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MVTec 3D Data

MVTec 3D Data#

MVTec 3D-AD Dataset (CC BY-NC-SA 4.0).

Description:

This script contains PyTorch Dataset, Dataloader and PyTorch Lightning DataModule for the MVTec 3D-AD dataset. If the dataset is not on the file system, the script downloads and extracts the dataset and create PyTorch data objects.

License:
MVTec 3D-AD dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

License (CC BY-NC-SA 4.0)(https://creativecommons.org/licenses/by-nc-sa/4.0/).

Reference:
  • Paul Bergmann, Xin Jin, David Sattlegger, Carsten Steger: The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly

    Detection and Localization in: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 202-213, 2022, DOI: 10.5220/ 0010865000003124.

class anomalib.data.depth.mvtec_3d.MVTec3D(root='./datasets/MVTec3D', category='bagel', train_batch_size=32, eval_batch_size=32, num_workers=8, task=TaskType.SEGMENTATION, image_size=None, transform=None, train_transform=None, eval_transform=None, test_split_mode=TestSplitMode.FROM_DIR, test_split_ratio=0.2, val_split_mode=ValSplitMode.SAME_AS_TEST, val_split_ratio=0.5, seed=None)#

Bases: AnomalibDataModule

MVTec Datamodule.

Parameters:

  • root (Path | str) – Path to the root of the dataset Defaults to "./datasets/MVTec3D".

  • category (str) – Category of the MVTec dataset (e.g. “bottle” or “cable”). Defaults to bagel.

  • train_batch_size (int, optional) – Training batch size. Defaults to 32.

  • eval_batch_size (int, optional) – Test batch size. Defaults to 32.

  • num_workers (int, optional) – Number of workers. Defaults to 8.

  • task (TaskType) – Task type, ‘classification’, ‘detection’ or ‘segmentation’ Defaults to TaskType.SEGMENTATION.

  • image_size (tuple[int, int], optional) – Size to which input images should be resized. Defaults to None.

  • transform (Transform, optional) – Transforms that should be applied to the input images. Defaults to None.

  • train_transform (Transform, optional) – Transforms that should be applied to the input images during training. Defaults to None.

  • eval_transform (Transform, optional) – Transforms that should be applied to the input images during evaluation. Defaults to None.

  • test_split_mode (TestSplitMode) – Setting that determines how the testing subset is obtained. Defaults to TestSplitMode.FROM_DIR.

  • test_split_ratio (float) – Fraction of images from the train set that will be reserved for testing. Defaults to 0.2.

  • val_split_mode (ValSplitMode) – Setting that determines how the validation subset is obtained. Defaults to ValSplitMode.SAME_AS_TEST.

  • val_split_ratio (float) – Fraction of train or test images that will be reserved for validation. Defaults to 0.5.

  • seed (int | None, optional) – Seed which may be set to a fixed value for reproducibility. Defaults to None.

prepare_data()#

Download the dataset if not available.

Return type:

None

class anomalib.data.depth.mvtec_3d.MVTec3DDataset(task, root='./datasets/MVTec3D', category='bagel', transform=None, split=None)#

Bases: AnomalibDepthDataset

MVTec 3D dataset class.

Parameters:

  • task (TaskType) – Task type, classification, detection or segmentation

  • root (Path | str) – Path to the root of the dataset Defaults to "./datasets/MVTec3D".

  • category (str) – Sub-category of the dataset, e.g. ‘bagel’ Defaults to "bagel".

  • transform (Transform, optional) – Transforms that should be applied to the input images. Defaults to None.

  • split (str | Split | None) – Split of the dataset, usually Split.TRAIN or Split.TEST Defaults to None.

anomalib.data.depth.mvtec_3d.make_mvtec_3d_dataset(root, split=None, extensions=None)#

Create MVTec 3D-AD samples by parsing the MVTec AD data file structure.

The files are expected to follow this structure: - path/to/dataset/split/category/image_filename.png - path/to/dataset/ground_truth/category/mask_filename.png

This function creates a DataFrame to store the parsed information. The DataFrame follows this format:

path

split

label

image_path

mask_path

label_index

0

datasets/name

test

defect

filename.png

ground_truth/defect/filename_mask.png

1
Parameters:

  • root (Path) – Path to the dataset.

  • split (str | Split | None, optional) – Dataset split (e.g., ‘train’ or ‘test’). Defaults to None.

  • extensions (Sequence[str] | None, optional) – List of file extensions to be included in the dataset. Defaults to None.

Examples

The following example shows how to get training samples from the MVTec 3D-AD ‘bagel’ category:

>>> from pathlib import Path
>>> root = Path('./MVTec3D')
>>> category = 'bagel'
>>> path = root / category
>>> print(path)
PosixPath('MVTec3D/bagel')

>>> samples = create_mvtec_3d_ad_samples(path, split='train')
>>> print(samples.head())
    path          split label image_path                          mask_path                        label_index
    MVTec3D/bagel train good MVTec3D/bagel/train/good/rgb/105.png MVTec3D/bagel/ground_truth/good/gt/105.png 0
    MVTec3D/bagel train good MVTec3D/bagel/train/good/rgb/017.png MVTec3D/bagel/ground_truth/good/gt/017.png 0
Returns:

An output DataFrame containing the samples of the dataset.

Return type:

DataFrame

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