Detect Open-Set Objects with Grounding DINO and DVC

                      
                     
"""
Production implementation for detecting open-set objects using Grounding DINO and DVC.
Provides secure, scalable operations for object detection in images.
"""

from typing import List, Dict, Any
import os
import logging
import requests
import time

# Set up logging for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to hold environment variables."""
    dvc_url: str = os.getenv('DVC_URL')
    model_endpoint: str = os.getenv('MODEL_ENDPOINT')

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 input data')
    return True

async def fetch_image(image_url: str) -> bytes:
    """Fetch image from a given URL.
    
    Args:
        image_url: URL of the image to fetch
    Returns:
        Image bytes
    Raises:
        RuntimeError: If fetching fails
    """
    try:
        response = requests.get(image_url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Fetched image successfully')
        return response.content
    except requests.RequestException as e:
        logger.error(f'Error fetching image: {e}')
        raise RuntimeError('Could not fetch image')

async def transform_image(image_bytes: bytes) -> Dict[str, Any]:
    """Transform image bytes to required format for model.
    
    Args:
        image_bytes: Raw image bytes
    Returns:
        Transformed image data
    """
    # Placeholder for transformation logic
    logger.info('Transforming image data')
    return {'data': image_bytes}  # Simulated transformation

async def call_model(image_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Call the model API for object detection.
    
    Args:
        image_data: Transformed image data
    Returns:
        List of detected objects
    Raises:
        RuntimeError: If model call fails
    """
    try:
        response = requests.post(Config.model_endpoint, json=image_data)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Model returned results successfully')
        return response.json()['detections']
    except requests.RequestException as e:
        logger.error(f'Error calling model: {e}')
        raise RuntimeError('Could not call model')

async def save_results(results: List[Dict[str, Any]], dvc_url: str) -> None:
    """Save detection results to DVC.
    
    Args:
        results: List of detected objects
        dvc_url: URL for DVC storage
    Raises:
        RuntimeError: If saving fails
    """
    try:
        # Placeholder for saving logic
        logger.info('Saving results to DVC')
        # Simulated save operation
        time.sleep(1)  # Simulate delay
        logger.info('Results saved successfully')
    except Exception as e:
        logger.error(f'Error saving results: {e}')
        raise RuntimeError('Could not save results')

async def process_open_set_detection(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Main processing function for detecting open-set objects.
    
    Args:
        data: Input data containing image_url
    Returns:
        List of detected objects
    Raises:
        Exception: If any step fails
    """
    await validate_input(data)  # Validate input
    image_bytes = await fetch_image(data['image_url'])  # Fetch the image
    image_data = await transform_image(image_bytes)  # Transform image
    detections = await call_model(image_data)  # Call the model
    await save_results(detections, Config.dvc_url)  # Save results
    return detections  # Return detected objects

if __name__ == '__main__':
    # Example usage
    sample_data = {'image_url': 'http://example.com/image.jpg'}
    try:
        results = process_open_set_detection(sample_data)
        logger.info(f'Detection results: {results}')
    except Exception as e:
        logger.error(f'Error in processing: {e}')  # Handle top-level errors