Expandable crack detection for composite materials.
This package provides an automated crack detection for tunneling off axis cracks in glass fiber reinforced materials. It relies on image processing and works with transilluminated white light images (TWLI). The basis of the crack detection method was first published by Glud et al. [1]. This implementation is aimed to provide a modular "batteries included" package for this method and extensions of it as well as image preprocessing functions.
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.
Installation:
pip install crackdect
Documentation:
https://crackdect.readthedocs.io/en/latest/
This package is written and tested in Python 3.8. The following packages must be installed.
- scikit-image 0.18.1
- numpy 1.18.5
- scipy 1.6.0
- matplotlib 3.3.4
- sqlalchemy 1.3.23
- numba 0.52.0
- psutil 5.8.0
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.
Clone the repository and add changes to it. Test the changes and make a pull request.
- Matthias Drvoderic
This project is licensed under the MIT License.
[1] J.A. Glud, J.M. Dulieu-Barton, O.T. Thomsen, L.C.T. Overgaard Automated counting of off-axis tunnelling cracks using digital image processing Compos. Sci. Technol., 125 (2016), pp. 80-89