def test_basic(self):
mesh_a = self.sphere_r1.copy()
mesh_a_save = self.sphere_r1.copy()
mesh_b = self.sphere_r2.copy()
mesh_b_save = self.sphere_r2.copy()
tmp = sdst.angle_difference(mesh_a, mesh_b)
assert (mesh_a.vertices == mesh_a_save.vertices).all()
assert (mesh_a.faces == mesh_a_save.faces).all()
assert (mesh_b.vertices == mesh_b_save.vertices).all()
assert (mesh_b.faces == mesh_b_save.faces).all()
tmp = sdst.area_difference(mesh_a, mesh_b)
assert (mesh_a.vertices == mesh_a_save.vertices).all()
assert (mesh_a.faces == mesh_a_save.faces).all()
assert (mesh_b.vertices == mesh_b_save.vertices).all()
assert (mesh_b.faces == mesh_b_save.faces).all()
tmp = sdst.edge_length_difference(mesh_a, mesh_b)
assert (mesh_a.vertices == mesh_a_save.vertices).all()
assert (mesh_a.faces == mesh_a_save.faces).all()
assert (mesh_b.vertices == mesh_b_save.vertices).all()
assert (mesh_b.faces == mesh_b_save.faces).all()
def test_correctness_area(self):
precisionA = .001
n_sub = 5
mesh_a = trimesh.creation.icosphere(subdivisions=n_sub, radius=1)
mesh_b = trimesh.creation.icosphere(subdivisions=n_sub, radius=2)
area_diff_estim = sdst.area_difference(mesh_a, mesh_b)
area_diff_estim = sum(abs(area_diff_estim))
area_diff_analytical = 4 * np.pi * 2 * 2 - 4 * np.pi * 1 * 1
assert (np.isclose(area_diff_estim, area_diff_analytical, precisionA))
################################################################################
# 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()
###############################################################################
# Computation of the angle difference between each faces of mesh and mesh_s
angle_diff = sdst.angle_difference(mesh, mesh_s)
angle_diff
###############################################################################
#
face_angle_dist = np.sum(np.abs(angle_diff), 1)
face_angle_dist
###############################################################################
# Computation of the area difference between each faces of mesh and mesh_s
area_diff = sdst.area_difference(mesh, mesh_s)
area_diff
###############################################################################
# Computation of the length difference between each edges of mesh and mesh_s
edge_diff = sdst.edge_length_difference(mesh, mesh_s)
edge_diff
def spherical_mapping(mesh,
mapping_type='laplacian_eigenvectors',
conformal_w=1,
authalic_w=1,
dt=0.01,
nb_it=10):
"""
ADD REF
:param mesh:
:param mapping_type:
:param conformal_w:
:param authalic_w:
:param dt:
:param nb_it:
:return:
"""
# computing spherical mapping based on laplacian eigenvectors
sph_vert = sdg.mesh_laplacian_eigenvectors(mesh, nb_vectors=3)
norm_sph_vert = np.sqrt(np.sum(sph_vert * sph_vert, 1))
sphere_vertices = sph_vert / np.tile(norm_sph_vert, (3, 1)).T
if mapping_type == 'laplacian_eigenvectors':
return trimesh.Trimesh(faces=mesh.faces,
vertices=sphere_vertices,
metadata=mesh.metadata,
process=False)
if mapping_type == 'conformal':
"""
Desbrun, M., Meyer, M., & Alliez, P. (2002).
Intrinsic parameterizations of surface meshes.
Computer Graphics Forum, 21(3), 209–218.
https://doi.org/10.1111/1467-8659.00580
"""
# options.symmetrize = 0;
# options.normalize = 1;
L, B = sdg.compute_mesh_laplacian(mesh, lap_type='conformal')
if mapping_type == 'authalic':
# options.symmetrize = 0;
# options.normalize = 1;
L, B = sdg.compute_mesh_laplacian(mesh, lap_type='authalic')
if mapping_type == 'combined':
# options.symmetrize = 0;
# options.normalize = 1;
Lconf, Bconf = sdg.compute_mesh_laplacian(mesh, lap_type='conformal')
Laut, Baut = sdg.compute_mesh_laplacian(mesh, lap_type='authalic')
L = conformal_w * Lconf + authalic_w * Laut
# continue the spherical mappig by minimizig the energy
evol = list()
for it in range(nb_it):
sphere_vertices = sphere_vertices - dt * L.dot(sphere_vertices)
# sphere_vertices * L
norm_sph_vert = np.sqrt(np.sum(sphere_vertices * sphere_vertices, 1))
sphere_vertices = sphere_vertices / np.tile(norm_sph_vert, (3, 1)).T
if it % 10 == 0:
sph = trimesh.Trimesh(faces=mesh.faces,
vertices=sphere_vertices,
process=False)
angle_diff = sdst.angle_difference(sph, mesh)
area_diff = sdst.area_difference(sph, mesh)
edge_diff = sdst.edge_length_difference(sph, mesh)
evol.append([
np.sum(np.abs(angle_diff.flatten())),
np.sum(np.abs(area_diff.flatten())),
np.sum(np.abs(edge_diff.flatten()))
])
# ind = 0;
# for it=1:nb_it
# % it = 1;
# % while sum(I(: ) < 0)
# % it = it + 1;
# % vertex1 = vertex1 * L;
# vertex1 = vertex1 - eta * vertex1 * L;
# vertex1 = vertex1. / repmat(sqrt(sum(vertex1. ^ 2, 1)), [3 1]);
# if mod(it, 100) == 0
# ind = ind + 1;
# NFV.vertices = vertex1
# ';
# [nb_inward, inward] = reversed_faces(NFV, 'sphere');
# bil_inward(ind) = nb_inward;
# w = zeros(1, m);
# E = zeros(1, m);
# for i=1:3
# i1 = mod(i, 3) + 1;
# % directed
# edge
# u = vertex1(:, faces(i,:)) - vertex1(:, faces(i1,:));
# % norm
# squared
# u = sum(u. ^ 2);
# % weights
# between
# the
# vertices
# for j=1:m
# w(j) = L(faces(i, j), faces(i1, j));
# end
# % w = W(faces(i,:) + (faces(i1,:) - 1)*n);
# E = E + w. * u;
#
#
# end
#
# bil_E(ind) = sum(E);
# if ind > 2
# disp(['Ratio of inverted triangles:' num2str(100 * nb_inward / m, 3)
# '% energy decrease:' num2str(bil_E(end - 1) - bil_E(end), 3)]);
# end
# end
# end
return trimesh.Trimesh(faces=mesh.faces,
vertices=sphere_vertices,
metadata=mesh.metadata,
process=False), evol
all_area_diff = list()
plane_proj_mesh = sphmap.stereo_projection(sphere_mesh, invert=False)
for i in range(8):
t += step
all_steps.append(t)
a = complex(t, 0)
plan_complex_transfo = sphmap.mobius_transformation(
a, b, c, d, plane_proj_mesh)
sphere_transformed_mesh = sphmap.inverse_stereo_projection(
plan_complex_transfo, invert=False)
all_spheres.append(sphere_transformed_mesh)
angle_diff = sdst.angle_difference(sphere_mesh,
sphere_transformed_mesh)
all_angle_diff.append(angle_diff)
area_diff = sdst.area_difference(sphere_mesh, sphere_transformed_mesh)
all_area_diff.append(area_diff)
poly_angles = meshPolygonAngles(sphere_transformed_mesh.vertices,
sphere_transformed_mesh.faces)
print(np.max(sphere_transformed_mesh.face_angles - poly_angles))
poly_areas = meshPolygonArea(sphere_transformed_mesh.vertices,
sphere_transformed_mesh.faces)
print(np.max(sphere_transformed_mesh.area_faces - poly_areas))
splt.pyglet_plot(sphere_transformed_mesh,
z_coord_texture,
caption="moebius transformed sphere")
int_area = [np.sum(np.abs(v)) for v in all_area_diff]
int_angle = [np.sum(np.abs(angle_diff).flatten()) for v in all_angle_diff]
fig, ax = plt.subplots(1, 1)
ax.set_xlabel('moebius scaling parameter')
pp = pp / np.vstack((nopp, np.vstack((nopp, nopp)))).transpose()
qq = qq / np.vstack((noqq, np.vstack((noqq, noqq)))).transpose()
angles_out[:, i] = np.arccos(np.sum(pp * qq, 1))
return angles_out
if __name__ == '__main__':
mesh = sio.load_mesh('data/example_mesh.gii')
sph, evol = smap.spherical_mapping(mesh,
mapping_type='conformal',
dt=0.01,
nb_it=3000)
sph.show()
angle_diff = sdst.angle_difference(sph, mesh)
area_diff = sdst.area_difference(sph, mesh)
edge_diff = sdst.edge_length_difference(sph, mesh)
aevol = np.array(evol)
f, ax = plt.subplots(1, 3)
ax[0].set_title('angles')
# ax[0].hist(angle_diff.flatten())
ax[0].plot(aevol[:, 0])
ax[0].grid(True)
ax[1].set_title('areas')
# ax[1].hist(area_diff.flatten())
ax[1].plot(aevol[:, 1])
ax[1].grid(True)
ax[2].set_title('edges')
# ax[2].hist(edge_diff.flatten())
ax[2].plot(aevol[:, 2])
def spherical_mapping(mesh,
mapping_type='laplacian_eigenvectors',
conformal_w=1,
authalic_w=1,
dt=0.01,
nb_it=10):
"""
Computes the mapping between the input mesh and a sphere centered on 0 and of radius=1
Several methods are implemented:
-for laplacian eigenvectors, see:
J. Lefèvre and G. Auzias, "Spherical Parameterization for Genus Zero Surfaces Using
Laplace-Beltrami Eigenfunctions," in 2nd Conference on Geometric Science of
Information, GSI, 2015, 121–29, https://doi.org/10.1007/978-3-319-25040-3_14.
-for conformal mapping, see:
Desbrun, M., Meyer, M., & Alliez, P., "Intrinsic parameterizations of surface meshes",
Computer Graphics Forum, 21(3), 2002, 209–218. https://doi.org/10.1111/1467-8659.00580
-for authalic and combined methods, see:
Rachel a Yotter, Paul M. Thompson, and Christian Gaser, “Algorithms to Improve the
Reparameterization of Spherical Mappings of Brain Surface Meshes.,” Journal of
Neuroimaging 21, no. 2 (April 2011): e134-47, https://doi.org/10.1111/j.1552-6569.2010.00484.x.
or
Ilja Friedel, Peter Schröder, and Mathieu Desbrun, “Unconstrained Spherical Parameterization”,
Journal of Graphics, GPU, and Game Tools 12, no. 1 (2007): 17–26.
:param mesh: Trimesh object, input mesh to be mapped onto a sphere
:param mapping_type: string, type of mapping method, possible options are:
'laplacian_eigenvectors', 'conformal', 'authalic' or 'combined'
:param conformal_w: Float, weight of the conformal constraint for the 'combined' method
:param authalic_w: Float, weight of the authalic constraint for the 'combined' method
:param dt: Float, discrtization step
:param nb_it: Int, number of iterations
:return: Trimesh object: the spherical representation of the input mesh, having the same
adjacency (faces, edges, vertex indexing) as the input mesh.
"""
# computing spherical mapping based on laplacian eigenvectors
sph_vert = sdg.mesh_laplacian_eigenvectors(mesh, nb_vectors=3)
norm_sph_vert = np.sqrt(np.sum(sph_vert * sph_vert, 1))
sphere_vertices = sph_vert / np.tile(norm_sph_vert, (3, 1)).T
if mapping_type == 'laplacian_eigenvectors':
return trimesh.Trimesh(faces=mesh.faces,
vertices=sphere_vertices,
metadata=mesh.metadata,
process=False)
if mapping_type == 'conformal':
L, B = sdg.compute_mesh_laplacian(mesh, lap_type='conformal')
if mapping_type == 'authalic':
L, B = sdg.compute_mesh_laplacian(mesh, lap_type='authalic')
if mapping_type == 'combined':
Lconf, Bconf = sdg.compute_mesh_laplacian(mesh, lap_type='conformal')
Laut, Baut = sdg.compute_mesh_laplacian(mesh, lap_type='authalic')
L = conformal_w * Lconf + authalic_w * Laut
# continue the spherical mappig by minimizig the energy
evol = list()
for it in range(nb_it):
sphere_vertices = sphere_vertices - dt * L.dot(sphere_vertices)
# sphere_vertices * L
norm_sph_vert = np.sqrt(np.sum(sphere_vertices * sphere_vertices, 1))
sphere_vertices = sphere_vertices / np.tile(norm_sph_vert, (3, 1)).T
if it % 10 == 0:
sph = trimesh.Trimesh(faces=mesh.faces,
vertices=sphere_vertices,
process=False)
angle_diff = sdst.angle_difference(sph, mesh)
area_diff = sdst.area_difference(sph, mesh)
edge_diff = sdst.edge_length_difference(sph, mesh)
evol.append([
np.sum(np.abs(angle_diff.flatten())),
np.sum(np.abs(area_diff.flatten())),
np.sum(np.abs(edge_diff.flatten()))
])
return trimesh.Trimesh(faces=mesh.faces,
vertices=sphere_vertices,
metadata=mesh.metadata,
process=False), evol
visb_sc.add_to_subplot(l_obj)
visb_sc.preview()
###############################################################################
# Mapping onto a planar disk
disk_mesh = smap.disk_conformal_mapping(open_mesh)
# Visualization
visb_sc2 = splt.visbrain_plot(mesh=disk_mesh, caption='disk mesh')
for bound in open_mesh_boundary:
points = disk_mesh.vertices[bound]
s_rad = SourceObj('rad',
points,
color='red',
symbol='square',
radius_min=10)
visb_sc2.add_to_subplot(s_rad)
lines = Line(pos=disk_mesh.vertices[bound], width=10, color='b')
# wrap the vispy object using visbrain
l_obj = VispyObj('line', lines)
visb_sc2.add_to_subplot(l_obj)
visb_sc2.preview()
###############################################################################
# Compute distortion measures between original and planar representations
angle_diff = sdst.angle_difference(disk_mesh, open_mesh)
area_diff = sdst.area_difference(disk_mesh, open_mesh)
edge_diff = sdst.edge_length_difference(disk_mesh, open_mesh)
print(np.mean(angle_diff))
print(np.mean(area_diff))
print(np.mean(edge_diff))