Abstract
It’s, finally, time to get our hands dirty and write some code to program the Kinect device. No matter how old I get, the enthusiasm of coding for such an exciting technology resembles the unique feeling of the first “Hello World” program I’ve ever written.
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Notes
- 1.
Keep in mind that the SDK version number may be different in your case.
- 2.
DNN stands for “Deep Neural Network,” and it’s a type of AI set of cognitive algorithms. ONNX stands for “Open Neural Network Exchange” and is an AI ecosystem. The ONNX Runtime is a low-level engine that processes the Machine Learning model to acquire its body-tracking results.
- 3.
Netron model visualizer: https://lutzroeder.github.io/netron/
- 4.
Mastering Azure Kinect source code: https://github.com/vangos/mastering-azure-kinect
- 5.
Mastering Azure Kinect Unity package: https://github.com/vangos/mastering-azure-kinect/releases/latest
- 6.
The video camera and the depth camera are different sensors; thus, they do not fire their frames at exactly the same time.
- 7.
To create a disposable object yourself, simply implement the IDisposable C# interface.
- 8.
The actual exception message would read “AzureKinectException: result = K4A_WAIT_RESULT_FAILED”.
- 9.
There is no such thing as a fully bulletproof application. Things may go wrong, even if you think you’ve checked everything. For example, what happens if the user manually unplugs the Kinect cable while the app is in use? In that case, you should add a background process that’s constantly checking for the number of connected devices. I’ll leave this as an exercise for you.
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© 2022 Vangos Pterneas
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Pterneas, V. (2022). Configuring the Device. In: Mastering the Microsoft Kinect . Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-8070-6_3
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DOI: https://doi.org/10.1007/978-1-4842-8070-6_3
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Publisher Name: Apress, Berkeley, CA
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Online ISBN: 978-1-4842-8070-6
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