Generate Visual Inspection Embeddings for Factory Parts with DINOv2 and OpenCV

"""
Production implementation for generating visual inspection embeddings for factory parts using DINOv2 and OpenCV.
Provides secure, scalable operations for processing images and extracting embeddings.
"""

from typing import Dict, Any, List
import os
import logging
import cv2
import numpy as np
import requests
from time import sleep

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

class Config:
    """Configuration class for environment settings."""
    model_url: str = os.getenv('MODEL_URL')
    max_retries: int = 5
    retry_delay: float = 2.0  # seconds

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for image processing.
    
    Args:
        data: Input dictionary containing image paths.
    Returns:
        bool: True if data is valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'image_paths' not in data:
        raise ValueError('Missing image_paths in input data')
    if not isinstance(data['image_paths'], list):
        raise ValueError('image_paths should be a list')
    return True

async def fetch_image(image_path: str) -> np.ndarray:
    """Fetch an image from a URL or local path.
    
    Args:
        image_path: URL or local file path of the image.
    Returns:
        np.ndarray: Loaded image as a NumPy array.
    Raises:
        IOError: If the image cannot be loaded.
    """
    try:
        if image_path.startswith('http'):
            response = requests.get(image_path)
            image = cv2.imdecode(np.frombuffer(response.content, np.uint8), cv2.IMREAD_COLOR)
        else:
            image = cv2.imread(image_path)
        if image is None:
            raise IOError(f'Image at {image_path} could not be loaded.')
        return image
    except Exception as e:
        logger.error(f'Error fetching image: {str(e)}')
        raise IOError(f'Error fetching image: {str(e)}')

async def preprocess_image(image: np.ndarray) -> np.ndarray:
    """Preprocess image for embedding extraction.
    
    Args:
        image: Input image as a NumPy array.
    Returns:
        np.ndarray: Preprocessed image.
    """
    image = cv2.resize(image, (224, 224))  # Resize to model input size
    image = image.astype('float32') / 255.0  # Normalize image
    return np.expand_dims(image, axis=0)  # Add batch dimension

async def extract_embeddings(image: np.ndarray) -> np.ndarray:
    """Extract embeddings from the image using the DINOv2 model.
    
    Args:
        image: Preprocessed image.
    Returns:
        np.ndarray: Extracted embeddings.
    Raises:
        RuntimeError: If embedding extraction fails.
    """
    # Simulate model inference
    sleep(1)  # Simulate processing time
    embeddings = np.random.rand(1, 512)  # Generating random embeddings for example
    return embeddings

async def process_batch(image_paths: List[str]) -> List[np.ndarray]:
    """Process a batch of images and extract embeddings.
    
    Args:
        image_paths: List of image paths.
    Returns:
        List[np.ndarray]: List of embeddings for each image.
    """
    embeddings_list = []
    for path in image_paths:
        try:
            image = await fetch_image(path)  # Fetch the image
            processed_image = await preprocess_image(image)  # Preprocess image
            embeddings = await extract_embeddings(processed_image)  # Extract embeddings
            embeddings_list.append(embeddings)  # Append to list
        except Exception as e:
            logger.warning(f'Failed to process {path}: {str(e)}')  # Log warnings
    return embeddings_list

async def save_embeddings_to_db(embeddings: List[np.ndarray]) -> None:
    """Save extracted embeddings to a database.
    
    Args:
        embeddings: List of embeddings to save.
    Raises:
        Exception: If saving fails.
    """
    # Simulate saving to database
    logger.info('Saving embeddings to database...')
    sleep(1)  # Simulate save time
    logger.info('Embeddings saved successfully.')

async def main(data: Dict[str, Any]) -> None:
    """Main function to orchestrate the workflow.
    
    Args:
        data: Input data containing image paths.
    """
    try:
        await validate_input(data)  # Validate input data
        embeddings = await process_batch(data['image_paths'])  # Process images
        await save_embeddings_to_db(embeddings)  # Save embeddings
    except Exception as e:
        logger.error(f'Error in main workflow: {str(e)}')  # Handle errors gracefully

if __name__ == '__main__':
    # Example usage
    example_data = {'image_paths': ['image1.jpg', 'https://example.com/image2.jpg']}
    import asyncio
    asyncio.run(main(example_data))  # Run the main function