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import os
from enum import Enum
from pathlib import Path
import datasets
from pandas import DataFrame
_HOMEPAGE = "https://www.mvtec.com/company/research/datasets/mvtec-ad"
_LICENSE = "cc-by-nc-sa-4.0"
_CITATION = """\
@misc{
the-mvtec-anomaly-detection-dataset,
title = { The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection },
type = { Open Source Dataset },
author = { Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger },
howpublished = { \\url{ https://link.springer.com/article/10.1007%2Fs11263-020-01400-4 } },
url = { https://link.springer.com/article/10.1007%2Fs11263-020-01400-4 },
}
"""
class LabelName(int, Enum):
NORMAL = 0
ABNORMAL = 1
class MVTECCapsule(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
_URL = "https://huggingface.co/datasets/alexsu52/mvtec_capsule/resolve/main/capsule.tar.xz"
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
"mask": datasets.Image(),
"label": datasets.ClassLabel(names=["normal", "abnormal"]),
}
)
return datasets.DatasetInfo(
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
folder_dir = dl_manager.download_and_extract(self._URL)
category_dir = os.path.join(folder_dir, "capsule")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"category_dir": category_dir,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"category_dir": category_dir,
"split": "test",
},
),
]
def _generate_examples(self, category_dir, split):
extensions = (".png", ".PNG")
root = Path(category_dir)
samples_list = [(str(root),) + f.parts[-3:] for f in root.glob(r"**/*") if f.suffix in extensions]
if not samples_list:
raise RuntimeError(f"Found 0 images in {root}")
samples = DataFrame(samples_list, columns=["path", "split", "label", "image_path"])
# Modify image_path column by converting to absolute path
samples["image_path"] = samples.path + "/" + samples.split + "/" + samples.label + "/" + samples.image_path
# Create label index for normal (0) and anomalous (1) images.
samples.loc[(samples.label == "good"), "label_index"] = LabelName.NORMAL
samples.loc[(samples.label != "good"), "label_index"] = LabelName.ABNORMAL
samples.label_index = samples.label_index.astype(int)
# separate masks from samples
mask_samples = samples.loc[samples.split == "ground_truth"].sort_values(by="image_path", ignore_index=True)
samples = samples[samples.split != "ground_truth"].sort_values(by="image_path", ignore_index=True)
# assign mask paths to anomalous test images
samples["mask_path"] = ""
samples.loc[
(samples.split == "test") & (samples.label_index == LabelName.ABNORMAL), "mask_path"
] = mask_samples.image_path.values
# assert that the right mask files are associated with the right test images
if len(samples.loc[samples.label_index == LabelName.ABNORMAL]):
assert (
samples.loc[samples.label_index == LabelName.ABNORMAL]
.apply(lambda x: Path(x.image_path).stem in Path(x.mask_path).stem, axis=1)
.all()
), "Mismatch between anomalous images and ground truth masks. Make sure the mask files in 'ground_truth' \
folder follow the same naming convention as the anomalous images in the dataset (e.g. image: \
'000.png', mask: '000.png' or '000_mask.png')."
if split:
samples = samples[samples.split == split].reset_index(drop=True)
for idx in range(len(samples)):
image_path = samples.iloc[idx].image_path
mask_path = samples.iloc[idx].mask_path
label_index = samples.iloc[idx].label_index
with open(image_path, "rb") as f:
image_bytes = f.read()
if mask_path:
with open(mask_path, "rb") as f:
mask_bytes = f.read()
else:
mask_bytes = bytes(len(image_bytes))
yield idx, {
"image": {"path": image_path, "bytes": image_bytes},
"mask": {"path": mask_path, "bytes": mask_bytes},
"label": label_index,
}
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