Merge 5d0d89a3f2 into ec3fb00494

perf(track): use sum()/len() instead of np.mean in average_boxes (#23521 )
* 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>
2026-11-05 21:30:30 +03:00 · 2026-06-23 23:14:38 +00:00 · 2026-06-23 15:47:04 -06:00 · 2026-06-23 15:24:05 -06:00 · 2026-06-23 15:23:18 -06:00 · 2026-05-11 22:56:50 +00:00
7 changed files with 152 additions and 181 deletions
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@@ -8796,9 +8747,9 @@
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@@ -8973,9 +8924,9 @@
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@@ -10499,6 +10450,16 @@
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+      "license": "MIT",
+      "workspaces": [
+        "docs",
+        "benchmarks"
+      ]
+    },
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@@ -13058,22 +13019,6 @@
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@@ -13190,9 +13135,9 @@
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+      "resolved": "https://registry.npmjs.org/lodash-es/-/lodash-es-4.18.1.tgz",
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    "node_modules/lodash.debounce": {
@@ -13840,31 +13785,32 @@
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+      "resolved": "https://registry.npmjs.org/mermaid/-/mermaid-11.15.0.tgz",
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      "license": "MIT",
      "dependencies": {
        "@braintree/sanitize-url": "^7.1.1",
-        "@iconify/utils": "^3.0.1",
-        "@mermaid-js/parser": "^0.6.3",
+        "@iconify/utils": "^3.0.2",
+        "@mermaid-js/parser": "^1.1.1",
        "@types/d3": "^7.4.3",
-        "cytoscape": "^3.29.3",
+        "@upsetjs/venn.js": "^2.0.0",
+        "cytoscape": "^3.33.1",
        "cytoscape-cose-bilkent": "^4.1.0",
        "cytoscape-fcose": "^2.2.0",
        "d3": "^7.9.0",
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-        "dayjs": "^1.11.18",
-        "dompurify": "^3.2.5",
-        "katex": "^0.16.22",
+        "dagre-d3-es": "7.0.14",
+        "dayjs": "^1.11.19",
+        "dompurify": "^3.3.1",
+        "es-toolkit": "^1.45.1",
+        "katex": "^0.16.25",
        "khroma": "^2.1.0",
-        "lodash-es": "^4.17.21",
-        "marked": "^16.2.1",
+        "marked": "^16.3.0",
        "roughjs": "^4.6.6",
        "stylis": "^4.3.6",
        "ts-dedent": "^2.2.0",
-        "uuid": "^11.1.0"
+        "uuid": "^11.1.0 || ^12 || ^13 || ^14.0.0"
      }
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@@ -20210,9 +20156,9 @@
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@@ -22561,55 +22507,6 @@
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@@ -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):
@@ -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()
@@ -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")
@@ -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:
@@ -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:
@@ -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
Author	SHA1	Message	Date
dependabot[bot]andGitHub	7b2402fbf6	Merge `5d0d89a3f2` into `ec3fb00494`	2026-06-23 23:14:38 +00:00
Daniel GitHub Claude Opus 4.8	ec3fb00494	perf(track): use sum()/len() instead of np.mean in average_boxes (#23521 ) CI / AMD64 Build (push) Has been cancelled Details CI / ARM Build (push) Has been cancelled Details CI / Jetson Jetpack 6 (push) Has been cancelled Details CI / AMD64 Extra Build (push) Has been cancelled Details CI / ARM Extra Build (push) Has been cancelled Details CI / Synaptics Build (push) Has been cancelled Details CI / Assemble and push default build (push) Has been cancelled Details * 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>	2026-06-23 15:47:04 -06:00
Daniel GitHub Claude Opus 4.8	081d6f95ef	perf(track): avoid per-frame allocations and list lookups in tracker (#23523 ) Two small per-object-per-frame improvements in the tracker hot path (match_and_update), both bit-identical: - get_stationary_threshold returned a freshly constructed StationaryThresholds (a dataclass plus a list) on every call for any label not in the three known lists - i.e. for common labels like person/dog. The default thresholds are constant and never mutated, so return a shared module-level singleton, as the other three cases already do. - untracked_object_boxes membership used `box not in [list of boxes]` (O(n)); build a set of box tuples for O(1) membership. Boxes are hashable as tuples and output is unchanged. get_stationary_threshold appeared in a live py-spy --gil profile of a camera process_frames worker. Adds tests for the threshold lookups. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-23 15:24:05 -06:00
Daniel GitHub Claude Opus 4.8	a2b46f5d84	perf(util): cut redundant work in per-frame detection consolidation (#23522 ) video/detect.py runs these for every frame: - get_cluster_candidates: used_boxes was a list with `in` membership tests inside the nested loop (O(n) per check). It is only ever membership-tested, so switching it to a set (O(1)) leaves output unchanged. - get_consolidated_object_detections: area(current_box) was recomputed on every inner-loop iteration though it is loop-invariant; hoist it to one call per outer detection. Both are bit-identical (verified against the previous implementations over randomized inputs). Measured in the release image, get_cluster_candidates on a frame of 30 detection boxes: 59.2 us -> 42.1 us (1.4x); the gain scales with the number of boxes per frame. Adds a partition-invariant test (every box index lands in exactly one cluster). Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-23 15:23:18 -06:00
dependabot[bot]andGitHub	5d0d89a3f2	Bump mermaid from 11.12.2 to 11.15.0 in /docs Bumps [mermaid](https://github.com/mermaid-js/mermaid) from 11.12.2 to 11.15.0. - [Release notes](https://github.com/mermaid-js/mermaid/releases) - [Commits](https://github.com/mermaid-js/mermaid/compare/mermaid@11.12.2...mermaid@11.15.0) --- updated-dependencies: - dependency-name: mermaid dependency-version: 11.15.0 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>	2026-05-11 22:56:50 +00:00