Strange output when using cv2.dft inside loops

edit

Edit: updated with proper working code to reproduce the issue.

Hello everyone! I'm having some very weird issues with the Python bindings of OpenCV. I'm trying to use the dft function (amongst others) to compute operations between long arrays of 1D data (think >=8192 samples).

The issue is, when setting up a certain combination of loops (see code reproducing the problem below), a dft that is supposed to give always the same answer can output different data in different iterations (array_fix_S).

Numerical values are just close to what I really use in practice/arbitrary, not trying to exploit anything:

import cv2
import numpy as np

t = np.r_[0:16384]
#Random vector 1 to test dft repeatability. Will stay the same all the iterations.
array_fix = np.random.rand(16384) 

for i in range (0,10):

    #This print is always the same, which is OK.
    print(np.max(array_fix))
    print(np.min(array_fix)) 
    acum    = np.zeros((np.size(50),np.size(t)))

    ####AT LEAST THE OPERATION BELOW IS GIVING TROUBLE####
    array_fix_S = cv2.dft(array_fix,flags=cv2.DFT_COMPLEX_OUTPUT)

    #The 2 prints below can change each iteration despite coming from the same DFT every time.
    #Pydev's debugger shows that the arrays do change between iterations when it happens.
    print(np.max(array_fix_S))
    print(np.min(array_fix_S))
    for iteration in range(0, 50):

        #I and Q would normally be real/imaginary readings from binary files that change
        #each iteration in my code. You can pull I & Q outside the loops like
        #array_fix and the problem in array_fix_S still persists.
        I=np.random.rand(16384)
        Q=np.random.rand(16384)

        #Channel merging to get cv2-friendly complex array.
        xchan = [I,Q]
        X = cv2.merge(xchan)

        #If I comment the loop below (where the other dft/idft operations appear) the problem
        #in the previous prints is GONE.
        for iteration2 in range(0, 50):
            interm = cv2.dft(X)
            sep = cv2.split(cv2.idft(cv2.mulSpectrums((interm),array_fix_S,0,conjB=True),flags=cv2.DFT_COMPLEX_OUTPUT))

Bear in mind that the equivalent code using numpy.fft does work as intended. The algorithm in principle is correct. I'm starting to think this is an OpenCV bug.

I tried with stock Ubuntu 14.04 and 15.04 64 bits.

Any useful advice? I will provide more information if needed.

Thank you very much!