About

Important

This work is directly related to the study [Schott2023] and [Schott20232]. This project is under construction and new functionalities are constantly added on this package.

For support do not hesitate to contact Florian Schott or Rajmund Mokso

Overview

The package is currently structured in 11 sections:

  • FoamQuant.Process

  • FoamQuant.FromBinary

  • FoamQuant.FromLabelled

  • FoamQuant.FromContact

  • FoamQuant.Tracking

  • FoamQuant.Passage

  • FoamQuant.Average

  • FoamQuant.Figure

  • FoamQuant.Movie

  • FoamQuant.VTK

  • FoamQuant.Helper

_images/Diagram.png

Current package structure. The functions in red are not yet included in FoamQuant.

Process

Wrapped functions for processing batch foam-like images: from raw images to bubble-segmented images.

  • Remove background (homogeneization)

  • Phase segmentation (binarization)

  • Masking (cylindrical or region of interest)

  • Remove small objects and holes (volume threshold)

  • Bubble segmentation (watershed)

  • Remove edge bubbles (edge of a mask if provided)

_images/Figure_segmentation.png

  1. raw reconstructed image, (b) phase segmented image and (c) bubble segmented image.

FromBinary

Functions to quantify the liquid fraction from a batch of phase segmented images.

_images/fromliqfrac.png

The liquid fraction along a cartesian mesh can be returned structured or unstructured.

FromLabelled

Functions to quantify the bubbles regions properties from a batch of labelled images.

_images/fromlab.png

The function save the regions properties in a .csv

Tracking

Functions to track the bubbles and their properties from a batch of labelled images.

_images/tracking.png

The color (from green to black) indicates the time index. The red points are the lost tracking positions.

Passage and Average

Functions to convert scalar, vectorial or tensorial properties from cartesian to cylindrical and spherical, and perform time/space averages.

_images/passage_average.png

In this example the displacement field is first expressed in a cylindrical basic and then averaged.

Two ways of measuring the internal strain field

_images/shape_texture_3d.PNG

Label traking

The tracking method was inspired by ID-track presented in [Ando2013].

_images/tracking_3d.PNG

Tracking of five bubbles, showing various tracked properties: elastic internal strain, number of neighbours, velocity, and volume.

References

[vanderWalt2014]

  1. van der Walt et al., scikit-image: Image processing in Python. PeerJ 2:e453 (2014) https://doi.org/10.7717/peerj.453

[Stamani2020]

Stamati et al., (2020). spam: Software for Practical Analysis of Materials. Journal of Open Source Software, 5(51), 2286, https://doi.org/10.21105/joss.02286

[Ando2013]

Andò,E. et al., Experimental micromechanics: grain-scale observation of sand deformation, Géotechnique Letters 2, 107–112, (2012) https://doi.org/10.1680/geolett.12.00027

[Hall2010]

    1. Hall et al., Discrete and continuum analysis of localised deformation in sand using X-ray μCT and volumetric digital image correlation. Géotechnique, 60(5), 315-322, (2010) https://doi.org/10.1680/geot.2010.60.5.315

[Graner2008] (1,2)

  1. Graner et al., Discrete rearranging disordered patterns, part I: Robust statistical tools in two or three dimensions, Eur. Phys. J. E 25, 349–369 (2008) https://doi.org/10.1140/epje/i2007-10298-8

[Raufaste2015]

Raufaste, C. et al., Three-dimensional foam flow resolved by fast X-ray tomographic microscopy, EPL, 111, 38004, (2015) https://doi.org/10.1209/0295-5075/111/38004

[Schott2023]

  1. Schott et al., Three-dimensional liquid foam flow through a hopper resolved by fast X-ray microtomography, Soft Matter, (2023) https://doi.org/10.1039/d2sm01299e

[Schott20232]

  1. Schott et al., Structural formation during bread baking in a combined microwave-convective oven determined by sub-second in-situ synchrotron X-ray microtomography, Food Research International, (2023) https://doi.org/10.1016/j.foodres.2023.113283