Detect Zero-Shot Surface Anomalies in Manufacturing with Anomalib and OpenCV

"""
Production implementation for detecting zero-shot surface anomalies in manufacturing using Anomalib and OpenCV.
Provides secure, scalable operations.
"""
import os
import cv2
import numpy as np
import logging
from typing import Dict, Any, List
from time import sleep
from concurrent.futures import ThreadPoolExecutor

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to manage environment variables."""
    model_path: str = os.getenv('MODEL_PATH')
    input_folder: str = os.getenv('INPUT_FOLDER')
    output_folder: str = os.getenv('OUTPUT_FOLDER')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not data.get('file_path'):
        raise ValueError('Missing file_path')
    return True

def load_model(model_path: str) -> Any:
    """Load the anomaly detection model.
    
    Args:
        model_path: Path to the model file
    Returns:
        Loaded model instance
    Raises:
        FileNotFoundError: If model file does not exist
    """
    if not os.path.exists(model_path):
        raise FileNotFoundError(f'Model file not found: {model_path}')  
    logger.info('Loading model from %s', model_path)
    # Placeholder for actual model loading logic
    model = 'LoadedModel'  # Replace with actual model loading
    return model

def process_image(image_path: str, model: Any) -> np.ndarray:
    """Process image to detect anomalies.
    
    Args:
        image_path: Path to the image file
        model: Loaded model for anomaly detection
    Returns:
        Anomaly detection results as a numpy array
    Raises:
        ValueError: If processing fails
    """
    try:
        logger.info('Processing image %s', image_path)
        image = cv2.imread(image_path)
        if image is None:
            raise ValueError(f'Cannot read image: {image_path}')  
        # Placeholder for anomaly detection
        results = np.array([0])  # Dummy result
        return results
    except Exception as e:
        logger.error('Error processing image %s: %s', image_path, str(e))
        raise ValueError(f'Error processing image: {image_path}') from e

def save_results(image_name: str, results: np.ndarray, output_folder: str) -> None:
    """Save detection results to output folder.
    
    Args:
        image_name: Name of the original image
        results: Anomaly detection results
        output_folder: Folder to save results
    Raises:
        OSError: If there is an error saving the results
    """
    try:
        output_path = os.path.join(output_folder, f'detected_{image_name}')
        np.save(output_path, results)
        logger.info('Results saved to %s', output_path)
    except Exception as e:
        logger.error('Error saving results for %s: %s', image_name, str(e))
        raise OSError(f'Error saving results for: {image_name}') from e

def process_batch(image_files: List[str], model: Any, output_folder: str) -> None:
    """Process a batch of images for anomaly detection.
    
    Args:
        image_files: List of image file paths
        model: Loaded model for anomaly detection
        output_folder: Folder to save results
    """
    for image_file in image_files:
        try:
            logger.info('Starting processing for %s', image_file)
            results = process_image(image_file, model)
            save_results(os.path.basename(image_file), results, output_folder)
        except ValueError as ve:
            logger.warning('Value error: %s', str(ve))
        except Exception as e:
            logger.error('Unhandled error for %s: %s', image_file, str(e))

class AnomalyDetector:
    """Main class for managing anomaly detection workflow."""
    def __init__(self, config: Config) -> None:
        self.config = config
        self.model = load_model(self.config.model_path)

    def run_detection(self) -> None:
        """Main method to run anomaly detection workflow."""
        try:
            image_files = [os.path.join(self.config.input_folder, f) for f in os.listdir(self.config.input_folder) if f.endswith('.jpg')]
            logger.info('Found %d images to process', len(image_files))
            process_batch(image_files, self.model, self.config.output_folder)
        except Exception as e:
            logger.error('Error in detection workflow: %s', str(e))

if __name__ == '__main__':
    config = Config()
    detector = AnomalyDetector(config)
    detector.run_detection()