snow_partitioning_parallel

Similar to snow_partitioning except that it performs SNOW algorithm in parallel and serial mode to save computational time and memory requirement respectively.

Import packages

import numpy as np
import porespy as ps
from porespy.tools import randomize_colors
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import time
ps.visualization.set_mpl_style()
np.random.seed(10)

im

Works on 2D and 3D images. We use 2D here because it is easier to visualize.

im = ps.generators.blobs(shape=[800, 800])

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

overlap, divs, and cores

cores is the number of cores to use. The more cores the faster the snow_partitioning performs. If overlap is None it is estimated using porespy.tools.estimate_overlap method. The domain is divided by 2 in each direction as supplied to divs.

start = time.time()
x1 = ps.filters.snow_partitioning_parallel(im, r_max=5, sigma=0.4, divs=2, overlap=None, cores=1)
pause = time.time()
x2 = ps.filters.snow_partitioning_parallel(im, r_max=5, sigma=0.4, divs=2, overlap=None, cores=4)
stop = time.time()

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

OPERATION TIME:
cores=1: 2.6860721111297607 seconds
cores=4: 0.47281813621520996 seconds

The snow algorithm returns several images

print(x1)

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Item                      Description
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im                        Image of size (800, 800)
dt                        Image of size (800, 800)
regions                   Image of size (800, 800)
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Plot the results

fix, ax = plt.subplots(1, 2, figsize=[12, 12])
ax[0].axis(False)
ax[0].imshow(x1.dt/im)
ax[0].set_title('Distance Transform', fontdict={'fontsize': 18});
ax[1].axis(False)
ax[1].imshow(x1.regions/im);
ax[1].set_title('Regions', fontdict={'fontsize': 18});