It's been a bit of a battle, with a steep Python learning curve, but finally I created a single Python file that enables a time stamp, detection class, confidence and bounding box coordinates to be sent to the Arduino base station. Obviously, there's still a number of dependancies in various directories - some close by and others deeply embedded in the Python system somewhere, but here's my 'top of the stack' code:

#!/usr/bin/python3

# ****************************************************************************
# Copyright(c) 2017 Intel Corporation. 
# License: MIT See LICENSE file in root directory.
# ****************************************************************************

# Detect objects on a LIVE camera feed using and send data over LoRa network
# Intel® Movidius™ Neural Compute Stick (NCS)

import os
import cv2
import sys
import numpy
import ntpath
import argparse

import mvnc.mvncapi as mvnc

from time import localtime, strftime
from utils import visualize_output
from utils import deserialize_output
from pySX127x_master import basic_test01

import datetime
import array as arr
import numpy as np
from time import sleep


from pySX127x_master.SX127x.LoRa import *
from pySX127x_master.SX127x.LoRaArgumentParser import LoRaArgumentParser
from pySX127x_master.SX127x.board_config import BOARD

#from pySX127x_master import Tegwyns_LoRa_Beacon

BOARD.setup()

parser = LoRaArgumentParser("A simple LoRa beacon")
parser.add_argument('--single', '-S', dest='single', default=False, action="store_true", help="Single transmission")
parser.add_argument('--wait', '-w', dest='wait', default=1, action="store", type=float, help="Waiting time between transmissions (default is 0s)")

myString = ""
#from pySX127x_master.Tegwyns_LoRa_Beacon import *

class LoRaBeacon(LoRa):

    def __init__(self, verbose=False):
        super(LoRaBeacon, self).__init__(verbose)
        self.set_mode(MODE.SLEEP)
        self.set_dio_mapping([1,0,0,0,0,0])
        
    def on_rx_done(self):
        print("\nRxDone")
        print(self.get_irq_flags())
        print(map(hex, self.read_payload(nocheck=True)))
        self.set_mode(MODE.SLEEP)
        self.reset_ptr_rx()
        self.set_mode(MODE.RXCONT)

    def on_cad_done(self):
        print("\non_CadDone")
        print(self.get_irq_flags())

    def on_rx_timeout(self):
        print("\non_RxTimeout")
        print(self.get_irq_flags())

    def on_valid_header(self):
        print("\non_ValidHeader")
        print(self.get_irq_flags())

    def on_payload_crc_error(self):
        print("\non_PayloadCrcError")
        print(self.get_irq_flags())

    def on_fhss_change_channel(self):
        print("\non_FhssChangeChannel")
        print(self.get_irq_flags())
            
    def start(self):
        global args
        sys.stdout.write("\rstart\r")
        stamp = str(datetime.datetime.now())
        text=bytearray('PING LoRa Test PI: ' + stamp + ('  ') + myString,'utf-8')
        self.write_payload([0x00, 0x00, 0x00, 0x00] + list(text))
        self.set_mode(MODE.TX)
        
lora = LoRaBeacon(verbose=False)
args = parser.parse_args(lora)

lora.set_pa_config(pa_select=1)
#lora.set_rx_crc(True)
#lora.set_agc_auto_on(True)
#lora.set_lna_gain(GAIN.NOT_USED)
#lora.set_coding_rate(CODING_RATE.CR4_6)
#lora.set_implicit_header_mode(False)
#lora.set_pa_config(max_power=0x04, output_power=0x0F)
#lora.set_pa_config(max_power=0x04, output_power=0b01000000)
#lora.set_low_data_rate_optim(True)
#lora.set_pa_ramp(PA_RAMP.RAMP_50_us)

#print(lora)
#assert(lora.get_lna()['lna_gain'] == GAIN.NOT_USED)
assert(lora.get_agc_auto_on() == 1)

print("Security cam config:")
print("  Wait %f s" % args.wait)
print("  Single tx = %s" % args.single)
print("")
                    
lora.start()

# "Class of interest" - Display detections only if they match this class ID
CLASS_PERSON         = 15

# Detection threshold: Minimum confidance to tag as valid detection
CONFIDANCE_THRESHOLD = 0.60 # 60% confidant

# Variable to store commandline arguments
ARGS                 = None

# OpenCV object for video capture
camera               = None

# ---- Step 1: Open the enumerated device and get a handle to it -------------

def open_ncs_device():

    # Look for enumerated NCS device(s); quit program if none found.
    devices = mvnc.EnumerateDevices()
    if len( devices ) == 0:
        print( "No devices found" )
        quit()

    # Get a handle to the first enumerated device and open it
    device = mvnc.Device( devices[0] )
    device.OpenDevice()

    return device

# ---- Step 2: Load a graph file onto the NCS device -------------------------

def load_graph( device ):

    # Read the graph file into a buffer
    with open( ARGS.graph, mode='rb' ) as f:
        blob = f.read()

    # Load the graph buffer into the NCS
    graph = device.AllocateGraph( blob )

    return graph

# ---- Step 3: Pre-process the images ----------------------------------------

def pre_process_image( frame ):

    # Resize image [Image size is defined by choosen network, during training]
    img = cv2.resize( frame, tuple( ARGS.dim ) )

    # Convert RGB to BGR [OpenCV reads image in BGR, some networks may need RGB]
    if( ARGS.colormode == "rgb" ):
        img = img[:, :, ::-1]

    # Mean subtraction & scaling [A common technique used to center the data]
    img = img.astype( numpy.float16 )
    img = ( img - numpy.float16( ARGS.mean ) ) * ARGS.scale

    return img

# ---- Step 4: Read & print inference results from the NCS -------------------

def infer_image( graph, img, frame ):
    #from pySX127x_master.Tegwyns_LoRa_Beacon import LoRaBeacon
    #from pySX127x_master import Tegwyns_LoRa_Beacon
    # Load the image as a half-precision floating point array
    graph.LoadTensor( img, 'user object' )

    # Get the results from NCS
    output, userobj = graph.GetResult()

    # Get execution time
    inference_time = graph.GetGraphOption( mvnc.GraphOption.TIME_TAKEN )

    # Deserialize the output into a python dictionary
    output_dict = deserialize_output.ssd( 
                      output, 
                      CONFIDANCE_THRESHOLD, 
                      frame.shape )

    # Print the results (each image/frame may have multiple objects)
    for i in range( 0, output_dict['num_detections'] ):

        # Filter a specific class/category
        if( output_dict.get( 'detection_classes_' + str(i) ) == CLASS_PERSON ):

            cur_time = strftime( "%Y_%m_%d_%H_%M_%S", localtime() )
            print( "Person detected on " + cur_time )
            print(".... Press q to quit ..... ")

            # Extract top-left & bottom-right coordinates of detected objects 
            (y1, x1) = output_dict.get('detection_boxes_' + str(i))[0]
            (y2, x2) = output_dict.get('detection_boxes_' + str(i))[1]
            #print (y1, x1)
            # Prep string to overlay on the image
            display_str = ( 
                labels[output_dict.get('detection_classes_' + str(i))]
                + ": "
                + str( output_dict.get('detection_scores_' + str(i) ) )
                + "%" )
            print (display_str)
            print (y1, x1)
            print (y2, x2)
            # Overlay bounding boxes, detection class and scores
            frame = visualize_output.draw_bounding_box( 
                        y1, x1, y2, x2, 
                        frame,
                        thickness=4,
                        color=(255, 255, 0),
                        display_str=display_str )
            global myString
            myString = display_str + " , " + "(" + str(y1) + "," + str(x1) +")" + "," + "(" + str(y2) + "," + str(x2) +")"
            
###########################################################################################
            lora.start()
###########################################################################################

            # Capture snapshots
            photo = ( os.path.dirname(os.path.realpath(__file__))
                      + "/captures/photo_"
                      + cur_time + ".jpg" )
            cv2.imwrite( photo, frame )

    # If a display is available, show the image on which inference was performed
    if 'DISPLAY' in os.environ:
        cv2.imshow( 'NCS live inference', frame )
        
# ---- Step 5: Unload the graph and close the device -------------------------

def close_ncs_device( device, graph ):
    graph.DeallocateGraph()
    device.CloseDevice()
    camera.release()
    cv2.destroyAllWindows()

# ---- Main function (entry point for this script ) --------------------------

def main():

    device = open_ncs_device()
    graph = load_graph( device )

    # Main loop: Capture live stream & send frames to NCS
    while( True ):
        ret, frame = camera.read()
        img = pre_process_image( frame )
        infer_image( graph, img, frame )

        # Display the frame for 5ms, and close the window so that the next
        # frame can be displayed. Close the window if 'q' or 'Q' is pressed.
        if( cv2.waitKey( 5 ) & 0xFF == ord( 'q' ) ):
            break

    close_ncs_device( device, graph )

# ---- Define 'main' function as the entry point for this script -------------

if __name__ == '__main__':

    parser = argparse.ArgumentParser(
                         description="DIY smart security camera PoC using \
                         Intel® Movidius™ Neural Compute Stick." )

    parser.add_argument( '-g', '--graph', type=str,
                         default='../../caffe/SSD_MobileNet/graph',
                         help="Absolute path to the neural network graph file." )

    parser.add_argument( '-v', '--video', type=int,
                         default=0,
                         help="Index of your computer's V4L2 video device. \
                               ex. 0 for /dev/video0" )

    parser.add_argument( '-l', '--labels', type=str,
                         default='../../caffe/SSD_MobileNet/labels.txt',
                         help="Absolute path to labels file." )

    parser.add_argument( '-M', '--mean', type=float,
                         nargs='+',
                         default=[127.5, 127.5, 127.5],
                         help="',' delimited floating point values for image mean." )

    parser.add_argument( '-S', '--scale', type=float,
                         default=0.00789,
                         help="Absolute path to labels file." )

    parser.add_argument( '-D', '--dim', type=int,
                         nargs='+',
                         default=[300, 300],
                         help="Image dimensions. ex. -D 224 224" )

    parser.add_argument( '-c', '--colormode', type=str,
                         default="bgr",
                         help="RGB vs BGR color sequence. This is network dependent." )

    ARGS = parser.parse_args()

    # Create a VideoCapture object
    camera = cv2.VideoCapture( ARGS.video )

    # Set camera resolution
    camera.set( cv2.CAP_PROP_FRAME_WIDTH, 620 )
    camera.set( cv2.CAP_PROP_FRAME_HEIGHT, 480 )

    # Load the labels file
    labels =[ line.rstrip('\n') for line in
              open( ARGS.labels ) if line != 'classes\n']

    main()

# ==== End of file ===========================================================