# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Image utility functions"""
import logging
from multiprocessing import Pool
from functools import partial
import numpy as np
from scipy.signal import fftconvolve
from scipy.ndimage.filters import gaussian_filter
from astropy.convolution import Gaussian2DKernel
__all__ = ["scale_cube"]
log = logging.getLogger(__name__)
def _fftconvolve_wrap(kernel, data):
# wrap gaussian filter as a special case, because the gain in
# performance is factor ~100
if isinstance(kernel, Gaussian2DKernel):
width = kernel.model.x_stddev.value
norm = kernel.array.sum()
return norm * gaussian_filter(data, width)
else:
return fftconvolve(data, kernel.array, mode="same")
[docs]def scale_cube(data, kernels, parallel=True):
"""
Compute scale space cube.
Compute scale space cube by convolving the data with a set of kernels and
stack the resulting images along the third axis.
Parameters
----------
data : `~numpy.ndarray`
Input data.
kernels: list of `~astropy.convolution.Kernel`
List of convolution kernels.
parallel : bool
Whether to use multiprocessing.
Returns
-------
cube : `~numpy.ndarray`
Array of the shape (len(kernels), data.shape)
"""
wrap = partial(_fftconvolve_wrap, data=data)
if parallel:
pool = Pool()
result = pool.map(wrap, kernels)
pool.close()
pool.join()
else:
result = map(wrap, kernels)
return np.dstack(result)