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>

frigate/util/builtin.py

@@ -2,7 +2,6 @@
 
 import ast
 import copy
-import datetime
 import logging
 import math
 import multiprocessing.queues
@@ -10,7 +9,9 @@ import queue
 import re
 import shlex
 import struct
+import time
 import urllib.parse
+from collections import deque
 from collections.abc import Mapping
 from multiprocessing.managers import ValueProxy
 from pathlib import Path
@@ -32,23 +33,20 @@ class EventsPerSecond:
         self._start = None
         self._max_events = max_events
         self._last_n_seconds = last_n_seconds
-        self._timestamps = []
+        self._timestamps: deque[float] = deque(maxlen=max_events)
 
     def start(self) -> None:
-        self._start = datetime.datetime.now().timestamp()
+        self._start = time.monotonic()
 
     def update(self) -> None:
-        now = datetime.datetime.now().timestamp()
+        now = time.monotonic()
         if self._start is None:
             self._start = now
         self._timestamps.append(now)
-        # truncate the list when it goes 100 over the max_size
-        if len(self._timestamps) > self._max_events + 100:
-            self._timestamps = self._timestamps[(1 - self._max_events) :]
         self.expire_timestamps(now)
 
     def eps(self) -> float:
-        now = datetime.datetime.now().timestamp()
+        now = time.monotonic()
         if self._start is None:
             self._start = now
         # compute the (approximate) events in the last n seconds
@@ -63,7 +61,7 @@ class EventsPerSecond:
     def expire_timestamps(self, now: float) -> None:
         threshold = now - self._last_n_seconds
         while self._timestamps and self._timestamps[0] < threshold:
-            del self._timestamps[0]
+            self._timestamps.popleft()
 
 
 class InferenceSpeed: