#[CVPR2020]Learning to Cartoonize Using White-box Cartoon Representations

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• Tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”.
• Improved method for facial images are now available:
• https://github.com/SystemErrorWang/FacialCartoonization

#Use cases

#Scenery

#Food

#Indoor Scenes

#People

#More Images Are Shown In The Supplementary Materials

#Online demo

• Some kind people made online demo for this project
• Demo link: https://cartoonize-lkqov62dia-de.a.run.app/cartoonize
• Code: https://github.com/experience-ml/cartoonize
• Sample Demo: https://www.youtube.com/watch?v=GqduSLcmhto&feature=emb_title

#Prerequisites

• Training code: Linux or Windows
• NVIDIA GPU + CUDA CuDNN for performance
• Inference code: Linux, Windows and MacOS

#How To Use

#Installation

• Assume you already have NVIDIA GPU and CUDA CuDNN installed
• Install tensorflow-gpu, we tested 1.12.0 and 1.13.0rc0
• Install scikit-image==0.14.5, other versions may cause problems

#Docker

You can run the cartoonization using Docker without installing any dependencies locally.

#Build the Docker image

docker build --platform linux/amd64 -t whitebox-cartoonization:latest .

#Run with default test images

docker run --rm --platform linux/amd64 whitebox-cartoonization:latest

#Run with custom images

Mount your input and output directories as volumes:

docker run --rm --platform linux/amd64 \
  -v /path/to/your/images:/app/test_code/test_images \
  -v /path/to/output:/app/test_code/cartoonized_images \
  whitebox-cartoonization:latest

#Docker environment details

• Base image: tensorflow/tensorflow:1.15.5-py3 (Ubuntu 18.04)
• Python: 3.6
• TensorFlow: 1.15.5
• Platform: linux/amd64 (works on Apple Silicon via emulation)

#Inference with Pre-trained Model

• Store test images in /test_code/test_images
• Run /test_code/cartoonize.py
• Results will be saved in /test_code/cartoonized_images

#Train

• Place your training data in corresponding folders in /dataset
• Run pretrain.py, results will be saved in /pretrain folder
• Run train.py, results will be saved in /train_cartoon folder
• Codes are cleaned from production environment and untested
• There may be minor problems but should be easy to resolve
• Pretrained VGG_19 model can be found at following url: https://drive.google.com/file/d/1j0jDENjdwxCDb36meP6-u5xDBzmKBOjJ/view?usp=sharing

#Datasets

• Due to copyright issues, we cannot provide cartoon images used for training
• However, these training datasets are easy to prepare
• Scenery images are collected from Shinkai Makoto, Miyazaki Hayao and Hosoda Mamoru films
• Clip films into frames and random crop and resize to 256x256
• Portrait images are from Kyoto animations and PA Works
• We use this repo(https://github.com/nagadomi/lbpcascade_animeface) to detect facial areas
• Manual data cleaning will greatly increace both datasets quality

#Acknowledgement

We are grateful for the help from Lvmin Zhang and Style2Paints Research

#License

• Copyright (C) Xinrui Wang All rights reserved. Licensed under the CC BY-NC-SA 4.0
• license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
• Commercial application is prohibited, please remain this license if you clone this repo

#Citation

If you use this code for your research, please cite our paper:

@InProceedings{Wang_2020_CVPR, author = {Wang, Xinrui and Yu, Jinze}, title = {Learning to Cartoonize Using White-Box Cartoon Representations}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} }

#中文社区

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