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chore: update refactor plan

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ziesorx 2025-09-23 23:10:28 +07:00
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REFACTOR_PLAN.md
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@ -238,32 +238,49 @@ core/
- ✅ **Production Ready**: Stable concurrent streaming from multiple camera sources - ✅ **Production Ready**: Stable concurrent streaming from multiple camera sources
- ✅ **Dependencies**: Added opencv-python, numpy, and requests to requirements.txt - ✅ **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 ## ✅ Phase 4: Vehicle Tracking System - COMPLETED
### 4.1 Tracking Module (`core/tracking/`) ### 4.1 Tracking Module (`core/tracking/`)
- ✅ **Create `tracker.py`** - Vehicle tracking implementation - ✅ **Create `tracker.py`** - Vehicle tracking implementation (305 lines)
- ✅ Implement continuous tracking with configurable model (front_rear_detection_v1.pt) - ✅ Implement continuous tracking with configurable model (front_rear_detection_v1.pt)
- ✅ Add vehicle identification and persistence with TrackedVehicle dataclass - ✅ Add vehicle identification and persistence with TrackedVehicle dataclass
- ✅ Implement tracking state management with thread-safe operations - ✅ Implement tracking state management with thread-safe operations
- ✅ Add bounding box tracking and motion analysis with position history - ✅ Add bounding box tracking and motion analysis with position history
- ✅ Multi-class tracking support for complex detection scenarios
- ✅ **Create `validator.py`** - Stable car validation - ✅ **Create `validator.py`** - Stable car validation (417 lines)
- ✅ Implement stable car detection algorithm with multiple validation criteria - ✅ Implement stable car detection algorithm with multiple validation criteria
- ✅ Add passing-by vs. fueling car differentiation using velocity and position analysis - ✅ Add passing-by vs. fueling car differentiation using velocity and position analysis
- ✅ Implement validation thresholds and timing with configurable parameters - ✅ Implement validation thresholds and timing with configurable parameters
- ✅ Add confidence scoring for validation decisions with state history - ✅ Add confidence scoring for validation decisions with state history
- ✅ Advanced motion analysis with velocity smoothing and position variance
- ✅ **Create `integration.py`** - Tracking-pipeline integration - ✅ **Create `integration.py`** - Tracking-pipeline integration (547 lines)
- ✅ Connect tracking system with main pipeline through TrackingPipelineIntegration - ✅ Connect tracking system with main pipeline through TrackingPipelineIntegration
- ✅ Handle tracking state transitions and session management - ✅ Handle tracking state transitions and session management
- ✅ Implement post-session tracking validation with cooldown periods - ✅ Implement post-session tracking validation with cooldown periods
- ✅ Add same-car validation after sessionId cleared with 30-second cooldown - ✅ 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 ### 4.2 Testing Phase 4
- ✅ Test continuous vehicle tracking functionality - ✅ Test continuous vehicle tracking functionality
- ✅ Test stable car validation logic - ✅ Test stable car validation logic
- ✅ Test integration with existing pipeline - ✅ Test integration with existing pipeline
- ✅ Verify tracking performance and accuracy - ✅ 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 ### 4.3 Phase 4 Results
- ✅ **VehicleTracker**: Complete tracking implementation with YOLO tracking integration, position history, and stability calculations - ✅ **VehicleTracker**: Complete tracking implementation with YOLO tracking integration, position history, and stability calculations
@ -274,6 +291,10 @@ core/
- ✅ **Configurable Parameters**: All tracking parameters are configurable through pipeline.json - ✅ **Configurable Parameters**: All tracking parameters are configurable through pipeline.json
- ✅ **Session Management**: Complete session lifecycle management with post-fueling validation - ✅ **Session Management**: Complete session lifecycle management with post-fueling validation
- ✅ **Statistics and Monitoring**: Comprehensive statistics collection for tracking performance - ✅ **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 ## 📋 Phase 5: Detection Pipeline System