mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-03 03:11:05 +08:00
Tony S Yu
92 lines
3.0 KiB
Cython
92 lines
3.0 KiB
Cython
#cython: cdivision=True
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#cython: boundscheck=False
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#cython: nonecheck=False
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#cython: wraparound=False
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import numpy as np
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cimport numpy as cnp
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from libc.float cimport DBL_MAX
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from skimage.color import rgb2grey
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from skimage.util import img_as_float
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def corner_moravec(image, Py_ssize_t window_size=1):
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"""Compute Moravec corner measure response image.
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This is one of the simplest corner detectors and is comparatively fast but
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has several limitations (e.g. not rotation invariant).
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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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window_size : int, optional
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Window size.
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Returns
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-------
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response : ndarray
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Moravec response image.
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References
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----------
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..[1] http://kiwi.cs.dal.ca/~dparks/CornerDetection/moravec.htm
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..[2] http://en.wikipedia.org/wiki/Corner_detection
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Examples
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--------
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>>> from skimage.feature import moravec, peak_local_max
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>>> square = np.zeros([7, 7])
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>>> square[3, 3] = 1
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>>> square
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array([[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 1., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.]])
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>>> moravec(square)
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array([[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 1., 1., 1., 0., 0.],
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[ 0., 0., 1., 2., 1., 0., 0.],
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[ 0., 0., 1., 1., 1., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0.]])
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"""
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cdef Py_ssize_t rows = image.shape[0]
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cdef Py_ssize_t cols = image.shape[1]
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cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] cimage, out
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if image.ndim == 3:
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cimage = rgb2grey(image)
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cimage = np.ascontiguousarray(img_as_float(image))
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out = np.zeros(image.shape, dtype=np.double)
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cdef double* image_data = <double*>cimage.data
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cdef double* out_data = <double*>out.data
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cdef double msum, min_msum
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cdef Py_ssize_t r, c, br, bc, mr, mc, a, b
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for r in range(2 * window_size, rows - 2 * window_size):
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for c in range(2 * window_size, cols - 2 * window_size):
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min_msum = DBL_MAX
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for br in range(r - window_size, r + window_size + 1):
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for bc in range(c - window_size, c + window_size + 1):
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if br != r and bc != c:
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msum = 0
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for mr in range(- window_size, window_size + 1):
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for mc in range(- window_size, window_size + 1):
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a = (r + mr) * cols + c + mc
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b = (br + mr) * cols + bc + mc
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msum += (image_data[a] - image_data[b]) ** 2
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min_msum = min(msum, min_msum)
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out_data[r * cols + c] = min_msum
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return out
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