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MAINT: move docstring and merge cython functions

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This commit is contained in:
François Boulogne
2016-05-26 21:48:01 +02:00
parent 07f753d081
commit 9992a4480b
2 changed files with 108 additions and 124 deletions
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skimage/measure/_ccomp.pyx
+16 -121
View File
@@ -8,21 +8,6 @@ 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
@@ -357,114 +342,19 @@ def undo_reshape_array(arr, swaps):
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`.
See Also
--------
regionprops
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]]
"""
def label_cython(input, neighbors=None, background=None, return_num=False,
connectivity=None):
# Connected components search as described in Fiorio et al.
# 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))
data = np.copy(input_corrected.flatten().astype(DTYPE))
forest = np.arange(data.size, dtype=DTYPE)
cdef DTYPE_t *forest_p = <DTYPE_t*>forest.data
@@ -473,12 +363,12 @@ def _label(input, neighbors=None, background=None, connectivity=None):
cdef shape_info shapeinfo
cdef bginfo bg
get_shape_info(input.shape, &shapeinfo)
get_shape_info(input_corrected.shape, &shapeinfo)
get_bginfo(background, &bg)
if neighbors is None and connectivity is None:
# use the full connectivity by default
connectivity = input.ndim
connectivity = input_corrected.ndim
elif neighbors is not None:
DeprecationWarning("The argument 'neighbors' is deprecated, use "
"'connectivity' instead")
@@ -486,15 +376,15 @@ def _label(input, neighbors=None, background=None, connectivity=None):
if neighbors == 4:
connectivity = 1
elif neighbors == 8:
connectivity = input.ndim
connectivity = input_corrected.ndim
else:
raise ValueError("Neighbors must be either 4 or 8, got '%d'.\n"
% neighbors)
if not 1 <= connectivity <= input.ndim:
if not 1 <= connectivity <= input_corrected.ndim:
raise ValueError(
"Connectivity below 1 or above %d is illegal."
% input.ndim)
% input_corrected.ndim)
scanBG(data_p, forest_p, &shapeinfo, &bg)
# the data are treated as degenerated 3D arrays if needed
@@ -509,9 +399,14 @@ def _label(input, neighbors=None, background=None, connectivity=None):
if data.dtype == np.int32:
data = data.view(np.int32)
res = data.reshape(input.shape)
res = data.reshape(input_corrected.shape)
return res, ctr
res_orig = undo_reshape_array(res, swaps)
if return_num:
return res_orig, ctr
else:
return res_orig
cdef DTYPE_t resolve_labels(DTYPE_t *data_p, DTYPE_t *forest_p,
skimage/measure/_label.py
+92 -3
View File
@@ -1,8 +1,97 @@
from ._ccomp import label as _label
from ._ccomp import label_cython as clabel
"""
References
----------
.. [1] Christophe Fiorio and Jens Gustedt, "Two linear time Union-Find
strategies for image processing", Theoretical Computer Science
154 (1996), pp. 165-181.
.. [2] 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
"""
def label(input, neighbors=None, background=None, return_num=False,
connectivity=None):
return _label(input, neighbors, background, return_num, connectivity)
r"""Label connected regions of an integer array.
label.__doc__ = _label.__doc__
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`.
See Also
--------
regionprops
Examples
--------
>>> import numpy as np
>>> x = np.eye(3).astype(int)
>>> print(x)
[[1 0 0]
[0 1 0]
[0 0 1]]
>>> 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]]
"""
return clabel(input, neighbors, background, return_num, connectivity)