Anime-Decensoring
Decensoring Hentai with Deep Neural Networks. Formerly named DeepMindBreak.
This project is a continuation of a discontinued project.
A deep learning-based tool to automatically replace censored artwork in hentai with plausible reconstructions.
The user colors cencored regions green in an image editing our program. A neural network fills in the censored regions.
Anime-Decensoring has a pre-built binary for Windows 64-bit available here.
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 (Beta)
- User interface (Beta)
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 maybe 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
To do
- Finish the user interface
- Update model with better quality data
- Add error log
Installation
Download Prebuilt Binaries
You can download the latest release here or find all previous releases here Binary only available for Windows 64-bit.
Run Code Yourself
If you want to run the code yourself, you can clone this repo and download the model from https://drive.google.com/open?id=1byrmn6wp0r27lSXcT9MC4j-RQ2R04P1Z. Unzip the file into the /models/ folder.
Dependencies (for running the code yourself)
- Python 3.6.7
- TensorFlow 1.10
- Keras 2.2.4
- Pillow
- h5py
No GPU required! Tested on Ubuntu 16.04 and Windows. Tensorflow on Windows is compatible with Python 3 and not Python 2. Tensorflow is not compatible with Python 3.7.
Tensorflow, Keras, Pillow, and h5py can all be installed by running in the command line
$ pip install -r requirements.txt
Run Code Yourself on CPUs that don't support AVX instructions
CPUs that don't support AVX instructions may experience this error when using the above install instructions:
ModuleNotFoundError: No module named '_pywrap_tensorflow_internal'
Follow these alternate install instructions if that happens:
- Start from a clean Python 3.6.7 install.
- Download a version of tensorflow that do support AVX instructions from (https://github.com/fo40225/tensorflow-windows-wheel/tree/master/1.10.0/py36/CPU/sse2). I assume you picked tensorflow-1.10.0-cp36-cp36m-win_amd64.whl for 64-bit and the other for 32-bit computers.
- Open the command line in the same directory as the file downloaded in step 2. Run
pip install tensorflow-1.10.0-cp36-cp36m-win_amd64.whl
or
pip install tensorflow-1.10.0-cp36-cp36m-win32.whl
depending on what you installed in step 2. 4. Open the command line in the directory of "DeepCreamPy-master" and run
pip install -r requirements.txt
Instructions are from https://github.com/deeppomf/DeepCreamPy/issues/26#issuecomment-434043166.
Follow me on Twitter @DoginUwU for project updates.
Initial projects credits to liaoxiong3x.
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. And liaoxiong3x.
See ACKNOWLEDGEMENTS.md for full license text of these projects.