用numpy在边界框内查找点 [英] Finding points within a bounding box with numpy

问题描述

我从多个点云文件中获取了数百万个xyz坐标，这些文件存储在二维numpy数组中: [[x1，y1，z1]，[x2，y2，z2]，...，[xn，yn，zn]] .

I have millions of xyz-coordinates from multiple point cloud files which I am storing inside a 2-dimensional numpy array: [[x1, y1, z1], [x2, y2, z2],..., [xn, yn, zn]].

我要过滤由4个坐标 [[x1，y1]，[x2，y2]] 描述的特定边界框内的所有点，即矩形.

I want to filter all the points which are inside a specific bounding box described by 4 coordinates [[x1, y1], [x2, y2]] i.e. the lower left and upper right coordinates of a rectangle.

我已经找到以下代码来用numpy过滤坐标，这几乎是我想要的.唯一的区别是(如果我做对了)我的二维数组也具有z坐标.

I have already found the following piece of code to filter coordinates with numpy and it's almost what I want. The only difference is (if I'm getting it right) that my 2-dimensional array also has got z-coordinates.

import random
import numpy as np

points = [(random.random(), random.random()) for i in range(100)]

bx1, bx2 = sorted([random.random(), random.random()])
by1, by2 = sorted([random.random(), random.random()])

pts = np.array(points)
ll = np.array([bx1, by1])  # lower-left
ur = np.array([bx2, by2])  # upper-right

inidx = np.all(np.logical_and(ll <= pts, pts <= ur), axis=1)
inbox = pts[inidx]
outbox = pts[np.logical_not(inidx)]

我如何修改上面的代码以使其与xyz坐标一起工作，并通过两个xy坐标所描述的边界框对其进行过滤?

How would I have to modifiy the code above to make it work with xyz-coordinates to be filtered by a bounding box described with two xy-coordinates?

推荐答案

选择点的X和Y坐标:

xy_pts = pts[:,[0,1]]

现在，在比较中只需使用 xy_pts 而不是 pts :

Now, simply use xy_pts instead of pts in the comparisons:

inidx = np.all((ll <= xy_pts) & (xy_pts <= ur), axis=1)

这篇关于用numpy在边界框内查找点的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！