Generating Chinese calligraphy using CycleGANs.
python 3.9.13
jupyter / ipython
opencv 4.6.0
numpy 1.23.4
matplotlib 3.6.1
tensorflow 2.10.0
pickle 0.7.5
pillow 9.2.0
tqdm 4.64.1
Plus their dependencies.
Can use tensorflow-cpu and directML.
Generally, the latest version of the modules should work. Python should be 3.9.
Data Generation. Lanting and Simkai are provided. To generatoe more datasets, run the loader.py file with arguments <font path> <out folder> [resolution=128] [characters]. Characters are given by the constant PRESETCHARACTERS if not otherwise specified.
Training. Training is done by running main.py. Edit the setup reion to set paths and domain.
The GAN has many parameters: all the loss parameters are either keras loss keyword strings, or loss functions. The generator type is determined by the Enum GenType (in model.py)
Then just run the file.
Testing/Manual Use. Manual interaction in predict.ipynb, run all the files manually, it should walk you through. Images are saved in images/ with a .png extension.
More automatic generation is done in analysis.ipynb, any automatically saved images will be done in images/genRuns/ prefixed with their model number.