Fast Checkerboard Demodulation (FCD) for Synthetic Schlieren imaging, in python
Almost completely based on the work from Sander Wildeman: https://github.com/swildeman/fcd
To apply the FCD to a single or series of images, run fcd.py as a program:
Run python fcd.py --help to see how to use it.
usage: fcd.py [-h] [--output-format {tiff,bmp,png,jpg,jpeg}] [--skip-existing]
output_folder reference_image definition_image
[definition_image ...]
positional arguments:
output_folder
reference_image
definition_image May contain wildcards
optional arguments:
-h, --help show this help message and exit
--output-format {tiff,bmp,png,jpg,jpeg}
The output format (default: tiff)
--skip-existing Skip processing an image if the output file already
exists (default: False)
If you don't have images to apply FCD on, you can generate one with generate_test_images.py:
run
python generate_test_images.py ripples
This program will generate a reference image, a distorted image and a height field to test against.
If you like smileys, you can replace ripples by smiley.
If you like to change some parameters (e.g. pattern frequency), run python generate_test_images.py --help to see what can be changed and how.
To apply the FCD, run:
python fcd.py test_output "test image reference.tiff" "test image displaced.tiff"
This will apply the FCD with the output file test_output/test image displaced.tiff, which should resemble the file test image height field.tiff
Python >= 3.7 and the following PIP packages:
- numpy
- scipy
- imageio
- more_itertools
- scikit-image
- Improve the performance. The current implementation is not very fast
Information on the FCD technique and the employed algorithms can be found in: Wildeman S., Real-time quantitative Schlieren imaging by fast Fourier demodulation of a checkered backdrop, Exp. Fluids (2018) 59: 97, https://doi.org/10.1007/s00348-018-2553-9, or https://arxiv.org/abs/1712.05679 (preprint)