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79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
import numpy as np
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from scipy import ndimage
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from skimage.color import rgb2grey
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from . import peak
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def harris(image, method='k', k=0.05, eps=1e-6, sigma=1):
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"""Compute Harris response image.
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Parameters
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----------
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image : ndarray
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Input image.
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method : {'k', 'eps'}, optional
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Method to
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k : float, optional
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Sensitivity factor to separate corners from edges, typically in range
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`[0, 0.2]`. Small values of k result in detection of sharp corners.
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eps : float, optional
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Normalisation factor (Noble's corner measure).
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sigma : float, optional
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Standard deviation used for the Gaussian kernel, which is used as
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weighting function for the auto-correlation matrix.
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Returns
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-------
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response : ndarray
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Harris response image.
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Examples
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-------
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>>> from skimage.feature import harris, peak_local_max
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>>> square = np.zeros([10, 10])
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>>> square[2:8,2:8] = 1
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>>> square
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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>>> peak_local_max(harris(square), min_distance=1)
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array([[3, 3],
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[3, 6],
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[6, 3],
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[6, 6]])
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"""
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if image.ndim == 3:
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image = rgb2grey(image)
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# derivatives
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imx = ndimage.sobel(image, axis=0, mode='constant', cval=0)
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imy = ndimage.sobel(image, axis=1, mode='constant', cval=0)
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Axx = ndimage.gaussian_filter(imx * imx, sigma,
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mode='constant', cval=0)
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Axy = ndimage.gaussian_filter(imx * imy, sigma,
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mode='constant', cval=0)
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Ayy = ndimage.gaussian_filter(imy * imy, sigma,
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mode='constant', cval=0)
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# determinant
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detA = Axx * Ayy - Axy**2
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# trace
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traceA = Axx + Ayy
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if method == 'k':
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harris = detA - k * traceA**2
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else:
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harris = 2 * detA / (traceA + eps)
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return harris
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