Samplewise Standardizer
tefla.da.standardizer.SamplewiseStandardizer (clip, channel_wise=False)
Args
- clip: max/min allowed value in the output image e.g.: 6
- channel_wise: perform standarization separately accross channels
Samplewise Standardizer
tefla.da.standardizer.SamplewiseStandardizerTF (clip, channel_wise=False)
Args
- clip: max/min allowed value in the output image e.g.: 6
- channel_wise: perform standarization separately accross channels
Aggregate Standardizer
tefla.da.standardizer.AggregateStandardizer (mean, std, u, ev, sigma=0.0, color_vec=None)
Creates a standardizer based on whole training dataset
Args
- mean: 1-D array, aggregate mean array e.g.: mean is calculated for each color channel, R, G, B
- std: 1-D array, aggregate standard deviation array e.g.: std is calculated for each color channel, R, G, B
- u: 2-D array, eigenvector for the color channel variation
- ev: 1-D array, eigenvalues
- sigma: float, noise factor
- color_vec: an optional color vector
Methods
augment_color (img, sigma=0.0, color_vec=None)
Args
- img: input image
- sigma: a float, noise factor
- color_vec: an optional color vec
Aggregate Standardizer
tefla.da.standardizer.AggregateStandardizerTF (mean, std, u, ev, sigma=0.0, color_vec=None)
Creates a standardizer based on whole training dataset
Args
- mean: 1-D array, aggregate mean array e.g.: mean is calculated for each color channel, R, G, B
- std: 1-D array, aggregate standard deviation array e.g.: std is calculated for each color channel, R, G, B
- u: 2-D array, eigenvector for the color channel variation
- ev: 1-D array, eigenvalues
- sigma: float, noise factor
- color_vec: an optional color vector
Methods
augment_color (img, sigma=0.0, color_vec=None)
Args
- img: input image
- sigma: a float, noise factor
- color_vec: an optional color vec