OAK

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OAK-D
OAK-D
OAK-D-PoE
OAK-D-PoE
OAK-D-Lite
OAK-D-Pro
OAK-D-Pro
OAK-D-Lite
OAK-D-S2
OAK-D-Lite

Overview

Onboard Intel® Movidius™ Myriad™ X vision processor, OAK-D is an AI vision intelligent kit designed and produced by the OpenCV team. Although it is tiny, it integrates a 4K RGB binocular depth camera, IMU and a high-performance AI processing chip to realize the binocular depth visual computing and neural network reasoning. The inertial navigation sensor is integrated into a single camera, allowing users to obtain binocular vision measurement positioning, AI neural network acceleration, and 4K H.265 30-frame real-time streaming with a low power consumption of 2.5W. It meets the needs of users in intelligent driving, intelligent transportation, intelligent security, robots, teaching competitions, etc.
OAK-D-PoE is based on the OAK-D with a PoE power supply circuit that allows a single Cat5e (or higher) Ethernet cable (up to 100 meters (328 feet)) to power and provides a 1,000 Mbps (1 Gbps) full-duplex connection to devices. With the IP67 protection grade shell, it is suitable for users to use in environments that have requirements.
OAK-D-Lite is the most cost-effective product in the OAK USB series. Except for no IMU, the performance is comparable to OAK-D, but the price is lower. It combines depth perception, object detection (neural reasoning), and object tracking, and helps you achieve these functions with a simple and easy-to-use Python API. This OAK-D-Lite includes three onboard cameras (a 4K/30fps RGB camera, two monochrome binocular cameras) and a USB3.0 Type-C interface, you can use it on an ordinary computer, Raspberry Pi, or other popular embedded host to access the OAK through the USB interface.
OAK-D-Pro is an upgraded version of the OAK-D with structured light ranging, featuring an IR laser dot matrix emitter (active depth vision), and IR illuminated LEDs (for "night vision"). It is also smaller, lighter, and more precise than the OAK-D. With built-in high-performance Myriad X VPU, it adopts the active binocular vision technology and structured light, which improves the positioning accuracy to the sub-millimeter level, meeting the needs of close-range high-precision positioning and identification, such as automatic welding robots, the positioning, identification, and calibration of surface defects of parts, etc. and enhancing the robot's perception capabilities.
OAK-D-S2 is more compact than ODK-D. Its functions, performance, and lenses are the same as OAK-D, but smaller in size and lighter in weight, and can be used in scenes where space is limited and quality is required. In addition, compared with OAK-D, OAK-D-S2 removes the 5V power port in the structure and has a larger depth measurement range than OAK-D in performance.

Features

  • Depth measuring range: 0.2 ~ 35m
  • Depth camera: Global shutter 120fps / 3MP 200fps
  • RGB camera: 12MP 60fps / 13MP 60fps
  • AI chip: Intel Myriad X 4TOPS computing performance
  • Video plug flow: 4K 30 fps H.265 plug flow
  • Interface: USB3.0 Type-C (OAK-D/OAK-D-Lite/OAK-D-Pro) / PoE (OAK-D-PoE)
  • Expansion interfaces: GPIO, SPI, UART
  • NN platform support: all platforms
  • Average power consumption: 2.5W (average)
  • Development language: Python, C++
  • Enclosure: Metal enclosure

Camera Specifications

OAK-D/OAK-D-PoE/OAK-D-S2
Camera Color camera Monochrome camera
Shutter Rolling Global
Sensor IMX378 OV9282
Max framerate 60fps 120fps
H.265 framerate 30fps /
Resolution 12MP (4056 × 3040 px/1.55um) 1MP (1280 × 800 px/3um)
FoV 81° DFoV – 68.8° HFoV 81° DFoV – 71.8° HFoV
Lens size 1/2.3 Inch 1/4 Inch
Focus 8cm – ∞ (AutoFocus) 19.6cm – ∞ (FixedFocus)
D-number 2.0 2.0
OAK-D-Lite
Camera Color camera Monochrome camera
Shutter Rolling Global
Sensor IMX214 OV7215
Max framerate 60fps 200fps
H.265 framerate 30fps /
Resolution 13MP (4208 × 3120 px) 0.3MP (640 × 480 px)
FoV 81.3° DFoV 85.6° DFoV
Lens size 1/2.3 Inch 1/2.3 Inch
Focus 8cm – ∞ (AutoFocus) 6.5cm – ∞ (FixedFocus)
D-number 2.2 2.2
OAK-D-Pro
Camera Color Camera Monochrome camera
Shutter Rolling Global
Sensor IMX378 OV9282
Max framerate 60fps 120fps
H.265 framerate 30fps /
Resolution 12MP (4032 × 3040px) 1MP (1280 × 4800px)
FoV 81°DFoV / 69°HFoV / 55°VFoV 81°DFoV / 72°HFoV / 49°VFoV
Lens Size 1/2.3 Inch 1/4 Inch
Focus range 8cm – ∞ (AutoFocus) 19.6cm – ∞ (FixedFocus)
D-number 2.0 2.2
Laser Transmitter Specifications
Laser Transmitter Specification
Model Belago1.1 Dot-Pattern
Number of dots 4700
HFOI*50% 78±7%
VFOI*50% 61°±7%
VSCEL wavelength 940nm
Operating temperature 10°C ~ 60°C
Storage temperature 0°C ~ 80°C
Laser Safety Standards EN/IEC 60825-1 3rd Edition (2014) Class 1 Laser Products

Supported NN

Caffe*
  • AlexNet
  • CaffeNet
  • GoogleNet (Inception) v1, v2, v4
  • VGG family (VGG16, VGG19)
  • SqueezeNet v1.0, v1.1
  • ResNet v1 family (18***, 50, 101, 152)
  • MobileNet (mobilenet-v1-1.0-224, mobilenet-v2)
  • Inception ResNet v2
  • DenseNet family (121,161,169,201)
  • SSD-300, SSD-512, SSD-MobileNet, SSD-GoogleNet, SSD-SqueezeNet
TensorFlow*
  • AlexNet
  • Inception v1, v2, v3, v4
  • Inception ResNet v2
  • MobileNet v1, v2
  • ResNet v1 family (50, 101, 152)
  • ResNet v2 family (50, 101, 152)
  • SqueezeNet v1.0, v1.1
  • VGG family (VGG16, VGG19)
  • Yolo family (yolo-v2, yolo-v3, tiny-yolo-v1, tiny-yolo-v2, tiny-yolo-v3)
  • faster_rcnn_inception_v2, faster_rcnn_resnet101
  • ssd_mobilenet_v1
  • DeepLab-v3+
MXNet*
  • AlexNet and CaffeNet
  • DenseNet family (121,161,169,201)
  • SqueezeNet v1.1
  • MobileNet v1, v2
  • NiN
  • ResNet v1 (101, 152)
  • ResNet v2 (101)
  • SqueezeNet v1.1
  • VGG family (VGG16, VGG19)
  • SSD-Inception-v3, SSD-MobileNet, SSD-ResNet-50, SSD-300

Hardware Connection

OAK-D

  • Connect the power supply to the OAK-D's power connector.
  • Use a Type-C cable to connect OAK-D to the USB3.0 port of a computer or other hosts.

OAK-D-PoE

  • To use OAK-D-PoE, you need to use a switch or router that complies with the 802.3af POE power supply standard.
  • Remove the plastic waterproof casing and connect the matching network cable to the switch. OAK-D-PoE needs to be connected to the Internet for normal use.
  • Note that OAK-D-PoE needs to be connected to the same LAN as the host computer, otherwise the program cannot identify the device.

OAK-D-Lite

  • Use a Type-C cable to connect OAK-D to the USB3.0 port of a computer or other hosts.

OAK-D-Pro

  • Connect the Y-Adapter to the OAK-D-Pro.
  • Use two Type-C cables to connect the Y-type connector, on the other side, connect a UB cable to the USB3,0 interface of other hosts, and connect the other one to the 5V/2A power supply.

OAK-D-S2

  • Use Type-C to connect OAK-D-S2 to the USB3.0 interface of the computer or other host.

Windows

  • Unzip the downloaded zip.
  • Double click the "exe" file.
  • Follow the prompts to install the OAKEnvironment software.
    • It is recommended to change the installation directory to another location.
  • Check to add environment variables.
  • Click "Install" and wait for the installation to complete.
  • After the installation is complete, a shortcut will be added to the desktop. Double-click to run the "depthai-demo.py" program directly.

Oak-windows-inputPath.png Oak-windows-install.png Oak-windows-meunDir.png Oak-windows-selectDir.png Oak-windows-success.png Oak-windows-depthaiDemoShow.png

Linux

If you use ubuntu system, you can take the following steps:

  • Install depthai
git clone https://gitee.com/oakchina/depthai.git
  • Install depthai-python
git clone https://gitee.com/oakchina/depthai-python.git
  • Install depthai-experiments
git clone https://gitee.com/oakchina/depthai-experiments.git
  • OAK device used for the first time requires the rule configuration.
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
  • Install dependency library
python3 -m pip install -r depthai/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
  • Program test
python3 depthai/depthai_demo.py

OAK-D Quick Start001.png


Raspberry Pi

  • At the beginning, we provided a Raspberry Pi image with a configured environment, and users can directly download and burn it.
  • Download the tools

OAK-D Quick Start01.png

  • Open the software and choose the downloaded oak image (note: unzip .img file) and the programmed it to the SD card.

OAK-D Quick Start02.png

  • Enable the Raspberry Pi and run the following demo:
cd depthai
python3 depthai_demo.py

Ubuntu

  • Install depthai
git clone https://gitee.com/oakchina/depthai.git
  • Install dependent libraries
cd depthai
python3 install_requirements.py
  • Run the program
python3 depthai-demo.py

Note: If opencv reports an error and displays an illegal command after installation, please run the command to add the environment and test again.

echo "export OPENBLAS_CORETYPE=ARMV8" >> ~/.bashrc
source ~/.bashrc

Jetson Platform

Note: Do not directly run the dependency scripts in the depthai package on the jetson platform, or OpenCV coverage that will cause other programs to fail to work properly.

  • Please program the system first according to the Jetson platform, and configure it completely and normally.
  • (Optional) If there is a problem with the subsequent configuration, you can update the package. Please do not do the second update for the first configuration.
sudo apt update && sudo apt upgrade
sudo reboot
  • S set SWAP
# Disable ZRAM:
sudo systemctl disable nvzramconfig
# Create 4GB swap file
sudo fallocate -l 4G /mnt/4GB.swap
sudo chmod 600 /mnt/4GB.swap
sudo mkswap /mnt/4GB.swap
  • Install pip3.
sudo -H apt install -y python3-pip
  • Install and configure the virtual environment.
sudo -H pip3 install virtualenv virtualenvwrapper
  • Add the setting to bash script.
sudo vi ~/.bashrc

# Add the following to the open document
export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
source /usr/local/bin/virtualenvwrapper.sh
  • Reload the script and wear the virtual environment depthAI:
source ~/.bashrc
mkvirtualenv depthAI -p python3
  • Install depthai, note that the installation needs to be done in a virtual environment, please enter the virtual environment first.
#download and install the dependencies script
sudo wget -qO- http://docs.luxonis.com/_static/install_dependencies.sh | bash

#clone depthai respository
git clone https://github.com/luxonis/depthai-python.git
cd depthai-python
  • Add environment configuration:
echo "export OPENBLAS_CORETYPE=ARMV8" >> ~/.bashrc
  • Go to the example folder and run the script to install the dependency library:
cd examples/
sudo python install_requirements.py
  • Run the test script.
sudo python rgb_preview.py

Resources

FAQ

 Answer:
The 3D positioning accuracy of OAK-D is at the centimeter level. Within three meters, about 1 to 2 centimeters, within nine meters, about 2 to 10 centimeters, the positioning accuracy is also affected by the texture of the object.
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Support

If you require technical support, please go to the Support page and open a ticket.