• 星瞳AI VISION软件内测!可以离线标注,训练,并生成OpenMV的模型。可以替代edge impulse https://forum.singtown.com/topic/8206
  • 我们只解决官方正版的OpenMV的问题(STM32),其他的分支有很多兼容问题,我们无法解决。
  • 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。
  • 垃圾分类openmv神经网络模型,openmv怎么准确识别垃圾的位置,让机械臂抓取呢?



    • Edge Impulse - OpenMV Image Classification Example

      import sensor, image, time, os, tf, uos, gc, pyb
      from pyb import UART,LED

      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((128, 128)) # Set 240x240 window.
      sensor.skip_frames(time=2000) # Let the camera adjust.
      LED(1).on()
      LED(2).on()
      LED(3).on()
      uart = UART(3,9600,timeout_char=1000)
      net = None
      labels = None

      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:
      print(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) + ')')

      clock = time.clock()
      while(True):
      clock.tick()
      img = sensor.snapshot()

      obj = net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5)

      # default settings just do one detection... change them to search the image...
      for obj in net.classify(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]))
      

      程序运行都是摄像头整幅画面



    • 用FOMO模型,可以识别到位置

      https://singtown.com/learn/50918/