在Windows下使用NumPy数组对图像进行快速傅立叶变换时出现MemoryError

Question

该代码可以在我的Ubuntu 11.04上从.tiff图像计算傅立叶变换。在Windows XP上，它会产生内存错误。要更改什么？非常感谢。

def fouriertransform(result):     #function for Fourier transform computation
    for filename in glob.iglob ('*.tif')
        imgfourier = scipy.misc.imread(filename) #read the image
        arrayfourier = numpy.array([imgfourier])#make an array 
        # Take the fourier transform of the image.
        F1 = fftpack.fft2(arrayfourier)
        # Now shift so that low spatial frequencies are in the center.
        F2 = fftpack.fftshift(F1)
        # the 2D power spectrum is:
        psd2D = np.abs(F2)**2
        L = psd2D
        np.set_printoptions(threshold=3)
        #np.set_printoptions(precision = 3, threshold = None, edgeitems = None, linewidth = 3, suppress = True, nanstr = None, infstr = None, formatter = None)
        for subarray in L:
            for array in subarray:
                for array in subarray:
                    for elem in array:
                        print '%3.10fn' % elem

错误输出为：

Traceback (most recent call last):
  File "C:Documents and SettingsHrenMudakМои документыМоя музыкаfourier.py", line 27, in <module>
    F1 = fftpack.fft2(arrayfourier)
  File "C:Python27libsite-packagesscipyfftpackbasic.py", line 571, in fft2
    return fftn(x,shape,axes,overwrite_x)
  File "C:Python27libsite-packagesscipyfftpackbasic.py", line 521, in fftn
    return _raw_fftn_dispatch(x, shape, axes, overwrite_x, 1)
  File "C:Python27libsite-packagesscipyfftpackbasic.py", line 535, in _raw_fftn_dispatch
    return _raw_fftnd(tmp,shape,axes,direction,overwrite_x,work_function)
  File "C:Python27libsite-packagesscipyfftpackbasic.py", line 463, in _raw_fftnd
    x, copy_made = _fix_shape(x, s[i], waxes[i])
  File "C:Python27libsite-packagesscipyfftpackbasic.py", line 134, in _fix_shape
    z = zeros(s,x.dtype.char)
MemoryError

Answer 1

我试过运行您的代码，只是用scipy.misc.imread函数替换了mahotas.imread，因为我没有那个库，而且我无法重现您的错误。

进一步说明：

• 你能试着用scipy.misc.imread函数代替mahotas函数吗？我想问题可能就在那里
• 抛出的实际异常是什么？（+其他输出？）
• 你的图像的尺寸是多少？灰度/RGB？打印大图像的所有值确实会占用相当多的内存，因此最好使用例如matplotlibs imshow函数来可视化结果