mirror of
https://github.com/Deepshift/DeepCreamPy.git
synced 2024-11-29 05:10:43 +00:00
118 lines
4.7 KiB
Markdown
118 lines
4.7 KiB
Markdown
# 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.
|
|
|
|
# **THIS PROJECT IS STILL IN DEVELOPMENT. DO NOT BE DISAPPOINTED IF THE RESULTS AREN'T AS GOOD AS YOU EXPECT.**
|
|
|
|
|
|
|
|
# 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
|
|
- Monochrome images
|
|
- Hentai containing screentones (e.g. printed hentai)
|
|
- Real life porn
|
|
- Mosaic censorship
|
|
- Censorship of nipples
|
|
- Censorship of anus
|
|
- Animated gifs/videos
|
|
|
|
In particular, if a vagina or penis is completely censored out, inpainting will be ineffective.
|
|
|
|
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.
|
|
|
|
# Dependencies
|
|
|
|
- Python 2
|
|
- TensorFlow 1.5
|
|
- Pillow
|
|
- tqdm
|
|
- scipy
|
|
- matplotlib (only for running test.py)
|
|
|
|
Tested on Ubuntu 16.04. Cannot run on Windows since Tensorflow on Windows is for Python 3.
|
|
|
|
Pillow, tqdm, and matplotlib can all be installed using pip.
|
|
|
|
# Model
|
|
The pretrained model can be downloaded from https://drive.google.com/open?id=1mWHYSj0LDSbJQQxjR4hUMykQkVve2U3Q.
|
|
|
|
Unzip the contents into the /models/ folder.
|
|
|
|
# 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
|
|
- Retrain for arbitrary shape censors
|
|
- 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). It also has a modified version of parosky's project [poissonblending](https://github.com/parosky/poissonblending).
|
|
|
|
---
|
|
|
|
Copyright (c) 2018 tadax, parosky, deeppomf
|
|
|
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
of this software and associated documentation files (the "Software"), to deal
|
|
in the Software without restriction, including without limitation the rights
|
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
copies of the Software, and to permit persons to whom the Software is
|
|
furnished to do so, subject to the following conditions:
|
|
|
|
The above copyright notice and this permission notice shall be included in all
|
|
copies or substantial portions of the Software.
|
|
|
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|
SOFTWARE.
|