请问你解决这个问题了吗

回复: 请问如何绘制赛道中心线
纯白色的赛道，两边有黑色边缘，无其它颜色的干扰，赛道只包含一个弯道。请问如何绘制赛道的中心线，然后再获取到摄像头最下方中心线点的坐标

ROIS = [ # [ROI, weight]
        (0, 0, 80, 120, 0.5), # You'll need to tweak the weights for you app
        (80, 0, 80, 120, 0.5) # depending on how your robot is setup.
       ]
weight_sum = 0 #权值和初始化
#for r in ROIS: weight_sum += r[2] # r[4] is the roi weight.A
       #计算权值和。遍历上面的三个矩形，r[4]即每个矩形的权值。
# This is called the fast linear regression because we use the least-squares
# method to fit the line. However, this method is NOT GOOD FOR ANY images that
# have a lot (or really any) outlier points which corrupt the line fit...

#设置阈值，（0，100）检测黑色线
THRESHOLD = (0, 100) # Grayscale threshold for dark things...

#设置是否使用img.binary()函数进行图像分割
BINARY_VISIBLE = True # Does binary first so you can see what the linear regression
                      # is being run on... might lower FPS though.

import sensor, image, time
import json
from pyb import UART
uart = UART(3, 9600)
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

while(True):
    clock.tick()
    img = sensor.snapshot().binary([THRESHOLD]) if BINARY_VISIBLE else sensor.snapshot()
    centroid_sum = 0
    for r in ROIS:
      blobs = img.find_blobs(THRESHOLD , roi=r[0:2], merge=True)
    # Returns a line object similar to line objects returned by find_lines() and
    # find_line_segments(). You have x1(), y1(), x2(), y2(), length(),
    # theta() (rotation in degrees), rho(), and magnitude().
    #
    # magnitude() represents how well the linear regression worked. It goes from
    # (0, INF] where 0 is returned for a circle. The more linear the
    # scene is the higher the magnitude.
    #函数返回回归后的线段对象line，有x1(), y1(), x2(), y2(), length(), theta(), rho(), magnitude()参数。
    #x1 y1 x2 y2分别代表线段的两个顶点坐标，length是线段长度，theta是线段的角度。
    #magnitude表示线性回归的效果，它是（0，+∞）范围内的一个数字，其中0代表一个圆。如果场景线性回归的越好，这个值越大。
    line = img.get_regression([(255,255) if BINARY_VISIBLE else THRESHOLD])
    if (line): img.draw_line(line.line(), color = 127)
    arveage_x1 += line.x1 ()*r[4]
    arveage_x2 += line.x2 ()*r[4]
    arveage_y1 += line.y1 ()*r[4]
    arveage_y2 += line.y2 ()*r[4]
    arveage_theta += line.theta()*r[4]
    img.draw_line(arveage_x1,arveage_y1,arveage_x2,arveage_y2, color = 80)
    print("FPS %f, mag = %s" % (clock.fps(), str(line.magnitude()) if (line) else "N/A"))

# About negative rho values:
#
# A [theta+0:-rho] tuple is the same as [theta+180:+rho].
    data_out = str(line.theta())
    uart.write(data_out )
    time.sleep(100)
    print("OUT",data_out)

注释r中说r[4]为每个矩形的权值，但我仍然不理解坐标乘以权值能得到什么，或者说有什么含义？

ROIS = [ # [ROI, weight]
        (0, 0, 80, 120, 0.5), # You'll need to tweak the weights for you app
        (80, 0, 80, 120, 0.5) # depending on how your robot is setup.
       ]
weight_sum = 0 #权值和初始化
#for r in ROIS: weight_sum += r[2] # r[4] is the roi weight.A
       #计算权值和。遍历上面的三个矩形，r[4]即每个矩形的权值。
# This is called the fast linear regression because we use the least-squares
# method to fit the line. However, this method is NOT GOOD FOR ANY images that
# have a lot (or really any) outlier points which corrupt the line fit...

#设置阈值，（0，100）检测黑色线
THRESHOLD = (0, 100) # Grayscale threshold for dark things...

#设置是否使用img.binary()函数进行图像分割
BINARY_VISIBLE = True # Does binary first so you can see what the linear regression
                      # is being run on... might lower FPS though.

import sensor, image, time
import json
from pyb import UART
uart = UART(3, 9600)
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

while(True):
    clock.tick()
    img = sensor.snapshot().binary([THRESHOLD]) if BINARY_VISIBLE else sensor.snapshot()
    centroid_sum = 0
    for r in ROIS:
      blobs = img.find_blobs(THRESHOLD , roi=r[0:2], merge=True)
    # Returns a line object similar to line objects returned by find_lines() and
    # find_line_segments(). You have x1(), y1(), x2(), y2(), length(),
    # theta() (rotation in degrees), rho(), and magnitude().
    #
    # magnitude() represents how well the linear regression worked. It goes from
    # (0, INF] where 0 is returned for a circle. The more linear the
    # scene is the higher the magnitude.
    #函数返回回归后的线段对象line，有x1(), y1(), x2(), y2(), length(), theta(), rho(), magnitude()参数。
    #x1 y1 x2 y2分别代表线段的两个顶点坐标，length是线段长度，theta是线段的角度。
    #magnitude表示线性回归的效果，它是（0，+∞）范围内的一个数字，其中0代表一个圆。如果场景线性回归的越好，这个值越大。
    line = img.get_regression([(255,255) if BINARY_VISIBLE else THRESHOLD])
    if (line): img.draw_line(line.line(), color = 127)
    arveage_x1 += line.x1 ()*r[4]
    arveage_x2 += line.x2 ()*r[4]
    arveage_y1 += line.y1 ()*r[4]
    arveage_y2 += line.y2 ()*r[4]
    arveage_theta += line.theta()*r[4]
    img.draw_line(arveage_x1,arveage_y1,arveage_x2,arveage_y2, color = 80)
    print("FPS %f, mag = %s" % (clock.fps(), str(line.magnitude()) if (line) else "N/A"))

# About negative rho values:
#
# A [theta+0:-rho] tuple is the same as [theta+180:+rho].
    data_out = str(line.theta())
    uart.write(data_out )
    time.sleep(100)
    print("OUT",data_out)