Python GStreamer Apps

QIM SDK provides Python GStreamer binding examples, demonstrating how to build AI/ML and multimedia pipelines using Python.

Prerequisites

• Completed QIM SDK Installation

Python examples are installed via the gstreamer1.0-qcom-python-examples package. Related Python dependencies (python3-gst-1.0, python3-numpy, python3-opencv) are installed automatically.

Available Examples

Application	Description
Camera Encode	Capture from camera and encode to H.264
OpenCV Camera Stream	Read camera stream via OpenCV
OpenCV Video Convert	OpenCV video format conversion
Concurrent Video Playback (Video Wall)	Multi-stream simultaneous playback
Multi-Camera Stream	Python-controlled multi-camera
Object Detection & Display	Run object detection and display results
RTSP Stream Decode & Detection	Decode from RTSP and detect
JPEG Image Decode	Decode JPEG images
Object Detection & Classification	Cascaded detection + classification
Convert & Encode Camera Stream	Camera stream transcoding
Camera Encode + Detection + Display	End-to-end pipeline
Detection + Classification + Segmentation	Multi-task AI
Parallel Inference	Multi-model parallel
Daisy-Chain Detection & Pose	Cascaded detection + pose

Basic Structure

Python GStreamer apps use PyGObject bindings:

import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GLib

Gst.init(None)

# Create pipeline
pipeline = Gst.parse_launch(
    "filesrc location=/etc/media/video.mp4 ! "
    "qtdemux ! h264parse ! v4l2h264dec ! waylandsink"
)

# Start
pipeline.set_state(Gst.State.PLAYING)

# Main loop
loop = GLib.MainLoop()
loop.run()

Get Source Code

radxa@airbox$

git clone https://github.com/quic/sample-apps-for-qualcomm-linux

Reference

• Build from Source — Compile custom C/C++ apps on-device