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  • TypeError: can't convert float to int ;一个循红线程序 roi 会报错



    • # 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)]
      
      #RED_THRESHOLD   = [(   30,   80,  0,   45,  10,   30)]
      
      #RED_THRESHOLD   = [(44, 75, 8, 77, -44, 21)]
      
      RED_THRESHOLD   = [(20, 34, -19, 72, 16, 37)]
      
      #frame_size_w = 320
      
      frame_size_w = 160
      
      #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)
      #        ]
      
      
      
      if frame_size_w == 160:
          #print(frame_size_w)
          width = frame_size_w /2
          frame_size_h = frame_size_w *3 /4
          height = (frame_size_h /10 )//3 * 10
      
          roi_x = (frame_size_w-width)/2
          roi_y_base = height
          roi_h = height *3/5
          roi_y_base_two = height *2
          print("height")
          print(height)
          print("test")
          print(roi_x)
          print(roi_y_base)
          print(roi_h)
          print(roi_y_base_two)
          ROIS = [ # [ROI, weight]
              (roi_x,  roi_y_base_two , width, roi_h, 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
              (roi_x, roi_y_base , width, roi_h, 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, frame_size_w, roi_h, 0)
             ]
      
          #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)
              #]
      
      if frame_size_w == 320:
          #print(frame_size_w)
          width = frame_size_w /2
          frame_size_h = frame_size_w *3 /4
          height = (frame_size_h /10 )//3 * 10
          print(height)
          ROIS = [ # [ROI, weight]
          ((frame_size_w-width)/2, height *2, width, height *3/5, 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
          ((frame_size_w-width)/2, height, width, height *3/5, 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, frame_size_w, height *3/5, 0)
         ]
      
      #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.
      
      
      
      if frame_size_w == 320:
          sensor.set_framesize(sensor.QVGA) # use QQVGA for speed.
      if frame_size_w == 160:
          sensor.set_framesize(sensor.QQVGA) # 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:
              find_blobs_num = 0
              error_x = 0 # - left; + right
      
              #blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=False)
              print(r[0])
              for i in range(4):
                  print(r[i])
              print("test")
              print(r[0:4])
              blobs = img.find_blobs(RED_THRESHOLD, roi=r[0:4], merge=False)
      
              #blobs = img.find_blobs(RED_THRESHOLD, roi=[40, 80, 80, 24] ,merge=False)
              ##blobs = img.find_blobs(RED_THRESHOLD, roi=(40.0, 40.0, 80.0, 24.0) ,merge=False)
              #blobs = img.find_blobs(RED_THRESHOLD, roi=[0, 0, 160, 24.0] ,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:
                  find_blobs_num +=1
                  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
                  #print("most_pixels")
                  #print(most_pixels)
      
                  # 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())
      
                  if frame_size_w == 320:
                      print(find_blobs_num)
                      error_x += blobs[largest_blob].cx() - frame_size_w / 2
                      print(error_x)
      
                  if frame_size_w == 160:
                      print(find_blobs_num)
                      error_x += blobs[largest_blob].cx() - frame_size_w / 2
                      print(error_x)
      
      
                  # if no turn angle ; can comment until (1)
      
                  #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)
      
      
      
          #for temp in blobs:
              #img.draw_rectangle(temp.rect())
      
      
      
          #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)
      
          # (1) if no turn angle ; can comment this
      
          print("error_x")
          print(error_x)
      
          if int (error_x) > 0 :
              error_x=int(error_x)*10
          else:
              error_x=abs(int(error_x))*10+1
          error_x_str=str(error_x)
      
          error_x_str_out = json.dumps(set(error_x_str))#将data转化为json
          uart.write('?'+error_x_str_out+'!')#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(0.1)#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.
      
      

      修改前 没有bug

      # 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)]
      
      #RED_THRESHOLD   = [(   30,   80,  0,   45,  10,   30)]
      
      #RED_THRESHOLD   = [(44, 75, 8, 77, -44, 21)]
      
      RED_THRESHOLD   = [(20, 34, -19, 72, 16, 37)]
      
      #frame_size_w = 320
      
      frame_size_w = 160
      
      #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)
      #        ]
      
      
      
      if frame_size_w == 160:
          #print(frame_size_w)
          width = frame_size_w /2
          frame_size_h = frame_size_w *3 /4
          height = (frame_size_h /10 )//3 * 10
      
          roi_x = (frame_size_w-width)/2
          roi_y_base = height
          roi_h = height *3/5
          roi_y_base_two = height *2
          print("height")
          print(height)
          print("test")
          print(roi_x)
          print(roi_y_base)
          print(roi_h)
          print(roi_y_base_two)
          #ROIS = [ # [ROI, weight]
              #(roi_x,  roi_y_base_two , width, roi_h, 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
              #(roi_x, roi_y_base , width, roi_h, 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, frame_size_w, roi_h, 0)
             #]
      
          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)
              ]
      
      if frame_size_w == 320:
          #print(frame_size_w)
          width = frame_size_w /2
          frame_size_h = frame_size_w *3 /4
          height = (frame_size_h /10 )//3 * 10
          print(height)
          ROIS = [ # [ROI, weight]
          ((frame_size_w-width)/2, height *2, width, height *3/5, 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
          ((frame_size_w-width)/2, height, width, height *3/5, 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, frame_size_w, height *3/5, 0)
         ]
      
      #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.
      
      
      
      if frame_size_w == 320:
          sensor.set_framesize(sensor.QVGA) # use QQVGA for speed.
      if frame_size_w == 160:
          sensor.set_framesize(sensor.QQVGA) # 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:
              find_blobs_num = 0
              error_x = 0 # - left; + right
      
              #blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=False)
              print(r[0])
              for i in range(4):
                  print(r[i])
              print("test")
              print(r[0:4])
              blobs = img.find_blobs(RED_THRESHOLD, roi=r[0:4], merge=False)
      
              #blobs = img.find_blobs(RED_THRESHOLD, roi=[40, 80, 80, 24] ,merge=False)
              ##blobs = img.find_blobs(RED_THRESHOLD, roi=(40.0, 40.0, 80.0, 24.0) ,merge=False)
              #blobs = img.find_blobs(RED_THRESHOLD, roi=[0, 0, 160, 24.0] ,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:
                  find_blobs_num +=1
                  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
                  #print("most_pixels")
                  #print(most_pixels)
      
                  # 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())
      
                  if frame_size_w == 320:
                      print(find_blobs_num)
                      error_x += blobs[largest_blob].cx() - frame_size_w / 2
                      print(error_x)
      
                  if frame_size_w == 160:
                      print(find_blobs_num)
                      error_x += blobs[largest_blob].cx() - frame_size_w / 2
                      print(error_x)
      
      
                  # if no turn angle ; can comment until (1)
      
                  #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)
      
      
      
          #for temp in blobs:
              #img.draw_rectangle(temp.rect())
      
      
      
          #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)
      
          # (1) if no turn angle ; can comment this
      
          print("error_x")
          print(error_x)
      
          if int (error_x) > 0 :
              error_x=int(error_x)*10
          else:
              error_x=abs(int(error_x))*10+1
          error_x_str=str(error_x)
      
          error_x_str_out = json.dumps(set(error_x_str))#将data转化为json
          uart.write('?'+error_x_str_out+'!')#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(0.1)#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.
      
      

      只改了

          #ROIS = [ # [ROI, weight]
              #(roi_x,  roi_y_base_two , width, roi_h, 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
              #(roi_x, roi_y_base , width, roi_h, 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, frame_size_w, roi_h, 0)
             #]
      
          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)
              ]
      

      在这里报错

      blobs = img.find_blobs(RED_THRESHOLD, roi=r[0:4], merge=False)
      


    • 67行到75行,90行到96行。所有的成员加上int转成整数。
      比如,roi_x 改为 int(roi_x)。

      注意:我只是在修改你的报错错误。逻辑需要自己考虑,我看不懂。