MinMaxNormalize#

class MinMaxNormalize[source]#

Bases: ImageOnlyTransform

Albumentations transform that normalizes image to desired min and max values.

This will shift and scale the image appropriately to achieve the desired min and max.

Attributes

`applied_in_replay`
`available_keys`	Returns set of available keys.
`call_backup`
`deterministic`
`replay_mode`
`save_key`
`strict`
`targets`
`targets_as_params`	Targets used to get params dependent on targets.

__init__(min_val=0.0, max_val=1.0, dtype=5, p=1.0)[source]#

Constructor.

Parameters

min_val – the minimum value that output should have
max_val – the maximum value that output should have
dtype – the dtype of output image

Methods

`__init__`([min_val, max_val, dtype, p])	Constructor.
`add_targets`(additional_targets)	Add targets to transform them the same way as one of existing targets.
`apply`(image, **params)	Apply transform on image.
`apply_to_images`(images, args, *params)	Apply transform on images.
`apply_to_volume`(volume, args, *params)	Apply transform slice by slice to a volume.
`apply_to_volumes`(volumes, args, *params)	Apply transform to multiple volumes.
`apply_with_params`(params, args, *kwargs)	Apply transforms with parameters.
`get_applied_params`()	Returns the parameters that were used in the last transform application.
`get_base_init_args`()	Returns base init args - p
`get_class_fullname`()
`get_dict_with_id`()
`get_params`()	Returns parameters independent of input.
`get_params_dependent_on_data`(params, data)	Returns parameters dependent on input.
`get_processor`(key)
`get_transform_init_args`()	Exclude seed from init args during serialization
`get_transform_init_args_names`()	Returns names of arguments that are used in __init__ method of the transform.
`is_serializable`()
`set_deterministic`(flag[, save_key])	Set transform to be deterministic.
`set_processors`(processors)
`set_random_seed`(seed)	Set random state from seed.
`set_random_state`(random_generator, py_random)	Set random state directly from generators.
`should_apply`([force_apply])
`to_dict`([on_not_implemented_error])	Take a transform pipeline and convert it to a serializable representation that uses only standard python data types: dictionaries, lists, strings, integers, and floats.
`to_dict_private`()	Returns a dictionary representation of the transform, excluding internal parameters.
`update_transform_params`(params, data)	Updates parameters with input shape and transform-specific params.

pydantic model InitSchema#

Bases: BaseTransformInitSchema

Show JSON schema

{
   "title": "InitSchema",
   "type": "object",
   "properties": {
      "p": {
         "maximum": 1.0,
         "minimum": 0.0,
         "title": "P",
         "type": "number"
      },
      "strict": {
         "default": false,
         "title": "Strict",
         "type": "boolean"
      }
   },
   "required": [
      "p"
   ]
}

Config

arbitrary_types_allowed: bool = True

Fields

p (float)
strict (bool)

field p: float [Required]#

Constraints

ge = 0
le = 1

field strict: bool = False#

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self#

Parameters

_fields_set (set[str] | None) –
values (Any) –

Return type

Self

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters

include (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.

Returns

A copy of the model with included, excluded and updated fields as specified.

Return type

Self

dict(*, include: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, exclude: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]#

Parameters

include (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) –
exclude (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) –
by_alias (bool) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –

Return type

Dict[str, Any]

classmethod from_orm(obj: Any) → Self#

Parameters: obj (Any) –
Return type: Self

json(*, include: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, exclude: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str#

Parameters

include (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) –
exclude (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) –
by_alias (bool) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –

Return type

str

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self#

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters

_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Returns

A new instance of the Model class with validated data.

Return type

Self

model_copy(*, update: Optional[Mapping[str, Any]] = None, deep: bool = False) → Self#

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters

update (Optional[Mapping[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Returns

New model instance.

Return type

Self

model_dump(*, mode: Union[Literal['json', 'python'], str] = 'python', include: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, exclude: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: Union[bool, Literal['none', 'warn', 'error']] = True, serialize_as_any: bool = False) → dict[str, Any]#

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) – A set of fields to include in the output.
exclude (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns

A dictionary representation of the model.

Return type

dict[str, Any]

model_dump_json(*, indent: int | None = None, include: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, exclude: Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]] = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: Union[bool, Literal['none', 'warn', 'error']] = True, serialize_as_any: bool = False) → str#

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) – Field(s) to include in the JSON output.
exclude (Optional[Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]]]]) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns

A JSON string representation of the model.

Return type

str

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any]#

Generates a JSON schema for a model class.

Parameters

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Returns

The JSON schema for the given model class.

Return type

dict[str, Any]

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str#

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Returns: String representing the new class where params are passed to cls as type variables.
Raises: TypeError – Raised when trying to generate concrete names for non-generic models.
Return type: str

model_post_init(_BaseModel__context: Any) → None#

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters: _BaseModel__context (Any) –
Return type: None

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None#

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (MappingNamespace | None) – The types namespace, defaults to None.

Returns

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

Return type

bool | None

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None) → Self#

Validate a pydantic model instance.

Parameters

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.

Raises

ValidationError – If the object could not be validated.

Returns

The validated model instance.

Return type

Self

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None) → Self#

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Raises

ValidationError – If json_data is not a JSON string or the object could not be validated.

Return type

Self

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None) → Self#

Validate the given object with string data against the Pydantic model.

Parameters

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Return type

Self

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

Parameters

path (str | Path) –
content_type (str | None) –
encoding (str) –
proto (DeprecatedParseProtocol | None) –
allow_pickle (bool) –

Return type

Self

classmethod parse_obj(obj: Any) → Self#

Parameters: obj (Any) –
Return type: Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

Parameters

b (str | bytes) –
content_type (str | None) –
encoding (str) –
proto (DeprecatedParseProtocol | None) –
allow_pickle (bool) –

Return type

Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]#

Parameters

by_alias (bool) –
ref_template (str) –

Return type

Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str#

Parameters

by_alias (bool) –
ref_template (str) –
dumps_kwargs (Any) –

Return type

str

classmethod update_forward_refs(**localns: Any) → None#

Parameters: localns (Any) –
Return type: None

classmethod validate(value: Any) → Self#

Parameters: value (Any) –
Return type: Self

property model_extra: dict[str, Any] | None#

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]#

Returns the set of fields that have been explicitly set on this model instance.

Returns

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

__init__(min_val=0.0, max_val=1.0, dtype=5, p=1.0)[source]#

Constructor.

Parameters

min_val – the minimum value that output should have
max_val – the maximum value that output should have
dtype – the dtype of output image

add_targets(additional_targets: dict[str, str]) → None#

Add targets to transform them the same way as one of existing targets. ex: {‘target_image’: ‘image’} ex: {‘obj1_mask’: ‘mask’, ‘obj2_mask’: ‘mask’} by the way you must have at least one object with key ‘image’

Parameters: additional_targets (dict) – keys - new target name, values - old target name. ex: {‘image2’: ‘image’}
Return type: None

apply(image, **params)[source]#: Apply transform on image.

apply_to_images(images: ndarray, *args: Any, **params: Any) → ndarray#

Apply transform on images.

Parameters

images (ndarray) – Input images as numpy array of shape: - (num_images, height, width, channels) - (num_images, height, width) for grayscale
*args (Any) – Additional positional arguments
**params (Any) – Additional parameters specific to the transform

Returns

Transformed images as numpy array in the same format as input

Return type

ndarray

apply_to_volume(volume: ndarray, *args: Any, **params: Any) → ndarray#

Apply transform slice by slice to a volume.

Parameters

volume (ndarray) – Input volume of shape (depth, height, width) or (depth, height, width, channels)
*args (Any) – Additional positional arguments
**params (Any) – Additional parameters specific to the transform

Returns

Transformed volume as numpy array in the same format as input

Return type

ndarray

apply_to_volumes(volumes: ndarray, *args: Any, **params: Any) → ndarray#

Apply transform to multiple volumes.

Parameters

volumes (ndarray) –
args (Any) –
params (Any) –

Return type

ndarray

apply_with_params(params: dict[str, Any], *args: Any, **kwargs: Any) → dict[str, Any]#

Apply transforms with parameters.

Parameters

params (dict[str, Any]) –
args (Any) –
kwargs (Any) –

Return type

dict[str, Any]

get_applied_params() → dict[str, Any]#

Returns the parameters that were used in the last transform application. Returns empty dict if transform was not applied.

Return type: dict[str, Any]

get_base_init_args() → dict[str, Any]#

Returns base init args - p

Return type: dict[str, Any]

classmethod get_class_fullname() → str#

Return type: str

get_dict_with_id() → dict[str, Any]#

Return type: dict[str, Any]

get_params() → dict[str, Any]#

Returns parameters independent of input.

Return type: dict[str, Any]

get_params_dependent_on_data(params: dict[str, Any], data: dict[str, Any]) → dict[str, Any]#

Returns parameters dependent on input.

Parameters

params (dict[str, Any]) –
data (dict[str, Any]) –

Return type

dict[str, Any]

get_processor(key: str) → albumentations.core.bbox_utils.BboxProcessor | albumentations.core.keypoints_utils.KeypointsProcessor | None#

Parameters: key (str) –
Return type: albumentations.core.bbox_utils.BboxProcessor | albumentations.core.keypoints_utils.KeypointsProcessor | None

get_transform_init_args() → dict[str, Any]#

Exclude seed from init args during serialization

Return type: dict[str, Any]

get_transform_init_args_names()[source]#: Returns names of arguments that are used in __init__ method of the transform.

classmethod is_serializable() → bool#

Return type: bool

set_deterministic(flag: bool, save_key: str = 'replay') → BasicTransform#

Set transform to be deterministic.

Parameters

flag (bool) –
save_key (str) –

Return type

BasicTransform

set_processors(processors: dict[str, albumentations.core.bbox_utils.BboxProcessor | albumentations.core.keypoints_utils.KeypointsProcessor]) → None#

Parameters: processors (dict[str, albumentations.core.bbox_utils.BboxProcessor | albumentations.core.keypoints_utils.KeypointsProcessor]) –
Return type: None

set_random_seed(seed: int | None) → None#

Set random state from seed.

Parameters: seed (int | None) – Random seed to use
Return type: None

set_random_state(random_generator: Generator, py_random: Random) → None#

Set random state directly from generators.

Parameters

random_generator (Generator) – numpy random generator to use
py_random (Random) – python random generator to use

Return type

None

should_apply(force_apply: bool = False) → bool#

Parameters: force_apply (bool) –
Return type: bool

to_dict(on_not_implemented_error: str = 'raise') → dict[str, Any]#

Take a transform pipeline and convert it to a serializable representation that uses only standard python data types: dictionaries, lists, strings, integers, and floats.

Parameters

self – A transform that should be serialized. If the transform doesn’t implement the to_dict method and on_not_implemented_error equals to ‘raise’ then NotImplementedError is raised. If on_not_implemented_error equals to ‘warn’ then NotImplementedError will be ignored but no transform parameters will be serialized.
on_not_implemented_error (str) – raise or warn.

Return type

dict[str, Any]

to_dict_private() → dict[str, Any]#

Returns a dictionary representation of the transform, excluding internal parameters.

Return type: dict[str, Any]

update_transform_params(params: dict[str, Any], data: dict[str, Any]) → dict[str, Any]#

Updates parameters with input shape and transform-specific params.

Parameters

params (dict[str, Any]) – Parameters to be updated
data (dict[str, Any]) – Input data dictionary containing images/volumes

Returns

Updated parameters dictionary with shape and transform-specific params

Return type

dict[str, Any]

applied_in_replay = False#

property available_keys: set[str]#: Returns set of available keys.

call_backup = None#

deterministic: bool = False#

fill: tuple[float, ...] | float#

fill_mask: tuple[float, ...] | float | None#

interpolation: int#

replay_mode = False#

save_key = 'replay'#

property strict: bool#

property targets: dict[str, Callable[..., Any]]#

property targets_as_params: list[str]#: Targets used to get params dependent on targets. This is used to check input has all required targets.