`regionprops_3D`#

This is similar to the regionprops in scikit-image but has some extra features that are relevant to 3D images. Note that the scikit-image version has been adding 3D capabilities, make the PoreSpy version less useful.

import porespy as ps
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as spim
ps.visualization.set_mpl_style()

[17:46:10] ERROR    PARDISO solver not installed, run `pip install pypardiso`. Otherwise,          _workspace.py:56
                    simulations will be slow. Apple M chips not supported.

im = ~ps.generators.random_spheres(shape=[80, 80, 80], r=10, clearance=3)
plt.imshow(im[20, ...], origin='lower', interpolation='none');

../../../_images/66b6a8e12e75dd81b9d528c1dd20a927fbab52b94ea9d133704255b41e7e0708.png

We need to label each sphere so they are recognized as individual regions:

regions = spim.label(im)[0]
plt.imshow(regions[20, ...], origin='lower', interpolation='none');

props = ps.metrics.regionprops_3D(regions)

props is a list of RegionProperties objects, subclassed from scikit-image. Each RegionProperties object possess the properties as attributes. Note that these are calculated on demand, so the regionsprops_3D function appears very quick, but the work has not yet been done.

props

[<porespy.metrics._regionprops.RegionPropertiesPS at 0x7f4fcc8194b0>]

The properties of regions 1 are on the RegionsProperties object located at position 0 in the props list:

for d in dir(props[0]):
    if not d.startswith('_'):
        print(d)

area
area_bbox
area_convex
area_filled
axis_major_length
axis_minor_length
bbox
bbox_volume
border
centroid
centroid_local
centroid_weighted
centroid_weighted_local
convex_volume
coords
coords_scaled
dt
eccentricity
equivalent_diameter_area
euler_number
extent
feret_diameter_max
image
image_convex
image_filled
image_intensity
inertia_tensor
inertia_tensor_eigvals
inscribed_sphere
intensity_max
intensity_mean
intensity_min
label
mask
moments
moments_central
moments_hu
moments_normalized
moments_weighted
moments_weighted_central
moments_weighted_hu
moments_weighted_normalized
num_pixels
orientation
perimeter
perimeter_crofton
skeleton
slice
slices
solidity
sphericity
surface_area
surface_mesh_simplices
surface_mesh_vertices
volume

Let’s check a few of the properties:

print(props[0].volume)

407557.0

Because the present function is meant for 3D images, we have added specific terminology, like volume, instead of allowing area to mean volume like the scikit-image version”

print(props[0].area)

407557.0

We do have a surface_area, which is also specific to 3D objects:

print(props[0].surface_area)

24389.740234375

In addition to scalar metrics, we also provide access to useful images of the region:

plt.imshow(props[0].dt[5, ...]);

../../../_images/579c7419e0ffe6eb87b91fec5e4542f8f32c0dfb40fd37302ea25dabf86ce21b.png

regionprops_3D#

`regionprops_3D`#