Odd camera output

jakaskerl

My script is pretty simple:

import cv2
import depthai as dai

# Create pipeline
pipeline = dai.Pipeline()

# Define source and output
camRgb = pipeline.create(dai.node.ColorCamera)
xoutRgb = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("rgb")

# Properties
camRgb.setPreviewSize(640, 640)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)

# Linking
camRgb.preview.link(xoutRgb.input)

# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    print('Connected cameras:', device.getConnectedCameraFeatures())
    # Print out usb speed
    print('Usb speed:', device.getUsbSpeed().name)
    # Bootloader version
    if device.getBootloaderVersion() is not None:
        print('Bootloader version:', device.getBootloaderVersion())
    # Device name
    print('Device name:', device.getDeviceName(), ' Product name:', device.getProductName())

    # Output queue will be used to get the rgb frames from the output defined above
    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)

    while True:
        inRgb = qRgb.get()  # blocking call, will wait until a new data has arrived

        # Retrieve 'bgr' (opencv format) frame
        cv2.imshow("bgr", inRgb.getCvFrame())

        if cv2.waitKey(1) == ord('q'):
            break

MernokAdriaan I think I tried it both ways and there was no difference.

jakaskerl It's an OAK 1 POE

I'm also running into another issue.

What's the workflow to run a YOLO model on the OAK camera? I'm trying the blob converter with the model exports from Ultralytics hub but that isn't working. I'll post it as a seperate issue, and perhaps you can help me on that as well.

Thank you for your time!

I managed to make it work using some Docs and mainly ChatGPT(!).

However, I can't seem to integrate the ObjectTracker node into this program without running into some error; there have just been too many.

from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
import time

# Get yolo v8n model blob file path
nnPath = str("03022025.blob")
if not Path(nnPath).exists():
    import sys
    raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')

# yolo v8 abel texts
labelMap = ["goat", "pig", "sheep"]

syncNN = True

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
detectionNetwork = pipeline.create(dai.node.YoloDetectionNetwork)
xoutRgb = pipeline.create(dai.node.XLinkOut)
nnOut = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("rgb")
nnOut.setStreamName("nn")

# Properties
camRgb.setPreviewSize(640, 640)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
camRgb.setFps(40)

# Network specific settings
detectionNetwork.setConfidenceThreshold(0.5)
detectionNetwork.setNumClasses(3)
detectionNetwork.setCoordinateSize(4)
detectionNetwork.setIouThreshold(0.5)
detectionNetwork.setBlobPath(nnPath)
detectionNetwork.setNumInferenceThreads(2) 
detectionNetwork.input.setBlocking(True) # From False

# Linking
camRgb.preview.link(detectionNetwork.input)
if syncNN:
    detectionNetwork.passthrough.link(xoutRgb.input)
else:
    camRgb.preview.link(xoutRgb.input)

detectionNetwork.out.link(nnOut.input)

# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)

    frame = None
    detections = []
    startTime = time.monotonic()
    counter = 0
    color2 = (255, 255, 255)

    # nn data, being the bounding box locations, are in <0..1> range - they need to be normalised with frame width/height
    def frameNorm(frame, bbox):
        normVals = np.full(len(bbox), frame.shape[0])
        normVals[::2] = frame.shape[1]
        return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)

    def displayFrame(name, frame):
        color = (255, 0, 0)
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.putText(frame, f"{detection.confidence}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 255)
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
        # Show the frame
        cv2.imshow(name, frame)

    while True:
        if syncNN:
            inRgb = qRgb.get()
            inDet = qDet.get()
        else:
            inRgb = qRgb.tryGet()
            inDet = qDet.tryGet()

        if inRgb is not None:
            frame = inRgb.getCvFrame()
            cv2.putText(frame, "NN fps: {:.2f}".format(counter / (time.monotonic() - startTime)),
                        (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color2)

        if inDet is not None:
            detections = inDet.detections
            counter += 1

        if frame is not None:
            displayFrame("rgb", frame)

        if cv2.waitKey(1) == ord('q'):
            break

This is my program, so far, any assistance into integrating ObjectTracking would be appreciated!

Thanks in advance.