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Upgrade Python on Jetson Nano - Tutorial
If you have a Jetson running JetPack 4, you can upgrade your Python version from the default Python 3.6. Looky here: Background The current JetPack 4 release installs Jetson Linux 32.7. Jetson Linux is a variant of Ubuntu, in this case version 18.04 LTS. Each Ubuntu release has a group of Python releases. In this case, 18.04 has Python version 3.6, 3.7 and 3.8. This article covers the Jetson…
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J2K codec performance on Jetson TX2
NVIDIA Jetson TX2 hardware is very promising for imaging and other embedded applications. That high-performance and low-power hardware is utilized in autonomous solutions, especially the industrial version Jetson TX2i. Since J2K compression is a common task for UAV (Unmanned Aerial Vehicle) applications, here we evaluate such a solution and its limitations.
Detailed info concerning our testing approach for JPEG2000 encoding and decoding on desktop/server NVIDIA GPUs you can find at the corresponding links. Here we follow exactly the same procedure, but it's applied to the Jetson hardware.
J2K encoding/decoding parameters
File format – JP2
Lossy JPEG2000 compression with CDF 9/7 wavelet
Lossless JPEG2000 compression with CDF 5/3 wavelet
Compression ratio (for lossy algorithm) ~ 12.0:1 which corresponds to visually lossless encoding
Subsampling mode – 4:4:4
Number of DWT resolutions – 7
Codeblock size – 32×32
MCT – on
PCRD – off
Tiling – off
Window – off
Quality layers – one
Progression order – LRCP (L = layer, R = resolution, C = component, P = position)
Modes of operation – single or multithreaded batch
2K test image (24-bit) – 2k_wild.ppm
4K test image (24-bit) – 4k_wild.ppm
It's obvious that in many cases compression ratio for visually lossless encoding could be much higher for JPEG2000 algorithm. So we would suggest testing different parameters to achieve the best compression ratio with an acceptable image quality. Decreasing the quality coefficient one can get not only better compression, but also higher framerate both for encoding and decoding. Our benchmarks show the performance results for the above images and parameters. It's not the maximum performance, which could be better in many other cases.
Hardware and software
NVIDIA Jetson TX2
CUDA Toolkit 10.2
JPEG2000 codec benchmarks on NVIDIA Jetson TX2
Jetson TX2 has 4-core ARM Cortex-A57 @ 2 GHz and 2-core Denver2 @ 2 GHz. These two types of cores have different performance, which should be taken into account. Since Tier-2 stage of JPEG2000 algorithm is implemented on CPU, the performance of both CPU and GPU cores determine the framerate. From that point of view, multithreading can be useful (we use up to 12 threads), but in the single mode we could get different performance depending on the CPU core used. So in the single mode we need to set affinity mask to ensure utilizing the fastest CPU core.
In the tests discussed we've restricted memory usage to 2 GB. This was done under an assumption that Jetson TX2 can have only 4 GB memory, so this is important limitation for the whole image processing solution.
Here we haven't considered the task of J2K transcoding to H.264 on Jetson. That task requires additional tests, though from our previous experience with desktop/server GPUs, performance of the transcoding should not differ significantly, because Jetson has hardware support of H.264 encoding (separate from GPU), which is accessible via V4L2 interface and can be used simultaneously with JPEG2000 decoder.
By request we could offer Fastvideo SDK for Jetson for evaluation - please fill the form below and send it to us.
Other info from Fastvideo concerning JPEG2000 and Jetson
JPEG2000 codec on GPU
JPEG2000 vs JPEG vs PNG: What's the Difference?
J2K encoding benchmarks
J2K decoding benchmarks
Fast FFmpeg J2K decoder on NVIDIA GPU
MXF Player
Jetson Benchmark Comparison: Nano vs TX2 vs Xavier
Jetson image processing for camera applications
Original article see at: https://www.fastcompression.com/blog/j2k-codec-on-jetson-tx2.htm
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JETSON TX2 Graphic Card
JETSON TX2 Graphic Card
The NVIDIA Jetson TX2 Developer Kit gives you a fast, easy way to develop hardware and software for the Jetson TX2 AI supercomputer on a module.
JETSON TX2 The NVIDIA Jetson TX2 Developer Kit gives you a fast, easy way to develop hardware and software for the Jetson TX2 AI supercomputer on a module. It exposes the hardware capabilities and interfaces of the module and is supported by NVIDIA Jetpack—a complete SDK that includes the BSP, libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more.
Evaluation of performance and power consumption! Results coming soon...
Revisa nuestro nuevo post en http://www.skgcl.com/news/nvidia-recibe-cuatro-premios-importantes-computex/
NVIDIA recibe cuatro premios importantes en Computex
Embedded Computer Vision Real Time? Nvidia Jetson TX2 + VisionWorks toolkit
Embedded Computer Vision Real Time? Nvidia Jetson TX2 + VisionWorks toolkit @nvidia @NVIDIAembedded @nvidia_IT
The Computer Vision is a really computational power requesting task, if you work with computer vision you know that to reach very high frame rates you need to maximize the parallelism of the algorithms and you know that massive parallelism is reachable only moving elaborations to GPUs. The embedded massive parallelism was a dream for computer vision until Nvidia launched Jetson TK1 board in 2014.…
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Embedded Computer Vision Real Time? Nvidia Jetson TX2 + VisionWorks toolkit
Embedded Computer Vision Real Time? Nvidia Jetson TX2 + VisionWorks toolkit @nvidia @NVIDIAEmbedded @NVIDIA_IT
The Computer Vision is a really computational power requesting task, if you work with computer vision you know that to reach very high frame rates you need to maximize the parallelism of the algorithms and you know that massive parallelism is reachable only moving elaborations to GPUs. The embedded massive parallelism was a dream for computer vision until Nvidia launched Jetson TK1 board in 2014.…
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