FAQs & How-To

Why Does DepthAI Exist?

In trying to solve an Embedded Spatial AI problem (details here), we discovered that although the perfect chip existed, there was no platform (hardware, firmware, or software) which allowed the chip to be used to solve such a Spatial AI & CV problem.

So we built the platform - known as DepthAI and the OpenCV AI Kit (OAK) - which allows folks to embed performant, spatial AI & CV into their products quickly and easily.

What is DepthAI?

DepthAI is the Embedded, Performant, Spatial AI+CV platform, composed of an open-source hardware, firmware, software ecosystem that provides turnkey embedded Spatial AI+CV and hardware-accelerated computer vision.

It gives embedded systems the super-power of human-like perception in real-time: what an object is and where it is in physical space.

It can be used with off-the-shelf AI models (how-to here) or with custom models using our completely-free training flow (how-to here).

An example of a custom-trained model is below, where DepthAI is used by a robot to autonomously pick and sort strawberries by ripeness.

Spatial AI Strawberry Example

It was trained to do so over the course of a weekend, by a student (for a student project), using our free online training resources.

DepthAI is also open-source (including hardware). This is done so that companies (and even individuals) can prototype and productize solutions quickly, autonomously, and at low risk.

See the summary of our (MIT-Licensed) Github repositories below, which include open-source hardware, firmware, software, and machine-learning training.

What is SpatialAI? What is 3D Object Localization?

First, it is necessary to define what ‘Object Detection’ is:

Object Detection

It is the technical term for finding the bounding box of an object of interest, in pixel space (i.e. pixel coordinates), in an image.

3D Object Localization (or 3D Object Detection), is all about finding such objects in physical space, instead of pixel space. This is useful when trying to real-time measure or interact with the physical world.

Below is a visualization to showcase the difference between Object Detection and 3D Object Localization:

Spatial AI Visualization

Spatial AI is then the super-set of such 2D-equivalent neural networks being extended with spatial information to give them 3D context. So in other words, it’s not limited to object detectors being extended to 3D object localizers. Other network types can be extended as well, including any network which returns results in pixel space.

An example of such an extension is using a facial landmark detector on DepthAI. With a normal camera this network returns the 2D coordinates of all 45 facial landmarks (contours of eyes, ears, mouth, eyebrows, etc.) Using this same network with DepthAI, each of these 45 facial landmarks is now a 3D point in physical space instead of 2D points in pixel space.

How is DepthAI Used? In What Industries is it Used?

DepthAI has been used in effectively every industry, from farming/ranch, to cleaning spots courts, to building personal-service robots. Here’s a quick list of some common use-cases of DepthAI:

  • Visual assistance (for visually impaired, or for aiding in fork-lift operation, etc.)

  • Aerial / subsea drones (fault detection, AI-based guidance/detection/routing)

  • E-scooter & micromobility (not allowing folks to ride rented e-scooters like jerks)

  • Cargo/transport/autonomy (fullness, status, navigation, hazard avoidance)

  • Sports monitoring (automatically losslessly zooming in on action)

  • Smart agriculture (e.g guiding lasers to kill weeds, pests, or targeting watering)

What Distinguishes OAK-D From Other Cameras?

DepthAI purpose is the tight fusion of real-time, hardware-accelerated depth estimation, neural inference, and computer vision into a single, simple to use interface. It is the equivalent of combining a 12MP/4K camera, a stereo depth camera, an AI processor into one product. And to boot, it has accelerated CV capabilities to tie this all together.

So this produces a smaller, lower power, more performant, significantly easier-to-use, and lower-cost solution than what would be otherwise required, which would be to purchase each of these components independently, and do the lifting to physically integrate them and also write the code to combine disparate codebases.

With DepthAI, this is all done for you, and is available in a device that you can buy and plug into a computer (as below) - and also a module (here) with all these capabilities that can be integrated into your product - to allow your products to have these capabilities built-in.

DepthAI comparison

How Does DepthAI Provide Spatial AI Results?

There are two ways to use DepthAI to get Spatial AI results:

  1. Monocular Neural Inference fused with Stereo Depth.

    In this mode the neural network is run on a single camera and fused with disparity depth results. The left, right, or RGB camera can be used to run the neural inference.

  2. Stereo Neural Inference.

    In this mode the neural network is run in parallel on both the left and right stereo cameras to produce 3D position data directly with the neural network.

In both of these cases, standard neural networks can be used. There is no need for the neural networks to be trained with 3D data.

DepthAI automatically provides the 3D results in both cases using standard 2D-trained networks, as detailed here. These modes have differing minimum depth-perception limits, detailed here.

Monocular Neural Inference fused with Stereo Depth

In this mode, DepthAI runs object detection on a single cameras (user’s choice: left, right, or RGB) and the results are fused with the stereo disparity depth results. The stereo disparity results are produced in parallel and in real-time on DepthAI (based on semi global matching (SGBM)).

DepthAI automatically fuses the disparity depth results with the object detector results and uses this depth data for each object in conjunction with the known intrinsics of the calibrated cameras to reproject the 3D position of the detected object in physical space (X, Y, Z coordinates in meters).

And all of these calculations are done onboard to DepthAI without any processing load to any other systems. This technique is great for object detectors as it provides the physical location of the centroid of the object - and takes advantage of the fact that most objects are usually many pixels so the disparity depth results can be averaged to produce a more accurate location.

A visualization of this mode is below.

Monocular AI plus Stereo Depth for Spatial AI

In this case the neural inference (20-class object detection per here) was run on the RGB camera and the results were overlaid onto the depth stream. The DepthAI reference Python script can be used to show this out (python3 depthai_demo.py -s metaout depth -bb is the command used to produce the video above).

And if you’d like to know more about the underlying math that DepthAI is using to perform the stereo depth, see this excellent blog post here here. And if you’d like to run the same example run in that blog, on DepthAI, see this depthai-experiment.

What is the Max Stereo Disparity Depth Resolution?

The maximum resolution for the depthai depth map is 1280x800 (1MP), with either a 96-pixel (default) or 191-pixel disparity search (when Extended Disparity is enabled) and either a full-pixel (default) or sub-pixel matching with precision of 32 sub-pixel steps (when Sub-Pixel Disparity is enabled), resulting in a maximum theoretical depth precision of 191 (extended disparity search mode) * 32 (sub-pixel disparity search enabled) of 6,112. However sub-pixel and extended disparity are not yet supported simultaneously, but should be available in the near future (Pull Request). More information on the disparity depth modes are below:

  1. Default (96-pixel disparity search, range: [0..95]): 1280x800 or 640x400, 96 depth steps

  2. Extended Disparity (191-pixel disparity search, range: [0..190]), here: 1280x800 or 640x400, 191 depth steps

  3. Subpixel Disparity (32 sub-pixel steps), here, 1280x800 or 640x400, 96 depth steps * 32 subpixel depth steps = 3,072 depth steps.

  4. LR-Check Disparity, here: 1280x800, with disparity run in both directions for allowing recentering of the depth.

(see Extended Disparity below)

Stereo Neural Inference

In this mode DepthAI runs the neural network in parallel on both the left and right stereo cameras. The disparity of the results are then triangulated with the calibrated camera intrinsics (programmed into the EEPROM of each DepthAI unit) to give 3D position of all the detected features.

This stereo neural inference mode affords accurate 3D Spatial AI for networks which produce single-pixel locations of features such as facial landmark estimation, pose estimation, or other meta-data which provides feature locations like this.

Examples include finding the 3D locations of:

  • Facial landmarks (eyes, ears, nose, edges of mouth, etc.)

  • Features on a product (screw holes, blemishes, etc.)

  • Joints on a person (e.g. elbow, knees, hips, etc.)

  • Features on a vehicle (e.g. mirrors, headlights, etc.)

  • Pests or disease on a plant (i.e. features that are too small for object detection + stereo depth)

Again, this mode does not require the neural networks to be trained with depth data. DepthAI takes standard, off-the-shelf 2D networks (which are significantly more common) and uses this stereo inference to produce accurate 3D results.

An example of stereo neural inference is below.

DepthAI parallel multi-stage inference

And this is actually an interesting case as it demonstrates two things on DepthAI:

  1. Stereo inference (i.e. running the neural network(s) running on both the left and right cameras in parallel)

  2. Multi-stage inference (i.e. face detection flowed directly into facial landmark directly on DepthAI)

We have a gen2-triangulation demo that performs the stereo neural interface. You should use the gen2 demo, as we are focusing only on the gen2.

If you would like to use the (old) gen1 API, you would have to download gen1 depthai library (python3 -mpip install depthai== and checkout the gen1_main branch of the depthai repo. After that, you can run

python3 depthai_demo.py -cnn face-detection-retail-0004 -cnn2 landmarks-regression-retail-0009 -cam left_right -dd -sh 12 -cmx 12 -nce 2 -monor 400 -monof 30

Where cam specifies to run the neural network on both cameras, -cnn specifies the first-stage network to run (face detection, in this case), -cnn2 specifies the second-stage network (facial landmark detection, in this case), and -dd disables running disparity depth calculations (since they are unused in this mode).


It is worth noting that monocular neural inference fused with stereo depth is possible for networks like facial-landmark detectors, pose estimators, etc. that return single-pixel locations (instead of for example bounding boxes of semantically-labeled pixels), but stereo neural inference is advised for these types of networks better results as unlike object detectors (where the object usually covers many pixels, typically hundreds, which can be averaged for an excellent depth/position estimation), landmark detectors typically return single-pixel locations. So if there doesn’t happen to be a good stereo-disparity result for that single pixel, the position can be wrong.

And so running stereo neural inference excels in these cases, as it does not rely on stereo disparity depth at all, and instead relies purely on the results of the neural network, which are robust at providing these single pixel results. And triangulation of the parallel left/right outputs results in very-accurate real-time landmark results in 3D space.

What is the Gen2 Pipeline Builder?

UPDATE: The Gen2 Pipeline Builder is now the standard release of DepthAI. This Gen2 API system was architected to be next-generation software suite for DepthAI and OAK. All DepthAI and OAK hardware work with Gen1 and Gen2 software, as Gen2 is purely a software re-write, no hardware changes. Gen2 is infinitely more flexible, and is the result of all that we learned from the customer deployments of Gen1. Amassing all the requests and need for flexibility from users of Gen1, we made Gen2. In short, Gen2 allows theoretically-infinite permutations of parallel and series CV + AI (neural inference) nodes, limited only by hardware capabilities, whereas Gen1 was limited for example to 2-series and 2-parallel neural inference. Full background on the Gen2 Pipeline Builder is here.

Several Gen2 Examples are here and also the docs for Gen2 are now available in the main docs page.

What is megaAI?

The monocular (single-camera) version of DepthAI is megaAI. Because not all solutions to embedded AI/CV problems require spatial information.

We named it mega because it’s tiny:


megaAI uses all the same hardware, firmware, software, and training stacks as DepthAI (and uses the same DepthAI Github repositories), it is simply the tiny single-camera variant.

More details - including open source 3D files and schematics, can be found here.

You can buy megaAI from our distributors and also our online store here.

Which Model Should I Order?

Embedded CV/AI requires all sorts of different shapes/sizes/permutations. And so we have a variety of options to meet these needs in our store. Below is a quick/dirty summary for the ~10,000-foot view of the options:

  • USB3C with Onboard Cameras and Depth (OAK-D) - Great for quickly using DepthAI with a computer. All cameras are onboard, and it has a USB3C connection which can be used with any USB3 or USB2 host.

  • USB3C with Single Camera (OAK-1) - This is just like the OAK-D, but for those who don’t need depth information. Single, small, plug-and-play USB3C AI/CV camera.

  • USB3C with Modular Cameras (OAK-FFC-3P) - Great for prototyping flexibility. Since the cameras are modular, you can place them at various stereo baselines. This flexibility comes with a trade - you have to figure out how/where you will mount them, and then once mounted, do a stereo calibration. This is not a TON of work, but keep this in mind, that it’s not ‘plug and play’ like other options - it’s more for applications that require custom mounting, custom baseline, or custom orientation of the cameras.

  • PoE models (OAK-D-PoE) - It is the equivalent of the OAK-D, with with PoE instead of USB. If you don’t need depth, we have OAK-1-PoE.

  • All in One Dev. Kits (OAK-D-CM4) - this one has a built-in Raspberry Pi Compute Module 4. So you literally plug it into power and HDMI, and it boots up showing off the power of DepthAI.

  • Embedded with WiFi/BT (OAK-D-IoT-40 and OAK-D-IoT-75) - We have two models that have additional 128MB NOR flash, so they can boot on their own out of the NOR flash, and no host needs to be present to run. In contrast, the OAK-D-CM4 can also run on its own, but it is still booting over USB from the Raspberry Pi. On OAK-D-IoT-40 and OAK-D-IoT-75, the Myriad X can run completely standalone and with no other devices. The built-in ESP32 then provides easy/convenient WiFi/BT support (more info here) as well as popular integrations like plug-and-play AWS-IoT support, great iOS/Android BT examples, etc.

More products in store.

More details - including open source 3D files and schematics, can be found in hardware documentation.

System on Modules

For designing products around DepthAI, we offer system on modules. You can then design your own variants, leveraging our open source hardware. There are three system on modules available:

  1. OAK-SoM - USB-boot system on module. For making devices which interface over USB to a host processor running Linux, MacOS, or Windows. In this case, the host processor stores everything, and the OAK-SoM boots up over USB from the host.

  2. OAK-SoM-IoT - NOR-flash boot (also capable of USB-boot). For making devices that run standalone, or work with embedded MCUs like ESP32, AVR, STM32F4, etc. Can also USB-boot if/as desirable.

  3. OAK-SoM-Pro - NOR flash, eMMC, SD-Card, and USB-boot (selectable via IO on the 2x 100-pin connectors). For making devices that run standalone and require onboard storage (16GB eMMC) and/or Ethernet Support (the onboard PCIE interface through one of the 2x 100-pin connectors, paired with an Ethernet-capable base-board provides Ethernet support).

Check our hardware documentation for more details.

How hard is it to get DepthAI running from scratch? What Platforms are Supported?

Not hard. Usually DepthAI is up/running on your platform within a couple minutes (most of which is download time). The requirements are Python and OpenCV (which are great to have on your system anyway!). see here for supported platforms and how to get up/running with them.

Raspbian, Ubuntu, macOS, Windows, and many others are supported and are easy to get up/running. For the list of supported platforms (and instructions on how to get started), click here.

It’s a matter of minutes to be up and running with the power of Spatial AI, on the platform of your choice. Below is DepthAI running on my Mac.

DepthAI on Mac

(Click on the image above to pull up the YouTube video.)

The command to get the above output is

python3 depthai_demo.py -s metaout previewout depth -ff -bb

Here is a single-camera version (megaAI) running with python3 depthai_demo.py -dd (to disable showing depth info):

DepthAI on Mac

Is DepthAI and MegaAI easy to use with Raspberry Pi?

Very. It’s designed for ease of setup and use, and to keep the Pi CPU not-busy.

See here to get up and running quickly!

Can all the models be used with the Raspberry Pi?

Yes, every model can be used, including:

  • OAK-D-CM4 - this one has a built-in Raspberry Pi Compute Module 4

  • OAK-D

  • OAK-FFC-3P

  • OAK-1

  • Raspberry Pi HAT (BW1094) - this can also be used with other hosts as its interface is USB3

We even have some basic ROS support going as well which can be used on the Pi also.

Does DepthAI Work on the Nvidia Jetson Series?

Yes, DepthAI and megaAI work cleanly on all the Jetson/Xavier series, and installation is easy. Jetson Nano, Jetson Tx1, Jetson Tx2, Jetson Xavier NX, Jetson AGX Xavier, etc. are all supported.

See below for DepthAI running on a Jetson Tx2 I have on my desk:

Jetson Tx2

Installing for NVIDIA Jetson and Xavier is now the same set of instructions as Ubuntu. See here and following the standard Ubuntu instructions.

Also don’t forget about the udev rules after you have that set up. And make sure to unplug and replug your depthai after having run the following commands (this allows Linux to execute the modification of the USB rules).

echo 'SUBSYSTEM=="usb", ATTRS{idVendor}=="03e7", MODE="0666"' | sudo tee /etc/udev/rules.d/80-movidius.rules
sudo udevadm control --reload-rules && sudo udevadm trigger

Can I Use Multiple DepthAI With One Host?

Yes. DepthAI is architected to put as-little-as-possible burden on the host. So even with a Raspberry Pi you can run a handful of DepthAI with the Pi and not burden the Pi CPU.

See here for instructions on how to do so.

Is DepthAI OpenVINO Compatible?

Yes. DepthAI is fully compatible with OpenVINO 2020.1, 2020.2, 2020.3, 2020.4, 2021.1 and 2021.2.

Can I Train My Own Models for DepthAI?


We have a tutorial around Google Colab notebooks you can even use for this. See here

Do I Need Depth Data to Train My Own Custom Model for DepthAI?


That’s the beauty of DepthAI. It takes standard object detectors (2D, pixel space) and fuses these neural networks with stereo disparity depth to give you 3D results in physical space.

Now, could you train a model to take advantage of depth information? Yes, and it would likely be even more accurate than the 2D version. To do so, record all the streams (left, right, and color) and retrain on all of those (which would require modifying the front-end of say MobileNet-SSD to allow 5 layers instead of 3 (1 for each grayscale, 3 for the color R, G, B)).

If I train my own network, which Neural Operations are supported by DepthAI?

See the VPU section here.

Anything that’s supported there under VPU will work on DepthAI. It’s worth noting that we haven’t tested all of these permutations though.

What network backbones are supported on DepthAI?

All the networks listed here are supported by DepthAI.

We haven’t tested all of them though. So if you have a problem, contact us and we’ll figure it out.

My Model Requires Pre-Processing (normalization, for example). How do I do that in DepthAI?

The OpenVINO toolkit allows adding these pre-processing steps to your model, and then these steps are performed automatically by DepthAI. See here for how to take advantage of this.

For instance, to scale frame pixels to the range [0,1], consider adding the following parameters to the model optimizer: --data_type=FP16 --scale_values [255,255,255]

To scale to the range [-1, 1], mean values should be added, e.g. for mobilenet: --scale_values [127.5, 127.5, 127.5] --mean_values [127.5, 127.5, 127.5]

More model converting options here

Can I Run Multiple Neural Models in Parallel or in Series (or Both)?

Yes. The Gen2 Pipeline Builder is what allows you to do this. And we have several example implementations of parallel, series, and parallel+series in depthai-experiments repository. A notable example is the Gaze estimation example, here, which shows series and parallel all together in one example.

Can DepthAI do Arbitrary Crop, Resize, Thumbnail, etc.?

Yes, see here for an example of how to do this, with WASD controls of a cropped section. And see here for extension of the cropping for non-rectangular crops, and warping those to be rectangular (which can be useful for OCR).

Can DepthAI Run Custom CV Code? Say CV Code From PyTorch?

Yes, although we have yet to personally do this. But folks in the community have. Rahul Ravikumar is one, and was quite nice to have written up the process on how to do this, see here. This code can then be run as a node in the Gen2 Pipeline Builder, to be paired with other CV nodes, neural inference, depth processing, etc. that are supported on the platform.

How do I Integrate DepthAI into Our Product?

How to integrate DepthAI depends on whether the product you are building includes:

  1. a processor running an operating system (Linux, MacOS, or Windows) or

  2. a microcontroller (MCU) with no operating system (or an RTOS like FreeRTOS) or

  3. no other processor or microcontroller (i.e. DepthAI is the only processor in the system).

We offer hardware to support all 3 use-cases, but firmware/software maturity varies across the 3 modes:

  1. Using our Python API and/or C++ API (equal capabilities)

  2. Using our C++ SPI API (see here),

  3. Using our standalone flashing utility to flash a depthai application for standalone boot (as part of Pipeline Builder Gen2, leveraging our SBR Util here).

In all cases, DepthAI is compatible with OpenVINO for neural models. The only thing that changes between the modalities is the communication (USB, Ethernet, SPI, etc.) and what (if any) other processor is involved.

Use-Case 1: DepthAI are a co-processor to a processor running Linux, MacOS, or Windows.

In this case, DepthAI can be used in two modalities:

  • NCS2 Mode (USB, here) - in this mode, the device appears as an NCS2 and the onboard cameras are not used and it’s as if they don’t exist. This mode is often use for initial prototyping, and in some cases, where a product simply needs an ‘integrated NCS2’ - accomplished by integrating a OAK-SoM.

  • DepthAI Mode (USB, using our USB API, here) - this uses the onboard cameras directly into the Myriad X, and boots the firmware over USB from a host processor running Linux, Mac, or Windows. This is the main use-case of DepthAI/megaAI when used with a host processor capable of running an operating system (e.g Raspberry Pi, i.MX8, etc.).

Use-Case 2: Using DepthAI with a MicroController like ESP32, ATTiny8, etc.

In this case, DepthAI boots off of internal flash on the OAK-SoM-IoT and communicates over SPI, allowing DepthAI to be used with microcontroller such as the STM32, MSP430, ESP32, ATMega/Arduino, etc. We even have an embedded reference design for ESP32 (OAK-D-IoT-40 (BW1092)) available on our store. And it’s open-source! You can check design files here.

We have prepared a guide and a demo on how to work with ESP32.

Use-Case 3: Using DepthAI as the Only Processor on a Device.

This is supported through running microPython directly on the OAK-SoM-Pro or OAK-SoM-IoT as nodes in the Gen2 Pipeline Builder.

The microPython nodes allow custom logic, driving I2C, SPI, GPIO, UART, etc. controls, letting direct controls of actuators, direct reading of sensors, etc. from/to the pipeline of CV/AI functions. A target example is making an entire autonomous, visually-controlled robotic platform with DepthAI as the only processor in the system.

Hardware for Each Case:

  • OAK-SoM: USB boot. So it is intended for working with a host processor running Linux, Mac, or Windows and this host processor boots the OAK-SoM over USB

  • OAK-SoM-IoT: USB boot or NOR-flash boot. This module can work with a host computer just like the OAK-SoM, but also has a 128MB NOR flash built-in and boot switches onboard - so that it can be programmed to boot off of NOR flash instead of USB. So this allows use of the DepthAI in pure-embedded applications where there is no operating system involved at all. So this module could be paired with an ATTiny8 for example, communicating over SPI, or an ESP32 like on the OAK-D-IoT-40 (which comes with the OAK-SoM-IoT pre-installed).

  • OAK-SoM-Pro: Supports multiple boot options: NOR (128MB), eMMC (SD-Card support), USB, Ethernet (EEPROM, 32KB). All those boot options make OAK-SoM-Pro very flexible in terms of use cases and most apropriate as a standalone device. It is designed for integration into a top-level system with a need for a low power AI vision system.

Getting Started with Development

Whether intending to use DepthAI with an OS-capable host, a microcontroller over SPI (in development), or completely standalone - we recommend starting with either NCS2 mode or with the DepthAI USB API for prototype/test/etc. as it allows faster iteration/feedback on neural model performance/etc. And in particular, with NCS2 mode, all the images/video can be used directly from the host (so that you don’t have to point the camera at the thing you want to test).

In DepthAI mode, theoretically, anything that will run in NCS2 mode will run - but sometimes it needs host-side processing if it’s a network we’ve never run before. And this work is usually not heavy lifting. See some examples here and in out Github.

For common object detector formats (MobileNet-SSD, (Tiny) YOLO V3/V4) there’s effectively no work to go from NCS2 mode to DepthAI mode because we have added the support for decoding their results on the device side. To use the device side decoding with gen2, have a look at YoloDetectionNetwork for YOLO (demo here) or MobileNetDetectionNetwork for MobileNet (demo here) decoding.

To use your own trained Yolo model with the DepthAI, you should start with the demo and modify its code a bit:

  • Change the labels at labelMap = ["label1", "label2", "..."], depending on your model

  • Set the number of classes at detectionNetwork.setNumClasses() depending on your model

  • If you haven’t compiled the model with the latest OpenVINO version, set the OpenVINO version

  • Don’t forget to change the path to the model (.blob file)

For MobileNet you should follow the same steps (skip the 2nd one) but start with the MobileNet demo.

Interested in how to train an object detector with your data? You can check our Yolo V4 training tutorial here!

What Hardware-Accelerated Capabilities Exist in DepthAI and/or megaAI?

The DepthAI system is a node-and-graph pipeline builder. Below are the hardware-accelerated nodes that exist in this builder.

Available in DepthAI API Today:

  • Neural Inference Node, which is compatible with OpenVINO (e.g. object detection, image classification, etc., including multi-stage inference, e.g. here and here)

  • Stereo Depth (including median filtering) (e.g. here)

  • Stereo Inference (with two-stage, e.g. here)

  • 3D Object Localization (augmenting 2D object detectors with 3D position in meters, e.g. here and here)

  • Object Tracking (e.g. here, including in 3D space)

  • H.264 and H.265 Encoding (HEVC, 1080p & 4K Video, e.g. here)

  • JPEG Encoding (e.g. here)

  • MJPEG Encoding

  • Warp/Dewarp (for RGB-depth alignment/etc.)

  • Enhanced Disparity Depth Modes (Sub-Pixel, LR-Check, and Extended Disparity), here

  • SPI Support, here

  • Arbitrary crop/rescale/reformat and ROI return (e.g. here)

  • Integrated Text Detection (e.g. here)

  • Pipeline Builder Gen2 (arbitrary series/parallel combination of neural nets and CV functions, background here and API documentation is here).

  • Lossless zoom (from 12MP full to 4K, 1080p, or 720p, here)

  • Improved Stereo Neural Inference Support (here)

  • Integrated IMU Support (here)

  • Edge Detection (here, video)

  • On-Device Python Scripting Support, here

  • Feature Tracking ( here, video)

The above features are available in the Luxonis Pipeline Builder Gen2 which is now the main API for DepthAI. The Gen1 API is still supported, and can be accessed via the version switcher at the bottom left of this page. See below for in-progress additional functionality/flexibility which will be added as modular nodes to the Luxonis pipeline builder for DepthAI.

On our Roadmap (Most are in development/integration)

  • Motion Estimation (here)

  • Background Subtraction (here)

  • AprilTags (PR here)

  • OpenCL Support (supported through OpenVINO (here))

And see our Github project here to follow along with the progress of these implementations.

Pipeline Builder Gen2

The 2nd-generation DepthAI pipeline builder which incorporates all the feedback we learned from our first Generation API. It is now the mainline way to use DepthAI.

It allows multi-stage neural networks to be pieced together in conjunction with CV functions (such as motion estimation or Harris filtering) and logical rules, all of which run on DepthAI/megaAI/OAK without any load on the host.

Are CAD Files Available?


The full designs (including source Altium files) for all the carrier boards are in our depthai-hardware Github.

How to enable depthai to perceive closer distances

If the depth results for close-in objects look weird, this is likely because they are below the minimum depth-perception distance of OAK-D.

For OAK-D, the standard-settings minimum depth is around 70cm.

This can be cut in 1/2 and 1/4 with the following options:

  1. Change the resolution to 640x400, instead of the standard 1280x800.

  2. Enable Extended Disparity.

See these examples for how to enable Extended Disparity.

For more information see the StereoDepth documentation.

What are the Minimum Depths Visible by DepthAI?

There are two ways to use DepthAI for 3D object detection and/or using neural information to get real-time 3D position of features (e.g. facial landmarks):

  1. Monocular Neural Inference fused with Stereo Depth

  2. Stereo Neural Inference

Monocular Neural Inference fused with Stereo Depth

In this mode, the AI (object detection) is run on the left, right, or RGB camera, and the results are fused with stereo disparity depth, based on semi global matching (SGBM). The minimum depth is limited by the maximum disparity search, which is by default 96, but is extendable to 191 in extended disparity modes (see Extended Disparity below).

To calculate the minimum distance in this mode, use the following formula:

min_distance = focal_length_in_pixels * base_line_dist / max_disparity_in_pixels

Where the focal_length_in_pixels is (HFOV of the grayscale global shutter cameras is 71.9 degrees):

focal_length_in_pixels = 1280 * 0.5 / tan(71.9 * 0.5 * PI / 180) = 882.5

Calculation here (and for disparity depth data, the value is stored in uint16, where 0 is a special value, meaning that distance is unknown.)

By using the formula above with the default settings of OAK-D (base_line_dist = 7.5cm, max_disparity_in_pixels = 95), we get:

min_distance = 882.5 * 7.5cm / 95 = 69.67cm

Note that this distance can be halved by either:

  • Changing the resolution to 640x400, instead of the standard 1280x800.

  • Enabling Extended Disparity - see these examples for how to enable Extended Disparity.

Extended disparity mode sets the max_disparity_in_pixels to 190, thus the min_distance for the above OAK-D example is:

min_distance = 882.5 * 7.5cm / 190 = 34.84cm

Note that applying both options is possible, but at such short distances, the minimum distance is limited by focal length, which is 19.6cm, so minumum distance cannot be lower than 19.6cm.

Calculation examples for OAK-D:

  • ~ 70cm with standard disparity (1280x800 resolution)

  • ~ 35cm with extended disparity (1280x800 resolution)

  • ~ 35cm with 640x400 resolution

  • ~ 19.6cm with extended disparity and 640x400 resolution

For a more detailed explanation refer to the StereoDepth documentation.

Stereo Neural Inference

In this mode, the neural inference (object detection, landmark detection, etc.) is run on the left and right cameras to produce stereo inference results. Unlike monocular neural inference fused with stereo depth - there is no max disparity search limit - so the minimum distance is purely limited by the greater of (a) horizontal field of view (HFOV) of the stereo cameras themselves and (b) the hyperfocal distance of the cameras.

The hyperfocal distance of the global shutter synchronized stereo pair is 19.6cm. So objects closer than 19.6cm will appear out of focus. This is effectively the minimum distance for this mode of operation, as in most cases (except for very wide stereo baselines with the OAK-FFC-3P-OG), this effective minimum distance is higher than the actual minimum distance as a result of the stereo camera field of views. For example, the objects will be fully out of the field of view of both grayscale cameras when less than 5.25cm from the OAK-D, but that is closer than the hyperfocal distance of the grayscale cameras (which is 19.6cm), so the actual minimum depth is this hyperfocal distance.

Accordingly, to calculate the minimum distance for this mode of operation, use the following formula:

min_distance = max(tan((90 - HFOV/2) * pi/2) * base_line_dist/2, 19.6)

This formula implements the maximum of the HFOV-imposed minimum distance, and 19.6cm, which is the hyperfocal-distance-imposed minimum distance.

Onboard Camera Minimum Depths

Below are the minimum depth perception possible in the disparity depth and stereo neural inference modes.

Monocular Neural Inference fused with Stereo Depth Mode

For DepthAI units with onboard cameras, this works out to the following minimum depths:

  • OAK-D-CM4 the minimum depth is 0.836 meters for full 1280x800 stereo resolution and 0.418 meters for 640x400 stereo resolution:

min_distance = 882.5 * 0.09 / 95 = 0.836 # m

calculation here

  • OAK-D is

    • 0.697 meters for standard disparity,

    • 0.348 meters for Extended Disparity (191 pixel) at 1280x800 resolution or standard disparity at 640x400 resolution, and

    • 0.196 meters for Extended Disparity at 640x400 resolution (this distance is limited by the focal distance of the cameras on OAK-D)

min_distance = 882.5 * 0.075 / 95 = 0.697 # m

calculation here

Stereo Neural Inference Mode

For DepthAI units with onboard cameras, all models (OAK-D-CM4 and OAK-D) are limited by the hyperfocal distance of the stereo cameras, so their minimum depth is 0.196 meters.

Modular Camera Minimum Depths:

Below are the minimum depth perception possible in the disparity depth and stereo neural inference modes.

Monocular Neural Inference fused with Stereo Depth Mode

For DepthAI units which use modular cameras, the minimum baseline is 2.5cm (see image below) which means the minimum perceivable depth 0.229 meters for full 1280x800 resolution and 0.196 meters for 640x400 resolution (limited by the minimum focal distance of the grayscale cameras, as in stereo neural inference mode).

The minimum baseline is set simply by how close the two boards can be spaced before they physically interfere:

Jetson Tx2

For any stereo baseline under 29 cm, the minimum depth is dictated by the hyperfocal distance (the distance above which objects are in focus) of 19.6cm.

For stereo baselines wider than 29 cm, the minimum depth is limited by the horizontal field of view (HFOV):

min_distance = tan((90-HFOV/2)*pi/2)*base_line_dist/2

Extended Disparity Depth Mode

The extended disparity mode affords a closer minimum distance for the given baseline. This increases the maximum disparity search from 96 to 191. So this cuts the minimum perceivable distance in half (given that the minimum distance is now focal_length * base_line_dist / 190 instead of focal_length * base_line_dist / 95).

See here for examples of how to use Extended Disparity Mode.

And for a bit more background as to how this mode is supported:

Extended disparity: allows detecting closer distance objects, without compromising on long distance values (integer disparity) by running the following flow.

  1. Computes disparity on the original size images (e.g. 1280x720)

  2. Computes disparity on 2x downscaled images (e.g. 640x360)

  3. Combines the two level disparities on Shave, effectively covering a total disparity range of 191 pixels (in relation to the original resolution).

Left-Right Check Depth Mode

Left-Right Check, or LR-Check is used to remove incorrectly calculated disparity pixels due to occlusions at object borders (Left and Right camera views are slightly different).

  1. Computes disparity by matching in R->L direction

  2. Computes disparity by matching in L->R direction

  3. Combines results from 1 and 2, running on Shave: each pixel d = disparity_LR(x,y) is compared with disparity_RL(x-d,y). If the difference is above a threshold, the pixel at (x,y) in final disparity map is invalidated.

To run LR-Check on DepthAI/OAK, use the example here.

What Are The Maximum Depths Visible by DepthAI?

The maximum depth perception for 3D object detection is practically limited by how far the object detector (or other neural network) can detect what it’s looking for. We’ve found that OpenVINO people detectors work to about 22 meters or so. But generally this distance will be limited by how far away the object detector can detect objects, and then after that, the minimum angle difference between the objects.

So if the object detector is not the limit, the maximum distance will be limited by the physics of the baseline and the number of pixels. So once an object is less than 0.056 degrees (which corresponds to 1 pixel difference) difference between one camera to the other, it is past the point where full-pixel disparity can be done. The formula used to calculate this distance is an approximation, but is as follows:

Dm = (baseline/2) * tan_d(90 - HFOV / HPixels)

For DepthAI HFOV = 71.9(+/-0.5) degrees, and HPixels = 1280.

So using this formula for existing models the theoretical max distance is:

  • OAK-D (7.5cm baseline): 38.4 meters

  • OAK-D-CM4 (9cm baseline): 46 meters

  • OAK-D-IOT-40 (4cm baseline): 20.4 meters

  • OAK-FFC-3P (Custom baseline): Dm = (baseline/2) * tan_d(90 - 71.9 / 1280)

But these theoretical maximums are not achievable in the real-world, as the disparity matching is not perfect, nor are the optics, image sensor, etc., so the actual maximum depth will be application-specific depending on lighting, neural model, feature sizes, baselines, etc.

We also support subpixel depth mode, which extend this theoretical max, but again this will likely not be the actual limit of the max object detection distance, but rather the neural network itself will be. And this subpixel use will likely have application-specific benefits.

For more information see the StereoDepth documentation.

Subpixel Disparity Depth Mode

Subpixel improves the precision and is especially useful for long range measurements. It also helps for better estimating surface normals (comparison of normal disparity vs. subpixel disparity is here).

Beside the integer disparity output, the Stereo engine is programmed to dump to memory the cost volume, that is 96 bytes (disparities) per pixel, then software interpolation is done on Shave, resulting a final disparity with 5 fractional bits, resulting in significantly more granular depth steps (32 additional steps between the integer-pixel depth steps), and also theoretically, longer-distance depth viewing - as the maximum depth is no longer limited by a feature being a full integer pixel-step apart, but rather 1/32 of a pixel.

Examples of the difference in depth steps from standard disparity to subpixel disparity are shown below:

Standard Disparity (96 depth steps):

Standard Disparity (96 depth steps)

Subpixel Disparity (3,072 depth steps):

Subpixel Disparity (3,072 depth steps) Subpixel Disparity (3,072 depth steps)

To run Subpixel on DepthAI/OAK, use the example here.

How Does DepthAI Calculate Disparity Depth?

DepthAI makes use of a combination of hardware-blocks (a semi-global-matching disparity (SGBM) hardware block) as well as accelerated vector processing code in the SHAVES of the Myriad X to produce the disparity depth. This block is accessible via the Gen2 Pipeline Builder system, with an example here.

The SGBM hardware-block can process up to 1280x800 pixels, this is its hardware limit. Using higher-resolution sensors is technically possible via downscaling. So for example, using the 12MP color camera with the 1280x800 grayscale camera is possible (and has been prototyped by some users with the Gen2 pipeline builder). Or 2x 12MP image sensors could be used for depth (theoretically). But in both cases, the image data needs to be either decimated down to 1280x800, or converted in some other way (e.g. selectively cropped/windowed).

What Is the Format of the Depth Data in depth stream?

The output array is in uint16, so 0 to 65,535 with direct mapping to millimeters (mm).

So a value of 3,141 in the array is 3,141 mm, or 3.141 meters. So this whole array is the z-dimension of each pixel off of the camera plane, where the center of the universe is the camera marked RIGHT.

And the specific value of 65,535 is a special value, meaning an invalid disparity/depth result.

What Disparity Depth Modes are Supported?

  1. Default (96-pixel disparity search)

  2. Extended Disparity (191-pixel disparity search), here

  3. Subpixel Disparity (32 sub-pixel steps), here

  4. LR-Check Disparity, here

How Do I Calculate Depth from Disparity?

DepthAI does convert to depth onboard for both the depth stream and also for object detectors like MobileNet-SSD, YOLO, etc.

But we also allow the actual disparity results to be retrieved so that if you would like to use the disparity map directly, you can.

To calculate the depth map from the disparity map, it is (approximately) depth = focal_length_in_pixels * baseline / disparity_in_pixels. Where the baseline is 7.5cm for OAK-D, 4.0cm for OAK-D-IoT-40, and 9.0cm for OAK-D-CM4, and the focal_length_in_pixels is 882.5 (focal_length = 1280 * 0.5 / tan(71.9 * 0.5 * PI / 180) = 882.5) for all current DepthAI models.

So for example, for a OAK-D-IoT-40 (stereo baseline of 4.0cm), a disparity measurement of 60 is a depth of 58.8cm (depth = 882.5 * 40mm / 60 = 588 mm (0.588m)).

For more information see the StereoDepth documentation.

How Do I Display Multiple Streams?

To specify which streams you would like displayed, use the -s option. For example for the raw disparity map (disparity), and for depth results (depth), use the following command:

python3 depthai_demo.py -s disparity depth
The available streams are:
  • nnInput - Neural Network passthrough frames on which inference was made on (300x300 in case of MobileNet)

  • color - 4K color camera, biggest camera on the board with lens

  • left - Left grayscale camera (marked L or LEFT on the board)

  • right - Right grayscale camera (marked R or RIGHT on the board)

  • rectifiedLeft - Rectified left camera frames

  • rectifiedRight - Rectified right camera frames

  • depth - Depth in uint16

  • depthRaw - Raw frames which are used to calculate depth

  • disparity - Raw disparity

  • disparityColor - Disparity colorized on the host (JET colorized visualization of depth)

Is It Possible to Have Access to the Raw Stereo Pair Stream on the Host?

Yes, to get the raw stereo pair stream on the host use the following command:

python3 depthai_demo.py -s left right

This will show the full RAW (uncompressed) 1280x720 stereo synchronized pair, as below:

RAW Stereo Pair Streams

How Do I Limit The FrameRate Per Stream?

So the simple way to select streams is to just use the -s option. But in some cases (say when you have a slow host or only USB2 connection and you want to display a lot of streams) it may be necessary to limit the frame rate of streams to not overwhelm the host/USB2 with too much data.

So to set streams to a specific frame rate to reduce the USB2 load and host load, simply specify the stream with -s streamname with a comma and FPS after the stream name like -s streamname,FPS.

So for limiting depth to 5 FPS, use the following command:

python3 depthai_demo.py -s depth,5

And this works equally for multiple streams:

python3 depthai_demo.py -s left,2 right,2 previewout depth,5

It’s worth noting that the frame rate limiting works best for lower rates. So if you’re say trying to hit 25FPS, it’s best to just leave no frame-rate specified and let the system go to full 30FPS instead.

Specifying no limit will default to 30FPS.

How do I Synchronize Streams and/or Meta Data (Neural Inference Results)

The -sync option is used to synchronize the neural inference results and the frames on which they were run. When this option is used, the device-side firmware makes a best effort to send metadata and frames in order of metadata first, immediately followed by the corresponding image.

When running heavier stereo neural inference, particularly with high host load, this system can break down, and there are two options which can keep synchronization:

  1. Reduce the frame rate of the cameras running the inference to the speed of the neural inference itself, or just below it.

  2. Or pull the timestamps or sequence numbers from the results (frames or metadata) and match them on the host.

Reducing the Camera Frame Rate

In the case of neural models which cannot be executed at the full 30FPS, this can cause lack of synchronization, particularly if stereo neural inference is being run using these models in parallel on the left and right grayscale image sensors.

A simple/easy way to regain synchronization is to reduce the frame rate to match, or be just below, the frame rate of the neural inference. This can be accomplished via the command line with the using -rgbf and -monof commands.

So for example to run a default model with both the RGB and both grayscale cameras set to 24FPS, use the following command:

./depthai_demo.py -rgbf 24 -monof 24 -sync

Synchronizing on the Host

The two methods FrameMetadata.getTimestamp() and FrameMetadata.getSequenceNum() can be used to guarantee the synchronization on host side.

The NNPackets and DataPackets are being sent separately from device side, and get into individual queues per stream on host side. The function CNNPipeline.get_available_nnet_and_data_packets() returns what’s available in the queues at the moment the function is called (it could be that just one NN packet is unread, or just one frame packet).

With the -sync CLI option from depthai.py, we are doing a best effort on the device side (i.e. on the Myriad X) to synchronize NN and previewout, and send them in order: first the NN packet is being sent (and in depthai.py it gets saved as the latest), then the previewout frame is being sent (and when received in depthai_demo.py, the latest saved NN data is overlaid on).

In most cases this works well, but there is a risk (especially under high system load on host side), that the packets may still get desynchronized, as the queues are handled by different threads (in the C++ library).

So in that case, getMetadata().getTimestamp() returns the device time (in seconds, as float) and is also used in the stereo calibration script to synchronize the Left and Right frames.

The timestamp corresponds to the moment the frames are captured from the camera, and is forwarded through the pipeline. And the method getMetadata().getSequenceNum() returns an incrementing number per camera frame. The same number is associated to the NN packet, so it could be an easier option to use, rather than comparing timestamps. The NN packet and Data packet sequence numbers should match.

Also, the left and right cameras will both have the same sequence number (timestamps will not be precisely the same, but few microseconds apart - that’s because the timestamp is assigned separately to each from different interrupt handlers. But the cameras are started at the same time using an I2C broadcast write, and also use the same MCLK source, so shouldn’t drift).

In this case we also need to check the camera source of the NN and Data packets. Currently, depthai.py uses getMetadata().getCameraName() for this purpose, that returns a string: rgb, left or right .

It is also possible to use getMetadata().getInstanceNum(), that returns a number: 0, 1 or 2 , respectively.

How do I Record (or Encode) Video with DepthAI?

DepthAI supports h.264 and h.265 (HEVC) and JPEG encoding directly itself - without any host support. The depthai_demo.py script shows and example of how to access this functionality.

Note that hardware limit for the encoder is: 3840x2160 pixels at 30FPS or 248 million pixels/second. The resolution and frame rate can be divided into multiple streams - but the sum of all the pixels/second needs to be below 248 million.

See our encoding examples for Gen2 (current main line), which uses VideoEncoder node:

  • RGB and Mono Encoding, here.

  • RGB Encoding and MobilenetSSD, here.

  • RGB Encoding and Mono with MobilenetSSD and Depth, here.

  • Encoding Max Limit, here.

Alternatively, to leverage this functionality from the depthai_demo.py script, use the -enc (or –encode) to specify which cameras to encode (record), with optional -encout arguemnt to specify path to directory where to store encoded files. An example is below:

python3 depthai_demo.py -enc left color -encout [path/to/output]

To then play the video in mp4/mkv format use the following muxing command:

ffmpeg -frame rate 30 -i [path/to/output/video.h264]

For more information about the script and its arguments, see our GitHub repository here.

By default there are keyframes every 1 second which resolve the previous issues with traversing the video as well as provide the capability to start recording anytime (worst case 1 second of video is lost if just missed the keyframe)

When running depthai_demo.py, one can record a JPEG of the current frame by hitting c on the keyboard.

An example video encoded on DepthAI OAK-D-CM3 (Raspberry Pi Compute Module Edition) is below. All DepthAI and megaAI units have the same 4K color camera, so will have equivalent performance to the video below.

4K Video in 3.125MB/s on DepthAI with Raspberry Pi 3B

What are the Capabilities of the Video Encoder on DepthAI?

The max total encoding for h.264 and h.265 has 3 limits: - 4096 pixel max width for a frame. - Maximum pixels per second of 248 MegaPixel/second. - Maximum of 3 parallel encoding streams

The JPEG encoder is capable of 16384x8192 resolution at 500Mpixel/second.

Note the processing resources of the encoder are shared between H.26x and JPEG and both the width and height should be a multiple of 8 (which is usually the case with standard resolutions).

What Is The Stream Latency?

When implementing robotic or mechatronic systems it is often quite useful to know how long it takes from light hitting an image sensor to when the results are available to a user, the photon-to-results latency.

So the following results are an approximation of this photon-to-results latency, and are likely an over-estimate as we tested by actually seeing when results were updated on a monitor, and the monitor itself has some latency, so the results below are likely overestimated by whatever the latency of the monitor is that we used during the test. And we have also since done several optimizations since these measurements, so the latency could be quite a bit lower than these.

Worst-case estimates of stream latency



avg latency, ms





left, right



left, right, depth



left, right, depth, metaout, previewout






metaout, previewout



left, right, depth, metaout, previewout






metaout, previewout



left, right, depth, metaout, previewout


How To Do a Letterboxing (Thumbnailing) on the Color Camera?

To get the black bars on the top and the bottom of the video, the y component of the points needs to be adjusted (for 16:9 to 1:1 transform).

The ImageMAnip node could be used for this kind of transform, e.g created as:

manip = pipeline.createImageManip()

There are two possible options:

  1. Using setResizeThumbnail (details)

manip.initialConfig.setResizeThumbnail(1280, 1280)
  1. Using setWarpTransformFourPoints (details)

adj = (16-9)/9 / 2
point2f_list = [depthai.Point2f(0,-adj), depthai.Point2f(1,-adj), depthai.Point2f(1,1+adj), depthai.Point2f(0,1+adj)]

normalized = True
manip.initialConfig.setWarpTransformFourPoints(point2f_list, normalized)
manip.setResize(1280, 1280)  # Optional, for a final resize of the frame

If large resolutions are set, this could result in a latency build-up. Performance-wise, instead of reducing FPS, it is recommended to configure the on-device queue as non-blocking with a single slot:


For now, it will work only for lower resolution (i.e. 1280x720), so we suggest resizing before starting the pipeline:

manip = pipeline.createImageManip()
manip.initialConfig.setResizeThumbnail(1280, 1280)

Note! Interleaved RGB input with ImageManip is not yet supported, so set:


Is it Possible to Use the RGB Camera and/or the Stereo Pair as a Regular UVC Camera?

Yes, but it is not currently implemented in our API. It’s on our roadmap, here

The why of our DepthAI API provides more flexibility in formats (unencoded, encoded, metadata, processing, frame-rate, etc.) and already works on any operating system (see here). So what we plan to do is to support UVC as part of our Gen2 Pipeline builder, so you can build a complex spatial AI/CV pipeline and then have the UVC endpoints output the results, so that DepthAI could then work on any system without drivers. For our embedded variants, this could then be flashed to the device so that the whole pipeline will automatically run on boot-up and show up to a computer a UVC device (a webcam).

Theoretically we can implement support for 3 UVC endpoints (so showing up as 3 UVC cameras), on for each of the 3 cameras.

We’ve prototyped 2x w/ internal proof of concept (but grayscale) but have not yet tried 3 but it would probably work. We could support a UVC stream per camera if it is of interest.

So if you would like this functionality please feel free to subscribe to the Github feature request here.

And in the meantime, if you would like to use depthai as a standard UVC camera, it is possible to use V4L2 loopback device (and some users have informed us that they have done so), but linking the output of the depthai API config into this loopback device on the host.

Check our quick guide on how to do that here.

How Do I Force USB2 Mode?

USB2 Communication may be desirable if you’d like to use extra-long USB cables and don’t need USB3 speeds.

You can force USB2 mode by setting usb2Mode to True when creating the device (note - it works for gen2):

dai.Device(pipeline, usb2Mode=True)

The other way is using the -usbs usb2 (or --usbSpeed usb2) command line option as below:

python3 depthai_demo.py -usbs usb2

Note that if you would like to use DepthAI at distances that are even greater than what USB2 can handle, we do have DepthAI PoE variants, see here, which allow DepthAI to use up to a 328.1 foot (100 meter) cable for both data and power - at 1 gigabit per second (1gbps).

What is “NCS2 Mode”?

All DepthAI devices come with support of what we call ‘NCS2 mode’. This allows any DepthAI device to pretend to be an NCS2.

So in fact, if you power your unit, plug it into a computer, and follow the instructions/examples/etc. of an NCS2 with OpenVINO, DepthAI device will behave identically.

We also have an example code here. It runs facial cartoonization model (IR format) on the device using OpenVINOs Inference Engine (IE).

This allows you to try out examples from OpenVINO directly as if our hardware is an NCS2. This can be useful when experimenting with models which are designed to operate on objects/items that you may not have available locally/physically. It also allows running inference in programmatic ways for quality assurance, refining model performance, etc., as the images are pushed from the host, instead of pulled from the onboard camera in this mode.

Another common use case to run your model with IE (Inference Engine) first is to check if your model conversion to OpenVINOs IR format (eg. from TF/ONNX) was successful. After you run it successfully with the IE you can then proceed with compiling the IR model into the .blob, which is required by the DepthAI library.

What Information is Stored on the DepthAI Boards

Initial Crowd Supply backers received boards which had literally nothing stored on them. All information was loaded from the host to the board. This includes the OAK-D-CM3, which had the calibration stored on the included microSD card.

So each hardware model which has stereo cameras (e.g. OAK-D-CM4, OAK-FFC-3P-OG, OAK-D, and BW1094) has the capability to store the calibration data and field-of-view, stereo baseline (L-R distance) and relative location of the color camera to the stereo cameras (L-RGB distance) as well as camera orientation (L/R swapped). To retrieve this information, simply run python3 depthai_demo.py and look for EEPROM data:.

Example of information pulled from a OAK-D is below:

EEPROM data: valid (v2)
  Board name     : BW1098OBC
  Board rev      : R0M0E0
  HFOV L/R       : 71.9 deg
  HFOV RGB       : 68.7938 deg
  L-R   distance : 7.5 cm
  L-RGB distance : 3.75 cm
  L/R swapped    : yes
  L/R crop region: top
  Calibration homography:
    1.002324,   -0.004016,   -0.552212,
    0.001249,    0.993829,   -1.710247,
    0.000008,   -0.000010,    1.000000,

Current (those April 2020 and after) DepthAI boards with on-board stereo cameras (OAK-D-CM4, OAK-D, and OAK-D-IoT-40) ship calibration and board parameters pre-programmed into DepthAI’s onboard EEPROM.

Dual-Homography vs. Single-Homography Calibration

As a result of some great feedback/insight from the OpenCV Spatial AI Competition we discovered and implemented many useful features (summary here).

Among those was the discovery that a dual-homography approach, although mathematically equivalent to a single-homography (as you can collapse the two homographies into one) actually outperforms single-homography in real-world practice.

As a result, we switched our calibration system in September 2020 to use dual-homography instead of single homography. So any units produced after September 2020 include dual homography. Any units with single homography can be recalibrated (see here) to use this updated dual-homography calibration.

What is the Field of View of OAK?

OAK devices use the same 12MP RGB Camera module based on the IMX378.

  • 12MP RGB Horizontal Field of View (HFOV): 68.7938 deg

  • 1MP Global Shutter Grayscale Camera Horizontal Field of View (HFOV): 71.9 deg

How Do I Get Different Field of View or Lenses for DepthAI and megaAI?

ArduCam has built a variety of camera modules specifically for Luxonis’ devices, including a variety of M12-mount options (so that the optics/view-angles/etc. are change-able by you the user).

  • M12-Mount IMX477 here

  • M12-Mount Global Shutter Grayscale OV9282 here

  • M12-Mount Global Shutter Color OV9782 here

  • Compact Camera Module (CCM) Fish-Eye OV9282 (for better SLAM) here

  • Mechanical, Optical, and Electrical equivalent OV9282 module with visible and IR capability here

  • Global-Shutter Color Camera (OV9782) with same intrinsics as OV9282 grayscale here is in progress.

  • C/CS-Mount IMX283 (1” diagonal sensor, which is huge) here is in progress.

With these, there will be a variety of options for view angle, focal length, filtering (IR, no IR, NDVI, etc.) and image sensor formats. Click here for more information.

What are the Highest Resolutions and Recording FPS Possible with DepthAI and megaAI?

MegaAI can be used to stream raw/uncompressed video with USB3. Gen1 USB3 is capable of 5gbps and Gen2 USB3 is capable of 10gbps. DepthAI and MegaAI are capable of both Gen1 and Gen2 USB3 - but not all USB3 hosts will support Gen2, so check your hosts specifications to see if Gen2 rates are possible.


USB3 Gen1 (5gbps)

USB3 Gen2 (10gbps)

12MP (4056x3040)

21.09fps (390MB/s)

41.2fps (762MB/s)

4K (3840x2160)

30.01fps (373MB/s)

60.0fps (746MB/s)

DepthAI and megaAI can do h.264 and h.265 (HEVC) encoding on-device. The max resolution/rate is 4K at 30FPS. With the default encoding settings in DepthAI/megaAI, this brings the throughput down from 373MB/s (raw/unencoded 4K/30) to 3.125MB/s (h.265/HEVC at 25mbps bit rate). An example video encoded on DepthAI OAK-D-CM3 (Raspberry Pi Compute Module Edition) is below:

4K Video on DepthAI with Raspberry Pi 3B

It’s worth noting that all DepthAI and megaAI products share the same color camera specs and encoding capabilities. So footage filmed on a DepthAI unit with the color camera will be identical to that taken with a megaAI unit.

  • 12MP (4056x3040) : JPEG Pictures/Stills

  • 4K (3840x2160) : 30.00fps (3.125MB/s)

What are the theoretical maximum transmission rate for USB3 Gen1 and Gen2?

The maximum bit rate (the PHY rate) for Gen1 is 5Gbps and for Gen2 is 10Gbps. But this is the line rate - meaning purely how fast the bits can change from 0 to 1 and vice-versa. So above this, there is the USB encoding of the data, and then above this the protocol that is being used.

This FAQ answers the maximum transmission rate of USB-encoded data being sent over USB3. Keep in mind that this is prior to whatever protocol is being used over USB3 (e.g. USB Video Class (UVC), or XLink). Actual use of USB3 will always involve some form of protocol, which means the actual throughput will be lower than the following. And the CPUs involved may not be able to handle this throughput and/or the handling of the protocol used above USB3 at these rates.

So that is to say, this is the absolute maximum possible data transmission through USB3:

  • Gen1 (8b/10b): 4Gbps (of 5Gbps PHY rate)

  • Gen2 (128b/132b): 9.697Gbps (of 10Gbps PHY rate)

So interestingly, in Gen2 USB3, not only is the PHY rate 2x as high, the encoding overhead is significantly lower, as in USB3 Gen1 - each 8 bits get 2 bits of encoding added on top, whereas in Gen2, this can be increased to 4 bits of overhead for every 128bits of data. So in other words, in Gen1, 20% of what is being sent over the line is USB overhead. And in Gen2, this USB encoding overhead can be reduced down from 20% to 3.03%.

What is the best way to get FullHD in good quality?

The solution for now is to configure the sensor to 4K, but downscale further in the pipeline:
  • with the existing FW, the ColorCamera preview set as colorCam.setPreviewSize(1920, 1080) - will output RGB/BGR. But the video output is still 4K (unless cropped).

  • using the branch stereo_fixes from here, it is possible to set an ISP downscale as colorCam.setIspScale(1, 2) , while keeping the sensor at 4K, and then both preview and video will act (resolution-wise) as if the sensor was configured at 1080p.

How to run OAK-D as video device

Luxonis devices do not appear as standard cameras. To run them as a video device, consider running them as UVC (USB Video Class). To run UVC directly (still needing the depthai library, but just for initialization), inside a clone of dephtai-python repo, run:

git fetch --all
git checkout origin/gen2_uvc
python3 examples/install_requirements.py
python3 examples/19_uvc_video.py

Keep the script open. It’s needed to periodically feed the device watchdog (would reset the device on close). And then open some UVC viewer. It should work all good on Linux. On macOS, it needs a workaround (having some app looking for devices and opening the stream quickly after the depthai pipeline is started), while on Windows, it doesn’t work yet.

You can also try using Flask Opencv Streamer to generate a local stream, pick it up with FFmpeg and pipe it to a v4l loopback device.

with dai.Device(pipeline) as device:

    video = device.getOutputQueue(name="video", maxSize=1, blocking=False)

    while True:
        video_capture = video.get()
        frame = video_capture.getCvFrame()

        if not streamer.is_streaming:

ffmpeg  -i "http://192.168.***.***:3030/video_feed" -vf format=yuv420p -f v4l2 /dev/video0

We work on adding UVC descriptor on-demand to a device preboot configuration as a feature.

How Much Compute Is Available? How Much Neural Compute is Available?

OAKs are built around the Intel Movidius Myriad X. More details/background on this part are here and also here.

A brief overview of the capabilities of DepthAI/megaAI hardware/compute capabilities:
  • Overall Compute: 4 Trillion Ops/sec (4 TOPS)

  • Neural Compute Engines (2x total): 1.4 TOPS (neural compute only)

  • 16x SHAVES: 1 TOPS available for additional neural compute or other CV functions (e.g. through OpenCL)

  • 20+ dedicated hardware-accelerated computer vision blocks including disparity-depth, feature matching/tracking, optical flow, median filtering, Harris filtering, WARP/de-warp, h.264/h.265/JPEG/MJPEG encoding, motion estimation, etc.

  • 500+ million pixels/second total processing (see max resolution and frame rates over USB here)

  • 450 GB/sec memory bandwidth

  • 512 MB LPDDR4 (contact us for 1GB LPDDR version if of interest)

How are resources allocated? How do I see allocation?

  • Resources are allocated automatically, based on the enabled nodes in the pipeline and their properties, before starting the pipeline. If there are no available resources an error will be thrown.

  • After distributing the SHAVE/CMX resources between nodes (except NN), NeuralNetwork receives the rest of the free resources.

  • There are 2 main CPUs, LeonOS and LeonRT, running Rtems OS, scheduling the tasks (USB, SHAVES, ISP etc.).

  • There are a total of 16 SHAVEs and 20 CMX slices, each slice 128KB, a total of 2.5MB, together with 512MB DDR.

  • CMX memory is super-fast SRAM compared to DRAM (DDR), used by Hardware CV filters, SHAVEs for highest performance and lowest latency.

  • SHAVEs are accelerator processors for CV, NN algorithms.

The allocated resources can be printed with DEPTHAI_LEVEL environment variable set to INFO. For example: DEPTHAI_LEVEL=info python3 26_1_spatial_mobilenet.py

  • [system] [info] ImageManip internal buffer size ‘80640’B, shave buffer size ‘19456’B

  • [system] [info] SpatialLocationCalculator shave buffer size ‘11264’B

  • [system] [info] SIPP (Signal Image Processing Pipeline) internal buffer size ‘143360’B

  • [system] [info] NeuralNetwork allocated resources: shaves: [0-12] cmx slices: [0-12]

  • [system] [info] ColorCamera allocated resources: no shaves; cmx slices: [13-15]

  • [system] [info] MonoCamera allocated resources: no shaves; cmx slices: [13-15]

  • [system] [info] StereoDepth allocated resources: shaves: [13-13] cmx slices: [13-15]

  • [system] [info] ImageManip allocated resources: shaves: [15-15] no cmx slices.

  • [system] [info] SpatialCalculator allocated resources: shaves: [14-14] no cmx slices.

  • ImageManip node requires 80640+19456 bytes of CMX memory and shave 15.

  • SpatialLocationCalculator node (used by SpatialDetectionNetwork requires 11264 bytes of CMX memory and shave 15).

  • SIPP (Signal Image Processing Pipeline) requires 143360 bytes of CMX memory, which is used by stereo node, camera ISP.

  • NeuralNetwork takes shaves [0-12] and cmx slices [0-12].

  • ColorCamera takes cmx slices [13-15], a total of 3 at 1080p. At 4k/12MP it requires 6 slices.

  • MonoCamera takes cmx slices [13-15].

  • StereoDepth takes cmx slices [13-15] and shave 13.

Each node requires its own pools in the memory where data is stored. In addition to SHAVE and CMX distribution, the CPU usage, DDR, CMX, heap memory allocations are exposed too at runtime.

  • [system] [info] Memory Usage - DDR: 74.12 / 414.56 MiB, CMX: 2.37 / 2.50 MiB, LeonOS Heap: 32.72 / 46.36 MiB, LeonRT Heap: 5.20 / 27.45 MiB

  • [system] [info] Temperatures - Average: 58.40 °C, CSS: 58.94 °C, MSS 58.30 °C, UPA: 59.36 °C, DSS: 57.01 °C

  • [system] [info] Cpu Usage - LeonOS 55.29%, LeonRT: 34.93%

What Auto-Focus Modes Are Supported? Is it Possible to Control Auto-Focus From the Host?

OAK-D, OAK-1, OAK-D-IoT-40, etc. all support continuous video autofocus (‘2’ below, where the system is constantly autonomously searching for the best focus) and also and auto mode which waits to focus until directed by the host, in addition to region-of-interest based focus, where the focus is automatically focused around a region provided to DepthAI (e.g. from a neural network bounding box, or some other real-time or apriori setting).

  • See here for an example of switching back/forth between autofocus and manual focus, and commanding specific manual-focus positions.

  • See here for autofocus controls, region of interest (to set autofocus to only consider a certain region), and triggering.

  • See here for the API for manually setting the focus level.

What is the Hyperfocal Distance of the Auto-Focus Color Camera?

The hyperfocal distance is important, as it’s the distance beyond which everything is in good focus. Some refer to this as ‘infinity focus’ colloquially.

The ‘hyperfocal distance’ (H) of DepthAI/megaAI’s color camera module is quite close because of it’s f.no and focal length.

From WIKIPEDIA, here, the hyperfocal distance is as follows:

Hyperfocal Distance


  • f = 4.52mm (the ‘effective focal length’ of the camera module)

  • N = 2.0 (+/- 5%, FWIW)

  • c = C=0.00578mm (see here, someone spelling it out for the 1/2.3” format, which is the sensor format of the IMX378)

So H = (4.52mm)^2/(2.0 * 0.00578mm) + 4.52mm ~= 1,772mm, or 1.772 meters (5.8 feet).

We are using the effective focal length, and since we’re not optics experts, we’re not 100% sure if this is appropriate here, but the total height of the color module is 6.05mm, so using that as a worst-case focal length, this still puts the hyperfocal distance at 10.4 feet.

So what does this mean for your application?

Anything further than 10 feet (~3m) away from OAK will be in focus when the focus is set to 10 feet or beyond. In other words, as long as you don’t have something closer than 10 feet which the camera is trying to focus on, everything 10 feet or beyond will be in focus.

Is it Possible to Control the Exposure and White Balance and Auto-Focus (3A) Settings of the RGB Camera From the Host?

Auto-Focus (AF)

  • See here for an example of switching back/forth between autofocus and manual focus, and commanding specific manual-focus positions.

  • See here for autofocus controls, region of interest (to set autofocus to only consider a certain region), and triggering.

  • See here for the API for manually setting the focus level.

Exposure (AE)

It is possible to set frame duration (us), exposure time (us), sensitivity (iso) via the API. And we have a small example for the color camera to show how to do this for the color camera, which is here: https://github.com/luxonis/depthai/pull/279

We have now made the Exposure commands more self-documenting (here). And see this example for controlling exposure, and setting auto or manual for exposure.

White Balance (AWB)

See here for Auto White Balance modes and controls.

What Are the Specifications of the Global Shutter Grayscale Cameras?

The stereo pair is composed of synchronized global shutter OV9282-based camera modules.

  • Effective Focal Length (EFL): 2.55

  • F-number (F.NO): 2.2 +/- 5%

  • Field of View (FOV): - Diagonal (DFOV): 82.6(+/-0.5) deg. - Horizontal (HFOV): 71.9(+/-0.5) deg. - Vertical (VFOV): 50.0(+/-0.5) deg.

  • Distortion: < 1%

  • Lens Size: 1/4 inch

  • Focusing: Fixed Focus, 0.196 meter (hyperfocal distance) to infinity

  • Resolution: 1280 x 800 pixel

  • Pixel Size: 3x3 micrometer (um)

Am I able to attach alternate lenses to the camera? What sort of mounting system? S mount? C mount?

The color camera on megaAI and DepthAI is a fully-integrated camera module, so the lens, auto-focus, auto-focus motor etc. are all self-contained and none of it is replaceable or serviceable.  You’ll see it’s all very small. It’s the same sort of camera you would find in a high-end smart phone.

So the recommended approach, if you’d like custom optics, say IR-capable, UV-capable, different field of view (FOV), etc. is to use the ArduCam M12 or CS mount series of OV9281 and/or IMX477 modules.

Note that these require an adapter (here), and below and this adapter connects to the RGB port of the DepthAI FFC. It is possible to make other adapters such that more than one of these cameras could be used at a time, or to modify the open-source OAK-FFC-3P-OG to accept the ArduCam FFC directly, but these have not yet been made.

That said, we have seen users attach the same sort of optics that they would to smartphones to widen field of view, zoom, etc. The auto-focus seems to work appropriately through these adapters. For example a team member has tested the Occipital Wide Vision Lens here to work with both megaAI and DepthAI color cameras. (We have not yet tried on the grayscale cameras.)

Also, see below for using DepthAI FFC with the Raspberry Pi HQ Camera to enable use of C- and CS-mount lenses.

Can I Power DepthAI Completely from USB?

So USB3 (capable of 900mA) is capable of providing enough power for the DepthAI models. However, USB2 (capable of 500mA) is not. So on DepthAI models power is provided by the 5V barrel jack power to prevent situations where DepthAI is plugged into USB2 and intermittent behavior occurs because of insufficient power (i.e. brownout) of the USB2 supply.

To power your DepthAI completely from USB (assuming you are confident your port can provide enough power), you can use this USB-A to barrel-jack adapter cable here. And we often use DepthAI with this USB power bank here.

What is the Screw Mount Specification on OAK-1 and OAK-D?

It is the standard 1/4-20 “Tripod” mount used on most cameras. More information on this type of mount on Wikipedia here.

How to use DepthAI under VirtualBox

If you want to use VirtualBox to run the DepthAI source code, please check our tutorial here.

What are the SHAVES?

The SHAVES are vector processors in DepthAI/OAK. The 2x NCE (neural compute engines) were architected for a slew of operations, but there are some that are not implemented. So the SHAVES take over these operations.

These SHAVES are also used for other things in the device, like handling reformatting of images, doing some ISP, etc.

So the higher the resolution, the more SHAVES are consumed for this.

  • For 1080p, 13 SHAVES (of 16) are free for neural network stuff.

  • For 4K sensor resolution, 10 SHAVES are available for neural operations.

There is an internal resource manager inside DepthAI firmware that coordinates the use of SHAVES, and warns if too many resources are requested by a given pipeline configuration.

How to increase NCE, SHAVES and CMX parameters?

If you want to specify how many Neural Compute Engines (NCE) to use, or how many SHAVE cores, or how many Connection MatriX blocks, you can do this with the DepthAI.

We have implemented the -nce, -sh and -cmx command line params in our example script. Just clone the DepthAI repository and do

./depthai_demo.py -nce 2 -sh 14 -cmx 14

And it will run the default MobilenetSSD, compiled to use 2 NCEs, 14 SHAVEs and 14 CMXes. Note that these values cannot be greater than the ones you can see above, so you cannot use 15 SHAVEs or 3 NCEs. 14 is the limit for both SHAVE and CMX parameters, and 2 is the limit for NCE.

You can try it out yourself either by following local OpenVINO model conversion tutorial or by using our online Myriad X blob converter. For more info, please see Converting model to MyriadX blob

Can I Use DepthAI with the New Raspberry Pi HQ Camera?

This is a particularly interesting application of DepthAI, as it allows the Raspberry Pi HQ camera to be encoded to h.265 4K video (and 12MP stills) even with a Raspberry Pi 1 or Raspberry Pi Zero - because DepthAI does all the encoding onboard - so the Pi only receives a 3.125 MB/s encoded 4K h.265 stream instead of the otherwise 373 MB/s 4K RAW stream coming off the IMX477 directly (which is too much data for the Pi to handle, and is why the Pi when used with the Pi HQ camera directly, can only do 1080p video and not 4K video recording).

OAK-FFC-3P and OAK-FFC-4P will work with the `Raspberry Pi HQ Camera < https://www.arducam.com/product/b0240-arducam-imx477-hq-quality-camera/>`__ without an adapter board, as you can connect the camera via the 22-26 pin adapter cable (SKU: A00403, which you get with the OAK-FFC-3P/OAK-FFC-4P) to the FFC board.

OAK-FFC-3P-OG model also works via an adapter board with the Raspberry Pi HQ camera (IMX477 based), which then does work with a ton of C- and CS-mount lenses (see here). And see here for the adapter board for OAK-FFC-3P-OG.

Raspberry Pi HQ with DepthAI FFC

Here are some quick images and videos of it in use:

Raspberry Pi HQ Camera Support in DepthAI Raspberry Pi HQ Camera Support in DepthAI Raspberry Pi HQ Camera Support in DepthAI

You can buy this adapter kit for the OAK-FFC-3P-OG here

Can I use DepthAI with Raspberry Pi Zero?

Yes, DepthAI is fully functional on it, you can see the example below:

pizerosetup pizeroruntime

Thanks to Connor Christie for his help building this setup!

And note that we now have a specific ARMv6 (Pi Zero) specific build of DepthAI.

How Much Power Does the DepthAI Raspberry Pi CME Consume?

The OAK-D-CM3 for short consumes around 2.5W idle and 5.5W to 6W when DepthAI is running full-out.

  • Idle: 2.5W (0.5A @ 5V)

  • DepthAI Full-Out: 6W (1.2A @ 5V)

Below is a quick video showing this:

OAK-D-CM3 Power Use

How To Unbind and Bind a Device?

In some cases, you may need to unbind and bind your device, i.e. a controller crashes with the following error messages:

[345692.730104] xhci_hcd 0000:02:00.0: xHCI host controller not responding, assume dead
[345692.730113] xhci_hcd 0000:02:00.0: HC died; cleaning up

or you encounter error, such as:

RuntimeError: Failed to find device after booting, error message: X_LINK_DEVICE_NOT_FOUND


Cannot enable. Maybe the USB cable is bad?

Instead of rebooting a host, you may unbind and bind a device.

Note! You’ll need to know the PCI ID of the USB host controller to replace the “0000:00:14.0” part from the command below.

echo -n "0000:00:14.0" | sudo tee /sys/bus/pci/drivers/xhci_hcd/unbind; sleep 1; echo -n "0000:00:14.0" | sudo tee /sys/bus/pci/drivers/xhci_hcd/bind

How Do I Get Shorter or Longer Flexible Flat Cables (FFC)?

For all cameras we use a 0.5mm 26-pin, same-side 152 mm contact flex cable. Follow the link for more details.

What are CSS MSS UPA and DSS Returned By meta_d2h?

  • CSS: CPU SubSystem (main cores)

  • MSS: Media SubSystem

  • UPA: Microprocessor(UP) Array – Shaves

  • DSS: DDR SubSystem

Where are the Github repositories? Is DepthAI Open Source?

DepthAI is an open-source platform across a variety of stacks, including hardware (electrical and mechanical), software, and machine-learning training using Google Colab.

See below for the pertinent Github repositories:


Embedded Use Case

The above examples include a few submodules of interest. You can read a bit more about them in their respective README files:

How Do I Build the C++ API?

Prebuilt binaries are available for Python bindings (or so called wheels).

We do not have prebuilt binaries for C++ core library.

One of the reasons is the vast number of different platforms and the second is that the library itself is quite lean so compiling along the other C++ source should not be a problem.

To compile the needed headers and a .dll follow this link: https://github.com/luxonis/depthai-core/tree/main#building Under - And for the dynamic version of the library

You can optionally also install it into a desired directory by appending this cmake flag:

cmake -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=[desired/installation/path]
And then calling the install target
cmake --build . --target install

This should result in the headers and the library being copied to that path.

Another option is integrating into your CMake project directly, for that see: https://github.com/luxonis/depthai-core-example

And a note on building for Windows: Windows does not use libusb, but rather uses Windows internal winusb.

Can I Use an IMU With DepthAI?

Yes, all of our System on Modules (OAK-SoM, OAK-SoM-IoT, and OAK-SoM-Pro) have support for the BNO086 (and BNO080/BNO085) IMU. And OAK-D, OAK-D-IoT-40, OAK-FFC-3P, OAK-D-IoT-75, OAK-D-PoE all have an integrated IMU onboard.

Can I Use Microphones with DepthAI?


  • The OAK-SoM-Pro SoM supports up to 3x I2S stereo inputs (up to 6x physical microphones) and one I2S stereo output (e.g. for a stereo speaker drive).

  • Any I2S mics should work, and may be possible to also use audio codecs, but those might need extra I2C config.

  • It is important to note that the OAK-SoM and OAK-SoM-IoT do not have I2S support.

We have tested audio input on the OAK-SoM-Pro using 3x CMM-4030D-261-I2S-TR and have found the audio quality to be good. Theoretically many other microphones should work, however we have not tested audio output.

Where are Product Brochures and/or Datasheets?

For more information about OAK devices, go to Hardware Documentation Page.


  • Editions Summary here

  • OAK-SoM (System on Module) here

  • OAK-FFC-3P-OG (USB3 Modular Cameras Edition) here

  • OAK-D-PCBA here

  • OAK-D-CM3 (Raspberry Pi Compute Edition Module) here

  • Raspberry Pi HAT (BW1094) here

  • OAK-1 here


  • DepthAI System on Module (OAK-SoM) here

  • DepthAI System on Module Pro (OAK-SoM-Pro) here

  • DepthAI System on Module IoT (OAK-SoM-IoT) here

  • PoE Modular Cameras Edition (BW2098FFC) here

How Much Does OAK Devices Weight?

  • OAK-D - 114.5 grams

  • OAK-1 - 53.1 grams

  • OAK-D-PCBA - 22 grams

  • OAK-D-IoT-40 - 45.5 grams

How Can I Cite Luxonis Products in Publications?

If DepthAI and OAK-D products has been significantly used in your research and if you would like to acknowledge the DepthAI and OAK-D in your academic publication, we suggest citing them using the following bibtex format.

title={ {DepthAI}: Embedded Machine learning and Computer vision api},
note={Software available from luxonis.com},

title={ {OAK-D}: Stereo camera with Edge AI},
note={Stereo Camera with Edge AI capabilities from Luxonis and OpenCV},

How Do I Talk to an Engineer?

At Luxonis we firmly believe in the value of customers being able to communicate directly with our engineers. It helps our engineering efficiency. And it does so by making us make the things that matter, in the ways that matter (i.e. usability in the right ways) to solve real problems.

As such, we have many mechanisms to allow direct communication:
  • Luxonis Community Discord. Use this for real-time communication with our engineers. We can even make dedicated channels for your project/effort public or private in here for discussions as needed.

  • Luxonis Github. Feel free to make Github issues in any/all of the pertinent repositories with questions, feature requests, or issue reports. We usually respond within a couple hours (and often w/in a couple minutes). For a summary of our Github repositories, see here.

  • discuss.luxonis.com. Use this for starting any public discussions, ideas, product requests, support requests etc. or generally to engage with the Luxonis Community. While you’re there, check out this awesome visual-assistance device being made with DepthAI for the visually-impaired, here.

Got questions?

We’re always happy to help with code or other questions you might have.