mirror of
https://github.com/wassname/scikit-image.git
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Nelle Varoquaux
117 lines
3.4 KiB
Cython
117 lines
3.4 KiB
Cython
"""
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:author: Damian Eads, 2009
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:license: modified BSD
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"""
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from __future__ import division
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import numpy as np
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cimport numpy as np
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cimport cython
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from cpython cimport bool
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STREL_DTYPE = np.uint8
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ctypedef np.uint8_t STREL_DTYPE_t
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IMAGE_DTYPE = np.uint8
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ctypedef np.uint8_t IMAGE_DTYPE_t
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cdef inline int int_max(int a, int b): return a if a >= b else b
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cdef inline int int_min(int a, int b): return a if a <= b else b
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@cython.boundscheck(False)
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def dilate(np.ndarray[IMAGE_DTYPE_t, ndim=2] image not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] selem not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] out,
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bool shift_x, bool shift_y):
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cdef int hw = selem.shape[0] // 2
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cdef int hh = selem.shape[1] // 2
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if shift_x:
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hh -= 1
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if shift_y:
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hw -= 1
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cdef int width = image.shape[0], height = image.shape[1]
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if out is None:
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out = np.zeros([width, height], dtype=IMAGE_DTYPE)
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assert out.shape[0] == image.shape[0]
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assert out.shape[1] == image.shape[1]
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cdef int x, y, ix, iy, cx, cy
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cdef IMAGE_DTYPE_t max_so_far
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cdef int sw = selem.shape[0], sh = selem.shape[1]
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cdef np.ndarray[np.int_t, ndim=2] xinc = np.zeros([sw, sh], dtype=np.int)
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cdef np.ndarray[np.int_t, ndim=2] yinc = np.zeros([sw, sh], dtype=np.int)
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for x in range(sw):
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for y in range(sh):
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xinc[x, y] = (x - hw)
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yinc[x, y] = (y - hh)
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for x in range(width):
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for y in range(height):
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max_so_far = 0
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for cx in range(0, sw):
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for cy in range(0, sh):
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ix = x + xinc[cx,cy]
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iy = y + yinc[cx,cy]
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if ix>=0 and iy>=0 and ix < width and iy < height \
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and selem[cx, cy] == 1 \
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and image[ix,iy] > max_so_far:
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max_so_far = image[ix,iy]
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out[x,y] = max_so_far
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return out
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@cython.boundscheck(False)
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def erode(np.ndarray[IMAGE_DTYPE_t, ndim=2] image not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] selem not None,
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np.ndarray[IMAGE_DTYPE_t, ndim=2] out,
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bool shift_x, bool shift_y):
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cdef int hw = selem.shape[0] // 2
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cdef int hh = selem.shape[1] // 2
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if shift_x:
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hh -= 1
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if shift_y:
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hw -= 1
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cdef int width = image.shape[0], height = image.shape[1]
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if out is None:
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out = np.zeros([width, height], dtype=IMAGE_DTYPE)
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assert out.shape[0] == image.shape[0]
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assert out.shape[1] == image.shape[1]
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cdef int x, y, ix, iy, cx, cy
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cdef IMAGE_DTYPE_t min_so_far
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cdef int sw = selem.shape[0], sh = selem.shape[1]
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cdef np.ndarray[np.int_t, ndim=2] xinc = np.zeros([sw, sh], dtype=np.int)
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cdef np.ndarray[np.int_t, ndim=2] yinc = np.zeros([sw, sh], dtype=np.int)
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for x in range(sw):
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for y in range(sh):
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xinc[x, y] = (x - hw)
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yinc[x, y] = (y - hh)
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for x in range(width):
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for y in range(height):
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min_so_far = 255
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for cx in range(0, sw):
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for cy in range(0, sh):
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ix = x + xinc[cx,cy]
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iy = y + yinc[cx,cy]
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if ix>=0 and iy>=0 and ix < width \
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and iy < height and selem[cx, cy] == 1 \
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and image[ix,iy] < min_so_far:
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min_so_far = image[ix,iy]
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out[x,y] = min_so_far
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return out
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