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opencv/modules/calib3d/src/calibration.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "hal_replacement.hpp"
#include "distortion_model.hpp"
#include "calib3d_c_api.h"
#include <stdio.h>
#include <iterator>
/*
This is straight-forward port v3 of Matlab calibration engine by Jean-Yves Bouguet
that is (in a large extent) based on the paper:
Z. Zhang. "A flexible new technique for camera calibration".
IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.
The 1st initial port was done by Valery Mosyagin.
*/
using namespace cv;
static const char* cvDistCoeffErr = "Distortion coefficients must be 1x4, 4x1, 1x5, 5x1, 1x8, 8x1, 1x12, 12x1, 1x14 or 14x1 floating-point vector";
CV_IMPL void cvInitIntrinsicParams2D( const CvMat* objectPoints,
const CvMat* imagePoints, const CvMat* npoints,
CvSize imageSize, CvMat* cameraMatrix,
double aspectRatio )
{
Ptr<CvMat> matA, _b, _allH;
int i, j, pos, nimages, ni = 0;
double a[9] = { 0, 0, 0, 0, 0, 0, 0, 0, 1 };
double H[9] = {0}, f[2] = {0};
CvMat _a = cvMat( 3, 3, CV_64F, a );
CvMat matH = cvMat( 3, 3, CV_64F, H );
CvMat _f = cvMat( 2, 1, CV_64F, f );
CV_Assert(npoints);
CV_Assert(CV_MAT_TYPE(npoints->type) == CV_32SC1);
CV_Assert(CV_IS_MAT_CONT(npoints->type));
nimages = npoints->rows + npoints->cols - 1;
if( (CV_MAT_TYPE(objectPoints->type) != CV_32FC3 &&
CV_MAT_TYPE(objectPoints->type) != CV_64FC3) ||
(CV_MAT_TYPE(imagePoints->type) != CV_32FC2 &&
CV_MAT_TYPE(imagePoints->type) != CV_64FC2) )
CV_Error( cv::Error::StsUnsupportedFormat, "Both object points and image points must be 2D" );
if( objectPoints->rows != 1 || imagePoints->rows != 1 )
CV_Error( cv::Error::StsBadSize, "object points and image points must be a single-row matrices" );
matA.reset(cvCreateMat( 2*nimages, 2, CV_64F ));
_b.reset(cvCreateMat( 2*nimages, 1, CV_64F ));
a[2] = (!imageSize.width) ? 0.5 : (imageSize.width - 1)*0.5;
a[5] = (!imageSize.height) ? 0.5 : (imageSize.height - 1)*0.5;
_allH.reset(cvCreateMat( nimages, 9, CV_64F ));
// extract vanishing points in order to obtain initial value for the focal length
for( i = 0, pos = 0; i < nimages; i++, pos += ni )
{
CV_DbgAssert(npoints->data.i);
CV_DbgAssert(matA && matA->data.db);
CV_DbgAssert(_b && _b->data.db);
double* Ap = matA->data.db + i*4;
double* bp = _b->data.db + i*2;
ni = npoints->data.i[i];
double h[3], v[3], d1[3], d2[3];
double n[4] = {0,0,0,0};
CvMat _m, matM;
cvGetCols( objectPoints, &matM, pos, pos + ni );
cvGetCols( imagePoints, &_m, pos, pos + ni );
cvFindHomography( &matM, &_m, &matH );
CV_DbgAssert(_allH && _allH->data.db);
memcpy( _allH->data.db + i*9, H, sizeof(H) );
H[0] -= H[6]*a[2]; H[1] -= H[7]*a[2]; H[2] -= H[8]*a[2];
H[3] -= H[6]*a[5]; H[4] -= H[7]*a[5]; H[5] -= H[8]*a[5];
for( j = 0; j < 3; j++ )
{
double t0 = H[j*3], t1 = H[j*3+1];
h[j] = t0; v[j] = t1;
d1[j] = (t0 + t1)*0.5;
d2[j] = (t0 - t1)*0.5;
n[0] += t0*t0; n[1] += t1*t1;
n[2] += d1[j]*d1[j]; n[3] += d2[j]*d2[j];
}
for( j = 0; j < 4; j++ )
n[j] = 1./std::sqrt(n[j]);
for( j = 0; j < 3; j++ )
{
h[j] *= n[0]; v[j] *= n[1];
d1[j] *= n[2]; d2[j] *= n[3];
}
Ap[0] = h[0]*v[0]; Ap[1] = h[1]*v[1];
Ap[2] = d1[0]*d2[0]; Ap[3] = d1[1]*d2[1];
bp[0] = -h[2]*v[2]; bp[1] = -d1[2]*d2[2];
}
cvSolve( matA, _b, &_f, CV_NORMAL + CV_SVD );
a[0] = std::sqrt(fabs(1./f[0]));
a[4] = std::sqrt(fabs(1./f[1]));
if( aspectRatio != 0 )
{
double tf = (a[0] + a[4])/(aspectRatio + 1.);
a[0] = aspectRatio*tf;
a[4] = tf;
}
cvConvert( &_a, cameraMatrix );
}
static void subMatrix(const cv::Mat& src, cv::Mat& dst, const std::vector<uchar>& cols,
const std::vector<uchar>& rows) {
int nonzeros_cols = cv::countNonZero(cols);
cv::Mat tmp(src.rows, nonzeros_cols, CV_64FC1);
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_64FC1);
for (int i = 0, j = 0; i < (int)rows.size(); i++)
{
if (rows[i])
{
tmp.row(i).copyTo(dst.row(j++));
}
}
}
static double cvCalibrateCamera2Internal( const CvMat* objectPoints,
const CvMat* imagePoints, const CvMat* npoints,
CvSize imageSize, int iFixedPoint, CvMat* cameraMatrix, CvMat* distCoeffs,
CvMat* rvecs, CvMat* tvecs, CvMat* newObjPoints, CvMat* stdDevs,
CvMat* perViewErrors, int flags, CvTermCriteria termCrit )
{
const int NINTRINSIC = CV_CALIB_NINTRINSIC;
double reprojErr = 0;
Matx33d A;
double k[14] = {0};
CvMat matA = cvMat(3, 3, CV_64F, A.val), _k;
int i, nimages, maxPoints = 0, ni = 0, pos, total = 0, nparams, npstep, cn;
double aspectRatio = 0.;
// 0. check the parameters & allocate buffers
if( !CV_IS_MAT(objectPoints) || !CV_IS_MAT(imagePoints) ||
!CV_IS_MAT(npoints) || !CV_IS_MAT(cameraMatrix) || !CV_IS_MAT(distCoeffs) )
CV_Error( cv::Error::StsBadArg, "One of required vector arguments is not a valid matrix" );
if( imageSize.width <= 0 || imageSize.height <= 0 )
CV_Error( cv::Error::StsOutOfRange, "image width and height must be positive" );
if( CV_MAT_TYPE(npoints->type) != CV_32SC1 ||
(npoints->rows != 1 && npoints->cols != 1) )
CV_Error( cv::Error::StsUnsupportedFormat,
"the array of point counters must be 1-dimensional integer vector" );
if(flags & CALIB_TILTED_MODEL)
{
//when the tilted sensor model is used the distortion coefficients matrix must have 14 parameters
if (distCoeffs->cols*distCoeffs->rows != 14)
CV_Error( cv::Error::StsBadArg, "The tilted sensor model must have 14 parameters in the distortion matrix" );
}
else
{
//when the thin prism model is used the distortion coefficients matrix must have 12 parameters
if(flags & CALIB_THIN_PRISM_MODEL)
if (distCoeffs->cols*distCoeffs->rows != 12)
CV_Error( cv::Error::StsBadArg, "Thin prism model must have 12 parameters in the distortion matrix" );
}
nimages = npoints->rows*npoints->cols;
npstep = npoints->rows == 1 ? 1 : npoints->step/CV_ELEM_SIZE(npoints->type);
if( rvecs )
{
cn = CV_MAT_CN(rvecs->type);
if( !CV_IS_MAT(rvecs) ||
(CV_MAT_DEPTH(rvecs->type) != CV_32F && CV_MAT_DEPTH(rvecs->type) != CV_64F) ||
((rvecs->rows != nimages || (rvecs->cols*cn != 3 && rvecs->cols*cn != 9)) &&
(rvecs->rows != 1 || rvecs->cols != nimages || cn != 3)) )
CV_Error( cv::Error::StsBadArg, "the output array of rotation vectors must be 3-channel "
"1xn or nx1 array or 1-channel nx3 or nx9 array, where n is the number of views" );
}
if( tvecs )
{
cn = CV_MAT_CN(tvecs->type);
if( !CV_IS_MAT(tvecs) ||
(CV_MAT_DEPTH(tvecs->type) != CV_32F && CV_MAT_DEPTH(tvecs->type) != CV_64F) ||
((tvecs->rows != nimages || tvecs->cols*cn != 3) &&
(tvecs->rows != 1 || tvecs->cols != nimages || cn != 3)) )
CV_Error( cv::Error::StsBadArg, "the output array of translation vectors must be 3-channel "
"1xn or nx1 array or 1-channel nx3 array, where n is the number of views" );
}
bool releaseObject = iFixedPoint > 0 && iFixedPoint < npoints->data.i[0] - 1;
if( stdDevs && !releaseObject )
{
cn = CV_MAT_CN(stdDevs->type);
if( !CV_IS_MAT(stdDevs) ||
(CV_MAT_DEPTH(stdDevs->type) != CV_32F && CV_MAT_DEPTH(stdDevs->type) != CV_64F) ||
((stdDevs->rows != (nimages*6 + NINTRINSIC) || stdDevs->cols*cn != 1) &&
(stdDevs->rows != 1 || stdDevs->cols != (nimages*6 + NINTRINSIC) || cn != 1)) )
#define STR__(x) #x
#define STR_(x) STR__(x)
CV_Error( cv::Error::StsBadArg, "the output array of standard deviations vectors must be 1-channel "
"1x(n*6 + NINTRINSIC) or (n*6 + NINTRINSIC)x1 array, where n is the number of views,"
" NINTRINSIC = " STR_(CV_CALIB_NINTRINSIC));
}
if( (CV_MAT_TYPE(cameraMatrix->type) != CV_32FC1 &&
CV_MAT_TYPE(cameraMatrix->type) != CV_64FC1) ||
cameraMatrix->rows != 3 || cameraMatrix->cols != 3 )
CV_Error( cv::Error::StsBadArg,
"Intrinsic parameters must be 3x3 floating-point matrix" );
if( (CV_MAT_TYPE(distCoeffs->type) != CV_32FC1 &&
CV_MAT_TYPE(distCoeffs->type) != CV_64FC1) ||
(distCoeffs->cols != 1 && distCoeffs->rows != 1) ||
(distCoeffs->cols*distCoeffs->rows != 4 &&
distCoeffs->cols*distCoeffs->rows != 5 &&
distCoeffs->cols*distCoeffs->rows != 8 &&
distCoeffs->cols*distCoeffs->rows != 12 &&
distCoeffs->cols*distCoeffs->rows != 14) )
CV_Error( cv::Error::StsBadArg, cvDistCoeffErr );
for( i = 0; i < nimages; i++ )
{
ni = npoints->data.i[i*npstep];
if( ni < 4 )
{
CV_Error_( cv::Error::StsOutOfRange, ("The number of points in the view #%d is < 4", i));
}
maxPoints = MAX( maxPoints, ni );
total += ni;
}
if( newObjPoints )
{
cn = CV_MAT_CN(newObjPoints->type);
if( !CV_IS_MAT(newObjPoints) ||
(CV_MAT_DEPTH(newObjPoints->type) != CV_32F && CV_MAT_DEPTH(newObjPoints->type) != CV_64F) ||
((newObjPoints->rows != maxPoints || newObjPoints->cols*cn != 3) &&
(newObjPoints->rows != 1 || newObjPoints->cols != maxPoints || cn != 3)) )
CV_Error( cv::Error::StsBadArg, "the output array of refined object points must be 3-channel "
"1xn or nx1 array or 1-channel nx3 array, where n is the number of object points per view" );
}
if( stdDevs && releaseObject )
{
cn = CV_MAT_CN(stdDevs->type);
if( !CV_IS_MAT(stdDevs) ||
(CV_MAT_DEPTH(stdDevs->type) != CV_32F && CV_MAT_DEPTH(stdDevs->type) != CV_64F) ||
((stdDevs->rows != (nimages*6 + NINTRINSIC + maxPoints*3) || stdDevs->cols*cn != 1) &&
(stdDevs->rows != 1 || stdDevs->cols != (nimages*6 + NINTRINSIC + maxPoints*3) || cn != 1)) )
CV_Error( cv::Error::StsBadArg, "the output array of standard deviations vectors must be 1-channel "
"1x(n*6 + NINTRINSIC + m*3) or (n*6 + NINTRINSIC + m*3)x1 array, where n is the number of views,"
" NINTRINSIC = " STR_(CV_CALIB_NINTRINSIC) ", m is the number of object points per view");
}
Mat matM( 1, total, CV_64FC3 );
Mat _m( 1, total, CV_64FC2 );
Mat allErrors(1, total, CV_64FC2);
if(CV_MAT_CN(objectPoints->type) == 3) {
cvarrToMat(objectPoints).convertTo(matM, CV_64F);
} else {
convertPointsHomogeneous(cvarrToMat(objectPoints), matM);
}
if(CV_MAT_CN(imagePoints->type) == 2) {
cvarrToMat(imagePoints).convertTo(_m, CV_64F);
} else {
convertPointsHomogeneous(cvarrToMat(imagePoints), _m);
}
nparams = NINTRINSIC + nimages*6;
if( releaseObject )
nparams += maxPoints * 3;
_k = cvMat( distCoeffs->rows, distCoeffs->cols, CV_MAKETYPE(CV_64F,CV_MAT_CN(distCoeffs->type)), k);
if( distCoeffs->rows*distCoeffs->cols*CV_MAT_CN(distCoeffs->type) < 8 )
{
if( distCoeffs->rows*distCoeffs->cols*CV_MAT_CN(distCoeffs->type) < 5 )
flags |= CALIB_FIX_K3;
flags |= CALIB_FIX_K4 | CALIB_FIX_K5 | CALIB_FIX_K6;
}
const double minValidAspectRatio = 0.01;
const double maxValidAspectRatio = 100.0;
// 1. initialize intrinsic parameters & LM solver
if( flags & CALIB_USE_INTRINSIC_GUESS )
{
cvConvert( cameraMatrix, &matA );
if( A(0, 0) <= 0 || A(1, 1) <= 0 )
CV_Error( cv::Error::StsOutOfRange, "Focal length (fx and fy) must be positive" );
if( A(0, 2) < 0 || A(0, 2) >= imageSize.width ||
A(1, 2) < 0 || A(1, 2) >= imageSize.height )
CV_Error( cv::Error::StsOutOfRange, "Principal point must be within the image" );
if( fabs(A(0, 1)) > 1e-5 )
CV_Error( cv::Error::StsOutOfRange, "Non-zero skew is not supported by the function" );
if( fabs(A(1, 0)) > 1e-5 || fabs(A(2, 0)) > 1e-5 ||
fabs(A(2, 1)) > 1e-5 || fabs(A(2,2)-1) > 1e-5 )
CV_Error( cv::Error::StsOutOfRange,
"The intrinsic matrix must have [fx 0 cx; 0 fy cy; 0 0 1] shape" );
A(0, 1) = A(1, 0) = A(2, 0) = A(2, 1) = 0.;
A(2, 2) = 1.;
if( flags & CALIB_FIX_ASPECT_RATIO )
{
aspectRatio = A(0, 0)/A(1, 1);
if( aspectRatio < minValidAspectRatio || aspectRatio > maxValidAspectRatio )
CV_Error( cv::Error::StsOutOfRange,
"The specified aspect ratio (= cameraMatrix[0][0] / cameraMatrix[1][1]) is incorrect" );
}
cvConvert( distCoeffs, &_k );
}
else
{
Scalar mean, sdv;
meanStdDev(matM, mean, sdv);
if( fabs(mean[2]) > 1e-5 || fabs(sdv[2]) > 1e-5 )
CV_Error( cv::Error::StsBadArg,
"For non-planar calibration rigs the initial intrinsic matrix must be specified" );
for( i = 0; i < total; i++ )
matM.at<Point3d>(i).z = 0.;
if( flags & CALIB_FIX_ASPECT_RATIO )
{
aspectRatio = cvmGet(cameraMatrix,0,0);
aspectRatio /= cvmGet(cameraMatrix,1,1);
if( aspectRatio < minValidAspectRatio || aspectRatio > maxValidAspectRatio )
CV_Error( cv::Error::StsOutOfRange,
"The specified aspect ratio (= cameraMatrix[0][0] / cameraMatrix[1][1]) is incorrect" );
}
CvMat _matM = cvMat(matM), m = cvMat(_m);
cvInitIntrinsicParams2D( &_matM, &m, npoints, imageSize, &matA, aspectRatio );
}
CvLevMarq solver( nparams, 0, termCrit );
Mat _Ji( maxPoints*2, NINTRINSIC, CV_64FC1, Scalar(0));
Mat _Je( maxPoints*2, 6, CV_64FC1 );
Mat _err( maxPoints*2, 1, CV_64FC1 );
const bool allocJo = (solver.state == CvLevMarq::CALC_J) || stdDevs || releaseObject;
Mat _Jo = allocJo ? Mat( maxPoints*2, maxPoints*3, CV_64FC1, Scalar(0) ) : Mat();
if(flags & CALIB_USE_LU) {
solver.solveMethod = DECOMP_LU;
}
else if(flags & CALIB_USE_QR) {
solver.solveMethod = DECOMP_QR;
}
{
double* param = solver.param->data.db;
uchar* mask = solver.mask->data.ptr;
param[0] = A(0, 0); param[1] = A(1, 1); param[2] = A(0, 2); param[3] = A(1, 2);
std::copy(k, k + 14, param + 4);
if(flags & CALIB_FIX_ASPECT_RATIO)
mask[0] = 0;
if( flags & CALIB_FIX_FOCAL_LENGTH )
mask[0] = mask[1] = 0;
if( flags & CALIB_FIX_PRINCIPAL_POINT )
mask[2] = mask[3] = 0;
if( flags & CALIB_ZERO_TANGENT_DIST )
{
param[6] = param[7] = 0;
mask[6] = mask[7] = 0;
}
if( !(flags & CALIB_RATIONAL_MODEL) )
flags |= CALIB_FIX_K4 + CALIB_FIX_K5 + CALIB_FIX_K6;
if( !(flags & CALIB_THIN_PRISM_MODEL))
flags |= CALIB_FIX_S1_S2_S3_S4;
if( !(flags & CALIB_TILTED_MODEL))
flags |= CALIB_FIX_TAUX_TAUY;
mask[ 4] = !(flags & CALIB_FIX_K1);
mask[ 5] = !(flags & CALIB_FIX_K2);
if( flags & CALIB_FIX_TANGENT_DIST )
{
mask[6] = mask[7] = 0;
}
mask[ 8] = !(flags & CALIB_FIX_K3);
mask[ 9] = !(flags & CALIB_FIX_K4);
mask[10] = !(flags & CALIB_FIX_K5);
mask[11] = !(flags & CALIB_FIX_K6);
if(flags & CALIB_FIX_S1_S2_S3_S4)
{
mask[12] = 0;
mask[13] = 0;
mask[14] = 0;
mask[15] = 0;
}
if(flags & CALIB_FIX_TAUX_TAUY)
{
mask[16] = 0;
mask[17] = 0;
}
if(releaseObject)
{
// copy object points
std::copy( matM.ptr<double>(), matM.ptr<double>( 0, maxPoints - 1 ) + 3,
param + NINTRINSIC + nimages * 6 );
// fix points
mask[NINTRINSIC + nimages * 6] = 0;
mask[NINTRINSIC + nimages * 6 + 1] = 0;
mask[NINTRINSIC + nimages * 6 + 2] = 0;
mask[NINTRINSIC + nimages * 6 + iFixedPoint * 3] = 0;
mask[NINTRINSIC + nimages * 6 + iFixedPoint * 3 + 1] = 0;
mask[NINTRINSIC + nimages * 6 + iFixedPoint * 3 + 2] = 0;
mask[nparams - 1] = 0;
}
}
Mat mask = cvarrToMat(solver.mask);
int nparams_nz = countNonZero(mask);
if (nparams_nz >= 2 * total)
CV_Error_(cv::Error::StsBadArg,
("There should be less vars to optimize (having %d) than the number of residuals (%d = 2 per point)", nparams_nz, 2 * total));
// 2. initialize extrinsic parameters
for( i = 0, pos = 0; i < nimages; i++, pos += ni )
{
CvMat _ri, _ti;
ni = npoints->data.i[i*npstep];
cvGetRows( solver.param, &_ri, NINTRINSIC + i*6, NINTRINSIC + i*6 + 3 );
cvGetRows( solver.param, &_ti, NINTRINSIC + i*6 + 3, NINTRINSIC + i*6 + 6 );
CvMat _Mi = cvMat(matM.colRange(pos, pos + ni));
CvMat _mi = cvMat(_m.colRange(pos, pos + ni));
Mat r_mat = cvarrToMat(&_ri), t_mat = cvarrToMat(&_ti);
findExtrinsicCameraParams2( cvarrToMat(&_Mi), cvarrToMat(&_mi), cvarrToMat(&matA),
cvarrToMat(&_k), r_mat, t_mat, /*useExtrinsicGuess=*/0 );
}
// 3. run the optimization
for(;;)
{
const CvMat* _param = 0;
CvMat *_JtJ = 0, *_JtErr = 0;
double* _errNorm = 0;
bool proceed = solver.updateAlt( _param, _JtJ, _JtErr, _errNorm );
double *param = solver.param->data.db, *pparam = solver.prevParam->data.db;
bool calcJ = solver.state == CvLevMarq::CALC_J || (!proceed && stdDevs);
if( flags & CALIB_FIX_ASPECT_RATIO )
{
param[0] = param[1]*aspectRatio;
pparam[0] = pparam[1]*aspectRatio;
}
A(0, 0) = param[0]; A(1, 1) = param[1]; A(0, 2) = param[2]; A(1, 2) = param[3];
std::copy(param + 4, param + 4 + 14, k);
if ( !proceed && !stdDevs && !perViewErrors )
break;
else if ( !proceed && stdDevs )
cvZero(_JtJ);
reprojErr = 0;
for( i = 0, pos = 0; i < nimages; i++, pos += ni )
{
CvMat _ri, _ti;
ni = npoints->data.i[i*npstep];
cvGetRows( solver.param, &_ri, NINTRINSIC + i*6, NINTRINSIC + i*6 + 3 );
cvGetRows( solver.param, &_ti, NINTRINSIC + i*6 + 3, NINTRINSIC + i*6 + 6 );
CvMat _Mi = cvMat(matM.colRange(pos, pos + ni));
if( releaseObject )
{
cvGetRows( solver.param, &_Mi, NINTRINSIC + nimages * 6,
NINTRINSIC + nimages * 6 + ni * 3 );
cvReshape( &_Mi, &_Mi, 3, 1 );
}
CvMat _mi = cvMat(_m.colRange(pos, pos + ni));
CvMat _me = cvMat(allErrors.colRange(pos, pos + ni));
_Je.resize(ni*2); _Ji.resize(ni*2); _err.resize(ni*2);
_Jo.resize(ni*2);
CvMat _mp = cvMat(_err.reshape(2, 1));
if( calcJ )
{
projectPoints( cvarrToMat(&_Mi), cvarrToMat(&_ri), cvarrToMat(&_ti), cvarrToMat(&matA),
cvarrToMat(&_k), cvarrToMat(&_mp), _Je.colRange(0, 3), _Je.colRange(3, 6),
(flags & CALIB_FIX_FOCAL_LENGTH) ? noArray() : _Ji.colRange(0, 2),
(flags & CALIB_FIX_PRINCIPAL_POINT) ? noArray() : _Ji.colRange(2, 4),
_Ji.colRange(4, 4 + _k.cols * _k.rows), (_Jo.empty()) ? noArray() : _Jo.colRange(0, ni * 3),
(flags & CALIB_FIX_ASPECT_RATIO) ? aspectRatio : 0);
}
else
projectPoints( cvarrToMat(&_Mi), cvarrToMat(&_ri), cvarrToMat(&_ti), cvarrToMat(&matA),
cvarrToMat(&_k), cvarrToMat(&_mp) );
cvSub( &_mp, &_mi, &_mp );
if (perViewErrors || stdDevs)
cvCopy(&_mp, &_me);
if( calcJ )
{
Mat JtJ(cvarrToMat(_JtJ)), JtErr(cvarrToMat(_JtErr));
// see HZ: (A6.14) for details on the structure of the Jacobian
JtJ(Rect(0, 0, NINTRINSIC, NINTRINSIC)) += _Ji.t() * _Ji;
JtJ(Rect(NINTRINSIC + i * 6, NINTRINSIC + i * 6, 6, 6)) = _Je.t() * _Je;
JtJ(Rect(NINTRINSIC + i * 6, 0, 6, NINTRINSIC)) = _Ji.t() * _Je;
if( releaseObject )
{
JtJ(Rect(NINTRINSIC + nimages * 6, 0, maxPoints * 3, NINTRINSIC)) += _Ji.t() * _Jo;
JtJ(Rect(NINTRINSIC + nimages * 6, NINTRINSIC + i * 6, maxPoints * 3, 6))
+= _Je.t() * _Jo;
JtJ(Rect(NINTRINSIC + nimages * 6, NINTRINSIC + nimages * 6, maxPoints * 3, maxPoints * 3))
+= _Jo.t() * _Jo;
}
JtErr.rowRange(0, NINTRINSIC) += _Ji.t() * _err;
JtErr.rowRange(NINTRINSIC + i * 6, NINTRINSIC + (i + 1) * 6) = _Je.t() * _err;
if( releaseObject )
{
JtErr.rowRange(NINTRINSIC + nimages * 6, nparams) += _Jo.t() * _err;
}
}
double viewErr = norm(_err, NORM_L2SQR);
if( perViewErrors )
perViewErrors->data.db[i] = std::sqrt(viewErr / ni);
reprojErr += viewErr;
}
if( _errNorm )
*_errNorm = reprojErr;
if( !proceed )
{
if( stdDevs )
{
Mat JtJinv, JtJN;
JtJN.create(nparams_nz, nparams_nz, CV_64F);
subMatrix(cvarrToMat(_JtJ), JtJN, mask, mask);
completeSymm(JtJN, false);
cv::invert(JtJN, JtJinv, DECOMP_SVD);
// an explanation of that denominator correction can be found here:
// R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, 2004, section 5.1.3, page 134
// see the discussion for more details: https://github.com/opencv/opencv/pull/22992
int nErrors = 2 * total - nparams_nz;
double sigma2 = norm(allErrors, NORM_L2SQR) / nErrors;
Mat stdDevsM = cvarrToMat(stdDevs);
int j = 0;
for ( int s = 0; s < nparams; s++ )
{
stdDevsM.at<double>(s) = mask.data[s] ? std::sqrt(JtJinv.at<double>(j,j) * sigma2) : 0.0;
if( mask.data[s] )
j++;
}
}
break;
}
}
// 4. store the results
cvConvert( &matA, cameraMatrix );
cvConvert( &_k, distCoeffs );
if( newObjPoints && releaseObject )
{
CvMat _Mi;
cvGetRows( solver.param, &_Mi, NINTRINSIC + nimages * 6,
NINTRINSIC + nimages * 6 + maxPoints * 3 );
cvReshape( &_Mi, &_Mi, 3, 1 );
cvConvert( &_Mi, newObjPoints );
}
for( i = 0, pos = 0; i < nimages; i++ )
{
CvMat src, dst;
if( rvecs )
{
src = cvMat( 3, 1, CV_64F, solver.param->data.db + NINTRINSIC + i*6 );
if( rvecs->rows == nimages && rvecs->cols*CV_MAT_CN(rvecs->type) == 9 )
{
dst = cvMat( 3, 3, CV_MAT_DEPTH(rvecs->type),
rvecs->data.ptr + rvecs->step*i );
Rodrigues( cvarrToMat(&src), cvarrToMat(&matA) );
cvConvert( &matA, &dst );
}
else
{
dst = cvMat( 3, 1, CV_MAT_DEPTH(rvecs->type), rvecs->rows == 1 ?
rvecs->data.ptr + i*CV_ELEM_SIZE(rvecs->type) :
rvecs->data.ptr + rvecs->step*i );
cvConvert( &src, &dst );
}
}
if( tvecs )
{
src = cvMat( 3, 1, CV_64F, solver.param->data.db + NINTRINSIC + i*6 + 3 );
dst = cvMat( 3, 1, CV_MAT_DEPTH(tvecs->type), tvecs->rows == 1 ?
tvecs->data.ptr + i*CV_ELEM_SIZE(tvecs->type) :
tvecs->data.ptr + tvecs->step*i );
cvConvert( &src, &dst );
}
}
return std::sqrt(reprojErr/total);
}
/* finds intrinsic and extrinsic camera parameters
from a few views of known calibration pattern */
CV_IMPL double cvCalibrateCamera2( const CvMat* objectPoints,
const CvMat* imagePoints, const CvMat* npoints,
CvSize imageSize, CvMat* cameraMatrix, CvMat* distCoeffs,
CvMat* rvecs, CvMat* tvecs, int flags, CvTermCriteria termCrit )
{
return cvCalibrateCamera2Internal(objectPoints, imagePoints, npoints, imageSize, -1, cameraMatrix,
distCoeffs, rvecs, tvecs, NULL, NULL, NULL, flags, termCrit);
}
//////////////////////////////// Stereo Calibration ///////////////////////////////////
static int dbCmp( const void* _a, const void* _b )
{
double a = *(const double*)_a;
double b = *(const double*)_b;
return (a > b) - (a < b);
}
static double cvStereoCalibrateImpl( const CvMat* _objectPoints, const CvMat* _imagePoints1,
const CvMat* _imagePoints2, const CvMat* _npoints,
CvMat* _cameraMatrix1, CvMat* _distCoeffs1,
CvMat* _cameraMatrix2, CvMat* _distCoeffs2,
CvSize imageSize, CvMat* matR, CvMat* matT,
CvMat* matE, CvMat* matF,
CvMat* rvecs, CvMat* tvecs, CvMat* perViewErr, int flags,
CvTermCriteria termCrit )
{
const int NINTRINSIC = 18;
Ptr<CvMat> npoints, imagePoints[2], objectPoints, RT0;
double reprojErr = 0;
double A[2][9], dk[2][14]={{0}}, rlr[9];
CvMat K[2], Dist[2], om_LR, T_LR;
CvMat R_LR = cvMat(3, 3, CV_64F, rlr);
int i, k, p, ni = 0, ofs, nimages, pointsTotal, maxPoints = 0;
int nparams;
bool recomputeIntrinsics = false;
double aspectRatio[2] = {0};
CV_Assert( CV_IS_MAT(_imagePoints1) && CV_IS_MAT(_imagePoints2) &&
CV_IS_MAT(_objectPoints) && CV_IS_MAT(_npoints) &&
CV_IS_MAT(matR) && CV_IS_MAT(matT) );
CV_Assert( CV_ARE_TYPES_EQ(_imagePoints1, _imagePoints2) &&
CV_ARE_DEPTHS_EQ(_imagePoints1, _objectPoints) );
CV_Assert( (_npoints->cols == 1 || _npoints->rows == 1) &&
CV_MAT_TYPE(_npoints->type) == CV_32SC1 );
nimages = _npoints->cols + _npoints->rows - 1;
npoints.reset(cvCreateMat( _npoints->rows, _npoints->cols, _npoints->type ));
cvCopy( _npoints, npoints );
for( i = 0, pointsTotal = 0; i < nimages; i++ )
{
maxPoints = MAX(maxPoints, npoints->data.i[i]);
pointsTotal += npoints->data.i[i];
}
objectPoints.reset(cvCreateMat( _objectPoints->rows, _objectPoints->cols,
CV_64FC(CV_MAT_CN(_objectPoints->type))));
cvConvert( _objectPoints, objectPoints );
cvReshape( objectPoints, objectPoints, 3, 1 );
if( rvecs )
{
int cn = CV_MAT_CN(rvecs->type);
if( !CV_IS_MAT(rvecs) ||
(CV_MAT_DEPTH(rvecs->type) != CV_32F && CV_MAT_DEPTH(rvecs->type) != CV_64F) ||
((rvecs->rows != nimages || (rvecs->cols*cn != 3 && rvecs->cols*cn != 9)) &&
(rvecs->rows != 1 || rvecs->cols != nimages || cn != 3)) )
CV_Error( cv::Error::StsBadArg, "the output array of rotation vectors must be 3-channel "
"1xn or nx1 array or 1-channel nx3 or nx9 array, where n is the number of views" );
}
if( tvecs )
{
int cn = CV_MAT_CN(tvecs->type);
if( !CV_IS_MAT(tvecs) ||
(CV_MAT_DEPTH(tvecs->type) != CV_32F && CV_MAT_DEPTH(tvecs->type) != CV_64F) ||
((tvecs->rows != nimages || tvecs->cols*cn != 3) &&
(tvecs->rows != 1 || tvecs->cols != nimages || cn != 3)) )
CV_Error( cv::Error::StsBadArg, "the output array of translation vectors must be 3-channel "
"1xn or nx1 array or 1-channel nx3 array, where n is the number of views" );
}
for( k = 0; k < 2; k++ )
{
const CvMat* points = k == 0 ? _imagePoints1 : _imagePoints2;
const CvMat* cameraMatrix = k == 0 ? _cameraMatrix1 : _cameraMatrix2;
const CvMat* distCoeffs = k == 0 ? _distCoeffs1 : _distCoeffs2;
int cn = CV_MAT_CN(_imagePoints1->type);
CV_Assert( (CV_MAT_DEPTH(_imagePoints1->type) == CV_32F ||
CV_MAT_DEPTH(_imagePoints1->type) == CV_64F) &&
((_imagePoints1->rows == pointsTotal && _imagePoints1->cols*cn == 2) ||
(_imagePoints1->rows == 1 && _imagePoints1->cols == pointsTotal && cn == 2)) );
K[k] = cvMat(3,3,CV_64F,A[k]);
Dist[k] = cvMat(1,14,CV_64F,dk[k]);
imagePoints[k].reset(cvCreateMat( points->rows, points->cols, CV_64FC(CV_MAT_CN(points->type))));
cvConvert( points, imagePoints[k] );
cvReshape( imagePoints[k], imagePoints[k], 2, 1 );
if( flags & (CALIB_FIX_INTRINSIC|CALIB_USE_INTRINSIC_GUESS|
CALIB_FIX_ASPECT_RATIO|CALIB_FIX_FOCAL_LENGTH) )
cvConvert( cameraMatrix, &K[k] );
if( flags & (CALIB_FIX_INTRINSIC|CALIB_USE_INTRINSIC_GUESS|
CALIB_FIX_K1|CALIB_FIX_K2|CALIB_FIX_K3|CALIB_FIX_K4|CALIB_FIX_K5|CALIB_FIX_K6|CALIB_FIX_TANGENT_DIST) )
{
CvMat tdist = cvMat( distCoeffs->rows, distCoeffs->cols,
CV_MAKETYPE(CV_64F,CV_MAT_CN(distCoeffs->type)), Dist[k].data.db );
cvConvert( distCoeffs, &tdist );
}
if( !(flags & (CALIB_FIX_INTRINSIC|CALIB_USE_INTRINSIC_GUESS)))
{
cvCalibrateCamera2( objectPoints, imagePoints[k],
npoints, imageSize, &K[k], &Dist[k], NULL, NULL, flags );
}
}
if( flags & CALIB_SAME_FOCAL_LENGTH )
{
static const int avg_idx[] = { 0, 4, 2, 5, -1 };
for( k = 0; avg_idx[k] >= 0; k++ )
A[0][avg_idx[k]] = A[1][avg_idx[k]] = (A[0][avg_idx[k]] + A[1][avg_idx[k]])*0.5;
}
if( flags & CALIB_FIX_ASPECT_RATIO )
{
for( k = 0; k < 2; k++ )
aspectRatio[k] = A[k][0]/A[k][4];
}
recomputeIntrinsics = (flags & CALIB_FIX_INTRINSIC) == 0;
Mat err( maxPoints*2, 1, CV_64F );
Mat Je( maxPoints*2, 6, CV_64F );
Mat J_LR( maxPoints*2, 6, CV_64F );
Mat Ji( maxPoints*2, NINTRINSIC, CV_64F, Scalar(0) );
// we optimize for the inter-camera R(3),t(3), then, optionally,
// for intrinisic parameters of each camera ((fx,fy,cx,cy,k1,k2,p1,p2) ~ 8 parameters).
nparams = 6*(nimages+1) + (recomputeIntrinsics ? NINTRINSIC*2 : 0);
CvLevMarq solver( nparams, 0, termCrit );
if(flags & CALIB_USE_LU) {
solver.solveMethod = DECOMP_LU;
}
if( recomputeIntrinsics )
{
uchar* imask = solver.mask->data.ptr + nparams - NINTRINSIC*2;
if( !(flags & CALIB_RATIONAL_MODEL) )
flags |= CALIB_FIX_K4 | CALIB_FIX_K5 | CALIB_FIX_K6;
if( !(flags & CALIB_THIN_PRISM_MODEL) )
flags |= CALIB_FIX_S1_S2_S3_S4;
if( !(flags & CALIB_TILTED_MODEL) )
flags |= CALIB_FIX_TAUX_TAUY;
if( flags & CALIB_FIX_ASPECT_RATIO )
imask[0] = imask[NINTRINSIC] = 0;
if( flags & CALIB_FIX_FOCAL_LENGTH )
imask[0] = imask[1] = imask[NINTRINSIC] = imask[NINTRINSIC+1] = 0;
if( flags & CALIB_FIX_PRINCIPAL_POINT )
imask[2] = imask[3] = imask[NINTRINSIC+2] = imask[NINTRINSIC+3] = 0;
if( flags & (CALIB_ZERO_TANGENT_DIST|CALIB_FIX_TANGENT_DIST) )
imask[6] = imask[7] = imask[NINTRINSIC+6] = imask[NINTRINSIC+7] = 0;
if( flags & CALIB_FIX_K1 )
imask[4] = imask[NINTRINSIC+4] = 0;
if( flags & CALIB_FIX_K2 )
imask[5] = imask[NINTRINSIC+5] = 0;
if( flags & CALIB_FIX_K3 )
imask[8] = imask[NINTRINSIC+8] = 0;
if( flags & CALIB_FIX_K4 )
imask[9] = imask[NINTRINSIC+9] = 0;
if( flags & CALIB_FIX_K5 )
imask[10] = imask[NINTRINSIC+10] = 0;
if( flags & CALIB_FIX_K6 )
imask[11] = imask[NINTRINSIC+11] = 0;
if( flags & CALIB_FIX_S1_S2_S3_S4 )
{
imask[12] = imask[NINTRINSIC+12] = 0;
imask[13] = imask[NINTRINSIC+13] = 0;
imask[14] = imask[NINTRINSIC+14] = 0;
imask[15] = imask[NINTRINSIC+15] = 0;
}
if( flags & CALIB_FIX_TAUX_TAUY )
{
imask[16] = imask[NINTRINSIC+16] = 0;
imask[17] = imask[NINTRINSIC+17] = 0;
}
}
// storage for initial [om(R){i}|t{i}] (in order to compute the median for each component)
RT0.reset(cvCreateMat( 6, nimages, CV_64F ));
/*
Compute initial estimate of pose
For each image, compute:
R(om) is the rotation matrix of om
om(R) is the rotation vector of R
R_ref = R(om_right) * R(om_left)'
T_ref_list = [T_ref_list; T_right - R_ref * T_left]
om_ref_list = {om_ref_list; om(R_ref)]
om = median(om_ref_list)
T = median(T_ref_list)
*/
for( i = ofs = 0; i < nimages; ofs += ni, i++ )
{
ni = npoints->data.i[i];
CvMat objpt_i;
double _om[2][3], r[2][9], t[2][3];
CvMat om[2], R[2], T[2], imgpt_i[2];
objpt_i = cvMat(1, ni, CV_64FC3, objectPoints->data.db + ofs*3);
for( k = 0; k < 2; k++ )
{
imgpt_i[k] = cvMat(1, ni, CV_64FC2, imagePoints[k]->data.db + ofs*2);
om[k] = cvMat(3, 1, CV_64F, _om[k]);
R[k] = cvMat(3, 3, CV_64F, r[k]);
T[k] = cvMat(3, 1, CV_64F, t[k]);
Mat r_mat = cvarrToMat(&om[k]), t_mat = cvarrToMat(&T[k]);
findExtrinsicCameraParams2( cvarrToMat(&objpt_i), cvarrToMat(&imgpt_i[k]),
cvarrToMat(&K[k]), cvarrToMat(&Dist[k]),
r_mat, t_mat, /*useExtrinsicGuess=*/0 );
Rodrigues( cvarrToMat(&om[k]), cvarrToMat(&R[k]) );
if( k == 0 )
{
// save initial om_left and T_left
solver.param->data.db[(i+1)*6] = _om[0][0];
solver.param->data.db[(i+1)*6 + 1] = _om[0][1];
solver.param->data.db[(i+1)*6 + 2] = _om[0][2];
solver.param->data.db[(i+1)*6 + 3] = t[0][0];
solver.param->data.db[(i+1)*6 + 4] = t[0][1];
solver.param->data.db[(i+1)*6 + 5] = t[0][2];
}
}
cvGEMM( &R[1], &R[0], 1, 0, 0, &R[0], CV_GEMM_B_T );
cvGEMM( &R[0], &T[0], -1, &T[1], 1, &T[1] );
Rodrigues( cvarrToMat(&R[0]), cvarrToMat(&T[0]) );
RT0->data.db[i] = t[0][0];
RT0->data.db[i + nimages] = t[0][1];
RT0->data.db[i + nimages*2] = t[0][2];
RT0->data.db[i + nimages*3] = t[1][0];
RT0->data.db[i + nimages*4] = t[1][1];
RT0->data.db[i + nimages*5] = t[1][2];
}
if(flags & CALIB_USE_EXTRINSIC_GUESS)
{
Vec3d R, T;
cvarrToMat(matT).convertTo(T, CV_64F);
if( matR->rows == 3 && matR->cols == 3 )
Rodrigues(cvarrToMat(matR), R);
else
cvarrToMat(matR).convertTo(R, CV_64F);
solver.param->data.db[0] = R[0];
solver.param->data.db[1] = R[1];
solver.param->data.db[2] = R[2];
solver.param->data.db[3] = T[0];
solver.param->data.db[4] = T[1];
solver.param->data.db[5] = T[2];
}
else
{
// find the medians and save the first 6 parameters
for( i = 0; i < 6; i++ )
{
qsort( RT0->data.db + i*nimages, nimages, CV_ELEM_SIZE(RT0->type), dbCmp );
solver.param->data.db[i] = nimages % 2 != 0 ? RT0->data.db[i*nimages + nimages/2] :
(RT0->data.db[i*nimages + nimages/2 - 1] + RT0->data.db[i*nimages + nimages/2])*0.5;
}
}
if( recomputeIntrinsics )
for( k = 0; k < 2; k++ )
{
double* iparam = solver.param->data.db + (nimages+1)*6 + k*NINTRINSIC;
if( flags & CALIB_ZERO_TANGENT_DIST )
dk[k][2] = dk[k][3] = 0;
iparam[0] = A[k][0]; iparam[1] = A[k][4]; iparam[2] = A[k][2]; iparam[3] = A[k][5];
iparam[4] = dk[k][0]; iparam[5] = dk[k][1]; iparam[6] = dk[k][2];
iparam[7] = dk[k][3]; iparam[8] = dk[k][4]; iparam[9] = dk[k][5];
iparam[10] = dk[k][6]; iparam[11] = dk[k][7];
iparam[12] = dk[k][8];
iparam[13] = dk[k][9];
iparam[14] = dk[k][10];
iparam[15] = dk[k][11];
iparam[16] = dk[k][12];
iparam[17] = dk[k][13];
}
om_LR = cvMat(3, 1, CV_64F, solver.param->data.db);
T_LR = cvMat(3, 1, CV_64F, solver.param->data.db + 3);
for(;;)
{
const CvMat* param = 0;
CvMat *JtJ = 0, *JtErr = 0;
double *_errNorm = 0;
double _omR[3], _tR[3];
double _dr3dr1[9], _dr3dr2[9], /*_dt3dr1[9],*/ _dt3dr2[9], _dt3dt1[9], _dt3dt2[9];
CvMat dr3dr1 = cvMat(3, 3, CV_64F, _dr3dr1);
CvMat dr3dr2 = cvMat(3, 3, CV_64F, _dr3dr2);
//CvMat dt3dr1 = cvMat(3, 3, CV_64F, _dt3dr1);
CvMat dt3dr2 = cvMat(3, 3, CV_64F, _dt3dr2);
CvMat dt3dt1 = cvMat(3, 3, CV_64F, _dt3dt1);
CvMat dt3dt2 = cvMat(3, 3, CV_64F, _dt3dt2);
CvMat om[2], T[2], imgpt_i[2];
if( !solver.updateAlt( param, JtJ, JtErr, _errNorm ))
break;
reprojErr = 0;
Rodrigues( cvarrToMat(&om_LR), cvarrToMat(&R_LR) );
om[1] = cvMat(3,1,CV_64F,_omR);
T[1] = cvMat(3,1,CV_64F,_tR);
if( recomputeIntrinsics )
{
double* iparam = solver.param->data.db + (nimages+1)*6;
double* ipparam = solver.prevParam->data.db + (nimages+1)*6;
if( flags & CALIB_SAME_FOCAL_LENGTH )
{
iparam[NINTRINSIC] = iparam[0];
iparam[NINTRINSIC+1] = iparam[1];
ipparam[NINTRINSIC] = ipparam[0];
ipparam[NINTRINSIC+1] = ipparam[1];
}
if( flags & CALIB_FIX_ASPECT_RATIO )
{
iparam[0] = iparam[1]*aspectRatio[0];
iparam[NINTRINSIC] = iparam[NINTRINSIC+1]*aspectRatio[1];
ipparam[0] = ipparam[1]*aspectRatio[0];
ipparam[NINTRINSIC] = ipparam[NINTRINSIC+1]*aspectRatio[1];
}
for( k = 0; k < 2; k++ )
{
A[k][0] = iparam[k*NINTRINSIC+0];
A[k][4] = iparam[k*NINTRINSIC+1];
A[k][2] = iparam[k*NINTRINSIC+2];
A[k][5] = iparam[k*NINTRINSIC+3];
dk[k][0] = iparam[k*NINTRINSIC+4];
dk[k][1] = iparam[k*NINTRINSIC+5];
dk[k][2] = iparam[k*NINTRINSIC+6];
dk[k][3] = iparam[k*NINTRINSIC+7];
dk[k][4] = iparam[k*NINTRINSIC+8];
dk[k][5] = iparam[k*NINTRINSIC+9];
dk[k][6] = iparam[k*NINTRINSIC+10];
dk[k][7] = iparam[k*NINTRINSIC+11];
dk[k][8] = iparam[k*NINTRINSIC+12];
dk[k][9] = iparam[k*NINTRINSIC+13];
dk[k][10] = iparam[k*NINTRINSIC+14];
dk[k][11] = iparam[k*NINTRINSIC+15];
dk[k][12] = iparam[k*NINTRINSIC+16];
dk[k][13] = iparam[k*NINTRINSIC+17];
}
}
for( i = ofs = 0; i < nimages; ofs += ni, i++ )
{
ni = npoints->data.i[i];
CvMat objpt_i;
om[0] = cvMat(3,1,CV_64F,solver.param->data.db+(i+1)*6);
T[0] = cvMat(3,1,CV_64F,solver.param->data.db+(i+1)*6+3);
if( JtJ || JtErr )
composeRT( cvarrToMat(&om[0]), cvarrToMat(&T[0]), cvarrToMat(&om_LR),
cvarrToMat(&T_LR), cvarrToMat(&om[1]), cvarrToMat(&T[1]),
cvarrToMat(&dr3dr1), noArray(), cvarrToMat(&dr3dr2),
noArray(), noArray(), cvarrToMat(&dt3dt1), cvarrToMat(&dt3dr2),
cvarrToMat(&dt3dt2 ) );
else
composeRT( cvarrToMat(&om[0]), cvarrToMat(&T[0]), cvarrToMat(&om_LR),
cvarrToMat(&T_LR), cvarrToMat(&om[1]), cvarrToMat(&T[1]) );
objpt_i = cvMat(1, ni, CV_64FC3, objectPoints->data.db + ofs*3);
err.resize(ni*2); Je.resize(ni*2); J_LR.resize(ni*2); Ji.resize(ni*2);
CvMat tmpimagePoints = cvMat(err.reshape(2, 1));
for( k = 0; k < 2; k++ )
{
imgpt_i[k] = cvMat(1, ni, CV_64FC2, imagePoints[k]->data.db + ofs*2);
if( JtJ || JtErr )
projectPoints( cvarrToMat(&objpt_i), cvarrToMat(&om[k]), cvarrToMat(&T[k]),
cvarrToMat(&K[k]), cvarrToMat(&Dist[k]),
err.reshape(2, 1), Je.colRange(0, 3), Je.colRange(3, 6),
Ji.colRange(0, 2), Ji.colRange(2, 4), Ji.colRange(4, 4 + Dist[k].cols * Dist[k].rows), noArray(),
(flags & CALIB_FIX_ASPECT_RATIO) ? aspectRatio[k] : 0);
else
projectPoints( cvarrToMat(&objpt_i), cvarrToMat(&om[k]), cvarrToMat(&T[k]),
cvarrToMat(&K[k]), cvarrToMat(&Dist[k]), cvarrToMat(&tmpimagePoints) );
cvSub( &tmpimagePoints, &imgpt_i[k], &tmpimagePoints );
if( solver.state == CvLevMarq::CALC_J )
{
int iofs = (nimages+1)*6 + k*NINTRINSIC, eofs = (i+1)*6;
CV_Assert( JtJ && JtErr );
Mat _JtJ(cvarrToMat(JtJ)), _JtErr(cvarrToMat(JtErr));
if( k == 1 )
{
// d(err_{x|y}R) ~ de3
// convert de3/{dr3,dt3} => de3{dr1,dt1} & de3{dr2,dt2}
for( p = 0; p < ni*2; p++ )
{
CvMat de3dr3 = cvMat( 1, 3, CV_64F, Je.ptr(p));
CvMat de3dt3 = cvMat( 1, 3, CV_64F, de3dr3.data.db + 3 );
CvMat de3dr2 = cvMat( 1, 3, CV_64F, J_LR.ptr(p) );
CvMat de3dt2 = cvMat( 1, 3, CV_64F, de3dr2.data.db + 3 );
double _de3dr1[3], _de3dt1[3];
CvMat de3dr1 = cvMat( 1, 3, CV_64F, _de3dr1 );
CvMat de3dt1 = cvMat( 1, 3, CV_64F, _de3dt1 );
cvMatMul( &de3dr3, &dr3dr1, &de3dr1 );
cvMatMul( &de3dt3, &dt3dt1, &de3dt1 );
cvMatMul( &de3dr3, &dr3dr2, &de3dr2 );
cvMatMulAdd( &de3dt3, &dt3dr2, &de3dr2, &de3dr2 );
cvMatMul( &de3dt3, &dt3dt2, &de3dt2 );
cvCopy( &de3dr1, &de3dr3 );
cvCopy( &de3dt1, &de3dt3 );
}
_JtJ(Rect(0, 0, 6, 6)) += J_LR.t()*J_LR;
_JtJ(Rect(eofs, 0, 6, 6)) = J_LR.t()*Je;
_JtErr.rowRange(0, 6) += J_LR.t()*err;
}
_JtJ(Rect(eofs, eofs, 6, 6)) += Je.t()*Je;
_JtErr.rowRange(eofs, eofs + 6) += Je.t()*err;
if( recomputeIntrinsics )
{
_JtJ(Rect(iofs, iofs, NINTRINSIC, NINTRINSIC)) += Ji.t()*Ji;
_JtJ(Rect(iofs, eofs, NINTRINSIC, 6)) += Je.t()*Ji;
if( k == 1 )
{
_JtJ(Rect(iofs, 0, NINTRINSIC, 6)) += J_LR.t()*Ji;
}
_JtErr.rowRange(iofs, iofs + NINTRINSIC) += Ji.t()*err;
}
}
double viewErr = norm(err, NORM_L2SQR);
if(perViewErr)
perViewErr->data.db[i*2 + k] = std::sqrt(viewErr/ni);
reprojErr += viewErr;
}
}
if(_errNorm)
*_errNorm = reprojErr;
}
Rodrigues( cvarrToMat(&om_LR), cvarrToMat(&R_LR) );
if( matR->rows == 1 || matR->cols == 1 )
cvConvert( &om_LR, matR );
else
cvConvert( &R_LR, matR );
cvConvert( &T_LR, matT );
if( recomputeIntrinsics )
{
cvConvert( &K[0], _cameraMatrix1 );
cvConvert( &K[1], _cameraMatrix2 );
for( k = 0; k < 2; k++ )
{
CvMat* distCoeffs = k == 0 ? _distCoeffs1 : _distCoeffs2;
CvMat tdist = cvMat( distCoeffs->rows, distCoeffs->cols,
CV_MAKETYPE(CV_64F,CV_MAT_CN(distCoeffs->type)), Dist[k].data.db );
cvConvert( &tdist, distCoeffs );
}
}
if( matE || matF )
{
double* t = T_LR.data.db;
double tx[] =
{
0, -t[2], t[1],
t[2], 0, -t[0],
-t[1], t[0], 0
};
CvMat Tx = cvMat(3, 3, CV_64F, tx);
double e[9], f[9];
CvMat E = cvMat(3, 3, CV_64F, e);
CvMat F = cvMat(3, 3, CV_64F, f);
cvMatMul( &Tx, &R_LR, &E );
if( matE )
cvConvert( &E, matE );
if( matF )
{
double ik[9];
CvMat iK = cvMat(3, 3, CV_64F, ik);
cvInvert(&K[1], &iK);
cvGEMM( &iK, &E, 1, 0, 0, &E, CV_GEMM_A_T );
cvInvert(&K[0], &iK);
cvMatMul(&E, &iK, &F);
cvConvertScale( &F, matF, fabs(f[8]) > 0 ? 1./f[8] : 1 );
}
}
CvMat tmp = cvMat(3, 3, CV_64F);
for( i = 0; i < nimages; i++ )
{
CvMat src, dst;
if( rvecs )
{
src = cvMat(3, 1, CV_64F, solver.param->data.db+(i+1)*6);
if( rvecs->rows == nimages && rvecs->cols*CV_MAT_CN(rvecs->type) == 9 )
{
dst = cvMat(3, 3, CV_MAT_DEPTH(rvecs->type),
rvecs->data.ptr + rvecs->step*i);
Rodrigues( cvarrToMat(&src), cvarrToMat(&tmp) );
cvConvert( &tmp, &dst );
}
else
{
dst = cvMat(3, 1, CV_MAT_DEPTH(rvecs->type), rvecs->rows == 1 ?
rvecs->data.ptr + i*CV_ELEM_SIZE(rvecs->type) :
rvecs->data.ptr + rvecs->step*i);
cvConvert( &src, &dst );
}
}
if( tvecs )
{
src = cvMat(3, 1,CV_64F,solver.param->data.db+(i+1)*6+3);
dst = cvMat(3, 1, CV_MAT_DEPTH(tvecs->type), tvecs->rows == 1 ?
tvecs->data.ptr + i*CV_ELEM_SIZE(tvecs->type) :
tvecs->data.ptr + tvecs->step*i);
cvConvert( &src, &dst );
}
}
return std::sqrt(reprojErr/(pointsTotal*2));
}
namespace cv
{
static void collectCalibrationData( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints1,
InputArrayOfArrays imagePoints2,
int iFixedPoint,
Mat& objPtMat, Mat& imgPtMat1, Mat* imgPtMat2,
Mat& npoints )
{
int nimages = (int)objectPoints.total();
int total = 0;
CV_Assert(nimages > 0);
CV_CheckEQ(nimages, (int)imagePoints1.total(), "");
if (imgPtMat2)
CV_CheckEQ(nimages, (int)imagePoints2.total(), "");
for (int i = 0; i < nimages; i++)
{
Mat objectPoint = objectPoints.getMat(i);
if (objectPoint.empty())
CV_Error(cv::Error::StsBadSize, "objectPoints should not contain empty vector of vectors of points");
int numberOfObjectPoints = objectPoint.checkVector(3, CV_32F);
if (numberOfObjectPoints <= 0)
CV_Error(cv::Error::StsUnsupportedFormat, "objectPoints should contain vector of vectors of points of type Point3f");
Mat imagePoint1 = imagePoints1.getMat(i);
if (imagePoint1.empty())
CV_Error(cv::Error::StsBadSize, "imagePoints1 should not contain empty vector of vectors of points");
int numberOfImagePoints = imagePoint1.checkVector(2, CV_32F);
if (numberOfImagePoints <= 0)
CV_Error(cv::Error::StsUnsupportedFormat, "imagePoints1 should contain vector of vectors of points of type Point2f");
CV_CheckEQ(numberOfObjectPoints, numberOfImagePoints, "Number of object and image points must be equal");
total += numberOfObjectPoints;
}
npoints.create(1, (int)nimages, CV_32S);
objPtMat.create(1, (int)total, CV_32FC3);
imgPtMat1.create(1, (int)total, CV_32FC2);
Point2f* imgPtData2 = 0;
if (imgPtMat2)
{
imgPtMat2->create(1, (int)total, CV_32FC2);
imgPtData2 = imgPtMat2->ptr<Point2f>();
}
Point3f* objPtData = objPtMat.ptr<Point3f>();
Point2f* imgPtData1 = imgPtMat1.ptr<Point2f>();
for (int i = 0, j = 0; i < nimages; i++)
{
Mat objpt = objectPoints.getMat(i);
Mat imgpt1 = imagePoints1.getMat(i);
int numberOfObjectPoints = objpt.checkVector(3, CV_32F);
npoints.at<int>(i) = numberOfObjectPoints;
for (int n = 0; n < numberOfObjectPoints; ++n)
{
objPtData[j + n] = objpt.ptr<Point3f>()[n];
imgPtData1[j + n] = imgpt1.ptr<Point2f>()[n];
}
if (imgPtData2)
{
Mat imgpt2 = imagePoints2.getMat(i);
int numberOfImage2Points = imgpt2.checkVector(2, CV_32F);
CV_CheckEQ(numberOfObjectPoints, numberOfImage2Points, "Number of object and image(2) points must be equal");
for (int n = 0; n < numberOfImage2Points; ++n)
{
imgPtData2[j + n] = imgpt2.ptr<Point2f>()[n];
}
}
j += numberOfObjectPoints;
}
int ni = npoints.at<int>(0);
bool releaseObject = iFixedPoint > 0 && iFixedPoint < ni - 1;
// check object points. If not qualified, report errors.
if( releaseObject )
{
for (int i = 1; i < nimages; i++)
{
if( npoints.at<int>(i) != ni )
{
CV_Error( cv::Error::StsBadArg, "All objectPoints[i].size() should be equal when "
"object-releasing method is requested." );
}
Mat ocmp = objPtMat.colRange(ni * i, ni * i + ni) != objPtMat.colRange(0, ni);
ocmp = ocmp.reshape(1);
if( countNonZero(ocmp) )
{
CV_Error( cv::Error::StsBadArg, "All objectPoints[i] should be identical when object-releasing"
" method is requested." );
}
}
}
}
static void collectCalibrationData( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints1,
InputArrayOfArrays imagePoints2,
Mat& objPtMat, Mat& imgPtMat1, Mat* imgPtMat2,
Mat& npoints )
{
collectCalibrationData( objectPoints, imagePoints1, imagePoints2, -1, objPtMat, imgPtMat1,
imgPtMat2, npoints );
}
static Mat prepareCameraMatrix(Mat& cameraMatrix0, int rtype, int flags)
{
Mat cameraMatrix = Mat::eye(3, 3, rtype);
if( cameraMatrix0.size() == cameraMatrix.size() )
cameraMatrix0.convertTo(cameraMatrix, rtype);
else if( flags & CALIB_USE_INTRINSIC_GUESS )
CV_Error(Error::StsBadArg, "CALIB_USE_INTRINSIC_GUESS flag is set, but the camera matrix is not 3x3");
return cameraMatrix;
}
static Mat prepareDistCoeffs(Mat& distCoeffs0, int rtype, int outputSize = 14)
{
CV_Assert((int)distCoeffs0.total() <= outputSize);
Mat distCoeffs = Mat::zeros(distCoeffs0.cols == 1 ? Size(1, outputSize) : Size(outputSize, 1), rtype);
if( distCoeffs0.size() == Size(1, 4) ||
distCoeffs0.size() == Size(1, 5) ||
distCoeffs0.size() == Size(1, 8) ||
distCoeffs0.size() == Size(1, 12) ||
distCoeffs0.size() == Size(1, 14) ||
distCoeffs0.size() == Size(4, 1) ||
distCoeffs0.size() == Size(5, 1) ||
distCoeffs0.size() == Size(8, 1) ||
distCoeffs0.size() == Size(12, 1) ||
distCoeffs0.size() == Size(14, 1) )
{
Mat dstCoeffs(distCoeffs, Rect(0, 0, distCoeffs0.cols, distCoeffs0.rows));
distCoeffs0.convertTo(dstCoeffs, rtype);
}
return distCoeffs;
}
} // namespace cv
cv::Mat cv::initCameraMatrix2D( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints,
Size imageSize, double aspectRatio )
{
CV_INSTRUMENT_REGION();
Mat objPt, imgPt, npoints, cameraMatrix(3, 3, CV_64F);
collectCalibrationData( objectPoints, imagePoints, noArray(),
objPt, imgPt, 0, npoints );
CvMat _objPt = cvMat(objPt), _imgPt = cvMat(imgPt), _npoints = cvMat(npoints), _cameraMatrix = cvMat(cameraMatrix);
cvInitIntrinsicParams2D( &_objPt, &_imgPt, &_npoints,
cvSize(imageSize), &_cameraMatrix, aspectRatio );
return cameraMatrix;
}
double cv::calibrateCamera( InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints,
Size imageSize, InputOutputArray _cameraMatrix, InputOutputArray _distCoeffs,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs, int flags, TermCriteria criteria )
{
CV_INSTRUMENT_REGION();
return calibrateCamera(_objectPoints, _imagePoints, imageSize, _cameraMatrix, _distCoeffs,
_rvecs, _tvecs, noArray(), noArray(), noArray(), flags, criteria);
}
double cv::calibrateCamera(InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints,
Size imageSize, InputOutputArray _cameraMatrix, InputOutputArray _distCoeffs,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs,
OutputArray stdDeviationsIntrinsics,
OutputArray stdDeviationsExtrinsics,
OutputArray _perViewErrors, int flags, TermCriteria criteria )
{
CV_INSTRUMENT_REGION();
return calibrateCameraRO(_objectPoints, _imagePoints, imageSize, -1, _cameraMatrix, _distCoeffs,
_rvecs, _tvecs, noArray(), stdDeviationsIntrinsics, stdDeviationsExtrinsics,
noArray(), _perViewErrors, flags, criteria);
}
double cv::calibrateCameraRO(InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints,
Size imageSize, int iFixedPoint, InputOutputArray _cameraMatrix,
InputOutputArray _distCoeffs,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs,
OutputArray newObjPoints,
int flags, TermCriteria criteria)
{
CV_INSTRUMENT_REGION();
return calibrateCameraRO(_objectPoints, _imagePoints, imageSize, iFixedPoint, _cameraMatrix,
_distCoeffs, _rvecs, _tvecs, newObjPoints, noArray(), noArray(),
noArray(), noArray(), flags, criteria);
}
double cv::calibrateCameraRO(InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints,
Size imageSize, int iFixedPoint, InputOutputArray _cameraMatrix,
InputOutputArray _distCoeffs,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs,
OutputArray newObjPoints,
OutputArray stdDeviationsIntrinsics,
OutputArray stdDeviationsExtrinsics,
OutputArray stdDeviationsObjPoints,
OutputArray _perViewErrors, int flags, TermCriteria criteria )
{
CV_INSTRUMENT_REGION();
int rtype = CV_64F;
CV_Assert( _cameraMatrix.needed() );
CV_Assert( _distCoeffs.needed() );
Mat cameraMatrix = _cameraMatrix.getMat();
cameraMatrix = prepareCameraMatrix(cameraMatrix, rtype, flags);
Mat distCoeffs = _distCoeffs.getMat();
distCoeffs = (flags & CALIB_THIN_PRISM_MODEL) && !(flags & CALIB_TILTED_MODEL) ? prepareDistCoeffs(distCoeffs, rtype, 12) :
prepareDistCoeffs(distCoeffs, rtype);
if( !(flags & CALIB_RATIONAL_MODEL) &&
(!(flags & CALIB_THIN_PRISM_MODEL)) &&
(!(flags & CALIB_TILTED_MODEL)))
distCoeffs = distCoeffs.rows == 1 ? distCoeffs.colRange(0, 5) : distCoeffs.rowRange(0, 5);
int nimages = int(_objectPoints.total());
CV_Assert( nimages > 0 );
Mat objPt, imgPt, npoints, rvecM, tvecM, stdDeviationsM, errorsM;
bool rvecs_needed = _rvecs.needed(), tvecs_needed = _tvecs.needed(),
stddev_needed = stdDeviationsIntrinsics.needed(), errors_needed = _perViewErrors.needed(),
stddev_ext_needed = stdDeviationsExtrinsics.needed();
bool newobj_needed = newObjPoints.needed();
bool stddev_obj_needed = stdDeviationsObjPoints.needed();
bool rvecs_mat_vec = _rvecs.isMatVector();
bool tvecs_mat_vec = _tvecs.isMatVector();
if( rvecs_needed )
{
_rvecs.create(nimages, 1, CV_64FC3);
if(rvecs_mat_vec)
rvecM.create(nimages, 3, CV_64F);
else
rvecM = _rvecs.getMat();
}
if( tvecs_needed )
{
_tvecs.create(nimages, 1, CV_64FC3);
if(tvecs_mat_vec)
tvecM.create(nimages, 3, CV_64F);
else
tvecM = _tvecs.getMat();
}
collectCalibrationData( _objectPoints, _imagePoints, noArray(), iFixedPoint,
objPt, imgPt, 0, npoints );
bool releaseObject = iFixedPoint > 0 && iFixedPoint < npoints.at<int>(0) - 1;
newobj_needed = newobj_needed && releaseObject;
int np = npoints.at<int>( 0 );
Mat newObjPt;
if( newobj_needed ) {
newObjPoints.create( 1, np, CV_32FC3 );
newObjPt = newObjPoints.getMat();
}
stddev_obj_needed = stddev_obj_needed && releaseObject;
bool stddev_any_needed = stddev_needed || stddev_ext_needed || stddev_obj_needed;
if( stddev_any_needed )
{
if( releaseObject )
stdDeviationsM.create(nimages*6 + CV_CALIB_NINTRINSIC + np * 3, 1, CV_64F);
else
stdDeviationsM.create(nimages*6 + CV_CALIB_NINTRINSIC, 1, CV_64F);
}
if( errors_needed )
{
_perViewErrors.create(nimages, 1, CV_64F);
errorsM = _perViewErrors.getMat();
}
CvMat c_objPt = cvMat(objPt), c_imgPt = cvMat(imgPt), c_npoints = cvMat(npoints);
CvMat c_cameraMatrix = cvMat(cameraMatrix), c_distCoeffs = cvMat(distCoeffs);
CvMat c_rvecM = cvMat(rvecM), c_tvecM = cvMat(tvecM), c_stdDev = cvMat(stdDeviationsM), c_errors = cvMat(errorsM);
CvMat c_newObjPt = cvMat( newObjPt );
double reprojErr = cvCalibrateCamera2Internal(&c_objPt, &c_imgPt, &c_npoints, cvSize(imageSize),
iFixedPoint,
&c_cameraMatrix, &c_distCoeffs,
rvecs_needed ? &c_rvecM : NULL,
tvecs_needed ? &c_tvecM : NULL,
newobj_needed ? &c_newObjPt : NULL,
stddev_any_needed ? &c_stdDev : NULL,
errors_needed ? &c_errors : NULL, flags, cvTermCriteria(criteria));
if( newobj_needed )
newObjPt.copyTo(newObjPoints);
if( stddev_needed )
{
stdDeviationsIntrinsics.create(CV_CALIB_NINTRINSIC, 1, CV_64F);
Mat stdDeviationsIntrinsicsMat = stdDeviationsIntrinsics.getMat();
std::memcpy(stdDeviationsIntrinsicsMat.ptr(), stdDeviationsM.ptr(),
CV_CALIB_NINTRINSIC*sizeof(double));
}
if ( stddev_ext_needed )
{
stdDeviationsExtrinsics.create(nimages*6, 1, CV_64F);
Mat stdDeviationsExtrinsicsMat = stdDeviationsExtrinsics.getMat();
std::memcpy(stdDeviationsExtrinsicsMat.ptr(),
stdDeviationsM.ptr() + CV_CALIB_NINTRINSIC*sizeof(double),
nimages*6*sizeof(double));
}
if( stddev_obj_needed )
{
stdDeviationsObjPoints.create( np * 3, 1, CV_64F );
Mat stdDeviationsObjPointsMat = stdDeviationsObjPoints.getMat();
std::memcpy( stdDeviationsObjPointsMat.ptr(), stdDeviationsM.ptr()
+ ( CV_CALIB_NINTRINSIC + nimages * 6 ) * sizeof( double ),
np * 3 * sizeof( double ) );
}
// overly complicated and inefficient rvec/ tvec handling to support vector<Mat>
for(int i = 0; i < nimages; i++ )
{
if( rvecs_needed && rvecs_mat_vec)
{
_rvecs.create(3, 1, CV_64F, i, true);
Mat rv = _rvecs.getMat(i);
memcpy(rv.ptr(), rvecM.ptr(i), 3*sizeof(double));
}
if( tvecs_needed && tvecs_mat_vec)
{
_tvecs.create(3, 1, CV_64F, i, true);
Mat tv = _tvecs.getMat(i);
memcpy(tv.ptr(), tvecM.ptr(i), 3*sizeof(double));
}
}
cameraMatrix.copyTo(_cameraMatrix);
distCoeffs.copyTo(_distCoeffs);
return reprojErr;
}
void cv::calibrationMatrixValues( InputArray _cameraMatrix, Size imageSize,
double apertureWidth, double apertureHeight,
double& fovx, double& fovy, double& focalLength,
Point2d& principalPoint, double& aspectRatio )
{
CV_INSTRUMENT_REGION();
if(_cameraMatrix.size() != Size(3, 3))
CV_Error(cv::Error::StsUnmatchedSizes, "Size of cameraMatrix must be 3x3!");
Matx33d K = _cameraMatrix.getMat();
CV_DbgAssert(imageSize.width != 0 && imageSize.height != 0 && K(0, 0) != 0.0 && K(1, 1) != 0.0);
/* Calculate pixel aspect ratio. */
aspectRatio = K(1, 1) / K(0, 0);
/* Calculate number of pixel per realworld unit. */
double mx, my;
if(apertureWidth != 0.0 && apertureHeight != 0.0) {
mx = imageSize.width / apertureWidth;
my = imageSize.height / apertureHeight;
} else {
mx = 1.0;
my = aspectRatio;
}
/* Calculate fovx and fovy. */
fovx = atan2(K(0, 2), K(0, 0)) + atan2(imageSize.width - K(0, 2), K(0, 0));
fovy = atan2(K(1, 2), K(1, 1)) + atan2(imageSize.height - K(1, 2), K(1, 1));
fovx *= 180.0 / CV_PI;
fovy *= 180.0 / CV_PI;
/* Calculate focal length. */
focalLength = K(0, 0) / mx;
/* Calculate principle point. */
principalPoint = Point2d(K(0, 2) / mx, K(1, 2) / my);
}
double cv::stereoCalibrate( InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints1,
InputArrayOfArrays _imagePoints2,
InputOutputArray _cameraMatrix1, InputOutputArray _distCoeffs1,
InputOutputArray _cameraMatrix2, InputOutputArray _distCoeffs2,
Size imageSize, OutputArray _Rmat, OutputArray _Tmat,
OutputArray _Emat, OutputArray _Fmat, int flags,
TermCriteria criteria)
{
if (flags & CALIB_USE_EXTRINSIC_GUESS)
CV_Error(Error::StsBadFlag, "stereoCalibrate does not support CALIB_USE_EXTRINSIC_GUESS.");
Mat Rmat, Tmat;
double ret = stereoCalibrate(_objectPoints, _imagePoints1, _imagePoints2, _cameraMatrix1, _distCoeffs1,
_cameraMatrix2, _distCoeffs2, imageSize, Rmat, Tmat, _Emat, _Fmat,
noArray(), flags, criteria);
Rmat.copyTo(_Rmat);
Tmat.copyTo(_Tmat);
return ret;
}
double cv::stereoCalibrate( InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints1,
InputArrayOfArrays _imagePoints2,
InputOutputArray _cameraMatrix1, InputOutputArray _distCoeffs1,
InputOutputArray _cameraMatrix2, InputOutputArray _distCoeffs2,
Size imageSize, InputOutputArray _Rmat, InputOutputArray _Tmat,
OutputArray _Emat, OutputArray _Fmat,
OutputArray _perViewErrors, int flags,
TermCriteria criteria)
{
return stereoCalibrate(_objectPoints, _imagePoints1, _imagePoints2, _cameraMatrix1, _distCoeffs1,
_cameraMatrix2, _distCoeffs2, imageSize, _Rmat, _Tmat, _Emat, _Fmat,
noArray(), noArray(), _perViewErrors, flags, criteria);
}
double cv::stereoCalibrate( InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints1,
InputArrayOfArrays _imagePoints2,
InputOutputArray _cameraMatrix1, InputOutputArray _distCoeffs1,
InputOutputArray _cameraMatrix2, InputOutputArray _distCoeffs2,
Size imageSize, InputOutputArray _Rmat, InputOutputArray _Tmat,
OutputArray _Emat, OutputArray _Fmat,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs, OutputArray _perViewErrors, int flags,
TermCriteria criteria)
{
int rtype = CV_64F;
Mat cameraMatrix1 = _cameraMatrix1.getMat();
Mat cameraMatrix2 = _cameraMatrix2.getMat();
Mat distCoeffs1 = _distCoeffs1.getMat();
Mat distCoeffs2 = _distCoeffs2.getMat();
cameraMatrix1 = prepareCameraMatrix(cameraMatrix1, rtype, flags);
cameraMatrix2 = prepareCameraMatrix(cameraMatrix2, rtype, flags);
distCoeffs1 = prepareDistCoeffs(distCoeffs1, rtype);
distCoeffs2 = prepareDistCoeffs(distCoeffs2, rtype);
if( !(flags & CALIB_RATIONAL_MODEL) &&
(!(flags & CALIB_THIN_PRISM_MODEL)) &&
(!(flags & CALIB_TILTED_MODEL)) )
{
distCoeffs1 = distCoeffs1.rows == 1 ? distCoeffs1.colRange(0, 5) : distCoeffs1.rowRange(0, 5);
distCoeffs2 = distCoeffs2.rows == 1 ? distCoeffs2.colRange(0, 5) : distCoeffs2.rowRange(0, 5);
}
if( (flags & CALIB_USE_EXTRINSIC_GUESS) == 0 )
{
_Rmat.create(3, 3, rtype);
_Tmat.create(3, 1, rtype);
}
int nimages = int(_objectPoints.total());
CV_Assert( nimages > 0 );
Mat objPt, imgPt, imgPt2, npoints, rvecLM, tvecLM;
collectCalibrationData( _objectPoints, _imagePoints1, _imagePoints2,
objPt, imgPt, &imgPt2, npoints );
CvMat c_objPt = cvMat(objPt), c_imgPt = cvMat(imgPt), c_imgPt2 = cvMat(imgPt2), c_npoints = cvMat(npoints);
CvMat c_cameraMatrix1 = cvMat(cameraMatrix1), c_distCoeffs1 = cvMat(distCoeffs1);
CvMat c_cameraMatrix2 = cvMat(cameraMatrix2), c_distCoeffs2 = cvMat(distCoeffs2);
Mat matR_ = _Rmat.getMat(), matT_ = _Tmat.getMat();
CvMat c_matR = cvMat(matR_), c_matT = cvMat(matT_), c_matE, c_matF, c_matErr;
bool E_needed = _Emat.needed(), F_needed = _Fmat.needed(), rvecs_needed = _rvecs.needed(), tvecs_needed = _tvecs.needed(), errors_needed = _perViewErrors.needed();
Mat matE_, matF_, matErr_;
if( E_needed )
{
_Emat.create(3, 3, rtype);
matE_ = _Emat.getMat();
c_matE = cvMat(matE_);
}
if( F_needed )
{
_Fmat.create(3, 3, rtype);
matF_ = _Fmat.getMat();
c_matF = cvMat(matF_);
}
bool rvecs_mat_vec = _rvecs.isMatVector();
bool tvecs_mat_vec = _tvecs.isMatVector();
if( rvecs_needed )
{
_rvecs.create(nimages, 1, CV_64FC3);
if( rvecs_mat_vec )
rvecLM.create(nimages, 3, CV_64F);
else
rvecLM = _rvecs.getMat();
}
if( tvecs_needed )
{
_tvecs.create(nimages, 1, CV_64FC3);
if( tvecs_mat_vec )
tvecLM.create(nimages, 3, CV_64F);
else
tvecLM = _tvecs.getMat();
}
CvMat c_rvecLM = cvMat(rvecLM), c_tvecLM = cvMat(tvecLM);
if( errors_needed )
{
_perViewErrors.create(nimages, 2, CV_64F);
matErr_ = _perViewErrors.getMat();
c_matErr = cvMat(matErr_);
}
double err = cvStereoCalibrateImpl(&c_objPt, &c_imgPt, &c_imgPt2, &c_npoints, &c_cameraMatrix1,
&c_distCoeffs1, &c_cameraMatrix2, &c_distCoeffs2, cvSize(imageSize), &c_matR,
&c_matT, E_needed ? &c_matE : NULL, F_needed ? &c_matF : NULL,
rvecs_needed ? &c_rvecLM : NULL, tvecs_needed ? &c_tvecLM : NULL,
errors_needed ? &c_matErr : NULL, flags, cvTermCriteria(criteria));
cameraMatrix1.copyTo(_cameraMatrix1);
cameraMatrix2.copyTo(_cameraMatrix2);
distCoeffs1.copyTo(_distCoeffs1);
distCoeffs2.copyTo(_distCoeffs2);
for(int i = 0; i < nimages; i++ )
{
if( rvecs_needed && rvecs_mat_vec )
{
_rvecs.create(3, 1, CV_64F, i, true);
Mat rv = _rvecs.getMat(i);
memcpy(rv.ptr(), rvecLM.ptr(i), 3*sizeof(double));
}
if( tvecs_needed && tvecs_mat_vec )
{
_tvecs.create(3, 1, CV_64F, i, true);
Mat tv = _tvecs.getMat(i);
memcpy(tv.ptr(), tvecLM.ptr(i), 3*sizeof(double));
}
}
return err;
}
/* End of file. */