OAK-D-IoT-75 with raspberry pi.

Here are some more explanation and photos of error. As i have mentioned above it works some times too. I have found out that if I turn on my RPi (after shutting it down for sometime) the script starts working and it works fine for some time before it starts giving the error after that nothing works on RPi even the simplest scripts. I do not know why it works after resting RPi for some time. I was working on it yesterday after it started giving me the error I turned off RPi and I turned it on just now and it again worked for like an hour and then again it started giving the error and right now nothing works on RPi and the RPi is giving a pretty detailed error I have pasted its photos below. It was giving same error yesterday too. Please have a look.

erik Hi Erik, I could not reply earlier. Here the minimum reproduceable code that crashes the device. I also explanined it a little earlier. Code has two script nodes one just arrange frames and order one post process output of NN node. Neural network is just digit recognition network. I have shared link for downloading its blob file. Please have a look. https://drive.google.com/file/d/1qYLMy7P6hGO9PQPCLKtD4_Inn-vuM2te/view?usp=sharing
Above link is for the blob file and below link is the python code file that is pasted below.
https://drive.google.com/file/d/1G7gWjvHbbZ_u9EWOujephRNNnN0xIvIA/view?usp=sharing

import depthai as dai
import numpy as np
import marshal
import time
import logging 
import os
import requests

whitelist=set('0123456789')
time_bb_cord=[0.3070,0.6312,0.6008,0.8187]
local_bb_cord=[0.2289,0.2812,0.3914,0.4537]
visit_bb_cord=[0.5492,0.2762,0.7164,0.4525]
# Create pipeline
def Manip_Frame(pipeline,region_bb_cord):
    manip_time = pipeline.create(dai.node.ImageManip)
    manip_time.initialConfig.setCropRect(region_bb_cord[0],region_bb_cord[1],region_bb_cord[2],region_bb_cord[3])
    manip_time.setKeepAspectRatio(False)
    manip_time.initialConfig.setResize(100,32)
    return manip_time
def NN_node(pipeline,path):
    nn = pipeline.create(dai.node.NeuralNetwork)
    nn.setBlobPath(path)
    return nn

pipeline = dai.Pipeline()
model_path = 'crnn_99_soft_no_norm_4.blob'

cam = pipeline.create(dai.node.MonoCamera)
cam.setBoardSocket(dai.CameraBoardSocket.RIGHT)
cam.setFps(6)
cam.setResolution(dai.MonoCameraProperties.SensorResolution.THE_800_P)


manip_time=Manip_Frame(pipeline,time_bb_cord)
cam.out.link(manip_time.inputImage)

manip_local=Manip_Frame(pipeline,local_bb_cord)
cam.out.link(manip_local.inputImage)

manip_visit=Manip_Frame(pipeline,visit_bb_cord)
cam.out.link(manip_visit.inputImage)

script=pipeline.create(dai.node.Script)
manip_time.out.link(script.inputs['time_frame'])
manip_local.out.link(script.inputs['local_frame'])
manip_visit.out.link(script.inputs['visit_frame'])

script.setScript("""
import marshal
f_list={}
while True:
    t_frame=node.io['time_frame'].get()
    l_frame=node.io['local_frame'].get()
    v_frame=node.io['visit_frame'].get()
    if t_frame is not None and 'time' not in f_list.keys():
        t_f_num=t_frame.getSequenceNum()
        f_list['time']=t_f_num
    if l_frame is not None and 'local' not in f_list.keys():
        l_f_num=t_frame.getSequenceNum()
        f_list['local']=l_f_num
    if v_frame is not None and 'visit' not in f_list.keys():
        v_f_num=t_frame.getSequenceNum()
        f_list['visit']=v_f_num
    if len(f_list.keys())==3 and f_list['time']==f_list['local'] and f_list['time']==f_list['visit']:
        f_list.clear()
        node.io['script_out'].send(t_frame)
        node.io['script_out1'].send(l_frame)
        node.io['script_out2'].send(v_frame)

        """)

recog_nn=NN_node(pipeline,model_path)

script.outputs['script_out'].link(recog_nn.input)
script.outputs['script_out1'].link(recog_nn.input)
script.outputs['script_out2'].link(recog_nn.input)

decoder_script=pipeline.create(dai.node.Script)
recog_nn.out.link(decoder_script.inputs['log_probs'])

decoder_script.setScript("""

import marshal
label2char={1: '0', 2: '1', 3: '2', 4: '3', 5: '4', 6: '5', 7: '6', 8: '7', 9: '8', 10: '9', 11: '.', 12: ':'}
def get_strings(nndata):
    outname=nndata.getAllLayerNames()[0]
    data=nndata.getLayerFp16(outname)
    raw_preds=[]
    for i in range(24):
        log_probs=data[i*13:i*13+13]
        raw_preds.append(log_probs.index(max(log_probs)))
        results=[]
    previous=None
    for l in raw_preds:
        if l != previous:
            results.append(l)
            previous = l
    results = [l for l in results if l != 0]
    f_results=''
    for r in range(len(results)):
        f_results=f_results+label2char[results[r]]
    return f_results
outs=[]
while True:
    time_=node.io['log_probs'].get()
    results=get_strings(time_)
    if len(results)==0:
        results='none'
    if len(outs)==0:
        outs.append(results)
    elif len(outs)>0:
        if results=='none':
            outs.append(results)
        elif results!=outs[-1]:
            outs.append(results)
    if len(outs)==3:
        x_serial = marshal.dumps(outs)
        b = Buffer(len(x_serial))
        b.setData(x_serial)
        outs.clear()
        node.io['f_time'].send(b)
""")

nn_Out = pipeline.create(dai.node.XLinkOut)
nn_Out.setStreamName("recogs1")
decoder_script.outputs['f_time'].link(nn_Out.input)

with dai.Device(pipeline) as device:
    nn_queue1 = device.getOutputQueue(name='recogs1', maxSize=4, blocking=False)
    while True:
        nn_out1 = nn_queue1.get()
        if nn_out1 is not None:
            score_dict=marshal.loads(nn_out1.getData())
            print(score_dict)
                `