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https://github.com/blakeblackshear/frigate.git
synced 2026-02-09 04:35:25 +03:00
Refactor activity api to send motion and audio data
This commit is contained in:
Nicolas Mowen
parent
a174d82eb9
commit
e88a0baccb
@ -5,12 +5,9 @@ import json
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import logging
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import os
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import traceback
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from collections import defaultdict
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from datetime import datetime, timedelta
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from functools import reduce
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import numpy as np
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import pandas as pd
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import requests
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from flask import (
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Blueprint,
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@ -632,102 +629,3 @@ def hourly_timeline():
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"hours": hours,
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}
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)
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@bp.route("/<camera_name>/recording/hourly/activity")
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def hourly_timeline_activity(camera_name: str):
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"""Get hourly summary for timeline."""
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if camera_name not in current_app.frigate_config.cameras:
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return make_response(
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jsonify({"success": False, "message": "Camera not found"}),
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404,
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)
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before = request.args.get("before", type=float, default=datetime.now())
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after = request.args.get(
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"after", type=float, default=datetime.now() - timedelta(hours=1)
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)
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tz_name = request.args.get("timezone", default="utc", type=str)
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_, minute_modifier, _ = get_tz_modifiers(tz_name)
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minute_offset = int(minute_modifier.split(" ")[0])
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all_recordings: list[Recordings] = (
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Recordings.select(
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Recordings.start_time,
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Recordings.duration,
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Recordings.objects,
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Recordings.motion,
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)
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.where(Recordings.camera == camera_name)
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.where(Recordings.motion > 0)
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.where((Recordings.start_time > after) & (Recordings.end_time < before))
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.order_by(Recordings.start_time.asc())
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.iterator()
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)
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# data format is ex:
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# {timestamp: [{ date: 1, count: 1, type: motion }]}] }}
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hours: dict[int, list[dict[str, any]]] = defaultdict(list)
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key = datetime.fromtimestamp(after).replace(second=0, microsecond=0) + timedelta(
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minutes=minute_offset
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)
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check = (key + timedelta(hours=1)).timestamp()
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# set initial start so data is representative of full hour
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hours[int(key.timestamp())].append(
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[
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key.timestamp(),
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0,
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False,
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]
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)
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for recording in all_recordings:
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if recording.start_time > check:
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hours[int(key.timestamp())].append(
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[
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(key + timedelta(minutes=59, seconds=59)).timestamp(),
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0,
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False,
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]
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)
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key = key + timedelta(hours=1)
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check = (key + timedelta(hours=1)).timestamp()
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hours[int(key.timestamp())].append(
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[
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key.timestamp(),
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0,
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False,
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]
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)
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data_type = recording.objects > 0
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count = recording.motion + recording.objects
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hours[int(key.timestamp())].append(
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[
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recording.start_time + (recording.duration / 2),
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0 if count == 0 else np.log2(count),
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data_type,
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]
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)
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# resample data using pandas to get activity on minute to minute basis
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for key, data in hours.items():
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df = pd.DataFrame(data, columns=["date", "count", "hasObjects"])
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# set date as datetime index
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df["date"] = pd.to_datetime(df["date"], unit="s")
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df.set_index(["date"], inplace=True)
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# normalize data
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df = df.resample("T").mean().fillna(0)
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# change types for output
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df.index = df.index.astype(int) // (10**9)
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df["count"] = df["count"].astype(int)
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df["hasObjects"] = df["hasObjects"].astype(bool)
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hours[key] = df.reset_index().to_dict("records")
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return jsonify(hours)
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@ -4,6 +4,8 @@ import logging
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from datetime import datetime, timedelta
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from functools import reduce
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import numpy as np
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import pandas as pd
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from flask import (
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Blueprint,
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jsonify,
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@ -12,7 +14,7 @@ from flask import (
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)
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from peewee import Case, DoesNotExist, fn, operator
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from frigate.models import ReviewSegment
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from frigate.models import Recordings, ReviewSegment
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from frigate.util.builtin import get_tz_modifiers
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logger = logging.getLogger(__name__)
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@ -258,3 +260,59 @@ def delete_reviews(ids: str):
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ReviewSegment.delete().where(ReviewSegment.id << list_of_ids).execute()
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return make_response(jsonify({"success": True, "message": "Delete reviews"}), 200)
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@ReviewBp.route("/review/activity")
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def review_activity():
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"""Get motion and audio activity."""
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before = request.args.get("before", type=float, default=datetime.now().timestamp())
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after = request.args.get(
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"after", type=float, default=(datetime.now() - timedelta(hours=1)).timestamp()
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)
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# get scale in seconds
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scale = request.args.get(
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"scale", type=int, default=30
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)
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all_recordings: list[Recordings] = (
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Recordings.select(
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Recordings.start_time,
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Recordings.duration,
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Recordings.objects,
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Recordings.motion,
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Recordings.dBFS,
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)
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.where((Recordings.start_time > after) & (Recordings.end_time < before))
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.order_by(Recordings.start_time.asc())
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.iterator()
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)
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# format is: { timestamp: segment_start_ts, motion: [0-100], audio: [0 - -100] }
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# periods where active objects / audio was detected will cause motion / audio to be scaled down
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data: list[dict[str, float]] = []
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for rec in all_recordings:
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factor = 0.1 if rec.objects > 0 else 1.0
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data.append({
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"date": rec.start_time,
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"motion": rec.motion * factor,
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"audio": rec.dBFS * factor,
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})
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# resample data using pandas to get activity on scaled basis
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df = pd.DataFrame(data, columns=["date", "motion", "audio"])
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# set date as datetime index
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df["date"] = pd.to_datetime(df["date"], unit="s")
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df.set_index(["date"], inplace=True)
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# normalize data
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df = df.resample(f"{scale}S").mean().fillna(0.0)
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df["motion"] = (df["motion"] - df["motion"].min()) / (df["motion"].max() - df["motion"].min()) * 100
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df["audio"] = (df["audio"] - df["audio"].max()) / (df["audio"].min() - df["audio"].max()) * -100
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# change types for output
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df.index = df.index.astype(int) // (10**9)
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normalized = df.reset_index().to_dict("records")
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return jsonify(normalized)
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