| |
| """ |
| CLI script: image → metric depth → 3D point cloud using DepthPro. |
| |
| Usage |
| ----- |
| python image_to_pointcloud.py photo.jpg output.ply --device cuda:0 --sample-step 2 |
| python image_to_pointcloud.py photo.jpg output.ply --colored --save-depth depth.png |
| """ |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
| sys.path.insert(0, str(Path(__file__).parent.parent)) |
|
|
| from depthpro_wrapper import ( |
| DepthProEstimator, |
| depth_to_point_cloud, |
| rgbd_to_point_cloud, |
| normals_from_depth, |
| load_image, |
| save_point_cloud, |
| ) |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser( |
| description="Apple DepthPro: image → metric depth → 3D point cloud" |
| ) |
| parser.add_argument("image", type=Path, help="Input RGB image") |
| parser.add_argument("output", type=Path, help="Output point cloud (.ply)") |
| parser.add_argument("--device", default="cuda:0", help="PyTorch device") |
| parser.add_argument("--dtype", choices=["float16", "float32"], default="float16", help="Inference dtype") |
| parser.add_argument("--colored", action="store_true", help="Include per-point RGB colours") |
| parser.add_argument("--normals", action="store_true", help="Include per-point normals") |
| parser.add_argument("--sample-step", type=int, default=1, help="Spatial sub-sampling (1 = full res, 2 = 1/4 points)") |
| parser.add_argument("--save-depth", type=Path, default=None, help="Also save depth map as .npy") |
| parser.add_argument("--save-confidence", type=Path, default=None, help="Also save confidence map as .npy") |
| args = parser.parse_args() |
|
|
| if not args.image.exists(): |
| parser.error(f"Input image not found: {args.image}") |
|
|
| |
| print(f"Loading DepthPro on {args.device} (dtype={args.dtype}) ...") |
| dtype = {"float16": "float16", "float32": "float32"}[args.dtype] |
| import torch |
| torch_dtype = torch.float16 if dtype == "float16" else torch.float32 |
|
|
| estimator = DepthProEstimator(device=args.device, dtype=torch_dtype) |
|
|
| print(f"Estimating depth for {args.image} ...") |
| result = estimator.estimate( |
| args.image, |
| return_confidence=args.save_confidence is not None, |
| ) |
|
|
| print(f" Image size: {result.width}×{result.height}") |
| print(f" Estimated focal length: {result.focal_length:.1f} px") |
| print(f" Estimated FOV: {result.field_of_view:.1f}°") |
| print(f" Depth range: {result.depth.min():.2f} m – {result.depth.max():.2f} m") |
|
|
| |
| if args.save_depth: |
| np.save(args.save_depth, result.depth) |
| print(f" Saved depth map → {args.save_depth}") |
|
|
| if args.save_confidence and result.confidence is not None: |
| np.save(args.save_confidence, result.confidence) |
| print(f" Saved confidence map → {args.save_confidence}") |
|
|
| |
| print("\nBack-projecting to 3D point cloud ...") |
|
|
| normals = None |
| if args.normals: |
| normals = normals_from_depth(result.depth, result.focal_length) |
|
|
| if args.colored: |
| points, colors = rgbd_to_point_cloud( |
| result.depth, |
| result.image, |
| result.focal_length, |
| sample_step=args.sample_step, |
| ) |
| if args.normals: |
| |
| H, W = result.depth.shape |
| v_idx = np.arange(0, H, args.sample_step) |
| u_idx = np.arange(0, W, args.sample_step) |
| valid = result.depth[v_idx[:, None], u_idx[None, :]] > 0 |
| normals = normals[v_idx[:, None], u_idx[None, :]] |
| normals = normals[valid] |
| print(f" Colored point cloud: {len(points):,} points") |
| save_point_cloud(args.output, points, colors=colors, normals=normals) |
| else: |
| points = depth_to_point_cloud( |
| result.depth, |
| result.focal_length, |
| sample_step=args.sample_step, |
| ) |
| if args.normals: |
| H, W = result.depth.shape |
| v_idx = np.arange(0, H, args.sample_step) |
| u_idx = np.arange(0, W, args.sample_step) |
| valid = result.depth[v_idx[:, None], u_idx[None, :]] > 0 |
| normals = normals[v_idx[:, None], u_idx[None, :]] |
| normals = normals[valid] |
| print(f" Point cloud: {len(points):,} points") |
| save_point_cloud(args.output, points, normals=normals) |
|
|
| print(f"\nDone — saved to {args.output}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
