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)

Return type:

CompositeTransform

konfai.utils.ITK.flatten_transform(transform_files)

Return type:

AffineTransform

konfai.utils.ITK.apply_to_image_rigid_transform(image, transform_files)

Return type:

Image

konfai.utils.ITK.apply_to_data_transform(data, transform_files)

Return type:

Image

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

Return type:

Image

konfai.utils.ITK.parametermap_to_transform(path_src)

Return type:

Transform | list[Transform]

konfai.utils.ITK.resample_isotropic(image, spacing=None)

Return type:

Image

konfai.utils.ITK.resample_resize(image, size=None)

konfai.utils.ITK.box_with_mask(mask, label, dilatations)

Return type:

ndarray

konfai.utils.ITK.crop_with_mask(image, box)

Return type:

Image

konfai.utils.ITK.format_mask_label(mask, labels)

Return type:

Image

konfai.utils.ITK.get_flat_label(mask, labels=None)

Return type:

Image

konfai.utils.ITK.clip_and_cast(image, min_value, max_value, dtype)

Return type:

Image

konfai.utils.config module

Configuration helpers that map YAML trees to KonfAI Python objects.

class konfai.utils.config.Config(key)

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)

Return type:

dict

merge(dict1, dict2)

Return type:

dict

get_value(name, default)

Return type:

object

konfai.utils.config.config(key=None)

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)

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)

Bases: dict[str, Any]

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

pop(key, default=None)

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

Return type:

Any

get_np_array(key)

Return type:

ndarray

get_tensor(key)

Return type:

Tensor

pop_np_array(key)

pop_tensor(key)

Return type:

Tensor

is_info(key, value)

Return type:

bool

konfai.utils.dataset.is_an_image(attributes)

Return whether the given attribute set contains image geometry metadata.

konfai.utils.dataset.data_to_image(data, attributes)

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

Return type:

Image

konfai.utils.dataset.image_to_data(image)

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

Return type:

tuple[ndarray, Attribute]

konfai.utils.dataset.get_infos(filename)

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)

Read Slicer-style fiducial landmarks from disk.

Return type:

ndarray | None

konfai.utils.dataset.write_landmarks(data, filename)

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

Return type:

None

class konfai.utils.dataset.Dataset(filename, file_format)

Bases: object

Filesystem or HDF5-backed dataset abstraction used across KonfAI.

class AbstractFile

Bases: ABC

abstractmethod file_to_data(group, name)

Return type:

tuple[ndarray, Attribute]

abstractmethod file_to_data_slice(group, name, slices)

Return type:

tuple[ndarray, Attribute]

abstractmethod file_to_data_statistics(group, name, channels=None)

Return type:

dict[str, float]

abstractmethod data_to_file(name, data, attributes=None)

Return type:

None

abstractmethod get_names(group)

Return type:

list[str]

abstractmethod get_group()

Return type:

list[str]

abstractmethod is_exist(group, name=None)

Return type:

bool

abstractmethod get_infos(group, name)

Return type:

tuple[list[int], Attribute]

class H5File(filename, read)

Bases: AbstractFile

file_to_data(groups, name)

Return type:

tuple[ndarray, Attribute]

file_to_data_slice(groups, name, slices)

Return type:

tuple[ndarray, Attribute]

file_to_data_statistics(groups, name, channels=None)

Return type:

dict[str, float]

data_to_file(name, data, attributes=None)

Return type:

None

is_exist(group, name=None)

Return type:

bool

get_names(groups, h5_group=None)

Return type:

list[str]

get_group()

Return type:

list[str]

get_infos(groups, name)

Return type:

tuple[list[int], Attribute]

class SitkFile(filename, read, file_format)

Bases: AbstractFile

file_to_data(group, name)

Return type:

tuple[ndarray, Attribute]

file_to_data_slice(group, name, slices)

Return type:

tuple[ndarray, Attribute]

file_to_data_statistics(group, name, channels=None)

Return type:

dict[str, float]

is_vtk_polydata(obj)

data_to_file(name, data, attributes=None)

Return type:

None

is_exist(group, name=None)

Return type:

bool

get_names(group)

Return type:

list[str]

get_group()

get_infos(group, name)

Return type:

tuple[list[int], Attribute]

class File(filename, read, file_format)

Bases: object

write(group, name, data, attributes=None)

read_data(groups, name)

Return type:

tuple[ndarray, Attribute]

read_data_slice(groups, name, slices)

Return type:

tuple[ndarray, Attribute]

read_data_statistics(groups, name, channels=None)

Return type:

dict[str, float]

read_transform(group, name)

Return type:

Transform

read_image(group, name)

get_size(group)

Return type:

int

is_group_exist(group)

Return type:

bool

is_dataset_exist(group, name)

Return type:

bool

get_names(groups, index=None)

Return type:

list[str]

get_group()

get_infos(groups, name)

Return type:

tuple[list[int], Attribute]

get_statistics(groups)

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)

Return type:

tuple[ModuleType, str]

konfai.utils.utils.get_patch_slices_from_nb_patch_per_dim(patch_size_tmp, nb_patch_per_dim, overlap)

Return type:

list[tuple[slice, ...]]

konfai.utils.utils.get_patch_slices_from_shape(patch_size, shape, overlap_tmp)

Return type:

tuple[list[tuple[slice, ...]], list[tuple[int, bool]]]

konfai.utils.utils.is_windows_absolute_path(path)

Return whether path looks like a Windows absolute path.

Return type:

bool

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

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, and runtime helpers.