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https://github.com/hukkelas/deep_privacy2
DeepPrivacy2 - A Toolbox for Realistic Image Anonymization
https://github.com/hukkelas/deep_privacy2
Last synced: 5 days ago
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DeepPrivacy2 - A Toolbox for Realistic Image Anonymization
- Host: GitHub
- URL: https://github.com/hukkelas/deep_privacy2
- Owner: hukkelas
- License: apache-2.0
- Created: 2022-08-26T11:14:21.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-01-28T09:59:18.000Z (12 months ago)
- Last Synced: 2024-12-31T05:12:16.751Z (12 days ago)
- Language: Python
- Homepage:
- Size: 13.4 MB
- Stars: 310
- Watchers: 6
- Forks: 35
- Open Issues: 11
-
Metadata Files:
- Readme: readme.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- awesome-MLSecOps - DeepPrivacy2
README
# DeepPrivacy2 - A Toolbox for Realistic Image Anonymization
[[Paper]](https://openaccess.thecvf.com/content/WACV2023/papers/Hukkelas_DeepPrivacy2_Towards_Realistic_Full-Body_Anonymization_WACV_2023_paper.pdf) [[Appendix]](https://openaccess.thecvf.com/content/WACV2023/supplemental/Hukkelas_DeepPrivacy2_Towards_Realistic_WACV_2023_supplemental.pdf)
[[Video Demo]](https://youtu.be/Kt3au719hhk)
[[Documentation]](http://hukkelas.no/deep_privacy2/)
[](https://huggingface.co/spaces/haakohu/deep_privacy2)
DeepPrivacy2 is a toolbox for realistic anonymization of humans, including a face and a full-body anonymizer.
DeepPrivacy first detects, then recursively anonymization all individuals in an image with a Generative Adversarial Network (GAN) that synthesizes one individual at a time.
## Published Papers
This repository provide source code for the following papers
- [[PDF]](https://openaccess.thecvf.com/content/WACV2023/papers/Hukkelas_DeepPrivacy2_Towards_Realistic_Full-Body_Anonymization_WACV_2023_paper.pdf) [[Appendix]](https://openaccess.thecvf.com/content/WACV2023/supplemental/Hukkelas_DeepPrivacy2_Towards_Realistic_WACV_2023_supplemental.pdf) [[Conference Presentation]](https://youtu.be/wwKRkkzxKuM) DeepPrivacy2: Towards Realistic Full-Body Anonymization. Published at WACV 2023.
- [[PDF]](https://openaccess.thecvf.com/content/CVPR2023W/WAD/papers/Hukkelas_Does_Image_Anonymization_Impact_Computer_Vision_Training_CVPRW_2023_paper.pdf) [[Appendix]](https://openaccess.thecvf.com/content/CVPR2023W/WAD/supplemental/Hukkelas_Does_Image_Anonymization_CVPRW_2023_supplemental.pdf) Does Image Anonymization Impact Computer Vision Training? Published at CVPR Workshop on Autonomous Driving 2023.
- [[PDF]](https://arxiv.org/pdf/2304.03164) [[Appendix]](https://api.loke.aws.unit.no/dlr-gui-backend-resources-content/v2/contents/links/09a4db16-7c09-4de5-890d-60ad608813b9ed8ba261-2b76-499d-ad50-f5351a5518e54be5b525-0280-4d04-97bd-8a701e3e5f42) Synthesizing Anyone, Anywhere, in Any Pose.
## DeepPrivacy2 vs [DeepPrivacy1](https://github.com/hukkelas/DeepPrivacy)
This repository improves over the original [DeepPrivacy](https://github.com/hukkelas/DeepPrivacy) repository with the following new features:
- **Full-body anonymization:** Anonymize the entire human body with a single generator
- **Improved Face Anonymization:** Improved quality and higher resolution (256x256 vs. 128x128) face anonymization without relying on facial landmark detection.
- **Attribute Guided Anonymiation:** Anonymize faces guided on text prompts using [StyleMC](https://github.com/catlab-team/stylemc) - [[Video Demo]](https://www.youtube.com/watch?v=faoNyaaORts).
- **Code cleanup and general improvements:** Extensive refactoring, bugfixes, and improvements yielding improved results and faster training.
## Useful Links
- [Installation instructions](https://www.hukkelas.no/deep_privacy2/#/README).
- [Anonymization Instructions](https://www.hukkelas.no/deep_privacy2/#/anonymization).
- [Training Instructions](https://www.hukkelas.no/deep_privacy2/#/training_and_development).
## Quick Start
### Installation
We recommend to setup and install pytorch with [anaconda](https://www.anaconda.com/) following the [pytorch installation instructions](https://pytorch.org/get-started/locally/).
1. Clone repository: `git clone https://github.com/hukkelas/deep_privacy2/`.
2. Install using `setup.py`:
```
pip install -e .
```
or:
```
pip install git+https://github.com/hukkelas/deep_privacy2/
```
See the [documentation](https://www.hukkelas.no/deep_privacy2/#/) for more installation instructions.
### Anonymization
[anonymize.py](anonymize.py) is the main script for anonymization.
**Full-Body Anonymization**
```
python3 anonymize.py configs/anonymizers/FB_cse.py -i media/regjeringen.jpg --output_path output.png --visualize
```
**Face Anonymization**
```
python3 anonymize.py configs/anonymizers/face.py -i media/regjeringen.jpg --output_path output.png --visualize
```
**Webcam anonymization**
```
python3 anonymize.py configs/anonymizers/FB_cse.py --webcam
```
See the [documentation](https://www.hukkelas.no/deep_privacy2/#/anonymization) for more detailed instructions for anonymization.
### Gradio Demos
The repository includes gradio demos to show of the capabilities of DeepPrivacy2.
**Face anonymization**. Test it on [Hugging Face](https://huggingface.co/spaces/haakohu/deep_privacy2_face).
```
python3 -m gradio_demos.face
```
**Full-body anonymization**. Test it on [Hugging Face](https://huggingface.co/spaces/haakohu/deep_privacy2).
```
python3 -m gradio_demos.body_cse
```
## License
This repsitory is released under [Apache 2.0 License](License), except for the following:.
- Code under `sg3_torch_utils/`. This code is modified from [github.com/NVlabs/stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch). Separate license is attached in the directory.
- Detection network: See [Detectron2 License](https://github.com/facebookresearch/detectron2/blob/main/LICENSE).
- All checkpoints follow the license of the datasets. See the respective datasets for more information.
- Code under `dp2/detection/models/vit_pose`. This code is modified from [https://github.com/gpastal24/ViTPose-Pytorch](https://github.com/gpastal24/ViTPose-Pytorch), where code is adapted from OpenMMLab. Original license is [Apache 2-0](https://github.com/open-mmlab/mmpose/blob/master/LICENSE).
## Citation
If you find this repository useful, please cite:
```
@inproceedings{hukkelas23DP2,
author={Hukkelås, Håkon and Lindseth, Frank},
booktitle={2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
title={DeepPrivacy2: Towards Realistic Full-Body Anonymization},
year={2023},
volume={},
number={},
pages={1329-1338},
doi={10.1109/WACV56688.2023.00138}}
```