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modified focal estimation function in opencv_stitching

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This commit is contained in:
Alexey Spizhevoy 2011-05-18 08:56:48 +00:00
parent 34e2c78cec
commit 60e1eda149
3 changed files with 55 additions and 118 deletions
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130
modules/stitching/autocalib.cpp
View File

@ -8,99 +8,63 @@ void focalsFromHomography(const Mat& H, double &f0, double &f1, bool &f0_ok, boo
{ {
CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3)); CV_Assert(H.type() == CV_64F && H.size() == Size(3, 3));
const double h[9] = const double* h = reinterpret_cast<const double*>(H.data);
{
H.at<double>(0, 0), H.at<double>(0, 1), H.at<double>(0, 2), double d1, d2; // Denominators
H.at<double>(1, 0), H.at<double>(1, 1), H.at<double>(1, 2), double v1, v2; // Focal squares value candidates
H.at<double>(2, 0), H.at<double>(2, 1), H.at<double>(2, 2)
};
f1_ok = true; f1_ok = true;
double denom1 = h[6] * h[7]; d1 = h[6] * h[7];
double denom2 = (h[7] - h[6]) * (h[7] + h[6]); d2 = (h[7] - h[6]) * (h[7] + h[6]);
if (max(abs(denom1), abs(denom2)) < 1e-5) v1 = -(h[0] * h[1] + h[3] * h[4]) / d1;
f1_ok = false; v2 = (h[0] * h[0] + h[3] * h[3] - h[1] * h[1] - h[4] * h[4]) / d2;
else if (v1 < v2) swap(v1, v2);
{ if (v1 > 0 && v2 > 0) f1 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
double val1 = -(h[0] * h[1] + h[3] * h[4]) / denom1; else if (v1 > 0) f1 = sqrt(v1);
double val2 = (h[0] * h[0] + h[3] * h[3] - h[1] * h[1] - h[4] * h[4]) / denom2; else f1_ok = false;
if (val1 < val2)
swap(val1, val2);
if (val1 > 0 && val2 > 0)
f1 = sqrt(abs(denom1) > abs(denom2) ? val1 : val2);
else if (val1 > 0)
f1 = sqrt(val1);
else
f1_ok = false;
}
f0_ok = true; f0_ok = true;
denom1 = h[0] * h[3] + h[1] * h[4]; d1 = h[0] * h[3] + h[1] * h[4];
denom2 = h[0] * h[0] + h[1] * h[1] - h[3] * h[3] - h[4] * h[4]; d2 = h[0] * h[0] + h[1] * h[1] - h[3] * h[3] - h[4] * h[4];
if (max(abs(denom1), abs(denom2)) < 1e-5) v1 = -h[2] * h[5] / d1;
f0_ok = false; v2 = (h[5] * h[5] - h[2] * h[2]) / d2;
else if (v1 < v2) swap(v1, v2);
{ if (v1 > 0 && v2 > 0) f0 = sqrt(abs(d1) > abs(d2) ? v1 : v2);
double val1 = -h[2] * h[5] / denom1; else if (v1 > 0) f0 = sqrt(v1);
double val2 = (h[5] * h[5] - h[2] * h[2]) / denom2; else f0_ok = false;
if (val1 < val2)
swap(val1, val2);
if (val1 > 0 && val2 > 0)
f0 = sqrt(abs(denom1) > abs(denom2) ? val1 : val2);
else if (val1 > 0)
f0 = sqrt(val1);
else
f0_ok = false;
}
} }
bool focalsFromFundamental(const Mat &F, double &f0, double &f1) double estimateFocal(const vector<Mat> &images, const vector<ImageFeatures> &/*features*/,
const vector<MatchesInfo> &pairwise_matches)
{ {
CV_Assert(F.type() == CV_64F); const int num_images = static_cast<int>(images.size());
CV_Assert(F.size() == Size(3, 3));
Mat Ft = F.t(); vector<double> focals;
Mat k = Mat::zeros(3, 1, CV_64F); for (int src_idx = 0; src_idx < num_images; ++src_idx)
k.at<double>(2, 0) = 1.f; {
for (int dst_idx = 0; dst_idx < num_images; ++dst_idx)
{
const MatchesInfo &m = pairwise_matches[src_idx*num_images + dst_idx];
if (m.H.empty())
continue;
// 1. Compute quantities double f0, f1;
double a = normL2sq(F*Ft*k) / normL2sq(Ft*k); bool f0ok, f1ok;
double b = normL2sq(Ft*F*k) / normL2sq(F*k); focalsFromHomography(m.H, f0, f1, f0ok, f1ok);
double c = sqr(k.dot(F*k)) / (normL2sq(Ft*k) * normL2sq(F*k)); if (f0ok && f1ok) focals.push_back(sqrt(f0*f1));
double d = k.dot(F*Ft*F*k) / k.dot(F*k); }
double A = 1/c + a - 2*d; }
double B = 1/c + b - 2*d;
double P = 2*(1/c - 2*d + 0.5*normL2sq(F));
double Q = -(A + B)/c + 0.5*(normL2sq(F*Ft) - 0.5*sqr(normL2sq(F)));
// 2. Solve quadratic equation Z*Z*a_ + Z*b_ + c_ = 0 if (focals.size() + 1 >= images.size())
double a_ = 1 + c*P; {
double b_ = -(c*P*P + 2*P + 4*c*Q); nth_element(focals.begin(), focals.end(), focals.begin() + focals.size()/2);
double c_ = P*P + 4*c*P*Q + 12*A*B; return focals[focals.size()/2];
double D = b_*b_ - 4*a_*c_; }
if (abs(D) < 1e-5)
D = 0;
else if (D < 0)
return false;
double D_sqrt = sqrt(D);
double Z0 = (-b_ - D_sqrt) / (2*a_);
double Z1 = (-b_ + D_sqrt) / (2*a_);
// 3. Choose solution LOGLN("Can't estimate focal length, will use naive approach");
double w0 = abs(Z0*Z0*Z0 - 3*P*Z0*Z0 + 2*(P*P + 2*Q)*Z0 - 4*(P*Q + 4*A*B/c)); double focals_sum = 0;
double w1 = abs(Z1*Z1*Z1 - 3*P*Z1*Z1 + 2*(P*P + 2*Q)*Z1 - 4*(P*Q + 4*A*B/c)); for (int i = 0; i < num_images; ++i)
double Z = Z0; focals_sum += images[i].rows + images[i].cols;
if (w1 < w0) return focals_sum / num_images;
Z = Z1;
// 4.
double X = -1/c*(1 + 2*B/(Z - P));
double Y = -1/c*(1 + 2*A/(Z - P));
// 5. Compute focal lengths
f0 = 1/sqrt(1 + X/normL2sq(Ft*k));
f1 = 1/sqrt(1 + Y/normL2sq(F*k));
return true;
} }

5
modules/stitching/autocalib.hpp
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@ -1,12 +1,15 @@
#ifndef __OPENCV_AUTOCALIB_HPP__ #ifndef __OPENCV_AUTOCALIB_HPP__
#define __OPENCV_AUTOCALIB_HPP__ #define __OPENCV_AUTOCALIB_HPP__
#include <vector>
#include <opencv2/core/core.hpp> #include <opencv2/core/core.hpp>
#include "matchers.hpp"
// See "Construction of Panoramic Image Mosaics with Global and Local Alignment" // See "Construction of Panoramic Image Mosaics with Global and Local Alignment"
// by Heung-Yeung Shum and Richard Szeliski. // by Heung-Yeung Shum and Richard Szeliski.
void focalsFromHomography(const cv::Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok); void focalsFromHomography(const cv::Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok);
bool focalsFromFundamental(const cv::Mat &F, double &f0, double &f1); double estimateFocal(const std::vector<cv::Mat> &images, const std::vector<ImageFeatures> &features,
const std::vector<MatchesInfo> &pairwise_matches);
#endif // __OPENCV_AUTOCALIB_HPP__ #endif // __OPENCV_AUTOCALIB_HPP__

38
modules/stitching/motion_estimators.cpp
View File

@ -64,46 +64,16 @@ struct CalcRotation
}; };
void HomographyBasedEstimator::estimate(const vector<Mat> &images, const vector<ImageFeatures> &/*features*/, void HomographyBasedEstimator::estimate(const vector<Mat> &images, const vector<ImageFeatures> &features,
const vector<MatchesInfo> &pairwise_matches, vector<CameraParams> &cameras) const vector<MatchesInfo> &pairwise_matches, vector<CameraParams> &cameras)
{ {
const int num_images = static_cast<int>(images.size()); const int num_images = static_cast<int>(images.size());
// Find focals from pair-wise homographies // Estimate focal length and set it for all cameras
vector<bool> is_focal_estimated(num_images, false); double focal = estimateFocal(images, features, pairwise_matches);
vector<double> focals;
for (int i = 0; i < num_images; ++i)
{
for (int j = 0; j < num_images; ++j)
{
int pair_idx = i * num_images + j;
if (pairwise_matches[pair_idx].H.empty())
continue;
double f_to, f_from;
bool f_to_ok, f_from_ok;
focalsFromHomography(pairwise_matches[pair_idx].H.inv(), f_to, f_from, f_to_ok, f_from_ok);
if (f_from_ok) focals.push_back(f_from);
if (f_to_ok) focals.push_back(f_to);
if (f_from_ok && f_to_ok)
{
is_focal_estimated[i] = true;
is_focal_estimated[j] = true;
}
}
}
is_focals_estimated_ = true;
for (int i = 0; i < num_images; ++i)
is_focals_estimated_ = is_focals_estimated_ && is_focal_estimated[i];
// Find focal median and use it as true focal length
nth_element(focals.begin(), focals.end(), focals.begin() + focals.size() / 2);
cameras.resize(num_images); cameras.resize(num_images);
for (int i = 0; i < num_images; ++i) for (int i = 0; i < num_images; ++i)
cameras[i].focal = focals[focals.size() / 2]; cameras[i].focal = focal;
// Restore global motion // Restore global motion
Graph span_tree; Graph span_tree;