NVIDIA Jetson Xavier - Multimedia API (MMAPI)

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NVIDIA®Jetson Xavier™
Introduction
SoM Overview Carrier Boards NVIDIA Xavier Developer Kit Connect Tech Rogue Connect Tech Mimic Software Support NVIDIA JetPack
JetPack 5.X
Getting Started Installing JetPack Flashing the board with GUI installer JetPack Components Compiling Source Code Flashing the Board from Cmdline Performance Tuning Evaluating Performance Set Values Manually Tuning Power Maximizing Performance
Development in the Board
Getting into the Board Using Serial Console Using SSH Using Ubuntu Login Installing Packages
Video Capture and Display
Description Supported Cameras Imx219 Driver Software Support Using GStreamer Using Libargus
Processors
HD Audio and Video Subystem Video Encoder Video Decoder JPEG Encoder and Decoder Video Image Compositor Audio Processing Engine Deep Learning Accelerator Volta GPU Description Using CUDA Using OpenGL
Interfaces
I2C SD/eMMC Controller USB Controller
GStreamer Pipelines
Capture and Display H264 H265 VP9 MPEG4 Simultaneous Streams Compositor
Deep Learning
TensorRT Using TensorRT integrated with Tensorflow Parsing a Tensorflow model for TensorRT Parsing Caffe model for TensorRT Building TensorRT API examples Deep Learning Accelerator Software Support Deep Learning Tutorials Jetson Inference Jetson Reinforcement
RidgeRun Products
GstPTZR GstCUDA GstColorTransfer GstWebRTC GstRtspSink GstInterpipe GstInference GStreamer Daemon GstShark
Reference Documentation
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Multimedia API also known as MMAPI is a collection of lower-level APIs that support application development. MMAPI is for developers who use a custom framework or wish to avoid a framework like GStreamer.

Downloading MMAPI

MMAPI should be downloaded separately from the NVIDIA Jetson Download Center L4T Multimedia API 31.0.1

Components

• Libargus for imaging applications.
• Buffer utility for buffer allocation, management and sharing.
• NVOSD for On-Screen display.
• V4l2 API extensions for video converter, decoder and encoder support.
• Aplication framework
• Samples that demostrate image processing with CUDA, object detection and classification with cuDNN, TensorRT and OpenCV4Tegra usage.