Drop interpolated_percentile, keep only average_boxes

Per review: reimplementing np.percentile hurts readability and risks
divergence from numpy (e.g. numpy 2.x). Revert update_position to
np.percentile and remove the helper; keep only the average_boxes change
(sum()/len() instead of np.mean), which stays bit-identical.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

frigate/test/test_obects.py

@@ -4,7 +4,7 @@ import unittest
 import numpy as np
 
 from frigate.track.tracked_object import TrackedObjectAttribute
-from frigate.util.object import average_boxes, interpolated_percentile
+from frigate.util.object import average_boxes
 
 
 class TestBoxStatistics(unittest.TestCase):
@@ -18,19 +18,6 @@ class TestBoxStatistics(unittest.TestCase):
             expected = [float(np.mean([b[i] for b in boxes])) for i in range(4)]
             self.assertEqual(average_boxes(boxes), expected)
 
-    def test_interpolated_percentile_matches_numpy(self) -> None:
-        rng = random.Random(1)
-        for _ in range(5000):
-            values = [rng.randint(0, 5000) for _ in range(rng.randint(1, 10))]
-            for q in (15, 85, 50):
-                self.assertEqual(
-                    interpolated_percentile(values, q),
-                    float(np.percentile(values, q)),
-                )
-
-    def test_interpolated_percentile_single_value(self) -> None:
-        self.assertEqual(interpolated_percentile([42], 15), 42.0)
-
 
 class TestAttribute(unittest.TestCase):
     def test_overlapping_object_selection(self) -> None:

frigate/track/norfair_tracker.py

@@ -27,11 +27,7 @@ from frigate.util.image import (
     get_histogram,
     intersection_over_union,
 )
-from frigate.util.object import (
+from frigate.util.object import average_boxes, median_of_boxes
-    average_boxes,
-    interpolated_percentile,
-    median_of_boxes,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -432,10 +428,10 @@ class NorfairTracker(ObjectTracker):
             position["xmaxs"].append(xmax)
             position["ymaxs"].append(ymax)
             # by using percentiles here, we hopefully remove outliers
-            position["xmin"] = interpolated_percentile(position["xmins"], 15)
+            position["xmin"] = np.percentile(position["xmins"], 15)
-            position["ymin"] = interpolated_percentile(position["ymins"], 15)
+            position["ymin"] = np.percentile(position["ymins"], 15)
-            position["xmax"] = interpolated_percentile(position["xmaxs"], 85)
+            position["xmax"] = np.percentile(position["xmaxs"], 85)
-            position["ymax"] = interpolated_percentile(position["ymaxs"], 85)
+            position["ymax"] = np.percentile(position["ymaxs"], 85)
 
         return True
 

frigate/util/object.py

@@ -354,25 +354,6 @@ def median_of_boxes(boxes: list[list[int, int, int, int]]) -> list[int, int, int
     return sorted_boxes[int(len(sorted_boxes) / 2.0)]
 
 
-def interpolated_percentile(values: list[int], q: float) -> float:
-    """Linear-interpolated percentile, matching numpy.percentile for the small
-    lists used in position smoothing without the per-call numpy overhead."""
-    ordered = sorted(values)
-    n = len(ordered)
-    if n == 1:
-        return float(ordered[0])
-    rank = (q / 100.0) * (n - 1)
-    lo = int(rank)
-    frac = rank - lo
-    if frac == 0.0:
-        return float(ordered[lo])
-    diff = ordered[lo + 1] - ordered[lo]
-    # numpy's lerp switches sides at frac >= 0.5 to limit rounding error
-    if frac < 0.5:
-        return ordered[lo] + diff * frac
-    return ordered[lo + 1] - diff * (1.0 - frac)
-
-
 def intersects_any(box_a, boxes):
     for box in boxes:
         if box_overlaps(box_a, box):