at<Point2f>(0, inlier_idx) = p;
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inlier_idx++;
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}
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// Rerun motion estimation on inliers only
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matches_info.H = findHomography(src_points, dst_points, CV_RANSAC);
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}
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