[英]How to create an increasing, multi-dimensional array in numpy
I am trying to perform inverse warping given a homography matrix, and to do that efficiently I need a numpy array that looks like this: 我试图在给出单应矩阵的情况下执行逆变形,为了有效地做到这一点,我需要一个看起来像这样的numpy数组:
([[0, 0, 1], [0, 1, 1], [0, 2, 1], ... [1, 0, 1], [1, 1, 1], ... [n, p, 1]])
Where n is an image's width ( im.shape[0]
) and p is the image's height ( im.shape[1]
). 其中n是图像的宽度(
im.shape[0]
),p是图像的高度( im.shape[1]
)。 Any idea on how to efficiently construct numpy arrays that look like that? 关于如何有效地构建看起来那样的numpy数组的任何想法?
Edit: 编辑:
There is some discussion on which is the fastest, if anyone has any info on that I think it'd be interesting to hear. 有一些讨论哪个是最快的,如果有人有任何信息,我认为听起来很有趣。 I appreciate everyone's help!
我感谢大家的帮助!
Using indices_merged_arr_generic_using_cp
by @unutbu - 使用
indices_merged_arr_generic_using_cp
的indices_merged_arr_generic_using_cp -
def indices_one_grid(n,p):
ar = np.ones((n,p),dtype=int)
return indices_merged_arr_generic_using_cp(ar)
Sample run - 样品运行 -
In [141]: indices_one_grid(n=3,p=4)
Out[141]:
array([[0, 0, 1],
[0, 1, 1],
[0, 2, 1],
[0, 3, 1],
[1, 0, 1],
[1, 1, 1],
[1, 2, 1],
[1, 3, 1],
[2, 0, 1],
[2, 1, 1],
[2, 2, 1],
[2, 3, 1]])
Other approaches - 其他方法 -
def MSeifert(n,p):
x, y = np.mgrid[:n, :p]
return np.stack([x.ravel(), y.ravel(), np.ones(x.size, dtype=int)], axis=1)
def DanielF(n,p):
return np.vstack([np.indices((n,p)), np.ones((1, n,p))]).reshape(3,-1).T
def Aaron(n,p):
arr = np.empty([n*p,3])
arr[:,0] = np.repeat(np.arange(n),p)
arr[:,1] = np.tile(np.arange(p),n)
arr[:,2] = 1
return arr
Timings - 计时 -
In [152]: n=1000;p=1000
In [153]: %timeit MSeifert(n,p)
...: %timeit DanielF(n,p)
...: %timeit Aaron(n,p)
...: %timeit indices_one_grid(n,p)
...:
100 loops, best of 3: 15.8 ms per loop
100 loops, best of 3: 8.46 ms per loop
100 loops, best of 3: 10.4 ms per loop
100 loops, best of 3: 4.78 ms per loop
You could use np.mgrid
to create the grid (first two entries of each subarray) with np.stack
to concatenate them: 你可以使用
np.mgrid
创建网格(每个子np.stack
前两个条目)和np.stack
来连接它们:
>>> x, y = np.mgrid[:3, :3] # assuming a 3x3 image
>>> np.stack([x.ravel(), y.ravel(), np.ones(x.size, dtype=int)], axis=1)
array([[0, 0, 1],
[0, 1, 1],
[0, 2, 1],
[1, 0, 1],
[1, 1, 1],
[1, 2, 1],
[2, 0, 1],
[2, 1, 1],
[2, 2, 1]])
In this case I used 3 as width and height but by altering the arguments for np.mgrid
you can change them. 在这种情况下,我使用3作为宽度和高度,但通过更改
np.mgrid
的参数,您可以更改它们。
In one line: 在一行中:
np.vstack([np.indices(im.shape), np.ones((1, *im.shape))]).reshape(3,-1).T
Basically, key to getting indices like this is using something like indices
, mgrid
/ meshgrid
or the like. 基本上,获得像这样的索引的关键是使用像
indices
, mgrid
/ meshgrid
之类的东西。
You can do this all without looping using numpy.tile
and numpy.repeat
and a pre-allocated container 你可以在不使用
numpy.tile
和numpy.repeat
以及预先分配的容器进行循环的情况下完成所有操作
import numpy as np
arr = np.empty([n*p,3])
arr[:,0] = np.repeat(np.arange(n),p)
arr[:,1] = np.tile(np.arange(p),n)
arr[:,2] = 1
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