Fix empty instance handling (#364)

This PR improves empty-instance handling in instance cropping method and
the `CenteredInstanceDataset` class. Previously, if an instance
contained only NaN keypoints, `find_instance_crop_size` would trigger a
“NaN values encountered” warning when attempting to compute its bounding
box. We now fixthis by only computing crop sizes for non-empty instances
(`instance.is_empty` is False). We also remove redundant empty-instance
filtering in `__getitem__()` for centered-instance models, since this is
already enforced when creating `instance_idx_list`.

Author: gitttt-1234

sleap_nn/data/custom_datasets.py

@@ -799,8 +799,9 @@ def __getitem__(self, index) -> Dict:
 
         instances = []
         for inst in instances_list:
-            if not inst.is_empty:
-                instances.append(inst.numpy())
+            instances.append(
+                inst.numpy()
+            )  # no need to filter empty instances; handled while creating instance_idx_list
         instances = np.stack(instances, axis=0)
 
         # Add singleton time dimension for single frames.
@@ -1046,8 +1047,9 @@ def __getitem__(self, index) -> Dict:
 
         instances = []
         for inst in instances_list:
-            if not inst.is_empty:
-                instances.append(inst.numpy())
+            instances.append(
+                inst.numpy()
+            )  # no need to filter empty instance (handled while creating instance_idx)
         instances = np.stack(instances, axis=0)
 
         # Add singleton time dimension for single frames.

sleap_nn/data/instance_cropping.py

@@ -44,15 +44,16 @@ def find_instance_crop_size(
     max_length = 0.0
     for lf in labels:
         for inst in lf.instances:
-            pts = inst.numpy()
-            pts *= input_scaling
-            diff_x = np.nanmax(pts[:, 0]) - np.nanmin(pts[:, 0])
-            diff_x = 0 if np.isnan(diff_x) else diff_x
-            max_length = np.maximum(max_length, diff_x)
-            diff_y = np.nanmax(pts[:, 1]) - np.nanmin(pts[:, 1])
-            diff_y = 0 if np.isnan(diff_y) else diff_y
-            max_length = np.maximum(max_length, diff_y)
-            max_length = np.maximum(max_length, min_crop_size_no_pad)
+            if not inst.is_empty:  # only if at least one point is not nan
+                pts = inst.numpy()
+                pts *= input_scaling
+                diff_x = np.nanmax(pts[:, 0]) - np.nanmin(pts[:, 0])
+                diff_x = 0 if np.isnan(diff_x) else diff_x
+                max_length = np.maximum(max_length, diff_x)
+                diff_y = np.nanmax(pts[:, 1]) - np.nanmin(pts[:, 1])
+                diff_y = 0 if np.isnan(diff_y) else diff_y
+                max_length = np.maximum(max_length, diff_y)
+                max_length = np.maximum(max_length, min_crop_size_no_pad)
 
     max_length += float(padding)
     crop_size = math.ceil(max_length / float(maximum_stride)) * maximum_stride