Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
File size: 4,849 Bytes
40bbfa3 | 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 | #!/usr/bin/env python3
"""Validate the public nuScenes-NRS release without requiring raw nuScenes data."""
from __future__ import annotations
import argparse
import hashlib
import json
import re
from pathlib import Path
import cv2
EXPECTED = {"training": 3182, "validation": 805}
TOKEN_RE = re.compile(r"^[0-9a-f]{32}$")
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def split_tokens(path: Path) -> list[str]:
rows = [line.strip() for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
if len(rows) != len(set(rows)):
raise AssertionError(f"duplicate token in {path}")
bad = [token for token in rows if not TOKEN_RE.fullmatch(token)]
if bad:
raise AssertionError(f"invalid token in {path}: {bad[0]}")
return rows
def validate_split(root: Path, split: str) -> list[str]:
tokens = split_tokens(root / "splits" / f"{split}.txt")
if len(tokens) != EXPECTED[split]:
raise AssertionError(f"{split}: expected {EXPECTED[split]} tokens, found {len(tokens)}")
files = sorted((root / split / "masks").glob("*.png"))
names = sorted(path.stem for path in files)
if names != sorted(tokens):
raise AssertionError(f"{split}: token list and mask filenames differ")
for index, path in enumerate(files, start=1):
image = cv2.imread(str(path), cv2.IMREAD_COLOR)
if image is None:
raise AssertionError(f"cannot read {path}")
if image.shape != (900, 1600, 3) or image.dtype.name != "uint8":
raise AssertionError(f"bad shape/dtype in {path}: {image.shape}, {image.dtype}")
# PNG is read as BGR: road must be pure red and all other channels zero.
if (image[:, :, 0] != 0).any() or (image[:, :, 1] != 0).any():
raise AssertionError(f"nonzero blue/green channel in {path}")
if not ((image[:, :, 2] == 0) | (image[:, :, 2] == 255)).all():
raise AssertionError(f"red channel is not binary in {path}")
if index % 500 == 0 or index == len(files):
print(f"checked {split}: {index}/{len(files)}")
return tokens
def validate_hashes(root: Path) -> None:
checksum_file = root / "SHA256SUMS.txt"
rows = []
for line in checksum_file.read_text(encoding="utf-8").splitlines():
if not line.strip():
continue
digest, relative = line.split(" ", 1)
rows.append((digest, relative))
if not rows:
raise AssertionError("SHA256SUMS.txt is empty")
for expected, relative in rows:
path = root / relative
if not path.is_file():
raise AssertionError(f"checksum target missing: {relative}")
actual = sha256_file(path)
if actual != expected:
raise AssertionError(f"checksum mismatch: {relative}")
print(f"checked SHA-256 entries: {len(rows)}")
def validate_scene_disjointness(root: Path, metadata_dir: Path | None) -> None:
if metadata_dir is None:
print("scene disjointness: skipped (no official metadata supplied)")
return
samples_path = metadata_dir / "sample.json"
scenes_path = metadata_dir / "scene.json"
samples = {row["token"]: row for row in json.loads(samples_path.read_text(encoding="utf-8"))}
scenes = {row["token"]: row for row in json.loads(scenes_path.read_text(encoding="utf-8"))}
train = {samples[token]["scene_token"] for token in split_tokens(root / "splits/training.txt")}
val = {samples[token]["scene_token"] for token in split_tokens(root / "splits/validation.txt")}
if train & val:
raise AssertionError("training/validation scene overlap")
unknown = (train | val) - scenes.keys()
if unknown:
raise AssertionError(f"unknown scene tokens: {len(unknown)}")
print(f"scene disjointness: PASS ({len(train)} training, {len(val)} validation scenes)")
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--root", type=Path, default=Path("."))
parser.add_argument(
"--metadata-dir",
type=Path,
default=None,
help="Optional official v1.0-trainval metadata directory for scene checks",
)
args = parser.parse_args()
root = args.root.resolve()
train = validate_split(root, "training")
val = validate_split(root, "validation")
if set(train) & set(val):
raise AssertionError("training/validation token overlap")
validate_scene_disjointness(root, args.metadata_dir.resolve() if args.metadata_dir else None)
validate_hashes(root)
print("nuScenes-NRS release validation: PASS")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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