0-Ph0ddlmZddlZdgZdddddZdS)Ncontingency_tableFmatrix) ignore_labels normalize sparse_typec|g}|d}|d}tj||dt}|r|tj|z}t j|||ff}|dkrt j|}n|dkrd|} 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`. NT)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 b/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/metrics/_contingency_table.pyrrs: ##I##I 79mD 9 9 9 @ @ G GD'  &&&  TIy#9: ; ;Dh &&   M MMoo K) scipy.sparsernumpyr __all__rrrr#sW  (,u(,,,,,,,r