| |
| """Utilities to convert the raw metadata dumps into viewer-friendly JSONL files.""" |
| from __future__ import annotations |
|
|
| import json |
| from dataclasses import asdict, dataclass |
| from pathlib import Path |
| from typing import Dict, Iterator, MutableMapping |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| RAW_DIR = ROOT / "raw" |
| OUTPUT_DIR = ROOT / "data" |
| HF_DATASET_ID = "ishwarbb23/cuebench" |
| HF_IMAGE_PREFIX = f"hf://datasets/{HF_DATASET_ID}/" |
|
|
| CONFIG_SOURCES: Dict[str, Path] = { |
| "clue": RAW_DIR / "clue_metadata.jsonl", |
| "mep": RAW_DIR / "mep_metadata.jsonl", |
| } |
|
|
| @dataclass |
| class BuildStats: |
| """Simple container for summary numbers we surface in README/stats.json.""" |
|
|
| num_examples: int |
| num_bytes: int |
| source_path: str |
| output_path: str |
|
|
| def as_dict(self) -> Dict[str, object]: |
| return asdict(self) |
|
|
|
|
| def _normalize_record(record: MutableMapping[str, object]) -> MutableMapping[str, object]: |
| """Add the columns expected by the README and dataset viewer.""" |
|
|
| image_id = record.get("aligned_id") or record.get("image_id") |
| if image_id is None: |
| seq = record.get("seq_name", "seq") |
| frame = record.get("frame_count", 0) |
| image_id = f"{seq}.{int(frame):05d}" |
| record["image_id"] = image_id |
|
|
| observed = record.get("observed_classes") or record.get("detected_classes") or [] |
| record["observed_classes"] = observed |
| |
| record.setdefault("detected_classes", observed) |
|
|
| record["target_classes"] = record.get("target_classes", []) |
|
|
| image_path = record.get("image_path") |
| record["image_path"] = image_path |
| if image_path: |
| normalized_path = str(Path(image_path)) |
| record["image"] = f"{HF_IMAGE_PREFIX}{normalized_path}" |
| else: |
| record["image"] = None |
| return record |
|
|
|
|
| def _iter_records(path: Path) -> Iterator[MutableMapping[str, object]]: |
| with path.open("r", encoding="utf-8") as src: |
| for line in src: |
| if not line.strip(): |
| continue |
| yield json.loads(line) |
|
|
|
|
| def build_split(config_name: str, source_path: Path, output_path: Path) -> BuildStats: |
| output_path.parent.mkdir(parents=True, exist_ok=True) |
| count = 0 |
| with output_path.open("w", encoding="utf-8") as dst: |
| for record in _iter_records(source_path): |
| normalized = _normalize_record(record) |
| dst.write(json.dumps(normalized, ensure_ascii=False) + "\n") |
| count += 1 |
| num_bytes = output_path.stat().st_size |
| return BuildStats( |
| num_examples=count, |
| num_bytes=num_bytes, |
| source_path=str(source_path.relative_to(ROOT)), |
| output_path=str(output_path.relative_to(ROOT)), |
| ) |
|
|
|
|
| def main() -> None: |
| stats: Dict[str, Dict[str, object]] = {} |
| for config_name, source in CONFIG_SOURCES.items(): |
| if not source.exists(): |
| raise FileNotFoundError(f"Missing source file for {config_name}: {source}") |
| output_path = OUTPUT_DIR / config_name / "train.jsonl" |
| summary = build_split(config_name, source, output_path) |
| stats[config_name] = summary.as_dict() |
| print( |
| f"[{config_name}] wrote {summary.num_examples} examples -> {summary.output_path} " |
| f"({summary.num_bytes} bytes)." |
| ) |
| stats_path = OUTPUT_DIR / "stats.json" |
| with stats_path.open("w", encoding="utf-8") as handle: |
| json.dump(stats, handle, indent=2) |
| handle.write("\n") |
| print(f"Wrote summary stats to {stats_path.relative_to(ROOT)}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
