mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
fbaab71d78
* Create timeline table * Fix indexes * Add other fields * Adjust schema to be less descriptive * Handle timeline queue from tracked object data * Setup timeline queue in events * Add source id for index * Add other fields * Fixes * Formatting * Store better data * Add api with filtering * Setup basic UI for timeline in events * Cleanups * Add recordings snapshot url * Start working on timeline ui * Add tooltip with info * Improve icons * Fix start time with clip * Move player logic back to clips * Make box in timeline relative coordinates * Make region relative * Get box overlay working * Remove overlay when playing again * Add disclaimer when selecting overlay points * Add docs for new apis * Fix mobile * Fix docs * Change color of bottom center box * Fix vscode formatting
141 lines
5.1 KiB
Python
141 lines
5.1 KiB
Python
"""Record events for object, audio, etc. detections."""
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import logging
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import threading
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import queue
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from enum import Enum
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from frigate.config import FrigateConfig
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from frigate.models import Timeline
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from multiprocessing.queues import Queue
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from multiprocessing.synchronize import Event as MpEvent
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logger = logging.getLogger(__name__)
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class TimelineSourceEnum(str, Enum):
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# api = "api"
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# audio = "audio"
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tracked_object = "tracked_object"
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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 == TimelineSourceEnum.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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if event_type == "start":
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Timeline.insert(
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timestamp=event_data["frame_time"],
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camera=camera,
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source="tracked_object",
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source_id=event_data["id"],
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class_type="visible",
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data={
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"box": [
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event_data["box"][0] / camera_config.detect.width,
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event_data["box"][1] / camera_config.detect.height,
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event_data["box"][2] / camera_config.detect.width,
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event_data["box"][3] / camera_config.detect.height,
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],
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"label": event_data["label"],
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"region": [
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event_data["region"][0] / camera_config.detect.width,
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event_data["region"][1] / camera_config.detect.height,
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event_data["region"][2] / camera_config.detect.width,
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event_data["region"][3] / camera_config.detect.height,
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],
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},
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).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.insert(
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timestamp=event_data["frame_time"],
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camera=camera,
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source="tracked_object",
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source_id=event_data["id"],
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class_type="entered_zone",
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data={
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"box": [
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event_data["box"][0] / camera_config.detect.width,
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event_data["box"][1] / camera_config.detect.height,
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event_data["box"][2] / camera_config.detect.width,
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event_data["box"][3] / camera_config.detect.height,
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],
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"label": event_data["label"],
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"region": [
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event_data["region"][0] / camera_config.detect.width,
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event_data["region"][1] / camera_config.detect.height,
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event_data["region"][2] / camera_config.detect.width,
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event_data["region"][3] / camera_config.detect.height,
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],
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"zones": event_data["current_zones"],
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},
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).execute()
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elif event_type == "end":
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Timeline.insert(
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timestamp=event_data["frame_time"],
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camera=camera,
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source="tracked_object",
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source_id=event_data["id"],
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class_type="gone",
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data={
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"box": [
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event_data["box"][0] / camera_config.detect.width,
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event_data["box"][1] / camera_config.detect.height,
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event_data["box"][2] / camera_config.detect.width,
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event_data["box"][3] / camera_config.detect.height,
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],
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"label": event_data["label"],
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"region": [
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event_data["region"][0] / camera_config.detect.width,
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event_data["region"][1] / camera_config.detect.height,
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event_data["region"][2] / camera_config.detect.width,
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event_data["region"][3] / camera_config.detect.height,
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],
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},
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).execute()
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