def build_tree(self):
tree = KDTree(self.nmax)
vnum = 0
for i,v in enumerate(self.obj.data.vertices):
tree.insert(v.co,i)
vnum += 1
tree.balance()
self.tree = tree
self.vnum = vnum
return
def make_blobs(context, gridob, groundob, samples2D, display_radius):
blob_group_clear(context)
blobs = []
imat = groundob.matrix_world.inverted()
blobtree = KDTree(len(gridob.data.vertices))
for i, v in enumerate(gridob.data.vertices):
co = gridob.matrix_world * v.co
# note: only using 2D coordinates, otherwise weights get distorted by z offset
blobtree.insert((co[0], co[1], 0.0), i)
blobtree.balance()
for v in gridob.data.vertices:
co = gridob.matrix_world * v.co
ok, loc, nor, poly_index = project_on_ground(groundob, co)
blobs.append(Blob(loc, nor, poly_index) if ok else None)
with progress.ProgressContext("Grouping Samples", 0, len(samples2D)):
mpolys = groundob.data.polygons
mverts = groundob.data.vertices
for xy in samples2D:
progress.progress_add(1)
# note: use only 2D coordinates for weighting, z component should be 0
index = assign_blob(blobtree, (xy[0], xy[1], 0.0), nor)
if index < 0:
continue
blob = blobs[index]
if blob is None:
continue
# project samples onto the ground object
ok, sloc, snor, spoly = project_on_ground(groundob, xy[0:2]+(0,))
if not ok:
continue
# calculate barycentric vertex weights on the poly
poly = mpolys[spoly]
sverts = list(poly.vertices)
# note: coordinate space has to be consistent, use sloc in object space
sweights = poly_3d_calc(tuple(mverts[i].co for i in sverts), imat * sloc)
blob.add_sample(sloc, snor, spoly, sverts, sweights)
blobs_to_customprops(groundob.meadow, blobs)
make_blob_visualizer(context, groundob, blobs, display_radius, hide=True)
def __init__(self, guide, ground, scale):
self.GUIDE_STRENGTH = 1.0 * scale
self.TURBULENCE_FREQUENCY = 10 * scale
self.TURBULENCE_STRENGTH = 1.0 * scale
self.AVOID_THRESHOLD = 0.01 * scale
self.AVOID_STRENGTH = 0.2 * scale
self.frame = 0
self.particles = []
self.guide = guide
# self.vertex_distance = (self.guide.data.vertices[0].co - self.guide.data.vertices[1].co).length_squared
self.guide_tree = KDTree(len(self.guide.data.vertices))
for v in self.guide.data.vertices:
self.guide_tree.insert(v.co, v.index)
self.guide_tree.balance()
self.ground = ground
self.scale = scale
# bpy.ops.mesh.primitive_ico_sphere_add(location=(0,0,0), size=0.01)
# self.instance_obj = bpy.context.object
# self.instance_obj = bpy.data.objects['Fleche']
self.instance_obj = bpy.data.objects[bpy.context.scene.ant_instance]
self.instance_mesh = self.instance_obj.data
def __build_src_kd(self):
mesh = self.decimated.data
size = len(mesh.vertices)
self.src_kd = KDTree(size)
for i, v in enumerate(mesh.vertices):
self.src_kd.insert(v.co, i)
self.src_kd.balance()
def build_tree(self):
tree = KDTree(self.nmax)
vnum = 0
for i,v in enumerate(self.obj.data.vertices):
tree.insert(v.co,i)
vnum += 1
#co,index,dist = kd.find(co)
#co,index,dist = kd.find(co,10)
#co, index, dist = kd.find_range(co,0.5)
tree.balance()
self.tree = tree
self.vnum = vnum
return
def connect_verts(bm, z_idx, v1, verts2_bm, connections, max_rho):
tree = KDTree(len(verts2_bm))
for i, v2 in enumerate(verts2_bm):
tree.insert(v2.co, i)
tree.balance()
for co, i, dist in tree.find_n(v1.co, connections):
if dist <= max_rho:
v2 = verts2_bm[i]
if bm.edges.get((v1, v2)) is None:
bm.edges.new((v1, v2))
bm.edges.ensure_lookup_table()
def _init(self):
self._faces_by_vertex = defaultdict(set)
self._faces_by_edge = defaultdict(set)
self._edges_by_vertex = defaultdict(set)
for face in self.solid.Faces:
for vertex in face.Vertexes:
self._faces_by_vertex[SvSolidTopology.Item(vertex)].add(
SvSolidTopology.Item(face))
for edge in face.Edges:
self._faces_by_edge[SvSolidTopology.Item(edge)].add(
SvSolidTopology.Item(face))
for edge in self.solid.Edges:
for vertex in edge.Vertexes:
self._edges_by_vertex[SvSolidTopology.Item(vertex)].add(
SvSolidTopology.Item(edge))
self._tree = KDTree(len(self.solid.Vertexes))
for i, vertex in enumerate(self.solid.Vertexes):
co = (vertex.X, vertex.Y, vertex.Z)
self._tree.insert(co, i)
self._tree.balance()
def gen(seed, num):
random.seed(seed)
data = []
tree = KDTree(0)
tree.balance()
for i in range(num):
best = best_candidate(num_candidates, tree)
if not best:
break
yield best
data.append((best[0], best[1], 0.0))
tree = KDTree(len(data))
for i, p in enumerate(data):
tree.insert(p, i)
tree.balance()
class Tile3DFinder:
def __init__(self, objects=None):
self.cached = {}
self.objects = objects or [
c for c in t3d.root.children if c.layers[t3d.layer]
]
size = len(self.objects)
self.kd = KDTree(size)
for i, obj in enumerate(self.objects):
self.kd.insert(obj.pos, i)
self.kd.balance()
def get_tiles_at(self, pos):
vec = pos.copy().freeze()
if vec in self.cached:
return self.cached[vec]
else:
objs = [
self.objects[index]
for pos, index, dist in self.kd.find_range(pos, TOLERANCE)
]
self.cached[vec] = objs
return objs
def build_kdtree_from_verts(verts):
# Create a kd-tree from verts
size = len(verts)
kd = KDTree(size)
for i, vtx in enumerate(verts):
# exclude hidden geometry
if not vtx.hide:
kd.insert(vtx.co, i)
kd.balance()
return kd
def set_gt_points(cls, gt_points: List[Vector] = None) -> None:
"""Set the ground truth point cloud. Automatically creates the KDTree to speed up point cloud operations.
Keyword Arguments:
gt_points {List[Vector]} -- ground truth point cloud. If {None} both the point cloud
and the KDTree are cleared. (default: {None})
"""
cls.unload_deleted()
#
cls.gt_points = gt_points
if gt_points is not None:
# build KDTree for target point cloud to speed up the nearest neighbor search
cls.gt_kdtree = KDTree(len(gt_points))
for i, v in enumerate(gt_points):
cls.gt_kdtree.insert(v, i)
cls.gt_kdtree.balance()
def step(
self,
speed,
):
new_tree = KDTree(len(self.particles))
self.draw_obj.commands.clear()
for id, particle in enumerate(self.particles):
particle.step(speed)
particle.draw()
new_tree.insert(particle.location, id)
new_tree.balance()
self.kd_tree = new_tree
def _execute(self, context):
ob = context.object
verts = ob.data.vertices
kd = KDTree(len(verts))
for i, v in enumerate(verts):
kd.insert(ob.matrix_world @ v.co, i)
kd.balance()
for o in context.selected_objects:
if ob is o or o.type != 'MESH':
continue
for v in o.data.vertices:
nearest = kd.find_range(o.matrix_world @ v.co, self.dist)
for _, idx, _ in nearest:
verts[idx].select = True
class ParticleManager:
_particle_types = {}
@classmethod
def register_particle_type(cls, type, name):
cls._particle_types[name] = type
@classmethod
def unregister_particle_type(cls, name):
del cls._particle_types[name]
def __init__(self, context):
self.obj = context.active_object
self.particles = []
self.edges = []
self.draw_layer = grease_draw.StrokeLayer(context)
self.kd_tree = None
def create_particle(self, type, location, radius=0.03):
if type in self._particle_types:
p = self._particle_types[type](radius, location, self)
self.particles.append(p)
return p
else:
raise KeyError("No such particle registered: %s" % type)
def build_kd_tree(self):
self.kd_tree = KDTree(len(self.particles))
for index, particle in enumerate(self.particles):
self.kd_tree.insert(particle.location, index)
self.kd_tree.balance()
def nearest_n_particles(self, location, n):
for location, index, distance in self.kd_tree.find_n(location, n):
particle = self.particles[index]
yield particle, distance
def nearest_n_tag_particles(self, location, n, tag):
def tag_filter(i):
return self.particles[i].tag is tag
for location, index, distance in self.kd_tree.find_n(
location, n, filter=tag_filter):
particle = self.particles[index]
yield particle, distance
def step(self, speed):
for particle in self.particles:
particle.step(speed)
def sample_obj(self, location):
return self.obj.closest_point_on_mesh(location)
def init():
nonlocal tree, total_faces, deleted_faces, face_index
bm.faces.ensure_lookup_table()
# tree = BVHTree.FromObject(ob, context.scene)
# tree = BVHTree.FromBMesh(bm, epsilon=0.00001)
tree = KDTree(len(bm.faces))
for i, face in enumerate(bm.faces):
tree.insert(face.calc_center_median(), i)
tree.balance()
total_faces = len(bm.faces)
deleted_faces = [False] * total_faces
face_index = 0
def my_handler(scene):
from_name = 'tire'
to_name = 'tire.001'
tgt_name = 'tire.002'
brush_name = 'Cube'
from_obj = scene.objects[from_name]
to_obj = scene.objects[to_name]
tgt_obj = scene.objects[tgt_name]
brush_obj = scene.objects[brush_name]
from_mesh = from_obj.data
to_mesh = to_obj.data
tgt_mesh = tgt_obj.data
brush_mesh = brush_obj.data
tgt_kd = KDTree(len(tgt_mesh.vertices))
for i in range(len(tgt_mesh.vertices)):
co = tgt_obj.matrix_world * tgt_mesh.vertices[i].co
tgt_kd.insert(co, i)
tgt_kd.balance()
# return tgt_kd
min_x = float('inf')
max_x = float('-inf') # brush_mesh.vertices[0].co.x
for i in range(len(brush_mesh.vertices)):
co = brush_mesh.vertices[i].co * brush_obj.matrix_world
max_x = max(co.x, max_x)
min_x = min(co.x, min_x)
radius = (max_x - min_x) / 2
# raise ValueError('min_x: %f, max_x: %f, radius: %f' % (min_x, max_x, radius))
pts = tgt_kd.find_range(brush_obj.location, radius)
# raise ValueError('len(pts): %d' % len(pts))
for (co, idx, dist) in pts:
tgt_obj.vertex_groups[0].add([idx], 1.0, 'REPLACE')
for i in range(len(from_mesh.vertices)):
t = tgt_obj.vertex_groups[0].weight(i)
co = from_mesh.vertices[i].co * (1 - t) + \
to_mesh.vertices[i].co * (t)
no = from_mesh.vertices[i].normal * (1 - t) + \
to_mesh.vertices[i].normal * (t)
tgt_mesh.vertices[i].co = co
tgt_mesh.vertices[i].normal = no
scene.update()
def make_blobs(context, gridob, groundob, samples2D, display_radius):
blob_group_clear(context)
blobs = []
imat = groundob.matrix_world.inverted()
blobtree = KDTree(len(gridob.data.vertices))
for i, v in enumerate(gridob.data.vertices):
co = gridob.matrix_world * v.co
# note: only using 2D coordinates, otherwise weights get distorted by z offset
blobtree.insert((co[0], co[1], 0.0), i)
blobtree.balance()
for v in gridob.data.vertices:
co = gridob.matrix_world * v.co
ok, loc, nor, poly_index = project_on_ground(groundob, co)
blobs.append(Blob(loc, nor, poly_index) if ok else None)
with progress.ProgressContext("Grouping Samples", 0, len(samples2D)):
mpolys = groundob.data.polygons
mverts = groundob.data.vertices
for xy in samples2D:
progress.progress_add(1)
# note: use only 2D coordinates for weighting, z component should be 0
index = assign_blob(blobtree, (xy[0], xy[1], 0.0), nor)
if index < 0:
continue
blob = blobs[index]
if blob is None:
continue
# project samples onto the ground object
ok, sloc, snor, spoly = project_on_ground(groundob,
xy[0:2] + (0, ))
if not ok:
continue
# calculate barycentric vertex weights on the poly
poly = mpolys[spoly]
sverts = list(poly.vertices)
# note: coordinate space has to be consistent, use sloc in object space
sweights = poly_3d_calc(tuple(mverts[i].co for i in sverts),
imat * sloc)
blob.add_sample(sloc, snor, spoly, sverts, sweights)
blobs_to_customprops(groundob.meadow, blobs)
make_blob_visualizer(context, groundob, blobs, display_radius, hide=True)
def unique_points(points, eps=1e-4):
kdt = KDTree(len(points))
for i, p in enumerate(points):
kdt.insert(p, i)
kdt.balance()
unique = []
repeating = []
mask = []
for p in points:
found = kdt.find_n(p, 2)
if len(found) > 1:
loc, idx, distance = found[1]
ok = distance > eps
mask.append(ok)
if ok:
unique.append(p)
else:
repeating.append(p)
return mask, unique, repeating
def init_guess(curve, points_from, samples=50):
u_min, u_max = curve.get_u_bounds()
us = np.linspace(u_min, u_max, num=samples)
points = curve.evaluate_array(us).tolist()
#print("P:", points)
kdt = KDTree(len(us))
for i, v in enumerate(points):
kdt.insert(v, i)
kdt.balance()
us_out = []
nearest_out = []
for point_from in points_from:
nearest, i, distance = kdt.find(point_from)
us_out.append(us[i])
nearest_out.append(tuple(nearest))
return us_out, nearest_out
def my_handler(scene):
from_name='tire'
to_name='tire.001'
tgt_name='tire.002'
brush_name = 'Cube'
from_obj=scene.objects[from_name]
to_obj=scene.objects[to_name]
tgt_obj=scene.objects[tgt_name]
brush_obj=scene.objects[brush_name]
from_mesh=from_obj.data
to_mesh=to_obj.data
tgt_mesh=tgt_obj.data
brush_mesh=brush_obj.data
tgt_kd = KDTree(len(tgt_mesh.vertices))
for i in range(len(tgt_mesh.vertices)):
co = tgt_obj.matrix_world * tgt_mesh.vertices[i].co
tgt_kd.insert(co, i)
tgt_kd.balance()
# return tgt_kd
min_x = float('inf')
max_x = float('-inf') # brush_mesh.vertices[0].co.x
for i in range(len(brush_mesh.vertices)):
co = brush_mesh.vertices[i].co * brush_obj.matrix_world
max_x = max(co.x, max_x)
min_x = min(co.x, min_x)
radius = (max_x - min_x) / 2
# raise ValueError('min_x: %f, max_x: %f, radius: %f' % (min_x, max_x, radius))
pts = tgt_kd.find_range(brush_obj.location, radius)
# raise ValueError('len(pts): %d' % len(pts))
for (co, idx, dist) in pts:
tgt_obj.vertex_groups[0].add([idx], 1.0, 'REPLACE')
for i in range(len(from_mesh.vertices)):
t = tgt_obj.vertex_groups[0].weight(i)
co = from_mesh.vertices[i].co * (1 - t) + \
to_mesh.vertices[i].co * (t)
no = from_mesh.vertices[i].normal * (1 - t) + \
to_mesh.vertices[i].normal * (t)
tgt_mesh.vertices[i].co = co
tgt_mesh.vertices[i].normal = no
scene.update()
def to_point(self, amplitude, coefficient, vertex, centers, direction):
vertex = Vector(vertex)
n = len(centers)
if self.point_mode == 'AVG' or n <= 1:
vectors = []
for center in centers:
vector = Vector(center) - vertex
vector = self.falloff(amplitude, coefficient,
vector.length) * vector.normalized()
vectors.append(vector)
result = get_avg_vector(vectors)
return result.length, result.normalized()
else:
kdt = KDTree(n)
for i, center in enumerate(centers):
kdt.insert(Vector(center), i)
kdt.balance()
nearest_co, nearest_idx, nearest_distance = kdt.find(vertex)
vector = nearest_co - vertex
coeff = self.falloff(amplitude, coefficient, nearest_distance)
return coeff, vector.normalized()
def invoke(self, context, event):
self.ob = context.active_object.data
self.bm = bmesh.new()
self.bm.from_mesh(self.ob)
self.bm.verts.ensure_lookup_table()
links = []
for vert in self.bm.verts:
l = []
links.append(l)
for v in n_ring(vert, 100):
l.append(v.index)
immediate_edges = [len(vert.link_edges) for vert in self.bm.verts]
bmesh.ops.triangulate(self.bm, faces=self.bm.faces)
self.bm.verts.ensure_lookup_table()
co = [tuple(v.co) for v in self.bm.verts]
t = [tuple(v.index for v in f.verts) for f in self.bm.faces]
kd = KDTree(len(self.bm.verts))
for vert in self.bm.verts:
kd.insert(vert.co, vert.index)
kd.balance()
x_mirr_table = [kd.find((vert.co[0] * -1, vert.co[1], vert.co[2]))[1] for vert in self.bm.verts]
self.engine = softwrap_core.ShapeEngine(co, t, links, co, t, immediate_edges, x_mirr_table)
self.engine.random_co()
self.engine.add_pin(co=(10, 0, 0), vert_index=0, stiffness=50, twisty=False, x_mirr=True)
context.window_manager.modal_handler_add(self)
return {"RUNNING_MODAL"}
def spread_step(self):
count = 0
new_particles = []
self.draw_obj.commands.clear()
for particle in self.particles:
new_particles += particle.spread()
count += len(new_particles)
for particle in self.particles:
if not particle.tag == "REMOVE":
new_particles.append(particle)
self.particles = new_particles
new_tree = KDTree(len(self.particles))
for id, particle in enumerate(self.particles):
particle.draw()
new_tree.insert(particle.location, id)
new_tree.balance()
self.kd_tree = new_tree
return count
def execute(self, vertices, clusters, connections, minDistance, maxDistance):
minDistance = max(0, minDistance)
maxDistance = max(minDistance, maxDistance)
verticesAmount = len(vertices)
kdTree = KDTree(verticesAmount)
for i, vector in enumerate(vertices):
kdTree.insert(vector, i)
kdTree.balance()
edges = []
for searchIndex in range(min(verticesAmount, clusters)):
added = 0
for (vector, foundIndex, distance) in kdTree.find_range(vertices[searchIndex], maxDistance):
if searchIndex != foundIndex and distance > minDistance:
if added >= connections: break
if foundIndex > searchIndex:
edge = (searchIndex, foundIndex)
else:
edge = (foundIndex, searchIndex)
edges.append(edge)
added += 1
return list(set(edges))
def getDefaultValue(cls):
kdTree = KDTree(0)
kdTree.balance()
return kdTree
def build_kdtree(self):
tree = KDTree(len(self.particles))
for id, p in enumerate(self.particles):
tree.insert(p.location, id)
tree.balance()
self.kd_tree = tree
class Converter(object):
TARGET_NUM_FACET = 2000
DEFAULT_OCTREE = 3
@elapsed
def __init__(self, src):
self.src = src
self.decimated = None
self.src_kd = None
self.voxel_list = Manager().list()
self.mesh_list = Manager().list()
self.color_dict = {}
self.parent = None
self.block_map = Manager().list()
self.unit = None
self.join = True
# Initial procedure
self.__calc_decimated()
self.__build_src_kd()
self.__create_color_dict()
bpy.ops.object.select_all(action="DESELECT")
@elapsed
def __calc_decimated(self):
num_facet = len(self.src.data.polygons)
ratio = float(Converter.TARGET_NUM_FACET) / float(num_facet)
mesh = bpy.data.meshes.new("Decimated")
self.decimated = bpy.data.objects.new("Decimated", mesh)
self.decimated.data = self.src.data.copy()
self.decimated.scale = self.src.scale
self.decimated.location = self.src.location
bpy.context.scene.objects.link(self.decimated)
self.decimated.select = True
self.decimated.modifiers.new("Decimate", "DECIMATE")
self.decimated.modifiers["Decimate"].ratio = ratio
bpy.ops.object.modifier_apply(apply_as="DATA", modifier="DECIMATE")
@elapsed
def __build_src_kd(self):
mesh = self.decimated.data
size = len(mesh.vertices)
self.src_kd = KDTree(size)
for i, v in enumerate(mesh.vertices):
self.src_kd.insert(v.co, i)
self.src_kd.balance()
@elapsed
def __create_color_dict(self):
for i, loop in enumerate(self.decimated.data.loops):
vi = loop.vertex_index
if vi not in self.color_dict:
self.color_dict[vi] = i
@elapsed
def apply_join(self):
if self.join:
bpy.ops.object.join()
@elapsed
def cleanup(self):
bpy.context.scene.objects.unlink(self.decimated)
@staticmethod
def create_new_octree(box):
box0 = (
box[0],
(box[0] + box[1])/2.0,
(box[0] + box[2])/2.0,
(box[0] + box[3])/2.0,
(box[0] + box[4])/2.0,
(box[0] + box[5])/2.0,
(box[0] + box[6])/2.0,
(box[0] + box[7])/2.0,
)
box1 = (
# Left side
(box[0] + box[1])/2.0,
box[1],
(box[1] + box[2])/2.0,
(box[0] + box[2])/2.0,
# Right side
(box[0] + box[5])/2.0,
(box[1] + box[5])/2.0,
(box[1] + box[6])/2.0,
(box[0] + box[6])/2.0,
)
box2 = (
# Left side
(box[0] + box[2])/2.0,
(box[1] + box[2])/2.0,
box[2],
(box[2] + box[3])/2.0,
# Right side
(box[0] + box[6])/2.0,
(box[1] + box[6])/2.0,
(box[2] + box[6])/2.0,
(box[3] + box[6])/2.0
)
box3 = (
# Left side
(box[0] + box[3])/2.0,
(box[0] + box[2])/2.0,
(box[2] + box[3])/2.0,
box[3],
# Right side
(box[0] + box[7])/2.0,
(box[0] + box[6])/2.0,
(box[3] + box[6])/2.0,
(box[3] + box[7])/2.0,
)
box4 = (
# Left side
(box[0] + box[4])/2.0,
(box[0] + box[5])/2.0,
(box[0] + box[6])/2.0,
(box[0] + box[7])/2.0,
# Right side
box[4],
(box[4] + box[5])/2.0,
(box[4] + box[6])/2.0,
(box[4] + box[7])/2.0,
)
box5 = (
# Left side
(box[0] + box[5])/2.0,
(box[1] + box[5])/2.0,
(box[1] + box[6])/2.0,
(box[0] + box[6])/2.0,
# Right side
(box[4] + box[5])/2.0,
box[5],
(box[5] + box[6])/2.0,
(box[4] + box[6])/2.0,
)
box6 = (
# Left side
(box[0] + box[6])/2.0,
(box[1] + box[6])/2.0,
(box[2] + box[6])/2.0,
(box[3] + box[6])/2.0,
# Right side
(box[4] + box[6])/2.0,
(box[5] + box[6])/2.0,
box[6],
(box[6] + box[7])/2.0,
)
box7 = (
# Left side
(box[0] + box[7])/2.0,
(box[0] + box[6])/2.0,
(box[3] + box[6])/2.0,
(box[3] + box[7])/2.0,
# Right side
(box[4] + box[7])/2.0,
(box[4] + box[6])/2.0,
(box[6] + box[7])/2.0,
box[7],
)
return box0, box1, box2, box3, box4, box5, box6, box7
@staticmethod
def get_bvhtree_from_box(box):
mesh_data = bpy.data.meshes.new("cube_mesh_data")
faces = [(0, 1, 2, 3),
(4, 7, 6, 5),
(0, 4, 5, 1),
(1, 5, 6, 2),
(2, 3, 7, 6),
(4, 0, 3, 7)]
mesh_data.from_pydata([x.to_tuple() for x in box], [], faces)
mesh_data.update()
bm = bmesh.new()
bm.from_mesh(mesh_data)
return bvh.BVHTree.FromBMesh(bm)
@staticmethod
def check_if_overlap(obj, box):
bvh_tree1 = bvh.BVHTree.FromObject(obj, bpy.context.scene)
bvh_tree2 = Converter.get_bvhtree_from_box(box)
return bvh_tree1.overlap(bvh_tree2)
@elapsed
def invoke(self, obj, box, max_depth):
try:
self.invoke_create_voxel(obj, box, max_depth)
self.draw_voxel(origin=box[0])
finally:
# Post procedure
self.apply_join()
self.cleanup()
return list(self.block_map)
@elapsed
def invoke_create_voxel(self, obj, box, max_depth):
# Calc unit length
self.unit = (box[1].z - box[0].z) / float(2 ** max_depth)
overlap = Converter.check_if_overlap(obj, box)
if overlap:
boxes = Converter.create_new_octree(box)
jobs = []
for child in boxes:
p = Process(
target=self.create_voxel,
args=(obj, child, 1, self.voxel_list, max_depth)
)
jobs.append(p)
p.start()
[job.join() for job in jobs]
def create_voxel(self, obj, box, depth, queue, max_depth=3):
"""For multiprocessing
:param obj:
:param box:
:param depth:
:param queue:
:param max_depth:
:return:
"""
depth += 1
overlap = Converter.check_if_overlap(obj, box)
if overlap:
if depth == max_depth:
queue.append([x.to_tuple() for x in box])
else:
boxes = Converter.create_new_octree(box)
for _child in boxes:
self.create_voxel(obj, _child, depth, queue, max_depth)
def calc_mesh_and_color(self, voxel_list, mesh_list, block_list, origin):
"""For multiprocessing
:param list voxel_list:
:param list mesh_list:
:param list block_list:
:param mathutils.Vector origin:
"""
faces = ((0, 1, 2, 3), (4, 7, 6, 5), (0, 4, 5, 1),
(1, 5, 6, 2), (2, 3, 7, 6), (4, 0, 3, 7))
for i, voxel in enumerate(voxel_list):
mesh = bpy.data.meshes.new("cube_mesh_data")
mesh.from_pydata(voxel, [], faces)
mesh.update()
# Find closest color
co, index, dist = self.src_kd.find(voxel[0])
if self.decimated.data.vertex_colors:
rgb = self.decimated.data.vertex_colors["Col"].data[self.color_dict[index]].color
else:
rgb = (1.0, 1.0, 1.0) # White
mesh_list.append((voxel, tuple(rgb)))
ix = int(round((voxel[0][0] - origin.x) / self.unit))
iy = int(round((voxel[0][1] - origin.y) / self.unit))
iz = int(round((voxel[0][2] - origin.z) / self.unit))
col_def = BlockDef.find_nearest_color_block(Vector(rgb))
block_list.append(BlockInfo(
has_block=True,
block_type=col_def.block_def[0],
color=col_def.block_def[1],
pos=(ix, iy, iz)
))
@elapsed
def draw_voxel(self, origin):
# Add null object
self.parent = bpy.data.objects.new("Voxcel", bpy.data.meshes.new("Voxcel"))
bpy.context.scene.objects.link(self.parent)
bpy.context.scene.objects.active = self.parent
self.parent.select = True
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i+n]
parallels = 8
chunk_list = chunks(
self.voxel_list,
len(self.voxel_list)//parallels
)
jobs = []
for chunk in chunk_list:
job = Process(
target=self.calc_mesh_and_color,
args=(chunk, self.mesh_list, self.block_map, origin)
)
jobs.append(job)
job.start()
[job.join() for job in jobs]
@elapsed
def add_voxels():
for i, item in enumerate(self.mesh_list):
vertices = item[0]
color = item[1]
name = "Cube.%010d" % i
voxel.Voxel(name, vertices, color).create().add(
scene=bpy.context.scene,
parent=self.parent
)
add_voxels()
def getValue(self):
kdTree = KDTree(0)
kdTree.balance()
return kdTree
def kd_from_points(points):
tree = KDTree(len(points))
for i, p in enumerate(points):
tree.insert(p, i)
tree.balance()
return tree
def simplify_mesh(self, bm):
class Ownership:
def __init__(self, particle, dist):
self.particle = particle
self.distance = dist
self.valid = False
bmesh.ops.triangulate(bm, faces=bm.faces)
last_edges = float("+inf")
while True:
edges = set()
for edge in bm.edges:
le = (edge.verts[0].co - edge.verts[1].co).length_squared
center = edge.verts[0].co + edge.verts[1].co
center /= 2
for p, dist in self.get_nearest(center, 1):
if p.radius**2 < le:
edges.add(edge)
if not len(edges) < last_edges:
break
last_edges = len(edges)
bmesh.ops.subdivide_edges(bm, edges=list(edges), cuts=1)
bmesh.ops.triangulate(bm, faces=bm.faces)
bm.faces.ensure_lookup_table()
bm.verts.ensure_lookup_table()
tree = KDTree(len(bm.verts))
for vert in bm.verts:
tree.insert(vert.co, vert.index)
tree.balance()
ownership_mapping = {}
ownership_validation_front = set()
for vert in bm.verts:
for p, dist in self.get_nearest(vert.co, 1):
ownership_mapping[vert] = Ownership(p, dist)
for particle in self.particles:
location, index, dist = tree.find(particle.location)
vert = bm.verts[index]
if vert in ownership_mapping:
if ownership_mapping[vert].particle == particle:
ownership_mapping[vert].valid = True
ownership_validation_front.add(vert)
while True:
new_front = set()
for vert in ownership_validation_front:
for edge in vert.link_edges:
other_vert = edge.other_vert(vert)
if other_vert not in ownership_mapping:
continue
if ownership_mapping[other_vert].valid:
continue
if other_vert in ownership_mapping:
if ownership_mapping[
vert].particle is ownership_mapping[
other_vert].particle:
new_front.add(other_vert)
ownership_mapping[other_vert].valid = True
ownership_validation_front = new_front
if not new_front:
break
new_bm = bmesh.new()
for particle in self.particles:
particle.vert = new_bm.verts.new(particle.location)
for face in bm.faces:
connections = set()
for vert in face.verts:
if vert in ownership_mapping:
if ownership_mapping[vert].valid:
p = ownership_mapping[vert].particle
connections.add(p)
if len(connections) == 3:
try:
new_bm.faces.new(
[particle.vert for particle in connections])
except ValueError:
pass
while True:
stop = True
for vert in new_bm.verts:
if len(vert.link_edges) < 3:
new_bm.verts.remove(vert)
stop = False
if stop:
break
bmesh.ops.holes_fill(new_bm, edges=new_bm.edges)
bmesh.ops.triangulate(new_bm, faces=new_bm.faces)
bmesh.ops.recalc_face_normals(new_bm, faces=new_bm.faces)
if not self.triangle_mode:
bmesh.ops.join_triangles(new_bm,
faces=new_bm.faces,
angle_face_threshold=1.0,
angle_shape_threshold=3.14)
return new_bm
class SvSolidTopology(object):
class Item(object):
def __init__(self, item):
self.item = item
def __hash__(self):
return self.item.hashCode()
def __eq__(self, other):
return self.item.isSame(other.item)
def __repr__(self):
return f"<Item: {type(self.item).__name__} #{self.item.hashCode()}>"
def __init__(self, solid):
self.solid = solid
self._init()
def __repr__(self):
v = len(self.solid.Vertexes)
e = len(self.solid.Edges)
f = len(self.solid.Faces)
return f"<Solid topology: {v} vertices, {e} edges, {f} faces>"
def _init(self):
self._faces_by_vertex = defaultdict(set)
self._faces_by_edge = defaultdict(set)
self._edges_by_vertex = defaultdict(set)
for face in self.solid.Faces:
for vertex in face.Vertexes:
self._faces_by_vertex[SvSolidTopology.Item(vertex)].add(
SvSolidTopology.Item(face))
for edge in face.Edges:
self._faces_by_edge[SvSolidTopology.Item(edge)].add(
SvSolidTopology.Item(face))
for edge in self.solid.Edges:
for vertex in edge.Vertexes:
self._edges_by_vertex[SvSolidTopology.Item(vertex)].add(
SvSolidTopology.Item(edge))
self._tree = KDTree(len(self.solid.Vertexes))
for i, vertex in enumerate(self.solid.Vertexes):
co = (vertex.X, vertex.Y, vertex.Z)
self._tree.insert(co, i)
self._tree.balance()
def tessellate(self, precision):
self._points_by_edge = defaultdict(list)
self._points_by_face = defaultdict(list)
for edge in self.solid.Edges:
points = edge.discretize(Deflection=precision)
i_edge = SvSolidTopology.Item(edge)
for pt in points:
self._points_by_edge[i_edge].append((pt.x, pt.y, pt.z))
for face in self.solid.Faces:
data = face.tessellate(precision)
i_face = SvSolidTopology.Item(face)
for pt in data[0]:
self._points_by_face[i_face].append((pt.x, pt.y, pt.z))
def calc_normals(self):
self._normals_by_face = dict()
for face in self.solid.Faces:
#face.tessellate(precision)
#u_min, u_max, v_min, v_max = face.ParameterRange
sum_normal = Base.Vector(0, 0, 0)
for u, v in face.getUVNodes():
normal = face.normalAt(u, v)
sum_normal = sum_normal + normal
sum_normal = np.array([sum_normal.x, sum_normal.y, sum_normal.z])
sum_normal = sum_normal / np.linalg.norm(sum_normal)
self._normals_by_face[SvSolidTopology.Item(face)] = sum_normal
def get_normal_by_face(self, face):
return self._normals_by_face[SvSolidTopology.Item(face)]
def get_vertices_by_location(self, condition):
to_tuple = lambda v: (v.X, v.Y, v.Z)
return [
to_tuple(v) for v in self.solid.Vertexes if condition(to_tuple(v))
]
def get_vertices_by_location_mask(self, condition):
to_tuple = lambda v: (v.X, v.Y, v.Z)
return [condition(to_tuple(v)) for v in self.solid.Vertexes]
def get_points_by_edge(self, edge):
return self._points_by_edge[SvSolidTopology.Item(edge)]
def get_points_by_face(self, face):
return self._points_by_face[SvSolidTopology.Item(face)]
def get_edges_by_location_mask(self, condition, include_partial):
# condition is vectorized
check = any if include_partial else all
mask = []
for edge in self.solid.Edges:
test = condition(
np.array(self._points_by_edge[SvSolidTopology.Item(edge)]))
mask.append(check(test))
return mask
def get_faces_by_location_mask(self, condition, include_partial):
# condition is vectorized
check = any if include_partial else all
mask = []
for face in self.solid.Faces:
test = condition(
np.array(self._points_by_face[SvSolidTopology.Item(face)]))
mask.append(check(test))
return mask
def get_faces_by_vertex(self, vertex):
return [
i.item for i in self._faces_by_vertex[SvSolidTopology.Item(vertex)]
]
def get_faces_by_vertices_mask(self, vertices, include_partial=True):
if include_partial:
good = set()
for vertex in vertices:
new = self._faces_by_vertex[SvSolidTopology.Item(vertex)]
good.update(new)
return [
SvSolidTopology.Item(face) in good for face in self.solid.Faces
]
else:
vertices = set([SvSolidTopology.Item(v) for v in vertices])
mask = []
for face in self.solid.Faces:
ok = all(
SvSolidTopology.Item(v) in vertices for v in face.Vertexes)
mask.append(ok)
return mask
def get_faces_by_edge(self, edge):
return [
i.item for i in self._faces_by_edge[SvSolidTopology.Item(edge)]
]
def get_faces_by_edges_mask(self, edges, include_partial=True):
if include_partial:
good = set()
for edge in edges:
new = self._faces_by_edge[SvSolidTopology.Item(edge)]
good.update(new)
return [
SvSolidTopology.Item(face) in good for face in self.solid.Faces
]
else:
edges = set([SvSolidTopology.Item(e) for e in edges])
mask = []
for face in self.solid.Faces:
ok = all(SvSolidTopology.Item(e) in edges for e in face.Edges)
mask.append(ok)
return mask
def get_edges_by_vertex(self, vertex):
return [
i.item for i in self._edges_by_vertex[SvSolidTopology.Item(vertex)]
]
def get_edges_by_vertices_mask(self, vertices, include_partial=True):
if include_partial:
good = set()
for vertex in vertices:
new = self._edges_by_vertex[SvSolidTopology.Item(vertex)]
good.update(new)
return [
SvSolidTopology.Item(edge) in good for edge in self.solid.Edges
]
else:
vertices = set([SvSolidTopology.Item(v) for v in vertices])
mask = []
for edge in self.solid.Edges:
ok = all(
SvSolidTopology.Item(v) in vertices for v in edge.Vertexes)
mask.append(ok)
return mask
def get_edges_by_faces_mask(self, faces):
good = set()
for face in faces:
new = set([SvSolidTopology.Item(e) for e in face.Edges])
good.update(new)
return [
SvSolidTopology.Item(edge) in good for edge in self.solid.Edges
]
def get_vertices_by_faces_mask(self, faces):
good = set()
for face in faces:
new = set([SvSolidTopology.Item(v) for v in face.Vertexes])
good.update(new)
return [
SvSolidTopology.Item(vertex) in good
for vertex in self.solid.Vertexes
]
def get_vertices_by_edges_mask(self, edges):
good = set()
for edge in edges:
new = set([SvSolidTopology.Item(v) for v in edge.Vertexes])
good.update(new)
return [
SvSolidTopology.Item(vertex) in good
for vertex in self.solid.Vertexes
]
def get_vertices_within_range(self, origin, distance):
found = self._tree.find_range(tuple(origin), distance)
idxs = [item[1] for item in found]
vertices = [self.solid.Vertexes[i] for i in idxs]
return vertices
def get_vertices_within_range_mask(self, origin, distance):
found = self._tree.find_range(tuple(origin), distance)
idxs = set([item[1] for item in found])
return [i in idxs for i in range(len(self.solid.Vertexes))]
def finish(self, context):
#ray cast the entire grid into
if 'Posterior Plane' in bpy.data.objects:
Plane = bpy.data.objects['Posterior Plane']
Plane.hide = False
else:
me = bpy.data.meshes.new('Posterior Plane')
Plane = bpy.data.objects.new('Posterior Plane', me)
context.scene.objects.link(Plane)
pbme = bmesh.new()
pbme.verts.ensure_lookup_table()
pbme.edges.ensure_lookup_table()
pbme.faces.ensure_lookup_table()
bmesh.ops.create_grid(pbme, x_segments = 200, y_segments = 200, size = 39.9)
pbme.to_mesh(Plane.data)
pt, pno = calculate_plane(self.crv.b_pts)
if self.splint.jaw_type == 'MANDIBLE':
Zw = Vector((0,0,-1))
Xw = Vector((1,0,0))
Yw = Vector((0,-1,1))
else:
Zw = Vector((0,0,1))
Xw = Vector((1,0,0))
Yw = Vector((0,1,0))
Z = pno
Z.normalize()
if Zw.dot(Z) < 0:
Z *= -1
Y = Z.cross(Xw)
X = Y.cross(Z)
R = Matrix.Identity(3) #make the columns of matrix U, V, W
R[0][0], R[0][1], R[0][2] = X[0] ,Y[0], Z[0]
R[1][0], R[1][1], R[1][2] = X[1], Y[1], Z[1]
R[2][0] ,R[2][1], R[2][2] = X[2], Y[2], Z[2]
R = R.to_4x4()
T = Matrix.Translation(pt - 5 * Z)
Plane.matrix_world = T * R
pmx = Plane.matrix_world
ipmx = pmx.inverted()
bme_pln = bmesh.new()
bme_pln.from_mesh(Plane.data)
bme_pln.verts.ensure_lookup_table()
bme_pln.edges.ensure_lookup_table()
bme_pln.faces.ensure_lookup_table()
bvh = BVHTree.FromBMesh(bme_pln)
#we are going to raycast the user world coordinate points
#into a grid, and identify all points in the grid from the local Z direction
#Then we will store the local location of the user picked coordinate in a dictionary
key_verts = {}
for loc in self.crv.b_pts:
res = bvh.ray_cast(ipmx * loc, -Z, 30)
if res[0] != None:
f = bme_pln.faces[res[2]]
for v in f.verts:
key_verts[v] = ipmx * loc
v.select_set(True)
continue
res = bvh.ray_cast(ipmx * loc, Z, 30)
if res[0] != None:
f = bme_pln.faces[res[2]]
for v in f.verts:
key_verts[v] = ipmx * loc
v.select_set(True)
continue
#bme_pln.to_mesh(Plane.data)
#bme_pln.free()
#return
kdtree = KDTree(len(key_verts))
for v in key_verts.keys():
kdtree.insert(v.co, v.index)
kdtree.balance()
#raycast the shell if we can
raycast_shell = False
if 'Splint Shell' in bpy.data.objects:
shell = bpy.data.objects.get('Splint Shell')
bvh_shell = BVHTree.FromObject(shell, context.scene)
mx_shell = shell.matrix_world
imx_shell = mx_shell.inverted()
Z_shell = imx_shell.to_3x3()*Z
raycast_shell = True
right_side = set()
left_side = set()
ray_casted = set()
to_delete = set()
for v in bme_pln.verts:
if v in key_verts:
v.co[2] = key_verts[v][2]
if v.co[1] > 0:
left_side.add(v)
else:
right_side.add(v)
continue
results = kdtree.find_range(v.co, .5)
if len(results):
N = len(results)
r_total = 0
v_new = Vector((0,0,0))
for res in results:
r_total += 1/res[2]
v_new += (1/res[2]) * key_verts[bme_pln.verts[res[1]]]
v_new *= 1/r_total
v.co[2] = v_new[2]
if v.co[1] > 0:
left_side.add(v)
else:
right_side.add(v)
continue
results = kdtree.find_range(v.co, 6)
if len(results):
N = len(results)
r_total = 0
v_new = Vector((0,0,0))
for res in results:
r_total += (1/res[2])**2
v_new += ((1/res[2])**2) * key_verts[bme_pln.verts[res[1]]]
v_new *= 1/r_total
v.co[2] = v_new[2]
if v.co[1] > 0:
left_side.add(v)
else:
right_side.add(v)
continue
loc, no, index, d = bvh_shell.ray_cast(imx_shell * pmx * v.co, Z_shell)
if loc:
ray_casted.add(v)
results = kdtree.find_n(v.co, 4)
N = len(results)
r_total = 0
v_new = Vector((0,0,0))
for res in results:
r_total += (1/res[2])**2
v_new += ((1/res[2])**2) * key_verts[bme_pln.verts[res[1]]]
v_new *= 1/r_total
v.co[2] = v_new[2]
continue
total_verts = ray_casted | left_side | right_side
ant_left = max(left_side, key = lambda x: x.co[0])
ant_right = max(right_side, key = lambda x: x.co[0])
new_verts = set()
dilation_verts = set()
for v in total_verts:
for ed in v.link_edges:
v_new = ed.other_vert(v)
if v_new in total_verts or v_new in new_verts:
continue
else:
new_verts.add(v_new)
print('adding %i new verts' % len(new_verts))
total_verts.update(new_verts)
dilation_verts.update(new_verts)
#for v in ray_casted:
# if v.co[1] > 0:
# if v.co[0] > ant_left.co[0]:
# to_delete.add(v)
# else:
# if v.co[0] > ant_right.co[0]:
# to_delete.add(v)
#print('added %i ray_casted' % len(ray_casted))
#total_verts = ray_casted | left_side | right_side
#total_verts.difference_update(to_delete)
#new_verts = set()
#for v in total_verts:
# for ed in v.link_edges:
# v_new = ed.other_vert(v)
# if v_new in total_verts: continue
# if v_new.co[1] > 0 and v_new.co[0] < ant_left.co[0]:
# if v in to_delete:
# new_verts.add(v)
# if v_new.co[1] <= 0 and v_new.co[0] < ant_right.co[0]:
# if v in to_delete:
# new_verts.add(v)
#to_delete.difference_update(new_verts)
#print('adding %i new verts' % len(new_verts))
for j in range(0,3):
newer_verts = set()
for v in new_verts:
for ed in v.link_edges:
v_new = ed.other_vert(v)
if v_new in total_verts or v_new in newer_verts:
continue
newer_verts.add(v_new)
total_verts.update(newer_verts)
dilation_verts.update(newer_verts)
new_verts = newer_verts
to_delete = set(bme_pln.verts[:]) - total_verts
#filter out anteior dilation
for v in dilation_verts:
if v.co[1] > 0 and v.co[0] > ant_left.co[0]:
to_delete.add(v)
continue
if v.co[1] <= 0 and v.co[0] > ant_right.co[0]:
to_delete.add(v)
continue
results = kdtree.find_n(v.co, 4)
N = len(results)
r_total = 0
v_new = Vector((0,0,0))
for res in results:
r_total += (1/res[2])**2
v_new += ((1/res[2])**2) * key_verts[bme_pln.verts[res[1]]]
v_new *= 1/r_total
v.co[2] = v_new[2]
#filter out anteior dilation
for v in ray_casted:
if v.co[1] > 0 and v.co[0] > ant_left.co[0]:
to_delete.add(v)
continue
if v.co[1] <= 0 and v.co[0] > ant_right.co[0]:
to_delete.add(v)
continue
bmesh.ops.delete(bme_pln, geom = list(to_delete), context = 1)
bme_pln.to_mesh(Plane.data)
Plane.data.update()
smod = Plane.modifiers.new('Smooth', type = 'SMOOTH')
smod.iterations = 5
smod.factor = 1
self.splint.ops_string += 'Mark Posterior Cusps:'
class ParticleManager:
def __init__(self, obj):
self.particles = []
self.obj = obj
self.field = vector_fields.FrameField(obj)
self.inv_mat = obj.matrix_world.inverted()
self.bm = self.field.bm
self.kd_tree = KDTree(0)
self.kd_tree.balance()
self.draw_obj = draw_3d.DrawObject()
self.triangle_mode = False
def build_field(self, context, use_gp, x_mirror):
self.field.build_major_curvatures()
frame = get_gp_frame(context)
if frame:
self.field.from_grease_pencil(frame,
mat=self.inv_mat,
x_mirror=x_mirror)
self.field.marching_growth()
self.field.smooth(2)
else:
self.field.erase_part(2)
self.field.marching_growth()
self.field.smooth()
def build_kdtree(self):
tree = KDTree(len(self.particles))
for id, p in enumerate(self.particles):
tree.insert(p.location, id)
tree.balance()
self.kd_tree = tree
def initialize_particles_from_gp(self, resolution, adaptive, context):
scale = max(self.obj.dimensions) / max(self.obj.scale)
target_resolution = scale / resolution
frame = get_gp_frame(context)
created_particles = 0
if not frame:
return created_particles
for stroke in frame.strokes:
co = self.inv_mat * stroke.points[0].co
last_particle = self.create_particle(Partile, co)
last_particle.target_resolution = target_resolution
last_particle.radius = target_resolution / (
last_particle.last_hit.curvature * adaptive + (1 - adaptive))
last_particle.adaptive = adaptive
created_particles += 1
for point in stroke.points:
co = self.inv_mat * point.co
if (co - last_particle.location
).length >= last_particle.radius * 2:
last_particle = self.create_particle(Partile, co)
last_particle.target_resolution = target_resolution
last_particle.radius = target_resolution / (
last_particle.last_hit.curvature * adaptive +
(1 - adaptive))
last_particle.adaptive = adaptive
created_particles += 1
return created_particles
def initialize_from_features(self,
verts,
resolution=20,
adaptive=0,
count=50):
scale = max(self.obj.dimensions) / max(self.obj.scale)
target_resolution = scale / resolution
verts = sorted(self.field.bm.verts,
key=lambda v: self.field.sharpness_field.get(
v.index, float("inf")),
reverse=True)
for i in range(min(count, len(self.field.bm.verts))):
vert = verts[i]
co = vert.co.copy()
p1 = self.create_particle(Partile, co)
p1.radius = target_resolution
p1.target_resolution = target_resolution
p1.adaptive = adaptive
def initialize_from_verts(self, verts, adaptive):
for vert in verts:
p = self.create_particle(Partile, vert.co)
p.adaptive = adaptive
def initialize_grid(self,
verts,
resolution=20,
use_x_mirror=True,
adaptive=0):
particle_locations = set()
scale = max(self.obj.dimensions)
target_resolution = 1 / ((1 / scale) * resolution)
for vert in verts:
co = vert.co.copy()
co /= scale
co *= resolution
x = int(co.x)
y = int(co.y)
z = int(co.z)
if use_x_mirror:
if x > 0:
particle_locations.add((x, y, z))
else:
particle_locations.add((x, y, z))
for location in particle_locations:
co = Vector(location)
co *= scale
co /= resolution
hit = self.sample_surface(co)
p1 = self.create_particle(Partile, hit.co)
p1.adaptive = adaptive
p1.target_resolution = target_resolution
if use_x_mirror:
hit.co.x *= -1
p2 = self.create_particle(Partile, hit.co)
p2.adaptive = adaptive
p2.target_resolution = target_resolution
p1.counter_pair, p2.counter_pair = p2, p1
def mirror_particles(self, any_side=False):
new_particles = []
for particle in self.particles:
if particle.location.x > particle.radius or any_side:
co = particle.location.copy()
co.x *= -1
p1 = particle
p2 = Partile(co, self)
p2.radius = p1.radius
p2.tag = p1.tag
p2.adaptive = p1.adaptive
p2.target_resolution = p1.target_resolution
p1.counter_pair, p2.counter_pair = p2, p1
new_particles.append(p2)
new_particles.append(p1)
elif -particle.radius * 0.5 < particle.location.x < particle.radius * 0.5:
new_particles.append(particle)
particle.lock_x = True
self.particles = new_particles
self.build_kdtree()
def create_particle(self, type, location, prepend=False):
p = type(location, self)
if not prepend:
self.particles.append(p)
else:
self.particles.insert(0, p)
return p
def remove_particle(self, particle):
self.particles.remove(particle)
def step(
self,
speed,
):
new_tree = KDTree(len(self.particles))
self.draw_obj.commands.clear()
for id, particle in enumerate(self.particles):
particle.step(speed)
particle.draw()
new_tree.insert(particle.location, id)
new_tree.balance()
self.kd_tree = new_tree
def spread_step(self):
count = 0
new_particles = []
self.draw_obj.commands.clear()
for particle in self.particles:
new_particles += particle.spread()
count += len(new_particles)
for particle in self.particles:
if not particle.tag == "REMOVE":
new_particles.append(particle)
self.particles = new_particles
new_tree = KDTree(len(self.particles))
for id, particle in enumerate(self.particles):
particle.draw()
new_tree.insert(particle.location, id)
new_tree.balance()
self.kd_tree = new_tree
return count
def get_nearest(self, location, n):
for location, index, dist in self.kd_tree.find_n(location, n):
yield self.particles[index], dist
def sample_surface(self, location):
return self.field.sample_point(location)
def draw(self):
self.draw_obj.commands.clear()
for particle in self.particles:
particle.draw()
def simplify_mesh(self, bm):
class Ownership:
def __init__(self, particle, dist):
self.particle = particle
self.distance = dist
self.valid = False
bmesh.ops.triangulate(bm, faces=bm.faces)
last_edges = float("+inf")
while True:
edges = set()
for edge in bm.edges:
le = (edge.verts[0].co - edge.verts[1].co).length_squared
center = edge.verts[0].co + edge.verts[1].co
center /= 2
for p, dist in self.get_nearest(center, 1):
if p.radius**2 < le:
edges.add(edge)
if not len(edges) < last_edges:
break
last_edges = len(edges)
bmesh.ops.subdivide_edges(bm, edges=list(edges), cuts=1)
bmesh.ops.triangulate(bm, faces=bm.faces)
bm.faces.ensure_lookup_table()
bm.verts.ensure_lookup_table()
tree = KDTree(len(bm.verts))
for vert in bm.verts:
tree.insert(vert.co, vert.index)
tree.balance()
ownership_mapping = {}
ownership_validation_front = set()
for vert in bm.verts:
for p, dist in self.get_nearest(vert.co, 1):
ownership_mapping[vert] = Ownership(p, dist)
for particle in self.particles:
location, index, dist = tree.find(particle.location)
vert = bm.verts[index]
if vert in ownership_mapping:
if ownership_mapping[vert].particle == particle:
ownership_mapping[vert].valid = True
ownership_validation_front.add(vert)
while True:
new_front = set()
for vert in ownership_validation_front:
for edge in vert.link_edges:
other_vert = edge.other_vert(vert)
if other_vert not in ownership_mapping:
continue
if ownership_mapping[other_vert].valid:
continue
if other_vert in ownership_mapping:
if ownership_mapping[
vert].particle is ownership_mapping[
other_vert].particle:
new_front.add(other_vert)
ownership_mapping[other_vert].valid = True
ownership_validation_front = new_front
if not new_front:
break
new_bm = bmesh.new()
for particle in self.particles:
particle.vert = new_bm.verts.new(particle.location)
for face in bm.faces:
connections = set()
for vert in face.verts:
if vert in ownership_mapping:
if ownership_mapping[vert].valid:
p = ownership_mapping[vert].particle
connections.add(p)
if len(connections) == 3:
try:
new_bm.faces.new(
[particle.vert for particle in connections])
except ValueError:
pass
while True:
stop = True
for vert in new_bm.verts:
if len(vert.link_edges) < 3:
new_bm.verts.remove(vert)
stop = False
if stop:
break
bmesh.ops.holes_fill(new_bm, edges=new_bm.edges)
bmesh.ops.triangulate(new_bm, faces=new_bm.faces)
bmesh.ops.recalc_face_normals(new_bm, faces=new_bm.faces)
if not self.triangle_mode:
bmesh.ops.join_triangles(new_bm,
faces=new_bm.faces,
angle_face_threshold=1.0,
angle_shape_threshold=3.14)
return new_bm
class Particle_system:
def __init__(self, guide, ground, scale):
self.GUIDE_STRENGTH = 1.0 * scale
self.TURBULENCE_FREQUENCY = 10 * scale
self.TURBULENCE_STRENGTH = 1.0 * scale
self.AVOID_THRESHOLD = 0.01 * scale
self.AVOID_STRENGTH = 0.2 * scale
self.frame = 0
self.particles = []
self.guide = guide
# self.vertex_distance = (self.guide.data.vertices[0].co - self.guide.data.vertices[1].co).length_squared
self.guide_tree = KDTree(len(self.guide.data.vertices))
for v in self.guide.data.vertices:
self.guide_tree.insert(v.co, v.index)
self.guide_tree.balance()
self.ground = ground
self.scale = scale
# bpy.ops.mesh.primitive_ico_sphere_add(location=(0,0,0), size=0.01)
# self.instance_obj = bpy.context.object
# self.instance_obj = bpy.data.objects['Fleche']
self.instance_obj = bpy.data.objects[bpy.context.scene.ant_instance]
self.instance_mesh = self.instance_obj.data
# self.instance_mesh.materials.append(bpy.data.materials['noir'])
def add_particles(self, particles_number):
'''Add a new particle to the system'''
for p in range(particles_number):
ind = randint(1, len(self.guide.data.vertices)-2)
self.particles.append(Particle(ind, self.scale, self.guide.data.vertices[ind].co))
def kill_particle(self, part):
self.particles.remove(part)
def create_tree(self):
self.parts_tree = KDTree(len(self.particles))
for i, p in enumerate(self.particles):
self.parts_tree.insert(p.location, i)
self.parts_tree.balance()
def step(self):
'''Simulate next frame'''
self.frame += 1
self.create_tree()
for part in self.particles:
if part.active:
previous_velocity = part.velocity.copy()
#guide vector
guide_vector = self.guide.data.vertices[part.guide_index].co - part.location
guide_vector = guide_vector.normalized() * self.GUIDE_STRENGTH
#turbulence vector
turbulence = noise.turbulence_vector(part.noise_seed+part.location, 2, False, 1, self.TURBULENCE_STRENGTH, self.TURBULENCE_FREQUENCY)
# part.noise_seed += turbulence / 50
# if part.velocity.length_squared < 0.0001:
# part.noise_seed = noise.random_unit_vector()
part.noise_seed.z += 0.01
#boid-like vector
too_close = self.parts_tree.find_range(part.location, self.AVOID_THRESHOLD)
avoid_vector = Vector()
for p in too_close:
other_vec = part.location - p[0]
if other_vec.length_squared < 0.0001:
continue
other_vec /= other_vec.length
avoid_vector += other_vec
# avoid_vector.normalize()
# avoid_vector -= part.velocity
avoid_vector *= self.AVOID_STRENGTH
#velocity change
part.velocity += avoid_vector
part.velocity += turbulence * (1.0-part.behaviour)
part.velocity += guide_vector * part.behaviour
#limit velocity (drag and shit)
if part.velocity.length > part.MAX_VEL:
part.velocity.length = part.MAX_VEL
# limit rotation
rotation_scalar = previous_velocity.dot(part.velocity) * 0.5 + 0.5 # normalized 0-1
# rotation_scalar **= 3
if rotation_scalar > 0.1:
rotation_scalar = 0.1
# rotation_scalar = 0
part.velocity *= (rotation_scalar)
part.velocity += previous_velocity * (1-rotation_scalar)
# put that shit on the ground
closest = self.ground.closest_point_on_mesh(part.location)
part.location = closest[0]
# velocity parallel to the ground
vel_norm = part.velocity.length
inter = part.velocity.cross(closest[1])
part.velocity = closest[1].cross(inter)
part.velocity.length = vel_norm
# print(part.velocity)
# SET NEW LOCATION
part.location += part.velocity
# behaviour change
part.behaviour += random()*0.1-0.05
if part.behaviour < 0.8:
part.behaviour = 0.8
if part.behaviour > 0.9:
part.behaviour = 0.9
# # set goal to next vertex if close enough
pt, ind, dist = self.guide_tree.find(part.location)
if fabs(ind - part.guide_index) < 2:
part.guide_index += part.direction
# if self.frame % 20 == 0:
# part.guide_index += part.direction
# if next_point_distance.length_squared < self.vertex_distance:
# part.guide_index += 1
# switch direction if end reached
if part.guide_index >= len(self.guide.data.vertices)-1 or part.guide_index == 1:
# part.active = False
# self.kill_particle(part)
part.direction = -part.direction
part.guide_index += part.direction
self.create_frame(self.frame)
def create_frame(self, frame):
'''
For each frame:
- create a new instance of the object to duplicate (eg. a sphere)
- get a list of vertices from particles' positions
- create a new generator objects, use the vertex list to generate mesh
- this object will be used for duplication
- parent the object to duplicate to the generator object
- animate the visibility of both objects
'''
instance_obj_frame = bpy.data.objects.new('instance_{:05}'.format(frame), self.instance_mesh)
bpy.context.scene.objects.link(instance_obj_frame)
vertices = [(p.location, p.velocity) for p in self.particles]
generator_mesh = bpy.data.meshes.new('generator_{:05}'.format(frame))
# generator_mesh.from_pydata(vertices, [], [])
## Track to camera
# cam = bpy.context.scene.camera
for v in vertices:
generator_mesh.vertices.add(1)
generator_mesh.vertices[-1].co = v[0]
generator_mesh.vertices[-1].normal = v[1]
# generator_mesh.vertices[-1].normal = cam.location - v
generator_obj = bpy.data.objects.new('generator_{:05}'.format(frame), generator_mesh)
bpy.context.scene.objects.link(generator_obj)
instance_obj_frame.parent = generator_obj
generator_obj.dupli_type = "VERTS"
generator_obj.use_dupli_vertices_rotation = True
#anim
generator_obj.keyframe_insert('hide', frame=frame)
generator_obj.keyframe_insert('hide_render', frame=frame)
generator_obj.hide = True
generator_obj.hide_render = True
generator_obj.keyframe_insert('hide', frame=frame+1)
generator_obj.keyframe_insert('hide_render', frame=frame+1)
generator_obj.keyframe_insert('hide', frame=frame-1)
generator_obj.keyframe_insert('hide_render', frame=frame-1)
def create_tree(self):
self.parts_tree = KDTree(len(self.particles))
for i, p in enumerate(self.particles):
self.parts_tree.insert(p.location, i)
self.parts_tree.balance()
def initialize(self):
self.frozen = True
nodes = self.nodes
tree = KDTree(len(nodes))
for i, node in enumerate(nodes):
tree.insert(node.point, i)
tree.balance()
processed = set()
final_nodes = []
groups = []
for i in range(len(nodes)):
if i in processed:
continue
# Find points to merge
pending = [i]
merge_set = set(pending)
while pending:
added = set()
for j in pending:
for co, idx, dist in tree.find_range(
nodes[j].point, self.epsilon):
added.add(idx)
pending = added.difference(merge_set)
merge_set.update(added)
assert merge_set.isdisjoint(processed)
processed.update(merge_set)
# Group the points
merge_list = [nodes[i] for i in merge_set]
merge_list.sort(key=lambda x: x.name)
group_class = merge_list[0].group_class
for item in merge_list[1:]:
cls = item.group_class
if issubclass(cls, group_class):
group_class = cls
elif not issubclass(group_class, cls):
raise MetarigError(
'Group class conflict: {} and {} from {} of {}'.format(
group_class,
cls,
item.name,
item.rig.base_bone,
))
group = group_class(merge_list)
group.build(final_nodes)
groups.append(group)
self.final_nodes = self.rigify_sub_objects = final_nodes
self.groups = groups
class BoundaryAlignedRemesher:
def get_hold_edges(self, obj):
sc = bpy.context.scene
props = sc.ba_remesh
split_edge_l = []
# create layer
if props.use_edge_bevel_weight:
if self.bm.edges.layers.bevel_weight:
bevelweight_Layer = self.bm.edges.layers.bevel_weight.verify()
if props.use_edge_crease:
if self.bm.edges.layers.crease:
crease_Layer = self.bm.edges.layers.crease.verify()
if props.use_edge_freestyle:
if self.bm.edges.layers.freestyle:
freestyle_Layer = self.bm.edges.layers.freestyle.verify()
# find edge
for edge in self.bm.edges:
# 選択
if props.use_edge_select:
if edge.select:
split_edge_l.append(edge)
# 角度
if props.use_edge_angle:
try:
if math.degrees(
edge.calc_face_angle()) >= props.edge_angle:
split_edge_l.append(edge)
except:
pass
# シーム
if props.use_edge_seam:
if edge.seam:
split_edge_l.append(edge)
# シャープ
if props.use_edge_sharp:
if not edge.smooth: # sharp
split_edge_l.append(edge)
# ベベルウェイト
if props.use_edge_bevel_weight:
if self.bm.edges.layers.bevel_weight:
if edge[bevelweight_Layer]:
split_edge_l.append(edge)
# クリース
if props.use_edge_crease:
if self.bm.edges.layers.crease:
if edge[crease_Layer]:
split_edge_l.append(edge)
# Freestyle
if props.use_edge_freestyle:
if self.bm.edges.layers.freestyle:
if edge[freestyle_Layer]:
split_edge_l.append(edge)
# 重複を削除
new_split_edge_l = []
for i in split_edge_l:
if not i in new_split_edge_l:
new_split_edge_l.append(i)
return new_split_edge_l
def split_feature_edges(self, obj):
new_split_edge_l = self.get_hold_edges(obj)
if new_split_edge_l:
bmesh.ops.split_edges(self.bm, edges=new_split_edge_l)
def __init__(self, obj):
self.obj = object
self.bm = bmesh.new()
self.bm.from_mesh(obj.data)
self.bvh = BVHTree.FromBMesh(self.bm)
# ホールドエッジ
self.split_feature_edges(obj)
# 開いたエッジのガイド
# Boundary_data is a list of directions and locations of boundaries.
# This data will serve as guidance for the alignment
self.boundary_data = []
# Fill the data using boundary edges as source of directional data.
for edge in self.bm.edges:
if edge.is_boundary:
vec = (edge.verts[0].co - edge.verts[1].co).normalized()
center = (edge.verts[0].co + edge.verts[1].co) / 2
self.boundary_data.append((center, vec))
# Create a Kd Tree to easily locate the nearest boundary point
self.boundary_kd_tree = KDTree(len(self.boundary_data))
for index, (center, vec) in enumerate(self.boundary_data):
self.boundary_kd_tree.insert(center, index)
self.boundary_kd_tree.balance()
def nearest_boundary_vector(self, location):
""" Gets the nearest boundary direction """
location, index, dist = self.boundary_kd_tree.find(location)
location, vec = self.boundary_data[index]
return vec
def enforce_edge_length(self, edge_length=0.05, bias=0.333):
""" Replicates dyntopo behaviour """
upper_length = edge_length + edge_length * bias
lower_length = edge_length - edge_length * bias
# Subdivide Long edges
subdivide = []
for edge in self.bm.edges:
if edge.calc_length() > upper_length:
subdivide.append(edge)
bmesh.ops.subdivide_edges(self.bm, edges=subdivide, cuts=1)
bmesh.ops.triangulate(self.bm, faces=self.bm.faces)
# Remove verts with less than 5 edges, this helps inprove mesh quality
dissolve_verts = []
for vert in self.bm.verts:
if len(vert.link_edges) < 5:
if not vert.is_boundary:
dissolve_verts.append(vert)
bmesh.ops.dissolve_verts(self.bm, verts=dissolve_verts)
bmesh.ops.triangulate(self.bm, faces=self.bm.faces)
# 外側エッジを固定
# Collapse short edges but ignore boundaries and never collapse two chained edges
lock_verts = set(vert for vert in self.bm.verts if vert.is_boundary)
collapse = []
for edge in self.bm.edges:
if edge.calc_length() < lower_length and not edge.is_boundary:
verts = set(edge.verts)
if verts & lock_verts:
continue
collapse.append(edge)
lock_verts |= verts
bmesh.ops.collapse(self.bm, edges=collapse)
bmesh.ops.beautify_fill(self.bm, faces=self.bm.faces, method="ANGLE")
def align_verts(self, rule=(-1, -2, -3, -4)):
# Align verts to the nearest boundary by averaging neigbor vert locations selected
# by a specific rule,
# Rules work by sorting edges by angle relative to the boundary.
# Eg1. (0, 1) stands for averagiing the biggest angle and the 2nd biggest angle edges.
# Eg2. (-1, -2, -3, -4), averages the four smallest angle edges
for vert in self.bm.verts:
if not vert.is_boundary:
vec = self.nearest_boundary_vector(vert.co)
neighbor_locations = [
edge.other_vert(vert).co for edge in vert.link_edges
]
best_locations = sorted(
neighbor_locations,
key=lambda n_loc: abs(
(n_loc - vert.co).normalized().dot(vec)))
co = vert.co.copy()
le = len(vert.link_edges)
for i in rule:
co += best_locations[i % le]
co /= len(rule) + 1
co -= vert.co
co -= co.dot(vert.normal) * vert.normal
vert.co += co
def reproject(self):
""" Recovers original shape """
for vert in self.bm.verts:
location, normal, index, dist = self.bvh.find_nearest(vert.co)
if location:
vert.co = location
def remesh(self, edge_length=0.05, iterations=30, quads=True):
""" Coordenates remeshing """
if quads:
rule = (-1, -2, 0, 1)
else:
rule = (0, 1, 2, 3)
for _ in range(iterations):
self.enforce_edge_length(edge_length=edge_length)
try:
self.align_verts(rule=rule)
except:
pass
self.reproject()
if quads:
bmesh.ops.join_triangles(self.bm,
faces=self.bm.faces,
angle_face_threshold=3.14,
angle_shape_threshold=3.14)
bmesh.ops.remove_doubles(self.bm, verts=self.bm.verts, dist=0.001)
return self.bm
def getDefaultValue(cls):
kdTree = KDTree(0)
kdTree.balance()
return kdTree
def __init__(self,
source_bm,
target_bm=None,
max_springs=300,
x_mirror=False,
immediate_edges_max=6):
self.max_springs = max_springs
self.immediate_edges_max = immediate_edges_max
self.bm = source_bm
self.target_bm = target_bm
self.n = len(source_bm.verts)
self.co = np.array(list(tuple(v.co) for v in source_bm.verts),
dtype=np.float64)
self.last_co = self.co.copy()
self.springs = np.zeros((self.n, max_springs), dtype=np.int64)
self.immediate_edges = np.full((self.n, immediate_edges_max),
-1,
dtype=np.int64)
self.lengths = np.zeros((self.n, max_springs), dtype=np.float64)
self.sizing = 1
self.pins = []
self.out_cache = DummyObj()
if target_bm:
target_bm.faces.ensure_lookup_table()
self.bvh = BVHTree.FromBMesh(target_bm)
else:
self.bvh = None
source_bm.verts.ensure_lookup_table()
source_bm.faces.ensure_lookup_table()
if x_mirror:
self.mirror_table = np.full((self.n, ), -1, dtype=np.int64)
self.x_mirr = True
kd = KDTree(self.n)
for vert in source_bm.verts:
kd.insert(vert.co, vert.index)
kd.balance()
else:
self.x_mirr = False
self._mirror_table = None
for vert in source_bm.verts:
for j, edge in enumerate(vert.link_edges):
if not j < immediate_edges_max:
break
other = edge.other_vert(vert)
if not vert.is_boundary or other.is_boundary == vert.is_boundary:
self.immediate_edges[vert.index, j] = other.index
for j, other in enumerate(n_ring(vert, self.max_springs)):
self.springs[vert.index, j] = other.index
self.lengths[vert.index, j] = (other.co - vert.co).length
if self.x_mirr:
co = vert.co.copy()
co.x *= -1
mirrco, mirri, dist = kd.find(co)
self.mirror_table[vert.index] = mirri
self.immediate_edges_invalid_places = self.immediate_edges == -1
self.immediate_edges_number = (
immediate_edges_max -
self.immediate_edges_invalid_places.sum(axis=1))
def execute(self, context):
source = context.object
try:
sdata = source.data
sgeom = sdata.polygons
except AttributeError:
self.report(
{'ERROR_INVALID_INPUT'},
"The active object needs to have a mesh data block.",
)
return {'CANCELLED'}
# get comparison values for source
scvals = get_vecs(sgeom, 'center')
if self.in_wrld_crds:
# transform source to world coordinates
mat = np.array(source.matrix_world)
scvals = transf_pts(mat, scvals)
# build KD-Tree from comparison values
kd = KDTree(len(sgeom))
for i, v in enumerate(scvals):
kd.insert(v, i)
kd.balance()
# get values to transfer from source
stvals = get_scalars(sgeom, 'material_index', np.int8)
all_meshless = True # for error-reporting
for target in context.selected_objects:
if target is source:
continue
try:
tdata = target.data
tgeom = tdata.polygons
all_meshless = False
except AttributeError:
continue
# get comparison values for target
tcvals = get_vecs(tgeom, 'center')
if self.in_wrld_crds:
# transform target to world coordinates
mat = np.array(target.matrix_world)
tcvals = transf_pts(mat, tcvals)
ttvals = np.empty(len(tgeom), dtype=np.int32)
# for every comparison point in target, find closest in
# source and copy over its transfer value
for ti, tv in enumerate(tcvals):
_, si, _ = kd.find(tv)
ttvals[ti] = stvals[si]
# set values to transfer to target
set_vals(tgeom, ttvals, 'material_index')
tmats = tdata.materials
if self.assign_mat:
# transfer assigned materials
for i, m in enumerate(sdata.materials):
if i < len(tmats):
tmats[i] = m
else:
tmats.append(m)
if all_meshless:
self.report(
{'ERROR_INVALID_INPUT'},
"No selected target object has a mesh data block.",
)
return {'CANCELLED'}
return {'FINISHED'}
class BoundaryAlignedRemesher:
def __init__(self, obj):
self.obj = obj
mode = obj.mode
self.edit_mode = mode == 'EDIT'
# hack to update the mesh data
bpy.ops.object.mode_set(mode='OBJECT')
bpy.ops.object.mode_set(mode=mode)
self.bm = bmesh.new()
self.bm.from_mesh(obj.data)
self.bm1 = None
if self.edit_mode:
self.bm1 = self.bm.copy()
remove, remove1 = [], []
for vert in self.bm.verts:
if all(not f.select for f in vert.link_faces):
remove.append(vert)
for vert in remove:
self.bm.verts.remove(vert)
for vert in self.bm1.verts:
if all(v.select for v in vert.link_faces):
remove1.append(vert)
for vert in remove1:
self.bm1.verts.remove(vert)
remove1 = [f for f in self.bm1.faces if f.select]
for face in remove1:
self.bm1.faces.remove(face)
self.bvh = BVHTree.FromBMesh(self.bm)
# Boundary_data is a list of directions and locations of boundaries.
# This data will serve as guidance for the alignment
self.boundary_data = []
# Fill the data using boundary edges as source of directional data.
for edge in self.bm.edges:
if edge.is_boundary:
vec = (edge.verts[0].co - edge.verts[1].co).normalized()
center = (edge.verts[0].co + edge.verts[1].co) / 2
self.boundary_data.append((center, vec))
# Create a Kd Tree to easily locate the nearest boundary point
self.boundary_kd_tree = KDTree(len(self.boundary_data))
for index, (center, vec) in enumerate(self.boundary_data):
self.boundary_kd_tree.insert(center, index)
self.boundary_kd_tree.balance()
def nearest_boundary_vector(self, location):
""" Gets the nearest boundary direction """
location, index, dist = self.boundary_kd_tree.find(location)
location, vec = self.boundary_data[index]
return vec
def enforce_edge_length(self, edge_length=0.05, bias=0.333):
""" Replicates dyntopo behavior """
upper_length = edge_length + edge_length * bias
lower_length = edge_length - edge_length * bias
# Subdivide Long edges
subdivide = []
for edge in self.bm.edges:
if edge.calc_length() > upper_length:
subdivide.append(edge)
bmesh.ops.subdivide_edges(self.bm, edges=subdivide, cuts=1)
bmesh.ops.triangulate(self.bm, faces=self.bm.faces)
if self.edit_mode:
subdivide = []
for edge in self.bm1.edges:
if edge.select and edge.calc_length() > upper_length:
subdivide.append(edge)
bmesh.ops.subdivide_edges(self.bm1, edges=subdivide, cuts=1)
# Remove verts with less than 5 edges, this helps inprove mesh quality
dissolve_verts = []
for vert in self.bm.verts:
if len(vert.link_edges) < 5:
if not vert.is_boundary:
dissolve_verts.append(vert)
bmesh.ops.dissolve_verts(self.bm, verts=dissolve_verts)
bmesh.ops.triangulate(self.bm, faces=self.bm.faces)
# Collapse short edges but ignore boundaries and never collapse two chained edges
lock_verts = set(vert for vert in self.bm.verts if vert.is_boundary)
collapse = []
for edge in self.bm.edges:
if edge.calc_length() < lower_length and not edge.is_boundary:
verts = set(edge.verts)
if verts & lock_verts:
continue
collapse.append(edge)
lock_verts |= verts
bmesh.ops.collapse(self.bm, edges=collapse)
bmesh.ops.beautify_fill(self.bm, faces=self.bm.faces, method="ANGLE")
def align_verts(self, rule=(-1, -2, -3, -4)):
# Align verts to the nearest boundary by averaging neigbor vert locations selected
# by a specific rule,
# Rules work by sorting edges by angle relative to the boundary.
# Eg1. (0, 1) stands for averagiing the biggest angle and the 2nd biggest angle edges.
# Eg2. (-1, -2, -3, -4), averages the four smallest angle edges
for vert in self.bm.verts:
if not vert.is_boundary:
# min_edge = min(vert.link_edges, key=lambda e: e.calc_length())
# other = min_edge.other_vert(vert)
# vec = other.co - vert.co
# vert.co -= vec * 0.1
#
vec = self.nearest_boundary_vector(vert.co)
neighbor_locations = [
edge.other_vert(vert).co for edge in vert.link_edges
]
best_locations = sorted(
neighbor_locations,
key=lambda n_loc: abs(
(n_loc - vert.co).normalized().dot(vec)))
co = vert.co.copy()
le = len(vert.link_edges)
for i in rule:
co += best_locations[i % le]
co /= len(rule) + 1
co -= vert.co
co -= co.dot(vert.normal) * vert.normal
vert.co += co
self.reproject()
def reproject(self):
""" Recovers original shape """
for vert in self.bm.verts:
if vert.is_boundary:
continue
location, normal, index, dist = self.bvh.find_nearest(vert.co)
if location:
vert.co = location
def remesh(self, edge_length=0.05, iterations=30, quads=True):
""" Coordenates remeshing """
if self.edit_mode:
bpy.ops.object.mode_set(mode='OBJECT')
if quads:
rule = (-1, -2, 0, 1)
else:
rule = (0, 1, 2, 3)
for _ in range(iterations):
self.enforce_edge_length(edge_length=edge_length)
self.align_verts(rule=rule)
self.reproject()
if quads:
bmesh.ops.join_triangles(self.bm,
faces=self.bm.faces,
angle_face_threshold=3.14,
angle_shape_threshold=3.14)
for vert in self.bm.verts:
vert.select = True
for face in self.bm.faces:
face.select = True
if self.bm1:
self.bm1.to_mesh(self.obj.data)
self.bm.from_mesh(self.obj.data)
bmesh.ops.remove_doubles(
self.bm,
verts=[v for v in self.bm.verts if v.select],
dist=0.00001)
self.bm.to_mesh(self.obj.data)
if self.edit_mode:
bpy.ops.object.mode_set(mode='EDIT')
def execute(self, context):
C = context
me = C.object.to_mesh(C.scene,
apply_modifiers=True,
settings='PREVIEW')
bme = bmesh.new()
bme.from_mesh(me)
bme.verts.ensure_lookup_table()
bme.edges.ensure_lookup_table()
#verts in order
loops = edge_loops_from_bmedges(bme, [ed.index for ed in bme.edges])
if len(loops) > 1:
print('need a single loop')
loop = loops[0]
loop.pop() #cyclic
#don't need that one any more
coords = [bme.verts[i].co for i in loop]
spaced_verts, spaced_eds = space_evenly_on_path(
coords, [(0, 1), (1, 0)], 300)
bme.free()
print(len(spaced_verts))
#build our search tree
kd = KDTree(len(spaced_verts))
for i, v in enumerate(spaced_verts):
kd.insert(v, i)
kd.balance()
bme2 = bmesh.new()
bme2.verts.ensure_lookup_table()
bme2.edges.ensure_lookup_table()
for v in spaced_verts:
bme2.verts.new(v)
bme2.verts.ensure_lookup_table()
bme2.edges.ensure_lookup_table()
for ed in spaced_eds:
v0, v1 = ed
bme2.edges.new((bme2.verts[v0], bme2.verts[v1]))
bme2.verts.index_update()
bme2.verts.ensure_lookup_table()
bme2.edges.index_update()
bme2.edges.ensure_lookup_table()
loops = edge_loops_from_bmedges(bme2, [ed.index for ed in bme2.edges])
loop = loops[0]
loop.pop()
def euc_dist(v1, v2):
return (v1.co - v2.co).length
def split_loop(vert_loop_inds):
best_pairs = {}
def geo_dist(v1, v2):
N = len(vert_loop_inds)
n = vert_loop_inds.index(v1.index)
m = vert_loop_inds.index(v2.index)
return min(math.fmod(N + m - n, N), math.fmod(N + n - m, N))
for i in vert_loop_inds:
v1 = bme2.verts[i]
pfactor = 0
match = None
link_verts = [ed.other_vert(v1) for ed in v1.link_edges]
for loc, ind, dist in kd.find_range(v1.co, self.max_edge):
if ind == i: continue #prevent divide by 0
if ind not in vert_loop_inds:
continue #filter by this loop
v2 = bme2.verts[ind]
if v2 in link_verts: continue #prevent neighbors
fac = geo_dist(v1, v2) / euc_dist(v1, v2)
if fac > pfactor: #if a better match is found, keep it
pfactor = fac
match = v2
best_pairs[v1] = (match, pfactor)
vs = [bme2.verts[i] for i in vert_loop_inds]
v1 = max(vs, key=lambda x: best_pairs[x][1])
#connect the best pair
v2, pfactor = best_pairs[v1]
try:
#split the index loop into 2
ind1 = min(vert_loop_inds.index(v1.index),
vert_loop_inds.index(v2.index))
ind2 = max(vert_loop_inds.index(v1.index),
vert_loop_inds.index(v2.index))
print('splitting loop at ind1: %i and ind2: %i' % (ind1, ind2))
print('creating edge between vert: %i and vert: %i' %
(v1.index, v2.index))
loop0 = vert_loop_inds[ind1:ind2 + 1]
loop1 = vert_loop_inds[0:ind1 + 1] + vert_loop_inds[ind2:]
print(vert_loop_inds)
print('\n')
print(loop0)
print('\n')
print(loop1)
bme2.edges.new((v1, v2))
bme2.verts.ensure_lookup_table()
bme2.edges.ensure_lookup_table()
return loop0, loop1
except:
print('cant add edge between vert: %i and vert: %i' %
(v1.index, v2.index))
return vert_loop_inds, []
loops = [loop]
for n in range(0, self.n_partitions):
print('\n')
print('PARTITION # %i' % (n + 1))
biggest_loop = max(loops, key=len)
if len(biggest_loop) < 20:
break
loop1, loop2 = split_loop(biggest_loop)
if loop2 != []:
loops.remove(biggest_loop)
loops += [loop1, loop2]
else:
break
new_faces = []
bme2.faces.ensure_lookup_table()
for loop in loops:
new_faces.append(bme2.faces.new([bme2.verts[i] for i in loop]))
bme2.faces.ensure_lookup_table()
bmesh.ops.triangulate(bme2, faces=new_faces)
new_ob = bpy.data.objects.new('Partitioned', me)
C.scene.objects.link(new_ob)
bme2.to_mesh(me)
bme2.free()
return {'FINISHED'}
def repeal_particles(self, iterations=20, factor=0.01):
particles = list(self.particles)
tree = KDTree(len(particles))
for index, particle in enumerate(particles):
tree.insert(particle.co, index)
tree.balance()
for i in range(iterations):
new_tree = KDTree(len(self.particles))
for index, particle in enumerate(particles):
if particle.tag in {"SHARP", "GREASE"}:
continue
d = Vector()
for loc, other_index, dist in tree.find_n(particle.co, 3):
if dist == 0:
continue
other = particles[other_index]
vec = particle.co - other.co
d += (vec / (dist ** 3))
if not self.triangle_mode:
u = particle.dir
v = u.cross(particle.normal)
for vec in (u + v, u - v, -u + v, -u - v):
vec *= particle.radius
vec += other.co
vec -= particle.co
dist = vec.length
d -= vec * 0.3 / (dist ** 3)
d.normalize()
location, normal, dir, s, c = self.field.sample_point(particle.co + (d * factor * particle.radius))
if location:
particle.co = location
particle.normal = normal
self.grid.update(particle)
particle.dir = dir
new_tree.insert(particle.co, index)
new_tree.balance()
tree = new_tree
yield i
def buildKDTree(self, points):
kdTree = KDTree(len(points))
for i, vector in enumerate(points):
kdTree.insert(vector, i)
kdTree.balance()
return kdTree
