Build Cross-View Factory Component Matching Pipelines with LightGlue and OpenCV

"""
Production implementation for building cross-view factory component matching pipelines using LightGlue and OpenCV.
Provides secure, scalable operations for image matching processes in manufacturing.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import cv2
import numpy as np
from time import sleep
from dataclasses import dataclass

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    lightglue_threshold: float = float(os.getenv('LIGHTGLUE_THRESHOLD', '0.8'))

@dataclass
class ImageData:
    """
    Data structure to hold image details.
    """
    path: str
    keypoints: List[cv2.KeyPoint]
    descriptors: np.ndarray

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for image paths.
    
    Args:
        data: Input dictionary containing image paths.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not isinstance(data, dict):
        raise ValueError('Input must be a dictionary.')
    required_keys = ['image1', 'image2']
    for key in required_keys:
        if key not in data:
            raise ValueError(f'Missing key: {key}')
    return True

def fetch_data(image_paths: Dict[str, str]) -> Tuple[ImageData, ImageData]:
    """Fetch and prepare image data for processing.
    
    Args:
        image_paths: Dictionary containing paths for two images.
    Returns:
        Tuple of ImageData for the two images.
    Raises:
        FileNotFoundError: If an image path is invalid.
    """
    images = []
    for path in image_paths.values():
        if not os.path.exists(path):
            raise FileNotFoundError(f'Image not found: {path}')
        image = cv2.imread(path)
        keypoints, descriptors = detect_keypoints(image)
        images.append(ImageData(path, keypoints, descriptors))
    return tuple(images)

def detect_keypoints(image: np.ndarray) -> Tuple[List[cv2.KeyPoint], np.ndarray]:
    """Detect keypoints and descriptors in an image using ORB.
    
    Args:
        image: Input image as a NumPy array.
    Returns:
        List of keypoints and corresponding descriptors.
    """
    orb = cv2.ORB_create()
    keypoints, descriptors = orb.detectAndCompute(image, None)
    return keypoints, descriptors

def match_images(image_data1: ImageData, image_data2: ImageData) -> List[cv2.DMatch]:
    """Match descriptors between two images using BFMatcher.
    
    Args:
        image_data1: First image data.
        image_data2: Second image data.
    Returns:
        List of matches found between the two images.
    """
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(image_data1.descriptors, image_data2.descriptors)
    matches = sorted(matches, key=lambda x: x.distance)
    return matches

def transform_records(matches: List[cv2.DMatch]) -> List[Dict[str, Any]]:
    """Transform match records into a more usable format.
    
    Args:
        matches: List of cv2.DMatch objects.
    Returns:
        List of dictionaries containing match info.
    """
    return [{'queryIdx': match.queryIdx, 'trainIdx': match.trainIdx, 'distance': match.distance} for match in matches]

def aggregate_metrics(matches: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate matching metrics for analysis.
    
    Args:
        matches: List of match records.
    Returns:
        Dictionary with aggregated metrics.
    """
    return {'total_matches': len(matches), 'average_distance': np.mean([m['distance'] for m in matches])}

def save_to_db(data: Dict[str, Any]) -> None:
    """Stub function to save results to the database.
    
    Args:
        data: Data to be saved.
    Raises:
        Exception: If saving fails (simulated).
    """
    logger.info('Saving to database...')
    # Simulate a database operation
    sleep(1)
    logger.info('Data saved successfully.')

class MatchingPipeline:
    """Class to orchestrate the matching pipeline workflow.
    """
    def __init__(self, config: Config):
        self.config = config

    def execute(self, image_paths: Dict[str, str]) -> Dict[str, Any]:
        """Execute the complete matching workflow.
        
        Args:
            image_paths: Dictionary with image paths.
        Returns:
            Aggregated metrics from the matching process.
        """
        try:
            validate_input(image_paths)
            image_data1, image_data2 = fetch_data(image_paths)
            matches = match_images(image_data1, image_data2)
            transformed_matches = transform_records(matches)
            metrics = aggregate_metrics(transformed_matches)
            save_to_db(metrics)
            return metrics
        except Exception as e:
            logger.error(f'Error during processing: {e}')
            return {'error': str(e)}

if __name__ == '__main__':
    # Example usage of the pipeline
    config = Config()
    pipeline = MatchingPipeline(config)
    paths = {'image1': 'path/to/image1.jpg', 'image2': 'path/to/image2.jpg'}
    result = pipeline.execute(paths)
    logger.info(f'Matching result: {result}')