Toolkit for Video Understanding tasks
Project description
Video Understanding Toolkit
This repository provides a collection of tools and utilities for video understanding tasks, including video classification, action recognition, and more. The toolkit is designed to be modular and extensible, allowing researchers and developers to easily integrate new models and datasets.
Features
TODO: Implement the features and tools in the toolkit.
Installation
You can install the toolkit using pip:
pip install vut
By default, no dependencies are installed. This is suitable for code snippet usage or minimal environments.
If you want to use all features with all dependencies, install with:
pip install vut[full]
This will install all dependencies required for full functionality.
Usage
TODO: Provide usage examples and documentation for the various features and tools in the toolkit.
Tools & Utilities
In addition to the main toolkit, we provide some useful tools and utilities:
Matplotlib Colormap Visualization
We've created an interactive web application for visualizing matplotlib colormaps. This tool helps you explore and choose the right colormap for your data visualization needs.
🌈 Visit the site: matplotlib-colormap.streamlit.app
This visualization tool provides:
- Interactive preview of all matplotlib colormaps
- Easy comparison between different colormaps
- Information about colormap properties and use cases
- Export capabilities for your selected colormaps
Development
This toolkit requires package management tool uv. You first need to install it:
curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.local/bin/env
Then, you can install the toolkit using the following command:
git clone https://github.com/kage1020/vut.git
cd vut
uv venv
source .venv/bin/activate
uv sync --all
uv sync --all command will install all the optional dependencies specified in the pyproject.toml file, including those for full functionality like PyTorch, NumPy, OpenCV, and more.
This will install all the required dependencies and set up the development environment.
License
The core functionality of this toolkit is licensed under the MIT License.
However, the models included in the vut/models directory may be subject to different licenses:
- Each model implementation in the
vut/modelsdirectory includes its own licensing information. - Please refer to the models README for specific license details of each model.
When using this toolkit, especially when incorporating the provided models, please make sure to comply with the respective licenses.
Contributing
We welcome contributions to the toolkit!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file vut-0.1.15.tar.gz.
File metadata
- Download URL: vut-0.1.15.tar.gz
- Upload date:
- Size: 41.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
52877386c29569c5db290d7495f52439cde5722bdb09faf76a079ad3c3409460
|
|
| MD5 |
29c45f19ee689cefc2fc24406d830295
|
|
| BLAKE2b-256 |
c372ea507e7386ace9099f228cda9a6ab608a2b553b5eb213782c4a6645e4e6f
|
File details
Details for the file vut-0.1.15-py3-none-any.whl.
File metadata
- Download URL: vut-0.1.15-py3-none-any.whl
- Upload date:
- Size: 33.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
75265617e041494cad9a935ff67a8cfe35eec3c432499b5cf7755a5848c15c55
|
|
| MD5 |
a76ab7d545f010611af5d3de38321da8
|
|
| BLAKE2b-256 |
610a9042f9df6dff250b103598697dc8650b6c0506790cdfdcbc44a9c6ab9ade
|