chunked_func

Performs the specfied operation one “chunk” at a time to save memory.

Import Packages

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

func

We will perform a dilation using scipy’s binary_dilation

func = spim.binary_dilation

im and strel

This function takes a binary image and a structuring element as arguments

im = ps.generators.blobs(shape=[500, 500])
strel = disk(3)

plt.figure(figsize=[6, 6])
plt.imshow(im)
plt.axis(False);

overlap, divs, and cores

If overlap is None it will always be the radius of the structuring element. divs is the number of chunks to divide the image into in each direction and cores is the number of cores to use. The more cores the faster.

start = time.time()
x1 = ps.filters.chunked_func(func=func, overlap=None, divs=4, cores=1, input=im, structure=disk(3))
pause = time.time()
x2 = ps.filters.chunked_func(func=func, overlap=None, divs=4, cores=4, input=im, structure=disk(3))
stop = time.time()

print('OPERATION TIME:')
print('cores=1:', pause-start, 'seconds')
print('cores=4:', stop-pause, 'seconds')

fix, ax = plt.subplots(1, 2, figsize=[12, 12])
ax[0].axis(False)
ax[0].imshow(x1)
ax[0].set_title('cores=1', fontdict={'fontsize': 18});
ax[1].axis(False)
ax[1].imshow(x2);
ax[1].set_title('cores=4', fontdict={'fontsize': 18});

OPERATION TIME:
cores=1: 0.03835129737854004 seconds
cores=4: 0.02741265296936035 seconds