RuntimeError: Communication exception

erik It worked, thank you so much, just last thing, you have used a different code, the code I have uploaded doesn't work at all it means. Does it? I want to get this spatial data to use it in another code. That is why am asking. thank you so much for helping me out.

erik Thank you, can I get spatial detection data in a string format?

jakaskerl Thank you for your answer,

Yes, I tested my model before the conversion. Actually, I converted the model using this tool: DepthAI Tools (luxonis.com). Then these were all my inputs:

and I did the same steps as the previous one(which worked successfully)

erik Could you help me with it?

Update, I ran my blob and json files with the code "main_api" from this link:

depthai-experiments/gen2-yolo/device-decoding at master · luxonis/depthai-experiments (github.com)

It worked properly, but I want to see the spatial coordination like this:

I ran the same code but spatial coordination didn't appear

jakaskerl Hello,

Can you give me an example code for a manually written spatial callback function?

Thanks,

MhmdBarazi Hello dear @erik , Do you have an idea about this error?

#!/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?

erik Hello dear,

Thank you so much it worked, but unfortunately there is a new problem:

the window just shows an image, the image that you are seeing know doesn't change. there is no video no motion.

Thanks,