QDomElement PCA_transformer::serialize() const{
QDomDocument doc;
QDomElement elem = doc.createElement("Transformer");
elem.setAttribute("type",this->classname().c_str());
elem.setAttribute("name",this->name().c_str());
QStringList qvariate_names;
for(int i=0; i< variate_names_.size(); ++i) {
qvariate_names.append(variate_names_[i].c_str());
}
elem.setAttribute("variableNames",qvariate_names.join(";"));
QStringList qstr_mean;
for(int i=0; i< means_.rows(); ++i) {
qstr_mean.append( QString("%1").arg(means_(i)) );
}
elem.setAttribute("means",qstr_mean.join(";"));
QStringList qstr_eigvalues;
for(int i=0; i< eigenvalues_.rows(); ++i) {
qstr_eigvalues.append( QString("%1").arg(eigenvalues_(i)) );
}
elem.setAttribute("EigenValues",qstr_eigvalues.join(";"));
QStringList qstr_eigvectors;
for(int i=0; i< eigenvectors_.rows(); ++i) {
for(int j=0; j< eigenvectors_.cols(); ++j) {
qstr_eigvectors.append( QString("%1").arg(eigenvectors_(i,j)) );
}
}
elem.setAttribute("EigenVectors",qstr_eigvectors.join(";"));
return elem;
}
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT>::computePointPrincipalCurvatures (
const pcl::PointCloud<PointNT> &normals, int p_idx, const std::vector<int> &indices,
float &pcx, float &pcy, float &pcz, float &pc1, float &pc2)
{
EIGEN_ALIGN16 Eigen::Matrix3f I = Eigen::Matrix3f::Identity ();
Eigen::Vector3f n_idx (normals.points[p_idx].normal[0], normals.points[p_idx].normal[1], normals.points[p_idx].normal[2]);
EIGEN_ALIGN16 Eigen::Matrix3f M = I - n_idx * n_idx.transpose (); // projection matrix (into tangent plane)
// Project normals into the tangent plane
Eigen::Vector3f normal;
projected_normals_.resize (indices.size ());
xyz_centroid_.setZero ();
for (size_t idx = 0; idx < indices.size(); ++idx)
{
normal[0] = normals.points[indices[idx]].normal[0];
normal[1] = normals.points[indices[idx]].normal[1];
normal[2] = normals.points[indices[idx]].normal[2];
projected_normals_[idx] = M * normal;
xyz_centroid_ += projected_normals_[idx];
}
// Estimate the XYZ centroid
xyz_centroid_ /= indices.size ();
// Initialize to 0
covariance_matrix_.setZero ();
double demean_xy, demean_xz, demean_yz;
// For each point in the cloud
for (size_t idx = 0; idx < indices.size (); ++idx)
{
demean_ = projected_normals_[idx] - xyz_centroid_;
demean_xy = demean_[0] * demean_[1];
demean_xz = demean_[0] * demean_[2];
demean_yz = demean_[1] * demean_[2];
covariance_matrix_(0, 0) += demean_[0] * demean_[0];
covariance_matrix_(0, 1) += demean_xy;
covariance_matrix_(0, 2) += demean_xz;
covariance_matrix_(1, 0) += demean_xy;
covariance_matrix_(1, 1) += demean_[1] * demean_[1];
covariance_matrix_(1, 2) += demean_yz;
covariance_matrix_(2, 0) += demean_xz;
covariance_matrix_(2, 1) += demean_yz;
covariance_matrix_(2, 2) += demean_[2] * demean_[2];
}
// Extract the eigenvalues and eigenvectors
//Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> ei_symm (covariance_matrix_);
//eigenvalues_ = ei_symm.eigenvalues ();
//eigenvectors_ = ei_symm.eigenvectors ();
pcl::eigen33 (covariance_matrix_, eigenvectors_, eigenvalues_);
pcx = eigenvectors_ (0, 2);
pcy = eigenvectors_ (1, 2);
pcz = eigenvectors_ (2, 2);
pc1 = eigenvalues_ (2);
pc2 = eigenvalues_ (1);
}
