def _run_interface(self, runtime):
import os.path as op
import nibabel.gifti as ng
import numpy as np
import skimage.measure as sm
import nilearn.image as nimg
import slam.io as sio
import slam.differential_geometry as sdg
from nipype.utils.filemanip import split_filename
# Generate output mesh filename from the input image name
_, fname, _ = split_filename(self.inputs.image_file)
gii_file = op.abspath(op.join(runtime.cwd, fname + ".gii"))
# Load the largest connected component of the input image
img = nimg.largest_connected_component_img(self.inputs.image_file)
# TODO: check if the input image is correct (binary)
# Run the marching cube algorithm
verts, faces, normals, values = sm.marching_cubes_lewiner(
img.get_data(), self.inputs.level)
# Convert vertices coordinates to image space
# TODO: check that is correct by plotting the mesh on the image
x, y, z = nimg.coord_transform(verts[:, 0], verts[:, 1], verts[:, 2],
img.affine)
mm_verts = np.array([x, y, z]).T
# Save the mesh as Gifti
# FIXME: FreeView can not open the mesh (but anatomist do)
gii = ng.GiftiImage(darrays=[
ng.GiftiDataArray(mm_verts, intent='NIFTI_INTENT_POINTSET'),
ng.GiftiDataArray(faces, intent='NIFTI_INTENT_TRIANGLE')
])
gii.meta = ng.GiftiMetaData().from_dict({
"volume_file":
self.inputs.image_file,
"marching_cube_level":
str(self.inputs.level),
"smoothing_iterations":
str(self.inputs.smoothing_iter),
"smoothing_dt":
str(self.inputs.smoothing_dt)
})
ng.write(gii, gii_file)
# Optional: Smooth the marching cube output with SLAM
if self.inputs.smoothing_iter > 0:
mesh = sdg.laplacian_mesh_smoothing(
sio.load_mesh(gii_file),
nb_iter=self.inputs.smoothing_iter,
dt=self.inputs.smoothing_dt)
sio.write_mesh(mesh, gii_file)
return runtime
def test_basic(self):
mesh_a = self.sphere_A.copy()
mesh_a_save = self.sphere_A.copy()
sio.write_mesh(mesh_a, "tests/data/io/temp.gii")
# Non modification
assert (mesh_a.vertices == mesh_a_save.vertices).all()
assert (mesh_a.faces == mesh_a_save.faces).all()
mesh_b = sio.load_mesh('tests/data/io/temp.gii')
precision_A = .00001
# Correctness
assert (np.isclose(mesh_a.vertices, mesh_b.vertices,
precision_A).all())
assert (np.isclose(mesh_a.faces, mesh_b.faces, precision_A).all())
def main(arguments):
if len(arguments)==3:
meshN = arguments[1]
texN = arguments[2]
mesh = sio.load_mesh(meshN)
maxCurv = maxAbsCurvature(mesh)
# visb_sc = splt.visbrain_plot(mesh=mesh, tex=maxCurv,
# caption='max absolute curvature',
# cblabel='max absolute curvature')
# visb_sc.preview()
sio.write_texture(stex.TextureND(darray=maxCurv), texN)
else:
print('Usage:')
print('python computeMaxCurvature.py mesh curvTex')
# Authors: Guillaume Auzias <*****@*****.**>
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 2
###############################################################################
# importation of slam modules
import slam.plot as splt
import slam.io as sio
import numpy as np
import trimesh
###############################################################################
# loading an two unregistered meshes
mesh_1 = sio.load_mesh('../examples/data/example_mesh.gii')
mesh_2 = sio.load_mesh('../examples/data/example_mesh_2.gii')
###############################################################################
# plot them to check the misalignment
joint_mesh = mesh_1 + mesh_2
joint_tex = np.ones((joint_mesh.vertices.shape[0], ))
joint_tex[:mesh_1.vertices.shape[0]] = 10
visb_sc = splt.visbrain_plot(mesh=joint_mesh,
tex=joint_tex,
caption='before registration')
visb_sc.preview()
###############################################################################
# compute ICP registration
transf_mat, cost = trimesh.registration.mesh_other(mesh_1,
print(np.min(mesh_coords))
print(np.max(mesh_coords))"""
main_folder = "/hpc/meca/users/souani.a/curvature/Compute_curvatures/Quadrics"
curv_types = ['Peyre', 'Rusinkiewicz', 'PetitJean', 'Taubin',
'Dong'] #, 'Peyre', 'fem', 'boix', 'bary']
curv_types_leg = curv_types.copy()
curv_types_leg.append('analytic')
meshes = list()
curv_mean = list()
Ks = [[-1, 1], [1, 1], [0, 1]]
for K in Ks:
meshes.append('hex_quad_k1_' + str(K[0]) + '_k2_' + str(K[1]) + '_100')
os.chdir(
r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Quadrics")
tmp_m = sio.load_mesh('hex_quad_k1_' + str(K[0]) + '_k2_' + str(K[1]) +
'_100.gii')
mesh_coords = tmp_m.vertices
print(np.shape(mesh_coords))
curv_mean.append(
gp.quadric_curv_mean(K[0], K[1])(mesh_coords[:, 0],
mesh_coords[:, 1]))
#meshes.append('FSsphere.ico7')
#meshes.append('KKI2009_113_MR1.lh.white')
for mesh_type, curv_ref in zip(meshes, curv_mean):
print(mesh_type)
print("Shaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaape", np.shape(curv_ref))
file_fig = os.path.join(main_folder,
mesh_type + '_curv_mean_comparison_hist.png')
curvatures = list()
integral = list()
class TestTopologyMethods(unittest.TestCase):
# Sphere cut by two planes, with new vertices added at the intersection
cutSphere_A = make_cut_sphere_a()
# Sphere cut by one plane, with new vertices added at the intersection
cutSphere_B = make_cut_sphere_b()
# Sphere cut by two planes, with no new vertices added at the intersection
cutSphere_C = sio.load_mesh("tests/data/topology/mesh_C.gii")
# A 3D disk with height 0 and radius 2
disk_radius_2 = sio.load_mesh("tests/data/topology/mesh_D.gii")
# hexagon 2 rings
K = 4
k_rings = create_K_rings(K)
def test_boundary_angles(self):
# for a k ring the angle between two consecutive sides is pi - pi/(3k)
N = len(self.k_rings.vertices)
coords = np.hstack([self.k_rings.vertices, np.zeros((N, 1))])
indices = range(N - 6 * (self.K - 1), N)
ang, norm = stop.boundary_angles(indices, coords)
assert (ang == 180 - 180 / (3 * (self.K - 1))).all
def test_boundaries_basic(self):
mesh_a = self.cutSphere_A
mesh_a_save = mesh_a.copy()
boundary = stop.mesh_boundary(mesh_a)
# Non modification
assert (mesh_a.vertices == mesh_a_save.vertices).all()
assert (mesh_a.faces == mesh_a_save.faces).all()
# Correctness
assert (len(boundary) == 2)
# Type
assert (isinstance(boundary, list))
assert (isinstance(boundary[0], list))
iterable = list(itertools.combinations(boundary, 2))
# Uniqueness
for b1, b2 in iterable:
assert set(b1).intersection(set(b2)) == set()
def test_boundaries_triangles(self):
mesh_a = distinct_triangles(5)
mesh_b = random_triangles(5)
boundary_a = stop.mesh_boundary(mesh_a)
boundary_b = stop.mesh_boundary(mesh_b)
iterable_a = list(itertools.combinations(boundary_a, 2))
iterable_b = list(itertools.combinations(boundary_b, 2))
# Correctness
assert (len(boundary_a) == 5)
assert (len(boundary_b) == 5)
# Uniqueness
for b1, b2 in iterable_a:
assert set(b1).intersection(set(b2)) == set()
for b1, b2 in iterable_b:
assert set(b1).intersection(set(b2)) == set()
def test_angle_norm_boundary(self):
# GET A DISK IN 3D
radius = 2
mesh_a = self.disk_radius_2
coords3D = mesh_a.vertices
# COMPUTE BOUNDARY
boundary = stop.mesh_boundary(mesh_a)
# Correctness
assert (len(boundary) == 1)
# COMPUTE ANGLE AND NORM VARIATIONS
angles, norms = stop.boundary_angles(boundary[0], coords3D)
angles = abs(180 - angles)
sum_angles, sum_norms = sum(angles), sum(norms)
# Computation on three arbitrary linked vertices
three_angles, three_norms = stop.boundary_angles([0, 1, 2], coords3D)
three_angles = abs(180 - three_angles)
perimeter = radius * np.pi * 2
precision_A = .001
precision_B = .001
# Coherence of the angle sum and norm sum
assert (np.isclose(sum_angles, 360, precision_A))
assert (np.isclose(sum_norms, perimeter, precision_B))
# Coherence of a partial angle and a partial norm variation
assert (np.isclose(three_angles[1], 360 / len(coords3D), precision_A))
assert (np.isclose(three_norms[1], perimeter / len(coords3D),
precision_B))
def test_boundaries_intersection_copy(self):
mesh_a = self.cutSphere_A
boundary = stop.mesh_boundary(mesh_a)
b1 = boundary[0]
b2 = boundary[1]
b2_save = b2.copy()
concat = b1 + b2
inter_1 = stop.boundaries_intersection([concat, b2])
inter_1 = inter_1[0][2]
# Non modification
assert (b2 == b2_save)
# Type
assert (isinstance(inter_1, list))
# Correctness
assert (set(inter_1) == set(b2))
def test_close_mesh(self):
mesh_a = self.cutSphere_C.copy()
boundary_prev = stop.mesh_boundary(mesh_a)
# Correctness
assert (len(boundary_prev) == 2)
mesh_a_closed, nvertices_added = stop.close_mesh(mesh_a)
# Coherence
assert (len(mesh_a_closed.vertices) == len(mesh_a.vertices) +
nvertices_added)
boundary_closed = stop.mesh_boundary(mesh_a_closed)
# Mesh is now closed
assert (len(boundary_closed) == 0)
# Non modification of the vertices which are not on the boundaries
sbase = (set(range(len(mesh_a.vertices))))
sbound1 = (set(boundary_prev[0]))
sbound2 = (set(boundary_prev[1]))
sbound = sbound1.union(sbound2)
snot_bound = (sbase.difference(sbound))
for i in snot_bound:
assert (mesh_a.vertices[i] == mesh_a_closed.vertices[i]).all()
def test_remove_mesh_boundary_faces(self):
mesh_a = self.cutSphere_A.copy()
mesh_a_save = mesh_a.copy()
boundary = stop.mesh_boundary(mesh_a)
mesh_processed = stop.remove_mesh_boundary_faces(mesh_a,
face_vertex_number=1)
# Non modification
assert (mesh_a.vertices == mesh_a_save.vertices).all()
# Correctness
# check that when removing all faces with any vertex on the boundary,
# all the boundary vertices are removed from the mesh
print(len(boundary[0]))
print(len(mesh_a.vertices))
print(len(mesh_processed.vertices))
assert (len(mesh_processed.vertices) == len(mesh_a.vertices) -
len(boundary[0]))
def test_k_ring_neighborhood(self):
mesh_hexagon = self.k_rings.copy()
# WARNING, 0 is not the center...
texture_2_rings = stop.k_ring_neighborhood(mesh_hexagon, index=0, k=2)
zero_ring = np.where(texture_2_rings == 0)[0]
one_ring = np.where(texture_2_rings == 1)[0]
two_ring = np.where(texture_2_rings == 2)[0]
assert (zero_ring == [0]).all()
assert (one_ring == [i for i in range(1, 7)]).all()
assert (two_ring == [i for i in range(7, 19)]).all()
def test_adjacency_matrix(self):
toy_graph = create_test_graph()
gd_truth_adja = []
gd_truth_adja.append([0, 1, 1, 0])
gd_truth_adja.append([1, 0, 1, 1])
gd_truth_adja.append([1, 1, 0, 1])
gd_truth_adja.append([0, 1, 1, 0])
gd_truth_adja = np.array(gd_truth_adja)
adja = stop.adjacency_matrix(toy_graph)
assert (adja == gd_truth_adja).all()
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 2
###############################################################################
# Importation of slam modules
import slam.distortion as sdst
import slam.differential_geometry as sdg
import slam.plot as splt
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)
##########################################################################
# 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()
###############################################################################
# Computation of the angle difference between each faces of mesh and mesh_s
angle_diff = sdst.angle_difference(mesh, mesh_s)
import os
import slam.io as sio
from scipy.stats import pearsonr
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
if __name__ == '__main__':
output_folder = '/hpc/meca/users/auzias/test_curvature/real_mesh_all_curvatures'
target_spherical_mesh = sio.load_mesh('/hpc/meca/softs/dev/auzias/FSsphere.ico7.gii')
curv_types = ['mean_Rusinkiewicz', 'mean_Dong', 'mean_PetitJean', 'mean_Peyre', 'mean_Taubin', 'gauss_Dong', 'gauss_PetitJean', 'gauss_Peyre', 'gauss_Rusinkiewicz', 'gauss_Taubin']
#'mean_Maillot', 'gauss_Maillot'
sides = ['lh']
files_list=os.listdir(output_folder)
correl = list()
diff = list()
for curv_type in curv_types:
print(curv_type+'------------------------------------------------')
for side in sides:
tex_files = list()
subjects = list()
for fil in files_list:
if curv_type in fil and 'NN_FSsphere.ico7.gii' in fil and side in fil:
tex_files.append(fil)
filename_split = fil.split('.')
subj = filename_split[0]
filename = filename_split[-1]
side = filename_split[1]
# script for calculating the 3D elongations
# based on the differences of the averages of the distances
# between the vertex of interest and its 4 neighbors since we are working on
# quadrilateral configurations
# Ref: Makki.K et al. A new geodesic-based feature for characterizationof 3D shapes:
# application to soft tissue organtemporal deformations
# ICPR International Conference on Pattern Recognition -- Milan 01/2021
output_path = '../Data/Features/elongation_all/'
path = '../Data/AF_Dyn3D_5SL_QuadMesh_Gifti/'
basename = 'output_AF_Dyn3D_5SL_3dRecbyReg'
mesh_set = glob.glob(path + basename + '*.gii')
mesh_set.sort()
print(mesh_set)
data_ref = sio.load_mesh('QuadMesh_AF.gii')
pcd_pts_ref = data_ref.vertices
nbrs = NearestNeighbors(n_neighbors=5, algorithm='ball_tree').fit(pcd_pts_ref)
distances, indices = nbrs.kneighbors(pcd_pts_ref)
print(distances.shape)
print(indices.shape)
average_dist_ref = np.mean(distances[:, 1:5], axis=1)
print(average_dist_ref)
distances_trg_i = np.zeros(distances.shape)
distances_trg_j = np.zeros(distances.shape)
for t in range(len(mesh_set) - 1):
data_dist_i = sio.load_mesh(mesh_set[t])
pcd_pts_dist_i = data_dist_i.vertices
import trimesh
import slam.io as sio
import slam.plot as splt
import nibabel as nb
import os
import numpy as np
if __name__ == '__main__':
fd = '/hpc/meca/users/bohi.a/Data/Models/week23_ref_surf/B0.txt'
coords = np.loadtxt(fd, skiprows=1, max_rows=50943)
faces = np.loadtxt(fd, skiprows=50945, dtype=np.int)
mesh = trimesh.Trimesh(faces=faces - 1, vertices=coords, process=False)
mesh.show()
os.chdir(r"/hpc/meca/users/souani.a/data/OASIS")
mesh1 = sio.load_mesh('OAS1_0277_Rwhite.gii')
mesh1.show()
# curv_gifti = nb.gifti.read('hex_quad_k1_0_k2_10_curv_mean_Dong.gii')
# curv_tex = curv_gifti.darrays[0].data.squeeze()
# splt.pyglet_plot(mesh1, curv_tex)
"""mesh1 = sio.load_mesh('example_1_curv_mean_Dong.gii')
mesh1.show()
mesh1 = sio.load_mesh('example_1_curv_gauss_PetitJean.gii')
mesh1.show()
mesh1 = sio.load_mesh('example_1_curv_mean_PetitJean.gii')
mesh1.show()
mesh1 = sio.load_mesh('example_1_curv_gauss_Peyre.gii')
mesh1.show()
mesh1 = sio.load_mesh('example_1_curv_mean_Peyre.gii')
mesh1.show()
# Estimate curvatures :
cc = 'hex_quad_k1_' + str(Ks[i][0]) + '_k2_' + str(Ks[i][1]) + '_' + str(nstep) + '_noise_' + str(
int(sigma * 1000))
# os.chdir(r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Noise")
# os.mkdir(cc )
os.chdir(r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Noise/"+cc)
sio.write_mesh(mesh, cc+'.gii')
list_exampels.append(cc)
print("done for " + cc)
data = list()
for curv_type in curv_types:
mesh_file = 'hex_quad_k1_' + str(Ks[i][0]) + '_k2_' + str(Ks[i][1]) + '_' + str(nstep) + '.gii'
texture_file = cc + '_curv_mean_' + curv_type + '.gii'
os.chdir("/hpc/meca/users/souani.a/curvature/Compute_curvatures/Quadrics")
mesh = sio.load_mesh(mesh_file)
# mesh.apply_transform(mesh.principal_inertia_transform)
os.chdir(
"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Noise/" + cc)
tex = sio.load_texture(texture_file)
# splt.pyglet_plot(mesh, tex.darray, plot_colormap=True)
# splt.pyglet_plot(mesh, tex.darray, 'hot', plot_colormap=True)
# print(tex.darray)
data.append(tex.darray)
xx = sps.quadric_curv_mean(Ks[i][0], Ks[i][1])(mesh.vertices[:, 0], mesh.vertices[:, 1])
xx = np.reshape(xx, (1, nstep ** 2))
data = np.array(data)
data_m = np.zeros((6, nstep ** 2))
data_m[:5, :] = data[:, :, 0]
for sigma in Sigma:
# Generate the quadric
# X, Y, faces, Zs = gqs.generate_quadric([ks], nstep=list_nbr_steps[i], ax=list_ax_ay[i][0ay=list_ax_ay[i][1])
# Z = Zs[0]
# coords = np.array([X, Y, Z]).transpose()
# mesh = trimesh.Trimesh(faces=faces, vertices=coords, process=False)
# mesh.show()
# print("mesh surface = ", mesh.area, "\n surface needed = ", list_surfaces[i])
os.chdir(
r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Curvatures/0Noise"
)
cc = 'hex_quad_k1_' + str(ks[0]) + '_k2_' + str(
ks[1]) + '_' + str(list_nbr_steps[i])
mesh = sio.load_mesh(cc + '.gii')
data = list()
xx = gqs.quadric_curv_mean(ks[0], ks[1])(mesh.vertices[:, 0],
mesh.vertices[:, 1])
print("-------------------------------" + cc + '_noise_' +
str(int(sigma * 1000)))
for curv_type in curv_types:
print(curv_type)
texture_file = cc + '_noise_' + str(int(
sigma * 1000)) + '_curv_mean_' + curv_type + '.gii'
# mesh.apply_transform(mesh.principal_inertia_transform)
os.chdir(
"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Curvatures/Noise/"
+ cc + '_noise_' + str(int(sigma * 1000)))
import real_mesh_compute_all_curvatures
import compute_all_curvatures
import slam.plot as splt
import numpy as np
import generate_quadric_surfaces as gqs
if __name__ == "__main__":
ccc = ["sub_mesh0", "sub_mesh1", "sub_mesh2", "sub_mesh3", "sub_mesh4"]
curv_types = ["Dong", "PetitJean", "Peyre", "Rusinkiewicz", "Taubin"]
mm = "lhwhite"
i = 0
for cc in ccc:
os.chdir(r"/hpc/meca/users/souani.a/curvature")
data = list()
mesh = sio.load_mesh(cc + ".gii")
for curv_type in curv_types:
print(curv_type)
os.chdir(r"/hpc/meca/users/souani.a/data/sub_m/sub_m" + str(i))
texture_mean = sio.load_texture(cc + "_curv_mean_" + curv_type +
".gii")
print(np.shape(texture_mean.darray))
data.append(texture_mean.darray)
# splt.pyglet_plot(mesh, texture_mean.darray, color_map='jet')
texture_gauss = sio.load_texture(cc + "_curv_gauss_" + curv_type +
".gii")
# splt.pyglet_plot(mesh, texture_gauss.darray, color_map='jet')
#data = np.array(data)
#print(data)
data = np.array(data)
print(np.shape(data))
class TestTopologyMethods(unittest.TestCase):
# Sphere cut by two planes, with new vertices added at the intersection
cutSphere_A = make_cut_sphere_a()
# Sphere cut by one plane, with new vertices added at the intersection
cutSphere_B = make_cut_sphere_b()
# Sphere cut by two planes, with no new vertices added at the intersection
cutSphere_C = sio.load_mesh("tests/data/topology/mesh_C.gii")
# A 3D disk with height 0 and radius 2
disk_radius_2 = sio.load_mesh("tests/data/topology/mesh_D.gii")
def test_boundaries_basic(self):
mesh_a = self.cutSphere_A
mesh_a_save = mesh_a.copy()
boundary = stop.mesh_boundary(mesh_a)
# Non modification
assert (mesh_a.vertices == mesh_a_save.vertices).all()
assert (mesh_a.faces == mesh_a_save.faces).all()
# Correctness
assert (len(boundary) == 2)
# Type
assert (isinstance(boundary, list))
assert (isinstance(boundary[0], list))
iterable = list(itertools.combinations(boundary, 2))
# Uniqueness
for b1, b2 in iterable:
assert set(b1).intersection(set(b2)) == set()
def test_boundaries_triangles(self):
mesh_a = distinct_triangles(5)
mesh_b = random_triangles(5)
boundary_a = stop.mesh_boundary(mesh_a)
boundary_b = stop.mesh_boundary(mesh_b)
iterable_a = list(itertools.combinations(boundary_a, 2))
iterable_b = list(itertools.combinations(boundary_b, 2))
# Correctness
assert (len(boundary_a) == 5)
assert (len(boundary_b) == 5)
# Uniqueness
for b1, b2 in iterable_a:
assert set(b1).intersection(set(b2)) == set()
for b1, b2 in iterable_b:
assert set(b1).intersection(set(b2)) == set()
def test_angle_norm_boundary(self):
# GET A DISK IN 3D
radius = 2
mesh_a = self.disk_radius_2
coords3D = mesh_a.vertices
# COMPUTE BOUNDARY
boundary = stop.mesh_boundary(mesh_a)
# Correctness
assert (len(boundary) == 1)
# COMPUTE ANGLE AND NORM VARIATIONS
angles, norms = stop.boundary_angles(boundary[0], coords3D)
angles = abs(180 - angles)
sum_angles, sum_norms = sum(angles), sum(norms)
# Computation on three arbitrary linked vertices
three_angles, three_norms = stop.boundary_angles([0, 1, 2], coords3D)
three_angles = abs(180 - three_angles)
perimeter = radius * np.pi * 2
precision_A = .001
precision_B = .001
# Coherence of the angle sum and norm sum
assert (np.isclose(sum_angles, 360, precision_A))
assert (np.isclose(sum_norms, perimeter, precision_B))
# Coherence of a partial angle and a partial norm variation
assert (np.isclose(three_angles[1], 360 / len(coords3D), precision_A))
assert (np.isclose(three_norms[1], perimeter / len(coords3D),
precision_B))
def test_boundaries_intersection_copy(self):
mesh_a = self.cutSphere_A
boundary = stop.mesh_boundary(mesh_a)
b1 = boundary[0]
b2 = boundary[1]
b2_save = b2.copy()
concat = b1 + b2
inter_1 = stop.boundaries_intersection([concat, b2])
inter_1 = inter_1[0][2]
# Non modification
assert (b2 == b2_save)
# Type
assert (isinstance(inter_1, list))
# Correctness
assert (set(inter_1) == set(b2))
def test_close_mesh(self):
mesh_a = self.cutSphere_C.copy()
boundary_prev = stop.mesh_boundary(mesh_a)
# Correctness
assert (len(boundary_prev) == 2)
mesh_a_closed, nvertices_added = stop.close_mesh(mesh_a)
# Coherence
assert (len(mesh_a_closed.vertices) == len(mesh_a.vertices) +
nvertices_added)
boundary_closed = stop.mesh_boundary(mesh_a_closed)
# Mesh is now closed
assert (len(boundary_closed) == 0)
# Non modification of the vertices which are not on the boundaries
sbase = (set(range(len(mesh_a.vertices))))
sbound1 = (set(boundary_prev[0]))
sbound2 = (set(boundary_prev[1]))
sbound = sbound1.union(sbound2)
snot_bound = (sbase.difference(sbound))
for i in snot_bound:
assert (mesh_a.vertices[i] == mesh_a_closed.vertices[i]).all()
def test_remove_mesh_boundary_faces(self):
mesh_a = self.cutSphere_A.copy()
mesh_a_save = mesh_a.copy()
boundary = stop.mesh_boundary(mesh_a)
mesh_processed = stop.remove_mesh_boundary_faces(mesh_a,
face_vertex_number=1)
# Non modification
assert (mesh_a.vertices == mesh_a_save.vertices).all()
# Correctness
# check that when removing all faces with any vertex on the boundary,
# all the boundary vertices are removed from the mesh
print(len(boundary[0]))
print(len(mesh_a.vertices))
print(len(mesh_processed.vertices))
assert (len(mesh_processed.vertices) == len(mesh_a.vertices) -
len(boundary[0]))
import numpy as np
import slam.io as sio
import os
if __name__ == '__main__':
list_str_meshes = [
'sub_mesh0', 'sub_mesh1', 'sub_mesh2', 'sub_mesh3', 'sub_mesh4'
]
data_path = "/hpc/meca/users/souani.a/curvature/"
list_surfaces, list_densities, list_nbr_vertex = list(), list(), list()
os.chdir(data_path)
for str_mesh in list_str_meshes:
mesh = sio.load_mesh(str_mesh + '.gii')
# mesh.show()
print(str_mesh, ' surface : ', mesh.area_faces.sum())
list_surfaces.append(mesh.area_faces.sum())
print(str_mesh, ' vertices nbr : ', np.shape(mesh.vertices)[0])
list_nbr_vertex.append(np.shape(mesh.vertices)[0])
print(str_mesh, ' density : ',
np.shape(mesh.vertices)[0] / mesh.area_faces.sum())
list_densities.append(
np.shape(mesh.vertices)[0] / mesh.area_faces.sum())
# Comparaison d'histogrammes
import slam.plot as splt
import slam.io as sio
import slam.remeshing as srem
###############################################################################
# source object files
source_mesh_file = 'data/example_mesh.gii'
source_texture_file = 'data/example_texture.gii'
source_spherical_mesh_file = 'data/example_mesh_spherical.gii'
###############################################################################
# target object files
target_mesh_file = 'data/example_mesh_2.gii'
target_spherical_mesh_file = 'data/example_mesh_2_spherical.gii'
source_mesh = sio.load_mesh(source_mesh_file)
source_tex = sio.load_texture(source_texture_file)
source_spherical_mesh = sio.load_mesh(source_spherical_mesh_file)
target_mesh = sio.load_mesh(target_mesh_file)
target_spherical_mesh = sio.load_mesh(target_spherical_mesh_file)
interpolated_tex_values = \
srem.spherical_interpolation_nearest_neigbhor(source_spherical_mesh,
target_spherical_mesh,
source_tex.darray[0])
###############################################################################
# plot
visb_sc = splt.visbrain_plot(mesh=source_mesh, tex=source_tex.darray[0],
caption='source with curvature',
cblabel='curvature')
plt.xlabel(' Noise')
plt.ylabel(' MSE ')
s = 0
for curv_type in curv_types:
err= list()
for sigma in Sigma:
cc = 'hex_quad_k1_' + str(Ks[i][0]) + '_k2_' + str(Ks[i][1]) + '_' + str(nstep) + '_noise_' + str(
int(sigma * 1000))
# os.chdir(r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Noise")
# os.mkdir(cc )
# os.chdir(r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Noise/"+cc)
# sio.write_mesh(mesh, cc+'.gii')
list_exampels.append(cc)
mesh_file = 'hex_quad_k1_' + str(Ks[i][0]) + '_k2_' + str(Ks[i][1]) + '_' + str(nstep) + '.gii'
os.chdir("/hpc/meca/users/souani.a/curvature/Compute_curvatures/Curvatures/0Noise")
mesh = sio.load_mesh(mesh_file)
xx = sps.quadric_curv_mean(Ks[i][0], Ks[i][1])(mesh.vertices[:, 0], mesh.vertices[:, 1])
xx_m = np.zeros((nstep ** 2, 1))
texture_file = cc + '_curv_mean_' + curv_type + '.gii'
# mesh.apply_transform(mesh.principal_inertia_transform)
os.chdir(
"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Curvatures/Noise/" + cc)
tex = sio.load_texture(texture_file)
err.append(mean_squared_error(xx, tex.darray))
plt.plot( Sigma,err, color=couleur[s] , marker= 'o', label=str(curv_types[s]))
print("Done for "+ cc[:-5]+ ' for '+ curv_type)
# plt.text(Sigma[0], err[0], curv_types[s], fontsize=9)
s += 1
# plt.show()
os.chdir(r"/hpc/meca/users/souani.a/curvature/Compute_curvatures/Curvatures/Figures/Mean_squarred_error_function_of_noise")
"marching_cube_level": self.input_spec.level,
"smoothing_iterations": self.input_spec.smoothing_iter,
"smoothing_dt": self.input_spec.smoothing_dt
=======
"volume_file": self.inputs.image_file,
"marching_cube_level": str(self.inputs.level),
"smoothing_iterations": str(self.inputs.smoothing_iter),
"smoothing_dt": str(self.inputs.smoothing_dt)
>>>>>>> 02924efd7fd9ff93e10f0e0030a43b878812e288
})
ng.write(gii, gii_file)
# Optional: Smooth the marching cube output with SLAM
<<<<<<< HEAD
if self.input_spec.smoothing_iter > 0:
mesh = sdg.laplacian_mesh_smoothing(
sio.load_mesh(gii_file),
nb_iter=self.input_spec.smoothing_iter,
dt=self.input_spec.smoothing_dt)
sio.write_mesh(mesh, gii_file)
=======
if self.inputs.smoothing_iter > 0:
mesh = sdg.laplacian_mesh_smoothing(
sio.load_mesh(gii_file),
nb_iter=self.inputs.smoothing_iter,
dt=self.inputs.smoothing_dt)
sio.write_mesh(mesh, gii_file)
return runtime
>>>>>>> 02924efd7fd9ff93e10f0e0030a43b878812e288
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 2
###############################################################################
# Importation of slam modules
import slam.distortion as sdst
import slam.differential_geometry as sdg
import slam.plot as splt
import slam.io as sio
import numpy as np
###############################################################################
# Loading an example mesh and a smoothed copy of it
mesh = sio.load_mesh('data/example_mesh.gii')
mesh_s = sdg.laplacian_mesh_smoothing(mesh, nb_iter=50, dt=0.1)
################################################################################
# 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=visb_sc)
visb_sc.preview()
###############################################################################
#--------------------------------------------------------------------
# quadrilateral to triangular mesh Converter
#
result_path = '../Data/MESH_DATA/trimesh_gifti_all/'
path = '../Data/MESH_DATA/mesh_gifti_all/TV_Dyn3D_5SL/'
basename = 'output_TV_Dyn3D_5SL_3dRecbyReg'
mesh_set = glob.glob(path+basename+'*.gii')
mesh_set.sort()
print(len(mesh_set))
for t in range(0,len(mesh_set)):
print(t)
prefix = mesh_set[t].split('/')[-1].split('.')[0]
print(prefix)
gifti_file = result_path+prefix+'.gii'
print(gifti_file)
mesh = sio.load_mesh(mesh_set[t])
#mesh = trimesh.load_mesh(mesh_set[t])
sio.write_mesh(mesh, gifti_file)
