Awesome
Comicolorization
This is an implementation of the Comicolorization : Semi-automatic Manga Colorization.
With this repository, you can
- run sample codes
- train colorization task
- use as colorization library
Sample Code
We prepare two sample codes, sample_painting.py
and sample_detecting_panels.py
.
sample_painting.py
is example of colorization the manga page.sample_detecting_panels.py
is example of detection auto panel rectangle.
First of all, install the requirements.
pip install -r requirements.txt
In addition, you must install OpenCV-Python.
Colorization
python sample/sample_painting.py
# Help
# python sample/sample_painting.py --help
Auto Panel Rectangle Detection
1. get manga-frame-extension
and build
git submodule init
git submodule update
cd manga-frame-extraction/MangaFrameExtraction
cmake ./
make
Please read manga-frame-extraction's README.md for details.
2. run
cd ../../
python sample/sample_detecting_panels.py
# Help
# python sample/sample_detecting_panels.py --help
Copyright
Following images are from Manga109 dataset.
<table style="border-collapse: collapse"> <tr> <td><img src="./sample/Belmondo-1.png" width="96px"></td> <td><img src="./sample/HinagikuKenzan_026.jpg" width="96px"></td> <td><img src="./sample/HinagikuKenzan-1.png" width="96px"></td> <td><img src="./sample/TasogareTsushin-1.png" width="96px"></td> </tr> <tr> <td>©Ishioka Shoei</td> <td>©Sakurano Minene</td> <td>©Sakurano Minene</td> <td>©Tanaka Masato</td> </tr> </table>Training
There are two training task.
- the colorization task for generate the low resolution colorized image.
- the super resolution task for generate the higher resolution colorized image.
First of all, install the requirements.
pip install -r requirements.txt
In addition, you must install OpenCV-Python.
Colorization Task
1. prepare dataset
For training, three type data are required.
- color images that Pillow can load
- json file written label ID list that has 'id' key
- ex.
{ "id": [ "a", "b", "c" ] }
- ex.
- json file written label ID list for each image
- the key is image file name without extension
- ex.
{ "imageX": ["a", "b"], "imageY": ["a"], "imageZ": ["c"], ... }
2. run
# run same as paper
python bin/train.py \
/path/to/images/directory \
/path/to/save \
--path_tag_list /path/to/label_ID_list.json \
--path_tag_list_each_image /path/to/label_ID_list_for_each_image.json \
--network_model LTBC \
--num_dataset_test 1000 \
--batchsize 30 \
--size_image 224 \
--augmentation True \
--size_image_augmentation 256 \
--save_result_iteration 1000 \
--random_seed_test 0 \
--loss_type Lab \
--alpha_ltbc_classification 0.00333333333 \
--ltbc_classification_loss_function multi_label \
--line_drawing_mode otsu_threshold \
--max_pixel_drawing 15 \
--use_adversarial_network \
--blend_adversarial_generator 1.0 \
--discriminator_first_pooling_size 2 \
--optimizer_adam_alpha 0.0001 \
--blend_mse_color 1.0 \
--mse_loss_mode color_space \
--weight_decay 0.0001 \
--log_interval 200 \
--gpu -1 \
--ltbc_classification_num_output_list 512 428 \ # last number should be same as number of labels
{color_feature} # other params depending on task, please see below
# Help
# python bin/train.py --help
When palette mode, change color_feature
to
--threshold_histogram_palette 0.0 \
--use_histogram_network \
--num_bins_histogram 6 \
--use_multidimensional_histogram
When histogram mode, change color_feature
to
--use_histogram_network \
--num_bins_histogram 6 \
--use_multidimensional_histogram
When without any color feature, color_feature
is empty.
Super Resolution Task
1. modify config file
Modify the information with these keys in bin/config_super_resolution.json
.
dataset/images_glob
: set the image file namesmodel/other/path_result_directory
: set the colorization model directoryproject/name
: set the name of this task (this will be the directory name)project/result_path
: set the path for the result directory
2. run
python bin/train_super_resolution.py bin/config_super_resolution.json
Use as colorization library
Install
pip install git+http://github.com/DwangoMediaVillage/Comicolorization
Download trained model
Please download model files at model
directory in this repository.
How to use
- import
comicolorization
andcomicolorization_sr
- initialize
Drawer
- load model
- initialize
PagePipeline
- call
process
method
Please see the sample code for more details.
License
MIT License, see LICENSE.