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Messier 2019-09-05 14:44:36 +08:00
parent f8d47e6f3a
commit fff6c84032
10 changed files with 821 additions and 797 deletions
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49
src/mynteye/api/camera_models/CtainBase.h
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@ -1,8 +1,17 @@
// Copyright 2018 Slightech Co., Ltd. All rights reserved.
// //
// Created by 顾涵彬 on 2019-08-28. // Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// //
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <cstdint>
#include "Matrix.h" #include "Matrix.h"
#include "SquareMatrix.h" #include "SquareMatrix.h"
#include "MatrixSolver.h" #include "MatrixSolver.h"
@ -12,28 +21,26 @@
namespace Ctain { namespace Ctain {
typedef SMatrix<double> Matrixd; typedef SMatrix<double> Matrixd;
typedef Matrix<double> MatrixXd; typedef Matrix<double> MatrixXd;
typedef Matrix<double> Matrix23d; typedef Matrix<double> Matrix23d;
typedef SMatrix<double> Matrix3d; typedef SMatrix<double> Matrix3d;
typedef SMatrix<double> Matrix4d; typedef SMatrix<double> Matrix4d;
typedef SMatrix<float> Matrixf; typedef SMatrix<float> Matrixf;
typedef Matrixf Matrix2f; typedef Matrixf Matrix2f;
typedef Matrixf Matrix3f; typedef Matrixf Matrix3f;
typedef Matrix<float> Vectorf; typedef Matrix<float> Vectorf;
typedef Vectorf Vector2f; typedef Vectorf Vector2f;
typedef Vectorf Vector3f; typedef Vectorf Vector3f;
typedef Matrix<double> Vectord; typedef Matrix<double> Vectord;
typedef Matrix<double> Vector2d; typedef Matrix<double> Vector2d;
typedef Matrix<double> Vector3d; typedef Matrix<double> Vector3d;
typedef Matrix<double> MatrixXcd; typedef Matrix<double> MatrixXcd;
typedef Quaternion<double> Quaterniond; typedef Quaternion<double> Quaterniond;
} // end namespace Ctain }
#endif // Ctain_CtainBASE_H #endif // Ctain_CtainBASE_H

786
src/mynteye/api/camera_models/Matrix.h
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@ -1,415 +1,421 @@
// Copyright 2018 Slightech Co., Ltd. All rights reserved.
// //
// Created by 顾涵彬 on 2019-08-28. // Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// //
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef MATRIX_MATRIX_H #ifndef SRC_MYNTEYE_API_CAMERA_MODELS_MATRIX_H_
#define MATRIX_MATRIX_H #define SRC_MYNTEYE_API_CAMERA_MODELS_MATRIX_H_
#include <cstring> #include <cstring>
#include <iostream> #include <iostream>
#include <cmath> #include <cmath>
namespace Ctain { namespace Ctain {
template<typename _Scalar> template<typename _Scalar>
class Matrix { class Matrix {
public: public:
Matrix(int Rows, int Cols) : Matrix(int Rows, int Cols) :
_Rows(Rows), _Cols(Cols), _isSub(0), input_id(0) { _Rows(Rows), _Cols(Cols), _isSub(0), input_id(0) {
_startRow = 0; _startRow = 0;
_startCol = 0; _startCol = 0;
_Rows_raw = Rows; _Rows_raw = Rows;
_Cols_raw = Cols; _Cols_raw = Cols;
data = new _Scalar [_Rows * _Cols]; data = new _Scalar[_Rows * _Cols];
memset(data, 0, _Rows * _Cols * sizeof(_Scalar)); memset(data, 0, _Rows * _Cols * sizeof(_Scalar));
} }
Matrix() : Matrix() :
_Rows(0), _Cols(0), _isSub(0), input_id(0) { _Rows(0), _Cols(0), _isSub(0), input_id(0) {
_startRow = 0; _startRow = 0;
_startCol = 0; _startCol = 0;
_Rows_raw = 0; _Rows_raw = 0;
_Cols_raw = 0; _Cols_raw = 0;
} }
Matrix(_Scalar _data[], int Rows, int Cols) : Matrix(_Scalar _data[], int Rows, int Cols) :
_Rows(Rows), _Cols(Cols), _isSub(0), input_id(0) { _Rows(Rows), _Cols(Cols), _isSub(0), input_id(0) {
_startRow = 0; _startRow = 0;
_startCol = 0; _startCol = 0;
_Rows_raw = Rows; _Rows_raw = Rows;
_Cols_raw = Cols; _Cols_raw = Cols;
data = new _Scalar [_Rows * _Cols]; data = new _Scalar[Rows * _Cols];
memcpy(data, _data, _Rows * _Cols * sizeof(_Scalar)); memcpy(data, _data, _Rows * _Cols * sizeof(_Scalar));
} }
Matrix(_Scalar **_data, int Rows, int Cols) : Matrix(_Scalar **_data, int Rows, int Cols) :
_Rows(Rows), _Cols(Cols), _isSub(0), input_id(0) { _Rows(Rows), _Cols(Cols), _isSub(0), input_id(0) {
_startRow = 0; _startRow = 0;
_startCol = 0; _startCol = 0;
_Rows_raw = Rows; _Rows_raw = Rows;
_Cols_raw = Cols; _Cols_raw = Cols;
data = new _Scalar [_Rows * _Cols]; data = new _Scalar[Rows * _Cols];
for (int i = 0; i < _Rows; ++i) { for (int i = 0; i < _Rows; ++i) {
memcpy(data + i * _Cols, *(_data + i), _Cols * sizeof(_Scalar)); memcpy(data + i * _Cols, *(_data + i), _Cols * sizeof(_Scalar));
}
}
template<typename T>
Matrix<T> cast() {
Matrix<T> res(_Rows, _Cols);
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
res(i,j) = cData(i, j);
}
}
return res;
}
void setIdentity() {
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
if(i == j) {
Data(i,j) = 1;
}else {
Data(i,j) = 0;
}
}
}
}
void setZero() {
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
Data(i, j) = 0;
}
}
}
inline int cols() const { return _Cols; }
inline int rows() const { return _Rows; }
inline int size() const { return cols() * rows(); }
_Scalar * addr() {
return data;
}
friend Matrix<_Scalar> &operator <<(Matrix<_Scalar> &m,_Scalar val) {
m.Data(m.input_id++) = val;
return m;
}
friend std::ostream &operator <<(std::ostream &os,const Matrix<_Scalar> &m) {
// os << std::endl;
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols - 1; j++) {
std::cout.width(10);
std::cout.setf(std::ios::left);
std::cout.precision(6);
os << m.cData(i,j) ;
}
std::cout.width(9);
std::cout.setf(std::ios::left);
std::cout.precision(8);
os << m.cData(i,m._Cols - 1) << std::endl;
}
return os;
}
friend Matrix<_Scalar> operator *(
double a, const Matrix<_Scalar> &m) {
Matrix<_Scalar> res;
res = m;
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
res.Data(i,j) *= a;
}
}
return res;
}
friend Matrix<_Scalar> operator *(
const Matrix<_Scalar> &m, double a) {
Matrix<_Scalar> res;
res = m;
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
res.Data(i,j) *= a;
}
}
return res;
}
friend Matrix<_Scalar> operator -(
const Matrix<_Scalar> &m) {
Matrix<_Scalar> res;
res = m;
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
res.Data(i,j) *= -1;
}
}
return res;
}
friend Matrix<_Scalar> operator +(
double a, const Matrix<_Scalar> &m) {
Matrix<_Scalar> res;
res = m;
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
res.Data(i,j) += a;
}
}
return res;
}
void operator =(Matrix<_Scalar> m);
// void operator =(Matrix<_Scalar> &m);
Matrix<_Scalar> operator +(const Matrix<_Scalar> &m) const;
Matrix<_Scalar> operator -(const Matrix<_Scalar> &m) const;
Matrix<_Scalar> operator *(const Matrix<_Scalar> &m) const;
Matrix<_Scalar> operator /(double m) const;
_Scalar &operator()(int i, int j) {
return Data(i,j);
}
_Scalar &operator()(int id) {
return Data(id);
}
_Scalar operator()(int id) const {
return cData(id);
}
Matrix<_Scalar> transpose() const;
Matrix<_Scalar> col(int Col) {
return block(0, Col, _Rows, 1);
}
Matrix<_Scalar> row(int Row) {
return block(Row, 0, 1, _Cols);
}
Matrix<_Scalar> block(int sRow, int sCol, int Rows, int Cols) {
Matrix<_Scalar> sub;
sub = *this;
sub.setSub(sRow, sCol, Rows, Cols, data);
return sub;
}
template<int Rows, int Cols>
Matrix<_Scalar> topLeftCorner() const {
Matrix<_Scalar> sub;
sub = *this;
sub.setSub(0, 0, Rows, Cols, data);
return sub;
}
template<int Rows, int Cols>
Matrix<_Scalar> topRightCorner() const {
Matrix<_Scalar> sub;
sub = *this;
sub.setSub(0, _Cols-Cols, Rows, Cols, data);
return sub;
}
void setSub(int sRow, int sCol, int Rows, int Cols, _Scalar *Data) {
_isSub = true;
_Rows_raw = _Rows;
_Cols_raw = _Cols;
_Rows = Rows;
_Cols = Cols;
_startRow = sRow;
_startCol = sCol;
data = Data;
}
void normalize();
double norm() const;
virtual ~Matrix() {
if(!data)
delete[] data;
}
// template<int _Rows, int _Cols>
// inline Matrix<_Scalar, _Rows, _Cols> block<_Rows, _Cols>(Index i,Index j) {
//
// }
inline _Scalar *Data() {
return data;
}
protected:
_Scalar *data;
int _Rows;
int _Cols;
bool _isSub;
int _startRow;
int _startCol;
int _Rows_raw;
int _Cols_raw;
int input_id ;
inline int id(int i, int j) const {
if(_isSub)
return (i + _startRow) * _Cols_raw + j + _startCol;
else
return i * _Cols + j;
}
inline _Scalar &Data(int i,int j) {
return data[id(i,j)];
}
inline _Scalar &Data(int id) {
int i = id / _Cols;
int j = id % _Cols;
int index;
if(_isSub)
index = (i + _startRow) * _Cols_raw + j + _startCol;
else
index = i * _Cols + j;
return data[index];
}
inline _Scalar cData(int id) const{
int i = id / _Cols;
int j = id % _Cols;
int index;
if(_isSub)
index = (i + _startRow) * _Cols_raw + j + _startCol;
else
index = i * _Cols + j;
return data[index];
}
inline _Scalar cData (int i,int j) const{
return data[id(i,j)];
}
}; //class Matrix end
template<typename _Scalar>
void Matrix<_Scalar>::operator =(Matrix<_Scalar> m) {
if(m._isSub) {
if(_isSub) {
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
Data(i,j) = m.cData(i, j);
}
}
return;
}
_isSub = true;
_Rows = m._Rows;
_Cols = m._Cols;
_Rows_raw = m._Rows_raw;
_Cols_raw = m._Cols_raw;
_startRow = m._startRow;
_startCol = m._startCol;
data = m.Data();
return;
}
if(!_isSub) {
if(size() != m.size()) {
if(size() > 0) {
delete[] data;
}
_Rows = m._Rows;
_Cols = m._Cols;
data = new _Scalar[_Rows * _Cols];
}else {
_Rows = m._Rows;
_Cols = m._Cols;
}
}
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
Data(i,j) = m.cData(i, j);
}
}
} }
}
template<typename _Scalar> template<typename T>
Matrix<_Scalar> Matrix<_Scalar>::operator +(const Matrix<_Scalar> &m) const{ Matrix<T> cast() {
Matrix<_Scalar> sum; Matrix<T> res(_Rows, _Cols);
sum = *this; for (int i = 0; i < _Rows; i++) {
for (int i = 0; i < _Rows * _Cols; i++) { for (int j = 0; j < _Cols; j++) {
sum.data[i] += m.data[i]; res(i, j) = cData(i, j);
} }
return sum;
} }
return res;
}
template<typename _Scalar> void setIdentity() {
Matrix<_Scalar> Matrix<_Scalar>::operator -(const Matrix<_Scalar> &m) const{ for (int i = 0; i < _Rows; i++) {
Matrix<_Scalar> sum; for (int j = 0; j < _Cols; j++) {
sum = *this; if (i == j) {
for (int i = 0; i < _Rows * _Cols; i++) { Data(i, j) = 1;
sum.data[i] -= m.data[i]; } else {
Data(i, j) = 0;
} }
return sum; }
} }
}
template<typename _Scalar> void setZero() {
Matrix<_Scalar> Matrix<_Scalar>::transpose() const for (int i = 0; i < _Rows; i++) {
{ for (int j = 0; j < _Cols; j++) {
Matrix<_Scalar> res(_Cols, _Rows); Data(i, j) = 0;
for (int i = 0; i < _Rows; i++) { }
for (int j = 0; j < _Cols; j++) {
res.Data(j, i) = cData(i, j);
}
}
return res;
} }
}
inline int cols() const { return _Cols; }
template<typename _Scalar> inline int rows() const { return _Rows; }
Matrix<_Scalar> Matrix<_Scalar>::operator *(const Matrix<_Scalar> &m) const {
if(_Cols != m._Rows) { inline int size() const { return cols() * rows(); }
//todo:output err inline _Scalar * addr() {
return m; return data;
} }
Matrix<_Scalar> res(_Rows, m._Cols);
for (int i = 0; i < _Rows; i++) { friend Matrix<_Scalar> &operator <<(Matrix<_Scalar> &m, _Scalar val) {
for (int j = 0; j < m._Cols; j++) { m.Data(m.input_id++) = val;
_Scalar sum = 0; return m;
for (int k = 0; k < _Cols; k++) { }
sum += cData(i, k) * m.cData(k, j);
} friend std::ostream &operator <<(std::ostream &os,
res.Data(i,j) = sum; const Matrix<_Scalar> &m) {
} for (int i = 0; i < m._Rows; i++) {
} for (int j = 0; j < m._Cols - 1; j++) {
return res; std::cout.width(10);
std::cout.setf(std::ios::left);
std::cout.precision(6);
os << m.cData(i, j);
}
std::cout.width(9);
std::cout.setf(std::ios::left);
std::cout.precision(8);
os << m.cData(i, m._Cols - 1) << std::endl;
} }
return os;
}
template<typename _Scalar> friend Matrix<_Scalar> operator *(
Matrix<_Scalar> Matrix<_Scalar>::operator /(double m) const { double a, const Matrix<_Scalar> &m) {
Matrix<_Scalar> res; Matrix<_Scalar> res;
res = *this; res = m;
for (int i = 0; i < _Rows; i++) { for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < _Cols; j++) { for (int j = 0; j < m._Cols; j++) {
res.Data(i,j) /= m; res.Data(i, j) *= a;
} }
}
return res;
} }
return res;
}
friend Matrix<_Scalar> operator *(
template<typename _Scalar> const Matrix<_Scalar> &m, double a) {
void Matrix<_Scalar>::normalize() { Matrix<_Scalar> res;
double sum = 0; res = m;
for (int i = 0; i < _Rows; i++) { for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < _Cols; j++) { for (int j = 0; j < m._Cols; j++) {
sum += Matrix::cData(i, j); res.Data(i, j) *= a;
} }
}
sum = sqrt(sum);
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
Matrix::Data(i, j) /= sum;
}
}
} }
return res;
}
template<typename _Scalar> friend Matrix<_Scalar> operator -(
double Matrix<_Scalar>::norm() const{ const Matrix<_Scalar> &m) {
double sum = 0; Matrix<_Scalar> res;
for (int i = 0; i < _Rows; i++) { res = m;
for (int j = 0; j < _Cols; j++) { for (int i = 0; i < m._Rows; i++) {
sum += Matrix::cData(i, j) * Matrix::cData(i, j); for (int j = 0; j < m._Cols; j++) {
} res.Data(i, j) *= -1;
} }
sum = sqrt(sum);
return sum;
} }
} //namespace Ctain end return res;
#endif //MATRIX_MATRIX_H }
friend Matrix<_Scalar> operator +(
double a, const Matrix<_Scalar> &m) {
Matrix<_Scalar> res;
res = m;
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
res.Data(i, j) += a;
}
}
return res;
}
virtual void operator =(Matrix<_Scalar> m);
Matrix<_Scalar> operator +(const Matrix<_Scalar> &m) const;
Matrix<_Scalar> operator -(const Matrix<_Scalar> &m) const;
Matrix<_Scalar> operator *(const Matrix<_Scalar> &m) const;
Matrix<_Scalar> operator /(double m) const;
_Scalar &operator()(int i, int j) {
return Data(i, j);
}
_Scalar &operator()(int id) {
return Data(id);
}
_Scalar operator()(int id) const {
return cData(id);
}
Matrix<_Scalar> transpose() const;
Matrix<_Scalar> col(int Col) {
return block(0, Col, _Rows, 1);
}
Matrix<_Scalar> row(int Row) {
return block(Row, 0, 1, _Cols);
}
Matrix<_Scalar> block(int sRow, int sCol, int Rows, int Cols) {
Matrix<_Scalar> sub;
sub = *this;
sub.setSub(sRow, sCol, Rows, Cols, data);
return sub;
}
template<int Rows, int Cols>
Matrix<_Scalar> topLeftCorner() {
Matrix<_Scalar> sub;
sub = *this;
sub.setSub(0, 0, Rows, Cols, data);
return sub;
}
template<int Rows, int Cols>
Matrix<_Scalar> topRightCorner() {
Matrix<_Scalar> sub;
sub = *this;
sub.setSub(0, _Cols-Cols, Rows, Cols, data);
return sub;
}
void setSub(int sRow, int sCol, int Rows, int Cols, _Scalar *Data) {
_isSub = true;
_Rows_raw = _Rows;
_Cols_raw = _Cols;
_Rows = Rows;
_Cols = Cols;
_startRow = sRow;
_startCol = sCol;
data = Data;
}
void normalize();
double norm() const;
virtual ~Matrix() {
if (!data)
delete[] data;
}
inline _Scalar *Data() {
return data;
}
protected:
_Scalar *data;
int _Rows;
int _Cols;
bool _isSub;
int _startRow;
int _startCol;
int _Rows_raw;
int _Cols_raw;
int input_id;
inline int id(int i, int j) const {
if (_isSub)
return (i + _startRow) * _Cols_raw + j + _startCol;
else
return i * _Cols + j;
}
inline _Scalar &Data(int i, int j) {
return data[id(i, j)];
}
inline _Scalar &Data(int id) {
int i = id / _Cols;
int j = id % _Cols;
int index;
if (_isSub)
index = (i + _startRow) * _Cols_raw + j + _startCol;
else
index = i * _Cols + j;
return data[index];
}
inline _Scalar cData(int id) const {
int i = id / _Cols;
int j = id % _Cols;
int index;
if (_isSub)
index = (i + _startRow) * _Cols_raw + j + _startCol;
else
index = i * _Cols + j;
return data[index];
}
inline _Scalar cData(int i, int j) const {
return data[id(i, j)];
}
}; // class Matrix
template<typename _Scalar>
void Matrix<_Scalar>::operator =(Matrix<_Scalar> m) {
if (m._isSub) {
if (_isSub) {
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
Data(i, j) = m.cData(i, j);
}
}
return;
}
_isSub = true;
_Rows = m._Rows;
_Cols = m._Cols;
_Rows_raw = m._Rows_raw;
_Cols_raw = m._Cols_raw;
_startRow = m._startRow;
_startCol = m._startCol;
data = m.Data();
return;
}
if (!_isSub) {
if (size() != m.size()) {
if (size() > 0) {
delete[] data;
}
_Rows = m._Rows;
_Cols = m._Cols;
data = new _Scalar[_Rows * _Cols];
} else {
_Rows = m._Rows;
_Cols = m._Cols;
}
}
for (int i = 0; i < m._Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
Data(i, j) = m.cData(i, j);
}
}
}
template<typename _Scalar>
Matrix<_Scalar> Matrix<_Scalar>::operator +(const Matrix<_Scalar> &m) const {
Matrix<_Scalar> sum;
sum = *this;
for (int i = 0; i < _Rows * _Cols; i++) {
sum.data[i] += m.data[i];
}
return sum;
}
template<typename _Scalar>
Matrix<_Scalar> Matrix<_Scalar>::operator -(const Matrix<_Scalar> &m) const {
Matrix<_Scalar> sum;
sum = *this;
for (int i = 0; i < _Rows * _Cols; i++) {
sum.data[i] -= m.data[i];
}
return sum;
}
template<typename _Scalar>
Matrix<_Scalar> Matrix<_Scalar>::transpose() const
{
Matrix<_Scalar> res(_Cols, _Rows);
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
res.Data(j, i) = cData(i, j);
}
}
return res;
}
template<typename _Scalar>
Matrix<_Scalar> Matrix<_Scalar>::operator *(const Matrix<_Scalar> &m) const {
if (_Cols != m._Rows) {
// todo:output err
return m;
}
Matrix<_Scalar> res(_Rows, m._Cols);
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < m._Cols; j++) {
_Scalar sum = 0;
for (int k = 0; k < _Cols; k++) {
sum += cData(i, k) * m.cData(k, j);
}
res.Data(i, j) = sum;
}
}
return res;
}
template<typename _Scalar>
Matrix<_Scalar> Matrix<_Scalar>::operator /(double m) const {
Matrix<_Scalar> res;
res = *this;
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
res.Data(i, j) /= m;
}
}
return res;
}
template<typename _Scalar>
void Matrix<_Scalar>::normalize() {
double sum = 0;
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
sum += Matrix::cData(i, j);
}
}
sum = sqrt(sum);
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
Matrix::Data(i, j) /= sum;
}
}
}
template<typename _Scalar>
double Matrix<_Scalar>::norm() const {
double sum = 0;
for (int i = 0; i < _Rows; i++) {
for (int j = 0; j < _Cols; j++) {
sum += Matrix::cData(i, j) * Matrix::cData(i, j);
}
}
sum = sqrt(sum);
return sum;
}
} // namespace Ctain
#endif // SRC_MYNTEYE_API_CAMERA_MODELS_MATRIX_H_

466
src/mynteye/api/camera_models/MatrixSolver.h
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@ -2,249 +2,241 @@
// Created by 顾涵彬 on 2019-08-30. // Created by 顾涵彬 on 2019-08-30.
// //
#ifndef MATRIX_MATRIXSOLVER_H #ifndef SRC_MYNTEYE_API_CAMERA_MODELS_MATRIXSOLVER_H_
#define MATRIX_MATRIXSOLVER_H #define SRC_MYNTEYE_API_CAMERA_MODELS_MATRIXSOLVER_H_
#include <cmath> #include <cmath>
#include <complex> #include <complex>
static bool Matrix_EigenValue(double *K1,int n,int LoopNumber,double Error1,double *Ret);
static void Matrix_Hessenberg(double *A1,int n,double *ret); static bool Matrix_EigenValue(double *K1, int n,
int LoopNumber, double Error1, double *Ret);
static void Matrix_Hessenberg(double *A1, int n, double *ret);
namespace Ctain { namespace Ctain {
class EigenSolver { class EigenSolver {
public: public:
EigenSolver(SMatrix<double> s) { explicit EigenSolver(SMatrix<double> s) {
double *A = new double[s.rows()*2]; double *A = new double[s.rows()*2];
double *B = new double[s.size()]; double *B = new double[s.size()];
for (int i = 0; i < s.size(); i++) for (int i = 0; i < s.size(); i++)
B[i] = s(i); B[i] = s(i);
memset(A, 0, sizeof(s.rows()*2)); memset(A, 0, sizeof(s.rows()*2));
Matrix_EigenValue(B, s.rows(),1000,1e-10,A); Matrix_EigenValue(B, s.rows(), 1000, 1e-10, A);
Matrix<double> tt(A, s.rows(), 2); Matrix<double> tt(A, s.rows(), 2);
t=tt; t = tt;
std::cout<<"s:"<<s; SMatrix<double> s2(A, s.rows());
SMatrix<double> s2(A, s.rows());
std::cout<<"tt:"<<tt;
std::cout<<"s2:"<<s2;
delete []A;
delete []B;
}
Matrix<double> eigenvalues() {
return t;
}
private:
Matrix<double> t;
};
} //namespace Ctain end
static void Matrix_Hessenberg(double *A1,int n,double *ret)
{
int MaxNumber;
double temp,*A;
A=new double[n*n];
memset(A, 0, sizeof(double)*n*n);
for (int i=0;i<n;i++) {
int k=i*n;
for (int j=0;j<n;j++)
{
A[k+j]=A1[k+j];
}
}
for (int k=1;k<n-1;k++) {
int i=k-1;
MaxNumber=k;
temp=fabs(A[k*n+i]);
for (int j=k+1;j<n;j++) {
if (fabs(A[j*n+i])>temp) {
temp=fabs(A[j*n+i]);
MaxNumber=j;
}
}
ret[0]=A[MaxNumber*n+i];
if (ret[0]!=0) {
if (MaxNumber!=k) {
for (int j=k-1;j<n;j++) {
temp=A[i*n+j];
A[i*n+j]=A[k*n+j];
A[k*n+j]=temp;
}
for (int j=0;j<n;j++) {
temp=A[j*n+i];
A[j*n+i]=A[j*n+k];
A[j*n+k]=temp;
}
}
for (int i=k+1;i<n;i++) {
temp=A[i*n+k-1]/ret[0];
A[i*n+k-1]=0;
for (int j=k;j<n;j++) {
A[i*n+j]-=temp*A[k*n+j];
}
for (int j=0;j<n;j++) {
A[j*n+k]+=temp*A[j*n+i];
}
}
}
}
for (int i=0;i<n;i++) {
int k=i*n;
for (int j=0;j<n;j++) {
ret[k+j]=A[k+j];
}
}
delete []A; delete []A;
delete []B;
}
Matrix<double> eigenvalues() {
return t;
}
private:
Matrix<double> t;
};
} // namespace Ctain
static void Matrix_Hessenberg(double *A1, int n, double *ret) {
int MaxNumber;
double temp, *A;
A = new double[n*n];
memset(A, 0, sizeof(double)*n*n);
for (int i = 0; i < n; i++) {
int k = i * n;
for (int j = 0; j < n; j++) {
A[k + j] = A1[k + j];
}
}
for (int k = 1; k < n-1; k++) {
int i = k-1;
MaxNumber = k;
temp = fabs(A[k*n+i]);
for (int j = k+1; j < n; j++) {
if (fabs(A[j * n + i]) > temp) {
temp = fabs(A[j * n + i]);
MaxNumber = j;
}
}
ret[0] = A[MaxNumber * n + i];
if (ret[0] != 0) {
if (MaxNumber != k) {
for (int j = k-1; j < n; j++) {
temp = A[i * n + j];
A[i * n + j] = A[k * n + j];
A[k * n + j] = temp;
}
for (int j = 0; j < n; j++) {
temp = A[j * n + i];
A[j * n + i] = A[j * n + k];
A[j * n + k] = temp;
}
}
for (int i = k + 1; i < n; i++) {
temp = A[i * n + k - 1] / ret[0];
A[i * n + k - 1] = 0;
for (int j = k; j < n; j++) {
A[i * n + j] -= temp * A[k * n + j];
}
for (int j = 0; j < n; j++) {
A[j * n + k] += temp * A[j * n + i];
}
}
}
}
for (int i = 0; i < n; i++) {
int k = i * n;
for (int j = 0; j < n; j++) {
ret[k + j] = A[k + j];
}
}
delete []A;
} }
static bool Matrix_EigenValue(double *K1,int n,int LoopNumber,double Error1,double *Ret) static bool Matrix_EigenValue(double *K1, int n,
{ int LoopNumber, double Error1, double *Ret) {
int i,j,k,t,m,Loop1; int i, j, k, t, m, Loop1;
double b,c,d,g,xy,p,q,r,x,s,e,f,z,y,temp,*A; double b, c, d, g, xy, p, q, r, x, s, e, f, z, y, temp, *A;
A=new double[n*n]; A = new double[n * n];
memset(A, 0, sizeof(double)*n*n); memset(A, 0, sizeof(double) * n * n);
Matrix_Hessenberg(K1,n,A); Matrix_Hessenberg(K1, n, A);
m=n; m = n;
Loop1=LoopNumber; Loop1 = LoopNumber;
while(m!=0) { while (m != 0) {
t=m-1; t = m - 1;
while(t>0) { while (t > 0) {
temp=fabs(A[(t-1)*n+t-1]); temp = fabs(A[(t - 1) * n + t - 1]);
temp+=fabs(A[t*n+t]); temp += fabs(A[t * n + t]);
temp=temp*Error1; temp = temp * Error1;
if (fabs(A[t*n+t-1])>temp) { if (fabs(A[t * n + t - 1]) > temp) {
t--; t--;
} } else {
else { break;
break; }
}
}
if (t==m-1) {
Ret[(m-1)*2]=A[(m-1)*n+m-1];
Ret[(m-1)*2+1]=0;
m-=1;
Loop1=LoopNumber;
}
else if(t==m-2) {
b=-A[(m-1)*n+m-1]-A[(m-2)*n+m-2];
c=A[(m-1)*n+m-1]*A[(m-2)*n+m-2]-A[(m-1)*n+m-2]*A[(m-2)*n+m-1];
d=b*b-4*c;
y=sqrt(fabs(d));
if (d>0) {
xy=1;
if (b<0) {
xy=-1;
}
Ret[(m-1)*2]=-(b+xy*y)/2;
Ret[(m-1)*2+1]=0;
Ret[(m-2)*2]=c/Ret[(m-1)*2];
Ret[(m-2)*2+1]=0;
}
else {
Ret[(m-1)*2]=-b/2;
Ret[(m-2)*2]=-b/2;
Ret[(m-1)*2+1]=y/2;
Ret[(m-2)*2+1]=-y/2;
}
m-=2;
Loop1=LoopNumber;
}
else {
if (Loop1<1) {
return false;
}
Loop1--;
j=t+2;
while (j<m) {
A[j*n+j-2]=0;
j++;
}
j=t+3;
while (j<m) {
A[j*n+j-3]=0;
j++;
}
k=t;
while (k<m-1) {
if (k!=t) {
p=A[k*n+k-1];
q=A[(k+1)*n+k-1];
if (k!=m-2) {
r=A[(k+2)*n+k-1];
}
else {
r=0;
}
}
else {
b=A[(m-1)*n+m-1];
c=A[(m-2)*n+m-2];
x=b+c;
y=b*c-A[(m-2)*n+m-1]*A[(m-1)*n+m-2];
p=A[t*n+t]*(A[t*n+t]-x)+A[t*n+t+1]*A[(t+1)*n+t]+y;
q=A[(t+1)*n+t]*(A[t*n+t]+A[(t+1)*n+t+1]-x);
r=A[(t+1)*n+t]*A[(t+2)*n+t+1];
}
if (p!=0 || q!=0 || r!=0) {
if (p<0) {
xy=-1;
}
else {
xy=1;
}
s=xy*sqrt(p*p+q*q+r*r);
if (k!=t) {
A[k*n+k-1]=-s;
}
e=-q/s;
f=-r/s;
x=-p/s;
y=-x-f*r/(p+s);
g=e*r/(p+s);
z=-x-e*q/(p+s);
for (j=k;j<m;j++) {
b=A[k*n+j];
c=A[(k+1)*n+j];
p=x*b+e*c;
q=e*b+y*c;
r=f*b+g*c;
if (k!=m-2) {
b=A[(k+2)*n+j];
p+=f*b;
q+=g*b;
r+=z*b;
A[(k+2)*n+j]=r;
}
A[(k+1)*n+j]=q;
A[k*n+j]=p;
}
j=k+3;
if (j>m-2) {
j=m-1;
}
for (i=t;i<j+1;i++) {
b=A[i*n+k];
c=A[i*n+k+1];
p=x*b+e*c;
q=e*b+y*c;
r=f*b+g*c;
if (k!=m-2) {
b=A[i*n+k+2];
p+=f*b;
q+=g*b;
r+=z*b;
A[i*n+k+2]=r;
}
A[i*n+k+1]=q;
A[i*n+k]=p;
}
}
k++;
}
}
} }
delete []A; if (t == m-1) {
return true; Ret[(m - 1) * 2] = A[(m - 1) * n + m - 1];
Ret[(m - 1) * 2 + 1] = 0;
m -= 1;
Loop1 = LoopNumber;
} else if (t == m - 2) {
b = -A[(m - 1) * n + m - 1] - A[(m - 2) * n + m - 2];
c = A[(m - 1) * n + m - 1] * A[(m - 2) * n + m - 2]
- A[(m - 1) * n + m - 2] * A[(m - 2) * n + m - 1];
d = b * b - 4 * c;
y = sqrt(fabs(d));
if (d > 0) {
xy = 1;
if (b < 0) {
xy = -1;
}
Ret[(m - 1) * 2] = -(b + xy * y) / 2;
Ret[(m - 1) * 2 + 1] = 0;
Ret[(m - 2) * 2] = c / Ret[(m - 1) * 2];
Ret[(m - 2) * 2 + 1] = 0;
} else {
Ret[(m - 1) * 2] = -b / 2;
Ret[(m - 2) * 2] = -b / 2;
Ret[(m - 1) * 2 + 1] = y / 2;
Ret[(m - 2) * 2 + 1] = -y / 2;
}
m -= 2;
Loop1 = LoopNumber;
} else {
if (Loop1 < 1) {
delete []A;
return false;
}
Loop1--;
j = t + 2;
while (j < m) {
A[j * n + j - 2] = 0;
j++;
}
j = t + 3;
while (j < m) {
A[j * n + j - 3] = 0;
j++;
}
k = t;
while (k < m - 1) {
if (k != t) {
p = A[k * n + k - 1];
q = A[(k + 1) * n + k - 1];
if (k != m - 2) {
r = A[(k + 2) * n + k - 1];
} else {
r = 0;
}
} else {
b = A[(m - 1) * n + m - 1];
c = A[(m - 2) * n + m - 2];
x = b + c;
y = b * c - A[(m - 2) * n + m - 1] * A[(m - 1) * n + m - 2];
p = A[t * n + t] * (A[t * n + t] - x) +
A[t * n + t + 1] * A[(t + 1) * n + t] + y;
q = A[(t + 1) * n + t] * (A[t * n + t] + A[(t + 1) * n + t + 1] - x);
r = A[(t + 1) * n + t] * A[(t + 2) * n + t + 1];
}
if (p != 0 || q != 0 || r != 0) {
if (p < 0) {
xy = -1;
} else {
xy = 1;
}
s = xy * sqrt(p * p + q * q + r * r);
if (k != t) {
A[k * n + k - 1]= -s;
}
e = -q / s;
f = -r / s;
x = -p / s;
y = -x - f * r / (p + s);
g = e * r / (p + s);
z = -x - e * q / (p + s);
for (j = k; j < m; j++) {
b = A[k * n + j];
c = A[(k + 1) * n + j];
p = x * b + e * c;
q = e * b + y * c;
r = f * b + g * c;
if (k != m - 2) {
b = A[(k + 2) * n + j];
p += f * b;
q += g * b;
r += z * b;
A[(k + 2) * n + j] = r;
}
A[(k + 1) * n + j] = q;
A[k * n + j] = p;
}
j = k + 3;
if (j > m - 2) {
j = m - 1;
}
for (i = t; i < j + 1; i++) {
b = A[i * n + k];
c = A[i * n + k + 1];
p = x * b + e * c;
q = e * b + y * c;
r = f * b + g * c;
if (k != m - 2) {
b = A[i * n + k + 2];
p += f * b;
q += g * b;
r += z * b;
A[i * n + k + 2] = r;
}
A[i * n + k + 1] = q;
A[i * n + k] = p;
}
}
k++;
}
}
}
delete []A;
return true;
} }
#endif //MATRIX_MATRIXSOLVER_H #endif // SRC_MYNTEYE_API_CAMERA_MODELS_MATRIXSOLVER_H_

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src/mynteye/api/camera_models/Quaternion.h
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@ -1,57 +1,66 @@
// Copyright 2018 Slightech Co., Ltd. All rights reserved.
// //
// Created by 顾涵彬 on 2019-08-30. // Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// //
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef MATRIX_QUATERNION_H #ifndef SRC_MYNTEYE_API_CAMERA_MODELS_QUATERNION_H_
#define MATRIX_QUATERNION_H #define SRC_MYNTEYE_API_CAMERA_MODELS_QUATERNION_H_
#include "SquareMatrix.h" #include "SquareMatrix.h"
#include <cmath> #include <cmath>
namespace Ctain { namespace Ctain {
// using SMatrix<double>; template<typename T>
template<typename T> class Quaternion {
class Quaternion { public:
public: Quaternion() {}
Quaternion(){} explicit Quaternion(SMatrix<double> m) {
Quaternion(SMatrix<double> m){ _w = sqrt(m(0) + m(4) + m(8) + 1) / 2;
//double f = 1e-10; _x = (m(5) - m(7)) / (4 * _w);
_w = sqrt(m(0)+m(4)+m(8)+1)/2; _y = (m(6) - m(2)) / (4 * _w);
_x = (m(5)-m(7))/(4*_w); _z = (m(1) - m(3)) / (4 * _w);
_y = (m(6)-m(2))/(4*_w); }
_z = (m(1)-m(3))/(4*_w); Quaternion(T X, T Y, T Z, T W) : _x(X), _y(Y), _z(Z), _w(W) {}
} void normalize() {
Quaternion(T X,T Y,T Z,T W) : _x(X),_y(Y),_z(Z),_w(W){} double len;
void normalize() { len = sqrt(_x * _x + _y * _y + _z * _z + _w * _w);
double len; _x = _x / len;
len = sqrt(_x*_x+_y*_y+_z*_z+_w*_w); _y = _y / len;
_x = _x / len; _z = _z / len;
_y = _y / len; _w = _w / len;
_z = _z / len; }
_w = _w / len; inline T x() {return _x;}
} inline T y() {return _y;}
T x(){return _x;} inline T z() {return _z;}
T y(){return _y;} inline T w() {return _w;}
T z(){return _z;} SMatrix<double> toRotationMatrix() const {
T w(){return _w;} SMatrix<double> r(3);
SMatrix<double> toRotationMatrix() const { double q0 = _w, q1 = _x, q2 = _y, q3 = _z;
SMatrix<double> r(3); r(0) = 1 - 2 * q2 * q2 - 2 * q3 * q3;
double q0=_w,q1=_x,q2=_y,q3=_z; r(1) = 2 * q1 * q2 + 2 * q0 * q3;
r(0) = 1 - 2*q2*q2-2*q3*q3; r(2) = 2 * q1 * q3 - 2 * q0 * q2;
r(1) = 2*q1*q2+2*q0*q3; r(3) = 2 * q1 * q2 - 2 * q0 * q3;
r(2) = 2*q1*q3-2*q0*q2; r(4) = 1 - 2 * q1 * q1 - 2 * q3 * q3;
r(3) = 2*q1*q2 - 2*q0*q3; r(5) = 2 * q2 * q3 + 2 * q0 * q1;
r(4) = 1-2*q1*q1 - 2*q3*q3; r(6) = 2 * q1 * q3 + 2 * q0 * q2;
r(5) = 2*q2*q3 +2*q0*q1; r(7) = 2 * q2 * q3 - 2 * q0 * q1;
r(6) = 2*q1*q3+2*q0*q2; r(8) = 1 - 2 * q1 * q1 - 2 * q2 * q2;
r(7) = 2*q2*q3 - 2*q0*q1; return r;
r(8) = 1-2*q1*q1 -2*q2*q2; }
return r;
} private:
private: T _x;
T _x; T _y;
T _y; T _z;
T _z; T _w;
T _w; };
}; } // namespace Ctain
} #endif // SRC_MYNTEYE_API_CAMERA_MODELS_QUATERNION_H_
#endif //MATRIX_QUATERNION_H

165
src/mynteye/api/camera_models/SquareMatrix.h
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@ -1,91 +1,96 @@
// Copyright 2018 Slightech Co., Ltd. All rights reserved.
// //
// Created by 顾涵彬 on 2019-08-29. // Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// //
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef MATRIX_SQUAREMATRIX_H #ifndef SRC_MYNTEYE_API_CAMERA_MODELS_SQUAREMATRIX_H_
#define MATRIX_SQUAREMATRIX_H #define SRC_MYNTEYE_API_CAMERA_MODELS_SQUAREMATRIX_H_
#include "Matrix.h" #include "Matrix.h"
namespace Ctain { namespace Ctain {
#define Matrix Matrix<_Scalar> #define Matrix_ Matrix<_Scalar>
template<typename _Scalar> template<typename _Scalar>
class SMatrix: public Matrix{ class SMatrix: public Matrix_{
public: public:
SMatrix(int D) : Matrix(D, D) {} explicit SMatrix(int D) : Matrix_(D, D) {}
SMatrix() : Matrix(0, 0) {} SMatrix() : Matrix_(0, 0) {}
SMatrix(_Scalar _data[], int D) : SMatrix(_Scalar _data[], int D) : Matrix_(_data, D, D) {}
Matrix(_data, D, D) {} SMatrix(_Scalar **_data, int D) : Matrix_(_data, D, D) {}
SMatrix(_Scalar **_data, int D) : explicit SMatrix(Matrix_ m) : Matrix_(m) {}
Matrix(_data, D, D) {} _Scalar determinant();
SMatrix(Matrix m) : _Scalar M(int m, int n);
Matrix(m) {} SMatrix<_Scalar> inverse() {
// void operator =(const Matrix &m){ SMatrix<_Scalar> res(Matrix_::_Rows);
// } _Scalar d = determinant();
_Scalar determinant(); for (int i = 0; i < Matrix_::_Rows; i++) {
_Scalar M(int m, int n); for (int j = 0; j < Matrix_::_Cols; j++) {
SMatrix<_Scalar> inverse() { res.Data(j, i) = 1.0 * M(i, j) / d;
SMatrix<_Scalar> res(Matrix::_Rows);
_Scalar d = determinant();
for (int i = 0; i < Matrix::_Rows; i++) {
for (int j = 0; j < Matrix::_Cols; j++) {
res.Data(j, i) = 1.0*M(i, j)/d;
}
}
return res;
} }
};//class Matrix end
template<typename _Scalar>
_Scalar SMatrix<_Scalar>::determinant() {
int r, c, m;
int lop = 0;
int n = Matrix::_Rows;
_Scalar result = 0;
_Scalar mid = 1;
if (n != 1) {
lop = (n == 2) ? 1 : n;
for (m = 0; m < lop; m++) {
mid = 1;
for (r = 0, c = m; r < n; r++, c++) {
mid = mid * (*(Matrix::data+r*n+c%n));
}
result += mid;
}
for (m = 0; m < lop; m++) {
mid = 1;
for (r = 0, c = n-1-m+n; r < n; r++, c--) {
mid = mid * (*(Matrix::data+r*n+c%n));
}
result -= mid;
}
}
else
result = Matrix::data[0];
return result;
} }
return res;
}
void operator =(Matrix<_Scalar> m) {
SMatrix t(m);
*this = t;
}
};
template<typename _Scalar> template<typename _Scalar>
_Scalar SMatrix<_Scalar>::M(int m, int n) { _Scalar SMatrix<_Scalar>::determinant() {
float mid_result = 0; int r, c, m;
int sign = 1; int lop = 0;
int k = Matrix::_Rows; int n = Matrix_::_Rows;
SMatrix mid(k-1); _Scalar result = 0;
int c = 0; _Scalar mid = 1;
for (int i = 0; i < k; i++) { if (n != 1) {
for (int j = 0; j < k; j++) { lop = (n == 2) ? 1 : n;
if (i != m && j != n) for (m = 0; m < lop; m++) {
{ mid = 1;
mid.Data(c++) = Matrix::cData(i,j); for (r = 0, c = m; r < n; r++, c++) {
} mid = mid * (*(Matrix_::data+r*n+c%n));
}
} }
sign = (m+n)%2 == 0 ? 1 : -1; result += mid;
mid_result = (float)sign*mid.determinant();
return mid_result;
} }
#undef Matrix for (m = 0; m < lop; m++) {
mid = 1;
for (r = 0, c = n-1-m+n; r < n; r++, c--) {
mid = mid * (*(Matrix_::data + r * n + c % n));
}
result -= mid;
}
} else {
result = Matrix_::data[0];
}
return result;
}
}//namespace Ctain end template<typename _Scalar>
#endif //MATRIX_SQUAREMATRIX_H _Scalar SMatrix<_Scalar>::M(int m, int n) {
float mid_result = 0;
int sign = 1;
int k = Matrix_::_Rows;
SMatrix mid(k - 1);
int c = 0;
for (int i = 0; i < k; i++) {
for (int j = 0; j < k; j++) {
if (i != m && j != n) {
mid.Data(c++) = Matrix_::cData(i, j);
}
}
}
sign = (m+n)%2 == 0 ? 1 : -1;
mid_result = static_cast<_Scalar>(sign) * mid.determinant();
return mid_result;
}
#undef Matrix_
} // namespace Ctain
#endif // SRC_MYNTEYE_API_CAMERA_MODELS_SQUAREMATRIX_H_

3
src/mynteye/api/camera_models/camera.h
View File

@ -127,7 +127,8 @@ class Camera {
void projectPoints( void projectPoints(
const std::vector<cv::Point3f> &objectPoints, const cv::Mat &rvec, const std::vector<cv::Point3f> &objectPoints, const cv::Mat &rvec,
const cv::Mat &tvec, std::vector<cv::Point2f> &imagePoints) const; const cv::Mat &tvec, std::vector<cv::Point2f> &imagePoints) const; // NOLINT
protected: protected:
cv::Mat m_mask; cv::Mat m_mask;
}; };

3
src/mynteye/api/camera_models/equidistant_camera.cc
View File

@ -587,8 +587,7 @@ void EquidistantCamera::fitOddPoly(
} }
Y(i, 0) = y.at(i); Y(i, 0) = y.at(i);
} }
Ctain::SMatrix<double> Tmp; Ctain::SMatrix<double> Tmp(X.transpose() * X);
Tmp = X.transpose() * X;
Ctain::MatrixXd A = Tmp.inverse() * X.transpose() * Y; Ctain::MatrixXd A = Tmp.inverse() * X.transpose() * Y;
coeffs.resize(A.rows()); coeffs.resize(A.rows());

10
src/mynteye/api/camera_models/equidistant_camera.h
View File

@ -127,8 +127,8 @@ class EquidistantCamera : public Camera {
// Projects 3D points to the image plane (Pi function) // Projects 3D points to the image plane (Pi function)
// and calculates jacobian // and calculates jacobian
void spaceToPlane( void spaceToPlane(
const Ctain::Vector3d &P, Ctain::Vector2d &p, const Ctain::Vector3d &P,Ctain::Vector2d &p, // NOLINT
Ctain::Matrix23d &J) const; Ctain::Matrix23d &J) const; // NOLINT
// %output p // %output p
// %output J // %output J
@ -161,7 +161,7 @@ class EquidistantCamera : public Camera {
void fitOddPoly( void fitOddPoly(
const std::vector<double> &x, const std::vector<double> &y, int n, const std::vector<double> &x, const std::vector<double> &y, int n,
std::vector<double> &coeffs) const; std::vector<double> &coeffs) const; // NOLINT
void backprojectSymmetric( void backprojectSymmetric(
const Ctain::Vector2d &p_u, double &theta, double &phi) const; // NOLINT const Ctain::Vector2d &p_u, double &theta, double &phi) const; // NOLINT
@ -172,7 +172,7 @@ class EquidistantCamera : public Camera {
}; };
typedef std::shared_ptr<EquidistantCamera> EquidistantCameraPtr; typedef std::shared_ptr<EquidistantCamera> EquidistantCameraPtr;
typedef std::shared_ptr<const EquidistantCamera> EquidistantCameraConstPtr; typedef std::shared_ptr<const EquidistantCamera> EquidistantCameraConstPtr; // NOLINT
template <typename T> template <typename T>
T EquidistantCamera::r(T k2, T k3, T k4, T k5, T theta) { T EquidistantCamera::r(T k2, T k3, T k4, T k5, T theta) {
@ -193,7 +193,7 @@ T EquidistantCamera::r(T k2, T k3, T k4, T k5, T theta) {
template <typename T> template <typename T>
void spaceToPlane( void spaceToPlane(
const T *const params, const T *const q, const T *const t, const T *const params, const T *const q, const T *const t,
const Ctain::Matrix<T> &P, Ctain::Matrix<T> &p) { const Ctain::Matrix<T> &P, Ctain::Matrix<T> &p) { // NOLINT
T P_w[3]; T P_w[3];
P_w[0] = T(P(0)); P_w[0] = T(P(0));
P_w[1] = T(P(1)); P_w[1] = T(P(1));

19
src/mynteye/api/processor/rectify_processor.cc
View File

@ -109,11 +109,15 @@ void RectifyProcessor::stereoRectify(models::CameraPtr leftOdo,
// these contain the relevant rectified image internal params (fx, fy=fx, cx, cy) // these contain the relevant rectified image internal params (fx, fy=fx, cx, cy)
double fc_new = DBL_MAX; double fc_new = DBL_MAX;
CvPoint2D64f cc_new[2] = {{0, 0}, {0, 0}}; CvPoint2D64f cc_new[2] = {{0, 0}, {0, 0}};
newImgSize = newImgSize.width * newImgSize.height != 0 ? newImgSize : imageSize; newImgSize = newImgSize.width * newImgSize.height != 0 ?
const double ratio_x = (double)newImgSize.width / imageSize.width / 2; newImgSize : imageSize;
const double ratio_y = (double)newImgSize.height / imageSize.height / 2; const double ratio_x = static_cast<double>(newImgSize.width) /
imageSize.width / 2;
const double ratio_y = static_cast<double>(newImgSize.height) /
imageSize.height / 2;
const double ratio = idx == 1 ? ratio_x : ratio_y; const double ratio = idx == 1 ? ratio_x : ratio_y;
fc_new = (cvmGet(K1, idx ^ 1, idx ^ 1) + cvmGet(K2, idx ^ 1, idx ^ 1)) * ratio; fc_new = (cvmGet(K1, idx ^ 1, idx ^ 1) +
cvmGet(K2, idx ^ 1, idx ^ 1)) * ratio;
for (k = 0; k < 2; k++) { for (k = 0; k < 2; k++) {
CvPoint2D32f _pts[4]; CvPoint2D32f _pts[4];
@ -126,8 +130,8 @@ void RectifyProcessor::stereoRectify(models::CameraPtr leftOdo,
Ctain::Vector3d b(3, 1); Ctain::Vector3d b(3, 1);
for (i = 0; i < 4; i++) { for (i = 0; i < 4; i++) {
int j = (i < 2) ? 0 : 1; int j = (i < 2) ? 0 : 1;
a(0) = (float)((i % 2)*(nx)); a(0) = static_cast<float>((i % 2)*(nx));
a(1) = (float)(j*(ny)); a(1) = static_cast<float>(j*(ny));
if (0 == k) { if (0 == k) {
leftOdo->liftProjective(a, b); leftOdo->liftProjective(a, b);
} else { } else {
@ -290,7 +294,8 @@ std::shared_ptr<struct CameraROSMsgInfoPair> RectifyProcessor::stereoRectify(
// Eigen::Matrix3d R = T.topLeftCorner<3, 3>(); // Eigen::Matrix3d R = T.topLeftCorner<3, 3>();
// Eigen::Vector3d t = T.topRightCorner<3, 1>(); // Eigen::Vector3d t = T.topRightCorner<3, 1>();
Ctain::Matrix4d T = loadT(ex_right_to_left); Ctain::Matrix4d T = loadT(ex_right_to_left);
Ctain::Matrix3d R = T.topLeftCorner<3, 3>(); Ctain::Matrix3d R;
R = T.topLeftCorner<3, 3>();
Ctain::Vector3d t = T.topRightCorner<3, 1>(); Ctain::Vector3d t = T.topRightCorner<3, 1>();
// cv::Mat cv_R, cv_t; // cv::Mat cv_R, cv_t;
// cv::eigen2cv(R, cv_R); // cv::eigen2cv(R, cv_R);