Numpy Resize/Rescale Image
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?
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)**
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_CUBIC- a bicubic interpolation over 4x4 pixel neighborhood
INTER_LANCZOS4- a Lanczos interpolation over 8x8 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.