mirror of
https://github.com/blakeblackshear/frigate.git
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472 lines
14 KiB
Python
472 lines
14 KiB
Python
"""Review apis."""
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import logging
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from datetime import datetime, timedelta
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from functools import reduce
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import pandas as pd
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from flask import Blueprint, jsonify, make_response, request
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from peewee import Case, DoesNotExist, fn, operator
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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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ReviewBp = Blueprint("reviews", __name__)
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@ReviewBp.route("/review")
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def review():
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cameras = request.args.get("cameras", "all")
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labels = request.args.get("labels", "all")
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reviewed = request.args.get("reviewed", type=int, default=0)
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limit = request.args.get("limit", type=int, default=None)
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severity = request.args.get("severity", None)
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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=18)).timestamp()
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)
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clauses = [((ReviewSegment.start_time > after) & (ReviewSegment.end_time < before))]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((ReviewSegment.camera << camera_list))
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if labels != "all":
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# use matching so segments with multiple labels
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# still match on a search where any label matches
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label_clauses = []
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filtered_labels = labels.split(",")
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for label in filtered_labels:
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label_clauses.append(
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(ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
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)
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label_clause = reduce(operator.or_, label_clauses)
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clauses.append((label_clause))
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if reviewed == 0:
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clauses.append((ReviewSegment.has_been_reviewed == False))
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if severity:
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clauses.append((ReviewSegment.severity == severity))
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review = (
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ReviewSegment.select()
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.where(reduce(operator.and_, clauses))
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.order_by(ReviewSegment.severity.asc())
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.order_by(ReviewSegment.start_time.desc())
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.limit(limit)
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.dicts()
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)
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return jsonify([r for r in review])
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@ReviewBp.route("/review/summary")
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def review_summary():
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tz_name = request.args.get("timezone", default="utc", type=str)
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hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(tz_name)
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day_ago = (datetime.now() - timedelta(hours=24)).timestamp()
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month_ago = (datetime.now() - timedelta(days=30)).timestamp()
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cameras = request.args.get("cameras", "all")
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labels = request.args.get("labels", "all")
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clauses = [(ReviewSegment.start_time > day_ago)]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((ReviewSegment.camera << camera_list))
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if labels != "all":
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# use matching so segments with multiple labels
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# still match on a search where any label matches
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label_clauses = []
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filtered_labels = labels.split(",")
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for label in filtered_labels:
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label_clauses.append(
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(ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
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)
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label_clause = reduce(operator.or_, label_clauses)
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clauses.append((label_clause))
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last_24 = (
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ReviewSegment.select(
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_detection"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "significant_motion"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_motion"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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1,
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)
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],
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0,
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)
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).alias("total_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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1,
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)
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],
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0,
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)
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).alias("total_detection"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "significant_motion"),
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1,
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)
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],
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0,
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)
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).alias("total_motion"),
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)
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.where(reduce(operator.and_, clauses))
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.dicts()
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.get()
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)
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clauses = [(ReviewSegment.start_time > month_ago)]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((ReviewSegment.camera << camera_list))
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if labels != "all":
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# use matching so segments with multiple labels
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# still match on a search where any label matches
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label_clauses = []
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filtered_labels = labels.split(",")
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for label in filtered_labels:
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label_clauses.append(
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(ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
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)
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label_clause = reduce(operator.or_, label_clauses)
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clauses.append((label_clause))
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last_month = (
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ReviewSegment.select(
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fn.strftime(
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"%Y-%m-%d",
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fn.datetime(
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ReviewSegment.start_time,
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"unixepoch",
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hour_modifier,
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minute_modifier,
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),
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).alias("day"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_detection"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "significant_motion"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_motion"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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1,
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)
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],
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0,
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)
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).alias("total_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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1,
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)
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],
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0,
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)
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).alias("total_detection"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "significant_motion"),
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1,
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)
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],
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0,
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)
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).alias("total_motion"),
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)
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.where(reduce(operator.and_, clauses))
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.group_by(
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(ReviewSegment.start_time + seconds_offset).cast("int") / (3600 * 24),
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)
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.order_by(ReviewSegment.start_time.desc())
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)
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data = {
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"last24Hours": last_24,
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}
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for e in last_month.dicts().iterator():
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data[e["day"]] = e
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return jsonify(data)
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@ReviewBp.route("/reviews/viewed", methods=("POST",))
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def set_multiple_reviewed():
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json: dict[str, any] = request.get_json(silent=True) or {}
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list_of_ids = json.get("ids", "")
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if not list_of_ids or len(list_of_ids) == 0:
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return make_response(
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jsonify({"success": False, "message": "Not a valid list of ids"}), 404
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)
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ReviewSegment.update(has_been_reviewed=True).where(
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ReviewSegment.id << list_of_ids
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).execute()
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return make_response(
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jsonify({"success": True, "message": "Reviewed multiple items"}), 200
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)
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@ReviewBp.route("/review/<id>/viewed", methods=("DELETE",))
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def set_not_reviewed(id):
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try:
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review: ReviewSegment = ReviewSegment.get(ReviewSegment.id == id)
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except DoesNotExist:
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return make_response(
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jsonify({"success": False, "message": "Review " + id + " not found"}), 404
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)
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review.has_been_reviewed = False
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review.save()
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return make_response(
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jsonify({"success": True, "message": "Reviewed " + id + " not viewed"}), 200
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)
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@ReviewBp.route("/reviews/<ids>", methods=("DELETE",))
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def delete_reviews(ids: str):
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list_of_ids = ids.split(",")
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if not list_of_ids or len(list_of_ids) == 0:
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return make_response(
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jsonify({"success": False, "message": "Not a valid list of ids"}), 404
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)
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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/motion")
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def motion_activity():
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"""Get motion and audio activity."""
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cameras = request.args.get("cameras", "all")
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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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clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((Recordings.camera << camera_list))
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data: list[Recordings] = (
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Recordings.select(
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Recordings.start_time,
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Recordings.motion,
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)
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.where(reduce(operator.and_, clauses))
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.order_by(Recordings.start_time.asc())
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.dicts()
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.iterator()
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)
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# get scale in seconds
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scale = request.args.get("scale", type=int, default=30)
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# resample data using pandas to get activity on scaled basis
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df = pd.DataFrame(data, columns=["start_time", "motion"])
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# set date as datetime index
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df["start_time"] = pd.to_datetime(df["start_time"], unit="s")
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df.set_index(["start_time"], inplace=True)
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# normalize data
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df = (
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df.resample(f"{scale}S")
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.apply(lambda x: max(x, key=abs, default=0.0))
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.fillna(0.0)
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)
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length = df.shape[0]
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chunk = int(60 * (60 / scale))
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for i in range(0, length, chunk):
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part = df.iloc[i : i + chunk]
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df.iloc[i : i + chunk, 0] = (
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(part["motion"] - part["motion"].min())
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/ (part["motion"].max() - part["motion"].min())
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* 100
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).fillna(0.0)
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# change types for output
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df.index = df.index.astype(int) // (10**9)
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normalized = df.reset_index().to_dict("records")
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return jsonify(normalized)
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@ReviewBp.route("/review/activity/audio")
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def audio_activity():
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"""Get motion and audio activity."""
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cameras = request.args.get("cameras", "all")
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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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clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((Recordings.camera << camera_list))
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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.dBFS,
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)
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.where(reduce(operator.and_, clauses))
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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 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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data.append(
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{
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"start_time": rec.start_time,
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"audio": rec.dBFS if rec.objects == 0 else 0,
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}
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)
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# get scale in seconds
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scale = request.args.get("scale", type=int, default=30)
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# resample data using pandas to get activity on scaled basis
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df = pd.DataFrame(data, columns=["start_time", "audio"])
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# set date as datetime index
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df["start_time"] = pd.to_datetime(df["start_time"], unit="s")
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df.set_index(["start_time"], 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["audio"] = (
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(df["audio"] - df["audio"].max())
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/ (df["audio"].min() - df["audio"].max())
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* -100
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)
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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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