File size: 4,485 Bytes
dadf189 | 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 | from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable
import torch
from PIL import Image
from torchvision import transforms
from .base import FingerprintSample
IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff"}
@dataclass
class NIST302Paths:
"""Root folders for three NIST SD302 subsets."""
root_302a: str
root_302b: str
root_302d: str
class NIST302Loader:
"""Unified loader for NIST SD302 A/B/D variants.
The loader parses metadata from path and filename patterns currently present
under the workspace dataset tree.
"""
def __init__(self, image_size: int = 224):
self.transform = transforms.Compose(
[
transforms.Resize((image_size, image_size)),
transforms.ToTensor(),
]
)
def discover(self, paths: NIST302Paths) -> list[dict[str, str]]:
records: list[dict[str, str]] = []
for root_str, dataset_name in [
(paths.root_302a, "nist_sd302a"),
(paths.root_302b, "nist_sd302b"),
(paths.root_302d, "nist_sd302d"),
]:
if not root_str: # skip empty roots — Path("") resolves to cwd and scans everything
continue
records.extend(self._scan_subset(Path(root_str), dataset_name))
return records
def iter_samples(
self, records: Iterable[dict[str, str]]
) -> Iterable[FingerprintSample]:
for rec in records:
image = Image.open(rec["image_path"]).convert("L")
tensor = self.transform(image)
yield {
"image": tensor,
"identity_id": rec["identity_id"],
"finger_id": rec["finger_id"],
"sensor_id": rec["sensor_id"],
"dataset": rec["dataset"],
"image_path": rec["image_path"],
}
def _scan_subset(self, subset_root: Path, dataset_name: str) -> list[dict[str, str]]:
if not subset_root.exists():
return []
records: list[dict[str, str]] = []
for path in sorted(subset_root.rglob("*")):
if not path.is_file() or path.suffix.lower() not in IMAGE_EXTS:
continue
meta = self._parse_metadata(path, subset_root, dataset_name)
if meta is not None:
records.append(meta)
return records
@staticmethod
def _parse_metadata(
file_path: Path,
subset_root: Path,
dataset_name: str,
) -> dict[str, str] | None:
rel_parts = file_path.relative_to(subset_root).parts
stem_tokens = file_path.stem.split("_")
# SD302-A filenames have 4 tokens: {subject}_{sensor}_{captype}_{finger}
# SD302-B/D filenames have 5 tokens: {subject}_{sensor}_{dpi}_{captype}_{finger}
if len(stem_tokens) < 4:
return None
identity_id = stem_tokens[0]
# NIST SD302 identity IDs are 8-digit numbers; reject non-fingerprint files early
if not (identity_id.isdigit() and len(identity_id) == 8):
return None
# Heuristic finger id from filename tail. This should be audited before
# strict cross-sensor experiments, matching the plan's risk note.
finger_token = stem_tokens[-1]
finger_id = f"F{int(finger_token):02d}" if finger_token.isdigit() else finger_token
# Sensor token is stabilized from the first 1-3 directory markers that
# capture device/capture style differences.
sensor_tokens = rel_parts[:-1]
sensor_id = "_".join(sensor_tokens[:3]) if sensor_tokens else "unknown"
return {
"identity_id": identity_id,
"finger_id": finger_id,
"sensor_id": sensor_id,
"dataset": dataset_name,
"image_path": str(file_path),
}
def to_batch(samples: list[FingerprintSample]) -> dict[str, torch.Tensor | list[str]]:
"""Collate helper for lists emitted by NIST302Loader.iter_samples."""
images = torch.stack([s["image"] for s in samples], dim=0)
return {
"images": images,
"identity_ids": [s["identity_id"] for s in samples],
"finger_ids": [s["finger_id"] for s in samples],
"sensor_ids": [s["sensor_id"] for s in samples],
"datasets": [s["dataset"] for s in samples],
"image_paths": [s["image_path"] for s in samples],
}
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