Add scripts/preprocess_step4_o3dml.py

scripts/preprocess_step4_o3dml.py

@@ -0,0 +1,79 @@
+#!/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()