/HDNet

This is the implementation of paper 'Hierarchical Dynamic Image Harmonization' (ACM MM'2023, Oral).

Primary LanguagePythonMIT LicenseMIT

Hierarchical Dynamic Image Harmonization

PWC

PWC

This is the official code of the ACM MM'23 oral paper: Hierarchical Dynamic Image Harmonization.

Hierarchical Dynamic Image Harmonization
Haoxing Chen, Zhangxuan Gu, Yaohui Li, Jun Lan, Changhua Meng, Weiqiang Wang, Huaxiong Li, ACM Multimedia 2023

Preparation

1. Clone this repo:

git clone https://github.com/chenhaoxing/HDNet
cd HDNet

2. Requirements

  • Both Linux and Windows are supported, but Linux is recommended for compatibility reasons.
  • We have tested on PyTorch 1.8.1+cu11.

install the required packages using pip:

pip3 install -r requirements.txt

or conda:

conda create -n rainnet python=3.8
conda activate rainnet
pip install -r requirements.txt

3. Prepare the data

Download iHarmony4 dataset in dataset folder and run data/preprocess_iharmony4.py to resize the images (eg, 512x512, or 256x256) and save the resized images in your local device.

Training and validation

We provide the code in train_evaluate.py, which supports the model training, evaluation and results saving in iHarmony4 dataset.

python train_evaluate.py --dataset_root <DATA_DIR> --save_dir results --batch_size 12 --device cuda 

Results

Citing HDNet

If you use HDNet in your research, please use the following BibTeX entry.

@inproceedings{MM23_HDNet,
      title={Hierarchical Dynamic Image Harmonization},
      author={Chen, Haoxing and Gu, Zhangxuan and Yaohui Li and Lan, Jun and Meng, Changhua and Wang, Weiqiang and Li, Huaxiong},
      booktitle={ACM Multimedia},
      year={2023}
}

Acknowledgement

Many thanks to the nice work of RainNet. Our codes and configs follow RainNet.

Contacts

Please feel free to contact us if you have any problems.

Email: haoxingchen@smail.nju.edu.cn or hx.chen@hotmail.com