Segment Industrial Defects with Florence-2 and Detectron2

                      
                     
"""
Production implementation for segmenting industrial defects using Florence-2 and Detectron2.
Provides secure, scalable operations for defect detection and segmentation.
"""

from typing import Dict, Any, List, Tuple, Optional
import os
import logging
import requests
import time
import numpy as np
from fastapi import FastAPI, HTTPException

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

class Config:
    """
    Configuration class for environment variables.
    """
    florence_url: str = os.getenv('FLORENCE_URL')
    detectron_url: str = os.getenv('DETECTRON_URL')

# Helper Functions
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_url' not in data:
        raise ValueError('Missing image_url in request data')
    return True

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

async def fetch_data(image_url: str) -> np.ndarray:
    """Fetch image data from the provided URL.
    
    Args:
        image_url: URL of the image
    Returns:
        Image data as a NumPy array
    Raises:
        HTTPException: If fetching the image fails
    """
    try:
        response = requests.get(image_url)
        response.raise_for_status()
        # Convert to NumPy array
        return np.frombuffer(response.content, np.uint8)
    except requests.exceptions.RequestException as e:
        logger.error(f"Failed to fetch image: {e}")
        raise HTTPException(status_code=400, detail="Failed to fetch image")

async def call_api(endpoint: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Call an external API with the given payload.
    
    Args:
        endpoint: API endpoint to call
        payload: Data to send
    Returns:
        Response data from the API
    Raises:
        HTTPException: If API call fails
    """
    for attempt in range(3):  # Retry logic with exponential backoff
        try:
            response = requests.post(endpoint, json=payload)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.warning(f"Attempt {attempt + 1} failed: {e}")
            time.sleep(2 ** attempt)  # Exponential backoff
    raise HTTPException(status_code=500, detail="API call failed after retries")

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of images for defect segmentation.
    
    Args:
        data: List of images to process
    Returns:
        List of processed results
    """
    results = []
    for item in data:
        try:
            sanitized = await sanitize_fields(item)
            await validate_input(sanitized)
            image_data = await fetch_data(sanitized['image_url'])
            payload = {'image': image_data.tolist()}  # Prepare payload
            result = await call_api(Config.detectron_url, payload)
            results.append(result)
        except Exception as e:
            logger.error(f"Error processing {item}: {e}")
            results.append({'error': str(e)})
    return results

async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save processed results to the database.
    
    Args:
        results: Results to save
    """
    # Here you would implement actual database saving logic
    logger.info(f"Saving {len(results)} results to database")

# Main Orchestrator
app = FastAPI()

@app.post("/segment_defects")
async def segment_defects(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Endpoint for segmenting industrial defects.
    
    Args:
        data: List of images to process
    Returns:
        List of results for each image
    Raises:
        HTTPException: If processing fails
    """
    try:
        results = await process_batch(data)
        await save_to_db(results)
        return results
    except Exception as e:
        logger.error(f"Error in segment_defects: {e}")
        raise HTTPException(status_code=500, detail="Processing failed")

if __name__ == '__main__':
    # Example usage of the segment_defects function
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)