@startuml Crowd Node - Request Processing Flow

!define COMPONENT_BG_COLOR #E3F2FD
!define API_BG_COLOR #FFF3E0
!define STORAGE_BG_COLOR #F3E5F5

title Crowd Node: Процесс обработки запроса на анализ изображения

participant "Scheduler" as Scheduler
participant "Redis Queue" as RedisQueue #FFCCCC
participant "tasks.py" as TaskListener COMPONENT_BG_COLOR
participant "analyzer.py" as Analyzer COMPONENT_BG_COLOR
participant "frames.py" as FramePuller COMPONENT_BG_COLOR
participant "LivePreview service" as LivePreview API_BG_COLOR
box #LightGreen
participant "PersonDetector" as PersonDetector 
participant "PersonDetectionService" as PersonDetectionService 
end box
participant "Redis Cache" as RedisCache #FFCCCC
participant "S3 Storage" as S3 STORAGE_BG_COLOR
participant "Central" as Central API_BG_COLOR

== Получение задания ==

Scheduler -> RedisQueue: push task\n{cmd: "analyze_crowd",\nparams: {uin, camera_id, zones, ...}}
activate RedisQueue

TaskListener -> RedisQueue: pull_task()
activate TaskListener
RedisQueue --> TaskListener: task data
deactivate RedisQueue

TaskListener -> Analyzer: analyze_crowd(uin, camera_id, zones, **options)
activate Analyzer

== Получение кадра ==

Analyzer -> FramePuller: pull(uin, camera)
activate FramePuller
FramePuller -> LivePreview: GET /internal/preview
LivePreview --> FramePuller: JPEG image bytes
FramePuller -> FramePuller: Frame(content)\n- создает PIL Image\n- генерирует image_id
FramePuller --> Analyzer: Frame object
deactivate FramePuller

group #LightGreen Новый подход
== Взаимодействие с новым сервисом==
    Analyzer -> PersonDetector: _run_detecotrs(uin, camera, frame, zones)
    activate PersonDetector
    PersonDetector -> PersonDetector: prepare() - Метод для подготовки\nданных для отправки в сервис
    PersonDetector -> PersonDetectionService: POST /picture/analyze\nМетод для анализа
    activate PersonDetectionService
    PersonDetectionService -> PersonDetectionService: Сервис анализирует изображение
    PersonDetectionService --> PersonDetector: {results:\n\t[detection_1, detection_2, ...]\n}
    deactivate PersonDetectionService
    PersonDetector -> PersonDetector: parse_response() - Метод\nдля приведения резултата в формат,\nкоторый был раньше
end
PersonDetector --> Analyzer: result dict\n{zone_id: {count, objects}, ...}
deactivate PersonDetector


== Форматирование результата ==
deactivate Detector

== Сохранение результата ==

Analyzer -> S3: storage.upload_fileobj(\n  image, bucket, key, ...)
S3 --> Analyzer: ObjRef

Analyzer -> S3: storage.generate_presigned_url(obj_ref)
S3 --> Analyzer: presigned_url

== Отправка результата в Central ==

Analyzer -> Central: central.send('new_measurement', {\n  timestamp,\n  camera_id,\n  measurement_id,\n  image: zones_url,\n  timings,\n  errors,\n  zones: zones_info\n})
note right
  Отправка через aio_broker
  в очередь 'overmind:input'
  с командой 'new_measurement'
end note
Central --> Analyzer: (async, no wait)

Analyzer -> Analyzer: Обновить БД zones_db:\ndetected_at = time.time()

Analyzer --> TaskListener: complete
deactivate Analyzer

TaskListener -> TaskListener: Ожидать следующую задачу
deactivate TaskListener

@enduml