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Nelle Varoquaux
119 lines
3.3 KiB
Python
119 lines
3.3 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, gaussian_deviation=1):
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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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Parameters
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----------
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image: ndarray of floats
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Input image
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eps: float, optional
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Normalisation factor
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gaussian_deviation: integer, optional
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Standard deviation used for the Gaussian kernel
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Returns
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--------
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image: (M, N) ndarray
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Harris image response
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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, gaussian_deviation)
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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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mask = (harris == harris_max)
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harris *= mask
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# Remove the image borders
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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(image, min_distance=10, threshold=0.1, eps=1e-6,
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gaussian_deviation=1):
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"""Return corners from a Harris response image
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Parameters
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----------
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image: ndarray of floats
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Input image
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min_distance: int, optional
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Minimum number of pixels separating interest points and image boundary
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threshold: float, optional
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Relative threshold impacting the number of interest points.
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eps: float, optional
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Normalisation factor
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gaussian_deviation: integer, optional
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Standard deviation used for the Gaussian kernel
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returns:
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--------
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array: coordinates of interest points
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"""
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harrisim = _compute_harris_response(image, eps=eps,
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gaussian_deviation=gaussian_deviation)
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# find top corner candidates above a threshold
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corner_threshold = np.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 = np.transpose(candidates)
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# ...and their values
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candidate_values = harrisim[candidates]
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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[tuple(coords[i])] == 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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