|h.ddlmZddlZdgZdddddZy)Ncontingency_tableFmatrix) ignore_labels normalize sparse_typecp|g}|jd}|jd}tj||djt}|r|tj |z}t j|||ff}|dk(rt j|}|S|dk7rd|} t| |S)a Return the contingency table for all regions in matched segmentations. Parameters ---------- im_true : ndarray of int Ground-truth label image, same shape as im_test. im_test : ndarray of int Test image. ignore_labels : sequence of int, optional Labels to ignore. Any part of the true image labeled with any of these values will not be counted in the score. normalize : bool Determines if the contingency table is normalized by pixel count. sparse_type : {"matrix", "array"}, optional The return type of `cont`, either `scipy.sparse.csr_array` or `scipy.sparse.csr_matrix` (default). Returns ------- cont : scipy.sparse.csr_matrix or scipy.sparse.csr_array A contingency table. `cont[i, j]` will equal the number of voxels labeled `i` in `im_true` and `j` in `im_test`. Depending on `sparse_type`, this can be returned as a `scipy.sparse.csr_array`. T)invertrarrayz/`sparse_type` must be 'array' or 'matrix', got ) reshapenpisinastypefloat count_nonzerosparse csr_array csr_matrix ValueError) im_trueim_testrrr im_test_r im_true_rdatacontmsgs a/var/www/html/test/engine/venv/lib/python3.12/site-packages/skimage/metrics/_contingency_table.pyrrs: #I#I 779mD 9 @ @ GD   &&   TIy#9: ;Dh  & K  ? }Mo K) scipy.sparsernumpyr __all__rrrr#s!  (,u(,r