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81 lines
4.6 KiB
Markdown
81 lines
4.6 KiB
Markdown
# DeepCreamPy
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*Decensoring Hentai with Deep Neural Networks.*
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[![GitHub release](https://img.shields.io/github/release/deeppomf/DeepCreamPy.svg)](https://github.com/deeppomf/DeepCreamPy/releases/latest)
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[![GitHub downloads](https://img.shields.io/github/downloads/deeppomf/DeepCreamPy/latest/total.svg)](https://github.com/deeppomf/DeepCreamPy/releases/latest)
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[![GitHub downloads](https://img.shields.io/github/downloads/deeppomf/DeepCreamPy/total.svg)](https://github.com/deeppomf/DeepCreamPy/releases)
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[![GitHub issues](https://img.shields.io/github/issues/deeppomf/DeepCreamPy.svg)](https://github.com/deeppomf/DeepCreamPy/issues)
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[![Donate with PayPal](https://img.shields.io/badge/paypal-donate-green.svg)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=SAM6C6DQRDBAE)
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[![Twitter Follow](https://img.shields.io/twitter/follow/deeppomf.svg?label=Follow&style=social)](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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Before DeepCreamPy can be used, the user must color censored regions in their hentai green with an image editing program like GIMP or Photoshop. DeepCreamPy takes the green colored images as input, and a neural network automatically fills in the censored regions.
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You can download the latest release for Windows 64-bit [here](https://github.com/deeppomf/DeepCreamPy/releases/latest).
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For users interested in compiling DeepCreamPy themselves, DeepCreamPy can run 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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- Decensoring of mosaic decensors
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- Limited support for decensoring black and white/monochrome images
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- **New September 2019** Generate multiple decensors from the same image (not yet added to the binary release)
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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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- 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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- Resolve all Tensorflow compatibility problems
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- Finish the user interface
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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
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Contributions are closed for the near future.
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Special thanks to ccppoo, IAmTheRedSpy, 0xb8, deniszh, Smethan, mrmajik45, harjitmoe, itsVale, StartleStars, and SoftArmpit for their contributions!
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## License
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Source code and official releases/binaries are distributed under the [EULA](EULA.txt).
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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 Forty-lock's project [PEPSI](https://github.com/Forty-lock/PEPSI), which is the official implementation of the paper [PEPSI : Fast Image Inpainting With Parallel Decoding Network](http://openaccess.thecvf.com/content_CVPR_2019/html/Sagong_PEPSI__Fast_Image_Inpainting_With_Parallel_Decoding_Network_CVPR_2019_paper.html). [PEPSI](https://github.com/Forty-lock/PEPSI) is licensed under the MIT license.
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Training 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 go towards keeping me alive. [![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=SAM6C6DQRDBAE)
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