def compute_edgelets(image, sigma=3):
"""Create edgelets as in the paper.
Uses canny edge detection and then finds (small) lines using probabilstic
hough transform as edgelets.
Parameters
----------
image: ndarray
Image for which edgelets are to be computed.
sigma: float
Smoothing to be used for canny edge detection.
Returns
-------
locations: ndarray of shape (n_edgelets, 2)
Locations of each of the edgelets.
directions: ndarray of shape (n_edgelets, 2)
Direction of the edge (tangent) at each of the edgelet.
strengths: ndarray of shape (n_edgelets,)
Length of the line segments detected for the edgelet.
"""
gray_img = color.rgb2gray(image)
edges = feature.canny(gray_img, sigma)
lines = transform.probabilistic_hough_line(edges, line_length=3,
line_gap=2)
locations = []
directions = []
strengths = []
for p0, p1 in lines:
p0, p1 = np.array(p0), np.array(p1)
locations.append((p0 + p1) / 2)
directions.append(p1 - p0)
strengths.append(np.linalg.norm(p1 - p0))
# convert to numpy arrays and normalize
locations = np.array(locations)
directions = np.array(directions)
strengths = np.array(strengths)
directions = np.array(directions) / \
np.linalg.norm(directions, axis=1)[:, np.newaxis]
return (locations, directions, strengths)
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