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| 1 | +#!/usr/bin/env python3 |
| 2 | +"""DynamicMap_Benchmark adapter for dynamic-object-removal (numpy-only). |
| 3 | +
|
| 4 | +Usage (from a cloned DynamicMap_Benchmark repo, after ``pip install dynamic-object-removal``): |
| 5 | + python main.py --data_dir /path/to/00 --algorithm range |
| 6 | + python main.py --data_dir /path/to/00 --algorithm scan_ratio |
| 7 | + python main.py --data_dir /path/to/00 --algorithm temporal |
| 8 | +
|
| 9 | +Writes ``dor_<algorithm>_output.pcd`` into ``data_dir`` (cleaned accumulated map). |
| 10 | +Run ``export_eval_pcd`` + ``evaluate_all.py`` in the benchmark repo for SA/DA/AA/HA. |
| 11 | +""" |
| 12 | + |
| 13 | +from __future__ import annotations |
| 14 | + |
| 15 | +import argparse |
| 16 | +from pathlib import Path |
| 17 | + |
| 18 | +import numpy as np |
| 19 | + |
| 20 | +try: |
| 21 | + import dynamic_object_removal as core |
| 22 | +except ImportError as exc: |
| 23 | + raise SystemExit('Install first: pip install "dynamic-object-removal>=0.3"') from exc |
| 24 | + |
| 25 | + |
| 26 | +def _load_sequence(pcd_dir: Path) -> tuple[np.ndarray, list[tuple[np.ndarray, np.ndarray]], list[tuple[int, int]]]: |
| 27 | + scan_files = sorted(pcd_dir.glob("*.pcd")) |
| 28 | + if not scan_files: |
| 29 | + raise SystemExit(f"No scans in {pcd_dir}") |
| 30 | + |
| 31 | + chunks: list[np.ndarray] = [] |
| 32 | + scans: list[tuple[np.ndarray, np.ndarray]] = [] |
| 33 | + slices: list[tuple[int, int]] = [] |
| 34 | + cursor = 0 |
| 35 | + for path in scan_files: |
| 36 | + scan = core.load_pcd_scan(path) |
| 37 | + if scan.viewpoint is None: |
| 38 | + raise SystemExit(f"Missing VIEWPOINT in {path}") |
| 39 | + origin = scan.viewpoint[:3] |
| 40 | + n = len(scan.points) |
| 41 | + chunks.append(scan.points) |
| 42 | + scans.append((scan.points, origin)) |
| 43 | + slices.append((cursor, cursor + n)) |
| 44 | + cursor += n |
| 45 | + return np.concatenate(chunks, axis=0), scans, slices |
| 46 | + |
| 47 | + |
| 48 | +def _clean( |
| 49 | + algorithm: str, |
| 50 | + acc_map: np.ndarray, |
| 51 | + scans: list[tuple[np.ndarray, np.ndarray]], |
| 52 | + slices: list[tuple[int, int]], |
| 53 | + *, |
| 54 | + h_res: float, |
| 55 | + v_res: float, |
| 56 | + voxel_size: float, |
| 57 | + temporal_min_hits: int, |
| 58 | + sr_min_votes: int | None, |
| 59 | +) -> np.ndarray: |
| 60 | + if algorithm == "range": |
| 61 | + ground_z = float(np.percentile(acc_map[:, 2], 2)) |
| 62 | + _, keep = core.clean_map_by_visibility( |
| 63 | + acc_map, |
| 64 | + scans, |
| 65 | + h_res_deg=h_res, |
| 66 | + v_res_deg=v_res, |
| 67 | + ground_z=ground_z, |
| 68 | + ) |
| 69 | + elif algorithm == "scan_ratio": |
| 70 | + # min_votes=None: the library scales the voting threshold to 15% of the scans. |
| 71 | + _, keep = core.clean_map_by_scan_ratio(acc_map, scans, min_votes=sr_min_votes) |
| 72 | + elif algorithm == "temporal": |
| 73 | + keep = np.ones(len(acc_map), dtype=bool) |
| 74 | + tfilter = core.TemporalConsistencyFilter( |
| 75 | + voxel_size=voxel_size, |
| 76 | + window_size=len(scans), |
| 77 | + min_hits=temporal_min_hits, |
| 78 | + ) |
| 79 | + # Two passes over the same scans: the first fills the hit counter |
| 80 | + # (window_size == number of scans, so nothing is evicted), the second |
| 81 | + # judges each scan against the full-sequence counts — re-inserting a |
| 82 | + # scan evicts its own first-pass copy, leaving the counter unchanged. |
| 83 | + for s, e in slices: |
| 84 | + tfilter.filter(acc_map[s:e]) |
| 85 | + for s, e in slices: |
| 86 | + _, keep_f = tfilter.filter(acc_map[s:e]) |
| 87 | + keep[s:e] = keep_f |
| 88 | + else: |
| 89 | + raise SystemExit(f"Unknown algorithm: {algorithm}") |
| 90 | + return acc_map[keep] |
| 91 | + |
| 92 | + |
| 93 | +def main() -> int: |
| 94 | + parser = argparse.ArgumentParser(description="DynamicMap_Benchmark adapter (numpy-only).") |
| 95 | + parser.add_argument("--data_dir", required=True, help="Sequence folder with pcd/ and gt_cloud.pcd.") |
| 96 | + parser.add_argument("--algorithm", choices=["range", "scan_ratio", "temporal"], default="range") |
| 97 | + parser.add_argument("--h-res", type=float, default=1.0) |
| 98 | + parser.add_argument("--v-res", type=float, default=1.0) |
| 99 | + parser.add_argument("--voxel-size", type=float, default=core.DEFAULT_TEMPORAL_VOXEL_SIZE) |
| 100 | + parser.add_argument("--temporal-min-hits", type=int, default=2) |
| 101 | + parser.add_argument("--sr-min-votes", type=int, default=None, |
| 102 | + help="Scans that must flag a point before removal (default: 15%% of scans).") |
| 103 | + args = parser.parse_args() |
| 104 | + |
| 105 | + data_dir = Path(args.data_dir) |
| 106 | + pcd_dir = data_dir / "pcd" |
| 107 | + acc_map, scans, slices = _load_sequence(pcd_dir) |
| 108 | + cleaned = _clean( |
| 109 | + args.algorithm, |
| 110 | + acc_map, |
| 111 | + scans, |
| 112 | + slices, |
| 113 | + h_res=args.h_res, |
| 114 | + v_res=args.v_res, |
| 115 | + voxel_size=args.voxel_size, |
| 116 | + temporal_min_hits=args.temporal_min_hits, |
| 117 | + sr_min_votes=args.sr_min_votes, |
| 118 | + ) |
| 119 | + out = data_dir / f"dor_{args.algorithm}_output.pcd" |
| 120 | + core.save_points(out, cleaned, fmt="pcd") |
| 121 | + print(f"Wrote {len(cleaned):,} points -> {out}") |
| 122 | + return 0 |
| 123 | + |
| 124 | + |
| 125 | +if __name__ == "__main__": |
| 126 | + raise SystemExit(main()) |
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