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358 lines
12 KiB
C++
358 lines
12 KiB
C++
#include <algorithm>
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#include <functional>
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#include <opencv2/calib3d/calib3d.hpp>
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#include <opencv2/gpu/gpu.hpp>
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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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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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inline 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 vector<Mat> &images, vector<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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void CpuSurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
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{
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// Make images gray
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vector<Mat> gray_images(images.size());
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for (size_t i = 0; i < images.size(); ++i)
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{
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CV_Assert(images[i].depth() == CV_8U);
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cvtColor(images[i], gray_images[i], CV_BGR2GRAY);
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}
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features.resize(images.size());
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// Find keypoints in all images
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for (size_t i = 0; i < images.size(); ++i)
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{
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detector_->detect(gray_images[i], features[i].keypoints);
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extractor_->compute(gray_images[i], features[i].keypoints, features[i].descriptors);
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}
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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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inline 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_.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 vector<Mat> &images, vector<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 GpuSurfFeaturesFinder::find(const vector<Mat> &images, vector<ImageFeatures> &features)
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{
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// Make images gray
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vector<GpuMat> gray_images(images.size());
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for (size_t i = 0; i < images.size(); ++i)
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{
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CV_Assert(images[i].depth() == CV_8U);
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cvtColor(GpuMat(images[i]), gray_images[i], CV_BGR2GRAY);
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}
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features.resize(images.size());
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// Find keypoints in all images
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GpuMat d_keypoints;
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GpuMat d_descriptors;
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for (size_t i = 0; i < images.size(); ++i)
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{
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surf_.nOctaves = num_octaves_;
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surf_.nOctaveLayers = num_layers_;
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surf_(gray_images[i], 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_images[i], GpuMat(), d_keypoints, d_descriptors, true);
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surf_.downloadKeypoints(d_keypoints, features[i].keypoints);
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d_descriptors.download(features[i].descriptors);
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}
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}
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}
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SurfFeaturesFinder::SurfFeaturesFinder(bool gpu_hint, 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 (gpu_hint && 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 vector<Mat> &images, vector<ImageFeatures> &features)
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{
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(*impl_)(images, 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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void FeaturesMatcher::operator ()(const vector<Mat> &images, const vector<ImageFeatures> &features,
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vector<MatchesInfo> &pairwise_matches)
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{
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pairwise_matches.resize(images.size() * images.size());
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for (size_t i = 0; i < images.size(); ++i)
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{
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LOGLN("Processing image " << i << "... ");
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for (size_t j = 0; j < images.size(); ++j)
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{
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if (i == j)
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continue;
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size_t pair_idx = i * images.size() + j;
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(*this)(images[i], features[i], images[j], features[j], pairwise_matches[pair_idx]);
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pairwise_matches[pair_idx].src_img_idx = i;
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pairwise_matches[pair_idx].dst_img_idx = j;
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////
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namespace
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{
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class CpuMatcher : public FeaturesMatcher
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{
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public:
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inline CpuMatcher(float match_conf) : match_conf_(match_conf) {}
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void match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, 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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void CpuMatcher::match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info)
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{
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matches_info.matches.clear();
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BruteForceMatcher< L2<float> > matcher;
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vector< vector<DMatch> > pair_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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matches_info.matches.push_back(m0);
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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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matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
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}
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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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inline GpuMatcher(float match_conf) : match_conf_(match_conf) {}
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void match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info);
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private:
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float match_conf_;
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GpuMat descriptors1_;
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GpuMat descriptors2_;
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GpuMat trainIdx_, distance_, allDist_;
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};
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void GpuMatcher::match(const cv::Mat&, const ImageFeatures &features1, const cv::Mat&, const ImageFeatures &features2, MatchesInfo& matches_info)
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{
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matches_info.matches.clear();
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BruteForceMatcher_GPU< L2<float> > matcher;
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descriptors1_.upload(features1.descriptors);
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descriptors2_.upload(features2.descriptors);
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vector< vector<DMatch> > pair_matches;
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// Find 1->2 matches
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matcher.knnMatch(descriptors1_, descriptors2_, trainIdx_, distance_, allDist_, 2);
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matcher.knnMatchDownload(trainIdx_, 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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CV_Assert(m0.queryIdx < static_cast<int>(features1.keypoints.size()));
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CV_Assert(m0.trainIdx < static_cast<int>(features2.keypoints.size()));
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if (m0.distance < (1.f - match_conf_) * m1.distance)
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matches_info.matches.push_back(m0);
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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_, trainIdx_, distance_, allDist_, 2);
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matcher.knnMatchDownload(trainIdx_, 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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CV_Assert(m0.trainIdx < static_cast<int>(features1.keypoints.size()));
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CV_Assert(m0.queryIdx < static_cast<int>(features2.keypoints.size()));
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if (m0.distance < (1.f - match_conf_) * m1.distance)
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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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}
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BestOf2NearestMatcher::BestOf2NearestMatcher(bool gpu_hint, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
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{
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if (gpu_hint && 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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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 Mat &img1, const ImageFeatures &features1, const Mat &img2, const ImageFeatures &features2,
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MatchesInfo &matches_info)
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{
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(*impl_)(img1, features1, img2, features2, matches_info);
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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 -= img1.cols * 0.5f;
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p.y -= img1.rows * 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 -= img2.cols * 0.5f;
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p.y -= img2.rows * 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 -= img1.cols * 0.5f;
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p.y -= img2.rows * 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 -= img2.cols * 0.5f;
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p.y -= img2.rows * 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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