Fix motion activity endpoint returning invalid timestamps after pandas 3.0 upgrade

pandas 3.0 changed DatetimeIndex internal storage from datetime64[ns]
(nanoseconds) to datetime64[us] (microseconds). The motion activity
endpoint in review.py converted DatetimeIndex to epoch seconds using:

    df.index = df.index.astype(int) // (10**9)

This assumed nanosecond resolution, dividing by 10^9 to get seconds.
With microsecond resolution the division produces values ~1000x too
small (e.g. 1774785 instead of 1774785600), causing every entry to
have a start_time near zero. The frontend timeline could not match
these timestamps to the visible range, so motion indicator bars
disappeared entirely — despite the underlying recording data being
correct.

Replace the resolution-dependent integer division with pandas
Timedelta arithmetic:

    df.index = (df.index - _EPOCH) // _ONE_SECOND

This is resolution-independent (produces correct results on
datetime64[s], [ms], [us], and [ns]), ~148x faster than the
per-element .timestamp() alternative, produces native Python int
types that serialize cleanly to JSON, and is backwards-compatible
with older pandas versions.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: brontide

frigate/api/review.py

@@ -40,6 +40,11 @@
 
 logger = logging.getLogger(__name__)
 
+# Pre-computed constants for resolution-independent datetime-to-epoch conversion
+# (pandas 3.0+ stores datetime64 as microseconds, not nanoseconds)
+_EPOCH = pd.Timestamp("1970-01-01")
+_ONE_SECOND = pd.Timedelta("1s")
+
 router = APIRouter(tags=[Tags.review])
 
 
@@ -659,7 +664,7 @@ def motion_activity(
             df.iloc[i : i + chunk, 0] = 0.0
 
     # change types for output
-    df.index = df.index.astype(int) // (10**9)
+    df.index = (df.index - _EPOCH) // _ONE_SECOND
     normalized = df.reset_index().to_dict("records")
     return JSONResponse(content=normalized)