protoclass.extraction.HaralickExtraction

class protoclass.extraction.HaralickExtraction(base_modality, distance=1, patch_size=(9, 9, 3), levels=256)

Haralick extraction from standalone modality.

Parameters:

base_modality : object

The base modality on which the normalization will be applied. The base modality should inherate from StandaloneModality class.

distance : int, optional (default=1)

The distance used to compute the cooccurence matrix

patch_size : int or tuple, optional (default=(9, 9, 3))

The size of the sliding used to extract patches later used to compute the cooccurence matrix.

levels : int, optional (default=256)

The input image should contain integers in [0, levels-1], where levels indicate the number of grey-levels counted (typically 256 for an 8-bit image). This argument is required for 16-bit images or higher and is typically the maximum of the image. As the output matrix is at least levels x levels, it might be preferable to use binning of the input image rather than large values for levels.

Attributes

base_modality_	(object) The base modality on which the normalization will be applied. The base modality should inherate from StandaloneModality class.
roi_data_	(ndarray, shape flexible) Corresponds to the index to consider in order to fit the data.

Methods

fit(modality[, ground_truth, cat])	Compute the images images.
load_from_pickles(filename)	Function to load a normalization object.
save_to_pickles(filename)	Function to save a normalizatio object using pickles.
transform(modality[, ground_truth, cat])	Extract the data from the given modality.

__init__(base_modality, distance=1, patch_size=(9, 9, 3), levels=256)

Methods

__init__(base_modality[, distance, ...])
fit(modality[, ground_truth, cat])	Compute the images images.
load_from_pickles(filename)	Function to load a normalization object.
save_to_pickles(filename)	Function to save a normalizatio object using pickles.
transform(modality[, ground_truth, cat])	Extract the data from the given modality.

fit(modality, ground_truth=None, cat=None)

Compute the images images.

Parameters:

modality : object of type TemporalModality

The modality object of interest.

ground-truth : object of type GTModality or None

The ground-truth of GTModality. If None, the whole data will be considered.

cat : str or None

String corresponding at the ground-truth of interest. Cannot be None if ground-truth is not None.

load_from_pickles(filename)

Function to load a normalization object.

Parameters:

filename : str

Filename to the pickle file. The extension should be .p.

Returns:

bpp : object

Returns the loaded object.

save_to_pickles(filename)

Function to save a normalizatio object using pickles.

Parameters:

filename : str

Filename to the pickle file. The extension should be .p.

Returns:

None

transform(modality, ground_truth=None, cat=None)

Extract the data from the given modality.

Parameters:

modality : object of type StandaloneModality

The modality object of interest.

ground-truth : object of type GTModality or None

The ground-truth of GTModality. If None, the whole data will be considered.

cat : str or None

String corresponding at the ground-truth of interest. Cannot be None if ground-truth is not None.

Returns

——

data : ndarray, shape (n_sample, n_feature)

A matrix containing the features extracted. The number of samples is equal to the number of positive label in the ground-truth.