Update some work schemes

This commit is contained in:
t0xa 2026-02-10 15:50:37 +03:00
parent 62e533fec5
commit d4f18b886f
15 changed files with 568 additions and 924 deletions

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@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

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@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\n(Task Listener)" as TaskListener COMPONENT_BG_COLOR
participant "analyzer.py\n(Main Analyzer)" as Analyzer COMPONENT_BG_COLOR
participant "frames.py\n(Frame Puller)" as FramePuller COMPONENT_BG_COLOR
participant "LivePreview service" as LivePreview API_BG_COLOR
participant "TevianHeadsDetector\n(detectors/tevian.py)" as Detector COMPONENT_BG_COLOR
participant "Redis Cache" as RedisCache #FFCCCC
participant "Tevian Cloud API\n(ext_api/tevian_api.py)" as TevianAPI API_BG_COLOR
participant "S3 Storage" as S3 STORAGE_BG_COLOR
participant "Central (crowd backend)" 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
activate LivePreview
LivePreview --> FramePuller: JPEG image bytes
deactivate LivePreview
FramePuller -> FramePuller: Frame(content)\n- создает PIL Image\n- генерирует image_id
FramePuller --> Analyzer: Frame object
deactivate FramePuller
== Запуск детектора ==
Analyzer -> Analyzer: _run_detectors(uin, camera, frame, zones)
Analyzer -> Detector: request(uin, camera, frame, zones)
activate Detector
== Подготовка Tevian (prepare) ==
Detector -> Detector: prepare(cam_name, zones)
note right
Подготовка включает:
1. Создание/получение камеры
2. Синхронизацию очередей (зон)
3. Обновление параметров зон
end note
Detector -> Detector: _get_or_create_camera(cam_name)
Detector -> RedisCache: get_camera(cam_name)
activate RedisCache
RedisCache --> Detector: TCamera or None
deactivate RedisCache
alt Камеры нет в кеше
Detector -> TevianAPI: TCamera.get_all()
activate TevianAPI
TevianAPI -> TevianAPI: _refresh_token() if needed
TevianAPI -> "Tevian Cloud": GET /api/cameras
"Tevian Cloud" --> TevianAPI: список камер [{id, name, ...}]
TevianAPI --> Detector: [TCamera, ...]
deactivate TevianAPI
Detector -> RedisCache: set_camera(cam) для каждой
alt Камера все еще не найдена
Detector -> TevianAPI: TCamera.create(cam_name)
activate TevianAPI
TevianAPI -> "Tevian Cloud": POST /api/cameras\n{name, rtsp, frequency_plan_id, ...}
"Tevian Cloud" --> TevianAPI: {id, name, status, ...}
TevianAPI --> Detector: TCamera
deactivate TevianAPI
Detector -> RedisCache: set_camera(cam)
end
end
== Синхронизация очередей (зон) ==
Detector -> Detector: _get_camera_queues(cam)
Detector -> RedisCache: get_queue(q_id)\nдля каждого queues_ids камеры
RedisCache --> Detector: TQueue objects
loop Для каждой зоны из запроса
Detector -> Detector: Конвертировать координаты\nв относительные (0..1)
alt Очередь не найдена
Detector -> TevianAPI: TQueue.create(cam_id, zone_id, polygon, min_head_size)
activate TevianAPI
TevianAPI -> "Tevian Cloud": POST /api/queues\n{name, camera_id, roi_polygon_relative, ...}
"Tevian Cloud" --> TevianAPI: {id, name, camera_id, ...}
TevianAPI --> Detector: TQueue
deactivate TevianAPI
Detector -> RedisCache: set_queue(queue)
else Параметры зоны изменились
Detector -> TevianAPI: queue.save()
activate TevianAPI
TevianAPI -> "Tevian Cloud": POST /api/queues/{id}\n{roi_polygon_relative, ...}
"Tevian Cloud" --> TevianAPI: updated queue
TevianAPI --> Detector: success
deactivate TevianAPI
Detector -> RedisCache: set_queue(queue)
end
end
loop Для старых очередей (не в списке зон)
Detector -> TevianAPI: TQueue.delete_by_id(queue_id)
activate TevianAPI
TevianAPI -> "Tevian Cloud": DELETE /api/queues/{id}
"Tevian Cloud" --> TevianAPI: success
TevianAPI --> Detector: success
deactivate TevianAPI
Detector -> RedisCache: delete_queue(queue_id)
end
Detector -> TevianAPI: cam.refresh()
note right
Обновляем состояние камеры
после изменения очередей
end note
activate TevianAPI
TevianAPI -> "Tevian Cloud": GET /api/cameras/{id}
"Tevian Cloud" --> TevianAPI: {status, is_accepting_snapshots, ...}
TevianAPI --> Detector: updated TCamera
deactivate TevianAPI
Detector -> RedisCache: set_camera(cam)
== Отправка снапшота и получение результатов ==
Detector -> Detector: Проверка rate limiting\n(FORCED_WAIT_PERIOD)
note right
Избегаем HTTP 429: Too Many Requests
Ждем если запрос слишком частый
end note
alt Слишком частые запросы
Detector -> Detector: asyncio.sleep(wait_for)
end
Detector -> TevianAPI: cam.send_snapshot(frame.data)
activate TevianAPI
TevianAPI -> "Tevian Cloud": POST /api/cameras/{id}/snapshots\nContent-Type: image/jpeg\nbody: <JPEG bytes>
"Tevian Cloud" --> TevianAPI: {snapshot_accepted_at: <timestamp>}
TevianAPI --> Detector: timestamp
deactivate TevianAPI
Detector -> Detector: asyncio.sleep(TEVIAN_RECOGNITION_DELAY)\n(рекомендуется 12 сек)
Detector -> TevianAPI: TRecognition.get_many(queues_ids, timestamp)
activate TevianAPI
loop Polling до получения результатов или timeout
TevianAPI -> "Tevian Cloud": GET /api/recognitions?\nqueues_ids={ids}&utc_timestamp={ts}
"Tevian Cloud" --> TevianAPI: [recognitions...]
alt Результатов меньше чем очередей
TevianAPI -> TevianAPI: await gen.sleep(2)\nи повторить
else Все результаты получены
TevianAPI -> TevianAPI: break
end
end
TevianAPI -> TevianAPI: Фильтровать detections:\nоставить только\nfiltered_status == 'passed_filters'
TevianAPI --> Detector: [TRecognition, ...]
deactivate TevianAPI
== Форматирование результата ==
loop Для каждого recognition
Detector -> RedisCache: get_queue(rec.queue_id)
RedisCache --> Detector: TQueue
Detector -> Detector: Форматировать objects:\n[{x, y, w, h}, ...]\nиз bbox данных
Detector -> Detector: result[queue.name] = {\n 'count': len(objects),\n 'objects': objects\n}
end
Detector --> Analyzer: result dict\n{zone_id: {count, objects}, ...}
deactivate Detector
Analyzer -> Analyzer: _build_zones_info(zones, detected_values)
note right
Объединяет данные зон с результатами
детекторов, определяет length_by_ai
end note
Analyzer -> Analyzer: _get_triggered_zones(zones_info, timestamp)
note right
Определяет зоны для подсветки
на основе trigger_at и trigger_type
end note
== Сохранение результата ==
Analyzer -> Analyzer: frame.draw_zones(triggered_zones)
note right
Рисует полигоны триггерных зон
на изображении с прозрачностью
end note
Analyzer -> Analyzer: resize_image(image, 640)
Analyzer -> S3: storage.upload_fileobj(\n image, bucket, key, ...)
activate S3
S3 --> Analyzer: ObjRef
deactivate S3
Analyzer -> S3: storage.generate_presigned_url(obj_ref)
activate S3
S3 --> Analyzer: presigned_url
deactivate S3
Analyzer -> Analyzer: Удалить query params\nиз URL (сделать публичным)
== 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})
activate Central
note right
Отправка через aio_broker
в очередь 'overmind:input'
с командой 'new_measurement'
end note
Central --> Analyzer: (async, no wait)
deactivate Central
Analyzer -> Analyzer: Обновить БД zones_db:\ndetected_at = time.time()
Analyzer --> TaskListener: complete
deactivate Analyzer
TaskListener -> TaskListener: Ожидать следующую задачу
deactivate TaskListener
@enduml

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@startuml Crowd Node - Request Processing Flow
!define COMPONENT_BG_COLOR #E3F2FD
!define API_BG_COLOR #FFF3E0
!define STORAGE_BG_COLOR #F3E5F5
participant "tasks.py" as TaskListener COMPONENT_BG_COLOR
participant "analyzer.py" as Analyzer COMPONENT_BG_COLOR
participant "PersonDetector" as PersonDetector
participant "PersonDetectionService" as PersonDetectionService
participant "Redis Queue" as RedisQueue #FFCCCC
participant "S3 Storage" as S3 STORAGE_BG_COLOR
participant "Central" as Central API_BG_COLOR
== Вариант 1: Получаем задачу в analyze, процессим синхронно и отдаем ответ ==
TaskListener -> Analyzer : Получена задача на процессинг
Analyzer -> PersonDetector : Отправка задачи на анализ
PersonDetector -> PersonDetectionService : Установка HTTP соединения
group Открытое HTTP соединение
PersonDetector -> PersonDetectionService : HTTP POST запрос
PersonDetectionService -> PersonDetectionService : Обарботка запроса
PersonDetectionService --> PersonDetector : Отправка в response результата
end group
PersonDetector --> Analyzer : Результаты задачи
== Вариант 2: Получаем задачу в analyze, кладем в очередь, процессим в очереди, формируем результат и отдаем в ответе analyze ==
TaskListener -> Analyzer : Получена задача на процессинг
Analyzer -> RedisQueue : Положили в очередь задачу на анализ изображения
group Polling
PersonDetector -> RedisQueue : полит очередь на предмет наличия задач
RedisQueue --> PersonDetector : Получает задачу на анализ
group Асинхронный запрос на анализ
PersonDetector -> PersonDetectionService : Дерганье API сервиса для анализа
PersonDetectionService -> PersonDetectionService : Обарботка запроса
PersonDetectionService --> PersonDetector : Отправка в response результата
end group
end group
group Polling
Analyzer -> RedisQueue : Полит в ожидании выполненных задач
RedisQueue --> Analyzer : Выполненные задачи анализа
end group
== Вариант 3: Получаем задачу в analyze, процессим синхронно, формируем результат, отдаем в ответе get_results ==
note over PersonDetector
Такое ощущение что это похоже не вариант 1
end note
== Вариант 4: Получаем задачу в analyze кладем в очередь, процессим в очереди, формируем результат и отдаем в ответе get_results ==
TaskListener -> Analyzer : Получена задача на процессинг
activate Analyzer
Analyzer -> PersonDetector : Отправка задачи на анализ\n<b>во внутреннюю очередь PersonDetector'a</b>
group Внутрянка PersonDetector'a
PersonDetector -> PersonDetector: как то хэндлит запросы на обработку
group Обработка в порядке очереди
PersonDetector -> PersonDetectionService : отправляет HTTP запросы на обработку\nпо внутренней логике
PersonDetectionService --> PersonDetector
PersonDetector --> Analyzer : Результат задачи на обработку
deactivate Analyzer
end group
end group
@enduml

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@ -1,540 +0,0 @@
@startuml Folder System Architecture
title Система папок/групп в Ivideon
package "Database" @startuml FolderSystemSimple
title Система папок в Ivideon
entity "folders" as folders_db {
* _id : ObjectId
--
* owner_id : string
* name : string
* parents : array
* objects : array
* root : boolean
}
entity "permission_grants" as grants_db {
* _id : ObjectId
--
* object_id : string
* object_type : string
* grantee_id : string
* permissions : array
}
entity "servers" as servers_db {
* _id : ObjectId
--
* owner_id : string
* cameras : object
}
entity "Folder" as folder_class {
+ get_objects(type)
+ add_object(obj)
+ remove_object(obj)
+ has_permissions(perm)
}
entity "FolderTree" as tree_class {
+ folders : dict
+ find_folders()
+ reload()
}
entity "Camera" as camera_node {
+ id : "server:index"
+ object_type : "camera"
}
folders_db ||--o{ folder_class
grants_db ||--o{ folder_class
servers_db ||--o{ camera_node
tree_class --> folder_class : manages
folder_class --> camera_node : contains
note right of folders_db
objects[] format:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note bottom of tree_class
Usage:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
@enduml
@startuml FolderSystemSimple
title Система папок в Ivideon
entity "folders" as folders_db {
* _id : ObjectId
--
* owner_id : string
* name : string
* parents : array
* objects : array
* root : boolean
}
entity "permission_grants" as grants_db {
* _id : ObjectId
--
* object_id : string
* object_type : string
* grantee_id : string
* permissions : array
}
entity "servers" as servers_db {
* _id : ObjectId
--
* owner_id : string
* cameras : object
}
entity "Folder" as folder_class {
+ get_objects(type)
+ add_object(obj)
+ remove_object(obj)
+ has_permissions(perm)
}
entity "FolderTree" as tree_class {
+ folders : dict
+ find_folders()
+ reload()
}
entity "Camera" as camera_node {
+ id : "server:index"
+ object_type : "camera"
}
folders_db ||--o{ folder_class
grants_db ||--o{ folder_class
servers_db ||--o{ camera_node
tree_class --> folder_class : manages
folder_class --> camera_node : contains
note right of folders_db
objects[] format:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note bottom of tree_class
Usage:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
@enduml
@startuml FolderSystemSimple
title Система папок в Ivideon
entity "folders" as folders_db {
* _id : ObjectId
--
* owner_id : string
* name : string
* parents : array
* objects : array
* root : boolean
}
entity "permission_grants" as grants_db {
* _id : ObjectId
--
* object_id : string
* object_type : string
* grantee_id : string
* permissions : array
}
entity "servers" as servers_db {
* _id : ObjectId
--
* owner_id : string
* cameras : object
}
entity "Folder" as folder_class {
+ get_objects(type)
+ add_object(obj)
+ remove_object(obj)
+ has_permissions(perm)
}
entity "FolderTree" as tree_class {
+ folders : dict
+ find_folders()
+ reload()
}
entity "Camera" as camera_node {
+ id : "server:index"
+ object_type : "camera"
}
folders_db ||--o{ folder_class
grants_db ||--o{ folder_class
servers_db ||--o{ camera_node
tree_class --> folder_class : manages
folder_class --> camera_node : contains
note right of folders_db
objects[] format:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note bottom of tree_class
Usage:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
@enduml
@startuml FolderSystemSimple
title Система папок в Ivideon
entity "folders" as folders_db {
* _id : ObjectId
--
* owner_id : string
* name : string
* parents : array
* objects : array
* root : boolean
}
entity "permission_grants" as grants_db {
* _id : ObjectId
--
* object_id : string
* object_type : string
* grantee_id : string
* permissions : array
}
entity "servers" as servers_db {
* _id : ObjectId
--
* owner_id : string
* cameras : object
}
entity "Folder" as folder_class {
+ get_objects(type)
+ add_object(obj)
+ remove_object(obj)
+ has_permissions(perm)
}
entity "FolderTree" as tree_class {
+ folders : dict
+ find_folders()
+ reload()
}
entity "Camera" as camera_node {
+ id : "server:index"
+ object_type : "camera"
}
folders_db ||--o{ folder_class
grants_db ||--o{ folder_class
servers_db ||--o{ camera_node
tree_class --> folder_class : manages
folder_class --> camera_node : contains
note right of folders_db
objects[] format:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note bottom of tree_class
Usage:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
@enduml
@startuml FolderSystemSimple
title Система папок в Ivideon
entity "folders" as folders_db {
* _id : ObjectId
--
* owner_id : string
* name : string
* parents : array
* objects : array
* root : boolean
}
entity "permission_grants" as grants_db {
* _id : ObjectId
--
* object_id : string
* object_type : string
* grantee_id : string
* permissions : array
}
entity "servers" as servers_db {
* _id : ObjectId
--
* owner_id : string
* cameras : object
}
entity "Folder" as folder_class {
+ get_objects(type)
+ add_object(obj)
+ remove_object(obj)
+ has_permissions(perm)
}
entity "FolderTree" as tree_class {
+ folders : dict
+ find_folders()
+ reload()
}
entity "Camera" as camera_node {
+ id : "server:index"
+ object_type : "camera"
}
folders_db ||--o{ folder_class
grants_db ||--o{ folder_class
servers_db ||--o{ camera_node
tree_class --> folder_class : manages
folder_class --> camera_node : contains
note right of folders_db
objects[] format:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note bottom of tree_class
Usage:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
@enduml
@startuml FolderSystemSimple
title Система папок в Ivideon
entity "folders" as folders_db {
* _id : ObjectId
--
* owner_id : string
* name : string
* parents : array
* objects : array
* root : boolean
}
entity "permission_grants" as grants_db {
* _id : ObjectId
--
* object_id : string
* object_type : string
* grantee_id : string
* permissions : array
}
entity "servers" as servers_db {
* _id : ObjectId
--
* owner_id : string
* cameras : object
}
entity "Folder" as folder_class {
+ get_objects(type)
+ add_object(obj)
+ remove_object(obj)
+ has_permissions(perm)
}
entity "FolderTree" as tree_class {
+ folders : dict
+ find_folders()
+ reload()
}
entity "Camera" as camera_node {
+ id : "server:index"
+ object_type : "camera"
}
folders_db ||--o{ folder_class
grants_db ||--o{ folder_class
servers_db ||--o{ camera_node
tree_class --> folder_class : manages
folder_class --> camera_node : contains
note right of folders_db
objects[] format:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note bottom of tree_class
Usage:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
@enduml
{
database folders_db as "folders collection"
database grants_db as "permission_grants"
database servers_db as "servers collection"
}
package "Folder Classes" {
class Folder {
+id: ObjectId
+owner_id: string
+name: string
+parents: List[string]
+objects: List[dict]
+root: boolean
--
+get_objects(type): List[string]
+add_object(obj)
+remove_object(obj)
+has_permissions(perm): boolean
}
class FolderTree {
+owner_id: string
+folders: Dict[id, Folder]
+objects: Dict[type, Dict[id, Node]]
+roots: List[Folder]
--
+find_folders(): List[Folder]
+reload()
}
class BaseNode {
+id: string
+owner_id: string
+grantee_id: string
+grants: Set[PermissionGrant]
--
+has_permissions(perm): boolean
+permissions: Tuple[string]
}
}
package "Permission System" {
class PermissionGrant {
+object_id: string
+object_type: string
+grantee_id: string
+permissions: List[string]
+shared_at: dict
}
}
package "Node Types" {
class Camera {
+id: "server:index"
+object_type: "camera"
}
}
' Relationships
Folder --|> BaseNode
FolderTree --> Folder : manages
Folder --> PermissionGrant : has grants
BaseNode --> PermissionGrant : uses
Folder --> folders_db : stored in
PermissionGrant --> grants_db : stored in
Camera --> servers_db : stored in
' Composition relationships
Folder --> Camera : "contains (objects[])"
Folder --> Folder : "contains subfolders"
note right of Folder
objects[] содержит:
[
{object_type: "camera",
object_id: "server:0"},
{object_type: "folder",
object_id: "subfolder_id"}
]
end note
note right of FolderTree
Главная точка доступа:
tree = FolderTree(user_id)
folder = tree.folders[folder_id]
cameras = folder.get_objects("camera")
end note
note left of PermissionGrant
Права доступа:
- admin (изменение)
- read (просмотр)
- Наследование по иерархии
end note
@enduml

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@ -1,326 +0,0 @@
@startuml _get_all_user_cameras Sequence Diagram
title Последовательность выполнения _get_all_user_cameras
participant "Caller" as caller
participant "_get_all_user_cameras" as main_func
participant "_get_servers" as get_servers
participant "MongoDB" as mongo
participant "ivideon.servers" as servers_collection
note over main_func
Входные параметры:
- user_id: int
- requested_cameras: list[str]
(формат: ["server1:0", "server1:1"])
- service_name: str (например: "crowd")
end note
caller -> main_func: _get_all_user_cameras(user_id, requested_cameras, service_name)
activate main_func
main_func -> main_func: cameras = {}
main_func -> get_servers: _get_servers(requested_cameras)
activate get_servers
note over get_servers
Извлекает server_ids из camera_ids:
["server1:0", "server1:1"]
→ ["server1", "server1"]
→ ["server1"]
end note
@startuml _get_all_user_cameras Activity Diagram
title Алгоритм работы _get_all_user_cameras
start
note right
**Входные параметры:**
• user_id: int
• requested_cameras: list[str]
(формат: ["server1:0", "server1:1"])
• service_name: str (например: "crowd")
end note
:Инициализация cameras = {};
:Извлечь server_ids из requested_cameras|
note right
["server1:0", "server1:1"]
→ ["server1"]
end note
:Построить MongoDB запрос:
query = {
'deleted': {'$ne': True},
'_id': {'$in': server_ids}
}|
:Задать проекцию полей:
projection = {
'_id': 1, 'owner_id': 1, 'name': 1,
'cameras': 1, 'cam_services': 1,
'info': 1, 'timezone': 1
}|
:Выполнить запрос к MongoDB:
servers = db.ivideon().servers.find(query, projection)|
partition "Обработка серверов" {
:Взять следующий server;
while (Есть серверы для обработки?) is (да)
:server_id = server['_id'];
:is_shared = server['owner_id'] != user_id;
:server_build_type = server.get('info', {}).get('build_type', '');
:is_server_embedded = server_build_type.endswith('camera');
:cam_services = server.get('cam_services', {});
partition "Обработка камер сервера" {
:Взять следующую камеру (camera_idx, camera_data);
while (Есть камеры на сервере?) is (да)
:service_info = cam_services.get(camera_idx, {})
.get(service_name, {});
if (service_info.get('active', False) == True?) then (да)
:camera_id = f'{server_id}:{camera_idx}';
if (is_server_embedded?) then (да)
:camera_name = server['name'];
else (нет)
:camera_name = camera_data.get('name');
endif
:cameras[camera_id] = {
'id': camera_id,
'owner_id': server['owner_id'],
'server': server_id,
'name': camera_name,
'is_shared': is_shared,
'timezone': server.get('timezone') or
server.get('timezone_default'),
'is_embedded': is_server_embedded
};
else (нет)
note right: Камера пропускается - сервис неактивен
endif
:Взять следующую камеру (camera_idx, camera_data);
endwhile (нет)
}
:Взять следующий server;
endwhile (нет)
}
:return cameras;
stop
note left
**Результат:** dict[camera_id, camera_info]
**Пример:**
{
"507f...439011:0": {
"id": "507f...439011:0",
"owner_id": "user123",
"server": "507f...439011",
"name": "Камера входа",
"is_shared": false,
"timezone": "Europe/Moscow",
"is_embedded": false
}
}
end note
@enduml
ggVG
get_servers -> get_servers: requested_server_ids = [camera_id.split(':')[0] \\nfor camera_id in requested_camera_ids]
get_servers -> get_servers: query = {\n 'deleted': {'$ne': True},\n '_id': {'$in': requested_server_ids}\n}
get_servers -> get_servers: projection = {\n '_id': 1, 'owner_id': 1, 'name': 1,\n 'cameras': 1, 'cam_services': 1,\n 'info': 1, 'timezone': 1\n}
get_servers -> mongo: db.ivideon().servers.find(query, projection)
activate mongo
mongo -> servers_collection: find documents
activate servers_collection
servers_collection -> mongo: return server documents
deactivate servers_collection
mongo -> get_servers: list[server_documents]
deactivate mongo
get_servers -> main_func: return servers_list
deactivate get_servers
loop for each server in servers_list
main_func -> main_func: server@startuml _get_all_user_cameras Activity Diagram
title Алгоритм работы _get_all_user_cameras
start
note right
**Входные параметры:**
• user_id: int
• requested_cameras: list[str]
(формат: ["server1:0", "server1:1"])
• service_name: str (например: "crowd")
end note
:Инициализация cameras = {};
:Извлечь server_ids из requested_cameras|
note right
["server1:0", "server1:1"]
→ ["server1"]
end note
:Построить MongoDB запрос:
query = {
'deleted': {'$ne': True},
'_id': {'$in': server_ids}
}|
:Задать проекцию полей:
projection = {
'_id': 1, 'owner_id': 1, 'name': 1,
'cameras': 1, 'cam_services': 1,
'info': 1, 'timezone': 1
}|
:Выполнить запрос к MongoDB:
servers = db.ivideon().servers.find(query, projection)|
partition "Обработка серверов" {
:Взять следующий server;
while (Есть серверы для обработки?) is (да)
:server_id = server['_id'];
:is_shared = server['owner_id'] != user_id;
:server_build_type = server.get('info', {}).get('build_type', '');
:is_server_embedded = server_build_type.endswith('camera');
:cam_services = server.get('cam_services', {});
partition "Обработка камер сервера" {
:Взять следующую камеру (camera_idx, camera_data);
while (Есть камеры на сервере?) is (да)
:service_info = cam_services.get(camera_idx, {})
.get(service_name, {});
if (service_info.get('active', False) == True?) then (да)
:camera_id = f'{server_id}:{camera_idx}';
if (is_server_embedded?) then (да)
:camera_name = server['name'];
else (нет)
:camera_name = camera_data.get('name');
endif
:cameras[camera_id] = {
'id': camera_id,
'owner_id': server['owner_id'],
'server': server_id,
'name': camera_name,
'is_shared': is_shared,
'timezone': server.get('timezone') or
server.get('timezone_default'),
'is_embedded': is_server_embedded
};
else (нет)
note right: Камера пропускается - сервис неактивен
endif
:Взять следующую камеру (camera_idx, camera_data);
endwhile (нет)
}
:Взять следующий server;
endwhile (нет)
}
:return cameras;
stop
note left
**Результат:** dict[camera_id, camera_info]
**Пример:**
{
"507f...439011:0": {
"id": "507f...439011:0",
"owner_id": "user123",
"server": "507f...439011",
"name": "Камера входа",
"is_shared": false,
"timezone": "Europe/Moscow",
"is_embedded": false
}
}
end note
@enduml
ggVG_id = server['_id']
main_func -> main_func: is_shared = server['owner_id'] != user_id
main_func -> main_func: server_build_type = server.get('info', {}).get('build_type', '')
main_func -> main_func: is_server_embedded = server_build_type.endswith('camera')
main_func -> main_func: cam_services = server.get('cam_services', {})
loop for camera_idx, camera_data in server.cameras.items()
main_func -> main_func: service_info = cam_services.get(camera_idx, {})\\n .get(service_name, {})
alt service_info.get('active', False) == True
main_func -> main_func: camera_id = f'{server_id}:{camera_idx}'
alt is_server_embedded == True
main_func -> main_func: camera_name = server['name']
else
main_func -> main_func: camera_name = camera_data.get('name')
end
main_func -> main_func: cameras[camera_id] = {\n 'id': camera_id,\n 'owner_id': server['owner_id'],\n 'server': server_id,\n 'name': camera_name,\n 'is_shared': is_shared,\n 'timezone': server.timezone,\n 'is_embedded': is_server_embedded\n}
note right
Создается полная информация
о камере для возврата
end note
else
note right
Камера пропускается:
сервис неактивен
end note
end
end
end
main_func -> caller: return cameras dict
deactivate main_func
note over caller
Результат: dict[camera_id, camera_info]
где camera_id = "server_id:camera_index"
Пример:
{
"507f...439011:0": {
"id": "507f...439011:0",
"owner_id": "user123",
"server": "507f...439011",
"name": "Камера входа",
"is_shared": false,
"timezone": "Europe/Moscow",
"is_embedded": false
}
}
end note
@enduml

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@ -1,58 +0,0 @@
@startuml
title Верхнеуровневые сущности сервиса Crowd
card api_concept{
entity "CrowdReport(APIObject)" as CRA{
+ id str
+ owner_id str
+ type str
+ name str
+ status str
+ created_at timestamp
+ updated_at timestamp
+ progress int
+ options dict
+ create() -> CrowdReport
}
}
card crowd_service{
card backend {
}
card bot_notifier {
}
card frontend {
card impl {
entity CrowdReport{
+ delete() -> None
+ create() -> CrowdReport
}
}
}
card node {
}
card protocols{
}
card report_builder{
}
card utils {
}
}
json options_dict {
"cameras": ["cam1", "cam2"],
"folders": ["folder1"],
"zones": ["zone1"]
}
CrowdReport ..|> CRA
CRA::options -- options_dict
@enduml

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@ -0,0 +1,83 @@
@startuml API Infrastructure with HAProxy
skinparam linetype ortho
title Архитектура развертывания API (с HAProxy)
actor "Клиент" as Client
cloud "Internet" as Internet
package "Kubernetes Cluster" {
package "Ingress Layer" {
component "Nginx Ingress\nController" as NginxIngress #lightblue
note right of NginxIngress
- HTTPS (443) termination
- TLS (Let's Encrypt)
- X-Forwarded-For
- Domains:
* api.ivideon.com
* api.stage-01.stg01-k8s.extcam.com
end note
}
package "Proxy Layer" {
component "HAProxy\n(haproxy-central)" as HAProxy #lightgreen
note right of HAProxy
- Port 80 (HTTP)
- ACL routing
- Health checks (/status)
- Backend: api4.service.ivideon:80
end note
}
package "Service Layer" {
component "api4 Service" as Service #lightyellow
note right of Service
- Kubernetes Service
- Port 80 → 8080
- Load balancing
- DNS: api4.service.ivideon
end note
}
package "Application Layer" {
collections "api4 Pods" as Pods
component "Pod 1" as Pod1 {
component "Tornado\nHTTP Server" as Tornado1 #orange
note bottom of Tornado1
- Port: 8080
- xheaders: true
- Workers: 4
end note
}
component "Pod 2-N" as PodN {
component "Tornado\nHTTP Server" as TornadoN #orange
}
}
}
database "MongoDB\n(main)" as MongoDB
database "MongoDB\n(user_registry)" as UserRegistry
Client --> Internet: HTTPS\nPOST /public/registration
Internet --> NginxIngress: 443 (HTTPS)
NginxIngress --> HAProxy: 80 (HTTP)\n+ X-Forwarded-For
HAProxy --> Service: api4.service.ivideon:80\n(ACL: !has_api5_components)
Service --> Pods: Round-robin LB
Pods --> Pod1: 8080
Pods --> PodN: 8080
Pod1 --> MongoDB: users.insert_one()
Pod1 --> UserRegistry: check duplicate
note bottom of HAProxy
**HAProxy ACL Routing:**
- use_backend api4 if host_api !has_api5_components
- Health check: GET /status
- server-template api-four-srv 4
- option redispatch
end note
@enduml

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@ -0,0 +1,85 @@
@startuml Folder Structure
package "MongoDB Collection: folders" {
object "Root Folder" as root {
_id = "root_abc123"
name = "__root__"
parents = []
objects = []
owner_id = "123456"
owner_name = "user@example.com"
root = true
}
object "Folder: Office" as office {
_id = "folder_office_xyz"
name = "Office"
parents = ["root_abc123"]
objects = [
{object_type: "camera", object_id: "cam_1"},
{object_type: "camera", object_id: "cam_2"}
]
owner_id = "123456"
}
object "Folder: Warehouse" as warehouse {
_id = "folder_warehouse_qwe"
name = "Warehouse"
parents = ["root_abc123"]
objects = [
{object_type: "camera", object_id: "cam_3"},
{object_type: "server", object_id: "srv_1"}
]
owner_id = "123456"
}
object "Subfolder: Entrance" as entrance {
_id = "folder_entrance_asd"
name = "Entrance"
parents = ["root_abc123", "folder_office_xyz"]
objects = [
{object_type: "camera", object_id: "cam_4"}
]
owner_id = "123456"
}
}
object "User" as user {
_id = 123456
login = "user@example.com"
root_folder = "root_abc123"
}
user --> root : root_folder
root --> office : subfolder
root --> warehouse : subfolder
office --> entrance : subfolder
note right of root
При создании пользователя
создается пустая root_folder
objects = []
parents = []
root = true
end note
note right of office
Пользователь может создавать
папки для организации камер:
- Офис
- Склад
- Парковка
и т.д.
end note
note right of entrance
Поддерживается вложенность:
parents = [root, office]
Уровень вложенности:
level = len(parents) = 2
end note
@enduml