fix(*): fix EigenSolver bug
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@ -123,6 +123,18 @@ namespace Ctain {
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return res;
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}
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friend Matrix<_Scalar> operator *(
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const Matrix<_Scalar> &m, double a) {
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Matrix<_Scalar> res;
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res = m;
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for(int i = 0; i < m._Rows; i++) {
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for(int j = 0; j < m._Cols; j++) {
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res.Data(i,j) *= a;
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}
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}
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return res;
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}
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friend Matrix<_Scalar> operator -(
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const Matrix<_Scalar> &m) {
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Matrix<_Scalar> res;
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@ -268,6 +280,14 @@ namespace Ctain {
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template<typename _Scalar>
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void Matrix<_Scalar>::operator =(Matrix<_Scalar> m) {
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if(m._isSub) {
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if(_isSub) {
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for (int i = 0; i < m._Rows; i++) {
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for (int j = 0; j < m._Cols; j++) {
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Data(i,j) = m.cData(i, j);
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}
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}
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return;
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}
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_isSub = true;
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_Rows = m._Rows;
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_Cols = m._Cols;
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@ -352,7 +372,8 @@ namespace Ctain {
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template<typename _Scalar>
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Matrix<_Scalar> Matrix<_Scalar>::operator /(double m) const {
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Matrix<_Scalar> res(_Rows, _Cols);
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Matrix<_Scalar> res;
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res = *this;
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for(int i = 0; i < _Rows; i++) {
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for(int j = 0; j < _Cols; j++) {
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res.Data(i,j) /= m;
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@ -7,20 +7,30 @@
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#include <cmath>
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#include <complex>
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static bool Matrix_EigenValue(double *K1,int n,int LoopNumber,double Error1,double *Ret);
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static void Matrix_Hessenberg(double *A1,int n,double *ret);
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namespace Ctain {
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class EigenSolver {
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public:
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EigenSolver(SMatrix<double> s) {
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_matrix = s;
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Matrix<double> tt(_matrix.rows(),2);
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Matrix_EigenValue(_matrix.addr(),_matrix.rows(),1000,1e-10,tt.addr());
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double *A = new double[s.rows()*2];
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double *B = new double[s.size()];
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for(int i = 0; i < s.size(); i++)
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B[i] = s(i);
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memset(A, 0, sizeof(s.rows()*2));
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Matrix_EigenValue(B, s.rows(),1000,1e-10,A);
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Matrix<double> tt(A, s.rows(), 2);
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t=tt;
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std::cout<<"s:"<<s;
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SMatrix<double> s2(A, s.rows());
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std::cout<<"tt:"<<tt;
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std::cout<<"s2:"<<s2;
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delete []A;
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delete []B;
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}
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Matrix<double> eigenvalues() {
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return t;
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}
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private:
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SMatrix<double> _matrix;
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Matrix<double> t;
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};
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@ -29,56 +39,58 @@ namespace Ctain {
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static void Matrix_Hessenberg(double *A1,int n,double *ret)
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{
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int i,j,k,MaxNumber;
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int MaxNumber;
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double temp,*A;
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A=new double[n*n];
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for (i=0;i<n;i++) {
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k=i*n;
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for (j=0;j<n;j++)
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memset(A, 0, sizeof(double)*n*n);
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for (int i=0;i<n;i++) {
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int k=i*n;
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for (int j=0;j<n;j++)
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{
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A[k+j]=A1[k+j];
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}
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}
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for (k=1;k<n-1;k++) {
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i=k-1;
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for (int k=1;k<n-1;k++) {
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int i=k-1;
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MaxNumber=k;
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temp=abs(A[k*n+i]);
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for (j=k+1;j<n;j++) {
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if (abs(A[j*n+i])>temp) {
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temp=abs(A[j*n+i]);
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temp=fabs(A[k*n+i]);
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for (int j=k+1;j<n;j++) {
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if (fabs(A[j*n+i])>temp) {
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temp=fabs(A[j*n+i]);
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MaxNumber=j;
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}
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}
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ret[0]=A[MaxNumber*n+i];
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i=MaxNumber;
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if (ret[0]!=0) {
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if (i!=k) {
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for(j=k-1;j<n;j++) {
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if (MaxNumber!=k) {
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for(int j=k-1;j<n;j++) {
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temp=A[i*n+j];
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A[i*n+j]=A[k*n+j];
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A[k*n+j]=temp;
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}
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for(j=0;j<n;j++) {
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for(int j=0;j<n;j++) {
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temp=A[j*n+i];
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A[j*n+i]=A[j*n+k];
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A[j*n+k]=temp;
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}
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}
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for (i=k+1;i<n;i++) {
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for (int i=k+1;i<n;i++) {
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temp=A[i*n+k-1]/ret[0];
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A[i*n+k-1]=0;
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for (j=k;j<n;j++) {
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for (int j=k;j<n;j++) {
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A[i*n+j]-=temp*A[k*n+j];
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}
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for (j=0;j<n;j++) {
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for (int j=0;j<n;j++) {
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A[j*n+k]+=temp*A[j*n+i];
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}
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}
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}
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}
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for (i=0;i<n;i++) {
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k=i*n;
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for (j=0;j<n;j++) {
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for (int i=0;i<n;i++) {
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int k=i*n;
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for (int j=0;j<n;j++) {
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ret[k+j]=A[k+j];
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}
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}
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@ -90,16 +102,17 @@ static bool Matrix_EigenValue(double *K1,int n,int LoopNumber,double Error1,doub
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int i,j,k,t,m,Loop1;
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double b,c,d,g,xy,p,q,r,x,s,e,f,z,y,temp,*A;
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A=new double[n*n];
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memset(A, 0, sizeof(double)*n*n);
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Matrix_Hessenberg(K1,n,A);
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m=n;
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Loop1=LoopNumber;
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while(m!=0) {
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t=m-1;
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while(t>0) {
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temp=abs(A[(t-1)*n+t-1]);
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temp+=abs(A[t*n+t]);
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temp=fabs(A[(t-1)*n+t-1]);
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temp+=fabs(A[t*n+t]);
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temp=temp*Error1;
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if (abs(A[t*n+t-1])>temp) {
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if (fabs(A[t*n+t-1])>temp) {
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t--;
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}
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else {
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@ -116,7 +129,7 @@ static bool Matrix_EigenValue(double *K1,int n,int LoopNumber,double Error1,doub
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b=-A[(m-1)*n+m-1]-A[(m-2)*n+m-2];
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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];
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d=b*b-4*c;
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y=sqrt(abs(d));
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y=sqrt(fabs(d));
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if (d>0) {
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xy=1;
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if (b<0) {
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@ -651,20 +651,25 @@ void EquidistantCamera::backprojectSymmetric(
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if (npow >= 9) {
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coeffs(9) = mParameters.k5();
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}
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std::cout << std::endl << std::endl << "coeffs:" << coeffs;
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if (npow == 1) {
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theta = p_u_norm;
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} else {
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// Get eigenvalues of companion matrix corresponding to polynomial.
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// Eigenvalues correspond to roots of polynomial.
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Ctain::MatrixXd A(npow, npow);
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Ctain::Matrixd A(npow);
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A.setZero();
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A.block(1, 0, npow - 1, npow - 1).setIdentity();
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A.col(npow - 1) = -coeffs.block(0, 0, npow, 1) / coeffs(npow);
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std::cout << std::endl <<"A:" << A;
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Ctain::EigenSolver es(A);
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Ctain::MatrixXcd eigval = es.eigenvalues();
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Ctain::Matrix<double> eigval(9, 2);
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eigval = es.eigenvalues();
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// Ctain::EigenSolver es(A);
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// Ctain::MatrixXcd eigval(npow, 2);
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// eigval = es.eigenvalues();
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std::cout << std::endl <<"eigval:" << eigval;
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std::vector<double> thetas;
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for (int i = 0; i < eigval.rows(); ++i) {
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if (fabs(eigval(i, 1)) > tol) { //imag
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@ -684,10 +689,17 @@ void EquidistantCamera::backprojectSymmetric(
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if (thetas.empty()) {
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theta = p_u_norm;
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std::cout<<std::endl<<"empty";
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} else {
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theta = *std::min_element(thetas.begin(), thetas.end());
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// theta = 1.3457661;
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std::cout<<"thetas[]:";
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for(auto t:thetas)
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std::cout<<t<<" ";
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}
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}
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std::cout << std::endl <<"thetas:" << theta <<" phi:"<<phi;
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}
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