import sensor, image, time, os, tf, math, uos, gc, ustruct,pyb#,thread
from pyb import UART,LED
from image import SEARCH_EX, SEARCH_DS
from pyb import LED
from pid import PID
rho_pid = PID(p=0.4, i=0)
theta_pid = PID(p=0.001, i=0)
THRESHOLD = (0, 36, -46, 50, 44, -83)
threshold = (245, 255)

sensor.reset()
sensor.set_vflip(False)
sensor.set_hmirror(False)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.set_windowing((240,240))
sensor.skip_frames(time=2000)
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_auto_gain(False) # 颜色跟踪必须关闭自动增益
sensor.set_auto_whitebal(False) # 颜色跟踪必须关闭白平衡
clock = time.clock()

roi = (10,0,30,30)#,(140,50,90,100)]
led = pyb.LED(4)
net = None
labels = None
min_confidence = 0.3

flag = 0
rho = 0
theta = 0
def track(rho,theta):        
    img = sensor.snapshot().binary([THRESHOLD])
    line = img.get_regression([(100,100)], robust = True)
    if (line):
         rho_err = abs(line.rho())-img.width()/2
         if line.theta()>90:
           theta_err = line.theta()-180
         else:
            theta_err = line.theta()
            img.draw_line(line.line(), color = 127)
            print(rho_err,line.magnitude(),rho_err)
         if line.magnitude()>8:
         #if -40<b_err<40 and -30<t_err<30:
            rho_output = rho_pid.get_pid(rho_err,1)
            theta_output = theta_pid.get_pid(theta_err,1)
            output = rho_output+theta_output
               # car.run(50+output, 50-output)
         else:
            car.run(0,0)
    else:
        car.run(50,-50)
        pass
        #print(clock.fps())
        print(rho_err,line.magnitude(),rho_err)
        outuart(rho,theta)
            


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),
]


uart = UART(3,115200)
uart.init(9600, bits=8, parity=None, stop=1)
def outuart(num):
    global uart
    data = ustruct.pack("<bbhhhhb",
                   0x2C,
                   0x12,
                   int(num),
                   int(88),
                   0x5B)
    uart.write(data)
    time.sleep_ms(1)
data = [0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00]

def UartReceiveDate():
    global Find_Task
    global Target_Num
    global x
    global data
    data[0] = uart.readchar()
    data[1] = uart.readchar()
    Target_Num=int(data[0]-48)*10+int(data[1]-48)
    #outuart(Target_Num)

clock = time.clock()

flag = 0
rho = 0
theta = 0
def track(rho,theta):
    img = sensor.snapshot().binary([THRESHOLD])
    line = img.get_regression([(100,100)], robust = True)
    if (line):
        rho_err = abs(line.rho())-img.width()/2
        if line.theta()>90:
            theta_err = line.theta()-180
        else:
            theta_err = line.theta()
        img.draw_line(line.line(), color = 127)
        print(rho_err,line.magnitude(),rho_err)

num = 0
temp_num = 0
UartReceiveDate
#track(rho,theta)
Target_Num = 0
def find_number(temp_num):
    global tem
    global num
    while(True):
        clock.tick()
        img = sensor.snapshot()
        if(num == 0):
            sensor.set_windowing(1,1,100,100)
            sensor.set_windowing(10,20,50,60)
        if(num == 1):
            sensor.set_windowing(60,40,180,180)
            sensor.set_windowing(90,40,50,60)
        for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
            if (i == 0): continue
            if (len(detection_list) == 0): continue
            print(" %s " % labels[i])
            tem = labels[i]
            templatei ="/"+str(labels[i])+".pgm"
            print(templatei)
            outuart(tem)
            num = num + 1
            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))
            sensor.set_windowing(240,240)
            if(num == 1):
                temp_num=int(tem)*10
                print(temp_num)
            elif(num == 2):
                temp_num+=int(tem)
                print(temp_num)
                outuart(temp_num)
                break
        if(num ==2):
            break
    if(Target_Num==temp_num):
        print("okk")
        outuart(1)
        led = pyb.LED(2)
    else:
        print("error")

UartReceiveDate()
while(True):
    clock.tick()
    img = sensor.snapshot() # 拍一张照片并返回图像
    blobs = img.find_blobs([threshold=(245, 255),roi=(10,0,30,30)])
    if blobs==1:
    #如果找到了目标颜色
        for b in blobs:
            #迭代找到的目标颜色区域
            track(rho,theta)
    else:
        outuart("stop")
        find_number(temp_num)
    #track(rho,theta)
    #find_number(temp_num)

import sensor, image, time, os, tf, math, uos, gc, ustruct, pyb
from pyb import UART,LED
from image import SEARCH_EX, SEARCH_DS
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.set_windowing((240,240))
sensor.skip_frames(time=2000)
sensor.set_contrast(1)
sensor.set_gainceiling(16)

led = pyb.LED(4) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.
'''
template1 = image.Image("/1.pgm")
template2 = image.Image("/2.pgm")
template3 = image.Image("/3.pgm")
template4 = image.Image("/4.pgm")
templates = ["/1.pgm","/2.pgm","/3.pgm","/4.pgm"]
'''
rois = [(20,20,100,150),(140,50,90,100)]
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),
]
uart = UART(3,115200)
uart.init(9600, bits=8, parity=None, stop=1)

def outuart(num):
	global uart
	data = ustruct.pack("<bbhhhhb",
				   0x2C,
				   0x12,
				   int(num),
				   int(88),
				   0x5B)
	uart.write(data)
	time.sleep_ms(1)
data = [0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00]
def UartReceiveDate():
	global Find_Task
	global Target_Num
	global x
	global data
	data[0] = uart.readchar()
	data[1] = uart.readchar()
	Target_Num=int(data[0]-48)*10+int(data[1]-48)
	outuart(Target_Num)
clock = time.clock()
flag = 0
num = 0
temp_num = 0


while(True):
	clock.tick()
	img = sensor.snapshot()
	UartReceiveDate()
	
	if(num == 0):
		sensor.set_windowing(1,1,100,100)
		sensor.set_windowing(10,20,50,60)
	if(num == 1):
		sensor.set_windowing(60,40,180,180)
		sensor.set_windowing(90,40,50,60)
		
	for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
		if (i == 0): continue
		if (len(detection_list) == 0): continue
		print(" %s " % labels[i])
		tem = labels[i]
		templatei ="/"+str(labels[i])+".pgm"
		print(templatei)
		outuart(tem)
		num = num + 1
		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))
			sensor.set_windowing(240,240)

			if(num == 1):
				temp_num=int(tem)*10
				print(temp_num)
			elif(num == 2):
				temp_num+=int(tem)
				print(temp_num)
				outuart(temp_num)
               
				
	if(num==2):
		break
		led.on()
        time.sleep_ms(150)
        led.off()
        time.sleep_ms(100)
        led.on()
        time.sleep_ms(150)
        led.off()
        time.sleep_ms(600)
				
				
'''	else:
		print(temp_num)
		temp_num = 0
if (Target_Num == temp_num):		 #发送回去
	print("ok")
	outuart("ok")
	#print(clock.fps(), "fps", end="\n\n")'''

import sensor, image, time, os, tf, math, uos, gc, ustruct
from pyb import UART,LED
from image import SEARCH_EX, SEARCH_DS

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.
sensor.set_contrast(1)
sensor.set_gainceiling(16)

net = None
labels = None
min_confidence = 0.5
rois = [(0,30,80,80),(70,30,85,85)]

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),
]

#rois = [(0,20,190,200),(150,35,160,170)]
for roi in rois:
  clock = time.clock()

  num = 0
  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])
          tem = labels[i]
          #print(tem)
          num = 1
       
          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)
           
      if (num != 0):break

# 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")

降低精度会误判，但是提高精度又会识别不出来。距离和角度都有随时调整但是很难判别出具体数字，应该怎么解决

还是这个吗？