Copying Numpy Array With '=' Operator. Why Is It Working?

According to this answer, B=A where A is a numpy array, B should be pointing to the same object A.

import cv2
import numpy as np

img = cv2.imread('rose.jpeg')
print("img.shape: ", np.shape(img))

img2 = img
img = cv2.resize(img, (250,100))
print("img.shape: ", img.shape)
print("img2.shape:", img2.shape)

Output:

img.shape:  (331, 500, 3)
img.shape:  (100, 250, 3)
img2.shape: (331, 500, 3)

It seems to be a very basic question, but I have been scratching my head over this. Could someone please explain what's happening behind it?

The "problem" is that your not using numpy here but opencv and while numpy array.resize() is in-place opencv img.resize() is not.

So your call to

    img = cv2.resize(img, (250,100))

creates a new object (image) with the given size. So here the img variable will point to a different object then before the call.

    img2 = img

adds a new name for the original object. Here img2 and img are refering to exactly the same object/piece of memory.

    img = cv2.resize(img, (250,100))

cv2.resize(img, (250,100)) creates a new object and the name img now refers to that new object/piece of memory.

    print("img.shape: ", img.shape)

gets you the size of the new object and

    print("img2.shape:", img2.shape)

the size of the original object as img2 still refers to the original object.

By the way in numpy the call a = a.resize(...) would be really bad - because a would then by None (return value of resize) instead of the resized array. There you would just do a.resize(...)