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opencv/apps/interactive-calibration/cvCalibrationFork.cpp
Vladislav Sovrasov 5a0c04409b Add interactive calibration app
2016-04-25 11:58:19 +03:00

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#include <opencv2/calib3d.hpp>
#include "linalg.hpp"
#include "cvCalibrationFork.hpp"
using namespace cv;
static void subMatrix(const cv::Mat& src, cv::Mat& dst, const std::vector<uchar>& cols,
const std::vector<uchar>& rows);
static const char* cvDistCoeffErr = "Distortion coefficients must be 1x4, 4x1, 1x5, 5x1, 1x8, 8x1, 1x12, 12x1, 1x14 or 14x1 floating-point vector";
static void cvEvaluateJtJ2(CvMat* _JtJ,
const CvMat* camera_matrix,
const CvMat* distortion_coeffs,
const CvMat* object_points,
const CvMat* param,
const CvMat* npoints,
int flags, int NINTRINSIC, double aspectRatio)
{
int i, pos, ni, total = 0, npstep = 0, maxPoints = 0;
npstep = npoints->rows == 1 ? 1 : npoints->step/CV_ELEM_SIZE(npoints->type);
int nimages = npoints->rows*npoints->cols;
for( i = 0; i < nimages; i++ )
{
ni = npoints->data.i[i*npstep];
if( ni < 4 )
{
CV_Error_( CV_StsOutOfRange, ("The number of points in the view #%d is < 4", i));
}
maxPoints = MAX( maxPoints, ni );
total += ni;
}
Mat _Ji( maxPoints*2, NINTRINSIC, CV_64FC1, Scalar(0));
Mat _Je( maxPoints*2, 6, CV_64FC1 );
Mat _err( maxPoints*2, 1, CV_64FC1 );
Mat _m( 1, total, CV_64FC2 );
const Mat matM = cvarrToMat(object_points);
cvZero(_JtJ);
for(i = 0, pos = 0; i < nimages; i++, pos += ni )
{
CvMat _ri, _ti;
ni = npoints->data.i[i*npstep];
cvGetRows( param, &_ri, NINTRINSIC + i*6, NINTRINSIC + i*6 + 3 );
cvGetRows( param, &_ti, NINTRINSIC + i*6 + 3, NINTRINSIC + i*6 + 6 );
CvMat _Mi(matM.colRange(pos, pos + ni));
CvMat _mi(_m.colRange(pos, pos + ni));
_Je.resize(ni*2); _Ji.resize(ni*2); _err.resize(ni*2);
CvMat _dpdr(_Je.colRange(0, 3));
CvMat _dpdt(_Je.colRange(3, 6));
CvMat _dpdf(_Ji.colRange(0, 2));
CvMat _dpdc(_Ji.colRange(2, 4));
CvMat _dpdk(_Ji.colRange(4, NINTRINSIC));
CvMat _mp(_err.reshape(2, 1));
cvProjectPoints2( &_Mi, &_ri, &_ti, camera_matrix, distortion_coeffs, &_mp, &_dpdr, &_dpdt,
(flags & CALIB_FIX_FOCAL_LENGTH) ? 0 : &_dpdf,
(flags & CALIB_FIX_PRINCIPAL_POINT) ? 0 : &_dpdc, &_dpdk,
(flags & CALIB_FIX_ASPECT_RATIO) ? aspectRatio : 0);
cvSub( &_mp, &_mi, &_mp );
Mat JtJ(cvarrToMat(_JtJ));
// 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;
}
}
double cvfork::cvCalibrateCamera2( const CvMat* objectPoints,
const CvMat* imagePoints, const CvMat* npoints,
CvSize imageSize, CvMat* cameraMatrix, CvMat* distCoeffs,
CvMat* rvecs, CvMat* tvecs, 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_StsBadArg, "One of required vector arguments is not a valid matrix" );
if( imageSize.width <= 0 || imageSize.height <= 0 )
CV_Error( CV_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_StsUnsupportedFormat,
"the array of point counters must be 1-dimensional integer vector" );
if(flags & CV_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_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 & CV_CALIB_THIN_PRISM_MODEL)
if (distCoeffs->cols*distCoeffs->rows != 12)
CV_Error( CV_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_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_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" );
}
if( stdDevs )
{
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)) )
CV_Error( CV_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" );
}
if( (CV_MAT_TYPE(cameraMatrix->type) != CV_32FC1 &&
CV_MAT_TYPE(cameraMatrix->type) != CV_64FC1) ||
cameraMatrix->rows != 3 || cameraMatrix->cols != 3 )
CV_Error( CV_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_StsBadArg, cvDistCoeffErr );
for( i = 0; i < nimages; i++ )
{
ni = npoints->data.i[i*npstep];
if( ni < 4 )
{
CV_Error_( CV_StsOutOfRange, ("The number of points in the view #%d is < 4", i));
}
maxPoints = MAX( maxPoints, ni );
total += ni;
}
Mat matM( 1, total, CV_64FC3 );
Mat _m( 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;
Mat _Ji( maxPoints*2, NINTRINSIC, CV_64FC1, Scalar(0));
Mat _Je( maxPoints*2, 6, CV_64FC1 );
Mat _err( maxPoints*2, 1, CV_64FC1 );
_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_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_StsOutOfRange, "Principal point must be within the image" );
if( fabs(A(0, 1)) > 1e-5 )
CV_Error( CV_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_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_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_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_StsOutOfRange,
"The specified aspect ratio (= cameraMatrix[0][0] / cameraMatrix[1][1]) is incorrect" );
}
CvMat _matM(matM), m(_m);
cvInitIntrinsicParams2D( &_matM, &m, npoints, imageSize, &matA, aspectRatio );
}
//CvLevMarq solver( nparams, 0, termCrit );
cvfork::CvLevMarqFork solver( nparams, 0, termCrit );
Mat allErrors(1, total, CV_64FC2);
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 & CV_CALIB_FIX_FOCAL_LENGTH )
mask[0] = mask[1] = 0;
if( flags & CV_CALIB_FIX_PRINCIPAL_POINT )
mask[2] = mask[3] = 0;
if( flags & CV_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 & CV_CALIB_THIN_PRISM_MODEL))
flags |= CALIB_FIX_S1_S2_S3_S4;
if( !(flags & CV_CALIB_TILTED_MODEL))
flags |= CALIB_FIX_TAUX_TAUY;
mask[ 4] = !(flags & CALIB_FIX_K1);
mask[ 5] = !(flags & CALIB_FIX_K2);
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;
}
}
// 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(matM.colRange(pos, pos + ni));
CvMat _mi(_m.colRange(pos, pos + ni));
cvFindExtrinsicCameraParams2( &_Mi, &_mi, &matA, &_k, &_ri, &_ti );
}
// 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;
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 ) {
//do errors estimation
if(stdDevs) {
Ptr<CvMat> JtJ(cvCreateMat(nparams, nparams, CV_64F));
CvMat cvMatM(matM);
cvEvaluateJtJ2(JtJ, &matA, &_k, &cvMatM, solver.param, npoints, flags, NINTRINSIC, aspectRatio);
Mat mask = cvarrToMat(solver.mask);
int nparams_nz = countNonZero(mask);
Mat JtJinv, JtJN;
JtJN.create(nparams_nz, nparams_nz, CV_64F);
subMatrix(cvarrToMat(JtJ), JtJN, mask, mask);
completeSymm(JtJN, false);
#ifndef USE_LAPACK
cv::invert(JtJN, JtJinv, DECOMP_SVD);
#else
cvfork::invert(JtJN, JtJinv, DECOMP_SVD);
#endif
double sigma2 = norm(allErrors, NORM_L2SQR) / (total - nparams_nz);
Mat stdDevsM = cvarrToMat(stdDevs);
int j = 0;
for (int s = 0; s < nparams; s++)
if(mask.data[s]) {
stdDevsM.at<double>(s) = std::sqrt(JtJinv.at<double>(j,j)*sigma2);
j++;
}
else
stdDevsM.at<double>(s) = 0;
}
break;
}
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(matM.colRange(pos, pos + ni));
CvMat _mi(_m.colRange(pos, pos + ni));
CvMat _me(allErrors.colRange(pos, pos + ni));
_Je.resize(ni*2); _Ji.resize(ni*2); _err.resize(ni*2);
CvMat _dpdr(_Je.colRange(0, 3));
CvMat _dpdt(_Je.colRange(3, 6));
CvMat _dpdf(_Ji.colRange(0, 2));
CvMat _dpdc(_Ji.colRange(2, 4));
CvMat _dpdk(_Ji.colRange(4, NINTRINSIC));
CvMat _mp(_err.reshape(2, 1));
if( solver.state == CvLevMarq::CALC_J )
{
cvProjectPoints2( &_Mi, &_ri, &_ti, &matA, &_k, &_mp, &_dpdr, &_dpdt,
(flags & CALIB_FIX_FOCAL_LENGTH) ? 0 : &_dpdf,
(flags & CALIB_FIX_PRINCIPAL_POINT) ? 0 : &_dpdc, &_dpdk,
(flags & CALIB_FIX_ASPECT_RATIO) ? aspectRatio : 0);
}
else
cvProjectPoints2( &_Mi, &_ri, &_ti, &matA, &_k, &_mp );
cvSub( &_mp, &_mi, &_mp );
if( solver.state == CvLevMarq::CALC_J )
{
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;
JtErr.rowRange(0, NINTRINSIC) += _Ji.t() * _err;
JtErr.rowRange(NINTRINSIC + i * 6, NINTRINSIC + (i + 1) * 6) = _Je.t() * _err;
}
if (stdDevs || perViewErrors)
cvCopy(&_mp, &_me);
reprojErr += norm(_err, NORM_L2SQR);
}
if( _errNorm )
*_errNorm = reprojErr;
}
// 4. store the results
cvConvert( &matA, cameraMatrix );
cvConvert( &_k, distCoeffs );
for( i = 0, pos = 0; i < nimages; i++)
{
CvMat src, dst;
if( perViewErrors )
{
ni = npoints->data.i[i*npstep];
perViewErrors->data.db[i] = std::sqrt(cv::norm(allErrors.colRange(pos, pos + ni), NORM_L2SQR) / ni);
pos+=ni;
}
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 );
cvRodrigues2( &src, &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);
}
}
static Mat prepareCameraMatrix(Mat& cameraMatrix0, int rtype)
{
Mat cameraMatrix = Mat::eye(3, 3, rtype);
if( cameraMatrix0.size() == cameraMatrix.size() )
cameraMatrix0.convertTo(cameraMatrix, rtype);
return cameraMatrix;
}
static Mat prepareDistCoeffs(Mat& distCoeffs0, int rtype)
{
Mat distCoeffs = Mat::zeros(distCoeffs0.cols == 1 ? Size(1, 14) : Size(14, 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;
}
static void collectCalibrationData( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints1,
InputArrayOfArrays imagePoints2,
Mat& objPtMat, Mat& imgPtMat1, Mat* imgPtMat2,
Mat& npoints )
{
int nimages = (int)objectPoints.total();
int i, j = 0, ni = 0, total = 0;
CV_Assert(nimages > 0 && nimages == (int)imagePoints1.total() &&
(!imgPtMat2 || nimages == (int)imagePoints2.total()));
for( i = 0; i < nimages; i++ )
{
ni = objectPoints.getMat(i).checkVector(3, CV_32F);
if( ni <= 0 )
CV_Error(CV_StsUnsupportedFormat, "objectPoints should contain vector of vectors of points of type Point3f");
int ni1 = imagePoints1.getMat(i).checkVector(2, CV_32F);
if( ni1 <= 0 )
CV_Error(CV_StsUnsupportedFormat, "imagePoints1 should contain vector of vectors of points of type Point2f");
CV_Assert( ni == ni1 );
total += ni;
}
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( i = 0; i < nimages; i++, j += ni )
{
Mat objpt = objectPoints.getMat(i);
Mat imgpt1 = imagePoints1.getMat(i);
ni = objpt.checkVector(3, CV_32F);
npoints.at<int>(i) = ni;
memcpy( objPtData + j, objpt.ptr(), ni*sizeof(objPtData[0]) );
memcpy( imgPtData1 + j, imgpt1.ptr(), ni*sizeof(imgPtData1[0]) );
if( imgPtData2 )
{
Mat imgpt2 = imagePoints2.getMat(i);
int ni2 = imgpt2.checkVector(2, CV_32F);
CV_Assert( ni == ni2 );
memcpy( imgPtData2 + j, imgpt2.ptr(), ni*sizeof(imgPtData2[0]) );
}
}
}
double cvfork::calibrateCamera(InputArrayOfArrays _objectPoints,
InputArrayOfArrays _imagePoints,
Size imageSize, InputOutputArray _cameraMatrix, InputOutputArray _distCoeffs,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs, OutputArray _stdDeviations, OutputArray _perViewErrors, int flags, TermCriteria criteria )
{
int rtype = CV_64F;
Mat cameraMatrix = _cameraMatrix.getMat();
cameraMatrix = prepareCameraMatrix(cameraMatrix, rtype);
Mat distCoeffs = _distCoeffs.getMat();
distCoeffs = 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 = _stdDeviations.needed(), errors_needed = _perViewErrors.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();
}
if( stddev_needed ) {
_stdDeviations.create(nimages*6 + CV_CALIB_NINTRINSIC, 1, CV_64F);
stdDeviationsM = _stdDeviations.getMat();
}
if( errors_needed) {
_perViewErrors.create(nimages, 1, CV_64F);
errorsM = _perViewErrors.getMat();
}
collectCalibrationData( _objectPoints, _imagePoints, noArray(),
objPt, imgPt, 0, npoints );
CvMat c_objPt = objPt, c_imgPt = imgPt, c_npoints = npoints;
CvMat c_cameraMatrix = cameraMatrix, c_distCoeffs = distCoeffs;
CvMat c_rvecM = rvecM, c_tvecM = tvecM, c_stdDev = stdDeviationsM, c_errors = errorsM;
double reprojErr = cvfork::cvCalibrateCamera2(&c_objPt, &c_imgPt, &c_npoints, imageSize,
&c_cameraMatrix, &c_distCoeffs,
rvecs_needed ? &c_rvecM : NULL,
tvecs_needed ? &c_tvecM : NULL,
stddev_needed ? &c_stdDev : NULL,
errors_needed ? &c_errors : NULL, flags, criteria );
// 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;
}
double cvfork::calibrateCameraCharuco(InputArrayOfArrays _charucoCorners, InputArrayOfArrays _charucoIds,
Ptr<aruco::CharucoBoard> &_board, Size imageSize,
InputOutputArray _cameraMatrix, InputOutputArray _distCoeffs,
OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs, OutputArray _stdDeviations, OutputArray _perViewErrors,
int flags, TermCriteria criteria) {
CV_Assert(_charucoIds.total() > 0 && (_charucoIds.total() == _charucoCorners.total()));
// Join object points of charuco corners in a single vector for calibrateCamera() function
std::vector< std::vector< Point3f > > allObjPoints;
allObjPoints.resize(_charucoIds.total());
for(unsigned int i = 0; i < _charucoIds.total(); i++) {
unsigned int nCorners = (unsigned int)_charucoIds.getMat(i).total();
CV_Assert(nCorners > 0 && nCorners == _charucoCorners.getMat(i).total()); //actually nCorners must be > 3 for calibration
allObjPoints[i].reserve(nCorners);
for(unsigned int j = 0; j < nCorners; j++) {
int pointId = _charucoIds.getMat(i).ptr< int >(0)[j];
CV_Assert(pointId >= 0 && pointId < (int)_board->chessboardCorners.size());
allObjPoints[i].push_back(_board->chessboardCorners[pointId]);
}
}
return cvfork::calibrateCamera(allObjPoints, _charucoCorners, imageSize, _cameraMatrix, _distCoeffs,
_rvecs, _tvecs, _stdDeviations, _perViewErrors, flags, criteria);
}
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++));
}
}
}
void cvfork::CvLevMarqFork::step()
{
using namespace cv;
const double LOG10 = log(10.);
double lambda = exp(lambdaLg10*LOG10);
int nparams = param->rows;
Mat _JtJ = cvarrToMat(JtJ);
Mat _mask = cvarrToMat(mask);
int nparams_nz = countNonZero(_mask);
if(!JtJN || JtJN->rows != nparams_nz) {
// prevent re-allocation in every step
JtJN.reset(cvCreateMat( nparams_nz, nparams_nz, CV_64F ));
JtJV.reset(cvCreateMat( nparams_nz, 1, CV_64F ));
JtJW.reset(cvCreateMat( nparams_nz, 1, CV_64F ));
}
Mat _JtJN = cvarrToMat(JtJN);
Mat _JtErr = cvarrToMat(JtJV);
Mat_<double> nonzero_param = cvarrToMat(JtJW);
subMatrix(cvarrToMat(JtErr), _JtErr, std::vector<uchar>(1, 1), _mask);
subMatrix(_JtJ, _JtJN, _mask, _mask);
if( !err )
completeSymm( _JtJN, completeSymmFlag );
#if 1
_JtJN.diag() *= 1. + lambda;
#else
_JtJN.diag() += lambda;
#endif
#ifndef USE_LAPACK
cv::solve(_JtJN, _JtErr, nonzero_param, solveMethod);
#else
cvfork::solve(_JtJN, _JtErr, nonzero_param, solveMethod);
#endif
int j = 0;
for( int i = 0; i < nparams; i++ )
param->data.db[i] = prevParam->data.db[i] - (mask->data.ptr[i] ? nonzero_param(j++) : 0);
}
cvfork::CvLevMarqFork::CvLevMarqFork(int nparams, int nerrs, CvTermCriteria criteria0, bool _completeSymmFlag)
{
init(nparams, nerrs, criteria0, _completeSymmFlag);
}
cvfork::CvLevMarqFork::~CvLevMarqFork()
{
clear();
}
bool cvfork::CvLevMarqFork::updateAlt( const CvMat*& _param, CvMat*& _JtJ, CvMat*& _JtErr, double*& _errNorm )
{
CV_Assert( !err );
if( state == DONE )
{
_param = param;
return false;
}
if( state == STARTED )
{
_param = param;
cvZero( JtJ );
cvZero( JtErr );
errNorm = 0;
_JtJ = JtJ;
_JtErr = JtErr;
_errNorm = &errNorm;
state = CALC_J;
return true;
}
if( state == CALC_J )
{
cvCopy( param, prevParam );
step();
_param = param;
prevErrNorm = errNorm;
errNorm = 0;
_errNorm = &errNorm;
state = CHECK_ERR;
return true;
}
assert( state == CHECK_ERR );
if( errNorm > prevErrNorm )
{
if( ++lambdaLg10 <= 16 )
{
step();
_param = param;
errNorm = 0;
_errNorm = &errNorm;
state = CHECK_ERR;
return true;
}
}
lambdaLg10 = MAX(lambdaLg10-1, -16);
if( ++iters >= criteria.max_iter ||
cvNorm(param, prevParam, CV_RELATIVE_L2) < criteria.epsilon )
{
//printf("iters %i\n", iters);
_param = param;
state = DONE;
return false;
}
prevErrNorm = errNorm;
cvZero( JtJ );
cvZero( JtErr );
_param = param;
_JtJ = JtJ;
_JtErr = JtErr;
state = CALC_J;
return true;
}
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