Motion timeline data (#10245)

* Refactor activity api to send motion and audio data

* Prepare for using motion data timeline

* Get working

* reduce to 0

* fix

* Formatting

* fix typing

* add motion data to timelines and allow motion cameas to be selectable

* Fix tests

* cleanup

* Fix not loading preview when changing hours

frigate/api/review.py

@@ -4,6 +4,7 @@ import logging
 from datetime import datetime, timedelta
 from functools import reduce
 
+import pandas as pd
 from flask import (
     Blueprint,
     jsonify,
@@ -12,7 +13,7 @@ from flask import (
 )
 from peewee import Case, DoesNotExist, fn, operator
 
-from frigate.models import ReviewSegment
+from frigate.models import Recordings, ReviewSegment
 from frigate.util.builtin import get_tz_modifiers
 
 logger = logging.getLogger(__name__)
@@ -258,3 +259,66 @@ def delete_reviews(ids: str):
     ReviewSegment.delete().where(ReviewSegment.id << list_of_ids).execute()
 
     return make_response(jsonify({"success": True, "message": "Delete reviews"}), 200)
+
+
+@ReviewBp.route("/review/activity")
+def review_activity():
+    """Get motion and audio activity."""
+    before = request.args.get("before", type=float, default=datetime.now().timestamp())
+    after = request.args.get(
+        "after", type=float, default=(datetime.now() - timedelta(hours=1)).timestamp()
+    )
+
+    # get scale in seconds
+    scale = request.args.get("scale", type=int, default=30)
+
+    all_recordings: list[Recordings] = (
+        Recordings.select(
+            Recordings.start_time,
+            Recordings.duration,
+            Recordings.objects,
+            Recordings.motion,
+            Recordings.dBFS,
+        )
+        .where((Recordings.start_time > after) & (Recordings.end_time < before))
+        .order_by(Recordings.start_time.asc())
+        .iterator()
+    )
+
+    # format is: { timestamp: segment_start_ts, motion: [0-100], audio: [0 - -100] }
+    # periods where active objects / audio was detected will cause motion / audio to be scaled down
+    data: list[dict[str, float]] = []
+
+    for rec in all_recordings:
+        data.append(
+            {
+                "start_time": rec.start_time,
+                "motion": rec.motion if rec.objects == 0 else 0,
+                "audio": rec.dBFS if rec.objects == 0 else 0,
+            }
+        )
+
+    # resample data using pandas to get activity on scaled basis
+    df = pd.DataFrame(data, columns=["start_time", "motion", "audio"])
+
+    # set date as datetime index
+    df["start_time"] = pd.to_datetime(df["start_time"], unit="s")
+    df.set_index(["start_time"], inplace=True)
+
+    # normalize data
+    df = df.resample(f"{scale}S").mean().fillna(0.0)
+    df["motion"] = (
+        (df["motion"] - df["motion"].min())
+        / (df["motion"].max() - df["motion"].min())
+        * 100
+    )
+    df["audio"] = (
+        (df["audio"] - df["audio"].max())
+        / (df["audio"].min() - df["audio"].max())
+        * -100
+    )
+
+    # change types for output
+    df.index = df.index.astype(int) // (10**9)
+    normalized = df.reset_index().to_dict("records")
+    return jsonify(normalized)