Repository of the Rodan wrapper for Background Removal
- scikit-image (0.19.2)
- opencv-python (4.5.5.64)
- numpy (1.21.6)
- tensorflow (2.5.1)
- keras (2.5.0rc0)
This background removal task belongs inside gpu-celery container.
For local usage, Sauvola Algorithm method and SAE Binarization method are separate.
Use BgRemovalLocalTask.py to run this job locally. Parameters:
- -psr
Path to folder with the original images(Default: datasets/images) - -out
Path to folder for output processed images(Default: datasets/output) - -pfx
Postfix for output files <image_name><output_postfix>.png(Default: _nBg) - -w
Window size for saulova algorithm. Must be an odd number integer.(Default: 15) - -k
Parameter for saulova algorithm. Must be positive(Default: 0.2) - -c
Amount to adjust contrast by. Can be negative.(Default: 127.0) - -b
Amount to adjust brightness by. Can be negative(Default: 0.0)
Example: python3 BgRemovalLocalTask.py -psr datasets/images/MS73 -out datasets/output/MS73 -pfx _Bgr -w 101 -k 0.15 -c 150.0 -b 5.0
The binarize.py script performs the binarization of an input image using a trained model. The parameters of this script are the following:
| Parameter | Default | Description |
|---|---|---|
-imgpath |
Path to the image to process | |
-modelpath |
(*) | Path to the model to load |
-w |
256 | Input window size |
-s |
-1 | Step size. -1 to use window size |
-f |
64 | Number of filters |
-k |
5 | Kernel size |
-drop |
0 | Dropout percentage |
-stride |
2 | Convolution stride size |
-every |
1 | Residual connections every x layers |
-th |
0.5 | Selectional threshold |
-save |
Output image filename |
(*) By default, the model trained with all datasets will be used.
The only mandatory parameter is -imgpath, the rest are optional. You also have to choose if you want to save (-save) the binarized image.
For example, to binarize the image img01.png you can run the following command:
$ python binarize.py -imgpath img01.png -save out.png