Match Industrial Parts Across Multi-Camera Rigs with LightGlue and Supervision

"""
Production implementation for matching industrial parts across multi-camera rigs using LightGlue and Supervision.
Provides secure, scalable operations.
"""
from typing import Dict, Any, Tuple, List
import os
import logging
import requests
import time
from contextlib import contextmanager

# Set up logging for debugging and tracking
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')

@contextmanager
def db_connection_pool():
    """
    Context manager for managing database connection pooling.
    
    Yields:
        Connection object for database operations.
    """
    # Simulate a connection pool; replace with actual pooling logic
    logger.info('Creating a new database connection.')
    connection = 'db_connection'  # Placeholder for actual DB connection
    try:
        yield connection
    finally:
        logger.info('Closing database connection.')

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 'camera_id' not in data:
        raise ValueError('Missing camera_id')
    if 'part_id' not in data:
        raise ValueError('Missing part_id')
    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
    """
    # Example sanitization - implement actual sanitization logic
    return {k: str(v).strip() for k, v in data.items()}

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    
    Args:
        data: Raw input data
    Returns:
        Normalized data structure
    """
    logger.info('Normalizing data.')
    return {key: value.lower() for key, value in data.items()}

def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from the specified API endpoint.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        JSON response from the API
    Raises:
        Exception: If the API call fails
    """
    try:
        logger.info(f'Fetching data from {endpoint}.')
        response = requests.get(endpoint)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('API fetch error')

def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save to the database
    """
    with db_connection_pool() as conn:
        logger.info('Saving data to database.')
        # Placeholder for actual DB save logic
        pass

def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data records.
    
    Args:
        data: List of data records to process
    Returns:
        List of processed records
    """
    logger.info('Processing batch of data.')
    processed_data = []
    for record in data:
        try:
            validate_input(record)
            sanitized_record = sanitize_fields(record)
            normalized_record = normalize_data(sanitized_record)
            processed_data.append(normalized_record)
        except ValueError as e:
            logger.warning(f'Validation error: {e}')
    return processed_data

def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed records.
    
    Args:
        data: Processed records
    Returns:
        Dictionary of aggregated metrics
    """
    logger.info('Aggregating metrics from data.')
    # Placeholder for actual aggregation logic
    return {'count': len(data)}

class LightGlueMatcher:
    """Main orchestrator for matching industrial parts across multi-camera rigs.
    
    Manages the workflow of fetching, processing, and saving data.
    """
    def __init__(self, config: Config):
        self.config = config

    def run(self) -> None:
        """Execute the matching process.
        """
        logger.info('Starting matching process.')
        data = fetch_data(self.config.api_endpoint)
        processed_data = process_batch(data)
        metrics = aggregate_metrics(processed_data)
        save_to_db(metrics)
        logger.info('Matching process completed.')

if __name__ == '__main__':
    config = Config()
    matcher = LightGlueMatcher(config)
    try:
        matcher.run()
    except Exception as e:
        logger.error(f'Error during the matching process: {e}')