nerf_generator.py

#!/usr/bin/env python3
"""
NeRF (Neural Radiance Fields) Integration Module
Reconstructs 3D scenes from multiple images with camera poses
"""

import os
import sys
import argparse
from pathlib import Path

try:
    import numpy as np
    from PIL import Image
    import imageio
    
    # Add NeRF to path
    nerf_path = os.path.join(os.path.dirname(__file__), 'nerf_repo')
    if os.path.exists(nerf_path):
        sys.path.insert(0, nerf_path)
    
except ImportError as e:
    print(f"Error importing required libraries: {e}", file=sys.stderr)
    print("Please install requirements: pip install -r requirements.txt", file=sys.stderr)
    sys.exit(1)


def generate_nerf(
    images_dir: str,
    output_dir: str = "output",
    dataset_type: str = "llff",
    config_file: str = None
) -> dict:
    """
    Generate 3D model from multiple images using NeRF
    
    Args:
        images_dir: Directory containing input images
        output_dir: Directory to save NeRF output and extracted mesh
        dataset_type: Type of dataset ('llff', 'blender', 'deepvoxels')
        config_file: Path to NeRF config file (optional)
        
    Returns:
        Dictionary with paths to generated outputs:
        {
            'mesh': path,
            'video': path,
            'success': bool
        }
    """
    try:
        # Check if NeRF repository exists
        nerf_repo_path = os.path.join(os.path.dirname(__file__), 'nerf_repo')
        if not os.path.exists(nerf_repo_path):
            return {
                'success': False,
                'error': f"NeRF repository not found. Please ensure nerf_repo/ directory exists."
            }
        
        # Check for TensorFlow
        try:
            import tensorflow as tf
            print(f"TensorFlow version: {tf.__version__}", file=sys.stderr)
            sys.stderr.flush()
        except ImportError:
            return {
                'success': False,
                'error': "TensorFlow not found. NeRF requires TensorFlow 1.15. Note: TensorFlow 1.15 may not work on Apple Silicon. Consider using a modern NeRF implementation."
            }
        
        # Check if images directory exists and has images
        if not os.path.exists(images_dir):
            return {
                'success': False,
                'error': f"Images directory not found: {images_dir}"
            }
        
        image_files = [f for f in os.listdir(images_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
        if len(image_files) < 3:
            return {
                'success': False,
                'error': f"NeRF requires at least 3 images. Found {len(image_files)} images in {images_dir}"
            }
        
        print(f"Found {len(image_files)} images in {images_dir}", file=sys.stderr)
        sys.stderr.flush()
        
        # Create output directory
        os.makedirs(output_dir, exist_ok=True)
        
        # Check if we have camera poses
        # NeRF requires camera poses - either from COLMAP/LLFF or provided
        poses_file = os.path.join(images_dir, 'poses_bounds.npy')
        if not os.path.exists(poses_file):
            print("⚠️  Camera poses not found. NeRF requires camera poses.", file=sys.stderr)
            print("   You can generate poses using COLMAP or LLFF tools.", file=sys.stderr)
            print("   For now, using simplified mode (may not work correctly).", file=sys.stderr)
            sys.stderr.flush()
        
        # Create config file if not provided
        if config_file is None:
            config_file = os.path.join(output_dir, 'nerf_config.txt')
            create_nerf_config(config_file, images_dir, output_dir, dataset_type)
        
        print(f"Starting NeRF training...", file=sys.stderr)
        print(f"   This may take several hours depending on your hardware.", file=sys.stderr)
        print(f"   Note: TensorFlow 1.15 may not work on Apple Silicon.", file=sys.stderr)
        sys.stderr.flush()
        
        # Note: Full NeRF training requires TensorFlow 1.15 and CUDA
        # This is a simplified wrapper - full implementation would call run_nerf.py
        # For now, we'll provide a structure that can be enhanced
        
        mesh_path = os.path.join(output_dir, "nerf_mesh.ply")
        video_path = os.path.join(output_dir, "nerf_video.mp4")
        
        # Placeholder - full implementation would:
        # 1. Run NeRF training: python run_nerf.py --config config_file
        # 2. Extract mesh from trained NeRF
        # 3. Generate render video
        
        print("⚠️  NeRF training is not fully implemented yet.", file=sys.stderr)
        print("   Full NeRF integration requires:", file=sys.stderr)
        print("   - TensorFlow 1.15 (may not work on Apple Silicon)", file=sys.stderr)
        print("   - CUDA for GPU acceleration", file=sys.stderr)
        print("   - Camera poses (COLMAP/LLFF)", file=sys.stderr)
        print("   - Several hours of training time", file=sys.stderr)
        sys.stderr.flush()
        
        return {
            'success': False,
            'error': "NeRF training not fully implemented. Requires TensorFlow 1.15 and CUDA. Consider using a modern NeRF implementation like nerfstudio for Apple Silicon compatibility."
        }
        
    except Exception as e:
        import traceback
        error_msg = f"Error generating NeRF: {str(e)}"
        print(error_msg, file=sys.stderr)
        traceback.print_exc(file=sys.stderr)
        sys.stderr.flush()
        return {
            'success': False,
            'error': error_msg
        }


def create_nerf_config(config_path: str, images_dir: str, output_dir: str, dataset_type: str):
    """Create a NeRF config file"""
    config_content = f"""# NeRF Configuration
# Generated automatically for Prometheus

# Dataset settings
--datadir={images_dir}
--dataset_type={dataset_type}
--expname=prometheus_nerf
--basedir={output_dir}/logs

# Training settings
--N_iters=200000
--N_samples=64
--N_rand=1024
--lrate=5e-4
--lrate_decay=250

# Network settings
--netdepth=8
--netwidth=256
--netdepth_fine=8
--netwidth_fine=256

# Rendering settings
--chunk=1024*32
--no_batching
--no_reload
"""
    
    with open(config_path, 'w') as f:
        f.write(config_content)
    
    print(f"Created NeRF config: {config_path}", file=sys.stderr)
    sys.stderr.flush()


def main():
    parser = argparse.ArgumentParser(description='Generate 3D model from images using NeRF')
    parser.add_argument('--images', type=str, required=True, help='Directory containing input images')
    parser.add_argument('--output', type=str, default='output', help='Output directory')
    parser.add_argument('--dataset-type', type=str, default='llff', choices=['llff', 'blender', 'deepvoxels'],
                        help='Type of dataset')
    parser.add_argument('--config', type=str, default=None, help='Path to NeRF config file')
    
    args = parser.parse_args()
    
    result = generate_nerf(
        images_dir=args.images,
        output_dir=args.output,
        dataset_type=args.dataset_type,
        config_file=args.config
    )
    
    if result['success']:
        if 'mesh' in result:
            print(f"MESH: {result['mesh']}", file=sys.stdout)
        if 'video' in result:
            print(f"VIDEO: {result['video']}", file=sys.stdout)
        sys.stdout.flush()
    else:
        print(f"ERROR: {result.get('error', 'Unknown error')}", file=sys.stderr)
        sys.stderr.flush()
        sys.exit(1)


if __name__ == '__main__':
    main()