Medial axis

class geoteqpy.MedialAxis(mesh: UnstructuredGrid, radius_ma: float, pca_method: str = 'sphere', cov_radius: float = 10000.0, cov_knearest: int = 100)

Class to compute the medial axis of a 3-dimensional shape. The medial axis is a set of points located at the centre of the shape. Medial axis is computed using the algorithm described in the paper:

Ma, J., Bae, S.W., Choi, S. (2019). 3D medial axis point approximation using nearest neighbors and the normal field. Computer-Aided Design, 114, 1-11. https://doi.org/10.1016/j.cad.2019.05.002

Parameters:

mesh (pyvista.UnstructuredGrid) – Mesh of the shape for which the medial axis is computed.
radius_ma (float) – Radius of research for the medial axis.
pca_method (str) –
Method to compute the covariance matrix and its eigenvectors. Available methods are "sphere" or "knearest".
- "sphere": compute the covariance matrix using a sphere of radius cov_radius.
- "knearest": compute the covariance matrix using the cov_knearest nearest neighbors.
cov_radius (float) – Radius of the sphere to select the points for the covariance matrix. Default is 1e4.
cov_knearest (int) – Number of nearest neighbors to select the points for the covariance matrix. Default is 100.

Attributes:

medial_axis: pyvista.PolyData: Pyvista mesh object of the medial axis points.

radius_ma: float: Radius of research for the medial axis.

pca: geoteqpy.PCA: PCA object to compute the covariance matrix and its eigenvectors.

Example:

import pyvista as pvs
import geoteqpy as gte

# Load a mesh
mesh = pvs.read("path/to/mesh.vtu")
# Create a MedialAxis object, compute the medial axis and the orientation vectors
ma = gte.MedialAxis(mesh=mesh,radius_ma=1e4,pca_method="knearest",cov_knearest=100)

Methods:

compute_medial_axis() → PolyData: Compute the medial axis of the shape. Attach the result to the class attribute medial_axis.

get_medial_axis_mesh() → PolyData

Get the medial axis mesh.

Returns:: Pyvista mesh object of the medial axis points.
Return type:: pyvista.PolyData

get_orientation_vectors(): Register the eigen vectors of the covariance matrix on the medial_axis mesh if they are not already present.