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