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I've been able to use the lewiner marching cubes algorithm in python. It outputs vertices, faces, and other attributes. I want to be sure that it is working correctly, so I'd like to plot a 3D image of what the function returns. However, I have not had any success so far. I have tried the following:

Successful retrieval of necessary fields:

verts, faces, normals, values = skimage.measure.marching_cubes_lewiner(var,20,spacing=(0.5,0.5,0.5))

And unsuccessful plots of the retrieved values:

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(projection='3d')

mesh = Poly3DCollection(verts[faces])
mesh.set_edgecolor('k')
ax.add_collection3d(mesh)

And also:

vv.mesh(np.fliplr(verts), faces, normals, values) # doctest: +SKIP

Hypothetically, I would like to use the verts, faces, etc in a machine-learning algorithm, but I'd like to be sure that the returned values are reliable. Any one have experience with anything like this?

McM
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1 Answers1

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I don't know if you're still looking for an answer to this, but I just experienced a problem for plotting the mesh through this function as well. I was getting no error but an empty plot. Should this be the case for you as well, I solved it by specifying the axis limits. The following did the trick for me:

import matplotlib.pyplot as plt
import numpy as np
from skimage.measure import marching_cubes_lewiner
from mpl_toolkits.mplot3d.art3d import Poly3DCollection

# mask is a currently stored binary 3D numpy array 
verts, faces, normals, values = marching_cubes_lewiner(mask)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection="3d")

ax.set_xlim(np.min(verts[:,0]), np.max(verts[:,0]))
ax.set_ylim(np.min(verts[:,1]), np.max(verts[:,1])) 
ax.set_zlim(np.min(verts[:,2]), np.max(verts[:,2]))

mesh = Poly3DCollection(verts[faces])
mesh.set_edgecolor('k')
ax.add_collection3d(mesh)
plt.tight_layout()
plt.show()
rvdaele
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  • I had figured this out ages ago, but thank you for your work. The way you set your axes limits, in particular, is helpful. – McM May 13 '20 at 15:05