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Open Source Computer Vision Library
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41 lines
1.1 KiB
41 lines
1.1 KiB
#!/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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