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scikit-image/skimage/measure/_ccomp.pyx
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emmanuelle a5a771a8e4 Modified label function so that background pixels are labeled with 0, and 
background=0 by default.

Modified label function so that background pixels are labeled with 0, and
background=0 by default. All tests of _ccomp.pyx pass

Modified a couple of files to be consistent with the new behavior of
measure.label

Modified doctring of label to pass doctest

Modified TODO.txt as well as release notes to mention the new behavior of
label.

Typo in docstring

Typo in docstring

Changed default value of kw argument background in measure.label

Removed unnecessary and outdated comment
2016-02-22 21:19:23 +01:00

827 lines
29 KiB
Cython
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#cython: cdivision=True
#cython: boundscheck=False
#cython: nonecheck=False
#cython: wraparound=False
import numpy as np
from .._shared.utils import warn
cimport numpy as cnp
"""
See also:
Christophe Fiorio and Jens Gustedt,
"Two linear time Union-Find strategies for image processing",
Theoretical Computer Science 154 (1996), pp. 165-181.
Kensheng Wu, Ekow Otoo and Arie Shoshani,
"Optimizing connected component labeling algorithms",
Paper LBNL-56864, 2005,
Lawrence Berkeley National Laboratory
(University of California),
http://repositories.cdlib.org/lbnl/LBNL-56864
"""
DTYPE = np.intp
# Short int - could be more graceful to the CPU cache
ctypedef cnp.int32_t INTS_t
cdef struct s_shpinfo
ctypedef s_shpinfo shape_info
ctypedef int (* fun_ravel)(int, int, int, shape_info *)
# For having stuff concerning background in one place
ctypedef struct bginfo:
## The value in the image (i.e. not the label!) that identifies
## the background.
DTYPE_t background_val
DTYPE_t background_node
## Identification of the background in the labelled image
DTYPE_t background_label
cdef void get_bginfo(background_val, bginfo *ret) except *:
if background_val is None:
ret.background_val = 0
else:
ret.background_val = background_val
# The node -999 doesn't exist, it will get subsituted by a meaningful value
# upon the first background pixel occurence
ret.background_node = -999
ret.background_label = 0
# A pixel has neighbors that have already been scanned.
# In the paper, the pixel is denoted by E and its neighbors:
#
# z=1 z=0 x
# ---------------------->
# | A B C F G H
# | D E . I J K
# | . . . L M N
# |
# y V
#
# D_ea represents offset of A from E etc. - see the definition of
# get_shape_info
cdef enum:
# the 0D neighbor
# D_ee, # We don't need D_ee
# the 1D neighbor
D_ed,
# 2D neighbors
D_ea, D_eb, D_ec,
# 3D neighbors
D_ef, D_eg, D_eh, D_ei, D_ej, D_ek, D_el, D_em, D_en,
D_COUNT
# Structure for centralised access to shape data
# Contains information related to the shape of the input array
cdef struct s_shpinfo:
INTS_t x
INTS_t y
INTS_t z
# Number of elements
DTYPE_t numels
# Number of of the input array
INTS_t ndim
# Offsets between elements recalculated to linear index increments
# DEX[D_ea] is offset between E and A (i.e. to the point to upper left)
# The name DEX is supposed to evoke DE., where . = A, B, C, D, F etc.
INTS_t DEX[D_COUNT]
# Function pointer to a function that recalculates multiindex to linear
# index. Heavily depends on dimensions of the input array.
fun_ravel ravel_index
cdef void get_shape_info(inarr_shape, shape_info *res) except *:
"""
Precalculates all the needed data from the input array shape
and stores them in the shape_info struct.
"""
res.y = 1
res.z = 1
res.ravel_index = ravel_index2D
# A shape (3, 1, 4) would make the algorithm crash, but the corresponding
# good_shape (i.e. the array with axis swapped) (1, 3, 4) is OK.
# Having an axis length of 1 when an axis on the left is longer than 1
# (in this case, it has length of 3) is NOT ALLOWED.
good_shape = tuple(sorted(inarr_shape))
res.ndim = len(inarr_shape)
if res.ndim == 1:
res.x = inarr_shape[0]
res.ravel_index = ravel_index1D
elif res.ndim == 2:
res.x = inarr_shape[1]
res.y = inarr_shape[0]
res.ravel_index = ravel_index2D
if res.x == 1 and res.y > 1:
# Should not occur, but better be safe than sorry
raise ValueError(
"Swap axis of your %s array so it has a %s shape"
% (inarr_shape, good_shape))
elif res.ndim == 3:
res.x = inarr_shape[2]
res.y = inarr_shape[1]
res.z = inarr_shape[0]
res.ravel_index = ravel_index3D
if ((res.x == 1 and res.y > 1)
or res.y == 1 and res.z > 1):
# Should not occur, but better be safe than sorry
raise ValueError(
"Swap axes of your %s array so it has a %s shape"
% (inarr_shape, good_shape))
else:
raise NotImplementedError(
"Only for images of dimension 1-3 are supported, got a %sD one"
% res.ndim)
res.numels = res.x * res.y * res.z
# When reading this for the first time, look at the diagram by the enum
# definition above (keyword D_ee)
# Difference between E and G is (x=0, y=-1, z=-1), E and A (-1, -1, 0) etc.
# Here, it is recalculated to linear (raveled) indices of flattened arrays
# with their last (=contiguous) dimension is x.
# So now the 1st (needed for 1D, 2D and 3D) part, y = 1, z = 1
res.DEX[D_ed] = -1
# Not needed, just for illustration
# res.DEX[D_ee] = 0
# So now the 2nd (needed for 2D and 3D) part, y = 0, z = 1
res.DEX[D_ea] = res.ravel_index(-1, -1, 0, res)
res.DEX[D_eb] = res.DEX[D_ea] + 1
res.DEX[D_ec] = res.DEX[D_eb] + 1
# And now the 3rd (needed only for 3D) part, z = 0
res.DEX[D_ef] = res.ravel_index(-1, -1, -1, res)
res.DEX[D_eg] = res.DEX[D_ef] + 1
res.DEX[D_eh] = res.DEX[D_ef] + 2
res.DEX[D_ei] = res.DEX[D_ef] - res.DEX[D_eb] # DEX[D_eb] = one row up, remember?
res.DEX[D_ej] = res.DEX[D_ei] + 1
res.DEX[D_ek] = res.DEX[D_ei] + 2
res.DEX[D_el] = res.DEX[D_ei] - res.DEX[D_eb]
res.DEX[D_em] = res.DEX[D_el] + 1
res.DEX[D_en] = res.DEX[D_el] + 2
cdef inline void join_trees_wrapper(DTYPE_t *data_p, DTYPE_t *forest_p,
DTYPE_t rindex, INTS_t idxdiff):
if data_p[rindex] == data_p[rindex + idxdiff]:
join_trees(forest_p, rindex, rindex + idxdiff)
cdef int ravel_index1D(int x, int y, int z, shape_info *shapeinfo):
"""
Ravel index of a 1D array - trivial. y and z are ignored.
"""
return x
cdef int ravel_index2D(int x, int y, int z, shape_info *shapeinfo):
"""
Ravel index of a 2D array. z is ignored
"""
cdef int ret = x + y * shapeinfo.x
return ret
cdef int ravel_index3D(int x, int y, int z, shape_info *shapeinfo):
"""
Ravel index of a 3D array
"""
cdef int ret = x + y * shapeinfo.x + z * shapeinfo.y * shapeinfo.x
return ret
# Tree operations implemented by an array as described in Wu et al.
# The term "forest" is used to indicate an array that stores one or more trees
# Consider a following tree:
#
# 5 ----> 3 ----> 2 ----> 1 <---- 6 <---- 7
# | |
# 4 >----/ \----< 8 <---- 9
#
# The vertices are a unique number, so the tree can be represented by an
# array where a the tuple (index, array[index]) represents an edge,
# so for our example, array[2] == 1, array[7] == 6 and array[1] == 1, because
# 1 is the root.
# Last but not least, one array can hold more than one tree as long as their
# indices are different. It is the case in this algorithm, so for that reason
# the array is referred to as the "forest" = multiple trees next to each
# other.
#
# In this algorithm, there are as many indices as there are elements in the
# array to label and array[x] == x for all x. As the labelling progresses,
# equivalence between so-called provisional (i.e. not final) labels is
# discovered and trees begin to surface.
# When we found out that label 5 and 3 are the same, we assign array[5] = 3.
cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) nogil:
"""Find the root of node n.
Given the example above, for any integer from 1 to 9, 1 is always returned
"""
cdef DTYPE_t root = n
while (forest[root] < root):
root = forest[root]
return root
cdef inline void set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) nogil:
"""
Set all nodes on a path to point to new_root.
Given the example above, given n=9, root=6, it would "reconnect" the tree.
so forest[9] = 6 and forest[8] = 6
The ultimate goal is that all tree nodes point to the real root,
which is element 1 in this case.
"""
cdef DTYPE_t j
while (forest[n] < n):
j = forest[n]
forest[n] = root
n = j
forest[n] = root
cdef inline void join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m) nogil:
"""Join two trees containing nodes n and m.
If we imagine that in the example tree, the root 1 is not known, we
rather have two disjoint trees with roots 2 and 6.
Joining them would mean that all elements of both trees become connected
to the element 2, so forest[9] == 2, forest[6] == 2 etc.
However, when the relationship between 1 and 2 can still be discovered later.
"""
cdef DTYPE_t root
cdef DTYPE_t root_m
if (n != m):
root = find_root(forest, n)
root_m = find_root(forest, m)
if (root > root_m):
root = root_m
set_root(forest, n, root)
set_root(forest, m, root)
def _get_swaps(shp):
"""
What axes to swap if we want to convert an illegal array shape
to a legal one.
Args:
shp (tuple-like): The array shape
Returns:
list: List of tuples to be passed to np.swapaxes
"""
shp = np.array(shp)
swaps = []
# Dimensions where the array is "flat"
ones = np.where(shp == 1)[0][::-1]
# The other dimensions
bigs = np.where(shp > 1)[0]
# We now go from opposite directions and swap axes if an index of a flat
# axis is higher than the one of a thick axis
for one, big in zip(ones, bigs):
if one < big:
# We are ordered already
break
else:
swaps.append((one, big))
# TODO: Add more swaps so the array is longer along x and shorter along y
return swaps
def _apply_swaps(arr, swaps):
arr2 = arr
for one, two in swaps:
arr2 = arr.swapaxes(one, two)
return arr2
def reshape_array(arr):
"""
"Rotates" the array so it gains a shape that the algorithm can work with.
An illegal shape is (3, 1, 4), and correct one is (1, 3, 4) or (1, 4, 3).
The point is to have all 1s of the shape at the beginning, not in the
middle of the shape tuple.
Note: The greater-than-one shape component should go from greatest to
lowest numbers since it is more friendly to the CPU cache (so (1, 3, 4) is
less optimal than (1, 4, 3). Keyword to this is "memory spatial locality"
Args:
arr (np.ndarray): The array we want to fix
Returns:
tuple (result, swaps): The result is the "fixed" array and a record
of what has been done with it.
"""
swaps = _get_swaps(arr.shape)
reshaped = _apply_swaps(arr, swaps)
return reshaped, swaps
def undo_reshape_array(arr, swaps):
"""
Does the opposite of what :func:`reshape_array` does
Args:
arr (np.ndarray): The array to "revert"
swaps (list): The second result of :func:`reshape_array`
Returns:
np.ndarray: The array of the original shape
"""
# It is safer to undo axes swaps in the opposite order
# than the application order
reshaped = _apply_swaps(arr, swaps[::-1])
return reshaped
# Connected components search as described in Fiorio et al.
def label(input, neighbors=None, background=0, return_num=False,
connectivity=None):
r"""Label connected regions of an integer array.
Two pixels are connected when they are neighbors and have the same value.
In 2D, they can be neighbors either in a 1- or 2-connected sense.
The value refers to the maximum number of orthogonal hops to consider a
pixel/voxel a neighbor::
1-connectivity 2-connectivity diagonal connection close-up
[ ] [ ] [ ] [ ] [ ]
| \ | / | <- hop 2
[ ]--[x]--[ ] [ ]--[x]--[ ] [x]--[ ]
| / | \ hop 1
[ ] [ ] [ ] [ ]
Parameters
----------
input : ndarray of dtype int
Image to label.
neighbors : {4, 8}, int, optional
Whether to use 4- or 8-"connectivity".
In 3D, 4-"connectivity" means connected pixels have to share face,
whereas with 8-"connectivity", they have to share only edge or vertex.
**Deprecated, use ``connectivity`` instead.**
background : int, optional
Consider all pixels with this value as background pixels, and label
them as 0. By default, 0-valued pixels are considered as background
pixels.
return_num : bool, optional
Whether to return the number of assigned labels.
connectivity : int, optional
Maximum number of orthogonal hops to consider a pixel/voxel
as a neighbor.
Accepted values are ranging from 1 to input.ndim. If ``None``, a full
connectivity of ``input.ndim`` is used.
Returns
-------
labels : ndarray of dtype int
Labeled array, where all connected regions are assigned the
same integer value.
num : int, optional
Number of labels, which equals the maximum label index and is only
returned if return_num is `True`.
Examples
--------
>>> import numpy as np
>>> x = np.eye(3).astype(int)
>>> print(x)
[[1 0 0]
[0 1 0]
[0 0 1]]
>>> from skimage.measure import label
>>> print(label(x, connectivity=1))
[[1 0 0]
[0 2 0]
[0 0 3]]
>>> print(label(x, connectivity=2))
[[1 0 0]
[0 1 0]
[0 0 1]]
>>> print(label(x, background=-1))
[[1 2 2]
[2 1 2]
[2 2 1]]
>>> x = np.array([[1, 0, 0],
... [1, 1, 5],
... [0, 0, 0]])
>>> print(label(x))
[[1 0 0]
[1 1 2]
[0 0 0]]
"""
# We have to ensure that the shape of the input can be handled by the
# algorithm the input if it is the case
input_corrected, swaps = reshape_array(input)
# Do the labelling
res, ctr = _label(input_corrected, neighbors, background, connectivity)
res_orig = undo_reshape_array(res, swaps)
if return_num:
return res_orig, ctr
else:
return res_orig
# Connected components search as described in Fiorio et al.
def _label(input, neighbors=None, background=None, connectivity=None):
cdef cnp.ndarray[DTYPE_t, ndim=1] data
cdef cnp.ndarray[DTYPE_t, ndim=1] forest
# Having data a 2D array slows down access considerably using linear
# indices even when using the data_p pointer :-(
data = np.copy(input.flatten().astype(DTYPE))
forest = np.arange(data.size, dtype=DTYPE)
cdef DTYPE_t *forest_p = <DTYPE_t*>forest.data
cdef DTYPE_t *data_p = <DTYPE_t*>data.data
cdef shape_info shapeinfo
cdef bginfo bg
get_shape_info(input.shape, &shapeinfo)
get_bginfo(background, &bg)
if neighbors is None and connectivity is None:
# use the full connectivity by default
connectivity = input.ndim
elif neighbors is not None:
DeprecationWarning("The argument 'neighbors' is deprecated, use "
"'connectivity' instead")
# backwards-compatible neighbors recalc to connectivity,
if neighbors == 4:
connectivity = 1
elif neighbors == 8:
connectivity = input.ndim
else:
raise ValueError("Neighbors must be either 4 or 8, got '%d'.\n"
% neighbors)
if not 1 <= connectivity <= input.ndim:
raise ValueError(
"Connectivity below 1 or above %d is illegal."
% input.ndim)
scanBG(data_p, forest_p, &shapeinfo, &bg)
# the data are treated as degenerated 3D arrays if needed
# witout any performance sacrifice
scan3D(data_p, forest_p, &shapeinfo, &bg, connectivity)
# Label output
cdef DTYPE_t ctr
ctr = resolve_labels(data_p, forest_p, &shapeinfo, &bg)
# Work around a bug in ndimage's type checking on 32-bit platforms
if data.dtype == np.int32:
data = data.view(np.int32)
res = data.reshape(input.shape)
return res, ctr
cdef DTYPE_t resolve_labels(DTYPE_t *data_p, DTYPE_t *forest_p,
shape_info *shapeinfo, bginfo *bg):
"""
We iterate through the provisional labels and assign final labels based on
our knowledge of prov. labels relationship.
We also track how many distinct final labels we have.
"""
cdef DTYPE_t counter = 1, i
for i in range(shapeinfo.numels):
if i == bg.background_node:
data_p[i] = bg.background_label
elif i == forest_p[i]:
# We have stumbled across a root which is something new to us (root
# is the LOWEST of all prov. labels that are equivalent to it)
data_p[i] = counter
counter += 1
else:
data_p[i] = data_p[forest_p[i]]
return counter - 1
cdef void scanBG(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo,
bginfo *bg):
"""
Settle all background pixels now and don't bother with them later.
Since this only requires one linar sweep through the array, it is fast
and it makes sense to do it separately.
The result of this function is update of forest_p and bg parameter.
"""
cdef DTYPE_t i, bgval = bg.background_val, firstbg
# We find the provisional label of the background, which is the index of
# the first background pixel
for i in range(shapeinfo.numels):
if data_p[i] == bgval:
firstbg = i
bg.background_node = firstbg
break
# And then we apply this provisional label to the whole background
for i in range(firstbg, shapeinfo.numels):
if data_p[i] == bgval:
forest_p[i] = firstbg
# Here, we work with flat arrays regardless whether the data is 1, 2 or 3D.
# The lookup to the neighbor in a 2D array is achieved by precalculating an
# offset and adding it to the index.
# The forward scan mask looks like this (the center point is actually E):
# (take a look at shape_info docs for more exhaustive info)
# A B C
# D E
#
# So if I am in the point E and want to take a look to A, I take the index of
# E and add shapeinfo.DEX[D_ea] to it and get the index of A.
# The 1D indices are "raveled" or "linear", that's where "rindex" comes from.
cdef void scan1D(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo,
bginfo *bg, DTYPE_t connectivity, DTYPE_t y, DTYPE_t z):
"""
Perform forward scan on a 1D object, usually the first row of an image
"""
# Initialize the first row
cdef DTYPE_t x, rindex, bgval = bg.background_val
cdef INTS_t *DEX = shapeinfo.DEX
rindex = shapeinfo.ravel_index(0, y, z, shapeinfo)
for x in range(1, shapeinfo.x):
rindex += 1
# Handle the first row
if data_p[rindex] == bgval:
# Nothing to do if we are background
continue
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
cdef void scan2D(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo,
bginfo *bg, DTYPE_t connectivity, DTYPE_t z):
"""
Perform forward scan on a 2D array.
"""
cdef DTYPE_t x, y, rindex, bgval = bg.background_val
cdef INTS_t *DEX = shapeinfo.DEX
scan1D(data_p, forest_p, shapeinfo, bg, connectivity, 0, z)
for y in range(1, shapeinfo.y):
# BEGINNING of x = 0
rindex = shapeinfo.ravel_index(0, y, 0, shapeinfo)
# Handle the first column
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec])
# END of x = 0
for x in range(1, shapeinfo.x - 1):
# We have just moved to another column (of the same row)
# so we increment the raveled index. It will be reset when we get
# to another row, so we don't have to worry about altering it here.
rindex += 1
if data_p[rindex] == bgval:
# Nothing to do if we are background
continue
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec])
# Finally, the last column
# BEGINNING of x = max
rindex += 1
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea])
# END of x = max
cdef void scan3D(DTYPE_t *data_p, DTYPE_t *forest_p, shape_info *shapeinfo,
bginfo *bg, DTYPE_t connectivity):
"""
Perform forward scan on a 2D array.
"""
cdef DTYPE_t x, y, z, rindex, bgval = bg.background_val
cdef INTS_t *DEX = shapeinfo.DEX
# Handle first plane
scan2D(data_p, forest_p, shapeinfo, bg, connectivity, 0)
for z in range(1, shapeinfo.z):
# Handle first row in 3D manner
# BEGINNING of y = 0
# BEGINNING of x = 0
rindex = shapeinfo.ravel_index(0, 0, z, shapeinfo)
if data_p[rindex] != bgval:
# Nothing to do if we are background
# Now we have pixels below
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en])
# END of x = 0
for x in range(1, shapeinfo.x - 1):
rindex += 1
# Handle the first row
if data_p[rindex] == bgval:
# Nothing to do if we are background
continue
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en])
# BEGINNING of x = max
rindex += 1
# Handle the last element of the first row
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el])
# END of x = max
# END of y = 0
# BEGINNING of y = ...
for y in range(1, shapeinfo.y - 1):
# BEGINNING of x = 0
rindex = shapeinfo.ravel_index(0, y, z, shapeinfo)
# Handle the first column in 3D manner
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en])
# END of x = 0
# Handle the rest of columns
for x in range(1, shapeinfo.x - 1):
rindex += 1
if data_p[rindex] == bgval:
# Nothing to do if we are background
continue
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_en])
# BEGINNING of x = max
rindex += 1
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_em])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_el])
# END of x = max
# END of y = ...
# BEGINNING of y = max
# BEGINNING of x = 0
rindex = shapeinfo.ravel_index(0, shapeinfo.y - 1, z, shapeinfo)
# Handle the first column in 3D manner
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh])
# END of x = 0
# Handle the rest of columns
for x in range(1, shapeinfo.x - 1):
rindex += 1
if data_p[rindex] == bgval:
# Nothing to do if we are background
continue
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ec])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ek])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eh])
# BEGINNING of x = max
rindex += 1
if data_p[rindex] != bgval:
# Nothing to do if we are background
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eb])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ed])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ej])
if connectivity >= 2:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ea])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_eg])
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ei])
if connectivity >= 3:
join_trees_wrapper(data_p, forest_p, rindex, DEX[D_ef])
# END of x = max
# END of y = max
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