MangaLineExtraction_PyTorch

The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

Usage

model_torch.py [source folder] [output folder]

Example:

model_torch.py ./pytorchTestCases/ ./pytorchResults/

The model weights (erika.pth)

Please refer to the release section of this repo. Alternatively, you may use this link:

https://www.dropbox.com/s/y8pulix3zs73y62/erika.pth?dl=0

Requirement

Python3
PyTorch (tested on version 1.9)
Python-opencv

How the model is prepared

The PyTorch weights are exactly the same as the theano(!) model. I make some efforts to convert the original weights to the new model and ensure the overall error is less than 1e-3 over the image range from 0-255.

Moreover, the functional PyTorch interface allows easier fine-tuning of this model. You can also take the whole model as a sub-module for your own work (e.g., use the on-the-fly extraction of lines as a structural constraint).

About model training

I really don't want to admit it, but the legacy code looks like some artworks by a two-years old. I will try my best to recover the code to py3 and share the screentone dataset. This won't take long, so please stay tuned.

Go beyond manga

Surprisingly, this model works quite well on color cartoons and other nijigen-like images. Simply load the image as grayscale(by default) and check out the results!

Gallery

I'm glad to share some of the results of this model. Some of the images are copyrighted, I will list the original sources below the images. Feel free to share your creaions with me in the issues section.

BibTeX:

@article{li-2017-deep,
    author   = {Chengze Li and Xueting Liu and Tien-Tsin Wong},
    title    = {Deep Extraction of Manga Structural Lines},
    journal  = {ACM Transactions on Graphics (SIGGRAPH 2017 issue)},
    month    = {July},
    year     = {2017},
    volume   = {36},
    number   = {4},
    pages    = {117:1--117:12},
}