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Artem-Darius Weber 5 months ago
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sequence-diagrams.md
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@ -8,18 +8,21 @@ sequenceDiagram
participant GradioUI as Gradio Interface
participant RegisterUC as RegisterFaceUseCase
participant UserRepo as MongoDBUserRepository
participant FaceService as FaceRecognitionService
participant S3 as S3ObjectStorage
participant TaskPub as TaskPublisher
participant MQ as RabbitMQ
participant StatusRepo as TaskStatusRepository
participant Worker as FaceWorker
participant Detector as RetinaFaceDetector
participant Recognizer as SFaceRecognizer
participant FaceRepo as MongoDBFaceRepository
participant VectorStore as ChromaDBVectorStore
participant FileStorage as FileSystemStorage
participant MongoDB as MongoDB Database
participant ChromaDB as ChromaDB Vector DB
User->>GradioUI: Upload images + username
GradioUI->>RegisterUC: execute(username, images)
RegisterUC->>UserRepo: get_user_by_name(username)
UserRepo->>MongoDB: Query user by name
MongoDB-->>UserRepo: User data or null
@ -31,60 +34,53 @@ sequenceDiagram
MongoDB-->>UserRepo: User ID
UserRepo-->>RegisterUC: User ID
end
loop For each image
RegisterUC->>FaceService: register_face(user_id, image)
FaceService->>Detector: detect_faces(image)
Detector-->>FaceService: List of detected faces
alt No faces detected
FaceService-->>RegisterUC: null
else Face detected
FaceService->>Detector: calculate_quality_score(best_face)
Detector-->>FaceService: Quality score
alt Quality too low
FaceService-->>RegisterUC: null
else Quality acceptable
FaceService->>Detector: align_face(image, landmarks)
Detector-->>FaceService: Aligned face image
FaceService->>Recognizer: extract_embedding(aligned_face)
Recognizer-->>FaceService: Face embedding
FaceService->>FileStorage: save_image(aligned_face, user_id)
FileStorage-->>FaceService: Image path
FaceService->>FaceRepo: save(face_entity)
FaceRepo->>MongoDB: Insert face record
MongoDB-->>FaceRepo: Success
FaceRepo-->>FaceService: Success
FaceService->>VectorStore: add(face_id, embedding, metadata)
VectorStore->>ChromaDB: Store embedding vector
ChromaDB-->>VectorStore: Success
VectorStore-->>FaceService: Success
FaceService-->>RegisterUC: Face ID
end
RegisterUC->>S3: put_object(image)
S3-->>RegisterUC: s3_uri
RegisterUC->>TaskPub: publish_register_task(user_id, s3_uri)
TaskPub->>MQ: enqueue(task)
end
RegisterUC->>StatusRepo: wait/poll for task_ids status
StatusRepo-->>RegisterUC: Completed/Failed + results
alt All tasks completed
RegisterUC->>StatusRepo: get_mean_embedding(user_id) / or compute later
RegisterUC-->>GradioUI: Success message + stats
else Some tasks failed
RegisterUC-->>GradioUI: Partial success + failed items
end
GradioUI-->>User: Registration result (async aware)
%% Worker side
loop Worker Pool
MQ-->>Worker: task(user_id, s3_uri)
Worker->>S3: get_object(s3_uri)
Worker->>Detector: detect_faces(image)
Detector-->>Worker: faces
alt No faces or low quality
Worker->>StatusRepo: update(task_id, failed_reason)
else Face OK
Worker->>Detector: align_face(image, landmarks)
Detector-->>Worker: aligned_face
Worker->>Recognizer: extract_embedding(aligned_face)
Recognizer-->>Worker: embedding
Worker->>FaceRepo: save(face_entity)
FaceRepo->>MongoDB: insert face record
MongoDB-->>FaceRepo: ok
Worker->>VectorStore: add(face_id, embedding, metadata)
VectorStore->>ChromaDB: store vector
ChromaDB-->>VectorStore: ok
Worker->>StatusRepo: update(task_id, success, embedding_id)
end
end
RegisterUC->>FaceService: update_user_mean_embedding(user_id)
FaceService->>FaceRepo: get_by_user_id(user_id)
FaceRepo->>MongoDB: Query faces by user_id
MongoDB-->>FaceRepo: Face records
FaceRepo-->>FaceService: Face entities
FaceService-->>RegisterUC: Mean embedding
RegisterUC->>UserRepo: update_user(user)
UserRepo->>MongoDB: Update user record
MongoDB-->>UserRepo: Success
UserRepo-->>RegisterUC: Success
RegisterUC-->>GradioUI: Success message
GradioUI-->>User: Registration result
```
## 2. Face Verification Flow
@ -95,7 +91,11 @@ sequenceDiagram
participant GradioUI as Gradio Interface
participant VerifyUC as VerifyFaceUseCase
participant UserRepo as MongoDBUserRepository
participant FaceService as FaceRecognitionService
participant S3 as S3ObjectStorage
participant TaskPub as TaskPublisher
participant MQ as RabbitMQ
participant StatusRepo as TaskStatusRepository
participant Worker as FaceWorker
participant Detector as RetinaFaceDetector
participant Recognizer as SFaceRecognizer
participant FaceRepo as MongoDBFaceRepository
@ -103,50 +103,53 @@ sequenceDiagram
User->>GradioUI: Upload image + username
GradioUI->>VerifyUC: execute(username, image)
VerifyUC->>UserRepo: get_user_by_name(username)
UserRepo->>MongoDB: Query user by name
MongoDB-->>UserRepo: User data or null
UserRepo-->>VerifyUC: User entity or null
alt User not found
VerifyUC-->>GradioUI: VerificationResult(false, 0.0)
GradioUI-->>User: result
else User found
VerifyUC->>FaceService: verify_face(user_id, image)
FaceService->>Detector: detect_faces(image)
Detector-->>FaceService: List of detected faces
alt No faces detected
FaceService-->>VerifyUC: (false, 0.0)
else Face detected
FaceService->>Detector: align_face(image, landmarks)
Detector-->>FaceService: Aligned face image
FaceService->>Recognizer: extract_embedding(aligned_face)
Recognizer-->>FaceService: Query embedding
FaceService->>FaceRepo: get_by_user_id(user_id)
FaceRepo->>MongoDB: Query registered faces
MongoDB-->>FaceRepo: Face records
FaceRepo-->>FaceService: User's face entities
alt No registered faces
FaceService-->>VerifyUC: (false, 0.0)
else Compare with registered faces
loop For each registered face
FaceService->>Recognizer: calculate_similarity(query_embedding, face_embedding)
Recognizer-->>FaceService: Similarity score
end
FaceService-->>VerifyUC: (is_verified, max_similarity)
end
end
VerifyUC->>S3: put_object(image)
S3-->>VerifyUC: s3_uri
VerifyUC->>TaskPub: publish_verify_task(user_id, s3_uri)
TaskPub->>MQ: enqueue(task)
VerifyUC->>StatusRepo: wait/poll task_id
StatusRepo-->>VerifyUC: (is_verified, score)
VerifyUC-->>GradioUI: VerificationResult
GradioUI-->>User: result
end
%% Worker side
MQ-->>Worker: task(user_id, s3_uri)
Worker->>S3: get_object(s3_uri)
Worker->>Detector: detect_faces(image)
Detector-->>Worker: faces
alt No faces
Worker->>StatusRepo: update(task_id, false, 0.0)
else
Worker->>Detector: align_face(image, landmarks)
Detector-->>Worker: aligned
Worker->>Recognizer: extract_embedding(aligned)
Recognizer-->>Worker: query_embedding
Worker->>FaceRepo: get_by_user_id(user_id)
FaceRepo->>MongoDB: query faces
MongoDB-->>FaceRepo: records
FaceRepo-->>Worker: embeddings
Worker->>Recognizer: calculate_similarity(query_embedding, user_embeddings)
Recognizer-->>Worker: max_score
Worker->>StatusRepo: update(task_id, max_score>=threshold, max_score)
end
GradioUI-->>User: Verification result
```
## 3. Face Identification Flow
@ -156,109 +159,124 @@ sequenceDiagram
participant User
participant GradioUI as Gradio Interface
participant IdentifyUC as IdentifyFaceUseCase
participant UserRepo as MongoDBUserRepository
participant FaceService as FaceRecognitionService
participant S3 as S3ObjectStorage
participant TaskPub as TaskPublisher
participant MQ as RabbitMQ
participant StatusRepo as TaskStatusRepository
participant Worker as FaceWorker
participant Detector as RetinaFaceDetector
participant Recognizer as SFaceRecognizer
participant VectorStore as ChromaDBVectorStore
participant ChromaDB as ChromaDB Vector DB
participant UserRepo as MongoDBUserRepository
participant MongoDB as MongoDB Database
User->>GradioUI: Upload image
GradioUI->>IdentifyUC: execute(image)
IdentifyUC->>FaceService: identify_face(image)
FaceService->>Detector: detect_faces(image)
Detector-->>FaceService: List of detected faces
alt No faces detected
FaceService-->>IdentifyUC: (null, 0.0, [])
else Face detected
FaceService->>Detector: align_face(image, landmarks)
Detector-->>FaceService: Aligned face image
FaceService->>Recognizer: extract_embedding(aligned_face)
Recognizer-->>FaceService: Query embedding
FaceService->>VectorStore: search_similar(query_embedding, top_k=10)
VectorStore->>ChromaDB: Vector similarity search
ChromaDB-->>VectorStore: Similar face vectors
VectorStore-->>FaceService: Similar faces with metadata
alt No similar faces above threshold
FaceService-->>IdentifyUC: (null, 0.0, [])
else Similar faces found
note over FaceService: Group by user_id and calculate best scores
FaceService-->>IdentifyUC: (best_user_id, confidence, candidates)
end
end
loop For each candidate user_id
IdentifyUC->>S3: put_object(image)
S3-->>IdentifyUC: s3_uri
IdentifyUC->>TaskPub: publish_identify_task(s3_uri, top_k=10)
TaskPub->>MQ: enqueue(task)
IdentifyUC->>StatusRepo: wait/poll task_id
StatusRepo-->>IdentifyUC: (best_user_id, confidence, candidate_user_ids)
loop For each candidate_user_id
IdentifyUC->>UserRepo: get_by_id(user_id)
UserRepo->>MongoDB: Query user by ID
MongoDB-->>UserRepo: User data
UserRepo->>MongoDB: query
MongoDB-->>UserRepo: user data
UserRepo-->>IdentifyUC: User entity
end
alt User identified
IdentifyUC->>UserRepo: get_by_id(best_user_id)
UserRepo->>MongoDB: Query identified user
MongoDB-->>UserRepo: User data
UserRepo-->>IdentifyUC: User entity
end
IdentifyUC-->>GradioUI: IdentificationResult
GradioUI-->>User: Identification result
GradioUI-->>User: result
%% Worker
MQ-->>Worker: task(s3_uri)
Worker->>S3: get_object(s3_uri)
Worker->>Detector: detect_faces(image)
Detector-->>Worker: faces
alt No faces
Worker->>StatusRepo: update(task_id, null, 0.0, [])
else
Worker->>Detector: align_face(image, landmarks)
Detector-->>Worker: aligned
Worker->>Recognizer: extract_embedding(aligned)
Recognizer-->>Worker: query_embedding
Worker->>VectorStore: search_similar(query_embedding, top_k=10)
VectorStore->>ChromaDB: search
ChromaDB-->>VectorStore: results with metadata (face_id, user_id, score)
VectorStore-->>Worker: candidates
alt No scores above threshold
Worker->>StatusRepo: update(task_id, null, 0.0, [])
else
note over Worker: Group by user_id, choose best_user_id + confidence
Worker->>StatusRepo: update(task_id, best_user_id, confidence, candidate_user_ids)
end
end
```
## 4. System Initialization Flow
```mermaid
sequenceDiagram
participant Main as main.py
participant Main as main.py (API)
participant Config as Settings
participant MongoDB as MongoDBFaceRepository
participant MongoDBRepo as MongoDBFaceRepository
participant MongoUserRepo as MongoDBUserRepository
participant ChromaDB as ChromaDBVectorStore
participant FileStorage as FileSystemStorage
participant Detector as RetinaFaceDetector
participant Recognizer as SFaceRecognizer
participant FaceService as FaceRecognitionService
participant UseCases as Use Cases
participant S3 as S3ObjectStorage
participant Rabbit as RabbitMQ
participant StatusRepo as TaskStatusRepository
participant Publisher as TaskPublisher
participant GradioApp as Gradio Interface
participant WorkerMain as worker_main.py (Workers)
participant WConfig as Settings
participant WDetector as RetinaFaceDetector
participant WRecognizer as SFaceRecognizer
participant WFaceService as FaceRecognitionService
participant WFaceRepo as MongoDBFaceRepository
participant WVector as ChromaDBVectorStore
participant WS3 as S3ObjectStorage
participant WRabbit as RabbitMQ
participant WStatusRepo as TaskStatusRepository
participant WPool as FaceWorkerPool
%% API init
Main->>Config: Load configuration
Config-->>Main: Settings object
Main->>MongoDB: Initialize with MongoDB URL
MongoDB-->>Main: Repository instance
Main->>ChromaDB: Initialize with Chroma host/port
ChromaDB-->>Main: Vector store instance
Main->>FileStorage: Initialize with upload directory
FileStorage-->>Main: File storage instance
Main->>Detector: Initialize RetinaFace
note over Detector: Load ONNX models, prepare ctx
Detector-->>Main: Detector instance
Main->>Recognizer: Initialize SFace
note over Recognizer: Download/load SFace ONNX model
Recognizer-->>Main: Recognizer instance
Main->>FaceService: Initialize with dependencies
FaceService-->>Main: Service instance
Main->>UseCases: Initialize use cases with service
UseCases-->>Main: Use case instances
Main->>GradioApp: create_gradio_interface(use_cases)
GradioApp-->>Main: Gradio app instance
Main->>GradioApp: launch(server_name, server_port)
note over GradioApp: Start web server on port 7860
GradioApp-->>Main: Server running
Main->>MongoDBRepo: Initialize with Mongo URL
Main->>MongoUserRepo: Initialize with Mongo URL
Main->>ChromaDB: Initialize with host/port
Main->>S3: Initialize with bucket/creds
Main->>Rabbit: Connect/create queues
Main->>StatusRepo: Initialize (Redis/Mongo)
Main->>Publisher: Initialize with Rabbit
Main->>GradioApp: create_gradio_interface(use_cases, publisher, status_repo)
Main->>GradioApp: launch(server_name, port)
%% Worker init (can be on separate machines)
WorkerMain->>WConfig: Load configuration
WConfig-->>WorkerMain: Settings
WorkerMain->>WRabbit: Connect/subscribe queues
WorkerMain->>WStatusRepo: Initialize
WorkerMain->>WDetector: Initialize RetinaFace
note over WDetector: Load ONNX models, prepare ctx
WorkerMain->>WRecognizer: Initialize SFace
note over WRecognizer: Load/Download ONNX model
WorkerMain->>WFaceRepo: Initialize with Mongo URL
WorkerMain->>WVector: Initialize with Chroma host/port
WorkerMain->>WS3: Initialize with bucket/creds
WorkerMain->>WFaceService: Initialize with dependencies
WorkerMain->>WPool: Start consuming tasks
```
## Flow Descriptions

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