modules/stitching/src/autocalib.cpp - 4.x - Alx89/OpenCV

modules/stitching/src/autocalib.cpp
202 строки6 KB
Lilit Grigoryan
Fix focal length estimation from homography matrix
14 фев 2023, 21:51
14 фев 2023, 21:51a87b9fb
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#include "precomp.hpp"
#include "opencv2/core/hal/hal.hpp"

using namespace cv;

namespace {

static inline bool decomposeCholesky(double* A, size_t astep, int m)
{
    if (!hal::Cholesky64f(A, astep, m, 0, 0, 0))
        return false;
    return true;
}

} // namespace


namespace cv {
namespace detail {

void focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok, bool &f1_ok)
{
    CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3));

    const double* h = H.ptr<double>();

    double d1, d2; // Denominators
    double v1, v2; // Focal squares value candidates

    f1_ok = true;
    d1 = h[6] * h[7];
    d2 = (h[7] - h[6]) * (h[7] + h[6]);
    v1 = -(h[0] * h[1] + h[3] * h[4]) / d1;
    v2 = (h[0] * h[0] + h[3] * h[3] - h[1] * h[1] - h[4] * h[4]) / d2;
    if (v1 < v2)
    {
        std::swap(v1, v2);
        std::swap(d1, d2);
    }
    if (v1 > 0 && v2 > 0) f1 = std::sqrt(std::abs(d1) > std::abs(d2) ? v1 : v2);
    else if (v1 > 0) f1 = std::sqrt(v1);
    else f1_ok = false;

    f0_ok = true;
    d1 = h[0] * h[3] + h[1] * h[4];
    d2 = h[0] * h[0] + h[1] * h[1] - h[3] * h[3] - h[4] * h[4];
    v1 = -h[2] * h[5] / d1;
    v2 = (h[5] * h[5] - h[2] * h[2]) / d2;
    if (v1 < v2)
    {
        std::swap(v1, v2);
        std::swap(d1, d2);
    }
    if (v1 > 0 && v2 > 0) f0 = std::sqrt(std::abs(d1) > std::abs(d2) ? v1 : v2);
    else if (v1 > 0) f0 = std::sqrt(v1);
    else f0_ok = false;
}


void estimateFocal(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
                       std::vector<double> &focals)
{
    const int num_images = static_cast<int>(features.size());
    focals.resize(num_images);

    std::vector<double> all_focals;

    for (int i = 0; i < num_images; ++i)
    {
        for (int j = 0; j < num_images; ++j)
        {
            const MatchesInfo &m = pairwise_matches[i*num_images + j];
            if (m.H.empty())
                continue;
            double f0, f1;
            bool f0ok, f1ok;
            focalsFromHomography(m.H, f0, f1, f0ok, f1ok);
            if (f0ok && f1ok)
                all_focals.push_back(std::sqrt(f0 * f1));
        }
    }

    if (static_cast<int>(all_focals.size()) >= num_images - 1)
    {
        double median;

        std::sort(all_focals.begin(), all_focals.end());
        if (all_focals.size() % 2 == 1)
            median = all_focals[all_focals.size() / 2];
        else
            median = (all_focals[all_focals.size() / 2 - 1] + all_focals[all_focals.size() / 2]) * 0.5;

        for (int i = 0; i < num_images; ++i)
            focals[i] = median;
    }
    else
    {
        LOGLN("Can't estimate focal length, will use naive approach");
        double focals_sum = 0;
        for (int i = 0; i < num_images; ++i)
            focals_sum += features[i].img_size.width + features[i].img_size.height;
        for (int i = 0; i < num_images; ++i)
            focals[i] = focals_sum / num_images;
    }
}


bool calibrateRotatingCamera(const std::vector<Mat> &Hs, Mat &K)
{
    int m = static_cast<int>(Hs.size());
    CV_Assert(m >= 1);

    std::vector<Mat> Hs_(m);
    for (int i = 0; i < m; ++i)
    {
        CV_Assert(Hs[i].size() == Size(3, 3) && Hs[i].type() == CV_64F);
        Hs_[i] = Hs[i] / std::pow(determinant(Hs[i]), 1./3.);
    }

    const int idx_map[3][3] = {{0, 1, 2}, {1, 3, 4}, {2, 4, 5}};
    Mat_<double> A(6*m, 6);
    A.setTo(0);

    int eq_idx = 0;
    for (int k = 0; k < m; ++k)
    {
        Mat_<double> H(Hs_[k]);
        for (int i = 0; i < 3; ++i)
        {
            for (int j = i; j < 3; ++j, ++eq_idx)
            {
                for (int l = 0; l < 3; ++l)
                {
                    for (int s = 0; s < 3; ++s)
                    {
                        int idx = idx_map[l][s];
                        A(eq_idx, idx) += H(i,l) * H(j,s);
                    }
                }
                A(eq_idx, idx_map[i][j]) -= 1;
            }
        }
    }

    Mat_<double> wcoef;
    SVD::solveZ(A, wcoef);

    Mat_<double> W(3,3);
    for (int i = 0; i < 3; ++i)
        for (int j = i; j < 3; ++j)
            W(i,j) = W(j,i) = wcoef(idx_map[i][j], 0) / wcoef(5,0);
    if (!decomposeCholesky(W.ptr<double>(), W.step, 3))
        return false;
    W(0,1) = W(0,2) = W(1,2) = 0;
    K = W.t();
    return true;
}

} // namespace detail
} // namespace cv