extract_regions

Import packages

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

[01:02:41] ERROR    PARDISO solver not installed, run `pip install pypardiso`. Otherwise,          _workspace.py:56
                    simulations will be slow. Apple M chips not supported.                                         

Generate image for testing

To illustrate this function, we need an image containing many labelled regions. This can obtained by generating some blobs, then using scipy.label.

np.random.seed(0)
im = ps.generators.blobs([500, 500], blobiness=2, porosity=0.4)
regions = spim.label(im)[0]
fig, ax = plt.subplots(1, 1, figsize=[4, 4])
ax.axis(False)
ax.imshow(regions);

Apply tool

In it’s basic form, this function is equivalent to just obtaining a boolean mask like regions == 22, but it has a few more features including extracting a sub-image that just contains the regions, and also finding multiple regions at once.

reg1 = ps.tools.extract_regions(regions=regions, labels=[22], trim=False)
reg2 = ps.tools.extract_regions(regions=regions, labels=[22], trim=True)
reg3 = ps.tools.extract_regions(regions=regions, labels=[22, 23], trim=True)
fig, ax = plt.subplots(1, 3, figsize=[8, 4])
ax[0].axis(False)
ax[0].imshow(reg1)
ax[1].axis(False)
ax[1].imshow(reg2);
ax[2].axis(False)
ax[2].imshow(reg3);