• 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。
  • 迁移学习代码显示ValueError: Expected model output height to be 1!



    • 0_1722409625344_95c7a8fdc0ed39498d0cd5f07ca2984.png

      import sensor, image, time, os, tf, uos, gc
      
      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 = 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()
          
          # 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]))
      
          print(clock.fps(), "fps")
      

      使用edge impluse网站的迁移学习训练出来的代码显示错误怎么解决呀?
      很急,有没有大佬帮忙看看



    • for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):这行代码显示的错误



    • 看上去你的代码和模型不匹配,你要用edge impulse下载下来的代码。