mantidimaging.core.operations.flat_fielding.flat_fielding module

class mantidimaging.core.operations.flat_fielding.flat_fielding.FlatFieldFilter

Bases: mantidimaging.core.operations.base_filter.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.

static execute_wrapper(flat_before_widget: mantidimaging.gui.widgets.dataset_selector.view.DatasetSelectorWidgetView, flat_after_widget: mantidimaging.gui.widgets.dataset_selector.view.DatasetSelectorWidgetView, dark_before_widget: mantidimaging.gui.widgets.dataset_selector.view.DatasetSelectorWidgetView, dark_after_widget: mantidimaging.gui.widgets.dataset_selector.view.DatasetSelectorWidgetView, selected_flat_fielding_widget: PyQt5.QtWidgets.QComboBox, use_dark_widget: PyQt5.QtWidgets.QCheckBox) → functools.partial

Should construct a partial call to _filter_func using values taken from the widgets passed to this function as kwargs. :param kwargs: widgets which contain values required for _filter_func. :return: a partial call to _filter_func using parameters taken from the input widgets.

static filter_func(images: mantidimaging.core.data.images.Images, flat_before: Optional[mantidimaging.core.data.images.Images] = None, flat_after: Optional[mantidimaging.core.data.images.Images] = None, dark_before: Optional[mantidimaging.core.data.images.Images] = None, dark_after: Optional[mantidimaging.core.data.images.Images] = None, selected_flat_fielding: Optional[str] = None, use_dark: bool = True, cores=None, chunksize=None, progress=None) → mantidimaging.core.data.images.Images

Do background correction with flat and dark images.

Parameters

• images – Sample data which is to be processed. Expected in radiograms

• flat_before – Flat (open beam) image to use in normalization, collected before the sample was imaged

• flat_after – Flat (open beam) image to use in normalization, collected after the sample was imaged

• dark_before – Dark image to use in normalization, collected before the sample was imaged

• dark_after – Dark image to use in normalization, collected after the sample was imaged

• selected_flat_fielding – Select which of the flat fielding methods to use, just Before stacks, just After stacks or combined.

• use_dark – Whether to use dark frame subtraction

• cores – The number of cores that will be used to process the data.

• chunksize – The number of chunks that each worker will receive.

Returns

Filtered data (stack of images)

filter_name = 'Flat-fielding'

static group_name() → mantidimaging.core.operations.base_filter.FilterGroup

static register_gui(form, on_change, view) → Dict[str, Any]

Adds any required input widgets to the given form and returns references to them.

The return values should be in a dict which can be unpacked as kwargs for a call to the operations execute_wrapper.

Parameters

• view –

• form – the layout to create input widgets in

• on_change – the filter view action to be bound to all created inputs

Returns

the widgets bound as kwargs which are needed to call execute_wrapper

static validate_execute_kwargs(kwargs)

mantidimaging.core.operations.flat_fielding.flat_fielding.enable_correct_fields_only(selected_flat_fielding_widget, flat_before_widget, flat_after_widget, dark_before_widget, dark_after_widget, use_dark_frame)