Grayscale image to NumPy array for Fourier transform
You want to use the mode 'L' instead of 'LA' as the parameter to the convert() method. 'LA' leaves an alpha channel and then the numpy.asarray doesn't work as you intended. If you need the alpha channel, then you will need a different method to convert to a numpy array. Otherwise, use mode 'L'.
It looks like you're using a version of PIL prior to 1.1.6, where they introduced the methods so that numpy would know what to do with an Image
. So you're just getting img_as_np
as a one-element array containing an Image
object (which is what Out[4]
is showing you).
You instead need to do something like np.asarray(img.getdata())
, which will give you a num_pixels x num_channels
array of integers between 0 and 255 (at least for the png I tried). You may want to do
img_as_np = np.asarray(img.getdata()).reshape(img.size[1], img.size[0], -1)
to lay it out like the image (transposed). You might also want to divide by 255 to get float values between 0 and 1, if that's the format you're expecting (as does e.g. matplotlib's imshow
).
Using this for an image:
>>> from PIL import Image>>> import numpy as np>>> import matplotlib.pyplot as plt>>> Image.__version__'1.1.7'>>> img = Image.open('lena.png').convert('L')>>> data = np.asarray(img.getdata()).reshape(img.size)>>> fft = np.fft.fft2(data)>>> fft[0, 0] = 0 # remove DC component for visualization>>> plt.imshow(np.abs(np.fft.fftshift(fft)), interpolation='nearest')<matplotlib.image.AxesImage object at 0x046012F0>>>> plt.show()>>> plt.imshow(np.abs(np.fft.fftshift(fft))[224:288, 224:288], interpolation='nearest')<matplotlib.image.AxesImage object at 0x0476ED70>>>> plt.show()