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[VIS2023✨] Large-Scale Data Embedding for Invertible Visualization

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# InvVis

This is the model code for [InvVis: Large-Scale Data Embedding for Invertible Visualization](https://arxiv.org/pdf/2307.16176.pdf).

## Pretrained Model

You can download our pretrained model  [here](https://drive.google.com/file/d/1VLlwfsqNCrCzwhcOHOxXeG4jUXL_m7WU/view?usp=sharing).

## Testing

You can use `test.py` to test your model.

The model checkpoint should be placed in `pretrained/` . The default checkpoint name is `DHN_4channel.pth`, you can modify this by changing the value of `pretrainedModelDir` in `config.yml` .

The test image should be placed in `data/test/`. Three images are expected for model test:

- `cover.png` : The cover image for data embedding, usually a visualization image.
- `data_image.png` : A 3-channel image, each channel of which is a data image generated with our **Data-to-Image (DTOI)** algorithm.
- `qr_image.png` : A QR Code image containing one or more QR Codes encoded with chart information.

More details are presented in our paper.

We have prepared some images in `data/test/` for a quick start.

Once the aboved mentioned data is prepared, you can test your model with:

```bash
python test.py
```

The result images can be found in `result/` .

## Training

You can also train the model with your own data.

The training data should be ordered like:

You can use more kinds of data image for training by modifying `dataloader.py` and `config.yml`.

Once the data is prepared, you can train your model with:

```bash
python train.py
```

The model checkpoints will be saved in `checkpoints/` .

## Citation

```bib
@article{ye2023invvis,
  title={InvVis: Large-scale data embedding for invertible visualization},
  author={Ye, Huayuan and Li, Chenhui and Li, Yang and Wang, Changbo},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2023},
  publisher={IEEE}
}
```

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