hough-line

hough_line

skimage.transform.hough_line(img, theta=None) [source]

Perform a straight line Hough transform.

Parameters:	img : (M, N) ndarray Input image with nonzero values representing edges. theta : 1D ndarray of double Angles at which to compute the transform, in radians. Defaults to -pi/2 .. pi/2
Returns:	H : 2-D ndarray of uint64 Hough transform accumulator. theta : ndarray Angles at which the transform was computed, in radians. distances : ndarray Distance values.

Notes

The origin is the top left corner of the original image. X and Y axis are horizontal and vertical edges respectively. The distance is the minimal algebraic distance from the origin to the detected line.

Examples

Generate a test image:

>>> img = np.zeros((100, 150), dtype=bool)
>>> img[30, :] = 1
>>> img[:, 65] = 1
>>> img[35:45, 35:50] = 1
>>> for i in range(90):
...     img[i, i] = 1
>>> img += np.random.random(img.shape) > 0.95

Apply the Hough transform:

>>> out, angles, d = hough_line(img)

import numpy as np
import matplotlib.pyplot as plt

from skimage.transform import hough_line
from skimage.draw import line

img = np.zeros((100, 150), dtype=bool)
img[30, :] = 1
img[:, 65] = 1
img[35:45, 35:50] = 1
rr, cc = line(60, 130, 80, 10)
img[rr, cc] = 1
img += np.random.random(img.shape) > 0.95

out, angles, d = hough_line(img)

plt.subplot(1, 2, 1)

plt.imshow(img, cmap=plt.cm.gray)
plt.title('Input image')

plt.subplot(1, 2, 2)
plt.imshow(out, cmap=plt.cm.bone,
           extent=(np.rad2deg(angles[-1]), np.rad2deg(angles[0]),
                   d[-1], d[0]))
plt.title('Hough transform')
plt.xlabel('Angle (degree)')
plt.ylabel('Distance (pixel)')

plt.subplots_adjust(wspace=0.4)
plt.show()

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