DeepMosaics

You can use it to automatically remove the mosaics in images and videos, or add mosaics to them.
This porject based on "semantic segmentation" and "Image-to-Image Translation".

• 中文版README
  

More example

origin	auto add mosaic	auto clean mosaic

• Compared with DeepCreamPy

mosaic image	DeepCreamPy	ours

• Style Transfer

origin	to Van Gogh	to winter

An interesting example:Ricardo Milos to cat

Run DeepMosaics

You can either run DeepMosaics via pre-built binary package or from source.

Pre-built binary package

For windows, we bulid a GUI version for easy test.
Download this version and pre-trained model via [Google Drive] [百度云,提取码1x0a]

• [How to use]
  

Attentions:

• Require Windows_x86_64, Windows10 is better.
  
• Different pre-trained models are suitable for different effects.[Introduction to pre-trained models]
  
• Run time depends on computer performance(The current version does not support gpu, if you need to use gpu please run source).
  
• If output video cannot be played, you can try with potplayer.
  
• GUI version update slower than source.
  

Run from source

Prerequisites

• Linux, Mac OS, Windows
• Python 3.6+
• ffmpeg 3.4.6
• Pytorch 1.0+
• CPU or NVIDIA GPU + CUDA CuDNN
  

Dependencies

This code depends on opencv-python, torchvision available via pip install.

Clone this repo

git clone https://github.com/HypoX64/DeepMosaics
cd DeepMosaics

Get pre-trained models

You can download pre_trained models and put them into './pretrained_models'.
[Google Drive] [百度云,提取码1x0a]
[Introduction to pre-trained models]

Simple example

• Add Mosaic (output media will save in './result')
  

python3 deepmosaic.py --media_path ./imgs/ruoruo.jpg --model_path ./pretrained_models/mosaic/add_face.pth --use_gpu 0

• Clean Mosaic (output media will save in './result')
  

python3 deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --use_gpu 0

More parameters

If you want to test other image or video, please refer to this file.
[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

Acknowledgments

This code borrows heavily from [pytorch-CycleGAN-and-pix2pix] [Pytorch-UNet] [pix2pixHD] [BiSeNet].