Image Super-Resolution for Anime-Style Art
fork from : https://github.com/nagadomi/waifu2x.git

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README.md

dev branch

This branch is work in progress.

waifu2x

Image Super-Resolution for anime-style-art using Deep Convolutional Neural Networks.

Demo-Application can be found at http://waifu2x.udp.jp/ .

Summary

Click to see the slide show.

slide

References

waifu2x is inspired by SRCNN [1]. 2D character picture (HatsuneMiku) is licensed under CC BY-NC by piapro [2].

Public AMI

AMI ID: ami-0be01e4f
AMI NAME: waifu2x-server
Instance Type: g2.2xlarge
Region: US West (N.California)
OS: Ubuntu 14.04
User: ubuntu
Created at: 2015-08-12

Third Party Software

Third-Party

Dependencies

Hardware

  • NVIDIA GPU

Platform

lualocks packages (excludes torch7's default packages)

  • lua-csnappy
  • md5
  • uuid
  • turbo

Installation

Setting Up the Command Line Tool Environment

(on Ubuntu 14.04)

Install CUDA

See: NVIDIA CUDA Getting Started Guide for Linux

Download CUDA

sudo dpkg -i cuda-repo-ubuntu1404_7.0-28_amd64.deb
sudo apt-get update
sudo apt-get install cuda

Install Package

sudo apt-get install libsnappy-dev

Install Torch7

See: Getting started with Torch

And install luarocks packages.

luarocks install lua-csnappy
luarocks install md5
luarocks install uuid
PREFIX=$HOME/torch/install luarocks install turbo # if you need web application
``

#### Validation

Test the waifu2x command line tool.

th waifu2x.lua


## Web Application

th web.lua


View at: http://localhost:8812/

## Command line tools

### Noise Reduction

th waifu2x.lua -m noise -noise_level 1 -i input_image.png -o output_image.png

th waifu2x.lua -m noise -noise_level 2 -i input_image.png -o output_image.png


### 2x Upscaling

th waifu2x.lua -m scale -i input_image.png -o output_image.png


### Noise Reduction + 2x Upscaling

th waifu2x.lua -m noise_scale -noise_level 1 -i input_image.png -o output_image.png

th waifu2x.lua -m noise_scale -noise_level 2 -i input_image.png -o output_image.png


See also `images/gen.sh`.

### Video Encoding

\* `avconv` is `ffmpeg` on Ubuntu 14.04.

Extracting images and audio from a video. (range: 00:09:00 ~ 00:12:00)

mkdir frames avconv -i data/raw.avi -ss 00:09:00 -t 00:03:00 -r 24 -f image2 frames/%06d.png avconv -i data/raw.avi -ss 00:09:00 -t 00:03:00 audio.mp3


Generating a image list.

find ./frames -name "*.png" |sort > data/frame.txt


waifu2x (for example, noise reduction)

mkdir new_frames th waifu2x.lua -m noise -noise_level 1 -resume 1 -l data/frame.txt -o new_frames/%d.png


Generating a video from waifu2xed images and audio.

avconv -f image2 -r 24 -i new_frames/%d.png -i audio.mp3 -r 24 -vcodec libx264 -crf 16 video.mp4


## Training Your Own Model

### Data Preparation

Genrating a file list.

find /path/to/image/dir -name "*.png" > data/image_list.txt

(You should use PNG! In my case, waifu2x is trained with 3000 high-resolution-noise-free-PNG images.)

Converting training data.

th convert_data.lua


### Training a Noise Reduction(level1) model

mkdir models/my_model th train.lua -model_dir models/my_model -method noise -noise_level 1 -test images/miku_noisy.png th cleanup_model.lua -model models/my_model/noise1_model.t7 -oformat ascii

usage

th waifu2x.lua -model_dir models/my_model -m noise -noise_level 1 -i images/miku_noisy.png -o output.png

You can check the performance of model with `models/my_model/noise1_best.png`.

### Training a Noise Reduction(level2) model

th train.lua -model_dir models/my_model -method noise -noise_level 2 -test images/miku_noisy.png th cleanup_model.lua -model models/my_model/noise2_model.t7 -oformat ascii

usage

th waifu2x.lua -model_dir models/my_model -m noise -noise_level 2 -i images/miku_noisy.png -o output.png

You can check the performance of model with `models/my_model/noise2_best.png`.

### Training a 2x UpScaling model

th train.lua -model_dir models/my_model -method scale -scale 2 -test images/miku_small.png th cleanup_model.lua -model models/my_model/scale2.0x_model.t7 -oformat ascii

usage

th waifu2x.lua -model_dir models/my_model -m scale -scale 2 -i images/miku_small.png -o output.png `` You can check the performance of model withmodels/my_model/scale2.0x_best.png`.