mirror of
https://github.com/blakeblackshear/frigate.git
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ab50d0b006
* Add isort and ruff linter Both linters are pretty common among modern python code bases. The isort tool provides stable sorting and grouping, as well as pruning of unused imports. Ruff is a modern linter, that is very fast due to being written in rust. It can detect many common issues in a python codebase. Removes the pylint dev requirement, since ruff replaces it. * treewide: fix issues detected by ruff * treewide: fix bare except clauses * .devcontainer: Set up isort * treewide: optimize imports * treewide: apply black * treewide: make regex patterns raw strings This is necessary for escape sequences to be properly recognized.
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
"""Record events for object, audio, etc. detections."""
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import logging
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import queue
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import threading
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from multiprocessing.queues 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.events.maintainer import EventTypeEnum
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from frigate.models import Timeline
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from frigate.util import to_relative_box
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logger = logging.getLogger(__name__)
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class TimelineProcessor(threading.Thread):
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"""Handle timeline queue and update DB."""
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def __init__(
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self,
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config: FrigateConfig,
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queue: Queue,
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stop_event: MpEvent,
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) -> None:
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threading.Thread.__init__(self)
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self.name = "timeline_processor"
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self.config = config
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self.queue = queue
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self.stop_event = stop_event
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def run(self) -> None:
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while not self.stop_event.is_set():
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try:
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(
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camera,
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input_type,
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event_type,
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prev_event_data,
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event_data,
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) = self.queue.get(timeout=1)
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except queue.Empty:
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continue
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if input_type == EventTypeEnum.tracked_object:
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self.handle_object_detection(
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camera, event_type, prev_event_data, event_data
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)
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def handle_object_detection(
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self,
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camera: str,
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event_type: str,
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prev_event_data: dict[any, any],
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event_data: dict[any, any],
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) -> None:
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"""Handle object detection."""
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camera_config = self.config.cameras[camera]
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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.data: {
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"box": to_relative_box(
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camera_config.detect.width,
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camera_config.detect.height,
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event_data["box"],
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),
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"label": event_data["label"],
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"region": to_relative_box(
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camera_config.detect.width,
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camera_config.detect.height,
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event_data["region"],
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),
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},
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}
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if event_type == "start":
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timeline_entry[Timeline.class_type] = "visible"
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Timeline.insert(timeline_entry).execute()
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elif (
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event_type == "update"
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and prev_event_data["current_zones"] != event_data["current_zones"]
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and len(event_data["current_zones"]) > 0
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):
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timeline_entry[Timeline.class_type] = "entered_zone"
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timeline_entry[Timeline.data]["zones"] = event_data["current_zones"]
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Timeline.insert(timeline_entry).execute()
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elif event_type == "end":
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timeline_entry[Timeline.class_type] = "gone"
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Timeline.insert(timeline_entry).execute()
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