| |
| """Export RealEstate10K-Cap to per-clip .npy files in the TKCAM directory layout. |
| |
| <out_dir>/ |
| trajectories/<id>.npy (N, 12) TKCAM format, or (N, 9) universal format; row i = frame i+1 |
| texts/<id>.txt "caption#word/POS ..." (TKCAM text loader format) |
| untagged_text/<id>.txt plain caption |
| train.txt / val.txt / test.txt |
| Mean.npy / Std.npy (12d only; over all exported clips, as in TKCAM) |
| |
| Formats: |
| 12d [x, y, z, dx, dy, dz, r11, r21, r31, r12, r22, r32] (as stored) |
| 9d [r11, r21, r31, r12, r22, r32, x, y, z] (CLaTr / benchmark format) |
| |
| Requirements: pip install numpy pyarrow |
| |
| Example: |
| python scripts/export_npy.py --data-dir data --out-dir RealEstate10K-Cap_npy --format 12d |
| # frames for TKCAM's --frame_dir: |
| python scripts/download_frames.py --data-dir data --out-dir RealEstate10K-Cap_frames |
| """ |
|
|
| import argparse |
| import glob |
| import os |
| import shutil |
|
|
| import numpy as np |
| import pyarrow.parquet as pq |
|
|
|
|
| def to_9d(traj12): |
| return np.concatenate([traj12[:, 6:12], traj12[:, 0:3]], axis=1) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) |
| parser.add_argument("--data-dir", default="data", help="folder with the *.parquet files") |
| parser.add_argument("--out-dir", required=True) |
| parser.add_argument("--format", choices=["12d", "9d"], default="12d") |
| parser.add_argument("--exclude-benchmark", action="store_true", |
| help="drop clips with in_benchmark_test=True from train/val") |
| args = parser.parse_args() |
|
|
| for sub in ("trajectories", "texts", "untagged_text"): |
| os.makedirs(os.path.join(args.out_dir, sub), exist_ok=True) |
|
|
| cols = ["id", "split", "caption", "caption_pos", "trajectory_12d", "in_benchmark_test"] |
| splits = {"train": [], "val": [], "test": []} |
| for f in sorted(glob.glob(os.path.join(args.data_dir, "*.parquet"))): |
| for row in pq.read_table(f, columns=cols).to_pylist(): |
| if args.exclude_benchmark and row["in_benchmark_test"] and row["split"] != "test": |
| continue |
| sid = row["id"] |
| traj = np.asarray(row["trajectory_12d"], dtype=np.float32) |
| np.save(os.path.join(args.out_dir, "trajectories", f"{sid}.npy"), |
| to_9d(traj) if args.format == "9d" else traj) |
| with open(os.path.join(args.out_dir, "texts", f"{sid}.txt"), "w") as fh: |
| fh.write(f"{row['caption']}#{row['caption_pos']}\n") |
| with open(os.path.join(args.out_dir, "untagged_text", f"{sid}.txt"), "w") as fh: |
| fh.write(row["caption"] + "\n") |
| splits[row["split"]].append(sid) |
|
|
| for split, ids in splits.items(): |
| with open(os.path.join(args.out_dir, f"{split}.txt"), "w") as fh: |
| fh.write("\n".join(sorted(ids)) + "\n") |
|
|
| if args.format == "12d": |
| stats_dir = os.path.join(os.path.dirname(os.path.abspath(args.data_dir)), "stats") |
| if not args.exclude_benchmark and os.path.isdir(stats_dir): |
| for name in ("Mean.npy", "Std.npy"): |
| shutil.copy2(os.path.join(stats_dir, name), os.path.join(args.out_dir, name)) |
| else: |
| frames = np.concatenate([np.load(os.path.join(args.out_dir, "trajectories", f"{s}.npy")) |
| for ids in splits.values() for s in ids]) |
| np.save(os.path.join(args.out_dir, "Mean.npy"), frames.mean(0)) |
| np.save(os.path.join(args.out_dir, "Std.npy"), frames.std(0)) |
|
|
| print({k: len(v) for k, v in splits.items()}, "->", args.out_dir) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
