Add function to filter oob

sleap/nn/data/instance_cropping.py

@@ -6,6 +6,7 @@
 from typing import Optional, List, Text
 import sleap
 from sleap.nn.config import InstanceCroppingConfig
+from sleap.nn.data.utils import filter_oob_points
 
 
 def find_instance_crop_size(
@@ -43,22 +44,20 @@ def find_instance_crop_size(
     min_crop_size_no_pad = min_crop_size - padding
     max_length = 0.0
     for lf in labels:
-        for inst in lf.user_instances:
-            pts = inst.points_array
+        for inst in lf:
+            if isinstance(inst, sleap.PredictedInstance):
+                continue
 
-            pts[pts < 0] = np.NaN
-            height, width = lf.image.shape[:2]
-            pts[:, 0][pts[:, 0] > width - 1] = np.NaN
-            pts[:, 1][pts[:, 1] > height - 1] = np.NaN
+            pts = filter_oob_points(inst.numpy(), lf.image.shape[:2])
 
             pts *= input_scaling
-            max_length = np.maximum(
-                max_length, np.nanmax(pts[:, 0]) - np.nanmin(pts[:, 0])
+            max_length: float = np.nanmax(
+                [max_length, np.nanmax(pts[:, 0]) - np.nanmin(pts[:, 0])]
             )
-            max_length = np.maximum(
-                max_length, np.nanmax(pts[:, 1]) - np.nanmin(pts[:, 1])
+            max_length: float = np.nanmax(
+                [max_length, np.nanmax(pts[:, 1]) - np.nanmin(pts[:, 1])]
             )
-            max_length = np.maximum(max_length, min_crop_size_no_pad)
+            max_length: float = np.nanmax([max_length, min_crop_size_no_pad])
 
     max_length += float(padding)
     crop_size = np.math.ceil(max_length / float(maximum_stride)) * maximum_stride

sleap/nn/data/providers.py

@@ -6,6 +6,7 @@
 from typing import Text, Optional, List, Sequence, Union, Tuple
 import sleap
 from sleap.instance import Instance
+from sleap.nn.data.utils import filter_oob_points
 
 
 @attr.s(auto_attribs=True)
@@ -199,8 +200,6 @@ def py_fetch_lf(ind):
             raw_image = lf.image
             raw_image_size = np.array(raw_image.shape).astype("int32")
 
-            height, width = raw_image_size[:2]
-
             if self.user_instances_only:
                 insts = lf.user_instances
             else:
@@ -211,11 +210,7 @@ def py_fetch_lf(ind):
             for inst in insts:
 
                 # Filter OOB
-                pts = inst.numpy()
-                pts[pts < 0] = np.NaN
-
-                pts[:, 0][pts[:, 0] > width - 1] = np.NaN
-                pts[:, 1][pts[:, 1] > height - 1] = np.NaN
+                pts = filter_oob_points(inst.numpy(), raw_image_size[:2])
 
                 instance = Instance.from_numpy(pts, inst.skeleton, inst.track)
 

sleap/nn/data/utils.py

@@ -6,6 +6,16 @@
 from typing import Any, List, Tuple, Dict, Text, Optional
 
 
+def filter_oob_points(pts: np.ndarray, img_hw: tuple) -> np.ndarray:
+    """Convert negative/ out-of-boundary pts to NaNs."""
+    pts[pts < 0] = np.NaN
+    height, width = img_hw
+    pts[:, 0][pts[:, 0] > width - 1] = np.NaN
+    pts[:, 1][pts[:, 1] > height - 1] = np.NaN
+
+    return pts
+
+
 def ensure_list(x: Any) -> List[Any]:
     """Convert the input into a list if it is not already."""
     if not isinstance(x, list):