fill_blind_pores

Remove any isolated void space (not connected to the boundary) from the image which is often needed such as for network extraction or direct numerical simulations

import numpy as np
import porespy as ps
import scipy.ndimage as spim
import matplotlib.pyplot as plt
ps.visualization.set_mpl_style()

/opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpnm/algorithms/_invasion_percolation.py:358: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def _find_trapped_pores(inv_seq, indices, indptr, outlets):  # pragma: no cover

import inspect
print(inspect.signature(ps.filters.fill_blind_pores))

(im, conn=None, surface=False)

im

The void phase is indicated by True values. Blind pores are considered as any cluster of void voxels not connected to the boundary of the image.

np.random.seed(2)
im = ps.generators.blobs([100, 100], porosity=0.5)
im1 = ps.filters.fill_blind_pores(im)

fig, ax = plt.subplots(1, 2, figsize=[12, 6])
ax[0].imshow(im, origin='lower', interpolation='none')
ax[0].axis(False)
ax[1].imshow(im1, origin='lower', interpolation='none')
ax[1].axis(False);

conn

Controls how ‘connected’ a group of voxels must be. In 2D the options are 4 and 8, while in 3D they are 6 and 26.

im1 = ps.filters.fill_blind_pores(im=im, conn=4)
im2 = ps.filters.fill_blind_pores(im=im, conn=8)

fig, ax = plt.subplots(1, 2, figsize=[12, 6]);
ax[0].imshow(im1, origin='lower', interpolation='none')
ax[0].axis(False)
ax[0].set_title('conn = 4')
ax[1].imshow(im2, origin='lower', interpolation='none')
ax[1].axis(False)
ax[1].set_title('conn = 8');

surface

This flag, then True also removes any void voxels that are on the surface but not connected to the main body of the void space. This option can produce mis-leading results since it basically just keeps the largest cluster. For better control it is recommended to use the trim_nonpercolation_paths function.

np.random.seed(0)
im = ps.generators.blobs([100, 100], porosity=0.5)
im1 = ps.filters.fill_blind_pores(im=im, surface=False)
im2 = ps.filters.fill_blind_pores(im=im, surface=True)

fig, ax = plt.subplots(1, 2, figsize=[12, 6]);
ax[0].imshow(im1, origin='lower', interpolation='none')
ax[0].axis(False)
ax[0].set_title('Keep surface pores')
ax[1].imshow(im2, origin='lower', interpolation='none')
ax[1].axis(False)
ax[1].set_title('Remove surface pores');