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/e3dc34115c986af36abc0c432e9f6474caec74293a8e9a3142e805650a55bd37.png

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/690fe32edcb342a11ec1b3d9c169459084cdadc8786129df8da10bc763f538c5.png

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/3b4fcf90ec26347f3c72a627afa3b3f14204debd832bb5b61f7cbea0a5ab45c7.png

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/21181c014614e2861efc73c40605c7ff306145ba3a4b12c1b1413537e9ed0f3b.png