text: #!/usr/bin/python
  
  '''
  This example illustrates how to use Hough Transform to find lines
  '''
  
  # Python 2/3 compatibility
  from __future__ import print_function
  
  import cv2
  import numpy as np
  import sys
  import math
  
  from tests_common import NewOpenCVTests
  
  def linesDiff(line1, line2):
  
      norm1 = cv2.norm(line1 - line2, cv2.NORM_L2)
      line3 = line1[2:4] + line1[0:2]
      norm2 = cv2.norm(line3 - line2, cv2.NORM_L2)
  
      return min(norm1, norm2)
  
  class houghlines_test(NewOpenCVTests):
  
      def test_houghlines(self):
  
          fn = "/samples/cpp/pic1.png"
  
          src = self.get_sample(fn)
          dst = cv2.Canny(src, 50, 200)
  
          lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)[0,:,:]
  
          eps = 5
          testLines = [
              #rect1
               [ 232,  25, 43, 25],
               [ 43, 129, 232, 129],
               [ 43, 129,  43,  25],
               [232, 129, 232,  25],
              #rect2
               [251,  86, 314, 183],
               [252,  86, 323,  40],
               [315, 183, 386, 137],
               [324,  40, 386, 136],
              #triangle
               [245, 205, 377, 205],
               [244, 206, 305, 278],
               [306, 279, 377, 205],
              #rect3
               [153, 177, 196, 177],
               [153, 277, 153, 179],
               [153, 277, 196, 277],
               [196, 177, 196, 277]]
  
          matches_counter = 0
  
          for i in range(len(testLines)):
              for j in range(len(lines)):
                  if linesDiff(testLines[i], lines[j]) < eps:
                      matches_counter += 1
  
          self.assertGreater(float(matches_counter) / len(testLines), .7)