openmv在IDE运行时，串口接收和发送数据都是正常的，但是脱机运行时，能正常发送数据却不能正常接收数据，这是什么原因？

import sensor, image, time, os, tf, math, uos, gc
from pyb import UART, Timer, LED
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.set_windowing((240, 240))
sensor.skip_frames(time=2000)
net = None
labels = None
min_confidence = 0.5
try:
	net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
	raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
try:
	labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
	raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
colors = [
	(255,   0,   0),
	(  0, 255,   0),
	(255, 255,   0),
	(  0,   0, 255),
	(255,   0, 255),
	(  0, 255, 255),
	(255, 255, 255),
]
clock = time.clock()
global uart_r
uart_r = [0xAA, 0xAF, 0x05, 0x02, 0x00, 0x00, 0x00, 0x00]
class Saveimage(object):
	def __init__(self):
		pass
	def Recognize(self,img):
		self.img = img
		for i, detection_list in enumerate(net.detect(self.img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
			if (i == 0): continue
			if (len(detection_list) == 0): continue
			print("recognize to: "+labels[i])
			if labels[i]=='lin':
				sensor.snapshot().save("lin.jpg")
				uart_bufl[6] = 0xAA
				print("Save lin.")
			elif labels[i]=='ke':
				sensor.snapshot().save("ke.jpg")
				uart_bufl[6] = 0xBB
				print("Save ke.")
			elif labels[i]=='da':
				 sensor.snapshot().save("da.jpg")
				 uart_bufl[6] = 0xCC
				 print("Save da.")
			sensor.snapshot().save("other.jpg")
			print("Save other.")
Saveimg=Saveimage()
uart = UART(3,115200)
uart.init(115200,8, parity=None, stop=1)
def ReceiveB():
	global uart_r
	size = uart.any()
	if size<=len(uart_r):
		for i in range(0,size):
			uart_r[i] = uart.readchar()
	if uart_r[0]!=0xAA:
		uart_r = [0xAA, 0xAF, 0x05, 0x02, 0x00, 0x00, 0x00, 0x00]
		LED(1).on()
		LED(2).off()
	elif uart_r[1]!=0xAF:
		uart_r = [0xAA, 0xAF, 0x05, 0x02, 0x00, 0x00, 0x00, 0x00]
		LED(1).on()
		LED(2).off()
	elif uart_r[2]!=0x05:
		uart_r = [0xAA, 0xAF, 0x05, 0x02, 0x00, 0x00, 0x00, 0x00]
		LED(1).on()
		LED(2).off()
	else:
		LED(1).off()
		LED(2).on()
uart_bufl = bytearray([0xAA,0xFF, 0xAA, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00])
def uartsend(timer):
	uart.write(uart_bufl)
def updatesend(custom, cx, cy, distance):
	i=0
	sum = 0
	add = 0
	uart_bufl[2] = custom;
	uart_bufl[4] = cx;
	uart_bufl[5] = cy;
	uart_bufl[6] = distance;
	uart_bufl[3] = len(uart_bufl)-6;
	while i<(len(uart_bufl)-2):
		sum = sum+uart_bufl[i]
		add = add+sum
		i+=1
	uart_bufl[-2] = sum;
	uart_bufl[-1] = add;
tim = Timer(2, freq=20)
tim.callback(uartsend)
while(True):
	clock.tick()
	ReceiveB()
	print(str(uart_r))
	if uart_r[5]==0xDD:
		img0 = sensor.snapshot()
		Saveimg.Recognize(img0)
		LED(3).on()
		LED(1).off()
		LED(2).off()
		print("Saving......")
	elif uart_r[5]==0xEE:
		if uart_r[4]!=0x06:
			print("Now is locking mode\r\n")
			img1 = sensor.snapshot()
			lockimg = ''
			for i, detection_list in enumerate(net.detect(img1, thresholds=[(math.ceil(min_confidence * 255), 255)])):
				if (i == 0): continue
				if (len(detection_list) == 0): continue
				lockimg = labels[i]
				uart_bufl[7] = 0xCC
				print('reading:'+lockimg)
		else:
			img2 = sensor.snapshot()
			for k, detection_list in enumerate(net.detect(img2, thresholds=[(math.ceil(min_confidence * 255), 255)])):
				if (k == 0): continue
				if (len(detection_list) == 0): continue
				if labels[k]==lockimg:
					print(lockimg+labels[k]+'\r\n')
					for d in detection_list:
						[x, y, w, h] = d.rect()
						center_x = math.floor(x + (w / 2))
						center_y = math.floor(y + (h / 2))
						distance = int((80-center_x)*100*0.0024/0.42)
						uart_bufl[4] = center_x
						uart_bufl[5] = center_y
						uart_bufl[6] = distance
						LED(3).on()
						LED(1).off()
						LED(2).off()
						print('%d',distance)
				else:
					uart_bufl[6] = 0x3C
					print("Don't get picture!")
	else:
		print("Wait enter a recognization mode!")
	print(clock.fps(), "fps", end="\n\n")

@kidswong999 我是用的FOMO模型啊，而且图片都没够200张

我是用的FOMO模型啊，而且图片都没够200张

请在这里粘贴代码

Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, uos, gc

sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.

net = None
labels = None
min_confidence = 0.5

try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
(255, 0, 0),
( 0, 255, 0),
(255, 255, 0),
( 0, 0, 255),
(255, 0, 255),
( 0, 255, 255),
(255, 255, 255),
]

clock = time.clock()
while(True):
clock.tick()

img = sensor.snapshot()

# detect() returns all objects found in the image (splitted out per class already)
# we skip class index 0, as that is the background, and then draw circles of the center
# of our objects

for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
    if (i == 0): continue # background class
    if (len(detection_list) == 0): continue # no detections for this class?

    print("********** %s **********" % labels[i])
    for d in detection_list:
        [x, y, w, h] = d.rect()
        center_x = math.floor(x + (w / 2))
        center_y = math.floor(y + (h / 2))
        print('x %d\ty %d' % (center_x, center_y))
        img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)

print(clock.fps(), "fps", end="\n\n")

请在这里粘贴代码

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    print(e)
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        img.draw_rectangle(obj.rect())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))

        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

    print(clock.fps(), "fps")