/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "precomp.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/calib3d/calib3d_c.h"
#include "opencv2/core/cvdef.h"

using namespace cv;
using namespace cv::detail;

namespace {

struct IncDistance
{
    IncDistance(std::vector<int> &vdists) : dists(&vdists[0]) {}
    void operator ()(const GraphEdge &edge) { dists[edge.to] = dists[edge.from] + 1; }
    int* dists;
};


struct CalcRotation
{
    CalcRotation(int _num_images, const std::vector<MatchesInfo> &_pairwise_matches, std::vector<CameraParams> &_cameras)
        : num_images(_num_images), pairwise_matches(&_pairwise_matches[0]), cameras(&_cameras[0]) {}

    void operator ()(const GraphEdge &edge)
    {
        int pair_idx = edge.from * num_images + edge.to;

        Mat_<double> K_from = Mat::eye(3, 3, CV_64F);
        K_from(0,0) = cameras[edge.from].focal;
        K_from(1,1) = cameras[edge.from].focal * cameras[edge.from].aspect;
        K_from(0,2) = cameras[edge.from].ppx;
        K_from(1,2) = cameras[edge.from].ppy;

        Mat_<double> K_to = Mat::eye(3, 3, CV_64F);
        K_to(0,0) = cameras[edge.to].focal;
        K_to(1,1) = cameras[edge.to].focal * cameras[edge.to].aspect;
        K_to(0,2) = cameras[edge.to].ppx;
        K_to(1,2) = cameras[edge.to].ppy;

        Mat R = K_from.inv() * pairwise_matches[pair_idx].H.inv() * K_to;
        cameras[edge.to].R = cameras[edge.from].R * R;
    }

    int num_images;
    const MatchesInfo* pairwise_matches;
    CameraParams* cameras;
};


/**
 * @brief Functor calculating final transformation by chaining linear transformations
 */
struct CalcAffineTransform
{
    CalcAffineTransform(int _num_images,
                      const std::vector<MatchesInfo> &_pairwise_matches,
                      std::vector<CameraParams> &_cameras)
    : num_images(_num_images), pairwise_matches(&_pairwise_matches[0]), cameras(&_cameras[0]) {}

    void operator()(const GraphEdge &edge)
    {
        int pair_idx = edge.from * num_images + edge.to;
        cameras[edge.to].R = cameras[edge.from].R * pairwise_matches[pair_idx].H;
    }

    int num_images;
    const MatchesInfo *pairwise_matches;
    CameraParams *cameras;
};


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

void calcDeriv(const Mat &err1, const Mat &err2, double h, Mat res)
{
    for (int i = 0; i < err1.rows; ++i)
        res.at<double>(i, 0) = (err2.at<double>(i, 0) - err1.at<double>(i, 0)) / h;
}

} // namespace


namespace cv {
namespace detail {

bool HomographyBasedEstimator::estimate(
        const std::vector<ImageFeatures> &features,
        const std::vector<MatchesInfo> &pairwise_matches,
        std::vector<CameraParams> &cameras)
{
    LOGLN("Estimating rotations...");
#if ENABLE_LOG
    int64 t = getTickCount();
#endif

    const int num_images = static_cast<int>(features.size());

#if 0
    // Robustly estimate focal length from rotating cameras
    std::vector<Mat> Hs;
    for (int iter = 0; iter < 100; ++iter)
    {
        int len = 2 + rand()%(pairwise_matches.size() - 1);
        std::vector<int> subset;
        selectRandomSubset(len, pairwise_matches.size(), subset);
        Hs.clear();
        for (size_t i = 0; i < subset.size(); ++i)
            if (!pairwise_matches[subset[i]].H.empty())
                Hs.push_back(pairwise_matches[subset[i]].H);
        Mat_<double> K;
        if (Hs.size() >= 2)
        {
            if (calibrateRotatingCamera(Hs, K))
                cin.get();
        }
    }
#endif

    if (!is_focals_estimated_)
    {
        // Estimate focal length and set it for all cameras
        std::vector<double> focals;
        estimateFocal(features, pairwise_matches, focals);
        cameras.assign(num_images, CameraParams());
        for (int i = 0; i < num_images; ++i)
            cameras[i].focal = focals[i];
    }
    else
    {
        for (int i = 0; i < num_images; ++i)
        {
            cameras[i].ppx -= 0.5 * features[i].img_size.width;
            cameras[i].ppy -= 0.5 * features[i].img_size.height;
        }
    }

    // Restore global motion
    Graph span_tree;
    std::vector<int> span_tree_centers;
    findMaxSpanningTree(num_images, pairwise_matches, span_tree, span_tree_centers);
    span_tree.walkBreadthFirst(span_tree_centers[0], CalcRotation(num_images, pairwise_matches, cameras));

    // As calculations were performed under assumption that p.p. is in image center
    for (int i = 0; i < num_images; ++i)
    {
        cameras[i].ppx += 0.5 * features[i].img_size.width;
        cameras[i].ppy += 0.5 * features[i].img_size.height;
    }

    LOGLN("Estimating rotations, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    return true;
}


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

bool AffineBasedEstimator::estimate(const std::vector<ImageFeatures> &features,
                                    const std::vector<MatchesInfo> &pairwise_matches,
                                    std::vector<CameraParams> &cameras)
{
    cameras.assign(features.size(), CameraParams());
    const int num_images = static_cast<int>(features.size());

    // find maximum spaning tree on pairwise matches
    cv::detail::Graph span_tree;
    std::vector<int> span_tree_centers;
    // uses number of inliers as weights
    findMaxSpanningTree(num_images, pairwise_matches, span_tree,
                      span_tree_centers);

    // compute final transform by chaining H together
    span_tree.walkBreadthFirst(
            span_tree_centers[0],
            CalcAffineTransform(num_images, pairwise_matches, cameras));
    // this estimator never fails
    return true;
}


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

bool BundleAdjusterBase::estimate(const std::vector<ImageFeatures> &features,
                                  const std::vector<MatchesInfo> &pairwise_matches,
                                  std::vector<CameraParams> &cameras)
{
    LOG_CHAT("Bundle adjustment");
#if ENABLE_LOG
    int64 t = getTickCount();
#endif

    num_images_ = static_cast<int>(features.size());
    features_ = &features[0];
    pairwise_matches_ = &pairwise_matches[0];

    setUpInitialCameraParams(cameras);

    // Leave only consistent image pairs
    edges_.clear();
    for (int i = 0; i < num_images_ - 1; ++i)
    {
        for (int j = i + 1; j < num_images_; ++j)
        {
            const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];
            if (matches_info.confidence > conf_thresh_)
                edges_.push_back(std::make_pair(i, j));
        }
    }

    // Compute number of correspondences
    total_num_matches_ = 0;
    for (size_t i = 0; i < edges_.size(); ++i)
        total_num_matches_ += static_cast<int>(pairwise_matches[edges_[i].first * num_images_ +
                                                                edges_[i].second].num_inliers);

    CvLevMarq solver(num_images_ * num_params_per_cam_,
                     total_num_matches_ * num_errs_per_measurement_,
                     cvTermCriteria(term_criteria_));

    Mat err, jac;
    CvMat matParams = cvMat(cam_params_);
    cvCopy(&matParams, solver.param);

    int iter = 0;
    for(;;)
    {
        const CvMat* _param = 0;
        CvMat* _jac = 0;
        CvMat* _err = 0;

        bool proceed = solver.update(_param, _jac, _err);

        cvCopy(_param, &matParams);

        if (!proceed || !_err)
            break;

        if (_jac)
        {
            calcJacobian(jac);
            CvMat tmp = cvMat(jac);
            cvCopy(&tmp, _jac);
        }

        if (_err)
        {
            calcError(err);
            LOG_CHAT(".");
            iter++;
            CvMat tmp = cvMat(err);
            cvCopy(&tmp, _err);
        }
    }

    LOGLN_CHAT("");
    LOGLN_CHAT("Bundle adjustment, final RMS error: " << std::sqrt(err.dot(err) / total_num_matches_));
    LOGLN_CHAT("Bundle adjustment, iterations done: " << iter);

    // Check if all camera parameters are valid
    bool ok = true;
    for (int i = 0; i < cam_params_.rows; ++i)
    {
        if (cvIsNaN(cam_params_.at<double>(i,0)))
        {
            ok = false;
            break;
        }
    }
    if (!ok)
        return false;

    obtainRefinedCameraParams(cameras);

    // Normalize motion to center image
    Graph span_tree;
    std::vector<int> span_tree_centers;
    findMaxSpanningTree(num_images_, pairwise_matches, span_tree, span_tree_centers);
    Mat R_inv = cameras[span_tree_centers[0]].R.inv();
    for (int i = 0; i < num_images_; ++i)
        cameras[i].R = R_inv * cameras[i].R;

    LOGLN_CHAT("Bundle adjustment, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
    return true;
}


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

void BundleAdjusterReproj::setUpInitialCameraParams(const std::vector<CameraParams> &cameras)
{
    cam_params_.create(num_images_ * 7, 1, CV_64F);
    SVD svd;
    for (int i = 0; i < num_images_; ++i)
    {
        cam_params_.at<double>(i * 7, 0) = cameras[i].focal;
        cam_params_.at<double>(i * 7 + 1, 0) = cameras[i].ppx;
        cam_params_.at<double>(i * 7 + 2, 0) = cameras[i].ppy;
        cam_params_.at<double>(i * 7 + 3, 0) = cameras[i].aspect;

        svd(cameras[i].R, SVD::FULL_UV);
        Mat R = svd.u * svd.vt;
        if (determinant(R) < 0)
            R *= -1;

        Mat rvec;
        Rodrigues(R, rvec);
        CV_Assert(rvec.type() == CV_32F);
        cam_params_.at<double>(i * 7 + 4, 0) = rvec.at<float>(0, 0);
        cam_params_.at<double>(i * 7 + 5, 0) = rvec.at<float>(1, 0);
        cam_params_.at<double>(i * 7 + 6, 0) = rvec.at<float>(2, 0);
    }
}


void BundleAdjusterReproj::obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const
{
    for (int i = 0; i < num_images_; ++i)
    {
        cameras[i].focal = cam_params_.at<double>(i * 7, 0);
        cameras[i].ppx = cam_params_.at<double>(i * 7 + 1, 0);
        cameras[i].ppy = cam_params_.at<double>(i * 7 + 2, 0);
        cameras[i].aspect = cam_params_.at<double>(i * 7 + 3, 0);

        Mat rvec(3, 1, CV_64F);
        rvec.at<double>(0, 0) = cam_params_.at<double>(i * 7 + 4, 0);
        rvec.at<double>(1, 0) = cam_params_.at<double>(i * 7 + 5, 0);
        rvec.at<double>(2, 0) = cam_params_.at<double>(i * 7 + 6, 0);
        Rodrigues(rvec, cameras[i].R);

        Mat tmp;
        cameras[i].R.convertTo(tmp, CV_32F);
        cameras[i].R = tmp;
    }
}


void BundleAdjusterReproj::calcError(Mat &err)
{
    err.create(total_num_matches_ * 2, 1, CV_64F);

    int match_idx = 0;
    for (size_t edge_idx = 0; edge_idx < edges_.size(); ++edge_idx)
    {
        int i = edges_[edge_idx].first;
        int j = edges_[edge_idx].second;
        double f1 = cam_params_.at<double>(i * 7, 0);
        double f2 = cam_params_.at<double>(j * 7, 0);
        double ppx1 = cam_params_.at<double>(i * 7 + 1, 0);
        double ppx2 = cam_params_.at<double>(j * 7 + 1, 0);
        double ppy1 = cam_params_.at<double>(i * 7 + 2, 0);
        double ppy2 = cam_params_.at<double>(j * 7 + 2, 0);
        double a1 = cam_params_.at<double>(i * 7 + 3, 0);
        double a2 = cam_params_.at<double>(j * 7 + 3, 0);

        double R1[9];
        Mat R1_(3, 3, CV_64F, R1);
        Mat rvec(3, 1, CV_64F);
        rvec.at<double>(0, 0) = cam_params_.at<double>(i * 7 + 4, 0);
        rvec.at<double>(1, 0) = cam_params_.at<double>(i * 7 + 5, 0);
        rvec.at<double>(2, 0) = cam_params_.at<double>(i * 7 + 6, 0);
        Rodrigues(rvec, R1_);

        double R2[9];
        Mat R2_(3, 3, CV_64F, R2);
        rvec.at<double>(0, 0) = cam_params_.at<double>(j * 7 + 4, 0);
        rvec.at<double>(1, 0) = cam_params_.at<double>(j * 7 + 5, 0);
        rvec.at<double>(2, 0) = cam_params_.at<double>(j * 7 + 6, 0);
        Rodrigues(rvec, R2_);

        const ImageFeatures& features1 = features_[i];
        const ImageFeatures& features2 = features_[j];
        const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];

        Mat_<double> K1 = Mat::eye(3, 3, CV_64F);
        K1(0,0) = f1; K1(0,2) = ppx1;
        K1(1,1) = f1*a1; K1(1,2) = ppy1;

        Mat_<double> K2 = Mat::eye(3, 3, CV_64F);
        K2(0,0) = f2; K2(0,2) = ppx2;
        K2(1,1) = f2*a2; K2(1,2) = ppy2;

        Mat_<double> H = K2 * R2_.inv() * R1_ * K1.inv();

        for (size_t k = 0; k < matches_info.matches.size(); ++k)
        {
            if (!matches_info.inliers_mask[k])
                continue;

            const DMatch& m = matches_info.matches[k];
            Point2f p1 = features1.keypoints[m.queryIdx].pt;
            Point2f p2 = features2.keypoints[m.trainIdx].pt;
            double x = H(0,0)*p1.x + H(0,1)*p1.y + H(0,2);
            double y = H(1,0)*p1.x + H(1,1)*p1.y + H(1,2);
            double z = H(2,0)*p1.x + H(2,1)*p1.y + H(2,2);

            err.at<double>(2 * match_idx, 0) = p2.x - x/z;
            err.at<double>(2 * match_idx + 1, 0) = p2.y - y/z;
            match_idx++;
        }
    }
}


void BundleAdjusterReproj::calcJacobian(Mat &jac)
{
    jac.create(total_num_matches_ * 2, num_images_ * 7, CV_64F);
    jac.setTo(0);

    double val;
    const double step = 1e-4;

    for (int i = 0; i < num_images_; ++i)
    {
        if (refinement_mask_.at<uchar>(0, 0))
        {
            val = cam_params_.at<double>(i * 7, 0);
            cam_params_.at<double>(i * 7, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 7, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7));
            cam_params_.at<double>(i * 7, 0) = val;
        }
        if (refinement_mask_.at<uchar>(0, 2))
        {
            val = cam_params_.at<double>(i * 7 + 1, 0);
            cam_params_.at<double>(i * 7 + 1, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 7 + 1, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7 + 1));
            cam_params_.at<double>(i * 7 + 1, 0) = val;
        }
        if (refinement_mask_.at<uchar>(1, 2))
        {
            val = cam_params_.at<double>(i * 7 + 2, 0);
            cam_params_.at<double>(i * 7 + 2, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 7 + 2, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7 + 2));
            cam_params_.at<double>(i * 7 + 2, 0) = val;
        }
        if (refinement_mask_.at<uchar>(1, 1))
        {
            val = cam_params_.at<double>(i * 7 + 3, 0);
            cam_params_.at<double>(i * 7 + 3, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 7 + 3, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7 + 3));
            cam_params_.at<double>(i * 7 + 3, 0) = val;
        }
        for (int j = 4; j < 7; ++j)
        {
            val = cam_params_.at<double>(i * 7 + j, 0);
            cam_params_.at<double>(i * 7 + j, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 7 + j, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 7 + j));
            cam_params_.at<double>(i * 7 + j, 0) = val;
        }
    }
}


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

void BundleAdjusterRay::setUpInitialCameraParams(const std::vector<CameraParams> &cameras)
{
    cam_params_.create(num_images_ * 4, 1, CV_64F);
    SVD svd;
    for (int i = 0; i < num_images_; ++i)
    {
        cam_params_.at<double>(i * 4, 0) = cameras[i].focal;

        svd(cameras[i].R, SVD::FULL_UV);
        Mat R = svd.u * svd.vt;
        if (determinant(R) < 0)
            R *= -1;

        Mat rvec;
        Rodrigues(R, rvec);
        CV_Assert(rvec.type() == CV_32F);
        cam_params_.at<double>(i * 4 + 1, 0) = rvec.at<float>(0, 0);
        cam_params_.at<double>(i * 4 + 2, 0) = rvec.at<float>(1, 0);
        cam_params_.at<double>(i * 4 + 3, 0) = rvec.at<float>(2, 0);
    }
}


void BundleAdjusterRay::obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const
{
    for (int i = 0; i < num_images_; ++i)
    {
        cameras[i].focal = cam_params_.at<double>(i * 4, 0);

        Mat rvec(3, 1, CV_64F);
        rvec.at<double>(0, 0) = cam_params_.at<double>(i * 4 + 1, 0);
        rvec.at<double>(1, 0) = cam_params_.at<double>(i * 4 + 2, 0);
        rvec.at<double>(2, 0) = cam_params_.at<double>(i * 4 + 3, 0);
        Rodrigues(rvec, cameras[i].R);

        Mat tmp;
        cameras[i].R.convertTo(tmp, CV_32F);
        cameras[i].R = tmp;
    }
}


void BundleAdjusterRay::calcError(Mat &err)
{
    err.create(total_num_matches_ * 3, 1, CV_64F);

    int match_idx = 0;
    for (size_t edge_idx = 0; edge_idx < edges_.size(); ++edge_idx)
    {
        int i = edges_[edge_idx].first;
        int j = edges_[edge_idx].second;
        double f1 = cam_params_.at<double>(i * 4, 0);
        double f2 = cam_params_.at<double>(j * 4, 0);

        double R1[9];
        Mat R1_(3, 3, CV_64F, R1);
        Mat rvec(3, 1, CV_64F);
        rvec.at<double>(0, 0) = cam_params_.at<double>(i * 4 + 1, 0);
        rvec.at<double>(1, 0) = cam_params_.at<double>(i * 4 + 2, 0);
        rvec.at<double>(2, 0) = cam_params_.at<double>(i * 4 + 3, 0);
        Rodrigues(rvec, R1_);

        double R2[9];
        Mat R2_(3, 3, CV_64F, R2);
        rvec.at<double>(0, 0) = cam_params_.at<double>(j * 4 + 1, 0);
        rvec.at<double>(1, 0) = cam_params_.at<double>(j * 4 + 2, 0);
        rvec.at<double>(2, 0) = cam_params_.at<double>(j * 4 + 3, 0);
        Rodrigues(rvec, R2_);

        const ImageFeatures& features1 = features_[i];
        const ImageFeatures& features2 = features_[j];
        const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];

        Mat_<double> K1 = Mat::eye(3, 3, CV_64F);
        K1(0,0) = f1; K1(0,2) = features1.img_size.width * 0.5;
        K1(1,1) = f1; K1(1,2) = features1.img_size.height * 0.5;

        Mat_<double> K2 = Mat::eye(3, 3, CV_64F);
        K2(0,0) = f2; K2(0,2) = features2.img_size.width * 0.5;
        K2(1,1) = f2; K2(1,2) = features2.img_size.height * 0.5;

        Mat_<double> H1 = R1_ * K1.inv();
        Mat_<double> H2 = R2_ * K2.inv();

        for (size_t k = 0; k < matches_info.matches.size(); ++k)
        {
            if (!matches_info.inliers_mask[k])
                continue;

            const DMatch& m = matches_info.matches[k];

            Point2f p1 = features1.keypoints[m.queryIdx].pt;
            double x1 = H1(0,0)*p1.x + H1(0,1)*p1.y + H1(0,2);
            double y1 = H1(1,0)*p1.x + H1(1,1)*p1.y + H1(1,2);
            double z1 = H1(2,0)*p1.x + H1(2,1)*p1.y + H1(2,2);
            double len = std::sqrt(x1*x1 + y1*y1 + z1*z1);
            x1 /= len; y1 /= len; z1 /= len;

            Point2f p2 = features2.keypoints[m.trainIdx].pt;
            double x2 = H2(0,0)*p2.x + H2(0,1)*p2.y + H2(0,2);
            double y2 = H2(1,0)*p2.x + H2(1,1)*p2.y + H2(1,2);
            double z2 = H2(2,0)*p2.x + H2(2,1)*p2.y + H2(2,2);
            len = std::sqrt(x2*x2 + y2*y2 + z2*z2);
            x2 /= len; y2 /= len; z2 /= len;

            double mult = std::sqrt(f1 * f2);
            err.at<double>(3 * match_idx, 0) = mult * (x1 - x2);
            err.at<double>(3 * match_idx + 1, 0) = mult * (y1 - y2);
            err.at<double>(3 * match_idx + 2, 0) = mult * (z1 - z2);

            match_idx++;
        }
    }
}


void BundleAdjusterRay::calcJacobian(Mat &jac)
{
    jac.create(total_num_matches_ * 3, num_images_ * 4, CV_64F);

    double val;
    const double step = 1e-3;

    for (int i = 0; i < num_images_; ++i)
    {
        for (int j = 0; j < 4; ++j)
        {
            val = cam_params_.at<double>(i * 4 + j, 0);
            cam_params_.at<double>(i * 4 + j, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 4 + j, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 4 + j));
            cam_params_.at<double>(i * 4 + j, 0) = val;
        }
    }
}

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

void BundleAdjusterAffine::setUpInitialCameraParams(const std::vector<CameraParams> &cameras)
{
    cam_params_.create(num_images_ * 6, 1, CV_64F);
    for (size_t i = 0; i < static_cast<size_t>(num_images_); ++i)
    {
        CV_Assert(cameras[i].R.type() == CV_32F);
        // cameras[i].R is
        //     a b tx
        //     c d ty
        //     0 0 1. (optional)
        // cam_params_ model for LevMarq is
        //     (a, b, tx, c, d, ty)
        Mat params (2, 3, CV_64F, cam_params_.ptr<double>() + i * 6);
        cameras[i].R.rowRange(0, 2).convertTo(params, CV_64F);
    }
}


void BundleAdjusterAffine::obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const
{
    for (int i = 0; i < num_images_; ++i)
    {
        // cameras[i].R will be
        //     a b tx
        //     c d ty
        //     0 0 1
        cameras[i].R = Mat::eye(3, 3, CV_32F);
        Mat params = cam_params_.rowRange(i * 6, i * 6 + 6).reshape(1, 2);
        params.convertTo(cameras[i].R.rowRange(0, 2), CV_32F);
    }
}


void BundleAdjusterAffine::calcError(Mat &err)
{
    err.create(total_num_matches_ * 2, 1, CV_64F);

    int match_idx = 0;
    for (size_t edge_idx = 0; edge_idx < edges_.size(); ++edge_idx)
    {
        size_t i = edges_[edge_idx].first;
        size_t j = edges_[edge_idx].second;

        const ImageFeatures& features1 = features_[i];
        const ImageFeatures& features2 = features_[j];
        const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];

        Mat H1 (2, 3, CV_64F, cam_params_.ptr<double>() + i * 6);
        Mat H2 (2, 3, CV_64F, cam_params_.ptr<double>() + j * 6);

        // invert H1
        Mat H1_inv;
        invertAffineTransform(H1, H1_inv);

        // convert to representation in homogeneous coordinates
        Mat last_row = Mat::zeros(1, 3, CV_64F);
        last_row.at<double>(2) = 1.;
        H1_inv.push_back(last_row);
        H2.push_back(last_row);

        Mat_<double> H = H1_inv * H2;

        for (size_t k = 0; k < matches_info.matches.size(); ++k)
        {
            if (!matches_info.inliers_mask[k])
                continue;

            const DMatch& m = matches_info.matches[k];
            const Point2f& p1 = features1.keypoints[m.queryIdx].pt;
            const Point2f& p2 = features2.keypoints[m.trainIdx].pt;

            double x = H(0,0)*p1.x + H(0,1)*p1.y + H(0,2);
            double y = H(1,0)*p1.x + H(1,1)*p1.y + H(1,2);

            err.at<double>(2 * match_idx + 0, 0) = p2.x - x;
            err.at<double>(2 * match_idx + 1, 0) = p2.y - y;

            ++match_idx;
        }
    }
}


void BundleAdjusterAffine::calcJacobian(Mat &jac)
{
    jac.create(total_num_matches_ * 2, num_images_ * 6, CV_64F);

    double val;
    const double step = 1e-4;

    for (int i = 0; i < num_images_; ++i)
    {
        for (int j = 0; j < 6; ++j)
        {
            val = cam_params_.at<double>(i * 6 + j, 0);
            cam_params_.at<double>(i * 6 + j, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 6 + j, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 6 + j));
            cam_params_.at<double>(i * 6 + j, 0) = val;
        }
    }
}


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

void BundleAdjusterAffinePartial::setUpInitialCameraParams(const std::vector<CameraParams> &cameras)
{
    cam_params_.create(num_images_ * 4, 1, CV_64F);
    for (size_t i = 0; i < static_cast<size_t>(num_images_); ++i)
    {
        CV_Assert(cameras[i].R.type() == CV_32F);
        // cameras[i].R is
        //     a -b tx
        //     b  a ty
        //     0  0 1. (optional)
        // cam_params_ model for LevMarq is
        //     (a, b, tx, ty)
        double *params = cam_params_.ptr<double>() + i * 4;
        params[0] = cameras[i].R.at<float>(0, 0);
        params[1] = cameras[i].R.at<float>(1, 0);
        params[2] = cameras[i].R.at<float>(0, 2);
        params[3] = cameras[i].R.at<float>(1, 2);
    }
}


void BundleAdjusterAffinePartial::obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const
{
    for (size_t i = 0; i < static_cast<size_t>(num_images_); ++i)
    {
        // cameras[i].R will be
        //     a -b tx
        //     b  a ty
        //     0  0 1
        // cam_params_ model for LevMarq is
        //     (a, b, tx, ty)
        const double *params = cam_params_.ptr<double>() + i * 4;
        double transform_buf[9] =
        {
            params[0], -params[1], params[2],
            params[1],  params[0], params[3],
            0., 0., 1.
        };
        Mat transform(3, 3, CV_64F, transform_buf);
        transform.convertTo(cameras[i].R, CV_32F);
    }
}


void BundleAdjusterAffinePartial::calcError(Mat &err)
{
    err.create(total_num_matches_ * 2, 1, CV_64F);

    int match_idx = 0;
    for (size_t edge_idx = 0; edge_idx < edges_.size(); ++edge_idx)
    {
        size_t i = edges_[edge_idx].first;
        size_t j = edges_[edge_idx].second;

        const ImageFeatures& features1 = features_[i];
        const ImageFeatures& features2 = features_[j];
        const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];

        const double *H1_ptr = cam_params_.ptr<double>() + i * 4;
        double H1_buf[9] =
        {
            H1_ptr[0], -H1_ptr[1], H1_ptr[2],
            H1_ptr[1],  H1_ptr[0], H1_ptr[3],
            0., 0., 1.
        };
        Mat H1 (3, 3, CV_64F, H1_buf);
        const double *H2_ptr = cam_params_.ptr<double>() + j * 4;
        double H2_buf[9] =
        {
            H2_ptr[0], -H2_ptr[1], H2_ptr[2],
            H2_ptr[1],  H2_ptr[0], H2_ptr[3],
            0., 0., 1.
        };
        Mat H2 (3, 3, CV_64F, H2_buf);

        // invert H1
        Mat H1_aff (H1, Range(0, 2));
        double H1_inv_buf[6];
        Mat H1_inv (2, 3, CV_64F, H1_inv_buf);
        invertAffineTransform(H1_aff, H1_inv);
        H1_inv.copyTo(H1_aff);

        Mat_<double> H = H1 * H2;

        for (size_t k = 0; k < matches_info.matches.size(); ++k)
        {
            if (!matches_info.inliers_mask[k])
                continue;

            const DMatch& m = matches_info.matches[k];
            const Point2f& p1 = features1.keypoints[m.queryIdx].pt;
            const Point2f& p2 = features2.keypoints[m.trainIdx].pt;

            double x = H(0,0)*p1.x + H(0,1)*p1.y + H(0,2);
            double y = H(1,0)*p1.x + H(1,1)*p1.y + H(1,2);

            err.at<double>(2 * match_idx + 0, 0) = p2.x - x;
            err.at<double>(2 * match_idx + 1, 0) = p2.y - y;

            ++match_idx;
        }
    }
}


void BundleAdjusterAffinePartial::calcJacobian(Mat &jac)
{
    jac.create(total_num_matches_ * 2, num_images_ * 4, CV_64F);

    double val;
    const double step = 1e-4;

    for (int i = 0; i < num_images_; ++i)
    {
        for (int j = 0; j < 4; ++j)
        {
            val = cam_params_.at<double>(i * 4 + j, 0);
            cam_params_.at<double>(i * 4 + j, 0) = val - step;
            calcError(err1_);
            cam_params_.at<double>(i * 4 + j, 0) = val + step;
            calcError(err2_);
            calcDeriv(err1_, err2_, 2 * step, jac.col(i * 4 + j));
            cam_params_.at<double>(i * 4 + j, 0) = val;
        }
    }
}


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

WaveCorrectKind autoDetectWaveCorrectKind(const std::vector<Mat> &rmats)
{
    std::vector<float> xs, ys;
    xs.reserve(rmats.size());
    ys.reserve(rmats.size());

    // Project a [0, 0, 1, 1] point to the camera image frame
    // Ignore intrinsic parameters and camera translation as they
    // have little influence
    // This also means we can simply use "rmat.col(2)" as the
    // projected point homogeneous coordinate
    for (const Mat& rmat: rmats)
    {
        CV_Assert(rmat.type() == CV_32F);
        xs.push_back(rmat.at<float>(0, 2) / rmat.at<float>(2, 2));
        ys.push_back(rmat.at<float>(1, 2) / rmat.at<float>(2, 2));
    }

    // Calculate the delta between the max and min values for
    // both the X and Y axis
    auto min_max_x = std::minmax_element(xs.begin(), xs.end());
    auto min_max_y = std::minmax_element(ys.begin(), ys.end());
    double delta_x = *min_max_x.second - *min_max_x.first;
    double delta_y = *min_max_y.second - *min_max_y.first;

    // If the Y delta is the biggest, it means the images
    // mostly span along the vertical axis: correct this axis
    if (delta_y > delta_x)
    {
        LOGLN("  using vertical wave correction");
        return WAVE_CORRECT_VERT;
    }
    else
    {
        LOGLN("  using horizontal wave correction");
        return WAVE_CORRECT_HORIZ;
    }
}

void waveCorrect(std::vector<Mat> &rmats, WaveCorrectKind kind)
{
    LOGLN("Wave correcting...");
#if ENABLE_LOG
    int64 t = getTickCount();
#endif
    if (rmats.size() <= 1)
    {
        LOGLN("Wave correcting, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
        return;
    }

    if (kind == WAVE_CORRECT_AUTO)
    {
        kind = autoDetectWaveCorrectKind(rmats);
    }

    Mat moment = Mat::zeros(3, 3, CV_32F);
    for (size_t i = 0; i < rmats.size(); ++i)
    {
        Mat col = rmats[i].col(0);
        moment += col * col.t();
    }

    Mat eigen_vals, eigen_vecs;
    eigen(moment, eigen_vals, eigen_vecs);

    Mat rg1;
    if (kind == WAVE_CORRECT_HORIZ)
        rg1 = eigen_vecs.row(2).t();
    else if (kind == WAVE_CORRECT_VERT)
        rg1 = eigen_vecs.row(0).t();
    else
        CV_Error(CV_StsBadArg, "unsupported kind of wave correction");

    Mat img_k = Mat::zeros(3, 1, CV_32F);
    for (size_t i = 0; i < rmats.size(); ++i)
        img_k += rmats[i].col(2);
    Mat rg0 = rg1.cross(img_k);
    double rg0_norm = norm(rg0);

    if( rg0_norm <= DBL_MIN )
    {
        return;
    }

    rg0 /= rg0_norm;

    Mat rg2 = rg0.cross(rg1);

    double conf = 0;
    if (kind == WAVE_CORRECT_HORIZ)
    {
        for (size_t i = 0; i < rmats.size(); ++i)
            conf += rg0.dot(rmats[i].col(0));
        if (conf < 0)
        {
            rg0 *= -1;
            rg1 *= -1;
        }
    }
    else if (kind == WAVE_CORRECT_VERT)
    {
        for (size_t i = 0; i < rmats.size(); ++i)
            conf -= rg1.dot(rmats[i].col(0));
        if (conf < 0)
        {
            rg0 *= -1;
            rg1 *= -1;
        }
    }

    Mat R = Mat::zeros(3, 3, CV_32F);
    Mat tmp = R.row(0);
    Mat(rg0.t()).copyTo(tmp);
    tmp = R.row(1);
    Mat(rg1.t()).copyTo(tmp);
    tmp = R.row(2);
    Mat(rg2.t()).copyTo(tmp);

    for (size_t i = 0; i < rmats.size(); ++i)
        rmats[i] = R * rmats[i];

    LOGLN("Wave correcting, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
}


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

String matchesGraphAsString(std::vector<String> &pathes, std::vector<MatchesInfo> &pairwise_matches,
                                float conf_threshold)
{
    std::stringstream str;
    str << "graph matches_graph{\n";

    const int num_images = static_cast<int>(pathes.size());
    std::set<std::pair<int,int> > span_tree_edges;
    DisjointSets comps(num_images);

    for (int i = 0; i < num_images; ++i)
    {
        for (int j = 0; j < num_images; ++j)
        {
            if (pairwise_matches[i*num_images + j].confidence < conf_threshold)
                continue;
            int comp1 = comps.findSetByElem(i);
            int comp2 = comps.findSetByElem(j);
            if (comp1 != comp2)
            {
                comps.mergeSets(comp1, comp2);
                span_tree_edges.insert(std::make_pair(i, j));
            }
        }
    }

    for (std::set<std::pair<int,int> >::const_iterator itr = span_tree_edges.begin();
         itr != span_tree_edges.end(); ++itr)
    {
        std::pair<int,int> edge = *itr;
        if (span_tree_edges.find(edge) != span_tree_edges.end())
        {
            String name_src = pathes[edge.first];
            size_t prefix_len = name_src.find_last_of("/\\");
            if (prefix_len != String::npos) prefix_len++; else prefix_len = 0;
            name_src = name_src.substr(prefix_len, name_src.size() - prefix_len);

            String name_dst = pathes[edge.second];
            prefix_len = name_dst.find_last_of("/\\");
            if (prefix_len != String::npos) prefix_len++; else prefix_len = 0;
            name_dst = name_dst.substr(prefix_len, name_dst.size() - prefix_len);

            int pos = edge.first*num_images + edge.second;
            str << "\"" << name_src.c_str() << "\" -- \"" << name_dst.c_str() << "\""
                << "[label=\"Nm=" << pairwise_matches[pos].matches.size()
                << ", Ni=" << pairwise_matches[pos].num_inliers
                << ", C=" << pairwise_matches[pos].confidence << "\"];\n";
        }
    }

    for (size_t i = 0; i < comps.size.size(); ++i)
    {
        if (comps.size[comps.findSetByElem((int)i)] == 1)
        {
            String name = pathes[i];
            size_t prefix_len = name.find_last_of("/\\");
            if (prefix_len != String::npos) prefix_len++; else prefix_len = 0;
            name = name.substr(prefix_len, name.size() - prefix_len);
            str << "\"" << name.c_str() << "\";\n";
        }
    }

    str << "}";
    return str.str().c_str();
}

std::vector<int> leaveBiggestComponent(std::vector<ImageFeatures> &features,  std::vector<MatchesInfo> &pairwise_matches,
                                      float conf_threshold)
{
    const int num_images = static_cast<int>(features.size());

    DisjointSets comps(num_images);
    for (int i = 0; i < num_images; ++i)
    {
        for (int j = 0; j < num_images; ++j)
        {
            if (pairwise_matches[i*num_images + j].confidence < conf_threshold)
                continue;
            int comp1 = comps.findSetByElem(i);
            int comp2 = comps.findSetByElem(j);
            if (comp1 != comp2)
                comps.mergeSets(comp1, comp2);
        }
    }

    int max_comp = static_cast<int>(std::max_element(comps.size.begin(), comps.size.end()) - comps.size.begin());

    std::vector<int> indices;
    std::vector<int> indices_removed;
    for (int i = 0; i < num_images; ++i)
        if (comps.findSetByElem(i) == max_comp)
            indices.push_back(i);
        else
            indices_removed.push_back(i);

    std::vector<ImageFeatures> features_subset;
    std::vector<MatchesInfo> pairwise_matches_subset;
    for (size_t i = 0; i < indices.size(); ++i)
    {
        features_subset.push_back(features[indices[i]]);
        for (size_t j = 0; j < indices.size(); ++j)
        {
            pairwise_matches_subset.push_back(pairwise_matches[indices[i]*num_images + indices[j]]);
            pairwise_matches_subset.back().src_img_idx = static_cast<int>(i);
            pairwise_matches_subset.back().dst_img_idx = static_cast<int>(j);
        }
    }

    if (static_cast<int>(features_subset.size()) == num_images)
        return indices;

    LOG("Removed some images, because can't match them or there are too similar images: (");
    LOG(indices_removed[0] + 1);
    for (size_t i = 1; i < indices_removed.size(); ++i)
        LOG(", " << indices_removed[i]+1);
    LOGLN(").");
    LOGLN("Try to decrease the match confidence threshold and/or check if you're stitching duplicates.");

    features = features_subset;
    pairwise_matches = pairwise_matches_subset;

    return indices;
}


void findMaxSpanningTree(int num_images, const std::vector<MatchesInfo> &pairwise_matches,
                             Graph &span_tree, std::vector<int> &centers)
{
    Graph graph(num_images);
    std::vector<GraphEdge> edges;

    // Construct images graph and remember its edges
    for (int i = 0; i < num_images; ++i)
    {
        for (int j = 0; j < num_images; ++j)
        {
            if (pairwise_matches[i * num_images + j].H.empty())
                continue;
            float conf = static_cast<float>(pairwise_matches[i * num_images + j].num_inliers);
            graph.addEdge(i, j, conf);
            edges.push_back(GraphEdge(i, j, conf));
        }
    }

    DisjointSets comps(num_images);
    span_tree.create(num_images);
    std::vector<int> span_tree_powers(num_images, 0);

    // Find maximum spanning tree
    sort(edges.begin(), edges.end(), std::greater<GraphEdge>());
    for (size_t i = 0; i < edges.size(); ++i)
    {
        int comp1 = comps.findSetByElem(edges[i].from);
        int comp2 = comps.findSetByElem(edges[i].to);
        if (comp1 != comp2)
        {
            comps.mergeSets(comp1, comp2);
            span_tree.addEdge(edges[i].from, edges[i].to, edges[i].weight);
            span_tree.addEdge(edges[i].to, edges[i].from, edges[i].weight);
            span_tree_powers[edges[i].from]++;
            span_tree_powers[edges[i].to]++;
        }
    }

    // Find spanning tree leafs
    std::vector<int> span_tree_leafs;
    for (int i = 0; i < num_images; ++i)
        if (span_tree_powers[i] == 1)
            span_tree_leafs.push_back(i);

    // Find maximum distance from each spanning tree vertex
    std::vector<int> max_dists(num_images, 0);
    std::vector<int> cur_dists;
    for (size_t i = 0; i < span_tree_leafs.size(); ++i)
    {
        cur_dists.assign(num_images, 0);
        span_tree.walkBreadthFirst(span_tree_leafs[i], IncDistance(cur_dists));
        for (int j = 0; j < num_images; ++j)
            max_dists[j] = std::max(max_dists[j], cur_dists[j]);
    }

    // Find min-max distance
    int min_max_dist = max_dists[0];
    for (int i = 1; i < num_images; ++i)
        if (min_max_dist > max_dists[i])
            min_max_dist = max_dists[i];

    // Find spanning tree centers
    centers.clear();
    for (int i = 0; i < num_images; ++i)
        if (max_dists[i] == min_max_dist)
            centers.push_back(i);
    CV_Assert(centers.size() > 0 && centers.size() <= 2);
}

} // namespace detail
} // namespace cv
