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refactored opencv_stitching

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This commit is contained in:
Alexey Spizhevoy
2011-05-16 05:11:09 +00:00
parent f80c93aa82
commit 79ed4e4c92
10 changed files with 78 additions and 77 deletions
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26
modules/stitching/motion_estimators.cpp
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@@ -1,7 +1,7 @@
#include <algorithm>
#include "opencv2/core/core_c.h"
#include <opencv2/calib3d/calib3d.hpp>
#include "focal_estimators.hpp"
#include "autocalib.hpp"
#include "motion_estimators.hpp"
#include "util.hpp"
@@ -11,7 +11,7 @@ using namespace cv;
//////////////////////////////////////////////////////////////////////////////
CameraParams::CameraParams() : focal(1), M(Mat::eye(3, 3, CV_64F)), t(Mat::zeros(3, 1, CV_64F)) {}
CameraParams::CameraParams() : focal(1), R(Mat::eye(3, 3, CV_64F)), t(Mat::zeros(3, 1, CV_64F)) {}
CameraParams::CameraParams(const CameraParams &other)
@@ -23,7 +23,7 @@ CameraParams::CameraParams(const CameraParams &other)
const CameraParams& CameraParams::operator =(const CameraParams &other)
{
focal = other.focal;
M = other.M.clone();
R = other.R.clone();
t = other.t.clone();
return *this;
}
@@ -60,7 +60,7 @@ struct CalcRotation
K_to.at<double>(1, 1) = f_to;
Mat R = K_from.inv() * pairwise_matches[pair_idx].H.inv() * K_to;
cameras[edge.to].M = cameras[edge.from].M * R;
cameras[edge.to].R = cameras[edge.from].R * R;
}
int num_images;
@@ -132,7 +132,7 @@ void BundleAdjuster::estimate(const vector<Mat> &images, const vector<ImageFeatu
for (int i = 0; i < num_images_; ++i)
{
cameras_.at<double>(i * 4, 0) = cameras[i].focal;
svd(cameras[i].M, SVD::FULL_UV);
svd(cameras[i].R, SVD::FULL_UV);
Mat R = svd.u * svd.vt;
if (determinant(R) < 0) R *= -1;
Mat rvec;
@@ -207,19 +207,19 @@ void BundleAdjuster::estimate(const vector<Mat> &images, const vector<ImageFeatu
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].M);
Rodrigues(rvec, cameras[i].R);
Mat Mf;
cameras[i].M.convertTo(Mf, CV_32F);
cameras[i].M = 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]].M.inv();
Mat R_inv = cameras[span_tree_centers[0]].R.inv();
for (int i = 0; i < num_images_; ++i)
cameras[i].M = R_inv * cameras[i].M;
cameras[i].R = R_inv * cameras[i].R;
}
@@ -255,12 +255,12 @@ void BundleAdjuster::calcError(Mat &err)
if (!matches_info.inliers_mask[k])
continue;
const DMatch& m = matches_info.matches[k];
const DMatch& r = matches_info.matches[k];
Point2d kp1 = features1.keypoints[m.queryIdx].pt;
Point2d kp1 = features1.keypoints[r.queryIdx].pt;
kp1.x -= 0.5 * images_[i].cols;
kp1.y -= 0.5 * images_[i].rows;
Point2d kp2 = features2.keypoints[m.trainIdx].pt;
Point2d kp2 = features2.keypoints[r.trainIdx].pt;
kp2.x -= 0.5 * images_[j].cols;
kp2.y -= 0.5 * images_[j].rows;
double len1 = sqrt(kp1.x * kp1.x + kp1.y * kp1.y + f1 * f1);