# Copyright (C) 2023 ISIS Rutherford Appleton Laboratory UKRI
# SPDX - License - Identifier: GPL-3.0-or-later
from __future__ import annotations
from functools import partial
from typing import Any, Dict, TYPE_CHECKING
from PyQt5.QtWidgets import QComboBox, QCheckBox
import numpy as np
from mantidimaging import helper as h
from mantidimaging.core.operations.base_filter import BaseFilter, FilterGroup
from mantidimaging.core.parallel import utility as pu, shared as ps
from mantidimaging.core.utility.progress_reporting import Progress
from mantidimaging.gui.utility.qt_helpers import Type
from mantidimaging.gui.widgets.dataset_selector import DatasetSelectorWidgetView
if TYPE_CHECKING:
from mantidimaging.core.data import ImageStack
# The smallest and largest allowed pixel value
MINIMUM_PIXEL_VALUE = 1e-9
MAXIMUM_PIXEL_VALUE = 1e9
valid_methods = ["Only Before", "Only After", "Both, concatenated"]
[docs]def enable_correct_fields_only(selected_flat_fielding_widget, flat_before_widget, flat_after_widget, dark_before_widget,
dark_after_widget, use_dark_frame):
text = selected_flat_fielding_widget.currentText()
use_dark = use_dark_frame.isChecked()
if text == "Only Before":
flat_before_widget.setEnabled(True)
flat_after_widget.setEnabled(False)
dark_before_widget.setEnabled(use_dark)
dark_after_widget.setEnabled(False)
elif text == "Only After":
flat_before_widget.setEnabled(False)
flat_after_widget.setEnabled(True)
dark_before_widget.setEnabled(False)
dark_after_widget.setEnabled(use_dark)
elif text == "Both, concatenated":
flat_before_widget.setEnabled(True)
flat_after_widget.setEnabled(True)
dark_before_widget.setEnabled(use_dark)
dark_after_widget.setEnabled(use_dark)
else:
raise RuntimeError("Unknown field parameter")
[docs]class FlatFieldFilter(BaseFilter):
"""Uses the flat (open beam) and dark images to normalise a stack of images (radiograms, projections),
and to correct for a beam profile, scintillator imperfections and/or detector inhomogeneities. This
operation produces images of transmission values.
In practice, several open beam and dark images are averaged in the flat-fielding process.
Intended to be used on: Projections
When: As one of the first pre-processing steps
Caution: Make sure the correct stacks are selected for flat and dark.
Caution: Check that the flat and dark images don't have any very bright pixels,
or this will introduce additional noise in the sample. Remove outliers before flat-fielding.
"""
filter_name = 'Flat-fielding'
[docs] @staticmethod
def filter_func(images: ImageStack,
flat_before: ImageStack = None,
flat_after: ImageStack = None,
dark_before: ImageStack = None,
dark_after: ImageStack = None,
selected_flat_fielding: str = None,
use_dark: bool = True,
progress=None) -> ImageStack:
"""Do background correction with flat and dark images.
:param images: Sample data which is to be processed. Expected in radiograms
:param flat_before: Flat (open beam) image to use in normalization, collected before the sample was imaged
:param flat_after: Flat (open beam) image to use in normalization, collected after the sample was imaged
:param dark_before: Dark image to use in normalization, collected before the sample was imaged
:param dark_after: Dark image to use in normalization, collected after the sample was imaged
:param selected_flat_fielding: Select which of the flat fielding methods to use, just Before stacks, just After
stacks or combined.
:param use_dark: Whether to use dark frame subtraction
:return: Filtered data (stack of images)
"""
h.check_data_stack(images)
if selected_flat_fielding == "Both, concatenated" and flat_after is not None and flat_before is not None \
and dark_after is not None and dark_before is not None:
flat_avg = (flat_before.data.mean(axis=0) + flat_after.data.mean(axis=0)) / 2.0
if use_dark:
dark_avg = (dark_before.data.mean(axis=0) + dark_after.data.mean(axis=0)) / 2.0
elif selected_flat_fielding == "Only Before" and flat_before is not None and dark_before is not None:
flat_avg = flat_before.data.mean(axis=0)
if use_dark:
dark_avg = dark_before.data.mean(axis=0)
elif selected_flat_fielding == "Only After" and flat_after is not None and dark_after is not None:
flat_avg = flat_after.data.mean(axis=0)
if use_dark:
dark_avg = dark_after.data.mean(axis=0)
else:
raise ValueError("selected_flat_fielding not in:", valid_methods)
if not use_dark:
dark_avg = np.zeros_like(flat_avg)
if flat_avg is not None and dark_avg is not None:
if 2 != flat_avg.ndim or 2 != dark_avg.ndim:
raise ValueError(
f"Incorrect shape of the flat image ({flat_avg.shape}) or dark image ({dark_avg.shape}) \
which should match the shape of the sample images ({images.data.shape})")
if not images.data.shape[1:] == flat_avg.shape == dark_avg.shape:
raise ValueError(f"Not all images are the expected shape: {images.data.shape[1:]}, instead "
f"flat had shape: {flat_avg.shape}, and dark had shape: {dark_avg.shape}")
progress = Progress.ensure_instance(progress,
num_steps=images.data.shape[0],
task_name='Background Correction')
_execute(images, flat_avg, dark_avg, progress)
h.check_data_stack(images)
return images
[docs] @staticmethod
def register_gui(form, on_change, view) -> Dict[str, Any]:
from mantidimaging.gui.utility import add_property_to_form
_, selected_flat_fielding_widget = add_property_to_form("Flat Fielding Method",
Type.CHOICE,
valid_values=valid_methods,
form=form,
filters_view=view,
on_change=on_change,
tooltip="Choosing which stacks to use during flat "
"fielding")
_, flat_before_widget = add_property_to_form("Flat Before",
Type.STACK,
form=form,
filters_view=view,
on_change=on_change,
tooltip="Flat images to be used for correcting the flat field.")
_, flat_after_widget = add_property_to_form("Flat After",
Type.STACK,
form=form,
filters_view=view,
on_change=on_change,
tooltip="Flat images to be used for correcting the flat field.")
_, use_dark_widget = add_property_to_form("Use Dark Frame",
Type.BOOL,
default_value=True,
form=form,
filters_view=view,
on_change=on_change,
tooltip="Use dark frame subtraction")
_, dark_before_widget = add_property_to_form("Dark Before",
Type.STACK,
form=form,
filters_view=view,
on_change=on_change,
tooltip="Dark images to be used for subtracting the background.")
_, dark_after_widget = add_property_to_form("Dark After",
Type.STACK,
form=form,
filters_view=view,
on_change=on_change,
tooltip="Dark images to be used for subtracting the background.")
assert isinstance(flat_before_widget, DatasetSelectorWidgetView)
flat_before_widget.setMaximumWidth(375)
flat_before_widget.subscribe_to_main_window(view.main_window)
flat_before_widget.try_to_select_relevant_stack("Flat")
flat_before_widget.try_to_select_relevant_stack("Flat Before")
assert isinstance(flat_after_widget, DatasetSelectorWidgetView)
flat_after_widget.setMaximumWidth(375)
flat_after_widget.subscribe_to_main_window(view.main_window)
flat_after_widget.try_to_select_relevant_stack("Flat After")
flat_after_widget.setEnabled(False)
assert isinstance(dark_before_widget, DatasetSelectorWidgetView)
dark_before_widget.setMaximumWidth(375)
dark_before_widget.subscribe_to_main_window(view.main_window)
dark_before_widget.try_to_select_relevant_stack("Dark")
dark_before_widget.try_to_select_relevant_stack("Dark Before")
assert isinstance(dark_after_widget, DatasetSelectorWidgetView)
dark_after_widget.setMaximumWidth(375)
dark_after_widget.subscribe_to_main_window(view.main_window)
dark_after_widget.try_to_select_relevant_stack("Dark After")
dark_after_widget.setEnabled(False)
# Ensure that fields that are not currently used are disabled
assert (isinstance(selected_flat_fielding_widget, QComboBox))
selected_flat_fielding_widget.currentTextChanged.connect(lambda text: enable_correct_fields_only(
selected_flat_fielding_widget, flat_before_widget, flat_after_widget, dark_before_widget, dark_after_widget,
use_dark_widget))
assert (isinstance(use_dark_widget, QCheckBox))
use_dark_widget.stateChanged.connect(lambda text: enable_correct_fields_only(
selected_flat_fielding_widget, flat_before_widget, flat_after_widget, dark_before_widget, dark_after_widget,
use_dark_widget))
return {
'selected_flat_fielding_widget': selected_flat_fielding_widget,
'flat_before_widget': flat_before_widget,
'flat_after_widget': flat_after_widget,
'dark_before_widget': dark_before_widget,
'dark_after_widget': dark_after_widget,
'use_dark_widget': use_dark_widget,
}
[docs] @staticmethod
def execute_wrapper( # type: ignore
flat_before_widget: DatasetSelectorWidgetView, flat_after_widget: DatasetSelectorWidgetView,
dark_before_widget: DatasetSelectorWidgetView, dark_after_widget: DatasetSelectorWidgetView,
selected_flat_fielding_widget: QComboBox, use_dark_widget: QCheckBox) -> partial:
flat_before_images = BaseFilter.get_images_from_stack(flat_before_widget, "flat before")
flat_after_images = BaseFilter.get_images_from_stack(flat_after_widget, "flat after")
dark_before_images = BaseFilter.get_images_from_stack(dark_before_widget, "dark before")
dark_after_images = BaseFilter.get_images_from_stack(dark_after_widget, "dark after")
selected_flat_fielding = selected_flat_fielding_widget.currentText()
use_dark = use_dark_widget.isChecked()
return partial(FlatFieldFilter.filter_func,
flat_before=flat_before_images,
flat_after=flat_after_images,
dark_before=dark_before_images,
dark_after=dark_after_images,
selected_flat_fielding=selected_flat_fielding,
use_dark=use_dark)
[docs] @staticmethod
def validate_execute_kwargs(kwargs):
# Validate something is in both path text inputs
if 'selected_flat_fielding_widget' not in kwargs:
return False
if 'flat_before_widget' not in kwargs and 'dark_before_widget' not in kwargs or\
'flat_after_widget' not in kwargs and 'dark_after_widget' not in kwargs:
return False
assert isinstance(kwargs["flat_before_widget"], DatasetSelectorWidgetView)
assert isinstance(kwargs["flat_after_widget"], DatasetSelectorWidgetView)
assert isinstance(kwargs["dark_before_widget"], DatasetSelectorWidgetView)
assert isinstance(kwargs["dark_after_widget"], DatasetSelectorWidgetView)
return True
[docs] @staticmethod
def group_name() -> FilterGroup:
return FilterGroup.Basic
def _divide(data, norm_divide):
np.true_divide(data, norm_divide, out=data)
def _subtract(data, dark=None):
# specify out to do in place, otherwise the data is copied
np.subtract(data, dark, out=data)
def _norm_divide(flat: np.ndarray, dark: np.ndarray) -> np.ndarray:
# subtract dark from flat
return np.subtract(flat, dark)
def _execute(images: ImageStack, flat=None, dark=None, progress=None):
"""A benchmark justifying the current implementation, performed on
500x2048x2048 images.
#1 Separate runs
Subtract (sequential with np.subtract(data, dark, out=data)) - 13s
Divide (par) - 1.15s
#2 Separate parallel runs
Subtract (par) - 5.5s
Divide (par) - 1.15s
#3 Added subtract into _divide so that it is:
np.true_divide(
np.subtract(data, dark, out=data), norm_divide, out=data)
Subtract then divide (par) - 55s
"""
with progress:
progress.update(msg="Applying background correction")
if images.uses_shared_memory:
shared_dark = pu.copy_into_shared_memory(dark)
norm_divide = pu.copy_into_shared_memory(_norm_divide(flat, dark))
else:
shared_dark = pu.SharedArray(dark, None)
norm_divide = pu.SharedArray(_norm_divide(flat, dark), None)
# prevent divide-by-zero issues, and negative pixels make no sense
norm_divide.array[norm_divide.array == 0] = MINIMUM_PIXEL_VALUE
# subtract the dark from all images
do_subtract = ps.create_partial(_subtract, fwd_function=ps.inplace_second_2d)
arrays = [images.shared_array, shared_dark]
ps.execute(do_subtract, arrays, images.data.shape[0], progress)
# divide the data by (flat - dark)
do_divide = ps.create_partial(_divide, fwd_function=ps.inplace_second_2d)
arrays = [images.shared_array, norm_divide]
ps.execute(do_divide, arrays, images.data.shape[0], progress)
return images