forked from Github/frigate
Improve graph using pandas (#9234)
* Ensure viewport is always full screen * Protect against hour with no cards and ensure data is consistent * Reduce grouped up image refreshes * Include current hour and fix scrubbing bugginess * Scroll initially selected timeline in to view * Expand timelne class type * Use poster image for preview on video player instead of using separate image view * Fix available streaming modes * Incrase timing for grouping timline items * Fix audio activity listener * Fix player not switching views correctly * Use player time to convert to timeline time * Update sub labels for previous timeline items * Show mini timeline bar for non selected items * Rewrite desktop timeline to use separate dynamic video player component * Extend improvements to mobile as well * Improve time formatting * Fix scroll * Fix no preview case * Mobile fixes * Audio toggle fixes * More fixes for mobile * Improve scaling of graph motion activity * Add keyboard shortcut hook and support shortcuts for playback page * Fix sizing of dialog * Improve height scaling of dialog * simplify and fix layout system for timeline * Fix timeilne items not working * Implement basic Frigate+ submitting from timeline
This commit is contained in:
Nicolas Mowen
committed by
Blake Blackshear
Blake Blackshear
parent
9c4b69191b
commit
af3f6dadcb
@@ -16,6 +16,7 @@ from urllib.parse import unquote
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import cv2
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import numpy as np
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import pandas as pd
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import pytz
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import requests
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from flask import (
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@@ -390,6 +391,17 @@ def set_sub_label(id):
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new_sub_label = json.get("subLabel")
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new_score = json.get("subLabelScore")
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if new_sub_label is None:
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return make_response(
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jsonify(
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{
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"success": False,
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"message": "A sub label must be supplied",
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}
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),
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400,
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)
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if new_sub_label and len(new_sub_label) > 100:
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return make_response(
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jsonify(
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@@ -415,6 +427,7 @@ def set_sub_label(id):
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)
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if not event.end_time:
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# update tracked object
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tracked_obj: TrackedObject = (
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current_app.detected_frames_processor.camera_states[
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event.camera
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@@ -424,6 +437,11 @@ def set_sub_label(id):
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if tracked_obj:
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tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
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# update timeline items
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Timeline.update(
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data=Timeline.data.update({"sub_label": (new_sub_label, new_score)})
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).where(Timeline.source_id == id).execute()
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event.sub_label = new_sub_label
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if new_score:
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@@ -739,41 +757,59 @@ def hourly_timeline_activity(camera_name: str):
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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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"date": key.timestamp(),
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"count": 0,
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"type": "motion",
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}
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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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"date": (key + timedelta(hours=1)).timestamp(),
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"count": 0,
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"type": "motion",
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}
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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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"date": key.timestamp(),
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"count": 0,
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"type": "motion",
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}
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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 = "motion" if recording.objects == 0 else "objects"
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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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"date": recording.start_time + (recording.duration / 2),
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"count": recording.motion,
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"type": data_type,
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}
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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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@@ -1840,6 +1876,7 @@ def recordings(camera_name):
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Recordings.segment_size,
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Recordings.motion,
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Recordings.objects,
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Recordings.duration,
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)
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.where(
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Recordings.camera == camera_name,
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@@ -7,7 +7,6 @@ from multiprocessing import Queue
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from multiprocessing.synchronize import Event as MpEvent
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from frigate.config import FrigateConfig
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from frigate.const import ALL_ATTRIBUTE_LABELS
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from frigate.events.maintainer import EventTypeEnum
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from frigate.models import Timeline
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from frigate.util.builtin import to_relative_box
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@@ -85,12 +84,13 @@ class TimelineProcessor(threading.Thread):
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"""Handle object detection."""
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save = False
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camera_config = self.config.cameras[camera]
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event_id = event_data["id"]
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timeline_entry = {
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Timeline.timestamp: event_data["frame_time"],
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Timeline.camera: camera,
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Timeline.source: "tracked_object",
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Timeline.source_id: event_data["id"],
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Timeline.source_id: event_id,
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Timeline.data: {
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"box": to_relative_box(
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camera_config.detect.width,
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@@ -107,6 +107,16 @@ class TimelineProcessor(threading.Thread):
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"attribute": "",
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},
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}
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# update sub labels for existing entries that haven't been added yet
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if (
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prev_event_data != None
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and prev_event_data["sub_label"] != event_data["sub_label"]
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and event_id in self.pre_event_cache.keys()
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):
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for e in self.pre_event_cache[event_id]:
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e[Timeline.data]["sub_label"] = event_data["sub_label"]
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if event_type == "start":
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timeline_entry[Timeline.class_type] = "visible"
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save = True
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@@ -129,13 +139,6 @@ class TimelineProcessor(threading.Thread):
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event_data["attributes"].keys()
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)[0]
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save = True
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elif not prev_event_data.get("sub_label") and event_data.get("sub_label"):
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sub_label = event_data["sub_label"][0]
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if sub_label not in ALL_ATTRIBUTE_LABELS:
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timeline_entry[Timeline.class_type] = "sub_label"
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timeline_entry[Timeline.data]["sub_label"] = sub_label
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save = True
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elif event_type == "end":
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timeline_entry[Timeline.class_type] = "gone"
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save = True
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@@ -378,7 +378,7 @@ def auto_detect_hwaccel() -> str:
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try:
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cuda = False
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vaapi = False
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resp = requests.get("http://192.168.50.106:1984/api/ffmpeg/hardware", timeout=3)
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resp = requests.get("http://127.0.0.1:1984/api/ffmpeg/hardware", timeout=3)
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if resp.status_code == 200:
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data: dict[str, list[dict[str, str]]] = resp.json()
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