python-detector-worker/REFACTOR_PLAN.md

Detector Worker Refactoring Plan

Project Overview

Transform the current monolithic structure (~4000 lines across app.py and siwatsystem/pympta.py) into a modular, maintainable system with clear separation of concerns. The goal is to make the sophisticated computer vision pipeline easily understandable for other engineers while maintaining all existing functionality.

Current System Flow Understanding

Validated System Flow

1. WebSocket Connection → Backend connects and sends setSubscriptionList
2. Model Management → Download unique .mpta files to models/ and extract
3. Tracking Phase → Continuous tracking with front_rear_detection_v1.pt
4. Validation Phase → Validate stable car (not just passing by)
5. Pipeline Execution →
   • Detect car with yolo11m.pt
   • Branch 1: Front/rear detection → crop frontal → save to Redis + brand classification
   • Branch 2: Body type classification from car crop
6. Communication → Send imageDetection → Backend generates sessionId → Fueling starts
7. Post-Fueling → Backend clears sessionId → Continue tracking same car to avoid re-pipeline

Core Responsibilities Identified

1. WebSocket Communication - Message handling and protocol compliance
2. Stream Management - RTSP/HTTP frame processing and buffering
3. Model Management - MPTA download, extraction, and loading
4. Pipeline Configuration - Parse pipeline.json and setup execution flow
5. Vehicle Tracking - Continuous tracking and car identification
6. Validation Logic - Stable car detection vs. passing-by cars
7. Detection Pipeline - Main ML pipeline with parallel branches
8. Data Persistence - Redis/PostgreSQL operations
9. Session Management - Handle session IDs and lifecycle

Proposed Directory Structure

core/
├── communication/
│   ├── __init__.py
│   ├── websocket.py      # WebSocket message handling & protocol
│   ├── messages.py       # Message types and validation
│   ├── models.py         # Message data structures
│   └── state.py          # Worker state management
├── streaming/
│   ├── __init__.py
│   ├── manager.py        # Stream coordination and lifecycle
│   ├── readers.py        # RTSP/HTTP frame readers
│   └── buffers.py        # Frame buffering and caching
├── models/
│   ├── __init__.py
│   ├── manager.py        # MPTA download and model loading
│   ├── pipeline.py       # Pipeline.json parser and config
│   └── inference.py      # YOLO model wrapper and optimization
├── tracking/
│   ├── __init__.py
│   ├── tracker.py        # Vehicle tracking with front_rear_detection_v1
│   ├── validator.py      # Stable car validation logic
│   └── integration.py    # Tracking-pipeline integration
├── detection/
│   ├── __init__.py
│   ├── pipeline.py       # Main detection pipeline orchestration
│   └── branches.py       # Parallel branch processing (brand/bodytype)
└── storage/
    ├── __init__.py
    ├── redis.py          # Redis operations and image storage
    └── database.py       # PostgreSQL operations (existing - will be moved)

Implementation Strategy (Feature-by-Feature Testing)

Phase 1: Communication Layer

• WebSocket message handling (setSubscriptionList, sessionId management)
• HTTP API endpoints (camera image retrieval)
• Worker state reporting

Phase 2: Pipeline Configuration Reader

• Parse pipeline.json
• Model dependency resolution
• Branch configuration setup

Phase 3: Tracking System

• Continuous vehicle tracking
• Car identification and persistence

Phase 4: Tracking Validator

• Stable car detection logic
• Passing-by vs. fueling car differentiation

Phase 5: Model Pipeline Execution

• Main detection pipeline
• Parallel branch processing
• Redis/DB integration

Phase 6: Post-Session Tracking Validation

• Same car validation after sessionId cleared
• Prevent duplicate pipeline execution

Key Preservation Requirements

• HTTP Endpoint: /camera/{camera_id}/image must remain unchanged
• WebSocket Protocol: Full compliance with worker.md specification
• MPTA Format: Maintain compatibility with existing model archives
• Database Schema: Keep existing PostgreSQL structure
• Redis Integration: Preserve image storage and pub/sub functionality
• Configuration: Maintain config.json compatibility
• Logging: Preserve structured logging format

Expected Benefits

• Maintainability: Single responsibility modules (~200-400 lines each)
• Testability: Independent testing of each component
• Readability: Clear separation of concerns
• Scalability: Easy to extend and modify individual components
• Documentation: Self-documenting code structure

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Comprehensive TODO List

✅ Phase 1: Project Setup & Communication Layer - COMPLETED

1.1 Project Structure Setup

• ✅ Create core/ directory structure
• ✅ Create all module directories and __init__.py files
• ✅ Set up logging configuration for new modules
• ✅ Update imports in existing files to prepare for migration

1.2 Communication Module (core/communication/)

• ✅ Create models.py - Message data structures

  • ✅ Define WebSocket message models (SubscriptionList, StateReport, etc.)
  • ✅ Add validation schemas for incoming messages
  • ✅ Create response models for outgoing messages

• ✅ Create messages.py - Message types and validation

  • ✅ Implement message type constants
  • ✅ Add message validation functions
  • ✅ Create message builders for common responses

• ✅ Create websocket.py - WebSocket message handling

  • ✅ Extract WebSocket connection management from app.py
  • ✅ Implement message routing and dispatching
  • ✅ Add connection lifecycle management (connect, disconnect, reconnect)
  • ✅ Handle setSubscriptionList message processing
  • ✅ Handle setSessionId and setProgressionStage messages
  • ✅ Handle requestState and patchSessionResult messages

• ✅ Create state.py - Worker state management

  • ✅ Extract state reporting logic from app.py
  • ✅ Implement system metrics collection (CPU, memory, GPU)
  • ✅ Manage active subscriptions state
  • ✅ Handle session ID mapping and storage

1.3 HTTP API Preservation

• ✅ Preserve /camera/{camera_id}/image endpoint
  • ✅ Extract REST API logic from app.py
  • ✅ Ensure frame caching mechanism works with new structure
  • ✅ Maintain exact same response format and error handling

1.4 Testing Phase 1

• ✅ Test WebSocket connection and message handling
• ✅ Test HTTP API endpoint functionality
• ✅ Verify state reporting works correctly
• ✅ Test session management functionality

1.5 Phase 1 Results

• ✅ Modular Architecture: Transformed ~900 lines into 4 focused modules (~200 lines each)
• ✅ WebSocket Protocol: Full compliance with worker.md specification
• ✅ System Metrics: Real-time CPU, memory, GPU monitoring
• ✅ State Management: Thread-safe subscription and session tracking
• ✅ Backward Compatibility: All existing endpoints preserved
• ✅ Modern FastAPI: Lifespan events, Pydantic v2 compatibility

✅ Phase 2: Pipeline Configuration & Model Management - COMPLETED

2.1 Models Module (core/models/)

• ✅ Create pipeline.py - Pipeline.json parser

  • ✅ Extract pipeline configuration parsing from pympta.py
  • ✅ Implement pipeline validation
  • ✅ Add configuration schema validation
  • ✅ Handle Redis and PostgreSQL configuration parsing

• ✅ Create manager.py - MPTA download and model loading

  • ✅ Extract MPTA download logic from pympta.py
  • ✅ Implement ZIP extraction and validation
  • ✅ Add model file management and caching
  • ✅ Handle model loading with GPU optimization
  • ✅ Implement model dependency resolution

• ✅ Create inference.py - YOLO model wrapper

  • ✅ Create unified YOLO model interface
  • ✅ Add inference optimization and caching
  • ✅ Implement batch processing capabilities
  • ✅ Handle model switching and memory management

2.2 Testing Phase 2

• ✅ Test MPTA file download and extraction
• ✅ Test pipeline.json parsing and validation
• ✅ Test model loading with different configurations
• ✅ Verify GPU optimization works correctly

2.3 Phase 2 Results

• ✅ ModelManager: Downloads, extracts, and manages MPTA files with model ID-based directory structure
• ✅ PipelineParser: Parses and validates pipeline.json with full support for Redis, PostgreSQL, tracking, and branches
• ✅ YOLOWrapper: Unified interface for YOLO models with caching, tracking, and classification support
• ✅ Model Caching: Shared model cache across instances to optimize memory usage
• ✅ Dependency Resolution: Automatically identifies and tracks all model file dependencies

✅ Phase 3: Streaming System - COMPLETED

3.1 Streaming Module (core/streaming/)

• ✅ Create readers.py - RTSP/HTTP frame readers

  • ✅ Extract frame_reader function from app.py
  • ✅ Extract snapshot_reader function from app.py
  • ✅ Add connection management and retry logic
  • ✅ Implement frame rate control and optimization

• ✅ Create buffers.py - Frame buffering and caching

  • ✅ Extract frame buffer management from app.py
  • ✅ Implement efficient frame caching for REST API
  • ✅ Add buffer size management and memory optimization

• ✅ Create manager.py - Stream coordination

  • ✅ Extract stream lifecycle management from app.py
  • ✅ Implement shared stream optimization
  • ✅ Add subscription reconciliation logic
  • ✅ Handle stream sharing across multiple subscriptions

3.2 Testing Phase 3

• ✅ Test RTSP stream reading and buffering
• ✅ Test HTTP snapshot capture functionality
• ✅ Test shared stream optimization
• ✅ Verify frame caching for REST API access

3.3 Phase 3 Results

• ✅ RTSPReader: OpenCV-based RTSP stream reader with automatic reconnection and frame callbacks
• ✅ HTTPSnapshotReader: Periodic HTTP snapshot capture with HTTPBasicAuth and HTTPDigestAuth support
• ✅ FrameBuffer: Thread-safe frame storage with automatic aging and cleanup
• ✅ CacheBuffer: Enhanced frame cache with cropping support and highest quality JPEG encoding (default quality=100)
• ✅ StreamManager: Complete stream lifecycle management with shared optimization and subscription reconciliation
• ✅ Authentication Support: Proper handling of credentials in URLs with automatic auth type detection
• ✅ Real Camera Testing: Verified with authenticated RTSP (1280x720) and HTTP snapshot (2688x1520) cameras
• ✅ Production Ready: Stable concurrent streaming from multiple camera sources
• ✅ Dependencies: Added opencv-python, numpy, and requests to requirements.txt

3.4 Recent Streaming Enhancements (Post-Phase 3)

• ✅ Format-Specific Optimization: Tailored for 1280x720@6fps RTSP streams and 2560x1440 HTTP snapshots
• ✅ H.264 Error Recovery: Enhanced error handling for corrupted frames with automatic stream recovery
• ✅ Frame Validation: Implemented corruption detection using edge density analysis
• ✅ Buffer Size Optimization: Adjusted buffer limits to 3MB for RTSP frames (1280x720x3 bytes)
• ✅ FFMPEG Integration: Added environment variables to suppress verbose H.264 decoder errors
• ✅ URL Preservation: Maintained clean RTSP URLs without parameter injection
• ✅ Type Detection: Automatic stream type detection based on frame dimensions
• ✅ Quality Settings: Format-specific JPEG quality (90% for RTSP, 95% for HTTP)

✅ Phase 4: Vehicle Tracking System - COMPLETED

4.1 Tracking Module (core/tracking/)

• ✅ Create tracker.py - Vehicle tracking implementation (305 lines)

  • ✅ Implement continuous tracking with configurable model (front_rear_detection_v1.pt)
  • ✅ Add vehicle identification and persistence with TrackedVehicle dataclass
  • ✅ Implement tracking state management with thread-safe operations
  • ✅ Add bounding box tracking and motion analysis with position history
  • ✅ Multi-class tracking support for complex detection scenarios

• ✅ Create validator.py - Stable car validation (417 lines)

  • ✅ Implement stable car detection algorithm with multiple validation criteria
  • ✅ Add passing-by vs. fueling car differentiation using velocity and position analysis
  • ✅ Implement validation thresholds and timing with configurable parameters
  • ✅ Add confidence scoring for validation decisions with state history
  • ✅ Advanced motion analysis with velocity smoothing and position variance

• ✅ Create integration.py - Tracking-pipeline integration (547 lines)

  • ✅ Connect tracking system with main pipeline through TrackingPipelineIntegration
  • ✅ Handle tracking state transitions and session management
  • ✅ Implement post-session tracking validation with cooldown periods
  • ✅ Add same-car validation after sessionId cleared with 30-second cooldown
  • ✅ Car abandonment detection with automatic timeout monitoring
  • ✅ Mock detection system for backend communication
  • ✅ Async pipeline execution with proper error handling

4.2 Testing Phase 4

• ✅ Test continuous vehicle tracking functionality
• ✅ Test stable car validation logic
• ✅ Test integration with existing pipeline
• ✅ Verify tracking performance and accuracy
• ✅ Test car abandonment detection with null detection messages
• ✅ Verify session management and progression stage handling

4.3 Phase 4 Results

• ✅ VehicleTracker: Complete tracking implementation with YOLO tracking integration, position history, and stability calculations
• ✅ StableCarValidator: Sophisticated validation logic using velocity, position variance, and state consistency
• ✅ TrackingPipelineIntegration: Full integration with pipeline system including session management and async processing
• ✅ StreamManager Integration: Updated streaming manager to process tracking on every frame with proper threading
• ✅ Thread-Safe Operations: All tracking operations are thread-safe with proper locking mechanisms
• ✅ Configurable Parameters: All tracking parameters are configurable through pipeline.json
• ✅ Session Management: Complete session lifecycle management with post-fueling validation
• ✅ Statistics and Monitoring: Comprehensive statistics collection for tracking performance
• ✅ Car Abandonment Detection: Automatic detection when cars leave without fueling, sends detection: null to backend
• ✅ Message Protocol: Fixed JSON serialization to include detection: null for abandonment notifications
• ✅ Streaming Optimization: Enhanced RTSP/HTTP readers for 1280x720@6fps RTSP and 2560x1440 HTTP snapshots
• ✅ Error Recovery: Improved H.264 error handling and corrupted frame detection

✅ Phase 5: Detection Pipeline System - COMPLETED

5.1 Detection Module (core/detection/) ✅

• ✅ Create pipeline.py - Main detection orchestration (574 lines)

  • ✅ Extracted main pipeline execution from pympta.py with full orchestration
  • ✅ Implemented detection flow coordination with async execution
  • ✅ Added pipeline state management with comprehensive statistics
  • ✅ Handled pipeline result aggregation with branch synchronization
  • ✅ Redis and database integration with error handling
  • ✅ Immediate and parallel action execution with template resolution

• ✅ Create branches.py - Parallel branch processing (442 lines)

  • ✅ Extracted parallel branch execution from pympta.py
  • ✅ Implemented ThreadPoolExecutor-based parallel processing
  • ✅ Added branch synchronization and result collection
  • ✅ Handled branch failure and retry logic with graceful degradation
  • ✅ Support for nested branches and model caching
  • ✅ Both detection and classification model support

5.2 Storage Module (core/storage/) ✅

• ✅ Create redis.py - Redis operations (410 lines)

  • ✅ Extracted Redis action execution from pympta.py
  • ✅ Implemented async image storage with region cropping
  • ✅ Added pub/sub messaging functionality with JSON support
  • ✅ Handled Redis connection management and retry logic
  • ✅ Added statistics tracking and health monitoring
  • ✅ Support for various image formats (JPEG, PNG) with quality control

• ✅ Move database.py - PostgreSQL operations (339 lines)

  • ✅ Moved existing archive/siwatsystem/database.py to core/storage/
  • ✅ Updated imports and integration points
  • ✅ Ensured compatibility with new module structure
  • ✅ Added session management and statistics methods
  • ✅ Enhanced error handling and connection management

5.3 Integration Updates ✅

• ✅ Updated core/tracking/integration.py
  • ✅ Added DetectionPipeline integration
  • ✅ Replaced placeholder _execute_pipeline with real implementation
  • ✅ Added detection pipeline initialization and cleanup
  • ✅ Integrated with existing tracking system flow
  • ✅ Maintained backward compatibility with test mode

5.4 Testing Phase 5 ✅

• ✅ Verified module imports work correctly
• ✅ All new modules follow established coding patterns
• ✅ Integration points properly connected
• ✅ Error handling and cleanup methods implemented
• ✅ Statistics and monitoring capabilities added

5.5 Phase 5 Results ✅

• ✅ DetectionPipeline: Complete detection orchestration with Redis/PostgreSQL integration, async execution, and comprehensive error handling
• ✅ BranchProcessor: Parallel branch execution with ThreadPoolExecutor, model caching, and nested branch support
• ✅ RedisManager: Async Redis operations with image storage, pub/sub messaging, and connection management
• ✅ DatabaseManager: Enhanced PostgreSQL operations with session management and statistics
• ✅ Module Integration: Seamless integration with existing tracking system while maintaining compatibility
• ✅ Error Handling: Comprehensive error handling and graceful degradation throughout all components
• ✅ Performance: Optimized parallel processing and caching for high-performance pipeline execution

📋 Phase 6: Integration & Final Testing

6.1 Main Application Refactoring

• Refactor app.py

  • Remove extracted functionality
  • Update to use new modular structure
  • Maintain FastAPI application structure
  • Update imports and dependencies

• Clean up siwatsystem/pympta.py

  • Remove extracted functionality
  • Keep only necessary legacy compatibility code
  • Update imports to use new modules

6.2 Post-Session Tracking Validation

• Implement same-car validation after sessionId cleared
• Add logic to prevent duplicate pipeline execution
• Test tracking persistence through session lifecycle
• Verify correct behavior during edge cases

6.3 Configuration & Documentation

• Update configuration handling for new structure
• Ensure config.json compatibility maintained
• Update logging configuration for all modules
• Add module-level documentation

6.4 Comprehensive Testing

• Integration Testing

  • Test complete system flow end-to-end
  • Test all WebSocket message types
  • Test HTTP API endpoints
  • Test error handling and recovery

• Performance Testing

  • Verify system performance is maintained
  • Test memory usage optimization
  • Test GPU utilization efficiency
  • Benchmark against original implementation

• Edge Case Testing

  • Test connection failures and reconnection
  • Test model loading failures
  • Test stream interruption handling
  • Test concurrent subscription management

6.5 Logging Optimization & Cleanup ✅

• ✅ Removed Debug Frame Saving

  • ✅ Removed hard-coded debug frame saving in core/detection/pipeline.py
  • ✅ Removed hard-coded debug frame saving in core/detection/branches.py
  • ✅ Eliminated absolute debug paths for production use

• ✅ Eliminated Test/Mock Functionality

  • ✅ Removed save_frame_for_testing function from core/streaming/buffers.py
  • ✅ Removed save_test_frames configuration from StreamConfig
  • ✅ Cleaned up test frame saving calls in stream manager
  • ✅ Updated module exports to remove test functions

• ✅ Reduced Verbose Logging

  • ✅ Commented out verbose frame storage logging (every frame)
  • ✅ Converted debug-level info logs to proper debug level
  • ✅ Reduced repetitive frame dimension logging
  • ✅ Maintained important model results and detection confidence logging
  • ✅ Kept critical pipeline execution and error messages

• ✅ Production-Ready Logging

  • ✅ Clean startup and initialization messages
  • ✅ Clear model loading and pipeline status
  • ✅ Preserved detection results with confidence scores
  • ✅ Maintained session management and tracking messages
  • ✅ Kept important error and warning messages

6.6 Final Cleanup

• Remove any remaining duplicate code
• Optimize imports across all modules
• Clean up temporary files and debugging code
• Update project documentation

📋 Post-Refactoring Tasks

Documentation Updates

• Update CLAUDE.md with new architecture
• Create module-specific documentation
• Update installation and deployment guides
• Add troubleshooting guide for new structure

Code Quality

• Add type hints to all new modules
• Implement proper error handling patterns
• Add logging consistency across modules
• Ensure proper resource cleanup

Future Enhancements (Optional)

• Add unit tests for each module
• Implement monitoring and metrics collection
• Add configuration validation
• Consider adding dependency injection container

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Success Criteria

✅ Modularity: Each module has a single, clear responsibility ✅ Testability: Each phase can be tested independently ✅ Maintainability: Code is easy to understand and modify ✅ Compatibility: All existing functionality preserved ✅ Performance: System performance is maintained or improved ✅ Documentation: Clear documentation for new architecture

Risk Mitigation

• Feature-by-feature testing ensures functionality is preserved at each step
• Gradual migration minimizes risk of breaking existing functionality
• Preserve critical interfaces (WebSocket protocol, HTTP endpoints)
• Maintain backward compatibility with existing configurations
• Comprehensive testing at each phase before proceeding