props_to_DataFrame

Extracts the scalar values from a regionprops_3D query and uses them to populate a pandas DataFrame.

import porespy as ps
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as spim

[01:03:59] ERROR    PARDISO solver not installed, run `pip install pypardiso`. Otherwise,          _workspace.py:56
                    simulations will be slow. Apple M chips not supported.                                         

np.random.seed(7)
im = ~ps.generators.overlapping_spheres([100, 100], r=10, porosity=0.6)
plt.imshow(im, origin='lower', interpolation='none');

regions = spim.label(im)[0]
props = ps.metrics.regionprops_3D(regions)
plt.imshow(regions, origin='lower', interpolation='none');

df = ps.metrics.props_to_DataFrame(props)

/home/runner/work/porespy/porespy/src/porespy/metrics/_regionprops.py:282: FutureWarning: `skeletonize_3d` is deprecated since version 0.23 and will be removed in version 0.25. Use `skimage.morphology.skeletonize` instead.
  return skeletonize_3d(self.mask)

df

	label	volume	bbox_volume	sphericity	surface_area	convex_volume	num_pixels	area	area_bbox	area_convex	...	euler_number	extent	feret_diameter_max	area_filled	axis_major_length	axis_minor_length	orientation	perimeter	perimeter_crofton	solidity
0	1	270.0	304	3.377624	59.811131	274.0	270	270.0	304.0	274.0	...	1	0.888158	20.615528	270.0	20.302892	17.144166	1.570796	58.970563	58.589116	0.985401
1	2	292.0	352	2.966109	71.760513	301.0	292	292.0	352.0	301.0	...	1	0.829545	24.186773	292.0	22.792054	16.944133	-1.332241	64.384776	63.722113	0.970100
2	3	305.0	361	2.578463	84.981247	313.0	305	305.0	361.0	313.0	...	1	0.844875	20.808652	305.0	19.710694	19.710694	-0.785398	62.627417	62.056032	0.974441
3	4	701.0	952	1.939290	196.782654	747.0	701	701.0	952.0	747.0	...	1	0.736345	35.440090	701.0	34.990789	26.707805	1.291699	105.840620	103.024717	0.938420
4	5	1237.0	2205	1.255313	443.927490	1665.0	1237	1237.0	2205.0	1665.0	...	1	0.560998	63.788714	1237.0	70.153363	29.934308	1.478716	202.539105	195.878727	0.742943
5	6	255.0	285	3.677297	52.882927	259.0	255	255.0	285.0	259.0	...	1	0.894737	20.615528	255.0	20.466711	16.191807	0.000000	58.142136	57.803718	0.984556
6	7	641.0	945	1.932482	186.040405	762.0	641	641.0	945.0	762.0	...	1	0.678307	47.434165	641.0	52.035208	18.629564	-1.383012	124.526912	120.740433	0.841207

7 rows × 22 columns