#!/usr/bin/env python3 """Build MVEB CompCars subset from a precomputed train/test image split JSON. Input JSON format: { "train": ["1/1103/2011/xxx.jpg", ...], "test": ["2/2201/2013/yyy.jpg", ...] } Output (when executed from MVEB root): ./MVEB-train/CompCars/{query.parquet,candidate.parquet,media-*.parquet,README.md} ./MVEB-test/CompCars/{query.parquet,candidate.parquet,media-*.parquet,README.md} """ from __future__ import annotations import argparse import json import shutil import sys from pathlib import Path from typing import Dict, List, Tuple _SCRIPTS_ROOT = Path(__file__).resolve().parent.parent if str(_SCRIPTS_ROOT) not in sys.path: sys.path.insert(0, str(_SCRIPTS_ROOT)) from pack_media_parquet import pack_dataset_with_media, resolve_split_output_dir QUERY_INSTRUCTION = "Represent the image with the following text." QUERY_TEXT = "Find a car of the same model, regardless of color." CANDIDATE_INSTRUCTION = "You are a helpful assistant." CANDIDATE_TEXT = "Find a car of the same model, regardless of color." def _make_annotations(rel_paths: List[str]) -> Tuple[List[dict], List[dict]]: """Create query/candidate annotations compatible with pack_media_parquet.""" candidate_rows: List[dict] = [] inst_to_ids: Dict[str, List[str]] = {} for idx, rel_path in enumerate(rel_paths): parts = rel_path.split("/") if len(parts) < 4: raise ValueError( f"Invalid CompCars relative path: {rel_path!r}. " "Expected at least make/model/year/file.jpg" ) inst_id = "_".join(parts[:3]) row = { "id": rel_path, "instance_id": inst_id, "image_path": rel_path, "instruction": CANDIDATE_INSTRUCTION, "text": CANDIDATE_TEXT, } candidate_rows.append(row) inst_to_ids.setdefault(inst_id, []).append(rel_path) query_rows: List[dict] = [] for row in candidate_rows: pos_ids = [x for x in inst_to_ids[row["instance_id"]] if x != row["id"]] if not pos_ids: # Skip single-image identities for query set, aligned with converter/comp_cars.py continue query_rows.append( { "id": row["id"], "instance_id": row["instance_id"], "image_path": row["image_path"], "instruction": QUERY_INSTRUCTION, "text": QUERY_TEXT, "pos_ids": pos_ids, } ) return query_rows, candidate_rows def _process_one_split( split_name: str, rel_paths: List[str], image_root: Path, output_root: Path, overwrite: bool, media_rows_per_shard: int, row_group_size: int, num_workers: int, ) -> None: out_dir = resolve_split_output_dir(output_root, split_name, "CompCars") if overwrite and out_dir.exists(): shutil.rmtree(out_dir) out_dir.mkdir(parents=True, exist_ok=True) unique_paths = sorted(set(rel_paths)) query_rows, candidate_rows = _make_annotations(unique_paths) stats = pack_dataset_with_media( query_annotations=query_rows, candidate_annotations=candidate_rows, image_dir=str(image_root), output_dir=str(out_dir), media_rows_per_shard=media_rows_per_shard, row_group_size=row_group_size, num_workers=num_workers, dataset_name="CompCars", data_split=split_name, write_subset_readme=True, show_progress=True, ) print( f"[{split_name}] done: media={stats['num_media']}, " f"query={stats['num_query']}, candidate={stats['num_candidate']}, " f"shards={stats['num_shards']} -> {out_dir}" ) def main() -> None: script_dir = Path(__file__).resolve().parent root_dir = script_dir.parent.parent.parent default_split_json = script_dir / "train_test_split.json" if not default_split_json.exists(): # Backward-compatible filename used in some local copies. default_split_json = script_dir / "train_test_split.json" parser = argparse.ArgumentParser(description="Process CompCars split JSON to MVEB parquet format.") parser.add_argument( "--split-json", type=Path, default=default_split_json, help="JSON containing train/test relative image paths.", ) parser.add_argument( "--image-root", type=Path, default=root_dir / "source" / "compcars" / "data" / "image", help="Root directory of raw CompCars images (contains make/model/year).", ) parser.add_argument( "--output-root", type=Path, default=root_dir, help="Output MVEB root directory (contains train/ and test/).", ) parser.add_argument("--overwrite", action="store_true", help="Delete existing output split dir before writing.") parser.add_argument("--media-rows-per-shard", type=int, default=5000) parser.add_argument("--row-group-size", type=int, default=100) parser.add_argument("--num-workers", type=int, default=1) args = parser.parse_args() if not args.split_json.exists(): raise FileNotFoundError(f"split json not found: {args.split_json}") if not args.image_root.exists(): raise FileNotFoundError(f"image root not found: {args.image_root}") with args.split_json.open("r", encoding="utf-8") as f: split_data = json.load(f) for key in ("train", "test"): if key not in split_data or not isinstance(split_data[key], list): raise ValueError(f"split json must contain key {key!r} with a list value") _process_one_split( "train", split_data["train"], args.image_root, args.output_root, args.overwrite, args.media_rows_per_shard, args.row_group_size, args.num_workers, ) _process_one_split( "test", split_data["test"], args.image_root, args.output_root, args.overwrite, args.media_rows_per_shard, args.row_group_size, args.num_workers, ) if __name__ == "__main__": main()