4. 如何让这个程序能：有垃圾放入才开始识别，而不是插电之后就开始识别摄像头照到的一切东西？
  • OpenMV VSCode 扩展发布了，在插件市场直接搜索OpenMV就可以安装
  • 如果有产品硬件故障问题，比如无法开机，论坛很难解决。可以直接找售后维修。
  • 发帖子之前，请确认看过所有的视频教程，https://singtown.com/learn/ 和所有的上手教程http://book.openmv.cc/
  • 每一个新的提问，单独发一个新帖子
  • 帖子需要目的，你要做什么？
  • 如果涉及代码，需要报错提示与全部代码文本，请注意不要贴代码图片
  • 必看：玩转星瞳论坛了解一下图片上传，代码格式等问题。

    • 如何让这个程序能：有垃圾放入才开始识别，而不是插电之后就开始识别摄像头照到的一切东西？

    • Q
       

      # 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)
      0

    • 你怎么判断是否有垃圾？是添加开关？

      0