Multibody Surface to Volumetric Mesh in PyVista
I have a project that involves importing surfaces meshes into PyVista and converting them to volumetric meshes using tetgen. Some of these surface meshes contain multiple bodies, being essentially split along a surface. These cannot be tetrahedralized because they are non-manifold, but I would like to find a way to repair them in a way that preserves both sections. Below is an example showing the type of mesh I am dealing with, and how one section is lost when meshfix.repair is used. (Apologies for the large data set, this problem doesn't seem to happen with simpler meshes)
Given an input mesh like this, how can I end up with a tetrahedralization of both sections?
Edit: Some more thoughts.
- The mesh has two distinct watertight sections. So maybe there could be an algorithm to break a surface mesh into separate watertight sections. Tried DatasetFilters.Connectivity but this requires the sections to not be touching.
- The wall elements are shared between the sections so the only non-manifold edges are around the outside of the surface. This makes it hard to select the dividing wall.
- Perhaps we could do some sort of iterative test checking whether a point is inside a watertight section, then deleting those walls...
import pyvista as pv
import tetgen
import pymeshfix
import numpy as np
points = np.array([[ 76.84349 , 2.718057 , 2.718057 ],
[ 82.79519 , 1.491148 , 1.491148 ],
[ 0. , 0. , 0. ],
[ 70.97732 , 4.304215 , 4.304215 ],
[ 88.81041 , 0.6280057, 0.6280057],
[ 94.3964 , 0.1571253, 0.1571253],
[100. , 0. , 0. ],
[ 54.10642 , 11.15306 , 11.15306 ],
[ 59.58774 , 8.529518 , 8.529518 ],
[ 65.21831 , 6.243754 , 6.243754 ],
[ 38.75704 , 20.94748 , 20.94748 ],
[ 43.6718 , 17.37353 , 17.37353 ],
[ 48.79456 , 14.1047 , 14.1047 ],
[ 25.43809 , 33.36277 , 33.36277 ],
[ 29.62332 , 28.95689 , 28.95689 ],
[ 34.06845 , 24.81337 , 24.81337 ],
[ 14.59111 , 47.9873 , 47.9873 ],
[ 17.90807 , 42.89558 , 42.89558 ],
[ 21.52819 , 38.01472 , 38.01472 ],
[ 6.575722 , 64.33623 , 64.33623 ],
[ 8.914448 , 58.72746 , 58.72746 ],
[ 11.58954 , 53.2711 , 53.2711 ],
[ 1.657672 , 81.86754 , 81.86754 ],
[ 2.940621 , 75.92768 , 75.92768 ],
[ 4.581992 , 70.07671 , 70.07671 ],
[ 0. , 100. , 100. ],
[ 0.1846403, 93.92597 , 93.92597 ],
[ 0.7378794, 87.87437 , 87.87437 ],
[ 0. , 0. , 100. ],
[ 88.81041 , 0. , 0.6280057],
[ 76.84349 , 0. , 2.718057 ],
[ 65.21831 , 0. , 6.243754 ],
[ 54.10642 , 0. , 11.15306 ],
[ 43.6718 , 0. , 17.37353 ],
[ 34.06845 , 0. , 24.81337 ],
[ 25.43809 , 0. , 33.36277 ],
[ 17.90807 , 0. , 42.89558 ],
[ 11.58954 , 0. , 53.2711 ],
[ 6.575722 , 0. , 64.33623 ],
[ 2.940621 , 0. , 75.92768 ],
[ 0.7378794, 0. , 87.87437 ],
[ 87.87437 , 0.7378794, 0.7378794],
[ 93.92597 , 0.1846403, 0.1846403],
[ 81.86754 , 1.657672 , 1.657672 ],
[ 75.92768 , 2.940621 , 2.940621 ],
[ 70.07671 , 4.581992 , 4.581992 ],
[ 64.33623 , 6.575722 , 6.575722 ],
[ 58.72746 , 8.914448 , 8.914448 ],
[ 53.2711 , 11.58954 , 11.58954 ],
[ 47.9873 , 14.59111 , 14.59111 ],
[ 42.89558 , 17.90807 , 17.90807 ],
[ 38.01472 , 21.52819 , 21.52819 ],
[ 33.36277 , 25.43809 , 25.43809 ],
[ 28.95689 , 29.62332 , 29.62332 ],
[ 24.81337 , 34.06845 , 34.06845 ],
[ 20.94748 , 38.75704 , 38.75704 ],
[ 17.37353 , 43.6718 , 43.6718 ],
[ 14.1047 , 48.79456 , 48.79456 ],
[ 11.15306 , 54.10642 , 54.10642 ],
[ 8.529518 , 59.58774 , 59.58774 ],
[ 6.243754 , 65.21831 , 65.21831 ],
[ 4.304215 , 70.97732 , 70.97732 ],
[ 2.718057 , 76.84349 , 76.84349 ],
[ 1.491148 , 82.79519 , 82.79519 ],
[ 0.6280057, 88.81041 , 88.81041 ],
[ 0.1571253, 94.3964 , 94.3964 ],
[ 0. , 100. , 0. ],
[100. , 100. , 0. ],
[ 87.87437 , 100. , 0.7378794],
[ 75.92768 , 100. , 2.940621 ],
[ 64.33623 , 100. , 6.575722 ],
[ 53.2711 , 100. , 11.58954 ],
[ 42.89558 , 100. , 17.90807 ],
[ 33.36277 , 100. , 25.43809 ],
[ 24.81337 , 100. , 34.06845 ],
[ 17.37353 , 100. , 43.6718 ],
[ 11.15306 , 100. , 54.10642 ],
[ 6.243754 , 100. , 65.21831 ],
[ 2.718057 , 100. , 76.84349 ],
[ 0.6280057, 100. , 88.81041 ]], dtype=float)
faces = np.array([ 3, 0, 1, 2, 3, 0, 2, 3, 3, 1, 4, 2, 3, 2, 4, 5, 3,
2, 5, 6, 3, 7, 8, 2, 3, 2, 8, 9, 3, 2, 9, 3, 3, 10,
11, 2, 3, 2, 11, 12, 3, 2, 12, 7, 3, 13, 14, 2, 3, 2, 14,
15, 3, 2, 15, 10, 3, 16, 17, 2, 3, 2, 17, 18, 3, 2, 18, 13,
3, 19, 20, 2, 3, 2, 20, 21, 3, 2, 21, 16, 3, 22, 23, 2, 3,
2, 23, 24, 3, 2, 24, 19, 3, 25, 26, 2, 3, 2, 26, 27, 3, 2,
27, 22, 3, 25, 2, 28, 3, 2, 6, 29, 3, 29, 30, 2, 3, 2, 30,
31, 3, 2, 31, 32, 3, 32, 33, 2, 3, 2, 33, 34, 3, 2, 34, 35,
3, 35, 36, 2, 3, 2, 36, 37, 3, 2, 37, 38, 3, 38, 39, 2, 3,
2, 39, 40, 3, 2, 40, 28, 3, 40, 27, 28, 3, 28, 27, 26, 3, 28,
26, 25, 3, 39, 38, 19, 3, 19, 24, 39, 3, 39, 24, 23, 3, 39, 23,
40, 3, 40, 23, 22, 3, 40, 22, 27, 3, 37, 36, 17, 3, 17, 16, 37,
3, 37, 16, 21, 3, 37, 21, 38, 3, 38, 21, 20, 3, 38, 20, 19, 3,
35, 34, 15, 3, 15, 14, 35, 3, 35, 14, 13, 3, 35, 13, 36, 3, 36,
13, 18, 3, 36, 18, 17, 3, 33, 32, 7, 3, 7, 12, 33, 3, 33, 12,
11, 3, 33, 11, 34, 3, 34, 11, 10, 3, 34, 10, 15, 3, 31, 30, 0,
3, 0, 3, 31, 3, 31, 3, 9, 3, 31, 9, 32, 3, 32, 9, 8, 3,
32, 8, 7, 3, 6, 5, 29, 3, 29, 5, 4, 3, 29, 4, 30, 3, 30,
4, 1, 3, 30, 1, 0, 3, 41, 2, 42, 3, 42, 2, 6, 3, 41, 43,
2, 3, 2, 43, 44, 3, 2, 44, 45, 3, 45, 46, 2, 3, 2, 46, 47,
3, 2, 47, 48, 3, 48, 49, 2, 3, 2, 49, 50, 3, 2, 50, 51, 3,
51, 52, 2, 3, 2, 52, 53, 3, 2, 53, 54, 3, 54, 55, 2, 3, 2,
55, 56, 3, 2, 56, 57, 3, 57, 58, 2, 3, 2, 58, 59, 3, 2, 59,
60, 3, 60, 61, 2, 3, 2, 61, 62, 3, 2, 62, 63, 3, 63, 64, 2,
3, 2, 64, 65, 3, 2, 65, 25, 3, 25, 66, 2, 3, 6, 67, 42, 3,
42, 67, 68, 3, 42, 68, 41, 3, 41, 68, 43, 3, 43, 68, 69, 3, 43,
69, 44, 3, 44, 69, 45, 3, 45, 69, 70, 3, 45, 70, 46, 3, 46, 70,
47, 3, 47, 70, 71, 3, 47, 71, 48, 3, 48, 71, 49, 3, 49, 71, 72,
3, 49, 72, 50, 3, 73, 74, 54, 3, 54, 53, 73, 3, 73, 53, 52, 3,
73, 52, 72, 3, 72, 52, 51, 3, 72, 51, 50, 3, 75, 76, 58, 3, 58,
57, 75, 3, 75, 57, 56, 3, 75, 56, 74, 3, 74, 56, 55, 3, 74, 55,
54, 3, 77, 78, 62, 3, 62, 61, 77, 3, 77, 61, 60, 3, 77, 60, 76,
3, 76, 60, 59, 3, 76, 59, 58, 3, 25, 65, 79, 3, 79, 65, 64, 3,
79, 64, 78, 3, 78, 64, 63, 3, 78, 63, 62, 3, 66, 25, 79, 3, 79,
78, 66, 3, 66, 78, 77, 3, 66, 77, 76, 3, 76, 75, 66, 3, 66, 75,
74, 3, 66, 74, 73, 3, 73, 72, 66, 3, 66, 72, 71, 3, 66, 71, 70,
3, 70, 69, 66, 3, 66, 69, 68, 3, 66, 68, 67, 3, 2, 66, 6, 3,
6, 66, 67], dtype=int)
mesh = pv.PolyData(points, faces)
mesh.rotate_x(90, inplace=True)
mesh.rotate_z(90, inplace=True)
tet = tetgen.TetGen(mesh)
meshfix = pymeshfix.MeshFix(tet.v, tet.f)
holes = meshfix.extract_holes()
meshfix.repair()
tet.v, tet.f = meshfix.v, meshfix.f
assert(tet.tetrahedralize())
p = pv.Plotter()
p.add_mesh(mesh, style="wireframe", color="k", label="Original Surface")
p.add_mesh(holes, color='r', label="Holes")
p.add_mesh(meshfix.mesh, label="Repaired surface")
p.add_legend()
p.show()
Solution
I ended up writing a library that can extract enclosed surfaces of a mesh, among a few other useful things. Documentation is available here: https://acreegan.github.io/pyvista_tools/
Below is a plot showing a surface mesh before and after identifying enclosed regions.
- WSGIServerException: [Errno 8] nodename nor servname provided, or not known
- Python/Flask Login form throws 500 error on IIS
- Matplotlib plt.show() isn't showing graph
- How to avoid duplicate processing in python API server?
- Extract points/coordinates from a polygon in Shapely
- How do I detect collision in pygame?
- Typehint a method that returns new instance using a superclass classmethod
- How to process requests from multiiple users using ML model and FastAPI?
- How can I clear the interpreter console?
- What are the reasons for the differences in output results when using the groupby function in the Python pandas package?
- How to antialias picture to get rid of the jerkiness in the image
- Is there any difference between list(array) and array.tolist() in Python?
- How to move x-axis on top of the plot in plotnine?
- Pulling Zillow Rent Data from Zillow API
- Safest way to incrementally append to a file?
- Unable to install mysqlclient package on EC2 instance
- FastAPI - How to get app instance inside a router?
- SQLAlchemy: engine, connection and session difference
- Generating binary arrays with alternating values based on change indices in NumPy
- How can i solve this error: ValueError: The feature names should match those that were passed
- 'super' object has no attribute '__sklearn_tags__'
- Python: How to copy all attibutes from base class to derived one
- mitm proxy is closed, but the port is still occupied
- How to find pg_config path
- Why does "pip install" inside Python raise a SyntaxError?
- Plotting a matplotlib pie chart using 2 columns
- ImportError: No module named langchain.llms
- How do I disable the security certificate check in Python requests
- How can I import Psycopg2 into a Lambda Function on AWS?
- Django built in Logout view `Method Not Allowed (GET): /users/logout/`