SNOW network extraction¶
The SNOW algorithm, published in Physical Review E, uses a marker-based watershed segmentation algorithm to partition an image into regions belonging to each pore. The main contribution of the SNOW algorithm is to find a suitable set of initial markers in the image so that the watershed is not over-segmented. SNOW is an acronym for Sub-Network of an Over-segmented Watershed. This code works on both 2D and 3D images. In this example a 2D image will
be segmented using the predefined
snow function in PoreSpy.
Start by importing the necessary packages:
import numpy as np import porespy as ps import openpnm as op import matplotlib.pyplot as plt ps.visualization.set_mpl_style() np.random.seed(10)
Generate an artificial 2D image for illustration purposes:
im = ps.generators.blobs(shape=[400, 400], porosity=0.6, blobiness=2) fig, ax = plt.subplots() ax.imshow(im);
SNOW is composed of a series of filters, but PoreSpy has a single function that applies all the necessary steps:
snow_output = ps.networks.snow2(im, voxel_size=1)
snow function returns a python dict that is suitable for use in OpenPNM.
pn = op.network.GenericNetwork() pn.update(snow_output.network) prj = pn.project
OpenPNM has the ability to output network to a VTK file suitable for view in Paraivew:
Finally, we can export the image in ‘vti’ format for visualization. PoreSpy offers a tool for this:
ps.io.to_vtk(np.array(im, dtype=int)[:, :, np.newaxis], 'im')
And the result after opening both files in ParaView is:
You can also overlay the network on the image natively in
porespy. Note that you need to transpose the image using
imshow uses matrix representation, e.g. a (10, 20)-shaped array is shown as 10 pixels in the y-axis, and 20 pixels in the x-axis.
fig, ax = plt.subplots() ax.imshow(im.T); op.topotools.plot_coordinates(fig=fig, network=pn, size_by=pn["pore.inscribed_diameter"], color_by=pn["pore.inscribed_diameter"], markersize=100) op.topotools.plot_connections(network=pn, fig=fig) ax.axis("off");