+ Ai6^RIHt^RItR.tRRRRRR/RltR#) Ncontingency_table ignore_labels normalizeF sparse_typematrixcVf.pVPR4pVPR4p\P!WbRR7P\4pV'dV\P !V4,p\ P!WvV334pVR8Xd\ P!V4pV#VR8wdRV 2p \V 4hV#)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 &&$$$ `/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/metrics/_contingency_table.pyrrs: #I#I 779D 9 @ @ GD   &&   Ty#9: ;Dh  & K  ? }Mo K) scipy.sparsernumpyr __all__rrrr#s0  ,'+,7<,JR,r