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136 lines
5.7 KiB
Markdown
136 lines
5.7 KiB
Markdown
# DeepMindBreak
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*Decensoring Hentai with Deep Neural Networks*
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This project applies an implementation of [Globally and Locally Consistent Image Completion](http://hi.cs.waseda.ac.jp/%7Eiizuka/projects/completion/data/completion_sig2017.pdf) to the problem of hentai decensorship. Using a deep fully convolutional neural network, DeepMindBreak can replace censored artwork in hentai with plausible reconstructions. The user needs to only specify the censored regions.
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# **THIS PROJECT IS STILL IN DEVELOPMENT. DO NOT BE DISAPPOINTED IF THE RESULTS AREN'T AS GOOD AS YOU EXPECT.**
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Next planned update on March 1: Incorporate GAN loss in training. Improve CLI.
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# Limitations
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This project is LIMITED in capability. It is a proof of concept of ongoing research.
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The decensorship is intended to ONLY work on color hentai images that have minor bar censorship of the penis or vagina.
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It does NOT work with:
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- Black and white images
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- Monochrome images
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- Hentai containing screentones (e.g. printed hentai)
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- Real life porn
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- Mosaic censorship
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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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In particular, if a vagina or penis is completely censored out, inpainting will be ineffective.
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Embarrassingly, because the neural network was trained to decensor horizontally and vertically oriented rectangles, it has trouble with angled rectangles. This will be fixed soon.
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# Dependencies
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- Python 2/3
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- TensorFlow 1.5
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- Pillow
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- tqdm
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- scipy
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- pyamg (only needed if poisson blending is enabled in decensor.py)
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- matplotlib (only for running test.py)
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No GPU required! Tested on Ubuntu 16.04 and Windows.
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Poisson blending is disabled by default since it has little effect on output quality.
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Pillow, tqdm, scipy, pyamg, and matplotlib can all be installed using pip.
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# Model
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The pretrained model can be downloaded from https://drive.google.com/open?id=1mWHYSj0LDSbJQQxjR4hUMykQkVve2U3Q.
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Unzip the contents into the /models/ folder.
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# Usage
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## I. Decensoring hentai
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The decensorship process is fairly involved. A user interface will eventually be released to streamline the process.
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Using image editing software like Photoshop or GIMP, paint the areas you want to decensor the color with RGB values of (0,255,0). For each censored region, crop 128 x 128 size images containing the censored regions from your images and save them as new ".png" images.
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Move the cropped images to the "decensor_input_images" directory. 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_images" directory. Paste the decensored images back into the original image.
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## II. Train the pretrained model on custom dataset
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You must have a GPU for training since training on a CPU will take weeks.
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Your custom dataset should be 128 x 128 images of uncensored vaginas and penises cropped from hentai. The more images, the better: I used 70,000 images for training. Censoring these images yourself is unnecessary.
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Put your custom dataset for training in the "training_data/images" directory and convert images to npy format.
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```
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$ cd training_data
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$ python to_npy.py
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```
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To train, run
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```
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$ python train.py
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```
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If desired, you can train the pretrained model on your custom dataset by running
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```
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$ python train.py --continue_training=True
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```
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Training can be done separately for mosaics with train_mosaic.py, but decensor.py is not yet compatible with mosaic decensorship models.
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# To do
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- ~~Add Python 3 compatibility~~
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- ~~Add random rotations in cropping rectangles~~
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- Retrain for arbitrary shape censors
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- Add a user interface
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- Incorporate GAN loss into training
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- Update the model to the new version
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Contributions are welcome! Special thanks to StartleStars for contributing code for mosaic decensorship.
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# License
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Example image by dannychoo under [CC BY-NC-SA 2.0 License](https://creativecommons.org/licenses/by-nc-sa/2.0/). The example image is modified from the original, which can be found [here](https://www.flickr.com/photos/dannychoo/16081096643/in/photostream/).
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Model is licensed under CC BY-NC 3.0 License.
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Code is licensed under MIT License and is modified from tadax's project [Globally and Locally Consistent Image Completion with TensorFlow ](https://github.com/tadax/glcic) and shinseung428's project [https://github.com/shinseung428/GlobalLocalImageCompletion_TF], which are implementations of the paper [Globally and Locally Consistent Image Completion](http://hi.cs.waseda.ac.jp/%7Eiizuka/projects/completion/data/completion_sig2017.pdf). It also has a modified version of parosky's project [poissonblending](https://github.com/parosky/poissonblending).
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---
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Copyright (c) 2018 tadax, Seung Shin, parosky, deeppomf
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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