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71 lines
2.3 KiB
Python
71 lines
2.3 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 skimage.util import img_as_float
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from . import peak
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def harris(image, eps=1e-6, gaussian_deviation=1):
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"""Compute 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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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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coordinates : (N, 2) array
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`(row, column)` coordinates of interest points.
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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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image = ndimage.gaussian_filter(image, gaussian_deviation, mode='constant',
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cval=0)
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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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Wxx = ndimage.gaussian_filter(imx * imx, 1.5, mode='constant', cval=0)
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Wxy = ndimage.gaussian_filter(imx * imy, 1.5, mode='constant', cval=0)
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Wyy = ndimage.gaussian_filter(imy * imy, 1.5, mode='constant', cval=0)
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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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# Alternate formula for Harris response.
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# Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989)
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harris = Wdet / (Wtr + eps)
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return harris
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