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Update the docstrings on all the tools files and start laying out the docs for real
This commit is contained in:
Abe Levitan
@@ -1,3 +1,12 @@
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"""Contains functions for basic image processing needs
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This module contains two kinds of image processing tools. The first type
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is specific tools for calculating commonly needed metrics (such as the
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centroid of an image), directly on complex-valued torch tensors. The second
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kind of tools perform common image manipulations on torch tensors, in such
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a way that it is safe to include them in automatic differentiation models.
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"""
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from __future__ import division, print_function, absolute_import
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import numpy as np
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import torch as t
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@@ -17,12 +26,17 @@ def centroid(im, dims=2):
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Beware that the meaning of the centroid is not well defined if your
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image contains values less than 0
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Args:
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im (t.Tensor) : An image or stack of images to calculate from
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dims (int) : Default 2, how many trailing dimensions to calculate for
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Returns:
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t.Tensor : An (i,j) index or stack of indices
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Parameters
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----------
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im : torch.Tensor
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An image or stack of images to calculate from
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dims : int
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Default 2, how many trailing dimensions to calculate with
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Returns
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-------
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centroid : torch.Tensor
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An (i,j) index or stack of indices
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"""
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# For some reason this needs to be a list
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indices = [t.arange(im.shape[-dims+i]).to(t.float32) for i in range(dims)]
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@@ -46,12 +60,19 @@ def centroid_sq(im, dims=2, comp=False):
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represents the real and imaginary part of a complex number, and the
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centroid will be calculated for the magnitude squared of those numbers
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Args:
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im (t.Tensor) : An image or stack of images to calculate from
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dims (int) : Default 2, how many trailing dimensions to calculate for
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comp (bool) : Default is False, whether the data represents complex numbers
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Returns:
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t.Tensor : An (i,j) index or stack of indices
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Parameters
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----------
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im : torch.Tensor
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An image or stack of images to calculate from
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dims : int
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Default 2, how many trailing dimensions to calculate for
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comp : bool
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Default is False, whether the data represents complex numbers
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Returns
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-------
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centroid: torch.Tensor
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An (i,j) index or stack of indices
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"""
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if comp:
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im_sq = cmath.cabssq(im)
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@@ -67,12 +88,17 @@ def sinc_subpixel_shift(im, shift):
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The subpixel shift is done circularly via a multiplication with a linear
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phase mask in Fourier space.
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Args:
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im (torch.Tensor) : A complex-valued tensor to perform the subpixel shift on
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shift (array_like) : A length-2 array_like object describing the shift to perform, in pixels
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Parameters
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----------
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im : torch.Tensor
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A complex-valued tensor to perform the subpixel shift on
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shift : array
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A length-2 array describing the shift to perform, in pixels
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Returns:
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(torch.Tensor) : The subpixel shifted tensor
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Returns
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-------
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shifted_im : torch.Tensor
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The subpixel shifted tensor
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"""
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i = t.arange(im.shape[0]) - im.shape[0]//2
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@@ -97,11 +123,21 @@ def find_subpixel_shift(im1, im2, search_around=(0,0), resolution=10):
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approach outlined in "Efficient subpixel image registration algorithms",
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Optics Express (2008) by Manual Guizar-Sicarios et al.
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Args:
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im1 (t.Tensor): The first real or complex-valued torch tensor
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im2 (t.Tensor): The second real or complex-valued torch tensor
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search_around (array_like) : Default (0,0), the shift to search in the vicinity of
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resolution (int): Default is 10, the resolution to calculate to in units of 1/n
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Parameters
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----------
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im1 : torch.Tensor
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The first real or complex-valued torch tensor
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im2 : torch.Tensor
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The second real or complex-valued torch tensor
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search_around : array
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Default (0,0), the shift to search in the vicinity of
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resolution : int
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Default is 10, the fraction of a pixel to calculate to
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Returns
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-------
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shift : torch.Tensor
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The relative shift (i,j) needed to best map im1 onto im2
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"""
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#
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# Here's my approach, perhaps it's a little unconventional. I will first
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@@ -111,7 +147,8 @@ def find_subpixel_shift(im1, im2, search_around=(0,0), resolution=10):
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# using an FFT with upsampling by a factor of resolution in reciprocal
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# space
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#
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# If last dimension is not 2, then convert to a complex tensor now
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# If last dimension is not 2, then convert to a complex tensor now
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if im1.shape[-1] != 2:
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im1 = t.stack((im1,t.zeros_like(im1)),dim=-1)
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if im2.shape[-1] != 2:
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@@ -164,11 +201,17 @@ def find_pixel_shift(im1, im2):
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to the amount that im1 would have to be shifted by to line up best with
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im2
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Args:
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im1 (t.Tensor): The first real or complex-valued torch tensor
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im2 (t.Tensor): The second real or complex-valued torch tensor
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search_around (array_like) : Default (0,0), the shift to search in the vicinity of
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resolution (int): Default is 10, the resolution to calculate to in units of 1/n
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Parameters
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----------
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im1 : torch.Tensor
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The first real or complex-valued torch tensor
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im2 : torch.Tensor
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The second real or complex-valued torch tensor
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Returns
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-------
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shift : torch.Tensor
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The integer-valued shift (i,j) that best maps im1 onto im2
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"""
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# If last dimension is not 2, then convert to a complex tensor now
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if im1.shape[-1] != 2:
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@@ -198,10 +241,19 @@ def find_shift(im1, im2, resolution=10):
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pixel resolution, and then searchers the nearby area to calculate a
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subpixel shift
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Args:
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im1 (t.Tensor): The first real or complex-valued torch tensor
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im2 (t.Tensor): The second real or complex-valued torch tensor
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resolution (int): Default is 10, the resolution to calculate to in units of 1/n
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Parameters
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----------
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im1 : torch.Tensor
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The first real or complex-valued torch tensor
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im2 : torch.Tensor
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The second real or complex-valued torch tensor
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resolution : int
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Default is 10, the fraction of a pixel to calculate to
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Returns
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-------
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shift : torch.Tensor
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The relative shift (i,j) needed to best map im1 onto im2
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"""
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integer_shift = find_pixel_shift(im1,im2)
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subpixel_shift = find_subpixel_shift(im1, im2, search_around=integer_shift,
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@@ -221,14 +273,21 @@ def convolve_1d(image, kernel, dim=0, fftshift_kernel=True):
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Otherwise, the image is assumed to be real. The image and kernel
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must either both be real or both be complex.
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Args:
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image (torch.Tensor) : The image to convolve
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kernel (torch.Tensor) : The 1d kernel to convolve with
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dim (int) : Default 0, the dimension to convolve along
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fftshift_kernel (bool) : Default True, whether to fftshift the kernel first.
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Parameters
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----------
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image : torch.Tensor
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The image to convolve
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kernel : torch.Tensor
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The 1d kernel to convolve with
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dim : int
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Default 0, the dimension to convolve along
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fftshift_kernel : bool
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Default True, whether to fftshift the kernel first.
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Returns:
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(torch.Tensor) : The convolved image
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Returns
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-------
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convolved_im : torch.Tensor
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The convolved image
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"""
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complex_things = 2
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