OAK-D-IoT-75 with raspberry pi.

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)
                `