用的是openmv3CamM7,最新版的固体版本

import sensor, image, time
from pyb import UART
import json
# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
yellow_threshold   = (65, 100, -10, 6, 24, 51)
# You may need to tweak the above settings for tracking green things...
# Select an area in the Framebuffer to copy the color settings.

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.

uart = UART(3, 115200)
def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob.pixels() > max_size:
            max_blob=blob
            max_size = blob.pixels()
    return max_blob

while(True):
    img = sensor.snapshot() # Take a picture and return the image.

    blobs = img.find_blobs([yellow_threshold])
    if blobs:
        max_blob=find_max(blobs)
        print('sum :', len(blobs))
        img.draw_rectangle(max_blob.rect())
        img.draw_cross(max_blob.cx(), max_blob.cy())

        output_str="[%d,%d]" % (max_blob.cx(),max_blob.cy()) #方式1
        #output_str=json.dumps([max_blob.cx(),max_blob.cy()]) #方式2
        print('you send:',output_str)
        uart.write(output_str+'\r\n')
    else:
        print('not found!')

现在没有影响了，但是感觉运行速度好慢，三十几秒才一个循环

import sensor, image, time, math
import pyb
from servo import Servos
from machine import I2C, Pin



sensor.reset() # 初始化摄像头
sensor.set_pixformat(sensor.RGB565) # 格式为 RGB565.
sensor.set_framesize(sensor.QQVGA) # 使用 QQVGA 速度快一些
sensor.skip_frames(time = 2000) # 跳过2000s，使新设置生效,并自动调节白平衡
sensor.skip_frames(10) # Let new settings take affect.

sensor.set_auto_gain(False) # 关闭自动自动增益。默认开启的，在颜色识别中，一定要关闭白平衡。
sensor.set_auto_whitebal(False)
#关闭白平衡。白平衡是默认开启的，在颜色识别中，一定要关闭白平衡。
clock = time.clock() # 追踪帧率

i2c = I2C(sda=Pin('P5'), scl=Pin('P4'))
servo = Servos(i2c, address=0x40, freq=50, min_us=650, max_us=2800, degrees=180)

red_threshold   = (87, 11, 127, 6, 2, 93)
K=1400#the value should be measured得到mm
x_chassis=0
y_chassis=0
x_camera=0
l_camera=0
i=0
arm_angle_goal=0
elbow_angle_goal=0
chassised_angle=0
chassised_angle_goal=0
chassis_angle_goal=0
arm_ball=0
chassis_ball=0
chassis_high=0
chassis_angle=0      #底盘舵机角度
arm_angle=0          #臂舵机角度
elbow_angle=0        #肘舵机角度
hand_angle=0         #手爪舵机角度


max_chassis_angle=180     #底盘舵机复位角度
min_chassis_angle=0       #底盘舵机有效角度
max_arm_angle=93        #臂舵机复位角度
min_arm_angle=0          #臂舵机有效角度
max_elbow_angle=180       #肘舵机复位角度
min_elbow_angle=0        #肘舵机有效角度
max_hand_angle=120        #手爪舵复位机角度
min_hand_angle=60         #手爪舵机有效角度

x_camera=0           #小球对于摄像头x坐标
y_camera=0           #小球对于摄像头y坐标
x_chassis=0          #小球对于底盘x坐标
y_chassis=0          #小球对于底盘y坐标
i=0


arm_angle_camera=0     #手臂和水平线角度
arm_ball=0             #臂舵机到球距离
elbow_angle_camera=0   #肘舵机于竖线的角度

chassis_high=50     #底座高度mm 90-20=70
arm_long=70        #手臂长度
elbow_hand_long=100 #手爪到肘长
chassis_ball=0      #底座到球距离


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


while(True):

    time.sleep(3000)
    print('进入')
    while(i<11):
        i=i+1
        clock.tick() # Track elapsed milliseconds between snapshots().
        img = sensor.snapshot() # 从感光芯片获得一张图像

        blobs = img.find_blobs([red_threshold])
        if blobs:
        #如果找到了目标颜色
            for b in blobs:
            #迭代找到的目标颜色区域
                # Draw a rect around the blob.
                img.draw_rectangle(b[0:4]) # rect
                #用矩形标记出目标颜色区域
                img.draw_cross(b[5], b[6]) # cx, cy
                #在目标颜色区域的中心画十字形标记
        Lm = b[2]#+b[3])/2
        length = K/Lm   #球到摄像头距离
        x_camera=x_camera+b[5]  #为了求x点在感兴趣区域的坐标x平均
        l_camera=l_camera+length #距离累加10次
        print('正在获取小球对于球的坐标，距离，x坐标：',length,int(length),b[5])
        #print(length)
        #print(b[5])
        time.sleep(30)
    x_camera=x_camera/10
    l_camera=l_camera/10
    x_chassis=math.sin((x_camera-80)*115/160)*l_camera
    x_chassis=math.fabs(x_chassis)
    y_chassis=math.cos((x_camera-80)*115/160)*l_camera+80
    #115是整个视野角度，平均到x方向每个像素就是115/160左边为负，右边为正，再乘以距离就是真实的偏移距离


    print('得到数据，距离平均值mm，x坐标平均值，x绝对坐标，y绝对坐标，角度')
    time.sleep(30)
    print(l_camera)
    print(x_camera)
    print(x_chassis)
    print(y_chassis)
    print((x_camera-80)*115/160)
    #break

    #进入抓球状态
    chassised_angle=math.atan(x_chassis/y_chassis) #得到相对弧度
    chassised_angle=chassised_angle*180/3.14   #转换成角度
    chassised_angle_goal=90-chassised_angle/0.34   #0.34=50/17  转换成舵机绝对角度
    print('底盘绝对角度是：')
    print( chassised_angle)

    #arm_angle_goal和elbow_angle_goal是目标角度
    chassis_ball=math.sqrt(x_chassis**2+y_chassis**2)
    arm_angle_goal=math.atan(chassis_high/chassis_ball)    #角度:底座——球 & ——水平线
    arm_ball=math.sqrt(chassis_ball*chassis_ball+chassis_high*chassis_high) #臂舵机到球距离
    #手臂和水平线角度余弦定理c*c = a*a + b*b - 2*a*b*cosC求反余弦减去臂舵机到球距离
    print( chassised_angle)
    arm_angle_goal=math.acos((arm_long**2 + arm_ball**2 - elbow_hand_long**2)/(2*arm_long*arm_ball))-arm_angle_goal
    arm_angle_goal= arm_angle_goal*180/math.pi #手臂和水平线角度

    elbow_angle_goal = math.acos((elbow_hand_long*elbow_hand_long + arm_long*arm_long - arm_ball*arm_ball )/(2 * elbow_hand_long * arm_long))*180/math.pi#臂肘之间的角度
    elbow_angle_goal = elbow_angle_goal-90+arm_angle_goal  #肘舵机于竖线的角度
    print('底座目标角度:')
    print(chassised_angle)
    print('手臂目标角度:')
    print(arm_angle_goal)
    print('肘部目标角度:')
    print(elbow_angle_goal)
    pass
    while(elbow_angle!=elbow_angle_goal or arm_angle!= arm_angle_goal or chassis_angle!= chassis_angle_goal or hand_angle!=min_hand_angle ):
        i=4
        while(i>=0):
            i=i-1
            if i==3 and hand_angle>min_hand_angle:
                servo.position(i,hand_angle)
                hand_angle-=1
            elif i==2 and elbow_angle <= elbow_angle_goal:
                pass
                servo.position(i,elbow_angle)
                elbow_angle+=1

            elif i==1 and arm_angle <= arm_angle_goal:
                pass
                servo.position(i,arm_angle)
                arm_angle+=1
            elif i==0 and chassis_angle > chassis_angle_goal:
                ervo.position(i,chassis_angle)
                arm_angle-=1
            elif i==0 and chassis_angle <= chassis_angle_goal:
                servo.position(i,chassis_angle)
                arm_angle+=1
    while(min_hand_angle<=hand_angle):
        hand_angle+=1
        servo.position(4,hand_angle)
        time.sleep(30)





    while(elbow_angle!=max_elbow_angle or arm_angle!=max_arm_angle or chassis_angle!=90):
        pass
        i=3
        while(i>=0):
        #底座，手臂，肘，手爪，对应0，1，2，3
            i=i-1
            if i==2 and elbow_angle<=max_elbow_angle:
                servo.position(i,elbow_angle)
                elbow_angle+=1
            elif i==1 and arm_angle<=max_arm_angle:
                    servo.position(i,arm_angle)
                    arm_angle+=1
            elif i==0 and chassis_angle!=90:
                if chassis_angle<90:
                    ervo.position(i,chassis_angle)
                    arm_angle+=1
                else:
                    ervo.position(i,chassis_angle)
                    arm_angle-=1

# 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
import car
from pid import PID

# You may need to tweak the above settings for tracking green things...
# Select an area in the Framebuffer to copy the color settings.

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # use RGB565.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.

# For color tracking to work really well you should ideally be in a very, very,
# very, controlled enviroment where the lighting is constant...
green_threshold   = (76, 96, -110, -30, 8, 66)
size_threshold = 2000
x_pid = PID(p=0.5, i=1, imax=100)
h_pid = PID(p=0.05, i=0.1, imax=50)

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

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.

    blobs = img.find_blobs([green_threshold])
    if blobs:
        max_blob = find_max(blobs)
        x_error = max_blob[5]-img.width()/2
        h_error = max_blob[2]*max_blob[3]-size_threshold
        print("x error: ", x_error)
        '''
        for b in blobs:
            # Draw a rect around the blob.
            img.draw_rectangle(b[0:4]) # rect
            img.draw_cross(b[5], b[6]) # cx, cy
        '''
        img.draw_rectangle(max_blob[0:4]) # rect
        img.draw_cross(max_blob[5], max_blob[6]) # cx, cy
        x_output=x_pid.get_pid(x_error,1)
        h_output=h_pid.get_pid(h_error,1)
        print("h_output",h_output)
        car.run(-h_output-x_output,-h_output+x_output)
    else:
        car.run(18,-18)