• 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。
  • 视神经网络左右差距?



    • # 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,os,tf
      from image import SEARCH_EX, SEARCH_DS
      from pyb import UART
      #从imgae模块引入SEARCH_EX和SEARCH_DS。使用from import仅仅引入SEARCH_EX,
      #SEARCH_DS两个需要的部分,而不把image模块全部引入。
      uart = UART(3, 9600)
      # Reset sensor
      sensor.reset()
      
      # Set sensor settings
      # 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)
      
      net = "trained.tflite"
      labels = [line.rstrip('\n') for line in open("labels.txt")]
      
      # Load template.
      # Template should be a small (eg. 32x32 pixels) grayscale image.
      template1 = ["/1.pgm"]
      template2 = ["/2.pgm"]
      template3 = ["/3.pgm","/3a.pgm","/3b.pgm"]
      template4 = ["/4.pgm","/4a.pgm","/4b.pgm"]
      template5 = ["/5.pgm","/5a.pgm","/5b.pgm"]
      template6 = ["/6.pgm","/6a.pgm","/6b.pgm"]
      template7 = ["/7.pgm","/7a.pgm","/7b.pgm"]
      template8 = ["/8.pgm","/8a.pgm","/8b.pgm"]
      
      A0=1
      A9=1
      B0=1
      A1=0
      A2=0
      A3=0
      A4=0
      A5=0
      A6=0
      A7=0
      A8=0
      B0=1
      C0=1
      C1=0
      C2=1
      D0=1
      D1=1
      
      
      #加载模板图片
      
      clock = time.clock()
      
      # Run template matching
      while (True):
              clock.tick()
              img = sensor.snapshot()
              while(A0):
                  clock.tick()
                  img = sensor.snapshot()
                  t1 = image.Image(template1[0])
                  r1 = img.find_template(t1, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r1:
                      img.draw_rectangle(r1)
                      A1=1
                      A0=0
                  t2 = image.Image(template2[0])
                  r2 = img.find_template(t2, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r2:
                      img.draw_rectangle(r2)
                      A2=1
                      A0=0
                  t3 = image.Image(template3[0])
                  r3 = img.find_template(t3, 0.85, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r3:
                      img.draw_rectangle(r3)
                      print('3') #打印模板名字
                      A3=1
                      A0=0
                  t4 = image.Image(template4[0])
                  r4 = img.find_template(t4, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r4:
                      img.draw_rectangle(r4)
                      print('4') #打印模板名字
                      A4=1
                      A0=0
                  t5 = image.Image(template5[0])
                  r5 = img.find_template(t5, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r5:
                      img.draw_rectangle(r5)
                      print('5') #打印模板名字
                      A5=1
                      A0=0
                  t6 = image.Image(template6[0])
                  r6 = img.find_template(t6, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r6:
                      img.draw_rectangle(r6)
                      print('6') #打印模板名字
                      A6=1
                      A0=0
                  t7 = image.Image(template7[0])
                  r7 = img.find_template(t7, 0.80, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r7:
                      img.draw_rectangle(r7)
                      print('7') #打印模板名字
                      A7=1
                      A0=0
                  t8 = image.Image(template8[0])
                  r8 = img.find_template(t8, 0.85, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
                  if r8:
                      img.draw_rectangle(r8)
                      print('8') #打印模板名字
                      A8=1
                      A0=0
      
              while(A9):
                  if A1==1:
                      uart.write('1')
                      print('1') #打印模板名字
                      num=1
                      A9=0
                  if A2==1:
                      uart.write('2')
                      print('2') #打印模板名字
                      num=2
                      A9=0
                  if A3==1:
                      uart.write('3')
                      print('3')
                      num=3
                      A9=0
                  if A4==1:
                      uart.write('4')
                      print('4')
                      num=4
                      A9=0
                  if A5==1:
                      uart.write('5')
                      print('5')
                      num=5
                      A9=0
                  if A6==1:
                      uart.write('6')
                      print('6')
                      num=6
                      A9=0
                  if A7==1:
                      uart.write('7')
                      print('7')
                      num=7
                      A9=0
                  if A8==1:
                      uart.write('8')
                      print('8')
                      num=8
                      A9=0
      
              while(True):
                  while(B0):
                          if (uart.any()):
                              B = uart.read()
                              print(B)
                              if B==b'1':
                                  print("1")
                                  B0=0
                                  C0=1
                  while(C0):
                      img = sensor.snapshot()
                      roiL=(20,43,53,60)
                      for obj in tf.classify(net, img, roiL,min_scale=1, 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())
                          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]))
                          num1=ord(predictions_list[i][0])-48
                          if predictions_list[i][1]>0.7 and num1==num:
                              uart.write('1')
                              B0=1
                              print('11')
                              C1=1
                              C0=0
                      roiR=(89,43,53,60)
                      for obj in tf.classify(net, img, roiR,min_scale=1, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
                          print("**********\nPredictions2 at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
                          img.draw_rectangle(obj.rect())
                          predictions_list2 = list(zip(labels, obj.output()))
                      for p in range(len(predictions_list2)):
                          print("%s = %f" % (predictions_list2[p][0], predictions_list2[p][1]))
                          num2=ord(predictions_list2[p][0])-48
                          if predictions_list2[p][1]>0.7 and num2 == num:
                              uart.write('2')
                              B0=1
                              print("22")
                              C1=1
                              C0=0
                      if C1==0:
                          uart.write('0')
                          print("00")
                  print(clock.fps(), "fps")
      
      
      

      我用这个程序跑神经网络 然后出现roiL 和roiR识别差距 拍的右边的模板进行训练但是是左边秒识别 右边一点都识别不了



    • roi参数必须有roi=

      tf.classify(net, img, roiR,min_scale=1, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):

      改为

      tf.classify(net, img, roi = roiR,min_scale=1, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):



    • @kidswong999 改了之后问题还是没有得到解决右边还是一点没有