NVIDIA Jetson Reference Designs
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About NVIDIA Jetson Reference Designs
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Each example comes with the source code and they are implemented in different languages and using different frameworks (bash scripts, python, C/C++, GStreamer, etc). You can find the example that is closer to your use case, and use it as a starting point for your demos and exploratory tests. Instructions for creating your own demos are provided so you can easily play around with it. The project is provided as a Docker image so it is easy to set up in your system and start playing around with the examples in no time. No additional and bothering system setup or package installation is required. The web service interface allows you to run the demos remotely from your cell phone or a desktop computer as well as locally on your Jetson board. Example Applications GalleryWhen you run the Docker container in your Jetson system, you will be able to access to the demos gallery by entering the to the following URL in the Jetson browser: http://<BOARD_IP>:8000 Note that BOARD_IP is the IP of the Jetson board. A web page with the demos gallery will show up as shown in the following figure:
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How to create your own demo applicationYou can use Jetson AI to generate your own demo applications and use the provided framework to test them without worrying about the system setup. The easiest way to add a new demo is to use any of the existent examples as a starting point. For this reason, we have provided several demos using different execution flavors such as:
And using different frameworks such as OpenCV, GStreamer, DeepStream, CUDA, etc. You only need to find the application that matches your use case and use it as a reference for your own design. Example applications are located at: <path to demos> Every demo is composed of four main components as shown in Figure 4. The arrows show information dependency/use between components. Error creating thumbnail: Unable to save thumbnail to destination Information File: A JSON file containing all the meta-information regarding the demos and how to execute it. The JSON file should look like follows: { "title": "My demo title", "category": "Detection", "hero": "demo_image.jpg", "description" : "This is a demo example", "entrypoint" :{ "cmd": "python3", "cmdargs": "examples/demo_directory/web_page.py", "redirect": "8004" } Title: The title of the demo application - this will be displayed in the main page. Category: A category for the application
Display Image: This is just an image file that will be used in the demo gallery as a background image. Demo Web Page: This is the entry point for the demo page and it is meant to be either loaded when executed from the main page or executed in a standalone fashion. It brings up the basic web interface for the demo and the corresponding controls. The demo web page exposes the necessary controls to run the specific demo as shown in the following image: Demo Source Code: This is the core of the demo, it contains the source code for the demo in the corresponding language or framework. It is meant to interface with the demo web page.
examples/ ├── my_example_app │ ├── example.jpg | ├── example.py | ├── web_page.py │ └── example.json ├── gstcuda_opencv_filters │ ├── gstcuda_opencv_filters.jpg │ ├── gstcuda_opencv_filters.json │ ├── gstcuda_opencv_filters.py │ └── web_page.py ├── gstcuda_opencv_warp │ ├── gstcuda_opencv_warp.jpg │ ├── gstcuda_opencv_warp.json │ ├── gstcuda_opencv_warp.py │ └── web_page.py └── object_detection_with_deepstream ├── deepstream_camera_detection.py ├── object_detection_with_deepstream.jpg ├── object_detection_with_deepstream.json └── web_page.py After this, feel free to modify the demo files to properly match your application. The demo will automatically appear on the main web page after reboot as follows: |