`skimage.metrics._contingency_table:contingency_table`

source: /dev/scikit-image/src/skimage/metrics/_contingency_table.py:7

Signature

def contingency_table(im_true, im_test, *, ignore_labels = None, normalize = False, sparse_type = matrix)

Return the contingency table for all regions in matched segmentations.

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).

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.