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* 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>
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
import random
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import unittest
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import numpy as np
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from frigate.track.tracked_object import TrackedObjectAttribute
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from frigate.util.object import average_boxes
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class TestBoxStatistics(unittest.TestCase):
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def test_average_boxes_matches_numpy(self) -> None:
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rng = random.Random(0)
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for _ in range(5000):
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boxes = [
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[rng.randint(0, 4000) for _ in range(4)]
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for _ in range(rng.randint(1, 10))
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]
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expected = [float(np.mean([b[i] for b in boxes])) for i in range(4)]
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self.assertEqual(average_boxes(boxes), expected)
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class TestAttribute(unittest.TestCase):
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def test_overlapping_object_selection(self) -> None:
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attribute = TrackedObjectAttribute(
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(
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"amazon",
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0.80078125,
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(847, 242, 883, 255),
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468,
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2.769230769230769,
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(702, 134, 1050, 482),
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)
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)
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objects = [
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{
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"label": "car",
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"score": 0.98828125,
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"box": (728, 223, 1266, 719),
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"area": 266848,
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"ratio": 1.0846774193548387,
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"region": (349, 0, 1397, 1048),
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"frame_time": 1727785394.498972,
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"centroid": (997, 471),
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"id": "1727785349.150633-408hal",
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"start_time": 1727785349.150633,
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"motionless_count": 362,
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"position_changes": 0,
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"score_history": [0.98828125, 0.95703125, 0.98828125, 0.98828125],
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},
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{
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"label": "person",
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"score": 0.76953125,
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"box": (826, 172, 939, 417),
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"area": 27685,
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"ratio": 0.46122448979591835,
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"region": (702, 134, 1050, 482),
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"frame_time": 1727785394.498972,
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"centroid": (882, 294),
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"id": "1727785390.499768-9fbhem",
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"start_time": 1727785390.499768,
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"motionless_count": 2,
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"position_changes": 1,
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"score_history": [0.8828125, 0.83984375, 0.91796875, 0.94140625],
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},
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]
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assert attribute.find_best_object(objects) == "1727785390.499768-9fbhem"
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