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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 pic1.png
'''
# Python 2/3 compatibility
from __future__ import print_function
import cv2 as cv
import numpy as np
import sys
import math
def main():
try:
fn = sys.argv[1]
except IndexError:
fn = 'pic1.png'
src = cv.imread(cv.samples.findFile(fn))
dst = cv.Canny(src, 50, 200)
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
if True: # HoughLinesP
lines = cv.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10)
a, b, _c = lines.shape
for i in range(a):
cv.line(cdst, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv.LINE_AA)
else: # HoughLines
lines = cv.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0)
if lines is not None:
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)) )
cv.line(cdst, pt1, pt2, (0, 0, 255), 3, cv.LINE_AA)
cv.imshow("detected lines", cdst)
cv.imshow("source", src)
cv.waitKey(0)
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()
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