README.md

Paints Chainer

Paints Chainer is line drawing colorizer using chainer. Using CNN, you can colorize your scketch automatically / semi-automatically .

Requirement

If not specified, versions are assumed to be recent LTS version.

• A Nvidia graphic card supporting CUDA (6.5, 7.0, 7.5, 8.0)
• Linux: gcc/ g++ 4.8
• Windows: "Microsoft Visual C++ Build Tools 2015" (NOT "Microsoft Visual Studio Community 2015")
• Python 3 (3.5 recommended) (Python 2.7 needs modifying web host (at least) )
• Numpy
• openCV "cv2" (Python 3 support possible, see installation guide)
• Chainer
• CUDA / cuDNN (If you use GPU)

Installation Guide

Option: Docker user

If you have docker, you may check https://hub.docker.com/r/liamjones/paintschainer-docker/ (not supported officially but thanks for volunteering)

Option: Fresh install

If not specified, follow instruction from their official website.

• Chainer/ Linux gcc: See http://docs.chainer.org/en/stable/install.html

• Microsoft Visual C++ Build Tools 2015 (Windows): See http://landinghub.visualstudio.com/visual-cpp-build-tools

• Python: See https://www.python.org/downloads/

• Numpy: pip install numpy. Check installed version after that.

• openCV (Windows): See https://www.solarianprogrammer.com/2016/09/17/install-opencv-3-with-python-3-on-windows/ (Pre-built libraries)

• openCV (Linux): See http://stackoverflow.com/questions/20953273/install-opencv-for-python-3-3 (Build from source)

• openCV (Anaconda): conda install -c menpo opencv3 (Pre-built libraries)

• Step by step guide for installing chainer (Windows): (Tested on Win10 64-bit, python 3.5)

• Step1: Install "Microsoft Visual C++ Build Tools". Uninstall "Visual Studio 2015" if you have it.

• Step2: Install "Nvidia CUDA"

• Step3: Register and download "NVIDIA Deep Learning SDK"

• Step4: pip install chainer --no-cache-dir -vvvv (<- Do this AT LAST!)

If you failed to perform the following steps, you will see this message. Uninstall chainer and install it again.

 Running command python setup.py egg_info
    Options: {'profile': False, 'annotate': False, 'linetrace': False, 'no_cuda': False}
    Include directories: ['C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\include']
    Library directories: ['C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\bin', 'C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64']
    Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools

Starting web host

UI is html based. using wPaint.js Server side is very basic python server

boot local server python server.py

access to localhost localhost:8000/

Learning

main code of colorization is in cgi-bin/paint_x2_unet

to train 1st layer using GPU 0 python train_128.py -g 0 to train 2nd layer using GPU 0 python train_x2.py -g 0

DEMO

http://paintschainer.preferred.tech/

License

Source code : MIT License

Pre-trained Model : All Rights Reserved

Pre-Trained Models

Download following model files to cgi-bin/paint_x2_unet/models/

http://paintschainer.preferred.tech/downloads/

(Copyright 2017 Taizan Yonetsuji All Rights Reserved.)

Acknowledgements

This project is powered by Preferred Networks.

Thanks a lot for rezoolab, mattya, okuta, ofk . This project could not be achived without their great support.

Line drawing of top image is by ioiori18.