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https://github.com/hkchengrex/Cutie
[CVPR 2024 Highlight] Putting the Object Back Into Video Object Segmentation
https://github.com/hkchengrex/Cutie
computer-vision cvpr2024 deep-learning pytorch segmentation video-editing video-object-segmentation video-segmentation
Last synced: 4 days ago
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[CVPR 2024 Highlight] Putting the Object Back Into Video Object Segmentation
- Host: GitHub
- URL: https://github.com/hkchengrex/Cutie
- Owner: hkchengrex
- License: mit
- Created: 2023-10-19T17:49:24.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-08T21:38:38.000Z (4 days ago)
- Last Synced: 2024-11-08T22:28:31.274Z (4 days ago)
- Topics: computer-vision, cvpr2024, deep-learning, pytorch, segmentation, video-editing, video-object-segmentation, video-segmentation
- Language: Python
- Homepage: https://hkchengrex.com/Cutie/
- Size: 2.74 MB
- Stars: 719
- Watchers: 4
- Forks: 70
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# [Putting the Object Back into Video Object Segmentation](https://hkchengrex.github.io/Cutie)
[Ho Kei Cheng](https://hkchengrex.github.io/), [Seoung Wug Oh](https://sites.google.com/view/seoungwugoh/), [Brian Price](https://www.brianpricephd.com/), [Joon-Young Lee](https://joonyoung-cv.github.io/), [Alexander Schwing](https://www.alexander-schwing.de/)
University of Illinois Urbana-Champaign and Adobe
CVPR 2024, Highlight
[[arXiV]](https://arxiv.org/abs/2310.12982) [[PDF]](https://arxiv.org/pdf/2310.12982.pdf) [[Project Page]](https://hkchengrex.github.io/Cutie/) [](https://colab.research.google.com/drive/1yo43XTbjxuWA7XgCUO9qxAi7wBI6HzvP?usp=sharing)
## Highlight
Cutie is a video object segmentation framework -- a follow-up work of [XMem](https://github.com/hkchengrex/XMem) with better consistency, robustness, and speed.
This repository contains code for standard video object segmentation and a GUI tool for interactive video segmentation.
The GUI tool additionally contains the "permanent memory" (from [XMem++](https://github.com/max810/XMem2)) option for better controllability.
## Demo Video
https://github.com/hkchengrex/Cutie/assets/7107196/83a8abd5-369e-41a9-bb91-d9cc1289af70
Source: https://raw.githubusercontent.com/hkchengrex/Cutie/main/docs/sources.txt
## Installation
Tested on Ubuntu only.
**Prerequisite:**
- Python 3.8+
- PyTorch 1.12+ and corresponding torchvision
**Clone our repository:**
```bash
git clone https://github.com/hkchengrex/Cutie.git
```
**Install with pip:**
```bash
cd Cutie
pip install -e .
```
(If you encounter the File "setup.py" not found error, upgrade your pip with pip install --upgrade pip)
**Download the pretrained models:**
```python
python cutie/utils/download_models.py
```
## Quick Start
### Scripting Demo
This is probably the best starting point if you want to use Cutie in your project. Hopefully, the script is self-explanatory (additional comments in `scripting_demo.py`). If not, feel free to open an issue. For more advanced usage, like adding or removing objects, see `scripting_demo_add_del_objects.py`.
```python
@torch.inference_mode()
@torch.cuda.amp.autocast()
def main():
cutie = get_default_model()
processor = InferenceCore(cutie, cfg=cutie.cfg)
# the processor matches the shorter edge of the input to this size
# you might want to experiment with different sizes, -1 keeps the original size
processor.max_internal_size = 480
image_path = './examples/images/bike'
images = sorted(os.listdir(image_path)) # ordering is important
mask = Image.open('./examples/masks/bike/00000.png')
palette = mask.getpalette()
objects = np.unique(np.array(mask))
objects = objects[objects != 0].tolist() # background "0" does not count as an object
mask = torch.from_numpy(np.array(mask)).cuda()
for ti, image_name in enumerate(images):
image = Image.open(os.path.join(image_path, image_name))
image = to_tensor(image).cuda().float()
if ti == 0:
output_prob = processor.step(image, mask, objects=objects)
else:
output_prob = processor.step(image)
# convert output probabilities to an object mask
mask = processor.output_prob_to_mask(output_prob)
# visualize prediction
mask = Image.fromarray(mask.cpu().numpy().astype(np.uint8))
mask.putpalette(palette)
mask.show() # or use mask.save(...) to save it somewhere
main()
```
### Interactive Demo
Start the interactive demo with:
```bash
python interactive_demo.py --video ./examples/example.mp4 --num_objects 1
```
[See more instructions here](docs/INTERACTIVE.md).
If you are running this on a remote server, X11 forwarding is possible. Start by using `ssh -X`. Additional configurations might be needed but Google would be more helpful than me.
(For single video evaluation, see the unofficial script `scripts/process_video.py` from https://github.com/hkchengrex/Cutie/pull/16)
## Training and Evaluation
1. [Running Cutie on video object segmentation data.](docs/EVALUATION.md)
2. [Training Cutie.](docs/TRAINING.md)
## Citation
```bibtex
@inproceedings{cheng2023putting,
title={Putting the Object Back into Video Object Segmentation},
author={Cheng, Ho Kei and Oh, Seoung Wug and Price, Brian and Lee, Joon-Young and Schwing, Alexander},
booktitle={arXiv},
year={2023}
}
```
## References
- The GUI tools uses [RITM](https://github.com/SamsungLabs/ritm_interactive_segmentation) for interactive image segmentation. This repository also contains a redistribution of their code in `gui/ritm`. That part of code follows RITM's license.
- For automatic video segmentation/integration with external detectors, see [DEVA](https://github.com/hkchengrex/Tracking-Anything-with-DEVA).
- The interactive demo is developed upon [IVS](https://github.com/seoungwugoh/ivs-demo), [MiVOS](https://github.com/hkchengrex/MiVOS), and [XMem](https://github.com/hkchengrex/XMem).
- We used [ProPainter](https://github.com/sczhou/ProPainter) in our video inpainting demo.
- Thanks to [RTIM](https://github.com/SamsungLabs/ritm_interactive_segmentation) and [XMem++](https://github.com/max810/XMem2) for making this possible.