Pore space segmentation

Once the void phase of a porous material has been segmented into individual pore clusters, PoreScene can render every cluster as a separate object and color each one on its own. This makes the connectivity of the pore space immediately visible and is a natural companion to the solid structure visualization.

This example shows how to turn a segmented 3D voxel image – in which every void voxel carries the integer label of the cluster it belongs to – into one mesh per cluster, load them into the scene, and give each cluster a random color from a colormap.

As an example, this tutorial uses the segmented void space of the same freeze-dried sugar solution captured with X-ray micro-computed tomographic imaging [1] that is used in the solid structure example, available in PoreScene’s repository on GitHub: data/img_seg.raw

The rendered result: the segmented pore space, with every void cluster colored individually from a random selection of the configured colormap.

Step-by-step guide

1. Import required modules

Make sure to have porescene installed (see Installation). At first, required modules need to be imported:

from pathlib import Path

import numpy as np

from porescene import io, utility
from porescene.color.palette import Colormap, Palette
from porescene.scene import Scene

Module utility provides a function that converts the segmented volume image into one mesh per cluster, which can be subsequently saved with functions from io. Palette and Colormap supply the colors, and Scene loads and renders the created meshes. File paths and directories are handled throughout PoreScene with the built-in pathlib module.

2. Set utility variables

After that, a directory for saving the mesh file and the rendered images is specified:

# data subdirectory
pth_data = Path.cwd() / "data"

Next, some required information about the volume image is saved:

# [m] edge length of a single voxel
L_vxl = 1e-6

# [vxl] image resolution
res_img = np.array((100, 100, 100))

# [m] domain dimensions
extent = res_img * L_vxl

extent is the physical extent of the volume in meters – here 100 voxels of 1 µm along each edge. It calibrates the axis ticks and sets the aspect ratio of the scene, so it always stays in meters regardless of the unit you display (see Concepts & Conventions).

3. Mesh the segmented volume

The segmented volume data is loaded from file and reshaped into its original resolution. Unlike the binarized solid image, it is stored as uint32, because each void voxel holds the label of its cluster. Label 0 is reserved for the surrounding solid phase, so the cluster labels are all non-zero values:

# load and reshape segmented volume image
img_seg = np.fromfile(pth_data / "img_seg.raw", dtype=np.uint32)
img_seg = img_seg.reshape(res_img)

# void clusters carry labels 1..N; label 0 is the surrounding solid phase
labels = np.unique(img_seg)
labels = labels[labels != 0]

volume2mesh() builds the meshes. With per_label=True it returns a separate mesh per label instead of merging everything into one surface, as a {label: Mesh} mapping. The name becomes the object name of each mesh, with the label appended – here "label_1", "label_2" and so on, the naming that create_clusters() expects. mesh2obj() then writes all clusters as separate objects into a single OBJ file. You can skip this step in case you already have such a geometric object file of your segmented pore space.

# one mesh per void cluster, named "label_1", "label_2", ...
meshes = utility.volume2mesh(img_seg, L_vxl, labels, per_label=True, name="label")

# export all clusters as separate objects in a single OBJ file
io.mesh2obj(pth_data / "segmentation-void.obj", meshes)

Tip

The clusters are written to OBJ rather than PLY because the OBJ format stores several named objects in one file, which is what create_clusters() relies on to keep the clusters apart and color them individually.

4. Scene setup

Scene initializes the rendering stage with camera and lighting. The scene is empty by default – every visible element is added explicitly in the next steps.

# initialize a new scene
sc = Scene(extent)

For the rendering, the axes and the previously generated cluster meshes are added to the Scene. apply_colors() then assigns a color to every cluster – here a random selection from the TURBO colormap, one color per label:

# add axes to the scene
sc.create_axes()

# add the void clusters to the scene
sc.create_clusters(pth_data / "segmentation-void.obj")

# color each cluster with a random color drawn from a colormap
sc.apply_colors("Clusters", Palette.load(Colormap.TURBO).random(len(labels)))

# render the scene
pth_img = sc.render(pth_data / "segmentation-void.png")

With the final line, Blender renders the scene and saves the image as segmentation-void.png in the given data directory.

Full script

The complete example, also available on GitHub: example/segmentation.py.

example/segmentation.py

from pathlib import Path

import numpy as np

from porescene import io, utility
from porescene.color.palette import Colormap, Palette
from porescene.scene import Scene

# =============================================================================
# Parameters

# data subdirectory
pth_data = Path.cwd() / "data"

# [m] edge length of a single voxel
L_vxl = 1e-6

# [vxl] image resolution
res_img = np.array((100, 100, 100))

# [m] domain dimensions
extent = res_img * L_vxl


# =============================================================================
# Meshing

# load and reshape segmented volume image
img_seg = np.fromfile(pth_data / "img_seg.raw", dtype=np.uint32)
img_seg = img_seg.reshape(res_img)

# void clusters carry labels 1..N; label 0 is the surrounding solid phase
labels = np.unique(img_seg)
labels = labels[labels != 0]

# one mesh per void cluster, named "label_1", "label_2", ...
meshes = utility.volume2mesh(img_seg, L_vxl, labels, per_label=True, name="label")

# export all clusters as separate objects in a single OBJ file
io.mesh2obj(pth_data / "segmentation-void.obj", meshes)


# =============================================================================
# Scene configuration and rendering

# initialize a new scene
sc = Scene(extent)

# add axes to the scene
sc.create_axes()

# add the void clusters to the scene
sc.create_clusters(pth_data / "segmentation-void.obj")

# color each cluster with a random color drawn from a colormap
sc.apply_colors("Clusters", Palette.load(Colormap.TURBO).random(len(labels)))

# render the scene
pth_img = sc.render(pth_data / "cluster-random.png")

References

[1]

Faber, F., Vorhauer-Huget, N., Thomik, M., Gruber, S., Först, P., & Tsotsas, E. (2025). Pore-scale study of coupled heat and mass transfer during primary freeze-drying using an irregular pore network model. Drying Technology, 43(1-2), 162–182. https://doi.org/10.1080/07373937.2024.2407062