File size: 2,894 Bytes
422e038 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | #!/usr/bin/env python3
"""
preprocess_step4_o3dml.py
==========================
NEST3D Pre-processing Step 4: Convert Pointcept format to Open3D-ML format
for RandLA-Net / KPConv.
Input: <pointcept-dir>/train|val|test/sampleXXX/{coord,color,segment}.npy
Output: <out-dir>/train|val|test/sampleXXX.npy [N, 7] = x, y, z, r, g, b, label
Uses the same train/val/test split as the Pointcept-format data (Step 3),
since it is derived directly from that data's folder structure.
Labels: 0=grass, 1=tree, 2=nest, -1=ignore (no remapping applied; ignore
points are passed through unchanged from the Pointcept-format segment.npy).
Usage:
python preprocess_step4_o3dml.py \\
--pointcept-dir /path/to/pointcept/format/data \\
--out-dir /path/to/o3dml/output
Author: NEST3D team
"""
import argparse
import numpy as np
from pathlib import Path
def main():
parser = argparse.ArgumentParser(description="NEST3D Step 4: convert to Open3D-ML format")
parser.add_argument("--pointcept-dir", type=Path, required=True,
help="Path to the Pointcept-format data (output of Step 3), "
"containing train/val/test subfolders")
parser.add_argument("--out-dir", type=Path, required=True,
help="Output directory for the Open3D-ML-format .npy files")
args = parser.parse_args()
nest_dir = args.pointcept_dir
out_dir = args.out_dir
for split in ["train", "val", "test"]:
(out_dir / split).mkdir(parents=True, exist_ok=True)
for split in ["train", "val", "test"]:
src_dir = nest_dir / split
print(f"\n=== {split.upper()} ===")
samples = sorted([p.name for p in src_dir.iterdir()
if p.is_dir() and p.name.startswith("sample")])
for sample_id in samples:
out_path = out_dir / split / f"{sample_id}.npy"
if out_path.exists():
print(f" [SKIP] {sample_id}")
continue
src = src_dir / sample_id
coord = np.load(src / "coord.npy")
color = np.load(src / "color.npy")
segment = np.load(src / "segment.npy")
lbl = segment.copy() # -1 (ignore) passed through unchanged
data = np.concatenate([
coord.astype(np.float32),
color.astype(np.float32),
lbl.reshape(-1,1).astype(np.float32)
], axis=1)
np.save(str(out_path), data)
n = len(data)
n0 = int((lbl==0).sum())
n1 = int((lbl==1).sum())
n2 = int((lbl==2).sum())
n_ign = int((lbl==-1).sum())
print(f" [OK] {sample_id}: {n:,} | grass={100*n0/n:.1f}% tree={100*n1/n:.1f}% "
f"nest={100*n2/n:.1f}% ignore={100*n_ign/n:.1f}%")
print("\nDone!")
if __name__ == "__main__":
main()
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