mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-02-14 00:17:05 +01:00
72209986b6
* utility functions * backend config * backend object speed tracking * draw speed on debug view * basic frontend zone editor * remove line sorting * fix types * highlight line on canvas when entering value in zone edit pane * rename vars and add validation * ensure speed estimation is disabled when user adds more than 4 points * pixel velocity in debug * unit_system in config * ability to define unit system in config * save max speed to db * frontend * docs * clarify docs * utility functions * backend config * backend object speed tracking * draw speed on debug view * basic frontend zone editor * remove line sorting * fix types * highlight line on canvas when entering value in zone edit pane * rename vars and add validation * ensure speed estimation is disabled when user adds more than 4 points * pixel velocity in debug * unit_system in config * ability to define unit system in config * save max speed to db * frontend * docs * clarify docs * fix duplicates from merge * include max_estimated_speed in api responses * add units to zone edit pane * catch undefined * add average speed * clarify docs * only track average speed when object is active * rename vars * ensure points and distances are ordered clockwise * only store the last 10 speeds like score history * remove max estimated speed * update docs * update docs * fix point ordering * improve readability * docs inertia recommendation * fix point ordering * check object frame time * add velocity angle to frontend * docs clarity * add frontend speed filter * fix mqtt docs * fix mqtt docs * don't try to remove distances if they weren't already defined * don't display estimates on debug view/snapshots if object is not in a speed tracking zone * docs * implement speed_threshold for zone presence * docs for threshold * better ground plane image * improve image zone size * add inertia to speed threshold example
758 lines
28 KiB
Python
758 lines
28 KiB
Python
import datetime
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import json
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import logging
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import os
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import queue
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import threading
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from collections import defaultdict
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from multiprocessing.synchronize import Event as MpEvent
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from typing import Callable, Optional
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import cv2
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import numpy as np
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from frigate.comms.detections_updater import DetectionPublisher, DetectionTypeEnum
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from frigate.comms.dispatcher import Dispatcher
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from frigate.comms.events_updater import EventEndSubscriber, EventUpdatePublisher
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from frigate.comms.inter_process import InterProcessRequestor
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from frigate.config import (
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FrigateConfig,
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MqttConfig,
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RecordConfig,
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SnapshotsConfig,
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ZoomingModeEnum,
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)
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from frigate.const import CLIPS_DIR, UPDATE_CAMERA_ACTIVITY
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from frigate.events.types import EventStateEnum, EventTypeEnum
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from frigate.ptz.autotrack import PtzAutoTrackerThread
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from frigate.track.tracked_object import TrackedObject
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from frigate.util.image import (
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SharedMemoryFrameManager,
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draw_box_with_label,
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draw_timestamp,
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is_better_thumbnail,
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is_label_printable,
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)
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logger = logging.getLogger(__name__)
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# Maintains the state of a camera
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class CameraState:
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def __init__(
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self,
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name,
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config: FrigateConfig,
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frame_manager: SharedMemoryFrameManager,
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ptz_autotracker_thread: PtzAutoTrackerThread,
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):
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self.name = name
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self.config = config
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self.camera_config = config.cameras[name]
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self.frame_manager = frame_manager
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self.best_objects: dict[str, TrackedObject] = {}
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self.tracked_objects: dict[str, TrackedObject] = {}
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self.frame_cache = {}
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self.zone_objects = defaultdict(list)
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self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
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self.current_frame_lock = threading.Lock()
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self.current_frame_time = 0.0
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self.motion_boxes = []
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self.regions = []
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self.previous_frame_id = None
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self.callbacks = defaultdict(list)
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self.ptz_autotracker_thread = ptz_autotracker_thread
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def get_current_frame(self, draw_options={}):
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with self.current_frame_lock:
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frame_copy = np.copy(self._current_frame)
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frame_time = self.current_frame_time
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tracked_objects = {k: v.to_dict() for k, v in self.tracked_objects.items()}
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motion_boxes = self.motion_boxes.copy()
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regions = self.regions.copy()
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frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
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# draw on the frame
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if draw_options.get("mask"):
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mask_overlay = np.where(self.camera_config.motion.mask == [0])
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frame_copy[mask_overlay] = [0, 0, 0]
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if draw_options.get("bounding_boxes"):
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# draw the bounding boxes on the frame
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for obj in tracked_objects.values():
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if obj["frame_time"] == frame_time:
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if obj["stationary"]:
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color = (220, 220, 220)
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thickness = 1
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else:
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thickness = 2
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color = self.config.model.colormap[obj["label"]]
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else:
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thickness = 1
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color = (255, 0, 0)
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# draw thicker box around ptz autotracked object
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if (
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self.camera_config.onvif.autotracking.enabled
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and self.ptz_autotracker_thread.ptz_autotracker.autotracker_init[
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self.name
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]
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and self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
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self.name
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]
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is not None
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and obj["id"]
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== self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
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self.name
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].obj_data["id"]
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and obj["frame_time"] == frame_time
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):
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thickness = 5
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color = self.config.model.colormap[obj["label"]]
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# debug autotracking zooming - show the zoom factor box
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if (
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self.camera_config.onvif.autotracking.zooming
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!= ZoomingModeEnum.disabled
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):
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max_target_box = self.ptz_autotracker_thread.ptz_autotracker.tracked_object_metrics[
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self.name
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]["max_target_box"]
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side_length = max_target_box * (
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max(
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self.camera_config.detect.width,
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self.camera_config.detect.height,
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)
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)
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centroid_x = (obj["box"][0] + obj["box"][2]) // 2
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centroid_y = (obj["box"][1] + obj["box"][3]) // 2
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top_left = (
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int(centroid_x - side_length // 2),
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int(centroid_y - side_length // 2),
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)
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bottom_right = (
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int(centroid_x + side_length // 2),
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int(centroid_y + side_length // 2),
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)
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cv2.rectangle(
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frame_copy,
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top_left,
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bottom_right,
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(255, 255, 0),
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2,
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)
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# draw the bounding boxes on the frame
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box = obj["box"]
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text = (
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obj["label"]
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if (
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not obj.get("sub_label")
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or not is_label_printable(obj["sub_label"][0])
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)
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else obj["sub_label"][0]
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)
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draw_box_with_label(
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frame_copy,
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box[0],
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box[1],
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box[2],
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box[3],
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text,
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f"{obj['score']:.0%} {int(obj['area'])}"
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+ (
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f" {float(obj['current_estimated_speed']):.1f}"
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if obj["current_estimated_speed"] != 0
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else ""
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),
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thickness=thickness,
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color=color,
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)
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# draw any attributes
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for attribute in obj["current_attributes"]:
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box = attribute["box"]
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draw_box_with_label(
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frame_copy,
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box[0],
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box[1],
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box[2],
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box[3],
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attribute["label"],
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f"{attribute['score']:.0%}",
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thickness=thickness,
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color=color,
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)
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if draw_options.get("regions"):
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for region in regions:
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cv2.rectangle(
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frame_copy,
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(region[0], region[1]),
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(region[2], region[3]),
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(0, 255, 0),
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2,
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)
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if draw_options.get("zones"):
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for name, zone in self.camera_config.zones.items():
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thickness = (
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8
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if any(
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name in obj["current_zones"] for obj in tracked_objects.values()
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)
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else 2
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)
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cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
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if draw_options.get("motion_boxes"):
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for m_box in motion_boxes:
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cv2.rectangle(
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frame_copy,
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(m_box[0], m_box[1]),
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(m_box[2], m_box[3]),
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(0, 0, 255),
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2,
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)
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if draw_options.get("timestamp"):
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color = self.camera_config.timestamp_style.color
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draw_timestamp(
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frame_copy,
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frame_time,
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self.camera_config.timestamp_style.format,
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font_effect=self.camera_config.timestamp_style.effect,
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font_thickness=self.camera_config.timestamp_style.thickness,
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font_color=(color.blue, color.green, color.red),
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position=self.camera_config.timestamp_style.position,
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)
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return frame_copy
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def finished(self, obj_id):
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del self.tracked_objects[obj_id]
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def on(self, event_type: str, callback: Callable[[dict], None]):
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self.callbacks[event_type].append(callback)
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def update(
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self,
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frame_name: str,
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frame_time: float,
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current_detections: dict[str, dict[str, any]],
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motion_boxes: list[tuple[int, int, int, int]],
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regions: list[tuple[int, int, int, int]],
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):
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current_frame = self.frame_manager.get(
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frame_name, self.camera_config.frame_shape_yuv
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)
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tracked_objects = self.tracked_objects.copy()
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current_ids = set(current_detections.keys())
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previous_ids = set(tracked_objects.keys())
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removed_ids = previous_ids.difference(current_ids)
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new_ids = current_ids.difference(previous_ids)
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updated_ids = current_ids.intersection(previous_ids)
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for id in new_ids:
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new_obj = tracked_objects[id] = TrackedObject(
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self.config.model,
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self.camera_config,
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self.config.ui,
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self.frame_cache,
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current_detections[id],
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)
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# call event handlers
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for c in self.callbacks["start"]:
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c(self.name, new_obj, frame_name)
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for id in updated_ids:
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updated_obj = tracked_objects[id]
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thumb_update, significant_update, autotracker_update = updated_obj.update(
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frame_time, current_detections[id], current_frame is not None
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)
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if autotracker_update or significant_update:
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for c in self.callbacks["autotrack"]:
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c(self.name, updated_obj, frame_name)
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if thumb_update and current_frame is not None:
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# ensure this frame is stored in the cache
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if (
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updated_obj.thumbnail_data["frame_time"] == frame_time
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and frame_time not in self.frame_cache
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):
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self.frame_cache[frame_time] = np.copy(current_frame)
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updated_obj.last_updated = frame_time
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# if it has been more than 5 seconds since the last thumb update
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# and the last update is greater than the last publish or
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# the object has changed significantly
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if (
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frame_time - updated_obj.last_published > 5
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and updated_obj.last_updated > updated_obj.last_published
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) or significant_update:
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# call event handlers
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for c in self.callbacks["update"]:
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c(self.name, updated_obj, frame_name)
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updated_obj.last_published = frame_time
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for id in removed_ids:
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# publish events to mqtt
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removed_obj = tracked_objects[id]
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if "end_time" not in removed_obj.obj_data:
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removed_obj.obj_data["end_time"] = frame_time
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for c in self.callbacks["end"]:
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c(self.name, removed_obj, frame_name)
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# TODO: can i switch to looking this up and only changing when an event ends?
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# maintain best objects
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camera_activity: dict[str, list[any]] = {
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"motion": len(motion_boxes) > 0,
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"objects": [],
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}
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for obj in tracked_objects.values():
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object_type = obj.obj_data["label"]
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active = obj.is_active()
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if not obj.false_positive:
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label = object_type
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sub_label = None
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if obj.obj_data.get("sub_label"):
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if (
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obj.obj_data.get("sub_label")[0]
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in self.config.model.all_attributes
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):
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label = obj.obj_data["sub_label"][0]
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else:
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label = f"{object_type}-verified"
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sub_label = obj.obj_data["sub_label"][0]
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camera_activity["objects"].append(
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{
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"id": obj.obj_data["id"],
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"label": label,
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"stationary": not active,
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"area": obj.obj_data["area"],
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"ratio": obj.obj_data["ratio"],
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"score": obj.obj_data["score"],
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"sub_label": sub_label,
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"current_zones": obj.current_zones,
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}
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)
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# if we don't have access to the current frame or
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# if the object's thumbnail is not from the current frame, skip
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if (
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current_frame is None
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or obj.thumbnail_data is None
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or obj.false_positive
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or obj.thumbnail_data["frame_time"] != frame_time
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):
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continue
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if object_type in self.best_objects:
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current_best = self.best_objects[object_type]
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now = datetime.datetime.now().timestamp()
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# if the object is a higher score than the current best score
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# or the current object is older than desired, use the new object
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if (
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is_better_thumbnail(
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object_type,
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current_best.thumbnail_data,
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obj.thumbnail_data,
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self.camera_config.frame_shape,
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)
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or (now - current_best.thumbnail_data["frame_time"])
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> self.camera_config.best_image_timeout
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):
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self.best_objects[object_type] = obj
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for c in self.callbacks["snapshot"]:
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c(self.name, self.best_objects[object_type], frame_name)
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else:
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self.best_objects[object_type] = obj
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for c in self.callbacks["snapshot"]:
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c(self.name, self.best_objects[object_type], frame_name)
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for c in self.callbacks["camera_activity"]:
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c(self.name, camera_activity)
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# cleanup thumbnail frame cache
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current_thumb_frames = {
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obj.thumbnail_data["frame_time"]
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for obj in tracked_objects.values()
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if not obj.false_positive and obj.thumbnail_data is not None
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}
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current_best_frames = {
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obj.thumbnail_data["frame_time"] for obj in self.best_objects.values()
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}
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thumb_frames_to_delete = [
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t
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for t in self.frame_cache.keys()
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if t not in current_thumb_frames and t not in current_best_frames
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]
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for t in thumb_frames_to_delete:
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del self.frame_cache[t]
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with self.current_frame_lock:
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self.tracked_objects = tracked_objects
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self.motion_boxes = motion_boxes
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self.regions = regions
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if current_frame is not None:
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self.current_frame_time = frame_time
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self._current_frame = current_frame
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if self.previous_frame_id is not None:
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self.frame_manager.close(self.previous_frame_id)
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self.previous_frame_id = frame_name
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class TrackedObjectProcessor(threading.Thread):
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def __init__(
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self,
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config: FrigateConfig,
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dispatcher: Dispatcher,
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tracked_objects_queue,
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ptz_autotracker_thread,
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stop_event,
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):
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super().__init__(name="detected_frames_processor")
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self.config = config
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self.dispatcher = dispatcher
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self.tracked_objects_queue = tracked_objects_queue
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self.stop_event: MpEvent = stop_event
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self.camera_states: dict[str, CameraState] = {}
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self.frame_manager = SharedMemoryFrameManager()
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self.last_motion_detected: dict[str, float] = {}
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self.ptz_autotracker_thread = ptz_autotracker_thread
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self.requestor = InterProcessRequestor()
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self.detection_publisher = DetectionPublisher(DetectionTypeEnum.video)
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self.event_sender = EventUpdatePublisher()
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self.event_end_subscriber = EventEndSubscriber()
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|
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self.camera_activity: dict[str, dict[str, any]] = {}
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|
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# {
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# 'zone_name': {
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# 'person': {
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# 'camera_1': 2,
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# 'camera_2': 1
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# }
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# }
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# }
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self.zone_data = defaultdict(lambda: defaultdict(dict))
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self.active_zone_data = defaultdict(lambda: defaultdict(dict))
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|
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def start(camera: str, obj: TrackedObject, frame_name: str):
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self.event_sender.publish(
|
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(
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EventTypeEnum.tracked_object,
|
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EventStateEnum.start,
|
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camera,
|
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frame_name,
|
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obj.to_dict(),
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)
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)
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|
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def update(camera: str, obj: TrackedObject, frame_name: str):
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obj.has_snapshot = self.should_save_snapshot(camera, obj)
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obj.has_clip = self.should_retain_recording(camera, obj)
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after = obj.to_dict()
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message = {
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"before": obj.previous,
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"after": after,
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"type": "new" if obj.previous["false_positive"] else "update",
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}
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self.dispatcher.publish("events", json.dumps(message), retain=False)
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obj.previous = after
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self.event_sender.publish(
|
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(
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EventTypeEnum.tracked_object,
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EventStateEnum.update,
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camera,
|
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frame_name,
|
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obj.to_dict(include_thumbnail=True),
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)
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)
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|
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def autotrack(camera: str, obj: TrackedObject, frame_name: str):
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self.ptz_autotracker_thread.ptz_autotracker.autotrack_object(camera, obj)
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|
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def end(camera: str, obj: TrackedObject, frame_name: str):
|
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# populate has_snapshot
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obj.has_snapshot = self.should_save_snapshot(camera, obj)
|
|
obj.has_clip = self.should_retain_recording(camera, obj)
|
|
|
|
# write the snapshot to disk
|
|
if obj.has_snapshot:
|
|
snapshot_config: SnapshotsConfig = self.config.cameras[camera].snapshots
|
|
jpg_bytes = obj.get_jpg_bytes(
|
|
timestamp=snapshot_config.timestamp,
|
|
bounding_box=snapshot_config.bounding_box,
|
|
crop=snapshot_config.crop,
|
|
height=snapshot_config.height,
|
|
quality=snapshot_config.quality,
|
|
)
|
|
if jpg_bytes is None:
|
|
logger.warning(f"Unable to save snapshot for {obj.obj_data['id']}.")
|
|
else:
|
|
with open(
|
|
os.path.join(CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"),
|
|
"wb",
|
|
) as j:
|
|
j.write(jpg_bytes)
|
|
|
|
# write clean snapshot if enabled
|
|
if snapshot_config.clean_copy:
|
|
png_bytes = obj.get_clean_png()
|
|
if png_bytes is None:
|
|
logger.warning(
|
|
f"Unable to save clean snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
with open(
|
|
os.path.join(
|
|
CLIPS_DIR,
|
|
f"{camera}-{obj.obj_data['id']}-clean.png",
|
|
),
|
|
"wb",
|
|
) as p:
|
|
p.write(png_bytes)
|
|
|
|
if not obj.false_positive:
|
|
message = {
|
|
"before": obj.previous,
|
|
"after": obj.to_dict(),
|
|
"type": "end",
|
|
}
|
|
self.dispatcher.publish("events", json.dumps(message), retain=False)
|
|
self.ptz_autotracker_thread.ptz_autotracker.end_object(camera, obj)
|
|
|
|
self.event_sender.publish(
|
|
(
|
|
EventTypeEnum.tracked_object,
|
|
EventStateEnum.end,
|
|
camera,
|
|
frame_name,
|
|
obj.to_dict(include_thumbnail=True),
|
|
)
|
|
)
|
|
|
|
def snapshot(camera, obj: TrackedObject, frame_name: str):
|
|
mqtt_config: MqttConfig = self.config.cameras[camera].mqtt
|
|
if mqtt_config.enabled and self.should_mqtt_snapshot(camera, obj):
|
|
jpg_bytes = obj.get_jpg_bytes(
|
|
timestamp=mqtt_config.timestamp,
|
|
bounding_box=mqtt_config.bounding_box,
|
|
crop=mqtt_config.crop,
|
|
height=mqtt_config.height,
|
|
quality=mqtt_config.quality,
|
|
)
|
|
|
|
if jpg_bytes is None:
|
|
logger.warning(
|
|
f"Unable to send mqtt snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
self.dispatcher.publish(
|
|
f"{camera}/{obj.obj_data['label']}/snapshot",
|
|
jpg_bytes,
|
|
retain=True,
|
|
)
|
|
|
|
def camera_activity(camera, activity):
|
|
last_activity = self.camera_activity.get(camera)
|
|
|
|
if not last_activity or activity != last_activity:
|
|
self.camera_activity[camera] = activity
|
|
self.requestor.send_data(UPDATE_CAMERA_ACTIVITY, self.camera_activity)
|
|
|
|
for camera in self.config.cameras.keys():
|
|
camera_state = CameraState(
|
|
camera, self.config, self.frame_manager, self.ptz_autotracker_thread
|
|
)
|
|
camera_state.on("start", start)
|
|
camera_state.on("autotrack", autotrack)
|
|
camera_state.on("update", update)
|
|
camera_state.on("end", end)
|
|
camera_state.on("snapshot", snapshot)
|
|
camera_state.on("camera_activity", camera_activity)
|
|
self.camera_states[camera] = camera_state
|
|
|
|
def should_save_snapshot(self, camera, obj: TrackedObject):
|
|
if obj.false_positive:
|
|
return False
|
|
|
|
snapshot_config: SnapshotsConfig = self.config.cameras[camera].snapshots
|
|
|
|
if not snapshot_config.enabled:
|
|
return False
|
|
|
|
# object never changed position
|
|
if obj.obj_data["position_changes"] == 0:
|
|
return False
|
|
|
|
# if there are required zones and there is no overlap
|
|
required_zones = snapshot_config.required_zones
|
|
if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
|
|
logger.debug(
|
|
f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
|
|
)
|
|
return False
|
|
|
|
return True
|
|
|
|
def should_retain_recording(self, camera: str, obj: TrackedObject):
|
|
if obj.false_positive:
|
|
return False
|
|
|
|
record_config: RecordConfig = self.config.cameras[camera].record
|
|
|
|
# Recording is disabled
|
|
if not record_config.enabled:
|
|
return False
|
|
|
|
# object never changed position
|
|
if obj.obj_data["position_changes"] == 0:
|
|
return False
|
|
|
|
# If the object is not considered an alert or detection
|
|
if obj.max_severity is None:
|
|
return False
|
|
|
|
return True
|
|
|
|
def should_mqtt_snapshot(self, camera, obj: TrackedObject):
|
|
# object never changed position
|
|
if obj.obj_data["position_changes"] == 0:
|
|
return False
|
|
|
|
# if there are required zones and there is no overlap
|
|
required_zones = self.config.cameras[camera].mqtt.required_zones
|
|
if len(required_zones) > 0 and not set(obj.entered_zones) & set(required_zones):
|
|
logger.debug(
|
|
f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
|
|
)
|
|
return False
|
|
|
|
return True
|
|
|
|
def update_mqtt_motion(self, camera, frame_time, motion_boxes):
|
|
# publish if motion is currently being detected
|
|
if motion_boxes:
|
|
# only send ON if motion isn't already active
|
|
if self.last_motion_detected.get(camera, 0) == 0:
|
|
self.dispatcher.publish(
|
|
f"{camera}/motion",
|
|
"ON",
|
|
retain=False,
|
|
)
|
|
|
|
# always updated latest motion
|
|
self.last_motion_detected[camera] = frame_time
|
|
elif self.last_motion_detected.get(camera, 0) > 0:
|
|
mqtt_delay = self.config.cameras[camera].motion.mqtt_off_delay
|
|
|
|
# If no motion, make sure the off_delay has passed
|
|
if frame_time - self.last_motion_detected.get(camera, 0) >= mqtt_delay:
|
|
self.dispatcher.publish(
|
|
f"{camera}/motion",
|
|
"OFF",
|
|
retain=False,
|
|
)
|
|
# reset the last_motion so redundant `off` commands aren't sent
|
|
self.last_motion_detected[camera] = 0
|
|
|
|
def get_best(self, camera, label):
|
|
# TODO: need a lock here
|
|
camera_state = self.camera_states[camera]
|
|
if label in camera_state.best_objects:
|
|
best_obj = camera_state.best_objects[label]
|
|
best = best_obj.thumbnail_data.copy()
|
|
best["frame"] = camera_state.frame_cache.get(
|
|
best_obj.thumbnail_data["frame_time"]
|
|
)
|
|
return best
|
|
else:
|
|
return {}
|
|
|
|
def get_current_frame(
|
|
self, camera: str, draw_options: dict[str, any] = {}
|
|
) -> Optional[np.ndarray]:
|
|
if camera == "birdseye":
|
|
return self.frame_manager.get(
|
|
"birdseye",
|
|
(self.config.birdseye.height * 3 // 2, self.config.birdseye.width),
|
|
)
|
|
|
|
if camera not in self.camera_states:
|
|
return None
|
|
|
|
return self.camera_states[camera].get_current_frame(draw_options)
|
|
|
|
def get_current_frame_time(self, camera) -> int:
|
|
"""Returns the latest frame time for a given camera."""
|
|
return self.camera_states[camera].current_frame_time
|
|
|
|
def run(self):
|
|
while not self.stop_event.is_set():
|
|
try:
|
|
(
|
|
camera,
|
|
frame_name,
|
|
frame_time,
|
|
current_tracked_objects,
|
|
motion_boxes,
|
|
regions,
|
|
) = self.tracked_objects_queue.get(True, 1)
|
|
except queue.Empty:
|
|
continue
|
|
|
|
camera_state = self.camera_states[camera]
|
|
|
|
camera_state.update(
|
|
frame_name, frame_time, current_tracked_objects, motion_boxes, regions
|
|
)
|
|
|
|
self.update_mqtt_motion(camera, frame_time, motion_boxes)
|
|
|
|
tracked_objects = [
|
|
o.to_dict() for o in camera_state.tracked_objects.values()
|
|
]
|
|
|
|
# publish info on this frame
|
|
self.detection_publisher.publish(
|
|
(
|
|
camera,
|
|
frame_name,
|
|
frame_time,
|
|
tracked_objects,
|
|
motion_boxes,
|
|
regions,
|
|
)
|
|
)
|
|
|
|
# cleanup event finished queue
|
|
while not self.stop_event.is_set():
|
|
update = self.event_end_subscriber.check_for_update(timeout=0.01)
|
|
|
|
if not update:
|
|
break
|
|
|
|
event_id, camera, _ = update
|
|
self.camera_states[camera].finished(event_id)
|
|
|
|
self.requestor.stop()
|
|
self.detection_publisher.stop()
|
|
self.event_sender.stop()
|
|
self.event_end_subscriber.stop()
|
|
logger.info("Exiting object processor...")
|