GstCUDA: RidgeRun presentation at NVIDIA GTC 2019 on GStreamer and CUDA integration.

RidgeRun engineers presented a GstCUDA, a framework developed by RidgeRun that provides an easy, flexible and powerful integration between GStreamer audio/video streaming infrastructure and CUDA hardware-accelerated video processing at NVIDIA GTC 2019.

GstCUDA: Easy GStreamer and CUDA Integration

Please Watch the Video

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RidgeRun's USB Video Class Gadget Library - LibGUVC v1.4.0 - NVidia Xavier and NXP iMX Support

The UVC Video Class Gadget Library or libguvc for short is a platform agnostic library that simplifies the development of UVC based gadget devices by encapsulating the most of the UVC communication leaving just the basic setup to the user. The USB video class gadget runs on top of the UVC function driver in the user space and takes care of the communication between the user application and the linux driver stack.

Release of libGUVC v1.4.0, is now supporting NVIDIA Jetson platform along with previously supported NXP-iMX6 family of processors, thanks to the new bulk transfer support.

It has never been easier to implement a UVC application on your hardware, libGUVC make it easy to interact with the UVC driver and expose a variety of useful features such as:

•     USB 2.0 and USB 3.0 support
•     Isochronous and bulk endpoint support
•     YUV2, MJPEG and H264 video streaming support
•     Extension Unit support
•     MMAP and UserPtr support.

Since the driver is agnostic to the platform, you can run it on almost any platform with quality USB and UVC drivers, making it really simple to convert it into a UVC capable device.

 libGuvc in action - Running libGuvc on NVidia Xavier

 

 libGuvc in action - Running libGuvc on NXP-iMX6

 

You can find more information about the library in this developer wiki from RidgeRun Engineering : USB Video Class Gadget Library - libguvc

For more technical information please contact us at support@ridgerun.com. Please post your inquiry at our Contact Us page for purchase related questions and also since the libguvc is platform agnostic, you can request for a custom demo image for any other platform using this link.

RidgeRun - NVIDIA Xavier - Deep Learning Tutorials using Jetson Inference

Jetson-inference is a training guide for inference on the TX1 and TX2 using NVIDIA Deep Learning GPU Training System (DIGITS). 

This blog details the summary of original Jetson-inference training from NVIDIA with a focus on inference part.

You can learn about following details in this developer wiki from RIdgeRun Engineering : NVIDIA Xavier - Deep Learning - Deep Learning Tutorials - Jetson Inference

Building jetson-inference.

Classifying Images with ImageNet.

Locating Object Coordinates using DetectNet.

Image Segmentation with SegNet.

and run a Live Demo.

With jetson-inference you can deploy deep learning examples on the NVIDIA Xavier in a matter of minutes. An example application is shown below.

The input is an image and it outputs the most likely class and the probability that the image belongs to that class using ImageNet classification network. ImageNet is a classification network trained with a database of 1000 objects.

Fig1: imagenet-console output image

It detects the image as 'Boston bull, Boston terrier' with imagenet class id of 0195 at 96.305% classification accuracy. Image recognition networks output a class probabilities corresponding to the entire input image.

Detection networks, on the other hand, find where in the image those objects are located. DetectNet accepts an input image, and outputs the class and coordinates of the detected bounding boxes.

                            Fig2: detectnet-console output image using coco-dog pretrained model


If you are new to the Xavier or planning on getting one, please visit our Jetson Xavier Wiki page.

Article related : 

Read this blog on deep reinforcement learning Deep Reinforcement Learning on the Jetson Xavier

For more information please contact us at support@ridgerun.com