def test_draco_two_layers():
n_points = 12
img = sphere_tissue_image(size=100, n_points=n_points)
draco = DracoMesh(img)
draco.construct_adjacency_complex()
triangular = ['star', 'remeshed', 'projected', 'regular', 'flat']
image_dual_topomesh = draco.dual_reconstruction(
reconstruction_triangulation=triangular, adjacency_complex_degree=3)
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_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)
world['adjacency_complex_vertices']['display_colorbar'] = False world['adjacency_complex_vertices']['polydata_colormap'] = load_colormaps()['glasbey'] world['adjacency_complex_vertices']['point_radius'] = 1.5 draco.adjacency_complex_optimization(n_iterations=3) world['adjacency_complex']['coef_3'] = 0.9 world['adjacency_complex']['display_3'] = True world['adjacency_complex_cells']['display_colorbar'] = False world['adjacency_complex_cells']['polydata_colormap'] = load_colormaps()['grey'] world['adjacency_complex_cells']['intensity_range'] = (-1,0) world['adjacency_complex_cells']['preserve_faces'] = True world['adjacency_complex_cells']['x_slice'] = (0,90) triangular = ['star','remeshed','projected','flat'] image_dual_topomesh = draco.dual_reconstruction(reconstruction_triangulation = triangular, adjacency_complex_degree=3, maximal_edge_length=5.1) from openalea.cellcomplex.property_topomesh.property_topomesh_analysis import compute_topomesh_property, compute_topomesh_vertex_property_from_faces compute_topomesh_property(image_dual_topomesh,'barycenter',2) compute_topomesh_property(image_dual_topomesh,'normal',2,normal_method='orientation') compute_topomesh_vertex_property_from_faces(image_dual_topomesh,'normal',adjacency_sigma=2,neighborhood=5) compute_topomesh_property(image_dual_topomesh,'mean_curvature',2) compute_topomesh_vertex_property_from_faces(image_dual_topomesh,'mean_curvature',adjacency_sigma=2,neighborhood=5) compute_topomesh_vertex_property_from_faces(image_dual_topomesh,'gaussian_curvature',adjacency_sigma=2,neighborhood=5) world.add(image_dual_topomesh ,'dual_reconstruction') world['dual_reconstruction']['coef_3'] = 0.99
