dask_image.ndfourier package¶
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dask_image.ndfourier.
fourier_gaussian
(image, sigma, n=- 1, axis=- 1)[source]¶ Multi-dimensional Gaussian fourier filter.
The array is multiplied with the fourier transform of a Gaussian kernel.
- Parameters
image (array_like) – The input image.
sigma (float or sequence) – The sigma of the Gaussian kernel. If a float, sigma is the same for all axes. If a sequence, sigma has to contain one value for each axis.
n (int, optional) – If n is negative (default), then the image is assumed to be the result of a complex fft. If n is larger than or equal to zero, the image is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.
axis (int, optional) – The axis of the real transform.
- Returns
fourier_gaussian
- Return type
Dask Array
Examples
>>> 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() >>> image = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_gaussian(image, sigma=4) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent)
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dask_image.ndfourier.
fourier_shift
(image, shift, n=- 1, axis=- 1)[source]¶ Multi-dimensional fourier shift filter.
The array is multiplied with the fourier transform of a shift operation.
- Parameters
image (array_like) – The input image.
shift (float or sequence) – The size of the box used for filtering. If a float, shift is the same for all axes. If a sequence, shift has to contain one value for each axis.
n (int, optional) – If n is negative (default), then the image is assumed to be the result of a complex fft. If n is larger than or equal to zero, the image is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.
axis (int, optional) – The axis of the real transform.
- Returns
fourier_shift
- Return type
Dask Array
Examples
>>> from scipy import ndimage, misc >>> import matplotlib.pyplot as plt >>> import numpy.fft >>> fig, (ax1, ax2) = plt.subplots(1, 2) >>> plt.gray() # show the filtered result in grayscale >>> ascent = misc.ascent() >>> image = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_shift(image, shift=200) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show()
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dask_image.ndfourier.
fourier_uniform
(image, size, n=- 1, axis=- 1)[source]¶ Multi-dimensional uniform fourier filter.
The array is multiplied with the fourier transform of a box of given size.
- Parameters
image (array_like) – The input image.
size (float or sequence) – The size of the box used for filtering. If a float, size is the same for all axes. If a sequence, size has to contain one value for each axis.
n (int, optional) – If n is negative (default), then the image is assumed to be the result of a complex fft. If n is larger than or equal to zero, the image is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.
axis (int, optional) – The axis of the real transform.
- Returns
fourier_uniform – The filtered image. If output is given as a parameter, None is returned.
- Return type
Dask Array
Examples
>>> 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() >>> image = numpy.fft.fft2(ascent) >>> result = ndimage.fourier_uniform(image, size=20) >>> result = numpy.fft.ifft2(result) >>> ax1.imshow(ascent) >>> ax2.imshow(result.real) # the imaginary part is an artifact >>> plt.show()