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Open Source Computer Vision Library
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54 lines
1.5 KiB
54 lines
1.5 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: |
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houghlines.py [<image_name>] |
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image argument defaults to ../data/pic1.png |
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''' |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import cv2 as cv |
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import numpy as np |
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import sys |
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import math |
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if __name__ == '__main__': |
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print(__doc__) |
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try: |
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fn = sys.argv[1] |
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except IndexError: |
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fn = "../data/pic1.png" |
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src = cv.imread(fn) |
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dst = cv.Canny(src, 50, 200) |
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cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR) |
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if True: # HoughLinesP |
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lines = cv.HoughLinesP(dst, 1, math.pi/180.0, 40, np.array([]), 50, 10) |
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a,b,c = lines.shape |
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for i in range(a): |
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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) |
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else: # HoughLines |
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lines = cv.HoughLines(dst, 1, math.pi/180.0, 50, np.array([]), 0, 0) |
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if lines is not None: |
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a,b,c = lines.shape |
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for i in range(a): |
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rho = lines[i][0][0] |
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theta = lines[i][0][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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cv.line(cdst, pt1, pt2, (0, 0, 255), 3, cv.LINE_AA) |
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cv.imshow("detected lines", cdst) |
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cv.imshow("source", src) |
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cv.waitKey(0)
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