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Minimal example: load one RoboCloth material from Hugging Face.
What this shows:
- downloading per-material files via huggingface_hub
- extracting one HDR PNG from hdr.tar without unpacking the whole archive
- reading the structured observations npz (xyz, rgbs, cam_pos, light_pos)
- reading the supporting JSON metadata (camera poses, scan log)
Run:
pip install huggingface_hub numpy pillow
python load_material.py --mid 145
"""
import argparse
import io
import json
import tarfile
import numpy as np
from huggingface_hub import hf_hub_download
REPO_ID = "koalapenguin/RoboCloth"
REPO_TYPE = "dataset"
def material_path(mid: str, filename: str) -> str:
return f"materials/{mid}/{filename}"
def download(mid: str, filename: str) -> str:
"""Download one per-material file; returns the local cache path."""
return hf_hub_download(
repo_id=REPO_ID,
repo_type=REPO_TYPE,
filename=material_path(mid, filename),
)
def main():
ap = argparse.ArgumentParser(description=__doc__)
ap.add_argument("--mid", default="145",
help="material id, 0-499 (e.g. 145)")
args = ap.parse_args()
mid = args.mid
print(f"=== loading material {mid} from {REPO_ID} ===\n")
# 1. JSON metadata (small, ~MB)
cam_path = download(mid, "rotated_camera.json")
with open(cam_path) as f:
cameras = json.load(f)
print(f"rotated_camera.json: {len(cameras)} camera poses")
print(f" first entry keys: {list(cameras[0].keys())}\n")
scan_path = download(mid, "scan_log.json")
with open(scan_path) as f:
scan_log = json.load(f)
print(f"scan_log.json: {len(scan_log)} scan entries")
print(f" first entry keys: {list(scan_log[0].keys())}\n")
# 2. Structured observations npz (~hundreds of MB)
obs_path = download(mid, "observations_structured.npz")
obs = np.load(obs_path)
print(f"observations_structured.npz arrays:")
for k in obs.files:
a = obs[k]
print(f" {k:14s} shape={a.shape} dtype={a.dtype}")
print()
# 3. Point positions (~tens of MB)
pts_path = download(mid, "point_positions.npz")
pts = np.load(pts_path)
print(f"point_positions.npz arrays:")
for k in pts.files:
a = pts[k]
print(f" {k:14s} shape={a.shape} dtype={a.dtype}")
print()
# 4. HDR images: one PNG extracted from the tar without full unpack
tar_path = download(mid, "hdr.tar")
with tarfile.open(tar_path, mode="r") as tf:
names = tf.getnames()
first_png = next((n for n in names if n.endswith(".png")), None)
print(f"hdr.tar: {len(names)} entries, first PNG: {first_png}")
if first_png:
member = tf.extractfile(first_png)
data = member.read()
print(f" first PNG size: {len(data) / 1024:.1f} KiB")
try:
from PIL import Image
img = Image.open(io.BytesIO(data))
print(f" decoded: mode={img.mode} size={img.size}")
except ImportError:
print(" (install Pillow to decode PNGs)")
if __name__ == "__main__":
main()
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