refactor: move old code to archive

archive/app.py

@@ -0,0 +1,903 @@
+from typing import Any, Dict
+import os
+import json
+import time
+import queue
+import torch
+import cv2
+import numpy as np
+import base64
+import logging
+import threading
+import requests
+import asyncio
+import psutil
+import zipfile
+from urllib.parse import urlparse
+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
+
+# Import shared pipeline functions
+from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
+
+app = FastAPI()
+
+# Global dictionaries to keep track of models and streams
+# "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)
+
+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.INFO,  # Set to INFO level for less verbose output
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    handlers=[
+        logging.FileHandler("detector_worker.log"),  # Write logs to a file
+        logging.StreamHandler()  # Also output to console
+    ]
+)
+
+# Create a logger specifically for this application
+logger = logging.getLogger("detector_worker")
+logger.setLevel(logging.DEBUG)  # Set app-specific logger to DEBUG level
+
+# Ensure all other libraries (including root) use at least INFO level
+logging.getLogger().setLevel(logging.INFO)
+
+logger.info("Starting detector worker application")
+logger.info(f"Configuration: Target FPS: {TARGET_FPS}, Max streams: {max_streams}, Max retries: {max_retries}")
+
+# Ensure the models directory exists
+os.makedirs("models", exist_ok=True)
+logger.info("Ensured models directory exists")
+
+# Constants for heartbeat and timeouts
+HEARTBEAT_INTERVAL = 2  # seconds
+WORKER_TIMEOUT_MS = 10000
+logger.debug(f"Heartbeat interval set to {HEARTBEAT_INTERVAL} seconds")
+
+# Locks for thread-safe operations
+streams_lock = threading.Lock()
+models_lock = threading.Lock()
+logger.debug("Initialized thread locks")
+
+# Add helper to download mpta ZIP file from a remote URL
+def download_mpta(url: str, dest_path: str) -> str:
+    try:
+        logger.info(f"Starting download of model from {url} to {dest_path}")
+        os.makedirs(os.path.dirname(dest_path), exist_ok=True)
+        response = requests.get(url, stream=True)
+        if response.status_code == 200:
+            file_size = int(response.headers.get('content-length', 0))
+            logger.info(f"Model file size: {file_size/1024/1024:.2f} MB")
+            downloaded = 0
+            with open(dest_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    if file_size > 0 and downloaded % (file_size // 10) < 8192:  # Log approximately every 10%
+                        logger.debug(f"Download progress: {downloaded/file_size*100:.1f}%")
+            logger.info(f"Successfully downloaded mpta file from {url} to {dest_path}")
+            return dest_path
+        else:
+            logger.error(f"Failed to download mpta file (status code {response.status_code}): {response.text}")
+            return None
+    except Exception as e:
+        logger.error(f"Exception downloading mpta file from {url}: {str(e)}", exc_info=True)
+        return None
+
+# Add helper to fetch snapshot image from HTTP/HTTPS URL
+def fetch_snapshot(url: str):
+    try:
+        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 {clean_url}, shape: {frame.shape}")
+                return frame
+            else:
+                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}): {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
+####################################################
+@app.websocket("/")
+async def detect(websocket: WebSocket):
+    logger.info("WebSocket connection accepted")
+    persistent_data_dict = {}
+
+    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()
+            
+            # Extract display identifier for session ID lookup
+            subscription_parts = stream["subscriptionIdentifier"].split(';')
+            display_identifier = subscription_parts[0] if subscription_parts else None
+            session_id = session_ids.get(display_identifier) if display_identifier else None
+            
+            # Create context for pipeline execution
+            pipeline_context = {
+                "camera_id": camera_id,
+                "display_id": display_identifier,
+                "session_id": session_id
+            }
+            
+            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)
+            if detection_result and isinstance(detection_result, dict) and "class" in detection_result and "confidence" in detection_result:
+                highest_confidence_detection = {
+                    "class": detection_result.get("class", "none"),
+                    "confidence": detection_result.get("confidence", 1.0),
+                    "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                }
+            # 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:
+                        highest_confidence_detection = {
+                            "class": highest_confidence_class.get("class", "none"),
+                            "confidence": highest_confidence_class.get("confidence", 1.0),
+                            "box": [0, 0, 0, 0]  # Empty bounding box for classifications
+                        }
+                    else:
+                        highest_confidence_detection = {
+                            "class": "none",
+                            "confidence": 1.0,
+                            "box": [0, 0, 0, 0]
+                        }
+                else:
+                    highest_confidence_detection = {
+                        "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]
+                }
+            
+            # Convert detection format to match protocol - flatten detection attributes
+            detection_dict = {}
+            
+            # Handle different detection result formats
+            if isinstance(highest_confidence_detection, dict):
+                # Copy all fields from the detection result
+                for key, value in highest_confidence_detection.items():
+                    if key not in ["box", "id"]:  # Skip internal fields
+                        detection_dict[key] = value
+            
+            detection_data = {
+                "type": "imageDetection",
+                "subscriptionIdentifier": stream["subscriptionIdentifier"],
+                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S.%fZ", time.gmtime()),
+                "data": {
+                    "detection": detection_dict,
+                    "modelId": stream["modelId"],
+                    "modelName": stream["modelName"]
+                }
+            }
+            
+            # Add session ID if available
+            if session_id is not None:
+                detection_data["sessionId"] = session_id
+            
+            if highest_confidence_detection["class"] != "none":
+                logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['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}")
+            return persistent_data
+        except Exception as e:
+            logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
+            return persistent_data
+
+    def frame_reader(camera_id, cap, buffer, stop_event):
+        retries = 0
+        logger.info(f"Starting frame reader thread for camera {camera_id}")
+        frame_count = 0
+        last_log_time = time.time()
+        
+        try:
+            # Log initial camera status and properties
+            if cap.isOpened():
+                width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                fps = cap.get(cv2.CAP_PROP_FPS)
+                logger.info(f"Camera {camera_id} opened successfully with resolution {width}x{height}, FPS: {fps}")
+            else:
+                logger.error(f"Camera {camera_id} failed to open initially")
+            
+            while not stop_event.is_set():
+                try:
+                    if not cap.isOpened():
+                        logger.error(f"Camera {camera_id} is not open before trying to read")
+                        # Attempt to reopen
+                        cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
+                        time.sleep(reconnect_interval)
+                        continue
+                    
+                    logger.debug(f"Attempting to read frame from camera {camera_id}")
+                    ret, frame = cap.read()
+                    
+                    if not ret:
+                        logger.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:
+                            logger.error(f"Max retries reached for camera: {camera_id}, stopping frame reader")
+                            break
+                        # Re-open
+                        logger.info(f"Attempting to reopen RTSP stream for camera: {camera_id}")
+                        cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
+                        if not cap.isOpened():
+                            logger.error(f"Failed to reopen RTSP stream for camera: {camera_id}")
+                            continue
+                        logger.info(f"Successfully reopened RTSP stream for camera: {camera_id}")
+                        continue
+                    
+                    # Successfully read a frame
+                    frame_count += 1
+                    current_time = time.time()
+                    # Log frame stats every 5 seconds
+                    if current_time - last_log_time > 5:
+                        logger.info(f"Camera {camera_id}: Read {frame_count} frames in the last {current_time - last_log_time:.1f} seconds")
+                        frame_count = 0
+                        last_log_time = current_time
+                    
+                    logger.debug(f"Successfully read frame from camera {camera_id}, shape: {frame.shape}")
+                    retries = 0
+                    
+                    # Overwrite old frame if buffer is full
+                    if not buffer.empty():
+                        try:
+                            buffer.get_nowait()
+                            logger.debug(f"[frame_reader] Removed old frame from buffer for camera {camera_id}")
+                        except queue.Empty:
+                            pass
+                    buffer.put(frame)
+                    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)
+                    
+                except cv2.error as e:
+                    logger.error(f"OpenCV error for camera {camera_id}: {e}", exc_info=True)
+                    cap.release()
+                    time.sleep(reconnect_interval)
+                    retries += 1
+                    if retries > max_retries and max_retries != -1:
+                        logger.error(f"Max retries reached after OpenCV error for camera {camera_id}")
+                        break
+                    logger.info(f"Attempting to reopen RTSP stream after OpenCV error for camera: {camera_id}")
+                    cap = cv2.VideoCapture(streams[camera_id]["rtsp_url"])
+                    if not cap.isOpened():
+                        logger.error(f"Failed to reopen RTSP stream for camera {camera_id} after OpenCV error")
+                        continue
+                    logger.info(f"Successfully reopened RTSP stream after OpenCV error for camera: {camera_id}")
+                except Exception as e:
+                    logger.error(f"Unexpected error for camera {camera_id}: {str(e)}", exc_info=True)
+                    cap.release()
+                    break
+        except Exception as e:
+            logger.error(f"Error in frame_reader thread for camera {camera_id}: {str(e)}", exc_info=True)
+        finally:
+            logger.info(f"Frame reader thread for camera {camera_id} is exiting")
+            if cap and cap.isOpened():
+                cap.release()
+
+    def snapshot_reader(camera_id, snapshot_url, snapshot_interval, buffer, stop_event):
+        """Frame reader that fetches snapshots from HTTP/HTTPS URL at specified intervals"""
+        retries = 0
+        logger.info(f"Starting snapshot reader thread for camera {camera_id} from {snapshot_url}")
+        frame_count = 0
+        last_log_time = time.time()
+        
+        try:
+            interval_seconds = snapshot_interval / 1000.0  # Convert milliseconds to seconds
+            logger.info(f"Snapshot interval for camera {camera_id}: {interval_seconds}s")
+            
+            while not stop_event.is_set():
+                try:
+                    start_time = time.time()
+                    frame = fetch_snapshot(snapshot_url)
+                    
+                    if frame is None:
+                        logger.warning(f"Failed to fetch snapshot for camera: {camera_id}, retry {retries+1}/{max_retries}")
+                        retries += 1
+                        if retries > max_retries and max_retries != -1:
+                            logger.error(f"Max retries reached for snapshot camera: {camera_id}, stopping reader")
+                            break
+                        time.sleep(min(interval_seconds, reconnect_interval))
+                        continue
+                    
+                    # Successfully fetched a frame
+                    frame_count += 1
+                    current_time = time.time()
+                    # Log frame stats every 5 seconds
+                    if current_time - last_log_time > 5:
+                        logger.info(f"Camera {camera_id}: Fetched {frame_count} snapshots in the last {current_time - last_log_time:.1f} seconds")
+                        frame_count = 0
+                        last_log_time = current_time
+                    
+                    logger.debug(f"Successfully fetched snapshot from camera {camera_id}, shape: {frame.shape}")
+                    retries = 0
+                    
+                    # Overwrite old frame if buffer is full
+                    if not buffer.empty():
+                        try:
+                            buffer.get_nowait()
+                            logger.debug(f"[snapshot_reader] Removed old snapshot from buffer for camera {camera_id}")
+                        except queue.Empty:
+                            pass
+                    buffer.put(frame)
+                    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
+                    sleep_time = max(interval_seconds - elapsed, 0)
+                    if sleep_time > 0:
+                        time.sleep(sleep_time)
+                
+                except Exception as e:
+                    logger.error(f"Unexpected error fetching snapshot for camera {camera_id}: {str(e)}", exc_info=True)
+                    retries += 1
+                    if retries > max_retries and max_retries != -1:
+                        logger.error(f"Max retries reached after error for snapshot camera {camera_id}")
+                        break
+                    time.sleep(min(interval_seconds, reconnect_interval))
+        except Exception as e:
+            logger.error(f"Error in snapshot_reader thread for camera {camera_id}: {str(e)}", exc_info=True)
+        finally:
+            logger.info(f"Snapshot reader thread for camera {camera_id} is exiting")
+
+    async def process_streams():
+        logger.info("Started processing streams")
+        try:
+            while True:
+                start_time = time.time()
+                with streams_lock:
+                    current_streams = list(streams.items())
+                    if current_streams:
+                        logger.debug(f"Processing {len(current_streams)} active streams")
+                    else:
+                        logger.debug("No active streams to process")
+                
+                for camera_id, stream in current_streams:
+                    buffer = stream["buffer"]
+                    if buffer.empty():
+                        logger.debug(f"Frame buffer is empty for camera {camera_id}")
+                        continue
+                    
+                    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:
+                            logger.warning(f"Model not found for camera {camera_id}, modelId {stream['modelId']}")
+                            continue
+                        logger.debug(f"Found model tree for camera {camera_id}, modelId {stream['modelId']}")
+                    
+                    key = (camera_id, stream["modelId"])
+                    persistent_data = persistent_data_dict.get(key, {})
+                    logger.debug(f"Starting detection for camera {camera_id} with modelId {stream['modelId']}")
+                    updated_persistent_data = await handle_detection(
+                        camera_id, stream, frame, websocket, model_tree, persistent_data
+                    )
+                    persistent_data_dict[key] = updated_persistent_data
+                
+                elapsed_time = (time.time() - start_time) * 1000  # ms
+                sleep_time = max(poll_interval - elapsed_time, 0)
+                logger.debug(f"Frame processing cycle: {elapsed_time:.2f}ms, sleeping for: {sleep_time:.2f}ms")
+                await asyncio.sleep(sleep_time / 1000.0)
+        except asyncio.CancelledError:
+            logger.info("Stream processing task cancelled")
+        except Exception as e:
+            logger.error(f"Error in process_streams: {str(e)}", exc_info=True)
+
+    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.utilization() if hasattr(torch.cuda, 'utilization') else None
+                    gpu_memory_usage = torch.cuda.memory_reserved() / (1024 ** 2)
+                else:
+                    gpu_usage = None
+                    gpu_memory_usage = None
+
+                camera_connections = [
+                    {
+                        "subscriptionIdentifier": stream["subscriptionIdentifier"],
+                        "modelId": stream["modelId"],
+                        "modelName": stream["modelName"],
+                        "online": True,
+                        **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
+                    }
+                    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))
+                logger.debug(f"Sent stateReport as heartbeat: CPU {cpu_usage:.1f}%, Memory {memory_usage:.1f}%, {len(camera_connections)} active cameras")
+                await asyncio.sleep(HEARTBEAT_INTERVAL)
+            except Exception as e:
+                logger.error(f"Error sending stateReport heartbeat: {e}")
+                break
+
+    async def on_message():
+        while True:
+            try:
+                msg = await websocket.receive_text()
+                logger.debug(f"Received message: {msg}")
+                data = json.loads(msg)
+                msg_type = data.get("type")
+
+                if msg_type == "subscribe":
+                    payload = data.get("payload", {})
+                    subscriptionIdentifier = payload.get("subscriptionIdentifier")
+                    rtsp_url = payload.get("rtspUrl")
+                    snapshot_url = payload.get("snapshotUrl")
+                    snapshot_interval = payload.get("snapshotInterval")
+                    model_url = payload.get("modelUrl")
+                    modelId = payload.get("modelId")
+                    modelName = payload.get("modelName")
+                    cropX1 = payload.get("cropX1")
+                    cropY1 = payload.get("cropY1")
+                    cropX2 = payload.get("cropX2")
+                    cropY2 = payload.get("cropY2")
+
+                    # Extract camera_id from subscriptionIdentifier (format: displayIdentifier;cameraIdentifier)
+                    parts = subscriptionIdentifier.split(';')
+                    if len(parts) != 2:
+                        logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}")
+                        continue
+                    
+                    display_identifier, camera_identifier = parts
+                    camera_id = subscriptionIdentifier  # Use full subscriptionIdentifier as camera_id for mapping
+
+                    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)
+                                # If model_url is remote, download it first.
+                                parsed = urlparse(model_url)
+                                if parsed.scheme in ("http", "https"):
+                                    logger.info(f"Downloading remote .mpta file from {model_url}")
+                                    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}")
+                                        error_response = {
+                                            "type": "error",
+                                            "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 .mpta file from {model_url}")
+                                    # Check if file exists before attempting to load
+                                    if not os.path.exists(model_url):
+                                        logger.error(f"Local .mpta file not found: {model_url}")
+                                        logger.debug(f"Current working directory: {os.getcwd()}")
+                                        error_response = {
+                                            "type": "error",
+                                            "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}")
+                                    error_response = {
+                                        "type": "error",
+                                        "subscriptionIdentifier": subscriptionIdentifier,
+                                        "error": f"Failed to load model {modelId}"
+                                    }
+                                    await websocket.send_json(error_response)
+                                    continue
+                                if camera_id not in models:
+                                    models[camera_id] = {}
+                                models[camera_id][modelId] = model_tree
+                                logger.info(f"Successfully loaded model {modelId} for camera {camera_id}")
+                                logger.debug(f"Model extraction directory: {extraction_dir}")
+                    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()
+                                    
+                                    if snapshot_url and snapshot_interval:
+                                        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()
+                                        mode = "snapshot"
+                                        
+                                        # Store shared stream info
+                                        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:
+                                        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}")
+                                            continue
+                                        thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
+                                        thread.daemon = True
+                                        thread.start()
+                                        mode = "rtsp"
+                                        
+                                        # Store shared stream info
+                                        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}")
+                                        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
+                                logger.info(f"Resubscribing to camera {camera_id}")
+                                # Note: Keep models in memory for reuse across subscriptions
+                elif msg_type == "unsubscribe":
+                    payload = data.get("payload", {})
+                    subscriptionIdentifier = payload.get("subscriptionIdentifier")
+                    camera_id = subscriptionIdentifier
+                    with streams_lock:
+                        if camera_id and camera_id in streams:
+                            stream = streams.pop(camera_id)
+                            camera_url = subscription_to_camera.pop(camera_id, None)
+                            
+                            if camera_url and camera_url in camera_streams:
+                                shared_stream = camera_streams[camera_url]
+                                shared_stream["ref_count"] -= 1
+                                
+                                # 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"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}")
+                            # Note: Keep models in memory for potential reuse
+                elif msg_type == "requestState":
+                    cpu_usage = psutil.cpu_percent()
+                    memory_usage = psutil.virtual_memory().percent
+                    if torch.cuda.is_available():
+                        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
+                        gpu_memory_usage = None
+
+                    camera_connections = [
+                        {
+                            "subscriptionIdentifier": stream["subscriptionIdentifier"],
+                            "modelId": stream["modelId"],
+                            "modelName": stream["modelName"],
+                            "online": True,
+                            **{k: v for k, v in get_crop_coords(stream).items() if v is not None}
+                        }
+                        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))
+                
+                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:
+                logger.error("Received invalid JSON message")
+            except (WebSocketDisconnect, ConnectionClosedError) as e:
+                logger.warning(f"WebSocket disconnected: {e}")
+                break
+            except Exception as e:
+                logger.error(f"Error handling message: {e}")
+                break
+    try:
+        await websocket.accept()
+        stream_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:
+        logger.error(f"Error in detect websocket: {e}")
+    finally:
+        stream_task.cancel()
+        await stream_task
+        with streams_lock:
+            # Clean up shared camera streams
+            for camera_url, shared_stream in camera_streams.items():
+                shared_stream["stop_event"].set()
+                shared_stream["thread"].join()
+                if "cap" in shared_stream:
+                    shared_stream["cap"].release()
+                while not shared_stream["buffer"].empty():
+                    try:
+                        shared_stream["buffer"].get_nowait()
+                    except queue.Empty:
+                        pass
+                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")

archive/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

archive/siwatsystem/pympta.py

@@ -0,0 +1,798 @@
+import os
+import json
+import logging
+import torch
+import cv2
+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 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):
+        logger.error(f"Model file {model_path} not found. Current directory: {os.getcwd()}")
+        logger.error(f"Directory content: {os.listdir(os.path.dirname(model_path))}")
+        raise FileNotFoundError(f"Model file {model_path} not found.")
+    logger.info(f"Loading model for node {node_config['modelId']} from {model_path}")
+    model = YOLO(model_path)
+    if torch.cuda.is_available():
+        logger.info(f"CUDA available. Moving model {node_config['modelId']} to GPU")
+        model.to("cuda")
+    else:
+        logger.info(f"CUDA not available. Using CPU for model {node_config['modelId']}")
+
+    # Prepare trigger class indices for optimization
+    trigger_classes = node_config.get("triggerClasses", [])
+    trigger_class_indices = None
+    if trigger_classes and hasattr(model, "names"):
+        # Convert class names to indices for the model
+        trigger_class_indices = [i for i, name in model.names.items() 
+                                if name in trigger_classes]
+        logger.debug(f"Converted trigger classes to indices: {trigger_class_indices}")
+
+    node = {
+        "modelId": node_config["modelId"],
+        "modelFile": node_config["modelFile"],
+        "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": [],
+        "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, redis_client, db_manager))
+    return node
+
+def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
+    logger.info(f"Attempting to load pipeline from {zip_source} to {target_dir}")
+    os.makedirs(target_dir, exist_ok=True)
+    zip_path = os.path.join(target_dir, "pipeline.mpta")
+    
+    # Parse the source; only local files are supported here.
+    parsed = urlparse(zip_source)
+    if parsed.scheme in ("", "file"):
+        local_path = parsed.path if parsed.scheme == "file" else zip_source
+        logger.debug(f"Checking if local file exists: {local_path}")
+        if os.path.exists(local_path):
+            try:
+                shutil.copy(local_path, zip_path)
+                logger.info(f"Copied local .mpta file from {local_path} to {zip_path}")
+            except Exception as e:
+                logger.error(f"Failed to copy local .mpta file from {local_path}: {str(e)}", exc_info=True)
+                return None
+        else:
+            logger.error(f"Local file {local_path} does not exist. Current directory: {os.getcwd()}")
+            # List all subdirectories of models directory to help debugging
+            if os.path.exists("models"):
+                logger.error(f"Content of models directory: {os.listdir('models')}")
+                for root, dirs, files in os.walk("models"):
+                    logger.error(f"Directory {root} contains subdirs: {dirs} and files: {files}")
+            else:
+                logger.error("The models directory doesn't exist")
+            return None
+    else:
+        logger.error(f"HTTP download functionality has been moved. Use a local file path here. Received: {zip_source}")
+        return None
+
+    try:
+        if not os.path.exists(zip_path):
+            logger.error(f"Zip file not found at expected location: {zip_path}")
+            return None
+            
+        logger.debug(f"Extracting .mpta file from {zip_path} to {target_dir}")
+        # Extract contents and track the directories created
+        extracted_dirs = []
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            file_list = zip_ref.namelist()
+            logger.debug(f"Files in .mpta archive: {file_list}")
+            
+            # Extract and track the top-level directories
+            for file_path in file_list:
+                parts = file_path.split('/')
+                if len(parts) > 1:
+                    top_dir = parts[0]
+                    if top_dir and top_dir not in extracted_dirs:
+                        extracted_dirs.append(top_dir)
+            
+            # Now extract the files
+            zip_ref.extractall(target_dir)
+            
+        logger.info(f"Successfully extracted .mpta file to {target_dir}")
+        logger.debug(f"Extracted directories: {extracted_dirs}")
+        
+        # Check what was actually created after extraction
+        actual_dirs = [d for d in os.listdir(target_dir) if os.path.isdir(os.path.join(target_dir, d))]
+        logger.debug(f"Actual directories created: {actual_dirs}")
+    except zipfile.BadZipFile as e:
+        logger.error(f"Bad zip file {zip_path}: {str(e)}", exc_info=True)
+        return None
+    except Exception as e:
+        logger.error(f"Failed to extract .mpta file {zip_path}: {str(e)}", exc_info=True)
+        return None
+    finally:
+        if os.path.exists(zip_path):
+            os.remove(zip_path)
+            logger.debug(f"Removed temporary zip file: {zip_path}")
+
+    # Use the first extracted directory if it exists, otherwise use the expected name
+    pipeline_name = os.path.basename(zip_source)
+    pipeline_name = os.path.splitext(pipeline_name)[0]
+    
+    # Find the directory with pipeline.json
+    mpta_dir = None
+    # First try the expected directory name
+    expected_dir = os.path.join(target_dir, pipeline_name)
+    if os.path.exists(expected_dir) and os.path.exists(os.path.join(expected_dir, "pipeline.json")):
+        mpta_dir = expected_dir
+        logger.debug(f"Found pipeline.json in the expected directory: {mpta_dir}")
+    else:
+        # Look through all subdirectories for pipeline.json
+        for subdir in actual_dirs:
+            potential_dir = os.path.join(target_dir, subdir)
+            if os.path.exists(os.path.join(potential_dir, "pipeline.json")):
+                mpta_dir = potential_dir
+                logger.info(f"Found pipeline.json in directory: {mpta_dir} (different from expected: {expected_dir})")
+                break
+    
+    if not mpta_dir:
+        logger.error(f"Could not find pipeline.json in any extracted directory. Directory content: {os.listdir(target_dir)}")
+        return None
+        
+    pipeline_json_path = os.path.join(mpta_dir, "pipeline.json")
+    if not os.path.exists(pipeline_json_path):
+        logger.error(f"pipeline.json not found in the .mpta file. Files in directory: {os.listdir(mpta_dir)}")
+        return None
+
+    try:
+        with open(pipeline_json_path, "r") as f:
+            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)}")
+        
+        # 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
+    except KeyError as e:
+        logger.error(f"Missing key in pipeline.json: {str(e)}", exc_info=True)
+        return None
+    except Exception as e:
+        logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
+        return None
+
+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 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":
+            results = node["model"].predict(frame, stream=False)
+            if not results:
+                return (None, None) if return_bbox else None
+
+            r = results[0]
+            probs = r.probs
+            if probs is None:
+                return (None, None) if return_bbox else None
+
+            top1_idx = int(probs.top1)
+            top1_conf = float(probs.top1conf)
+            class_name = node["model"].names[top1_idx]
+
+            det = {
+                "class": class_name,
+                "confidence": top1_conf,
+                "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 ──────────────────
+        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,
+            persist=True,
+            **({"classes": tk} if tk else {})
+        )[0]
+
+        # Collect all detections above confidence threshold
+        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]
+            
+            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
+
+        # ─── Multi-class validation ─────────────────────────────────
+        if node.get("multiClass", False) and node.get("expectedClasses"):
+            expected_classes = node["expectedClasses"]
+            detected_classes = list(regions_dict.keys())
+            
+            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")
+
+        # ─── 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"]:
+                trigger_classes = br.get("triggerClasses", [])
+                min_conf = br.get("minConfidence", 0)
+                
+                logger.debug(f"Evaluating branch {br['modelId']}: trigger_classes={trigger_classes}, min_conf={min_conf}")
+                
+                # Check if any detected class matches branch trigger
+                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 to primary detection for compatibility
+        if "branch_results" in detection_result:
+            primary_detection["branch_results"] = detection_result["branch_results"]
+        
+        return (primary_detection, primary_bbox) if return_bbox else primary_detection
+
+    except Exception as e:
+        logger.error(f"Error in node {node.get('modelId')}: {e}")
+        traceback.print_exc()
+        return (None, None) if return_bbox else None