why OpenCV cv2.resize gives different answer than

Question

I'm transferring a MATLAB code into python and trying to downscale an image using OpenCV function cv2.resize, But I get a different results from what MATLAB outputs.

To make sure that my code is not doing anything wrong before the resize, I used a small example on both functions and compared the output.

I first created the following array in both Python and MATLAB and upsampled it:

Python - NumPy and OpenCV

    x = cv2.resize(np.array([[1.,2],[3,4]]),(4,4), interpolation=cv2.INTER_LINEAR)
    print x

    [[ 1.    1.25  1.75  2.  ]
     [ 1.5   1.75  2.25  2.5 ]
     [ 2.5   2.75  3.25  3.5 ]
     [ 3.    3.25  3.75  4.  ]]

MATLAB

    x = imresize([1,2;3,4],[4,4],'bilinear')

    ans =

    1.0000    1.2500    1.7500    2.0000
    1.5000    1.7500    2.2500    2.5000
    2.5000    2.7500    3.2500    3.5000
    3.0000    3.2500    3.7500    4.0000

Then I took the answers and resized them back to the original 2x2 size.

Python:

    cv2.resize(x,(2,2), interpolation=cv2.INTER_LINEAR)

    ans = 

     [[ 1.375,  2.125],
      [ 2.875,  3.625]]

MATLAB:

    imresize(x,[2,2],'bilinear')

    ans =

      1.5625    2.1875
      2.8125    3.4375

They are clearly not the same, and when numbers are larger, the answers are a lot more different.

Any explanation or resources would be appreciated.

Answer 1

MATLAB's imresize has anti-aliasing enabled by default:

>> imresize(x,[2,2],'bilinear')
ans =
    1.5625    2.1875
    2.8125    3.4375
>> imresize(x,[2,2],'bilinear','AntiAliasing',false)
ans =
    1.3750    2.1250
    2.8750    3.6250

This has tripped me up in the past, while trying to reproduce the results of imresize using just interp2.