• 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。
  • 我想先识别白纸,然后在白纸上识别圆形,应该怎样改动这个代码呢?



    • # Single Color RGB565 Blob Tracking Example
      #
      # This example shows off single color RGB565 tracking using the OpenMV Cam.
      
      import sensor, image, time, math, lcd
      
      threshold_index = 0 # 0 for red, 1 for green, 2 for blue
      
      # Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)
      # The below thresholds track in general red/green/blue things. You may wish to tune them...
      thresholds = [(67, 100, -128, 37, -128, 69)] # generic_blue_thresholds
      blob=[0,0,16000,16000]
      sensor.reset()
      sensor.set_pixformat(sensor.RGB565)
      sensor.set_framesize(sensor.QVGA)
      sensor.skip_frames(time = 2000)
      sensor.set_auto_gain(False) # must be turned off for color tracking
      sensor.set_auto_whitebal(False) # must be turned off for color tracking
      clock = time.clock()
      lcd.init()
      # Only blobs that with more pixels than "pixel_threshold" and more area than "area_threshold" are
      # returned by "find_blobs" below. Change "pixels_threshold" and "area_threshold" if you change the
      # camera resolution. "merge=True" merges all overlapping blobs in the image.
      
      while(True):
          clock.tick()
          img = sensor.snapshot()
          for blob in img.find_blobs([thresholds[threshold_index]],pixels_threshold=200, area_threshold=19000, merge=True):
              # These values depend on the blob not being circular - otherwise they will be shaky.
              if blob.elongation() > 0.5:
                  img.draw_edges(blob.min_corners(), color=(255,0,0))
                  img.draw_line(blob.major_axis_line(), color=(0,255,0))
                  img.draw_line(blob.minor_axis_line(), color=(0,0,255))
              # These values are stable all the time.
              for c in img.find_circles(threshold=3500,  left_roi = [blob[0],blob[1],blob[2],blob[3]], x_margin=10, y_margin=10, r_margin=10, r_min=2, r_max=100, r_step=2):
                  img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0))
                  img.draw_rectangle(blob.rect())
                  img.draw_cross(blob.cx(), blob.cy())
              # Note - the blob rotation is unique to 0-180 only.
                  img.draw_keypoints([(blob.cx(), blob.cy(), int(math.degrees(blob.rotation())))], size=20)
          
                  img.draw_string(0,0,'x='+str(blob.cx()), color=(255,0,0))
                  img.draw_string(0,10,'y='+str(blob.cy()), color=(255,0,0))
                  img.draw_string(0,20,'h='+str(blob.h()), color=(255,0,0))
                  img.draw_string(0,30,'w='+str(blob.w()), color=(255,0,0))
                  lcd.display(img) # Take a picture and display the image.
          print(clock.fps())
      
      


    • 有没有实际图片?



    • @kidswong999 0_1653556695490_D2674E3374216F5614E4611DA07B5F5D.jpg 大概是这样的



    • 你这个就直接找红色的色块就行了。