File size: 5,321 Bytes
68afb88 | 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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | #!/usr/bin/env python3
"""
preprocess_step3_ptv3.py
=========================
NEST3D Pre-processing Step 3: Convert corrected PLY files to Pointcept format.
Input: <data-dir>/sampleXXX/sampleXXX_corrected.ply
<split-dir>/{train,val,test}.txt (one sample ID per line)
Output: <out-dir>/train|val|test/sampleXXX/{coord.npy, color.npy, segment.npy}
Format:
coord.npy : (N, 3) float32 - XYZ coordinates, centered on the scene
centroid and normalized to the unit
sphere (max radius = 1)
color.npy : (N, 3) float32 - RGB colors scaled to [0, 1]
segment.npy : (N,) int32 - semantic labels: 0=grass, 1=tree, 2=nest,
-1=ignore
Semantic labels (0/1/2) are preserved as-is from the corrected PLY files;
the ignore label is remapped from 255 (CloudCompare convention) to -1
(Pointcept convention).
The train/val/test split is defined by train.txt, val.txt, and test.txt
(one sample ID per line) and is not hardcoded here -- this script simply
reads and applies whatever split those files define. This keeps the split
definition in one authoritative place.
Usage:
python preprocess_step3_ptv3.py \\
--data-dir /path/to/reconstructions \\
--split-dir /path/to/split/txt/files \\
--out-dir /path/to/output
Author: NEST3D team
"""
import argparse
import numpy as np
from pathlib import Path
from plyfile import PlyData
# ── Helpers ───────────────────────────────────────────────────────────────────
def load_split(split_dir, split_name):
path = split_dir / f"{split_name}.txt"
with open(path) as f:
samples = [line.strip() for line in f if line.strip()]
return samples
def load_ply(path):
ply = PlyData.read(str(path))
v = ply["vertex"]
xyz = np.stack([v["x"], v["y"], v["z"]], axis=1).astype(np.float32)
rgb = np.stack([v["red"], v["green"], v["blue"]], axis=1).astype(np.float32) / 255.0
lbl = np.array(v["scalar_Classification"], dtype=np.float32).astype(np.int32)
return xyz, rgb, lbl
def unit_sphere(xyz):
centroid = xyz.mean(axis=0)
xyz = xyz - centroid
max_radius = np.sqrt((xyz**2).sum(axis=1)).max()
if max_radius > 0:
xyz = xyz / max_radius
return xyz.astype(np.float32)
def remap_ignore(lbl):
"""Only remap 255 -> -1 (ignore). Keep 0, 1, 2 as-is."""
out = lbl.copy()
out[lbl == 255] = -1
return out.astype(np.int32)
def save_sample(xyz, rgb, lbl, out_dir):
out_dir.mkdir(parents=True, exist_ok=True)
np.save(str(out_dir / "coord.npy"), xyz)
np.save(str(out_dir / "color.npy"), rgb)
np.save(str(out_dir / "segment.npy"), lbl)
# ── Main ──────────────────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(description="NEST3D Step 3: convert to Pointcept format")
parser.add_argument("--data-dir", type=Path, default=Path("./reconstructions"),
help="Path to reconstructions/ containing sampleXXX_corrected.ply files")
parser.add_argument("--split-dir", type=Path, default=Path("."),
help="Path to the directory containing train.txt, val.txt, test.txt")
parser.add_argument("--out-dir", type=Path, default=Path("./nest"),
help="Output directory for the Pointcept-format data")
args = parser.parse_args()
recon_dir = args.data_dir
out_dir = args.out_dir
all_splits = [(name, load_split(args.split_dir, name)) for name in ["train", "val", "test"]]
for split, samples in all_splits:
(out_dir / split).mkdir(parents=True, exist_ok=True)
print(f"\n=== {split.upper()} ({len(samples)} samples) ===")
for sample_id in samples:
sample_out_dir = out_dir / split / sample_id
ply_path = recon_dir / sample_id / f"{sample_id}_corrected.ply"
if all((sample_out_dir / f).exists() for f in ["coord.npy","color.npy","segment.npy"]):
print(f" [SKIP] {sample_id}")
continue
if not ply_path.exists():
print(f" [MISSING] {sample_id}: {ply_path.name} not found")
continue
print(f" [PROC] {sample_id} ...", end=" ", flush=True)
xyz, rgb, lbl = load_ply(ply_path)
lbl = remap_ignore(lbl)
xyz = unit_sphere(xyz)
n_total = len(lbl)
n_grass = int((lbl==0).sum())
n_tree = int((lbl==1).sum())
n_nest = int((lbl==2).sum())
n_ign = int((lbl==-1).sum())
save_sample(xyz, rgb, lbl, sample_out_dir)
print(f"n={n_total:,} | grass={100*n_grass/n_total:.1f}% "
f"tree={100*n_tree/n_total:.1f}% nest={100*n_nest/n_total:.1f}% "
f"ignore={100*n_ign/n_total:.1f}%")
print(f"\nDone! Output: {out_dir}")
for split, samples in all_splits:
print(f" {split}: {len(samples)} samples")
if __name__ == "__main__":
main()
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