How do I convert a PyAV video frame to a NumPy array?

You can use the 'frame.to_ndarray(format='rgb24')' method. This skill provides optimized patterns for various formats including BGR for OpenCV and YUV for native processing.

Can this skill help resolve PyAV installation errors?

Absolutely. It covers binary wheel installation, building from source on Linux/macOS/Windows, and troubleshooting shared library dependencies like libavcodec and libavformat.

Does this skill support the latest FFmpeg 8.0 features?

Yes, it includes specific compatibility data, version mapping, and build instructions to leverage FFmpeg 8.0 features like Whisper AI filters and VVC/H.266 decoding.

What is the main benefit of using PyAV over the FFmpeg CLI?

PyAV provides programmatic, frame-level access to media data within Python, which is essential for tasks like AI analysis, real-time computer vision, or complex frame manipulation that the CLI cannot handle efficiently.

PyAV FFmpeg Integration

Name: PyAV FFmpeg Integration
Author: JosiahSiegel

byJosiahSiegel

•

数据科学与机器学习

Provides comprehensive guidance and code patterns for PyAV, the high-performance Pythonic bindings for FFmpeg media libraries.

This skill serves as a master reference for integrating PyAV into Python projects, bridging the gap between low-level FFmpeg C libraries and high-level Python code. It offers proactive assistance for complex tasks such as building PyAV against custom FFmpeg versions (including 8.0+), managing memory and thread safety in media pipelines, and seamless conversion between video frames and NumPy arrays. Whether you are building an AI video processing pipeline, a custom media transcoder, or a real-time streaming application, this skill provides the type-safe examples and installation logic needed to handle containers, streams, and filters efficiently.

主要功能

01FFmpeg 7.0 and 8.0+ compatibility matrix with custom source build guides

0213 GitHub stars

03Complete PyAV API patterns for high-performance video/audio decoding and encoding

04Advanced filter graph implementation and multi-threaded performance optimization

05Seamless NumPy and Pillow integration for frame-level data processing

06Cross-platform installation strategies for Ubuntu, macOS, Windows, and Docker

使用场景

01Implementing low-latency RTSP streaming and network media processing in Python

02Developing AI-driven video analysis tools requiring direct frame access via NumPy

03Building custom media transcoding services with fine-grained codec and container control

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add josiahsiegel/claude-plugin-marketplace ffmpeg-pyav-integration

For use in Claude.ai and ChatGPT

Download Skill