• 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。
  • 这个问题是我的缓冲区太小了吗,为什么加载不了神经网络的模型?怎么解决这个问题呢?



    • 0_1657980966628_1.png

      
      

      请在这里粘贴代码

      Edge Impulse - OpenMV Object Detection Example

      import sensor, image, time, os, tf, math, 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
      min_confidence = 0.5

      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:
      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) + ')')

      colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
      (255, 0, 0),
      ( 0, 255, 0),
      (255, 255, 0),
      ( 0, 0, 255),
      (255, 0, 255),
      ( 0, 255, 255),
      (255, 255, 255),
      ]

      clock = time.clock()
      while(True):
      clock.tick()

      img = sensor.snapshot()
      
      # detect() returns all objects found in the image (splitted out per class already)
      # we skip class index 0, as that is the background, and then draw circles of the center
      # of our objects
      
      for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
          if (i == 0): continue # background class
          if (len(detection_list) == 0): continue # no detections for this class?
      
          print("********** %s **********" % labels[i])
          for d in detection_list:
              [x, y, w, h] = d.rect()
              center_x = math.floor(x + (w / 2))
              center_y = math.floor(y + (h / 2))
              print('x %d\ty %d' % (center_x, center_y))
              img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
      
      print(clock.fps(), "fps", end="\n\n")
      
      
      

      请在这里粘贴代码



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

      对于OpenMV4 H7 使用小模型,看视频。



    • 我是用的FOMO模型啊,而且图片都没够200张



    • @kidswong999 我是用的FOMO模型啊,而且图片都没够200张
      0_1658055329731_89928edb-dd07-4067-877f-31ee432b39b2-image.png



    • 看视频了吗?视频里说了。



    • @kidswong999 0_1658127745567_1aff8240-d07a-4808-892f-b79157ef6de7-image.png 这个是怎么回事



    • @sxft 我都说了,如果是OpenMV4 H7,要用小模型。看视频,里面都有讲。