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Folder 3D Data

Folder 3D Data#

Custom Folder Dataset.

This script creates a custom dataset from a folder.

class anomalib.data.depth.folder_3d.Folder3D(name, normal_dir, root, abnormal_dir=None, normal_test_dir=None, mask_dir=None, normal_depth_dir=None, abnormal_depth_dir=None, normal_test_depth_dir=None, extensions=None, 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.FROM_TEST, val_split_ratio=0.5, seed=None)#

Bases: AnomalibDataModule

Folder DataModule.

Parameters:

  • name (str) – Name of the dataset. This is used to name the datamodule, especially when logging/saving.

  • normal_dir (str | Path) – Name of the directory containing normal images.

  • root (str | Path | None) – Path to the root folder containing normal and abnormal dirs. Defaults to None.

  • abnormal_dir (str | Path | None) – Name of the directory containing abnormal images. Defaults to abnormal.

  • normal_test_dir (str | Path | None, optional) – Path to the directory containing normal images for the test dataset. Defaults to None.

  • mask_dir (str | Path | None, optional) – Path to the directory containing the mask annotations. Defaults to None.

  • normal_depth_dir (str | Path | None, optional) – Path to the directory containing normal depth images for the test dataset. Normal test depth images will be a split of normal_dir

  • abnormal_depth_dir (str | Path | None, optional) – Path to the directory containing abnormal depth images for the test dataset.

  • normal_test_depth_dir (str | Path | None, optional) – Path to the directory containing normal depth images for the test dataset. Normal test images will be a split of normal_dir if None. Defaults to None.

  • normal_split_ratio (float, optional) – Ratio to split normal training images and add to the test set in case test set doesn’t contain any normal images. Defaults to 0.2.

  • extensions (tuple[str, ...] | None, optional) – Type of the image extensions to read from the directory. Defaults to None.

  • 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, optional) – Task type. Could be 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.FROM_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 used during random subset splitting. Defaults to None.

property name: str#

Name of the datamodule.

Folder3D datamodule overrides the name property to provide a custom name.

class anomalib.data.depth.folder_3d.Folder3DDataset(name, task, normal_dir, root=None, abnormal_dir=None, normal_test_dir=None, mask_dir=None, normal_depth_dir=None, abnormal_depth_dir=None, normal_test_depth_dir=None, transform=None, split=None, extensions=None)#

Bases: AnomalibDepthDataset

Folder dataset.

Parameters:

  • name (str) – Name of the dataset.

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

  • transform (Transform, optional) – Transforms that should be applied to the input images.

  • normal_dir (str | Path) – Path to the directory containing normal images.

  • root (str | Path | None) – Root folder of the dataset. Defaults to None.

  • abnormal_dir (str | Path | None, optional) – Path to the directory containing abnormal images. Defaults to None.

  • normal_test_dir (str | Path | None, optional) – Path to the directory containing normal images for the test dataset. Defaults to None.

  • mask_dir (str | Path | None, optional) – Path to the directory containing the mask annotations. Defaults to None.

  • normal_depth_dir (str | Path | None, optional) – Path to the directory containing normal depth images for the test dataset. Normal test depth images will be a split of normal_dir Defaults to None.

  • abnormal_depth_dir (str | Path | None, optional) – Path to the directory containing abnormal depth images for the test dataset. Defaults to None.

  • normal_test_depth_dir (str | Path | None, optional) – Path to the directory containing normal depth images for the test dataset. Normal test images will be a split of normal_dir if None. Defaults to None.

  • transform – Transforms that should be applied to the input images. Defaults to None.

  • split (str | Split | None) – Fixed subset split that follows from folder structure on file system. Choose from [Split.FULL, Split.TRAIN, Split.TEST] Defaults to None.

  • extensions (tuple[str, ...] | None, optional) – Type of the image extensions to read from the directory. Defaults to None.

Raises:

ValueError – When task is set to classification and mask_dir is provided. When mask_dir is provided, task should be set to segmentation.

property name: str#

Name of the dataset.

Folder3D dataset overrides the name property to provide a custom name.

anomalib.data.depth.folder_3d.make_folder3d_dataset(normal_dir, root=None, abnormal_dir=None, normal_test_dir=None, mask_dir=None, normal_depth_dir=None, abnormal_depth_dir=None, normal_test_depth_dir=None, split=None, extensions=None)#

Make Folder Dataset.

Parameters:

  • normal_dir (str | Path) – Path to the directory containing normal images.

  • root (str | Path | None) – Path to the root directory of the dataset. Defaults to None.

  • abnormal_dir (str | Path | None, optional) – Path to the directory containing abnormal images. Defaults to None.

  • normal_test_dir (str | Path | None, optional) – Path to the directory containing normal images for the test

  • None. (dataset. Normal test images will be a split of normal_dir if) – Defaults to None.

  • mask_dir (str | Path | None, optional) – Path to the directory containing the mask annotations. Defaults to None.

  • normal_depth_dir (str | Path | None, optional) – Path to the directory containing normal depth images for the test dataset. Normal test depth images will be a split of normal_dir Defaults to None.

  • abnormal_depth_dir (str | Path | None, optional) – Path to the directory containing abnormal depth images for the test dataset. Defaults to None.

  • normal_test_depth_dir (str | Path | None, optional) – Path to the directory containing normal depth images for the test dataset. Normal test images will be a split of normal_dir if None. Defaults to None.

  • split (str | Split | None, optional) – Dataset split (ie., Split.FULL, Split.TRAIN or Split.TEST). Defaults to None.

  • extensions (tuple[str, ...] | None, optional) – Type of the image extensions to read from the directory. Defaults to None.

Returns:

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

Return type:

DataFrame

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