在 3d numpy 數組中查找 k 個最近鄰

Question

所以我試圖從示例網格中找到 pyvista numpy 數組中的 k 個最近鄰居。 收到鄰居后，我想在我的 3d model 中實現一些增長的區域。

但不幸的是，我收到了一些奇怪的 output，您可以在下圖中看到。 似乎我在KDTree實現上遺漏了一些東西。 我正在關注類似問題的答案： https://stackoverflow.com/a/2486341/9812286

import numpy as np 
from sklearn.neighbors import KDTree

import pyvista as pv

from pyvista import examples

# Example dataset with normals
mesh = examples.load_random_hills()

smooth = mesh

NDIM = 3
X = smooth.points
point = X[5000]

tree = KDTree(X, leaf_size=X.shape[0]+1)
# ind = tree.query_radius([point], r=10) # indices of neighbors within distance 0.3
distances, ind = tree.query([point], k=1000)

p = pv.Plotter()
p.add_mesh(smooth)

ids = np.arange(smooth.n_points)[ind[0]]
top = smooth.extract_cells(ids)
random_color = np.random.random(3)
p.add_mesh(top, color=random_color)

p.show()

Answer 1

你快到了:) 問題是你正在使用網格中的點來構建樹，然后提取單元格。 當然，這些是無關的，因為點的索引在用作單元格的索引時會給你帶來廢話。

要么你必須extract_points ：

import numpy as np 
from sklearn.neighbors import KDTree

import pyvista as pv

from pyvista import examples

# Example dataset with normals
mesh = examples.load_random_hills()

smooth = mesh

NDIM = 3
X = smooth.points
point = X[5000]

tree = KDTree(X, leaf_size=X.shape[0]+1)
# ind = tree.query_radius([point], r=10) # indices of neighbors within distance 0.3
distances, ind = tree.query([point], k=1000)

p = pv.Plotter()
p.add_mesh(smooth)

ids = np.arange(smooth.n_points)[ind[0]]
top = smooth.extract_points(ids)  # changed here!
random_color = np.random.random(3)
p.add_mesh(top, color=random_color)

p.show()

或者您必須首先與細胞中心合作：

import numpy as np 
from sklearn.neighbors import KDTree

import pyvista as pv

from pyvista import examples

# Example dataset with normals
mesh = examples.load_random_hills()

smooth = mesh

NDIM = 3
X = smooth.cell_centers().points  # changed here!
point = X[5000]

tree = KDTree(X, leaf_size=X.shape[0]+1)
# ind = tree.query_radius([point], r=10) # indices of neighbors within distance 0.3
distances, ind = tree.query([point], k=1000)

p = pv.Plotter()
p.add_mesh(smooth)

ids = np.arange(smooth.n_points)[ind[0]]
top = smooth.extract_cells(ids)
random_color = np.random.random(3)
p.add_mesh(top, color=random_color)

p.show()

如您所見，這兩個結果不同，因為索引 5000（我們用於參考點）在索引點或索引單元格時意味着其他東西。