def get_delaunay_triangulation(verts_in):
pt_list = [Site(pt[0], pt[1]) for pt in verts_in]
res = computeDelaunayTriangulation(pt_list)
polys_in = [tri for tri in res if -1 not in tri]
#all faces hase normals -Z, should be reversed
polys_in = [pol[::-1] for pol in polys_in]
edges_in = pols_edges([polys_in], unique_edges=True)[0]
return edges_in, polys_in
def delaunay_triangulatrion(samples_u, samples_v, us_list, vs_list, u_coeff,
v_coeff, epsilon):
#if delaunay_2d_cdt is None:
# Pure-python implementation
points_uv = [
Site(u * u_coeff, v * v_coeff) for u, v in zip(us_list, vs_list)
]
faces = computeDelaunayTriangulation(points_uv)
return faces
def process(self):
points_in = []
if not ('Polygons' in self.outputs and self.outputs['Polygons'].is_linked):
return
if 'Vertices' in self.inputs and self.inputs['Vertices'].is_linked:
points_in = SvGetSocketAnyType(self, self.inputs['Vertices'])
tris_out = []
for obj in points_in:
pt_list = [Site(pt[0], pt[1]) for pt in obj]
res = computeDelaunayTriangulation(pt_list)
tris_out.append([tri for tri in res if -1 not in tri])
if 'Polygons' in self.outputs and self.outputs['Polygons'].is_linked:
SvSetSocketAnyType(self, 'Polygons', tris_out)
def process(self):
if not self.inputs['Vertices'].is_linked:
return
if not self.outputs['Polygons'].is_linked:
return
tris_out = []
points_in = []
points_in = self.inputs['Vertices'].sv_get()
for obj in points_in:
pt_list = [Site(pt[0], pt[1]) for pt in obj]
res = computeDelaunayTriangulation(pt_list)
tris_out.append([tri for tri in res if -1 not in tri])
self.outputs['Polygons'].sv_set(tris_out)
def process(self):
points_in = []
if not ('Polygons' in self.outputs
and self.outputs['Polygons'].is_linked):
return
if 'Vertices' in self.inputs and self.inputs['Vertices'].is_linked:
points_in = SvGetSocketAnyType(self, self.inputs['Vertices'])
tris_out = []
for obj in points_in:
pt_list = [Site(pt[0], pt[1]) for pt in obj]
res = computeDelaunayTriangulation(pt_list)
tris_out.append([tri for tri in res if -1 not in tri])
if 'Polygons' in self.outputs and self.outputs['Polygons'].is_linked:
SvSetSocketAnyType(self, 'Polygons', tris_out)
def process(self):
if not self.inputs['Vertices'].is_linked:
return
if not self.outputs['Polygons'].is_linked:
return
tris_out = []
points_in = []
points_in = self.inputs['Vertices'].sv_get()
for obj in points_in:
pt_list = [Site(pt[0], pt[1]) for pt in obj]
res = computeDelaunayTriangulation(pt_list)
tris_out.append([tri for tri in res if -1 not in tri])
self.outputs['Polygons'].sv_set(tris_out)
def delaunay_triangulatrion(samples_u, samples_v, us_list, vs_list, u_coeff,
v_coeff, epsilon):
if delaunay_2d_cdt is None:
# Pure-python implementation
points_uv = [
Site(u * u_coeff, v * v_coeff) for u, v in zip(us_list, vs_list)
]
faces = computeDelaunayTriangulation(points_uv)
return faces
else:
points_scaled = [(u * u_coeff, v * v_coeff)
for u, v in zip(us_list, vs_list)]
INNER = 1
# delaunay_2d_cdt function wont' work if we do not provide neither edges nor faces.
# So let's just construct the outer faces of the rectangular grid
# (indices in `edges' depend on the fact that in `adaptive_subdivide` we
# add randomly generated points to the end of us_list/vs_list).
edges = make_outer_edges(samples_v, samples_u)
vert_coords, edges, faces, orig_verts, orig_edges, orig_faces = delaunay_2d_cdt(
points_scaled, edges, [], INNER, epsilon)
return faces
