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- #!/usr/bin/python
- '''
- This example illustrates how to use Hough Transform to find lines
- Usage:
- houghlines.py [<image_name>]
- image argument defaults to ../data/pic1.png
- '''
- # Python 2/3 compatibility
- from __future__ import print_function
- import cv2
- import numpy as np
- import sys
- import math
- if __name__ == '__main__':
- print(__doc__)
- try:
- fn = sys.argv[1]
- except IndexError:
- fn = "../data/pic1.png"
- src = cv2.imread(fn)
- dst = cv2.Canny(src, 50, 200)
- cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)
- if True: # HoughLinesP
- lines = cv2.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)
- a,b,c = lines.shape
- for i in range(a):
- 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)
- else: # HoughLines
- lines = cv2.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
- a,b,c = lines.shape
- for i in range(a):
- rho = lines[i][0][0]
- theta = lines[i][0][1]
- a = math.cos(theta)
- b = math.sin(theta)
- x0, y0 = a*rho, b*rho
- pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
- pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
- cv2.line(cdst, pt1, pt2, (0, 0, 255), 3, cv2.LINE_AA)
- cv2.imshow("source", src)
- cv2.imshow("detected lines", cdst)
- cv2.waitKey(0)
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