| """Dependency-light Gaussian preview exporter for early pipeline tests.""" | |
| from __future__ import annotations | |
| from pathlib import Path | |
| import numpy as np | |
| def image_billboard_points(image: np.ndarray, max_side: int = 96): | |
| """Create an explicitly labeled 2.5D preview, not a learned reconstruction.""" | |
| image = np.asarray(image.convert("RGB") if hasattr(image, "convert") else image, dtype=np.uint8) | |
| height, width = image.shape[:2] | |
| scale = min(1.0, max_side / max(height, width)) | |
| new_w, new_h = max(1, round(width * scale)), max(1, round(height * scale)) | |
| yy, xx = np.mgrid[0:new_h, 0:new_w] | |
| source_y = np.minimum((yy / max(new_h, 1) * height).astype(int), height - 1) | |
| source_x = np.minimum((xx / max(new_w, 1) * width).astype(int), width - 1) | |
| colors = image[source_y, source_x].reshape(-1, 3) | |
| x = (xx.reshape(-1) / max(new_w - 1, 1) - 0.5).astype(np.float32) | |
| y = -(yy.reshape(-1) / max(new_h - 1, 1) - 0.5).astype(np.float32) | |
| z = np.ones_like(x, dtype=np.float32) | |
| return np.column_stack([x, y, z]), colors | |
| def write_preview_ply(path: str | Path, points: np.ndarray, colors: np.ndarray) -> Path: | |
| path = Path(path) | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| with path.open("w", encoding="utf-8") as handle: | |
| handle.write("ply\nformat ascii 1.0\n") | |
| handle.write(f"element vertex {len(points)}\n") | |
| handle.write("property float x\nproperty float y\nproperty float z\n") | |
| handle.write("property uchar red\nproperty uchar green\nproperty uchar blue\n") | |
| handle.write("end_header\n") | |
| for point, color in zip(points, colors): | |
| handle.write(f"{point[0]:.6f} {point[1]:.6f} {point[2]:.6f} {int(color[0])} {int(color[1])} {int(color[2])}\n") | |
| return path | |