skimage2.metrics.contingency_table

skimage2.metrics.contingency_table(im_true, im_test, *, ignore_labels=None, normalize=False, sparse_type='matrix')

Return the contingency table for all regions in matched segmentations.

Parameters:

im_truendarray of int

Ground-truth label image, same shape as im_test.

im_testndarray of int

Test image.

ignore_labelssequence 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.

normalizebool

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:

contscipy.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.