First steps with DepthAI¶

This guide will go through the first steps with OAK camera and DepthAI library:

Installing DepthAI
Device setup - connecting the OAK camera to your host computer
Running DepthAI Demo a Python-based GUI application
Next steps; examples, demos, API docs

Installing DepthAI¶

Follow instructions below to install DepthAI (and its dependencies/requirements) with an installer.

Execute the script below to install DepthAI on macOS:

bash -c "$(curl -fL https://docs.luxonis.com/install_depthai.sh)"

Please refer to this documentation if any issues occur.

Windows 10/11 users can install DepthAI with the Windows Installer. After installer finishes, you can directly run the DepthAI application from the list of applications, which will run the DepthAI demo. You can skip Setup section (as Installer performs the whole setup) of this tutorial and go directly to Default run.

Execute the script below to install DepthAI on Linux systems:

sudo wget -qO- https://docs.luxonis.com/install_depthai.sh | bash

Please refer to Installation documentation if any issues occur.

If you would like to avoid using installer and would prefer manually installing dependencies, requirements and DepthAI, see Manual DepthAI installation.

Device setup¶

Now that we have installed requirements, we can setup the device. OAK cameras can be separated into two categories depending on how you connect to them; either via ethernet (OAK PoE cameras) or via USB (all others).

If your OAK came with an included USB cable, we suggest using that to connect the OAK camera to the host computer.

Warning

Make sure to use USB3 cable, as this is has been a very common culprit of OAK connectivity issues. If you aren’t using USB3 cable, force USB2 communication.

USB3 cable is colored blue in the inside of the USB-A connector of the USB-C cable. If it’s not blue, it might be USB2 charging cable.

Make sure that the device is connected to your host (which can be a PC or Raspberry Pi or another capable computer) directly to a USB3 port, or via a powered USB hub.

If you are using OAK PoE device, you will first need to connect the device to a PoE switch or a PoE injector. We recommend following the Getting started with OAK PoE devices for a step-by-step tutorial.

Default run¶

After installer finishes, it should automatically run the DepthAI Demo script. You can also manually run with command below:

$ python3 depthai_demo.py

Running the demo for the first time, the script will compile and download a default mobilenet-ssd model, configure the OAK camera and then show a default color preview that will contain a scaled preview from the RGB camera from your device.

Change preview¶

To see other previews from the device, you can use the preview switcher that is visible in the top-left section of the GUI

Available previews¶
Name	Description	Limitations
`color`	Shows preview from color camera
`nnInput`	Shows preview from right mono camera	Disabled if no AI model is running
`left`	Shows preview from left mono camera	Stereo required
`right`	Shows preview from right mono camera	Stereo required
`depth`	Shows disparity map calculated from `depthRaw` preview and JET colored. Best for visualizing depth	Stereo required
`depthRaw`	Shows raw depth map. Best for depth-based calculations	Stereo required
`disparity`	Shows disparity map produced on device	Stereo required
`disparityColor`	Shows disparity map produced on device and JET colored. Should be the same as `depth` preview but produced on the device.	Stereo required
`rectifiedLeft`	Rectified left camera frames	Stereo required
`rectifiedRight`	Rectified right camera frames	Stereo required

Default model¶

While the demo was running, you could see the detection results - and if you were standing in front of the camera, you should see yourself detected as a person with a pretty high probability.

The model that is used by default is a MobileNetv2 SSD object detector trained on the PASCAL 2007 VOC classes, which are:

Person: person
Animal: bird, cat, cow, dog, horse, sheep
Vehicle: airplane, bicycle, boat, bus, car, motorbike, train
Indoor: bottle, chair, dining table, potted plant, sofa, TV/monitor

So give it a try to detect different objects, like bottles or apples

Note

See for DepthAI Demo additional documentation about DepthAI demo.

Next steps¶

In the previous sections, we learned how to preview basic DepthAI features. From this point, you can explore the DepthAI world further

Usecases

Check our Example Use Cases for ready to use applications that solve a specific problem on DepthAI
Getting started with coding

Be sure to check hello world tutorial on API section for a step-by-step introduction to the API
Train and deploy a custom model to OAK

Visit Custom training page for ready to use Colab notebooks
Already built apps for OAK devices

See luxonis/depthai-experiments repository for apps built with depthai library
Depthai API library repository

See luxonis/depthai-python repository which contains Python bindings for the depthai API library, Code samples and various utility programs.

Got questions?

Head over to Discussion Forum for technical support or any other questions you might have.