39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import scipy.sparse as sparse
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import numpy as np
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__all__ = ['contingency_table']
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def contingency_table(im_true, im_test, *, ignore_labels=None, normalize=False):
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"""
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Return the contingency table for all regions in matched segmentations.
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Parameters
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----------
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im_true : ndarray of int
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Ground-truth label image, same shape as im_test.
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im_test : ndarray of int
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Test image.
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ignore_labels : sequence of int, optional
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Labels to ignore. Any part of the true image labeled with any of these
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values will not be counted in the score.
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normalize : bool
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Determines if the contingency table is normalized by pixel count.
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Returns
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-------
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cont : scipy.sparse.csr_matrix
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A contingency table. `cont[i, j]` will equal the number of voxels
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labeled `i` in `im_true` and `j` in `im_test`.
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"""
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if ignore_labels is None:
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ignore_labels = []
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im_test_r = im_test.reshape(-1)
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im_true_r = im_true.reshape(-1)
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data = np.isin(im_true_r, ignore_labels, invert=True).astype(float)
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if normalize:
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data /= np.count_nonzero(data)
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cont = sparse.coo_matrix((data, (im_true_r, im_test_r))).tocsr()
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return cont
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