RuntimeError: Communication exception

#!/usr/bin/env python3

from pathlib import Path

import sys

import cv2

import depthai as dai

import numpy as np

import time

import os

'''

Spatial detection network demo.

Performs inference on RGB camera and retrieves spatial location coordinates: x,y,z relative to the center of depth map.

'''

# Get argument first

nnBlobPath = str((os.path.dirname(os.path.abspath("file")) / Path('/home/apakgrup/depthai-python/examples/spatial/best_openvino_2022.1_7shave.blob')).resolve().absolute())

if len(sys.argv) > 1:

nnBlobPath = sys.argv[1]

if not Path(nnBlobPath).exists():

import sys

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

# MobilenetSSD label texts

labelMap = ["fire"]

syncNN = True

# Create pipeline

pipeline = dai.Pipeline()

# Define sources and outputs

camRgb = pipeline.create(dai.node.ColorCamera)

spatialDetectionNetwork = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)

monoLeft = pipeline.create(dai.node.MonoCamera)

monoRight = pipeline.create(dai.node.MonoCamera)

stereo = pipeline.create(dai.node.StereoDepth)

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

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

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

xoutRgb.setStreamName("rgb")

xoutNN.setStreamName("detections")

xoutDepth.setStreamName("depth")

# Properties

camRgb.setPreviewSize(640,640)

camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)

camRgb.setInterleaved(False)

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

monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)

monoLeft.setCamera("left")

monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)

monoRight.setCamera("right")

# Setting node configs

stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)

# Align depth map to the perspective of RGB camera, on which inference is done

stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A)

stereo.setSubpixel(True)

stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())

spatialDetectionNetwork.setBlobPath(nnBlobPath)

spatialDetectionNetwork.setConfidenceThreshold(0.5)

spatialDetectionNetwork.input.setBlocking(False)

spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)

spatialDetectionNetwork.setDepthLowerThreshold(100)

spatialDetectionNetwork.setDepthUpperThreshold(5000)

# Linking

monoLeft.out.link(stereo.left)

monoRight.out.link(stereo.right)

camRgb.preview.link(spatialDetectionNetwork.input)

if syncNN:

spatialDetectionNetwork.passthrough.link(xoutRgb.input)

else:

camRgb.preview.link(xoutRgb.input)

spatialDetectionNetwork.out.link(xoutNN.input)

stereo.depth.link(spatialDetectionNetwork.inputDepth)

spatialDetectionNetwork.passthroughDepth.link(xoutDepth.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

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

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

depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)

startTime = time.monotonic()

counter = 0

fps = 0

color = (255, 255, 255)

while True:

    inPreview = previewQueue.get()

    inDet = detectionNNQueue.get()

    depth = depthQueue.get()

    counter+=1

    current_time = time.monotonic()

    if (current_time - startTime) > 1 :

        fps = counter / (current_time - startTime)

        counter = 0

        startTime = current_time

    frame = inPreview.getCvFrame()

    depthFrame = depth.getFrame() # depthFrame values are in millimeters

    depth_downscaled = depthFrame[::4]

    min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)

    max_depth = np.percentile(depth_downscaled, 99)

    depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)

    depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)

    detections = inDet.detections

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

    height = frame.shape[0]

    width  = frame.shape[1]

    for detection in detections:

        roiData = detection.boundingBoxMapping

        roi = roiData.roi

        roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])

        topLeft = roi.topLeft()

        bottomRight = roi.bottomRight()

        xmin = int(topLeft.x)

        ymin = int(topLeft.y)

        xmax = int(bottomRight.x)

        ymax = int(bottomRight.y)

        cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, 1)

        # 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:

            label = detection.label

        cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

        cv2.putText(frame, "{:.2f}".format(detection.confidence\*100), (x1 + 10, y1 + 35), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

        cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

        cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

        cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

        cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX)

    cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, (255,255,255))

    cv2.imshow("depth", depthFrameColor)

    cv2.imshow("preview", frame)

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

        break

I ran this code from depth-ai examples then the same error appeared. this is my last blob and json file:

any help please?