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Merge pull request #726 from jni/fix-relabel-doc
Fix errors in relabel_from_one docs; PEP8
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@@ -1,5 +1,6 @@
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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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@@ -41,6 +42,7 @@ def join_segmentations(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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@@ -50,14 +52,29 @@ def relabel_from_one(label_field):
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Parameters
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----------
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label_field : numpy ndarray (integer type)
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label_field : numpy array of int, arbitrary shape
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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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relabeled : numpy array of int, same shape as `label_field`
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The input label field with labels mapped to
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{1, ..., number_of_labels}.
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forward_map : numpy array of int, shape ``(label_field.max() + 1,)``
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The map from the original label space to the returned label
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space. Can be used to re-apply the same mapping. See examples
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for usage.
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inverse_map : numpy array of int, shape ``(len(np.unique(label_field)),)``
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The map from the new label space to the original space. This
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can be used to reconstruct the original label field from the
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relabeled one.
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Notes
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-----
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The forward map can be extremely big for some inputs, since its
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length is given by the maximum of the label field. However, in most
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situations, ``label_field.max()`` is much smaller than
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``label_field.size``, and in these cases the forward map is
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guaranteed to be smaller than either the input or output images.
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Examples
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--------
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@@ -91,3 +108,4 @@ def relabel_from_one(label_field):
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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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