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162 lines
7.8 KiB
Markdown
162 lines
7.8 KiB
Markdown
# DeepCreamPy
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*Decensoring Hentai with Deep Neural Networks. Formerly named DeepMindBreak.*
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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 specifies the censored regions in each image by coloring those regions green in a separate image editing program like GIMP or Photoshop. A neural network handles the hard part of filling in the censored regions.
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DeepCreamPy has a pre-built binary for Windows 64-bit. DeepCreamPy works on Windows, Mac, and Linux.
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## What's New?
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- Decensoring images of ANY size
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- Decensoring censors of ANY shape (e.g. bunch of black lines, pink hearts, etc.)
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- Higher quality decensors
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- Support for mosaic decensors (still a WIP and not very usable)
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- User interface (not usable)
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## Installation
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### Download Prebuilt Binaries
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You can download the latest release [here](https://github.com/deeppomf/DeepCreamPy/releases/latest) or find all previous releases [here](https://github.com/deeppomf/DeepCreamPy/releases).
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Binary only available for Windows 64-bit.
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### Run Code Yourself
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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.
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#### Dependencies (for running the code yourself)
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- Python 3.6.7
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- TensorFlow 1.10
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- Keras 2.2.4
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- Pillow
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- h5py
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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.
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Tensorflow, Keras, Pillow, and h5py can all be installed by running in the command line
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```
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$ pip install -r requirements.txt
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```
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## Limitations
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The decensorship is intended to work on 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 containing 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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## Usage
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### I. Decensoring bar censors
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For each image you want to decensor, using image editing software like Photoshop or GIMP to color the areas you want to decensor the green color (0,255,0), which is a very bright green color.
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*I strongly recommend you use the pencil tool and NOT the brush tool.*
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*If you aren't using the pencil tool, BE SURE TO TURN OFF ANTI-ALIASING on the tool you are using.*
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I personally use the wand selection tool with anti-aliasing turned off to select the censored regions. I then expand the selections slightly, pick the color (0,255,0), and use the paint bucket tool on the selected regions.
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To expand selections in Photoshop, do Selection > Modify > Expand or Contract.
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To expand selections in GIMP, do Select > Grow.
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Save these images in the PNG format to the "decensor_input" folder.
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#### A. Using the binary
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Decensor the images by double-clicking on the decensor file.
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#### B. Running from scratch
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Decensor the images by running
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```
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$ python decensor.py
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```
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Decensored images will be saved to the "decensor_output" folder. Decensoring takes a few minutes per image.
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### II. Decensoring mosaic censors
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As with decensoring bar censors, perform the same steps of coloring the censored regions green and putting the colored image into the "decensor_input" folder.
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In addition, move the original, uncolored images into the "decensor_input_original" folder. Ensure each original image has the same names as their corresponding colored version in the "decensor_input" folder.
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For example, if the original image is called "mermaid.jpg," then you want to put this image in the "decensor_input_original" folder and, after you colored the censored regions, name the colored image "mermaid.png" and move it to the "decensor_input" folder.
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#### A. Using the binary
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Decensor the images by double-clicking on the decensor_mosaic file.
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#### B. Running from scratch
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Decensor the images by running
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```
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$ python decensor.py --is_mosaic=True
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```
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Decensored images will be saved to the "decensor_output" folder. Decensoring takes a few minutes per image.
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### III. Decensoring with the user interface
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To be implemented.
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## Troubleshooting
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### Installation
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```
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ModuleNotFoundError: No module named '_pywrap_tensorflow_internal'
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```
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See https://github.com/deeppomf/DeepCreamPy/issues/26#issuecomment-434043166
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### Decensoring
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If your decensor output looks like this, then the censored regions were not colored correctly.
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*Make sure you have antialiasing off.*
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Here are some examples of bad and good colorings:
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|Image|Zoom|Comment|
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|--- | --- | ---|
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|||Some censored pixels was left uncolored. Expand your selections to fully cover all censored regions.|
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|||Some pixels around the edges of the green regions are not pure green. This will cause the green to bleed into the decensors. Make sure anti-aliasing is off and to use a pencil tool and not a brush tool if possible.|
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|||Perfect coloring!|
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## FAQ
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- Q: Why aren't black and white images supported? Aren't black and white images easier to decensor than color images?
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- A: Black and white images are harder to decensor than color images because neural networks struggle to replicate screentone patterns.
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## To do
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- Finish the user interface (sometime in November)
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- Update model with better quality data (sometime in November)
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- Add support for black and white images
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- Add error log
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Contributions are welcome! Special thanks to StartleStars for contributing code for mosaic decensorship and SoftArmpit for greatly simplifying decensoring!
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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).
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See [ACKNOWLEDGEMENTS.md](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: [](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=SAM6C6DQRDBAE) |