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import numpy as np
from .._shared.utils import deprecated
def join_segmentations(s1, s2):
"""Return the join of the two input segmentations.
The join J of S1 and S2 is defined as the segmentation in which two
voxels are in the same segment if and only if they are in the same
segment in *both* S1 and S2.
Parameters
----------
s1, s2 : numpy arrays
s1 and s2 are label fields of the same shape.
Returns
-------
j : numpy array
The join segmentation of s1 and s2.
Examples
--------
>>> from skimage.segmentation import join_segmentations
>>> s1 = np.array([[0, 0, 1, 1],
... [0, 2, 1, 1],
... [2, 2, 2, 1]])
>>> s2 = np.array([[0, 1, 1, 0],
... [0, 1, 1, 0],
... [0, 1, 1, 1]])
>>> join_segmentations(s1, s2)
array([[0, 1, 3, 2],
[0, 5, 3, 2],
[4, 5, 5, 3]])
"""
if s1.shape != s2.shape:
raise ValueError("Cannot join segmentations of different shape. " +
"s1.shape: %s, s2.shape: %s" % (s1.shape, s2.shape))
s1 = relabel_sequential(s1)[0]
s2 = relabel_sequential(s2)[0]
j = (s2.max() + 1) * s1 + s2
j = relabel_sequential(j)[0]
return j
@deprecated('relabel_sequential')
def relabel_from_one(label_field):
"""Convert labels in an arbitrary label field to {1, ... number_of_labels}.
This function is deprecated, see ``relabel_sequential`` for more.
"""
return relabel_sequential(label_field, offset=1)
def relabel_sequential(label_field, offset=1):
"""Relabel arbitrary labels to {`offset`, ... `offset` + number_of_labels}.
This function also returns the forward map (mapping the original labels to
the reduced labels) and the inverse map (mapping the reduced labels back
to the original ones).
Parameters
----------
label_field : numpy array of int, arbitrary shape
An array of labels.
offset : int, optional
The return labels will start at `offset`, which should be
strictly positive.
Returns
-------
relabeled : numpy array of int, same shape as `label_field`
The input label field with labels mapped to
{offset, ..., number_of_labels + offset - 1}.
forward_map : numpy array of int, shape ``(label_field.max() + 1,)``
The map from the original label space to the returned label
space. Can be used to re-apply the same mapping. See examples
for usage.
inverse_map : 1D numpy array of int, of length offset + number of labels
The map from the new label space to the original space. This
can be used to reconstruct the original label field from the
relabeled one.
Notes
-----
The label 0 is assumed to denote the background and is never remapped.
The forward map can be extremely big for some inputs, since its
length is given by the maximum of the label field. However, in most
situations, ``label_field.max()`` is much smaller than
``label_field.size``, and in these cases the forward map is
guaranteed to be smaller than either the input or output images.
Examples
--------
>>> from skimage.segmentation import relabel_sequential
>>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])
>>> relab, fw, inv = relabel_sequential(label_field)
>>> relab
array([1, 1, 2, 2, 3, 5, 4])
>>> fw
array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 5])
>>> inv
array([ 0, 1, 5, 8, 42, 99])
>>> (fw[label_field] == relab).all()
True
>>> (inv[relab] == label_field).all()
True
>>> relab, fw, inv = relabel_sequential(label_field, offset=5)
>>> relab
array([5, 5, 6, 6, 7, 9, 8])
"""
m = label_field.max()
if not np.issubdtype(label_field.dtype, np.signedinteger):
new_type = np.min_scalar_type(int(m))
label_field = label_field.astype(new_type)
m = m.astype(new_type) # Ensures m is an integer
labels = np.unique(label_field)
labels0 = labels[labels != 0]
if m == len(labels0): # nothing to do, already 1...n labels
return label_field, labels, labels
forward_map = np.zeros(m + 1, int)
forward_map[labels0] = np.arange(offset, offset + len(labels0))
if not (labels == 0).any():
labels = np.concatenate(([0], labels))
inverse_map = np.zeros(offset - 1 + len(labels), dtype=np.intp)
inverse_map[(offset - 1):] = labels
relabeled = forward_map[label_field]
return relabeled, forward_map, inverse_map