1#!/usr/bin/python
2'''
3This example illustrates how to use Hough Transform to find lines
4Usage: ./houghlines.py [<image_name>]
5image argument defaults to ../data/pic1.png
6'''
7import cv2
8import numpy as np
9import sys
10import math
11
12try:
13    fn = sys.argv[1]
14except:
15    fn = "../data/pic1.png"
16print __doc__
17src = cv2.imread(fn)
18dst = cv2.Canny(src, 50, 200)
19cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
20
21if True: # HoughLinesP
22    lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)
23    a,b,c = lines.shape
24    for i in range(a):
25        cv2.line(cdst, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv2.LINE_AA)
26
27else:    # HoughLines
28    lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
29    a,b,c = lines.shape
30    for i in range(a):
31        rho = lines[i][0][0]
32        theta = lines[i][0][1]
33        a = math.cos(theta)
34        b = math.sin(theta)
35        x0, y0 = a*rho, b*rho
36        pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
37        pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
38        cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
39
40
41cv2.imshow("source", src)
42cv2.imshow("detected lines", cdst)
43cv2.waitKey(0)
44