numpy with python：将3d数组转换为2d

问题描述

假设我有一个彩色图像，当然这将由python中的三维数组表示，比如说形状（nxmx 3）并将其称为img。

Say that I have a color image, and naturally this will be represented by a 3-dimensional array in python, say of shape (n x m x 3) and call it img.

我想要一个新的2-d数组，将其称为narray以具有形状（3，nxm），使得该数组的每一行分别包含R，G和B通道的扁平版本。此外，它应该具有我可以轻松地重建任何原始频道的属性，例如

I want a new 2-d array, call it "narray" to have a shape (3,nxm), such that each row of this array contains the "flattened" version of R,G,and B channel respectively. Moreover, it should have the property that I can easily reconstruct back any of the original channel by something like

narray[0,].reshape(img.shape[0:2])    #so this should reconstruct back the R channel.

问题是如何从img构建narray？简单的img.reshape（3，-1）不起作用，因为元素的顺序对我来说是不可取的。

The question is how can I construct the "narray" from "img"? The simple img.reshape(3,-1) does not work as the order of the elements are not desirable for me.

谢谢

推荐答案

您需要使用重新排列维度。现在， nxmx 3 将被转换为 3 x（n * m），因此将最后一个轴发送到前面并向右移动剩余轴的顺序（0,1）。最后，重塑为 3 行。因此，实现将是 -

You need to use np.transpose to rearrange dimensions. Now, n x m x 3 is to be converted to 3 x (n*m), so send the last axis to the front and shift right the order of the remaining axes (0,1). Finally , reshape to have 3 rows. Thus, the implementation would be -

img.transpose(2,0,1).reshape(3,-1)

样品运行 -

In [16]: img
Out[16]:
array([[[155,  33, 129],
        [161, 218,   6]],

       [[215, 142, 235],
        [143, 249, 164]],

       [[221,  71, 229],
        [ 56,  91, 120]],

       [[236,   4, 177],
        [171, 105,  40]]])

In [17]: img.transpose(2,0,1).reshape(3,-1)
Out[17]:
array([[155, 161, 215, 143, 221,  56, 236, 171],
       [ 33, 218, 142, 249,  71,  91,   4, 105],
       [129,   6, 235, 164, 229, 120, 177,  40]])

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