apply_padded

Applies padding to an image before passing through specified function

import numpy as np
import porespy as ps
import scipy.ndimage as spim
import matplotlib.pyplot as plt
import skimage
from edt import edt
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

im

Generate a test image. Can be 2D or 3D image.

im = np.zeros([300, 300])
im = ps.generators.rsa(im, r=20, volume_fraction=0.4) == 0

plt.figure(figsize=[6, 6])
plt.axis(False)
plt.imshow(im);

/tmp/ipykernel_2422/1290140162.py:2: DeprecationWarning: Call to deprecated function (or staticmethod) rsa. (This function will be renamed random_spheres in a future version)
  im = ps.generators.rsa(im, r=20, volume_fraction=0.4) == 0

func

A good use case is skeletonize_3d, which has edge artifacts that can be reduced if the image is pre-padded. It works on 2D or 3D images.

func = skimage.morphology.skeletonize_3d

pad_width

The amount of padding to add to each axis

x1 = ps.filters.apply_padded(im=im, pad_width=1, func=func, pad_val=1)
x2 = ps.filters.apply_padded(im=im, pad_width=100, func=func, pad_val=1)

fig, ax = plt.subplots(1, 2, figsize=[12, 12]);
ax[0].imshow(x1);
ax[0].axis(False)
ax[0].set_title('pad_width = 1')
ax[1].imshow(x2);
ax[1].axis(False)
ax[1].set_title('pad_width = 100');

It’s also useful for morphological operations such as binary_opening

func = skimage.morphology.binary_opening

x3 = ps.filters.apply_padded(im=im, pad_width=0, func=func, pad_val=1, footprint=ps.tools.ps_disk(15))
x4 = ps.filters.apply_padded(im=im, pad_width=50, func=func, pad_val=1, footprint=ps.tools.ps_disk(15))

fig, ax = plt.subplots(1, 2, figsize=[12, 12]);
ax[0].imshow(x3)
ax[0].axis(False)
ax[0].set_title('With no padding')
ax[1].imshow(x4)
ax[1].axis(False)
ax[1].set_title('With padding');

pad_val

The value to be placed in padded voxels. It is almost always 1 but setting to 0 may be useful for applying a distance transform.

func = edt

x5 = ps.filters.apply_padded(im=im, pad_width=10, func=func, pad_val=1)
x6 = ps.filters.apply_padded(im=im, pad_width=10, func=func, pad_val=0)

fig, ax = plt.subplots(1, 2, figsize=[12, 12]);
ax[0].imshow(x5);
ax[0].axis(False)
ax[0].set_title('pad_val = 1')
ax[1].imshow(x6);
ax[1].axis(False)
ax[1].set_title('pad_val = 0');