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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!

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

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

You can simply try to remove the mosaic on the face at this website.

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

• [Help document]
  
• Video tutorial => [youtube] [bilibili]

Attentions:

• Requires Windows_x86_64, Windows10 is better.
  
• Different pre-trained models are suitable for different effects.[Introduction to pre-trained models]
  
• 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.
  
• GUI version updates slower than source.
  

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

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

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

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]

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

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')
  

python deepmosaic.py --media_path ./result/ruoruo_add.jpg --model_path ./pretrained_models/mosaic/clean_face_HD.pth --gpu_id 0

If you want to test other images or videos, please refer to this file.
[options_introduction.md]

If you want to train with your own dataset, please refer to training_with_your_own_dataset.md

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