OAK-1-PoE problem with read stream

jakaskerl
I open port but still it didn't help.
On third script run, model start work, but with out mikrotik, run always.
(Also, seems strange to me (perhaps it's ok), you save set the gateway to 1.254) - i use different input internet router, but it didn't help.

BR,
Edmunds

jakaskerl
I get that info from raspberry

"pi@pi:~$ ip rout

default via 192.168.1.254 dev wlan0 proto dhcp metric 600

default via 169.254.1.1 dev eth0 proto static metric 20100

169.254.0.0/16 dev eth0 proto kernel scope link src 169.254.1.250 metric 100

192.168.1.0/24 dev wlan0 proto kernel scope link src 192.168.1.104 metric 600 "

Can be that problem if i don't have last line?

jakaskerl
Yes, i can ping, but when start model get that error.
https://failiem.lv/u/tqcezh8pdy
Video for watch.

jakaskerl
Hi, I found one problem, when configuration range 10-100Mb camaera work good. If I put 1Gb, then momentaly get error.
But 100MB, give only 17 FPS, not 26 FPS in 1Gb mode.

jakaskerl Test code is not large, put it all.
currant-yolov8-1107-480.blob - got 1600 picture 5,79 MB file size.
Also run small *.blob file = 600 picture ~4MB file size, but problem same.

from pathlib import Path

import sys

import cv2

import depthai as dai

import numpy as np

import time

# Get argument first

nnBlobPath = str((Path(_file_).parent / Path('/home/pi/Desktop/WEB_weeder/weights/currant_08052024_server.blob')).resolve().absolute())

if not Path(nnBlobPath).exists():

raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')

# Label texts

labelMap = ["currant"] # Assuming there is only one class

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)

xoutNN = pipeline.create(dai.node.XLinkOut)

#nnNetworkOut = pipeline.create(dai.node.XLinkOut)

xoutRgb.setStreamName("rgb")

xoutNN.setStreamName("detections")

#nnNetworkOut.setStreamName("nnNetwork")

# Properties

camRgb.setPreviewSize(448,448)

camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_720_P)

#camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) #Var lietot šo resolution

camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)

camRgb.setFps(30)

camRgb.setInterleaved(False)

detectionNetwork.setBlobPath(nnBlobPath)

detectionNetwork.setConfidenceThreshold(0.8)

detectionNetwork.input.setBlocking(False)

detectionNetwork.setNumInferenceThreads(2)

#detectionNetwork.input.setBlocking(False)

# Yolo specific parameters

detectionNetwork.setNumClasses(1)

detectionNetwork.setCoordinateSize(4)

#detectionNetwork.setNumInferenceThreads(2)

#detectionNetwork.setIouThreshold(0.5)

# Linking

camRgb.preview.link(detectionNetwork.input)

#if syncNN:

detectionNetwork.passthrough.link(xoutRgb.input)

#else:

# camRgb.preview.link(xoutRgb.input)

detectionNetwork.out.link(xoutNN.input)

#detectionNetwork.outNetwork.link(nnNetworkOut.input)

print(1)

# Connect to device and start pipeline

with dai.Device(pipeline) as device:

print(2)

# Output queues will be used to get the rgb frames and nn data from the outputs defined above

previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)

detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False)

#networkQueue = device.getOutputQueue(name="nnNetwork", maxSize=4, blocking=False)

startTime = time.monotonic()

counter = 0

fps = 0

color = (0, 150, 255)

#printOutputLayersOnce = True

while True:

print("loop")

inPreview = previewQueue.get()

inDet = detectionNNQueue.get()

#inNN = networkQueue.get()

#if printOutputLayersOnce:

# toPrint = 'Output layer names:'

# for ten in inNN.getAllLayerNames():

# toPrint = f'{toPrint} {ten},'

# print(toPrint)

# printOutputLayersOnce = False

frame = inPreview.getCvFrame()

counter += 1

current_time = time.monotonic()

if (current_time - startTime) > 1:

fps = counter / (current_time - startTime)

counter = 0

startTime = current_time

detections = inDet.detections

# If the frame is available, draw bounding boxes on it and show the frame

height, width = frame.shape[:2]

for detection in detections:

# Denormalize bounding box

x1 = int(detection.xmin * width)

x2 = int(detection.xmax * width)

y1 = int(detection.ymin * height)

y2 = int(detection.ymax * height)

try:

label = labelMap[detection.label]

except IndexError:

label = f'Label {detection.label}'

cv2.putText(frame, label, (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)

cv2.putText(frame, f"{detection.confidence*100:.2f}%", (x1 + 10, y1 + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)

cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)

cv2.putText(frame, f"NN fps: {fps:.2f}", (2, height - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)

cv2.imshow("rgb", frame)

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

break

cv2.destroyAllWindows()

time.sleep(0.01)