scikit-video: Video Processing Routines for SciPy
This repository profile is provided by osrepos.com, an open source repository discovery platform.

Summary
scikit-video is a Python library designed for video processing, offering a suite of routines for tasks like I/O, quality metrics, and temporal filtering. Intended as a companion to scikit-image, it provides video-specific algorithms and aims for flexibility and GPU compute capabilities. This project offers a research-oriented alternative to existing frameworks, built entirely in Python.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
scikit-video is a Python-based library focused on video processing algorithms. Positioned as a companion to scikit-image, it aims to provide a comprehensive set of tools specifically for video, including I/O operations, quality metrics, temporal filtering, motion detection, and motion estimation. The project emphasizes a pure Python approach, leveraging technologies like Numba and Theano for performance, offering a flexible and research-oriented alternative to more traditional C/C++ frameworks like OpenCV.
Installation
As scikit-video is currently in a BETA stage, installation typically involves standard Python package management. Users are encouraged to check the official repository for the most up-to-date installation instructions and dependencies.
pip install scikit-video
Note that additional dependencies for video I/O, such as ffmpeg or avconv, may be required and should be installed separately on your system.
Examples
While specific code examples are not provided in the README, scikit-video is designed for tasks such as reading video files, applying various filters, calculating video quality metrics, and detecting motion or objects within video sequences. Users should consult the project's GitHub repository for detailed usage examples and API documentation as the project evolves.
Why Use scikit-video?
scikit-video stands out for several reasons:
- Python-centric: It's built entirely in Python, making it potentially easier to hack on and integrate into Python-based research workflows.
- Research-Oriented: The project aims to be a flexible platform for experimenting with new video algorithms, contrasting with more rigid frameworks.
- Performance: By utilizing tools like Numba and Theano, it seeks to achieve high performance, with future potential for GPU acceleration.
- Video-Specific Focus: It fills a niche by providing algorithms specifically tailored for video processing, complementing image processing libraries.
Links
- GitHub Repository: https://github.com/aizvorski/scikit-video
Related repositories
Similar repositories that may be relevant next.

Open Index: A Deterministic Memory Layer for Your AI Agents
September 16, 2026
Open Index is a powerful tool for building domain-specific, accurate, and structured data that AI agents can effectively operate on. It enables the creation of a "brain," a searchable and continuously improving context graph tailored to any domain. This system ensures agents have access to reliable, up-to-date information, enhancing their capabilities and decision-making processes.
tooltrim: Drastically Reduce LLM Agent Tool Output Tokens, Improve Accuracy
September 16, 2026
tooltrim provides drop-in compression for LLM agent tool outputs, drastically cutting tokens while often improving answer accuracy. This provider-agnostic solution offers content-aware compression, faithfulness benchmarks, and seamless integration with popular frameworks or as an OpenAI-compatible proxy.

AgentShield: Python Firewall for AI Agent Spend Control
September 16, 2026
AgentShield is a pure Python library designed to prevent runaway AI agents from exceeding budget limits. It offers 10 composable spend rules, evaluated in under 1ms, providing robust cost control. Although its core development has transitioned to sipi.bot, the AgentShield Python package remains available for existing users and its test fixtures are open-source.

DA-Forge: Streamlining Declarative Agent Creation for Copilot Notebooks
September 12, 2026
DA-Forge is a Python-based tool by Microsoft designed to automate the creation and deployment of Declarative Agents for Copilot Notebooks. It significantly reduces the manual effort and time required to set up AI assistants with specific grounding references, transforming an 85-minute process into just a few minutes. This tool is essential for developers and researchers working with Copilot Notebooks and Declarative Agents.
Source repository
Open the original repository on GitHub.
13 counted GitHub visits