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README.md

image

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".

More example

origin auto add mosaic auto clean mosaic
image image image
image image image
mosaic image DeepCreamPy ours
image image image
image image image
  • Style Transfer
origin to Van Gogh to winter
image image image

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]

image
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

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].

项目简介

使用深度学习方法去掉图片或视频中的马赛克

发行版本

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贡献者 2

HypoX64 @weixin_36721459
H HypoX64 @HypoX64

开发语言

  • Python 100.0 %