Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
#include "autocalib.hpp"
#include "util.hpp"
using namespace std;
using namespace cv;
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 = reinterpret_cast<const double*>(H.data);
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) swap(v1, v2);
if (v1 > 0 && v2 > 0) f1 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
else if (v1 > 0) f1 = 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) swap(v1, v2);
if (v1 > 0 && v2 > 0) f0 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
else if (v1 > 0) f0 = sqrt(v1);
else f0_ok = false;
}
void estimateFocal(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
vector<double> &focals)
{
const int num_images = static_cast<int>(features.size());
focals.resize(num_images);
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(sqrt(f0 * f1));
}
}
if (static_cast<int>(all_focals.size()) >= num_images - 1)
{
nth_element(all_focals.begin(), all_focals.begin() + all_focals.size()/2, all_focals.end());
for (int i = 0; i < num_images; ++i)
focals[i] = all_focals[all_focals.size()/2];
}
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;
}
}
namespace
{
template<typename _Tp> static inline bool
decomposeCholesky(_Tp* A, size_t astep, int m)
{
if (!Cholesky(A, astep, m, 0, 0, 0))
return false;
astep /= sizeof(A[0]);
for (int i = 0; i < m; ++i)
A[i*astep + i] = (_Tp)(1./A[i*astep + i]);
return true;
}
} // namespace
bool calibrateRotatingCamera(const vector<Mat> &Hs, Mat &K)
{
int m = static_cast<int>(Hs.size());
CV_Assert(m >= 1);
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] / 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;
}