feat: add ArcNet & modify frontend

# Restoration-of-Cataract-Images-via-Domain-Adaptation

There is little access to large datasets of cataract images paired with their corresponding clear ones. Therefore, it is unlikely to build a restoration model for cataract images through supervised learning.

Here, we propose an unsupervised restoration method via cataract-like image simulation and domain adaptation, and an annotation-free restoration network for cataractous fundus images. The source code for both has been released.

## Results

Li H ,  Liu H ,  Hu Y , et al. Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation[C]// 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021.

**Result：**

![Output](images/Output.png)

A comparison of the restored fundus images. (a) cataract image. (b) clear fundus image after surgery. (c) dark channel prior. (d) SGRIF [2]. (e) pix2pix [4]. (f) CycleGAN [5]. (g) the proposed method [8].

Li H, Liu H, Hu Y, et al. An Annotation-free Restoration Network for Cataractous Fundus Images[J]. IEEE Transactions on Medical Imaging, 2022.

![arcnet](./images/arcnet.png)

Visual comparison of images restored from cataract ones. (a) cataract image. (b) clear fundus image after surgery. (c) Mitra et al. [1]. (d)SGRIF [2]. (e) Cao et al. [3]. (f) pix2pix [4]. (g) CycleGAN [5]. (h) Luo et al. [6]. (i) CofeNet [7]. (j) ArcNet [10].

# Prerequisites

\- Win10

\- Python 3

\- CPU or NVIDIA GPU + CUDA CuDNN

# Environment (Using conda)

```

conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing opencv-python

conda install pytorch torchvision -c pytorch # add cuda90 if CUDA 9

conda install visdom dominate -c conda-forge # install visdom and dominate

```

# Simulate cataract-like images

Use the script in ./utils/catacact_simulation.py

# Visualization when training

python -m visdom.server

# To open this link in the browser

http://localhost:8097/

# Dataset preparation

To set up your own dataset constructed like images/cataract_dataset. Note that the number of source images should be bigger than the number of target images, or you can design you own data loader.

## Trained model's weight

For the model of "Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation", please download the pretrained model from this link:

https://drive.google.com/file/d/1Ystqt3RQVfIPPukE7ZdzzFM_hBqB0lr0/view?usp=sharing

or use link: https://pan.baidu.com/s/1Ax18-10dpJDToieqvcXGxQ , code: ak7c

Then, place the document in project_root/checkpoints/pixDA_sobel, so that we can get the file like project_root/checkpoints/cataract_model/latest_net_G.pth

For the model of "An Annotation-free Restoration Network for Cataractous Fundus Images", please download the pretrained model from this link:

https://drive.google.com/file/d/1VJ-_W7rRmy90AcgeAJtt_z7fgeBpC4Id/view?usp=share_link

or use link: https://pan.baidu.com/s/1hFt0bMpBb5V0Gj0ogYHGbA , code: 3xg0

Then, place the document in project_root/checkpoints/arcnet, so that we can get the file like project_root/checkpoints/arcnet/latest_net_G.pth

# Command to run

Please note that root directory is the project root directory.

## Train

```

python train.py --dataroot ./datasets/cataract_dataset --name train_pixDA_sobel --model pixDA_sobel --netG unet_256 --direction AtoB --dataset_mode cataract --norm batch --batch_size 8 --n_epochs 150 --n_epochs_decay 50 --input_nc 6 --output_nc 3

```

or

```

python train.py --dataroot ./images/cataract_dataset --name train_arcnet --model arcnet --netG unet_256 --input_nc 6 --direction AtoB --dataset_mode cataract_guide_padding --norm batch --batch_size 8 --lr_policy step --n_epochs 100 --n_epochs_decay 0 --lr_decay_iters 80

```

## Test & Visualization

```

python test.py --dataroot ./datasets/cataract_dataset --name pixDA_sobel --model pixDA_sobel --netG unet_256 --direction AtoB --dataset_mode cataract --norm batch --input_nc 6 --output_nc 3

```

or

```

python test.py --dataroot ./images/cataract_dataset --name arcnet --model arcnet --netG unet_256 --input_nc 6 --direction AtoB --dataset_mode cataract_guide_padding --norm batch

```

# Reference

[1] A. Mitra, S. Roy, S. Roy, and S. K. Setua, “Enhancement and restoration of non-uniform illuminated fundus image of retina obtained through thin layer of cataract,” Computer methods and programs in biomedicine, vol. 156, pp. 169–178, 2018.

[2] Cheng J ,  Li Z ,  Gu Z , et al. Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING MI, 2018.

[3] L. Cao, H. Li, and Y. Zhang, “Retinal image enhancement using lowpass filtering and α-rooting,” Signal Processing, vol. 170, p. 107445, 2020.

[4] Isola P ,  Zhu J Y ,  Zhou T , et al. Image-to-Image Translation with Conditional Adversarial Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.

[5] Zhu J Y ,  Park T ,  Isola P , et al. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks[J]. IEEE, 2017.

[6] Luo Y ,  K  Chen,  Liu L , et al. Dehaze of Cataractous Retinal Images Using an Unpaired Generative Adversarial Network[J]. IEEE Journal of Biomedical and Health Informatics, 2020, PP(99):1-1.

[7] Z. Shen, H. Fu, J. Shen, and L. Shao, “Modeling and enhancing lowquality retinal fundus images,” IEEE transactions on medical imaging, vol. 40, no. 3, pp. 996–1006, 2020.

[8] Li H ,  Liu H ,  Hu Y , et al. Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation[C]// 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021.

[9] Li H, Liu H, Hu Y, et al. An Annotation-free Restoration Network for Cataractous Fundus Images[J]. IEEE Transactions on Medical Imaging, 2022.

# Citation

```

@article{li2022annotation,

  title={An Annotation-free Restoration Network for Cataractous Fundus Images},

  author={Li, Heng and Liu, Haofeng and Hu, Yan and Fu, Huazhu and Zhao, Yitian and Miao, Hanpei and Liu, Jiang},

  journal={IEEE Transactions on Medical Imaging},

  year={2022},

  publisher={IEEE}

}

@inproceedings{li2021restoration,

  title={Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation},

  author={Li, Heng and Liu, Haofeng and Hu, Yan and Higashita, Risa and Zhao, Yitian and Qi, Hong and Liu, Jiang},

  booktitle={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},

  pages={516--520},

  year={2021},

  organization={IEEE}

}

```

 No newline at end of file

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            opt.name = opt.name + suffix

        self.print_options(opt)

        #self.print_options(opt)

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        str_ids = opt.gpu_ids.split(',')

        #str_ids = opt.gpu_ids.split(',')

        opt.gpu_ids = []

        for str_id in str_ids:

            id = int(str_id)

            if id >= 0:

                opt.gpu_ids.append(id)

        if len(opt.gpu_ids) > 0:

            torch.cuda.set_device(opt.gpu_ids[0])

        # for str_id in str_ids:

        #     id = int(str_id)

        #     if id >= 0:

        #         opt.gpu_ids.append(id)

        # if len(opt.gpu_ids) > 0:

        #     torch.cuda.set_device(opt.gpu_ids[0])

        self.opt = opt

        return self.opt

torch>=0.4.1

torchvision>=0.2.1

dominate>=2.3.1

visdom>=0.1.8.3