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:

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>>> 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:

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

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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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