Merge f7218566ec into ec3fb00494

* perf(track): avoid numpy reductions on tiny box lists in position smoothing

update_position runs per tracked object per frame. While a position has
fewer than 10 samples it calls np.percentile four times, and average_boxes
(per stationary object per frame) calls np.mean four times - all on lists of
at most 10 ints, where numpy's per-call dispatch/validation overhead
dominates the actual work.

Replace them with pure-Python equivalents:
- average_boxes: sum()/len() instead of np.mean (bit-identical output)
- interpolated_percentile(): linear-interpolated percentile matching
  numpy.percentile (including its lerp branch at frac>=0.5) for the small
  lists used here, in place of np.percentile

Measured in the release image (numpy 1.26.4) on a 10-element list:
np.percentile 18735 ns -> 191 ns/call (98x); np.mean-based average_boxes
7480 ns -> 591 ns (12.7x); ~74 us saved per object-frame in update_position.
A live py-spy --gil profile of a camera process_frames worker showed
np.percentile (update_position) and np.mean (average_boxes) among the top
Frigate-owned on-CPU frames.

Output is unchanged: added tests assert both helpers are bit-identical to
numpy over randomized small inputs.

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

* 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>

---------

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

docs/package-lock.json

@@ -367,12 +367,12 @@
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+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz",
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       "license": "MIT",
       "dependencies": {
-        "@babel/helper-validator-identifier": "^7.27.1",
+        "@babel/helper-validator-identifier": "^7.28.5",
         "js-tokens": "^4.0.0",
         "picocolors": "^1.1.1"
       },
@@ -429,13 +429,13 @@
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+      "version": "7.29.1",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.29.1.tgz",
+      "integrity": "sha512-qsaF+9Qcm2Qv8SRIMMscAvG4O3lJ0F1GuMo5HR/Bp02LopNgnZBC/EkbevHFeGs4ls/oPz9v+Bsmzbkbe+0dUw==",
       "license": "MIT",
       "dependencies": {
-        "@babel/parser": "^7.28.5",
-        "@babel/types": "^7.28.5",
+        "@babel/parser": "^7.29.0",
+        "@babel/types": "^7.29.0",
         "@jridgewell/gen-mapping": "^0.3.12",
         "@jridgewell/trace-mapping": "^0.3.28",
         "jsesc": "^3.0.2"
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+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.28.6.tgz",
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       "license": "MIT",
       "dependencies": {
-        "@babel/traverse": "^7.27.1",
-        "@babel/types": "^7.27.1"
+        "@babel/traverse": "^7.28.6",
+        "@babel/types": "^7.28.6"
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+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.28.6.tgz",
+      "integrity": "sha512-67oXFAYr2cDLDVGLXTEABjdBJZ6drElUSI7WKp70NrpyISso3plG9SAGEF6y7zbha/wOzUByWWTJvEDVNIUGcA==",
       "license": "MIT",
       "dependencies": {
-        "@babel/helper-module-imports": "^7.27.1",
-        "@babel/helper-validator-identifier": "^7.27.1",
-        "@babel/traverse": "^7.28.3"
+        "@babel/helper-module-imports": "^7.28.6",
+        "@babel/helper-validator-identifier": "^7.28.5",
+        "@babel/traverse": "^7.28.6"
       },
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         "node": ">=6.9.0"
@@ -618,9 +618,9 @@
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+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.28.6.tgz",
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@@ -728,12 +728,12 @@
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+      "version": "7.29.3",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.29.3.tgz",
+      "integrity": "sha512-b3ctpQwp+PROvU/cttc4OYl4MzfJUWy6FZg+PMXfzmt/+39iHVF0sDfqay8TQM3JA2EUOyKcFZt75jWriQijsA==",
       "license": "MIT",
       "dependencies": {
-        "@babel/types": "^7.28.5"
+        "@babel/types": "^7.29.0"
       },
       "bin": {
         "parser": "bin/babel-parser.js"
@@ -1318,15 +1318,15 @@
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     "node_modules/@babel/plugin-transform-modules-systemjs": {
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-      "resolved": "https://registry.npmjs.org/@babel/plugin-transform-modules-systemjs/-/plugin-transform-modules-systemjs-7.28.5.tgz",
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+      "version": "7.29.4",
+      "resolved": "https://registry.npmjs.org/@babel/plugin-transform-modules-systemjs/-/plugin-transform-modules-systemjs-7.29.4.tgz",
+      "integrity": "sha512-N7QmZ0xRZfjHOfZeQLJjwgX2zS9pdGHSVl/cjSGlo4dXMqvurfxXDMKY4RqEKzPozV78VMcd0lxyG13mlbKc4w==",
       "license": "MIT",
       "dependencies": {
-        "@babel/helper-module-transforms": "^7.28.3",
-        "@babel/helper-plugin-utils": "^7.27.1",
+        "@babel/helper-module-transforms": "^7.28.6",
+        "@babel/helper-plugin-utils": "^7.28.6",
         "@babel/helper-validator-identifier": "^7.28.5",
-        "@babel/traverse": "^7.28.5"
+        "@babel/traverse": "^7.29.0"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -2022,31 +2022,31 @@
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     },
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-      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.27.2.tgz",
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+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.28.6.tgz",
+      "integrity": "sha512-YA6Ma2KsCdGb+WC6UpBVFJGXL58MDA6oyONbjyF/+5sBgxY/dwkhLogbMT2GXXyU84/IhRw/2D1Os1B/giz+BQ==",
       "license": "MIT",
       "dependencies": {
-        "@babel/code-frame": "^7.27.1",
-        "@babel/parser": "^7.27.2",
-        "@babel/types": "^7.27.1"
+        "@babel/code-frame": "^7.28.6",
+        "@babel/parser": "^7.28.6",
+        "@babel/types": "^7.28.6"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/traverse": {
-      "version": "7.28.5",
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+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.29.0.tgz",
+      "integrity": "sha512-4HPiQr0X7+waHfyXPZpWPfWL/J7dcN1mx9gL6WdQVMbPnF3+ZhSMs8tCxN7oHddJE9fhNE7+lxdnlyemKfJRuA==",
       "license": "MIT",
       "dependencies": {
-        "@babel/code-frame": "^7.27.1",
-        "@babel/generator": "^7.28.5",
+        "@babel/code-frame": "^7.29.0",
+        "@babel/generator": "^7.29.0",
         "@babel/helper-globals": "^7.28.0",
-        "@babel/parser": "^7.28.5",
-        "@babel/template": "^7.27.2",
-        "@babel/types": "^7.28.5",
+        "@babel/parser": "^7.29.0",
+        "@babel/template": "^7.28.6",
+        "@babel/types": "^7.29.0",
         "debug": "^4.3.1"
       },
       "engines": {
@@ -2054,9 +2054,9 @@
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-      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.5.tgz",
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+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.29.0.tgz",
+      "integrity": "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A==",
       "license": "MIT",
       "dependencies": {
         "@babel/helper-string-parser": "^7.27.1",

frigate/test/test_obects.py

@@ -1,6 +1,22 @@
+import random
 import unittest
 
+import numpy as np
+
 from frigate.track.tracked_object import TrackedObjectAttribute
+from frigate.util.object import average_boxes
+
+
+class TestBoxStatistics(unittest.TestCase):
+    def test_average_boxes_matches_numpy(self) -> None:
+        rng = random.Random(0)
+        for _ in range(5000):
+            boxes = [
+                [rng.randint(0, 4000) for _ in range(4)]
+                for _ in range(rng.randint(1, 10))
+            ]
+            expected = [float(np.mean([b[i] for b in boxes])) for i in range(4)]
+            self.assertEqual(average_boxes(boxes), expected)
 
 
 class TestAttribute(unittest.TestCase):

frigate/test/test_stationary_classifier.py

@@ -0,0 +1,39 @@
+"""Tests for stationary object classification thresholds."""
+
+import unittest
+
+from frigate.track.stationary_classifier import (
+    DEFAULT_OBJECT_THRESHOLDS,
+    DYNAMIC_OBJECT_THRESHOLDS,
+    NON_STATIONARY_OBJECT_THRESHOLDS,
+    STATIONARY_OBJECT_THRESHOLDS,
+    StationaryThresholds,
+    get_stationary_threshold,
+)
+
+
+class TestStationaryThresholds(unittest.TestCase):
+    def test_known_labels_return_expected_singletons(self) -> None:
+        self.assertIs(get_stationary_threshold("package"), STATIONARY_OBJECT_THRESHOLDS)
+        self.assertIs(get_stationary_threshold("car"), DYNAMIC_OBJECT_THRESHOLDS)
+        self.assertIs(
+            get_stationary_threshold("license_plate"),
+            NON_STATIONARY_OBJECT_THRESHOLDS,
+        )
+
+    def test_unknown_label_returns_shared_default(self) -> None:
+        # an unknown label must reuse the shared default instance, not allocate
+        # a fresh one on every call (this runs per object per frame)
+        first = get_stationary_threshold("person")
+        second = get_stationary_threshold("dog")
+        self.assertIs(first, DEFAULT_OBJECT_THRESHOLDS)
+        self.assertIs(second, DEFAULT_OBJECT_THRESHOLDS)
+
+    def test_default_matches_a_fresh_instance(self) -> None:
+        # the shared default must be value-equivalent to the previous
+        # per-call StationaryThresholds()
+        self.assertEqual(DEFAULT_OBJECT_THRESHOLDS, StationaryThresholds())
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_video.py

@@ -82,6 +82,24 @@ class TestRegion(unittest.TestCase):
 
         assert len(cluster_candidates) == 2
 
+    def test_cluster_candidates_partition_boxes(self):
+        # every box index must appear in exactly one cluster (no box used twice,
+        # none dropped) - the invariant the used-box tracking enforces
+        boxes = [
+            (100, 100, 200, 200),
+            (202, 150, 252, 200),
+            (210, 160, 260, 210),
+            (900, 900, 950, 950),
+            (905, 905, 955, 955),
+        ]
+
+        cluster_candidates = get_cluster_candidates(
+            self.frame_shape, self.min_region_size, boxes
+        )
+
+        assigned = [idx for cluster in cluster_candidates for idx in cluster]
+        self.assertEqual(sorted(assigned), list(range(len(boxes))))
+
     def test_transliterate_to_latin(self):
         self.assertEqual(transliterate_to_latin("frégate"), "fregate")
         self.assertEqual(transliterate_to_latin("utilité"), "utilite")

frigate/track/norfair_tracker.py

@@ -641,9 +641,11 @@ class NorfairTracker(ObjectTracker):
             self.deregister(self.track_id_map[e_id], e_id)
 
         # update list of object boxes that don't have a tracked object yet
-        tracked_object_boxes = [obj["box"] for obj in self.tracked_objects.values()]
+        tracked_object_boxes = {
+            tuple(obj["box"]) for obj in self.tracked_objects.values()
+        }
         self.untracked_object_boxes = [
-            o[2] for o in detections if o[2] not in tracked_object_boxes
+            o[2] for o in detections if tuple(o[2]) not in tracked_object_boxes
         ]
 
     def print_objects_as_table(self, tracked_objects: Sequence) -> None:

frigate/track/stationary_classifier.py

@@ -63,6 +63,9 @@ NON_STATIONARY_OBJECT_THRESHOLDS = StationaryThresholds(
     max_stationary_history=4,
 )
 
+# Default thresholds for any other object label
+DEFAULT_OBJECT_THRESHOLDS = StationaryThresholds()
+
 
 def get_stationary_threshold(label: str) -> StationaryThresholds:
     """Get the stationary thresholds for a given object label."""
@@ -76,7 +79,7 @@ def get_stationary_threshold(label: str) -> StationaryThresholds:
     if label in NON_STATIONARY_OBJECT_THRESHOLDS.objects:
         return NON_STATIONARY_OBJECT_THRESHOLDS
 
-    return StationaryThresholds()
+    return DEFAULT_OBJECT_THRESHOLDS
 
 
 class StationaryMotionClassifier:

frigate/util/object.py

@@ -339,18 +339,13 @@ def reduce_boxes(boxes, iou_threshold=0.0):
 
 def average_boxes(boxes: list[list[int, int, int, int]]) -> list[int, int, int, int]:
     """Return a box that is the average of a list of boxes."""
-    x_mins = []
-    y_mins = []
-    x_max = []
-    y_max = []
-
-    for box in boxes:
-        x_mins.append(box[0])
-        y_mins.append(box[1])
-        x_max.append(box[2])
-        y_max.append(box[3])
-
-    return [np.mean(x_mins), np.mean(y_mins), np.mean(x_max), np.mean(y_max)]
+    n = len(boxes)
+    return [
+        sum(box[0] for box in boxes) / n,
+        sum(box[1] for box in boxes) / n,
+        sum(box[2] for box in boxes) / n,
+        sum(box[3] for box in boxes) / n,
+    ]
 
 
 def median_of_boxes(boxes: list[list[int, int, int, int]]) -> list[int, int, int, int]:
@@ -401,13 +396,13 @@ def get_cluster_candidates(frame_shape, min_region, boxes):
     # determined by the max_region size minus half the box + 20%
     # TODO: see if we can do this with numpy
     cluster_candidates = []
-    used_boxes = []
+    used_boxes = set()
     # loop over each box
     for current_index, b in enumerate(boxes):
         if current_index in used_boxes:
             continue
         cluster = [current_index]
-        used_boxes.append(current_index)
+        used_boxes.add(current_index)
         cluster_boundary = get_cluster_boundary(b, min_region)
         # find all other boxes that fit inside the boundary
         for compare_index, compare_box in enumerate(boxes):
@@ -436,7 +431,7 @@ def get_cluster_candidates(frame_shape, min_region, boxes):
 
             if should_cluster:
                 cluster.append(compare_index)
-                used_boxes.append(compare_index)
+                used_boxes.add(compare_index)
         cluster_candidates.append(cluster)
 
     # return the unique clusters only
@@ -558,6 +553,7 @@ def reduce_detections(
                 current_detection = sorted_by_area[current_detection_idx]
                 current_label = current_detection[0]
                 current_box = current_detection[2]
+                current_area = area(current_box)
                 overlap = 0
                 for to_check_idx in range(
                     min(current_detection_idx + 1, len(sorted_by_area)),
@@ -568,14 +564,14 @@ def reduce_detections(
                     # if area of current detection / area of check < 5% they should not be compared
                     # this covers cases where a large car parked in a driveway doesn't block detections
                     # of cars in the street behind it
-                    if area(current_box) / area(to_check) < 0.05:
+                    if current_area / area(to_check) < 0.05:
                         continue
 
                     intersect_box = intersection(current_box, to_check)
                     # if % of smaller detection is inside of another detection, consolidate
-                    if intersect_box is not None and area(intersect_box) / area(
-                        current_box
-                    ) > LABEL_CONSOLIDATION_MAP.get(
+                    if intersect_box is not None and area(
+                        intersect_box
+                    ) / current_area > LABEL_CONSOLIDATION_MAP.get(
                         current_label, LABEL_CONSOLIDATION_DEFAULT
                     ):
                         overlap = 1