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');
../../../_images/b6fac067f5a227fd5f6201c8e9c4d640d8d90e8dd9c4ebae190ff9bbdde66207.png
regions = spim.label(im)[0]
props = ps.metrics.regionprops_3D(regions)
plt.imshow(regions, origin='lower', interpolation='none');
../../../_images/651c489520522bcd5fa197967c5a0f9a70ce4f2020bdc13ffee8a1a4996e70c6.png
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