Hello there,
I'm currently try to setup the OAK-D SR PoE in a basic setup and am running into same issues,
maybe someone here can help me out.
When running the following example, I do receive some image data, although there seems to be some missalignment.
Can someone point out what the problem might be, as the image seems good when using depthai-viewer.
Thanks in advance!
depthai version 2.29.0.0
Bootloader 0.0.28
if __name__ == '__main__':
pipeline = dai.Pipeline()
# First, we want the Color camera as the output
cam_rgb = pipeline.createColorCamera()
# 300x300 will be the preview frame size, available as 'preview' output of the node
cam_rgb.setPreviewSize(300, 300)
cam_rgb.setInterleaved(False)
cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
detection_nn = pipeline.createMobileNetDetectionNetwork()
# Blob is the Neural Network file, compiled for MyriadX.
# It contains both the definition and weights of the model
# We're using a blobconverter tool to retrieve the MobileNetSSD blob
# automatically from OpenVINO Model Zoo
detection_nn.setBlobPath(
blobconverter.from_zoo(name='mobilenet-ssd', shaves=6),
)
# Next, we filter out the detections that are below a confidence threshold.
# Confidence can be anywhere between <0..1>
detection_nn.setConfidenceThreshold(0.5)
# XLinkOut is a "way out" from the device. Any data you want to transfer
# to host need to be send via XLink
x_out_rgb = pipeline.createXLinkOut()
x_out_rgb.setStreamName("rgb")
x_out_nn = pipeline.createXLinkOut()
x_out_nn.setStreamName("nn")
cam_rgb.preview.link(x_out_rgb.input)
cam_rgb.preview.link(detection_nn.input)
detection_nn.out.link(x_out_nn.input)
# Pipeline is now finished, and we need to find an available device to run
# our pipeline. We are using context manager here that will dispose the
# device after we stop using it
with dai.Device(pipeline) as device:
# From this point, the Device will be in "running" mode and will start
# sending data via XLink
# To consume the device results, we get two output queues from the
# device, with stream names we assigned earlier
q_rgb = device.getOutputQueue(name="rgb")
q_nn = device.getOutputQueue("nn")
# Here, some of the default values are defined. Frame will be an image
# from "rgb" stream, detections will contain nn results
frame = None
detections = []
# Since the detections returned by nn have values from <0..1> range,
# they need to be multiplied by frame width/height to receive the
# actual position of the bounding box on the image
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)
while True:
# we try to fetch the data from nn/rgb queues. tryGet will return
# either the data packet or None if there isn't any
in_rgb = q_rgb.get()
in_nn = q_nn.get()
if in_rgb is not None:
# If the packet from RGB camera is present, we're retrieving
# the frame in OpenCV format using getCvFrame
frame = in_rgb.getCvFrame() # type: ignore
cv2.imshow("preview", frame)
if in_nn is not None:
# when data from nn is received, we take the detections array
# that contains mobilenet-ssd results
detections = in_nn.detections # type: ignore
if frame is not None:
for detection in detections:
# for each bounding box, we first normalize it to match
# the frame size
bbox = frameNorm(
frame,
(
detection.xmin,
detection.ymin,
detection.xmax,
detection.ymax,
),
)
# and then draw a rectangle on the frame to show the
# actual result
cv2.rectangle(
frame,
(bbox[0], bbox[1]),
(bbox[2], bbox[3]),
(255, 0, 0), 2,
)
# After all the drawing is finished, we show the frame on the
# screen
cv2.imshow("preview", frame)
cv2.imwrite("log\\test_frame.jpg", frame)
# at any time, you can press "q" and exit the main loop,
# therefore exiting the program itself
if cv2.waitKey(1) == ord('q'):
break
