如何用线性插值插值一个numpy数组 [英] how to interpolate a numpy array with linear interpolation

问题描述

我有一个numpy数组，其形状如下:(1、128、160、1).

I have a numpy array which has the following shape: (1, 128, 160, 1).

现在，我有一个图像，其形状为(200，200).

Now, I have an image which has the shape: (200, 200).

因此，我执行以下操作:

So, I do the following:

orig = np.random.rand(1, 128, 160, 1)
orig = np.squeeze(orig)

现在，我要做的是获取原始数组，并使用线性插值将其插值为与输入图像相同的大小，即(200, 200).我想我必须指定应在其上评估numpy数组的网格，但我无法弄清楚如何做到这一点.

Now, what I want to do is take my original array and interpolate it to be of the same size as the input image i.e. (200, 200) using linear interpolation. I think I have to specify the grid on which the numpy array should be evaluated but I am unable to figure out how to do it.

推荐答案

您可以使用scipy.interpolate.interp2d来做到这一点:

You can do it with scipy.interpolate.interp2d like this:

from scipy import interpolate

# Make a fake image - you can use yours.
image = np.ones((200,200))

# Make your orig array (skipping the extra dimensions).
orig = np.random.rand(128, 160)

# Make its coordinates; x is horizontal.
x = np.linspace(0, image.shape[1], orig.shape[1])
y = np.linspace(0, image.shape[0], orig.shape[0])

# Make the interpolator function.
f = interpolate.interp2d(x, y, orig, kind='linear')

# Construct the new coordinate arrays.
x_new = np.arange(0, image.shape[1])
y_new = np.arange(0, image.shape[0])

# Do the interpolation.
new_orig = f(x_new, y_new)

在形成x和y时，注意对坐标范围的-1调整.这样可以确保图像坐标从0到199(包括0和199).

Note the -1 adjustment to the coordinate range when forming x and y. This ensures that the image coordinates go from 0 to 199 inclusive.

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