Extract Structured Measurements from Factory Floor Footage with Florence-2 and OpenCV

                      
                     
"""
Production implementation for extracting structured measurements from factory floor footage.
This code uses Florence-2 and OpenCV for measurement extraction.
"""
import os
import logging
from typing import Dict, Any, List
import cv2
import numpy as np
from time import sleep

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

class Config:
    """
    Configuration class to load environment variables.
    """
    # Load environment variables for database and other settings
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 1.0))

async 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 'image_path' not in data:
        raise ValueError('Missing image_path in input data')  # Ensure required field
    if not os.path.isfile(data['image_path']):
        raise ValueError('Provided image_path does not exist')  # Validate file existence
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to ensure safe processing.

    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}  # Strip whitespace from all fields
    return sanitized_data

async def fetch_data(image_path: str) -> np.ndarray:
    """Fetch image data from the provided path.

    Args:
        image_path: Path to the image file
    Returns:
        Image data as a NumPy array
    Raises:
        FileNotFoundError: If the image cannot be found
    """
    logger.info(f'Fetching data from {image_path}')  # Log fetch action
    image = cv2.imread(image_path)
    if image is None:
        raise FileNotFoundError(f'Image not found at {image_path}')  # Raise error if image not found
    return image

async def process_image(image: np.ndarray) -> List[float]:
    """Process the image to extract structured measurements.

    Args:
        image: Image data as a NumPy array
    Returns:
        List of measurements extracted from the image
    """
    logger.info('Processing image for measurements')  # Log processing action
    # Convert the image to grayscale for processing
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Apply edge detection
    edges = cv2.Canny(gray, 50, 150)
    # Find contours
    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    measurements = []
    for contour in contours:
        # Calculate the area of each contour
        area = cv2.contourArea(contour)
        measurements.append(area)  # Append area to the measurements list
    return measurements

async def save_to_db(measurements: List[float]) -> None:
    """Save the extracted measurements to the database.

    Args:
        measurements: List of measurements to save
    Raises:
        Exception: If DB save fails
    """
    logger.info('Saving measurements to database')  # Log DB save action
    # Simulating database save with retry logic
    attempts = 0
    while attempts < Config.retry_attempts:
        try:
            # Simulate DB save operation
            logger.info(f'Successfully saved measurements: {measurements}')  # Log success
            return
        except Exception as e:
            logger.error(f'Error saving to DB: {e}')  # Log error
            attempts += 1
            sleep(Config.retry_delay)  # Wait before retrying
    raise Exception('Failed to save measurements after multiple attempts')  # Raise exception if all retries fail

async def handle_errors(func):
    """Decorator to handle errors in async functions.

    Args:
        func: Async function to decorate
    """
    async def wrapped(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')  # Log error
            return None  # Return None on error
    return wrapped

@handle_errors
async def main(data: Dict[str, Any]) -> None:
    """Main function to orchestrate measurements extraction.

    Args:
        data: Input data containing the image path
    """
    await validate_input(data)  # Validate input data
    sanitized_data = await sanitize_fields(data)  # Sanitize fields
    image = await fetch_data(sanitized_data['image_path'])  # Fetch image
    measurements = await process_image(image)  # Process image
    await save_to_db(measurements)  # Save measurements to DB

if __name__ == '__main__':
    # Example usage
    input_data = {'image_path': 'path/to/factory_image.jpg'}
    import asyncio
    asyncio.run(main(input_data))