37 lines
881 B
Python
37 lines
881 B
Python
import mathutils
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# create a kd-tree from a mesh
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from bpy import context
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obj = context.object
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# 3d cursor relative to the object data
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co_find = context.scene.cursor_location * obj.matrix_world.inverted()
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mesh = obj.data
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size = len(mesh.vertices)
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kd = mathutils.kdtree.KDTree(size)
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for i, v in enumerate(mesh.vertices):
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kd.insert(v.co, i)
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kd.balance()
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# Find the closest point to the center
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co_find = (0.0, 0.0, 0.0)
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co, index, dist = kd.find(co_find)
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print("Close to center:", co, index, dist)
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# Find the closest 10 points to the 3d cursor
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print("Close 10 points")
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for (co, index, dist) in kd.find_n(co_find, 10):
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print(" ", co, index, dist)
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# Find points within a radius of the 3d cursor
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print("Close points within 0.5 distance")
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co_find = context.scene.cursor_location
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for (co, index, dist) in kd.find_range(co_find, 0.5):
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print(" ", co, index, dist)
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