mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 17:45:20 +08:00
STY: move main functions to the top of the file
This commit is contained in:
Evgeni Burovski
+131
-131
@@ -40,6 +40,137 @@ cimport cython
|
||||
ctypedef npy_uint8 pixel_type
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
def _compute_thin_image(pixel_type[:, :, ::1] img not None):
|
||||
"""Compute a thin image.
|
||||
|
||||
Loop through the image multiple times, removing "simple" points, i.e.
|
||||
those point which can be removed without changing local connectivity in the
|
||||
3x3x3 neighborhood of a point.
|
||||
|
||||
This routine implements the two-pass algorthim of [Lee94]. Namely,
|
||||
for each of the six border types (positive and negative x-, y- and z-),
|
||||
the algorithm first collects all possibly deletable points, and then
|
||||
performs a sequential rechecking.
|
||||
|
||||
The input, `img`, is assumed to be a 3D binary image in the
|
||||
(p, r, c) format [i.e., C ordered array], filled by zeros (background) and
|
||||
ones. Furthermore, `img` is assumed to be padded by zeros from all
|
||||
directions --- this way the zero boundary conditions are authomatic
|
||||
and there is need to guard against out-of-bounds access.
|
||||
|
||||
"""
|
||||
cdef:
|
||||
int unchanged_borders = 0, curr_border, num_borders
|
||||
int borders[6]
|
||||
npy_intp p, r, c
|
||||
bint no_change
|
||||
list simple_border_points
|
||||
pixel_type neighb[27]
|
||||
borders[:] = [4, 3, 2, 1, 5, 6]
|
||||
|
||||
# no need to worry about the z direction if the original image is 2D.
|
||||
if img.shape[0] == 3:
|
||||
num_borders = 4
|
||||
else:
|
||||
num_borders = 6
|
||||
|
||||
# loop through the image several times until there is no change for all
|
||||
# the six border types
|
||||
while unchanged_borders < num_borders:
|
||||
unchanged_borders = 0
|
||||
for j in range(num_borders):
|
||||
curr_border = borders[j]
|
||||
|
||||
simple_border_points = _loop_through(img, curr_border)
|
||||
## print(curr_border, " : ", simple_border_points, '\n')
|
||||
|
||||
# sequential re-checking to preserve connectivity when deleting
|
||||
# in a parallel way
|
||||
no_change = True
|
||||
for pt in simple_border_points:
|
||||
p, r, c = pt
|
||||
get_neighborhood(img, p, r, c, neighb)
|
||||
if is_simple_point(neighb):
|
||||
img[p, r, c] = 0
|
||||
no_change = False
|
||||
else:
|
||||
pass
|
||||
## print(" *** ", pt, " is not simple.")
|
||||
|
||||
if no_change:
|
||||
unchanged_borders += 1
|
||||
simple_border_points = []
|
||||
|
||||
return np.asarray(img)
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
cdef list _loop_through(pixel_type[:, :, ::1] img,
|
||||
int curr_border):
|
||||
"""Inner loop of compute_thin_image.
|
||||
|
||||
The algorithm of [Lee94] proceeds in two steps: (1) six directions are
|
||||
checked for simple border points to remove, and (2) these candidates are
|
||||
sequentially rechecked, see Sec 3 of [Lee94] for rationale and discussion.
|
||||
|
||||
This routine implements the first step above: it loops over the image
|
||||
for a given direction and assembles candidates for removal.
|
||||
|
||||
"""
|
||||
# This routine looks like it could be nogil, but actually it cannot be,
|
||||
# because of `simple_border_points` being a python list which is being
|
||||
# mutated.
|
||||
cdef:
|
||||
list simple_border_points = []
|
||||
pixel_type neighborhood[27]
|
||||
npy_intp p, r, c
|
||||
bint is_border_pt
|
||||
|
||||
# loop through the image
|
||||
# NB: each loop is from 1 to size-1: img is padded from all sides
|
||||
for p in range(1, img.shape[0] - 1):
|
||||
for r in range(1, img.shape[1] - 1):
|
||||
for c in range(1, img.shape[2] - 1):
|
||||
|
||||
# check if pixel is foreground
|
||||
if img[p, r, c] != 1:
|
||||
continue
|
||||
|
||||
is_border_pt = (curr_border == 1 and img[p, r, c-1] <= 0 or #N
|
||||
curr_border == 2 and img[p, r, c+1] <= 0 or #S
|
||||
curr_border == 3 and img[p, r+1, c] <= 0 or #E
|
||||
curr_border == 4 and img[p, r-1, c] <= 0 or #W
|
||||
curr_border == 5 and img[p+1, r, c] <= 0 or #U
|
||||
curr_border == 6 and img[p-1, r, c] <= 0) #B
|
||||
if not is_border_pt:
|
||||
# current point is not deletable
|
||||
continue
|
||||
|
||||
get_neighborhood(img, p, r, c, neighborhood)
|
||||
|
||||
# check if (p, r, c) is an endpoint (then it's not deletable.)
|
||||
if is_endpoint(neighborhood):
|
||||
continue
|
||||
|
||||
# check if point is Euler invariant (condition 1 in [Lee94]):
|
||||
# if it is not, it's not deletable.
|
||||
if not is_Euler_invariant(neighborhood):
|
||||
continue
|
||||
|
||||
# check if point is simple (i.e., deletion does not
|
||||
# change connectivity in the 3x3x3 neighborhood)
|
||||
# this are conditions 2 and 3 in [Lee94]
|
||||
if not is_simple_point(neighborhood):
|
||||
continue
|
||||
|
||||
# ok, add (p, r, c) to the list of simple border points
|
||||
simple_border_points.append((p, r, c))
|
||||
return simple_border_points
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
cdef void get_neighborhood(pixel_type[:, :, ::1] img,
|
||||
@@ -622,134 +753,3 @@ cdef void octree_labeling(int octant, int label, pixel_type cube[]):
|
||||
octree_labeling(7, label, cube)
|
||||
if cube[25] == 1:
|
||||
cube[25] = label
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
cdef list _loop_through(pixel_type[:, :, ::1] img,
|
||||
int curr_border):
|
||||
"""Inner loop of compute_thin_image.
|
||||
|
||||
The algorithm of [Lee94] proceeds in two steps: (1) six directions are
|
||||
checked for simple border points to remove, and (2) these candidates are
|
||||
sequentially rechecked, see Sec 3 of [Lee94] for rationale and discussion.
|
||||
|
||||
This routine implements the first step above: it loops over the image
|
||||
for a given direction and assembles candidates for removal.
|
||||
|
||||
"""
|
||||
# This routine looks like it could be nogil, but actually it cannot be,
|
||||
# because of `simple_border_points` being a python list which is being
|
||||
# mutated.
|
||||
cdef:
|
||||
list simple_border_points = []
|
||||
pixel_type neighborhood[27]
|
||||
npy_intp p, r, c
|
||||
bint is_border_pt
|
||||
|
||||
# loop through the image
|
||||
# NB: each loop is from 1 to size-1: img is padded from all sides
|
||||
for p in range(1, img.shape[0] - 1):
|
||||
for r in range(1, img.shape[1] - 1):
|
||||
for c in range(1, img.shape[2] - 1):
|
||||
|
||||
# check if pixel is foreground
|
||||
if img[p, r, c] != 1:
|
||||
continue
|
||||
|
||||
is_border_pt = (curr_border == 1 and img[p, r, c-1] <= 0 or #N
|
||||
curr_border == 2 and img[p, r, c+1] <= 0 or #S
|
||||
curr_border == 3 and img[p, r+1, c] <= 0 or #E
|
||||
curr_border == 4 and img[p, r-1, c] <= 0 or #W
|
||||
curr_border == 5 and img[p+1, r, c] <= 0 or #U
|
||||
curr_border == 6 and img[p-1, r, c] <= 0) #B
|
||||
if not is_border_pt:
|
||||
# current point is not deletable
|
||||
continue
|
||||
|
||||
get_neighborhood(img, p, r, c, neighborhood)
|
||||
|
||||
# check if (p, r, c) is an endpoint (then it's not deletable.)
|
||||
if is_endpoint(neighborhood):
|
||||
continue
|
||||
|
||||
# check if point is Euler invariant (condition 1 in [Lee94]):
|
||||
# if it is not, it's not deletable.
|
||||
if not is_Euler_invariant(neighborhood):
|
||||
continue
|
||||
|
||||
# check if point is simple (i.e., deletion does not
|
||||
# change connectivity in the 3x3x3 neighborhood)
|
||||
# this are conditions 2 and 3 in [Lee94]
|
||||
if not is_simple_point(neighborhood):
|
||||
continue
|
||||
|
||||
# ok, add (p, r, c) to the list of simple border points
|
||||
simple_border_points.append((p, r, c))
|
||||
return simple_border_points
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
def _compute_thin_image(pixel_type[:, :, ::1] img not None):
|
||||
"""Compute a thin image.
|
||||
|
||||
Loop through the image multiple times, removing "simple" points, i.e.
|
||||
those point which can be removed without changing local connectivity in the
|
||||
3x3x3 neighborhood of a point.
|
||||
|
||||
This routine implements the two-pass algorthim of [Lee94]. Namely,
|
||||
for each of the six border types (positive and negative x-, y- and z-),
|
||||
the algorithm first collects all possibly deletable points, and then
|
||||
performs a sequential rechecking.
|
||||
|
||||
The input, `img`, is assumed to be a 3D binary image in the
|
||||
(p, r, c) format [i.e., C ordered array], filled by zeros (background) and
|
||||
ones. Furthermore, `img` is assumed to be padded by zeros from all
|
||||
directions --- this way the zero boundary conditions are authomatic
|
||||
and there is need to guard against out-of-bounds access.
|
||||
|
||||
"""
|
||||
cdef:
|
||||
int unchanged_borders = 0, curr_border, num_borders
|
||||
int borders[6]
|
||||
npy_intp p, r, c
|
||||
bint no_change
|
||||
list simple_border_points
|
||||
pixel_type neighb[27]
|
||||
borders[:] = [4, 3, 2, 1, 5, 6]
|
||||
|
||||
# no need to worry about the z direction if the original image is 2D.
|
||||
if img.shape[0] == 3:
|
||||
num_borders = 4
|
||||
else:
|
||||
num_borders = 6
|
||||
|
||||
# loop through the image several times until there is no change for all
|
||||
# the six border types
|
||||
while unchanged_borders < num_borders:
|
||||
unchanged_borders = 0
|
||||
for j in range(num_borders):
|
||||
curr_border = borders[j]
|
||||
|
||||
simple_border_points = _loop_through(img, curr_border)
|
||||
## print(curr_border, " : ", simple_border_points, '\n')
|
||||
|
||||
# sequential re-checking to preserve connectivity when deleting
|
||||
# in a parallel way
|
||||
no_change = True
|
||||
for pt in simple_border_points:
|
||||
p, r, c = pt
|
||||
get_neighborhood(img, p, r, c, neighb)
|
||||
if is_simple_point(neighb):
|
||||
img[p, r, c] = 0
|
||||
no_change = False
|
||||
else:
|
||||
pass
|
||||
## print(" *** ", pt, " is not simple.")
|
||||
|
||||
if no_change:
|
||||
unchanged_borders += 1
|
||||
simple_border_points = []
|
||||
|
||||
return np.asarray(img)
|
||||
|
||||
Reference in New Issue
Block a user