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  • 标志位变化出现问题 无法依靠标志位 正确初始化其他变量



    • 标志位变量定义为i时 增减会出错
      但是定义为flag 则正确增减
      是因为与 此处 临时变量i冲突导致的吗?

      for i in range(len(blobs)):
                  #目标区域找到的颜色块(线段块)可能不止一个,找到最大的一个,作为本区域内的目标直线
                      if blobs[i].pixels() > most_pixels:
                          most_pixels = blobs[i].pixels()
                          #merged_blobs[i][4]是这个颜色块的像素总数,如果此颜色块像素总数大于                     #most_pixels,则把本区域作为像素总数最大的颜色块。更新most_pixels和largest_blob
                          largest_blob = i
      

      代码目的是循迹

      出现bug的代码

      # Black Grayscale Line Following Example
      #
      # Making a line following robot requires a lot of effort. This example script
      # shows how to do the machine vision part of the line following robot. You
      # can use the output from this script to drive a differential drive robot to
      # follow a line. This script just generates a single turn value that tells
      # your robot to go left or right.
      #
      # For this script to work properly you should point the camera at a line at a
      # 45 or so degree angle. Please make sure that only the line is within the
      # camera's field of view.
      
      import sensor, image, time, math#调用声明
      from pyb import UART
      import json
      
      # Tracks a black line. Use [(128, 255)] for a tracking a white line.
      # GRAYSCALE_THRESHOLD = [(0, 50)]
      GRAYSCALE_THRESHOLD = [(0, 80)]
      #设置阈值,如果是黑线,GRAYSCALE_THRESHOLD = [(0, 64)];
      #如果是白线,GRAYSCALE_THRESHOLD = [(128,255)]
      #GRAYSCALE_THRESHOLD = [(190,255)]
      
      
      # Each roi is (x, y, w, h). The line detection algorithm will try to find the
      # centroid of the largest blob in each roi. The x position of the centroids
      # will then be averaged with different weights where the most weight is assigned
      # to the roi near the bottom of the image and less to the next roi and so on.
      # ROIS = [ # [ROI, weight]
      #         (0, 40, 80, 15, 0.7), # You'll need to tweak the weights for you app
      #         (0, 20, 80, 15, 0.3), # depending on how your robot is setup.
      #         (0, 00, 80, 15, 0.1)
      #        ]
      
      ROIS = [ # [ROI, weight]
              (0, 100, 160, 30, 0.7), # You'll need to tweak the weights for you app
              # 根据线宽 修改高度
              # 远处给的权重更大
              (0, 050, 160, 30, 0.3), # depending on how your robot is setup.
              # 权重后期需要调整
              # 应该需要使用全宽度160(即分辨率对应的最大值) 否则转弯貌似无法识别
              (0, 000, 160, 30, 0.1)
             ]
      
      # ROIS = [ # [ROI, weight]
      #         (20, 40, 40, 15, 0.1), # You'll need to tweak the weights for you app
      #         (20, 20, 40, 15, 0.3), # depending on how your robot is setup.
      #         (20, 00, 40, 15, 0.7)
      #        ]
      #roi代表三个取样区域,(x,y,w,h,weight),代表左上顶点(x,y)宽高分别为w和h的矩形,
      #weight为当前矩形的权值。注意本例程采用的QQVGA图像大小为160x120,roi即把图像横分成三个矩形。
      #三个矩形的阈值要根据实际情况进行调整,离机器人视野最近的矩形权值要最大,
      #如上图的最下方的矩形,即(0, 100, 160, 20, 0.7)
      
      # Compute the weight divisor (we're computing this so you don't have to make weights add to 1).
      weight_sum = 0 #权值和初始化
      for r in ROIS: weight_sum += r[4] # r[4] is the roi weight.
      print("weight_sum_max")
      print(weight_sum)
      weight_sum = 0 #权值和初始化
      current_x=current_y=0
      
      #计算权值和。遍历上面的三个矩形,r[4]即每个矩形的权值。
      
      
      # Camera setup...
      sensor.reset() # Initialize the camera sensor.
      # sensor.set_pixformat(sensor.RGB565) # use grayscale.
      sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.
      sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
      # sensor.set_framesize(sensor.QQQVGA) # use QQVGA for speed.
      sensor.skip_frames(300) # Let new settings take affect.
      #不要太小 否则容易卡顿
      
      sensor.set_auto_gain(False) # must be turned off for color tracking
      sensor.set_auto_whitebal(False) # must be turned off for color tracking
      #关闭白平衡
      clock = time.clock() # Tracks FPS.
      
      uart = UART(3, 9600)#波特率两边要设置成一样
      
      
      while(True):
          clock.tick() # Track elapsed milliseconds between snapshots().
          img = sensor.snapshot() # Take a picture and return the image.
          # uart = UART(3,19200)
          # uart.init(19200,bits=8,parity=None,stop=1)#init with given parameters
      
          centroid_sum = centroid_sum_y = 0
          i=0
          #print("i")
          #print(i)
          #利用颜色识别分别寻找三个矩形区域内的线段
          weight_sum = 1 #权值和初始化
          for r in ROIS:
      
              blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=False)
              # blobs = img.find_blobs(GRAYSCALE_THRESHOL, roi=r[0:4], merge=True)
      
              # r[0:4] is roi tuple.
              #找到视野中的线,merge=true,将找到的图像区域合并成一个
      
              # 在每一个ROI都会执行下述语句 所以总共4个ROI
      
              #目标区域找到直线
              if blobs:
                  i+=1
                  print("flag ")
                  print(i)
                  if i ==1 :
                      weight_sum = 0
                  # Find the index of the blob with the most pixels.
                  most_pixels = 0
                  largest_blob = 0
                  for i in range(len(blobs)):
                  #目标区域找到的颜色块(线段块)可能不止一个,找到最大的一个,作为本区域内的目标直线
                      if blobs[i].pixels() > most_pixels:
                          most_pixels = blobs[i].pixels()
                          #merged_blobs[i][4]是这个颜色块的像素总数,如果此颜色块像素总数大于                     #most_pixels,则把本区域作为像素总数最大的颜色块。更新most_pixels和largest_blob
                          largest_blob = i
      
                  # Draw a rect around the blob.
                  img.draw_rectangle(blobs[largest_blob].rect())
                  img.draw_rectangle((0,0,20, 20))
                  #将此区域的像素数最大的颜色块画矩形和十字形标记出来
                  img.draw_cross(blobs[largest_blob].cx(),
                                 blobs[largest_blob].cy())
      
                  centroid_sum += blobs[largest_blob].cx() * r[4] # r[4] is the roi weight.
                  #计算centroid_sum,centroid_sum等于每个区域的最大颜色块的中心点的x坐标值乘本区域的权值
                  centroid_sum_y += blobs[largest_blob].cy() * r[4] # r[4] is the roi weight.
      
                  print("centroid_x_proportion")
                  print(centroid_sum)
                  weight_sum += r[4]
                  print("weight_sum_temp")
                  print(weight_sum)
                  if(r[4]==0.1):
                      # 近处权重更小 计算当前位置
                      current_x=blobs[largest_blob].cx()
                      current_y=blobs[largest_blob].cy()
                      print("current_x")
                      print(current_x)
                      print("current_y")
                      print(current_y)
          print("weight ")
          print(weight_sum)
          # print("s")
          center_pos = (centroid_sum / weight_sum) # Determine center of line.
          center_pos_y = (centroid_sum_y / weight_sum)
          #中间公式
          print("center_pos_x ")
      
          print(center_pos)
          print("center_pos_y ")
          print(center_pos_y)
      
      
      
          # Convert the center_pos to a deflection angle. We're using a non-linear
          # operation so that the response gets stronger the farther off the line we
          # are. Non-linear operations are good to use on the output of algorithms
          # like this to cause a response "trigger".
          deflection_angle = 0
          #机器人应该转的角度
      
          # The 80 is from half the X res, the 60 is from half the Y res. The
          # equation below is just computing the angle of a triangle where the
          # opposite side of the triangle is the deviation of the center position
          # from the center and the adjacent side is half the Y res. This limits
          # the angle output to around -45 to 45. (It's not quite -45 and 45).
      
          #deflection_angle = -math.atan((center_pos-80)/60)
          # deflection_angle = -math.atan((center_pos-40)/30)
          if center_pos_y-current_y !=0 :
              deflection_angle = -math.atan((center_pos-current_x)/(center_pos_y-current_y))
          #角度计算.80 60 分别为图像宽和高的一半,图像大小为QQVGA 160x120.
          #注意计算得到的是弧度值
          print("arc")
      
          print(deflection_angle)
      
          #1 Convert angle in radians to degrees.
          deflection_angle = math.degrees(deflection_angle)
          print("angle")
      
          print(deflection_angle)
      
          #将计算结果的弧度值转化为角度值
          A=deflection_angle
          print("Turn Angle: %d" % int (A))#输出时强制转换类型为int
          #print("Turn Angle: %d" % char (A))
          # Now you have an angle telling you how much to turn the robot by which
          # incorporates the part of the line nearest to the robot and parts of
          # the line farther away from the robot for a better prediction.
          print("Turn Angle: %f" % deflection_angle)
          #将结果打印在terminal中
          # uart_buf = bytearray([int (A)])
          # #uart_buf = bytearray([char (A)])
          # #uart.write(uart_buf)#区别于uart.writechar是输出字符型,这个函数可以输出int型
          # uart.write(uart_buf)
          # uart.writechar(0x41)#通信协议帧尾
          # uart.writechar(0x42)
      
          if int (A) > 0 :
              A=int(A)*10
          else:
              A=abs(int(A))*10+1
          data=str(A)
          print("transform")
          print(data)
          data_out = json.dumps(set(data))#将data转化为json
          uart.write('?'+data+'!')#写到缓冲区,由arduino进行读取
          # uart.write('?'+data+';'+data+'!')#写到缓冲区,由arduino进行读取
      
          #print('you send:',data_out)#写到串口监视端,让你能够看到数据
      
          time.sleep(1)#延时
          print("clock ")
      
          print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
          # connected to your computer. The FPS should increase once disconnected.
      
      

      修改后的代码

      # Black Grayscale Line Following Example
      #
      # Making a line following robot requires a lot of effort. This example script
      # shows how to do the machine vision part of the line following robot. You
      # can use the output from this script to drive a differential drive robot to
      # follow a line. This script just generates a single turn value that tells
      # your robot to go left or right.
      #
      # For this script to work properly you should point the camera at a line at a
      # 45 or so degree angle. Please make sure that only the line is within the
      # camera's field of view.
      
      import sensor, image, time, math#调用声明
      from pyb import UART
      import json
      
      # Tracks a black line. Use [(128, 255)] for a tracking a white line.
      # GRAYSCALE_THRESHOLD = [(0, 50)]
      GRAYSCALE_THRESHOLD = [(0, 80)]
      #设置阈值,如果是黑线,GRAYSCALE_THRESHOLD = [(0, 64)];
      #如果是白线,GRAYSCALE_THRESHOLD = [(128,255)]
      #GRAYSCALE_THRESHOLD = [(190,255)]
      
      
      # Each roi is (x, y, w, h). The line detection algorithm will try to find the
      # centroid of the largest blob in each roi. The x position of the centroids
      # will then be averaged with different weights where the most weight is assigned
      # to the roi near the bottom of the image and less to the next roi and so on.
      # ROIS = [ # [ROI, weight]
      #         (0, 40, 80, 15, 0.7), # You'll need to tweak the weights for you app
      #         (0, 20, 80, 15, 0.3), # depending on how your robot is setup.
      #         (0, 00, 80, 15, 0.1)
      #        ]
      
      ROIS = [ # [ROI, weight]
              (0, 100, 160, 30, 0.7), # You'll need to tweak the weights for you app
              # 根据线宽 修改高度
              # 远处给的权重更大
              (0, 050, 160, 30, 0.3), # depending on how your robot is setup.
              # 权重后期需要调整
              # 应该需要使用全宽度160(即分辨率对应的最大值) 否则转弯貌似无法识别
              (0, 000, 160, 30, 0.1)
             ]
      
      # ROIS = [ # [ROI, weight]
      #         (20, 40, 40, 15, 0.1), # You'll need to tweak the weights for you app
      #         (20, 20, 40, 15, 0.3), # depending on how your robot is setup.
      #         (20, 00, 40, 15, 0.7)
      #        ]
      #roi代表三个取样区域,(x,y,w,h,weight),代表左上顶点(x,y)宽高分别为w和h的矩形,
      #weight为当前矩形的权值。注意本例程采用的QQVGA图像大小为160x120,roi即把图像横分成三个矩形。
      #三个矩形的阈值要根据实际情况进行调整,离机器人视野最近的矩形权值要最大,
      #如上图的最下方的矩形,即(0, 100, 160, 20, 0.7)
      
      # Compute the weight divisor (we're computing this so you don't have to make weights add to 1).
      weight_sum = 0 #权值和初始化
      for r in ROIS: weight_sum += r[4] # r[4] is the roi weight.
      print("weight_sum_max")
      print(weight_sum)
      weight_sum = 0 #权值和初始化
      current_x=current_y=0
      
      #计算权值和。遍历上面的三个矩形,r[4]即每个矩形的权值。
      
      
      # Camera setup...
      sensor.reset() # Initialize the camera sensor.
      # sensor.set_pixformat(sensor.RGB565) # use grayscale.
      sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.
      sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
      # sensor.set_framesize(sensor.QQQVGA) # use QQVGA for speed.
      sensor.skip_frames(300) # Let new settings take affect.
      #不要太小 否则容易卡顿
      
      sensor.set_auto_gain(False) # must be turned off for color tracking
      sensor.set_auto_whitebal(False) # must be turned off for color tracking
      #关闭白平衡
      clock = time.clock() # Tracks FPS.
      
      uart = UART(3, 9600)#波特率两边要设置成一样
      
      
      while(True):
          clock.tick() # Track elapsed milliseconds between snapshots().
          img = sensor.snapshot() # Take a picture and return the image.
          # uart = UART(3,19200)
          # uart.init(19200,bits=8,parity=None,stop=1)#init with given parameters
      
          centroid_sum = centroid_sum_y = 0
          flag=0
          #print("i")
          #print(i)
          #利用颜色识别分别寻找三个矩形区域内的线段
          weight_sum = 1 #权值和初始化
          for r in ROIS:
      
              blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=False)
              # blobs = img.find_blobs(GRAYSCALE_THRESHOL, roi=r[0:4], merge=True)
      
              # r[0:4] is roi tuple.
              #找到视野中的线,merge=true,将找到的图像区域合并成一个
      
              # 在每一个ROI都会执行下述语句 所以总共4个ROI
      
              #目标区域找到直线
              if blobs:
                  flag+=1
                  # 不要用i 用flag
                  print("flag ")
                  print(flag)
                  if flag ==1 :
                      weight_sum = 0
                  # Find the index of the blob with the most pixels.
                  most_pixels = 0
                  largest_blob = 0
                  for i in range(len(blobs)):
                  #目标区域找到的颜色块(线段块)可能不止一个,找到最大的一个,作为本区域内的目标直线
                      if blobs[i].pixels() > most_pixels:
                          most_pixels = blobs[i].pixels()
                          #merged_blobs[i][4]是这个颜色块的像素总数,如果此颜色块像素总数大于                     #most_pixels,则把本区域作为像素总数最大的颜色块。更新most_pixels和largest_blob
                          largest_blob = i
      
                  # Draw a rect around the blob.
                  img.draw_rectangle(blobs[largest_blob].rect())
                  img.draw_rectangle((0,0,20, 20))
                  #将此区域的像素数最大的颜色块画矩形和十字形标记出来
                  img.draw_cross(blobs[largest_blob].cx(),
                                 blobs[largest_blob].cy())
      
                  centroid_sum += blobs[largest_blob].cx() * r[4] # r[4] is the roi weight.
                  #计算centroid_sum,centroid_sum等于每个区域的最大颜色块的中心点的x坐标值乘本区域的权值
                  centroid_sum_y += blobs[largest_blob].cy() * r[4] # r[4] is the roi weight.
      
                  print("centroid_x_proportion")
                  print(centroid_sum)
                  weight_sum += r[4]
                  print("weight_sum_temp")
                  print(weight_sum)
                  if(r[4]==0.1):
                      # 近处权重更小 计算当前位置
                      current_x=blobs[largest_blob].cx()
                      current_y=blobs[largest_blob].cy()
                      print("current_x")
                      print(current_x)
                      print("current_y")
                      print(current_y)
          print("weight ")
          print(weight_sum)
          # print("s")
          center_pos = (centroid_sum / weight_sum) # Determine center of line.
          center_pos_y = (centroid_sum_y / weight_sum)
          #中间公式
          print("center_pos_x ")
      
          print(center_pos)
          print("center_pos_y ")
          print(center_pos_y)
      
      
      
          # Convert the center_pos to a deflection angle. We're using a non-linear
          # operation so that the response gets stronger the farther off the line we
          # are. Non-linear operations are good to use on the output of algorithms
          # like this to cause a response "trigger".
          deflection_angle = 0
          #机器人应该转的角度
      
          # The 80 is from half the X res, the 60 is from half the Y res. The
          # equation below is just computing the angle of a triangle where the
          # opposite side of the triangle is the deviation of the center position
          # from the center and the adjacent side is half the Y res. This limits
          # the angle output to around -45 to 45. (It's not quite -45 and 45).
      
          #deflection_angle = -math.atan((center_pos-80)/60)
          # deflection_angle = -math.atan((center_pos-40)/30)
          if center_pos_y-current_y !=0 :
              deflection_angle = -math.atan((center_pos-current_x)/(center_pos_y-current_y))
          #角度计算.80 60 分别为图像宽和高的一半,图像大小为QQVGA 160x120.
          #注意计算得到的是弧度值
          print("arc")
      
          print(deflection_angle)
      
          #1 Convert angle in radians to degrees.
          deflection_angle = math.degrees(deflection_angle)
          print("angle")
      
          print(deflection_angle)
      
          #将计算结果的弧度值转化为角度值
          A=deflection_angle
          print("Turn Angle: %d" % int (A))#输出时强制转换类型为int
          #print("Turn Angle: %d" % char (A))
          # Now you have an angle telling you how much to turn the robot by which
          # incorporates the part of the line nearest to the robot and parts of
          # the line farther away from the robot for a better prediction.
          print("Turn Angle: %f" % deflection_angle)
          #将结果打印在terminal中
          # uart_buf = bytearray([int (A)])
          # #uart_buf = bytearray([char (A)])
          # #uart.write(uart_buf)#区别于uart.writechar是输出字符型,这个函数可以输出int型
          # uart.write(uart_buf)
          # uart.writechar(0x41)#通信协议帧尾
          # uart.writechar(0x42)
      
          if int (A) > 0 :
              A=int(A)*10
          else:
              A=abs(int(A))*10+1
          data=str(A)
          print("transform")
          print(data)
          data_out = json.dumps(set(data))#将data转化为json
          uart.write('?'+data+'!')#写到缓冲区,由arduino进行读取
          # uart.write('?'+data+';'+data+'!')#写到缓冲区,由arduino进行读取
      
          #print('you send:',data_out)#写到串口监视端,让你能够看到数据
      
          time.sleep(1)#延时
          print("clock ")
      
          print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
          # connected to your computer. The FPS should increase once disconnected.
      
      


    • 变量i的范围是本函数,你第90行定义的i,范围是整个文件,相当于全局变量。第115行是直接把i给改动了。