Python routines using Open CV2 and Mahotas image processing libraries to extract a binary image of cracks (lines of a single pixel in thickness) from a 2D greyscale image, and provide statistics of their lengths and spatial density.
This is the code for the automated microcrack analysis and quantification presented in Griffiths et al. (2017) and the corresponding supplementary materials--pdfs of both are provided in /References.
Griffiths, L., Heap, M. J., Baud, P., & Schmittbuhl, J. (2017). Quantification of microcrack characteristics and implications for stiffness and strength of granite. International Journal of Rock Mechanics and Mining Sciences, 100, 138–150. https://doi.org/10.1016/j.ijrmms.2017.10.013
First download or clone this folder.
Make sure the following are installed:
- mahotas
- open cv2
- matplotlib
- numpy
- scipy ndimage
- skimage
- PIL (Pillow)
If you are using conda, this can be acheived by making an environment (named crackenv) from the 'environment.yml' file
conda env create -f environment.yml
And activate the environment using
conda activate crackenv
The processing is split into two modules:
- extract_cracks.py: segmentation of the input image to create a binary image of pixel-thick cracks
- count_cracks.py: crack lengths and their spatial densities are calculated from the binary image. These values are provided both globally for the whole image, and within multiple windows, to assess their spatial variability.
Images are saved in ./output at each stage of the processing.
Run the example script:
'example.py' demonstrates the image processing on an example file located at '/input/example_micrograph.png', using the default parameters.
You can then adapt this file to run image files of your choosing, adjusting the parameters accordingly. The signification of these parameters, and guidelines for choosing them, are provided in the documentation of extract_cracks.py and in Griffiths et al. 2017 and supplementary materials, in /References.