KDTree Utilities (mathutils.kdtree)¶
Generic 3-dimentional kd-tree to perform spatial searches.
import mathutils
# create a kd-tree from a mesh
from bpy import context
obj = context.object
# 3d cursor relative to the object data
co_find = context.scene.cursor_location * obj.matrix_world.inverted()
mesh = obj.data
size = len(mesh.vertices)
kd = mathutils.kdtree.KDTree(size)
for i, v in enumerate(mesh.vertices):
kd.insert(v.co, i)
kd.balance()
# Find the closest point to the center
co_find = (0.0, 0.0, 0.0)
co, index, dist = kd.find(co_find)
print("Close to center:", co, index, dist)
# Find the closest 10 points to the 3d cursor
print("Close 10 points")
for (co, index, dist) in kd.find_n(co_find, 10):
print(" ", co, index, dist)
# Find points within a radius of the 3d cursor
print("Close points within 0.5 distance")
co_find = context.scene.cursor_location
for (co, index, dist) in kd.find_range(co_find, 0.5):
print(" ", co, index, dist)
-
class
mathutils.kdtree.
KDTree
¶ KdTree(size) -> new kd-tree initialized to hold
size
items.Note
KDTree.balance
must have been called before using any of thefind
methods.-
balance
()¶ Balance the tree.
Note
This builds the entire tree, avoid calling after each insertion.
-
find
(co, filter=None)¶ Find nearest point to
co
.Parameters: - co (float triplet) – 3d coordinates.
- filter (callable) – function which takes an index and returns True for indices to include in the search.
Returns: Returns (
Vector
, index, distance).Return type: tuple
-
find_n
(co, n)¶ Find nearest
n
points toco
.Parameters: - co (float triplet) – 3d coordinates.
- n (int) – Number of points to find.
Returns: Returns a list of tuples (
Vector
, index, distance).Return type: list
-