849 lines
29 KiB
C++
849 lines
29 KiB
C++
/*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.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "precomp.hpp"
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
using namespace cv::detail;
|
|
|
|
namespace {
|
|
|
|
struct IncDistance
|
|
{
|
|
IncDistance(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 vector<MatchesInfo> &_pairwise_matches, 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(0,2) = 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(0,2) = 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;
|
|
};
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
|
|
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 {
|
|
|
|
void HomographyBasedEstimator::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
|
|
vector<CameraParams> &cameras)
|
|
{
|
|
LOGLN("Estimating rotations...");
|
|
int64 t = getTickCount();
|
|
|
|
const int num_images = static_cast<int>(features.size());
|
|
|
|
#if 0
|
|
// Robustly estimate focal length from rotating cameras
|
|
vector<Mat> Hs;
|
|
for (int iter = 0; iter < 100; ++iter)
|
|
{
|
|
int len = 2 + rand()%(pairwise_matches.size() - 1);
|
|
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
|
|
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;
|
|
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");
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
|
|
void BundleAdjusterBase::estimate(const vector<ImageFeatures> &features,
|
|
const vector<MatchesInfo> &pairwise_matches,
|
|
vector<CameraParams> &cameras)
|
|
{
|
|
LOG_CHAT("Bundle adjustment");
|
|
int64 t = getTickCount();
|
|
|
|
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(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_,
|
|
term_criteria_);
|
|
|
|
Mat err, jac;
|
|
CvMat matParams = 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 = jac;
|
|
cvCopy(&tmp, _jac);
|
|
}
|
|
|
|
if (_err)
|
|
{
|
|
calcError(err);
|
|
LOG_CHAT(".");
|
|
iter++;
|
|
CvMat tmp = err;
|
|
cvCopy(&tmp, _err);
|
|
}
|
|
}
|
|
|
|
LOGLN_CHAT("");
|
|
LOGLN_CHAT("Bundle adjustment, final RMS error: " << sqrt(err.dot(err) / total_num_matches_));
|
|
LOGLN_CHAT("Bundle adjustment, iterations done: " << iter);
|
|
|
|
obtainRefinedCameraParams(cameras);
|
|
|
|
// Normalize motion to center image
|
|
Graph span_tree;
|
|
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");
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
|
|
void BundleAdjusterReproj::setUpInitialCameraParams(const 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(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 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(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 = 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 = sqrt(x2*x2 + y2*y2 + z2*z2);
|
|
x2 /= len; y2 /= len; z2 /= len;
|
|
|
|
double mult = 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 waveCorrect(vector<Mat> &rmats, WaveCorrectKind kind)
|
|
{
|
|
LOGLN("Wave correcting...");
|
|
int64 t = getTickCount();
|
|
|
|
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);
|
|
rg0 /= norm(rg0);
|
|
|
|
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(vector<string> &pathes, vector<MatchesInfo> &pairwise_matches,
|
|
float conf_threshold)
|
|
{
|
|
stringstream str;
|
|
str << "graph matches_graph{\n";
|
|
|
|
const int num_images = static_cast<int>(pathes.size());
|
|
set<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(make_pair(i, j));
|
|
}
|
|
}
|
|
}
|
|
|
|
for (set<pair<int,int> >::const_iterator itr = span_tree_edges.begin();
|
|
itr != span_tree_edges.end(); ++itr)
|
|
{
|
|
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 << "\" -- \"" << name_dst << "\""
|
|
<< "[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 << "\";\n";
|
|
}
|
|
}
|
|
|
|
str << "}";
|
|
return str.str();
|
|
}
|
|
|
|
vector<int> leaveBiggestComponent(vector<ImageFeatures> &features, 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>(max_element(comps.size.begin(), comps.size.end()) - comps.size.begin());
|
|
|
|
vector<int> indices;
|
|
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);
|
|
|
|
vector<ImageFeatures> features_subset;
|
|
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 --match_conf value and/or check if you're stitching duplicates.");
|
|
|
|
features = features_subset;
|
|
pairwise_matches = pairwise_matches_subset;
|
|
|
|
return indices;
|
|
}
|
|
|
|
|
|
void findMaxSpanningTree(int num_images, const vector<MatchesInfo> &pairwise_matches,
|
|
Graph &span_tree, vector<int> ¢ers)
|
|
{
|
|
Graph graph(num_images);
|
|
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);
|
|
vector<int> span_tree_powers(num_images, 0);
|
|
|
|
// Find maximum spanning tree
|
|
sort(edges.begin(), edges.end(), 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
|
|
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
|
|
vector<int> max_dists(num_images, 0);
|
|
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] = 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
|
|
|