solved mypy issue

src/laptrack/metric_utils.py

@@ -1,5 +1,7 @@
 """Utilities for metric calculation."""
+from typing import List
 from typing import Tuple
+from typing import Union
 
 import numpy as np
 import pandas as pd
@@ -32,12 +34,12 @@ def _union_bbox(self, r1, r2):
             bbox.append((y0, y1))
         return bbox
 
-    def __init__(self, label_images: IntArray):
+    def __init__(self, label_images: Union[IntArray, List[IntArray]]):
         """Summarise the segmentation properties and initialize the object.
 
         Parameters
         ----------
-        label_images : IntArray
+        label_images : Union[IntArray,List[IntArray]]
             The labeled images. The first dimension is interpreted as the frame dimension.
         """
         if not isinstance(label_images, np.ndarray):

tests/test_metric_utils.py

@@ -1,28 +1,32 @@
 from itertools import product
+from typing import List
+from typing import Union
 
 import numpy as np
 
+from laptrack._typing_utils import IntArray
 from laptrack.metric_utils import LabelOverlap
 
 
 def test_label_overlap() -> None:
-    labels = np.array(
-        [
-            [[[0, 1, 1, 1, 0], [0, 1, 2, 2, 2]], [[0, 1, 2, 2, 2], [3, 3, 3, 1, 0]]],
-            [[[0, 1, 1, 1, 2], [0, 4, 1, 2, 2]], [[0, 4, 4, 4, 4], [0, 4, 4, 4, 4]]],
-            [[[0, 1, 1, 1, 0], [5, 5, 5, 5, 5]], [[0, 1, 1, 1, 0], [0, 1, 1, 1, 0]]],
-        ]
-    )
-    labelss = [labels, list(labels)]
+    labels = [
+        [[[0, 1, 1, 1, 0], [0, 1, 2, 2, 2]], [[0, 1, 2, 2, 2], [3, 3, 3, 1, 0]]],
+        [[[0, 1, 1, 1, 2], [0, 4, 1, 2, 2]], [[0, 4, 4, 4, 4], [0, 4, 4, 4, 4]]],
+        [[[0, 1, 1, 1, 0], [5, 5, 5, 5, 5]], [[0, 1, 1, 1, 0], [0, 1, 1, 1, 0]]],
+    ]
+    labelss: List[Union[IntArray, List[IntArray]]] = [
+        np.array(labels),
+        [np.array(label).astype(np.int64) for label in labels],
+    ]
 
-    for labels in labelss:
-        lo = LabelOverlap(labels)
-        frame_labels = [np.unique(label) for label in labels]
+    for _labels in labelss:
+        lo = LabelOverlap(_labels)
+        frame_labels = [np.unique(label) for label in _labels]
         frame_labels = [x[x > 0] for x in frame_labels]
         for f1, f2 in [(0, 0), (0, 1), (1, 2)]:
             for l1, l2 in product(frame_labels[f1], frame_labels[f2]):
-                b1 = labels[f1] == l1
-                b2 = labels[f2] == l2
+                b1 = _labels[f1] == l1
+                b2 = _labels[f2] == l2
 
                 intersect = np.sum(b1 & b2)
                 union = np.sum(b1 | b2)