CrackDect: Expandable crack detection for composite materials.
This package provides crack detection algorithms for tunneling off axis cracks in glass fiber reinforced materials.
If you use this package in publications, please cite the paper.
In this package, crack detection algorithms based on the works of Glud et al. 1 and Bender et al. 2 are implemented. This implementation is aimed to provide a modular “batteries included” package for this crack detection algorithms as well as a framework to preprocess image series to suite the prerequisites of the different crack detection algorithms.
Quick start
To install CrackDect, check at first the Prerequisites of your python installation. Upon meeting all the criteria, the package can be installed with pip, or you can clone or download the repo. If the installed python version or certain necessary packages are not compatible we recommend the use of virtual environments by virtualenv or Conda. See the conda guide for infos about creating and managing Conda environments.
Installation:
Open a command line and check if python is available
$ python --version
This displays the version of the global python environment. If this does not return the python version, something is not working and you need to fix your global python environment.
If all the prerequisites are met CrackDect can be installed in the global environment via pip
$ pip install crackdect
Quick Start shows an illustrative example of the crack detection.
Crack Detection provides a quick theoretical introduction into the crack detection algorithm.
Prerequisites
It is recommended to use virtual environments (anaconda). This package is written and tested in Python 3.8 and relies on here listed packages.
And if the visualization module is used PyQt5 is also needed.
Motivation
Most algorithms and methods for scientific research are implemented as in-house code and not accessible for other researchers. Code rarely gets published and implementation details are often not included in papers presenting the results of these algorithms. Our motivation is to provide transparent and modular code with high level functions for crack detection in composite materials and the framework to efficiently apply it to experimental evaluations.
Contributing
Clone the repository and add changes to it. Test the changes and make a pull request.
Modules
This module provides the core functionality for handling a stack of images at once. |
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Preprocessing functions for single images |
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Routines for preprocessing image stacks. |
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IO module |
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Crack detection algorithms |
License
This project is licensed under the MIT License