# DeepMosaics
**English | [中文](./README_CN.md)**
You can use it to automatically remove the mosaics in images and videos, or add mosaics to them.
This project is based on "semantic segmentation" and "Image-to-Image Translation".
Try it at this [website](http://118.89.27.46:5000/)!
### Examples
origin | auto add mosaic | auto clean mosaic
:-:|:-:|:-:
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* Compared with [DeepCreamPy](https://github.com/deeppomf/DeepCreamPy)
mosaic image | DeepCreamPy | ours
:-:|:-:|:-:
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* Style Transfer
origin | to Van Gogh | to winter
:-:|:-:|:-:
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An interesting example:[Ricardo Milos to cat](https://www.bilibili.com/video/BV1Q7411W7n6)
## Run DeepMosaics
You can either run DeepMosaics via a pre-built binary package, or from source.
### Try it on web
You can simply try to remove the mosaic on the **face** at this [website](http://118.89.27.46:5000/).
### Pre-built binary package
For Windows, we bulid a GUI version for easy testing.
Download this version, and a pre-trained model via [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs) [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ)
* [[Help document]](./docs/exe_help.md)
* Video tutorial => [[youtube]](https://www.youtube.com/watch?v=1kEmYawJ_vk) [[bilibili]](https://www.bilibili.com/video/BV1QK4y1a7Av)
Attentions:
- Requires Windows_x86_64, Windows10 is better.
- Different pre-trained models are suitable for different effects.[[Introduction to pre-trained models]](./docs/pre-trained_models_introduction.md)
- Run time depends on computers performance (GPU version has better performance but requires CUDA to be installed).
- If output video cannot be played, you can try with [potplayer](https://daumpotplayer.com/download/).
- GUI version updates slower than source.
### Run From Source
#### Prerequisites
- Linux, Mac OS, Windows
- Python 3.6+
- [ffmpeg 3.4.6](http://ffmpeg.org/)
- [Pytorch 1.0+](https://pytorch.org/)
- CPU or NVIDIA GPU + CUDA CuDNN
#### Dependencies
This code depends on opencv-python, torchvision available via pip install.
#### Clone this repo
```bash
git clone https://github.com/HypoX64/DeepMosaics.git
cd DeepMosaics
```
#### Get Pre-Trained Models
You can download pre_trained models and put them into './pretrained_models'.
[[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs) [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ)
[[Introduction to pre-trained models]](./docs/pre-trained_models_introduction.md)
#### Simple Example
* Add Mosaic (output media will save in './result')
```bash
python deepmosaic.py --media_path ./imgs/ruoruo.jpg --model_path ./pretrained_models/mosaic/add_face.pth --gpu_id 0
```
* Clean Mosaic (output media will save in './result')
```bash
python deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --gpu_id 0
```
#### More Parameters
If you want to test other images or videos, please refer to this file.
[[options_introduction.md]](./docs/options_introduction.md)
## Training With Your Own Dataset
If you want to train with your own dataset, please refer to [training_with_your_own_dataset.md](./docs/training_with_your_own_dataset.md)
## Acknowledgements
This code borrows heavily from [[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet) [[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD) [[BiSeNet]](https://github.com/ooooverflow/BiSeNet) [[DFDNet]](https://github.com/csxmli2016/DFDNet) [[GFRNet_pytorch_new]](https://github.com/sonack/GFRNet_pytorch_new).
