9.4 KiB
9.4 KiB
🚦 Qt Traffic Monitoring Application - Detailed File Contents Analysis
📁 Project Overview
Location: D:\Downloads\finale6\khatam\qt_app_pyside\
Type: PySide6-based Traffic Monitoring System with Real-time AI Violation Detection
🚀 Main Application Entry Points
main.py (52 lines)
- Purpose: Primary application launcher
- Contents: QApplication setup, splash screen integration, MainWindow loading
- Key Features: Error handling for UI loading, fallback mechanisms
- Imports: PySide6.QtWidgets, splash screen utilities
launch.py (44 lines)
- Purpose: Enhanced launcher using subprocess
- Contents: Subprocess-based app launching to avoid encoding issues
- Key Features: Path validation, error handling, cross-platform compatibility
- Functionality: Checks main.py existence before launching
run_app.py (115 lines)
- Purpose: Environment preparation and path fixing
- Contents: Import path verification, missing file creation, fallback handling
- Key Features: Dynamic path fixing, init.py creation, enhanced_annotation_utils verification
- Debugging: Comprehensive path and import checking
enhanced_main_window.py (131 lines)
- Purpose: Main window controller patch for enhanced video processing
- Contents: EnhancedVideoController integration, import fallbacks
- Key Features: Advanced controller switching, compatibility layer
- Architecture: MVC pattern enhancement
🖥️ User Interface Layer (ui/ Directory)
main_window.py (641 lines)
- Purpose: Primary application window framework
- Contents: QMainWindow implementation, tab management, menu system
- Key Components: LiveTab, AnalyticsTab, ViolationsTab, ExportTab, ConfigPanel
- Features: Settings management, configuration loading, performance overlay integration
live_tab.py
- Purpose: Real-time video monitoring interface
- Contents: Video stream display, control buttons, status indicators
- Features: Multi-source support, real-time violation overlay
fixed_live_tab.py
- Purpose: Stabilized version of live video display
- Contents: Bug fixes for video rendering, improved stability
- Improvements: Memory leak fixes, thread safety
enhanced_simple_live_display.py
- Purpose: Optimized live video rendering component
- Contents: Hardware-accelerated rendering, reduced latency display
- Features: GPU acceleration, frame buffering
simple_live_display.py
- Purpose: Basic video display component
- Contents: Standard OpenCV video rendering
- Usage: Fallback display when enhanced features unavailable
analytics_tab.py
- Purpose: Violation analytics and reporting dashboard
- Contents: Charts, graphs, violation statistics, trend analysis
- Features: Real-time data visualization, export capabilities
violations_tab.py
- Purpose: Detailed violation management interface
- Contents: Violation list, filtering, detailed view, evidence management
- Features: Search, sort, export individual violations
export_tab.py
- Purpose: Data export and reporting functionality
- Contents: Multiple export formats, report generation, scheduling
- Formats: PDF, CSV, JSON, video clips
config_panel.py
- Purpose: System configuration interface
- Contents: Camera settings, detection parameters, model selection
- Features: Real-time parameter adjustment, configuration validation
🎮 Controllers Layer (controllers/ Directory)
enhanced_video_controller.py (687 lines)
- Purpose: Advanced video processing with AI integration
- Contents: Async inference, FPS tracking, OpenVINO integration
- Key Features:
- OpenVINOVehicleDetector integration
- Traffic light color detection
- Enhanced annotation utilities
- Performance monitoring
- Thread-safe processing
video_controller.py & video_controller_new.py
- Purpose: Standard and upgraded video stream management
- Contents: Video capture, frame processing, detection pipeline
- Features: Multiple video sources, recording capabilities
analytics_controller.py
- Purpose: Violation data analysis and reporting controller
- Contents: Data aggregation, statistical analysis, trend calculation
- Features: Real-time analytics, database integration
model_manager.py
- Purpose: AI model loading and management
- Contents: Model initialization, switching, performance optimization
- Models Supported: OpenVINO, ONNX, PyTorch models
performance_overlay.py
- Purpose: Real-time performance monitoring display
- Contents: FPS counter, memory usage, CPU/GPU utilization
- Features: Live system metrics, performance alerts
red_light_violation_detector.py
- Purpose: Specialized red light detection logic
- Contents: Traffic light state detection, violation triggering
- Algorithm: HSV color detection, temporal analysis
🛠️ Utility Modules (utils/ Directory)
annotation_utils.py & enhanced_annotation_utils.py
- Purpose: Video annotation and overlay functions
- Contents: Bounding box drawing, text overlay, color coding
- Functions:
enhanced_draw_detections()draw_performance_overlay()enhanced_cv_to_qimage()enhanced_cv_to_pixmap()
traffic_light_utils.py
- Purpose: Traffic light state detection algorithms
- Contents: HSV color space analysis, circle detection
- Functions:
detect_traffic_light_color()draw_traffic_light_status()
crosswalk_utils.py
- Purpose: Crosswalk area detection and analysis
- Contents: Edge detection, template matching, polygon definition
- Features: Dynamic crosswalk boundary detection
embedder_openvino.py
- Purpose: OpenVINO model embedder for inference acceleration
- Contents: Model optimization, feature extraction
- Features: Hardware acceleration, batch processing
helpers.py
- Purpose: Common utility functions
- Contents: Configuration loading, file I/O, data conversion
- Functions:
load_configuration(),save_configuration(),save_snapshot()
🤖 AI Models & Processing Files
mobilenetv2.bin & mobilenetv2.xml
- Purpose: OpenVINO MobileNetV2 model files
- Contents: Pre-trained weights and network architecture
- Usage: Feature extraction, object classification
mobilenetv2_embedder/ (Directory)
- Purpose: Feature extraction utilities for MobileNetV2
- Contents: Embedding generation, similarity calculation
red_light_violation_pipeline.py
- Purpose: Specialized red light detection pipeline
- Contents: End-to-end red light violation detection
- Features: Auto-learned stop lines, Kalman filtering
⚙️ Configuration & Setup Files
config.json
- Purpose: Main application configuration
- Contents: Camera settings, detection thresholds, model paths
- Structure: JSON format with nested configuration objects
requirements.txt
- Purpose: Python dependencies specification
- Contents: Required packages with version numbers
- Packages: PySide6, OpenCV, NumPy, OpenVINO, PyTorch
🎨 Resources (resources/ Directory)
splash.png
- Purpose: Application splash screen image
- Format: PNG image file
- Usage: Displayed during application startup
style.qss
- Purpose: Qt stylesheet for application theming
- Contents: CSS-like styling rules for UI components
- Features: Dark theme, custom colors, responsive design
generate_resources.py
- Purpose: Resource generation and compilation script
- Contents: QRC file processing, resource compilation
icons/ & themes/ (Directories)
- Purpose: UI graphics and visual configurations
- Contents: Application icons, theme files, visual assets
🧪 Testing & Development Files
test_redlight_violation.py
- Purpose: Red light detection testing and validation
- Contents: Unit tests, integration tests, performance benchmarks
- Features: Automated testing, result validation
kernel.errors.txt
- Purpose: Error logging and debugging information
- Contents: Runtime errors, stack traces, debug output
- Usage: Troubleshooting and development
present.md
- Purpose: Presentation documentation
- Contents: Project overview, feature highlights, demo scripts
update_controller.py
- Purpose: Controller update and management utilities
- Contents: Dynamic controller switching, version management
📊 Complete File Summary
- Total Files: 60+ across all directories
- Main Application: 4 entry point files
- UI Components: 11 interface files
- Controllers: 8 processing files
- Utilities: 7 helper modules
- AI Models: 3 model files + embedder directory
- Violation Detection: 20+ specialized detection files
- Configuration: 4 config/setup files
- Resources: 4 asset directories
- Testing: 4 development/testing files
Total Code: ~8,000+ lines of production-ready Python code with comprehensive AI integration, real-time processing, and modular architecture.