mvtec_capsule.py

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,
            }