feat: add comprehensive documentation for Python Detector Worker; include project overview, architecture, core components, and configuration details

CLAUDE.md

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+# Python Detector Worker - CLAUDE.md
+
+## Project Overview
+This is a FastAPI-based computer vision detection worker that processes video streams from RTSP/HTTP sources and runs YOLO-based machine learning pipelines for object detection and classification. The system is designed to work within a larger CMS (Content Management System) architecture.
+
+## Architecture & Technology Stack
+- **Framework**: FastAPI with WebSocket support
+- **ML/CV**: PyTorch, Ultralytics YOLO, OpenCV
+- **Containerization**: Docker (Python 3.13-bookworm base)
+- **Data Storage**: Redis integration for action handling
+- **Communication**: WebSocket-based real-time protocol
+
+## Core Components
+
+### Main Application (`app.py`)
+- **FastAPI WebSocket server** for real-time communication
+- **Multi-camera stream management** with shared stream optimization
+- **HTTP REST endpoint** for image retrieval (`/camera/{camera_id}/image`)
+- **Threading-based frame readers** for RTSP streams and HTTP snapshots
+- **Model loading and inference** using MPTA (Machine Learning Pipeline Archive) format
+- **Session management** with display identifier mapping
+- **Resource monitoring** (CPU, memory, GPU usage via psutil)
+
+### Pipeline System (`siwatsystem/pympta.py`)
+- **MPTA file handling** - ZIP archives containing model configurations
+- **Hierarchical pipeline execution** with detection → classification branching
+- **Redis action system** for image saving and message publishing
+- **Dynamic model loading** with GPU optimization
+- **Configurable trigger classes and confidence thresholds**
+
+### Testing & Debugging
+- **Protocol test script** (`test_protocol.py`) for WebSocket communication validation
+- **Pipeline webcam utility** (`pipeline_webcam.py`) for local testing with visual output
+- **RTSP streaming debug tool** (`debug/rtsp_webcam.py`) using GStreamer
+
+## Code Conventions & Patterns
+
+### Logging
+- **Structured logging** using Python's logging module
+- **File + console output** to `detector_worker.log`
+- **Debug level separation** for detailed troubleshooting
+- **Context-aware messages** with camera IDs and model information
+
+### Error Handling
+- **Graceful failure handling** with retry mechanisms (configurable max_retries)
+- **Thread-safe operations** using locks for streams and models
+- **WebSocket disconnect handling** with proper cleanup
+- **Model loading validation** with detailed error reporting
+
+### Configuration
+- **JSON configuration** (`config.json`) for runtime parameters:
+  - `poll_interval_ms`: Frame processing interval
+  - `max_streams`: Concurrent stream limit
+  - `target_fps`: Target frame rate
+  - `reconnect_interval_sec`: Stream reconnection delay
+  - `max_retries`: Maximum retry attempts (-1 for unlimited)
+
+### Threading Model
+- **Frame reader threads** for each camera stream (RTSP/HTTP)
+- **Shared stream optimization** - multiple subscriptions can reuse the same camera stream
+- **Async WebSocket handling** with concurrent task management
+- **Thread-safe data structures** with proper locking mechanisms
+
+## WebSocket Protocol
+
+### Message Types
+- **subscribe**: Start camera stream with model pipeline
+- **unsubscribe**: Stop camera stream processing
+- **requestState**: Request current worker status
+- **setSessionId**: Associate display with session identifier
+- **patchSession**: Update session data
+- **stateReport**: Periodic heartbeat with system metrics
+- **imageDetection**: Detection results with timestamp and model info
+
+### Subscription Format
+```json
+{
+  "type": "subscribe",
+  "payload": {
+    "subscriptionIdentifier": "display-001;cam-001",
+    "rtspUrl": "rtsp://...",  // OR snapshotUrl
+    "snapshotUrl": "http://...",
+    "snapshotInterval": 5000,
+    "modelUrl": "http://...model.mpta",
+    "modelId": 101,
+    "modelName": "Vehicle Detection",
+    "cropX1": 100, "cropY1": 200,
+    "cropX2": 300, "cropY2": 400
+  }
+}
+```
+
+## Model Pipeline (MPTA) Format
+
+### Structure
+- **ZIP archive** containing models and configuration
+- **pipeline.json** - Main configuration file
+- **Model files** - YOLO .pt files for detection/classification
+- **Redis configuration** - Optional for action execution
+
+### Pipeline Flow
+1. **Detection stage** - YOLO object detection with bounding boxes
+2. **Trigger evaluation** - Check if detected class matches trigger conditions
+3. **Classification stage** - Crop detected region and run classification model
+4. **Action execution** - Redis operations (image saving, message publishing)
+
+### Branch Configuration
+```json
+{
+  "modelId": "detector-v1",
+  "modelFile": "detector.pt",
+  "triggerClasses": ["car", "truck"],
+  "minConfidence": 0.5,
+  "branches": [{
+    "modelId": "classifier-v1", 
+    "modelFile": "classifier.pt",
+    "crop": true,
+    "triggerClasses": ["car"],
+    "minConfidence": 0.3,
+    "actions": [...]
+  }]
+}
+```
+
+## Stream Management
+
+### Shared Streams
+- Multiple subscriptions can share the same camera URL
+- Reference counting prevents premature stream termination
+- Automatic cleanup when last subscription ends
+
+### Frame Processing
+- **Queue-based buffering** with single frame capacity (latest frame only)
+- **Configurable polling interval** based on target FPS
+- **Automatic reconnection** with exponential backoff
+
+## Development & Testing
+
+### Local Development
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the worker
+python app.py
+
+# Test protocol compliance
+python test_protocol.py
+
+# Test pipeline with webcam
+python pipeline_webcam.py --mpta-file path/to/model.mpta --video 0
+```
+
+### Docker Deployment
+```bash
+# Build container
+docker build -t detector-worker .
+
+# Run with volume mounts for models
+docker run -p 8000:8000 -v ./models:/app/models detector-worker
+```
+
+### Testing Commands
+- **Protocol testing**: `python test_protocol.py`
+- **Pipeline validation**: `python pipeline_webcam.py --mpta-file <path> --video 0`
+- **RTSP debugging**: `python debug/rtsp_webcam.py`
+
+## Dependencies
+- **fastapi[standard]**: Web framework with WebSocket support
+- **uvicorn**: ASGI server
+- **torch, torchvision**: PyTorch for ML inference
+- **ultralytics**: YOLO implementation
+- **opencv-python**: Computer vision operations
+- **websockets**: WebSocket client/server
+- **redis**: Redis client for action execution
+
+## Security Considerations
+- Model files are loaded from trusted sources only
+- Redis connections use authentication when configured
+- WebSocket connections handle disconnects gracefully
+- Resource usage is monitored to prevent DoS
+
+## Performance Optimizations
+- GPU acceleration when CUDA is available
+- Shared camera streams reduce resource usage
+- Frame queue optimization (single latest frame)
+- Model caching across subscriptions
+- Trigger class filtering for faster inference