Example of morphological distortion in slam

# Authors:
# Guillaume Auzias <guillaume.auzias@univ-amu.fr>
# Julien Barrès <julien.barres@etu.univ-amu.fr>

# License: MIT
# sphinx_gallery_thumbnail_number = 2

NOTE: there is no visualization tool in slam, but we provide at the

end of this script exemplare code to do the visualization with an external solution

---

Importation of slam modules

import slam.distortion as sdst
import slam.differential_geometry as sdg
import slam.io as sio
import numpy as np

Loading an example mesh and a smoothed copy of it

mesh = sio.load_mesh("../examples/data/example_mesh.gii")
mesh_s = sdg.laplacian_mesh_smoothing(mesh, nb_iter=50, dt=0.1)

    Smoothing mesh
  Computing Laplacian
    Computing mesh weights of type fem
    -edge length threshold needed for  0  values =  0.0  %
    -number of Nan in weights:  0  =  0.0  %
    -number of Negative values in weights:  936  =  6.706792777300086  %
    -nb Nan in Laplacian :  0
    -nb Inf in Laplacian :  0
0
10
20
30
40
    OK

Computation of the angle difference between each faces of mesh and mesh_s

angle_diff = sdst.angle_difference(mesh, mesh_s)
angle_diff

array([[-0.18881895,  0.12117287,  0.06764609],
       [ 0.05797368,  0.03686888, -0.09484256],
       [ 0.07230468,  0.16239874, -0.23470342],
       ...,
       [-0.08685515,  0.03678422,  0.05007093],
       [ 0.0635167 , -0.10415994,  0.04064324],
       [ 0.05023476, -0.0978278 ,  0.04759305]])

face_angle_dist = np.sum(np.abs(angle_diff), 1)
face_angle_dist

array([0.3776379 , 0.18968511, 0.46940683, ..., 0.1737103 , 0.20831988,
       0.19565561])

Computation of the area difference between each faces of mesh and mesh_s

area_diff = sdst.area_difference(mesh, mesh_s)
area_diff

array([0.42263817, 0.20383547, 0.26206739, ..., 0.08069364, 0.08235209,
       0.18712064])

Computation of the length difference between each edges of mesh and mesh_s

edge_diff = sdst.edge_length_difference(mesh, mesh_s)
edge_diff

TrackedArray([0.2547129 , 0.23347774, 0.18442651, ..., 0.25986046,
              0.1005251 , 0.08211042])

VISUALIZATION USING EXTERNAL TOOLS

# Visualization of the original mesh visb_sc = splt.visbrain_plot(mesh=mesh, caption=”original mesh”) visb_sc.preview() ############################################################################ # Visualization of the smoothed mesh visb_sc = splt.visbrain_plot(mesh=mesh_s, caption=”smoothed mesh”) visb_sc.preview()