@startuml Crowd Analyzer Flow

  title Поток анализа очередей в Crowd Analyzer

  actor "Redis Queue" as redis
  participant "analyze_crowd()" as entry
  participant "_analyze_crowd()" as main
  participant "frames.pull()" as frames_mod
  participant "_run_detectors()" as detectors
  participant "TevianHeadsDetector" as tevian
  participant "_build_zones_info()" as zones_builder
  participant "_get_triggered_zones()" as trigger_checker
  participant "Storage" as storage
  participant "central.send()" as central
  participant "zones_db" as db

  redis -> entry: task(uin, camera_id, zones)
  activate entry

  entry -> entry: acquire TASKS_LICENCES
  entry -> main: _analyze_crowd(uin, camera, zones, server_id)
  activate main

  main -> main: _filter_frequent_tasks()
  alt tasks too frequent
    main -> entry: return (skip)
  else proceed
    main -> frames_mod: pull(uin, camera)
    activate frames_mod
    frames_mod -> main: Frame object
    deactivate frames_mod

    opt rotation_angle provided
      main -> main: frame.rotate(rotation_angle)
    end

    main -> detectors: _run_detectors(uin, camera, frame, zones)
    activate detectors

    detectors -> detectors: check tevian enabled & used
    detectors -> tevian: request(uin, camera, frame, zones)
    activate tevian
    tevian -> detectors: detection results
    deactivate tevian

    detectors -> main: detected_values, timings, errors
    deactivate detectors

    main -> zones_builder: _build_zones_info(zones, detected_values)
    activate zones_builder
    zones_builder -> main: zones_info (with AI results)
    deactivate zones_builder

    main -> trigger_checker: _get_triggered_zones(zones_info, timestamp)
    activate trigger_checker

    loop for each zone
      trigger_checker -> trigger_checker: check trigger conditions
      trigger_checker -> trigger_checker: check schedule
      trigger_checker -> trigger_checker: check grace period
    end

    trigger_checker -> main: triggered_zones
    deactivate trigger_checker

    main -> main: draw zones on frame
    main -> storage: upload_fileobj(image_with_zones)
    activate storage
    storage -> main: zones_url
    deactivate storage

    main -> central: send('new_measurement', data)
    activate central
    central -> main: measurement sent
    deactivate central

    main -> db: update({'_id': f'{uin}/{camera}'}, detected_at)
    activate db
    db -> main: updated
    deactivate db
  end

  main -> entry: analysis complete
  deactivate main

  entry -> entry: release TASKS_LICENCES
  entry -> redis: task finished
  deactivate entry

  @enduml