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105 lines
2.9 KiB
Python
105 lines
2.9 KiB
Python
#
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# Harris detector
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#
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# Inspired from Solem's implementation
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# http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html
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import numpy as np
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from scipy import ndimage
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def _compute_harris_response(image, eps=1e-6):
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"""Compute the Harris corner detector response function
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for each pixel in the image
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Params
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-------
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image: ndarray
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eps: float, optional, default: 1e-6
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normalisation factor
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Returns
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--------
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ndarray
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"""
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if len(image.shape) == 3:
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image = image.mean(axis=2)
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# derivatives
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image = ndimage.gaussian_filter(image, 1)
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imx = ndimage.sobel(image, axis=0, mode='constant')
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imy = ndimage.sobel(image, axis=1, mode='constant')
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Wxx = ndimage.gaussian_filter(imx * imx, 1.5, mode='constant')
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Wxy = ndimage.gaussian_filter(imx * imy, 1.5, mode='constant')
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Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant')
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# determinant and trace
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Wdet = Wxx * Wyy - Wxy ** 2
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Wtr = Wxx + Wyy
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harris = Wdet / (Wtr + eps)
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# Non maximum filter of size 3
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harris_max = ndimage.maximum_filter(harris, 3, mode='constant')
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harris *= harris == harris_max
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# Remove the image corners
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harris[:3] = 0
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harris[-3:] = 0
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harris[:, :3] = 0
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harris[:, -3:] = 0
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return harris
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def harris_corner_detector(image, min_distance=10, threshold=0.1, eps=1e-6):
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"""Return corners from a Harris response image
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params
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-------
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harrisim: ndarray of floats
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min_distance: int, optional, default: 10
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minimum number of pixels separating corners and image boundary
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threshold: float, optional, default: 0.1
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eps: float, optional, default: 1e-6
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returns:
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--------
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array: coordinates
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"""
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harrisim = _compute_harris_response(image, eps=eps)
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corner_threshold = np.max(harrisim.ravel()) * threshold
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# find top corner candidates above a threshold
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# corner_threshold = max(harrisim.ravel()) * threshold
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harrisim_t = (harrisim >= corner_threshold) * 1
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# get coordinates of candidates
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candidates = harrisim_t.nonzero()
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coords = [(candidates[0][c], candidates[1][c]) for c
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in range(len(candidates[0]))]
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# ...and their values
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candidate_values = [harrisim[c[0]][c[1]] for c in coords]
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# sort candidates
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index = np.argsort(candidate_values)
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# store allowed point locations in array
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allowed_locations = np.zeros(harrisim.shape)
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allowed_locations[min_distance:-min_distance,
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min_distance:-min_distance] = 1
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# select the best points taking min_distance into account
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filtered_coords = []
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for i in index:
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if allowed_locations[coords[i][0]][coords[i][1]] == 1:
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filtered_coords.append(coords[i])
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allowed_locations[
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(coords[i][0] - min_distance):(coords[i][0] + min_distance),
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(coords[i][1] - min_distance):(coords[i][1] + min_distance)] = 0
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return np.array(filtered_coords)
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