a6d.ddlmZddlZdgZddddZdS)Ncontingency_tableF) ignore_labels normalizecF|g}|d}|d}tj||dt}|r|tj|z}t j|||ff}|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. Returns ------- cont : scipy.sparse.csr_matrix A contingency table. `cont[i, j]` will equal the number of voxels labeled `i` in `im_true` and `j` in `im_test`. NT)invert) reshapenpisinastypefloat count_nonzerosparse coo_matrixtocsr)im_trueim_testrr im_test_r im_true_rdataconts Blib/python3.11/site-packages/skimage/metrics/_contingency_table.pyrrs0 ##I##I 79mD 9 9 9 @ @ G GD'  &&&  dY $:; < < B B D DD K) scipy.sparsernumpyr __all__rrrrsV  :> %       r