Deep learning for NeuroImaging in Python.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

class surfify.augmentation.base.SurfBlur(vertices, triangles, sigma, neighs=None, cachedir=None)[source]¶

An icosahedron texture Gaussian blur implementation. It uses the DiNe convolution filter for speed. The receptive field is controlled by sigma, the standard deviation of the kernel.

Init class.

Parameters:

vertices : array (N, 3)

icosahedron’s vertices.

triangles : array (M, 3)

icosahdron’s triangles.

sigma : float

sigma parameter of the gaussian filter.

neighs : dict, default None

optionnaly specify the DiNe neighboors of each vertex as build with sufify.utils.neighbors, ie. a dictionary with vertices row index as keys and a dictionary of neighbors vertices row indexes organized by rings as values.

cachedir : str, default None

the optional path to cache the neighbors function output.

run(data)[source]¶

Applies the augmentation to the data.

Parameters:: data : array (N, )

input data/texture.
Returns:: data : array (N, )

blurred output data.