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 -1

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 -1
```
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].

DeepMosaics

More example

Run DeepMosaics

Pre-built binary package

Run from source

Prerequisites

Dependencies

Clone this repo

Get pre-trained models

Simple example

More parameters

Training with your own dataset

Acknowledgments