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  • OSError:ROI does not overlap on the image



    • 0_1560854718972_a75c1709-e6ba-4572-9d91-e7538f9383a7-image.png
      摄像头运行过程中时不时会出现这个问题,求解答!



    • 如果涉及代码,需要报错提示与全部代码文本,请注意不要贴代码图片



    • 我也想知道为什么?



    • +1 这为啥会出现这个问题?



    • 如果涉及代码,需要报错提示与全部代码文本,请注意不要贴代码图片



    • 我也遇到了相同的问题
      为了提高运行速度,修改了图片分辨率之后
      sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
      改为
      sensor.set_framesize(sensor.QQQVGA) # use QQVGA for speed.
      运行过程中出现了上述问题
      以下为修改后的代码

      # 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
      
      # Tracks a black line. Use [(128, 255)] for a tracking a white line.
      GRAYSCALE_THRESHOLD = [(0, 40)]
      #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 (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.
      #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.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.
      
      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 = 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], 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 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((0,0,30, 30))
                  #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
                  print(centroid_sum)
          print("s")
          center_pos = (centroid_sum / weight_sum) # Determine center of line.
          #中间公式
          print(center_pos)
      
      
          # 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
          print(deflection_angle)
          #1 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
          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)
      
          time.sleep(1)#延时
          print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
          # connected to your computer. The FPS should increase once disconnected.
          print("k")
      
      


    • @kenr 这不很明显吗?你的图片是80x60,但是roi是160x20,肯定放不下。



    • 是因为我的ROI重叠了
      所以报错 提示我ROI does not overlap 是吗?



    • 是因为你的ROI不能重叠在图像上。所以提示ROI does not overlap。



    • @kidswong999 就是互相有交集 是吗?



    • 不是,错误原因是你的ROI不能覆盖到图片。就是ROI太大了,或者太偏了,导致ROI超出了图片的范围。



    • 了解了 谢谢 Thanks♪(・ω・)ノ