关于边缘检测的基础来自于一个事实，即在边缘部分，像素值出现”跳跃“或者较大的变化。如果在此边缘部分求取一阶导数，就会看到极值的出现。

Canny 算法可以对直线边界做出很好的检测；

关于 Canny 算法原理的描述，可参考：

• Canny 边缘检测

  import cv2.cv as cv
  import math
   
  im=cv.LoadImage('img/road.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
   
  pi = math.pi #Pi value
   
  dst = cv.CreateImage(cv.GetSize(im), 8, 1)
   
  cv.Canny(im, dst, 200, 200)
  cv.Threshold(dst, dst, 100, 255, cv.CV_THRESH_BINARY)
   
  #---- Standard ----
  color_dst_standard = cv.CreateImage(cv.GetSize(im), 8, 3)
  cv.CvtColor(im, color_dst_standard, cv.CV_GRAY2BGR)#Create output image in RGB to put red lines
   
  lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_STANDARD, 1, pi / 180, 100, 0, 0)
  for (rho, theta) in lines[:100]:
      a = math.cos(theta) #Calculate orientation in order to print them
      b = math.sin(theta)
      x0 = a * rho
      y0 = b * rho
      pt1 = (cv.Round(x0 + 1000*(-b)), cv.Round(y0 + 1000*(a)))
      pt2 = (cv.Round(x0 - 1000*(-b)), cv.Round(y0 - 1000*(a)))
      cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2, 4) #Draw the line
   
  #---- Probabilistic ----
  color_dst_proba = cv.CreateImage(cv.GetSize(im), 8, 3)
  cv.CvtColor(im, color_dst_proba, cv.CV_GRAY2BGR) # idem
   
  rho=1
  theta=pi/180
  thresh = 50
  minLength= 120 # Values can be changed approximately to fit your image edges
  maxGap= 20
   
  lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_PROBABILISTIC, rho, theta, thresh, minLength, maxGap)
  for line in lines:
      cv.Line(color_dst_proba, line[0], line[1], cv.CV_RGB(255, 0, 0), 2, 8)
   
  cv.ShowImage('Image',im)
  cv.ShowImage("Cannied", dst)
  cv.ShowImage("Hough Standard", color_dst_standard)
  cv.ShowImage("Hough Probabilistic", color_dst_proba)
  cv.WaitKey(0)

原图



使用 Canny 算法处理之后



标记出标准的直线



标记出所有可能的直线