Score and Filter Inspection Frames for Active Learning with FiftyOne and Ultralytics

"""
Production implementation for scoring and filtering inspection frames for active learning.
Utilizes FiftyOne for data visualization and Ultralytics for model inference.
"""

from typing import Dict, Any, List
import os
import logging
import time
import fiftyone as fo
import ultralytics

# Setting up logging for tracking application flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    Holds database URLs and model configurations.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    model_path: str = os.getenv('MODEL_PATH', 'yolov8.pt')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming data for scoring.
    
    Args:
        data: Input dictionary containing frames and parameters
    Returns:
        bool: True if valid
    Raises:
        ValueError: If the validation fails
    """
    if 'frames' not in data or not isinstance(data['frames'], list):
        raise ValueError('Missing or invalid frames data')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize fields in input data.
    
    Args:
        data: The input dictionary to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def normalize_data(frames: List[str]) -> List[str]:
    """Normalize frame paths for processing.
    
    Args:
        frames: List of frame paths
    Returns:
        List[str]: Normalized paths
    """
    return [frame.replace(' ', '_') for frame in frames]

async def process_batch(frames: List[str]) -> List[Dict[str, Any]]:
    """Process a batch of frames using the Ultralytics model.
    
    Args:
        frames: List of frame paths to process
    Returns:
        List[Dict[str, Any]]: Processed results
    """
    model = ultralytics.YOLO(Config.model_path)
    results = []

    for frame in frames:
        # Perform inference on each frame and store the result
        result = model(frame)
        results.append(result)
        logger.info(f'Processed frame: {frame}')  # Log each processed frame
    return results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of inference results
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    metrics = {'total_frames': len(results), 'valid_frames': 0}
    for result in results:
        if result['valid']:
            metrics['valid_frames'] += 1
    return metrics

async def fetch_data() -> List[str]:
    """Fetch frame data from the database.
    
    Returns:
        List[str]: List of frame paths
    """
    # Simulating a database fetch with a placeholder
    logger.info('Fetching data from database')
    return ['frame1.jpg', 'frame2.jpg', 'frame3.jpg']

async def save_to_db(metrics: Dict[str, Any]) -> None:
    """Save metrics to the database.
    
    Args:
        metrics: The metrics dictionary to save
    """
    logger.info('Saving metrics to database')
    # Placeholder for actual database saving logic

async def call_api(data: Dict[str, Any]) -> None:
    """Call external API with processed data.
    
    Args:
        data: Data to send to the API
    """
    logger.info('Calling external API')
    # Placeholder for actual API call logic

async def format_output(metrics: Dict[str, Any]) -> str:
    """Format metrics for output.
    
    Args:
        metrics: The metrics to format
    Returns:
        str: Formatted output string
    """
    return f"Total Frames: {metrics['total_frames']}, Valid Frames: {metrics['valid_frames']}"

class InspectionFrameProcessor:
    """Main orchestrator class for frame processing.
    
    Manages the entire workflow from fetching data to processing and saving.
    """

    async def run(self) -> None:
        try:
            # Fetch the data
            raw_data = await fetch_data()
            # Validate the data
            await validate_input({'frames': raw_data})
            # Sanitize the data
            sanitized_data = await sanitize_fields({'frames': raw_data})
            # Normalize the data
            normalized_frames = await normalize_data(sanitized_data['frames'])
            # Process the batch
            results = await process_batch(normalized_frames)
            # Aggregate metrics
            metrics = await aggregate_metrics(results)
            # Save metrics to the database
            await save_to_db(metrics)
            # Call external API
            await call_api(metrics)
            # Format and log the output
            output = await format_output(metrics)
            logger.info(output)
        except Exception as e:
            logger.error(f'Error in processing: {str(e)}')
            raise

if __name__ == '__main__':
    # Example usage of the InspectionFrameProcessor
    processor = InspectionFrameProcessor()
    import asyncio
    asyncio.run(processor.run())