PyMCubes is an implementation of the marching cubes algorithm to extract
isosurfaces from volumetric data. The volumetric data can be given as a
three-dimensional NumPy array or as a Python function f(x, y, z). The first
option is much faster, but it requires more memory and becomes unfeasible for
very large volumes.
PyMCubes also provides a function to export the results of the marching cubes as
COLLADA (.dae) files. This requires the PyCollada library.
Use pip:
$ pip install --upgrade PyMCubes
The following example creates a NumPy volume with spherical isosurfaces and
extracts one of them (i.e., a sphere) with PyMCubes. The result is exported to
sphere.dae:
>>> import numpy as np
>>> import mcubes
# Create a data volume (30 x 30 x 30)
>>> X, Y, Z = np.mgrid[:30, :30, :30]
>>> u = (X-15)**2 + (Y-15)**2 + (Z-15)**2 - 8**2
# Extract the 0-isosurface
>>> vertices, triangles = mcubes.marching_cubes(u, 0)
# Export the result to sphere.dae
>>> mcubes.export_mesh(vertices, triangles, "sphere.dae", "MySphere")The second example uses a function to represent the volume instead of a NumPy array.
>>> import numpy as np
>>> import mcubes
# Create the volume
>>> f = lambda x, y, z: x**2 + y**2 + z**2
# Extract the 16-isosurface
>>> vertices, triangles = mcubes.marching_cubes_func((-10,-10,-10), (10,10,10),
... 100, 100, 100, f, 16)
# Export the result to sphere2.dae
>>> mcubes.export_mesh(vertices, triangles, "sphere2.dae", "MySphere")Note that using a function to represent the volumetric data is much slower than using a NumPy array.