mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 03:54:28 +08:00
Juan Nunez-Iglesias
94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
import numpy as np
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def join_segmentations(s1, s2):
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"""Return the join of the two input segmentations.
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The join J of S1 and S2 is defined as the segmentation in which two voxels
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are in the same segment in J if and only if they are in the same segment
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in *both* S1 and S2.
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Parameters
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----------
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s1, s2 : numpy arrays
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s1 and s2 are label fields of the same shape.
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Returns
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-------
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j : numpy array
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The join segmentation of s1 and s2.
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Examples
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--------
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>>> import numpy as np
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>>> from skimage.segmentation import join_segmentations
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>>> s1 = np.array([[0, 0, 1, 1],
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... [0, 2, 1, 1],
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... [2, 2, 2, 1]])
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>>> s2 = np.array([[0, 1, 1, 0],
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... [0, 1, 1, 0],
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... [0, 1, 1, 1]])
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>>> join_segmentations(s1, s2)
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array([[0, 1, 3, 2],
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[0, 5, 3, 2],
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[4, 5, 5, 3]])
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"""
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if s1.shape != s2.shape:
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raise ValueError("Cannot join segmentations of different shape. " +
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"s1.shape: %s, s2.shape: %s" % (s1.shape, s2.shape))
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s1 = relabel_from_one(s1)[0]
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s2 = relabel_from_one(s2)[0]
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j = (s2.max() + 1) * s1 + s2
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j = relabel_from_one(j)[0]
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return j
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def relabel_from_one(label_field):
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"""Convert labels in an arbitrary label field to {1, ... number_of_labels}.
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This function also returns the forward map (mapping the original labels to
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the reduced labels) and the inverse map (mapping the reduced labels back
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to the original ones).
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Parameters
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----------
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label_field : numpy ndarray (integer type)
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Returns
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-------
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relabeled : numpy array of same shape as ar
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forward_map : 1d numpy array of length np.unique(ar) + 1
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inverse_map : 1d numpy array of length len(np.unique(ar))
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The length is len(np.unique(ar)) + 1 if 0 is not in np.unique(ar)
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Examples
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--------
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>>> import numpy as np
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>>> from skimage.segmentation import relabel_from_one
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>>> label_field = array([1, 1, 5, 5, 8, 99, 42])
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>>> relab, fw, inv = relabel_from_one(label_field)
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>>> relab
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array([1, 1, 2, 2, 3, 5, 4])
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>>> fw
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array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 5])
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>>> inv
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array([ 0, 1, 5, 8, 42, 99])
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>>> (fw[label_field] == relab).all()
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True
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>>> (inv[relab] == label_field).all()
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True
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"""
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labels = np.unique(label_field)
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labels0 = labels[labels != 0]
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m = labels.max()
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if m == len(labels0): # nothing to do, already 1...n labels
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return label_field, labels, labels
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forward_map = np.zeros(m+1, int)
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forward_map[labels0] = np.arange(1, len(labels0) + 1)
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if not (labels == 0).any():
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labels = np.concatenate(([0], labels))
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inverse_map = labels
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return forward_map[label_field], forward_map, inverse_map
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