pysegcnn.core.dataset.Cloud95Dataset

class pysegcnn.core.dataset.Cloud95Dataset(root_dir, use_bands=[], tile_size=None, pad=False, gt_pattern='(.*)gt\\.tif', sort=False, seed=0, transforms=[])[source]

Class for the Cloud-95 dataset by Mohajerani & Saeedi (2020).

Parameters
  • root_dir (str) – The root directory, path to the dataset.

  • use_bands (list [str], optional) – A list of the spectral bands to use. The default is [].

  • tile_size (int or None, optional) – The size of the tiles. If not None, each scene is divided into square tiles of shape (tile_size, tile_size). The default is None.

  • pad (bool, optional) – Whether to center pad the input image. Set pad = True, if the images are not evenly divisible by the tile_size. The image data is padded with a constant padding value of zero. For each image, the corresponding ground truth image is padded with a “no data” label. The default is False.

  • gt_pattern (str, optional) – A regural expression to match the ground truth naming convention. All directories and subdirectories in root_dir are searched for files matching gt_pattern. The default is ‘(.*)gt\.tif’.

  • sort (bool, optional) – Whether to chronologically sort the samples. Useful for time series data. The default is False.

  • seed (int, optional) – The random seed. Used to split the dataset into training, validation and test set. Useful for reproducibility. The default is 0.

  • transforms (list [pysegcnn.core.split.Augment], optional) – List of pysegcnn.core.split.Augment instances. Each item in transforms generates a distinct transformed version of the dataset. The total dataset is composed of the original untransformed dataset together with each transformed version of it. If transforms = [], only the original dataset is used. The default is [].

Returns

Return type

None.

Methods

build_samples(scene)

Stack the bands of a sample in a single array.

compose_scenes()

Build the list of samples of the dataset.

get_labels()

Class labels of the Cloud-95 dataset.

get_sensor()

Landsat 8 bands of the Cloud-95 dataset.

get_size()

Image size of the Cloud-95 dataset.

parse_scene_id(scene_id)

Parse Sparcs scene identifiers (Landsat 8).

preprocess(data, gt)

Preprocess Cloud-95 dataset images.

read_scene(idx)

Read the data of the sample with index idx.

to_tensor(x, dtype)

Convert x to torch.Tensor.

__init__(root_dir, use_bands=[], tile_size=None, pad=False, gt_pattern='(.*)gt\\.tif', sort=False, seed=0, transforms=[])[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(root_dir[, use_bands, tile_size, …])

Initialize self.

build_samples(scene)

Stack the bands of a sample in a single array.

compose_scenes()

Build the list of samples of the dataset.

get_labels()

Class labels of the Cloud-95 dataset.

get_sensor()

Landsat 8 bands of the Cloud-95 dataset.

get_size()

Image size of the Cloud-95 dataset.

parse_scene_id(scene_id)

Parse Sparcs scene identifiers (Landsat 8).

preprocess(data, gt)

Preprocess Cloud-95 dataset images.

read_scene(idx)

Read the data of the sample with index idx.

to_tensor(x, dtype)

Convert x to torch.Tensor.