cv2.warpAffine() results in an image shifted by 0.5 pixel

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Warping an image with cv2.warpAffine() results in an image shifted by 0.5 pixel.

Is this behaviour expected? If so, what's the reasons behind?

Code

Below is a simple example where an image is scaled with affine transform.

import cv2
import numpy as np

def warpScale(im, scale):
    affine = np.array([
        [scale, 0, 0],
        [0, scale, 0],
        [0, 0, 1],
    ], np.float32)
    return cv2.warpAffine(im, affine[:2, :], (int(im.shape[1]*scale), int(im.shape[0]*scale)))

im = cv2.imread("0-in.png")     # 2x2
scale = 8

# Scale up
im1 = warpScale(im, scale)
cv2.imwrite("1.png", im1)     # 16x16

# Scale down
im2 = warpScale(im1, 1. / scale)
cv2.imwrite("2.png", im2)     # 2x2

Input 2x2 image:

Output-1 (scaled up by a factor of 8):

Output-2 (scaled down back to the original):

Workaround

When I rewrite the warpScale() function as blow, it becomes as I expected. It's like doing the following operations in order:

• Shift the original image by (+0.5, +0.5)
• Apply the intended affine transform
• Shift the transformed image by (-0.5, -0.5)

:

def warpScale(im, scale):
    affine = np.dot(
        np.array([
            [1, 0, -0.5],
            [0, 1, -0.5],
            [0, 0, 1],
        ], np.float32),
        np.dot(
            np.array([
                [scale, 0, 0],
                [0, scale, 0],
                [0, 0, 1],
            ], np.float32),
            np.array([
                [1, 0, 0.5],
                [0, 1, 0.5],
                [0, 0, 1],
            ], np.float32)
        )
    )
    return cv2.warpAffine(im, affine[:2, :], (int(im.shape[1]*scale), int(im.shape[0]*scale)))

Output-1 (scaled up by a factor of 8):

Output-2 (scaled down back to the original):