:py:mod:`anomalib.data.btech`
=============================

.. py:module:: anomalib.data.btech

.. autoapi-nested-parse::

   BTech Dataset.

   This script contains PyTorch Lightning DataModule for the BTech dataset.

   If the dataset is not on the file system, the script downloads and
   extracts the dataset and create PyTorch data objects.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.data.btech.BTechDataset
   anomalib.data.btech.BTech



Functions
~~~~~~~~~

.. autoapisummary::

   anomalib.data.btech.make_btech_dataset



Attributes
~~~~~~~~~~

.. autoapisummary::

   anomalib.data.btech.logger


.. py:data:: logger
   

   

.. py:function:: make_btech_dataset(path: pathlib.Path, split: Optional[str] = None, split_ratio: float = 0.1, seed: Optional[int] = None, create_validation_set: bool = False) -> pandas.core.frame.DataFrame

   Create BTech samples by parsing the BTech data file structure.

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

   :param path: Path to dataset
   :type path: Path
   :param split: Dataset split (ie., either train or test). Defaults to None.
   :type split: str, optional
   :param split_ratio: Ratio to split normal training images and add to the
                       test set in case test set doesn't contain any normal images.
                       Defaults to 0.1.
   :type split_ratio: float, optional
   :param seed: Random seed to ensure reproducibility when splitting. Defaults to 0.
   :type seed: int, optional
   :param create_validation_set: Boolean to create a validation set from the test set.
                                 BTech dataset does not contain a validation set. Those wanting to create a validation set
                                 could set this flag to ``True``.
   :type create_validation_set: bool, optional

   .. rubric:: Example

   The following example shows how to get training samples from BTech 01 category:

   >>> root = Path('./BTech')
   >>> category = '01'
   >>> path = root / category
   >>> path
   PosixPath('BTech/01')

   >>> samples = make_btech_dataset(path, split='train', split_ratio=0.1, seed=0)
   >>> samples.head()
      path     split label image_path                  mask_path                     label_index
   0  BTech/01 train 01    BTech/01/train/ok/105.bmp BTech/01/ground_truth/ok/105.png      0
   1  BTech/01 train 01    BTech/01/train/ok/017.bmp BTech/01/ground_truth/ok/017.png      0
   ...

   :returns: an output dataframe containing samples for the requested split (ie., train or test)
   :rtype: DataFrame


.. py:class:: BTechDataset(root: Union[pathlib.Path, str], category: str, pre_process: anomalib.pre_processing.PreProcessor, split: str, task: str = 'segmentation', seed: Optional[int] = None, create_validation_set: bool = False)

   Bases: :py:obj:`torchvision.datasets.folder.VisionDataset`

   BTech PyTorch Dataset.

   .. py:method:: __len__() -> int

      Get length of the dataset.


   .. py:method:: __getitem__(index: int) -> Dict[str, Union[str, torch.Tensor]]

      Get dataset item for the index ``index``.

      :param index: Index to get the item.
      :type index: int

      :returns:

                Dict of image tensor during training.
                    Otherwise, Dict containing image path, target path, image tensor, label and transformed bounding box.
      :rtype: Union[Dict[str, Tensor], Dict[str, Union[str, Tensor]]]



.. py:class:: BTech(root: str, category: str, image_size: Optional[Union[int, Tuple[int, int]]] = None, train_batch_size: int = 32, test_batch_size: int = 32, num_workers: int = 8, task: str = 'segmentation', transform_config_train: Optional[Union[str, albumentations.Compose]] = None, transform_config_val: Optional[Union[str, albumentations.Compose]] = None, seed: Optional[int] = None, create_validation_set: bool = False)

   Bases: :py:obj:`pytorch_lightning.core.datamodule.LightningDataModule`

   BTechDataModule Lightning Data Module.

   .. py:method:: prepare_data() -> None

      Download the dataset if not available.


   .. py:method:: setup(stage: Optional[str] = None) -> None

      Setup train, validation and test data.

      BTech dataset uses BTech dataset structure, which is the reason for
      using `anomalib.data.btech.BTech` class to get the dataset items.

      :param stage: Optional[str]:  Train/Val/Test stages. (Default value = None)


   .. py:method:: train_dataloader() -> pytorch_lightning.utilities.types.TRAIN_DATALOADERS

      Get train dataloader.


   .. py:method:: val_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get validation dataloader.


   .. py:method:: test_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get test dataloader.


   .. py:method:: predict_dataloader() -> pytorch_lightning.utilities.types.EVAL_DATALOADERS

      Get predict dataloader.