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NVIDIA Jetson is now indispensable for applications that require video processing. NVIDIA has a wide range of software such as DeepStream SDK and NVIDIA Isaac SDK, as well as peripheral tools optimized for NVIDIA GPUs. We recommend that you However, depending on the application to be developed, there may be cases where a suitable SDK cannot be found or these SDKs are not suitable due to special requirements. In that case, it is necessary to develop an application that directly calls the API of the library included in JetPack.
In this series of articles, we assume that the system will process the image information captured from the camera with the GPU in the Jetson module and display the results. I will explain. In Part 1, let's consider the software configuration of the system built on Jetson.
[Jetson video processing programming]
第1話 NVIDIA提供 JetPackとSDKでできること
Episode 2 Video input (CSI-connected Libargus-compliant camera)
Episode 3 Video input (USB-connected V4L2-compliant camera)
Episode 4 Resize and format conversion
Jetson Software Development Kit
NVIDIA provides JetPack as a software development kit (SDK) for Jetson. By introducing JetPack into the Jetson developer kit, you can immediately start developing application software for Jetson. JetPack includes the following software: These software are general-purpose ones that do not specifically select the field of application.
| Operating system (L4T) |
|
| TensorRT |
|
| cuDNN |
|
| CUDA toolkit |
|
| Multimeida API |
|
| computer vision | |
| development tools (installed on host PC) |
|
Use of application domain-specific SDKs
More application-specific SDKs can be deployed on top of JetPack. Typical examples are as follows. These SDKs are sometimes called "frameworks," "toolkits," "platforms," etc. If you can use an SDK that is specific to your application domain, you may be able to build your application with less programming effort. On the other hand, fine control may be difficult. It is a trade-off between taking programming man-hours and taking the ease of creating application-specific parts. Also, if the SDK is difficult to use, it may cost a lot to learn, so be careful.
|
SDKs |
Usage |
Features |
| NVIDIA DeepStream SDK | Intelligent video analytics |
Provided as part of NVIDIA Metropolis and can be used in combination with pre-trained models and Transfer Learning Toolkit, a tool for transfer learning
|
| NVIDIA Isaac SDK | AI-powered robot | A simulator is also provided. |
| Open SDK ROS etc. |
Different for each SDK | Enhancing the ecosystem |
| Jetson-compatible SDKs sold by third parties | Different for each SDK | Each third party has its own characteristics |
If you can't find an application-specific SDK or it isn't right for you
冒頭にもありましたが、基本的にはNVIDIA DeepStream SDKやNVIDIA Isaac SDKの利用をお勧めしていますが、JetPackに含まれるライブラリーのAPIを直接コールする方法を知っていると今後のアプリケーション開発に役立てていただくことが可能です。
想定するビデオ処理ステップと利用するライブラリー/APIは以下のとおりです。
|
process |
Libraries/APIs used |
| Video input (CSI-connected Libargus compliant camera) |
|
| Video input (V4L2 compliant camera with USB connection) |
|
| Resize and format conversion |
|
| image display |
|
| video encoding |
|
| video decoding |
|
| Image processing |
|
| deep learning inference |
|
Next time, I will explain the "video input" method that should be considered first in a video processing system!
連載記事「Jetsonビデオ処理プログラミング」の第1話、NVIDIA提供 JetPackとSDKでできることをご紹介しましたがいかがでしたでしょうか。
次回はいよいよビデオの入力方法についてご紹介します。
If you have any questions, please feel free to contact us.
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Related information
![[Knowledge required for AI image analysis application development] Episode 1 Thumbnail image of NVIDIA DeepStream SDK](/business/semiconductor/articles/134117_thumb_r_6.png)
![[Acceleration of deep learning inference] Episode 1 Thumbnail image of the overview of NVIDIA TensorRT](/business/semiconductor/articles/136485_thumb_1.png)