Merge c238e62ad7 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 23:26:56 +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 · 2026-05-01 13:18:56 +00:00
8 changed files with 148 additions and 491 deletions
@@ -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
@@ -120,7 +120,7 @@
        "postcss": "^8.5.8",
        "prettier": "^3.3.3",
        "prettier-plugin-tailwindcss": "^0.6.5",
-        "tailwindcss": "^3.4.9",
+        "tailwindcss": "^4.2.4",
        "typescript": "^5.9.3",
        "vite": "^6.4.2",
        "vitest": "^3.0.7"
@@ -142,17 +142,6 @@
      "dev": true,
      "license": "MIT"
    },
-    "node_modules/@alloc/quick-lru": {
-      "version": "5.2.0",
-      "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
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-      "engines": {
-        "node": ">=10"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
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      "version": "2.3.0",
      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz",
@@ -1322,6 +1311,7 @@
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      "integrity": "sha512-IzL8ZoEDIBRWEzlCcRhOaCupYyN5gdIK+Q6fbFdPDg6HqX6jpkItn7DFIpW9LQzXG6Df9sA7+OKnq0qlz/GaQg==",
+      "dev": true,
      "license": "MIT",
      "dependencies": {
        "@jridgewell/set-array": "^1.2.1",
@@ -1336,6 +1326,7 @@
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+      "dev": true,
      "engines": {
        "node": ">=6.0.0"
      }
@@ -1344,6 +1335,7 @@
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      "resolved": "https://registry.npmjs.org/@jridgewell/set-array/-/set-array-1.2.1.tgz",
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+      "dev": true,
      "license": "MIT",
      "engines": {
        "node": ">=6.0.0"
@@ -1353,12 +1345,14 @@
      "version": "1.5.0",
      "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.5.0.tgz",
      "integrity": "sha512-gv3ZRaISU3fjPAgNsriBRqGWQL6quFx04YMPW/zD8XMLsU32mhCCbfbO6KZFLjvYpCZ8zyDEgqsgf+PwPaM7GQ==",
+      "dev": true,
      "license": "MIT"
    },
    "node_modules/@jridgewell/trace-mapping": {
      "version": "0.3.25",
      "resolved": "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.25.tgz",
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+      "dev": true,
      "dependencies": {
        "@jridgewell/resolve-uri": "^3.1.0",
        "@jridgewell/sourcemap-codec": "^1.4.14"
@@ -1402,6 +1396,7 @@
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      "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz",
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+      "dev": true,
      "dependencies": {
        "@nodelib/fs.stat": "2.0.5",
        "run-parallel": "^1.1.9"
@@ -1414,6 +1409,7 @@
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      "resolved": "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz",
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+      "dev": true,
      "engines": {
        "node": ">= 8"
      }
@@ -1422,6 +1418,7 @@
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      "resolved": "https://registry.npmjs.org/@nodelib/fs.walk/-/fs.walk-1.2.8.tgz",
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+      "dev": true,
      "dependencies": {
        "@nodelib/fs.scandir": "2.1.5",
        "fastq": "^1.6.0"
@@ -6321,23 +6318,6 @@
        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
      }
    },
-    "node_modules/any-promise": {
-      "version": "1.3.0",
-      "resolved": "https://registry.npmjs.org/any-promise/-/any-promise-1.3.0.tgz",
-      "integrity": "sha512-7UvmKalWRt1wgjL1RrGxoSJW/0QZFIegpeGvZG9kjp8vrRu55XTHbwnqq2GpXm9uLbcuhxm3IqX9OB4MZR1b2A=="
-    },
-    "node_modules/anymatch": {
-      "version": "3.1.3",
-      "resolved": "https://registry.npmjs.org/anymatch/-/anymatch-3.1.3.tgz",
-      "integrity": "sha512-KMReFUr0B4t+D+OBkjR3KYqvocp2XaSzO55UcB6mgQMd3KbcE+mWTyvVV7D/zsdEbNnV6acZUutkiHQXvTr1Rw==",
-      "dependencies": {
-        "normalize-path": "^3.0.0",
-        "picomatch": "^2.0.4"
-      },
-      "engines": {
-        "node": ">= 8"
-      }
-    },
    "node_modules/apexcharts": {
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      "resolved": "https://registry.npmjs.org/apexcharts/-/apexcharts-3.52.0.tgz",
@@ -6353,11 +6333,6 @@
        "svg.select.js": "^3.0.1"
      }
    },
-    "node_modules/arg": {
-      "version": "5.0.2",
-      "resolved": "https://registry.npmjs.org/arg/-/arg-5.0.2.tgz",
-      "integrity": "sha512-PYjyFOLKQ9y57JvQ6QLo8dAgNqswh8M1RMJYdQduT6xbWSgK36P/Z/v+p888pM69jMMfS8Xd8F6I1kQ/I9HUGg=="
-    },
    "node_modules/argparse": {
      "version": "2.0.1",
      "resolved": "https://registry.npmjs.org/argparse/-/argparse-2.0.1.tgz",
@@ -6481,7 +6456,8 @@
    "node_modules/balanced-match": {
      "version": "1.0.2",
      "resolved": "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.2.tgz",
-      "integrity": "sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw=="
+      "integrity": "sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==",
+      "dev": true
    },
    "node_modules/big-integer": {
      "version": "1.6.52",
@@ -6492,14 +6468,6 @@
        "node": ">=0.6"
      }
    },
-    "node_modules/binary-extensions": {
-      "version": "2.2.0",
-      "resolved": "https://registry.npmjs.org/binary-extensions/-/binary-extensions-2.2.0.tgz",
-      "integrity": "sha512-jDctJ/IVQbZoJykoeHbhXpOlNBqGNcwXJKJog42E5HDPUwQTSdjCHdihjj0DlnheQ7blbT6dHOafNAiS8ooQKA==",
-      "engines": {
-        "node": ">=8"
-      }
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    "node_modules/bplist-parser": {
      "version": "0.2.0",
      "resolved": "https://registry.npmjs.org/bplist-parser/-/bplist-parser-0.2.0.tgz",
@@ -6516,6 +6484,7 @@
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+      "dev": true,
      "dependencies": {
        "balanced-match": "^1.0.0",
        "concat-map": "0.0.1"
@@ -6525,6 +6494,7 @@
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+      "dev": true,
      "dependencies": {
        "fill-range": "^7.0.1"
      },
@@ -6649,14 +6619,6 @@
        "node": ">=6"
      }
    },
-    "node_modules/camelcase-css": {
-      "version": "2.0.1",
-      "resolved": "https://registry.npmjs.org/camelcase-css/-/camelcase-css-2.0.1.tgz",
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-      "engines": {
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-      }
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    "node_modules/caniuse-lite": {
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      "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001757.tgz",
@@ -6778,43 +6740,6 @@
        "node": ">= 16"
      }
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-    "node_modules/chokidar": {
-      "version": "3.5.3",
-      "resolved": "https://registry.npmjs.org/chokidar/-/chokidar-3.5.3.tgz",
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-      "funding": [
-        {
-          "type": "individual",
-          "url": "https://paulmillr.com/funding/"
-        }
-      ],
-      "dependencies": {
-        "anymatch": "~3.1.2",
-        "braces": "~3.0.2",
-        "glob-parent": "~5.1.2",
-        "is-binary-path": "~2.1.0",
-        "is-glob": "~4.0.1",
-        "normalize-path": "~3.0.0",
-        "readdirp": "~3.6.0"
-      },
-      "engines": {
-        "node": ">= 8.10.0"
-      },
-      "optionalDependencies": {
-        "fsevents": "~2.3.2"
-      }
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-      "dependencies": {
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-      },
-      "engines": {
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    "node_modules/ci-info": {
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      "resolved": "https://registry.npmjs.org/ci-info/-/ci-info-3.9.0.tgz",
@@ -6976,14 +6901,6 @@
        "url": "https://github.com/sponsors/wooorm"
      }
    },
-    "node_modules/commander": {
-      "version": "4.1.1",
-      "resolved": "https://registry.npmjs.org/commander/-/commander-4.1.1.tgz",
-      "integrity": "sha512-NOKm8xhkzAjzFx8B2v5OAHT+u5pRQc2UCa2Vq9jYL/31o2wi9mxBA7LIFs3sV5VSC49z6pEhfbMULvShKj26WA==",
-      "engines": {
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@@ -6992,7 +6909,8 @@
    "node_modules/concat-map": {
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+      "integrity": "sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==",
+      "dev": true
    },
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Author	SHA1	Message	Date
dependabot[bot]andGitHub	a67d12c682	Merge `c238e62ad7` into `ec3fb00494`	2026-06-23 23:26:56 +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	c238e62ad7	Bump tailwindcss from 3.4.9 to 4.2.4 in /web Bumps [tailwindcss](https://github.com/tailwindlabs/tailwindcss/tree/HEAD/packages/tailwindcss) from 3.4.9 to 4.2.4. - [Release notes](https://github.com/tailwindlabs/tailwindcss/releases) - [Changelog](https://github.com/tailwindlabs/tailwindcss/blob/main/CHANGELOG.md) - [Commits](https://github.com/tailwindlabs/tailwindcss/commits/v4.2.4/packages/tailwindcss) --- updated-dependencies: - dependency-name: tailwindcss dependency-version: 4.2.4 dependency-type: direct:development update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>	2026-05-01 13:18:56 +00:00