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  • 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。
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  • 帖子需要目的,你要做什么?
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  • 必看:玩转星瞳论坛了解一下图片上传,代码格式等问题。
  • 巡线例程运行不了的问题。



    • AttributeError: 'module' object has no attribute'set_ whitebal'

      AttributeError: 'lmage' object has no attribute'find markers'
      的问题
      这一次的问题很简单。。就是巡线的例程,死活运行不了,我寻思我应该都声明了啊😂
      另附,例程地址:http://kaizhi-xu.com/post/openmvli-cheng-jiang-jie/openmvli-cheng-jiang-jie-53

      # Line Following Example
      #
      # Making a line following robot requires a lot of effort. This example script
      # shows how to do the computer 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
      
      # Tracks a white line. Use [(0, 64)] for a tracking a black line.
      GRAYSCALE_THRESHOLD = [(0, 64)]
      #设置阈值,如果是黑线,GRAYSCALE_THRESHOLD = [(0, 64)];如果是白线,GRAYSCALE_THRESHOLD = [(128,255)]
      
      # 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, 100, 160, 20, 0.7), # You'll need to tweak the weights for you app
              (0, 050, 160, 20, 0.3), # depending on how your robot is setup.
              (0, 000, 160, 20, 0.1)
             ]
      #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
      weight_sum = 0 #权值和初始化
      for r in ROIS: weight_sum += r[4]
      #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.GRAYSCALE) # use grayscale.
      sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
      sensor.skip_frames(10) # Let new settings take affect.
      #sensor.set_whitebal(False) # turn this off.
      #关闭白平衡
      clock = time.clock() # Tracks FPS.
      
      while(True):
          clock.tick() # Track elapsed milliseconds between snapshots().
          img = sensor.snapshot() # Take a picture and return the image.
      
          centroid_sum = 0
          #利用颜色识别分别寻找三个矩形区域内的线段
          for r in ROIS:
              blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4]) # r[0:4] is roi tuple.
              #找到视野中的线
              merged_blobs = img.find_markers(blobs) # merge overlapping blobs
              #将找到的图像区域合并成一个。
      
              #目标区域找到直线
              if merged_blobs:
                  # Find the index of the blob with the most pixels.
                  most_pixels = 0
                  largest_blob = 0
                  for i in range(len(merged_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 merged_blobs[i][4] > most_pixels:
                          most_pixels = merged_blobs[i][4] # [4] is 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.
                  #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_rectangle(merged_blobs[largest_blob][0:4]) # rect
                  img.draw_cross(merged_blobs[largest_blob][5], # cx
                                 merged_blobs[largest_blob][6]) # cy
      
                  # [5] of the blob is the x centroid - r[4] is the weight.
                  centroid_sum += merged_blobs[largest_blob][5] * r[4]
                  #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
      
          center_pos = (centroid_sum / weight_sum) # Determine center of line.
          #中间公式
      
          # 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)
          #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
      
          # Convert angle in radians to degrees.
          deflection_angle = math.degrees(deflection_angle)
          #将计算结果的弧度值转化为角度值
      
          # 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
      
          print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
          # connected to your computer. The FPS should increase once disconnected.