5 min. read | With gstreamer appsrc element it easy to push buffers (ex.: numpy arrays) into gstreamer pipeline. Developer can benefit from a variety of already implemented gstreamer plugins and display image in window, write frame to a video file or send buffers over TCP/HTTP.
5 min. read | The most convenient way to integrate video streaming pipeline into your application is through Gstreamer’s appsink plugin. In the following post we’ll explore Python GObject API on how to receive video frames from gstreamer pipeline in Python.
3 min. read | Development for Gstreamer could be quite challenging at the beginning. But there is a lifeboard, – Gstreamer Python Bindings. With Python it is much easier to understand basic Gstreamer concepts and at the same time to speed up applications development.
10 min. read | Next guide shows steps to write Gstreamer Plugin in Python for any Computer Vision, Image Processing task and use it in standard Gstreamer pipeline from command line. Additionally this post explains how to get/set own properties from implemented Gstreamer plugin.
Gstreamer with Python is highly flexible for data visualization. Any video streaming pipeline could be extended with custom Python plugins that allows you to draw any information (bounding boxes, confidences, class names, etc.). Next information is simple comparison of two most popular approaches to draw on gstreamer buffer: PyCairo, OpenCV.
Gstreamer is flexible plugin based data streaming framework. One of the main advantange of plugins is that developer can pass not only data buffer, but custom metadata which can describe buffer and store any information about buffer. Next information is a simple guide with code templates on how to pass any data from