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  • 红线左边识别到数字发送L,右边识别到数字发送R



    • 0_1654436025670_微信图片_20220605213332.jpg



    • 或者说识别到多数字时怎么判断想要的数字是在左边还是右边



    • @dymb红线左边识别到数字发送L,右边识别到数字发送R 中说:

      或者说识别到多数字时怎么判断想要的数字是在左边还是右边

      一般是通过x坐标判断。



    • @kidswong999 哦哦已经想到了用坐标,但是不知道用什么语句



    • @dymb 你先发一下你的代码。我看看你用的什么算法。



    • @kidswong999 # Template Matching Example - Normalized Cross Correlation (NCC)

      #
      # This example shows off how to use the NCC feature of your OpenMV Cam to match
      # image patches to parts of an image... expect for extremely controlled enviorments
      # NCC is not all to useful.
      #
      # WARNING: NCC supports needs to be reworked! As of right now this feature needs
      # a lot of work to be made into somethin useful. This script will reamin to show
      # that the functionality exists, but, in its current state is inadequate.
      
      import time, sensor, image
      from image import SEARCH_EX, SEARCH_DS
      
      # Reset sensor
      sensor.reset()
      
      # Set sensor settings
      sensor.set_contrast(1)
      sensor.set_gainceiling(16)
      # Max resolution for template matching with SEARCH_EX is QQVGA
      sensor.set_framesize(sensor.QQVGA)
      # You can set windowing to reduce the search image.
      #sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60))
      sensor.set_pixformat(sensor.GRAYSCALE)
      from pyb import UART
      uart = UART(3, 115200)
      # Load template.
      # Template should be a small (eg. 32x32 pixels) grayscale image.
      template1 = image.Image("/1.pgm")
      template2 = image.Image("/2.pgm")
      
      clock = time.clock()
      flag=0
      
      # Run template matching
      while (True):
          clock.tick()
          img = sensor.snapshot()
      
      
      
          # find_template(template, threshold, [roi, step, search])
          # ROI: The region of interest tuple (x, y, w, h).
          # Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster.
          # Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search
          #
          # Note1: ROI has to be smaller than the image and bigger than the template.
          # Note2: In diamond search, step and ROI are both ignored.
      
      
      
          
          if flag==0:
              r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
              r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
              if r1:
                  img.draw_rectangle(r1)
                  flag=1
              if r2:
                  img.draw_rectangle(r2)
                  flag=2
          print(clock.fps())
      
          if flag==1:
              uart.write("1")
              flag=999
      
          if flag==2:
              uart.write("2")
              flag=998
              
          r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
          r2 = img.find_template(template2, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
          if flag==999
              if r1:
                  img.draw_rectangle(r1)
                      if
      


    • @dymb 最下面是要开始判断左右了,但是不会,请求大佬帮助一下😔



    • r1 = img.find_template(template1, 0.70, step=4, search=SEARCH_EX)
      cx = r1[0] + r1[2]/2
      
      if cx < img.width()/2:
          print(左边)
      else:
          print(右边)
      


    • @kidswong999 大佬,cx=r1【0】+r1【2】/2出问题了

      新发一个帖子,附上全部的代码。