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DepthAI V3

This is the documentation for DepthAI v3, which supports both OAK and OAK4 cameras. If you want to use DepthAI v2 (which supports only RVC2 devices), please switch to Depthai V2 documentation.

Depthai V2 vs V3

See key differences between V2 and V3 API.
V2 vs V3

Code

API Reference

Installation

Linux / MacOS
Windows

Linux / MacOS

1. Install DepthAI v3

Command Line
1git clone https://github.com/luxonis/depthai-core.git && cd depthai-core
2python3 -m venv venv
3source venv/bin/activate
4# Installs library and requirements
5python3 examples/python/install_requirements.py
or via pip:
Command Line
1pip install depthai --force-reinstall

2. Run an example

After installing the library, you can run an example, eg. Detection Network example or Display all cameras:
Command Line
1cd examples/python
2# Run YoloV6 detection example
3python3 DetectionNetwork/detection_network.py
4# Display all camera streams
5python3 Camera/camera_all.py

Developing with C++?

DepthAI v3 is largely written in C++. The build instructions are available on the depthai-core repository.
Github page

Components

DepthAI Components

  • Nodes represent a sensor, accelerated hardware, or some compute function
  • Pipeline consists of linked nodes and gets deployed to the device where it runs on accelerated hardware blocks
  • Messages are used for communication between nodes. They hold data and metadata
  • Device represents Luxonis' device - OAK or OAK4 camera. It handles connectivity and communication
  • Bootloader handles logic when booting RVC2 devices and makes them accessible for connection
  • Luxonis OS is a custom Linux distro for RVC4 devices (OAK4)

Deploying AI models

Pretrained models

HubAI model zoo has many pre-trained models that can be deployed to the OAK4 device directly. Besides examples, we also have some NN examples/apps at oak-examples.

Custom models

You can convert your custom model either:If you're using .dlc, you can deploy it to OAK4 by editing NeuralNetwork example with the following snippet:
Python
1nn = pipeline.create(dai.node.NeuralNetwork)
2nn.setModelPath('my_model.dlc')
3nn.setBackend("snpe") # Specify SNPE NN backend. This usually gets set under the hood
4# Specify SNPE (RVC4) specific settings, like DSP runtime and NN performance profile
5nn.setBackendProperties({"runtime": "dsp", "performance_profile": "default"})
Or, if you're using archive.tar.xz, you can edit that example with this snippet:
Python
1cam = pipeline.create(dai.node.Camera).build(socket)
2# If your nn model requires 640x640 input size (BGR):
3cam_out = cam.requestOutput((640, 640), dai.ImgFrame.Type.BGR888p)
4
5nn_archive = dai.NNArchive('./my_nn_archive.tar.xz')
6nn = pipeline.create(dai.node.NeuralNetwork).build(cam_out, nn_archive)