10 min. read | Video Acceleration API (VAAPI) is yet another way to use hardware acceleration to improve video processing performance (1.5-2 times faster than on CPU). The following guide helps to install Gstreamer VAAPI plugins on Ubuntu and provides solutions to most common issues.
7 min. read | Gstreamer’s plugins avdec_h264 (h264 video decoding) and x264enc (h264 video encoding) optimized for CPU. Meanwhile with Nvidia Gstreamer plugins (nvenc, nvdec) we can benefit from GPU capabilities and performance. With GPU based plugins applications can process videos up to 3 times faster.
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.
3 min. read | Youtube is a great platform with useful video content. With Gstreamer capabilities and your creativity you can watch same Youtube videos but with original filters (blurring, sharpening), transformations (style transfer), enhancements (coloring, super resolution).
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.
3 min. read | Video analytics applications (ex.: smart cities, retail, industries, etc.) consist of two main parts: Video Streaming and Computer Vision/ Deep Learning Frameworks. In here we’ll go through available frameworks that allow developers to focus on providing analytics part and hide nuances of video streaming.
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.