class TlseHypDataSet.utils.transforms.GaborFilters(n_frequencies: int = 4, n_thetas: int = 6)

Like-wise torchvision.transforms class to compute Gabor filters

class TlseHypDataSet.utils.transforms.SpectralIndices(wv: numpy.ndarray)

Like-wise torchvision.transforms class to compute spectral indices

class TlseHypDataSet.utils.transforms.GaussianFilter(bbl: numpy.ndarray, sigma: float)

Like-wise torchvision.transforms class to apply 1D Gaussian filters on the spectral dimension

class TlseHypDataSet.utils.transforms.Concat(transforms_)

Concatenate several data transformations

class TlseHypDataSet.utils.transforms.Stats

Like-wise torchvision.transforms class to compute statistics (mean, standard deviation, first and third quartiles, first and last deciles, minimum and maximum) over a 2D image

The example below shows how data transformations were combined to qualitatively compare the Toulouse Hyperspectral Data Set to other data sets (see comparison at

import torch
from torchvision import transforms
from TlseHypDataSet.tlse_hyp_data_set import TlseHypDataSet
from TlseHypDataSet.utils.transforms import GaussianFilter, SpectralIndices,\
                                            GaborFilters, Concat, Stats

dataset = TlseHypDataSet('/path/to/dataset/', pred_mode='patch', patch_size=64)
dataset.transform = transforms.Compose([
                GaussianFilter(dataset.bbl, sigma=1.5),