Recognize Equipment Components with CLIP and OpenCV

                      
                     
"""
Production implementation for recognizing equipment components using CLIP and OpenCV.
Provides secure, scalable operations for identifying components in images.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import cv2
import numpy as np
import requests
from PIL import Image
import torch
from torchvision import transforms

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

class Config:
    """
    Configuration class for environment variables.
    """
    model_url: str = os.getenv('MODEL_URL', 'http://localhost:5000/model')
    api_key: str = os.getenv('API_KEY')

async def validate_input(image_path: str) -> bool:
    """Validate the image path input.
    Args:
        image_path: Path to the input image
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not os.path.isfile(image_path):
        raise ValueError(f'Input image not found: {image_path}')
    return True

async def preprocess_image(image_path: str) -> torch.Tensor:
    """Preprocess the image for CLIP model input.
    Args:
        image_path: Path to the input image
    Returns:
        Preprocessed image tensor
    Raises:
        Exception: If image processing fails
    """
    try:
        image = Image.open(image_path).convert('RGB')
        preprocess = transforms.Compose([
            transforms.Resize((224, 224)),  # Resize to model input size
            transforms.ToTensor(),  # Convert image to tensor
            transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])  # Normalize
        ])
        return preprocess(image).unsqueeze(0)  # Add batch dimension
    except Exception as e:
        logger.error(f'Error processing image: {e}')
        raise

async def fetch_model_output(image_tensor: torch.Tensor) -> Dict[str, Any]:
    """Fetch model output from CLIP API.
    Args:
        image_tensor: Preprocessed image tensor
    Returns:
        Model output containing recognized equipment
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post(Config.model_url, json={'image': image_tensor.tolist()}, headers={'Authorization': f'Bearer {Config.api_key}'})
        response.raise_for_status()
        return response.json()
    except Exception as e:
        logger.error(f'Failed to fetch model output: {e}')
        raise

async def process_output(output: Dict[str, Any]) -> List[str]:
    """Process and extract relevant information from model output.
    Args:
        output: JSON response from the model
    Returns:
        List of recognized equipment components
    Raises:
        KeyError: If expected keys are missing
    """
    try:
        return output['components']  # Example of extracting components
    except KeyError:
        logger.error('Missing expected keys in output')
        raise

async def recognize_equipment(image_path: str) -> List[str]:
    """Main function to recognize equipment from an image.
    Args:
        image_path: Path to the input image
    Returns:
        List of recognized equipment components
    Raises:
        Exception: If any step fails
    """
    await validate_input(image_path)  # Validate input
    image_tensor = await preprocess_image(image_path)  # Preprocess the image
    output = await fetch_model_output(image_tensor)  # Fetch model output
    return await process_output(output)  # Process the output

if __name__ == '__main__':
    import asyncio
    try:
        # Example usage
        image_path = 'path/to/equipment_image.jpg'
        components = asyncio.run(recognize_equipment(image_path))
        logger.info(f'Recognized components: {components}')
    except Exception as e:
        logger.error(f'Error during recognition: {e}')