def fourierFilter(fileName,sigma,num):
workData = getData(fileName,num)
print 'INput sigma'
#sigma = float(raw_input())
fourFilter1 = ndimage.fourier_uniform(workData,sigma)
fourFilter = ndimage.fourier_uniform(fourFilter1,sigma)
mfSave = Image.fromarray(fourFilter)
mfSave = mfSave.convert('L')
mfSave.save('Fourier Filter.jpg')
imageGUI.imdisplay('Fourier Filter.jpg','Fourier Filter',1)
def transform_image(dimensions, image):
size = dimensions[0]
image = image.copy() / 255.
for channel in range(image.shape[2]):
image[:, :, channel] = fftpack.ifft2(
fourier_uniform(fftpack.fft2(image[:, :, channel]), size)).real
return np.clip(image * 255., 0., 255.)
def test_fourier_uniform_complex01(self, shape, dtype, dec):
a = numpy.zeros(shape, dtype)
a[0, 0] = 1.0
a = fft.fft(a, shape[0], 0)
a = fft.fft(a, shape[1], 1)
a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
a = fft.ifft(a, shape[1], 1)
a = fft.ifft(a, shape[0], 0)
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
def test_fourier_ellipsoid_1d_complex(self):
# expected result of 1d ellipsoid is the same as for fourier_uniform
for shape in [(32, ), (31, )]:
for type_, dec in zip([numpy.complex64, numpy.complex128],
[5, 14]):
x = numpy.ones(shape, dtype=type_)
a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
b = ndimage.fourier_uniform(x, 5, -1, 0)
assert_array_almost_equal(a, b, decimal=dec)
import scipy.ndimage as ndi
import scipy.misc as misc
import matplotlib.pyplot as plt
import numpy as np
img = misc.ascent()
noisy = img + 0.09 * img.std() * np.random.random(img.shape)
fe = ndi.fourier_ellipsoid(img, 1)
fg = ndi.fourier_gaussian(img, 1)
fs = ndi.fourier_shift(img, 1)
fu = ndi.fourier_uniform(img, 1)
title = ['original', 'Noisy', 'Fourier Ellipsoid', 'Fourier Gaussian', 'Fourier Shift', 'Fourier uniform']
output = [img, noisy, fe, fg, fs, fu]
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.imshow(np.float64(output[i]), cmap='gray')
plt.title(title[i])
plt.axis('off')
plt.show()
def fourier(img, size=100):
return [
ndimage.fourier_ellipsoid(img, size),
ndimage.fourier_shift(img, size),
ndimage.fourier_uniform(img, size)
]
def fourier_uniform(left):
left = ndimage.fourier_uniform(left, size=3, axis=0)
return left
def fourierut(f):
f1 = ndimage.fourier_uniform(f, size=1.25)
return (f1)
from scipy import ndimage, misc import numpy.fft import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(1, 2) plt.gray() # show the filtered result in grayscale ascent = misc.ascent() input_ = numpy.fft.fft2(ascent) result = ndimage.fourier_uniform(input_, size=20) result = numpy.fft.ifft2(result) ax1.imshow(ascent) ax2.imshow(result.real) # the imaginary part is an artifact plt.show()