GeoRel-100k-subset / scripts /build_viewer_dataset.py
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#!/usr/bin/env python3
"""Build the lightweight Hugging Face Dataset Viewer split.
The full dataset payload lives in scene ZIP archives. This script creates a
small Parquet table that embeds RGB preview images and records the archive
member paths for the heavier modalities.
"""
from __future__ import annotations
import argparse
import json
import re
from pathlib import Path
from zipfile import ZipFile
from datasets import Dataset, Features, Image, Sequence, Value
CAMERA_RE = re.compile(
r"cam(?P<camera_index>\d+)_az(?P<azimuth>-?\d+(?:\.\d+)?)_el(?P<elevation>-?\d+(?:\.\d+)?)"
)
FEATURES = Features(
{
"sample_id": Value("string"),
"scene_zip_id": Value("string"),
"scene_id": Value("string"),
"arrangement_id": Value("string"),
"camera_label": Value("string"),
"camera_index": Value("int32"),
"azimuth_deg": Value("float32"),
"elevation_deg": Value("float32"),
"image": Image(),
"archive": Value("string"),
"rgb_path": Value("string"),
"depth_z_path": Value("string"),
"depth_euclidean_path": Value("string"),
"normal_worldspace_path": Value("string"),
"normal_camspace_path": Value("string"),
"mask_combined_path": Value("string"),
"object_mask_paths": Sequence(Value("string")),
"metadata_path": Value("string"),
"scene_graph_path": Value("string"),
"blend_path": Value("string"),
"colmap_cameras_path": Value("string"),
"colmap_images_path": Value("string"),
"colmap_points3d_path": Value("string"),
"render_width": Value("int32"),
"render_height": Value("int32"),
"num_cameras": Value("int32"),
"num_objects": Value("int32"),
"num_edges": Value("int32"),
"camera_fx_px": Value("float32"),
"camera_fy_px": Value("float32"),
"camera_fov_h_deg": Value("float32"),
"camera_fov_v_deg": Value("float32"),
"camera_position_world_m": Sequence(Value("float32")),
"object_ids": Sequence(Value("int32")),
"object_names": Sequence(Value("string")),
"object_classes": Sequence(Value("string")),
"relation_triplets": Sequence(Value("string")),
}
)
def load_json(zf: ZipFile, member: str) -> dict:
return json.loads(zf.read(member).decode("utf-8"))
def path_if_present(names: set[str], path: str) -> str | None:
return path if path in names else None
def camera_from_metadata(metadata: dict, camera_label: str) -> dict:
for camera in metadata.get("cameras", []):
if camera.get("label") == camera_label:
return camera
return {}
def relation_triplets(scene_graph: dict) -> list[str]:
triplets = []
for edge in scene_graph.get("edges", []):
relations = edge.get("relations", {})
relation_text = "/".join(
str(relations.get(key, "unknown")) for key in ("proximity", "vertical", "horizontal")
)
triplets.append(f"{edge.get('source')}->{edge.get('target')}:{relation_text}")
return triplets
def arrangement_dirs(names: set[str]) -> list[str]:
return sorted({name.split("/", 1)[0] for name in names if name.startswith("arr_") and "/" in name})
def build_rows(repo_root: Path) -> list[dict]:
rows: list[dict] = []
zip_paths = sorted(repo_root.glob("scene_*.zip"))
if not zip_paths:
raise FileNotFoundError("No scene_*.zip archives found")
for zip_path in zip_paths:
scene_zip_id = zip_path.stem
with ZipFile(zip_path) as zf:
names = set(zf.namelist())
for arrangement_id in arrangement_dirs(names):
metadata_path = f"{arrangement_id}/metadata.json"
scene_graph_path = f"{arrangement_id}/scene_graph.json"
if metadata_path not in names or scene_graph_path not in names:
continue
metadata = load_json(zf, metadata_path)
scene_graph = load_json(zf, scene_graph_path)
render = metadata.get("render", {})
nodes = scene_graph.get("nodes", [])
relation_rows = relation_triplets(scene_graph)
object_ids = [int(node["id"]) for node in nodes if "id" in node]
object_names = [str(node.get("object_name", "")) for node in nodes]
object_classes = [str(node.get("class", "")) for node in nodes]
rgb_paths = sorted(
name
for name in names
if name.startswith(f"{arrangement_id}/rgb/") and name.endswith(".png")
)
for rgb_path in rgb_paths:
camera_label = Path(rgb_path).stem
camera_match = CAMERA_RE.fullmatch(camera_label)
if camera_match is None:
continue
camera = camera_from_metadata(metadata, camera_label)
intrinsics = camera.get("intrinsics", {})
extrinsics = camera.get("extrinsics", {})
stem = camera_label
object_mask_prefix = f"{arrangement_id}/masks/{stem}_obj"
rows.append(
{
"sample_id": f"{scene_zip_id}_{arrangement_id}_{camera_label}",
"scene_zip_id": scene_zip_id,
"scene_id": str(scene_graph.get("scene_id", f"{scene_zip_id}_{arrangement_id}")),
"arrangement_id": arrangement_id,
"camera_label": camera_label,
"camera_index": int(camera_match.group("camera_index")),
"azimuth_deg": float(camera_match.group("azimuth")),
"elevation_deg": float(camera_match.group("elevation")),
"image": {
"bytes": zf.read(rgb_path),
"path": f"{zip_path.name}::{rgb_path}",
},
"archive": zip_path.name,
"rgb_path": rgb_path,
"depth_z_path": path_if_present(
names, f"{arrangement_id}/depth/{stem}_zdepth.exr"
),
"depth_euclidean_path": path_if_present(
names, f"{arrangement_id}/depth/{stem}_euclidean.exr"
),
"normal_worldspace_path": path_if_present(
names, f"{arrangement_id}/normals/{stem}_worldspace.exr"
),
"normal_camspace_path": path_if_present(
names, f"{arrangement_id}/normals/{stem}_camspace.exr"
),
"mask_combined_path": path_if_present(
names, f"{arrangement_id}/masks/{stem}_combined.exr"
),
"object_mask_paths": sorted(
name
for name in names
if name.startswith(object_mask_prefix) and name.endswith(".exr")
),
"metadata_path": metadata_path,
"scene_graph_path": scene_graph_path,
"blend_path": path_if_present(names, f"{arrangement_id}/scene.blend"),
"colmap_cameras_path": path_if_present(
names, f"{arrangement_id}/colmap/cameras.txt"
),
"colmap_images_path": path_if_present(
names, f"{arrangement_id}/colmap/images.txt"
),
"colmap_points3d_path": path_if_present(
names, f"{arrangement_id}/colmap/points3D.txt"
),
"render_width": int(render.get("resolution_x", 0)),
"render_height": int(render.get("resolution_y", 0)),
"num_cameras": int(render.get("num_cameras", 0)),
"num_objects": int(scene_graph.get("num_objects", len(nodes))),
"num_edges": len(scene_graph.get("edges", [])),
"camera_fx_px": intrinsics.get("fx_px"),
"camera_fy_px": intrinsics.get("fy_px"),
"camera_fov_h_deg": intrinsics.get("fov_h_deg"),
"camera_fov_v_deg": intrinsics.get("fov_v_deg"),
"camera_position_world_m": extrinsics.get("position_world_m", []),
"object_ids": object_ids,
"object_names": object_names,
"object_classes": object_classes,
"relation_triplets": relation_rows,
}
)
return rows
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--repo-root", type=Path, default=Path("."), help="Dataset repository root")
parser.add_argument(
"--output",
type=Path,
default=Path("data/viewer/train.parquet"),
help="Output Parquet file, relative to --repo-root unless absolute",
)
args = parser.parse_args()
repo_root = args.repo_root.resolve()
output_path = args.output if args.output.is_absolute() else repo_root / args.output
output_path.parent.mkdir(parents=True, exist_ok=True)
rows = build_rows(repo_root)
dataset = Dataset.from_list(rows, features=FEATURES)
bytes_written = dataset.to_parquet(
output_path,
batch_size=100,
compression="zstd",
write_page_index=True,
)
print(f"Wrote {len(dataset)} rows to {output_path} ({bytes_written} bytes)")
if __name__ == "__main__":
main()