ó  ‰\c@sJddlZddlmZd„Zedƒd„ƒZdd„ZdS( iÿÿÿÿNi(t deprecatedcCs|j|jkr5tdd|j|jfƒ‚nt|ƒd}t|ƒd}|jƒd||}t|ƒd}|S(sSReturn 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]]) s.Cannot join segmentations of different shape. ss1.shape: %s, s2.shape: %sii(tshapet ValueErrortrelabel_sequentialtmax(ts1ts2tj((s9lib/python2.7/site-packages/skimage/segmentation/_join.pytjoin_segmentationssRcCst|ddƒS(s”Convert labels in an arbitrary label field to {1, ... number_of_labels}. This function is deprecated, see ``relabel_sequential`` for more. toffseti(R(t label_field((s9lib/python2.7/site-packages/skimage/segmentation/_join.pytrelabel_from_one.sic CsF|jƒ}tj|jtjƒsZtjt|ƒƒ}|j|ƒ}|j|ƒ}ntj|ƒ}||dk}|t |ƒkr˜|||fStj |dtƒ}tj ||t |ƒƒ||<|dkj ƒsûtj dg|fƒ}ntj |dt |ƒdtjƒ}|||d)||}|||fS(sŸ 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]) iitdtype(Rtnpt issubdtypeR t signedintegertmin_scalar_typetinttastypetuniquetlentzerostarangetanyt concatenatetintp( R R tmtnew_typetlabelstlabels0t forward_mapt inverse_mapt relabeled((s9lib/python2.7/site-packages/skimage/segmentation/_join.pyR7s">   & (tnumpyR t _shared.utilsRRR R(((s9lib/python2.7/site-packages/skimage/segmentation/_join.pyts  )