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UCSD Data

UCSD Data#

UCSD Pedestrian dataset.

class anomalib.data.video.ucsd_ped.UCSDped(root='./datasets/ucsd', category='UCSDped2', clip_length_in_frames=2, frames_between_clips=10, target_frame=VideoTargetFrame.LAST, task=TaskType.SEGMENTATION, image_size=None, transform=None, train_transform=None, eval_transform=None, train_batch_size=8, eval_batch_size=8, num_workers=8, val_split_mode=ValSplitMode.SAME_AS_TEST, val_split_ratio=0.5, seed=None)#

Bases: AnomalibVideoDataModule

UCSDped DataModule class.

Parameters:

  • root (Path | str) – Path to the root of the dataset

  • category (str) – Sub-category of the dataset, e.g. “UCSDped1” or “UCSDped2”

  • clip_length_in_frames (int, optional) – Number of video frames in each clip.

  • frames_between_clips (int, optional) – Number of frames between each consecutive video clip.

  • target_frame (VideoTargetFrame) – Specifies the target frame in the video clip, used for ground truth retrieval

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

  • 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.

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

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

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

prepare_data()#

Download the dataset if not available.

Return type:

None

class anomalib.data.video.ucsd_ped.UCSDpedClipsIndexer(video_paths, mask_paths, clip_length_in_frames=2, frames_between_clips=1)#

Bases: ClipsIndexer

Clips class for UCSDped dataset.

get_clip(idx)#

Get a subclip from a list of videos.

Parameters:

idx (int) – index of the subclip. Must be between 0 and num_clips().

Return type:

tuple[Tensor, Tensor, dict[str, Any], int]

Returns:

video (torch.Tensor) audio (torch.Tensor) info (dict) video_idx (int): index of the video in video_paths

get_mask(idx)#

Retrieve the masks from the file system.

Return type:

ndarray | None

class anomalib.data.video.ucsd_ped.UCSDpedDataset(task, root, category, split, clip_length_in_frames=2, frames_between_clips=10, target_frame=VideoTargetFrame.LAST, transform=None)#

Bases: AnomalibVideoDataset

UCSDped Dataset class.

Parameters:

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

  • root (Path | str) – Path to the root of the dataset

  • category (str) – Sub-category of the dataset, e.g. “UCSDped1” or “UCSDped2”

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

  • clip_length_in_frames (int, optional) – Number of video frames in each clip.

  • frames_between_clips (int, optional) – Number of frames between each consecutive video clip.

  • target_frame (VideoTargetFrame) – Specifies the target frame in the video clip, used for ground truth retrieval.

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

anomalib.data.video.ucsd_ped.make_ucsd_dataset(path, split=None)#

Create UCSD Pedestrian dataset by parsing the file structure.

The files are expected to follow the structure:

path/to/dataset/category/split/video_id/image_filename.tif path/to/dataset/category/split/video_id_gt/mask_filename.bmp

Parameters:

  • path (Path) – Path to dataset

  • split (str | Split | None, optional) – Dataset split (ie., either train or test). Defaults to None.

Example

The following example shows how to get testing samples from UCSDped2 category:

>>> root = Path('./UCSDped')
>>> category = 'UCSDped2'
>>> path = root / category
>>> path
PosixPath('UCSDped/UCSDped2')

>>> samples = make_ucsd_dataset(path, split='test')
>>> samples.head()
   root             folder image_path                    mask_path                         split
0  UCSDped/UCSDped2 Test   UCSDped/UCSDped2/Test/Test001 UCSDped/UCSDped2/Test/Test001_gt  test
1  UCSDped/UCSDped2 Test   UCSDped/UCSDped2/Test/Test002 UCSDped/UCSDped2/Test/Test002_gt  test
...
Returns:

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

Return type:

DataFrame

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