# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Restoration computation module
------------------------------
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# Note:
# ----
# All dataset classes must also be imported in the cdl.computation.param module.
from __future__ import annotations
import guidata.dataset as gds
import pywt
from skimage import morphology
from skimage.restoration import denoise_bilateral, denoise_tv_chambolle, denoise_wavelet
from cdl.computation.image import Wrap11Func, dst_11, restore_data_outside_roi
from cdl.computation.image.morphology import MorphologyParam
from cdl.config import _
from cdl.obj import ImageObj
[docs]
class DenoiseTVParam(gds.DataSet):
"""Total Variation denoising parameters"""
weight = gds.FloatItem(
_("Denoising weight"),
default=0.1,
min=0,
nonzero=True,
help=_(
"The greater weight, the more denoising "
"(at the expense of fidelity to input)."
),
)
eps = gds.FloatItem(
"Epsilon",
default=0.0002,
min=0,
nonzero=True,
help=_(
"Relative difference of the value of the cost function that "
"determines the stop criterion. The algorithm stops when: "
"(E_(n-1) - E_n) < eps * E_0"
),
)
max_num_iter = gds.IntItem(
_("Max. iterations"),
default=200,
min=0,
nonzero=True,
help=_("Maximal number of iterations used for the optimization"),
)
[docs]
def compute_denoise_tv(src: ImageObj, p: DenoiseTVParam) -> ImageObj:
"""Compute Total Variation denoising
with :py:func:`skimage.restoration.denoise_tv_chambolle`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
return Wrap11Func(
denoise_tv_chambolle, weight=p.weight, eps=p.eps, max_num_iter=p.max_num_iter
)(src)
[docs]
class DenoiseBilateralParam(gds.DataSet):
"""Bilateral filter denoising parameters"""
sigma_spatial = gds.FloatItem(
"σ<sub>spatial</sub>",
default=1.0,
min=0,
nonzero=True,
unit="pixels",
help=_(
"Standard deviation for range distance. "
"A larger value results in averaging of pixels "
"with larger spatial differences."
),
)
modes = ("constant", "edge", "symmetric", "reflect", "wrap")
mode = gds.ChoiceItem(_("Mode"), list(zip(modes, modes)), default="constant")
cval = gds.FloatItem(
"cval",
default=0,
help=_(
"Used in conjunction with mode 'constant', "
"the value outside the image boundaries."
),
)
[docs]
def compute_denoise_bilateral(src: ImageObj, p: DenoiseBilateralParam) -> ImageObj:
"""Compute bilateral filter denoising
with :py:func:`skimage.restoration.denoise_bilateral`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
return Wrap11Func(
denoise_bilateral, sigma_spatial=p.sigma_spatial, mode=p.mode, cval=p.cval
)(src)
[docs]
class DenoiseWaveletParam(gds.DataSet):
"""Wavelet denoising parameters"""
wavelets = pywt.wavelist()
wavelet = gds.ChoiceItem(
_("Wavelet"), list(zip(wavelets, wavelets)), default="sym9"
)
modes = ("soft", "hard")
mode = gds.ChoiceItem(_("Mode"), list(zip(modes, modes)), default="soft")
methods = ("BayesShrink", "VisuShrink")
method = gds.ChoiceItem(
_("Method"), list(zip(methods, methods)), default="VisuShrink"
)
[docs]
def compute_denoise_wavelet(src: ImageObj, p: DenoiseWaveletParam) -> ImageObj:
"""Compute Wavelet denoising
with :py:func:`skimage.restoration.denoise_wavelet`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
return Wrap11Func(denoise_wavelet, wavelet=p.wavelet, mode=p.mode, method=p.method)(
src
)
[docs]
def compute_denoise_tophat(src: ImageObj, p: MorphologyParam) -> ImageObj:
"""Denoise using White Top-Hat
with :py:func:`skimage.morphology.white_tophat`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(src, "denoise_tophat", f"radius={p.radius}")
dst.data = src.data - morphology.white_tophat(src.data, morphology.disk(p.radius))
restore_data_outside_roi(dst, src)
return dst
