1-Ph4ddlZddlmZmZd defdZd dZdS) N) map_arrayArrayMapFreturn_mappingc |j|jkrtd|jd|jt|\}}}t|\}}}|t jdz}||z|z} t| \} }} |s| St j| } t j| |\} }t| j || }t| j ||}| ||fS)aReturn 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. return_mapping : bool, optional If true, return mappings for joined segmentation labels to the original labels. Returns ------- j : numpy array The join segmentation of s1 and s2. map_j_to_s1 : ArrayMap, optional Mapping from labels of the joined segmentation j to labels of s1. map_j_to_s2 : ArrayMap, optional Mapping from labels of the joined segmentation j to labels of 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]]) >>> j, m1, m2 = join_segmentations(s1, s2, return_mapping=True) >>> m1 ArrayMap(array([0, 1, 2, 3, 4, 5]), array([0, 0, 1, 1, 2, 2])) >>> np.all(m1[j] == s1) True >>> np.all(m2[j] == s2) True z8Cannot join segmentations of different shape. s1.shape: z , s2.shape: ) shape ValueErrorrelabel_sequentialmaxnpuint8uniquedivmodr in_values)s1s2r s1_relabeled_ backward_map1 s2_relabeled backward_map2factor j_initialjmap_j_to_j_initiallabels_jlabels_s1_relabeledlabels_s2_relabeled map_j_to_s1 map_j_to_s2s Z/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/segmentation/_join.pyjoin_segmentationsr#s0X x28 : : :/1x : :   &8%;%;"L!]%7%;%;"L!] VVXX #F% 4I1)<<Aq  y##H/1y6/J/J,,$m4G&HK$m4G&HK k; &&rc V|dkrtdtj|dkrtdt|}tj|}|ddkr>tjdgtj||t|zdz g}n%tj||t|z}|j}|j dvrtdtj |d}|j |j kr|}n(|dtj |jkr|}n|}tj|j|}||}t%|||| t'||}t'||} ||| fS) u 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]) rz!Offset must be strictly positive.z3Cannot relabel array that contains negative values.riuz&label_field must have an integer dtype)dtype)out)r r minintr concatenatearangelenr(kind TypeErrormin_scalar_typeitemsizeiinfor emptyr astyperr) label_fieldoffsetin_valsout_vals input_type required_type output_type out_arrayfw_mapinv_maps r"r r LsN{{<=== vkQNOOO [[Fi $$GqzQ>A3 &&3w<<:ORS:S(T(T"UVV9VVc'll%:;;"Jd""@AAA&x|44M]333# B<"(:..2 2 2$KK'K*+>>>I{++H k7H)<<<< gx ( (Fx))G fg %%r$)F)r)numpyr util._map_arrayrrboolr#r r$r"rDst11111111C'C'tC'C'C'C'Ll&l&l&l&l&l&r$