该怎么控制它转动的角度呢？怎么让它随停呢？

@kidswong999 如果要自己写一个舵机控制函数的话，要怎么写啊？

# main.py -- put your code here!
import pyb, time
from pyb import Servo
import sensor, image, time, os, tf
led = pyb.LED(3)
usb = pyb.USB_VCP()
# while (usb.isconnected()==False):
led.on()
  # time.sleep(150)
  # led.off()
  # time.sleep(100)
  # led.on()
  # time.sleep(150)
  # led.off()
  # time.sleep(600)

# Edge Impulse - OpenMV Image Classification Example


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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")
    a = obj.output()
    maxx = max(a)
    print(maxx)
    maxxx = a.index(max(a))
    print(maxxx)
    s1 = Servo(1)
    s2 = Servo(2)
    if maxxx == 3:
        s1.angle(90)
        time.sleep_ms(1000)
        s1.angle(0)
        time.sleep_ms(1000)
        s2.angle(90)
        time.sleep_ms(1000)
        s2.angle(0)
        time.sleep_ms(3000)
        s1.angle(180)
        time.sleep_ms(1000)
        s1.angle(0)

@kidswong999 不行，运行你这个也只能是舵机转90度之后再回到0度。

from pyb import Servo
import time
s1 = Servo(1)
s2 = Servo(2)
s1.angle(180)
time.sleep_ms(1000)

import pyb
s1 = pyb.Servo(1)   # create a servo object on position P7 #在P7引脚创建servo对象
s1.angle(45)        # move servo 1 to 45 degrees #将servo1移动到45度

@kidswong999 加了冒号还是这个问题啊

# Edge Impulse - OpenMV Image Classification Example
from pyb import Servo
import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")
    a = obj.output()
    maxx = max(a)
    print(maxx)
    maxxx = a.index(max(a))
    print(maxxx)
    s1 = Servo(1)
    s2 = Servo(2)
    tim = pyb.Timer(4) #选一个时钟即可

    #初始化这个时钟
    tim.init(freq = 200) #假设每秒想判断舵机状态 200 次

    #设定中断执行的内容为函数 `timCallback`
    tim.callback(timCallback)

    #设定状态标识与舵机转动一次最长时间
    #参数意义：
    #    - isDetect：是否检测到有垃圾
    #    - isInTask：舵机是否在转动
    #    - servoAngle：舵机应该去的角度
    #    - threadCount：计数器，用于计算时间
    #    - whereIsServo: 舵机到达的位置：0 为原位，1 为转到的目的
    trash_thread = {'isDetect': True, 'isInTask': False ,'servoAngle': 0, 'threadCount': 0, 'whereIsServo': 0}
    servoTimeLim = 400  #例如 400ms，应该需要实验

    while(1):
        #推断balabala

        #中断在不断进行，把全局变量的值赋给目标即可
        s1.angle(trash_thread['servoAngle'])


    def timCallback
        #判断转动任务是否执行中，如果没有：
        if (trash_thread['isInTask'] == False):
            #判定是否有垃圾，有就根据最大概率设定舵机目标角度
            #判断语句，判定后赋值给 trash_thread['isDetect'] True 或 False
            #随后就是有的话判断舵机该去的角度
            if (trash_thread['isDetect']):
                #设定为任务执行状态中
                trash_thread['isInTask'] = True

                if (maxxx == 0):
                    trash_thread['servoAngle'] = 22
                elif (maxxx == 1):
                    trash_thread['servoAngle'] = 45
                elif (maxxx == 2):
                    trash_thread['servoAngle'] = 67
                elif (maxxx == 3):
                    trash_thread['servoAngle'] = 90
                #状态归位
                trash_thread['isDetect'] = False
                trash_thread['threadCount'] = 0
                trash_thread['whereIsServo'] = 0
            else:
                #无检测则保持原位
                trash_thread['servoAngle'] = 0
        #判断转动任务是否执行中，如果是：
        else:
            #开始转动后，每次中断计数器加一；随后计算开始旋转后的时间，若超出限制则认为到达位置
            trash_thread['threadCount'] += 1
            if (trash_thread['threadCount'] * 50 >= servoTimeLim):
                #到达后改变记录的舵机位置
                trash_thread['whereIsServo'] += 1
                #如果此次目标是回归原位
                if (trash_thread['whereIsServo'] == 2):
                    trash_thread['whereIsServo'] = 0
                    trash_thread['isInTask'] = False
                    trash_thread['threadCount'] = 0
                #如果此次目标是转到目标位置
                elif (trash_thread['whereIsServo'] == 1):
                    trash_thread['servoAngle'] = 0
            else :
                pass

# Edge Impulse - OpenMV Image Classification Example
from pyb import Servo
import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")
    a = obj.output()
    maxx = max(a) 
    print(maxx)
    maxxx = a.index(max(a))
    print(maxxx)
    s1 = Servo(1)
    s2 = Servo(2)
    if maxxx == 3:
        s1.angle(180)
        s2.angle(90)

@ynmo 是的，辽宁省的

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")

加了：之后它就提升if那一行有这种问题了

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")

@kidswong999 那样就能把定时器中断打开，那个里面运行识别部分的程序作为一个后台，然后舵机的部分就接受它的输出最大值了嘛