konfai.utils package#

Submodules#

konfai.utils.ITK module#

SimpleITK-based helpers for geometric transforms, resampling, and masking.

konfai.utils.ITK.compose_transform(transform_files, image=None)[source]#

Return type:: sitk.CompositeTransform

konfai.utils.ITK.flatten_transform(transform_files)[source]#

Return type:: sitk.AffineTransform

konfai.utils.ITK.apply_to_image_rigid_transform(image, transform_files)[source]#

Return type:: sitk.Image

konfai.utils.ITK.apply_to_data_transform(data, transform_files)[source]#

Return type:: np.ndarray

konfai.utils.ITK.resample_itk(image_reference, image, transform_files, mask=False, default_pixel_value=None, torch_resample=False)[source]#

Return type:: sitk.Image

konfai.utils.ITK.parametermap_to_transform(path_src)[source]#

Return type:: sitk.Transform | list[sitk.Transform]

konfai.utils.ITK.resample_isotropic(image, spacing=None)[source]#

Return type:: Image

konfai.utils.ITK.resample_resize(image, size=None)[source]#

konfai.utils.ITK.box_with_mask(mask, label, dilatations)[source]#

Return type:: ndarray

konfai.utils.ITK.crop_with_mask(image, box)[source]#

Return type:: Image

konfai.utils.ITK.format_mask_label(mask, labels)[source]#

Return type:: Image

konfai.utils.ITK.get_flat_label(mask, labels=None)[source]#

Return type:: Image

konfai.utils.ITK.clip_and_cast(image, min_value, max_value, dtype)[source]#

Return type:: Image

konfai.utils.config module#

Configuration helpers that map YAML trees to KonfAI Python objects.

class konfai.utils.config.Range(min, max)[source]#

Bases: object

UI hint attached to a parameter’s type — its inclusive numeric bounds.

Use Annotated[int, Range(0, 100)] (or float) in a config-bound signature: the binder ignores the metadata and validates the base type, while a UI reads the bounds to size a spinbox. Introspection-only.

min: float#

max: float#

class konfai.utils.config.Choices(values)[source]#

Bases: object

UI hint attached to a parameter’s type — its allowed values.

Use Annotated[str, Choices([...])] for a fixed list, or Annotated[str, Choices(resolver)] where resolver is a zero-arg callable the app owns (e.g. one that lists a model registry it already fetches). resolve() returns the list — a reader calls it lazily, so the app resolves its own values and no tool re-fetches. Introspection-only; the binder ignores it (a value outside the list is still accepted, e.g. a local path). For a small FIXED, binder-validated set, prefer Literal[...].

resolve()[source]#

Return type:: list

class konfai.utils.config.Config(key)[source]#

Bases: object

Context manager for reading and updating a subtree of the active YAML config.

Parameters:: key (str) – Dot-separated path pointing to the configuration subtree to inspect or materialize.

create_dictionary(data, keys, i)[source]#

Return type:: dict

merge(dict1, dict2)[source]#

Return type:: dict

get_value(name, default)[source]#

Return type:: object

konfai.utils.config.config(key=None)[source]#

Attach a KonfAI configuration key to a class or callable.

Parameters:: key (str | None) – Configuration branch handled by the decorated object.
Returns:: Decorator storing the key on the decorated object.
Return type:: Callable

konfai.utils.config.apply_config(konfai_args=None)[source]#

Recursively instantiate callables from the active KonfAI configuration.

Parameters:: konfai_args (str | None) – Root configuration path used to resolve nested constructor arguments.
Returns:: Decorator that injects configuration-backed arguments at call time.
Return type:: Callable

konfai.utils.dataset module#

Dataset file abstractions and image conversion utilities for KonfAI.

class konfai.utils.dataset.Attribute(attributes=None)[source]#

Bases: dict[str, Any]

Metadata container storing repeated values with a stack-like naming scheme.

pop(key, default=None)[source]#

If the key is not found, return the default if given; otherwise, raise a KeyError.

Return type:: Any

get_np_array(key)[source]#

Return type:: ndarray

get_tensor(key)[source]#

Return type:: Tensor

pop_np_array(key)[source]#

Return type:: ndarray

pop_tensor(key)[source]#

Return type:: Tensor

is_info(key, value)[source]#

Return type:: bool

konfai.utils.dataset.is_an_image(attributes)[source]#

Return whether the given attribute set contains image geometry metadata.

Return type:: bool

konfai.utils.dataset.data_to_image(data, attributes)[source]#

Convert a NumPy array and KonfAI attributes into a SimpleITK image.

Return type:: Image

konfai.utils.dataset.image_to_data(image)[source]#

Convert a SimpleITK image into a channel-first NumPy array and attributes.

Return type:: tuple[ndarray, Attribute]

konfai.utils.dataset.get_infos(filename)[source]#

Read shape and metadata from an image file without loading its full pixel data.

Return type:: tuple[list[int], Attribute]

konfai.utils.dataset.read_landmarks(filename)[source]#

Read Slicer-style fiducial landmarks from disk.

Return type:: ndarray | None

konfai.utils.dataset.write_landmarks(data, filename)[source]#

Write landmarks to the Slicer Markups fiducial CSV-like format.

Return type:: None

class konfai.utils.dataset.Dataset(filename, file_format)[source]#

Bases: object

Filesystem or HDF5-backed dataset abstraction used across KonfAI.

class AbstractFile[source]#

Bases: ABC

abstractmethod file_to_data(group, name)[source]#

Return type:: tuple[ndarray, Attribute]

abstractmethod file_to_data_slice(group, name, slices)[source]#

Return type:: tuple[ndarray, Attribute]

abstractmethod file_to_data_statistics(group, name, channels=None)[source]#

Return type:: dict[str, float]

abstractmethod data_to_file(name, data, attributes=None)[source]#

Return type:: None

abstractmethod get_names(group)[source]#

Return type:: list[str]

abstractmethod get_group()[source]#

Return type:: list[str]

abstractmethod is_exist(group, name=None)[source]#

Return type:: bool

abstractmethod get_infos(group, name)[source]#

Return type:: tuple[list[int], Attribute]

class H5File(filename, read)[source]#

Bases: AbstractFile

file_to_data(groups, name)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_slice(groups, name, slices)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_statistics(groups, name, channels=None)[source]#

Return type:: dict[str, float]

data_to_file(name, data, attributes=None)[source]#

Return type:: None

is_exist(group, name=None)[source]#

Return type:: bool

get_names(groups, h5_group=None)[source]#

get_group()[source]#

Return type:: list[str]

get_infos(groups, name)[source]#

Return type:: tuple[list[int], Attribute]

class SitkFile(filename, read, file_format)[source]#

Bases: AbstractFile

file_to_data(group, name)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_slice(group, name, slices)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_statistics(group, name, channels=None)[source]#

Return type:: dict[str, float]

is_vtk_polydata(obj)[source]#

Return type:: bool

data_to_file(name, data, attributes=None)[source]#

Return type:: None

is_exist(group, name=None)[source]#

Return type:: bool

get_names(group)[source]#

Return type:: list[str]

get_group()[source]#

Return type:: list[str]

get_infos(group, name)[source]#

Return type:: tuple[list[int], Attribute]

class OmeZarrFile(filename, read, level=0)[source]#

Bases: AbstractFile

OME-NGFF backend using chunked Zarr reads for KonfAI patches.

level selects the multiscale pyramid resolution to read (0 = full resolution, higher = coarser); it comes from the omezarr@<level> dataset-spec suffix.

file_to_data(group, name)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_slice(group, name, slices)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_statistics(group, name, channels=None)[source]#

Return type:: dict[str, float]

data_to_file(name, data, attributes=None)[source]#

Return type:: None

get_names(group)[source]#

Return type:: list[str]

get_group()[source]#

Return type:: list[str]

is_exist(group, name=None)[source]#

Return type:: bool

get_infos(group, name)[source]#

Return type:: tuple[list[int], Attribute]

class DicomFile(filename, read)[source]#

Bases: AbstractFile

DICOM series backend with header-only metadata and slice-level reads.

file_to_data(group, name)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_slice(group, name, slices)[source]#

Return type:: tuple[ndarray, Attribute]

file_to_data_statistics(group, name, channels=None)[source]#

Return type:: dict[str, float]

data_to_file(name, data, attributes=None)[source]#

Return type:: None

get_names(group)[source]#

Return type:: list[str]

get_group()[source]#

Return type:: list[str]

is_exist(group, name=None)[source]#

Return type:: bool

get_infos(group, name)[source]#

Return type:: tuple[list[int], Attribute]

class File(filename, read, file_format, level=0)[source]#: Bases: object

write(group, name, data, attributes=None)[source]#

Return type:: None

read_data(groups, name)[source]#

Return type:: tuple[ndarray, Attribute]

read_data_slice(groups, name, slices)[source]#

Return type:: tuple[ndarray, Attribute]

read_data_statistics(groups, name, channels=None)[source]#

Return type:: dict[str, float]

read_transform(group, name)[source]#

Return type:: Transform

read_image(group, name)[source]#

Return type:: Image

get_size(group)[source]#

Return type:: int

is_group_exist(group)[source]#

Return type:: bool

is_dataset_exist(group, name)[source]#

Return type:: bool

get_names(groups, index=None)[source]#

Return type:: list[str]

get_group()[source]#

Return type:: list[str]

get_infos(groups, name)[source]#

Return type:: tuple[list[int], Attribute]

get_statistics(groups)[source]#

Return type:: dict[str, dict[str, dict[str, float | list[float]]]]

konfai.utils.utils module#

Compatibility facade for KonfAI utility helpers and lightweight array utilities.

konfai.utils.utils.get_module(classpath, default_classpath)[source]#

Import the module a classpath names and return it with the name to take from it.

A : separates the module from the name: everything before the last one is the module, so torch:nn:L1Loss and torch.nn:L1Loss name the same class. Without one, the name is taken from the kind’s own package, and the dots between them lead there: Dice is that package’s own, segmentation.UNet.UNet is under two of its subpackages.

Return type:: tuple[ModuleType, str]

konfai.utils.utils.get_patch_slices_from_nb_patch_per_dim(patch_size_tmp, nb_patch_per_dim, overlap)[source]#

Return type:: list[tuple[slice, ...]]

konfai.utils.utils.get_patch_slices_from_shape(patch_size, shape, overlap_tmp)[source]#

Return type:: tuple[list[tuple[slice, ...]], list[tuple[int, bool]]]

konfai.utils.utils.is_windows_absolute_path(path)[source]#

Return whether path looks like a Windows absolute path.

Return type:: bool

konfai.utils.utils.split_format_level(file_format)[source]#

Split an optional pyramid-level suffix from a format token.

Used by the OME-Zarr backend to pick a multiscale resolution directly in the dataset spec, e.g. omezarr@2 selects pyramid level 2 (coarser), independently of any transform. Returns (base_format, level) and defaults to level 0 (full resolution) when no @<int> suffix is present.

Return type:: tuple[str, int]

konfai.utils.utils.split_path_spec(value, *, default_format='mha', allowed_flags=None, supported_extensions=None)[source]#

Split a KonfAI path[:flag]:format spec without breaking Windows paths.

KonfAI accepts dataset-like strings such as:

./Dataset
./Dataset:mha
./Dataset:a:mha
C:\Data\Dataset:mha
C:\Data\Dataset:a:mha

Parsing is performed from the right so the drive separator in Windows paths is preserved.

Return type:: tuple[str, str | None, str]

Module contents#

Utility modules supporting configuration, datasets, ITK, DICOM, OME-Zarr, and runtime helpers.