mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 20:22:51 +08:00
Marianne Corvellec
110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
"""
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Harris corner detector
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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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"""
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from scipy import ndimage
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from . import peak
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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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# 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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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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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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>>> 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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>>> harris(square, min_distance=1)
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Corners of the square
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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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harrisim = _compute_harris_response(image, eps=eps,
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gaussian_deviation=gaussian_deviation)
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coordinates = peak.peak_local_max(harrisim, min_distance=min_distance,
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threshold_rel=threshold)
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return coordinates
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