forked from 170010011/fr
155 lines
5.7 KiB
Python
155 lines
5.7 KiB
Python
import numpy as np
|
|
from ..util._map_array import map_array, ArrayMap
|
|
|
|
|
|
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
|
|
|
|
|
|
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, which must be non-negative integers.
|
|
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}.
|
|
The data type will be the same as `label_field`, except when
|
|
offset + number_of_labels causes overflow of the current data type.
|
|
forward_map : ArrayMap
|
|
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. The output data type will be the same as `relabeled`.
|
|
inverse_map : ArrayMap
|
|
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. The output data type will be the same as `label_field`.
|
|
|
|
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])
|
|
>>> print(fw)
|
|
ArrayMap:
|
|
1 → 1
|
|
5 → 2
|
|
8 → 3
|
|
42 → 4
|
|
99 → 5
|
|
>>> np.array(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])
|
|
>>> np.array(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])
|
|
"""
|
|
if offset <= 0:
|
|
raise ValueError("Offset must be strictly positive.")
|
|
if np.min(label_field) < 0:
|
|
raise ValueError("Cannot relabel array that contains negative values.")
|
|
offset = int(offset)
|
|
in_vals = np.unique(label_field)
|
|
if in_vals[0] == 0:
|
|
# always map 0 to 0
|
|
out_vals = np.concatenate(
|
|
[[0], np.arange(offset, offset+len(in_vals)-1)]
|
|
)
|
|
else:
|
|
out_vals = np.arange(offset, offset+len(in_vals))
|
|
input_type = label_field.dtype
|
|
|
|
# Some logic to determine the output type:
|
|
# - we don't want to return a smaller output type than the input type,
|
|
# ie if we get uint32 as labels input, don't return a uint8 array.
|
|
# - but, in some cases, using the input type could result in overflow. The
|
|
# input type could be a signed integer (e.g. int32) but
|
|
# `np.min_scalar_type` will always return an unsigned type. We check for
|
|
# that by casting the largest output value to the input type. If it is
|
|
# unchanged, we use the input type, else we use the unsigned minimum
|
|
# required type
|
|
required_type = np.min_scalar_type(out_vals[-1])
|
|
if input_type.itemsize < required_type.itemsize:
|
|
output_type = required_type
|
|
else:
|
|
if input_type.type(out_vals[-1]) == out_vals[-1]:
|
|
output_type = input_type
|
|
else:
|
|
output_type = required_type
|
|
out_array = np.empty(label_field.shape, dtype=output_type)
|
|
out_vals = out_vals.astype(output_type)
|
|
map_array(label_field, in_vals, out_vals, out=out_array)
|
|
fw_map = ArrayMap(in_vals, out_vals)
|
|
inv_map = ArrayMap(out_vals, in_vals)
|
|
return out_array, fw_map, inv_map
|