Fix Python – Numpy Resize/Rescale Image

Question 1

I would like to take an image and change the scale of the image, while it is a numpy array.

For example I have this image of a coca-cola bottle:
bottle-1

Which translates to a numpy array of shape (528, 203, 3) and I want to resize that to say the size of this second image:
bottle-2

Which has a shape of (140, 54, 3).

How do I change the size of the image to a certain shape while still maintaining the original image? Other answers suggest stripping every other or third row out, but what I want to do is basically shrink the image how you would via an image editor but in python code. Are there any libraries to do this in numpy/SciPy?

Question 2

Yeah, you can install opencv (this is a library used for image processing, and computer vision), and use the cv2.resize function. And for instance use:

import cv2
import numpy as np

img = cv2.imread('your_image.jpg')
res = cv2.resize(img, dsize=(54, 140), interpolation=cv2.INTER_CUBIC)

Here img is thus a numpy array containing the original image, whereas res is a numpy array containing the resized image. An important aspect is the interpolation parameter: there are several ways how to resize an image. Especially since you scale down the image, and the size of the original image is not a multiple of the size of the resized image. Possible interpolation schemas are:

INTER_NEAREST – a nearest-neighbor interpolation

INTER_LINEAR – a bilinear interpolation (used by default)

INTER_AREA – resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free
results. But when the image is zoomed, it is similar to the
INTER_NEAREST method.

INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood

INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood

Like with most options, there is no “best” option in the sense that for every resize schema, there are scenarios where one strategy can be preferred over another.

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