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

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);
../../../_images/af36d71a8b92ca6e389b4b61105cf0d1d75d60156b415c67b50cd4604eebf37f.png

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);
../../../_images/59a50aa4fe3b26f101d84f9f3d0fc2fbefd919c0e26b499b51470b4fc9ea7309.png

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');
../../../_images/8a90c56964b40d15f4d2d0aa7364d675978df8ba51469d57f33616ec6cfd78ee.png
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);
../../../_images/f55458d439493d87575c40f61a08e432ea9bfda7733844b206c263011178398e.png

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);
../../../_images/ab78a2d3cc06c6bfb839efb74c63b8c1d85d8dc39aa42eb79b63dda03fc3901d.png
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);
../../../_images/910a7d301d537e6209795cb58a13b3decfdfbf85581f24605e36a1d990c33786.png

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);
../../../_images/f005ffdcac7dc26089192afc01f0f2826a374ec10fe7055174494662360fc5bb.png