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Author	SHA1	Message	Date
3 people	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>	2026-06-23 15:47:04 -06:00
3 people	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
3 people	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
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
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