blobs

blobs is a simple function to generate a test image. It works by generating random noise, applying a guassian blur, renormalizing the result back to a uniform distribution, then thresholding the result to produce a binary image.

import porespy as ps
import matplotlib.pyplot as plt
import numpy as np

/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

shape

Both 2D and 3D images can be generated. Sending a scalar (shape=100) will produce a 3D image of size 100^3.

shape = [200, 200]
im = ps.generators.blobs(shape=shape)
fig, ax = plt.subplots(1, 1, figsize=[4, 4])
ax.imshow(im, origin='lower', interpolation='none')
ax.axis(False);

porosity

The fraction of the True to False pores can be set by specifying the porosity. The default is 0.5.

e = 0.75
im = ps.generators.blobs(shape=[500, 500], porosity=e)
fig, ax = plt.subplots(1, 1, figsize=[4, 4])
ax.imshow(im, origin='lower', interpolation='none')
ax.axis(False);

Specifying porosity=None will return a greyscale image which can then be thresholded to get a boolean image. The greyscale value are adjusted to a unifrom distribution:

im = ps.generators.blobs(shape=[500, 500], porosity=None)
fig, ax = plt.subplots(1, 2, figsize=[8, 4])

ax[0].imshow(im, origin='lower', interpolation='none')
ax[0].axis(False)

ax[1].hist(im.flatten(), edgecolor='k');

im = ps.generators.blobs(shape=[500, 500], porosity=None, blobiness=1)
fig, ax = plt.subplots(1, 3, figsize=[12, 4])
ax[0].imshow(im, origin='lower', interpolation='none')
ax[0].axis(False)
ax[1].imshow(im < 0.5, origin='lower', interpolation='none')
ax[1].axis(False)
ax[2].imshow(im < 0.75, origin='lower', interpolation='none')
ax[2].axis(False);

blobiness

The size and anisotropy of the blobs can be changed:

fig, ax = plt.subplots(1, 2, figsize=[8, 4])

im1 = ps.generators.blobs(shape=shape, porosity=e, blobiness=1)
ax[0].imshow(im1, origin='lower', interpolation='none')
ax[0].axis(False)

im2 = ps.generators.blobs(shape=shape, porosity=e, blobiness=2)
ax[1].imshow(im2, origin='lower', interpolation='none')
ax[1].axis(False);

fig, ax = plt.subplots(1, 2, figsize=[8, 4])

im1 = ps.generators.blobs(shape=shape, porosity=e, blobiness=[2, 1])
ax[0].imshow(im1, origin='lower', interpolation='none')
ax[0].axis(False)

im2 = ps.generators.blobs(shape=shape, porosity=e, blobiness=[1, 3])
ax[1].imshow(im2, origin='lower', interpolation='none')
ax[1].axis(False);

Reproducing Images

The random is controlled by numpy’s random number generator so the same image can be produced by setting the seed:

np.random.seed(0)
im1 = ps.generators.blobs(shape=[500, 500], porosity=0.5)
np.random.seed(0)
im2 = ps.generators.blobs(shape=[500, 500], porosity=0.5)
fig, ax = plt.subplots(1, 2, figsize=[8, 4])
ax[0].imshow(im1, origin='lower', interpolation='none')
ax[0].axis(False)
ax[1].imshow(im2, origin='lower', interpolation='none')
ax[1].axis(False);