matchers.cpp

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#include "precomp.hpp"

#include "opencv2/core/opencl/ocl_defs.hpp"

using namespace cv;
using namespace cv::detail;
using namespace cv::cuda;

#ifdef HAVE_OPENCV_XFEATURES2D
#include "opencv2/xfeatures2d.hpp"
using xfeatures2d::SURF;
using xfeatures2d::SIFT;
#else
#  if defined(_MSC_VER)
#    pragma warning(disable:4702)  // unreachable code
#  endif
#endif

#ifdef HAVE_OPENCV_CUDAIMGPROC
#  include "opencv2/cudaimgproc.hpp"
#endif

namespace {

struct DistIdxPair
{
    bool operator<(const DistIdxPair &other) const { return dist < other.dist; }
    double dist;
    int idx;
};


struct MatchPairsBody : ParallelLoopBody
{
    MatchPairsBody(FeaturesMatcher &_matcher, const std::vector<ImageFeatures> &_features,
                   std::vector<MatchesInfo> &_pairwise_matches, std::vector<std::pair<int,int> > &_near_pairs)
            : matcher(_matcher), features(_features),
              pairwise_matches(_pairwise_matches), near_pairs(_near_pairs) {}

    void operator ()(const Range &r) const CV_OVERRIDE
    {
        cv::RNG rng = cv::theRNG(); // save entry rng state
        const int num_images = static_cast<int>(features.size());
        for (int i = r.start; i < r.end; ++i)
        {
            cv::theRNG() = cv::RNG(rng.state + i); // force "stable" RNG seed for each processed pair

            int from = near_pairs[i].first;
            int to = near_pairs[i].second;
            int pair_idx = from*num_images + to;

            matcher(features[from], features[to], pairwise_matches[pair_idx]);
            pairwise_matches[pair_idx].src_img_idx = from;
            pairwise_matches[pair_idx].dst_img_idx = to;

            size_t dual_pair_idx = to*num_images + from;

            pairwise_matches[dual_pair_idx] = pairwise_matches[pair_idx];
            pairwise_matches[dual_pair_idx].src_img_idx = to;
            pairwise_matches[dual_pair_idx].dst_img_idx = from;

            if (!pairwise_matches[pair_idx].H.empty())
                pairwise_matches[dual_pair_idx].H = pairwise_matches[pair_idx].H.inv();

            for (size_t j = 0; j < pairwise_matches[dual_pair_idx].matches.size(); ++j)
                std::swap(pairwise_matches[dual_pair_idx].matches[j].queryIdx,
                          pairwise_matches[dual_pair_idx].matches[j].trainIdx);
            LOG(".");
        }
    }

    FeaturesMatcher &matcher;
    const std::vector<ImageFeatures> &features;
    std::vector<MatchesInfo> &pairwise_matches;
    std::vector<std::pair<int,int> > &near_pairs;

private:
    void operator =(const MatchPairsBody&);
};


struct FindFeaturesBody : ParallelLoopBody
{
    FindFeaturesBody(FeaturesFinder &finder, InputArrayOfArrays images,
                     std::vector<ImageFeatures> &features, const std::vector<std::vector<cv::Rect> > *rois)
            : finder_(finder), images_(images), features_(features), rois_(rois) {}

    void operator ()(const Range &r) const CV_OVERRIDE
    {
        for (int i = r.start; i < r.end; ++i)
        {
            Mat image = images_.getMat(i);
            if (rois_)
                finder_(image, features_[i], (*rois_)[i]);
            else
                finder_(image, features_[i]);
        }
    }

private:
    FeaturesFinder &finder_;
    InputArrayOfArrays images_;
    std::vector<ImageFeatures> &features_;
    const std::vector<std::vector<cv::Rect> > *rois_;

    // to cease visual studio warning
    void operator =(const FindFeaturesBody&);
};


//////////////////////////////////////////////////////////////////////////////

typedef std::set<std::pair<int,int> > MatchesSet;

// These two classes are aimed to find features matches only, not to
// estimate homography

class CpuMatcher CV_FINAL : public FeaturesMatcher
{
public:
    CpuMatcher(float match_conf) : FeaturesMatcher(true), match_conf_(match_conf) {}
    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info) CV_OVERRIDE;

private:
    float match_conf_;
};

#ifdef HAVE_OPENCV_CUDAFEATURES2D
class GpuMatcher CV_FINAL : public FeaturesMatcher
{
public:
    GpuMatcher(float match_conf) : match_conf_(match_conf) {}
    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info);

    void collectGarbage();

private:
    float match_conf_;
    GpuMat descriptors1_, descriptors2_;
    GpuMat train_idx_, distance_, all_dist_;
    std::vector< std::vector<DMatch> > pair_matches;
};
#endif


void CpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
{
    CV_INSTRUMENT_REGION();

    CV_Assert(features1.descriptors.type() == features2.descriptors.type());
    CV_Assert(features2.descriptors.depth() == CV_8U || features2.descriptors.depth() == CV_32F);

    matches_info.matches.clear();

    Ptr<cv::DescriptorMatcher> matcher;
#if 0 // TODO check this
    if (ocl::isOpenCLActivated())
    {
        matcher = makePtr<BFMatcher>((int)NORM_L2);
    }
    else
#endif
    {
        Ptr<flann::IndexParams> indexParams = makePtr<flann::KDTreeIndexParams>();
        Ptr<flann::SearchParams> searchParams = makePtr<flann::SearchParams>();

        if (features2.descriptors.depth() == CV_8U)
        {
            indexParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
            searchParams->setAlgorithm(cvflann::FLANN_INDEX_LSH);
        }

        matcher = makePtr<FlannBasedMatcher>(indexParams, searchParams);
    }
    std::vector< std::vector<DMatch> > pair_matches;
    MatchesSet matches;

    // Find 1->2 matches
    matcher->knnMatch(features1.descriptors, features2.descriptors, pair_matches, 2);
    for (size_t i = 0; i < pair_matches.size(); ++i)
    {
        if (pair_matches[i].size() < 2)
            continue;
        const DMatch& m0 = pair_matches[i][0];
        const DMatch& m1 = pair_matches[i][1];
        if (m0.distance < (1.f - match_conf_) * m1.distance)
        {
            matches_info.matches.push_back(m0);
            matches.insert(std::make_pair(m0.queryIdx, m0.trainIdx));
        }
    }
    LOG("\n1->2 matches: " << matches_info.matches.size() << endl);

    // Find 2->1 matches
    pair_matches.clear();
    matcher->knnMatch(features2.descriptors, features1.descriptors, pair_matches, 2);
    for (size_t i = 0; i < pair_matches.size(); ++i)
    {
        if (pair_matches[i].size() < 2)
            continue;
        const DMatch& m0 = pair_matches[i][0];
        const DMatch& m1 = pair_matches[i][1];
        if (m0.distance < (1.f - match_conf_) * m1.distance)
            if (matches.find(std::make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
                matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
    }
    LOG("1->2 & 2->1 matches: " << matches_info.matches.size() << endl);
}

#ifdef HAVE_OPENCV_CUDAFEATURES2D
void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo& matches_info)
{
    CV_INSTRUMENT_REGION();

    matches_info.matches.clear();

    ensureSizeIsEnough(features1.descriptors.size(), features1.descriptors.type(), descriptors1_);
    ensureSizeIsEnough(features2.descriptors.size(), features2.descriptors.type(), descriptors2_);

    descriptors1_.upload(features1.descriptors);
    descriptors2_.upload(features2.descriptors);

    //TODO: NORM_L1 allows to avoid matcher crashes for ORB features, but is not absolutely correct for them.
    //      The best choice for ORB features is NORM_HAMMING, but it is incorrect for SURF features.
    //      More accurate fix in this place should be done in the future -- the type of the norm
    //      should be either a parameter of this method, or a field of the class.
    Ptr<cuda::DescriptorMatcher> matcher = cuda::DescriptorMatcher::createBFMatcher(NORM_L1);

    MatchesSet matches;

    // Find 1->2 matches
    pair_matches.clear();
    matcher->knnMatch(descriptors1_, descriptors2_, pair_matches, 2);
    for (size_t i = 0; i < pair_matches.size(); ++i)
    {
        if (pair_matches[i].size() < 2)
            continue;
        const DMatch& m0 = pair_matches[i][0];
        const DMatch& m1 = pair_matches[i][1];
        if (m0.distance < (1.f - match_conf_) * m1.distance)
        {
            matches_info.matches.push_back(m0);
            matches.insert(std::make_pair(m0.queryIdx, m0.trainIdx));
        }
    }

    // Find 2->1 matches
    pair_matches.clear();
    matcher->knnMatch(descriptors2_, descriptors1_, pair_matches, 2);
    for (size_t i = 0; i < pair_matches.size(); ++i)
    {
        if (pair_matches[i].size() < 2)
            continue;
        const DMatch& m0 = pair_matches[i][0];
        const DMatch& m1 = pair_matches[i][1];
        if (m0.distance < (1.f - match_conf_) * m1.distance)
            if (matches.find(std::make_pair(m0.trainIdx, m0.queryIdx)) == matches.end())
                matches_info.matches.push_back(DMatch(m0.trainIdx, m0.queryIdx, m0.distance));
    }
}

void GpuMatcher::collectGarbage()
{
    descriptors1_.release();
    descriptors2_.release();
    train_idx_.release();
    distance_.release();
    all_dist_.release();
    std::vector< std::vector<DMatch> >().swap(pair_matches);
}
#endif

} // namespace


namespace cv {
namespace detail {

void FeaturesFinder::operator ()(InputArray  image, ImageFeatures &features)
{
    find(image, features);
    features.img_size = image.size();
}


void FeaturesFinder::operator ()(InputArray image, ImageFeatures &features, const std::vector<Rect> &rois)
{
    std::vector<ImageFeatures> roi_features(rois.size());
    size_t total_kps_count = 0;
    int total_descriptors_height = 0;

    for (size_t i = 0; i < rois.size(); ++i)
    {
        find(image.getUMat()(rois[i]), roi_features[i]);
        total_kps_count += roi_features[i].keypoints.size();
        total_descriptors_height += roi_features[i].descriptors.rows;
    }

    features.img_size = image.size();
    features.keypoints.resize(total_kps_count);
    features.descriptors.create(total_descriptors_height,
                                roi_features[0].descriptors.cols,
                                roi_features[0].descriptors.type());

    int kp_idx = 0;
    int descr_offset = 0;
    for (size_t i = 0; i < rois.size(); ++i)
    {
        for (size_t j = 0; j < roi_features[i].keypoints.size(); ++j, ++kp_idx)
        {
            features.keypoints[kp_idx] = roi_features[i].keypoints[j];
            features.keypoints[kp_idx].pt.x += (float)rois[i].x;
            features.keypoints[kp_idx].pt.y += (float)rois[i].y;
        }
        UMat subdescr = features.descriptors.rowRange(
                descr_offset, descr_offset + roi_features[i].descriptors.rows);
        roi_features[i].descriptors.copyTo(subdescr);
        descr_offset += roi_features[i].descriptors.rows;
    }
}


void FeaturesFinder::operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features)
{
    size_t count = images.total();
    features.resize(count);

    FindFeaturesBody body(*this, images, features, NULL);
    if (isThreadSafe())
        parallel_for_(Range(0, static_cast<int>(count)), body);
    else
        body(Range(0, static_cast<int>(count)));
}


void FeaturesFinder::operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features,
                                  const std::vector<std::vector<cv::Rect> > &rois)
{
    CV_Assert(rois.size() == images.total());
    size_t count = images.total();
    features.resize(count);

    FindFeaturesBody body(*this, images, features, &rois);
    if (isThreadSafe())
        parallel_for_(Range(0, static_cast<int>(count)), body);
    else
        body(Range(0, static_cast<int>(count)));
}


bool FeaturesFinder::isThreadSafe() const
{
#ifdef HAVE_OPENCL
    if (ocl::isOpenCLActivated())
    {
        return false;
    }
#endif
    if (dynamic_cast<const SurfFeaturesFinder*>(this))
    {
        return true;
    }
    else if (dynamic_cast<const OrbFeaturesFinder*>(this))
    {
        return true;
    }
    else
    {
        return false;
    }
}


SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int num_layers,
                                       int num_octaves_descr, int num_layers_descr)
{
#ifdef HAVE_OPENCV_XFEATURES2D
    if (num_octaves_descr == num_octaves && num_layers_descr == num_layers)
    {
        Ptr<SURF> surf_ = SURF::create();
        if( !surf_ )
            CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
        surf_->setHessianThreshold(hess_thresh);
        surf_->setNOctaves(num_octaves);
        surf_->setNOctaveLayers(num_layers);
        surf = surf_;
    }
    else
    {
        Ptr<SURF> sdetector_ = SURF::create();
        Ptr<SURF> sextractor_ = SURF::create();

        if( !sdetector_ || !sextractor_ )
            CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );

        sdetector_->setHessianThreshold(hess_thresh);
        sdetector_->setNOctaves(num_octaves);
        sdetector_->setNOctaveLayers(num_layers);

        sextractor_->setNOctaves(num_octaves_descr);
        sextractor_->setNOctaveLayers(num_layers_descr);

        detector_ = sdetector_;
        extractor_ = sextractor_;
    }
#else
    CV_UNUSED(hess_thresh);
    CV_UNUSED(num_octaves);
    CV_UNUSED(num_layers);
    CV_UNUSED(num_octaves_descr);
    CV_UNUSED(num_layers_descr);
    CV_Error( Error::StsNotImplemented, "OpenCV was built without SURF support" );
#endif
}

void SurfFeaturesFinder::find(InputArray image, ImageFeatures &features)
{
    UMat gray_image;
    CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC1));
    if(image.type() == CV_8UC3)
    {
        cvtColor(image, gray_image, COLOR_BGR2GRAY);
    }
    else
    {
        gray_image = image.getUMat();
    }
    if (!surf)
    {
        detector_->detect(gray_image, features.keypoints);
        extractor_->compute(gray_image, features.keypoints, features.descriptors);
    }
    else
    {
        UMat descriptors;
        surf->detectAndCompute(gray_image, Mat(), features.keypoints, descriptors);
        features.descriptors = descriptors.reshape(1, (int)features.keypoints.size());
    }
}

SiftFeaturesFinder::SiftFeaturesFinder()
{
#ifdef HAVE_OPENCV_XFEATURES2D
    Ptr<SIFT> sift_ = SIFT::create();
    if( !sift_ )
        CV_Error( Error::StsNotImplemented, "OpenCV was built without SIFT support" );
    sift = sift_;
#else
    CV_Error( Error::StsNotImplemented, "OpenCV was built without SIFT support" );
#endif
}

void SiftFeaturesFinder::find(InputArray image, ImageFeatures &features)
{
    UMat gray_image;
    CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC1));
    if(image.type() == CV_8UC3)
    {
        cvtColor(image, gray_image, COLOR_BGR2GRAY);
    }
    else
    {
        gray_image = image.getUMat();
    }
    UMat descriptors;
    sift->detectAndCompute(gray_image, Mat(), features.keypoints, descriptors);
    features.descriptors = descriptors.reshape(1, (int)features.keypoints.size());
}

OrbFeaturesFinder::OrbFeaturesFinder(Size _grid_size, int n_features, float scaleFactor, int nlevels)
{
    grid_size = _grid_size;
    orb = ORB::create(n_features * (99 + grid_size.area())/100/grid_size.area(), scaleFactor, nlevels);
}

void OrbFeaturesFinder::find(InputArray image, ImageFeatures &features)
{
    UMat gray_image;

    CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC4) || (image.type() == CV_8UC1));

    if (image.type() == CV_8UC3) {
        cvtColor(image, gray_image, COLOR_BGR2GRAY);
    } else if (image.type() == CV_8UC4) {
        cvtColor(image, gray_image, COLOR_BGRA2GRAY);
    } else if (image.type() == CV_8UC1) {
        gray_image = image.getUMat();
    } else {
        CV_Error(Error::StsUnsupportedFormat, "");
    }

    if (grid_size.area() == 1)
        orb->detectAndCompute(gray_image, Mat(), features.keypoints, features.descriptors);
    else
    {
        features.keypoints.clear();
        features.descriptors.release();

        std::vector<KeyPoint> points;
        Mat _descriptors;
        UMat descriptors;

        for (int r = 0; r < grid_size.height; ++r)
            for (int c = 0; c < grid_size.width; ++c)
            {
                int xl = c * gray_image.cols / grid_size.width;
                int yl = r * gray_image.rows / grid_size.height;
                int xr = (c+1) * gray_image.cols / grid_size.width;
                int yr = (r+1) * gray_image.rows / grid_size.height;

                // LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
                //     << " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
                //     << " yl=" << yl << ", yr=" << yr << ", "
                //     << " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
                //     << "gray_image.dims=" << gray_image.dims << "\n");

                UMat gray_image_part=gray_image(Range(yl, yr), Range(xl, xr));
                // LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
                //     << " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
                //     << " gray_image_part.dims=" << gray_image_part.dims << ", "
                //     << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");

                orb->detectAndCompute(gray_image_part, UMat(), points, descriptors);

                features.keypoints.reserve(features.keypoints.size() + points.size());
                for (std::vector<KeyPoint>::iterator kp = points.begin(); kp != points.end(); ++kp)
                {
                    kp->pt.x += xl;
                    kp->pt.y += yl;
                    features.keypoints.push_back(*kp);
                }
                _descriptors.push_back(descriptors.getMat(ACCESS_READ));
            }

        // TODO optimize copyTo()
        //features.descriptors = _descriptors.getUMat(ACCESS_READ);
        _descriptors.copyTo(features.descriptors);
    }
}

AKAZEFeaturesFinder::AKAZEFeaturesFinder(AKAZE::DescriptorType descriptor_type,
                                         int descriptor_size,
                                         int descriptor_channels,
                                         float threshold,
                                         int nOctaves,
                                         int nOctaveLayers,
                                         KAZE::DiffusivityType diffusivity)
{
    akaze = AKAZE::create(descriptor_type, descriptor_size, descriptor_channels,
                          threshold, nOctaves, nOctaveLayers, diffusivity);
}

void AKAZEFeaturesFinder::find(InputArray image, detail::ImageFeatures &features)
{
    CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC1));
    akaze->detectAndCompute(image, noArray(), features.keypoints, features.descriptors);
}

#ifdef HAVE_OPENCV_XFEATURES2D
SurfFeaturesFinderGpu::SurfFeaturesFinderGpu(double hess_thresh, int num_octaves, int num_layers,
                                             int num_octaves_descr, int num_layers_descr)
{
    surf_.keypointsRatio = 0.1f;
    surf_.hessianThreshold = hess_thresh;
    surf_.extended = false;
    num_octaves_ = num_octaves;
    num_layers_ = num_layers;
    num_octaves_descr_ = num_octaves_descr;
    num_layers_descr_ = num_layers_descr;
}


void SurfFeaturesFinderGpu::find(InputArray image, ImageFeatures &features)
{
    CV_Assert(image.depth() == CV_8U);

    ensureSizeIsEnough(image.size(), image.type(), image_);
    image_.upload(image);

    ensureSizeIsEnough(image.size(), CV_8UC1, gray_image_);

#ifdef HAVE_OPENCV_CUDAIMGPROC
    cv::cuda::cvtColor(image_, gray_image_, COLOR_BGR2GRAY);
#else
    cvtColor(image_, gray_image_, COLOR_BGR2GRAY);
#endif

    surf_.nOctaves = num_octaves_;
    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();
}
#endif


//////////////////////////////////////////////////////////////////////////////

MatchesInfo::MatchesInfo() : src_img_idx(-1), dst_img_idx(-1), num_inliers(0), confidence(0) {}

MatchesInfo::MatchesInfo(const MatchesInfo &other) { *this = other; }

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 std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
                                  const UMat &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.getMat(ACCESS_READ));
    if (mask_.empty())
        mask_ = Mat::ones(num_images, num_images, CV_8U);

    std::vector<std::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(std::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)
{
    CV_UNUSED(try_use_gpu);

#ifdef HAVE_OPENCV_CUDAFEATURES2D
    if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
    {
        impl_ = makePtr<GpuMatcher>(match_conf);
    }
    else
#endif
    {
        impl_ = makePtr<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)
{
    CV_INSTRUMENT_REGION();

    (*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, RANSAC);
    if (matches_info.H.empty() || std::abs(determinant(matches_info.H)) < std::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, RANSAC);
}

void BestOf2NearestMatcher::collectGarbage()
{
    impl_->collectGarbage();
}


BestOf2NearestRangeMatcher::BestOf2NearestRangeMatcher(int range_width, bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2): BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh2)
{
    range_width_ = range_width;
}


void BestOf2NearestRangeMatcher::operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
                                  const UMat &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.getMat(ACCESS_READ));
    if (mask_.empty())
        mask_ = Mat::ones(num_images, num_images, CV_8U);

    std::vector<std::pair<int,int> > near_pairs;
    for (int i = 0; i < num_images - 1; ++i)
        for (int j = i + 1; j < std::min(num_images, i + range_width_); ++j)
            if (features[i].keypoints.size() > 0 && features[j].keypoints.size() > 0 && mask_(i, j))
                near_pairs.push_back(std::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("");
}


void AffineBestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
                                        MatchesInfo &matches_info)
{
    (*impl_)(features1, features2, matches_info);

    // Check if it makes sense to find transform
    if (matches_info.matches.size() < static_cast<size_t>(num_matches_thresh1_))
        return;

    // Construct point-point correspondences for transform 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 cv::DMatch &m = matches_info.matches[i];
        src_points.at<Point2f>(0, static_cast<int>(i)) = features1.keypoints[m.queryIdx].pt;
        dst_points.at<Point2f>(0, static_cast<int>(i)) = features2.keypoints[m.trainIdx].pt;
    }

    // Find pair-wise motion
    if (full_affine_)
        matches_info.H = estimateAffine2D(src_points, dst_points, matches_info.inliers_mask);
    else
        matches_info.H = estimateAffinePartial2D(src_points, dst_points, matches_info.inliers_mask);

    if (matches_info.H.empty()) {
        // could not find transformation
        matches_info.confidence = 0;
        matches_info.num_inliers = 0;
        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());

    /* should we remove matches between too close images? */
    // matches_info.confidence = matches_info.confidence > 3. ? 0. : matches_info.confidence;

    // extend H to represent linear transformation in homogeneous coordinates
    matches_info.H.push_back(Mat::zeros(1, 3, CV_64F));
    matches_info.H.at<double>(2, 2) = 1;
}


} // namespace detail
} // namespace cv