#!/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()