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scikit-image/skimage/feature/interest.py
T

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2.2 KiB
Python

import numpy as np
from scipy import ndimage
from . import peak
def harris(image, eps=1e-6, sigma=1):
"""Compute Harris response image.
Parameters
----------
image : ndarray
Input image.
eps : float, optional
Normalisation factor.
sigma : float, optional
Standard deviation used for the Gaussian kernel.
Returns
-------
response : ndarray
Moravec response image.
Examples
-------
>>> from skimage.feature import harris, peak_local_max
>>> square = np.zeros([10, 10])
>>> square[2:8,2:8] = 1
>>> square
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> peak_local_max(harris(square), min_distance=1)
array([[3, 3],
[3, 6],
[6, 3],
[6, 6]])
"""
if image.ndim == 3:
image = rgb2grey(image)
# derivatives
image = ndimage.gaussian_filter(image, sigma, mode='constant', cval=0)
imx = ndimage.sobel(image, axis=0, mode='constant', cval=0)
imy = ndimage.sobel(image, axis=1, mode='constant', cval=0)
Wxx = ndimage.gaussian_filter(imx * imx, sigma,
mode='constant', cval=0)
Wxy = ndimage.gaussian_filter(imx * imy, sigma,
mode='constant', cval=0)
Wyy = ndimage.gaussian_filter(imy * imy, sigma,
mode='constant', cval=0)
# determinant and trace
Wdet = Wxx * Wyy - Wxy**2
Wtr = Wxx + Wyy
# Alternate formula for Harris response.
# Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989)
harris = Wdet / (Wtr + eps)
return harris