File size: 5,536 Bytes
d18d6b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import ast
import json
from collections import Counter
from pathlib import Path
import sys
from typing import Any
import pyarrow as pa
import pyarrow.parquet as pq
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
import dataset_catalog as catalog
def _to_rel(base_dir: Path, value: str) -> str:
text = str(value or "").strip()
if not text:
return ""
p = Path(text)
if not p.is_absolute():
return p.as_posix()
try:
return p.resolve().relative_to(base_dir).as_posix()
except ValueError:
return p.as_posix()
def _build_rows(base_dir: Path, dataset: list[dict[str, Any]]) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
for item in dataset:
row = dict(item)
row["video_rel_path"] = str(item.get("path", "")).strip()
row["image_gt_rel_path"] = _to_rel(base_dir, str(item.get("image_gt", "")).strip())
image_paths = item.get("image_paths") if isinstance(item.get("image_paths"), list) else []
row["image_paths_rel"] = [_to_rel(base_dir, str(path)) for path in image_paths]
rows.append(row)
rows.sort(key=lambda r: str(r.get("path", "")))
return rows
def _load_default_descriptions(base_dir: Path) -> dict[str, str]:
app_path = base_dir / "app.py"
if not app_path.exists():
return {}
tree = ast.parse(app_path.read_text(encoding="utf-8"))
for node in tree.body:
if isinstance(node, ast.Assign):
for target in node.targets:
if isinstance(target, ast.Name) and target.id == "DEFAULT_DESCRIPTIONS":
value = ast.literal_eval(node.value)
if isinstance(value, dict):
return {str(k): str(v) for k, v in value.items()}
return {}
def _validate_rows(base_dir: Path, rows: list[dict[str, Any]]) -> dict[str, Any]:
missing_videos: list[str] = []
for row in rows:
rel = str(row.get("path", "")).strip()
if not rel:
continue
if not (base_dir / rel).exists():
missing_videos.append(rel)
split_counter = Counter(str(row.get("data_split", "")).strip().lower() for row in rows)
category_counter = Counter(str(row.get("category", "")).strip() for row in rows)
major_counter = Counter(str(row.get("major", "")).strip() for row in rows)
return {
"total_rows": len(rows),
"split_counts": dict(sorted(split_counter.items())),
"category_counts": dict(sorted(category_counter.items())),
"major_counts": dict(sorted(major_counter.items())),
"missing_video_paths": missing_videos,
}
def main() -> int:
parser = argparse.ArgumentParser(
description="Build Parquet aligned with Streamlit app _build_dataset(meta) output."
)
parser.add_argument(
"--base-dir",
default=str(Path(__file__).resolve().parents[1]),
help="Repository base directory (default: script parent parent).",
)
parser.add_argument(
"--out-parquet",
default="dist/streamlit_aligned.parquet",
help="Parquet output path relative to base dir.",
)
parser.add_argument(
"--out-stats",
default="dist/streamlit_aligned_stats.json",
help="Stats output path relative to base dir.",
)
parser.add_argument(
"--strict-files",
action="store_true",
help="Fail if any video file in rows is missing on disk.",
)
args = parser.parse_args()
base_dir = Path(args.base_dir).resolve()
video_dir = base_dir / "videos"
dataset_test_path = base_dir / "video_dataset.json"
dataset_train_path = base_dir / "video_dataset_train.json"
legacy_path = base_dir / "video_texts.json"
out_parquet = Path(args.out_parquet)
if not out_parquet.is_absolute():
out_parquet = (base_dir / out_parquet).resolve()
out_stats = Path(args.out_stats)
if not out_stats.is_absolute():
out_stats = (base_dir / out_stats).resolve()
out_parquet.parent.mkdir(parents=True, exist_ok=True)
out_stats.parent.mkdir(parents=True, exist_ok=True)
meta, _, _ = catalog.load_meta(
base_dir=base_dir,
dataset_test_path=dataset_test_path,
dataset_train_path=dataset_train_path,
legacy_path=legacy_path,
)
default_descriptions = _load_default_descriptions(base_dir)
dataset = catalog.build_dataset(
base_dir=base_dir,
video_dir=video_dir,
meta=meta,
default_descriptions=default_descriptions,
)
rows = _build_rows(base_dir, dataset)
stats = _validate_rows(base_dir, rows)
if args.strict_files and stats["missing_video_paths"]:
sample = stats["missing_video_paths"][:20]
raise SystemExit(
f"Missing {len(stats['missing_video_paths'])} video paths. Sample: {sample}"
)
table = pa.Table.from_pylist(rows)
pq.write_table(table, out_parquet, compression="zstd")
out_stats.write_text(json.dumps(stats, ensure_ascii=False, indent=2), encoding="utf-8")
print(f"Rows: {stats['total_rows']}")
print(f"Split counts: {stats['split_counts']}")
print(f"Parquet: {out_parquet}")
print(f"Stats: {out_stats}")
if stats["missing_video_paths"]:
print(f"Missing video paths: {len(stats['missing_video_paths'])}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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