# ReversibleHalftoning

**Repository Path**: zs-derrick/ReversibleHalftoning

## Basic Information

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

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-05-21
- **Last Updated**: 2024-05-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep Halftoning with Reversible Binary Pattern
### [ICCV Paper](https://openaccess.thecvf.com/content/ICCV2021/html/Xia_Deep_Halftoning_With_Reversible_Binary_Pattern_ICCV_2021_paper.html) | [Project Website](https://www.cse.cuhk.edu.hk/~ttwong/papers/invhalftone/invhalftone.html) | [BibTex](#citation) 
<a href="https://replicate.ai/menghanxia/reversiblehalftoning"><img src="https://img.shields.io/static/v1?label=Replicate&message=Demo and Docker Image&color=blue"></a> 
[![Huggingface ReversibleHalftoning](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/menghanxia/ReversibleHalftoning)

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## Overview
Existing halftoning algorithms usually drop colors and fine details when dithering color images with binary dot patterns, which makes it extremely difficult to recover the original information. To dispense the recovery trouble in future, we propose a novel halftoning technique that dithers a color image into binary halftone with decent restorability to the original input. The key idea is to implicitly embed those previously dropped information into the binary dot patterns. So, the halftone pattern not only serves to reproduce the image tone, maintain the blue-noise randomness, but also represents the color information and fine details.

<div align="center">
	<img src="examples/demo.JPG" width="95%">
</div>


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## Run 
1. **Requirements**:
    * Basic variant infomation: Python 3.7 and Pytorch 1.0.1.
    * Create a virutal environment with satisfied requirements:
      ```bash
      conda env create -f requirement.yaml
      ```

2. **Training**:
    * Place your training set/validation set under `dataset/` per the exampled file organization.
    * Warm-up stage (optional):
	  Pre-download the checkpoint of feature extractor [Here](https://drive.google.com/file/d/1IStui2DK9BssFrW0s2xKdqTjiDVERmPh/view?usp=sharing).
	  ```bash
	  python train_warm.py --config scripts/invhalf_warm.json
	  ```
	  If this stage skipped, please download the [pretrained warm-up weight](https://drive.google.com/file/d/1GMRkqeNJtygoZsPy2toibuoZpcWhep6h/view?usp=sharing) and place it in `checkpoints/`, which is required at joint-train stage.
    * Joint-train stage:
	  ```bash
	  python train.py --config scripts/invhalf_full.json
	  ```

3. **Testing**:
	* Download the [pretrained weight](https://drive.google.com/file/d/1kw-FoS8lF_tgdiCkGG51UaUtmCcFvKiD/view?usp=sharing) below and put it under `checkpoints/`.
    * Place your images in any accesible directory, e.g. `test_imgs/`.
    * [Halftoning]: Dither the input images into binary halftones
	  ```bash
	  python inference.py --model checkpoints/model_best.pth.tar --data_dir ./test_imgs --save_dir ./result
	  ```
	* [Restoration]: Restore the generated halftone back to RGB images
	  ```bash
	  python inference.py --model checkpoints/model_best.pth.tar --data_dir ./test_imgs --save_dir ./result --decoding
	  ```

## Copyright and License
You are granted with the [LICENSE](./LICENSE) for both academic and commercial usages.

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## Citation
If any part of our paper and code is helpful to your work, please generously cite with:
```
@inproceedings{xia-2021-inverthalf,
	author   = {Menghan Xia and Wenbo Hu and Xueting Liu and Tien-Tsin Wong},
	title    = {Deep Halftoning with Reversible Binary Pattern},
	booktitle = {{IEEE/CVF} International Conference on Computer Vision (ICCV)},
	year = {2021}
}
```