Classify Factory Workspace Geometries from 3D Point Clouds with Open3D and Supervision

"""
Production implementation for Classifying Factory Workspace Geometries from 3D Point Clouds using Open3D.
Provides secure, scalable operations for geometrical classification.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import open3d as o3d
import numpy as np
from time import sleep

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    point_cloud_directory: str = os.getenv('POINT_CLOUD_DIRECTORY', './point_clouds/')
    output_directory: str = os.getenv('OUTPUT_DIRECTORY', './outputs/')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', '3'))
    retry_delay: float = float(os.getenv('RETRY_DELAY', '2'))

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for point cloud classification.
    
    Args:
        data: Input dictionary containing point cloud data.
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'points' not in data:
        raise ValueError('Missing points in input data')
    if not isinstance(data['points'], list) or not data['points']:
        raise ValueError('Points must be a non-empty list')
    return True

async def load_point_cloud(file_path: str) -> o3d.geometry.PointCloud:
    """Load a point cloud from a file.
    
    Args:
        file_path: Path to the point cloud file.
    Returns:
        o3d.geometry.PointCloud: Loaded point cloud object
    Raises:
        FileNotFoundError: If the file does not exist
    """
    try:
        pcd = o3d.io.read_point_cloud(file_path)
        if pcd.is_empty():
            raise ValueError('Loaded point cloud is empty')
        return pcd
    except Exception as e:
        logger.error(f'Error loading point cloud: {e}')
        raise

async def preprocess_point_cloud(pcd: o3d.geometry.PointCloud) -> o3d.geometry.PointCloud:
    """Preprocess the point cloud for classification.
    
    Args:
        pcd: Input point cloud.
    Returns:
        o3d.geometry.PointCloud: Preprocessed point cloud
    """
    # Downsample point cloud
    pcd = pcd.voxel_down_sample(voxel_size=0.05)
    logger.info('Point cloud downsampled')
    return pcd

async def classify_geometry(pcd: o3d.geometry.PointCloud) -> str:
    """Classify the geometry type of the point cloud.
    
    Args:
        pcd: Input point cloud.
    Returns:
        str: Classified geometry type
    """
    # Placeholder for classification logic
    # Here we would implement machine learning or heuristic rules
    logger.info('Classifying geometry...')
    # Simulating classification
    sleep(1)
    return 'Unknown Geometry'

async def save_classification_results(output_path: str, classification: str) -> None:
    """Save the classification results to a file.
    
    Args:
        output_path: Path to save the results.
        classification: The classification result.
    Raises:
        IOError: If file writing fails
    """
    try:
        with open(output_path, 'w') as f:
            f.write(f'Classification Result: {classification}\n')
        logger.info(f'Saved classification results to {output_path}')
    except Exception as e:
        logger.error(f'Error saving results: {e}')
        raise

async def process_point_cloud(file_path: str) -> None:
    """Main processing function for a point cloud file.
    
    Args:
        file_path: Path to the point cloud file.
    """
    # Load point cloud
    pcd = await load_point_cloud(file_path)
    # Preprocess
    pcd = await preprocess_point_cloud(pcd)
    # Classify
    classification = await classify_geometry(pcd)
    # Save results
    output_path = os.path.join(Config.output_directory, 'classification_results.txt')
    await save_classification_results(output_path, classification)

async def main() -> None:
    """Main entry point for the application.
    
    """
    # Example input data
    input_data = {'points': [[0, 0, 0], [1, 1, 1]]}  # Placeholder data
    try:
        await validate_input(input_data)
        # Process all point clouds in directory
        for file_name in os.listdir(Config.point_cloud_directory):
            if file_name.endswith('.ply') or file_name.endswith('.pcd'):
                file_path = os.path.join(Config.point_cloud_directory, file_name)
                await process_point_cloud(file_path)
    except ValueError as ve:
        logger.error(f'Validation error: {ve}')
    except Exception as e:
        logger.error(f'An error occurred: {e}')

if __name__ == '__main__':
    # Entry point for the async program
    import asyncio
    asyncio.run(main())