app.py

@@ -35,6 +35,8 @@ session_ids: Dict[str, int] = {}
 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)
@@ -109,20 +111,60 @@ def download_mpta(url: str, dest_path: str) -> str:
 # Add helper to fetch snapshot image from HTTP/HTTPS URL
 def fetch_snapshot(url: str):
     try:
-        response = requests.get(url, timeout=10)
+        from requests.auth import HTTPBasicAuth, HTTPDigestAuth
+        
+        # Parse URL to extract credentials
+        parsed = urlparse(url)
+        
+        # Prepare headers - some cameras require User-Agent
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (compatible; DetectorWorker/1.0)'
+        }
+        
+        # Reconstruct URL without credentials
+        clean_url = f"{parsed.scheme}://{parsed.hostname}"
+        if parsed.port:
+            clean_url += f":{parsed.port}"
+        clean_url += parsed.path
+        if parsed.query:
+            clean_url += f"?{parsed.query}"
+        
+        auth = None
+        if parsed.username and parsed.password:
+            # Try HTTP Digest authentication first (common for IP cameras)
+            try:
+                auth = HTTPDigestAuth(parsed.username, parsed.password)
+                response = requests.get(clean_url, auth=auth, headers=headers, timeout=10)
+                if response.status_code == 200:
+                    logger.debug(f"Successfully authenticated using HTTP Digest for {clean_url}")
+                elif response.status_code == 401:
+                    # If Digest fails, try Basic auth
+                    logger.debug(f"HTTP Digest failed, trying Basic auth for {clean_url}")
+                    auth = HTTPBasicAuth(parsed.username, parsed.password)
+                    response = requests.get(clean_url, auth=auth, headers=headers, timeout=10)
+                    if response.status_code == 200:
+                        logger.debug(f"Successfully authenticated using HTTP Basic for {clean_url}")
+            except Exception as auth_error:
+                logger.debug(f"Authentication setup error: {auth_error}")
+                # Fallback to original URL with embedded credentials
+                response = requests.get(url, headers=headers, timeout=10)
+        else:
+            # No credentials in URL, make request as-is
+            response = requests.get(url, headers=headers, timeout=10)
+        
         if response.status_code == 200:
             # Convert response content to numpy array
             nparr = np.frombuffer(response.content, np.uint8)
             # Decode image
             frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
             if frame is not None:
-                logger.debug(f"Successfully fetched snapshot from {url}, shape: {frame.shape}")
+                logger.debug(f"Successfully fetched snapshot from {clean_url}, shape: {frame.shape}")
                 return frame
             else:
-                logger.error(f"Failed to decode image from snapshot URL: {url}")
+                logger.error(f"Failed to decode image from snapshot URL: {clean_url}")
                 return None
         else:
-            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {url}")
+            logger.error(f"Failed to fetch snapshot (status code {response.status_code}): {clean_url}")
             return None
     except Exception as e:
         logger.error(f"Exception fetching snapshot from {url}: {str(e)}")
@@ -146,26 +188,24 @@ 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")
             
-            stream = streams[camera_id]
-            buffer = stream["buffer"]
-            logger.debug(f"Camera '{camera_id}' buffer size: {buffer.qsize()}, buffer empty: {buffer.empty()}")
-            logger.debug(f"Buffer queue contents: {getattr(buffer, 'queue', None)}")
-            
-            if buffer.empty():
-                logger.warning(f"No frame available for camera '{camera_id}'. Buffer is empty.")
+            # 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}")
             
-            # Get the latest frame (non-blocking)
-            try:
-                frame = buffer.queue[-1]  # Get the most recent frame without removing it
-            except IndexError:
-                logger.warning(f"Buffer queue is empty for camera '{camera_id}' when trying to access last frame.")
-                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:
@@ -199,7 +239,20 @@ async def detect(websocket: WebSocket):
             
             logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
             start_time = time.time()
-            detection_result = run_pipeline(cropped_frame, model_tree)
+            
+            # 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")
             
@@ -258,11 +311,6 @@ async def detect(websocket: WebSocket):
                     if key not in ["box", "id"]:  # Skip internal fields
                         detection_dict[key] = value
             
-            # 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
-            
             detection_data = {
                 "type": "imageDetection",
                 "subscriptionIdentifier": stream["subscriptionIdentifier"],
@@ -282,9 +330,6 @@ async def detect(websocket: WebSocket):
                 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
-                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
                 if session_id:
                     logger.debug(f"Detection associated with session ID: {session_id}")
             
@@ -476,6 +521,10 @@ async def detect(websocket: WebSocket):
                     logger.debug(f"Got frame from buffer for camera {camera_id}")
                     frame = buffer.get()
                     
+                    # Cache the frame for REST API access
+                    latest_frames[camera_id] = frame.copy()
+                    logger.debug(f"Cached frame for REST API access for camera {camera_id}")
+                    
                     with models_lock:
                         model_tree = models.get(camera_id, {}).get(stream["modelId"])
                         if not model_tree:
@@ -647,7 +696,7 @@ async def detect(websocket: WebSocket):
                                     
                                     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_identifier, snapshot_url, snapshot_interval, buffer, stop_event))
+                                        thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
                                         thread.daemon = True
                                         thread.start()
                                         mode = "snapshot"
@@ -670,7 +719,7 @@ async def detect(websocket: WebSocket):
                                         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_identifier, cap, buffer, stop_event))
+                                        thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
                                         thread.daemon = True
                                         thread.start()
                                         mode = "rtsp"
@@ -744,6 +793,8 @@ async def detect(websocket: WebSocket):
                                 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":
@@ -847,5 +898,6 @@ async def detect(websocket: WebSocket):
             subscription_to_camera.clear()
         with models_lock:
             models.clear()
+        latest_frames.clear()
         session_ids.clear()
         logger.info("WebSocket connection closed")

requirements.txt

@@ -7,3 +7,7 @@ opencv-python
 websockets
 fastapi[standard]
 redis
+urllib3<2.0.0
+psycopg2-binary
+scipy
+filterpy

siwatsystem/database.py

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

siwatsystem/pympta.py

@@ -3,20 +3,72 @@ import json
 import logging
 import torch
 import cv2
-import requests
 import zipfile
 import shutil
 import traceback
 import redis
 import time
 import uuid
+import concurrent.futures
 from ultralytics import YOLO
 from urllib.parse import urlparse
+from .database import DatabaseManager
 
 # Create a logger specifically for this module
 logger = logging.getLogger("detector_worker.pympta")
 
-def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
+def validate_redis_config(redis_config: dict) -> bool:
+    """Validate Redis configuration parameters."""
+    required_fields = ["host", "port"]
+    for field in required_fields:
+        if field not in redis_config:
+            logger.error(f"Missing required Redis config field: {field}")
+            return False
+    
+    if not isinstance(redis_config["port"], int) or redis_config["port"] <= 0:
+        logger.error(f"Invalid Redis port: {redis_config['port']}")
+        return False
+    
+    return True
+
+def validate_postgresql_config(pg_config: dict) -> bool:
+    """Validate PostgreSQL configuration parameters."""
+    required_fields = ["host", "port", "database", "username", "password"]
+    for field in required_fields:
+        if field not in pg_config:
+            logger.error(f"Missing required PostgreSQL config field: {field}")
+            return False
+    
+    if not isinstance(pg_config["port"], int) or pg_config["port"] <= 0:
+        logger.error(f"Invalid PostgreSQL port: {pg_config['port']}")
+        return False
+    
+    return True
+
+def crop_region_by_class(frame, regions_dict, class_name):
+    """Crop a specific region from frame based on detected class."""
+    if class_name not in regions_dict:
+        logger.warning(f"Class '{class_name}' not found in detected regions")
+        return None
+    
+    bbox = regions_dict[class_name]['bbox']
+    x1, y1, x2, y2 = bbox
+    cropped = frame[y1:y2, x1:x2]
+    
+    if cropped.size == 0:
+        logger.warning(f"Empty crop for class '{class_name}' with bbox {bbox}")
+        return None
+    
+    return cropped
+
+def format_action_context(base_context, additional_context=None):
+    """Format action context with dynamic values."""
+    context = {**base_context}
+    if additional_context:
+        context.update(additional_context)
+    return context
+
+def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client, db_manager=None) -> dict:
     # Recursively load a model node from configuration.
     model_path = os.path.join(mpta_dir, node_config["modelFile"])
     if not os.path.exists(model_path):
@@ -46,16 +98,22 @@ def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
         "triggerClasses": trigger_classes,
         "triggerClassIndices": trigger_class_indices,
         "crop": node_config.get("crop", False),
+        "cropClass": node_config.get("cropClass"),
         "minConfidence": node_config.get("minConfidence", None),
+        "multiClass": node_config.get("multiClass", False),
+        "expectedClasses": node_config.get("expectedClasses", []),
+        "parallel": node_config.get("parallel", False),
         "actions": node_config.get("actions", []),
+        "parallelActions": node_config.get("parallelActions", []),
         "model": model,
         "branches": [],
-        "redis_client": redis_client
+        "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))
+        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:
@@ -168,6 +226,9 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
         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"],
@@ -182,7 +243,25 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
                     logger.error(f"Failed to connect to Redis: {e}")
                     redis_client = None
         
-        return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client)
+        # 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
@@ -193,22 +272,53 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
         logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
         return None
 
-def execute_actions(node, frame, detection_result):
+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)
-                _, buffer = cv2.imencode('.jpg', frame)
+                
+                # 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())
@@ -216,60 +326,244 @@ def execute_actions(node, frame, detection_result):
                 else:
                     node["redis_client"].set(key, buffer.tobytes())
                     logger.info(f"Saved image to Redis with key: {key}")
-                # Add the generated key to the context for subsequent actions
                 action_context["image_key"] = key
             elif action["type"] == "redis_publish":
                 channel = action["channel"]
-                message = action["message"].format(**action_context)
-                node["redis_client"].publish(channel, message)
+                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 run_pipeline(frame, node: dict, return_bbox: bool=False):
+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):
     """
-    - For detection nodes (task != 'classify'):
-        • runs `track(..., classes=triggerClassIndices)`
-        • picks top box ≥ minConfidence
-        • optionally crops & resizes → recurse into child
-        • else returns (det_dict, bbox)
-    - For classify nodes:
-        • runs `predict()`
-        • returns top (class,confidence) and no bbox
+    Enhanced pipeline that supports:
+    - Multi-class detection (detecting multiple classes simultaneously)
+    - Parallel branch processing
+    - Region-based actions and cropping
+    - Context passing for session/camera information
     """
     try:
         task = getattr(node["model"], "task", None)
 
         # ─── Classification stage ───────────────────────────────────
         if task == "classify":
-            # run the classifier and grab its top-1 directly via the Probs API
             results = node["model"].predict(frame, stream=False)
-            # nothing returned?
             if not results:
                 return (None, None) if return_bbox else None
 
-            # take the first result's probs object
             r = results[0]
             probs = r.probs
             if probs is None:
                 return (None, None) if return_bbox else None
 
-            # get the top-1 class index and its confidence
             top1_idx = int(probs.top1)
             top1_conf = float(probs.top1conf)
+            class_name = node["model"].names[top1_idx]
 
             det = {
-                "class": node["model"].names[top1_idx],
+                "class": class_name,
                 "confidence": top1_conf,
-                "id": None
+                "id": None,
+                class_name: class_name  # Add class name as key for backward compatibility
             }
+            
+            # Add specific field mappings for database operations based on model type
+            model_id = node.get("modelId", "").lower()
+            if "brand" in model_id or "brand_cls" in model_id:
+                det["brand"] = class_name
+            elif "bodytype" in model_id or "body" in model_id:
+                det["body_type"] = class_name
+            elif "color" in model_id:
+                det["color"] = class_name
+            
             execute_actions(node, frame, det)
             return (det, None) if return_bbox else det
 
-
-        # ─── Detection stage ────────────────────────────────────────
-        # only look for your triggerClasses
+        # ─── 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,
@@ -277,48 +571,228 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False):
             **({"classes": tk} if tk else {})
         )[0]
 
-        dets, boxes = [], []
-        for box in res.boxes:
+        # 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]
-            if conf < node["minConfidence"]:
-                continue
-            xy = box.cpu().xyxy[0]
-            x1,y1,x2,y2 = map(int, xy)
-            dets.append({"class": name, "confidence": conf,
-                         "id": box.id.item() if hasattr(box, "id") else None})
-            boxes.append((x1, y1, x2, y2))
             
-        if not dets:
+            logger.debug(f"Detection {i}: class='{name}' (id={cid}), confidence={conf:.3f}, threshold={node['minConfidence']}")
+            
+            if conf < node["minConfidence"]:
+                logger.debug(f"  -> REJECTED: confidence {conf:.3f} < threshold {node['minConfidence']}")
+                continue
+                
+            xy = box.cpu().xyxy[0]
+            x1, y1, x2, y2 = map(int, xy)
+            bbox = (x1, y1, x2, y2)
+            
+            detection = {
+                "class": name,
+                "confidence": conf,
+                "id": box.id.item() if hasattr(box, "id") else None,
+                "bbox": bbox
+            }
+            
+            all_detections.append(detection)
+            all_boxes.append(bbox)
+            
+            logger.debug(f"  -> ACCEPTED: {name} with confidence {conf:.3f}, bbox={bbox}")
+            
+            # Store highest confidence detection for each class
+            if name not in regions_dict or conf > regions_dict[name]["confidence"]:
+                regions_dict[name] = {
+                    "bbox": bbox,
+                    "confidence": conf,
+                    "detection": detection
+                }
+                logger.debug(f"  -> Updated regions_dict['{name}'] with confidence {conf:.3f}")
+
+        logger.info(f"Detection summary: {len(all_detections)} accepted detections from {len(res.boxes) if res.boxes is not None else 0} total")
+        logger.info(f"Detected classes: {list(regions_dict.keys())}")
+
+        if not all_detections:
+            logger.warning("No detections above confidence threshold - returning null")
             return (None, None) if return_bbox else None
 
-        # take highest‐confidence
-        best_idx = max(range(len(dets)), key=lambda i: dets[i]["confidence"])
-        best_det = dets[best_idx]
-        best_box = boxes[best_idx]
+        # ─── Multi-class validation ─────────────────────────────────
+        if node.get("multiClass", False) and node.get("expectedClasses"):
+            expected_classes = node["expectedClasses"]
+            detected_classes = list(regions_dict.keys())
             
-        # ─── Branch (classification) ───────────────────────────────
+            logger.info(f"Multi-class validation: expected={expected_classes}, detected={detected_classes}")
+            
+            # Check if at least one expected class is detected (flexible mode)
+            matching_classes = [cls for cls in expected_classes if cls in detected_classes]
+            missing_classes = [cls for cls in expected_classes if cls not in detected_classes]
+            
+            logger.debug(f"Matching classes: {matching_classes}, Missing classes: {missing_classes}")
+            
+            if not matching_classes:
+                # No expected classes found at all
+                logger.warning(f"PIPELINE REJECTED: No expected classes detected. Expected: {expected_classes}, Detected: {detected_classes}")
+                return (None, None) if return_bbox else None
+            
+            if missing_classes:
+                logger.info(f"Partial multi-class detection: {matching_classes} found, {missing_classes} missing")
+            else:
+                logger.info(f"Complete multi-class detection success: {detected_classes}")
+        else:
+            logger.debug("No multi-class validation - proceeding with all detections")
+
+        # ─── Execute actions with region information ────────────────
+        detection_result = {
+            "detections": all_detections,
+            "regions": regions_dict,
+            **(context or {})
+        }
+        
+        # ─── Create initial database record when Car+Frontal detected ────
+        if node.get("db_manager") and node.get("multiClass", False):
+            # Only create database record if we have both Car and Frontal
+            has_car = "Car" in regions_dict
+            has_frontal = "Frontal" in regions_dict
+            
+            if has_car and has_frontal:
+                # Generate UUID session_id since client session is None for now
+                import uuid as uuid_lib
+                from datetime import datetime
+                generated_session_id = str(uuid_lib.uuid4())
+                
+                # Insert initial detection record
+                display_id = detection_result.get("display_id", "unknown")
+                timestamp = datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+                
+                inserted_session_id = node["db_manager"].insert_initial_detection(
+                    display_id=display_id,
+                    captured_timestamp=timestamp,
+                    session_id=generated_session_id
+                )
+                
+                if inserted_session_id:
+                    # Update detection_result with the generated session_id for actions and branches
+                    detection_result["session_id"] = inserted_session_id
+                    detection_result["timestamp"] = timestamp  # Update with proper timestamp
+                    logger.info(f"Created initial database record with session_id: {inserted_session_id}")
+            else:
+                logger.debug(f"Database record not created - missing required classes. Has Car: {has_car}, Has Frontal: {has_frontal}")
+        
+        execute_actions(node, frame, detection_result, regions_dict)
+
+        # ─── Parallel branch processing ─────────────────────────────
+        if node["branches"]:
+            branch_results = {}
+            
+            # Filter branches that should be triggered
+            active_branches = []
             for br in node["branches"]:
-            if (best_det["class"] in br["triggerClasses"]
-                    and best_det["confidence"] >= br["minConfidence"]):
-                # crop if requested
-                sub = frame
-                if br["crop"]:
-                    x1,y1,x2,y2 = best_box
-                    sub = frame[y1:y2, x1:x2]
-                    sub = cv2.resize(sub, (224, 224))
+                trigger_classes = br.get("triggerClasses", [])
+                min_conf = br.get("minConfidence", 0)
                 
-                det2, _ = run_pipeline(sub, br, return_bbox=True)
-                if det2:
-                    # return classification result + original bbox
-                    execute_actions(br, sub, det2)
-                    return (det2, best_box) if return_bbox else det2
+                logger.debug(f"Evaluating branch {br['modelId']}: trigger_classes={trigger_classes}, min_conf={min_conf}")
                 
-        # ─── No branch matched → return this detection ─────────────
-        execute_actions(node, frame, best_det)
-        return (best_det, best_box) if return_bbox else best_det
+                # 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:
-        logging.error(f"Error in node {node.get('modelId')}: {e}")
+        logger.error(f"Error in node {node.get('modelId')}: {e}")
+        traceback.print_exc()
         return (None, None) if return_bbox else None