Initialize triangular mesh in e.g. VTK based on np array with coordinates of vertices and np array with triangles

Question

Could you tell me how do I initialize triangular mesh based on np array with coordinates of vertices and np array with triangles (it contains triples of indices of vertices from first array)?

Could be done in VTK or any other Python library, the final goal is to do decimation on this mesh.

Thank you.

Answer 1

A solution using vedo which is based on vtk:

https://github.com/marcomusy/vedo/blob/master/examples/basic/buildmesh.py

from vedo import Mesh, show

verts = [(50,50,50), (70,40,50), (50,40,80), (80,70,50)]
faces = [(0,1,2), (2,1,3), (1,0,3)]
# (the first triangle face is formed by vertex 0, 1 and 2)

# Build the polygonal Mesh object:
mesh = Mesh([verts, faces])
#mesh.decimate()
show(mesh)

Answer 2

You can use several libraries:

Trimesh

Trimesh is one of the bests; It can be used on Colab to show:

import trimesh

verts = [[50,50,50], [70,40,50], [50,40,80], [80,70,50]]
faces = [[0,1,2], [2,1,3], [1,0,3]]

mesh = trimesh.Trimesh(vertices=verts, faces=faces)
mesh.show()

# for facet in mesh.facets:
#     mesh.visual.face_colors[facet] = trimesh.visual.random_color()
# mesh.split()
# mesh.show()

SciPy

1. ConvexHull:

import scipy as sp
from scipy.spatial import ConvexHull
import matplotlib as mpl
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d as a3

verts = np.array(verts)

hull = ConvexHull(verts)
indices = hull.simplices
faces = verts[indices]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.set_xlim([35, 85])
ax.set_ylim([35, 85])
ax.set_zlim([35, 85])
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')

for f in faces:
    face = a3.art3d.Poly3DCollection([f])
    face.set_color(mpl.colors.rgb2hex(np.random.rand(3)))
    face.set_edgecolor('k')
    face.set_alpha(0.5)
    ax.add_collection3d(face)

plt.show()

2. Delaunay:

import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay

verts = np.array(verts)
tri = Delaunay(verts)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.azim = 70
ax.plot_trisurf(verts[:, 0], verts[:, 1], verts[:, 2], triangles=tri.simplices, cmap=plt.cm.Spectral)
plt.show()