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Datasets

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VOC

paz.datasets.voc.VOC(path=None, split='train', class_names='all', name='VOC2007', with_difficult_samples=True, evaluate=False)

Dataset loader for the falling things dataset (FAT).

Arguments

  • data_path: Data path to VOC2007 annotations
  • split: String determining the data split to load. e.g. train, val or test
  • class_names: all or list. If list it should contain as elements strings indicating each class name.
  • name: String or list indicating with dataset or datasets to load. e.g. VOC2007 or [''VOC2007'', VOC2012].
  • with_difficult_samples: Boolean. If True flagged difficult boxes will be added to the returned data.
  • evaluate: Boolean. If True returned data will be loaded without normalization for a direct evaluation.

Return

  • data: List of dictionaries with keys corresponding to the image paths

and values numpy arrays of shape [num_objects, 4 + 1] where the + 1 contains the class_arg and num_objects refers to the amount of boxes in the image.

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FAT

paz.datasets.fat.FAT(path, split='train', class_names='all')

Dataset loader for the falling things dataset (FAT).

Arguments

  • path: String indicating full path to dataset e.g. /home/user/fat/
  • split: String determining the data split to load. e.g. train, val or test
  • class_names: all or list. If list it should contain as elements strings indicating each class name.

References

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FER

paz.datasets.fer.FER(path, split='train', class_names='all', image_size=(48, 48))

Class for loading FER2013 emotion classification dataset. Arguments

  • path: String. Full path to fer2013.csv file.
  • split: String. Valid option contain 'train', 'val' or 'test'.
  • class_names: String or list: If 'all' then it loads all default class names.
  • image_size: List of length two. Indicates the shape in which the image will be resized.

References

-FER2013 Dataset and Challenge

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FERPlus

paz.datasets.ferplus.FERPlus(path, split='train', class_names='all', image_size=(48, 48))

Class for loading FER2013 emotion classification dataset. with FERPlus labels. Arguments

  • path: String. Path to directory that has inside the files: fer2013.csv and fer2013new.csv
  • split: String. Valid option contain 'train', 'val' or 'test'.
  • class_names: String or list: If 'all' then it loads all default class names.
  • image_size: List of length two. Indicates the shape in which the image will be resized.

References

[source]

OpenImages

paz.datasets.open_images.OpenImages(path, split='train', class_names='all')

Dataset loader for the OpenImagesV4 dataset.

Arguments

  • path: String indicating full path to dataset e.g. /home/user/open_images/
  • split: String determining the data split to load. e.g. train, val or test
  • class_names: all or list. If list it should contain as elements the strings of the class names.

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CityScapes

paz.datasets.cityscapes.CityScapes(image_path, label_path, split, class_names='all')

CityScapes data manager for loading the paths of the RGB and segmentation masks.

Arguments

  • image_path: String. Path to RGB images e.g. '/home/user/leftImg8bit/'
  • label_path: String. Path to label masks e.g. '/home/user/gtFine/'
  • split: String. Valid option contain 'train', 'val' or 'test'.
  • class_names: String or list: If 'all' then it loads all default class names.

References

-The Cityscapes Dataset for Semantic Urban Scene Understanding

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Shapes

paz.datasets.shapes.Shapes(num_samples, image_size, split='train', class_names='all', iou_thresh=0.3, max_num_shapes=3)

Loader for shapes synthetic dataset.

Arguments

  • num_samples: Int indicating number of samples to load.
  • image_size: (height, width) of input image to load.
  • split: String determining the data split to load. e.g. train, val or test
  • class_names: List of strings or all.
  • iou_thresh: Float intersection over union.
  • max_num_shapes: Int. maximum number of shapes in the image.

Returns

List of dictionaries with keys image, mask, box_data containing

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Omniglot

paz.datasets.omniglot.Omniglot(split, shape, flat=True)

Loads omniglot dataset for in between and within alphabet sampling.

Arguments

  • split: String. Either train or test. Indicates which split to load.
  • shape: List of two integers indicating resize shape (H, W).
  • flat: Boolean. If True the returned data dictionary is organized using each possible character as a class, with each key being a number having as value an image array. If False the returned data dictionary is organized using as keys the language names and as value another dictionary with keys being the character number, and as value the image array. This is to perform either sampling between alpahabet (flat=True) or to perform sampling within alphabet (flat=False). Usually, neural few-shot learning algorithms have been tested using in between alphabet sampling, but the original authors tested using the more challenging within alphabet sampling.

Returns

dictionary with class names as keys and image numpy arrays as values.