| .github/ISSUE_TEMPLATE | ||
| decensor_input | ||
| decensor_input_original | ||
| decensor_output | ||
| docker | ||
| docs | ||
| icons | ||
| libs | ||
| models | ||
| readme_images | ||
| config.py | ||
| decensor.py | ||
| LICENSE.txt | ||
| README.md | ||
| requirements.txt | ||
| TROUBLESHOOTING_INSTALL.md | ||
| ui.py | ||
| USAGE.md | ||
DeepCreamPy
Decensoring Hentai with Deep Neural Networks.
A deep learning-based tool to automatically replace censored artwork in hentai with plausible reconstructions.
The user colors censored regions green in an image editing program like GIMP or Photoshop. A neural network fills in the censored regions.
DeepCreamPy has a pre-built binary for Windows 64-bit available here. DeepCreamPy works on Windows, Mac, and Linux.
Features
- Decensoring images of ANY size
- Decensoring of ANY shaped censor (e.g. black lines, pink hearts, etc.)
- Higher quality decensors
- Support for mosaic decensors (WIP)
- User interface (WIP)
Limitations
The decensorship is for color hentai images that have minor to moderate censorship of the penis or vagina. If a vagina or penis is completely censored out, decensoring will be ineffective.
It does NOT work with:
- Black and white/Monochrome image
- Hentai with screentones (e.g. printed hentai)
- Real life porn
- Censorship of nipples
- Censorship of anus
- Animated gifs/videos
Table of Contents
Setup:
Usage:
Miscellaneous:
To do
- Finish the user interface (estimated November)
Update model with better quality data (estimated November)- Add support for black and white images
- Add error log
Follow me on Twitter @deeppomf for project updates.
Contributions are welcome! Special thanks to IAmTheRedSpy, 0xb8, deniszh, Smethan, mrmajik45, harjitmoe, itsVale, StartleStars, and SoftArmpit!
License
This project is licensed under GNU Affero General Public License v3.0.
See LICENSE.txt for more information about the license.
Acknowledgements
Example mermaid image by Shurajo & AVALANCHE Game Studio under CC BY 3.0 License. The example image is modified from the original, which can be found here.
Neural network code is modified from MathiasGruber's project Partial Convolutions for Image Inpainting using Keras, which is an unofficial implementation of the paper Image Inpainting for Irregular Holes Using Partial Convolutions. Partial Convolutions for Image Inpainting using Keras is licensed under the MIT license.
User interface code is modified from Packt's project Tkinter GUI Application Development Blueprints - Second Edition. Tkinter GUI Application Development Blueprints - Second Edition is licensed under the MIT license.
Data is modified from gwern's project Danbooru2017: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset.
See ACKNOWLEDGEMENTS.md for full license text of these projects.
Donations
If you like the work I do, you can donate to me via Paypal. The funds will mainly go towards purchasing better GPUs to accelerate training. 