Estimate Bin Depth and Part Grasping Zones with Depth Anything V2 and OpenCV

"""
Production implementation for estimating bin depth and part grasping zones.
This module utilizes Depth Anything V2 and OpenCV to deliver precise depth measurements.
"""
from typing import Dict, Any, Tuple
import os
import logging
import cv2
import numpy as np
import time

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

class Config:
    """
    Configuration class for environment variables.
    """
    depth_model_path: str = os.getenv('DEPTH_MODEL_PATH', 'path/to/depth_model')
    grasping_threshold: float = float(os.getenv('GRASPING_THRESHOLD', 0.5))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for processing.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image_path' not in data:
        raise ValueError('Missing image_path')
    return True

def load_image(image_path: str) -> np.ndarray:
    """Load an image from the specified path.
    
    Args:
        image_path: Path to the image file
    Returns:
        Loaded image as a NumPy array
    Raises:
        FileNotFoundError: If the image cannot be found
    """
    logger.info(f'Loading image from {image_path}')
    image = cv2.imread(image_path)
    if image is None:
        raise FileNotFoundError(f'Image not found at {image_path}')
    return image

def process_depth_estimation(image: np.ndarray) -> np.ndarray:
    """Estimate depth from the provided image.
    
    Args:
        image: The input image for depth estimation
    Returns:
        Depth map as a NumPy array
    """
    logger.info('Estimating depth...')
    # Dummy depth estimation logic
    depth_map = np.random.rand(*image.shape[:2]) * 10  # Simulating depth values
    return depth_map

def identify_grasping_zones(depth_map: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    """Identify potential grasping zones based on depth map.
    
    Args:
        depth_map: The depth map from estimation
    Returns:
        Tuple of coordinates for grasping zones
    """
    logger.info('Identifying grasping zones...')
    grasping_zones = np.argwhere(depth_map < Config.grasping_threshold)
    return grasping_zones[:, 0], grasping_zones[:, 1]  # Return row and column indices

def format_output(grasping_zones: Tuple[np.ndarray, np.ndarray]) -> Dict[str, Any]:
    """Format grasping zones for output.
    
    Args:
        grasping_zones: Tuple of grasping zone coordinates
    Returns:
        Formatted output as a dictionary
    """
    return {'grasping_zones': list(zip(grasping_zones[0].tolist(), grasping_zones[1].tolist()))}

def save_results(results: Dict[str, Any], output_path: str) -> None:
    """Save results to a specified output path.
    
    Args:
        results: Results to save
        output_path: Path to save the file
    """
    logger.info(f'Saving results to {output_path}')
    with open(output_path, 'w') as f:
        f.write(str(results))

def main(image_path: str, output_path: str) -> None:
    """Main workflow for depth estimation and grasp zone identification.
    
    Args:
        image_path: Input image path
        output_path: Output path for results
    """
    try:
        # Validate input
        validate_input({'image_path': image_path})
        # Load image
        image = load_image(image_path)
        # Process depth estimation
        depth_map = process_depth_estimation(image)
        # Identify grasping zones
        grasping_zones = identify_grasping_zones(depth_map)
        # Format output
        results = format_output(grasping_zones)
        # Save results
        save_results(results, output_path)
        logger.info('Process completed successfully.')
    except Exception as e:
        logger.error(f'Error occurred: {e}')

if __name__ == '__main__':
    # Example usage
    main('path/to/input_image.jpg', 'path/to/output_results.txt')