Move test code to official tests

sleap/info/metrics.py

@@ -238,46 +238,3 @@ def point_match_count(dist_array: np.ndarray, thresh: float = 5) -> int:
 def point_nonmatch_count(dist_array: np.ndarray, thresh: float = 5) -> int:
     """Given an array of distances, returns number which are not <= threshold."""
     return dist_array.shape[0] - point_match_count(dist_array, thresh)
-
-
-if __name__ == "__main__":
-
-    labels_gt = Labels.load_json("tests/data/json_format_v1/centered_pair.json")
-    labels_pr = Labels.load_json(
-        "tests/data/json_format_v2/centered_pair_predictions.json"
-    )
-
-    # OPTION 1
-
-    # Match each ground truth instance node to the closest corresponding node
-    # from any predicted instance in the same frame.
-
-    nodewise_matching_func = match_instance_lists_nodewise
-
-    # OPTION 2
-
-    # Match each ground truth instance to a distinct predicted instance:
-    # We want to maximize the number of "matching" points between instances,
-    # where "match" means the points are within some threshold distance.
-    # Note that each sorted list will be as long as the shorted input list.
-
-    instwise_matching_func = lambda gt_list, pr_list: match_instance_lists(
-        gt_list, pr_list, point_nonmatch_count
-    )
-
-    # PICK THE FUNCTION
-
-    inst_matching_func = nodewise_matching_func
-    # inst_matching_func = instwise_matching_func
-
-    # Calculate distances
-    frame_idxs, D, points_gt, points_pr = matched_instance_distances(
-        labels_gt, labels_pr, inst_matching_func
-    )
-
-    # Show mean difference for each node
-    node_names = labels_gt.skeletons[0].node_names
-
-    for node_idx, node_name in enumerate(node_names):
-        mean_d = np.nanmean(D[..., node_idx])
-        print(f"{node_name}\t\t{mean_d}")

tests/info/test_metrics.py

@@ -0,0 +1,58 @@
+import numpy as np
+
+from sleap import Labels
+from sleap.info.metrics import (
+    match_instance_lists_nodewise,
+    matched_instance_distances,
+)
+
+
+def test_matched_instance_distances(centered_pair_labels, centered_pair_predictions):
+    labels_gt = centered_pair_labels
+    labels_pr = centered_pair_predictions
+
+    # Match each ground truth instance node to the closest corresponding node
+    # from any predicted instance in the same frame.
+
+    inst_matching_func = match_instance_lists_nodewise
+
+    # Calculate distances
+    frame_idxs, D, points_gt, points_pr = matched_instance_distances(
+        labels_gt, labels_pr, inst_matching_func
+    )
+
+    # Show mean difference for each node
+    node_names = labels_gt.skeletons[0].node_names
+
+    for node_idx, node_name in enumerate(node_names):
+        mean_d = np.nanmean(D[..., node_idx])
+        # print(f"{node_name}\t\t{mean_d}")
+
+        """Expected values (instance-wise matching):
+
+        head            0.872426920709296
+        neck            0.8016280746914615
+        thorax          0.8602021363390538
+        abdomen         1.01012200038258
+        wingL           1.1297727023475939
+        wingR           1.0869857897008424
+        forelegL1               0.780584225081443
+        forelegL2               1.170805798894702
+        forelegL3               1.1020486509389473
+        forelegR1               0.9014698776116817
+        forelegR2               0.9448001033112047
+        forelegR3               1.308385214215777
+        midlegL1                0.9095691623265347
+        midlegL2                1.2203595627907582
+        midlegL3                0.9813843358470163
+        midlegR1                0.9871017182813739
+        midlegR2                1.0209829335569256
+        midlegR3                1.0990681234096988
+        hindlegL1               1.0005335192834348
+        hindlegL2               1.273539518539708
+        hindlegL3               1.1752245985832817
+        hindlegR1               1.1402833959265248
+        hindlegR2               1.3143221301212737
+        hindlegR3               1.0441458592503365"""
+        assert mean_d > 0.77
+        assert mean_d < 1.32