在 3d numpy 数组中查找 k 个最近邻

Question

So I'm trying to find the k nearest neighbors in a pyvista numpy array from an example mesh. With the neighbors received, I want to implement some region growing in my 3d model.

But unfortunaley I receive some weird output, which you can see in the following picture. It seems like I'm missing something on the KDTree implementation. I was following the answer on a similar question: 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

You're almost there:) The problem is that you are using the points in the mesh to build the tree, but then extracting cells . Of course these are unrelated in the sense that indices for points will give you nonsense when applied as indices of cells.

Either you have to 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()

Or you have to work with cell centers to begin with:

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()

As you can see, the two results differ, since index 5000 (which we used for the reference point) means something else when indexing points or when indexing cells.