问题
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]]
.
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.
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)]
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?
回答1:
Select the X and Y coordinates of your points:
xy_pts = pts[:,[0,1]]
Now, simply use xy_pts
instead of pts
in the comparisons:
inidx = np.all((ll <= xy_pts) & (xy_pts <= ur), axis=1)
回答2:
I'm writing a Python library for working with point clouds and I have this function that I think that should work for you:
def bounding_box(points, min_x=-np.inf, max_x=np.inf, min_y=-np.inf,
max_y=np.inf, min_z=-np.inf, max_z=np.inf):
""" Compute a bounding_box filter on the given points
Parameters
----------
points: (n,3) array
The array containing all the points's coordinates. Expected format:
array([
[x1,y1,z1],
...,
[xn,yn,zn]])
min_i, max_i: float
The bounding box limits for each coordinate. If some limits are missing,
the default values are -infinite for the min_i and infinite for the max_i.
Returns
-------
bb_filter : boolean array
The boolean mask indicating wherever a point should be keeped or not.
The size of the boolean mask will be the same as the number of given points.
"""
bound_x = np.logical_and(points[:, 0] > min_x, points[:, 0] < max_x)
bound_y = np.logical_and(points[:, 1] > min_y, points[:, 1] < max_y)
bound_z = np.logical_and(points[:, 2] > min_z, points[:, 2] < max_z)
bb_filter = np.logical_and(np.logical_and(bound_x, bound_y), bound_z)
return bb_filter
Here is an example of what you are asking:
10 million points:
points = np.random.rand(10000000, 3)
Rectangle in the format you specify:
rectangle = np.array([[0.2, 0.2],
[0.4, 0.4]])
Unpack the rectangle:
min_x = rectangle[:,0].min()
max_x = rectangle[:,0].max()
min_y = rectangle[:,1].min()
max_y = rectangle[:,1].max()
Get boolean array marking points inside the box:
%%timeit
inside_box = bounding_box(points, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
1 loop, best of 3: 247 ms per loop
This way you can use the array as follows:
points_inside_box = points[inside_box]
points_outside_box = points[~inside_box]
来源:https://stackoverflow.com/questions/42352622/finding-points-within-a-bounding-box-with-numpy