# region_volumes#

Calculates the volume of each region in a labeled image. This method works by applying mesh_volume on each region to calculate the volume based on meshing the regions (marching cube) or by applying a summation on the number of voxels within each region.

[1]:

import matplotlib.pyplot as plt
import numpy as np
import porespy as ps
import inspect
inspect.signature(ps.metrics.region_volumes)

[1]:

<Signature (regions, mode='marching_cubes')>


## regions#

The input image of the pore space partitioned into individual pore regions. Note that zeros in the image (solid phase) will not be considered for volume calculation.

[2]:

np.random.seed(10)
im = ps.generators.blobs(shape=[50,50,50])
snow = ps.filters.snow_partitioning(im)
regions = snow.regions
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
ax.imshow(regions[:,:,20], origin='lower', interpolation='none')
ax.axis(False);


The region_volumes returns an array containing the volume of each region, offset by 1, such that the volume of region 1 is stored in element 0 of the array. We can visualize each region’s volume by assigning the returned values to each region:

[3]:

vols = ps.metrics.region_volumes(regions=regions)
regions_vol = np.copy(regions)
for i in range(0, len(vols)):
mask = np.array(np.where(regions_vol == i, regions_vol, 0), dtype=bool)
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
plt.imshow(regions_vol[:,:,10], origin='lower', interpolation='none')
cbar = plt.colorbar(ax=ax, shrink=0.8)
ax.axis(False);


## mode#

By default this function uses marching cube method to mesh each region and calculate the volume of each region. We can choose voxel mode where the function calculates the volume of each region by summing the number of voxels in each region.

[4]:

vols = ps.metrics.region_volumes(regions=regions, mode='voxel')
regions_vol = np.copy(regions)
for i in range(0, len(vols)):
mask = np.array(np.where(regions_vol == i, regions_vol, 0), dtype=bool)