# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Exposure 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
from skimage import exposure
from cdl.computation.image import VALID_DTYPES_STRLIST, dst_11, restore_data_outside_roi
from cdl.config import _
from cdl.obj import ImageObj
[docs]
class AdjustGammaParam(gds.DataSet):
"""Gamma adjustment parameters"""
gamma = gds.FloatItem(
_("Gamma"),
default=1.0,
min=0.0,
help=_("Gamma correction factor (higher values give more contrast)."),
)
gain = gds.FloatItem(
_("Gain"),
default=1.0,
min=0.0,
help=_("Gain factor (higher values give more contrast)."),
)
[docs]
def compute_adjust_gamma(src: ImageObj, p: AdjustGammaParam) -> ImageObj:
"""Gamma correction with :py:func:`skimage.exposure.adjust_gamma`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(src, "adjust_gamma", f"gamma={p.gamma}, gain={p.gain}")
dst.data = exposure.adjust_gamma(src.data, gamma=p.gamma, gain=p.gain)
restore_data_outside_roi(dst, src)
return dst
[docs]
class AdjustLogParam(gds.DataSet):
"""Logarithmic adjustment parameters"""
gain = gds.FloatItem(
_("Gain"),
default=1.0,
min=0.0,
help=_("Gain factor (higher values give more contrast)."),
)
inv = gds.BoolItem(
_("Inverse"),
default=False,
help=_("If True, apply inverse logarithmic transformation."),
)
[docs]
def compute_adjust_log(src: ImageObj, p: AdjustLogParam) -> ImageObj:
"""Compute log correction with :py:func:`skimage.exposure.adjust_log`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(src, "adjust_log", f"gain={p.gain}, inv={p.inv}")
dst.data = exposure.adjust_log(src.data, gain=p.gain, inv=p.inv)
restore_data_outside_roi(dst, src)
return dst
[docs]
class AdjustSigmoidParam(gds.DataSet):
"""Sigmoid adjustment parameters"""
cutoff = gds.FloatItem(
_("Cutoff"),
default=0.5,
min=0.0,
max=1.0,
help=_("Cutoff value (higher values give more contrast)."),
)
gain = gds.FloatItem(
_("Gain"),
default=10.0,
min=0.0,
help=_("Gain factor (higher values give more contrast)."),
)
inv = gds.BoolItem(
_("Inverse"),
default=False,
help=_("If True, apply inverse sigmoid transformation."),
)
[docs]
def compute_adjust_sigmoid(src: ImageObj, p: AdjustSigmoidParam) -> ImageObj:
"""Compute sigmoid correction with :py:func:`skimage.exposure.adjust_sigmoid`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(
src, "adjust_sigmoid", f"cutoff={p.cutoff}, gain={p.gain}, inv={p.inv}"
)
dst.data = exposure.adjust_sigmoid(
src.data, cutoff=p.cutoff, gain=p.gain, inv=p.inv
)
restore_data_outside_roi(dst, src)
return dst
[docs]
class RescaleIntensityParam(gds.DataSet):
"""Intensity rescaling parameters"""
_dtype_list = ["image", "dtype"] + VALID_DTYPES_STRLIST
in_range = gds.ChoiceItem(
_("Input range"),
list(zip(_dtype_list, _dtype_list)),
default="image",
help=_(
"Min and max intensity values of input image ('image' refers to input "
"image min/max levels, 'dtype' refers to input image data type range)."
),
)
out_range = gds.ChoiceItem(
_("Output range"),
list(zip(_dtype_list, _dtype_list)),
default="dtype",
help=_(
"Min and max intensity values of output image ('image' refers to input "
"image min/max levels, 'dtype' refers to input image data type range).."
),
)
[docs]
def compute_rescale_intensity(src: ImageObj, p: RescaleIntensityParam) -> ImageObj:
"""Rescale image intensity levels
with :py:func:`skimage.exposure.rescale_intensity`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(
src,
"rescale_intensity",
f"in_range={p.in_range}, out_range={p.out_range}",
)
dst.data = exposure.rescale_intensity(
src.data, in_range=p.in_range, out_range=p.out_range
)
restore_data_outside_roi(dst, src)
return dst
[docs]
class EqualizeHistParam(gds.DataSet):
"""Histogram equalization parameters"""
nbins = gds.IntItem(
_("Number of bins"),
min=1,
default=256,
help=_("Number of bins for image histogram."),
)
[docs]
def compute_equalize_hist(src: ImageObj, p: EqualizeHistParam) -> ImageObj:
"""Histogram equalization with :py:func:`skimage.exposure.equalize_hist`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(src, "equalize_hist", f"nbins={p.nbins}")
dst.data = exposure.equalize_hist(src.data, nbins=p.nbins)
restore_data_outside_roi(dst, src)
return dst
[docs]
class EqualizeAdaptHistParam(EqualizeHistParam):
"""Adaptive histogram equalization parameters"""
clip_limit = gds.FloatItem(
_("Clipping limit"),
default=0.01,
min=0.0,
max=1.0,
help=_("Clipping limit (higher values give more contrast)."),
)
[docs]
def compute_equalize_adapthist(src: ImageObj, p: EqualizeAdaptHistParam) -> ImageObj:
"""Adaptive histogram equalization
with :py:func:`skimage.exposure.equalize_adapthist`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
dst = dst_11(
src, "equalize_adapthist", f"nbins={p.nbins}, clip_limit={p.clip_limit}"
)
dst.data = exposure.equalize_adapthist(
src.data, clip_limit=p.clip_limit, nbins=p.nbins
)
restore_data_outside_roi(dst, src)
return dst
