https://github.com/t04glovern/deep-dune-coloring
Automatic coloring and shading of Dune coloring book using manga-style lineart model from deepcolor. Makes use of Tensorflow + cGANs
https://github.com/t04glovern/deep-dune-coloring
cgan dune gan tensorflow
Last synced: 3 months ago
JSON representation
Automatic coloring and shading of Dune coloring book using manga-style lineart model from deepcolor. Makes use of Tensorflow + cGANs
- Host: GitHub
- URL: https://github.com/t04glovern/deep-dune-coloring
- Owner: t04glovern
- Created: 2019-03-24T10:07:45.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-25T12:47:52.000Z (over 6 years ago)
- Last Synced: 2025-07-19T15:44:23.217Z (3 months ago)
- Topics: cgan, dune, gan, tensorflow
- Language: Python
- Homepage: https://devopstar.com/2019/03/25/dune-coloring-book-using-cgan-tensorflow/
- Size: 5.2 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DeepDune Coloring
Automatic coloring and shading of Dune coloring book using manga-style lineart model from deepcolor. Makes use of Tensorflow + cGANs
## Dune PDF
First download a copy of the coloring book from my S3 bucket
```bash
aws s3 cp s3://devopstar/resources/deep-dune-coloring/dune-coloring-book-remaster.pdf dune-coloring-book-remaster.pdf
```
### Optional - Convert the PDF into pages
```bash
./convert.sh
```
This will dump out the Dune book images to the `dune` folder along with splitting the pages. You will need to have `imagemagick` to do this. The alternative is you can download these pages from my S3 bucket.
```bash
aws s3 sync s3://devopstar/resources/deep-dune-coloring/dune dune/
```
## Deepcolor
Setup Deepcolor using the handy version built by [Kevin Frans](https://github.com/kvfrans) that I've tweaked slightly and put in this repository
### Setup Repo
Create the output directories that will be needed
```bash
cd deepcolor
mkdir results
mkdir imgs
mkdir samples
```
### Python Environment
#### Conda
```bash
## GPU
conda create -n tensorflow_gpuenv_py27 tensorflow-gpu python=2.7 numpy
conda activate tensorflow_gpuenv_py27
pip install opencv-python untangle bottle
```
#### Alternatives
The requirements you will need are:
- Python 2.7
- Tensorflow 1.12
- numpy, opencv-python, untangle, bottle
### Training
If you would like to train your own version you will need to download the training data from Safebooru. This can be done by running the following script.
```bash
# From within deepcolor/
python download_images.py
```
Alternatively you can sync the training set I used down from S3 (If this gets hammers I will remove acces to it)
```bash
# From within deepcolor/
aws s3 sync s3://devopstar/resources/deep-dune-coloring/imgs imgs/
```
At this point you can start training by running the following
```bash
python main.py train
```
### Pre-trained model
If you would like to use the pre-trained model you can pull down a copy from my S3 (If this gets hammers I will remove access to it. Contact me @nathangloverAUS on twitter if you would like access)
```bash
# From within deepcolor/
aws s3 sync s3://devopstar/resources/deep-dune-coloring/checkpoint checkpoint/
```
You should have a folder structure as follows:
```bash
# From within deepcolor/
checkpoint/
tr/
checkpoint
model-10900500.index
model-10900500.data-00000-of-00001
model-10900500.meta
```
### Web Interface
Once you have either run the training task (and have a checkpoint folder) or have downloaded the pretrained model using the command above you should be able to run the web interface that can be used to interact with the model
```bash
# From within deepcolor/
python server.py
```
Open up the web interface on [http://localhost:8000](http://localhost:8000)
## Attribution
- [Deepcolor: automatic coloring and shading of manga-style lineart](http://kvfrans.com/coloring-and-shading-line-art-automatically-through-conditional-gans/)
- [kvfrans/deepcolor](https://github.com/kvfrans/deepcolor)
- [burness/tensorflow-101](https://github.com/burness/tensorflow-101)
- [pix2pix implementation](https://github.com/yenchenlin/pix2pix-tensorflow)