在 3d numpy 数组中寻找 k 个最近的邻居

Question

所以我试图从示例网格中找到 pyvista numpy 数组中的 k 个最近邻居。收到邻居后，我想在我的 3d 模型中实现一些区域增长。

但不幸的是，我收到了一些奇怪的输出，您可以在下图中看到。似乎我在 KDTree 实施中遗漏了一些东西。我正在关注类似问题的答案：

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:)问题是你在网格中使用点来构建树，然后提取单元。当然，这些是不相关的，因为点的索引在用作单元格索引时会给你带来废话。

你要么 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()

或者您必须先与细胞中心合作：

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()

如您所见，这两个结果不同，因为索引 5000（我们用作参考点）在索引点或索引单元格时有其他含义。

在 3d numpy 数组中寻找 k 个最近的邻居

Finding k nearest neighbors in 3d numpy array

python

numpy

kdtree

pyvista