get_border¶

Import packages¶

[1]:

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¶

[2]:

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


Demonstrate function¶

thickness¶

[3]:

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

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’.

[4]:

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


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:

[5]:

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.

[6]:

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

[7]:

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:

[8]:

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

[9]:

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

[9]:

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