def subdivide_catmull_2d(vertices):
newNodes = []
for i in range(len(vertices)):
a = vertices[i]
newNodes.append(Vertex(a.x,a.y,a.z))
b = vertices[(i + 1) % len(vertices)]
center = utils_vertex.vertex_add(a, b)
newNodes.append(utils_vertex.vertex_scale(center,0.5))
newNodes2 = []
for i in range(len(newNodes)):
iPrev = i - 1
if iPrev < 0:
iPrev = len(newNodes) - 1
iNext = i + 1
if iNext >= len(newNodes):
iNext = 0
a = newNodes[iPrev]
b = newNodes[i]
c = newNodes[iNext]
average = Vertex()
# [average.add(v) for v in [a,b,b,c]]
average.add(a)
average.add(b)
average.add(b)
average.add(c)
average.divide(4.0)
# average = utils_vertex.vertex_add(average,a)
# average = utils_vertex.vertex_add(average,b)
# average = utils_vertex.vertex_add(average,b)
# average = utils_vertex.vertex_add(average,c)
# average /= 4
# average = utils_vertex.vertex_divide(average,4.0)
newNodes2.append(average)
return newNodes2
def mesh_smooth_laplacian(mesh, factor=0.3):
smoothed = mesh.copy()
#smoothed.update_topology()
for i, v in enumerate(mesh.vertices):
adjacent_vertices = [e.other_vertex(v) for e in v.edges]
v_sum = Vertex()
[v_sum.add(av) for av in adjacent_vertices]
v_sum.divide(len(adjacent_vertices))
delta = v_sum - v
sv = smoothed.vertices[i]
delta.scale(factor)
sv.add(delta)
return smoothed
def mesh_smooth_laplacian(mesh, factor=0.3):
"""
Applies Laplacian smoothing to a mesh.
It works by moving each vertex in the direction of the average position of its neighbors.
Note: this does not increase the face count.
"""
smoothed = mesh.copy()
#smoothed.update_topology()
for i, v in enumerate(mesh.vertices):
adjacent_vertices = [e.other_vertex(v) for e in v.edges]
v_sum = Vertex()
[v_sum.add(av) for av in adjacent_vertices]
v_sum.divide(len(adjacent_vertices))
delta = v_sum - v
sv = smoothed.vertices[i]
delta.scale(factor)
sv.add(delta)
return smoothed
