8 Best Python Video Processing Libraries

Here are the best open-source Python video processing libraries for your applications. You can use these to build intelligent applications with a wide range of functionality, such as object detection, image segmentation, and motion analysis.

Python has many open-source libraries that can be used for various image and video processing tasks. These support several file formats and image data types. Also, other video editing libraries allow developers to manipulate video files by using a simple Python interface and simplifying tasks such as cropping, concatenating, and adding effects to videos. Moreover, Python video processing libraries offer a simple and powerful interface for reading and writing various image and video formats, including GIF, MP4, AVI, and more. Moreover, some libraries can be used for tasks such as generating thumbnails, creating GIFs, and converting video formats. Also, developers get powerful tools for working with multimedia files, including video and audio codecs.

We have handpicked the top and tending open-source Python libraries that offer a wide range of capabilities, from essential video file reading and writing to advanced computer vision and machine learning algorithms for your applications.

OpenCV:

• Used for computer vision and image processing tasks.
• Supports a wide range of video processing functions.
• Offers support for cross-platform application development.

FFmpeg:

• Used in User Interface, Graphics applications, etc.
• Offers a complete, cross-platform solution to record, convert, and stream audio and video.
• Supports multiple formats like WebM, MP4, H.264, AAC, and MP3 encoders.

MoviePy:

• Used in telecommunications, media, entertainment, video, video Utils applications, etc.
• Allows to process video files and create animations.
• Compatible with Windows/Mac/Linux, with Python 3.6+.

PyAV:

• Used for efficient and high-performance video and audio processing.
• A Pythonic binding for the FFmpeg multimedia framework.
• Allows media access via containers, streams, packets, codecs, and frames.

PySceneDetect:

• Used to perform scene detection on video files.
• Provides scene-by-scene breakdowns of video content.
• Requires FFmpeg/mkvmerge for video splitting support.

scikit-video:

• Used in Artificial Intelligence, Computer Vision applications, etc.
• Built on the popular scikit-learn machine learning library.
• Key features include algorithm processing, quality metrics, temporal filtering, motion/object detection, and motion estimation.
• Created as a companion to the scikit-image library.

PyDub:

• Used to manipulate audio in different formats.
• Provides a simple and easy-to-use high-level interface.
• Makes it easy to work with audio and video formats.

Vidgear:

• Used for multiple high-performance video processing functions and capabilities.
• Provides a special internal video stabilization wrapper.
• Offers multiple APIs, known as Gears, each with some unique functionality.