146 lines
5.4 KiB
Python
146 lines
5.4 KiB
Python
|
from itertools import product
|
||
|
import numpy as np
|
||
|
from .._shared.utils import warn
|
||
|
|
||
|
try:
|
||
|
import dask
|
||
|
dask_available = True
|
||
|
except ImportError:
|
||
|
dask_available = False
|
||
|
|
||
|
|
||
|
def _generate_shifts(ndim, multichannel, max_shifts, shift_steps=1):
|
||
|
"""Returns all combinations of shifts in n dimensions over the specified
|
||
|
max_shifts and step sizes.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> s = list(_generate_shifts(2, False, max_shifts=(1, 2), shift_steps=1))
|
||
|
>>> print(s)
|
||
|
[(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)]
|
||
|
"""
|
||
|
mc = int(multichannel)
|
||
|
if np.isscalar(max_shifts):
|
||
|
max_shifts = (max_shifts, ) * (ndim - mc) + (0, ) * mc
|
||
|
elif multichannel and len(max_shifts) == ndim - 1:
|
||
|
max_shifts = tuple(max_shifts) + (0, )
|
||
|
elif len(max_shifts) != ndim:
|
||
|
raise ValueError("max_shifts should have length ndim")
|
||
|
|
||
|
if np.isscalar(shift_steps):
|
||
|
shift_steps = (shift_steps, ) * (ndim - mc) + (1, ) * mc
|
||
|
elif multichannel and len(shift_steps) == ndim - 1:
|
||
|
shift_steps = tuple(shift_steps) + (1, )
|
||
|
elif len(shift_steps) != ndim:
|
||
|
raise ValueError("max_shifts should have length ndim")
|
||
|
|
||
|
if any(s < 1 for s in shift_steps):
|
||
|
raise ValueError("shift_steps must all be >= 1")
|
||
|
|
||
|
if multichannel and max_shifts[-1] != 0:
|
||
|
raise ValueError(
|
||
|
"Multichannel cycle spinning should not have shifts along the "
|
||
|
"last axis.")
|
||
|
|
||
|
return product(*[range(0, s + 1, t) for
|
||
|
s, t in zip(max_shifts, shift_steps)])
|
||
|
|
||
|
|
||
|
def cycle_spin(x, func, max_shifts, shift_steps=1, num_workers=None,
|
||
|
multichannel=False, func_kw={}):
|
||
|
"""Cycle spinning (repeatedly apply func to shifted versions of x).
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
x : array-like
|
||
|
Data for input to ``func``.
|
||
|
func : function
|
||
|
A function to apply to circularly shifted versions of ``x``. Should
|
||
|
take ``x`` as its first argument. Any additional arguments can be
|
||
|
supplied via ``func_kw``.
|
||
|
max_shifts : int or tuple
|
||
|
If an integer, shifts in ``range(0, max_shifts+1)`` will be used along
|
||
|
each axis of ``x``. If a tuple, ``range(0, max_shifts[i]+1)`` will be
|
||
|
along axis i.
|
||
|
shift_steps : int or tuple, optional
|
||
|
The step size for the shifts applied along axis, i, are::
|
||
|
``range((0, max_shifts[i]+1, shift_steps[i]))``. If an integer is
|
||
|
provided, the same step size is used for all axes.
|
||
|
num_workers : int or None, optional
|
||
|
The number of parallel threads to use during cycle spinning. If set to
|
||
|
``None``, the full set of available cores are used.
|
||
|
multichannel : bool, optional
|
||
|
Whether to treat the final axis as channels (no cycle shifts are
|
||
|
performed over the channels axis).
|
||
|
func_kw : dict, optional
|
||
|
Additional keyword arguments to supply to ``func``.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
avg_y : np.ndarray
|
||
|
The output of ``func(x, **func_kw)`` averaged over all combinations of
|
||
|
the specified axis shifts.
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
Cycle spinning was proposed as a way to approach shift-invariance via
|
||
|
performing several circular shifts of a shift-variant transform [1]_.
|
||
|
|
||
|
For a n-level discrete wavelet transforms, one may wish to perform all
|
||
|
shifts up to ``max_shifts = 2**n - 1``. In practice, much of the benefit
|
||
|
can often be realized with only a small number of shifts per axis.
|
||
|
|
||
|
For transforms such as the blockwise discrete cosine transform, one may
|
||
|
wish to evaluate shifts up to the block size used by the transform.
|
||
|
|
||
|
References
|
||
|
----------
|
||
|
.. [1] R.R. Coifman and D.L. Donoho. "Translation-Invariant De-Noising".
|
||
|
Wavelets and Statistics, Lecture Notes in Statistics, vol.103.
|
||
|
Springer, New York, 1995, pp.125-150.
|
||
|
:DOI:`10.1007/978-1-4612-2544-7_9`
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
>>> import skimage.data
|
||
|
>>> from skimage import img_as_float
|
||
|
>>> from skimage.restoration import denoise_wavelet, cycle_spin
|
||
|
>>> img = img_as_float(skimage.data.camera())
|
||
|
>>> sigma = 0.1
|
||
|
>>> img = img + sigma * np.random.standard_normal(img.shape)
|
||
|
>>> denoised = cycle_spin(img, func=denoise_wavelet,
|
||
|
... max_shifts=3)
|
||
|
|
||
|
"""
|
||
|
x = np.asanyarray(x)
|
||
|
all_shifts = _generate_shifts(x.ndim, multichannel, max_shifts,
|
||
|
shift_steps)
|
||
|
all_shifts = list(all_shifts)
|
||
|
roll_axes = tuple(range(x.ndim))
|
||
|
|
||
|
def _run_one_shift(shift):
|
||
|
# shift, apply function, inverse shift
|
||
|
xs = np.roll(x, shift, axis=roll_axes)
|
||
|
tmp = func(xs, **func_kw)
|
||
|
return np.roll(tmp, tuple(-s for s in shift), axis=roll_axes)
|
||
|
|
||
|
if not dask_available and (num_workers is None or num_workers > 1):
|
||
|
num_workers = 1
|
||
|
warn('The optional dask dependency is not installed. '
|
||
|
'The number of workers is set to 1. To silence '
|
||
|
'this warning, install dask or explicitly set `num_workers=1` '
|
||
|
'when calling the `cycle_spin` function')
|
||
|
# compute a running average across the cycle shifts
|
||
|
if num_workers == 1:
|
||
|
# serial processing
|
||
|
mean = _run_one_shift(all_shifts[0])
|
||
|
for shift in all_shifts[1:]:
|
||
|
mean += _run_one_shift(shift)
|
||
|
mean /= len(all_shifts)
|
||
|
else:
|
||
|
# multithreaded via dask
|
||
|
futures = [dask.delayed(_run_one_shift)(s) for s in all_shifts]
|
||
|
mean = sum(futures) / len(futures)
|
||
|
mean = mean.compute(num_workers=num_workers)
|
||
|
return mean
|