get_border

Import packages

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

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

Generate image for testing

im2d = np.random.rand(60, 60)
im3d = np.random.rand(60, 60, 60)

Demonstrate function

thickness

im1 = ps.tools.get_border(shape=im2d.shape)
im2 = ps.tools.get_border(shape=im2d.shape, thickness=4, mode='edges',)

fig, ax = plt.subplots(1, 2, figsize=[8, 4])
ax[0].axis(False)
ax[0].imshow(im1)
ax[0].set_title('default')
ax[1].axis(False)
ax[1].imshow(im2)
ax[1].set_title('thickness = 20');

mode

The options are ‘faces’, ‘edges’, and ‘corners’.

im1 = ps.tools.get_border(shape=im3d.shape, thickness=5, mode='faces')
im2 = ps.tools.get_border(shape=im3d.shape, thickness=5, mode='edges')
im3 = ps.tools.get_border(shape=im3d.shape, thickness=5, mode='corners')

The visualization below using the show_3D function which gives a very rough idea of how things look in 3D. It rotates the image using scipy.ndimage.rotate, then does a projection along the z-axis. The results are a bit fuzzy do to the interpolation when rotating, but you can see how the borders look:

fig, ax = plt.subplots(1, 3, figsize=[8, 4])
ax[0].imshow(ps.visualization.show_3D(~im1[..., 20:]))
ax[0].axis(False)
ax[0].set_title('faces')
ax[1].imshow(ps.visualization.show_3D(~im2[..., 20:]))
ax[1].axis(False)
ax[1].set_title('edges')
ax[2].imshow(ps.visualization.show_3D(~im3[..., 20:]))
ax[2].axis(False)
ax[2].set_title('corners');

For 2D images, the mode of ‘faces’ and ‘edges’ both return the same thing.

im1 = ps.tools.get_border(shape=im2d.shape, thickness=10, mode='faces')
im2 = ps.tools.get_border(shape=im2d.shape, thickness=10, mode='edges')
im3 = ps.tools.get_border(shape=im2d.shape, thickness=10, mode='corners')

fig, ax = plt.subplots(1, 3, figsize=[8, 4])
ax[0].axis(False)
ax[0].imshow(im1)
ax[0].set_title('faces')
ax[1].axis(False)
ax[1].imshow(im2)
ax[1].set_title('edges')
ax[2].axis(False)
ax[2].imshow(im3)
ax[2].set_title('corners');

return_indices

Instead of returning a boolean array, the fuction can optionally return indices into im where the border would be:

inds = ps.tools.get_border(shape=im2d.shape, thickness=5, mode='corners')
im2d[inds] = 0

fig, ax = plt.subplots(1, 1, figsize=[4, 4])
ax.axis(False)
ax.imshow(im2d)
ax.set_title('image with zeroed borders')

Text(0.5, 1.0, 'image with zeroed borders')