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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Threshold computation module
----------------------------
This module provides various thresholding techniques for image segmentation.
Thresholding is a simple yet effective method to separate objects from the background
in an image by converting it into a binary image based on a specified threshold value.
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# Note:
# ----
# - All `guidata.dataset.DataSet` parameter classes must also be imported
# in the `sigima.params` module.
# - All functions decorated by `computation_function` must be imported in the upper
# level `sigima.proc.image` module.
from __future__ import annotations
import guidata.dataset as gds
import skimage.util
from skimage import filters
from sigima.config import _
from sigima.objects.image import ImageObj
from sigima.proc.decorator import computation_function
from sigima.proc.image.base import dst_1_to_1, restore_data_outside_roi
# NOTE: Only parameter classes DEFINED in this module should be included in __all__.
# Parameter classes imported from other modules (like sigima.proc.base) should NOT
# be re-exported to avoid Sphinx cross-reference conflicts. The sigima.params module
# serves as the central API point that imports and re-exports all parameter classes.
__all__ = [
"ThresholdParam",
"threshold",
"threshold_isodata",
"threshold_li",
"threshold_mean",
"threshold_minimum",
"threshold_otsu",
"threshold_triangle",
"threshold_yen",
]
class ThresholdParam(gds.DataSet):
"""Histogram threshold parameters"""
methods = (
("manual", _("Manual")),
("isodata", "ISODATA"),
("li", "Li"),
("mean", _("Mean")),
("minimum", _("Minimum")),
("otsu", "Otsu"),
("triangle", _("Triangle")),
("yen", "Yen"),
)
_method_prop = gds.GetAttrProp("method")
method = gds.ChoiceItem(_("Threshold method"), methods, default="manual").set_prop(
"display", store=_method_prop
)
bins = gds.IntItem(_("Number of bins"), default=256, min=1).set_prop(
"display",
active=gds.FuncProp(_method_prop, lambda x: x not in ("li", "mean", "manual")),
)
value = gds.FloatItem(_("Threshold value"), default=0.0).set_prop(
"display", active=gds.FuncProp(_method_prop, lambda x: x == "manual")
)
operation = gds.ChoiceItem(
_("Operation"),
((">", _("Greater than")), ("<", _("Less than"))),
default=">",
)
@computation_function()
def threshold(src: ImageObj, p: ThresholdParam) -> ImageObj:
"""Compute the threshold, using one of the available algorithms:
- Manual: a fixed threshold value
- ISODATA: :py:func:`skimage.filters.threshold_isodata`
- Li: :py:func:`skimage.filters.threshold_li`
- Mean: :py:func:`skimage.filters.threshold_mean`
- Minimum: :py:func:`skimage.filters.threshold_minimum`
- Otsu: :py:func:`skimage.filters.threshold_otsu`
- Triangle: :py:func:`skimage.filters.threshold_triangle`
- Yen: :py:func:`skimage.filters.threshold_yen`
Args:
src: input image object
p: parameters
Returns:
Output image object
"""
if p.method == "manual":
suffix = f"value={p.value}"
value = p.value
else:
suffix = f"method={p.method}"
if p.method not in ("li", "mean"):
suffix += f", nbins={p.bins}"
func = getattr(filters, f"threshold_{p.method}")
args = [] if p.method in ("li", "mean") else [p.bins]
value = func(src.data, *args)
suffix += f", op='{p.operation}'"
dst = dst_1_to_1(src, "threshold", suffix)
data = src.data > value if p.operation == ">" else src.data < value
dst.data = skimage.util.img_as_ubyte(data)
dst.zscalemin, dst.zscalemax = 0, 255 # LUT range
dst.set_metadata_option("colormap", "gray")
restore_data_outside_roi(dst, src)
return dst
@computation_function()
def threshold_isodata(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Isodata algorithm with default parameters,
see :py:func:`skimage.filters.threshold_isodata`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="isodata"))
@computation_function()
def threshold_li(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Li algorithm with default parameters,
see :py:func:`skimage.filters.threshold_li`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="li"))
@computation_function()
def threshold_mean(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Mean algorithm,
see :py:func:`skimage.filters.threshold_mean`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="mean"))
@computation_function()
def threshold_minimum(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Minimum algorithm with default parameters,
see :py:func:`skimage.filters.threshold_minimum`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="minimum"))
@computation_function()
def threshold_otsu(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Otsu algorithm with default parameters,
see :py:func:`skimage.filters.threshold_otsu`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="otsu"))
@computation_function()
def threshold_triangle(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Triangle algorithm with default parameters,
see :py:func:`skimage.filters.threshold_triangle`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="triangle"))
@computation_function()
def threshold_yen(src: ImageObj) -> ImageObj:
"""Compute the threshold using the Yen algorithm with default parameters,
see :py:func:`skimage.filters.threshold_yen`
Args:
src: input image object
Returns:
Output image object
"""
return threshold(src, ThresholdParam.create(method="yen"))
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