def test_draco():
n_points = 12
img = sphere_tissue_image(size=100, n_points=n_points)
draco = DracoMesh(img)
assert draco.point_topomesh.nb_wisps(0) == n_points + 1
draco.delaunay_adjacency_complex(surface_cleaning_criteria=[])
image_tetrahedra = np.sort(draco.image_cell_vertex.keys())
image_tetrahedra = image_tetrahedra[image_tetrahedra[:, 0] != 1]
draco_tetrahedra = np.sort([
list(draco.triangulation_topomesh.borders(3, t, 3))
for t in draco.triangulation_topomesh.wisps(3)
])
delaunay_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra)
draco.adjacency_complex_optimization(n_iterations=2)
assert draco.triangulation_topomesh.nb_region_neighbors(0, 2) == n_points
image_tetrahedra = np.sort(draco.image_cell_vertex.keys())
image_tetrahedra = image_tetrahedra[image_tetrahedra[:, 0] != 1]
draco_tetrahedra = np.sort([
list(draco.triangulation_topomesh.borders(3, t, 3))
for t in draco.triangulation_topomesh.wisps(3)
])
draco_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra)
# print delaunay_consistency,' -> ',draco_consistency
assert draco_consistency == 1 or (draco_consistency >= 0.9 and
draco_consistency > delaunay_consistency)
triangular = ['star', 'remeshed', 'projected', 'regular', 'flat']
image_dual_topomesh = draco.dual_reconstruction(
reconstruction_triangulation=triangular, adjacency_complex_degree=3)
image_volumes = array_dict(
nd.sum(np.ones_like(img), img, index=np.unique(img)[1:]) *
np.prod(img.voxelsize),
np.unique(img)[1:])
compute_topomesh_property(image_dual_topomesh, 'volume', 3)
draco_volumes = image_dual_topomesh.wisp_property('volume', 3)
for c in image_dual_topomesh.wisps(3):
assert np.isclose(image_volumes[c], draco_volumes[c], 0.33)
def test_tetrahedrization_optimization():
topomesh = octahedron_tetrahedra()
compute_tetrahedrization_topological_properties(topomesh)
compute_tetrahedrization_geometrical_properties(topomesh)
tetrahedrization_topomesh_add_exterior(topomesh)
target_tetrahedra = []
target_tetrahedra += [(2, 3, 5, 6)]
target_tetrahedra += [(3, 4, 5, 6)]
target_tetrahedra += [(2, 3, 5, 7)]
target_tetrahedra += [(3, 4, 5, 7)]
target_tetrahedra += [(1, 2, 3, 6)]
target_tetrahedra += [(1, 3, 4, 6)]
target_tetrahedra += [(1, 4, 5, 6)]
target_tetrahedra += [(1, 2, 5, 6)]
target_tetrahedra += [(1, 2, 3, 7)]
target_tetrahedra += [(1, 3, 4, 7)]
target_tetrahedra += [(1, 4, 5, 7)]
target_tetrahedra += [(1, 2, 5, 7)]
target_tetrahedra = dict(zip(target_tetrahedra, np.zeros((4, 3))))
kwargs = {}
kwargs['simulated_annealing_initial_temperature'] = 0.1
kwargs['simulated_annealing_lambda'] = 0.9
kwargs['simulated_annealing_minimal_temperature'] = 0.09
kwargs['n_iterations'] = 1
topomesh = tetrahedrization_topomesh_topological_optimization(
topomesh,
image_cell_vertex=target_tetrahedra,
omega_energies=dict(image=1.0),
**kwargs)
tetrahedrization_topomesh_remove_exterior(topomesh)
assert topomesh.nb_wisps(3) == 4
for t in topomesh.wisps(3):
print t, list(topomesh.borders(3, t, 3))
optimized_tetras = np.sort(
[list(topomesh.borders(3, t, 3)) for t in topomesh.wisps(3)])
target_tetras = np.sort(target_tetrahedra.keys())
target_tetras = target_tetras[target_tetras[:, 0] != 1]
assert jaccard_index(optimized_tetras, target_tetras) == 1.0
def test_draco():
n_points = 12
img = sphere_tissue_image(size=100,n_points=n_points)
draco = DracoMesh(img)
assert draco.point_topomesh.nb_wisps(0) == n_points+1
draco.delaunay_adjacency_complex(surface_cleaning_criteria = [])
image_tetrahedra = np.sort(draco.image_cell_vertex.keys())
image_tetrahedra = image_tetrahedra[image_tetrahedra[:,0] != 1]
draco_tetrahedra = np.sort([list(draco.triangulation_topomesh.borders(3,t,3)) for t in draco.triangulation_topomesh.wisps(3)])
delaunay_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra)
draco.adjacency_complex_optimization(n_iterations=2)
assert draco.triangulation_topomesh.nb_region_neighbors(0,2) == n_points
image_tetrahedra = np.sort(draco.image_cell_vertex.keys())
image_tetrahedra = image_tetrahedra[image_tetrahedra[:,0] != 1]
draco_tetrahedra = np.sort([list(draco.triangulation_topomesh.borders(3,t,3)) for t in draco.triangulation_topomesh.wisps(3)])
draco_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra)
# print delaunay_consistency,' -> ',draco_consistency
assert draco_consistency == 1 or (draco_consistency >= 0.9 and draco_consistency > delaunay_consistency)
triangular = ['star','remeshed','projected','regular','flat']
image_dual_topomesh = draco.dual_reconstruction(reconstruction_triangulation = triangular, adjacency_complex_degree=3)
image_volumes = array_dict(nd.sum(np.ones_like(img),img,index=np.unique(img)[1:])*np.prod(img.resolution),np.unique(img)[1:])
compute_topomesh_property(image_dual_topomesh,'volume',3)
draco_volumes = image_dual_topomesh.wisp_property('volume',3)
for c in image_dual_topomesh.wisps(3):
assert np.isclose(image_volumes[c],draco_volumes[c],0.33)
def test_tetrahedrization_optimization():
topomesh = octahedron_tetrahedra()
compute_tetrahedrization_topological_properties(topomesh)
compute_tetrahedrization_geometrical_properties(topomesh)
tetrahedrization_topomesh_add_exterior(topomesh)
target_tetrahedra = []
target_tetrahedra += [(2,3,5,6)]
target_tetrahedra += [(3,4,5,6)]
target_tetrahedra += [(2,3,5,7)]
target_tetrahedra += [(3,4,5,7)]
target_tetrahedra += [(1,2,3,6)]
target_tetrahedra += [(1,3,4,6)]
target_tetrahedra += [(1,4,5,6)]
target_tetrahedra += [(1,2,5,6)]
target_tetrahedra += [(1,2,3,7)]
target_tetrahedra += [(1,3,4,7)]
target_tetrahedra += [(1,4,5,7)]
target_tetrahedra += [(1,2,5,7)]
target_tetrahedra = dict(zip(target_tetrahedra,np.zeros((4,3))))
kwargs = {}
kwargs['simulated_annealing_initial_temperature'] = 0.1
kwargs['simulated_annealing_lambda'] = 0.9
kwargs['simulated_annealing_minimal_temperature'] = 0.09
kwargs['n_iterations'] = 1
topomesh = tetrahedrization_topomesh_topological_optimization(topomesh,image_cell_vertex=target_tetrahedra,omega_energies=dict(image=1.0),**kwargs)
tetrahedrization_topomesh_remove_exterior(topomesh)
assert topomesh.nb_wisps(3) == 4
for t in topomesh.wisps(3):
print t, list(topomesh.borders(3,t,3))
optimized_tetras = np.sort([list(topomesh.borders(3,t,3)) for t in topomesh.wisps(3)])
target_tetras = np.sort(target_tetrahedra.keys())
target_tetras = target_tetras[target_tetras[:,0] != 1]
assert jaccard_index(optimized_tetras,target_tetras) == 1.0
