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Just draw a bounding box and you can remove the object you want to remove.
| Date | Stars |
|---|---|
| 2026-07-24 | 2709 |
| 2026-07-25 | 2709 |
| 2026-07-28 | 2709 |
| 2026-07-30 | 2709 |
| 2026-08-06 | 2709 |
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# video-object-removal
Just draw a bounding box and you can remove the object you want to remove.
## Installation
All the code has been tested on Ubuntu 16.04, Python 3.5, **Pytorch 0.4.0**, CUDA 8.0, GTX1080Ti GPU.
- Clone the repository
```shell
git clone https://github.com/zllrunning/video-object-removal.git
cd video-object-removal
cd get_mask
bash make.sh
cd ../inpainting
bash install.sh
cd ..
```
## Demo
- Download pretrained models of [SiamMask](http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth) and [Inpainting](https://drive.google.com/file/d/1KAi9oQVBaJU9ytr7dYr2WwEcO5NLiJvo/view?usp=sharing)
- Put them in `cp/` folder
- Then just run:
```
python demo.py --data data/Human6
```
- It also **supports video file**.
```
python demo.py --data data/bag.avi
```
- Another optional parameter : `--mask-dilation`
```
python demo.py --data data/Human6 --mask-dilation 24
```
This parameter controls the size of the dilation kernel used for the mask. The role is to expand the range of the mask to avoid edge problems. Please see `inpainting/davis.py` for more details.
---
**1. Just draw a bounding box like this:**
**2. The objected will be removed and the inpainted video will be saved in `results/inpainting` folder.** (The Gif image loading takes some time, please wait a moment.)
### Examples
---
## Acknowledgement
- This repo is based on [SiamMask](https://github.com/foolwood/SiamMask) and [Deep-Video-Inpainting](https://github.com/mcahny/Deep-Video-Inpainting). Many thanks to the excellent repo.
## Citation
```
@article{Wang2019SiamMask,
title={Fast Online Object Tracking and Segmentation: A Unifying Approach},
author={Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip HS},
journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
```
```bibtex
@inproceedings{kim2019deep,
title={Deep Video Inpainting},
author={Kim, Dahun and Woo, Sanghyun and Lee, Joon-Young and So Kweon, In},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5792--5801},
year={2019}
```
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zll · China
12
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2854dc43318f4a1c, topic:inpainting, readme:inpainting
matched fp:2854dc43318f4a1c, topic:pytorch