Refactor: bring back original download_mpta

Reviewed-on: #3

.gitea/workflows/build.yml

@@ -1,13 +1,68 @@
-name: Build Backend Application and Docker Image
+name: Build Worker Base and Application Images
 
 on:
   push:
     branches:
       - main
+      - dev
   workflow_dispatch:
+    inputs:
+      force_base_build:
+        description: 'Force base image build regardless of changes'
+        required: false
+        default: 'false'
+        type: boolean
 
 jobs:
+  check-base-changes:
+    runs-on: ubuntu-latest
+    outputs:
+      base-changed: ${{ steps.changes.outputs.base-changed }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      - name: Check for base changes
+        id: changes
+        run: |
+          if git diff HEAD^ HEAD --name-only | grep -E "(Dockerfile\.base|requirements\.base\.txt)" > /dev/null; then
+            echo "base-changed=true" >> $GITHUB_OUTPUT
+          else
+            echo "base-changed=false" >> $GITHUB_OUTPUT
+          fi
+
+  build-base:
+    needs: check-base-changes
+    if: needs.check-base-changes.outputs.base-changed == 'true' || (github.event_name == 'workflow_dispatch' && github.event.inputs.force_base_build == 'true')
+    runs-on: ubuntu-latest
+    permissions:
+      packages: write
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: git.siwatsystem.com
+          username: ${{ github.actor }}
+          password: ${{ secrets.RUNNER_TOKEN }}
+        
+      - name: Build and push base Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: ./Dockerfile.base
+          push: true
+          tags: git.siwatsystem.com/adsist-cms/worker-base:latest
+
   build-docker:
+    needs: [check-base-changes, build-base]
+    if: always() && (needs.build-base.result == 'success' || needs.build-base.result == 'skipped')
     runs-on: ubuntu-latest
     permissions:
       packages: write
@@ -31,4 +86,27 @@ jobs:
           context: .
           file: ./Dockerfile
           push: true
-          tags: git.siwatsystem.com/adsist-cms/worker:latest
+          tags: git.siwatsystem.com/adsist-cms/worker:${{ github.ref_name == 'main' && 'latest' || 'dev' }}
+
+  deploy-stack:
+    needs: build-docker
+    runs-on: adsist
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+      - name: Set up SSH connection
+        run: |
+          mkdir -p ~/.ssh
+          echo "${{ secrets.DEPLOY_KEY_CMS }}" > ~/.ssh/id_rsa
+          chmod 600 ~/.ssh/id_rsa
+          ssh-keyscan -H ${{ vars.DEPLOY_HOST_CMS }} >> ~/.ssh/known_hosts
+      - name: Deploy stack
+        run: |
+          echo "Pulling and starting containers on server..."
+          if [ "${{ github.ref_name }}" = "main" ]; then
+            echo "Deploying production stack..."
+            ssh -i ~/.ssh/id_rsa ${{ vars.DEPLOY_USER_CMS }}@${{ vars.DEPLOY_HOST_CMS }} "cd ~/cms-system-k8s && docker compose -f docker-compose.production.yml pull && docker compose -f docker-compose.production.yml up -d"
+          else
+            echo "Deploying staging stack..."
+            ssh -i ~/.ssh/id_rsa ${{ vars.DEPLOY_USER_CMS }}@${{ vars.DEPLOY_HOST_CMS }} "cd ~/cms-system-k8s && docker compose -f docker-compose.staging.yml pull && docker compose -f docker-compose.staging.yml up -d"
+          fi

.gitignore

@@ -10,3 +10,6 @@ mptas
 detector_worker.log
 .gitignore
 no_frame_debug.log
+
+feeder/
+.venv/

CLAUDE.md

@@ -0,0 +1,277 @@
+# 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 advanced YOLO-based machine learning pipelines for multi-class object detection and parallel classification. The system features comprehensive database integration, Redis support, and hierarchical pipeline execution designed to work within a larger CMS (Content Management System) architecture.
+
+### Key Features
+- **Multi-Class Detection**: Simultaneous detection of multiple object classes (e.g., Car + Frontal)
+- **Parallel Processing**: Concurrent execution of classification branches using ThreadPoolExecutor
+- **Database Integration**: Automatic PostgreSQL schema management and record updates
+- **Redis Actions**: Image storage with region cropping and pub/sub messaging
+- **Pipeline Synchronization**: Branch coordination with `waitForBranches` functionality
+- **Dynamic Field Mapping**: Template-based field resolution for database operations
+
+## 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 + PostgreSQL for persistent storage
+- **Database**: Automatic schema management with gas_station_1 database
+- **Parallel Processing**: ThreadPoolExecutor for concurrent classification
+- **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
+- **Multi-class detection** - Simultaneous detection of multiple classes (Car + Frontal)
+- **Parallel processing** - Concurrent classification branches with ThreadPoolExecutor
+- **Redis action system** - Image saving with region cropping and message publishing
+- **PostgreSQL integration** - Automatic table creation and combined updates
+- **Dynamic model loading** with GPU optimization
+- **Configurable trigger classes and confidence thresholds**
+- **Branch synchronization** - waitForBranches coordination for database updates
+
+### Database System (`siwatsystem/database.py`)
+- **DatabaseManager class** for PostgreSQL operations
+- **Automatic table creation** with gas_station_1.car_frontal_info schema
+- **Combined update operations** with field mapping from branch results
+- **Session management** with UUID generation
+- **Error handling** and connection management
+
+### 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
+
+### Enhanced Structure
+- **ZIP archive** containing models and configuration
+- **pipeline.json** - Main configuration file with Redis + PostgreSQL settings
+- **Model files** - YOLO .pt files for detection/classification
+- **Multi-model support** - Detection + multiple classification models
+
+### Advanced Pipeline Flow
+1. **Multi-class detection stage** - YOLO detection of Car + Frontal simultaneously
+2. **Validation stage** - Check for expected classes (flexible matching)
+3. **Database initialization** - Create initial record with session_id
+4. **Redis actions** - Save cropped frontal images with expiration
+5. **Parallel classification** - Concurrent brand and body type classification
+6. **Branch synchronization** - Wait for all classification branches to complete
+7. **Database update** - Combined update with all classification results
+
+### Enhanced Branch Configuration
+```json
+{
+  "modelId": "car_frontal_detection_v1",
+  "modelFile": "car_frontal_detection_v1.pt",
+  "multiClass": true,
+  "expectedClasses": ["Car", "Frontal"],
+  "triggerClasses": ["Car", "Frontal"],
+  "minConfidence": 0.8,
+  "actions": [
+    {
+      "type": "redis_save_image",
+      "region": "Frontal",
+      "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+      "expire_seconds": 600
+    }
+  ],
+  "branches": [
+    {
+      "modelId": "car_brand_cls_v1",
+      "modelFile": "car_brand_cls_v1.pt",
+      "parallel": true,
+      "crop": true,
+      "cropClass": "Frontal",
+      "triggerClasses": ["Frontal"],
+      "minConfidence": 0.85
+    }
+  ],
+  "parallelActions": [
+    {
+      "type": "postgresql_update_combined",
+      "table": "car_frontal_info",
+      "key_field": "session_id",
+      "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+      "fields": {
+        "car_brand": "{car_brand_cls_v1.brand}",
+        "car_body_type": "{car_bodytype_cls_v1.body_type}"
+      }
+    }
+  ]
+}
+```
+
+## 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
+- **psycopg2-binary**: PostgreSQL database adapter
+- **scipy**: Scientific computing for advanced algorithms
+- **filterpy**: Kalman filtering and state estimation
+
+## 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
+
+## Database Integration
+
+### Schema Management
+The system automatically creates and manages PostgreSQL tables:
+
+```sql
+CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+    display_id VARCHAR(255),
+    captured_timestamp VARCHAR(255),
+    session_id VARCHAR(255) PRIMARY KEY,
+    license_character VARCHAR(255) DEFAULT NULL,
+    license_type VARCHAR(255) DEFAULT 'No model available',
+    car_brand VARCHAR(255) DEFAULT NULL,
+    car_model VARCHAR(255) DEFAULT NULL,
+    car_body_type VARCHAR(255) DEFAULT NULL,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+1. **Detection**: When both "Car" and "Frontal" are detected, create initial database record with UUID session_id
+2. **Redis Storage**: Save cropped frontal image to Redis with session_id in key
+3. **Parallel Processing**: Run brand and body type classification concurrently
+4. **Synchronization**: Wait for all branches to complete using `waitForBranches`
+5. **Database Update**: Update record with combined classification results using field mapping
+
+### Field Mapping
+Templates like `{car_brand_cls_v1.brand}` are resolved to actual classification results:
+- `car_brand_cls_v1.brand` → "Honda"
+- `car_bodytype_cls_v1.body_type` → "Sedan"
+
+## 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
+- Parallel processing with ThreadPoolExecutor for classification branches
+- Multi-class detection reduces inference passes
+- Region-based cropping minimizes processing overhead
+- Database connection pooling and prepared statements
+- Redis image storage with automatic expiration

Dockerfile

@@ -1,19 +1,11 @@
-# Use the official Python image from the Docker Hub
+# Use our pre-built base image with ML dependencies
-FROM python:3.13-bookworm
+FROM git.siwatsystem.com/adsist-cms/worker-base:latest
 
-# Set the working directory in the container
+# Copy and install application requirements (frequently changing dependencies)
-WORKDIR /app
-
-# Copy the requirements file into the container at /app
 COPY requirements.txt .
-
-# Update apt, install libgl1, and clear apt cache
-RUN apt update && apt install -y libgl1 && rm -rf /var/lib/apt/lists/*
-
-# Install any dependencies specified in requirements.txt
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy the rest of the application code into the container at /app
+# Copy the application code
 COPY . .
 
 # Run the application

Dockerfile.base

@@ -0,0 +1,15 @@
+# Base image with all ML dependencies
+FROM python:3.13-bookworm
+
+# Install system dependencies
+RUN apt update && apt install -y libgl1 && rm -rf /var/lib/apt/lists/*
+
+# Copy and install base requirements (ML dependencies that rarely change)
+COPY requirements.base.txt .
+RUN pip install --no-cache-dir -r requirements.base.txt
+
+# Set working directory
+WORKDIR /app
+
+# This base image will be reused for all worker builds
+CMD ["python3", "-m", "fastapi", "run", "--host", "0.0.0.0", "--port", "8000"]

app.py

@@ -13,9 +13,16 @@ import requests
 import asyncio
 import psutil
 import zipfile
+import ssl
+import urllib3
+import subprocess
+import tempfile
 from urllib.parse import urlparse
-from fastapi import FastAPI, WebSocket
+from requests.adapters import HTTPAdapter
+from urllib3.util.ssl_ import create_urllib3_context
+from fastapi import FastAPI, WebSocket, HTTPException
 from fastapi.websockets import WebSocketDisconnect
+from fastapi.responses import Response
 from websockets.exceptions import ConnectionClosedError
 from ultralytics import YOLO
 
@@ -28,6 +35,14 @@ app = FastAPI()
 # "models" now holds a nested dict: { camera_id: { modelId: model_tree } }
 models: Dict[str, Dict[str, Any]] = {}
 streams: Dict[str, Dict[str, Any]] = {}
+# Store session IDs per display
+session_ids: Dict[str, int] = {}
+# Track shared camera streams by camera URL
+camera_streams: Dict[str, Dict[str, Any]] = {}
+# Map subscriptions to their camera URL
+subscription_to_camera: Dict[str, str] = {}
+# Store latest frames for REST API access (separate from processing buffer)
+latest_frames: Dict[str, Any] = {}
 
 with open("config.json", "r") as f:
     config = json.load(f)
@@ -102,25 +117,115 @@ def download_mpta(url: str, dest_path: str) -> str:
 # Add helper to fetch snapshot image from HTTP/HTTPS URL
 def fetch_snapshot(url: str):
     try:
-        response = requests.get(url, timeout=10)
+        from requests.auth import HTTPBasicAuth, HTTPDigestAuth
+        
+        # Parse URL to extract credentials
+        parsed = urlparse(url)
+        
+        # Prepare headers - some cameras require User-Agent
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (compatible; DetectorWorker/1.0)'
+        }
+        
+        # Reconstruct URL without credentials
+        clean_url = f"{parsed.scheme}://{parsed.hostname}"
+        if parsed.port:
+            clean_url += f":{parsed.port}"
+        clean_url += parsed.path
+        if parsed.query:
+            clean_url += f"?{parsed.query}"
+        
+        auth = None
+        if parsed.username and parsed.password:
+            # Try HTTP Digest authentication first (common for IP cameras)
+            try:
+                auth = HTTPDigestAuth(parsed.username, parsed.password)
+                response = requests.get(clean_url, auth=auth, headers=headers, timeout=10)
+                if response.status_code == 200:
+                    logger.debug(f"Successfully authenticated using HTTP Digest for {clean_url}")
+                elif response.status_code == 401:
+                    # If Digest fails, try Basic auth
+                    logger.debug(f"HTTP Digest failed, trying Basic auth for {clean_url}")
+                    auth = HTTPBasicAuth(parsed.username, parsed.password)
+                    response = requests.get(clean_url, auth=auth, headers=headers, timeout=10)
+                    if response.status_code == 200:
+                        logger.debug(f"Successfully authenticated using HTTP Basic for {clean_url}")
+            except Exception as auth_error:
+                logger.debug(f"Authentication setup error: {auth_error}")
+                # Fallback to original URL with embedded credentials
+                response = requests.get(url, headers=headers, timeout=10)
+        else:
+            # No credentials in URL, make request as-is
+            response = requests.get(url, headers=headers, timeout=10)
+        
         if response.status_code == 200:
             # Convert response content to numpy array
             nparr = np.frombuffer(response.content, np.uint8)
             # Decode image
             frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
             if frame is not None:
-                logger.debug(f"Successfully fetched snapshot from {url}, shape: {frame.shape}")
+                logger.debug(f"Successfully fetched snapshot from {clean_url}, shape: {frame.shape}")
                 return frame
             else:
-                logger.error(f"Failed to decode image from snapshot URL: {url}")
+                logger.error(f"Failed to decode image from snapshot URL: {clean_url}")
                 return None
         else:
-            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {url}")
+            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {clean_url}")
             return None
     except Exception as e:
         logger.error(f"Exception fetching snapshot from {url}: {str(e)}")
         return None
 
+# Helper to get crop coordinates from stream
+def get_crop_coords(stream):
+    return {
+        "cropX1": stream.get("cropX1"),
+        "cropY1": stream.get("cropY1"),
+        "cropX2": stream.get("cropX2"),
+        "cropY2": stream.get("cropY2")
+    }
+
+####################################################
+# REST API endpoint for image retrieval
+####################################################
+@app.get("/camera/{camera_id}/image")
+async def get_camera_image(camera_id: str):
+    """
+    Get the current frame from a camera as JPEG image
+    """
+    try:
+        # URL decode the camera_id to handle encoded characters like %3B for semicolon
+        from urllib.parse import unquote
+        original_camera_id = camera_id
+        camera_id = unquote(camera_id)
+        logger.debug(f"REST API request: original='{original_camera_id}', decoded='{camera_id}'")
+        
+        with streams_lock:
+            if camera_id not in streams:
+                logger.warning(f"Camera ID '{camera_id}' not found in streams. Current streams: {list(streams.keys())}")
+                raise HTTPException(status_code=404, detail=f"Camera {camera_id} not found or not active")
+            
+            # Check if we have a cached frame for this camera
+            if camera_id not in latest_frames:
+                logger.warning(f"No cached frame available for camera '{camera_id}'.")
+                raise HTTPException(status_code=404, detail=f"No frame available for camera {camera_id}")
+            
+            frame = latest_frames[camera_id]
+            logger.debug(f"Retrieved cached frame for camera '{camera_id}', frame shape: {frame.shape}")
+        # Encode frame as JPEG
+        success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
+        if not success:
+            raise HTTPException(status_code=500, detail="Failed to encode image as JPEG")
+        
+        # Return image as binary response
+        return Response(content=buffer_img.tobytes(), media_type="image/jpeg")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error retrieving image for camera {camera_id}: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
+
 ####################################################
 # Detection and frame processing functions
 ####################################################
@@ -131,73 +236,118 @@ async def detect(websocket: WebSocket):
 
     async def handle_detection(camera_id, stream, frame, websocket, model_tree, persistent_data):
         try:
+            # Apply crop if specified
+            cropped_frame = frame
+            if all(coord is not None for coord in [stream.get("cropX1"), stream.get("cropY1"), stream.get("cropX2"), stream.get("cropY2")]):
+                cropX1, cropY1, cropX2, cropY2 = stream["cropX1"], stream["cropY1"], stream["cropX2"], stream["cropY2"]
+                cropped_frame = frame[cropY1:cropY2, cropX1:cropX2]
+                logger.debug(f"Applied crop coordinates ({cropX1}, {cropY1}, {cropX2}, {cropY2}) to frame for camera {camera_id}")
+            
             logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
             start_time = time.time()
-            detection_result = run_pipeline(frame, model_tree)
+            
+            # Extract display identifier for pipeline context
+            subscription_parts = stream["subscriptionIdentifier"].split(';')
+            display_identifier = subscription_parts[0] if subscription_parts else None
+            
+            # Create context for pipeline execution (session_id will be generated by pipeline)
+            pipeline_context = {
+                "camera_id": camera_id,
+                "display_id": display_identifier
+            }
+            
+            detection_result = run_pipeline(cropped_frame, model_tree, context=pipeline_context)
             process_time = (time.time() - start_time) * 1000
             logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms")
             
             # Log the raw detection result for debugging
             logger.debug(f"Raw detection result for camera {camera_id}:\n{json.dumps(detection_result, indent=2, default=str)}")
             
-            # Direct class result (no detections/classifications structure)
+            # Extract session_id from pipeline result (generated during database record creation)
-            if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
+            session_id = None
-                highest_confidence_detection = {
+            if detection_result and isinstance(detection_result, dict):
-                    "class": detection_result.get("class", "none"),
+                # Check if pipeline generated a session_id (happens when Car+Frontal detected together)
-                    "confidence": detection_result.get("confidence", 1.0),
+                if "session_id" in detection_result:
-                    "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                    session_id = detection_result["session_id"]
-                }
+                    logger.debug(f"Extracted session_id from pipeline result: {session_id}")
-            # Handle case when no detections found or result is empty
-            elif not detection_result or not detection_result.get("detections"):
-                # Check if we have classification results
-                if detection_result and detection_result.get("classifications"):
-                    # Get the highest confidence classification
-                    classifications = detection_result.get("classifications", [])
-                    highest_confidence_class = max(classifications, key=lambda x: x.get("confidence", 0)) if classifications else None
             
-                    if highest_confidence_class:
+            # Process detection result - run_pipeline returns the primary detection directly
-                        highest_confidence_detection = {
+            if detection_result and isinstance(detection_result, dict) and "class" in detection_result:
-                            "class": highest_confidence_class.get("class", "none"),
+                highest_confidence_detection = detection_result
-                            "confidence": highest_confidence_class.get("confidence", 1.0),
-                            "box": [0, 0, 0, 0]  # Empty bounding box for classifications
-                        }
             else:
+                # No detection found
                 highest_confidence_detection = {
                     "class": "none",
                     "confidence": 1.0,
-                            "box": [0, 0, 0, 0]
+                    "bbox": [0, 0, 0, 0],
+                    "branch_results": {}
                 }
+            
+            # Convert detection format to match backend expectations exactly as in worker.md section 4.2
+            detection_dict = {
+                "carModel": None,
+                "carBrand": None,
+                "carYear": None,
+                "bodyType": None,
+                "licensePlateText": None,
+                "licensePlateConfidence": None
+            }
+            
+            # Extract and process branch results from parallel classification
+            branch_results = highest_confidence_detection.get("branch_results", {})
+            if branch_results:
+                logger.debug(f"Processing branch results: {branch_results}")
+                
+                # Transform branch results into backend-expected detection attributes
+                for branch_id, branch_data in branch_results.items():
+                    if isinstance(branch_data, dict):
+                        logger.debug(f"Processing branch {branch_id}: {branch_data}")
+                        
+                        # Map common classification fields to backend-expected names
+                        if "brand" in branch_data:
+                            detection_dict["carBrand"] = branch_data["brand"]
+                        if "body_type" in branch_data:
+                            detection_dict["bodyType"] = branch_data["body_type"]
+                        if "class" in branch_data:
+                            class_name = branch_data["class"]
+                            
+                            # Map based on branch/model type
+                            if "brand" in branch_id.lower():
+                                detection_dict["carBrand"] = class_name
+                            elif "bodytype" in branch_id.lower() or "body" in branch_id.lower():
+                                detection_dict["bodyType"] = class_name
+                
+                logger.info(f"Detection payload after branch processing: {detection_dict}")
             else:
-                    highest_confidence_detection = {
+                logger.debug("No branch results found in detection result")
-                        "class": "none",
-                        "confidence": 1.0,
-                        "box": [0, 0, 0, 0]
-                    }
-            else:
-                # Find detection with highest confidence
-                detections = detection_result.get("detections", [])
-                highest_confidence_detection = max(detections, key=lambda x: x.get("confidence", 0)) if detections else {
-                    "class": "none",
-                    "confidence": 1.0,
-                    "box": [0, 0, 0, 0]
-                }
             
             detection_data = {
                 "type": "imageDetection",
-                "cameraIdentifier": camera_id,
+                "subscriptionIdentifier": stream["subscriptionIdentifier"],
-                "timestamp": time.time(),
+                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S.%fZ", time.gmtime()),
                 "data": {
-                    "detection": highest_confidence_detection,  # Send only the highest confidence detection
+                    "detection": detection_dict,
                     "modelId": stream["modelId"],
                     "modelName": stream["modelName"]
                 }
             }
             
-            if highest_confidence_detection["class"] != "none":
+            # Add session ID if available (generated by pipeline when Car+Frontal detected)
-                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
+            if session_id is not None:
+                detection_data["sessionId"] = session_id
+                logger.debug(f"Added session_id to WebSocket response: {session_id}")
+            
+            if highest_confidence_detection.get("class") != "none":
+                confidence = highest_confidence_detection.get("confidence", 0.0)
+                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {confidence:.2f} using model {stream['modelName']}")
+                
+                # Log session ID if available
+                if session_id:
+                    logger.debug(f"Detection associated with session ID: {session_id}")
             
             await websocket.send_json(detection_data)
-            logger.debug(f"Sent detection data to client for camera {camera_id}:\n{json.dumps(detection_data, indent=2)}")
+            logger.debug(f"Sent detection data to client for camera {camera_id}")
+            logger.debug(f"Sent this detection data: {detection_data}")
             return persistent_data
         except Exception as e:
             logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
@@ -264,12 +414,11 @@ async def detect(websocket: WebSocket):
                     if not buffer.empty():
                         try:
                             buffer.get_nowait()
-                            logger.debug(f"Removed old frame from buffer for camera {camera_id}")
+                            logger.debug(f"[frame_reader] Removed old frame from buffer for camera {camera_id}")
                         except queue.Empty:
                             pass
-                    
                     buffer.put(frame)
-                    logger.debug(f"Added new frame to buffer for camera {camera_id}")
+                    logger.debug(f"[frame_reader] Added new frame to buffer for camera {camera_id}. Buffer size: {buffer.qsize()}")
                     
                     # Short sleep to avoid CPU overuse
                     time.sleep(0.01)
@@ -340,12 +489,11 @@ async def detect(websocket: WebSocket):
                     if not buffer.empty():
                         try:
                             buffer.get_nowait()
-                            logger.debug(f"Removed old snapshot from buffer for camera {camera_id}")
+                            logger.debug(f"[snapshot_reader] Removed old snapshot from buffer for camera {camera_id}")
                         except queue.Empty:
                             pass
-                    
                     buffer.put(frame)
-                    logger.debug(f"Added new snapshot to buffer for camera {camera_id}")
+                    logger.debug(f"[snapshot_reader] Added new snapshot to buffer for camera {camera_id}. Buffer size: {buffer.qsize()}")
                     
                     # Wait for the specified interval
                     elapsed = time.time() - start_time
@@ -365,6 +513,199 @@ async def detect(websocket: WebSocket):
         finally:
             logger.info(f"Snapshot reader thread for camera {camera_id} is exiting")
 
+    async def reconcile_subscriptions(desired_subscriptions, websocket):
+        """
+        Declarative reconciliation: Compare desired vs current subscriptions and make changes
+        """
+        logger.info(f"Reconciling subscriptions: {len(desired_subscriptions)} desired")
+        
+        with streams_lock:
+            # Get current subscriptions
+            current_subscription_ids = set(streams.keys())
+            desired_subscription_ids = set(sub["subscriptionIdentifier"] for sub in desired_subscriptions)
+            
+            # Find what to add and remove
+            to_add = desired_subscription_ids - current_subscription_ids
+            to_remove = current_subscription_ids - desired_subscription_ids
+            to_check_for_changes = current_subscription_ids & desired_subscription_ids
+            
+            logger.info(f"Reconciliation: {len(to_add)} to add, {len(to_remove)} to remove, {len(to_check_for_changes)} to check for changes")
+            
+            # Remove subscriptions that are no longer wanted
+            for subscription_id in to_remove:
+                await unsubscribe_internal(subscription_id)
+                
+            # Check existing subscriptions for parameter changes
+            for subscription_id in to_check_for_changes:
+                desired_sub = next(sub for sub in desired_subscriptions if sub["subscriptionIdentifier"] == subscription_id)
+                current_stream = streams[subscription_id]
+                
+                # Check if parameters changed
+                if has_subscription_changed(desired_sub, current_stream):
+                    logger.info(f"Parameters changed for {subscription_id}, resubscribing")
+                    await unsubscribe_internal(subscription_id)
+                    await subscribe_internal(desired_sub, websocket)
+                    
+            # Add new subscriptions
+            for subscription_id in to_add:
+                desired_sub = next(sub for sub in desired_subscriptions if sub["subscriptionIdentifier"] == subscription_id)
+                await subscribe_internal(desired_sub, websocket)
+
+    def has_subscription_changed(desired_sub, current_stream):
+        """Check if subscription parameters have changed"""
+        return (
+            desired_sub.get("rtspUrl") != current_stream.get("rtsp_url") or
+            desired_sub.get("snapshotUrl") != current_stream.get("snapshot_url") or  
+            desired_sub.get("snapshotInterval") != current_stream.get("snapshot_interval") or
+            desired_sub.get("cropX1") != current_stream.get("cropX1") or
+            desired_sub.get("cropY1") != current_stream.get("cropY1") or
+            desired_sub.get("cropX2") != current_stream.get("cropX2") or
+            desired_sub.get("cropY2") != current_stream.get("cropY2") or
+            desired_sub.get("modelId") != current_stream.get("modelId") or
+            desired_sub.get("modelName") != current_stream.get("modelName")
+        )
+
+    async def subscribe_internal(subscription, websocket):
+        """Internal subscription logic extracted from original subscribe handler"""
+        subscriptionIdentifier = subscription.get("subscriptionIdentifier")
+        rtsp_url = subscription.get("rtspUrl") 
+        snapshot_url = subscription.get("snapshotUrl")
+        snapshot_interval = subscription.get("snapshotInterval")
+        model_url = subscription.get("modelUrl")
+        modelId = subscription.get("modelId")
+        modelName = subscription.get("modelName")
+        cropX1 = subscription.get("cropX1")
+        cropY1 = subscription.get("cropY1")
+        cropX2 = subscription.get("cropX2")
+        cropY2 = subscription.get("cropY2")
+
+        # Extract camera_id from subscriptionIdentifier
+        parts = subscriptionIdentifier.split(';')
+        if len(parts) != 2:
+            logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}")
+            return
+        
+        display_identifier, camera_identifier = parts
+        camera_id = subscriptionIdentifier
+
+        # Load model if needed
+        if model_url:
+            with models_lock:
+                if (camera_id not in models) or (modelId not in models[camera_id]):
+                    logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}")
+                    extraction_dir = os.path.join("models", camera_identifier, str(modelId))
+                    os.makedirs(extraction_dir, exist_ok=True)
+                    
+                    # Handle model loading (same as original)
+                    parsed = urlparse(model_url)
+                    if parsed.scheme in ("http", "https"):
+                        filename = os.path.basename(parsed.path) or f"model_{modelId}.mpta"
+                        local_mpta = os.path.join(extraction_dir, filename)
+                        local_path = download_mpta(model_url, local_mpta)
+                        if not local_path:
+                            logger.error(f"Failed to download model from {model_url}")
+                            return
+                        model_tree = load_pipeline_from_zip(local_path, extraction_dir)
+                    else:
+                        if not os.path.exists(model_url):
+                            logger.error(f"Model file not found: {model_url}")
+                            return
+                        model_tree = load_pipeline_from_zip(model_url, extraction_dir)
+                    
+                    if model_tree is None:
+                        logger.error(f"Failed to load model {modelId}")
+                        return
+                        
+                    if camera_id not in models:
+                        models[camera_id] = {}
+                    models[camera_id][modelId] = model_tree
+
+        # Create stream (same logic as original)
+        if camera_id and (rtsp_url or snapshot_url) and len(streams) < max_streams:
+            camera_url = snapshot_url if snapshot_url else rtsp_url
+            
+            # Check if we already have a stream for this camera URL
+            shared_stream = camera_streams.get(camera_url)
+            
+            if shared_stream:
+                # Reuse existing stream
+                buffer = shared_stream["buffer"]
+                stop_event = shared_stream["stop_event"] 
+                thread = shared_stream["thread"]
+                mode = shared_stream["mode"]
+                shared_stream["ref_count"] = shared_stream.get("ref_count", 0) + 1
+            else:
+                # Create new stream
+                buffer = queue.Queue(maxsize=1)
+                stop_event = threading.Event()
+                
+                if snapshot_url and snapshot_interval:
+                    thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
+                    thread.daemon = True
+                    thread.start()
+                    mode = "snapshot"
+                    shared_stream = {
+                        "buffer": buffer, "thread": thread, "stop_event": stop_event,
+                        "mode": mode, "url": snapshot_url, "snapshot_interval": snapshot_interval, "ref_count": 1
+                    }
+                    camera_streams[camera_url] = shared_stream
+                elif rtsp_url:
+                    cap = cv2.VideoCapture(rtsp_url)
+                    if not cap.isOpened():
+                        logger.error(f"Failed to open RTSP stream for camera {camera_id}")
+                        return
+                    thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
+                    thread.daemon = True
+                    thread.start()
+                    mode = "rtsp"
+                    shared_stream = {
+                        "buffer": buffer, "thread": thread, "stop_event": stop_event,
+                        "mode": mode, "url": rtsp_url, "cap": cap, "ref_count": 1
+                    }
+                    camera_streams[camera_url] = shared_stream
+                else:
+                    logger.error(f"No valid URL provided for camera {camera_id}")
+                    return
+
+            # Create stream info
+            stream_info = {
+                "buffer": buffer, "thread": thread, "stop_event": stop_event,
+                "modelId": modelId, "modelName": modelName, "subscriptionIdentifier": subscriptionIdentifier,
+                "cropX1": cropX1, "cropY1": cropY1, "cropX2": cropX2, "cropY2": cropY2,
+                "mode": mode, "camera_url": camera_url, "modelUrl": model_url
+            }
+            
+            if mode == "snapshot":
+                stream_info["snapshot_url"] = snapshot_url
+                stream_info["snapshot_interval"] = snapshot_interval
+            elif mode == "rtsp":
+                stream_info["rtsp_url"] = rtsp_url
+                stream_info["cap"] = shared_stream["cap"]
+            
+            streams[camera_id] = stream_info
+            subscription_to_camera[camera_id] = camera_url
+            logger.info(f"Subscribed to camera {camera_id}")
+
+    async def unsubscribe_internal(subscription_id):
+        """Internal unsubscription logic"""
+        if subscription_id in streams:
+            stream = streams.pop(subscription_id)
+            camera_url = subscription_to_camera.pop(subscription_id, None)
+            
+            if camera_url and camera_url in camera_streams:
+                shared_stream = camera_streams[camera_url]
+                shared_stream["ref_count"] -= 1
+                
+                if shared_stream["ref_count"] <= 0:
+                    shared_stream["stop_event"].set()
+                    shared_stream["thread"].join()
+                    if "cap" in shared_stream:
+                        shared_stream["cap"].release()
+                    del camera_streams[camera_url]
+            
+            latest_frames.pop(subscription_id, None)
+            logger.info(f"Unsubscribed from camera {subscription_id}")
+
     async def process_streams():
         logger.info("Started processing streams")
         try:
@@ -386,6 +727,10 @@ async def detect(websocket: WebSocket):
                     logger.debug(f"Got frame from buffer for camera {camera_id}")
                     frame = buffer.get()
                     
+                    # Cache the frame for REST API access
+                    latest_frames[camera_id] = frame.copy()
+                    logger.debug(f"Cached frame for REST API access for camera {camera_id}")
+                    
                     with models_lock:
                         model_tree = models.get(camera_id, {}).get(stream["modelId"])
                         if not model_tree:
@@ -416,18 +761,23 @@ async def detect(websocket: WebSocket):
                 cpu_usage = psutil.cpu_percent()
                 memory_usage = psutil.virtual_memory().percent
                 if torch.cuda.is_available():
-                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # MB
+                    gpu_usage = torch.cuda.utilization() if hasattr(torch.cuda, 'utilization') else None
-                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # MB
+                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)
                 else:
                     gpu_usage = None
                     gpu_memory_usage = None
 
                 camera_connections = [
                     {
-                        "cameraIdentifier": camera_id,
+                        "subscriptionIdentifier": stream["subscriptionIdentifier"],
                         "modelId": stream["modelId"],
                         "modelName": stream["modelName"],
-                        "online": True
+                        "online": True,
+                        # Include all subscription parameters for proper change detection
+                        "rtspUrl": stream.get("rtsp_url"),
+                        "snapshotUrl": stream.get("snapshot_url"),
+                        "snapshotInterval": stream.get("snapshot_interval"),
+                        **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
                     }
                     for camera_id, stream in streams.items()
                 ]
@@ -455,58 +805,87 @@ async def detect(websocket: WebSocket):
                 data = json.loads(msg)
                 msg_type = data.get("type")
 
-                if msg_type == "subscribe":
+                if msg_type == "setSubscriptionList":
-                    payload = data.get("payload", {})
+                    # Declarative approach: Backend sends list of subscriptions this worker should have
-                    camera_id = payload.get("cameraIdentifier")
+                    desired_subscriptions = data.get("subscriptions", [])
-                    rtsp_url = payload.get("rtspUrl")
+                    logger.info(f"Received subscription list with {len(desired_subscriptions)} subscriptions")
-                    snapshot_url = payload.get("snapshotUrl")
-                    snapshot_interval = payload.get("snapshotInterval")  # in milliseconds
-                    model_url = payload.get("modelUrl")  # may be remote or local
-                    modelId = payload.get("modelId")
-                    modelName = payload.get("modelName")
                     
+                    await reconcile_subscriptions(desired_subscriptions, websocket)
+                    
+                elif msg_type == "subscribe":
+                    # Legacy support - convert single subscription to list
+                    payload = data.get("payload", {})
+                    await reconcile_subscriptions([payload], websocket)
+                    
+                elif msg_type == "unsubscribe":
+                    # Legacy support - remove subscription
+                    payload = data.get("payload", {})
+                    subscriptionIdentifier = payload.get("subscriptionIdentifier")
+                    # Remove from current subscriptions and reconcile
+                    current_subs = []
+                    with streams_lock:
+                        for camera_id, stream in streams.items():
+                            if stream["subscriptionIdentifier"] != subscriptionIdentifier:
+                                # Convert stream back to subscription format
+                                current_subs.append({
+                                    "subscriptionIdentifier": stream["subscriptionIdentifier"],
+                                    "rtspUrl": stream.get("rtsp_url"),
+                                    "snapshotUrl": stream.get("snapshot_url"), 
+                                    "snapshotInterval": stream.get("snapshot_interval"),
+                                    "modelId": stream["modelId"],
+                                    "modelName": stream["modelName"],
+                                    "modelUrl": stream.get("modelUrl", ""),
+                                    "cropX1": stream.get("cropX1"),
+                                    "cropY1": stream.get("cropY1"),
+                                    "cropX2": stream.get("cropX2"),
+                                    "cropY2": stream.get("cropY2")
+                                })
+                    await reconcile_subscriptions(current_subs, websocket)
+                    
+                elif msg_type == "old_subscribe_logic_removed":
                     if model_url:
                         with models_lock:
                             if (camera_id not in models) or (modelId not in models[camera_id]):
                                 logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}")
-                                extraction_dir = os.path.join("models", camera_id, str(modelId))
+                                extraction_dir = os.path.join("models", camera_identifier, str(modelId))
                                 os.makedirs(extraction_dir, exist_ok=True)
                                 # If model_url is remote, download it first.
                                 parsed = urlparse(model_url)
                                 if parsed.scheme in ("http", "https"):
-                                    logger.info(f"Downloading remote model from {model_url}")
+                                    logger.info(f"Downloading remote .mpta file from {model_url}")
-                                    local_mpta = os.path.join(extraction_dir, os.path.basename(parsed.path))
+                                    filename = os.path.basename(parsed.path) or f"model_{modelId}.mpta"
+                                    local_mpta = os.path.join(extraction_dir, filename)
                                     logger.debug(f"Download destination: {local_mpta}")
                                     local_path = download_mpta(model_url, local_mpta)
                                     if not local_path:
-                                        logger.error(f"Failed to download the remote mpta file from {model_url}")
+                                        logger.error(f"Failed to download the remote .mpta file from {model_url}")
                                         error_response = {
                                             "type": "error",
-                                            "cameraIdentifier": camera_id,
+                                            "subscriptionIdentifier": subscriptionIdentifier,
                                             "error": f"Failed to download model from {model_url}"
                                         }
                                         await websocket.send_json(error_response)
                                         continue
                                     model_tree = load_pipeline_from_zip(local_path, extraction_dir)
                                 else:
-                                    logger.info(f"Loading local model from {model_url}")
+                                    logger.info(f"Loading local .mpta file from {model_url}")
                                     # Check if file exists before attempting to load
                                     if not os.path.exists(model_url):
-                                        logger.error(f"Local model file not found: {model_url}")
+                                        logger.error(f"Local .mpta file not found: {model_url}")
                                         logger.debug(f"Current working directory: {os.getcwd()}")
                                         error_response = {
                                             "type": "error",
-                                            "cameraIdentifier": camera_id,
+                                            "subscriptionIdentifier": subscriptionIdentifier,
                                             "error": f"Model file not found: {model_url}"
                                         }
                                         await websocket.send_json(error_response)
                                         continue
                                     model_tree = load_pipeline_from_zip(model_url, extraction_dir)
                                 if model_tree is None:
-                                    logger.error(f"Failed to load model {modelId} from mpta file for camera {camera_id}")
+                                    logger.error(f"Failed to load model {modelId} from .mpta file for camera {camera_id}")
                                     error_response = {
                                         "type": "error",
-                                        "cameraIdentifier": camera_id,
+                                        "subscriptionIdentifier": subscriptionIdentifier,
                                         "error": f"Failed to load model {modelId}"
                                     }
                                     await websocket.send_json(error_response)
@@ -515,37 +894,52 @@ async def detect(websocket: WebSocket):
                                     models[camera_id] = {}
                                 models[camera_id][modelId] = model_tree
                                 logger.info(f"Successfully loaded model {modelId} for camera {camera_id}")
-                                success_response = {
+                                logger.debug(f"Model extraction directory: {extraction_dir}")
-                                    "type": "modelLoaded",
-                                    "cameraIdentifier": camera_id,
-                                    "modelId": modelId
-                                }
-                                await websocket.send_json(success_response)
                     if camera_id and (rtsp_url or snapshot_url):
                         with streams_lock:
+                            # Determine camera URL for shared stream management
+                            camera_url = snapshot_url if snapshot_url else rtsp_url
+                            
                             if camera_id not in streams and len(streams) < max_streams:
+                                # Check if we already have a stream for this camera URL
+                                shared_stream = camera_streams.get(camera_url)
+                                
+                                if shared_stream:
+                                    # Reuse existing stream
+                                    logger.info(f"Reusing existing stream for camera URL: {camera_url}")
+                                    buffer = shared_stream["buffer"]
+                                    stop_event = shared_stream["stop_event"]
+                                    thread = shared_stream["thread"]
+                                    mode = shared_stream["mode"]
+                                    
+                                    # Increment reference count
+                                    shared_stream["ref_count"] = shared_stream.get("ref_count", 0) + 1
+                                else:
+                                    # Create new stream
                                     buffer = queue.Queue(maxsize=1)
                                     stop_event = threading.Event()
                                     
-                                # Choose between snapshot and RTSP based on availability
                                     if snapshot_url and snapshot_interval:
-                                    logger.info(f"Using snapshot mode for camera {camera_id}: {snapshot_url}")
+                                        logger.info(f"Creating new snapshot stream for camera {camera_id}: {snapshot_url}")
                                         thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
                                         thread.daemon = True
                                         thread.start()
-                                    streams[camera_id] = {
+                                        mode = "snapshot"
+                                        
+                                        # Store shared stream info
+                                        shared_stream = {
                                             "buffer": buffer,
                                             "thread": thread,
-                                        "snapshot_url": snapshot_url,
-                                        "snapshot_interval": snapshot_interval,
                                             "stop_event": stop_event,
-                                        "modelId": modelId,
+                                            "mode": mode,
-                                        "modelName": modelName,
+                                            "url": snapshot_url,
-                                        "mode": "snapshot"
+                                            "snapshot_interval": snapshot_interval,
+                                            "ref_count": 1
                                         }
-                                    logger.info(f"Subscribed to camera {camera_id} (snapshot mode) with modelId {modelId}, modelName {modelName}, URL {snapshot_url}, interval {snapshot_interval}ms")
+                                        camera_streams[camera_url] = shared_stream
+                                        
                                     elif rtsp_url:
-                                    logger.info(f"Using RTSP mode for camera {camera_id}: {rtsp_url}")
+                                        logger.info(f"Creating new RTSP stream for camera {camera_id}: {rtsp_url}")
                                         cap = cv2.VideoCapture(rtsp_url)
                                         if not cap.isOpened():
                                             logger.error(f"Failed to open RTSP stream for camera {camera_id}")
@@ -553,57 +947,86 @@ async def detect(websocket: WebSocket):
                                         thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
                                         thread.daemon = True
                                         thread.start()
-                                    streams[camera_id] = {
+                                        mode = "rtsp"
-                                        "cap": cap,
+                                        
+                                        # Store shared stream info
+                                        shared_stream = {
                                             "buffer": buffer,
                                             "thread": thread,
-                                        "rtsp_url": rtsp_url,
                                             "stop_event": stop_event,
-                                        "modelId": modelId,
+                                            "mode": mode,
-                                        "modelName": modelName,
+                                            "url": rtsp_url,
-                                        "mode": "rtsp"
+                                            "cap": cap,
+                                            "ref_count": 1
                                         }
-                                    logger.info(f"Subscribed to camera {camera_id} (RTSP mode) with modelId {modelId}, modelName {modelName}, URL {rtsp_url}")
+                                        camera_streams[camera_url] = shared_stream
                                     else:
                                         logger.error(f"No valid URL provided for camera {camera_id}")
                                         continue
+                                
+                                # Create stream info for this subscription
+                                stream_info = {
+                                    "buffer": buffer,
+                                    "thread": thread,
+                                    "stop_event": stop_event,
+                                    "modelId": modelId,
+                                    "modelName": modelName,
+                                    "subscriptionIdentifier": subscriptionIdentifier,
+                                    "cropX1": cropX1,
+                                    "cropY1": cropY1,
+                                    "cropX2": cropX2,
+                                    "cropY2": cropY2,
+                                    "mode": mode,
+                                    "camera_url": camera_url
+                                }
+                                
+                                if mode == "snapshot":
+                                    stream_info["snapshot_url"] = snapshot_url
+                                    stream_info["snapshot_interval"] = snapshot_interval
+                                elif mode == "rtsp":
+                                    stream_info["rtsp_url"] = rtsp_url
+                                    stream_info["cap"] = shared_stream["cap"]
+                                
+                                streams[camera_id] = stream_info
+                                subscription_to_camera[camera_id] = camera_url
+                                
                             elif camera_id and camera_id in streams:
                                 # If already subscribed, unsubscribe first
-                                stream = streams.pop(camera_id)
+                                logger.info(f"Resubscribing to camera {camera_id}")
-                                stream["stop_event"].set()
+                                # Note: Keep models in memory for reuse across subscriptions
-                                stream["thread"].join()
-                                if "cap" in stream:
-                                    stream["cap"].release()
-                                logger.info(f"Unsubscribed from camera {camera_id} for resubscription")
-                                with models_lock:
-                                    if camera_id in models and modelId in models[camera_id]:
-                                        del models[camera_id][modelId]
-                                        if not models[camera_id]:
-                                            del models[camera_id]
                 elif msg_type == "unsubscribe":
                     payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
+                    subscriptionIdentifier = payload.get("subscriptionIdentifier")
-                    logger.debug(f"Unsubscribing from camera {camera_id}")
+                    camera_id = subscriptionIdentifier
                     with streams_lock:
                         if camera_id and camera_id in streams:
                             stream = streams.pop(camera_id)
-                            stream["stop_event"].set()
+                            camera_url = subscription_to_camera.pop(camera_id, None)
-                            stream["thread"].join()
+                            
-                            # Only release cap if it exists (RTSP mode)
+                            if camera_url and camera_url in camera_streams:
-                            if "cap" in stream:
+                                shared_stream = camera_streams[camera_url]
-                                stream["cap"].release()
+                                shared_stream["ref_count"] -= 1
-                                logger.info(f"Released RTSP capture for camera {camera_id}")
+                                
+                                # If no more references, stop the shared stream
+                                if shared_stream["ref_count"] <= 0:
+                                    logger.info(f"Stopping shared stream for camera URL: {camera_url}")
+                                    shared_stream["stop_event"].set()
+                                    shared_stream["thread"].join()
+                                    if "cap" in shared_stream:
+                                        shared_stream["cap"].release()
+                                    del camera_streams[camera_url]
                                 else:
-                                logger.info(f"Released snapshot reader for camera {camera_id}")
+                                    logger.info(f"Shared stream for {camera_url} still has {shared_stream['ref_count']} references")
+                            
+                            # Clean up cached frame
+                            latest_frames.pop(camera_id, None)
                             logger.info(f"Unsubscribed from camera {camera_id}")
-                            with models_lock:
+                            # Note: Keep models in memory for potential reuse
-                                if camera_id in models:
-                                    del models[camera_id]
                 elif msg_type == "requestState":
                     cpu_usage = psutil.cpu_percent()
                     memory_usage = psutil.virtual_memory().percent
                     if torch.cuda.is_available():
-                        gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)
+                        gpu_usage = torch.cuda.utilization() if hasattr(torch.cuda, 'utilization') else None
                         gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)
                     else:
                         gpu_usage = None
@@ -611,10 +1034,15 @@ async def detect(websocket: WebSocket):
 
                     camera_connections = [
                         {
-                            "cameraIdentifier": camera_id,
+                            "subscriptionIdentifier": stream["subscriptionIdentifier"],
                             "modelId": stream["modelId"],
                             "modelName": stream["modelName"],
-                            "online": True
+                            "online": True,
+                            # Include all subscription parameters for proper change detection
+                            "rtspUrl": stream.get("rtsp_url"),
+                            "snapshotUrl": stream.get("snapshot_url"),
+                            "snapshotInterval": stream.get("snapshot_interval"),
+                            **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
                         }
                         for camera_id, stream in streams.items()
                     ]
@@ -628,6 +1056,37 @@ async def detect(websocket: WebSocket):
                         "cameraConnections": camera_connections
                     }
                     await websocket.send_text(json.dumps(state_report))
+                
+                elif msg_type == "setSessionId":
+                    payload = data.get("payload", {})
+                    display_identifier = payload.get("displayIdentifier")
+                    session_id = payload.get("sessionId")
+                    
+                    if display_identifier:
+                        # Store session ID for this display
+                        if session_id is None:
+                            session_ids.pop(display_identifier, None)
+                            logger.info(f"Cleared session ID for display {display_identifier}")
+                        else:
+                            session_ids[display_identifier] = session_id
+                            logger.info(f"Set session ID {session_id} for display {display_identifier}")
+                
+                elif msg_type == "patchSession":
+                    session_id = data.get("sessionId")
+                    patch_data = data.get("data", {})
+                    
+                    # For now, just acknowledge the patch - actual implementation depends on backend requirements
+                    response = {
+                        "type": "patchSessionResult",
+                        "payload": {
+                            "sessionId": session_id,
+                            "success": True,
+                            "message": "Session patch acknowledged"
+                        }
+                    }
+                    await websocket.send_json(response)
+                    logger.info(f"Acknowledged patch for session {session_id}")
+                
                 else:
                     logger.error(f"Unknown message type: {msg_type}")
             except json.JSONDecodeError:
@@ -638,7 +1097,6 @@ async def detect(websocket: WebSocket):
             except Exception as e:
                 logger.error(f"Error handling message: {e}")
                 break
-
     try:
         await websocket.accept()
         stream_task = asyncio.create_task(process_streams())
@@ -651,19 +1109,24 @@ async def detect(websocket: WebSocket):
         stream_task.cancel()
         await stream_task
         with streams_lock:
-            for camera_id, stream in streams.items():
+            # Clean up shared camera streams
-                stream["stop_event"].set()
+            for camera_url, shared_stream in camera_streams.items():
-                stream["thread"].join()
+                shared_stream["stop_event"].set()
-                # Only release cap if it exists (RTSP mode)
+                shared_stream["thread"].join()
-                if "cap" in stream:
+                if "cap" in shared_stream:
-                    stream["cap"].release()
+                    shared_stream["cap"].release()
-                while not stream["buffer"].empty():
+                while not shared_stream["buffer"].empty():
                     try:
-                        stream["buffer"].get_nowait()
+                        shared_stream["buffer"].get_nowait()
                     except queue.Empty:
                         pass
-                logger.info(f"Released camera {camera_id} and cleaned up resources")
+                logger.info(f"Released shared camera stream for {camera_url}")
+            
             streams.clear()
+            camera_streams.clear()
+            subscription_to_camera.clear()
         with models_lock:
             models.clear()
+        latest_frames.clear()
+        session_ids.clear()
         logger.info("WebSocket connection closed")

app_single.py

@@ -1,366 +0,0 @@
-from typing import List
-from fastapi import FastAPI, WebSocket
-from fastapi.websockets import WebSocketDisconnect
-from websockets.exceptions import ConnectionClosedError
-from ultralytics import YOLO
-import torch
-import cv2
-import base64
-import numpy as np
-import json
-import logging
-import threading
-import queue
-import os
-import requests
-from urllib.parse import urlparse
-import asyncio
-import psutil
-
-app = FastAPI()
-
-models = {}
-
-with open("config.json", "r") as f:
-    config = json.load(f)
-
-poll_interval = config.get("poll_interval_ms", 100)
-reconnect_interval = config.get("reconnect_interval_sec", 5) 
-TARGET_FPS = config.get("target_fps", 10)
-poll_interval = 1000 / TARGET_FPS
-logging.info(f"Poll interval: {poll_interval}ms")
-max_streams = config.get("max_streams", 5)
-max_retries = config.get("max_retries", 3)
-
-# Configure logging
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] %(message)s",
-    handlers=[
-        logging.FileHandler("app.log"),
-        logging.StreamHandler()
-    ]
-)
-
-# Ensure the models directory exists
-os.makedirs("models", exist_ok=True)
-
-# Add constants for heartbeat
-HEARTBEAT_INTERVAL = 2  # seconds
-WORKER_TIMEOUT_MS = 10000
-
-# Add a lock for thread-safe operations on shared resources
-streams_lock = threading.Lock()
-models_lock = threading.Lock()
-
-@app.websocket("/")
-async def detect(websocket: WebSocket):
-    import asyncio
-    import time
-
-    logging.info("WebSocket connection accepted")
-
-    streams = {}
-
-    # This function is user-modifiable
-    # Save data you want to persist across frames in the persistent_data dictionary
-    async def handle_detection(camera_id, stream, frame, websocket, model: YOLO, persistent_data):
-        try:
-            highest_conf_box = None
-            max_conf = -1
-            
-            for r in model.track(frame, stream=False, persist=True):
-                for box in r.boxes:
-                    box_cpu = box.cpu()
-                    conf = float(box_cpu.conf[0])
-                    if conf > max_conf and hasattr(box, "id") and box.id is not None:
-                        max_conf = conf
-                        highest_conf_box = {
-                            "class": model.names[int(box_cpu.cls[0])],
-                            "confidence": conf,
-                            "id": box.id.item(),
-                        }
-            
-            # Broadcast to all subscribers of this URL
-            detection_data = {
-                "type": "imageDetection",
-                "cameraIdentifier": camera_id,
-                "timestamp": time.time(),
-                "data": {
-                    "detections": highest_conf_box if highest_conf_box else None,
-                    "modelId": stream['modelId'],
-                    "modelName": stream['modelName']
-                }
-            }
-            logging.debug(f"Sending detection data for camera {camera_id}: {detection_data}")
-            await websocket.send_json(detection_data)
-            return persistent_data
-        except Exception as e:
-            logging.error(f"Error in handle_detection for camera {camera_id}: {e}")
-            return persistent_data
-
-    def frame_reader(camera_id, cap, buffer, stop_event):
-        import time
-        retries = 0
-        try:
-            while not stop_event.is_set():
-                try:
-                    ret, frame = cap.read()
-                    if not ret:
-                        logging.warning(f"Connection lost for camera: {camera_id}, retry {retries+1}/{max_retries}")
-                        cap.release()
-                        time.sleep(reconnect_interval)
-                        retries += 1
-                        if retries > max_retries and max_retries != -1:
-                            logging.error(f"Max retries reached for camera: {camera_id}")
-                            break
-                        # Re-open the VideoCapture
-                        cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
-                        if not cap.isOpened():
-                            logging.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
-                            continue
-                        continue
-                    retries = 0  # Reset on success
-                    if not buffer.empty():
-                        try:
-                            buffer.get_nowait()  # Discard the old frame
-                        except queue.Empty:
-                            pass
-                    buffer.put(frame)
-                except cv2.error as e:
-                    logging.error(f"OpenCV error for camera {camera_id}: {e}")
-                    cap.release()
-                    time.sleep(reconnect_interval)
-                    retries += 1
-                    if retries > max_retries and max_retries != -1:
-                        logging.error(f"Max retries reached after OpenCV error for camera: {camera_id}")
-                        break
-                    # Re-open the VideoCapture
-                    cap = cv2.VideoCapture(streams[camera_id]['rtsp_url'])
-                    if not cap.isOpened():
-                        logging.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
-                        continue
-                except Exception as e:
-                    logging.error(f"Unexpected error for camera {camera_id}: {e}")
-                    cap.release()
-                    break
-        except Exception as e:
-            logging.error(f"Error in frame_reader thread for camera {camera_id}: {e}")
-
-    async def process_streams():
-        global models
-        logging.info("Started processing streams")
-        persistent_data_dict = {} 
-        try:
-            while True:
-                start_time = time.time()
-                # Round-robin processing
-                with streams_lock:
-                    current_streams = list(streams.items())
-                for camera_id, stream in current_streams:
-                    buffer = stream['buffer']
-                    if not buffer.empty():
-                        frame = buffer.get()
-                        with models_lock:
-                            model = models.get(camera_id, {}).get(stream['modelId'])
-                        key = (camera_id, stream['modelId'])
-                        persistent_data = persistent_data_dict.get(key, {})
-                        updated_persistent_data = await handle_detection(camera_id, stream, frame, websocket, model, persistent_data)
-                        persistent_data_dict[key] = updated_persistent_data
-                elapsed_time = (time.time() - start_time) * 1000  # in ms
-                sleep_time = max(poll_interval - elapsed_time, 0)
-                logging.debug(f"Elapsed time: {elapsed_time}ms, sleeping for: {sleep_time}ms")
-                await asyncio.sleep(sleep_time / 1000.0)
-        except asyncio.CancelledError:
-            logging.info("Stream processing task cancelled")
-        except Exception as e:
-            logging.error(f"Error in process_streams: {e}")
-
-    async def send_heartbeat():
-        while True:
-            try:
-                cpu_usage = psutil.cpu_percent()
-                memory_usage = psutil.virtual_memory().percent
-                if torch.cuda.is_available():
-                    gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
-                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
-                else:
-                    gpu_usage = None
-                    gpu_memory_usage = None
-            
-                camera_connections = [
-                    {
-                        "cameraIdentifier": camera_id,
-                        "modelId": stream['modelId'],
-                        "modelName": stream['modelName'],
-                        "online": True
-                    }
-                    for camera_id, stream in streams.items()
-                ]
-            
-                state_report = {
-                    "type": "stateReport",
-                    "cpuUsage": cpu_usage,
-                    "memoryUsage": memory_usage,
-                    "gpuUsage": gpu_usage,
-                    "gpuMemoryUsage": gpu_memory_usage,
-                    "cameraConnections": camera_connections
-                }
-                await websocket.send_text(json.dumps(state_report))
-                logging.debug("Sent stateReport as heartbeat")
-                await asyncio.sleep(HEARTBEAT_INTERVAL)
-            except Exception as e:
-                logging.error(f"Error sending stateReport heartbeat: {e}")
-                break
-
-    async def on_message():
-        global models
-        while True:
-            try:
-                msg = await websocket.receive_text()
-                logging.debug(f"Received message: {msg}")
-                print(f"Received message: {msg}")
-                data = json.loads(msg)
-                msg_type = data.get("type")
-
-                if msg_type == "subscribe":
-                    payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
-                    rtsp_url = payload.get("rtspUrl")
-                    model_url = payload.get("modelUrl")
-                    modelId = payload.get("modelId")
-                    modelName = payload.get("modelName")
-    
-                    if model_url:
-                        with models_lock:
-                            if camera_id not in models:
-                                models[camera_id] = {}
-                            if modelId not in models[camera_id]:
-                                print(f"Downloading model from {model_url}")
-                                parsed_url = urlparse(model_url)
-                                filename = os.path.basename(parsed_url.path)    
-                                model_filename = os.path.join("models", filename)
-                                # Download the model
-                                response = requests.get(model_url, stream=True)
-                                if response.status_code == 200:
-                                    with open(model_filename, 'wb') as f:
-                                        for chunk in response.iter_content(chunk_size=8192):
-                                            f.write(chunk)
-                                    logging.info(f"Downloaded model from {model_url} to {model_filename}")
-                                    model = YOLO(model_filename)
-                                    if torch.cuda.is_available():
-                                        model.to('cuda')
-                                    models[camera_id][modelId] = model
-                                    logging.info(f"Loaded model {modelId} for camera {camera_id}")
-                                else:
-                                    logging.error(f"Failed to download model from {model_url}")
-                                    continue
-                    if camera_id and rtsp_url:
-                        with streams_lock:
-                            if camera_id not in streams and len(streams) < max_streams:
-                                cap = cv2.VideoCapture(rtsp_url)
-                                if not cap.isOpened():
-                                    logging.error(f"Failed to open RTSP stream for camera {camera_id}")
-                                    continue
-                                buffer = queue.Queue(maxsize=1)
-                                stop_event = threading.Event()
-                                thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
-                                thread.daemon = True
-                                thread.start()
-                                streams[camera_id] = {
-                                    'cap': cap,
-                                    'buffer': buffer,
-                                    'thread': thread,
-                                    'rtsp_url': rtsp_url,
-                                    'stop_event': stop_event,
-                                    'modelId': modelId,
-                                    'modelName': modelName
-                                }
-                                logging.info(f"Subscribed to camera {camera_id} with modelId {modelId}, modelName {modelName} and URL {rtsp_url}")
-                            elif camera_id and camera_id in streams:
-                                stream = streams.pop(camera_id)
-                                stream['cap'].release()
-                                logging.info(f"Unsubscribed from camera {camera_id}")
-                                if camera_id in models and modelId in models[camera_id]:
-                                    del models[camera_id][modelId]
-                                    if not models[camera_id]:
-                                        del models[camera_id]
-                elif msg_type == "unsubscribe":
-                    payload = data.get("payload", {})
-                    camera_id = payload.get("cameraIdentifier")
-                    logging.debug(f"Unsubscribing from camera {camera_id}")
-                    with streams_lock:
-                        if camera_id and camera_id in streams:
-                            stream = streams.pop(camera_id)
-                            stream['stop_event'].set()
-                            stream['thread'].join()
-                            stream['cap'].release()
-                            logging.info(f"Unsubscribed from camera {camera_id}")
-                            if camera_id in models and modelId in models[camera_id]:
-                                del models[camera_id][modelId]
-                                if not models[camera_id]:
-                                    del models[camera_id]
-                elif msg_type == "requestState":
-                    # Handle state request
-                    cpu_usage = psutil.cpu_percent()
-                    memory_usage = psutil.virtual_memory().percent
-                    if torch.cuda.is_available():
-                        gpu_usage = torch.cuda.memory_allocated() / (1024 ** 2)  # Convert to MB
-                        gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)  # Convert to MB
-                    else:
-                        gpu_usage = None
-                        gpu_memory_usage = None
-                    
-                    camera_connections = [
-                        {
-                            "cameraIdentifier": camera_id,
-                            "modelId": stream['modelId'],
-                            "modelName": stream['modelName'],
-                            "online": True
-                        }
-                        for camera_id, stream in streams.items()
-                    ]
-                    
-                    state_report = {
-                        "type": "stateReport",
-                        "cpuUsage": cpu_usage,
-                        "memoryUsage": memory_usage,
-                        "gpuUsage": gpu_usage,
-                        "gpuMemoryUsage": gpu_memory_usage,
-                        "cameraConnections": camera_connections
-                    }
-                    await websocket.send_text(json.dumps(state_report))
-                else:
-                    logging.error(f"Unknown message type: {msg_type}")
-            except json.JSONDecodeError:
-                logging.error("Received invalid JSON message")
-            except (WebSocketDisconnect, ConnectionClosedError) as e:
-                logging.warning(f"WebSocket disconnected: {e}")
-                break 
-            except Exception as e:
-                logging.error(f"Error handling message: {e}")
-                break
-
-    try:
-        await websocket.accept()
-        task = asyncio.create_task(process_streams())
-        heartbeat_task = asyncio.create_task(send_heartbeat())
-        message_task = asyncio.create_task(on_message())
-
-        await asyncio.gather(heartbeat_task, message_task)
-    except Exception as e:
-        logging.error(f"Error in detect websocket: {e}")
-    finally:
-        task.cancel()
-        await task
-        with streams_lock:
-            for camera_id, stream in streams.items():
-                stream['stop_event'].set()
-                stream['thread'].join()
-                stream['cap'].release()
-                stream['buffer'].queue.clear()
-                logging.info(f"Released camera {camera_id} and cleaned up resources")
-            streams.clear()
-        with models_lock:
-            models.clear()
-        logging.info("WebSocket connection closed")

pympta.md

@@ -0,0 +1,327 @@
+# pympta: Modular Pipeline Task Executor
+
+`pympta` is a Python module designed to load and execute modular, multi-stage AI pipelines defined in a special package format (`.mpta`). It is primarily used within the detector worker to run complex computer vision tasks where the output of one model can trigger a subsequent model on a specific region of interest.
+
+## Core Concepts
+
+### 1. MPTA Package (`.mpta`)
+
+An `.mpta` file is a standard `.zip` archive with a different extension. It bundles all the necessary components for a pipeline to run.
+
+A typical `.mpta` file has the following structure:
+
+```
+my_pipeline.mpta/
+├── pipeline.json
+├── model1.pt
+├── model2.pt
+└── ...
+```
+
+- **`pipeline.json`**: (Required) The manifest file that defines the structure of the pipeline, the models to use, and the logic connecting them.
+- **Model Files (`.pt`, etc.)**: The actual pre-trained model files (e.g., PyTorch, ONNX). The pipeline currently uses `ultralytics.YOLO` models.
+
+### 2. Pipeline Structure
+
+A pipeline is a tree-like structure of "nodes," defined in `pipeline.json`.
+
+- **Root Node**: The entry point of the pipeline. It processes the initial, full-frame image.
+- **Branch Nodes**: Child nodes that are triggered by specific detection results from their parent. For example, a root node might detect a "vehicle," which then triggers a branch node to detect a "license plate" within the vehicle's bounding box.
+
+This modular structure allows for creating complex and efficient inference logic, avoiding the need to run every model on every frame.
+
+## `pipeline.json` Specification
+
+This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition, optional `redis` key for Redis configuration, and optional `postgresql` key for database integration.
+
+### Top-Level Object Structure
+
+| Key          | Type   | Required | Description                                             |
+| ------------ | ------ | -------- | ------------------------------------------------------- |
+| `pipeline`   | Object | Yes      | The root node object of the pipeline.                   |
+| `redis`      | Object | No       | Configuration for connecting to a Redis server.         |
+| `postgresql` | Object | No       | Configuration for connecting to a PostgreSQL database.  |
+
+### Redis Configuration (`redis`)
+
+| Key        | Type   | Required | Description                                             |
+| ---------- | ------ | -------- | ------------------------------------------------------- |
+| `host`     | String | Yes      | The hostname or IP address of the Redis server.         |
+| `port`     | Number | Yes      | The port number of the Redis server.                    |
+| `password` | String | No       | The password for Redis authentication.                  |
+| `db`       | Number | No       | The Redis database number to use. Defaults to `0`.      |
+
+### PostgreSQL Configuration (`postgresql`)
+
+| Key        | Type   | Required | Description                                             |
+| ---------- | ------ | -------- | ------------------------------------------------------- |
+| `host`     | String | Yes      | The hostname or IP address of the PostgreSQL server.    |
+| `port`     | Number | Yes      | The port number of the PostgreSQL server.              |
+| `database` | String | Yes      | The database name to connect to.                       |
+| `username` | String | Yes      | The username for database authentication.              |
+| `password` | String | Yes      | The password for database authentication.              |
+
+### Node Object Structure
+
+| Key                 | Type          | Required | Description                                                                                                                            |
+| ------------------- | ------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- |
+| `modelId`           | String        | Yes      | A unique identifier for this model node (e.g., "vehicle-detector").                                                                    |
+| `modelFile`         | String        | Yes      | The path to the model file within the `.mpta` archive (e.g., "yolov8n.pt").                                                            |
+| `minConfidence`     | Float         | Yes      | The minimum confidence score (0.0 to 1.0) required for a detection to be considered valid and potentially trigger a branch.              |
+| `triggerClasses`    | Array<String> | Yes      | A list of class names that, when detected by the parent, can trigger this node. For the root node, this lists all classes of interest. |
+| `crop`              | Boolean       | No       | If `true`, the image is cropped to the parent's detection bounding box before being passed to this node's model. Defaults to `false`.    |
+| `cropClass`         | String        | No       | The specific class to use for cropping (e.g., "Frontal" for frontal view cropping).                                                   |
+| `multiClass`        | Boolean       | No       | If `true`, enables multi-class detection mode where multiple classes can be detected simultaneously.                                   |
+| `expectedClasses`   | Array<String> | No       | When `multiClass` is true, defines which classes are expected. At least one must be detected for processing to continue.              |
+| `parallel`          | Boolean       | No       | If `true`, this branch will be processed in parallel with other parallel branches.                                                    |
+| `branches`          | Array<Node>   | No       | A list of child node objects that can be triggered by this node's detections.                                                          |
+| `actions`           | Array<Action> | No       | A list of actions to execute upon a successful detection in this node.                                                                 |
+| `parallelActions`   | Array<Action> | No       | A list of actions to execute after all specified branches have completed.                                                             |
+
+### Action Object Structure
+
+Actions allow the pipeline to interact with Redis and PostgreSQL databases. They are executed sequentially for a given detection.
+
+#### Action Context & Dynamic Keys
+
+All actions have access to a dynamic context for formatting keys and messages. The context is created for each detection event and includes:
+
+- All key-value pairs from the detection result (e.g., `class`, `confidence`, `id`).
+- `{timestamp_ms}`: The current Unix timestamp in milliseconds.
+- `{timestamp}`: Formatted timestamp string (YYYY-MM-DDTHH-MM-SS).
+- `{uuid}`: A unique identifier (UUID4) for the detection event.
+- `{filename}`: Generated filename with UUID.
+- `{camera_id}`: Full camera subscription identifier.
+- `{display_id}`: Display identifier extracted from subscription.
+- `{session_id}`: Session ID for database operations.
+- `{image_key}`: If a `redis_save_image` action has already been executed for this event, this placeholder will be replaced with the key where the image was stored.
+
+#### `redis_save_image`
+
+Saves the current image frame (or cropped sub-image) to a Redis key.
+
+| Key              | Type   | Required | Description                                                                                             |
+| ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`           | String | Yes      | Must be `"redis_save_image"`.                                                                           |
+| `key`            | String | Yes      | The Redis key to save the image to. Can contain any of the dynamic placeholders.                        |
+| `region`         | String | No       | Specific detected region to crop and save (e.g., "Frontal").                                           |
+| `format`         | String | No       | Image format: "jpeg" or "png". Defaults to "jpeg".                                                     |
+| `quality`        | Number | No       | JPEG quality (1-100). Defaults to 90.                                                                  |
+| `expire_seconds` | Number | No       | If provided, sets an expiration time (in seconds) for the Redis key.                                    |
+
+#### `redis_publish`
+
+Publishes a message to a Redis channel.
+
+| Key       | Type   | Required | Description                                                                                             |
+| --------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`    | String | Yes      | Must be `"redis_publish"`.                                                                              |
+| `channel` | String | Yes      | The Redis channel to publish the message to.                                                            |
+| `message` | String | Yes      | The message to publish. Can contain any of the dynamic placeholders, including `{image_key}`.           |
+
+#### `postgresql_update_combined`
+
+Updates PostgreSQL database with results from multiple branches after they complete.
+
+| Key                | Type          | Required | Description                                                                                             |
+| ------------------ | ------------- | -------- | ------------------------------------------------------------------------------------------------------- |
+| `type`             | String        | Yes      | Must be `"postgresql_update_combined"`.                                                                 |
+| `table`            | String        | Yes      | The database table name (will be prefixed with `gas_station_1.` schema).                              |
+| `key_field`        | String        | Yes      | The field to use as the update key (typically "session_id").                                           |
+| `key_value`        | String        | Yes      | Template for the key value (e.g., "{session_id}").                                                     |
+| `waitForBranches`  | Array<String> | Yes      | List of branch model IDs to wait for completion before executing update.                               |
+| `fields`           | Object        | Yes      | Field mapping object where keys are database columns and values are templates (e.g., "{branch.field}").|
+
+### Complete Example `pipeline.json`
+
+This example demonstrates a comprehensive pipeline for vehicle detection with parallel classification and database integration:
+
+```json
+{
+  "redis": {
+    "host": "10.100.1.3",
+    "port": 6379,
+    "password": "your-redis-password",
+    "db": 0
+  },
+  "postgresql": {
+    "host": "10.100.1.3",
+    "port": 5432,
+    "database": "inference",
+    "username": "root",
+    "password": "your-db-password"
+  },
+  "pipeline": {
+    "modelId": "car_frontal_detection_v1",
+    "modelFile": "car_frontal_detection_v1.pt",
+    "crop": false,
+    "triggerClasses": ["Car", "Frontal"],
+    "minConfidence": 0.8,
+    "multiClass": true,
+    "expectedClasses": ["Car", "Frontal"],
+    "actions": [
+      {
+        "type": "redis_save_image",
+        "region": "Frontal",
+        "key": "inference:{display_id}:{timestamp}:{session_id}:{filename}",
+        "expire_seconds": 600,
+        "format": "jpeg",
+        "quality": 90
+      },
+      {
+        "type": "redis_publish",
+        "channel": "car_detections",
+        "message": "{\"event\":\"frontal_detected\"}"
+      }
+    ],
+    "branches": [
+      {
+        "modelId": "car_brand_cls_v1",
+        "modelFile": "car_brand_cls_v1.pt",
+        "crop": true,
+        "cropClass": "Frontal",
+        "resizeTarget": [224, 224],
+        "triggerClasses": ["Frontal"],
+        "minConfidence": 0.85,
+        "parallel": true,
+        "branches": []
+      },
+      {
+        "modelId": "car_bodytype_cls_v1",
+        "modelFile": "car_bodytype_cls_v1.pt",
+        "crop": true,
+        "cropClass": "Car",
+        "resizeTarget": [224, 224],
+        "triggerClasses": ["Car"],
+        "minConfidence": 0.85,
+        "parallel": true,
+        "branches": []
+      }
+    ],
+    "parallelActions": [
+      {
+        "type": "postgresql_update_combined",
+        "table": "car_frontal_info",
+        "key_field": "session_id",
+        "key_value": "{session_id}",
+        "waitForBranches": ["car_brand_cls_v1", "car_bodytype_cls_v1"],
+        "fields": {
+          "car_brand": "{car_brand_cls_v1.brand}",
+          "car_body_type": "{car_bodytype_cls_v1.body_type}"
+        }
+      }
+    ]
+  }
+}
+```
+
+## API Reference
+
+The `pympta` module exposes two main functions.
+
+### `load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict`
+
+Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes Redis and PostgreSQL connections if configured in `pipeline.json`.
+
+- **Parameters:**
+  - `zip_source` (str): The file path to the local `.mpta` zip archive.
+  - `target_dir` (str): A directory path where the archive's contents will be extracted.
+- **Returns:**
+  - A dictionary representing the root node of the pipeline, ready to be used with `run_pipeline`. Returns `None` if loading fails.
+
+### `run_pipeline(frame, node: dict, return_bbox: bool = False, context: dict = None)`
+
+Executes the inference pipeline on a single image frame.
+
+- **Parameters:**
+  - `frame`: The input image frame (e.g., a NumPy array from OpenCV).
+  - `node` (dict): The pipeline node to execute (typically the root node returned by `load_pipeline_from_zip`).
+  - `return_bbox` (bool): If `True`, the function returns a tuple `(detection, bounding_box)`. Otherwise, it returns only the `detection`.
+  - `context` (dict): Optional context dictionary containing camera_id, display_id, session_id for action formatting.
+- **Returns:**
+  - The final detection result from the last executed node in the chain. A detection is a dictionary like `{'class': 'car', 'confidence': 0.95, 'id': 1}`. If no detection meets the criteria, it returns `None` (or `(None, None)` if `return_bbox` is `True`).
+
+## Database Integration
+
+The pipeline system includes automatic PostgreSQL database management:
+
+### Table Schema (`gas_station_1.car_frontal_info`)
+
+The system automatically creates and manages the following table structure:
+
+```sql
+CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+    display_id VARCHAR(255),
+    captured_timestamp VARCHAR(255),
+    session_id VARCHAR(255) PRIMARY KEY,
+    license_character VARCHAR(255) DEFAULT NULL,
+    license_type VARCHAR(255) DEFAULT 'No model available',
+    car_brand VARCHAR(255) DEFAULT NULL,
+    car_model VARCHAR(255) DEFAULT NULL,
+    car_body_type VARCHAR(255) DEFAULT NULL,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+```
+
+### Workflow
+
+1. **Initial Record Creation**: When both "Car" and "Frontal" are detected, an initial database record is created with a UUID session_id.
+2. **Redis Storage**: Vehicle images are stored in Redis with keys containing the session_id.
+3. **Parallel Classification**: Brand and body type classification run concurrently.
+4. **Database Update**: After all branches complete, the database record is updated with classification results.
+
+## Usage Example
+
+This snippet shows how to use `pympta` with the enhanced features:
+
+```python
+import cv2
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
+# 1. Define paths
+MPTA_FILE = "path/to/your/pipeline.mpta"
+CACHE_DIR = ".mptacache"
+
+# 2. Load the pipeline from the .mpta file
+# This reads pipeline.json and loads the YOLO models into memory.
+model_tree = load_pipeline_from_zip(MPTA_FILE, CACHE_DIR)
+
+if not model_tree:
+    print("Failed to load pipeline.")
+    exit()
+
+# 3. Open a video source
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # 4. Run the pipeline on the current frame with context
+    context = {
+        "camera_id": "display-001;cam-001",
+        "display_id": "display-001", 
+        "session_id": None  # Will be generated automatically
+    }
+    
+    detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True, context=context)
+
+    # 5. Display the results
+    if detection_result:
+        print(f"Detected: {detection_result['class']} with confidence {detection_result['confidence']:.2f}")
+        if bounding_box:
+            x1, y1, x2, y2 = bounding_box
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(frame, detection_result['class'], (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+    cv2.imshow("Pipeline Output", frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+```

requirements.base.txt

@@ -0,0 +1,7 @@
+torch
+torchvision
+ultralytics
+opencv-python
+scipy
+filterpy
+psycopg2-binary

requirements.txt

@@ -1,8 +1,6 @@
 fastapi
 uvicorn
-torch
-torchvision
-ultralytics
-opencv-python
 websockets
 fastapi[standard]
+redis
+urllib3<2.0.0

siwatsystem/database.py

@@ -0,0 +1,211 @@
+import psycopg2
+import psycopg2.extras
+from typing import Optional, Dict, Any
+import logging
+import uuid
+
+logger = logging.getLogger(__name__)
+
+class DatabaseManager:
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.connection: Optional[psycopg2.extensions.connection] = None
+        
+    def connect(self) -> bool:
+        try:
+            self.connection = psycopg2.connect(
+                host=self.config['host'],
+                port=self.config['port'],
+                database=self.config['database'],
+                user=self.config['username'],
+                password=self.config['password']
+            )
+            logger.info("PostgreSQL connection established successfully")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to connect to PostgreSQL: {e}")
+            return False
+    
+    def disconnect(self):
+        if self.connection:
+            self.connection.close()
+            self.connection = None
+            logger.info("PostgreSQL connection closed")
+    
+    def is_connected(self) -> bool:
+        try:
+            if self.connection and not self.connection.closed:
+                cur = self.connection.cursor()
+                cur.execute("SELECT 1")
+                cur.fetchone()
+                cur.close()
+                return True
+        except:
+            pass
+        return False
+    
+    def update_car_info(self, session_id: str, brand: str, model: str, body_type: str) -> bool:
+        if not self.is_connected():
+            if not self.connect():
+                return False
+        
+        try:
+            cur = self.connection.cursor()
+            query = """
+            INSERT INTO car_frontal_info (session_id, car_brand, car_model, car_body_type, updated_at)
+            VALUES (%s, %s, %s, %s, NOW())
+            ON CONFLICT (session_id) 
+            DO UPDATE SET 
+                car_brand = EXCLUDED.car_brand,
+                car_model = EXCLUDED.car_model,
+                car_body_type = EXCLUDED.car_body_type,
+                updated_at = NOW()
+            """
+            cur.execute(query, (session_id, brand, model, body_type))
+            self.connection.commit()
+            cur.close()
+            logger.info(f"Updated car info for session {session_id}: {brand} {model} ({body_type})")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to update car info: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return False
+    
+    def execute_update(self, table: str, key_field: str, key_value: str, fields: Dict[str, str]) -> bool:
+        if not self.is_connected():
+            if not self.connect():
+                return False
+        
+        try:
+            cur = self.connection.cursor()
+            
+            # Build the UPDATE query dynamically
+            set_clauses = []
+            values = []
+            
+            for field, value in fields.items():
+                if value == "NOW()":
+                    set_clauses.append(f"{field} = NOW()")
+                else:
+                    set_clauses.append(f"{field} = %s")
+                    values.append(value)
+            
+            # Add schema prefix if table doesn't already have it
+            full_table_name = table if '.' in table else f"gas_station_1.{table}"
+            
+            query = f"""
+            INSERT INTO {full_table_name} ({key_field}, {', '.join(fields.keys())})
+            VALUES (%s, {', '.join(['%s'] * len(fields))})
+            ON CONFLICT ({key_field})
+            DO UPDATE SET {', '.join(set_clauses)}
+            """
+            
+            # Add key_value to the beginning of values list
+            all_values = [key_value] + list(fields.values()) + values
+            
+            cur.execute(query, all_values)
+            self.connection.commit()
+            cur.close()
+            logger.info(f"Updated {table} for {key_field}={key_value}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to execute update on {table}: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return False
+    
+    def create_car_frontal_info_table(self) -> bool:
+        """Create the car_frontal_info table in gas_station_1 schema if it doesn't exist."""
+        if not self.is_connected():
+            if not self.connect():
+                return False
+        
+        try:
+            cur = self.connection.cursor()
+            
+            # Create schema if it doesn't exist
+            cur.execute("CREATE SCHEMA IF NOT EXISTS gas_station_1")
+            
+            # Create table if it doesn't exist
+            create_table_query = """
+            CREATE TABLE IF NOT EXISTS gas_station_1.car_frontal_info (
+                display_id VARCHAR(255),
+                captured_timestamp VARCHAR(255),
+                session_id VARCHAR(255) PRIMARY KEY,
+                license_character VARCHAR(255) DEFAULT NULL,
+                license_type VARCHAR(255) DEFAULT 'No model available',
+                car_brand VARCHAR(255) DEFAULT NULL,
+                car_model VARCHAR(255) DEFAULT NULL,
+                car_body_type VARCHAR(255) DEFAULT NULL,
+                updated_at TIMESTAMP DEFAULT NOW()
+            )
+            """
+            
+            cur.execute(create_table_query)
+            
+            # Add columns if they don't exist (for existing tables)
+            alter_queries = [
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_brand VARCHAR(255) DEFAULT NULL",
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_model VARCHAR(255) DEFAULT NULL", 
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS car_body_type VARCHAR(255) DEFAULT NULL",
+                "ALTER TABLE gas_station_1.car_frontal_info ADD COLUMN IF NOT EXISTS updated_at TIMESTAMP DEFAULT NOW()"
+            ]
+            
+            for alter_query in alter_queries:
+                try:
+                    cur.execute(alter_query)
+                    logger.debug(f"Executed: {alter_query}")
+                except Exception as e:
+                    # Ignore errors if column already exists (for older PostgreSQL versions)
+                    if "already exists" in str(e).lower():
+                        logger.debug(f"Column already exists, skipping: {alter_query}")
+                    else:
+                        logger.warning(f"Error in ALTER TABLE: {e}")
+            
+            self.connection.commit()
+            cur.close()
+            logger.info("Successfully created/verified car_frontal_info table with all required columns")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Failed to create car_frontal_info table: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return False
+    
+    def insert_initial_detection(self, display_id: str, captured_timestamp: str, session_id: str = None) -> str:
+        """Insert initial detection record and return the session_id."""
+        if not self.is_connected():
+            if not self.connect():
+                return None
+        
+        # Generate session_id if not provided
+        if not session_id:
+            session_id = str(uuid.uuid4())
+        
+        try:
+            # Ensure table exists
+            if not self.create_car_frontal_info_table():
+                logger.error("Failed to create/verify table before insertion")
+                return None
+            
+            cur = self.connection.cursor()
+            insert_query = """
+            INSERT INTO gas_station_1.car_frontal_info 
+            (display_id, captured_timestamp, session_id, license_character, license_type, car_brand, car_model, car_body_type)
+            VALUES (%s, %s, %s, NULL, 'No model available', NULL, NULL, NULL)
+            ON CONFLICT (session_id) DO NOTHING
+            """
+            
+            cur.execute(insert_query, (display_id, captured_timestamp, session_id))
+            self.connection.commit()
+            cur.close()
+            logger.info(f"Inserted initial detection record with session_id: {session_id}")
+            return session_id
+            
+        except Exception as e:
+            logger.error(f"Failed to insert initial detection record: {e}")
+            if self.connection:
+                self.connection.rollback()
+            return None

siwatsystem/pympta.py

@@ -3,17 +3,72 @@ import json
 import logging
 import torch
 import cv2
-import requests
 import zipfile
 import shutil
 import traceback
+import redis
+import time
+import uuid
+import concurrent.futures
 from ultralytics import YOLO
 from urllib.parse import urlparse
+from .database import DatabaseManager
 
 # Create a logger specifically for this module
 logger = logging.getLogger("detector_worker.pympta")
 
-def load_pipeline_node(node_config: dict, mpta_dir: str) -> dict:
+def validate_redis_config(redis_config: dict) -> bool:
+    """Validate Redis configuration parameters."""
+    required_fields = ["host", "port"]
+    for field in required_fields:
+        if field not in redis_config:
+            logger.error(f"Missing required Redis config field: {field}")
+            return False
+    
+    if not isinstance(redis_config["port"], int) or redis_config["port"] <= 0:
+        logger.error(f"Invalid Redis port: {redis_config['port']}")
+        return False
+    
+    return True
+
+def validate_postgresql_config(pg_config: dict) -> bool:
+    """Validate PostgreSQL configuration parameters."""
+    required_fields = ["host", "port", "database", "username", "password"]
+    for field in required_fields:
+        if field not in pg_config:
+            logger.error(f"Missing required PostgreSQL config field: {field}")
+            return False
+    
+    if not isinstance(pg_config["port"], int) or pg_config["port"] <= 0:
+        logger.error(f"Invalid PostgreSQL port: {pg_config['port']}")
+        return False
+    
+    return True
+
+def crop_region_by_class(frame, regions_dict, class_name):
+    """Crop a specific region from frame based on detected class."""
+    if class_name not in regions_dict:
+        logger.warning(f"Class '{class_name}' not found in detected regions")
+        return None
+    
+    bbox = regions_dict[class_name]['bbox']
+    x1, y1, x2, y2 = bbox
+    cropped = frame[y1:y2, x1:x2]
+    
+    if cropped.size == 0:
+        logger.warning(f"Empty crop for class '{class_name}' with bbox {bbox}")
+        return None
+    
+    return cropped
+
+def format_action_context(base_context, additional_context=None):
+    """Format action context with dynamic values."""
+    context = {**base_context}
+    if additional_context:
+        context.update(additional_context)
+    return context
+
+def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client, db_manager=None) -> dict:
     # Recursively load a model node from configuration.
     model_path = os.path.join(mpta_dir, node_config["modelFile"])
     if not os.path.exists(model_path):
@@ -43,14 +98,22 @@ def load_pipeline_node(node_config: dict, mpta_dir: str) -> dict:
         "triggerClasses": trigger_classes,
         "triggerClassIndices": trigger_class_indices,
         "crop": node_config.get("crop", False),
+        "cropClass": node_config.get("cropClass"),
         "minConfidence": node_config.get("minConfidence", None),
+        "multiClass": node_config.get("multiClass", False),
+        "expectedClasses": node_config.get("expectedClasses", []),
+        "parallel": node_config.get("parallel", False),
+        "actions": node_config.get("actions", []),
+        "parallelActions": node_config.get("parallelActions", []),
         "model": model,
-        "branches": []
+        "branches": [],
+        "redis_client": redis_client,
+        "db_manager": db_manager
     }
     logger.debug(f"Configured node {node_config['modelId']} with trigger classes: {node['triggerClasses']}")
     for child in node_config.get("branches", []):
         logger.debug(f"Loading branch for parent node {node_config['modelId']}")
-        node["branches"].append(load_pipeline_node(child, mpta_dir))
+        node["branches"].append(load_pipeline_node(child, mpta_dir, redis_client, db_manager))
     return node
 
 def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
@@ -158,7 +221,47 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
             pipeline_config = json.load(f)
         logger.info(f"Successfully loaded pipeline configuration from {pipeline_json_path}")
         logger.debug(f"Pipeline config: {json.dumps(pipeline_config, indent=2)}")
-        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir)
+        
+        # Establish Redis connection if configured
+        redis_client = None
+        if "redis" in pipeline_config:
+            redis_config = pipeline_config["redis"]
+            if not validate_redis_config(redis_config):
+                logger.error("Invalid Redis configuration, skipping Redis connection")
+            else:
+                try:
+                    redis_client = redis.Redis(
+                        host=redis_config["host"],
+                        port=redis_config["port"],
+                        password=redis_config.get("password"),
+                        db=redis_config.get("db", 0),
+                        decode_responses=True
+                    )
+                    redis_client.ping()
+                    logger.info(f"Successfully connected to Redis at {redis_config['host']}:{redis_config['port']}")
+                except redis.exceptions.ConnectionError as e:
+                    logger.error(f"Failed to connect to Redis: {e}")
+                    redis_client = None
+        
+        # Establish PostgreSQL connection if configured
+        db_manager = None
+        if "postgresql" in pipeline_config:
+            pg_config = pipeline_config["postgresql"]
+            if not validate_postgresql_config(pg_config):
+                logger.error("Invalid PostgreSQL configuration, skipping database connection")
+            else:
+                try:
+                    db_manager = DatabaseManager(pg_config)
+                    if db_manager.connect():
+                        logger.info(f"Successfully connected to PostgreSQL at {pg_config['host']}:{pg_config['port']}")
+                    else:
+                        logger.error("Failed to connect to PostgreSQL")
+                        db_manager = None
+                except Exception as e:
+                    logger.error(f"Error initializing PostgreSQL connection: {e}")
+                    db_manager = None
+        
+        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client, db_manager)
     except json.JSONDecodeError as e:
         logger.error(f"Error parsing pipeline.json: {str(e)}", exc_info=True)
         return None
@@ -169,49 +272,357 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
         logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
         return None
 
-def run_pipeline(frame, node: dict, return_bbox: bool=False):
+def execute_actions(node, frame, detection_result, regions_dict=None):
+    if not node["redis_client"] or not node["actions"]:
+        return
+
+    # Create a dynamic context for this detection event
+    from datetime import datetime
+    action_context = {
+        **detection_result,
+        "timestamp_ms": int(time.time() * 1000),
+        "uuid": str(uuid.uuid4()),
+        "timestamp": datetime.now().strftime("%Y-%m-%dT%H-%M-%S"),
+        "filename": f"{uuid.uuid4()}.jpg"
+    }
+
+    for action in node["actions"]:
+        try:
+            if action["type"] == "redis_save_image":
+                key = action["key"].format(**action_context)
+                
+                # Check if we need to crop a specific region
+                region_name = action.get("region")
+                image_to_save = frame
+                
+                if region_name and regions_dict:
+                    cropped_image = crop_region_by_class(frame, regions_dict, region_name)
+                    if cropped_image is not None:
+                        image_to_save = cropped_image
+                        logger.debug(f"Cropped region '{region_name}' for redis_save_image")
+                    else:
+                        logger.warning(f"Could not crop region '{region_name}', saving full frame instead")
+                
+                # Encode image with specified format and quality (default to JPEG)
+                img_format = action.get("format", "jpeg").lower()
+                quality = action.get("quality", 90)
+                
+                if img_format == "jpeg":
+                    encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
+                    success, buffer = cv2.imencode('.jpg', image_to_save, encode_params)
+                elif img_format == "png":
+                    success, buffer = cv2.imencode('.png', image_to_save)
+                else:
+                    success, buffer = cv2.imencode('.jpg', image_to_save, [cv2.IMWRITE_JPEG_QUALITY, quality])
+                
+                if not success:
+                    logger.error(f"Failed to encode image for redis_save_image")
+                    continue
+                
+                expire_seconds = action.get("expire_seconds")
+                if expire_seconds:
+                    node["redis_client"].setex(key, expire_seconds, buffer.tobytes())
+                    logger.info(f"Saved image to Redis with key: {key} (expires in {expire_seconds}s)")
+                else:
+                    node["redis_client"].set(key, buffer.tobytes())
+                    logger.info(f"Saved image to Redis with key: {key}")
+                action_context["image_key"] = key
+            elif action["type"] == "redis_publish":
+                channel = action["channel"]
+                try:
+                    # Handle JSON message format by creating it programmatically
+                    message_template = action["message"]
+                    
+                    # Check if the message is JSON-like (starts and ends with braces)
+                    if message_template.strip().startswith('{') and message_template.strip().endswith('}'):
+                        # Create JSON data programmatically to avoid formatting issues
+                        json_data = {}
+                        
+                        # Add common fields
+                        json_data["event"] = "frontal_detected"
+                        json_data["display_id"] = action_context.get("display_id", "unknown")
+                        json_data["session_id"] = action_context.get("session_id")
+                        json_data["timestamp"] = action_context.get("timestamp", "")
+                        json_data["image_key"] = action_context.get("image_key", "")
+                        
+                        # Convert to JSON string
+                        message = json.dumps(json_data)
+                    else:
+                        # Use regular string formatting for non-JSON messages
+                        message = message_template.format(**action_context)
+                    
+                    # Publish to Redis
+                    if not node["redis_client"]:
+                        logger.error("Redis client is None, cannot publish message")
+                        continue
+                        
+                    # Test Redis connection
+                    try:
+                        node["redis_client"].ping()
+                        logger.debug("Redis connection is active")
+                    except Exception as ping_error:
+                        logger.error(f"Redis connection test failed: {ping_error}")
+                        continue
+                    
+                    result = node["redis_client"].publish(channel, message)
+                    logger.info(f"Published message to Redis channel '{channel}': {message}")
+                    logger.info(f"Redis publish result (subscribers count): {result}")
+                    
+                    # Additional debug info
+                    if result == 0:
+                        logger.warning(f"No subscribers listening to channel '{channel}'")
+                    else:
+                        logger.info(f"Message delivered to {result} subscriber(s)")
+                    
+                except KeyError as e:
+                    logger.error(f"Missing key in redis_publish message template: {e}")
+                    logger.debug(f"Available context keys: {list(action_context.keys())}")
+                except Exception as e:
+                    logger.error(f"Error in redis_publish action: {e}")
+                    logger.debug(f"Message template: {action['message']}")
+                    logger.debug(f"Available context keys: {list(action_context.keys())}")
+                    import traceback
+                    logger.debug(f"Full traceback: {traceback.format_exc()}")
+        except Exception as e:
+            logger.error(f"Error executing action {action['type']}: {e}")
+
+def execute_parallel_actions(node, frame, detection_result, regions_dict):
+    """Execute parallel actions after all required branches have completed."""
+    if not node.get("parallelActions"):
+        return
+    
+    logger.debug("Executing parallel actions...")
+    branch_results = detection_result.get("branch_results", {})
+    
+    for action in node["parallelActions"]:
+        try:
+            action_type = action.get("type")
+            logger.debug(f"Processing parallel action: {action_type}")
+            
+            if action_type == "postgresql_update_combined":
+                # Check if all required branches have completed
+                wait_for_branches = action.get("waitForBranches", [])
+                missing_branches = [branch for branch in wait_for_branches if branch not in branch_results]
+                
+                if missing_branches:
+                    logger.warning(f"Cannot execute postgresql_update_combined: missing branch results for {missing_branches}")
+                    continue
+                
+                logger.info(f"All required branches completed: {wait_for_branches}")
+                
+                # Execute the database update
+                execute_postgresql_update_combined(node, action, detection_result, branch_results)
+            else:
+                logger.warning(f"Unknown parallel action type: {action_type}")
+                
+        except Exception as e:
+            logger.error(f"Error executing parallel action {action.get('type', 'unknown')}: {e}")
+            import traceback
+            logger.debug(f"Full traceback: {traceback.format_exc()}")
+
+def execute_postgresql_update_combined(node, action, detection_result, branch_results):
+    """Execute a PostgreSQL update with combined branch results."""
+    if not node.get("db_manager"):
+        logger.error("No database manager available for postgresql_update_combined action")
+        return
+        
+    try:
+        table = action["table"]
+        key_field = action["key_field"]
+        key_value_template = action["key_value"]
+        fields = action["fields"]
+        
+        # Create context for key value formatting
+        action_context = {**detection_result}
+        key_value = key_value_template.format(**action_context)
+        
+        logger.info(f"Executing database update: table={table}, {key_field}={key_value}")
+        
+        # Process field mappings
+        mapped_fields = {}
+        for db_field, value_template in fields.items():
+            try:
+                mapped_value = resolve_field_mapping(value_template, branch_results, action_context)
+                if mapped_value is not None:
+                    mapped_fields[db_field] = mapped_value
+                    logger.debug(f"Mapped field: {db_field} = {mapped_value}")
+                else:
+                    logger.warning(f"Could not resolve field mapping for {db_field}: {value_template}")
+            except Exception as e:
+                logger.error(f"Error mapping field {db_field} with template '{value_template}': {e}")
+        
+        if not mapped_fields:
+            logger.warning("No fields mapped successfully, skipping database update")
+            return
+            
+        # Execute the database update
+        success = node["db_manager"].execute_update(table, key_field, key_value, mapped_fields)
+        
+        if success:
+            logger.info(f"Successfully updated database: {table} with {len(mapped_fields)} fields")
+        else:
+            logger.error(f"Failed to update database: {table}")
+            
+    except KeyError as e:
+        logger.error(f"Missing required field in postgresql_update_combined action: {e}")
+    except Exception as e:
+        logger.error(f"Error in postgresql_update_combined action: {e}")
+        import traceback
+        logger.debug(f"Full traceback: {traceback.format_exc()}")
+
+def resolve_field_mapping(value_template, branch_results, action_context):
+    """Resolve field mapping templates like {car_brand_cls_v1.brand}."""
+    try:
+        # Handle simple context variables first (non-branch references)
+        if not '.' in value_template:
+            return value_template.format(**action_context)
+        
+        # Handle branch result references like {model_id.field}
+        import re
+        branch_refs = re.findall(r'\{([^}]+\.[^}]+)\}', value_template)
+        
+        resolved_template = value_template
+        for ref in branch_refs:
+            try:
+                model_id, field_name = ref.split('.', 1)
+                
+                if model_id in branch_results:
+                    branch_data = branch_results[model_id]
+                    if field_name in branch_data:
+                        field_value = branch_data[field_name]
+                        resolved_template = resolved_template.replace(f'{{{ref}}}', str(field_value))
+                        logger.debug(f"Resolved {ref} to {field_value}")
+                    else:
+                        logger.warning(f"Field '{field_name}' not found in branch '{model_id}' results. Available fields: {list(branch_data.keys())}")
+                        return None
+                else:
+                    logger.warning(f"Branch '{model_id}' not found in results. Available branches: {list(branch_results.keys())}")
+                    return None
+            except ValueError as e:
+                logger.error(f"Invalid branch reference format: {ref}")
+                return None
+        
+        # Format any remaining simple variables
+        try:
+            final_value = resolved_template.format(**action_context)
+            return final_value
+        except KeyError as e:
+            logger.warning(f"Could not resolve context variable in template: {e}")
+            return resolved_template
+            
+    except Exception as e:
+        logger.error(f"Error resolving field mapping '{value_template}': {e}")
+        return None
+
+def validate_pipeline_execution(node, regions_dict):
     """
-    - For detection nodes (task != 'classify'):
+    Pre-validate that all required branches will execute successfully before 
-        • runs `track(..., classes=triggerClassIndices)`
+    committing to Redis actions and database records.
-        • picks top box ≥ minConfidence
+    
-        • optionally crops & resizes → recurse into child
+    Returns:
-        • else returns (det_dict, bbox)
+        - (True, []) if pipeline can execute completely
-    - For classify nodes:
+        - (False, missing_branches) if some required branches won't execute
-        • runs `predict()`
+    """
-        • returns top (class,confidence) and no bbox
+    # Get all branches that parallel actions are waiting for
+    required_branches = set()
+    
+    for action in node.get("parallelActions", []):
+        if action.get("type") == "postgresql_update_combined":
+            wait_for_branches = action.get("waitForBranches", [])
+            required_branches.update(wait_for_branches)
+    
+    if not required_branches:
+        # No parallel actions requiring specific branches
+        logger.debug("No parallel actions with waitForBranches - validation passes")
+        return True, []
+    
+    logger.debug(f"Pre-validation: checking if required branches {list(required_branches)} will execute")
+    
+    # Check each required branch
+    missing_branches = []
+    
+    for branch in node.get("branches", []):
+        branch_id = branch["modelId"]
+        
+        if branch_id not in required_branches:
+            continue  # This branch is not required by parallel actions
+            
+        # Check if this branch would be triggered
+        trigger_classes = branch.get("triggerClasses", [])
+        min_conf = branch.get("minConfidence", 0)
+        
+        branch_triggered = False
+        for det_class in regions_dict:
+            det_confidence = regions_dict[det_class]["confidence"]
+            
+            if (det_class in trigger_classes and det_confidence >= min_conf):
+                branch_triggered = True
+                logger.debug(f"Pre-validation: branch {branch_id} WILL be triggered by {det_class} (conf={det_confidence:.3f} >= {min_conf})")
+                break
+        
+        if not branch_triggered:
+            missing_branches.append(branch_id)
+            logger.warning(f"Pre-validation: branch {branch_id} will NOT be triggered - no matching classes or insufficient confidence")
+            logger.debug(f"  Required: {trigger_classes} with min_conf={min_conf}")
+            logger.debug(f"  Available: {[(cls, regions_dict[cls]['confidence']) for cls in regions_dict]}")
+    
+    if missing_branches:
+        logger.error(f"Pipeline pre-validation FAILED: required branches {missing_branches} will not execute")
+        return False, missing_branches
+    else:
+        logger.info(f"Pipeline pre-validation PASSED: all required branches {list(required_branches)} will execute")
+        return True, []
+
+def run_pipeline(frame, node: dict, return_bbox: bool=False, context=None):
+    """
+    Enhanced pipeline that supports:
+    - Multi-class detection (detecting multiple classes simultaneously)
+    - Parallel branch processing
+    - Region-based actions and cropping
+    - Context passing for session/camera information
     """
     try:
         task = getattr(node["model"], "task", None)
 
         # ─── Classification stage ───────────────────────────────────
         if task == "classify":
-            # run the classifier and grab its top-1 directly via the Probs API
             results = node["model"].predict(frame, stream=False)
-            # nothing returned?
             if not results:
                 return (None, None) if return_bbox else None
 
-            # take the first result's probs object
             r = results[0]
             probs = r.probs
             if probs is None:
                 return (None, None) if return_bbox else None
 
-            # get the top-1 class index and its confidence
             top1_idx = int(probs.top1)
             top1_conf = float(probs.top1conf)
+            class_name = node["model"].names[top1_idx]
 
             det = {
-                "class": node["model"].names[top1_idx],
+                "class": class_name,
                 "confidence": top1_conf,
-                "id": None
+                "id": None,
+                class_name: class_name  # Add class name as key for backward compatibility
             }
+            
+            # Add specific field mappings for database operations based on model type
+            model_id = node.get("modelId", "").lower()
+            if "brand" in model_id or "brand_cls" in model_id:
+                det["brand"] = class_name
+            elif "bodytype" in model_id or "body" in model_id:
+                det["body_type"] = class_name
+            elif "color" in model_id:
+                det["color"] = class_name
+            
+            execute_actions(node, frame, det)
             return (det, None) if return_bbox else det
 
-
+        # ─── Detection stage - Multi-class support ──────────────────
-        # ─── Detection stage ────────────────────────────────────────
-        # only look for your triggerClasses
         tk = node["triggerClassIndices"]
+        logger.debug(f"Running detection for node {node['modelId']} with trigger classes: {node.get('triggerClasses', [])} (indices: {tk})")
+        logger.debug(f"Node configuration: minConfidence={node['minConfidence']}, multiClass={node.get('multiClass', False)}")
+        
         res = node["model"].track(
             frame,
             stream=False,
@@ -219,46 +630,238 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False):
             **({"classes": tk} if tk else {})
         )[0]
 
-        dets, boxes = [], []
+        # Collect all detections above confidence threshold
-        for box in res.boxes:
+        all_detections = []
+        all_boxes = []
+        regions_dict = {}
+        
+        logger.debug(f"Raw detection results from model: {len(res.boxes) if res.boxes is not None else 0} detections")
+        
+        for i, box in enumerate(res.boxes):
             conf = float(box.cpu().conf[0])
             cid = int(box.cpu().cls[0])
             name = node["model"].names[cid]
-            if conf < node["minConfidence"]:
-                continue
-            xy = box.cpu().xyxy[0]
-            x1,y1,x2,y2 = map(int, xy)
-            dets.append({"class": name, "confidence": conf,
-                         "id": box.id.item() if hasattr(box, "id") else None})
-            boxes.append((x1, y1, x2, y2))
             
-        if not dets:
+            logger.debug(f"Detection {i}: class='{name}' (id={cid}), confidence={conf:.3f}, threshold={node['minConfidence']}")
+            
+            if conf < node["minConfidence"]:
+                logger.debug(f"  -> REJECTED: confidence {conf:.3f} < threshold {node['minConfidence']}")
+                continue
+                
+            xy = box.cpu().xyxy[0]
+            x1, y1, x2, y2 = map(int, xy)
+            bbox = (x1, y1, x2, y2)
+            
+            detection = {
+                "class": name,
+                "confidence": conf,
+                "id": box.id.item() if hasattr(box, "id") else None,
+                "bbox": bbox
+            }
+            
+            all_detections.append(detection)
+            all_boxes.append(bbox)
+            
+            logger.debug(f"  -> ACCEPTED: {name} with confidence {conf:.3f}, bbox={bbox}")
+            
+            # Store highest confidence detection for each class
+            if name not in regions_dict or conf > regions_dict[name]["confidence"]:
+                regions_dict[name] = {
+                    "bbox": bbox,
+                    "confidence": conf,
+                    "detection": detection
+                }
+                logger.debug(f"  -> Updated regions_dict['{name}'] with confidence {conf:.3f}")
+
+        logger.info(f"Detection summary: {len(all_detections)} accepted detections from {len(res.boxes) if res.boxes is not None else 0} total")
+        logger.info(f"Detected classes: {list(regions_dict.keys())}")
+
+        if not all_detections:
+            logger.warning("No detections above confidence threshold - returning null")
             return (None, None) if return_bbox else None
 
-        # take highest‐confidence
+        # ─── Multi-class validation ─────────────────────────────────
-        best_idx = max(range(len(dets)), key=lambda i: dets[i]["confidence"])
+        if node.get("multiClass", False) and node.get("expectedClasses"):
-        best_det = dets[best_idx]
+            expected_classes = node["expectedClasses"]
-        best_box = boxes[best_idx]
+            detected_classes = list(regions_dict.keys())
             
-        # ─── Branch (classification) ───────────────────────────────
+            logger.info(f"Multi-class validation: expected={expected_classes}, detected={detected_classes}")
+            
+            # Check if at least one expected class is detected (flexible mode)
+            matching_classes = [cls for cls in expected_classes if cls in detected_classes]
+            missing_classes = [cls for cls in expected_classes if cls not in detected_classes]
+            
+            logger.debug(f"Matching classes: {matching_classes}, Missing classes: {missing_classes}")
+            
+            if not matching_classes:
+                # No expected classes found at all
+                logger.warning(f"PIPELINE REJECTED: No expected classes detected. Expected: {expected_classes}, Detected: {detected_classes}")
+                return (None, None) if return_bbox else None
+            
+            if missing_classes:
+                logger.info(f"Partial multi-class detection: {matching_classes} found, {missing_classes} missing")
+            else:
+                logger.info(f"Complete multi-class detection success: {detected_classes}")
+        else:
+            logger.debug("No multi-class validation - proceeding with all detections")
+
+        # ─── Pre-validate pipeline execution ────────────────────────
+        pipeline_valid, missing_branches = validate_pipeline_execution(node, regions_dict)
+        
+        if not pipeline_valid:
+            logger.error(f"Pipeline execution validation FAILED - required branches {missing_branches} cannot execute")
+            logger.error("Aborting pipeline: no Redis actions or database records will be created")
+            return (None, None) if return_bbox else None
+
+        # ─── Execute actions with region information ────────────────
+        detection_result = {
+            "detections": all_detections,
+            "regions": regions_dict,
+            **(context or {})
+        }
+        
+        # ─── Create initial database record when Car+Frontal detected ────
+        if node.get("db_manager") and node.get("multiClass", False):
+            # Only create database record if we have both Car and Frontal
+            has_car = "Car" in regions_dict
+            has_frontal = "Frontal" in regions_dict
+            
+            if has_car and has_frontal:
+                # Generate UUID session_id since client session is None for now
+                import uuid as uuid_lib
+                from datetime import datetime
+                generated_session_id = str(uuid_lib.uuid4())
+                
+                # Insert initial detection record
+                display_id = detection_result.get("display_id", "unknown")
+                timestamp = datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+                
+                inserted_session_id = node["db_manager"].insert_initial_detection(
+                    display_id=display_id,
+                    captured_timestamp=timestamp,
+                    session_id=generated_session_id
+                )
+                
+                if inserted_session_id:
+                    # Update detection_result with the generated session_id for actions and branches
+                    detection_result["session_id"] = inserted_session_id
+                    detection_result["timestamp"] = timestamp  # Update with proper timestamp
+                    logger.info(f"Created initial database record with session_id: {inserted_session_id}")
+            else:
+                logger.debug(f"Database record not created - missing required classes. Has Car: {has_car}, Has Frontal: {has_frontal}")
+        
+        execute_actions(node, frame, detection_result, regions_dict)
+
+        # ─── Parallel branch processing ─────────────────────────────
+        if node["branches"]:
+            branch_results = {}
+            
+            # Filter branches that should be triggered
+            active_branches = []
             for br in node["branches"]:
-            if (best_det["class"] in br["triggerClasses"]
+                trigger_classes = br.get("triggerClasses", [])
-                    and best_det["confidence"] >= br["minConfidence"]):
+                min_conf = br.get("minConfidence", 0)
-                # crop if requested
-                sub = frame
-                if br["crop"]:
-                    x1,y1,x2,y2 = best_box
-                    sub = frame[y1:y2, x1:x2]
-                    sub = cv2.resize(sub, (224, 224))
                 
-                det2, _ = run_pipeline(sub, br, return_bbox=True)
+                logger.debug(f"Evaluating branch {br['modelId']}: trigger_classes={trigger_classes}, min_conf={min_conf}")
-                if det2:
-                    # return classification result + original bbox
-                    return (det2, best_box) if return_bbox else det2
                 
-        # ─── No branch matched → return this detection ─────────────
+                # Check if any detected class matches branch trigger
-        return (best_det, best_box) if return_bbox else best_det
+                branch_triggered = False
+                for det_class in regions_dict:
+                    det_confidence = regions_dict[det_class]["confidence"]
+                    logger.debug(f"  Checking detected class '{det_class}' (confidence={det_confidence:.3f}) against triggers {trigger_classes}")
+                    
+                    if (det_class in trigger_classes and det_confidence >= min_conf):
+                        active_branches.append(br)
+                        branch_triggered = True
+                        logger.info(f"Branch {br['modelId']} activated by class '{det_class}' (conf={det_confidence:.3f} >= {min_conf})")
+                        break
+                
+                if not branch_triggered:
+                    logger.debug(f"Branch {br['modelId']} not triggered - no matching classes or insufficient confidence")
+            
+            if active_branches:
+                if node.get("parallel", False) or any(br.get("parallel", False) for br in active_branches):
+                    # Run branches in parallel
+                    with concurrent.futures.ThreadPoolExecutor(max_workers=len(active_branches)) as executor:
+                        futures = {}
+                        
+                        for br in active_branches:
+                            crop_class = br.get("cropClass", br.get("triggerClasses", [])[0] if br.get("triggerClasses") else None)
+                            sub_frame = frame
+                            
+                            logger.info(f"Starting parallel branch: {br['modelId']}, crop_class: {crop_class}")
+                            
+                            if br.get("crop", False) and crop_class:
+                                cropped = crop_region_by_class(frame, regions_dict, crop_class)
+                                if cropped is not None:
+                                    sub_frame = cv2.resize(cropped, (224, 224))
+                                    logger.debug(f"Successfully cropped {crop_class} region for {br['modelId']}")
+                                else:
+                                    logger.warning(f"Failed to crop {crop_class} region for {br['modelId']}, skipping branch")
+                                    continue
+                            
+                            future = executor.submit(run_pipeline, sub_frame, br, True, context)
+                            futures[future] = br
+                        
+                        # Collect results
+                        for future in concurrent.futures.as_completed(futures):
+                            br = futures[future]
+                            try:
+                                result, _ = future.result()
+                                if result:
+                                    branch_results[br["modelId"]] = result
+                                    logger.info(f"Branch {br['modelId']} completed: {result}")
+                            except Exception as e:
+                                logger.error(f"Branch {br['modelId']} failed: {e}")
+                else:
+                    # Run branches sequentially  
+                    for br in active_branches:
+                        crop_class = br.get("cropClass", br.get("triggerClasses", [])[0] if br.get("triggerClasses") else None)
+                        sub_frame = frame
+                        
+                        logger.info(f"Starting sequential branch: {br['modelId']}, crop_class: {crop_class}")
+                        
+                        if br.get("crop", False) and crop_class:
+                            cropped = crop_region_by_class(frame, regions_dict, crop_class)
+                            if cropped is not None:
+                                sub_frame = cv2.resize(cropped, (224, 224))
+                                logger.debug(f"Successfully cropped {crop_class} region for {br['modelId']}")
+                            else:
+                                logger.warning(f"Failed to crop {crop_class} region for {br['modelId']}, skipping branch")
+                                continue
+                        
+                        try:
+                            result, _ = run_pipeline(sub_frame, br, True, context)
+                            if result:
+                                branch_results[br["modelId"]] = result
+                                logger.info(f"Branch {br['modelId']} completed: {result}")
+                            else:
+                                logger.warning(f"Branch {br['modelId']} returned no result")
+                        except Exception as e:
+                            logger.error(f"Error in sequential branch {br['modelId']}: {e}")
+                            import traceback
+                            logger.debug(f"Branch error traceback: {traceback.format_exc()}")
+
+            # Store branch results in detection_result for parallel actions
+            detection_result["branch_results"] = branch_results
+
+        # ─── Execute Parallel Actions ───────────────────────────────
+        if node.get("parallelActions") and "branch_results" in detection_result:
+            execute_parallel_actions(node, frame, detection_result, regions_dict)
+
+        # ─── Return detection result ────────────────────────────────
+        primary_detection = max(all_detections, key=lambda x: x["confidence"])
+        primary_bbox = primary_detection["bbox"]
+        
+        # Add branch results and session_id to primary detection for compatibility
+        if "branch_results" in detection_result:
+            primary_detection["branch_results"] = detection_result["branch_results"]
+        if "session_id" in detection_result:
+            primary_detection["session_id"] = detection_result["session_id"]
+        
+        return (primary_detection, primary_bbox) if return_bbox else primary_detection
 
     except Exception as e:
-        logging.error(f"Error in node {node.get('modelId')}: {e}")
+        logger.error(f"Error in node {node.get('modelId')}: {e}")
+        traceback.print_exc()
         return (None, None) if return_bbox else None

test_protocol.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""
+Test script to verify the worker implementation follows the protocol
+"""
+import json
+import asyncio
+import websockets
+import time
+
+async def test_protocol():
+    """Test the worker protocol implementation"""
+    uri = "ws://localhost:8000"
+    
+    try:
+        async with websockets.connect(uri) as websocket:
+            print("✓ Connected to worker")
+            
+            # Test 1: Check if we receive heartbeat (stateReport)
+            print("\n1. Testing heartbeat...")
+            try:
+                message = await asyncio.wait_for(websocket.recv(), timeout=5)
+                data = json.loads(message)
+                if data.get("type") == "stateReport":
+                    print("✓ Received stateReport heartbeat")
+                    print(f"  - CPU Usage: {data.get('cpuUsage', 'N/A')}%")
+                    print(f"  - Memory Usage: {data.get('memoryUsage', 'N/A')}%")
+                    print(f"  - Camera Connections: {len(data.get('cameraConnections', []))}")
+                else:
+                    print(f"✗ Expected stateReport, got {data.get('type')}")
+            except asyncio.TimeoutError:
+                print("✗ No heartbeat received within 5 seconds")
+            
+            # Test 2: Request state
+            print("\n2. Testing requestState...")
+            await websocket.send(json.dumps({"type": "requestState"}))
+            try:
+                message = await asyncio.wait_for(websocket.recv(), timeout=5)
+                data = json.loads(message)
+                if data.get("type") == "stateReport":
+                    print("✓ Received stateReport response")
+                else:
+                    print(f"✗ Expected stateReport, got {data.get('type')}")
+            except asyncio.TimeoutError:
+                print("✗ No response to requestState within 5 seconds")
+            
+            # Test 3: Set session ID
+            print("\n3. Testing setSessionId...")
+            session_message = {
+                "type": "setSessionId",
+                "payload": {
+                    "displayIdentifier": "display-001",
+                    "sessionId": 12345
+                }
+            }
+            await websocket.send(json.dumps(session_message))
+            print("✓ Sent setSessionId message")
+            
+            # Test 4: Test patchSession
+            print("\n4. Testing patchSession...")
+            patch_message = {
+                "type": "patchSession",
+                "sessionId": 12345,
+                "data": {
+                    "currentCar": {
+                        "carModel": "Civic",
+                        "carBrand": "Honda"
+                    }
+                }
+            }
+            await websocket.send(json.dumps(patch_message))
+            
+            # Wait for patchSessionResult
+            try:
+                message = await asyncio.wait_for(websocket.recv(), timeout=5)
+                data = json.loads(message)
+                if data.get("type") == "patchSessionResult":
+                    print("✓ Received patchSessionResult")
+                    print(f"  - Success: {data.get('payload', {}).get('success')}")
+                    print(f"  - Message: {data.get('payload', {}).get('message')}")
+                else:
+                    print(f"✗ Expected patchSessionResult, got {data.get('type')}")
+            except asyncio.TimeoutError:
+                print("✗ No patchSessionResult received within 5 seconds")
+            
+            # Test 5: Test subscribe message format (without actual camera)
+            print("\n5. Testing subscribe message format...")
+            subscribe_message = {
+                "type": "subscribe",
+                "payload": {
+                    "subscriptionIdentifier": "display-001;cam-001",
+                    "snapshotUrl": "http://example.com/snapshot.jpg",
+                    "snapshotInterval": 5000,
+                    "modelUrl": "http://example.com/model.mpta",
+                    "modelName": "Test Model",
+                    "modelId": 101,
+                    "cropX1": 100,
+                    "cropY1": 200,
+                    "cropX2": 300,
+                    "cropY2": 400
+                }
+            }
+            await websocket.send(json.dumps(subscribe_message))
+            print("✓ Sent subscribe message (will fail without actual camera/model)")
+            
+            # Listen for a few more messages to catch any errors
+            print("\n6. Listening for additional messages...")
+            for i in range(3):
+                try:
+                    message = await asyncio.wait_for(websocket.recv(), timeout=2)
+                    data = json.loads(message)
+                    msg_type = data.get("type")
+                    print(f"  - Received {msg_type}")
+                    if msg_type == "error":
+                        print(f"    Error: {data.get('error')}")
+                except asyncio.TimeoutError:
+                    break
+            
+            print("\n✓ Protocol test completed successfully!")
+            
+    except Exception as e:
+        print(f"✗ Connection failed: {e}")
+        print("Make sure the worker is running on localhost:8000")
+
+if __name__ == "__main__":
+    asyncio.run(test_protocol())

worker.md

@@ -0,0 +1,495 @@
+# Worker Communication Protocol
+
+This document outlines the WebSocket-based communication protocol between the CMS backend and a detector worker. As a worker developer, your primary responsibility is to implement a WebSocket server that adheres to this protocol.
+
+## 1. Connection
+
+The worker must run a WebSocket server, preferably on port `8000`. The backend system, which is managed by a container orchestration service, will automatically discover and establish a WebSocket connection to your worker.
+
+Upon a successful connection from the backend, you should begin sending `stateReport` messages as heartbeats.
+
+## 2. Communication Overview
+
+Communication is bidirectional and asynchronous. All messages are JSON objects with a `type` field that indicates the message's purpose, and an optional `payload` field containing the data.
+
+- **Worker -> Backend:** You will send messages to the backend to report status, forward detection events, or request changes to session data.
+- **Backend -> Worker:** The backend will send commands to you to manage camera subscriptions.
+
+## 3. Dynamic Configuration via MPTA File
+
+To enable modularity and dynamic configuration, the backend will send you a URL to a `.mpta` file when it issues a `subscribe` command. This file is a renamed `.zip` archive that contains everything your worker needs to perform its task.
+
+**Your worker is responsible for:**
+
+1. Fetching this file from the provided URL.
+2. Extracting its contents.
+3. Interpreting the contents to configure its internal pipeline.
+
+**The contents of the `.mpta` file are entirely up to the user who configures the model in the CMS.** This allows for maximum flexibility. For example, the archive could contain:
+
+- AI/ML Models: Pre-trained models for libraries like TensorFlow, PyTorch, or ONNX.
+- Configuration Files: A `config.json` or `pipeline.yaml` that defines a sequence of operations, specifies model paths, or sets detection thresholds.
+- Scripts: Custom Python scripts for pre-processing or post-processing.
+- API Integration Details: A JSON file with endpoint information and credentials for interacting with third-party detection services.
+
+Essentially, the `.mpta` file is a self-contained package that tells your worker _how_ to process the video stream for a given subscription.
+
+## 4. Messages from Worker to Backend
+
+These are the messages your worker is expected to send to the backend.
+
+### 4.1. State Report (Heartbeat)
+
+This message is crucial for the backend to monitor your worker's health and status, including GPU usage.
+
+- **Type:** `stateReport`
+- **When to Send:** Periodically (e.g., every 2 seconds) after a connection is established.
+
+**Payload:**
+
+```json
+{
+  "type": "stateReport",
+  "cpuUsage": 75.5,
+  "memoryUsage": 40.2,
+  "gpuUsage": 60.0,
+  "gpuMemoryUsage": 25.1,
+  "cameraConnections": [
+    {
+      "subscriptionIdentifier": "display-001;cam-001",
+      "modelId": 101,
+      "modelName": "General Object Detection",
+      "online": true,
+      "cropX1": 100,
+      "cropY1": 200,
+      "cropX2": 300,
+      "cropY2": 400
+    }
+  ]
+}
+```
+
+> **Note:**
+>
+> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) should be included in each camera connection to indicate the crop coordinates for that subscription.
+
+### 4.2. Image Detection
+
+Sent when the worker detects a relevant object. The `detection` object should be flat and contain key-value pairs corresponding to the detected attributes.
+
+- **Type:** `imageDetection`
+
+**Payload Example:**
+
+```json
+{
+  "type": "imageDetection",
+  "subscriptionIdentifier": "display-001;cam-001",
+  "timestamp": "2025-07-14T12:34:56.789Z",
+  "data": {
+    "detection": {
+      "carModel": "Civic",
+      "carBrand": "Honda",
+      "carYear": 2023,
+      "bodyType": "Sedan",
+      "licensePlateText": "ABCD1234",
+      "licensePlateConfidence": 0.95
+    },
+    "modelId": 101,
+    "modelName": "US-LPR-and-Vehicle-ID"
+  }
+}
+```
+
+### 4.3. Patch Session
+
+> **Note:** Patch messages are only used when the worker can't keep up and needs to retroactively send detections. Normally, detections should be sent in real-time using `imageDetection` messages. Use `patchSession` only to update session data after the fact.
+
+Allows the worker to request a modification to an active session's data. The `data` payload must be a partial object of the `DisplayPersistentData` structure.
+
+- **Type:** `patchSession`
+
+**Payload Example:**
+
+```json
+{
+  "type": "patchSession",
+  "sessionId": 12345,
+  "data": {
+    "currentCar": {
+      "carModel": "Civic",
+      "carBrand": "Honda",
+      "licensePlateText": "ABCD1234"
+    }
+  }
+}
+```
+
+The backend will respond with a `patchSessionResult` command.
+
+#### `DisplayPersistentData` Structure
+
+The `data` object in the `patchSession` message is merged with the existing `DisplayPersistentData` on the backend. Here is its structure:
+
+```typescript
+interface DisplayPersistentData {
+  progressionStage:
+    | 'welcome'
+    | 'car_fueling'
+    | 'car_waitpayment'
+    | 'car_postpayment'
+    | null;
+  qrCode: string | null;
+  adsPlayback: {
+    playlistSlotOrder: number; // The 'order' of the current slot
+    adsId: number | null;
+    adsUrl: string | null;
+  } | null;
+  currentCar: {
+    carModel?: string;
+    carBrand?: string;
+    carYear?: number;
+    bodyType?: string;
+    licensePlateText?: string;
+    licensePlateType?: string;
+  } | null;
+  fuelPump: {
+    /* FuelPumpData structure */
+  } | null;
+  weatherData: {
+    /* WeatherResponse structure */
+  } | null;
+  sessionId: number | null;
+}
+```
+
+#### Patching Behavior
+
+- The patch is a **deep merge**.
+- **`undefined`** values are ignored.
+- **`null`** values will set the corresponding field to `null`.
+- Nested objects are merged recursively.
+
+## 5. Commands from Backend to Worker
+
+These are the commands your worker will receive from the backend.
+
+### 5.1. Subscribe to Camera
+
+Instructs the worker to process a camera's RTSP stream using the configuration from the specified `.mpta` file.
+
+- **Type:** `subscribe`
+
+**Payload:**
+
+```json
+{
+  "type": "subscribe",
+  "payload": {
+    "subscriptionIdentifier": "display-001;cam-002",
+    "rtspUrl": "rtsp://user:pass@host:port/stream",
+    "snapshotUrl": "http://go2rtc/snapshot/1",
+    "snapshotInterval": 5000,
+    "modelUrl": "http://storage/models/us-lpr.mpta",
+    "modelName": "US-LPR-and-Vehicle-ID",
+    "modelId": 102,
+    "cropX1": 100,
+    "cropY1": 200,
+    "cropX2": 300,
+    "cropY2": 400
+  }
+}
+```
+
+> **Note:**
+>
+> - `cropX1`, `cropY1`, `cropX2`, `cropY2` (optional, integer) specify the crop coordinates for the camera stream. These values are configured per display and passed in the subscription payload. If not provided, the worker should process the full frame.
+>
+> **Important:**
+> If multiple displays are bound to the same camera, your worker must ensure that only **one stream** is opened per camera. When you receive multiple subscriptions for the same camera (with different `subscriptionIdentifier` values), you should:
+>
+> - Open the RTSP stream **once** for that camera if using RTSP.
+> - Capture each snapshot only once per cycle, and reuse it for all display subscriptions sharing that camera.
+> - Capture each frame/image only once per cycle.
+> - Reuse the same captured image and snapshot for all display subscriptions that share the camera, processing and routing detection results separately for each display as needed.
+>   This avoids unnecessary load and bandwidth usage, and ensures consistent detection results and snapshots across all displays sharing the same camera.
+
+### 5.2. Unsubscribe from Camera
+
+Instructs the worker to stop processing a camera's stream.
+
+- **Type:** `unsubscribe`
+
+**Payload:**
+
+```json
+{
+  "type": "unsubscribe",
+  "payload": {
+    "subscriptionIdentifier": "display-001;cam-002"
+  }
+}
+```
+
+### 5.3. Request State
+
+Direct request for the worker's current state. Respond with a `stateReport` message.
+
+- **Type:** `requestState`
+
+**Payload:**
+
+```json
+{
+  "type": "requestState"
+}
+```
+
+### 5.4. Patch Session Result
+
+Backend's response to a `patchSession` message.
+
+- **Type:** `patchSessionResult`
+
+**Payload:**
+
+```json
+{
+  "type": "patchSessionResult",
+  "payload": {
+    "sessionId": 12345,
+    "success": true,
+    "message": "Session updated successfully."
+  }
+}
+```
+
+### 5.5. Set Session ID
+
+Allows the backend to instruct the worker to associate a session ID with a subscription. This is useful for linking detection events to a specific session. The session ID can be `null` to indicate no active session.
+
+- **Type:** `setSessionId`
+
+**Payload:**
+
+```json
+{
+  "type": "setSessionId",
+  "payload": {
+    "displayIdentifier": "display-001",
+    "sessionId": 12345
+  }
+}
+```
+
+Or to clear the session:
+
+```json
+{
+  "type": "setSessionId",
+  "payload": {
+    "displayIdentifier": "display-001",
+    "sessionId": null
+  }
+}
+```
+
+> **Note:**
+>
+> - The worker should store the session ID for the given subscription and use it in subsequent detection or patch messages as appropriate. If `sessionId` is `null`, the worker should treat the subscription as having no active session.
+
+## Subscription Identifier Format
+
+The `subscriptionIdentifier` used in all messages is constructed as:
+
+```
+displayIdentifier;cameraIdentifier
+```
+
+This uniquely identifies a camera subscription for a specific display.
+
+### Session ID Association
+
+When the backend sends a `setSessionId` command, it will only provide the `displayIdentifier` (not the full `subscriptionIdentifier`).
+
+**Worker Responsibility:**
+
+- The worker must match the `displayIdentifier` to all active subscriptions for that display (i.e., all `subscriptionIdentifier` values that start with `displayIdentifier;`).
+- The worker should set or clear the session ID for all matching subscriptions.
+
+## 6. Example Communication Log
+
+This section shows a typical sequence of messages between the backend and the worker. Patch messages are not included, as they are only used when the worker cannot keep up.
+
+> **Note:** Unsubscribe is triggered when a user removes a camera or when the node is too heavily loaded and needs rebalancing.
+
+1.  **Connection Established** & **Heartbeat**
+    - **Worker -> Backend**
+    ```json
+    {
+      "type": "stateReport",
+      "cpuUsage": 70.2,
+      "memoryUsage": 38.1,
+      "gpuUsage": 55.0,
+      "gpuMemoryUsage": 20.0,
+      "cameraConnections": []
+    }
+    ```
+2.  **Backend Subscribes Camera**
+    - **Backend -> Worker**
+    ```json
+    {
+      "type": "subscribe",
+      "payload": {
+        "subscriptionIdentifier": "display-001;entry-cam-01",
+        "rtspUrl": "rtsp://192.168.1.100/stream1",
+        "modelUrl": "http://storage/models/vehicle-id.mpta",
+        "modelName": "Vehicle Identification",
+        "modelId": 201
+      }
+    }
+    ```
+3.  **Worker Acknowledges in Heartbeat**
+    - **Worker -> Backend**
+    ```json
+    {
+      "type": "stateReport",
+      "cpuUsage": 72.5,
+      "memoryUsage": 39.0,
+      "gpuUsage": 57.0,
+      "gpuMemoryUsage": 21.0,
+      "cameraConnections": [
+        {
+          "subscriptionIdentifier": "display-001;entry-cam-01",
+          "modelId": 201,
+          "modelName": "Vehicle Identification",
+          "online": true
+        }
+      ]
+    }
+    ```
+4.  **Worker Detects a Car**
+    - **Worker -> Backend**
+    ```json
+    {
+      "type": "imageDetection",
+      "subscriptionIdentifier": "display-001;entry-cam-01",
+      "timestamp": "2025-07-15T10:00:00.000Z",
+      "data": {
+        "detection": {
+          "carBrand": "Honda",
+          "carModel": "CR-V",
+          "bodyType": "SUV",
+          "licensePlateText": "GEMINI-AI",
+          "licensePlateConfidence": 0.98
+        },
+        "modelId": 201,
+        "modelName": "Vehicle Identification"
+      }
+    }
+    ```
+    - **Worker -> Backend**
+    ```json
+    {
+      "type": "imageDetection",
+      "subscriptionIdentifier": "display-001;entry-cam-01",
+      "timestamp": "2025-07-15T10:00:01.000Z",
+      "data": {
+        "detection": {
+          "carBrand": "Toyota",
+          "carModel": "Corolla",
+          "bodyType": "Sedan",
+          "licensePlateText": "CMS-1234",
+          "licensePlateConfidence": 0.97
+        },
+        "modelId": 201,
+        "modelName": "Vehicle Identification"
+      }
+    }
+    ```
+    - **Worker -> Backend**
+    ```json
+    {
+      "type": "imageDetection",
+      "subscriptionIdentifier": "display-001;entry-cam-01",
+      "timestamp": "2025-07-15T10:00:02.000Z",
+      "data": {
+        "detection": {
+          "carBrand": "Ford",
+          "carModel": "Focus",
+          "bodyType": "Hatchback",
+          "licensePlateText": "CMS-5678",
+          "licensePlateConfidence": 0.96
+        },
+        "modelId": 201,
+        "modelName": "Vehicle Identification"
+      }
+    }
+    ```
+5.  **Backend Unsubscribes Camera**
+    - **Backend -> Worker**
+    ```json
+    {
+      "type": "unsubscribe",
+      "payload": {
+        "subscriptionIdentifier": "display-001;entry-cam-01"
+      }
+    }
+    ```
+6.  **Worker Acknowledges Unsubscription**
+    - **Worker -> Backend**
+    ```json
+    {
+      "type": "stateReport",
+      "cpuUsage": 68.0,
+      "memoryUsage": 37.0,
+      "gpuUsage": 50.0,
+      "gpuMemoryUsage": 18.0,
+      "cameraConnections": []
+    }
+    ```
+
+## 7. HTTP API: Image Retrieval
+
+In addition to the WebSocket protocol, the worker exposes an HTTP endpoint for retrieving the latest image frame from a camera.
+
+### Endpoint
+
+```
+GET /camera/{camera_id}/image
+```
+
+- **`camera_id`**: The full `subscriptionIdentifier` (e.g., `display-001;cam-001`).
+
+### Response
+
+- **Success (200):** Returns the latest JPEG image from the camera stream.
+
+  - `Content-Type: image/jpeg`
+  - Binary JPEG data.
+
+- **Error (404):** If the camera is not found or no frame is available.
+
+  - JSON error response.
+
+- **Error (500):** Internal server error.
+
+### Example Request
+
+```
+GET /camera/display-001;cam-001/image
+```
+
+### Example Response
+
+- **Headers:**
+  ```
+  Content-Type: image/jpeg
+  ```
+- **Body:** Binary JPEG image.
+
+### Notes
+
+- The endpoint returns the most recent frame available for the specified camera subscription.
+- If multiple displays share the same camera, each subscription has its own buffer; the endpoint uses the buffer for the given `camera_id`.
+- This API is useful for debugging, monitoring, or integrating with external systems that require direct image access.