Luxonis OAK

Deploy your Roboflow Train model to your OpenCV AI Kit with Myriad X VPU acceleration.

The is an edge device that is popularly used for the deployment of embedded computer vision systems.

OAK devices are paired with a host machine that drives the operation of the downstream application. For some exciting inspiration, see and .

By the way: if you don't have your OAK device yet, you can to get a 10% discount.

Task Support

The following task types are supported by the hosted API:

Task Type

Supported by Luxonis OAK Deployment

Object Detection:

YOLOv8 models, trained on Roboflow, both Fast and Accurate
YOLOv11 models trained on Roboflow

Classification

Instance Segmentation

Semantic Segmentation

Deploy a Model to the Luxonis OAK

Supported Luxonis Devices and Host Requirements

The Roboflow Inference Server supports the following devices:

OAK-D
OAK-D-Lite
OAK-D-POE
OAK-1 (no depth)

Installation

Install the roboflowoak, depthai, and opencv-python packages:

pip install roboflowoak
pip install depthai
pip install opencv-python

Now you can use the roboflowoak package to run your custom trained Roboflow model.

Running Inference: Deployment

If you are deploying to an OAK device without Depth capabilities, set depth=False when instantiating (creating) the rf object. OAK's with Depth have a "D" attached to the model name, i.e OAK-D and OAK-D-Lite.

Also, comment out max_depth = np.amax(depth) and cv2.imshow("depth", depth/max_depth)

from roboflowoak import RoboflowOak
import cv2
import time
import numpy as np

if __name__ == '__main__':
    # instantiating an object (rf) with the RoboflowOak module
    rf = RoboflowOak(model="YOUR-MODEL-ID", confidence=0.05, overlap=0.5,
    version="YOUR-MODEL-VERSION-#", api_key="YOUR-PRIVATE_API_KEY", rgb=True,
    depth=True, device=None, blocking=True)
    # Running our model and displaying the video output with detections
    while True:
        t0 = time.time()
        # The rf.detect() function runs the model inference
        result, frame, raw_frame, depth = rf.detect()
        predictions = result["predictions"]
        #{
        #    predictions:
        #    [ {
        #        x: (middle),
        #        y:(middle),
        #        width:
        #        height:
        #        depth: ###->
        #        confidence:
        #        class:
        #        mask: {
        #    ]
        #}
        #frame - frame after preprocs, with predictions
        #raw_frame - original frame from your OAK
        #depth - depth map for raw_frame, center-rectified to the center camera
        
        # timing: for benchmarking purposes
        t = time.time()-t0
        print("FPS ", 1/t)
        print("PREDICTIONS ", [p.json() for p in predictions])

        # setting parameters for depth calculation
        # comment out the following 2 lines out if you're using an OAK without Depth
        max_depth = np.amax(depth)
        cv2.imshow("depth", depth/max_depth)
        # displaying the video feed as successive frames
        cv2.imshow("frame", frame)
    
        # how to close the OAK inference window / stop inference: CTRL+q or CTRL+c
        if cv2.waitKey(1) == ord('q'):
            break

Enter the code below (after replacing the placeholder text with the path to your Python script)

# To close the window (interrupt or end inference), enter CTRL+c on your keyboard
python3 /path/to/[YOUR-PYTHON-FILE].py

The inference speed (in milliseconds) with the Apple Macbook Air (M1) as the host device averaged around 15 ms, or 66 FPS. Note: The host device used with OAK will drastically impact FPS. Take this into consideration when creating your system.

Troubleshooting

from roboflowoak import RoboflowOak import cv2 import time import numpy as np if __name__ == '__main__': # instantiating an object (rf) with the RoboflowOak module rf = RoboflowOak(model="YOUR-MODEL-ID", confidence=0.05, overlap=0.5, version="YOUR-MODEL-VERSION-#", api_key="YOUR-PRIVATE_API_KEY", rgb=True, depth=True, device=None, blocking=True) # Running our model and displaying the video output with detections while True: t0 = time.time() # The rf.detect() function runs the model inference result, frame, raw_frame, depth = rf.detect() predictions = result["predictions"] #{ # predictions: # [ { # x: (middle), # y:(middle), # width: # height: # depth: ###-> # confidence: # class: # mask: { # ] #} #frame - frame after preprocs, with predictions #raw_frame - original frame from your OAK #depth - depth map for raw_frame, center-rectified to the center camera # timing: for benchmarking purposes t = time.time()-t0 print("FPS ", 1/t) print("PREDICTIONS ", [p.json() for p in predictions]) # setting parameters for depth calculation # comment out the following 2 lines out if you're using an OAK without Depth max_depth = np.amax(depth) cv2.imshow("depth", depth/max_depth) # displaying the video feed as successive frames cv2.imshow("frame", frame) # how to close the OAK inference window / stop inference: CTRL+q or CTRL+c if cv2.waitKey(1) == ord('q'): break

Luxonis OAK

Task Support

Deploy a Model to the Luxonis OAK

Supported Luxonis Devices and Host Requirements

Installation

Running Inference: Deployment

Troubleshooting

See Also

Task Support

Deploy a Model to the Luxonis OAK

Supported Luxonis Devices and Host Requirements

Installation

Running Inference: Deployment

Troubleshooting

See Also