Segment and Track Industrial Defects Across Frames with SAM 2 and Ultralytics

"""
Production implementation for Segment and Track Industrial Defects Across Frames with SAM 2 and Ultralytics.
Provides secure, scalable operations for defect tracking and reporting.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import cv2
import requests
import numpy as np
from functools import wraps

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    sam_model_path: str = os.getenv('SAM_MODEL_PATH', 'path/to/sam/model')
    ultralytics_model_path: str = os.getenv('ULTRALYTICS_MODEL_PATH', 'path/to/ultralytics/model')

def retry(exception_to_check, tries: int = 3, delay: float = 1, backoff: float = 2):
    """
    Retry decorator for handling transient errors.
    
    Args:
        exception_to_check: Exception type to check against
        tries: Number of attempts to retry
        delay: Initial delay before retrying
        backoff: Multiplier for subsequent delays
    """
    def deco_retry(f):
        @wraps(f)
        def f_retry(*args, **kwargs):
            mtries, mdelay = tries, delay
            while mtries > 1:
                try:
                    return f(*args, **kwargs)
                except exception_to_check as e:
                    logger.warning(f'Retrying {f.__name__} due to {e}')
                    time.sleep(mdelay)
                    mtries -= 1
                    mdelay *= backoff
            return f(*args, **kwargs)
        return f_retry
    return deco_retry

@retry(Exception)
def fetch_frame(frame_url: str) -> np.ndarray:
    """
    Fetch a frame from a given URL.
    
    Args:
        frame_url: URL of the frame to fetch
    Returns:
        Numpy array of the fetched frame
    Raises:
        ValueError: If the frame cannot be fetched
    """
    response = requests.get(frame_url)
    if response.status_code != 200:
        raise ValueError(f'Error fetching frame: {response.status_code}')
    return cv2.imdecode(np.frombuffer(response.content, np.uint8), cv2.IMREAD_COLOR)

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields for processing.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Normalize the input data for processing.
    
    Args:
        data: List of input data to normalize
    Returns:
        Normalized list of data
    """
    # Example normalization (could be complex depending on the use case)
    return [{k: v.lower() for k, v in item.items()} for item in data]

def process_defects(frame: np.ndarray) -> List[Dict[str, Any]]:
    """
    Process the frame to identify defects using SAM 2 and Ultralytics.
    
    Args:
        frame: Image frame to process
    Returns:
        List of detected defects with coordinates
    """
    # Placeholder for actual SAM 2 and Ultralytics processing
    defects = []  # This would normally contain the output of model inference
    logger.info('Processing frame for defects')
    return defects

def aggregate_metrics(defects: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Aggregate metrics from detected defects.
    
    Args:
        defects: List of defects detected
    Returns:
        Dictionary of metrics
    """
    metrics = {'total_defects': len(defects)}
    logger.info('Aggregating defect metrics')
    return metrics

def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save defect data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    # Placeholder for actual DB save operation
    logger.info('Saving data to database')

class DefectTracker:
    """
    Main orchestrator for defect tracking operations.
    """
    def __init__(self):
        self.config = Config()

    def track_defects(self, frame_url: str) -> None:
        """
        Main workflow for tracking defects in a given frame.
        
        Args:
            frame_url: URL of the frame to analyze
        """
        logger.info('Starting defect tracking workflow')
        try:
            frame = fetch_frame(frame_url)  # Fetch the frame
            defects = process_defects(frame)  # Process the frame
            metrics = aggregate_metrics(defects)  # Aggregate results
            save_to_db(metrics)  # Save results to the DB
        except Exception as e:
            logger.error(f'Error in defect tracking: {e}')
            handle_errors(e)  # Handle errors gracefully

    def handle_errors(self, error: Exception) -> None:
        """
        Handle errors gracefully.
        
        Args:
            error: The error to handle
        """
        logger.error(f'Handled error: {error}')

if __name__ == '__main__':
    tracker = DefectTracker()
    tracker.track_defects('http://example.com/frame.jpg')  # Example usage