Numpy Resize/Rescale Image

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.