Build Compact Industrial Vision Encoders with EUPE and OpenCV

                      
                     
"""
Production implementation for building compact industrial vision encoders using EUPE and OpenCV.
Provides secure, scalable operations for image processing and encoding tasks.
"""

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

# Setup logger for monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class that retrieves environment variables.
    """
    image_dir: str = os.getenv('IMAGE_DIR', '/path/to/images')
    output_dir: str = os.getenv('OUTPUT_DIR', '/path/to/output')

def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate request data for encoder.
    
    Args:
        data: Input data to validate, must contain required fields.
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image_name' not in data:
        raise ValueError('Missing image_name key in input data.')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize.
    Returns:
        Sanitized data dictionary.
    """
    return {key: str(value).strip() for key, value in data.items()}

@retry(stop_max_attempt_number=3, wait_exponential_multiplier=1000)
def fetch_data(image_name: str) -> np.ndarray:
    """Fetch image data from the specified directory.
    
    Args:
        image_name: Name of the image file to fetch.
    Returns:
        Image data as a NumPy array.
    Raises:
        FileNotFoundError: If the image is not found.
    """
    image_path = os.path.join(Config.image_dir, image_name)
    logger.info(f'Fetching image: {image_path}')
    image = cv2.imread(image_path)
    if image is None:
        raise FileNotFoundError(f'Image {image_name} not found.')
    return image

def normalize_data(image: np.ndarray) -> np.ndarray:
    """Normalize the image data for processing.
    
    Args:
        image: Input image as a NumPy array.
    Returns:
        Normalized image data.
    """
    return cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)

def transform_records(image: np.ndarray) -> np.ndarray:
    """Transform image records (e.g., resizing, color conversion).
    
    Args:
        image: Input image as a NumPy array.
    Returns:
        Transformed image data.
    """
    return cv2.resize(image, (640, 480))  # Resize to 640x480

def process_batch(images: List[np.ndarray]) -> List[np.ndarray]:
    """Process a batch of images.
    
    Args:
        images: List of images to process.
    Returns:
        List of processed images.
    """
    return [transform_records(normalize_data(image)) for image in images]

def save_to_db(encoded_data: Any) -> None:
    """Simulate saving encoded data to a database.
    
    Args:
        encoded_data: Encoded data to save.
    """
    logger.info('Saving encoded data to database...')
    # Here you would implement the actual database saving logic

def call_api(encoded_data: Any) -> None:
    """Simulate calling an external API with encoded data.
    
    Args:
        encoded_data: Encoded data to send.
    """
    logger.info('Calling external API with encoded data...')
    # Implement actual API call logic here

def format_output(encoded_data: Any) -> str:
    """Format the encoded data for output.
    
    Args:
        encoded_data: Data to format.
    Returns:
        Formatted output string.
    """
    return f'Encoded data: {encoded_data}'

class IndustrialVisionEncoder:
    """Main class for encoding images.
    """

    def __init__(self, image_name: str):
        self.image_name = image_name
        self.encoded_data = None

    def run(self) -> None:
        """Run the encoding process.
        """
        try:
            # Validate and fetch data
            input_data = {'image_name': self.image_name}
            validate_input_data(input_data)
            sanitized_data = sanitize_fields(input_data)
            image = fetch_data(sanitized_data['image_name'])

            # Process image
            processed_images = process_batch([image])
            # Simulate encoding (just for the example)
            self.encoded_data = processed_images[0].tobytes()

            # Save and call API
            save_to_db(self.encoded_data)
            response = call_api(self.encoded_data)
            logger.info(format_output(self.encoded_data))
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            handle_errors(e)

    def handle_errors(self, error: Exception) -> None:
        """Handle errors gracefully.
        
        Args:
            error: The caught exception.
        """
        logger.error(f'Handling error: {error}')

if __name__ == '__main__':
    # Example usage with an image name
    encoder = IndustrialVisionEncoder(image_name='example_image.jpg')
    encoder.run()