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87 lines
5.1 KiB
Markdown
87 lines
5.1 KiB
Markdown
# DeepCreamPy
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*Decensoring Hentai with Deep Neural Networks.*
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*DeepCreamPyV2 coming in Summer 2019*
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[](https://github.com/deeppomf/DeepCreamPy/releases/latest)
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[](https://github.com/deeppomf/DeepCreamPy/releases/latest)
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[](https://github.com/deeppomf/DeepCreamPy/releases)
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[](https://github.com/deeppomf/DeepCreamPy/issues)
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[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=SAM6C6DQRDBAE)
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[](https://raw.githubusercontent.com/deeppomf/DeepCreamPy/master/LICENSE.txt)
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[](https://twitter.com/deeppomf/)
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A deep learning-based tool to automatically replace censored artwork in hentai with plausible reconstructions.
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The user colors censored regions green in an image editing program like GIMP or Photoshop. A neural network fills in the censored regions.
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DeepCreamPy has a pre-built binary for Windows 64-bit available [here](https://github.com/deeppomf/DeepCreamPy/releases/latest). DeepCreamPy's code works on Windows, Mac, and Linux.
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Please before you open a new issue check [closed issues](https://github.com/deeppomf/DeepCreamPy/issues?q=is%3Aissue+is%3Aclosed) and check the [table of contents](https://github.com/deeppomf/DeepCreamPy#table-of-contents).
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<p align="center">
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<img src="https://github.com/deeppomf/DeepCreamPy/blob/master/readme_images/mermaid_collage.png" width="800">
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</p>
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## Features
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- Decensoring images of ANY size
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- Decensoring of ANY shaped censor (e.g. black lines, pink hearts, etc.)
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- Higher quality decensors
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- Support for mosaic decensors (WIP)
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## Limitations
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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.
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It does NOT work with:
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- Black and white/Monochrome image
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- Hentai with screentones (e.g. printed hentai)
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- Real life porn
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- Censorship of nipples
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- Censorship of anus
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- Animated gifs/videos
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## Table of Contents
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Setup:
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* [Running latest Window 64-bit release](docs/INSTALLATION_BINARY.md)
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* [Running code yourself](docs/INSTALLATION.md)
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Usage:
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* [Decensoring tutorial](docs/USAGE.md)
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* [Troubleshooting for installing](docs/TROUBLESHOOTING.md)
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* [Troubleshooting for poor quality decensors](docs/TROUBLESHOOTING_DECENSORS.md)
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Miscellaneous:
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* [FAQ](docs/FAQ.md)
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## To do
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- Switch to Deepfillv2 model
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- Enhance data collection and extraction
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- Use new training techniques
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- Resolve all Tensorflow compatibility problems
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- Finish the user interface
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- Add support for black and white images
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- Add error log
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Follow me on Twitter [@deeppomf](https://twitter.com/deeppomf) (NSFW Tweets) for project updates.
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Contributions are welcome! Special thanks to ccppoo, IAmTheRedSpy, 0xb8, deniszh, Smethan, mrmajik45, harjitmoe, itsVale, StartleStars, and SoftArmpit!
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## License
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This project is licensed under GNU Affero General Public License v3.0.
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See [LICENSE.txt](LICENSE.txt) for more information about the license.
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## Acknowledgements
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Example mermaid image by Shurajo & AVALANCHE Game Studio under [CC BY 3.0 License](https://creativecommons.org/licenses/by/3.0/). The example image is modified from the original, which can be found [here](https://opengameart.org/content/mermaid).
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Neural network code is modified from MathiasGruber's project [Partial Convolutions for Image Inpainting using Keras](https://github.com/MathiasGruber/PConv-Keras), which is an unofficial implementation of the paper [Image Inpainting for Irregular Holes Using Partial Convolutions](https://arxiv.org/abs/1804.07723). [Partial Convolutions for Image Inpainting using Keras](https://github.com/MathiasGruber/PConv-Keras) is licensed under the MIT license.
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User interface code is modified from Packt's project [Tkinter GUI Application Development Blueprints - Second Edition](https://github.com/PacktPublishing/Tkinter-GUI-Application-Development-Blueprints-Second-Edition). [Tkinter GUI Application Development Blueprints - Second Edition](https://github.com/PacktPublishing/Tkinter-GUI-Application-Development-Blueprints-Second-Edition) is licensed under the MIT license.
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Data is modified from gwern's project [Danbooru2017: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset](https://www.gwern.net/Danbooru2017) and other sources.
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See [ACKNOWLEDGEMENTS.md](docs/ACKNOWLEDGEMENTS.md) for full license text of these projects.
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## Donations
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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. [](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=SAM6C6DQRDBAE)
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