原来是这样！解决了，太感谢了

# Blob Detection Example
#
# This example shows off how to use the find_blobs function to find color
# blobs in the image. This example in particular looks for dark green objects.

import sensor, image, time         #导入模块
from pid import PID                   #导入PID控制模块
from pyb import Pin, Timer      #导入引脚和定时器
from pyb import UART
import display

uart = UART(3, 115200)
lcd = display.SPIDisplay()  # Initialize the lcd screen.
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QQVGA2) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
sensor.set_auto_gain(False) # turn this off.

#################阈值定义################################
fenbian_thresholds   = [ (0, 100, 18, 127, 10, 127),#000-红
                 (0, 46, -128, -13, 6, 127)  ,#001-绿
                 (0, 39, -128, 127, -128, -10) ]#020-蓝

yidong_thresholds   = [ (44, 100, 15, 127, -9, 127),#000-红
                 (64, 100, -16, 3, 0, 127)  ,#001-绿
                 (65, 100, 6, 127, -128, -18) ]#020-紫
#######################################################
#########电机控制函数##############################################
def run(left_speed, right_speed):
    if inverse_left==True:              #判断左轮是否需要反转
        left_speed=(-left_speed)
    if inverse_right==True:              #判断右轮是否需要反转
        right_speed=(-right_speed)

    #设置通道PWM，应改为串口发送
    l_speed="#006P"+str(int(abs(left_speed)))+"T1000!"
    uart.write(l_speed)
    r_speed="#007P"+str(int(abs(right_speed)))+"T1000!"
    uart.write(r_speed)

#########寻找最大色块函数###########################################
####返回最大色块max_blob的blob参数######
def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob

########变量定义###################################################

fenbian_mode=0    #方块 分辨模式
yidong_mode=000     #移动 分辨区域模式

inverse_left=False  #将其更改为True以反转左轮
inverse_right=False #将其更改为True以反转右轮

size_threshold = 2000
x_pid = PID(p=0.5, i=1, imax=100)
h_pid = PID(p=0.05, i=0.1, imax=50)

yidong_finish_flag=0
################################################################

clock = time.clock() # Tracks FPS.

#############主循环###############################################
while(True):

    clock.tick() # Track elapsed milliseconds between snapshots().
      
    img = sensor.snapshot() #获取一张图片
    lcd.write(img)
    blobs = img.find_blobs([yidong_thresholds[0]])
    if blobs and yidong_mode == 000 and yidong_finish_flag==0 :     ##########分辨红色###########
        yidong_blobs = img.find_blobs([yidong_thresholds[0]])
        yidong_max_blob = find_max(yidong_blobs)
        img.draw_rectangle(yidong_max_blob[0:4]) #绘制矩形
        img.draw_cross(yidong_max_blob[5], yidong_max_blob[6]) #绘制中心十字
        print('test3')
        img.draw_string(10,10,"test3",scale=1.2,mono_space=False)
        if fenbian_mode == 0 :
            max_blob = find_max(blobs)
            x_error = max_blob[5]-img.width()/2     #计算x轴离中心点位置
            h_error = max_blob[2]*max_blob[3]-size_threshold     #距离计算，计算色块面积-滤波面积
            img.draw_string(10,50,f"x_error:{x_error}",scale=1.2,mono_space=False)
            img.draw_rectangle(max_blob[0:4]) #绘制矩形
            img.draw_cross(max_blob[5], max_blob[6]) #绘制中心十字
            x_output=x_pid.get_pid(x_error,1)   #速度计算
            h_output=h_pid.get_pid(h_error,1)   #速度计算
            run(-h_output-x_output+1500,-h_output+x_output-1500)
            print('test1')
            print(-h_output-x_output+1500,-h_output+x_output-1500)
            img.draw_string(10,30,"test1",scale=1.2,mono_space=False)
            if yidong_max_blob[4]>=3000:
                run(1500,1500)
                fenbian_mode=1
                yidong_finish_flag=1


    elif blobs and yidong_finish_flag==1 :      ##################进入分辨模式##################  色块像素大于3000进入
        for i in range (0,3) :
            fenbian_blobs = img.find_blobs([fenbian_thresholds[i]])
            if fenbian_blobs :
                fenbian_max_blob = find_max(fenbian_blobs)
                img.draw_rectangle(fenbian_max_blob[0:4]) #绘制矩形
                img.draw_cross(fenbian_max_blob[5], fenbian_max_blob[6]) #绘制中心十字
                print('test2')
                img.draw_string(10,30,"test2",scale=1.2,mono_space=False)
#    else:
#        run(18,-18)     #寻找不到色块则原地自转

#        if blobs and fenbian_mode==0 :
#            max_blob = find_max(blobs)
#            x_error = max_blob[5]-img.width()/2     #计算x轴离中心点位置
#            h_error = max_blob[2]*max_blob[3]-size_threshold     #滤波，计算色块面积-滤波面积
#            img.draw_string(10,10,f"x_error:{x_error}",scale=1.2,mono_space=False)

#            img.draw_rectangle(max_blob[0:4]) #绘制矩形
#            img.draw_cross(max_blob[5], max_blob[6]) #绘制中心十字

#            x_output=x_pid.get_pid(x_error,1)   #速度计算
#            h_output=h_pid.get_pid(h_error,1)   #速度计算
#            run(-h_output-x_output,-h_output+x_output)
#        else:
#            run(18,-18)     #寻找不到色块则原地自转![0_1699003044429_IMG_20231103_171641.jpg](https://fcdn.singtown.com/73475913-7e75-4b5a-8299-e9410042ab90.jpg) 

# Single Color RGB565 Blob Tracking Example
#
# This example shows off single color RGB565 tracking using the OpenMV Cam.

import sensor, image, time, math

threshold_index = 0 # 0 for red, 1 for green, 2 for blue

# Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)
# The below thresholds track in general red/green/blue things. You may wish to tune them...
thresholds = [(67, 82, -128, 127, 28, 127)] # generic_red_thresholds   阈值设置，代表LAB的最大值和最小值


sensor.reset()  #重置感光元件/重置摄像机
sensor.set_pixformat(sensor.RGB565) #设置模式为彩图
sensor.set_framesize(sensor.QVGA)   #设置图像大小为QVGA
sensor.skip_frames(time = 2000)     #使以上操作生效
sensor.set_auto_gain(False) # must be turned off for color tracking     关闭颜色增益
sensor.set_auto_whitebal(False) # must be turned off for color tracking 关闭白平衡
clock = time.clock()    

# Only blobs that with more pixels than "pixel_threshold" and more area than "area_threshold" are
# returned by "find_blobs" below. Change "pixels_threshold" and "area_threshold" if you change the
# camera resolution. "merge=True" merges all overlapping blobs in the image.

while(True):
    clock.tick()    #开始追踪运行时间
    img = sensor.snapshot()     #在感光元件截取一张图
    for blob in img.find_blobs([thresholds[threshold_index]], pixels_threshold=320, area_threshold=400, merge=True):
        #img.find_blobs()函数创建一个class image.blob对象，返回一个包含多个元素的字典
        # These values depend on the blob not being circular - otherwise they will be shaky.
        if blob.elongation() > 0.5:     #返回一个介于0和1之间的值，该值表示对象的长度（不是圆形）。一条线将是1。
            img.draw_edges(blob.min_corners(), color=(255,0,0))
            img.draw_line(blob.major_axis_line(), color=(0,255,0))
            img.draw_line(blob.minor_axis_line(), color=(0,0,255))
        # These values are stable all the time.
        img.draw_rectangle(blob.rect())         #识别到颜色则画框框起
        img.draw_cross(blob.cx (), blob.cy())   #识别到颜色则画十字
        # Note - the blob rotation is unique to 0-180 only.
        img.draw_keypoints([(blob.cx(), blob.cy(), int(math.degrees(blob.rotation())))], size=20)
        print(blob)    
#    print(clock.fps())#停止追踪运行时间，并返回当前帧数

# Single Color RGB565 Blob Tracking Example
#
# This example shows off single color RGB565 tracking using the OpenMV Cam.

import sensor, image, time, math

threshold_index = 0 # 0 for red, 1 for green, 2 for blue

# Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)
# The below thresholds track in general red/green/blue things. You may wish to tune them...
thresholds = [(67, 82, -128, 127, 28, 127)] # generic_red_thresholds   阈值设置，代表LAB的最大值和最小值


sensor.reset()  #重置感光元件/重置摄像机
sensor.set_pixformat(sensor.RGB565) #设置模式为彩图
sensor.set_framesize(sensor.QVGA)   #设置图像大小为QVGA
sensor.skip_frames(time = 2000)     #使以上操作生效
sensor.set_auto_gain(False) # must be turned off for color tracking     关闭颜色增益
sensor.set_auto_whitebal(False) # must be turned off for color tracking 关闭白平衡
clock = time.clock()    

# Only blobs that with more pixels than "pixel_threshold" and more area than "area_threshold" are
# returned by "find_blobs" below. Change "pixels_threshold" and "area_threshold" if you change the
# camera resolution. "merge=True" merges all overlapping blobs in the image.

while(True):
    clock.tick()    #开始追踪运行时间
    img = sensor.snapshot()     #在感光元件截取一张图
    for blob in img.find_blobs([thresholds[threshold_index]], pixels_threshold=320, area_threshold=400, merge=True):
        #img.find_blobs()函数创建一个class image.blob对象，返回一个包含多个元素的字典
        # These values depend on the blob not being circular - otherwise they will be shaky.
        if blob.elongation() > 0.5:     #返回一个介于0和1之间的值，该值表示对象的长度（不是圆形）。一条线将是1。
            img.draw_edges(blob.min_corners(), color=(255,0,0))
            img.draw_line(blob.major_axis_line(), color=(0,255,0))
            img.draw_line(blob.minor_axis_line(), color=(0,0,255))
        # These values are stable all the time.
        img.draw_rectangle(blob.rect())         #识别到颜色则画框框起
        img.draw_cross(blob.cx (), blob.cy())   #识别到颜色则画十字
        # Note - the blob rotation is unique to 0-180 only.
        img.draw_keypoints([(blob.cx(), blob.cy(), int(math.degrees(blob.rotation())))], size=20)
        print(blob)    
#    print(clock.fps())#停止追踪运行时间，并返回当前帧数