Detect Assembly Line Defects in Real Time with Ultralytics and Supervision

                      
                     
"""\nProduction implementation for detecting assembly line defects in real time using Ultralytics and Supervision.\nProvides secure, scalable operations with efficient image processing and logging.\n"""\nfrom typing import Dict, Any, List\nimport os\nimport logging\nimport time\nimport numpy as np\nimport cv2\nfrom ultralytics import YOLO\n\n# Set up logging configuration\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nclass Config:\n    database_url: str = os.getenv('DATABASE_URL')  # Database connection string\n    model_path: str = os.getenv('MODEL_PATH', 'yolov8.pt')  # Default model path\n\nasync def validate_input(data: Dict[str, Any]) -> bool:\n    """Validate request data.\n    \n    Args:\n        data: Input to validate\n    Returns:\n        True if valid\n    Raises:\n        ValueError: If validation fails\n    """\n    if 'image' not in data:\n        raise ValueError('Missing image data')  # Ensure image data is present\n    return True\n\nasync def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:\n    """Sanitize input fields.\n    \n    Args:\n        data: Input data to sanitize\n    Returns:\n        Sanitized data\n    """\n    return {key: str(value).strip() for key, value in data.items()}\n\nasync def fetch_data(source: str) -> np.ndarray:\n    """Fetch image data from the specified source.\n    \n    Args:\n        source: Path or URL of the image\n    Returns:\n        Image data as a NumPy array\n    """\n    image = cv2.imread(source)  # Read image from file path\n    if image is None:\n        raise FileNotFoundError(f'Image not found: {source}')  # Handle file not found\n    return image\n\nasync def process_batch(images: List[np.ndarray]) -> List[Dict[str, Any]]:\n    """Process a batch of images for defect detection.\n    \n    Args:\n        images: List of images to process\n    Returns:\n        List of detection results\n    """\n    model = YOLO(Config.model_path)  # Load the detection model\n    results = []\n    for image in images:\n        detections = model(image)  # Perform detection\n        results.append(detections)  # Store results\n    return results\n\nasync def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:\n    """Aggregate detection metrics from results.\n    \n    Args:\n        results: Detection results list\n    Returns:\n        Aggregated metrics\n    """\n    metrics = {'defects': 0}  # Initialize metrics\n    for result in results:\n        metrics['defects'] += len(result)  # Count detected defects\n    return metrics\n\nasync def save_to_db(metrics: Dict[str, Any]) -> None:\n    """Save aggregated metrics to the database.\n    \n    Args:\n        metrics: Aggregated metrics to save\n    """\n    # Simulated database save operation\n    logger.info(f'Saving metrics to database: {metrics}')\n\nasync def handle_errors(func):\n    """Error handling decorator for asynchronous functions.\n    \n    Args:\n        func: Function to wrap\n    Returns:\n        Wrapped function\n    """\n    async def wrapper(*args, **kwargs):\n        try:\n            return await func(*args, **kwargs)\n        except Exception as e:\n            logger.error(f'Error in function {func.__name__}: {str(e)}')\n            raise\n    return wrapper\n\nclass DefectDetectionOrchestrator:\n    """Main orchestrator for defect detection workflow.\n    """\n    @handle_errors\n    async def run(self, image_path: str) -> None:\n        """Run the defect detection process.\n        \n        Args:\n            image_path: Path to the image to process\n        """\n        data = await fetch_data(image_path)  # Fetch image data\n        await validate_input({'image': data})  # Validate input\n        sanitized_data = await sanitize_fields({'image': image_path})  # Sanitize fields\n        detections = await process_batch([data])  # Process batch\n        metrics = await aggregate_metrics(detections)  # Aggregate metrics\n        await save_to_db(metrics)  # Save metrics to DB\n\nif __name__ == '__main__':\n    orchestrator = DefectDetectionOrchestrator()\n    # Example usage with a sample image\n    sample_image = 'path/to/sample/image.jpg'\n    orchestrator.run(sample_image)\n