https://github.com/EutropicAI/ccvfi
an inference lib for video frame interpolation with VapourSynth support
https://github.com/EutropicAI/ccvfi
artificial-intelligence basicsr computer-vision low-level-vision opencv python3 pytorch vapoursynth video-frame-interpolation video-processing
Last synced: 9 months ago
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an inference lib for video frame interpolation with VapourSynth support
- Host: GitHub
- URL: https://github.com/EutropicAI/ccvfi
- Owner: EutropicAI
- License: mit
- Created: 2024-11-07T02:47:46.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-11T00:47:04.000Z (over 1 year ago)
- Last Synced: 2025-09-04T07:32:03.522Z (10 months ago)
- Topics: artificial-intelligence, basicsr, computer-vision, low-level-vision, opencv, python3, pytorch, vapoursynth, video-frame-interpolation, video-processing
- Language: Python
- Homepage:
- Size: 7.01 MB
- Stars: 5
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
> **🚨 Deprecation Notice**
> This repository is **no longer maintained**. All features have been integrated and further developed in [cccv](https://github.com/EutropicAI/cccv). Please migrate to the new repository for future updates and support.
# ccvfi
[](https://codecov.io/gh/EutropicAI/ccvfi)
[](https://github.com/EutropicAI/ccvfi/actions/workflows/CI-test.yml)
[](https://github.com/EutropicAI/ccvfi/actions/workflows/Release.yml)
[](https://badge.fury.io/py/ccvfi)
an inference lib for video frame interpolation with VapourSynth support
### Install
Make sure you have Python >= 3.9 and PyTorch >= 1.13 installed
```bash
pip install ccvfi
```
- Install VapourSynth (optional)
### Start
#### cv2
a simple example to use the RIFE (ECCV2022-RIFE) model to process an image sequence.
```python
import cv2
import numpy as np
from ccvfi import AutoModel, ConfigType, VFIBaseModel
model: VFIBaseModel = AutoModel.from_pretrained(
pretrained_model_name=ConfigType.RIFE_IFNet_v426_heavy,
)
img0 = cv2.imdecode(np.fromfile("01.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
img1 = cv2.imdecode(np.fromfile("02.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
out = model.inference_image_list(img_list=[img0, img1])[0]
cv2.imwrite("test_out.jpg", out)
```
#### VapourSynth
a simple example to use the VFI (Video Frame-Interpolation) model to process a video (DRBA)
```python
import vapoursynth as vs
from vapoursynth import core
from ccvfi import AutoModel, BaseModelInterface, ConfigType
model: BaseModelInterface = AutoModel.from_pretrained(
pretrained_model_name=ConfigType.DRBA_IFNet,
)
clip = core.bs.VideoSource(source="s.mp4")
clip = core.resize.Bicubic(clip=clip, matrix_in_s="709", format=vs.RGBH)
clip = model.inference_video(clip, tar_fps=60)
clip = core.resize.Bicubic(clip=clip, matrix_s="709", format=vs.YUV420P16)
clip.set_output()
```
See more examples in the [example](./example) directory, ccvfi can register custom configurations and models to extend the functionality
### Current Support
It still in development, the following models are supported:
- [Architecture](./ccvfi/type/arch.py)
- [Model](./ccvfi/type/model.py)
- [Weight(Config)](./ccvfi/type/config.py)
### Reference
- [PyTorch](https://github.com/pytorch/pytorch)
- [BasicSR](https://github.com/XPixelGroup/BasicSR)
- [mmcv](https://github.com/open-mmlab/mmcv)
- [huggingface transformers](https://github.com/huggingface/transformers)
- [VapourSynth](https://www.vapoursynth.com/)
- [HolyWu's functions](https://github.com/HolyWu)
### License
This project is licensed under the MIT - see
the [LICENSE file](./LICENSE) for details.