DeepCreamPy/README.md

# DeepMindBreak
*Decensoring Hentai with Deep Neural Networks*

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.

![Censored, decensored][/readme_images/collage.png]

# Limitations

This project is LIMITED in capability. It is a proof of concept of ongoing research.

The decensorship works ONLY with color hentai images that have minor bar censorship of the penis or vagina.

It does NOT work with:
- Black and white images
- Scanned hentai
- Real life porn
- Mosaic censorship
- Censorship of nipples
- Animated gifs/videos

In particular, if a vagina or penis is completely censored out, inpainting will be ineffective.

# Dependencies

- Python 2
- TensorFlow 1.5
- Pillow
- tqdm
- matplotlib (only for running test.py)

# Model
The pretrained model can be downloaded from https://drive.google.com/open?id=1mWHYSj0LDSbJQQxjR4hUMykQkVve2U3Q.

# Usage

## I. Decensoring hentai

The decensorship process is fairly involved. A user interface will eventually be released to streamline the process.

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.

Move the cropped images to the "decensor_input_images" directory. Decensor the images by running

```
$ python decensor.py
```

Decensored images will be saved to the "decensor_output_images" directory. Paste the decensored images back into the original image.

## II. Train the pretrained model on custom dataset

Put your custom dataset for training in the "data/images" directory and convert images to npy format.

```
$ cd training_data
$ python to_npy.py
```

Train pretrained model on your custom dataset.

```
$ python train.py
```

# To do
- Add Python 3 compatibility
- Add a user interface
- Incorporate GAN loss into training

Contributions are welcome!

# License

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/).

Model is licensed under CC BY-NC 3.0 License.

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), which is an implementation of the paper [Globally and Locally Consistent Image Completion](http://hi.cs.waseda.ac.jp/%7Eiizuka/projects/completion/data/completion_sig2017.pdf).

---

Copyright (c) 2018 tadax, deeppomf

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