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()

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');
../../../_images/543ced12732bb142a5a3e848fe0df33a25e783c66fbf127e124dc68df541922d.svg

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');
../../../_images/e8b8208524675cd1d5b0e20266e811f12fe73776c10e8a6fa4ed5cae4560b9d4.svg

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');
../../../_images/2cfcb7a788667f0039222e7aeea6d5cde5fc56a75b50cae67a93b28b6713ac22.svg

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')
../../../_images/c0e07a382899faeac441a433e8c68ed457c1a16d5b2bace11ce64ce5124fb2eb.svg