Source code for image_registration.fft_tools.correlate2d
import numpy as np
#from .convolve_nd import convolvend as convolve
from astropy.convolution import convolve_fft as convolve
from . import fast_ffts
__all__ = ['correlate2d']
[docs]def correlate2d(im1, im2, boundary='wrap', nthreads=1, use_numpy_fft=False,
**kwargs):
"""
Cross-correlation of two images of arbitrary size. Returns an image
cropped to the largest of each dimension of the input images
Parameters
----------
return_fft - if true, return fft(im1)*fft(im2[::-1,::-1]), which is the power
spectral density
fftshift - if true, return the shifted psd so that the DC component is in
the center of the image
pad - Default on. Zero-pad image to the nearest 2^n
crop - Default on. Return an image of the size of the largest input image.
If the images are asymmetric in opposite directions, will return the largest
image in both directions.
boundary: str, optional
A flag indicating how to handle boundaries:
* 'fill' : set values outside the array boundary to fill_value
(default)
* 'wrap' : periodic boundary
nthreads : bool
Number of threads to use for fft (only matters if you have fftw
installed)
use_numpy_fft : bool
Force use numpy's fft over fftw? (only matters if you have fftw
installed)
WARNING: Normalization may be arbitrary if you use the PSD
"""
fftn,ifftn = fast_ffts.get_ffts(nthreads=nthreads, use_numpy_fft=use_numpy_fft)
return convolve(np.conjugate(im1), im2[::-1, ::-1], normalize_kernel=False,
fftn=fftn, ifftn=ifftn,
boundary=boundary, nan_treatment='fill', **kwargs)