rotation in frequency domain - question about question 64441200

Question

Following : How to rotate a non-squared image in frequency domain

I just ran this exact code (copy pasted from the author's code with just an additional normalization of the resulting image between 0 an 255 as shown) ... but I get horrible "aliasing" artifacts ... How is this possible ? I see that the OP shows nice unartifacted images from the rotation in frequency space... I would be very curious to know how to obtain that, surely you did not show all your code?

import numpy as np
import cv2
from numpy.fft import fftshift as fftshift
from numpy.fft import ifftshift as ifftshift

angle = 30
M = cv2.imread("phantom.png")
M = cv2.cvtColor(M, cv2.COLOR_BGR2GRAY)
M=np.float32(M)
hanning=cv2.createHanningWindow((M.shape[1],M.shape[0]),cv2.CV_32F)
M=hanning*M
sM = fftshift(M)
rotation_center=(M.shape[1]/2,M.shape[0]/2)
rot_matrix=cv2.getRotationMatrix2D(rotation_center,angle,1.0)

FsM = fftshift(cv2.dft(sM,flags = cv2.DFT_COMPLEX_OUTPUT))
rFsM=cv2.warpAffine(FsM,rot_matrix,(FsM.shape[1],FsM.shape[0]),flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
IrFsM = ifftshift(cv2.idft(ifftshift(rFsM),flags=cv2.DFT_REAL_OUTPUT))

x = IrFsM
x = ((x-np.min(x[:]))/(np.max(x[:])-np.min(x[:])))*255.0
cv2.imwrite('rotated_phantom.png',x)

the output image is:

Also, i ve always been told that it was impossible to correctly rotate (in discrete, not continuous) Fourier space because of interpolation, so how can you explain that?