regions_to_network#

The regions_to_network function is part of the snow2 network extraction process. This function analyzes an image of labeled regions, such as that produced by a watershed segmentation, and extracts topological connections and geometric properties. The inner workings of this function are described by Gostick. To summarize, the function analyzes one region at a time, dilates that regions to see what other regions it is neighbors with to establish topology, then analyzes the distance transform within that region to determine geometric properties.

import porespy as ps
import matplotlib.pyplot as plt
import numpy as np
from edt import edt
np.random.seed(13)
im = ps.generators.overlapping_spheres([100, 100], r=7, porosity=0.7)
plt.imshow(im, origin='lower', interpolation='none');
../../../_images/b496fb9506d2de4acdd64763734ab32f1d199bc7957d64d9038fcd036e4b47e6.png
print(ps.networks.regions_to_network.__doc__)
    Analyzes an image that has been partitioned into pore regions and extracts
    the pore and throat geometry as well as network connectivity.

    Parameters
    ----------
    regions : ndarray
        An image of the material partitioned into individual regions.
        Zeros in this image are ignored.
    phases : ndarray, optional
        An image indicating to which phase each voxel belongs. The returned
        network contains a 'pore.phase' array with the corresponding value.
        If not given a value of 1 is assigned to every pore.
    voxel_size : scalar (default = 1)
        The resolution of the image, expressed as the length of one side of a
        voxel, so the volume of a voxel would be **voxel_size**-cubed.
    accuracy : string
        Controls how accurately certain properties are calculated. Options are:

        'standard' (default)
            Computes the surface areas and perimeters by simply counting
            voxels.  This is *much* faster but does not properly account
            for the rough, voxelated nature of the surfaces.
        'high'
            Computes surface areas using the marching cube method, and
            perimeters using the fast marching method.  These are substantially
            slower but better account for the voxelated nature of the images.

    Returns
    -------
    net : dict
        A dictionary containing all the pore and throat size data, as well as
        the network topological information.  The dictionary names use the
        OpenPNM convention (i.e. 'pore.coords', 'throat.conns').

    Notes
    -----
    The meaning of each of the values returned in ``net`` are outlined below:

    'pore.region_label'
        The region labels corresponding to the watershed extraction. The
        pore indices and regions labels will be offset by 1, so pore 0
        will be region 1.
    'throat.conns'
        An *Nt-by-2* array indicating which pores are connected to each other
    'pore.region_label'
        Mapping of regions in the watershed segmentation to pores in the
        network
    'pore.local_peak'
        The coordinates of the location of the maxima of the distance transform
        performed on the pore region in isolation
    'pore.global_peak'
        The coordinates of the location of the maxima of the distance transform
        performed on the full image
    'pore.geometric_centroid'
        The center of mass of the pore region as calculated by
        ``skimage.measure.center_of_mass``
    'throat.global_peak'
        The coordinates of the location of the maxima of the distance transform
        performed on the full image
    'pore.region_volume'
        The volume of the pore region computed by summing the voxels
    'pore.volume'
        The volume of the pore found by as volume of a mesh obtained from the
        marching cubes algorithm
    'pore.surface_area'
        The surface area of the pore region as calculated by either counting
        voxels or using the fast marching method to generate a tetramesh (if
        ``accuracy`` is set to ``'high'``.)
    'throat.cross_sectional_area'
        The cross-sectional area of the throat found by either counting
        voxels or using the fast marching method to generate a tetramesh (if
        ``accuracy`` is set to ``'high'``.)
    'throat.perimeter'
        The perimeter of the throat found by counting voxels on the edge of
        the region defined by the intersection of two regions.
    'pore.inscribed_diameter'
        The diameter of the largest sphere inscribed in the pore region. This
        is found as the maximum of the distance transform on the region in
        isolation.
    'pore.extended_diameter'
        The diamter of the largest sphere inscribed in overal image, which
        can extend outside the pore region. This is found as the local maximum
        of the distance transform on the full image.
    'throat.inscribed_diameter'
        The diameter of the largest sphere inscribed in the throat.  This
        is found as the local maximum of the distance transform in the area
        where to regions meet.
    'throat.total_length'
        The length between pore centered via the throat center
    'throat.direct_length'
        The length between two pore centers on a straight line between them
        that does not pass through the throat centroid.

    Examples
    --------
    `Click here
    <https://porespy.org/examples/networks/reference/regions_to_network.html>`_
    to view online example.

    

regions#

This is the watershed segmentation of the image, presumably computed by ps.filters.snow_partitioning but could actually be any watershed image.

snow = ps.filters.snow_partitioning(im)
plt.imshow(snow.regions/im, origin='lower', interpolation='none');
../../../_images/f92f04b5f042c4e07a5fa4c8af498c55688ee62016afe50ab2ed665ee243c2ba.png
net1 = ps.networks.regions_to_network(regions=snow.regions)

The regions_to_network functions returns dict containing all the network information in OpenPNM format:

for item in net1.keys():
    print(item)
throat.conns
pore.coords
pore.all
throat.all
pore.region_label
pore.phase
throat.phases
pore.region_volume
pore.equivalent_diameter
pore.local_peak
pore.global_peak
pore.geometric_centroid
throat.global_peak
pore.inscribed_diameter
pore.extended_diameter
throat.inscribed_diameter
throat.total_length
throat.direct_length
throat.perimeter
pore.volume
pore.surface_area
throat.cross_sectional_area
throat.equivalent_diameter

phases#

Optionally it is possible to perform a multiphase extraction, by passing an image with each phase labelled as follows:

snow2 = ps.filters.snow_partitioning(~im)
plt.imshow(snow2.regions, origin='lower', interpolation='none');
../../../_images/86acb9a5911b99f0f229b5294faba3ba7ab33a60d8d775ca00bbb2d802a75a40.png

We can manually combine these two watersheds:

ws = snow.regions + (snow2.regions + snow.regions.max())*~im
plt.imshow(ws, origin='lower', interpolation='none');
../../../_images/18568bd7e3f547b2daa04ca4d2bc9c3fa2e996075d6e69c223ec0e3e915ac9aa.png

Now we can send this combined watershed image, along with an image with each phase labelled as 1 and 2 to regions_to_network:

net2 = ps.networks.regions_to_network(regions=ws, phases=im.astype(int)+1)
for item in net2.keys():
    print(item)
throat.conns
pore.coords
pore.all
throat.all
pore.region_label
pore.phase
throat.phases
pore.region_volume
pore.equivalent_diameter
pore.local_peak
pore.global_peak
pore.geometric_centroid
throat.global_peak
pore.inscribed_diameter
pore.extended_diameter
throat.inscribed_diameter
throat.total_length
throat.direct_length
throat.perimeter
pore.volume
pore.surface_area
throat.cross_sectional_area
throat.equivalent_diameter

Both net1 and net2 have 'pore.phase' and 'throat.phases', but the difference is that in net1 the values are all 1 since only a single phase was presents, while in net2 these arrays contain boths 1’s and 2’s:

net1['pore.phase']
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
net2['pore.phase']
array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

These two arrays are used by the label_phases function, which takes that information and creates actual labels that can be used in OpenPNM.

voxel_size#

This is the voxel size of the image, as in the length of one side of a voxel. Typical microtomography images are in the range of 1-5 um per voxel. NanoCT can be as low as 16 nm per voxel. FIB-SEM might be 4 nm per voxel. Note that all properties returned from the snow2 function, like ‘pore.volume’ will be in the same units are this value. It is strongly recommended to use SI, which is assumed in OpenPNM for most of the fluid property calculations.

accuracy#

This argument can either take the value of 'high' or 'standard', with standard being the default. It is not recommended to use 'high' since this uses some rather slow functions to measure the surface areas and perimeters. The increase in accuracy is basically futile since (a) the image has already been reduced to a coarse voxelization and (b) the pore network abstraction discards information about the actual microstructure in exchange for speed, so spending ages on the extraction step would undermine this purpose.