# HypergraphsImageInpainting

**Repository Path**: zyabo/HypergraphsImageInpainting

## Basic Information

- **Project Name**: HypergraphsImageInpainting
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-17
- **Last Updated**: 2021-02-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Hyperrealistic Image Inpainting with Hypergraphs

This repository contains the implmentation Image Inpainting method proposed in the paper

Gourav Wadhwa, Abhinav Dhall, Subrahmanyam Murala, and Usman Tariq, Hyperrealistic Image Inpainting with Hypergraphs.In IEEE Winter Conference on Computer Vision (WACV), 2021.

[Paper](https://openaccess.thecvf.com/content/WACV2021/papers/Wadhwa_Hyperrealistic_Image_Inpainting_With_Hypergraphs_WACV_2021_paper.pdf) | [Supplementary Material](https://openaccess.thecvf.com/content/WACV2021/supplemental/Wadhwa_Hyperrealistic_Image_Inpainting_WACV_2021_supplemental.pdf) | [BibTex](#reference)

<img src="./Examples/Teaser.png" width="100%" alt="Teaser">
<img src="./Examples/demo.gif" width="40%" alt="demo">

## Dependencies
* Python 3.5 or higher
* Tensorflow 2.x (tested on 2.0.0, 2.1.0, 2.2.0)
* Numpy
* Pillow
* Matplotlib
* tqdm
* OpenCV
* Scipy

## Our Framework

We use a two stage coarse-to-refine network for the task of image inpainting

<img src="./Examples/network.png" width="100%" alt="network">

## Hypergraph Layer 
<img src="./Examples/hypergraph_layer.png" width="60%" alt="hypergraph_layer">

## Installation

```bash
git clone https://github.com/GouravWadhwa/Hypergraphs-Image-Inpainting.git
cd Hypergraphs-Image-Inpainting
```

## Testing
Download the pretrained models from the following links
* CelebA-HQ ([Center Mask](https://drive.google.com/drive/folders/1sS3umEYN-wK-srVwcyYUD_AcXbqBEPd-?usp=sharing), [Random Mask](https://drive.google.com/drive/folders/1_BHYB5f-VwdOAB1q2NutrzPGecjWQz1p?usp=sharing))
* Places2 ([Center Mask](https://drive.google.com/drive/folders/1T7uLBwXHRKJWUHVNACkYutvpAyDlbJdD?usp=sharing), [Random Mask](https://drive.google.com/drive/folders/1dk1zSm1FxZVaafOtvoud8aAdZ6Ubs4oU?usp=sharing))
* Paris Street View ([Center Mask](https://drive.google.com/drive/folders/1KHnXT-QmfDkpy2tgo3faBAt6it-9ZUsU?usp=sharing), [Random Mask](https://drive.google.com/drive/folders/1ecrjYXCL8FHnfZ-JMRVCUluuvvkuNGTK?usp=sharing))
* Facades Dataset ([Center Mask](https://drive.google.com/drive/folders/1kf5SacEoBpr9li4F7Wwnj2XiX_uQwhRr?usp=sharing), [Random Mask](https://drive.google.com/drive/folders/1uJWJjWn7RXEhVwUYZycnv4IZzUaHomAW?usp=sharing))

Put the checkpoints in the folder `pretrained_models/`. To test images in a folder, specify the path to the folder using `--test_dir` and specify the model to be loaded using `--checkpoint_prefix`.

For example (for CelebA-HQ dataset on Random Mask) :

```bash
python test.py --dataset celeba-hq --pretrained_model_dir pretrained_models/ --checkpoint_prefix celeba_hq_256x256_random_mask --random_mask 1 --test_dir [Testing Folder Path]
```

Note - For all predicted images, 1st image represent the input image, 2nd represent the ground truth, 3rd represents the coarse network output and the final image is our final prediction.

You can use `evaluate.py` to determine SSIM and PSNR of the predicted images. 

## Reference

If you find this work useful or gives you some insights, please cite:
```
@InProceedings{Wadhwa_2021_WACV,
    author={Wadhwa, Gourav and Dhall, Abhinav and Murala, Subrahmanyam and Tariq, Usman},
    title={Hyperrealistic Image Inpainting With Hypergraphs},
    booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month={January},
    year={2021},
    pages={3912-3921}
}
```
## Contact

For any further queries please contact at 2017eeb1206@iitrpr.ac.in 
(The code is under development.)