Numpy 从 np 数组中删除一个维度 [英] Numpy remove a dimension from np array

问题描述

我有一些我想处理的图像，问题是有两种图像都是 106 x 106 像素，一些是彩色的，一些是黑白的.

I have some images I want to work with, the problem is that there are two kinds of images both are 106 x 106 pixels, some are in color and some are black and white.

只有两 (2) 个维度:

one with only two (2) dimensions:

(106,106)

一与三 (3)

(106,106,3)

(106,106,3)

有没有办法去掉这最后一个维度?

Is there a way I can strip this last dimension?

我试过 np.delete，但似乎没有用.

I tried np.delete, but it did not seem to work.

np.shape(np.delete(Xtrain[0], [2] , 2))
Out[67]: (106, 106, 2)

推荐答案

您可以使用 numpy 的花哨索引(Python 内置切片符号的扩展):

You could use numpy's fancy indexing (an extension to Python's built-in slice notation):

x = np.zeros( (106, 106, 3) )
result = x[:, :, 0]
print(result.shape)

印刷品

(106, 106)

(106, 106, 3) 的形状意味着你有 3 组形状为 (106, 106) 的东西.因此，为了剥离"最后一个维度，您只需选择其中一个(这就是花哨的索引所做的).

A shape of (106, 106, 3) means you have 3 sets of things that have shape (106, 106). So in order to "strip" the last dimension, you just have to pick one of these (that's what the fancy indexing does).

你可以保留任何你想要的切片.我随意选择保留第 0 个，因为您没有指定您想要的内容.因此， result = x[:, :, 1] 和 result = x[:, :, 2] 也会给出所需的形状:这一切都取决于您需要保留哪个切片.

You can keep any slice you want. I arbitrarily choose to keep the 0th, since you didn't specify what you wanted. So, result = x[:, :, 1] and result = x[:, :, 2] would give the desired shape as well: it all just depends on which slice you need to keep.

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