Curate and De-duplicate Industrial Vision Datasets for Training with FiftyOne and Supervision

"""
Production implementation for Curating and De-duplicating Industrial Vision Datasets.
Provides secure, scalable operations using FiftyOne and Supervision.
"""
from typing import Dict, Any, List
import os
import logging
import time
import fiftyone as fo
from fiftyone.utils import Dataset

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

class Config:
    """
    Configuration class for managing environment variables.
    """
    dataset_name: str = os.getenv('DATASET_NAME', 'industrial_datasets')
    db_url: str = os.getenv('DATABASE_URL', 'sqlite:///data.db')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data structure.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'images' not in data or not isinstance(data['images'], List):
        raise ValueError('Invalid data: Missing or invalid images list')
    logger.info('Input data validation passed')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to avoid security issues.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}  # Strip whitespace
    logger.info('Sanitized input fields')
    return sanitized_data

async def normalize_data(images: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize image data for consistency.
    
    Args:
        images: List of image records
    Returns:
        Normalized image data
    """
    normalized_images = []
    for image in images:
        normalized_image = {
            'filepath': image['filepath'],
            'label': image.get('label', 'unknown').lower(),  # Normalize label to lowercase
        }
        normalized_images.append(normalized_image)
    logger.info('Normalized image data')
    return normalized_images

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of images for deduplication.
    
    Args:
        batch: List of image records to process
    """
    logger.info('Processing batch of images')
    dataset = Dataset(Config.dataset_name)
    for image in batch:
        # Check for existing image in dataset
        if not dataset.contains(image['filepath']):
            dataset.add(image)
            logger.info(f'Added image {image["filepath"]} to dataset')
        else:
            logger.warning(f'Image {image["filepath"]} already exists in dataset')

async def aggregate_metrics(dataset: Dataset) -> Dict[str, Any]:
    """Aggregate metrics from the dataset.
    
    Args:
        dataset: Dataset to aggregate metrics from
    Returns:
        Dictionary of aggregated metrics
    """
    metrics = {
        'total_images': len(dataset),
        'unique_labels': len(set([img['label'] for img in dataset]))
    }
    logger.info('Aggregated metrics from the dataset')
    return metrics

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save aggregated metrics to a database.
    
    Args:
        data: Metrics data to save
    """
    # Here we would save the data to a database
    logger.info('Saving metrics to the database')

async def handle_errors(func):
    """Decorator for handling errors in async functions.
    
    Args:
        func: The function to wrap
    """    
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {str(e)}')
            raise
    return wrapper

class DatasetManager:
    def __init__(self, config: Config) -> None:
        self.config = config

    async def curate_dataset(self, data: Dict[str, Any]) -> None:
        """Curate the dataset based on input data.
        
        Args:
            data: Input data for curation
        """    
        await validate_input(data)
        sanitized_data = await sanitize_fields(data)
        normalized_images = await normalize_data(sanitized_data['images'])
        await process_batch(normalized_images)
        dataset = Dataset(self.config.dataset_name)
        metrics = await aggregate_metrics(dataset)
        await save_to_db(metrics)
        logger.info('Dataset curation completed')

if __name__ == '__main__':
    config = Config()
    manager = DatasetManager(config)
    sample_data = {'images': [{'filepath': 'path/to/image1.jpg', 'label': 'Car'}, {'filepath': 'path/to/image2.jpg', 'label': 'Truck'}]}
    # Example usage of dataset curation
    import asyncio
    asyncio.run(manager.curate_dataset(sample_data))