Showing posts with label Jetson TX1/TX2. Show all posts
Showing posts with label Jetson TX1/TX2. Show all posts

Sunday, December 8, 2019

NVIDIA Jetson TX2-Camera image capture latency measurement techniques.

For better understanding of this latest blog from RidgeRun, please read the RidgeRun blog on Diving deep into NVIDIA Jetson TX2 - Video Input system and camera image capture latency.

The techniques used to measure the Jetson TX2 image capture latency are outlined in this blog.

Two timestamps are used to measure the latency :

SOF : The first timestamp is taken when the pixels of a frame start to arrive at the VI (t0), this is the start-of-frame (SOF).
The second timestamp is usually obtained at the time when the frame is available on a userspace application (t1).

Techniques can be divided into two main categories, techniques that use the CHANSEL_PXL_SOF (referred to as SOF) and the techniques that do not use the SOF .

Outline of both the methods:

Techniques that use the SOF timestamp




CHANSEL_PXL_SOF can be used as t0 and then, t1 can be the timestamp obtained at the instant when the frame arrives to userspace using ideally the same clock used to set the CHANSEL_PXL_SOF, then the latency can be computed as follows:
Latency = t1 - t0

Techniques that do not use the SOF timestamp

LED test
This test involves the use of a led connected to the TX2 GPIO.
The camera must start capturing when the led is still off, and then a kernel module must turn the led on and then record the current CLOCK_MONOTONIC timestamp to be used as t0. Each image that arrives to userspace must be timestamped also with the CLOCK_MONOTONIC at the instant when it's available, this will be t1.



More technical details about the techniques, working with TSC (Time Stamping System Clock) for the latency measurements and rtcpu, v4l2 tracing details are explained in the RidgeRun developer wiki page : NVIDIA Jetson TX2 - VI Latency Measurement Techniques

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Wednesday, September 18, 2019

GStreamer Video Stabilizer for NVIDIA Jetson Boards



Many applications require the removal of undesired camera movement. Professional video stitching, medical imaging such as colonoscopy or endoscopy and localization of unmanned vehicles are a few examples of use cases that benefit from video stabilization. Unfortunately, this is a very resource consuming technique that may be unfeasible for real time operations on resource constrained systems such as embedded systems.
The following video provides a hands-on overview of GstNvStabilize on the works!



GstNvStabilize is GStreamer based video stabilizer for NVIDIA Jetson boards. It's based on VisionWorks and OpenVX hardware processing units to accelerate the stabilization for real time applications.

Latest v0.4.0 release include:
- Region-of-interest configuration via GStreamer caps - Smoothing level configuration via GStreamer property - Smart compensation limit to avoid black borders - GPU acceleration - Supported platforms: - NVIDIA Jetson Xavier - NVIDIA Jetson TX1/TX2 - NVIDIA Jetson Nano

Learn more in our developer's wiki:
https://developer.ridgerun.com/wiki/index.php?title=GStreamer_Video_Stabilizer_for_NVIDIA_Jetson_
Boards

Purchase directly from our website:
https://shop.ridgerun.com/products/gstnvstabilize?_pos=1&_sid=0951b9cf7&_ss=r 

Contact Us
Please visit our Main Website for the RidgeRun online store or Contact Us for pricing information of 
the engineering support, product and services. 
You can also send an email to support@ridgerun.com for a technical support, more information about 
the features, evaluation version (if available) or for a details about how to sponsor a new feature.