Merge remote-tracking branch 'origin/dev' into feat/tracker

Reviewed-on: #8

.gitignore

@@ -10,3 +10,17 @@ mptas
 detector_worker.log
 .gitignore
 no_frame_debug.log
+
+feeder/
+.venv/
+.vscode/
+dist/
+websocket_comm.log
+temp_debug/
+.claude
+
+# Video Test
+video_rtsp/
+multi_stream_viewer.py
+multi_camera_simulator.py
+start_4_cameras.bat

Dockerfile.base

@@ -1,8 +1,17 @@
 # Base image with all ML dependencies
-FROM python:3.13-bookworm
+FROM pytorch/pytorch:2.8.0-cuda12.6-cudnn9-runtime
 
 # Install system dependencies
-RUN apt update && apt install -y libgl1 && rm -rf /var/lib/apt/lists/*
+RUN apt update && apt install -y \
+    libgl1 \
+    libglib2.0-0 \
+    libgstreamer1.0-0 \
+    libgtk-3-0 \
+    libavcodec58 \
+    libavformat58 \
+    libswscale5 \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
 
 # Copy and install base requirements (ML dependencies that rarely change)
 COPY requirements.base.txt .

config.json

@@ -1,6 +1,6 @@
 {
   "poll_interval_ms": 100,
-  "max_streams": 5,
+  "max_streams": 999,
   "target_fps": 2,
   "reconnect_interval_sec": 5,
   "max_retries": -1

debug/test_camera_indices.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Test script to check available camera indices
+"""
+
+import cv2
+import logging
+import sys
+import subprocess
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
+)
+logger = logging.getLogger("camera_index_test")
+
+def test_camera_index(index):
+    """Test if a camera index is available"""
+    try:
+        cap = cv2.VideoCapture(index)
+        if cap.isOpened():
+            ret, frame = cap.read()
+            if ret and frame is not None:
+                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)
+                
+                cap.release()
+                return True, f"{width}x{height} @ {fps}fps"
+            else:
+                cap.release()
+                return False, "Can open but cannot read frames"
+        else:
+            cap.release()
+            return False, "Cannot open camera"
+    except Exception as e:
+        return False, f"Error: {str(e)}"
+
+def get_windows_cameras_ffmpeg():
+    """Get available cameras on Windows using FFmpeg"""
+    try:
+        result = subprocess.run(['ffmpeg', '-f', 'dshow', '-list_devices', 'true', '-i', 'dummy'], 
+                              capture_output=True, text=True, timeout=10, encoding='utf-8', errors='ignore')
+        output = result.stderr
+        
+        lines = output.split('\n')
+        video_devices = []
+        
+        # Parse the output - look for lines with (video) that contain device names in quotes
+        for line in lines:
+            if '[dshow @' in line and '(video)' in line and '"' in line:
+                # Extract device name between first pair of quotes
+                start = line.find('"') + 1
+                end = line.find('"', start)
+                if start > 0 and end > start:
+                    device_name = line[start:end]
+                    video_devices.append(device_name)
+        
+        logger.info(f"FFmpeg detected video devices: {video_devices}")
+        return video_devices
+    except Exception as e:
+        logger.error(f"Failed to get Windows camera names: {e}")
+        return []
+
+def main():
+    logger.info("=== Camera Index Test ===")
+    
+    # Check FFmpeg availability for Windows device detection
+    ffmpeg_available = False
+    try:
+        result = subprocess.run(['ffmpeg', '-version'], capture_output=True, text=True, timeout=5)
+        if result.returncode == 0:
+            ffmpeg_available = True
+            logger.info("FFmpeg is available")
+    except:
+        logger.info("FFmpeg not available")
+    
+    # Get Windows camera names if possible
+    if sys.platform.startswith('win') and ffmpeg_available:
+        logger.info("\n=== Windows Camera Devices (FFmpeg) ===")
+        cameras = get_windows_cameras_ffmpeg()
+        if cameras:
+            for i, camera in enumerate(cameras):
+                logger.info(f"Device {i}: {camera}")
+        else:
+            logger.info("No cameras detected via FFmpeg")
+    
+    # Test camera indices 0-9
+    logger.info("\n=== Testing Camera Indices ===")
+    available_cameras = []
+    
+    for index in range(10):
+        logger.info(f"Testing camera index {index}...")
+        is_available, info = test_camera_index(index)
+        
+        if is_available:
+            logger.info(f"✓ Camera {index}: AVAILABLE - {info}")
+            available_cameras.append(index)
+        else:
+            logger.info(f"✗ Camera {index}: NOT AVAILABLE - {info}")
+    
+    # Summary
+    logger.info("\n=== Summary ===")
+    if available_cameras:
+        logger.info(f"Available camera indices: {available_cameras}")
+        logger.info(f"Default camera index to use: {available_cameras[0]}")
+        
+        # Test the first available camera more thoroughly
+        logger.info(f"\n=== Detailed Test for Camera {available_cameras[0]} ===")
+        cap = cv2.VideoCapture(available_cameras[0])
+        if cap.isOpened():
+            # Get properties
+            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)
+            backend = cap.getBackendName()
+            
+            logger.info(f"Resolution: {width}x{height}")
+            logger.info(f"FPS: {fps}")
+            logger.info(f"Backend: {backend}")
+            
+            # Test frame capture
+            ret, frame = cap.read()
+            if ret and frame is not None:
+                logger.info(f"Frame capture: SUCCESS")
+                logger.info(f"Frame shape: {frame.shape}")
+                logger.info(f"Frame dtype: {frame.dtype}")
+            else:
+                logger.info(f"Frame capture: FAILED")
+            
+            cap.release()
+    else:
+        logger.error("No cameras available!")
+        logger.info("Possible solutions:")
+        logger.info("1. Check if camera is connected and not used by another application")
+        logger.info("2. Check camera permissions")
+        logger.info("3. Try different camera indices")
+        logger.info("4. Install camera drivers")
+
+if __name__ == "__main__":
+    main()

requirements.base.txt

@@ -1,7 +1,12 @@
-torch
+ultralytics>=8.3.0
-torchvision
+opencv-python>=4.6.0
-ultralytics
+scipy>=1.9.0
-opencv-python
+filterpy>=1.4.0
-scipy
+psycopg2-binary>=2.9.0
-filterpy
+easydict
-psycopg2-binary
+loguru
+pyzmq
+gitpython
+gdown
+lap
+pynvml

requirements.txt

@@ -1,6 +1,5 @@
-fastapi
+fastapi[standard]
 uvicorn
 websockets
-fastapi[standard]
 redis
 urllib3<2.0.0

siwatsystem/database.py

@@ -80,37 +80,50 @@ class DatabaseManager:
         try:
             cur = self.connection.cursor()
             
-            # Build the UPDATE query dynamically
+            # Build the INSERT and UPDATE query dynamically
+            insert_placeholders = []
+            insert_values = [key_value]  # Start with key_value
+            
             set_clauses = []
-            values = []
+            update_values = []
             
             for field, value in fields.items():
                 if value == "NOW()":
+                    # Special handling for NOW()
+                    insert_placeholders.append("NOW()")
                     set_clauses.append(f"{field} = NOW()")
                 else:
+                    insert_placeholders.append("%s")
+                    insert_values.append(value)
                     set_clauses.append(f"{field} = %s")
-                    values.append(value)
+                    update_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}"
             
+            # Build the complete query
             query = f"""
             INSERT INTO {full_table_name} ({key_field}, {', '.join(fields.keys())})
-            VALUES (%s, {', '.join(['%s'] * len(fields))})
+            VALUES (%s, {', '.join(insert_placeholders)})
             ON CONFLICT ({key_field})
             DO UPDATE SET {', '.join(set_clauses)}
             """
             
-            # Add key_value to the beginning of values list
+            # Combine values for the query: insert_values + update_values
-            all_values = [key_value] + list(fields.values()) + values
+            all_values = insert_values + update_values
+            
+            logger.debug(f"SQL Query: {query}")
+            logger.debug(f"Values: {all_values}")
             
             cur.execute(query, all_values)
             self.connection.commit()
             cur.close()
-            logger.info(f"Updated {table} for {key_field}={key_value}")
+            logger.info(f"✅ Updated {table} for {key_field}={key_value} with fields: {fields}")
             return True
         except Exception as e:
-            logger.error(f"Failed to execute update on {table}: {e}")
+            logger.error(f"❌ Failed to execute update on {table}: {e}")
+            logger.debug(f"Query: {query if 'query' in locals() else 'Query not built'}")
+            logger.debug(f"Values: {all_values if 'all_values' in locals() else 'Values not prepared'}")
             if self.connection:
                 self.connection.rollback()
             return False

siwatsystem/model_registry.py

@@ -0,0 +1,242 @@
+"""
+Shared Model Registry for Memory Optimization
+
+This module implements a global shared model registry to prevent duplicate model loading
+in memory when multiple cameras use the same model. This significantly reduces RAM and
+GPU VRAM usage by ensuring only one instance of each unique model is loaded.
+
+Key Features:
+- Thread-safe model loading and access
+- Reference counting for proper cleanup
+- Automatic model lifecycle management
+- Maintains compatibility with existing pipeline system
+"""
+
+import os
+import threading
+import logging
+from typing import Dict, Any, Optional, Set
+import torch
+from ultralytics import YOLO
+
+# Create a logger for this module
+logger = logging.getLogger("detector_worker.model_registry")
+
+class ModelRegistry:
+    """
+    Singleton class for managing shared YOLO models across multiple cameras.
+    
+    This registry ensures that each unique model is loaded only once in memory,
+    dramatically reducing RAM and GPU VRAM usage when multiple cameras use the
+    same model.
+    """
+    
+    _instance = None
+    _lock = threading.Lock()
+    
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super(ModelRegistry, cls).__new__(cls)
+                    cls._instance._initialized = False
+        return cls._instance
+    
+    def __init__(self):
+        if self._initialized:
+            return
+            
+        self._initialized = True
+        
+        # Thread-safe storage for loaded models
+        self._models: Dict[str, YOLO] = {}  # modelId -> YOLO model instance
+        self._model_files: Dict[str, str] = {}  # modelId -> file path
+        self._reference_counts: Dict[str, int] = {}  # modelId -> reference count
+        self._model_lock = threading.RLock()  # Reentrant lock for nested calls
+        
+        logger.info("🏭 Shared Model Registry initialized - ready for memory-optimized model loading")
+    
+    def get_model(self, model_id: str, model_file_path: str) -> YOLO:
+        """
+        Get or load a YOLO model. Returns shared instance if already loaded.
+        
+        Args:
+            model_id: Unique identifier for the model
+            model_file_path: Path to the model file
+            
+        Returns:
+            YOLO model instance (shared across all callers)
+        """
+        with self._model_lock:
+            if model_id in self._models:
+                # Model already loaded - increment reference count and return
+                self._reference_counts[model_id] += 1
+                logger.info(f"📖 Model '{model_id}' reused (ref_count: {self._reference_counts[model_id]}) - SAVED MEMORY!")
+                return self._models[model_id]
+            
+            # Model not loaded yet - load it
+            logger.info(f"🔄 Loading NEW model '{model_id}' from {model_file_path}")
+            
+            if not os.path.exists(model_file_path):
+                raise FileNotFoundError(f"Model file {model_file_path} not found")
+            
+            try:
+                # Load the YOLO model
+                model = YOLO(model_file_path)
+                
+                # Move to GPU if available
+                if torch.cuda.is_available():
+                    logger.info(f"🚀 CUDA available. Moving model '{model_id}' to GPU VRAM")
+                    model.to("cuda")
+                else:
+                    logger.info(f"💻 CUDA not available. Using CPU for model '{model_id}'")
+                
+                # Store in registry
+                self._models[model_id] = model
+                self._model_files[model_id] = model_file_path
+                self._reference_counts[model_id] = 1
+                
+                logger.info(f"✅ Model '{model_id}' loaded and registered (ref_count: 1)")
+                self._log_registry_status()
+                
+                return model
+                
+            except Exception as e:
+                logger.error(f"❌ Failed to load model '{model_id}' from {model_file_path}: {e}")
+                raise
+    
+    def release_model(self, model_id: str) -> None:
+        """
+        Release a reference to a model. If reference count reaches zero,
+        the model may be unloaded to free memory.
+        
+        Args:
+            model_id: Unique identifier for the model to release
+        """
+        with self._model_lock:
+            if model_id not in self._reference_counts:
+                logger.warning(f"⚠️ Attempted to release unknown model '{model_id}'")
+                return
+            
+            self._reference_counts[model_id] -= 1
+            logger.info(f"📉 Model '{model_id}' reference count decreased to {self._reference_counts[model_id]}")
+            
+            # For now, keep models in memory even when ref count reaches 0
+            # This prevents reload overhead if the same model is needed again soon
+            # In the future, we could implement LRU eviction policy
+            # if self._reference_counts[model_id] <= 0:
+            #     logger.info(f"💤 Model '{model_id}' has 0 references but keeping in memory for reuse")
+                # Optionally: self._unload_model(model_id)
+    
+    def _unload_model(self, model_id: str) -> None:
+        """
+        Internal method to unload a model from memory.
+        Currently not used to prevent reload overhead.
+        """
+        with self._model_lock:
+            if model_id in self._models:
+                logger.info(f"🗑️ Unloading model '{model_id}' from memory")
+                
+                # Clear GPU memory if model was on GPU
+                model = self._models[model_id]
+                if hasattr(model, 'model') and hasattr(model.model, 'cuda'):
+                    try:
+                        # Move model to CPU before deletion to free GPU memory
+                        model.to('cpu')
+                    except Exception as e:
+                        logger.warning(f"⚠️ Failed to move model '{model_id}' to CPU: {e}")
+                
+                # Remove from registry
+                del self._models[model_id]
+                del self._model_files[model_id]
+                del self._reference_counts[model_id]
+                
+                # Force garbage collection
+                import gc
+                gc.collect()
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+                
+                logger.info(f"✅ Model '{model_id}' unloaded and memory freed")
+                self._log_registry_status()
+    
+    def get_registry_status(self) -> Dict[str, Any]:
+        """
+        Get current status of the model registry.
+        
+        Returns:
+            Dictionary with registry statistics
+        """
+        with self._model_lock:
+            return {
+                "total_models": len(self._models),
+                "models": {
+                    model_id: {
+                        "file_path": self._model_files[model_id],
+                        "reference_count": self._reference_counts[model_id]
+                    }
+                    for model_id in self._models
+                },
+                "total_references": sum(self._reference_counts.values())
+            }
+    
+    def _log_registry_status(self) -> None:
+        """Log current registry status for debugging."""
+        status = self.get_registry_status()
+        logger.info(f"📊 Model Registry Status: {status['total_models']} unique models, {status['total_references']} total references")
+        for model_id, info in status['models'].items():
+            logger.debug(f"   📋 '{model_id}': refs={info['reference_count']}, file={os.path.basename(info['file_path'])}")
+    
+    def cleanup_all(self) -> None:
+        """
+        Clean up all models from the registry. Used during shutdown.
+        """
+        with self._model_lock:
+            model_ids = list(self._models.keys())
+            logger.info(f"🧹 Cleaning up {len(model_ids)} models from registry")
+            
+            for model_id in model_ids:
+                self._unload_model(model_id)
+            
+            logger.info("✅ Model registry cleanup complete")
+
+
+# Global singleton instance
+_registry = ModelRegistry()
+
+def get_shared_model(model_id: str, model_file_path: str) -> YOLO:
+    """
+    Convenience function to get a shared model instance.
+    
+    Args:
+        model_id: Unique identifier for the model
+        model_file_path: Path to the model file
+        
+    Returns:
+        YOLO model instance (shared across all callers)
+    """
+    return _registry.get_model(model_id, model_file_path)
+
+def release_shared_model(model_id: str) -> None:
+    """
+    Convenience function to release a shared model reference.
+    
+    Args:
+        model_id: Unique identifier for the model to release
+    """
+    _registry.release_model(model_id)
+
+def get_registry_status() -> Dict[str, Any]:
+    """
+    Convenience function to get registry status.
+    
+    Returns:
+        Dictionary with registry statistics
+    """
+    return _registry.get_registry_status()
+
+def cleanup_registry() -> None:
+    """
+    Convenience function to cleanup the entire registry.
+    """
+    _registry.cleanup_all()

siwatsystem/mpta_manager.py

@@ -0,0 +1,375 @@
+"""
+Shared MPTA Manager for Disk Space Optimization
+
+This module implements shared MPTA file management to prevent duplicate downloads
+and extractions when multiple cameras use the same model. MPTA files are stored
+in modelId-based directories and shared across all cameras using that model.
+
+Key Features:
+- Thread-safe MPTA downloading and extraction
+- ModelId-based directory structure: models/{modelId}/
+- Reference counting for proper cleanup
+- Eliminates duplicate MPTA downloads
+- Maintains compatibility with existing pipeline system
+"""
+
+import os
+import threading
+import logging
+import shutil
+import requests
+from typing import Dict, Set, Optional
+from urllib.parse import urlparse
+from .pympta import load_pipeline_from_zip
+
+# Create a logger for this module
+logger = logging.getLogger("detector_worker.mpta_manager")
+
+class MPTAManager:
+    """
+    Singleton class for managing shared MPTA files across multiple cameras.
+    
+    This manager ensures that each unique modelId is downloaded and extracted
+    only once, dramatically reducing disk usage and download time when multiple
+    cameras use the same model.
+    """
+    
+    _instance = None
+    _lock = threading.Lock()
+    
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super(MPTAManager, cls).__new__(cls)
+                    cls._instance._initialized = False
+        return cls._instance
+    
+    def __init__(self):
+        if self._initialized:
+            return
+            
+        self._initialized = True
+        
+        # Thread-safe storage for MPTA management
+        self._model_paths: Dict[int, str] = {}  # modelId -> shared_extraction_path
+        self._mpta_file_paths: Dict[int, str] = {}  # modelId -> local_mpta_file_path
+        self._reference_counts: Dict[int, int] = {}  # modelId -> reference count  
+        self._download_locks: Dict[int, threading.Lock] = {}  # modelId -> download lock
+        self._cameras_using_model: Dict[int, Set[str]] = {}  # modelId -> set of camera_ids
+        self._manager_lock = threading.RLock()  # Reentrant lock for nested calls
+        
+        logger.info("🏭 Shared MPTA Manager initialized - ready for disk-optimized MPTA management")
+    
+    def get_or_download_mpta(self, model_id: int, model_url: str, camera_id: str) -> Optional[tuple[str, str]]:
+        """
+        Get or download an MPTA file. Returns (extraction_path, mpta_file_path) if successful.
+        
+        Args:
+            model_id: Unique identifier for the model
+            model_url: URL to download the MPTA file from
+            camera_id: Identifier for the requesting camera
+            
+        Returns:
+            Tuple of (extraction_path, mpta_file_path), or None if failed
+        """
+        with self._manager_lock:
+            # Track camera usage
+            if model_id not in self._cameras_using_model:
+                self._cameras_using_model[model_id] = set()
+            self._cameras_using_model[model_id].add(camera_id)
+            
+            # Check if model directory already exists on disk (from previous sessions)
+            if model_id not in self._model_paths:
+                potential_path = f"models/{model_id}"
+                if os.path.exists(potential_path) and os.path.isdir(potential_path):
+                    # Directory exists from previous session, find the MPTA file
+                    mpta_files = [f for f in os.listdir(potential_path) if f.endswith('.mpta')]
+                    if mpta_files:
+                        # Use the first .mpta file found
+                        mpta_file_path = os.path.join(potential_path, mpta_files[0])
+                        self._model_paths[model_id] = potential_path
+                        self._mpta_file_paths[model_id] = mpta_file_path
+                        self._reference_counts[model_id] = 0  # Will be incremented below
+                        logger.info(f"📂 Found existing MPTA modelId {model_id} from previous session")
+            
+            # Check if already available
+            if model_id in self._model_paths:
+                shared_path = self._model_paths[model_id]
+                mpta_file_path = self._mpta_file_paths.get(model_id)
+                if os.path.exists(shared_path) and mpta_file_path and os.path.exists(mpta_file_path):
+                    self._reference_counts[model_id] += 1
+                    logger.info(f"📂 MPTA modelId {model_id} reused for camera {camera_id} (ref_count: {self._reference_counts[model_id]}) - SAVED DOWNLOAD!")
+                    return (shared_path, mpta_file_path)
+                else:
+                    # Path was deleted externally, clean up our records
+                    logger.warning(f"⚠️ MPTA path for modelId {model_id} was deleted externally, will re-download")
+                    del self._model_paths[model_id]
+                    self._mpta_file_paths.pop(model_id, None)
+                    self._reference_counts.pop(model_id, 0)
+            
+            # Need to download - get or create download lock for this modelId
+            if model_id not in self._download_locks:
+                self._download_locks[model_id] = threading.Lock()
+            
+        # Download with model-specific lock (released _manager_lock to allow other models)
+        download_lock = self._download_locks[model_id]
+        with download_lock:
+            # Double-check after acquiring download lock
+            with self._manager_lock:
+                if model_id in self._model_paths and os.path.exists(self._model_paths[model_id]):
+                    mpta_file_path = self._mpta_file_paths.get(model_id)
+                    if mpta_file_path and os.path.exists(mpta_file_path):
+                        self._reference_counts[model_id] += 1
+                        logger.info(f"📂 MPTA modelId {model_id} became available during wait (ref_count: {self._reference_counts[model_id]})")
+                        return (self._model_paths[model_id], mpta_file_path)
+            
+            # Actually download and extract
+            shared_path = f"models/{model_id}"
+            logger.info(f"🔄 Downloading NEW MPTA for modelId {model_id} from {model_url}")
+            
+            try:
+                # Ensure directory exists
+                os.makedirs(shared_path, exist_ok=True)
+                
+                # Download MPTA file
+                mpta_filename = self._extract_filename_from_url(model_url) or f"model_{model_id}.mpta"
+                local_mpta_path = os.path.join(shared_path, mpta_filename)
+                
+                if not self._download_file(model_url, local_mpta_path):
+                    logger.error(f"❌ Failed to download MPTA for modelId {model_id}")
+                    return None
+                
+                # Extract MPTA
+                pipeline_tree = load_pipeline_from_zip(local_mpta_path, shared_path)
+                if pipeline_tree is None:
+                    logger.error(f"❌ Failed to extract MPTA for modelId {model_id}")
+                    return None
+                
+                # Success - register in manager
+                with self._manager_lock:
+                    self._model_paths[model_id] = shared_path
+                    self._mpta_file_paths[model_id] = local_mpta_path
+                    self._reference_counts[model_id] = 1
+                    
+                    logger.info(f"✅ MPTA modelId {model_id} downloaded and registered (ref_count: 1)")
+                    self._log_manager_status()
+                
+                return (shared_path, local_mpta_path)
+                
+            except Exception as e:
+                logger.error(f"❌ Error downloading/extracting MPTA for modelId {model_id}: {e}")
+                # Clean up partial download
+                if os.path.exists(shared_path):
+                    shutil.rmtree(shared_path, ignore_errors=True)
+                return None
+    
+    def release_mpta(self, model_id: int, camera_id: str) -> None:
+        """
+        Release a reference to an MPTA. If reference count reaches zero,
+        the MPTA directory may be cleaned up to free disk space.
+        
+        Args:
+            model_id: Unique identifier for the model to release
+            camera_id: Identifier for the camera releasing the reference
+        """
+        with self._manager_lock:
+            if model_id not in self._reference_counts:
+                logger.warning(f"⚠️ Attempted to release unknown MPTA modelId {model_id} for camera {camera_id}")
+                return
+            
+            # Remove camera from usage tracking
+            if model_id in self._cameras_using_model:
+                self._cameras_using_model[model_id].discard(camera_id)
+            
+            self._reference_counts[model_id] -= 1
+            logger.info(f"📉 MPTA modelId {model_id} reference count decreased to {self._reference_counts[model_id]} (released by {camera_id})")
+            
+            # Clean up if no more references
+            # if self._reference_counts[model_id] <= 0:
+            #     self._cleanup_mpta(model_id)
+    
+    def _cleanup_mpta(self, model_id: int) -> None:
+        """
+        Internal method to clean up an MPTA directory and free disk space.
+        """
+        if model_id in self._model_paths:
+            shared_path = self._model_paths[model_id]
+            
+            try:
+                if os.path.exists(shared_path):
+                    shutil.rmtree(shared_path)
+                    logger.info(f"🗑️ Cleaned up MPTA directory: {shared_path}")
+                
+                # Remove from tracking
+                del self._model_paths[model_id]
+                self._mpta_file_paths.pop(model_id, None)
+                del self._reference_counts[model_id]
+                self._cameras_using_model.pop(model_id, None)
+                
+                # Clean up download lock (optional, could keep for future use)
+                self._download_locks.pop(model_id, None)
+                
+                logger.info(f"✅ MPTA modelId {model_id} fully cleaned up and disk space freed")
+                self._log_manager_status()
+                
+            except Exception as e:
+                logger.error(f"❌ Error cleaning up MPTA modelId {model_id}: {e}")
+    
+    def get_shared_path(self, model_id: int) -> Optional[str]:
+        """
+        Get the shared extraction path for a modelId without downloading.
+        
+        Args:
+            model_id: Model identifier to look up
+            
+        Returns:
+            Shared path if available, None otherwise
+        """
+        with self._manager_lock:
+            return self._model_paths.get(model_id)
+    
+    def get_manager_status(self) -> Dict:
+        """
+        Get current status of the MPTA manager.
+        
+        Returns:
+            Dictionary with manager statistics
+        """
+        with self._manager_lock:
+            return {
+                "total_mpta_models": len(self._model_paths),
+                "models": {
+                    str(model_id): {
+                        "shared_path": path,
+                        "reference_count": self._reference_counts.get(model_id, 0),
+                        "cameras_using": list(self._cameras_using_model.get(model_id, set()))
+                    }
+                    for model_id, path in self._model_paths.items()
+                },
+                "total_references": sum(self._reference_counts.values()),
+                "active_downloads": len(self._download_locks)
+            }
+    
+    def _log_manager_status(self) -> None:
+        """Log current manager status for debugging."""
+        status = self.get_manager_status()
+        logger.info(f"📊 MPTA Manager Status: {status['total_mpta_models']} unique models, {status['total_references']} total references")
+        for model_id, info in status['models'].items():
+            cameras_str = ','.join(info['cameras_using'][:3])  # Show first 3 cameras
+            if len(info['cameras_using']) > 3:
+                cameras_str += f"+{len(info['cameras_using'])-3} more"
+            logger.debug(f"   📋 ModelId {model_id}: refs={info['reference_count']}, cameras=[{cameras_str}]")
+    
+    def cleanup_all(self) -> None:
+        """
+        Clean up all MPTA directories. Used during shutdown.
+        """
+        with self._manager_lock:
+            model_ids = list(self._model_paths.keys())
+            logger.info(f"🧹 Cleaning up {len(model_ids)} MPTA directories")
+            
+            for model_id in model_ids:
+                self._cleanup_mpta(model_id)
+            
+            # Clear all tracking data
+            self._download_locks.clear()
+            logger.info("✅ MPTA manager cleanup complete")
+    
+    def _download_file(self, url: str, local_path: str) -> bool:
+        """
+        Download a file from URL to local path with progress logging.
+        
+        Args:
+            url: URL to download from
+            local_path: Local path to save to
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            logger.info(f"⬇️ Starting download from {url}")
+            
+            response = requests.get(url, stream=True)
+            response.raise_for_status()
+            
+            total_size = int(response.headers.get('content-length', 0))
+            if total_size > 0:
+                logger.info(f"📦 File size: {total_size / 1024 / 1024:.2f} MB")
+            
+            downloaded = 0
+            last_logged_progress = 0
+            with open(local_path, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+                        downloaded += len(chunk)
+                        
+                        if total_size > 0:
+                            progress = int((downloaded / total_size) * 100)
+                            # Log at 10% intervals (10%, 20%, 30%, etc.)
+                            if progress >= last_logged_progress + 10 and progress <= 100:
+                                logger.debug(f"Download progress: {progress}%")
+                                last_logged_progress = progress
+            
+            logger.info(f"✅ Successfully downloaded to {local_path}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"❌ Download failed: {e}")
+            # Clean up partial file
+            if os.path.exists(local_path):
+                os.remove(local_path)
+            return False
+    
+    def _extract_filename_from_url(self, url: str) -> Optional[str]:
+        """Extract filename from URL."""
+        try:
+            parsed = urlparse(url)
+            filename = os.path.basename(parsed.path)
+            return filename if filename else None
+        except Exception:
+            return None
+
+
+# Global singleton instance
+_mpta_manager = MPTAManager()
+
+def get_or_download_mpta(model_id: int, model_url: str, camera_id: str) -> Optional[tuple[str, str]]:
+    """
+    Convenience function to get or download a shared MPTA.
+    
+    Args:
+        model_id: Unique identifier for the model
+        model_url: URL to download the MPTA file from
+        camera_id: Identifier for the requesting camera
+        
+    Returns:
+        Tuple of (extraction_path, mpta_file_path), or None if failed
+    """
+    return _mpta_manager.get_or_download_mpta(model_id, model_url, camera_id)
+
+def release_mpta(model_id: int, camera_id: str) -> None:
+    """
+    Convenience function to release a shared MPTA reference.
+    
+    Args:
+        model_id: Unique identifier for the model to release
+        camera_id: Identifier for the camera releasing the reference
+    """
+    _mpta_manager.release_mpta(model_id, camera_id)
+
+def get_mpta_manager_status() -> Dict:
+    """
+    Convenience function to get MPTA manager status.
+    
+    Returns:
+        Dictionary with manager statistics
+    """
+    return _mpta_manager.get_manager_status()
+
+def cleanup_mpta_manager() -> None:
+    """
+    Convenience function to cleanup the entire MPTA manager.
+    """
+    _mpta_manager.cleanup_all()

test/test.py

@@ -0,0 +1,60 @@
+from ultralytics import YOLO
+import cv2
+import os
+
+# Load the model
+# model = YOLO('../models/webcam-local-01/4/bangchak_poc/yolo11n.pt')
+model = YOLO('yolo11m.pt')
+
+def test_image(image_path):
+    """Test a single image with YOLO model"""
+    if not os.path.exists(image_path):
+        print(f"Image not found: {image_path}")
+        return
+    
+    # Run inference - filter for car class only (class 2 in COCO)
+    results = model(image_path, classes=[2, 5, 7])  # 2, 5, 7 = car, bus, truck in COCO dataset
+    
+    # Display results
+    for r in results:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        
+        # Resize image for display (max width/height 800px)
+        height, width = im_array.shape[:2]
+        max_dimension = 800
+        if width > max_dimension or height > max_dimension:
+            if width > height:
+                new_width = max_dimension
+                new_height = int(height * (max_dimension / width))
+            else:
+                new_height = max_dimension
+                new_width = int(width * (max_dimension / height))
+            im_array = cv2.resize(im_array, (new_width, new_height))
+        
+        # Show image with predictions
+        cv2.imshow('YOLO Test - Car Detection Only', im_array)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        
+        # Print detection info
+        print(f"\nDetections for {image_path}:")
+        if r.boxes is not None and len(r.boxes) > 0:
+            for i, box in enumerate(r.boxes):
+                cls = int(box.cls[0])
+                conf = float(box.conf[0])
+                original_class = model.names[cls]  # Original class name (car/bus/truck)
+                # Get bounding box coordinates
+                x1, y1, x2, y2 = box.xyxy[0].tolist()
+                # Rename all vehicle types to "car"
+                print(f"Detection {i+1}: car (was: {original_class}) - Confidence: {conf:.3f} - BBox: ({x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f})")
+            print(f"Total cars detected: {len(r.boxes)}")
+        else:
+            print("No cars detected in the image")
+
+if __name__ == "__main__":
+    # Test with an image file
+    image_path = input("Enter image path (or press Enter for default test): ")
+    if not image_path:
+        image_path = "sample.png"  # Default test image
+    
+    test_image(image_path)

test/test_botsort_zone_track.py

@@ -0,0 +1,352 @@
+import cv2
+import torch
+import numpy as np
+import time
+from collections import defaultdict
+from ultralytics import YOLO
+
+def point_in_polygon(point, polygon):
+    """Check if a point is inside a polygon using ray casting algorithm"""
+    x, y = point
+    n = len(polygon)
+    inside = False
+    
+    p1x, p1y = polygon[0]
+    for i in range(1, n + 1):
+        p2x, p2y = polygon[i % n]
+        if y > min(p1y, p2y):
+            if y <= max(p1y, p2y):
+                if x <= max(p1x, p2x):
+                    if p1y != p2y:
+                        xinters = (y - p1y) * (p2x - p1x) / (p2y - p1y) + p1x
+                    if p1x == p2x or x <= xinters:
+                        inside = not inside
+        p1x, p1y = p2x, p2y
+    
+    return inside
+
+def draw_zone(frame, zone_polygon, color=(255, 0, 0), thickness=3):
+    """Draw tracking zone on frame"""
+    pts = np.array(zone_polygon, np.int32)
+    pts = pts.reshape((-1, 1, 2))
+    cv2.polylines(frame, [pts], True, color, thickness)
+    
+    # Add semi-transparent fill
+    overlay = frame.copy()
+    cv2.fillPoly(overlay, [pts], color)
+    cv2.addWeighted(overlay, 0.2, frame, 0.8, 0, frame)
+
+def setup_video_writer(output_path, fps, width, height):
+    """Setup video writer for output"""
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    return cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+def write_frame_to_video(video_writer, frame, repeat_count):
+    """Write frame to video with specified repeat count"""
+    for _ in range(repeat_count):
+        video_writer.write(frame)
+
+def finalize_video(video_writer):
+    """Release video writer"""
+    video_writer.release()
+
+def main():
+    video_path = "sample2.mp4"
+    yolo_model = "bangchakv2/yolov8n.pt"
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    
+    print("Loading YOLO model...")
+    model = YOLO(yolo_model)
+    
+    print("Opening video...")
+    cap = cv2.VideoCapture(video_path)
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    
+    print(f"Video info: {width}x{height}, {fps} FPS, {total_frames} frames")
+    
+    # Define tracking zone - Gas station floor area (trapezoidal shape)
+    # Based on the perspective of the gas station floor from your image
+    # width 2560, height 1440
+    
+    tracking_zone = [
+        (423, 974),   # Point 1
+        (1540, 1407), # Point 2
+        (1976, 806),  # Point 3
+        (1364, 749)   # Point 4
+    ]
+    
+    print(f"🎯 Tracking zone defined: {tracking_zone}")
+    
+    # CONTINUOUS TRACKING: Process every 118 frames (~2.0s intervals)
+    frame_skip = 118
+    
+    print(f"🎯 CONTINUOUS MODE: Processing every {frame_skip} frames ({frame_skip/fps:.2f}s intervals)")
+    print(f"🎬 Output video will have same duration as input (each processed frame shown for 2 seconds)")
+    print("🔥 ZONE-FIRST TRACKING: Only cars entering the zone will be tracked!")
+    print("Requires 5 consecutive detections IN ZONE for verification")
+    print("🕐 24/7 MODE: Memory reset every hour to prevent overflow")
+    print("Press 'q' to quit")
+    
+    # Setup video writer for output (same fps as input for normal playback speed)
+    output_path = "tracking_output_botsort_zone_track.mp4"
+    output_fps = fps  # Use same fps as input video
+    out = setup_video_writer(output_path, output_fps, width, height)
+    
+    # Track car IDs and their consecutive detections
+    car_id_counts = defaultdict(int)
+    successful_cars = set()
+    last_positions = {}
+    processed_count = 0
+    
+    # ID remapping for clean sequential zone IDs
+    tracker_to_zone_id = {}  # Maps tracker IDs to clean zone IDs
+    next_zone_id = 1  # Next clean zone ID to assign
+    
+    # Store previous frame detections to filter tracking inputs
+    previous_zone_cars = set()
+    
+    # 24/7 operation: Reset every hour (1800 snapshots at 2-sec intervals = 1 hour)
+    RESET_INTERVAL = 1800  # Reset every 1800 processed frames (1 hour)
+    
+    frame_idx = 0
+    
+    while True:
+        # Skip frames to maintain interval
+        for _ in range(frame_skip):
+            ret, frame = cap.read()
+            if not ret:
+                print("\nNo more frames to read")
+                cap.release()
+                cv2.destroyAllWindows()
+                return
+            frame_idx += 1
+        
+        processed_count += 1
+        current_time = frame_idx / fps
+        
+        print(f"\n🎬 Frame {frame_idx} at {current_time:.2f}s (processed #{processed_count})")
+        
+        # 24/7 Memory Management: Reset every hour
+        if processed_count % RESET_INTERVAL == 0:
+            print(f"🕐 HOURLY RESET: Clearing all tracking data (processed {processed_count} frames)")
+            print(f"   📊 Before reset: {len(tracker_to_zone_id)} tracked cars, next Zone ID was {next_zone_id}")
+            
+            # Clear all tracking data
+            tracker_to_zone_id.clear()
+            car_id_counts.clear()
+            successful_cars.clear()
+            last_positions.clear()
+            next_zone_id = 1  # Reset to 1
+            
+            # Reset BoT-SORT tracker state
+            try:
+                model.reset()
+                print(f"   ✅ BoT-SORT tracker reset successfully")
+            except:
+                print(f"   ⚠️ BoT-SORT reset not available (continuing without reset)")
+            
+            print(f"   🆕 Zone IDs will start from 1 again")
+        
+        # Draw tracking zone on frame
+        draw_zone(frame, tracking_zone, color=(0, 255, 255), thickness=3)  # Yellow zone
+        
+        # First run YOLO detection (without tracking) to find cars in zone
+        detection_results = model(frame, verbose=False, conf=0.7, classes=[2])
+        
+        # Find cars currently in the tracking zone
+        current_zone_cars = []
+        total_detections = 0
+        
+        if detection_results[0].boxes is not None:
+            boxes = detection_results[0].boxes.xyxy.cpu()
+            scores = detection_results[0].boxes.conf.cpu()
+            
+            total_detections = len(boxes)
+            print(f"  🔍 Total car detections: {total_detections}")
+            
+            for i in range(len(boxes)):
+                x1, y1, x2, y2 = boxes[i]
+                conf = float(scores[i])
+                
+                # Check if detection is in zone (using bottom center)
+                box_bottom = ((x1 + x2) / 2, y2)
+                if point_in_polygon(box_bottom, tracking_zone):
+                    current_zone_cars.append({
+                        'bbox': [float(x1), float(y1), float(x2), float(y2)],
+                        'conf': conf,
+                        'center': ((x1 + x2) / 2, (y1 + y2) / 2),
+                        'bottom': box_bottom
+                    })
+        
+        print(f"  🎯 Cars in zone: {len(current_zone_cars)}")
+        
+        # Only run tracking if there are cars in the zone
+        detected_car_ids = set()
+        
+        if current_zone_cars:
+            # Run tracking on the full frame (let tracker handle associations)
+            # But we'll filter results to only zone cars afterward
+            results = model.track(
+                frame, 
+                persist=True,
+                verbose=False,
+                conf=0.7,
+                classes=[2],
+                tracker="botsort_reid.yaml"
+            )
+            
+            if results[0].boxes is not None and results[0].boxes.id is not None:
+                boxes = results[0].boxes.xyxy.cpu()
+                scores = results[0].boxes.conf.cpu()
+                track_ids = results[0].boxes.id.cpu().int()
+                
+                print(f"  📊 Total tracked objects: {len(track_ids)}")
+                
+                # Filter tracked objects to only those in zone
+                zone_tracks = []
+                for i, track_id in enumerate(track_ids):
+                    x1, y1, x2, y2 = boxes[i]
+                    conf = float(scores[i])
+                    
+                    # Check if this tracked object is in our zone
+                    box_bottom = ((x1 + x2) / 2, y2)
+                    if point_in_polygon(box_bottom, tracking_zone):
+                        zone_tracks.append({
+                            'id': int(track_id),
+                            'bbox': [int(x1), int(y1), int(x2), int(y2)],
+                            'conf': conf,
+                            'center': ((x1 + x2) / 2, (y1 + y2) / 2),
+                            'bottom': box_bottom
+                        })
+                
+                print(f"  ✅ Zone tracks: {len(zone_tracks)}")
+                
+                # Process each zone track
+                for track in zone_tracks:
+                    tracker_id = track['id']  # Original tracker ID
+                    x1, y1, x2, y2 = track['bbox']
+                    conf = track['conf']
+                    box_center = track['center']
+                    
+                    # Map tracker ID to clean zone ID
+                    if tracker_id not in tracker_to_zone_id:
+                        tracker_to_zone_id[tracker_id] = next_zone_id
+                        print(f"  🆕 New car: Tracker ID {tracker_id} → Zone ID {next_zone_id}")
+                        next_zone_id += 1
+                    
+                    zone_id = tracker_to_zone_id[tracker_id]  # Clean sequential ID
+                    
+                    # Validate track continuity (use tracker_id for internal logic)
+                    is_valid = True
+                    
+                    # Check for suspicious jumps
+                    if tracker_id in last_positions:
+                        last_center = last_positions[tracker_id]
+                        distance = np.sqrt((box_center[0] - last_center[0])**2 + 
+                                         (box_center[1] - last_center[1])**2)
+                        
+                        if distance > 400:  # pixels in ~2.0s
+                            is_valid = False
+                            print(f"  ⚠️ Zone ID {zone_id} (Tracker {tracker_id}): suspicious jump {distance:.0f}px")
+                    
+                    # Skip already successful cars (use zone_id for user logic)
+                    if zone_id in successful_cars:
+                        is_valid = False
+                        print(f"  ✅ Zone ID {zone_id}: already successful, skipping")
+                    
+                    # Only process valid, high-confidence zone tracks
+                    if is_valid and conf > 0.7:
+                        detected_car_ids.add(zone_id)  # Use zone_id for display
+                        car_id_counts[zone_id] += 1
+                        last_positions[tracker_id] = box_center  # Track by tracker_id internally
+                        
+                        # Draw tracking results with clean zone ID
+                        zone_color = (0, 255, 0)  # Green for zone cars
+                        cv2.rectangle(frame, (x1, y1), (x2, y2), zone_color, 2)
+                        cv2.putText(frame, f'ZONE ID:{zone_id}', 
+                                  (x1, y1-30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, zone_color, 2)
+                        cv2.putText(frame, f'#{car_id_counts[zone_id]} {conf:.2f}', 
+                                  (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, zone_color, 2)
+                        
+                        # Draw center point
+                        cv2.circle(frame, (int(track['bottom'][0]), int(track['bottom'][1])), 5, zone_color, -1)
+                        
+                        print(f"  ✅ Zone ID {zone_id} (Tracker {tracker_id}): ZONE detection #{car_id_counts[zone_id]} (conf: {conf:.2f})")
+                        
+                        # Check for success (5 consecutive detections IN ZONE)
+                        if car_id_counts[zone_id] == 5:
+                            print(f"🏆 SUCCESS: Zone ID {zone_id} achieved 5 continuous ZONE detections - TRIGGER NEXT MODEL!")
+                            successful_cars.add(zone_id)
+                            
+                            # Add success indicator to frame
+                            cv2.putText(frame, f"SUCCESS: Zone Car {zone_id}!", 
+                                      (50, height-50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 3)
+        else:
+            print("  📋 No cars in zone - no tracking performed")
+            
+            # Draw any cars outside the zone in red (for reference)
+            if detection_results[0].boxes is not None:
+                boxes = detection_results[0].boxes.xyxy.cpu()
+                scores = detection_results[0].boxes.conf.cpu()
+                
+                for i in range(len(boxes)):
+                    x1, y1, x2, y2 = boxes[i]
+                    conf = float(scores[i])
+                    
+                    box_bottom = ((x1 + x2) / 2, y2)
+                    if not point_in_polygon(box_bottom, tracking_zone):
+                        # Draw cars outside zone in red (not tracked)
+                        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 1)
+                        cv2.putText(frame, f'OUT {conf:.2f}', 
+                                  (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
+        
+        # Display results
+        if detected_car_ids:
+            print(f"  📋 Active Zone IDs: {sorted(detected_car_ids)} (Clean sequential IDs)")
+        
+        # Show ID mapping for debugging
+        if tracker_to_zone_id:
+            mapping_str = ", ".join([f"Tracker{k}→Zone{v}" for k, v in tracker_to_zone_id.items()])
+            print(f"  🔄 ID Mapping: {mapping_str}")
+        
+        # Add annotations to frame
+        cv2.putText(frame, f"BoT-SORT Zone-First Tracking | Frame: {frame_idx} | {current_time:.2f}s", 
+                   (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+        cv2.putText(frame, f"Zone Cars: {len(current_zone_cars)} | Active Tracks: {len(detected_car_ids)}", 
+                   (10, 65), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+        cv2.putText(frame, f"Successful Cars: {len(successful_cars)}", 
+                   (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+        cv2.putText(frame, "TRACKING ZONE", 
+                   (tracking_zone[0][0], tracking_zone[0][1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+        
+        # Write annotated frame to output video (repeat for 2 seconds duration)
+        write_frame_to_video(out, frame, frame_skip)
+        
+        # Show video with zone tracking info
+        display_frame = cv2.resize(frame, (960, 540))
+        cv2.imshow('BoT-SORT Zone-First Tracking', display_frame)
+        
+        # Quick check for quit
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord('q'):
+            break
+        
+        # Small delay to see results
+        time.sleep(0.1)
+    
+    cap.release()
+    finalize_video(out)
+    cv2.destroyAllWindows()
+    print(f"\n🎯 BoT-SORT zone-first tracking completed!")
+    print(f"📊 Processed {processed_count} frames with {frame_skip/fps:.2f}s intervals")
+    print(f"🏆 Successfully tracked {len(successful_cars)} unique cars IN ZONE")
+    print(f"💾 Annotated video saved to: {output_path}")
+
+if __name__ == "__main__":
+    main()

view_redis_images.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+Script to view frontal images saved in Redis
+"""
+import redis
+import cv2
+import numpy as np
+import sys
+from datetime import datetime
+
+# Redis connection config (from pipeline.json)
+REDIS_CONFIG = {
+    "host": "10.100.1.3",
+    "port": 6379,
+    "password": "FBQgi0i5RevAAMO5Hh66",
+    "db": 0
+}
+
+def connect_redis():
+    """Connect to Redis server."""
+    try:
+        client = redis.Redis(
+            host=REDIS_CONFIG["host"],
+            port=REDIS_CONFIG["port"],
+            password=REDIS_CONFIG["password"],
+            db=REDIS_CONFIG["db"],
+            decode_responses=False  # Keep bytes for images
+        )
+        client.ping()
+        print(f"✅ Connected to Redis at {REDIS_CONFIG['host']}:{REDIS_CONFIG['port']}")
+        return client
+    except redis.exceptions.ConnectionError as e:
+        print(f"❌ Failed to connect to Redis: {e}")
+        return None
+
+def list_image_keys(client):
+    """List all image keys in Redis."""
+    try:
+        # Look for keys matching the inference pattern
+        keys = client.keys("inference:*")
+        print(f"\n📋 Found {len(keys)} image keys:")
+        for i, key in enumerate(keys):
+            key_str = key.decode() if isinstance(key, bytes) else key
+            print(f"{i+1}. {key_str}")
+        return keys
+    except Exception as e:
+        print(f"❌ Error listing keys: {e}")
+        return []
+
+def view_image(client, key):
+    """View a specific image from Redis."""
+    try:
+        # Get image data from Redis
+        image_data = client.get(key)
+        if image_data is None:
+            print(f"❌ No data found for key: {key}")
+            return
+        
+        print(f"📸 Image size: {len(image_data)} bytes")
+        
+        # Convert bytes to numpy array
+        nparr = np.frombuffer(image_data, np.uint8)
+        
+        # Decode image
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        if img is None:
+            print("❌ Failed to decode image data")
+            return
+        
+        print(f"🖼️  Image dimensions: {img.shape[1]}x{img.shape[0]} pixels")
+        
+        # Display image
+        key_str = key.decode() if isinstance(key, bytes) else key
+        cv2.imshow(f'Redis Image: {key_str}', img)
+        print("👁️  Image displayed. Press any key to close...")
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        
+        # Ask if user wants to save the image
+        save = input("💾 Save image to file? (y/n): ").lower().strip()
+        if save == 'y':
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"redis_image_{timestamp}.jpg"
+            cv2.imwrite(filename, img)
+            print(f"💾 Image saved as: {filename}")
+        
+    except Exception as e:
+        print(f"❌ Error viewing image: {e}")
+
+def monitor_new_images(client):
+    """Monitor for new images being added to Redis."""
+    print("👀 Monitoring for new images... (Press Ctrl+C to stop)")
+    try:
+        # Subscribe to Redis pub/sub for car detections
+        pubsub = client.pubsub()
+        pubsub.subscribe('car_detections')
+        
+        for message in pubsub.listen():
+            if message['type'] == 'message':
+                data = message['data'].decode()
+                print(f"🚨 New detection: {data}")
+                
+                # Try to extract image key from message
+                import json
+                try:
+                    detection_data = json.loads(data)
+                    image_key = detection_data.get('image_key')
+                    if image_key:
+                        print(f"🖼️  New image available: {image_key}")
+                        view_choice = input("View this image now? (y/n): ").lower().strip()
+                        if view_choice == 'y':
+                            view_image(client, image_key)
+                except json.JSONDecodeError:
+                    pass
+                    
+    except KeyboardInterrupt:
+        print("\n👋 Stopping monitor...")
+    except Exception as e:
+        print(f"❌ Monitor error: {e}")
+
+def main():
+    """Main function."""
+    print("🔍 Redis Image Viewer")
+    print("=" * 50)
+    
+    # Connect to Redis
+    client = connect_redis()
+    if not client:
+        return
+    
+    while True:
+        print("\n📋 Options:")
+        print("1. List all image keys")
+        print("2. View specific image")
+        print("3. Monitor for new images")
+        print("4. Exit")
+        
+        choice = input("\nEnter choice (1-4): ").strip()
+        
+        if choice == '1':
+            keys = list_image_keys(client)
+        elif choice == '2':
+            keys = list_image_keys(client)
+            if keys:
+                try:
+                    idx = int(input(f"\nEnter image number (1-{len(keys)}): ")) - 1
+                    if 0 <= idx < len(keys):
+                        view_image(client, keys[idx])
+                    else:
+                        print("❌ Invalid selection")
+                except ValueError:
+                    print("❌ Please enter a valid number")
+        elif choice == '3':
+            monitor_new_images(client)
+        elif choice == '4':
+            print("👋 Goodbye!")
+            break
+        else:
+            print("❌ Invalid choice")
+
+if __name__ == "__main__":
+    main()

webcam_rtsp_server.py

@@ -0,0 +1,325 @@
+#!/usr/bin/env python3
+"""
+Enhanced webcam server that provides both RTSP streaming and HTTP snapshot endpoints
+Compatible with CMS UI requirements for camera configuration
+"""
+
+import cv2
+import threading
+import time
+import logging
+import socket
+from http.server import BaseHTTPRequestHandler, HTTPServer
+import subprocess
+import sys
+import os
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
+)
+logger = logging.getLogger("webcam_rtsp_server")
+
+# Global webcam capture object
+webcam_cap = None
+rtsp_process = None
+
+class WebcamHTTPHandler(BaseHTTPRequestHandler):
+    """HTTP handler for snapshot requests"""
+    
+    def do_GET(self):
+        if self.path == '/snapshot' or self.path == '/snapshot.jpg':
+            try:
+                # Capture fresh frame from webcam for each request
+                ret, frame = webcam_cap.read()
+                if ret and frame is not None:
+                    # Encode as JPEG
+                    success, buffer = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
+                    if success:
+                        self.send_response(200)
+                        self.send_header('Content-Type', 'image/jpeg')
+                        self.send_header('Content-Length', str(len(buffer)))
+                        self.send_header('Cache-Control', 'no-cache, no-store, must-revalidate')
+                        self.send_header('Pragma', 'no-cache')
+                        self.send_header('Expires', '0')
+                        self.end_headers()
+                        self.wfile.write(buffer.tobytes())
+                        logger.debug(f"Served webcam snapshot, size: {len(buffer)} bytes")
+                        return
+                    else:
+                        logger.error("Failed to encode frame as JPEG")
+                else:
+                    logger.error("Failed to capture frame from webcam")
+                
+                # Send error response
+                self.send_response(500)
+                self.send_header('Content-Type', 'text/plain')
+                self.end_headers()
+                self.wfile.write(b'Failed to capture webcam frame')
+                
+            except Exception as e:
+                logger.error(f"Error serving snapshot: {e}")
+                self.send_response(500)
+                self.send_header('Content-Type', 'text/plain')
+                self.end_headers()
+                self.wfile.write(f'Error: {str(e)}'.encode())
+        
+        elif self.path == '/status':
+            # Status endpoint for health checking
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+            
+            width = int(webcam_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(webcam_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fps = webcam_cap.get(cv2.CAP_PROP_FPS)
+            
+            status = f'{{"status": "online", "width": {width}, "height": {height}, "fps": {fps}}}'
+            self.wfile.write(status.encode())
+        
+        else:
+            # 404 for other paths
+            self.send_response(404)
+            self.send_header('Content-Type', 'text/plain')
+            self.end_headers()
+            self.wfile.write(b'Not Found - Available endpoints: /snapshot, /snapshot.jpg, /status')
+    
+    def log_message(self, format, *args):
+        # Suppress default HTTP server logging to avoid spam
+        pass
+
+def check_ffmpeg():
+    """Check if FFmpeg is available for RTSP streaming"""
+    try:
+        result = subprocess.run(['ffmpeg', '-version'], 
+                              capture_output=True, text=True, timeout=5)
+        if result.returncode == 0:
+            logger.info("FFmpeg found and working")
+            return True
+    except (subprocess.TimeoutExpired, FileNotFoundError, subprocess.SubprocessError):
+        pass
+    
+    logger.warning("FFmpeg not found. RTSP streaming will not be available.")
+    logger.info("To enable RTSP streaming, install FFmpeg:")
+    logger.info("  Windows: Download from https://ffmpeg.org/download.html")
+    logger.info("  Linux: sudo apt install ffmpeg")
+    logger.info("  macOS: brew install ffmpeg")
+    return False
+
+def get_windows_camera_name():
+    """Get the actual camera device name on Windows"""
+    try:
+        # List video devices using FFmpeg with proper encoding handling
+        result = subprocess.run(['ffmpeg', '-f', 'dshow', '-list_devices', 'true', '-i', 'dummy'], 
+                              capture_output=True, text=True, timeout=10, encoding='utf-8', errors='ignore')
+        output = result.stderr  # FFmpeg outputs device list to stderr
+        
+        # Look for video devices in the output
+        lines = output.split('\n')
+        video_devices = []
+        
+        # Parse the output - look for lines with (video) that contain device names in quotes
+        for line in lines:
+            if '[dshow @' in line and '(video)' in line and '"' in line:
+                # Extract device name between first pair of quotes
+                start = line.find('"') + 1
+                end = line.find('"', start)
+                if start > 0 and end > start:
+                    device_name = line[start:end]
+                    video_devices.append(device_name)
+        
+        logger.info(f"Found Windows video devices: {video_devices}")
+        if video_devices:
+            # Force use the first device (index 0) which is the Logitech HD webcam
+            return video_devices[0]  # This will be "罗技高清网络摄像机 C930c"
+        else:
+            logger.info("No devices found via FFmpeg detection, using fallback")
+            # Fall through to fallback names
+        
+    except Exception as e:
+        logger.debug(f"Failed to get Windows camera name: {e}")
+    
+    # Try common camera device names as fallback
+    # Prioritize Integrated Camera since that's what's working now
+    common_names = [
+        "Integrated Camera",  # This is working for the current setup
+        "USB Video Device",  # Common name for USB cameras
+        "USB2.0 Camera",
+        "C930c",  # Direct model name
+        "HD Pro Webcam C930c",  # Full Logitech name
+        "Logitech",  # Brand name
+        "USB Camera",
+        "Webcam"
+    ]
+    logger.info(f"Using fallback camera names: {common_names}")
+    return common_names[0]  # Return "Integrated Camera" first
+
+def start_rtsp_stream(webcam_index=0, rtsp_port=8554):
+    """Start RTSP streaming using FFmpeg"""
+    global rtsp_process
+    
+    if not check_ffmpeg():
+        return None
+    
+    try:
+        # Get the actual camera device name for Windows
+        if sys.platform.startswith('win'):
+            camera_name = get_windows_camera_name()
+            logger.info(f"Using Windows camera device: {camera_name}")
+        
+        # FFmpeg command to stream webcam via RTSP
+        if sys.platform.startswith('win'):
+            cmd = [
+                'ffmpeg',
+                '-f', 'dshow',
+                '-i', f'video={camera_name}',  # Use detected camera name
+                '-c:v', 'libx264',
+                '-preset', 'veryfast',
+                '-tune', 'zerolatency',
+                '-r', '30',
+                '-s', '1280x720',
+                '-f', 'rtsp',
+                f'rtsp://localhost:{rtsp_port}/stream'
+            ]
+        elif sys.platform.startswith('linux'):
+            cmd = [
+                'ffmpeg',
+                '-f', 'v4l2',
+                '-i', f'/dev/video{webcam_index}',
+                '-c:v', 'libx264',
+                '-preset', 'veryfast',
+                '-tune', 'zerolatency',
+                '-r', '30',
+                '-s', '1280x720',
+                '-f', 'rtsp',
+                f'rtsp://localhost:{rtsp_port}/stream'
+            ]
+        else:  # macOS
+            cmd = [
+                'ffmpeg',
+                '-f', 'avfoundation',
+                '-i', f'{webcam_index}:',
+                '-c:v', 'libx264',
+                '-preset', 'veryfast',
+                '-tune', 'zerolatency',
+                '-r', '30',
+                '-s', '1280x720',
+                '-f', 'rtsp',
+                f'rtsp://localhost:{rtsp_port}/stream'
+            ]
+        
+        logger.info(f"Starting RTSP stream on rtsp://localhost:{rtsp_port}/stream")
+        logger.info(f"FFmpeg command: {' '.join(cmd)}")
+        
+        rtsp_process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True
+        )
+        
+        # Give FFmpeg a moment to start
+        time.sleep(2)
+        
+        # Check if process is still running
+        if rtsp_process.poll() is None:
+            logger.info("RTSP streaming started successfully")
+            return rtsp_process
+        else:
+            # Get error output if process failed
+            stdout, stderr = rtsp_process.communicate(timeout=2)
+            logger.error("RTSP streaming failed to start")
+            logger.error(f"FFmpeg stdout: {stdout}")
+            logger.error(f"FFmpeg stderr: {stderr}")
+            return None
+            
+    except Exception as e:
+        logger.error(f"Failed to start RTSP stream: {e}")
+        return None
+
+def get_local_ip():
+    """Get the Wireguard IP address for external access"""
+    # Use Wireguard IP for external access
+    return "10.101.1.4"
+
+def main():
+    global webcam_cap, rtsp_process
+    
+    # Configuration - Force use index 0 for Logitech HD webcam
+    webcam_index = 0  # Logitech HD webcam C930c (1920x1080@30fps)
+    http_port = 8080
+    rtsp_port = 8554
+    
+    logger.info("=== Webcam RTSP & HTTP Server ===")
+    
+    # Initialize webcam
+    logger.info("Initializing webcam...")
+    webcam_cap = cv2.VideoCapture(webcam_index)
+    
+    if not webcam_cap.isOpened():
+        logger.error(f"Failed to open webcam at index {webcam_index}")
+        logger.info("Try different webcam indices (0, 1, 2, etc.)")
+        return
+    
+    # Set webcam properties - Use high resolution for Logitech HD webcam
+    webcam_cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
+    webcam_cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
+    webcam_cap.set(cv2.CAP_PROP_FPS, 30)
+    
+    width = int(webcam_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(webcam_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = webcam_cap.get(cv2.CAP_PROP_FPS)
+    
+    logger.info(f"Webcam initialized: {width}x{height} @ {fps}fps")
+    
+    # Get local IP for CMS configuration
+    local_ip = get_local_ip()
+    
+    # Start RTSP streaming (optional, requires FFmpeg)
+    rtsp_process = start_rtsp_stream(webcam_index, rtsp_port)
+    
+    # Start HTTP server for snapshots
+    server_address = ('0.0.0.0', http_port)  # Bind to all interfaces
+    http_server = HTTPServer(server_address, WebcamHTTPHandler)
+    
+    logger.info("\n=== Server URLs for CMS Configuration ===")
+    logger.info(f"HTTP Snapshot URL: http://{local_ip}:{http_port}/snapshot")
+    
+    if rtsp_process:
+        logger.info(f"RTSP Stream URL: rtsp://{local_ip}:{rtsp_port}/stream")
+    else:
+        logger.info("RTSP Stream: Not available (FFmpeg not found)")
+        logger.info("HTTP-only mode: Use Snapshot URL for camera input")
+    
+    logger.info(f"Status URL: http://{local_ip}:{http_port}/status")
+    logger.info("\n=== CMS Configuration Suggestions ===")
+    logger.info(f"Camera Identifier: webcam-local-01")
+    logger.info(f"RTSP Stream URL: rtsp://{local_ip}:{rtsp_port}/stream")
+    logger.info(f"Snapshot URL: http://{local_ip}:{http_port}/snapshot")
+    logger.info(f"Snapshot Interval: 2000 (ms)")
+    logger.info("\nPress Ctrl+C to stop all servers")
+    
+    try:
+        # Start HTTP server
+        http_server.serve_forever()
+    except KeyboardInterrupt:
+        logger.info("Shutting down servers...")
+    finally:
+        # Clean up
+        if webcam_cap:
+            webcam_cap.release()
+        
+        if rtsp_process:
+            logger.info("Stopping RTSP stream...")
+            rtsp_process.terminate()
+            try:
+                rtsp_process.wait(timeout=5)
+            except subprocess.TimeoutExpired:
+                rtsp_process.kill()
+        
+        http_server.server_close()
+        logger.info("All servers stopped")
+
+if __name__ == "__main__":
+    main()