Source code for ncempy.eval.stack_align

import numpy as np

from ncempy.eval import multicorr

[docs]def stack_align(stack, align_type='static', **kwargs): """ Align a series of images by correlation using multicorr. All images are aligned to the start image or as dynamically starting with the start image and then each successive image. keyword arguments are passed to multicorr. Shifting is done in Fourier space which is very accurate, but wraps edges. Parameters ---------- stack : ndarray, 3D, Stack of images to align. Shape [num, Y, X] align_type: str static or dynamic alignment. Static aligns all images to the first image. Dynamic aligns each image to the previous image starting with the first image Returns ------- : tuple, aligned stack, shifts A tuple containing the aligned images as a 3D ndarray of shape [num, Y, X] and shifts as a 2D ndarray of shape [num, 2] """ if align_type is not 'static' and align_type is not 'dynamic': raise KeyError('Incorrect align type. Must be static or dynamic') # Pre-allocate the arrays aligned = np.zeros_like(stack) # shifted data array shifts = np.zeros((stack.shape[0], 2)) # the applied shifts aligned[0, :, :] = stack[0, :, :] # Align positive angles ref_fft = np.fft.fft2(stack[0, :, :]) ref_sh = np.zeros((2,)) for ii in range(1, stack.shape[0]): cur_fft = np.fft.fft2(stack[ii, :, :]) sh = multicorr.multicorr(cur_fft, ref_fft, **kwargs) sh += ref_sh image_shifted = np.real(np.fft.ifft2(multicorr.imageShifter(cur_fft, sh))) aligned[ii, :, :] = image_shifted shifts[ii, :] = sh if align_type is 'dynamic': ref_fft = cur_fft ref_sh = sh return aligned, shifts