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Author	SHA1	Message	Date
DanielandGitHub	2dfd58911c	Merge d80d020c86 into bb5056a68a	2026-06-22 12:06:26 -04:00
3 people	d982b3a782	perf(util): use monotonic clock and bounded deque in EventsPerSecond (#23520) ... * perf(util): use monotonic clock and bounded deque in EventsPerSecond EventsPerSecond is updated on every captured frame, every detection and every processed frame across all cameras and detectors. The previous implementation derived timestamps from datetime.now().timestamp() (wall clock), so an NTP or manual clock adjustment could skew the rolling-window expiry; it also stored timestamps in a list and expired them with del self._timestamps[0] (O(n) per removal) plus a periodic slice-copy to cap growth. Switch to time.monotonic() for the interval math (correct by construction and immune to wall-clock jumps) and a collections.deque(maxlen=...) so expiry is O(1) (popleft) and retention is bounded automatically. This mirrors the deque-based expiry already used in video/ffmpeg.py and watchdog.py. Observable output is unchanged. Adds frigate/test/test_builtin.py covering rate calculation, window expiry and the memory bound. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test: drop test_timestamps_are_memory_bounded It only asserted that deque(maxlen=) caps length, which is stdlib behavior rather than something this change needs to verify. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-21 07:38:41 -06:00
Daniel-dev22andClaude Opus 4.8	d80d020c86	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>	2026-06-20 09:25:04 -04:00
DanielandNicolas Mowen	d93d6262ce	Use 127.0.0.1 for chroma (#12135)	2024-08-29 20:19:50 -06:00
DanielandGitHub	7e5eb82882	Delete download-models (#10755)	2024-03-30 13:23:32 -06:00
DanielandGitHub	cc6e049966	Change multiselect camera icon (#8016) ... * CenterFocusString icon * Add CenterFocusString to multiselect * Rename CenterFocusString.jsx * Rename icon and make it smaller * Rename icon * Fix lint and use icon for speech * remove unused vars * Remove unused import	2023-10-08 14:30:53 -05:00