Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/metrics/_contingency_table.py

@@ -0,0 +1,40 @@
+import scipy.sparse as sparse
+import numpy as np
+
+__all__ = ['contingency_table']
+
+
+def contingency_table(im_true, im_test, *, ignore_labels=(), normalize=False):
+    """
+    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.
+
+    Returns
+    -------
+    cont : scipy.sparse.csr_matrix
+        A contingency table. `cont[i, j]` will equal the number of voxels
+        labeled `i` in `im_true` and `j` in `im_test`.
+    """
+
+    im_test_r = im_test.ravel()
+    im_true_r = im_true.ravel()
+    ignored = np.zeros(im_true_r.shape, np.bool)
+    for label in ignore_labels:
+        ignored[im_true_r == label] = True
+    data = np.ones(im_true_r.shape)
+    data[ignored] = 0
+    if normalize:
+        data = data / im_true.size
+    cont = sparse.coo_matrix((data, (im_true_r, im_test_r))).tocsr()
+    return cont