mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-29 13:47:32 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
1649c8f6d0
opencv/modules/stitching/motion_estimators.cpp

516 lines
18 KiB
C++
Raw Blame History

/*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 <algorithm>
#include "autocalib.hpp"
#include "motion_estimators.hpp"
#include "util.hpp"
using namespace std;
using namespace cv;
//////////////////////////////////////////////////////////////////////////////
CameraParams::CameraParams() : focal(1), R(Mat::eye(3, 3, CV_64F)), t(Mat::zeros(3, 1, CV_64F)) {}
CameraParams::CameraParams(const CameraParams &other) { *this = other; }
const CameraParams& CameraParams::operator =(const CameraParams &other)
{
focal = other.focal;
R = other.R.clone();
t = other.t.clone();
return *this;
}
//////////////////////////////////////////////////////////////////////////////
struct IncDistance
{
IncDistance(vector<int> &dists) : dists(&dists[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;
double f_from = cameras[edge.from].focal;
double f_to = cameras[edge.to].focal;
Mat K_from = Mat::eye(3, 3, CV_64F);
K_from.at<double>(0, 0) = f_from;
K_from.at<double>(1, 1) = f_from;
Mat K_to = Mat::eye(3, 3, CV_64F);
K_to.at<double>(0, 0) = f_to;
K_to.at<double>(1, 1) = f_to;
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 HomographyBasedEstimator::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
vector<CameraParams> &cameras)
{
const int num_images = static_cast<int>(features.size());
// Estimate focal length and set it for all cameras
vector<double> focals;
estimateFocal(features, pairwise_matches, focals);
cameras.resize(num_images);
for (int i = 0; i < num_images; ++i)
cameras[i].focal = focals[i];
// 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));
}
//////////////////////////////////////////////////////////////////////////////
void BundleAdjuster::estimate(const vector<ImageFeatures> &features, const vector<MatchesInfo> &pairwise_matches,
vector<CameraParams> &cameras)
{
num_images_ = static_cast<int>(features.size());
features_ = &features[0];
pairwise_matches_ = &pairwise_matches[0];
// Prepare focals and rotations
cameras_.create(num_images_ * 4, 1, CV_64F);
SVD svd;
for (int i = 0; i < num_images_; ++i)
{
cameras_.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);
cameras_.at<double>(i * 4 + 1, 0) = rvec.at<float>(0, 0);
cameras_.at<double>(i * 4 + 2, 0) = rvec.at<float>(1, 0);
cameras_.at<double>(i * 4 + 3, 0) = rvec.at<float>(2, 0);
}
// Select only consistent image pairs for futher adjustment
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_ * 4, total_num_matches_ * 3,
cvTermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 1000, DBL_EPSILON));
CvMat matParams = cameras_;
cvCopy(&matParams, solver.param);
int count = 0;
for(;;)
{
const CvMat* _param = 0;
CvMat* _J = 0;
CvMat* _err = 0;
bool proceed = solver.update(_param, _J, _err);
cvCopy( _param, &matParams );
if( !proceed || !_err )
break;
if( _J )
{
calcJacobian();
CvMat matJ = J_;
cvCopy( &matJ, _J );
}
if (_err)
{
calcError(err_);
LOG(".");
count++;
CvMat matErr = err_;
cvCopy( &matErr, _err );
}
}
LOGLN("");
LOGLN("Bundle adjustment, final error: " << sqrt(err_.dot(err_)));
LOGLN("Bundle adjustment, iterations done: " << count);
// Obtain global motion
for (int i = 0; i < num_images_; ++i)
{
cameras[i].focal = cameras_.at<double>(i * 4, 0);
Mat rvec(3, 1, CV_64F);
rvec.at<double>(0, 0) = cameras_.at<double>(i * 4 + 1, 0);
rvec.at<double>(1, 0) = cameras_.at<double>(i * 4 + 2, 0);
rvec.at<double>(2, 0) = cameras_.at<double>(i * 4 + 3, 0);
Rodrigues(rvec, cameras[i].R);
Mat Mf;
cameras[i].R.convertTo(Mf, CV_32F);
cameras[i].R = Mf;
}
// 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;
}
void BundleAdjuster::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 = cameras_.at<double>(i * 4, 0);
double f2 = cameras_.at<double>(j * 4, 0);
double R1[9], R2[9];
Mat R1_(3, 3, CV_64F, R1), R2_(3, 3, CV_64F, R2);
Mat rvec(3, 1, CV_64F);
rvec.at<double>(0, 0) = cameras_.at<double>(i * 4 + 1, 0);
rvec.at<double>(1, 0) = cameras_.at<double>(i * 4 + 2, 0);
rvec.at<double>(2, 0) = cameras_.at<double>(i * 4 + 3, 0);
Rodrigues(rvec, R1_); CV_Assert(R1_.type() == CV_64F);
rvec.at<double>(0, 0) = cameras_.at<double>(j * 4 + 1, 0);
rvec.at<double>(1, 0) = cameras_.at<double>(j * 4 + 2, 0);
rvec.at<double>(2, 0) = cameras_.at<double>(j * 4 + 3, 0);
Rodrigues(rvec, R2_); CV_Assert(R2_.type() == CV_64F);
const ImageFeatures& features1 = features_[i];
const ImageFeatures& features2 = features_[j];
const MatchesInfo& matches_info = pairwise_matches_[i * num_images_ + j];
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];
Point2d kp1 = features1.keypoints[m.queryIdx].pt;
kp1.x -= 0.5 * features1.img_size.width;
kp1.y -= 0.5 * features1.img_size.height;
Point2d kp2 = features2.keypoints[m.trainIdx].pt;
kp2.x -= 0.5 * features2.img_size.width;
kp2.y -= 0.5 * features2.img_size.height;
double len1 = sqrt(kp1.x * kp1.x + kp1.y * kp1.y + f1 * f1);
double len2 = sqrt(kp2.x * kp2.x + kp2.y * kp2.y + f2 * f2);
Point3d p1(kp1.x / len1, kp1.y / len1, f1 / len1);
Point3d p2(kp2.x / len2, kp2.y / len2, f2 / len2);
Point3d d1(p1.x * R1[0] + p1.y * R1[1] + p1.z * R1[2],
p1.x * R1[3] + p1.y * R1[4] + p1.z * R1[5],
p1.x * R1[6] + p1.y * R1[7] + p1.z * R1[8]);
Point3d d2(p2.x * R2[0] + p2.y * R2[1] + p2.z * R2[2],
p2.x * R2[3] + p2.y * R2[4] + p2.z * R2[5],
p2.x * R2[6] + p2.y * R2[7] + p2.z * R2[8]);
double mult = 1;
if (cost_space_ == FOCAL_RAY_SPACE)
mult = sqrt(f1 * f2);
err.at<double>(3 * match_idx, 0) = mult * (d1.x - d2.x);
err.at<double>(3 * match_idx + 1, 0) = mult * (d1.y - d2.y);
err.at<double>(3 * match_idx + 2, 0) = mult * (d1.z - d2.z);
match_idx++;
}
}
}
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;
}
void BundleAdjuster::calcJacobian()
{
J_.create(total_num_matches_ * 3, num_images_ * 4, CV_64F);
double f, r;
const double df = 0.001; // Focal length step
const double dr = 0.001; // Angle step
for (int i = 0; i < num_images_; ++i)
{
f = cameras_.at<double>(i * 4, 0);
cameras_.at<double>(i * 4, 0) = f - df;
calcError(err1_);
cameras_.at<double>(i * 4, 0) = f + df;
calcError(err2_);
calcDeriv(err1_, err2_, 2 * df, J_.col(i * 4));
cameras_.at<double>(i * 4, 0) = f;
r = cameras_.at<double>(i * 4 + 1, 0);
cameras_.at<double>(i * 4 + 1, 0) = r - dr;
calcError(err1_);
cameras_.at<double>(i * 4 + 1, 0) = r + dr;
calcError(err2_);
calcDeriv(err1_, err2_, 2 * dr, J_.col(i * 4 + 1));
cameras_.at<double>(i * 4 + 1, 0) = r;
r = cameras_.at<double>(i * 4 + 2, 0);
cameras_.at<double>(i * 4 + 2, 0) = r - dr;
calcError(err1_);
cameras_.at<double>(i * 4 + 2, 0) = r + dr;
calcError(err2_);
calcDeriv(err1_, err2_, 2 * dr, J_.col(i * 4 + 2));
cameras_.at<double>(i * 4 + 2, 0) = r;
r = cameras_.at<double>(i * 4 + 3, 0);
cameras_.at<double>(i * 4 + 3, 0) = r - dr;
calcError(err1_);
cameras_.at<double>(i * 4 + 3, 0) = r + dr;
calcError(err2_);
calcDeriv(err1_, err2_, 2 * dr, J_.col(i * 4 + 3));
cameras_.at<double>(i * 4 + 3, 0) = r;
}
}
//////////////////////////////////////////////////////////////////////////////
void waveCorrect(vector<Mat> &rmats)
{
float data[9];
Mat r0(1, 3, CV_32F, data);
Mat r1(1, 3, CV_32F, data + 3);
Mat r2(1, 3, CV_32F, data + 6);
Mat R(3, 3, CV_32F, data);
Mat cov = Mat::zeros(3, 3, CV_32F);
for (size_t i = 0; i < rmats.size(); ++i)
{
Mat r0 = rmats[i].col(0);
cov += r0 * r0.t();
}
SVD svd;
svd(cov, SVD::FULL_UV);
svd.vt.row(2).copyTo(r1);
if (determinant(svd.vt) < 0) r1 *= -1;
Mat avgz = Mat::zeros(3, 1, CV_32F);
for (size_t i = 0; i < rmats.size(); ++i)
avgz += rmats[i].col(2);
r1.cross(avgz.t()).copyTo(r0);
normalize(r0, r0);
r1.cross(r0).copyTo(r2);
if (determinant(R) < 0) R *= -1;
for (size_t i = 0; i < rmats.size(); ++i)
rmats[i] = R * rmats[i];
}
//////////////////////////////////////////////////////////////////////////////
vector<int> leaveBiggestComponent(vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches,
float conf_threshold)
{
const int num_images = static_cast<int>(features.size());
DjSets 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.find(i);
int comp2 = comps.find(j);
if (comp1 != comp2)
comps.merge(comp1, comp2);
}
}
int max_comp = 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.find(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 = i;
pairwise_matches_subset.back().dst_img_idx = j;
}
}
if (static_cast<int>(features_subset.size()) == num_images)
return indices;
LOG("Removed some images, because can't match them: (");
LOG(indices_removed[0]+1);
for (size_t i = 1; i < indices_removed.size(); ++i)
LOG(", " << indices_removed[i]+1);
LOGLN("). Try decrease --match_conf value.");
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> &centers)
{
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));
}
}
DjSets 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.find(edges[i].from);
int comp2 = comps.find(edges[i].to);
if (comp1 != comp2)
{
comps.merge(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);
}
Reference in New Issue View Git Blame Copy Permalink