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562c15cbe9
Cast to (int) to avoid warnings Add test for different points number case in fisheye calibration function
1624 lines
63 KiB
C++
1624 lines
63 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// loss of use, data, or profits; or business interruption) however caused
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "fisheye.hpp"
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namespace cv { namespace
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{
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struct JacobianRow
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{
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Vec2d df, dc;
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Vec4d dk;
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Vec3d dom, dT;
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double dalpha;
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};
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void subMatrix(const Mat& src, Mat& dst, const std::vector<uchar>& cols, const std::vector<uchar>& rows);
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}}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/// cv::fisheye::projectPoints
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void cv::fisheye::projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine,
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InputArray K, InputArray D, double alpha, OutputArray jacobian)
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{
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projectPoints(objectPoints, imagePoints, affine.rvec(), affine.translation(), K, D, alpha, jacobian);
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}
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void cv::fisheye::projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray _rvec,
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InputArray _tvec, InputArray _K, InputArray _D, double alpha, OutputArray jacobian)
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{
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// will support only 3-channel data now for points
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CV_Assert(objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3);
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imagePoints.create(objectPoints.size(), CV_MAKETYPE(objectPoints.depth(), 2));
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size_t n = objectPoints.total();
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CV_Assert(_rvec.total() * _rvec.channels() == 3 && (_rvec.depth() == CV_32F || _rvec.depth() == CV_64F));
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CV_Assert(_tvec.total() * _tvec.channels() == 3 && (_tvec.depth() == CV_32F || _tvec.depth() == CV_64F));
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CV_Assert(_tvec.getMat().isContinuous() && _rvec.getMat().isContinuous());
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Vec3d om = _rvec.depth() == CV_32F ? (Vec3d)*_rvec.getMat().ptr<Vec3f>() : *_rvec.getMat().ptr<Vec3d>();
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Vec3d T = _tvec.depth() == CV_32F ? (Vec3d)*_tvec.getMat().ptr<Vec3f>() : *_tvec.getMat().ptr<Vec3d>();
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CV_Assert(_K.size() == Size(3,3) && (_K.type() == CV_32F || _K.type() == CV_64F) && _D.type() == _K.type() && _D.total() == 4);
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cv::Vec2d f, c;
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if (_K.depth() == CV_32F)
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{
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Matx33f K = _K.getMat();
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f = Vec2f(K(0, 0), K(1, 1));
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c = Vec2f(K(0, 2), K(1, 2));
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}
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else
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{
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Matx33d K = _K.getMat();
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f = Vec2d(K(0, 0), K(1, 1));
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c = Vec2d(K(0, 2), K(1, 2));
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}
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Vec4d k = _D.depth() == CV_32F ? (Vec4d)*_D.getMat().ptr<Vec4f>(): *_D.getMat().ptr<Vec4d>();
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JacobianRow *Jn = 0;
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if (jacobian.needed())
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{
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int nvars = 2 + 2 + 1 + 4 + 3 + 3; // f, c, alpha, k, om, T,
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jacobian.create(2*(int)n, nvars, CV_64F);
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Jn = jacobian.getMat().ptr<JacobianRow>(0);
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}
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Matx33d R;
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Matx<double, 3, 9> dRdom;
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Rodrigues(om, R, dRdom);
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Affine3d aff(om, T);
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const Vec3f* Xf = objectPoints.getMat().ptr<Vec3f>();
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const Vec3d* Xd = objectPoints.getMat().ptr<Vec3d>();
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Vec2f *xpf = imagePoints.getMat().ptr<Vec2f>();
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Vec2d *xpd = imagePoints.getMat().ptr<Vec2d>();
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for(size_t i = 0; i < n; ++i)
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{
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Vec3d Xi = objectPoints.depth() == CV_32F ? (Vec3d)Xf[i] : Xd[i];
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Vec3d Y = aff*Xi;
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Vec2d x(Y[0]/Y[2], Y[1]/Y[2]);
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double r2 = x.dot(x);
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double r = std::sqrt(r2);
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// Angle of the incoming ray:
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double theta = atan(r);
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double theta2 = theta*theta, theta3 = theta2*theta, theta4 = theta2*theta2, theta5 = theta4*theta,
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theta6 = theta3*theta3, theta7 = theta6*theta, theta8 = theta4*theta4, theta9 = theta8*theta;
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double theta_d = theta + k[0]*theta3 + k[1]*theta5 + k[2]*theta7 + k[3]*theta9;
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double inv_r = r > 1e-8 ? 1.0/r : 1;
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double cdist = r > 1e-8 ? theta_d * inv_r : 1;
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Vec2d xd1 = x * cdist;
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Vec2d xd3(xd1[0] + alpha*xd1[1], xd1[1]);
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Vec2d final_point(xd3[0] * f[0] + c[0], xd3[1] * f[1] + c[1]);
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if (objectPoints.depth() == CV_32F)
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xpf[i] = final_point;
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else
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xpd[i] = final_point;
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if (jacobian.needed())
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{
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//Vec3d Xi = pdepth == CV_32F ? (Vec3d)Xf[i] : Xd[i];
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//Vec3d Y = aff*Xi;
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double dYdR[] = { Xi[0], Xi[1], Xi[2], 0, 0, 0, 0, 0, 0,
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0, 0, 0, Xi[0], Xi[1], Xi[2], 0, 0, 0,
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0, 0, 0, 0, 0, 0, Xi[0], Xi[1], Xi[2] };
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Matx33d dYdom_data = Matx<double, 3, 9>(dYdR) * dRdom.t();
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const Vec3d *dYdom = (Vec3d*)dYdom_data.val;
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Matx33d dYdT_data = Matx33d::eye();
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const Vec3d *dYdT = (Vec3d*)dYdT_data.val;
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//Vec2d x(Y[0]/Y[2], Y[1]/Y[2]);
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Vec3d dxdom[2];
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dxdom[0] = (1.0/Y[2]) * dYdom[0] - x[0]/Y[2] * dYdom[2];
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dxdom[1] = (1.0/Y[2]) * dYdom[1] - x[1]/Y[2] * dYdom[2];
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Vec3d dxdT[2];
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dxdT[0] = (1.0/Y[2]) * dYdT[0] - x[0]/Y[2] * dYdT[2];
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dxdT[1] = (1.0/Y[2]) * dYdT[1] - x[1]/Y[2] * dYdT[2];
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//double r2 = x.dot(x);
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Vec3d dr2dom = 2 * x[0] * dxdom[0] + 2 * x[1] * dxdom[1];
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Vec3d dr2dT = 2 * x[0] * dxdT[0] + 2 * x[1] * dxdT[1];
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//double r = std::sqrt(r2);
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double drdr2 = r > 1e-8 ? 1.0/(2*r) : 1;
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Vec3d drdom = drdr2 * dr2dom;
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Vec3d drdT = drdr2 * dr2dT;
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// Angle of the incoming ray:
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//double theta = atan(r);
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double dthetadr = 1.0/(1+r2);
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Vec3d dthetadom = dthetadr * drdom;
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Vec3d dthetadT = dthetadr * drdT;
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//double theta_d = theta + k[0]*theta3 + k[1]*theta5 + k[2]*theta7 + k[3]*theta9;
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double dtheta_ddtheta = 1 + 3*k[0]*theta2 + 5*k[1]*theta4 + 7*k[2]*theta6 + 9*k[3]*theta8;
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Vec3d dtheta_ddom = dtheta_ddtheta * dthetadom;
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Vec3d dtheta_ddT = dtheta_ddtheta * dthetadT;
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Vec4d dtheta_ddk = Vec4d(theta3, theta5, theta7, theta9);
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//double inv_r = r > 1e-8 ? 1.0/r : 1;
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//double cdist = r > 1e-8 ? theta_d / r : 1;
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Vec3d dcdistdom = inv_r * (dtheta_ddom - cdist*drdom);
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Vec3d dcdistdT = inv_r * (dtheta_ddT - cdist*drdT);
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Vec4d dcdistdk = inv_r * dtheta_ddk;
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//Vec2d xd1 = x * cdist;
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Vec4d dxd1dk[2];
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Vec3d dxd1dom[2], dxd1dT[2];
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dxd1dom[0] = x[0] * dcdistdom + cdist * dxdom[0];
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dxd1dom[1] = x[1] * dcdistdom + cdist * dxdom[1];
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dxd1dT[0] = x[0] * dcdistdT + cdist * dxdT[0];
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dxd1dT[1] = x[1] * dcdistdT + cdist * dxdT[1];
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dxd1dk[0] = x[0] * dcdistdk;
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dxd1dk[1] = x[1] * dcdistdk;
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//Vec2d xd3(xd1[0] + alpha*xd1[1], xd1[1]);
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Vec4d dxd3dk[2];
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Vec3d dxd3dom[2], dxd3dT[2];
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dxd3dom[0] = dxd1dom[0] + alpha * dxd1dom[1];
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dxd3dom[1] = dxd1dom[1];
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dxd3dT[0] = dxd1dT[0] + alpha * dxd1dT[1];
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dxd3dT[1] = dxd1dT[1];
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dxd3dk[0] = dxd1dk[0] + alpha * dxd1dk[1];
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dxd3dk[1] = dxd1dk[1];
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Vec2d dxd3dalpha(xd1[1], 0);
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//final jacobian
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Jn[0].dom = f[0] * dxd3dom[0];
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Jn[1].dom = f[1] * dxd3dom[1];
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Jn[0].dT = f[0] * dxd3dT[0];
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Jn[1].dT = f[1] * dxd3dT[1];
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Jn[0].dk = f[0] * dxd3dk[0];
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Jn[1].dk = f[1] * dxd3dk[1];
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Jn[0].dalpha = f[0] * dxd3dalpha[0];
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Jn[1].dalpha = 0; //f[1] * dxd3dalpha[1];
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Jn[0].df = Vec2d(xd3[0], 0);
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Jn[1].df = Vec2d(0, xd3[1]);
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Jn[0].dc = Vec2d(1, 0);
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Jn[1].dc = Vec2d(0, 1);
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//step to jacobian rows for next point
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Jn += 2;
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/// cv::fisheye::distortPoints
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void cv::fisheye::distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha)
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{
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// will support only 2-channel data now for points
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CV_Assert(undistorted.type() == CV_32FC2 || undistorted.type() == CV_64FC2);
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distorted.create(undistorted.size(), undistorted.type());
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size_t n = undistorted.total();
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CV_Assert(K.size() == Size(3,3) && (K.type() == CV_32F || K.type() == CV_64F) && D.total() == 4);
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cv::Vec2d f, c;
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if (K.depth() == CV_32F)
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{
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Matx33f camMat = K.getMat();
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f = Vec2f(camMat(0, 0), camMat(1, 1));
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c = Vec2f(camMat(0, 2), camMat(1, 2));
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}
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else
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{
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Matx33d camMat = K.getMat();
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f = Vec2d(camMat(0, 0), camMat(1, 1));
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c = Vec2d(camMat(0 ,2), camMat(1, 2));
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}
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Vec4d k = D.depth() == CV_32F ? (Vec4d)*D.getMat().ptr<Vec4f>(): *D.getMat().ptr<Vec4d>();
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const Vec2f* Xf = undistorted.getMat().ptr<Vec2f>();
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const Vec2d* Xd = undistorted.getMat().ptr<Vec2d>();
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Vec2f *xpf = distorted.getMat().ptr<Vec2f>();
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Vec2d *xpd = distorted.getMat().ptr<Vec2d>();
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for(size_t i = 0; i < n; ++i)
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{
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Vec2d x = undistorted.depth() == CV_32F ? (Vec2d)Xf[i] : Xd[i];
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double r2 = x.dot(x);
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double r = std::sqrt(r2);
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// Angle of the incoming ray:
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double theta = atan(r);
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double theta2 = theta*theta, theta3 = theta2*theta, theta4 = theta2*theta2, theta5 = theta4*theta,
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theta6 = theta3*theta3, theta7 = theta6*theta, theta8 = theta4*theta4, theta9 = theta8*theta;
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double theta_d = theta + k[0]*theta3 + k[1]*theta5 + k[2]*theta7 + k[3]*theta9;
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double inv_r = r > 1e-8 ? 1.0/r : 1;
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double cdist = r > 1e-8 ? theta_d * inv_r : 1;
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Vec2d xd1 = x * cdist;
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Vec2d xd3(xd1[0] + alpha*xd1[1], xd1[1]);
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Vec2d final_point(xd3[0] * f[0] + c[0], xd3[1] * f[1] + c[1]);
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if (undistorted.depth() == CV_32F)
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xpf[i] = final_point;
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else
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xpd[i] = final_point;
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/// cv::fisheye::undistortPoints
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void cv::fisheye::undistortPoints( InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray R, InputArray P)
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{
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// will support only 2-channel data now for points
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CV_Assert(distorted.type() == CV_32FC2 || distorted.type() == CV_64FC2);
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undistorted.create(distorted.size(), distorted.type());
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CV_Assert(P.empty() || P.size() == Size(3, 3) || P.size() == Size(4, 3));
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CV_Assert(R.empty() || R.size() == Size(3, 3) || R.total() * R.channels() == 3);
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CV_Assert(D.total() == 4 && K.size() == Size(3, 3) && (K.depth() == CV_32F || K.depth() == CV_64F));
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cv::Vec2d f, c;
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if (K.depth() == CV_32F)
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{
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Matx33f camMat = K.getMat();
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f = Vec2f(camMat(0, 0), camMat(1, 1));
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c = Vec2f(camMat(0, 2), camMat(1, 2));
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}
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else
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{
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Matx33d camMat = K.getMat();
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f = Vec2d(camMat(0, 0), camMat(1, 1));
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c = Vec2d(camMat(0, 2), camMat(1, 2));
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}
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Vec4d k = D.depth() == CV_32F ? (Vec4d)*D.getMat().ptr<Vec4f>(): *D.getMat().ptr<Vec4d>();
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cv::Matx33d RR = cv::Matx33d::eye();
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if (!R.empty() && R.total() * R.channels() == 3)
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{
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cv::Vec3d rvec;
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R.getMat().convertTo(rvec, CV_64F);
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RR = cv::Affine3d(rvec).rotation();
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}
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else if (!R.empty() && R.size() == Size(3, 3))
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R.getMat().convertTo(RR, CV_64F);
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if(!P.empty())
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{
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cv::Matx33d PP;
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P.getMat().colRange(0, 3).convertTo(PP, CV_64F);
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RR = PP * RR;
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}
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// start undistorting
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const cv::Vec2f* srcf = distorted.getMat().ptr<cv::Vec2f>();
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const cv::Vec2d* srcd = distorted.getMat().ptr<cv::Vec2d>();
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cv::Vec2f* dstf = undistorted.getMat().ptr<cv::Vec2f>();
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cv::Vec2d* dstd = undistorted.getMat().ptr<cv::Vec2d>();
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size_t n = distorted.total();
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int sdepth = distorted.depth();
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for(size_t i = 0; i < n; i++ )
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{
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Vec2d pi = sdepth == CV_32F ? (Vec2d)srcf[i] : srcd[i]; // image point
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Vec2d pw((pi[0] - c[0])/f[0], (pi[1] - c[1])/f[1]); // world point
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double scale = 1.0;
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double theta_d = sqrt(pw[0]*pw[0] + pw[1]*pw[1]);
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if (theta_d > 1e-8)
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{
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// compensate distortion iteratively
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double theta = theta_d;
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for(int j = 0; j < 10; j++ )
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{
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double theta2 = theta*theta, theta4 = theta2*theta2, theta6 = theta4*theta2, theta8 = theta6*theta2;
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theta = theta_d / (1 + k[0] * theta2 + k[1] * theta4 + k[2] * theta6 + k[3] * theta8);
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}
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scale = std::tan(theta) / theta_d;
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}
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Vec2d pu = pw * scale; //undistorted point
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// reproject
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Vec3d pr = RR * Vec3d(pu[0], pu[1], 1.0); // rotated point optionally multiplied by new camera matrix
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Vec2d fi(pr[0]/pr[2], pr[1]/pr[2]); // final
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if( sdepth == CV_32F )
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dstf[i] = fi;
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else
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dstd[i] = fi;
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/// cv::fisheye::undistortPoints
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|
|
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void cv::fisheye::initUndistortRectifyMap( InputArray K, InputArray D, InputArray R, InputArray P,
|
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const cv::Size& size, int m1type, OutputArray map1, OutputArray map2 )
|
|
{
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CV_Assert( m1type == CV_16SC2 || m1type == CV_32F || m1type <=0 );
|
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map1.create( size, m1type <= 0 ? CV_16SC2 : m1type );
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map2.create( size, map1.type() == CV_16SC2 ? CV_16UC1 : CV_32F );
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CV_Assert((K.depth() == CV_32F || K.depth() == CV_64F) && (D.depth() == CV_32F || D.depth() == CV_64F));
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CV_Assert((P.empty() || P.depth() == CV_32F || P.depth() == CV_64F) && (R.empty() || R.depth() == CV_32F || R.depth() == CV_64F));
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CV_Assert(K.size() == Size(3, 3) && (D.empty() || D.total() == 4));
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CV_Assert(R.empty() || R.size() == Size(3, 3) || R.total() * R.channels() == 3);
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CV_Assert(P.empty() || P.size() == Size(3, 3) || P.size() == Size(4, 3));
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cv::Vec2d f, c;
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if (K.depth() == CV_32F)
|
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{
|
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Matx33f camMat = K.getMat();
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f = Vec2f(camMat(0, 0), camMat(1, 1));
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c = Vec2f(camMat(0, 2), camMat(1, 2));
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}
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else
|
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{
|
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Matx33d camMat = K.getMat();
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f = Vec2d(camMat(0, 0), camMat(1, 1));
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c = Vec2d(camMat(0, 2), camMat(1, 2));
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}
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Vec4d k = Vec4d::all(0);
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if (!D.empty())
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k = D.depth() == CV_32F ? (Vec4d)*D.getMat().ptr<Vec4f>(): *D.getMat().ptr<Vec4d>();
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cv::Matx33d RR = cv::Matx33d::eye();
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if (!R.empty() && R.total() * R.channels() == 3)
|
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{
|
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cv::Vec3d rvec;
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R.getMat().convertTo(rvec, CV_64F);
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RR = Affine3d(rvec).rotation();
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}
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else if (!R.empty() && R.size() == Size(3, 3))
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R.getMat().convertTo(RR, CV_64F);
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cv::Matx33d PP = cv::Matx33d::eye();
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if (!P.empty())
|
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P.getMat().colRange(0, 3).convertTo(PP, CV_64F);
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cv::Matx33d iR = (PP * RR).inv(cv::DECOMP_SVD);
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for( int i = 0; i < size.height; ++i)
|
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{
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float* m1f = map1.getMat().ptr<float>(i);
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float* m2f = map2.getMat().ptr<float>(i);
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short* m1 = (short*)m1f;
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ushort* m2 = (ushort*)m2f;
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double _x = i*iR(0, 1) + iR(0, 2),
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_y = i*iR(1, 1) + iR(1, 2),
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_w = i*iR(2, 1) + iR(2, 2);
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for( int j = 0; j < size.width; ++j)
|
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{
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double x = _x/_w, y = _y/_w;
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double r = sqrt(x*x + y*y);
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double theta = atan(r);
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double theta2 = theta*theta, theta4 = theta2*theta2, theta6 = theta4*theta2, theta8 = theta4*theta4;
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double theta_d = theta * (1 + k[0]*theta2 + k[1]*theta4 + k[2]*theta6 + k[3]*theta8);
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|
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double scale = (r == 0) ? 1.0 : theta_d / r;
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double u = f[0]*x*scale + c[0];
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double v = f[1]*y*scale + c[1];
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if( m1type == CV_16SC2 )
|
|
{
|
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int iu = cv::saturate_cast<int>(u*cv::INTER_TAB_SIZE);
|
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int iv = cv::saturate_cast<int>(v*cv::INTER_TAB_SIZE);
|
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m1[j*2+0] = (short)(iu >> cv::INTER_BITS);
|
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m1[j*2+1] = (short)(iv >> cv::INTER_BITS);
|
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m2[j] = (ushort)((iv & (cv::INTER_TAB_SIZE-1))*cv::INTER_TAB_SIZE + (iu & (cv::INTER_TAB_SIZE-1)));
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}
|
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else if( m1type == CV_32FC1 )
|
|
{
|
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m1f[j] = (float)u;
|
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m2f[j] = (float)v;
|
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}
|
|
|
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_x += iR(0, 0);
|
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_y += iR(1, 0);
|
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_w += iR(2, 0);
|
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}
|
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}
|
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}
|
|
|
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/// cv::fisheye::undistortImage
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|
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void cv::fisheye::undistortImage(InputArray distorted, OutputArray undistorted,
|
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InputArray K, InputArray D, InputArray Knew, const Size& new_size)
|
|
{
|
|
Size size = new_size.area() != 0 ? new_size : distorted.size();
|
|
|
|
cv::Mat map1, map2;
|
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fisheye::initUndistortRectifyMap(K, D, cv::Matx33d::eye(), Knew, size, CV_16SC2, map1, map2 );
|
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cv::remap(distorted, undistorted, map1, map2, INTER_LINEAR, BORDER_CONSTANT);
|
|
}
|
|
|
|
|
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
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/// cv::fisheye::estimateNewCameraMatrixForUndistortRectify
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|
|
|
void cv::fisheye::estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R,
|
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OutputArray P, double balance, const Size& new_size, double fov_scale)
|
|
{
|
|
CV_Assert( K.size() == Size(3, 3) && (K.depth() == CV_32F || K.depth() == CV_64F));
|
|
CV_Assert(D.empty() || ((D.total() == 4) && (D.depth() == CV_32F || D.depth() == CV_64F)));
|
|
|
|
int w = image_size.width, h = image_size.height;
|
|
balance = std::min(std::max(balance, 0.0), 1.0);
|
|
|
|
cv::Mat points(1, 4, CV_64FC2);
|
|
Vec2d* pptr = points.ptr<Vec2d>();
|
|
pptr[0] = Vec2d(w/2, 0);
|
|
pptr[1] = Vec2d(w, h/2);
|
|
pptr[2] = Vec2d(w/2, h);
|
|
pptr[3] = Vec2d(0, h/2);
|
|
|
|
#if 0
|
|
const int N = 10;
|
|
cv::Mat points(1, N * 4, CV_64FC2);
|
|
Vec2d* pptr = points.ptr<Vec2d>();
|
|
for(int i = 0, k = 0; i < 10; ++i)
|
|
{
|
|
pptr[k++] = Vec2d(w/2, 0) - Vec2d(w/8, 0) + Vec2d(w/4/N*i, 0);
|
|
pptr[k++] = Vec2d(w/2, h-1) - Vec2d(w/8, h-1) + Vec2d(w/4/N*i, h-1);
|
|
|
|
pptr[k++] = Vec2d(0, h/2) - Vec2d(0, h/8) + Vec2d(0, h/4/N*i);
|
|
pptr[k++] = Vec2d(w-1, h/2) - Vec2d(w-1, h/8) + Vec2d(w-1, h/4/N*i);
|
|
}
|
|
#endif
|
|
|
|
fisheye::undistortPoints(points, points, K, D, R);
|
|
cv::Scalar center_mass = mean(points);
|
|
cv::Vec2d cn(center_mass.val);
|
|
|
|
double aspect_ratio = (K.depth() == CV_32F) ? K.getMat().at<float >(0,0)/K.getMat().at<float> (1,1)
|
|
: K.getMat().at<double>(0,0)/K.getMat().at<double>(1,1);
|
|
|
|
// convert to identity ratio
|
|
cn[0] *= aspect_ratio;
|
|
for(size_t i = 0; i < points.total(); ++i)
|
|
pptr[i][1] *= aspect_ratio;
|
|
|
|
double minx = DBL_MAX, miny = DBL_MAX, maxx = -DBL_MAX, maxy = -DBL_MAX;
|
|
for(size_t i = 0; i < points.total(); ++i)
|
|
{
|
|
miny = std::min(miny, pptr[i][1]);
|
|
maxy = std::max(maxy, pptr[i][1]);
|
|
minx = std::min(minx, pptr[i][0]);
|
|
maxx = std::max(maxx, pptr[i][0]);
|
|
}
|
|
|
|
#if 0
|
|
double minx = -DBL_MAX, miny = -DBL_MAX, maxx = DBL_MAX, maxy = DBL_MAX;
|
|
for(size_t i = 0; i < points.total(); ++i)
|
|
{
|
|
if (i % 4 == 0) miny = std::max(miny, pptr[i][1]);
|
|
if (i % 4 == 1) maxy = std::min(maxy, pptr[i][1]);
|
|
if (i % 4 == 2) minx = std::max(minx, pptr[i][0]);
|
|
if (i % 4 == 3) maxx = std::min(maxx, pptr[i][0]);
|
|
}
|
|
#endif
|
|
|
|
double f1 = w * 0.5/(cn[0] - minx);
|
|
double f2 = w * 0.5/(maxx - cn[0]);
|
|
double f3 = h * 0.5 * aspect_ratio/(cn[1] - miny);
|
|
double f4 = h * 0.5 * aspect_ratio/(maxy - cn[1]);
|
|
|
|
double fmin = std::min(f1, std::min(f2, std::min(f3, f4)));
|
|
double fmax = std::max(f1, std::max(f2, std::max(f3, f4)));
|
|
|
|
double f = balance * fmin + (1.0 - balance) * fmax;
|
|
f *= fov_scale > 0 ? 1.0/fov_scale : 1.0;
|
|
|
|
cv::Vec2d new_f(f, f), new_c = -cn * f + Vec2d(w, h * aspect_ratio) * 0.5;
|
|
|
|
// restore aspect ratio
|
|
new_f[1] /= aspect_ratio;
|
|
new_c[1] /= aspect_ratio;
|
|
|
|
if (new_size.area() > 0)
|
|
{
|
|
double rx = new_size.width /(double)image_size.width;
|
|
double ry = new_size.height/(double)image_size.height;
|
|
|
|
new_f[0] *= rx; new_f[1] *= ry;
|
|
new_c[0] *= rx; new_c[1] *= ry;
|
|
}
|
|
|
|
Mat(Matx33d(new_f[0], 0, new_c[0],
|
|
0, new_f[1], new_c[1],
|
|
0, 0, 1)).convertTo(P, P.empty() ? K.type() : P.type());
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
/// cv::fisheye::stereoRectify
|
|
|
|
void cv::fisheye::stereoRectify( InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size& imageSize,
|
|
InputArray _R, InputArray _tvec, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2,
|
|
OutputArray Q, int flags, const Size& newImageSize, double balance, double fov_scale)
|
|
{
|
|
CV_Assert((_R.size() == Size(3, 3) || _R.total() * _R.channels() == 3) && (_R.depth() == CV_32F || _R.depth() == CV_64F));
|
|
CV_Assert(_tvec.total() * _tvec.channels() == 3 && (_tvec.depth() == CV_32F || _tvec.depth() == CV_64F));
|
|
|
|
|
|
cv::Mat aaa = _tvec.getMat().reshape(3, 1);
|
|
|
|
Vec3d rvec; // Rodrigues vector
|
|
if (_R.size() == Size(3, 3))
|
|
{
|
|
cv::Matx33d rmat;
|
|
_R.getMat().convertTo(rmat, CV_64F);
|
|
rvec = Affine3d(rmat).rvec();
|
|
}
|
|
else if (_R.total() * _R.channels() == 3)
|
|
_R.getMat().convertTo(rvec, CV_64F);
|
|
|
|
Vec3d tvec;
|
|
_tvec.getMat().convertTo(tvec, CV_64F);
|
|
|
|
// rectification algorithm
|
|
rvec *= -0.5; // get average rotation
|
|
|
|
Matx33d r_r;
|
|
Rodrigues(rvec, r_r); // rotate cameras to same orientation by averaging
|
|
|
|
Vec3d t = r_r * tvec;
|
|
Vec3d uu(t[0] > 0 ? 1 : -1, 0, 0);
|
|
|
|
// calculate global Z rotation
|
|
Vec3d ww = t.cross(uu);
|
|
double nw = norm(ww);
|
|
if (nw > 0.0)
|
|
ww *= acos(fabs(t[0])/cv::norm(t))/nw;
|
|
|
|
Matx33d wr;
|
|
Rodrigues(ww, wr);
|
|
|
|
// apply to both views
|
|
Matx33d ri1 = wr * r_r.t();
|
|
Mat(ri1, false).convertTo(R1, R1.empty() ? CV_64F : R1.type());
|
|
Matx33d ri2 = wr * r_r;
|
|
Mat(ri2, false).convertTo(R2, R2.empty() ? CV_64F : R2.type());
|
|
Vec3d tnew = ri2 * tvec;
|
|
|
|
// calculate projection/camera matrices. these contain the relevant rectified image internal params (fx, fy=fx, cx, cy)
|
|
Matx33d newK1, newK2;
|
|
estimateNewCameraMatrixForUndistortRectify(K1, D1, imageSize, R1, newK1, balance, newImageSize, fov_scale);
|
|
estimateNewCameraMatrixForUndistortRectify(K2, D2, imageSize, R2, newK2, balance, newImageSize, fov_scale);
|
|
|
|
double fc_new = std::min(newK1(1,1), newK2(1,1));
|
|
Point2d cc_new[2] = { Vec2d(newK1(0, 2), newK1(1, 2)), Vec2d(newK2(0, 2), newK2(1, 2)) };
|
|
|
|
// Vertical focal length must be the same for both images to keep the epipolar constraint use fy for fx also.
|
|
// For simplicity, set the principal points for both cameras to be the average
|
|
// of the two principal points (either one of or both x- and y- coordinates)
|
|
if( flags & cv::CALIB_ZERO_DISPARITY )
|
|
cc_new[0] = cc_new[1] = (cc_new[0] + cc_new[1]) * 0.5;
|
|
else
|
|
cc_new[0].y = cc_new[1].y = (cc_new[0].y + cc_new[1].y)*0.5;
|
|
|
|
Mat(Matx34d(fc_new, 0, cc_new[0].x, 0,
|
|
0, fc_new, cc_new[0].y, 0,
|
|
0, 0, 1, 0), false).convertTo(P1, P1.empty() ? CV_64F : P1.type());
|
|
|
|
Mat(Matx34d(fc_new, 0, cc_new[1].x, tnew[0]*fc_new, // baseline * focal length;,
|
|
0, fc_new, cc_new[1].y, 0,
|
|
0, 0, 1, 0), false).convertTo(P2, P2.empty() ? CV_64F : P2.type());
|
|
|
|
if (Q.needed())
|
|
Mat(Matx44d(1, 0, 0, -cc_new[0].x,
|
|
0, 1, 0, -cc_new[0].y,
|
|
0, 0, 0, fc_new,
|
|
0, 0, -1./tnew[0], (cc_new[0].x - cc_new[1].x)/tnew[0]), false).convertTo(Q, Q.empty() ? CV_64F : Q.depth());
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
/// cv::fisheye::calibrate
|
|
|
|
double cv::fisheye::calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size,
|
|
InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
|
|
int flags , cv::TermCriteria criteria)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && !imagePoints.empty() && objectPoints.total() == imagePoints.total());
|
|
CV_Assert(objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3);
|
|
CV_Assert(imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2);
|
|
CV_Assert(K.empty() || (K.size() == Size(3,3)));
|
|
CV_Assert(D.empty() || (D.total() == 4));
|
|
CV_Assert(rvecs.empty() || (rvecs.channels() == 3));
|
|
CV_Assert(tvecs.empty() || (tvecs.channels() == 3));
|
|
|
|
CV_Assert((!K.empty() && !D.empty()) || !(flags & CALIB_USE_INTRINSIC_GUESS));
|
|
|
|
using namespace cv::internal;
|
|
//-------------------------------Initialization
|
|
IntrinsicParams finalParam;
|
|
IntrinsicParams currentParam;
|
|
IntrinsicParams errors;
|
|
|
|
finalParam.isEstimate[0] = 1;
|
|
finalParam.isEstimate[1] = 1;
|
|
finalParam.isEstimate[2] = flags & CALIB_FIX_PRINCIPAL_POINT ? 0 : 1;
|
|
finalParam.isEstimate[3] = flags & CALIB_FIX_PRINCIPAL_POINT ? 0 : 1;
|
|
finalParam.isEstimate[4] = flags & CALIB_FIX_SKEW ? 0 : 1;
|
|
finalParam.isEstimate[5] = flags & CALIB_FIX_K1 ? 0 : 1;
|
|
finalParam.isEstimate[6] = flags & CALIB_FIX_K2 ? 0 : 1;
|
|
finalParam.isEstimate[7] = flags & CALIB_FIX_K3 ? 0 : 1;
|
|
finalParam.isEstimate[8] = flags & CALIB_FIX_K4 ? 0 : 1;
|
|
|
|
const int recompute_extrinsic = flags & CALIB_RECOMPUTE_EXTRINSIC ? 1: 0;
|
|
const int check_cond = flags & CALIB_CHECK_COND ? 1 : 0;
|
|
|
|
const double alpha_smooth = 0.4;
|
|
const double thresh_cond = 1e6;
|
|
double change = 1;
|
|
Vec2d err_std;
|
|
|
|
Matx33d _K;
|
|
Vec4d _D;
|
|
if (flags & CALIB_USE_INTRINSIC_GUESS)
|
|
{
|
|
K.getMat().convertTo(_K, CV_64FC1);
|
|
D.getMat().convertTo(_D, CV_64FC1);
|
|
finalParam.Init(Vec2d(_K(0,0), _K(1, 1)),
|
|
Vec2d(_K(0,2), _K(1, 2)),
|
|
Vec4d(flags & CALIB_FIX_K1 ? 0 : _D[0],
|
|
flags & CALIB_FIX_K2 ? 0 : _D[1],
|
|
flags & CALIB_FIX_K3 ? 0 : _D[2],
|
|
flags & CALIB_FIX_K4 ? 0 : _D[3]),
|
|
_K(0, 1) / _K(0, 0));
|
|
}
|
|
else
|
|
{
|
|
finalParam.Init(Vec2d(max(image_size.width, image_size.height) / CV_PI, max(image_size.width, image_size.height) / CV_PI),
|
|
Vec2d(image_size.width / 2.0 - 0.5, image_size.height / 2.0 - 0.5));
|
|
}
|
|
|
|
errors.isEstimate = finalParam.isEstimate;
|
|
|
|
std::vector<Vec3d> omc(objectPoints.total()), Tc(objectPoints.total());
|
|
|
|
CalibrateExtrinsics(objectPoints, imagePoints, finalParam, check_cond, thresh_cond, omc, Tc);
|
|
|
|
|
|
//-------------------------------Optimization
|
|
for(int iter = 0; ; ++iter)
|
|
{
|
|
if ((criteria.type == 1 && iter >= criteria.maxCount) ||
|
|
(criteria.type == 2 && change <= criteria.epsilon) ||
|
|
(criteria.type == 3 && (change <= criteria.epsilon || iter >= criteria.maxCount)))
|
|
break;
|
|
|
|
double alpha_smooth2 = 1 - std::pow(1 - alpha_smooth, iter + 1.0);
|
|
|
|
Mat JJ2, ex3;
|
|
ComputeJacobians(objectPoints, imagePoints, finalParam, omc, Tc, check_cond,thresh_cond, JJ2, ex3);
|
|
|
|
Mat G;
|
|
solve(JJ2, ex3, G);
|
|
currentParam = finalParam + alpha_smooth2*G;
|
|
|
|
change = norm(Vec4d(currentParam.f[0], currentParam.f[1], currentParam.c[0], currentParam.c[1]) -
|
|
Vec4d(finalParam.f[0], finalParam.f[1], finalParam.c[0], finalParam.c[1]))
|
|
/ norm(Vec4d(currentParam.f[0], currentParam.f[1], currentParam.c[0], currentParam.c[1]));
|
|
|
|
finalParam = currentParam;
|
|
|
|
if (recompute_extrinsic)
|
|
{
|
|
CalibrateExtrinsics(objectPoints, imagePoints, finalParam, check_cond,
|
|
thresh_cond, omc, Tc);
|
|
}
|
|
}
|
|
|
|
//-------------------------------Validation
|
|
double rms;
|
|
EstimateUncertainties(objectPoints, imagePoints, finalParam, omc, Tc, errors, err_std, thresh_cond,
|
|
check_cond, rms);
|
|
|
|
//-------------------------------
|
|
_K = Matx33d(finalParam.f[0], finalParam.f[0] * finalParam.alpha, finalParam.c[0],
|
|
0, finalParam.f[1], finalParam.c[1],
|
|
0, 0, 1);
|
|
|
|
if (K.needed()) cv::Mat(_K).convertTo(K, K.empty() ? CV_64FC1 : K.type());
|
|
if (D.needed()) cv::Mat(finalParam.k).convertTo(D, D.empty() ? CV_64FC1 : D.type());
|
|
if (rvecs.isMatVector())
|
|
{
|
|
int N = (int)objectPoints.total();
|
|
|
|
if(rvecs.empty())
|
|
rvecs.create(N, 1, CV_64FC3);
|
|
|
|
if(tvecs.empty())
|
|
tvecs.create(N, 1, CV_64FC3);
|
|
|
|
for(int i = 0; i < N; i++ )
|
|
{
|
|
rvecs.create(3, 1, CV_64F, i, true);
|
|
tvecs.create(3, 1, CV_64F, i, true);
|
|
memcpy(rvecs.getMat(i).ptr(), omc[i].val, sizeof(Vec3d));
|
|
memcpy(tvecs.getMat(i).ptr(), Tc[i].val, sizeof(Vec3d));
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if (rvecs.needed()) cv::Mat(omc).convertTo(rvecs, rvecs.empty() ? CV_64FC3 : rvecs.type());
|
|
if (tvecs.needed()) cv::Mat(Tc).convertTo(tvecs, tvecs.empty() ? CV_64FC3 : tvecs.type());
|
|
}
|
|
|
|
return rms;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
/// cv::fisheye::stereoCalibrate
|
|
|
|
double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
|
|
InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize,
|
|
OutputArray R, OutputArray T, int flags, TermCriteria criteria)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && !imagePoints1.empty() && !imagePoints2.empty());
|
|
CV_Assert(objectPoints.total() == imagePoints1.total() || imagePoints1.total() == imagePoints2.total());
|
|
CV_Assert(objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3);
|
|
CV_Assert(imagePoints1.type() == CV_32FC2 || imagePoints1.type() == CV_64FC2);
|
|
CV_Assert(imagePoints2.type() == CV_32FC2 || imagePoints2.type() == CV_64FC2);
|
|
|
|
CV_Assert(K1.empty() || (K1.size() == Size(3,3)));
|
|
CV_Assert(D1.empty() || (D1.total() == 4));
|
|
CV_Assert(K2.empty() || (K1.size() == Size(3,3)));
|
|
CV_Assert(D2.empty() || (D1.total() == 4));
|
|
|
|
CV_Assert((!K1.empty() && !K2.empty() && !D1.empty() && !D2.empty()) || !(flags & CALIB_FIX_INTRINSIC));
|
|
|
|
//-------------------------------Initialization
|
|
|
|
const int threshold = 50;
|
|
const double thresh_cond = 1e6;
|
|
const int check_cond = 1;
|
|
|
|
int n_points = (int)objectPoints.getMat(0).total();
|
|
int n_images = (int)objectPoints.total();
|
|
|
|
double change = 1;
|
|
|
|
cv::internal::IntrinsicParams intrinsicLeft;
|
|
cv::internal::IntrinsicParams intrinsicRight;
|
|
|
|
cv::internal::IntrinsicParams intrinsicLeft_errors;
|
|
cv::internal::IntrinsicParams intrinsicRight_errors;
|
|
|
|
Matx33d _K1, _K2;
|
|
Vec4d _D1, _D2;
|
|
if (!K1.empty()) K1.getMat().convertTo(_K1, CV_64FC1);
|
|
if (!D1.empty()) D1.getMat().convertTo(_D1, CV_64FC1);
|
|
if (!K2.empty()) K2.getMat().convertTo(_K2, CV_64FC1);
|
|
if (!D2.empty()) D2.getMat().convertTo(_D2, CV_64FC1);
|
|
|
|
std::vector<Vec3d> rvecs1(n_images), tvecs1(n_images), rvecs2(n_images), tvecs2(n_images);
|
|
|
|
if (!(flags & CALIB_FIX_INTRINSIC))
|
|
{
|
|
calibrate(objectPoints, imagePoints1, imageSize, _K1, _D1, rvecs1, tvecs1, flags, TermCriteria(3, 20, 1e-6));
|
|
calibrate(objectPoints, imagePoints2, imageSize, _K2, _D2, rvecs2, tvecs2, flags, TermCriteria(3, 20, 1e-6));
|
|
}
|
|
|
|
intrinsicLeft.Init(Vec2d(_K1(0,0), _K1(1, 1)), Vec2d(_K1(0,2), _K1(1, 2)),
|
|
Vec4d(_D1[0], _D1[1], _D1[2], _D1[3]), _K1(0, 1) / _K1(0, 0));
|
|
|
|
intrinsicRight.Init(Vec2d(_K2(0,0), _K2(1, 1)), Vec2d(_K2(0,2), _K2(1, 2)),
|
|
Vec4d(_D2[0], _D2[1], _D2[2], _D2[3]), _K2(0, 1) / _K2(0, 0));
|
|
|
|
if ((flags & CALIB_FIX_INTRINSIC))
|
|
{
|
|
cv::internal::CalibrateExtrinsics(objectPoints, imagePoints1, intrinsicLeft, check_cond, thresh_cond, rvecs1, tvecs1);
|
|
cv::internal::CalibrateExtrinsics(objectPoints, imagePoints2, intrinsicRight, check_cond, thresh_cond, rvecs2, tvecs2);
|
|
}
|
|
|
|
intrinsicLeft.isEstimate[0] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicLeft.isEstimate[1] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicLeft.isEstimate[2] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicLeft.isEstimate[3] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicLeft.isEstimate[4] = flags & (CALIB_FIX_SKEW | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicLeft.isEstimate[5] = flags & (CALIB_FIX_K1 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicLeft.isEstimate[6] = flags & (CALIB_FIX_K2 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicLeft.isEstimate[7] = flags & (CALIB_FIX_K3 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicLeft.isEstimate[8] = flags & (CALIB_FIX_K4 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
|
|
intrinsicRight.isEstimate[0] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicRight.isEstimate[1] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicRight.isEstimate[2] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicRight.isEstimate[3] = flags & CALIB_FIX_INTRINSIC ? 0 : 1;
|
|
intrinsicRight.isEstimate[4] = flags & (CALIB_FIX_SKEW | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicRight.isEstimate[5] = flags & (CALIB_FIX_K1 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicRight.isEstimate[6] = flags & (CALIB_FIX_K2 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicRight.isEstimate[7] = flags & (CALIB_FIX_K3 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
intrinsicRight.isEstimate[8] = flags & (CALIB_FIX_K4 | CALIB_FIX_INTRINSIC) ? 0 : 1;
|
|
|
|
intrinsicLeft_errors.isEstimate = intrinsicLeft.isEstimate;
|
|
intrinsicRight_errors.isEstimate = intrinsicRight.isEstimate;
|
|
|
|
std::vector<uchar> selectedParams;
|
|
std::vector<int> tmp(6 * (n_images + 1), 1);
|
|
selectedParams.insert(selectedParams.end(), intrinsicLeft.isEstimate.begin(), intrinsicLeft.isEstimate.end());
|
|
selectedParams.insert(selectedParams.end(), intrinsicRight.isEstimate.begin(), intrinsicRight.isEstimate.end());
|
|
selectedParams.insert(selectedParams.end(), tmp.begin(), tmp.end());
|
|
|
|
//Init values for rotation and translation between two views
|
|
cv::Mat om_list(1, n_images, CV_64FC3), T_list(1, n_images, CV_64FC3);
|
|
cv::Mat om_ref, R_ref, T_ref, R1, R2;
|
|
for (int image_idx = 0; image_idx < n_images; ++image_idx)
|
|
{
|
|
cv::Rodrigues(rvecs1[image_idx], R1);
|
|
cv::Rodrigues(rvecs2[image_idx], R2);
|
|
R_ref = R2 * R1.t();
|
|
T_ref = cv::Mat(tvecs2[image_idx]) - R_ref * cv::Mat(tvecs1[image_idx]);
|
|
cv::Rodrigues(R_ref, om_ref);
|
|
om_ref.reshape(3, 1).copyTo(om_list.col(image_idx));
|
|
T_ref.reshape(3, 1).copyTo(T_list.col(image_idx));
|
|
}
|
|
cv::Vec3d omcur = cv::internal::median3d(om_list);
|
|
cv::Vec3d Tcur = cv::internal::median3d(T_list);
|
|
|
|
cv::Mat J = cv::Mat::zeros(4 * n_points * n_images, 18 + 6 * (n_images + 1), CV_64FC1),
|
|
e = cv::Mat::zeros(4 * n_points * n_images, 1, CV_64FC1), Jkk, ekk;
|
|
|
|
for(int iter = 0; ; ++iter)
|
|
{
|
|
if ((criteria.type == 1 && iter >= criteria.maxCount) ||
|
|
(criteria.type == 2 && change <= criteria.epsilon) ||
|
|
(criteria.type == 3 && (change <= criteria.epsilon || iter >= criteria.maxCount)))
|
|
break;
|
|
|
|
J.create(4 * n_points * n_images, 18 + 6 * (n_images + 1), CV_64FC1);
|
|
e.create(4 * n_points * n_images, 1, CV_64FC1);
|
|
Jkk.create(4 * n_points, 18 + 6 * (n_images + 1), CV_64FC1);
|
|
ekk.create(4 * n_points, 1, CV_64FC1);
|
|
|
|
cv::Mat omr, Tr, domrdomckk, domrdTckk, domrdom, domrdT, dTrdomckk, dTrdTckk, dTrdom, dTrdT;
|
|
|
|
for (int image_idx = 0; image_idx < n_images; ++image_idx)
|
|
{
|
|
Jkk = cv::Mat::zeros(4 * n_points, 18 + 6 * (n_images + 1), CV_64FC1);
|
|
|
|
cv::Mat object = objectPoints.getMat(image_idx).clone();
|
|
cv::Mat imageLeft = imagePoints1.getMat(image_idx).clone();
|
|
cv::Mat imageRight = imagePoints2.getMat(image_idx).clone();
|
|
cv::Mat jacobians, projected;
|
|
|
|
//left camera jacobian
|
|
cv::Mat rvec = cv::Mat(rvecs1[image_idx]);
|
|
cv::Mat tvec = cv::Mat(tvecs1[image_idx]);
|
|
cv::internal::projectPoints(object, projected, rvec, tvec, intrinsicLeft, jacobians);
|
|
cv::Mat(cv::Mat((imageLeft - projected).t()).reshape(1, 1).t()).copyTo(ekk.rowRange(0, 2 * n_points));
|
|
jacobians.colRange(8, 11).copyTo(Jkk.colRange(24 + image_idx * 6, 27 + image_idx * 6).rowRange(0, 2 * n_points));
|
|
jacobians.colRange(11, 14).copyTo(Jkk.colRange(27 + image_idx * 6, 30 + image_idx * 6).rowRange(0, 2 * n_points));
|
|
jacobians.colRange(0, 2).copyTo(Jkk.colRange(0, 2).rowRange(0, 2 * n_points));
|
|
jacobians.colRange(2, 4).copyTo(Jkk.colRange(2, 4).rowRange(0, 2 * n_points));
|
|
jacobians.colRange(4, 8).copyTo(Jkk.colRange(5, 9).rowRange(0, 2 * n_points));
|
|
jacobians.col(14).copyTo(Jkk.col(4).rowRange(0, 2 * n_points));
|
|
|
|
//right camera jacobian
|
|
cv::internal::compose_motion(rvec, tvec, omcur, Tcur, omr, Tr, domrdomckk, domrdTckk, domrdom, domrdT, dTrdomckk, dTrdTckk, dTrdom, dTrdT);
|
|
rvec = cv::Mat(rvecs2[image_idx]);
|
|
tvec = cv::Mat(tvecs2[image_idx]);
|
|
|
|
cv::internal::projectPoints(object, projected, omr, Tr, intrinsicRight, jacobians);
|
|
cv::Mat(cv::Mat((imageRight - projected).t()).reshape(1, 1).t()).copyTo(ekk.rowRange(2 * n_points, 4 * n_points));
|
|
cv::Mat dxrdom = jacobians.colRange(8, 11) * domrdom + jacobians.colRange(11, 14) * dTrdom;
|
|
cv::Mat dxrdT = jacobians.colRange(8, 11) * domrdT + jacobians.colRange(11, 14)* dTrdT;
|
|
cv::Mat dxrdomckk = jacobians.colRange(8, 11) * domrdomckk + jacobians.colRange(11, 14) * dTrdomckk;
|
|
cv::Mat dxrdTckk = jacobians.colRange(8, 11) * domrdTckk + jacobians.colRange(11, 14) * dTrdTckk;
|
|
|
|
dxrdom.copyTo(Jkk.colRange(18, 21).rowRange(2 * n_points, 4 * n_points));
|
|
dxrdT.copyTo(Jkk.colRange(21, 24).rowRange(2 * n_points, 4 * n_points));
|
|
dxrdomckk.copyTo(Jkk.colRange(24 + image_idx * 6, 27 + image_idx * 6).rowRange(2 * n_points, 4 * n_points));
|
|
dxrdTckk.copyTo(Jkk.colRange(27 + image_idx * 6, 30 + image_idx * 6).rowRange(2 * n_points, 4 * n_points));
|
|
jacobians.colRange(0, 2).copyTo(Jkk.colRange(9 + 0, 9 + 2).rowRange(2 * n_points, 4 * n_points));
|
|
jacobians.colRange(2, 4).copyTo(Jkk.colRange(9 + 2, 9 + 4).rowRange(2 * n_points, 4 * n_points));
|
|
jacobians.colRange(4, 8).copyTo(Jkk.colRange(9 + 5, 9 + 9).rowRange(2 * n_points, 4 * n_points));
|
|
jacobians.col(14).copyTo(Jkk.col(9 + 4).rowRange(2 * n_points, 4 * n_points));
|
|
|
|
//check goodness of sterepair
|
|
double abs_max = 0;
|
|
for (int i = 0; i < 4 * n_points; i++)
|
|
{
|
|
if (fabs(ekk.at<double>(i)) > abs_max)
|
|
{
|
|
abs_max = fabs(ekk.at<double>(i));
|
|
}
|
|
}
|
|
|
|
CV_Assert(abs_max < threshold); // bad stereo pair
|
|
|
|
Jkk.copyTo(J.rowRange(image_idx * 4 * n_points, (image_idx + 1) * 4 * n_points));
|
|
ekk.copyTo(e.rowRange(image_idx * 4 * n_points, (image_idx + 1) * 4 * n_points));
|
|
}
|
|
|
|
cv::Vec6d oldTom(Tcur[0], Tcur[1], Tcur[2], omcur[0], omcur[1], omcur[2]);
|
|
|
|
//update all parameters
|
|
cv::subMatrix(J, J, selectedParams, std::vector<uchar>(J.rows, 1));
|
|
int a = cv::countNonZero(intrinsicLeft.isEstimate);
|
|
int b = cv::countNonZero(intrinsicRight.isEstimate);
|
|
cv::Mat deltas;
|
|
solve(J.t() * J, J.t()*e, deltas);
|
|
intrinsicLeft = intrinsicLeft + deltas.rowRange(0, a);
|
|
intrinsicRight = intrinsicRight + deltas.rowRange(a, a + b);
|
|
omcur = omcur + cv::Vec3d(deltas.rowRange(a + b, a + b + 3));
|
|
Tcur = Tcur + cv::Vec3d(deltas.rowRange(a + b + 3, a + b + 6));
|
|
for (int image_idx = 0; image_idx < n_images; ++image_idx)
|
|
{
|
|
rvecs1[image_idx] = cv::Mat(cv::Mat(rvecs1[image_idx]) + deltas.rowRange(a + b + 6 + image_idx * 6, a + b + 9 + image_idx * 6));
|
|
tvecs1[image_idx] = cv::Mat(cv::Mat(tvecs1[image_idx]) + deltas.rowRange(a + b + 9 + image_idx * 6, a + b + 12 + image_idx * 6));
|
|
}
|
|
|
|
cv::Vec6d newTom(Tcur[0], Tcur[1], Tcur[2], omcur[0], omcur[1], omcur[2]);
|
|
change = cv::norm(newTom - oldTom) / cv::norm(newTom);
|
|
}
|
|
|
|
double rms = 0;
|
|
const Vec2d* ptr_e = e.ptr<Vec2d>();
|
|
for (size_t i = 0; i < e.total() / 2; i++)
|
|
{
|
|
rms += ptr_e[i][0] * ptr_e[i][0] + ptr_e[i][1] * ptr_e[i][1];
|
|
}
|
|
|
|
rms /= ((double)e.total() / 2.0);
|
|
rms = sqrt(rms);
|
|
|
|
_K1 = Matx33d(intrinsicLeft.f[0], intrinsicLeft.f[0] * intrinsicLeft.alpha, intrinsicLeft.c[0],
|
|
0, intrinsicLeft.f[1], intrinsicLeft.c[1],
|
|
0, 0, 1);
|
|
|
|
_K2 = Matx33d(intrinsicRight.f[0], intrinsicRight.f[0] * intrinsicRight.alpha, intrinsicRight.c[0],
|
|
0, intrinsicRight.f[1], intrinsicRight.c[1],
|
|
0, 0, 1);
|
|
|
|
Mat _R;
|
|
Rodrigues(omcur, _R);
|
|
|
|
if (K1.needed()) cv::Mat(_K1).convertTo(K1, K1.empty() ? CV_64FC1 : K1.type());
|
|
if (K2.needed()) cv::Mat(_K2).convertTo(K2, K2.empty() ? CV_64FC1 : K2.type());
|
|
if (D1.needed()) cv::Mat(intrinsicLeft.k).convertTo(D1, D1.empty() ? CV_64FC1 : D1.type());
|
|
if (D2.needed()) cv::Mat(intrinsicRight.k).convertTo(D2, D2.empty() ? CV_64FC1 : D2.type());
|
|
if (R.needed()) _R.convertTo(R, R.empty() ? CV_64FC1 : R.type());
|
|
if (T.needed()) cv::Mat(Tcur).convertTo(T, T.empty() ? CV_64FC1 : T.type());
|
|
|
|
return rms;
|
|
}
|
|
|
|
namespace cv{ namespace {
|
|
void subMatrix(const Mat& src, Mat& dst, const std::vector<uchar>& cols, const std::vector<uchar>& rows)
|
|
{
|
|
CV_Assert(src.channels() == 1);
|
|
|
|
int nonzeros_cols = cv::countNonZero(cols);
|
|
Mat tmp(src.rows, nonzeros_cols, CV_64F);
|
|
|
|
for (int i = 0, j = 0; i < (int)cols.size(); i++)
|
|
{
|
|
if (cols[i])
|
|
{
|
|
src.col(i).copyTo(tmp.col(j++));
|
|
}
|
|
}
|
|
|
|
int nonzeros_rows = cv::countNonZero(rows);
|
|
dst.create(nonzeros_rows, nonzeros_cols, CV_64F);
|
|
for (int i = 0, j = 0; i < (int)rows.size(); i++)
|
|
{
|
|
if (rows[i])
|
|
{
|
|
tmp.row(i).copyTo(dst.row(j++));
|
|
}
|
|
}
|
|
}
|
|
|
|
}}
|
|
|
|
cv::internal::IntrinsicParams::IntrinsicParams():
|
|
f(Vec2d::all(0)), c(Vec2d::all(0)), k(Vec4d::all(0)), alpha(0), isEstimate(9,0)
|
|
{
|
|
}
|
|
|
|
cv::internal::IntrinsicParams::IntrinsicParams(Vec2d _f, Vec2d _c, Vec4d _k, double _alpha):
|
|
f(_f), c(_c), k(_k), alpha(_alpha), isEstimate(9,0)
|
|
{
|
|
}
|
|
|
|
cv::internal::IntrinsicParams cv::internal::IntrinsicParams::operator+(const Mat& a)
|
|
{
|
|
CV_Assert(a.type() == CV_64FC1);
|
|
IntrinsicParams tmp;
|
|
const double* ptr = a.ptr<double>();
|
|
|
|
int j = 0;
|
|
tmp.f[0] = this->f[0] + (isEstimate[0] ? ptr[j++] : 0);
|
|
tmp.f[1] = this->f[1] + (isEstimate[1] ? ptr[j++] : 0);
|
|
tmp.c[0] = this->c[0] + (isEstimate[2] ? ptr[j++] : 0);
|
|
tmp.alpha = this->alpha + (isEstimate[4] ? ptr[j++] : 0);
|
|
tmp.c[1] = this->c[1] + (isEstimate[3] ? ptr[j++] : 0);
|
|
tmp.k[0] = this->k[0] + (isEstimate[5] ? ptr[j++] : 0);
|
|
tmp.k[1] = this->k[1] + (isEstimate[6] ? ptr[j++] : 0);
|
|
tmp.k[2] = this->k[2] + (isEstimate[7] ? ptr[j++] : 0);
|
|
tmp.k[3] = this->k[3] + (isEstimate[8] ? ptr[j++] : 0);
|
|
|
|
tmp.isEstimate = isEstimate;
|
|
return tmp;
|
|
}
|
|
|
|
cv::internal::IntrinsicParams& cv::internal::IntrinsicParams::operator =(const Mat& a)
|
|
{
|
|
CV_Assert(a.type() == CV_64FC1);
|
|
const double* ptr = a.ptr<double>();
|
|
|
|
int j = 0;
|
|
|
|
this->f[0] = isEstimate[0] ? ptr[j++] : 0;
|
|
this->f[1] = isEstimate[1] ? ptr[j++] : 0;
|
|
this->c[0] = isEstimate[2] ? ptr[j++] : 0;
|
|
this->c[1] = isEstimate[3] ? ptr[j++] : 0;
|
|
this->alpha = isEstimate[4] ? ptr[j++] : 0;
|
|
this->k[0] = isEstimate[5] ? ptr[j++] : 0;
|
|
this->k[1] = isEstimate[6] ? ptr[j++] : 0;
|
|
this->k[2] = isEstimate[7] ? ptr[j++] : 0;
|
|
this->k[3] = isEstimate[8] ? ptr[j++] : 0;
|
|
|
|
return *this;
|
|
}
|
|
|
|
void cv::internal::IntrinsicParams::Init(const cv::Vec2d& _f, const cv::Vec2d& _c, const cv::Vec4d& _k, const double& _alpha)
|
|
{
|
|
this->c = _c;
|
|
this->f = _f;
|
|
this->k = _k;
|
|
this->alpha = _alpha;
|
|
}
|
|
|
|
void cv::internal::projectPoints(cv::InputArray objectPoints, cv::OutputArray imagePoints,
|
|
cv::InputArray _rvec,cv::InputArray _tvec,
|
|
const IntrinsicParams& param, cv::OutputArray jacobian)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && objectPoints.type() == CV_64FC3);
|
|
Matx33d K(param.f[0], param.f[0] * param.alpha, param.c[0],
|
|
0, param.f[1], param.c[1],
|
|
0, 0, 1);
|
|
fisheye::projectPoints(objectPoints, imagePoints, _rvec, _tvec, K, param.k, param.alpha, jacobian);
|
|
}
|
|
|
|
void cv::internal::ComputeExtrinsicRefine(const Mat& imagePoints, const Mat& objectPoints, Mat& rvec,
|
|
Mat& tvec, Mat& J, const int MaxIter,
|
|
const IntrinsicParams& param, const double thresh_cond)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && objectPoints.type() == CV_64FC3);
|
|
CV_Assert(!imagePoints.empty() && imagePoints.type() == CV_64FC2);
|
|
Vec6d extrinsics(rvec.at<double>(0), rvec.at<double>(1), rvec.at<double>(2),
|
|
tvec.at<double>(0), tvec.at<double>(1), tvec.at<double>(2));
|
|
double change = 1;
|
|
int iter = 0;
|
|
|
|
while (change > 1e-10 && iter < MaxIter)
|
|
{
|
|
std::vector<Point2d> x;
|
|
Mat jacobians;
|
|
projectPoints(objectPoints, x, rvec, tvec, param, jacobians);
|
|
|
|
Mat ex = imagePoints - Mat(x).t();
|
|
ex = ex.reshape(1, 2);
|
|
|
|
J = jacobians.colRange(8, 14).clone();
|
|
|
|
SVD svd(J, SVD::NO_UV);
|
|
double condJJ = svd.w.at<double>(0)/svd.w.at<double>(5);
|
|
|
|
if (condJJ > thresh_cond)
|
|
change = 0;
|
|
else
|
|
{
|
|
Vec6d param_innov;
|
|
solve(J, ex.reshape(1, (int)ex.total()), param_innov, DECOMP_SVD + DECOMP_NORMAL);
|
|
|
|
Vec6d param_up = extrinsics + param_innov;
|
|
change = norm(param_innov)/norm(param_up);
|
|
extrinsics = param_up;
|
|
iter = iter + 1;
|
|
|
|
rvec = Mat(Vec3d(extrinsics.val));
|
|
tvec = Mat(Vec3d(extrinsics.val+3));
|
|
}
|
|
}
|
|
}
|
|
|
|
cv::Mat cv::internal::ComputeHomography(Mat m, Mat M)
|
|
{
|
|
int Np = m.cols;
|
|
|
|
if (m.rows < 3)
|
|
{
|
|
vconcat(m, Mat::ones(1, Np, CV_64FC1), m);
|
|
}
|
|
if (M.rows < 3)
|
|
{
|
|
vconcat(M, Mat::ones(1, Np, CV_64FC1), M);
|
|
}
|
|
|
|
divide(m, Mat::ones(3, 1, CV_64FC1) * m.row(2), m);
|
|
divide(M, Mat::ones(3, 1, CV_64FC1) * M.row(2), M);
|
|
|
|
Mat ax = m.row(0).clone();
|
|
Mat ay = m.row(1).clone();
|
|
|
|
double mxx = mean(ax)[0];
|
|
double myy = mean(ay)[0];
|
|
|
|
ax = ax - mxx;
|
|
ay = ay - myy;
|
|
|
|
double scxx = mean(abs(ax))[0];
|
|
double scyy = mean(abs(ay))[0];
|
|
|
|
Mat Hnorm (Matx33d( 1/scxx, 0.0, -mxx/scxx,
|
|
0.0, 1/scyy, -myy/scyy,
|
|
0.0, 0.0, 1.0 ));
|
|
|
|
Mat inv_Hnorm (Matx33d( scxx, 0, mxx,
|
|
0, scyy, myy,
|
|
0, 0, 1 ));
|
|
Mat mn = Hnorm * m;
|
|
|
|
Mat L = Mat::zeros(2*Np, 9, CV_64FC1);
|
|
|
|
for (int i = 0; i < Np; ++i)
|
|
{
|
|
for (int j = 0; j < 3; j++)
|
|
{
|
|
L.at<double>(2 * i, j) = M.at<double>(j, i);
|
|
L.at<double>(2 * i + 1, j + 3) = M.at<double>(j, i);
|
|
L.at<double>(2 * i, j + 6) = -mn.at<double>(0,i) * M.at<double>(j, i);
|
|
L.at<double>(2 * i + 1, j + 6) = -mn.at<double>(1,i) * M.at<double>(j, i);
|
|
}
|
|
}
|
|
|
|
if (Np > 4) L = L.t() * L;
|
|
SVD svd(L);
|
|
Mat hh = svd.vt.row(8) / svd.vt.row(8).at<double>(8);
|
|
Mat Hrem = hh.reshape(1, 3);
|
|
Mat H = inv_Hnorm * Hrem;
|
|
|
|
if (Np > 4)
|
|
{
|
|
Mat hhv = H.reshape(1, 9)(Rect(0, 0, 1, 8)).clone();
|
|
for (int iter = 0; iter < 10; iter++)
|
|
{
|
|
Mat mrep = H * M;
|
|
Mat J = Mat::zeros(2 * Np, 8, CV_64FC1);
|
|
Mat MMM;
|
|
divide(M, Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 2, mrep.cols, 1)), MMM);
|
|
divide(mrep, Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 2, mrep.cols, 1)), mrep);
|
|
Mat m_err = m(Rect(0,0, m.cols, 2)) - mrep(Rect(0,0, mrep.cols, 2));
|
|
m_err = Mat(m_err.t()).reshape(1, m_err.cols * m_err.rows);
|
|
Mat MMM2, MMM3;
|
|
multiply(Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 0, mrep.cols, 1)), MMM, MMM2);
|
|
multiply(Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0, 1, mrep.cols, 1)), MMM, MMM3);
|
|
|
|
for (int i = 0; i < Np; ++i)
|
|
{
|
|
for (int j = 0; j < 3; ++j)
|
|
{
|
|
J.at<double>(2 * i, j) = -MMM.at<double>(j, i);
|
|
J.at<double>(2 * i + 1, j + 3) = -MMM.at<double>(j, i);
|
|
}
|
|
|
|
for (int j = 0; j < 2; ++j)
|
|
{
|
|
J.at<double>(2 * i, j + 6) = MMM2.at<double>(j, i);
|
|
J.at<double>(2 * i + 1, j + 6) = MMM3.at<double>(j, i);
|
|
}
|
|
}
|
|
divide(M, Mat::ones(3, 1, CV_64FC1) * mrep(Rect(0,2,mrep.cols,1)), MMM);
|
|
Mat hh_innov = (J.t() * J).inv() * (J.t()) * m_err;
|
|
Mat hhv_up = hhv - hh_innov;
|
|
Mat tmp;
|
|
vconcat(hhv_up, Mat::ones(1,1,CV_64FC1), tmp);
|
|
Mat H_up = tmp.reshape(1,3);
|
|
hhv = hhv_up;
|
|
H = H_up;
|
|
}
|
|
}
|
|
return H;
|
|
}
|
|
|
|
cv::Mat cv::internal::NormalizePixels(const Mat& imagePoints, const IntrinsicParams& param)
|
|
{
|
|
CV_Assert(!imagePoints.empty() && imagePoints.type() == CV_64FC2);
|
|
|
|
Mat distorted((int)imagePoints.total(), 1, CV_64FC2), undistorted;
|
|
const Vec2d* ptr = imagePoints.ptr<Vec2d>();
|
|
Vec2d* ptr_d = distorted.ptr<Vec2d>();
|
|
for (size_t i = 0; i < imagePoints.total(); ++i)
|
|
{
|
|
ptr_d[i] = (ptr[i] - param.c).mul(Vec2d(1.0 / param.f[0], 1.0 / param.f[1]));
|
|
ptr_d[i][0] -= param.alpha * ptr_d[i][1];
|
|
}
|
|
cv::fisheye::undistortPoints(distorted, undistorted, Matx33d::eye(), param.k);
|
|
return undistorted;
|
|
}
|
|
|
|
void cv::internal::InitExtrinsics(const Mat& _imagePoints, const Mat& _objectPoints, const IntrinsicParams& param, Mat& omckk, Mat& Tckk)
|
|
{
|
|
CV_Assert(!_objectPoints.empty() && _objectPoints.type() == CV_64FC3);
|
|
CV_Assert(!_imagePoints.empty() && _imagePoints.type() == CV_64FC2);
|
|
|
|
Mat imagePointsNormalized = NormalizePixels(_imagePoints, param).reshape(1).t();
|
|
Mat objectPoints = _objectPoints.reshape(1).t();
|
|
Mat objectPointsMean, covObjectPoints;
|
|
Mat Rckk;
|
|
int Np = imagePointsNormalized.cols;
|
|
calcCovarMatrix(objectPoints, covObjectPoints, objectPointsMean, COVAR_NORMAL | COVAR_COLS);
|
|
SVD svd(covObjectPoints);
|
|
Mat R(svd.vt);
|
|
if (norm(R(Rect(2, 0, 1, 2))) < 1e-6)
|
|
R = Mat::eye(3,3, CV_64FC1);
|
|
if (determinant(R) < 0)
|
|
R = -R;
|
|
Mat T = -R * objectPointsMean;
|
|
Mat X_new = R * objectPoints + T * Mat::ones(1, Np, CV_64FC1);
|
|
Mat H = ComputeHomography(imagePointsNormalized, X_new(Rect(0,0,X_new.cols,2)));
|
|
double sc = .5 * (norm(H.col(0)) + norm(H.col(1)));
|
|
H = H / sc;
|
|
Mat u1 = H.col(0).clone();
|
|
u1 = u1 / norm(u1);
|
|
Mat u2 = H.col(1).clone() - u1.dot(H.col(1).clone()) * u1;
|
|
u2 = u2 / norm(u2);
|
|
Mat u3 = u1.cross(u2);
|
|
Mat RRR;
|
|
hconcat(u1, u2, RRR);
|
|
hconcat(RRR, u3, RRR);
|
|
Rodrigues(RRR, omckk);
|
|
Rodrigues(omckk, Rckk);
|
|
Tckk = H.col(2).clone();
|
|
Tckk = Tckk + Rckk * T;
|
|
Rckk = Rckk * R;
|
|
Rodrigues(Rckk, omckk);
|
|
}
|
|
|
|
void cv::internal::CalibrateExtrinsics(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
|
|
const IntrinsicParams& param, const int check_cond,
|
|
const double thresh_cond, InputOutputArray omc, InputOutputArray Tc)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3));
|
|
CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2));
|
|
CV_Assert(omc.type() == CV_64FC3 || Tc.type() == CV_64FC3);
|
|
|
|
if (omc.empty()) omc.create(1, (int)objectPoints.total(), CV_64FC3);
|
|
if (Tc.empty()) Tc.create(1, (int)objectPoints.total(), CV_64FC3);
|
|
|
|
const int maxIter = 20;
|
|
|
|
for(int image_idx = 0; image_idx < (int)imagePoints.total(); ++image_idx)
|
|
{
|
|
Mat omckk, Tckk, JJ_kk;
|
|
Mat image, object;
|
|
|
|
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
|
|
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
|
|
|
|
bool imT = image.rows < image.cols;
|
|
bool obT = object.rows < object.cols;
|
|
|
|
InitExtrinsics(imT ? image.t() : image, obT ? object.t() : object, param, omckk, Tckk);
|
|
|
|
ComputeExtrinsicRefine(!imT ? image.t() : image, !obT ? object.t() : object, omckk, Tckk, JJ_kk, maxIter, param, thresh_cond);
|
|
if (check_cond)
|
|
{
|
|
SVD svd(JJ_kk, SVD::NO_UV);
|
|
CV_Assert(svd.w.at<double>(0) / svd.w.at<double>((int)svd.w.total() - 1) < thresh_cond);
|
|
}
|
|
omckk.reshape(3,1).copyTo(omc.getMat().col(image_idx));
|
|
Tckk.reshape(3,1).copyTo(Tc.getMat().col(image_idx));
|
|
}
|
|
}
|
|
|
|
void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
|
|
const IntrinsicParams& param, InputArray omc, InputArray Tc,
|
|
const int& check_cond, const double& thresh_cond, Mat& JJ2, Mat& ex3)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3));
|
|
CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2));
|
|
|
|
CV_Assert(!omc.empty() && omc.type() == CV_64FC3);
|
|
CV_Assert(!Tc.empty() && Tc.type() == CV_64FC3);
|
|
|
|
int n = (int)objectPoints.total();
|
|
|
|
JJ2 = Mat::zeros(9 + 6 * n, 9 + 6 * n, CV_64FC1);
|
|
ex3 = Mat::zeros(9 + 6 * n, 1, CV_64FC1 );
|
|
|
|
for (int image_idx = 0; image_idx < n; ++image_idx)
|
|
{
|
|
Mat image, object;
|
|
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
|
|
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
|
|
|
|
bool imT = image.rows < image.cols;
|
|
Mat om(omc.getMat().col(image_idx)), T(Tc.getMat().col(image_idx));
|
|
|
|
std::vector<Point2d> x;
|
|
Mat jacobians;
|
|
projectPoints(object, x, om, T, param, jacobians);
|
|
Mat exkk = (imT ? image.t() : image) - Mat(x);
|
|
|
|
Mat A(jacobians.rows, 9, CV_64FC1);
|
|
jacobians.colRange(0, 4).copyTo(A.colRange(0, 4));
|
|
jacobians.col(14).copyTo(A.col(4));
|
|
jacobians.colRange(4, 8).copyTo(A.colRange(5, 9));
|
|
|
|
A = A.t();
|
|
|
|
Mat B = jacobians.colRange(8, 14).clone();
|
|
B = B.t();
|
|
|
|
JJ2(Rect(0, 0, 9, 9)) += A * A.t();
|
|
JJ2(Rect(9 + 6 * image_idx, 9 + 6 * image_idx, 6, 6)) = B * B.t();
|
|
|
|
JJ2(Rect(9 + 6 * image_idx, 0, 6, 9)) = A * B.t();
|
|
JJ2(Rect(0, 9 + 6 * image_idx, 9, 6)) = JJ2(Rect(9 + 6 * image_idx, 0, 6, 9)).t();
|
|
|
|
ex3.rowRange(0, 9) += A * exkk.reshape(1, 2 * exkk.rows);
|
|
ex3.rowRange(9 + 6 * image_idx, 9 + 6 * (image_idx + 1)) = B * exkk.reshape(1, 2 * exkk.rows);
|
|
|
|
if (check_cond)
|
|
{
|
|
Mat JJ_kk = B.t();
|
|
SVD svd(JJ_kk, SVD::NO_UV);
|
|
CV_Assert(svd.w.at<double>(0) / svd.w.at<double>(svd.w.rows - 1) < thresh_cond);
|
|
}
|
|
}
|
|
|
|
std::vector<uchar> idxs(param.isEstimate);
|
|
idxs.insert(idxs.end(), 6 * n, 1);
|
|
|
|
subMatrix(JJ2, JJ2, idxs, idxs);
|
|
subMatrix(ex3, ex3, std::vector<uchar>(1, 1), idxs);
|
|
}
|
|
|
|
void cv::internal::EstimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
|
|
const IntrinsicParams& params, InputArray omc, InputArray Tc,
|
|
IntrinsicParams& errors, Vec2d& std_err, double thresh_cond, int check_cond, double& rms)
|
|
{
|
|
CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3));
|
|
CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2));
|
|
|
|
CV_Assert(!omc.empty() && omc.type() == CV_64FC3);
|
|
CV_Assert(!Tc.empty() && Tc.type() == CV_64FC3);
|
|
|
|
int total_ex = 0;
|
|
for (int image_idx = 0; image_idx < (int)objectPoints.total(); ++image_idx)
|
|
{
|
|
total_ex += (int)objectPoints.getMat(image_idx).total();
|
|
}
|
|
Mat ex(total_ex, 1, CV_64FC2);
|
|
int insert_idx = 0;
|
|
for (int image_idx = 0; image_idx < (int)objectPoints.total(); ++image_idx)
|
|
{
|
|
Mat image, object;
|
|
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
|
|
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
|
|
|
|
bool imT = image.rows < image.cols;
|
|
|
|
Mat om(omc.getMat().col(image_idx)), T(Tc.getMat().col(image_idx));
|
|
|
|
std::vector<Point2d> x;
|
|
projectPoints(object, x, om, T, params, noArray());
|
|
Mat ex_ = (imT ? image.t() : image) - Mat(x);
|
|
ex_.copyTo(ex.rowRange(insert_idx, insert_idx + ex_.rows));
|
|
insert_idx += ex_.rows;
|
|
}
|
|
|
|
meanStdDev(ex, noArray(), std_err);
|
|
std_err *= sqrt((double)ex.total()/((double)ex.total() - 1.0));
|
|
|
|
Vec<double, 1> sigma_x;
|
|
meanStdDev(ex.reshape(1, 1), noArray(), sigma_x);
|
|
sigma_x *= sqrt(2.0 * (double)ex.total()/(2.0 * (double)ex.total() - 1.0));
|
|
|
|
Mat JJ2, ex3;
|
|
ComputeJacobians(objectPoints, imagePoints, params, omc, Tc, check_cond, thresh_cond, JJ2, ex3);
|
|
|
|
sqrt(JJ2.inv(), JJ2);
|
|
|
|
errors = 3 * sigma_x(0) * JJ2.diag();
|
|
rms = sqrt(norm(ex, NORM_L2SQR)/ex.total());
|
|
}
|
|
|
|
void cv::internal::dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB)
|
|
{
|
|
CV_Assert(A.getMat().cols == B.getMat().rows);
|
|
CV_Assert(A.type() == CV_64FC1 && B.type() == CV_64FC1);
|
|
|
|
int p = A.getMat().rows;
|
|
int n = A.getMat().cols;
|
|
int q = B.getMat().cols;
|
|
|
|
dABdA.create(p * q, p * n, CV_64FC1);
|
|
dABdB.create(p * q, q * n, CV_64FC1);
|
|
|
|
dABdA.getMat() = Mat::zeros(p * q, p * n, CV_64FC1);
|
|
dABdB.getMat() = Mat::zeros(p * q, q * n, CV_64FC1);
|
|
|
|
for (int i = 0; i < q; ++i)
|
|
{
|
|
for (int j = 0; j < p; ++j)
|
|
{
|
|
int ij = j + i * p;
|
|
for (int k = 0; k < n; ++k)
|
|
{
|
|
int kj = j + k * p;
|
|
dABdA.getMat().at<double>(ij, kj) = B.getMat().at<double>(k, i);
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < q; ++i)
|
|
{
|
|
A.getMat().copyTo(dABdB.getMat().rowRange(i * p, i * p + p).colRange(i * n, i * n + n));
|
|
}
|
|
}
|
|
|
|
void cv::internal::JRodriguesMatlab(const Mat& src, Mat& dst)
|
|
{
|
|
Mat tmp(src.cols, src.rows, src.type());
|
|
if (src.rows == 9)
|
|
{
|
|
Mat(src.row(0).t()).copyTo(tmp.col(0));
|
|
Mat(src.row(1).t()).copyTo(tmp.col(3));
|
|
Mat(src.row(2).t()).copyTo(tmp.col(6));
|
|
Mat(src.row(3).t()).copyTo(tmp.col(1));
|
|
Mat(src.row(4).t()).copyTo(tmp.col(4));
|
|
Mat(src.row(5).t()).copyTo(tmp.col(7));
|
|
Mat(src.row(6).t()).copyTo(tmp.col(2));
|
|
Mat(src.row(7).t()).copyTo(tmp.col(5));
|
|
Mat(src.row(8).t()).copyTo(tmp.col(8));
|
|
}
|
|
else
|
|
{
|
|
Mat(src.col(0).t()).copyTo(tmp.row(0));
|
|
Mat(src.col(1).t()).copyTo(tmp.row(3));
|
|
Mat(src.col(2).t()).copyTo(tmp.row(6));
|
|
Mat(src.col(3).t()).copyTo(tmp.row(1));
|
|
Mat(src.col(4).t()).copyTo(tmp.row(4));
|
|
Mat(src.col(5).t()).copyTo(tmp.row(7));
|
|
Mat(src.col(6).t()).copyTo(tmp.row(2));
|
|
Mat(src.col(7).t()).copyTo(tmp.row(5));
|
|
Mat(src.col(8).t()).copyTo(tmp.row(8));
|
|
}
|
|
dst = tmp.clone();
|
|
}
|
|
|
|
void cv::internal::compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2,
|
|
Mat& om3, Mat& T3, Mat& dom3dom1, Mat& dom3dT1, Mat& dom3dom2,
|
|
Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2)
|
|
{
|
|
Mat om1 = _om1.getMat();
|
|
Mat om2 = _om2.getMat();
|
|
Mat T1 = _T1.getMat().reshape(1, 3);
|
|
Mat T2 = _T2.getMat().reshape(1, 3);
|
|
|
|
//% Rotations:
|
|
Mat R1, R2, R3, dR1dom1(9, 3, CV_64FC1), dR2dom2;
|
|
Rodrigues(om1, R1, dR1dom1);
|
|
Rodrigues(om2, R2, dR2dom2);
|
|
JRodriguesMatlab(dR1dom1, dR1dom1);
|
|
JRodriguesMatlab(dR2dom2, dR2dom2);
|
|
R3 = R2 * R1;
|
|
Mat dR3dR2, dR3dR1;
|
|
dAB(R2, R1, dR3dR2, dR3dR1);
|
|
Mat dom3dR3;
|
|
Rodrigues(R3, om3, dom3dR3);
|
|
JRodriguesMatlab(dom3dR3, dom3dR3);
|
|
dom3dom1 = dom3dR3 * dR3dR1 * dR1dom1;
|
|
dom3dom2 = dom3dR3 * dR3dR2 * dR2dom2;
|
|
dom3dT1 = Mat::zeros(3, 3, CV_64FC1);
|
|
dom3dT2 = Mat::zeros(3, 3, CV_64FC1);
|
|
|
|
//% Translations:
|
|
Mat T3t = R2 * T1;
|
|
Mat dT3tdR2, dT3tdT1;
|
|
dAB(R2, T1, dT3tdR2, dT3tdT1);
|
|
Mat dT3tdom2 = dT3tdR2 * dR2dom2;
|
|
T3 = T3t + T2;
|
|
dT3dT1 = dT3tdT1;
|
|
dT3dT2 = Mat::eye(3, 3, CV_64FC1);
|
|
dT3dom2 = dT3tdom2;
|
|
dT3dom1 = Mat::zeros(3, 3, CV_64FC1);
|
|
}
|
|
|
|
double cv::internal::median(const Mat& row)
|
|
{
|
|
CV_Assert(row.type() == CV_64FC1);
|
|
CV_Assert(!row.empty() && row.rows == 1);
|
|
Mat tmp = row.clone();
|
|
sort(tmp, tmp, 0);
|
|
if ((int)tmp.total() % 2) return tmp.at<double>((int)tmp.total() / 2);
|
|
else return 0.5 *(tmp.at<double>((int)tmp.total() / 2) + tmp.at<double>((int)tmp.total() / 2 - 1));
|
|
}
|
|
|
|
cv::Vec3d cv::internal::median3d(InputArray m)
|
|
{
|
|
CV_Assert(m.depth() == CV_64F && m.getMat().rows == 1);
|
|
Mat M = Mat(m.getMat().t()).reshape(1).t();
|
|
return Vec3d(median(M.row(0)), median(M.row(1)), median(M.row(2)));
|
|
}
|