Run Any Raspberry Pi AI Project FASTER! OAK-D Lite Camera, Getting Starting With Raspberry Pi

Ever needed a performance boost when running Machine Learnt AI Systems (like facial recognition) with a Raspberry Pi Single Board Computer? Or wanted some Depth Data from your Camera feed? Then the OAK-D Lite is for you!

OpenCV AI Kit Depth = Oak-D

Related Information

This is a single USB-C powered module that sports 3 Cameras and a suite of circuitry. It is like a Google Coral but with auto-focusing cameras. It has an autofocus RGB high-resolution 4K central camera that can run at 60FPS and two 480P Binocular Vision (Stereo) Cameras that can run at 200 FPS. There is also internal circuitry to the Oak-D Lite that does the hard yards of machine-learned processing and provides the Raspberry Pi with the results. Thus this offloads the computational operations to the module and leaves your Raspberry Pi free and capable to perform other tasks. The brain of the OAK-D Lite is an Intel® Myriad™ X Visual Processing Unit (VPU).

OAK-D was the world’s first Spatial AI camera and OAK-D Lite is Luxonis's most recent offering. The two stereo cameras pointed forwards enable the OAK-D Lite to create depth maps and determine accurately the distance to identified objects. This is similar to human binocular vision, except much more accurate. They both can run multiple neural networks simultaneously for visual perception tasks like object detection, image classification, segmentation, pose estimation, text recognition, and more while performing depth estimation in real-time. Also, Happy Birthday to me! A number of these machine-learned systems I have explored before running directly on a Raspberry Pi 4 Model B, Face Recognition with the Raspberry Pi, Hand and Gesture Control with Raspberry PI, and Object Detection with the Raspberry Pi (to name a few). But none of these guides run at the high FPS (frames per second) or high resolution like the OAK-D Lite. This higher FPS speed unlocks new AI capabilities and ways to stack multiple AI systems in a single Python script that I have not been able to explore with previous hardware.

This guide will demonstrate exactly how this kind of AI processing can be harnessed by the everyday maker, whilst allowing them a peek in the programming back room to see and understand exactly what levers there are to adjust. By the end of this guide, you will have a Raspberry Pi single-board computer with a Spatial AI Camera capable of keeping up with the most current advances in Edge Machine Learnt technologies. Edge computing is the idea of pushing computing and data closer to where they are used, which means no cloud computing.

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0:00 Intro
0:33 Oak-D Lite Overview
1:04 Depth Overview
1:35 OAK-D Lite on LEFT, Oak-D Original in MIDDLE, Raspberry Pi on RIGHT
2:07 What You Need
3:12 Assembly of Hardware
4:40 Software Set Up
5:55 Luxonis GUI Demo
7:59 GUI Demo Exploration
11:45 Available Open Source AI Systems
12:15 Remote Heartbeat Monitoring with AI
14:20 Face Blurring with AI
15:20 VR Projected CAD Control
16:12 Where to Now
16:49 Outro

[jyothishkumar]

Actually, the Raspberry Pi 4 has all USB ports connected to the CPU using a single PCIe Gen 2 lane. That means a total of 4 Gbps shared across all USB ports. Even though it's correct to plug in OAK to the USB3 port, plugging other devices to other ports can still take away from the USB 3 port's bandwidth.

I found this while trying to design a multi-camera system.

[gearscodeandfire]

Dude, owning an oak-d lite, this is a really good breakdown of this incredibly powerful combo, bravo 🤘

[mermoqqueijo]

Man this is great!

Just realized that here on the lab I work we have a robot with this camera attached. Gonna try to figure out how to make both work together

[johnreskusich]

Very cool demo. Just getting started with mine and it was a useful overview.
Thank you.

[HIBAW]

Really great overview! I appreciate all the relevant context throughout. ty.

[jeffschroeder]

I regret that I am only allowed to click the like button once! Muy bueno!

[liamjonah]

Thank you for the videos you have been posting we are learning a lot from them.

[louvoodoo]

Outstanding video, thank you so much for making this!

[BigJim]

Great video. So many ideas swirling :)

[Midhun]

Damn, that's a lot to learn, thank you. Haven't tried it but will do. Love from India

[klsh]

Thanks for this video! Do you know the max depth distance by any chance? Is it more than a Kinect 2.0?

[rahaf.r]

Which one is better using YOLOv8 and Raspberry Pi4 : Raspberry Pi HQ camera or that one in this video?

[philipptietjen]

Nice! Can the camera be used for pose estimation too? I saw your other video on pose and face detection. Would great to combine heart rate and pose detection for tracking and guiding workouts. Is the built in pose estimation here better than Open CV?

[tld]

why is the audio choppy. artificially add in background noise so silence is not so jarring

[qzorn]

wow this is so exciting! 🥳 I have a Raspberry Pi 4 order. Will the OAK-D Lite camera run on the Orange Pi 800 all-in-one-keyboard or the new Orange Pi 5? 😎 Thanks a lot.

[kapildevpalanisamy]

how can i find the best pre-trained models in open sourse

[Florencio]

hi I would like to know how to make a video with a Rasp 3 + where I can also overlay information from different Arduino sensors so that everything is coordinated on a screen that is showing the HDMI output of the Raspeberry
Another question is posible split in multiple sectors the data in the video is for my project to a starship for my child thanks

[Alpha_fitz]

I'm going to write a program to sense who is looking at people's butts. Ima call it buttlooker. I called dibs.

[ivando]

Can this camera detect the terrain on an image on TV screen?

[PattyCali]

Say i want to track animals such as deer, do i need an ai camera like this one or are there cheaper alternatives, like a regular pi camera to do the same thing just lower quality. Thanks 🙏