Recognize Equipment Components with CLIP and OpenCV

                      
                     
"""
Production implementation for recognizing equipment components using CLIP and OpenCV.
This module provides secure and scalable operations to identify and classify various equipment components.
"""

import os
import logging
import cv2
import numpy as np
from typing import List, Dict, Any, Tuple

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    model_path: str = os.getenv('MODEL_PATH')
    confidence_threshold: float = float(os.getenv('CONFIDENCE_THRESHOLD', 0.5))

def validate_input(image_path: str) -> None:
    """Validate input image path.
    
    Args:
        image_path: Path to the image file
    Raises:
        ValueError: If the image path is invalid
    """
    if not os.path.isfile(image_path):
        raise ValueError(f'Invalid image path: {image_path}')  # Raise error for invalid path

def load_image(image_path: str) -> np.ndarray:
    """Load an image from the specified path.
    
    Args:
        image_path: Path to the image file
    Returns:
        Loaded image as a numpy array
    Raises:
        FileNotFoundError: If the image does not exist
    """
    try:
        image = cv2.imread(image_path)
        if image is None:
            raise FileNotFoundError('Image not found or could not be loaded.')
        return image  # Return loaded image
    except Exception as e:
        logger.error(f'Error loading image: {e}')  # Log error
        raise

def preprocess_image(image: np.ndarray) -> np.ndarray:
    """Preprocess the image for model input.
    
    Args:
        image: Original image as a numpy array
    Returns:
        Preprocessed image as a numpy array
    """
    image_resized = cv2.resize(image, (224, 224))  # Resize image to match model input
    image_normalized = image_resized / 255.0  # Normalize pixel values
    return image_normalized

def recognize_components(image: np.ndarray) -> Dict[str, Any]:
    """Recognize equipment components in the image using a pre-trained model.
    
    Args:
        image: Preprocessed image
    Returns:
        Dictionary of recognized components and their confidence scores
    """
    model = load_model(Config.model_path)  # Load the CLIP model
    predictions = model.predict(image[np.newaxis, ...])  # Predict components
    recognized = {}  # Dictionary to hold recognized components
    for component, score in predictions.items():
        if score > Config.confidence_threshold:
            recognized[component] = score  # Store component if above threshold
    return recognized

def load_model(model_path: str) -> Any:
    """Load the machine learning model from the specified path.
    
    Args:
        model_path: Path to the model file
    Returns:
        Loaded model
    Raises:
        Exception: If model loading fails
    """
    try:
        # Pseudo code for loading a CLIP model
        model = ...  # Load your CLIP model
        return model  # Return loaded model
    except Exception as e:
        logger.error(f'Error loading model: {e}')  # Log error
        raise

def main(image_path: str) -> None:
    """Main function to orchestrate the recognition process.
    
    Args:
        image_path: Path to the image file
    """
    try:
        validate_input(image_path)  # Validate input image
        image = load_image(image_path)  # Load the image
        processed_image = preprocess_image(image)  # Preprocess the image
        results = recognize_components(processed_image)  # Recognize components
        logger.info(f'Recognition results: {results}')  # Log results
    except Exception as e:
        logger.error(f'Error in processing: {e}')  # Log error

if __name__ == '__main__':
    # Example usage
    main('path/to/equipment_image.jpg')