amberwangyili/neurop-pytorch

A Pytorch Implementation of paper: "Neural Color Operators for Sequential Image Retouching", ECCV 2022

Neural Color Operators for Sequential Image Retouching (Pytorch Implementation)

Yili Wang, Xin Li, Kun Xu, Dongliang He, Qi Zhang, Fu Li, Errui Ding

[arXiv] [project] [doi]

Get Started

• Clone this repo

  git clone https://github.com/amberwangyili/neurop-pytorch
  

• Download the Dataset from 百度网盘 (code:jvvq) and unzip in project folder

  tree -L 2 neurop-pytorch/datasets
  # the output should be like the following:
  datasets/
  ├── dataset-dark
  │   ├── testA
  │   ├── testB
  │   ├── trainA
  │   └── trainB
  ├── dataset-init
  │   ├── BC
  │   ├── EX
  │   └── VB
  ├── dataset-lite
  │   ├── testA
  │   ├── testB
  │   ├── trainA
  │   └── trainB
  └── dataset-ppr
      ├── ppr-a
      ├── ppr-b
      ├── ppr-c
      ├── testA
      ├── testM
      ├── trainA
      └── trainM

• Install Dependencies

  cd neurop-pytorch/codes
  pip install -r requirements.txt 

Test

1. We provide pretrained model weights for MIT-Adobe FiveK and PPR10K in neurop-pytorch/pretrain_models/

2. Run command:

   python test.py -config configs/test/<configuaration-name>.yaml 

3. The evaluation results will be in the neurop-pytorch/results folder

Train

1. Initialization individual neural color operators:

   python train.py -config ./configs/init_neurop.yaml 

2. Finetune with strength predictors:

   python train.py -config ./configs/train/<configuration-name>.yaml