# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A collection of "vanilla" transforms for utility functions.
"""
from __future__ import annotations
import logging
import sys
import time
import warnings
from collections.abc import Mapping, Sequence
from copy import deepcopy
from functools import partial
from typing import Any, Callable
import numpy as np
import torch
import torch.nn as nn
from monai.config import DtypeLike
from monai.config.type_definitions import NdarrayOrTensor
from monai.data.meta_obj import get_track_meta
from monai.data.meta_tensor import MetaTensor
from monai.data.utils import is_no_channel, no_collation
from monai.networks.layers.simplelayers import (
ApplyFilter,
EllipticalFilter,
GaussianFilter,
LaplaceFilter,
MeanFilter,
SavitzkyGolayFilter,
SharpenFilter,
median_filter,
)
from monai.transforms.inverse import InvertibleTransform
from monai.transforms.traits import MultiSampleTrait
from monai.transforms.transform import Randomizable, RandomizableTrait, RandomizableTransform, Transform
from monai.transforms.utils import (
extreme_points_to_image,
get_extreme_points,
map_binary_to_indices,
map_classes_to_indices,
)
from monai.transforms.utils_pytorch_numpy_unification import concatenate, in1d, moveaxis, unravel_indices
from monai.utils import (
MetaKeys,
TraceKeys,
convert_data_type,
convert_to_cupy,
convert_to_numpy,
convert_to_tensor,
ensure_tuple,
look_up_option,
min_version,
optional_import,
)
from monai.utils.enums import TransformBackends
from monai.utils.misc import is_module_ver_at_least
from monai.utils.type_conversion import convert_to_dst_type, get_equivalent_dtype
PILImageImage, has_pil = optional_import("PIL.Image", name="Image")
pil_image_fromarray, _ = optional_import("PIL.Image", name="fromarray")
cp, has_cp = optional_import("cupy")
__all__ = [
"Identity",
"RandIdentity",
"AsChannelLast",
"AddCoordinateChannels",
"EnsureChannelFirst",
"EnsureType",
"RepeatChannel",
"RemoveRepeatedChannel",
"SplitDim",
"CastToType",
"ToTensor",
"ToNumpy",
"ToPIL",
"Transpose",
"SqueezeDim",
"DataStats",
"SimulateDelay",
"Lambda",
"RandLambda",
"LabelToMask",
"FgBgToIndices",
"ClassesToIndices",
"ConvertToMultiChannelBasedOnBratsClasses",
"AddExtremePointsChannel",
"TorchVision",
"MapLabelValue",
"IntensityStats",
"ToDevice",
"CuCIM",
"RandCuCIM",
"ToCupy",
"ImageFilter",
"RandImageFilter",
]
[docs]
class Identity(Transform):
"""
Do nothing to the data.
As the output value is same as input, it can be used as a testing tool to verify the transform chain,
Compose or transform adaptor, etc.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`.
"""
return img
class RandIdentity(RandomizableTrait):
"""
Do nothing to the data. This transform is random, so can be used to stop the caching of any
subsequent transforms.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __call__(self, data: Any) -> Any:
return data
[docs]
class AsChannelLast(Transform):
"""
Change the channel dimension of the image to the last dimension.
Some of other 3rd party transforms assume the input image is in the channel-last format with shape
(spatial_dim_1[, spatial_dim_2, ...], num_channels).
This transform could be used to convert, for example, a channel-first image array in shape
(num_channels, spatial_dim_1[, spatial_dim_2, ...]) into the channel-last format,
so that MONAI transforms can construct a chain with other 3rd party transforms together.
Args:
channel_dim: which dimension of input image is the channel, default is the first dimension.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(self, channel_dim: int = 0) -> None:
if not (isinstance(channel_dim, int) and channel_dim >= -1):
raise ValueError(f"invalid channel dimension ({channel_dim}).")
self.channel_dim = channel_dim
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`.
"""
out: NdarrayOrTensor = convert_to_tensor(moveaxis(img, self.channel_dim, -1), track_meta=get_track_meta())
return out
[docs]
class EnsureChannelFirst(Transform):
"""
Adjust or add the channel dimension of input data to ensure `channel_first` shape.
This extracts the `original_channel_dim` info from provided meta_data dictionary or MetaTensor input. This value
should state which dimension is the channel dimension so that it can be moved forward, or contain "no_channel" to
state no dimension is the channel and so a 1-size first dimension is to be added.
Args:
strict_check: whether to raise an error when the meta information is insufficient.
channel_dim: This argument can be used to specify the original channel dimension (integer) of the input array.
It overrides the `original_channel_dim` from provided MetaTensor input.
If the input array doesn't have a channel dim, this value should be ``'no_channel'``.
If this is set to `None`, this class relies on `img` or `meta_dict` to provide the channel dimension.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(self, strict_check: bool = True, channel_dim: None | str | int = None):
self.strict_check = strict_check
self.input_channel_dim = channel_dim
[docs]
def __call__(self, img: torch.Tensor, meta_dict: Mapping | None = None) -> torch.Tensor:
"""
Apply the transform to `img`.
"""
if not isinstance(img, MetaTensor) and not isinstance(meta_dict, Mapping):
if self.input_channel_dim is None:
msg = "Metadata not available and channel_dim=None, EnsureChannelFirst is not in use."
if self.strict_check:
raise ValueError(msg)
warnings.warn(msg)
return img
else:
img = MetaTensor(img)
if isinstance(img, MetaTensor):
meta_dict = img.meta
channel_dim = meta_dict.get(MetaKeys.ORIGINAL_CHANNEL_DIM, None) if isinstance(meta_dict, Mapping) else None
if self.input_channel_dim is not None:
channel_dim = float("nan") if self.input_channel_dim == "no_channel" else self.input_channel_dim
if channel_dim is None:
msg = "Unknown original_channel_dim in the MetaTensor meta dict or `meta_dict` or `channel_dim`."
if self.strict_check:
raise ValueError(msg)
warnings.warn(msg)
return img
# track the original channel dim
if isinstance(meta_dict, dict):
meta_dict[MetaKeys.ORIGINAL_CHANNEL_DIM] = channel_dim
if is_no_channel(channel_dim):
result = img[None]
else:
result = moveaxis(img, int(channel_dim), 0) # type: ignore
return convert_to_tensor(result, track_meta=get_track_meta()) # type: ignore
[docs]
class RepeatChannel(Transform):
"""
Repeat channel data to construct expected input shape for models.
The `repeats` count includes the origin data, for example:
``RepeatChannel(repeats=2)([[1, 2], [3, 4]])`` generates: ``[[1, 2], [1, 2], [3, 4], [3, 4]]``
Args:
repeats: the number of repetitions for each element.
"""
backend = [TransformBackends.TORCH]
def __init__(self, repeats: int) -> None:
if repeats <= 0:
raise ValueError(f"repeats count must be greater than 0, got {repeats}.")
self.repeats = repeats
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is a "channel-first" array.
"""
repeat_fn = torch.repeat_interleave if isinstance(img, torch.Tensor) else np.repeat
return convert_to_tensor(repeat_fn(img, self.repeats, 0), track_meta=get_track_meta()) # type: ignore
[docs]
class RemoveRepeatedChannel(Transform):
"""
RemoveRepeatedChannel data to undo RepeatChannel
The `repeats` count specifies the deletion of the origin data, for example:
``RemoveRepeatedChannel(repeats=2)([[1, 2], [1, 2], [3, 4], [3, 4]])`` generates: ``[[1, 2], [3, 4]]``
Args:
repeats: the number of repetitions to be deleted for each element.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(self, repeats: int) -> None:
if repeats <= 0:
raise ValueError(f"repeats count must be greater than 0, got {repeats}.")
self.repeats = repeats
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is a "channel-first" array.
"""
if img.shape[0] < 2:
raise ValueError(f"Image must have more than one channel, got {img.shape[0]} channels.")
out: NdarrayOrTensor = convert_to_tensor(img[:: self.repeats, :], track_meta=get_track_meta())
return out
[docs]
class SplitDim(Transform, MultiSampleTrait):
"""
Given an image of size X along a certain dimension, return a list of length X containing
images. Useful for converting 3D images into a stack of 2D images, splitting multichannel inputs into
single channels, for example.
Note: `torch.split`/`np.split` is used, so the outputs are views of the input (shallow copy).
Args:
dim: dimension on which to split
keepdim: if `True`, output will have singleton in the split dimension. If `False`, this
dimension will be squeezed.
update_meta: whether to update the MetaObj in each split result.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(self, dim: int = -1, keepdim: bool = True, update_meta=True) -> None:
self.dim = dim
self.keepdim = keepdim
self.update_meta = update_meta
[docs]
def __call__(self, img: torch.Tensor) -> list[torch.Tensor]:
"""
Apply the transform to `img`.
"""
n_out = img.shape[self.dim]
if isinstance(img, torch.Tensor):
outputs = list(torch.split(img, 1, self.dim))
else:
outputs = np.split(img, n_out, self.dim)
for idx, item in enumerate(outputs):
if not self.keepdim:
outputs[idx] = item.squeeze(self.dim)
if self.update_meta and isinstance(img, MetaTensor):
if not isinstance(item, MetaTensor):
item = MetaTensor(item, meta=img.meta)
if self.dim == 0: # don't update affine if channel dim
continue
ndim = len(item.affine)
shift = torch.eye(ndim, device=item.affine.device, dtype=item.affine.dtype)
shift[self.dim - 1, -1] = idx
item.affine = item.affine @ shift
return outputs
[docs]
class CastToType(Transform):
"""
Cast the Numpy data to specified numpy data type, or cast the PyTorch Tensor to
specified PyTorch data type.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs]
def __init__(self, dtype=np.float32) -> None:
"""
Args:
dtype: convert image to this data type, default is `np.float32`.
"""
self.dtype = dtype
[docs]
def __call__(self, img: NdarrayOrTensor, dtype: DtypeLike | torch.dtype = None) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is a numpy array or PyTorch Tensor.
Args:
dtype: convert image to this data type, default is `self.dtype`.
Raises:
TypeError: When ``img`` type is not in ``Union[numpy.ndarray, torch.Tensor]``.
"""
return convert_data_type(img, output_type=type(img), dtype=dtype or self.dtype)[0] # type: ignore
[docs]
class ToTensor(Transform):
"""
Converts the input image to a tensor without applying any other transformations.
Input data can be PyTorch Tensor, numpy array, list, dictionary, int, float, bool, str, etc.
Will convert Tensor, Numpy array, float, int, bool to Tensor, strings and objects keep the original.
For dictionary, list or tuple, convert every item to a Tensor if applicable and `wrap_sequence=False`.
Args:
dtype: target data type to when converting to Tensor.
device: target device to put the converted Tensor data.
wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.
track_meta: whether to convert to `MetaTensor` or regular tensor, default to `None`,
use the return value of ``get_track_meta``.
"""
backend = [TransformBackends.TORCH]
def __init__(
self,
dtype: torch.dtype | None = None,
device: torch.device | str | None = None,
wrap_sequence: bool = True,
track_meta: bool | None = None,
) -> None:
super().__init__()
self.dtype = dtype
self.device = device
self.wrap_sequence = wrap_sequence
self.track_meta = get_track_meta() if track_meta is None else bool(track_meta)
[docs]
def __call__(self, img: NdarrayOrTensor):
"""
Apply the transform to `img` and make it contiguous.
"""
if isinstance(img, MetaTensor):
img.applied_operations = [] # drops tracking info
return convert_to_tensor(
img, dtype=self.dtype, device=self.device, wrap_sequence=self.wrap_sequence, track_meta=self.track_meta
)
[docs]
class EnsureType(Transform):
"""
Ensure the input data to be a PyTorch Tensor or numpy array, support: `numpy array`, `PyTorch Tensor`,
`float`, `int`, `bool`, `string` and `object` keep the original.
If passing a dictionary, list or tuple, still return dictionary, list or tuple will recursively convert
every item to the expected data type if `wrap_sequence=False`.
Args:
data_type: target data type to convert, should be "tensor" or "numpy".
dtype: target data content type to convert, for example: np.float32, torch.float, etc.
device: for Tensor data type, specify the target device.
wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
E.g., if `False`, `[1, 2]` -> `[tensor(1), tensor(2)]`, if `True`, then `[1, 2]` -> `tensor([1, 2])`.
track_meta: if `True` convert to ``MetaTensor``, otherwise to Pytorch ``Tensor``,
if ``None`` behave according to return value of py:func:`monai.data.meta_obj.get_track_meta`.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(
self,
data_type: str = "tensor",
dtype: DtypeLike | torch.dtype = None,
device: torch.device | None = None,
wrap_sequence: bool = True,
track_meta: bool | None = None,
) -> None:
self.data_type = look_up_option(data_type.lower(), {"tensor", "numpy"})
self.dtype = dtype
self.device = device
self.wrap_sequence = wrap_sequence
self.track_meta = get_track_meta() if track_meta is None else bool(track_meta)
[docs]
def __call__(self, data: NdarrayOrTensor, dtype: DtypeLike | torch.dtype = None):
"""
Args:
data: input data can be PyTorch Tensor, numpy array, list, dictionary, int, float, bool, str, etc.
will ensure Tensor, Numpy array, float, int, bool as Tensors or numpy arrays, strings and
objects keep the original. for dictionary, list or tuple, ensure every item as expected type
if applicable and `wrap_sequence=False`.
dtype: target data content type to convert, for example: np.float32, torch.float, etc.
"""
if self.data_type == "tensor":
output_type = MetaTensor if self.track_meta else torch.Tensor
else:
output_type = np.ndarray # type: ignore
out: NdarrayOrTensor
out, *_ = convert_data_type(
data=data,
output_type=output_type, # type: ignore
dtype=self.dtype if dtype is None else dtype,
device=self.device,
wrap_sequence=self.wrap_sequence,
)
return out
[docs]
class ToNumpy(Transform):
"""
Converts the input data to numpy array, can support list or tuple of numbers and PyTorch Tensor.
Args:
dtype: target data type when converting to numpy array.
wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.
"""
backend = [TransformBackends.NUMPY]
def __init__(self, dtype: DtypeLike = None, wrap_sequence: bool = True) -> None:
super().__init__()
self.dtype = dtype
self.wrap_sequence = wrap_sequence
[docs]
def __call__(self, img: NdarrayOrTensor):
"""
Apply the transform to `img` and make it contiguous.
"""
return convert_to_numpy(img, dtype=self.dtype, wrap_sequence=self.wrap_sequence)
[docs]
class ToCupy(Transform):
"""
Converts the input data to CuPy array, can support list or tuple of numbers, NumPy and PyTorch Tensor.
Args:
dtype: data type specifier. It is inferred from the input by default.
if not None, must be an argument of `numpy.dtype`, for more details:
https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.
wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.
"""
backend = [TransformBackends.CUPY]
def __init__(self, dtype: np.dtype | None = None, wrap_sequence: bool = True) -> None:
super().__init__()
self.dtype = dtype
self.wrap_sequence = wrap_sequence
[docs]
def __call__(self, data: NdarrayOrTensor):
"""
Create a CuPy array from `data` and make it contiguous
"""
return convert_to_cupy(data, dtype=self.dtype, wrap_sequence=self.wrap_sequence)
[docs]
class ToPIL(Transform):
"""
Converts the input image (in the form of NumPy array or PyTorch Tensor) to PIL image
"""
backend = [TransformBackends.NUMPY]
[docs]
def __call__(self, img):
"""
Apply the transform to `img`.
"""
if isinstance(img, PILImageImage):
return img
if isinstance(img, torch.Tensor):
img = img.detach().cpu().numpy()
return pil_image_fromarray(img)
[docs]
class Transpose(Transform):
"""
Transposes the input image based on the given `indices` dimension ordering.
"""
backend = [TransformBackends.TORCH]
def __init__(self, indices: Sequence[int] | None) -> None:
self.indices = None if indices is None else tuple(indices)
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`.
"""
img = convert_to_tensor(img, track_meta=get_track_meta())
return img.permute(self.indices or tuple(range(img.ndim)[::-1])) # type: ignore
[docs]
class SqueezeDim(Transform):
"""
Squeeze a unitary dimension.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs]
def __init__(self, dim: int | None = 0, update_meta=True) -> None:
"""
Args:
dim: dimension to be squeezed. Default = 0
"None" works when the input is numpy array.
update_meta: whether to update the meta info if the input is a metatensor. Default is ``True``.
Raises:
TypeError: When ``dim`` is not an ``Optional[int]``.
"""
if dim is not None and not isinstance(dim, int):
raise TypeError(f"dim must be None or a int but is {type(dim).__name__}.")
self.dim = dim
self.update_meta = update_meta
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Args:
img: numpy arrays with required dimension `dim` removed
"""
img = convert_to_tensor(img, track_meta=get_track_meta())
if self.dim is None:
if self.update_meta:
warnings.warn("update_meta=True is ignored when dim=None.")
return img.squeeze()
dim = (self.dim + len(img.shape)) if self.dim < 0 else self.dim
# for pytorch/numpy unification
if img.shape[dim] != 1:
raise ValueError(f"Can only squeeze singleton dimension, got shape {img.shape[dim]} of {img.shape}.")
img = img.squeeze(dim)
if self.update_meta and isinstance(img, MetaTensor) and dim > 0 and len(img.affine.shape) == 2:
h, w = img.affine.shape
affine, device = img.affine, img.affine.device if isinstance(img.affine, torch.Tensor) else None
if h > dim:
affine = affine[torch.arange(0, h, device=device) != dim - 1]
if w > dim:
affine = affine[:, torch.arange(0, w, device=device) != dim - 1]
if (affine.shape[0] == affine.shape[1]) and not np.linalg.det(convert_to_numpy(affine, wrap_sequence=True)):
warnings.warn(f"After SqueezeDim, img.affine is ill-posed: \n{img.affine}.")
img.affine = affine
return img
[docs]
class DataStats(Transform):
"""
Utility transform to show the statistics of data for debug or analysis.
It can be inserted into any place of a transform chain and check results of previous transforms.
It support both `numpy.ndarray` and `torch.tensor` as input data,
so it can be used in pre-processing and post-processing.
It gets logger from `logging.getLogger(name)`, we can setup a logger outside first with the same `name`.
If the log level of `logging.RootLogger` is higher than `INFO`, will add a separate `StreamHandler`
log handler with `INFO` level and record to `stdout`.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs]
def __init__(
self,
prefix: str = "Data",
data_type: bool = True,
data_shape: bool = True,
value_range: bool = True,
data_value: bool = False,
additional_info: Callable | None = None,
name: str = "DataStats",
) -> None:
"""
Args:
prefix: will be printed in format: "{prefix} statistics".
data_type: whether to show the type of input data.
data_shape: whether to show the shape of input data.
value_range: whether to show the value range of input data.
data_value: whether to show the raw value of input data.
a typical example is to print some properties of Nifti image: affine, pixdim, etc.
additional_info: user can define callable function to extract additional info from input data.
name: identifier of `logging.logger` to use, defaulting to "DataStats".
Raises:
TypeError: When ``additional_info`` is not an ``Optional[Callable]``.
"""
if not isinstance(prefix, str):
raise ValueError(f"prefix must be a string, got {type(prefix)}.")
self.prefix = prefix
self.data_type = data_type
self.data_shape = data_shape
self.value_range = value_range
self.data_value = data_value
if additional_info is not None and not callable(additional_info):
raise TypeError(f"additional_info must be None or callable but is {type(additional_info).__name__}.")
self.additional_info = additional_info
self._logger_name = name
_logger = logging.getLogger(self._logger_name)
_logger.setLevel(logging.INFO)
if logging.root.getEffectiveLevel() > logging.INFO:
# Avoid duplicate stream handlers to be added when multiple DataStats are used in a chain.
has_console_handler = any(
hasattr(h, "is_data_stats_handler") and h.is_data_stats_handler for h in _logger.handlers
)
if not has_console_handler:
# if the root log level is higher than INFO, set a separate stream handler to record
console = logging.StreamHandler(sys.stdout)
console.setLevel(logging.INFO)
console.is_data_stats_handler = True # type:ignore[attr-defined]
_logger.addHandler(console)
[docs]
def __call__(
self,
img: NdarrayOrTensor,
prefix: str | None = None,
data_type: bool | None = None,
data_shape: bool | None = None,
value_range: bool | None = None,
data_value: bool | None = None,
additional_info: Callable | None = None,
) -> NdarrayOrTensor:
"""
Apply the transform to `img`, optionally take arguments similar to the class constructor.
"""
lines = [f"{prefix or self.prefix} statistics:"]
if self.data_type if data_type is None else data_type:
lines.append(f"Type: {type(img)} {img.dtype if hasattr(img, 'dtype') else None}")
if self.data_shape if data_shape is None else data_shape:
lines.append(f"Shape: {img.shape if hasattr(img, 'shape') else None}")
if self.value_range if value_range is None else value_range:
if isinstance(img, np.ndarray):
lines.append(f"Value range: ({np.min(img)}, {np.max(img)})")
elif isinstance(img, torch.Tensor):
lines.append(f"Value range: ({torch.min(img)}, {torch.max(img)})")
else:
lines.append(f"Value range: (not a PyTorch or Numpy array, type: {type(img)})")
if self.data_value if data_value is None else data_value:
lines.append(f"Value: {img}")
additional_info = self.additional_info if additional_info is None else additional_info
if additional_info is not None:
lines.append(f"Additional info: {additional_info(img)}")
separator = "\n"
output = f"{separator.join(lines)}"
logging.getLogger(self._logger_name).info(output)
return img
[docs]
class SimulateDelay(Transform):
"""
This is a pass through transform to be used for testing purposes. It allows
adding fake behaviors that are useful for testing purposes to simulate
how large datasets behave without needing to test on large data sets.
For example, simulating slow NFS data transfers, or slow network transfers
in testing by adding explicit timing delays. Testing of small test data
can lead to incomplete understanding of real world issues, and may lead
to sub-optimal design choices.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs]
def __init__(self, delay_time: float = 0.0) -> None:
"""
Args:
delay_time: The minimum amount of time, in fractions of seconds,
to accomplish this delay task.
"""
super().__init__()
self.delay_time: float = delay_time
[docs]
def __call__(self, img: NdarrayOrTensor, delay_time: float | None = None) -> NdarrayOrTensor:
"""
Args:
img: data remain unchanged throughout this transform.
delay_time: The minimum amount of time, in fractions of seconds,
to accomplish this delay task.
"""
time.sleep(self.delay_time if delay_time is None else delay_time)
return img
[docs]
class Lambda(InvertibleTransform):
"""
Apply a user-defined lambda as a transform.
For example:
.. code-block:: python
:emphasize-lines: 2
image = np.ones((10, 2, 2))
lambd = Lambda(func=lambda x: x[:4, :, :])
print(lambd(image).shape)
(4, 2, 2)
Args:
func: Lambda/function to be applied.
inv_func: Lambda/function of inverse operation, default to `lambda x: x`.
track_meta: If `False`, then standard data objects will be returned (e.g., torch.Tensor` and `np.ndarray`)
as opposed to MONAI's enhanced objects. By default, this is `True`.
Raises:
TypeError: When ``func`` is not an ``Optional[Callable]``.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(
self, func: Callable | None = None, inv_func: Callable = no_collation, track_meta: bool = True
) -> None:
if func is not None and not callable(func):
raise TypeError(f"func must be None or callable but is {type(func).__name__}.")
self.func = func
self.inv_func = inv_func
self.track_meta = track_meta
[docs]
def __call__(self, img: NdarrayOrTensor, func: Callable | None = None):
"""
Apply `self.func` to `img`.
Args:
func: Lambda/function to be applied. Defaults to `self.func`.
Raises:
TypeError: When ``func`` is not an ``Optional[Callable]``.
"""
fn = func if func is not None else self.func
if not callable(fn):
raise TypeError(f"func must be None or callable but is {type(fn).__name__}.")
out = fn(img)
# convert to MetaTensor if necessary
if isinstance(out, (np.ndarray, torch.Tensor)) and not isinstance(out, MetaTensor) and self.track_meta:
out = MetaTensor(out)
if isinstance(out, MetaTensor):
self.push_transform(out)
return out
[docs]
def inverse(self, data: torch.Tensor):
if isinstance(data, MetaTensor):
self.pop_transform(data)
return self.inv_func(data)
[docs]
class RandLambda(Lambda, RandomizableTransform):
"""
Randomizable version :py:class:`monai.transforms.Lambda`, the input `func` may contain random logic,
or randomly execute the function based on `prob`.
Args:
func: Lambda/function to be applied.
prob: probability of executing the random function, default to 1.0, with 100% probability to execute.
inv_func: Lambda/function of inverse operation, default to `lambda x: x`.
track_meta: If `False`, then standard data objects will be returned (e.g., torch.Tensor` and `np.ndarray`)
as opposed to MONAI's enhanced objects. By default, this is `True`.
For more details, please check :py:class:`monai.transforms.Lambda`.
"""
backend = Lambda.backend
def __init__(
self,
func: Callable | None = None,
prob: float = 1.0,
inv_func: Callable = no_collation,
track_meta: bool = True,
) -> None:
Lambda.__init__(self=self, func=func, inv_func=inv_func, track_meta=track_meta)
RandomizableTransform.__init__(self=self, prob=prob)
[docs]
def __call__(self, img: NdarrayOrTensor, func: Callable | None = None):
self.randomize(img)
out = deepcopy(super().__call__(img, func) if self._do_transform else img)
# convert to MetaTensor if necessary
if not isinstance(out, MetaTensor) and self.track_meta:
out = MetaTensor(out)
if isinstance(out, MetaTensor):
lambda_info = self.pop_transform(out) if self._do_transform else {}
self.push_transform(out, extra_info=lambda_info)
return out
[docs]
def inverse(self, data: torch.Tensor):
do_transform = self.get_most_recent_transform(data).pop(TraceKeys.DO_TRANSFORM)
if do_transform:
data = super().inverse(data)
else:
self.pop_transform(data)
return data
[docs]
class LabelToMask(Transform):
"""
Convert labels to mask for other tasks. A typical usage is to convert segmentation labels
to mask data to pre-process images and then feed the images into classification network.
It can support single channel labels or One-Hot labels with specified `select_labels`.
For example, users can select `label value = [2, 3]` to construct mask data, or select the
second and the third channels of labels to construct mask data.
The output mask data can be a multiple channels binary data or a single channel binary
data that merges all the channels.
Args:
select_labels: labels to generate mask from. for 1 channel label, the `select_labels`
is the expected label values, like: [1, 2, 3]. for One-Hot format label, the
`select_labels` is the expected channel indices.
merge_channels: whether to use `np.any()` to merge the result on channel dim. if yes,
will return a single channel mask with binary data.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__( # pytype: disable=annotation-type-mismatch
self, select_labels: Sequence[int] | int, merge_channels: bool = False
) -> None: # pytype: disable=annotation-type-mismatch
self.select_labels = ensure_tuple(select_labels)
self.merge_channels = merge_channels
[docs]
def __call__(
self, img: NdarrayOrTensor, select_labels: Sequence[int] | int | None = None, merge_channels: bool = False
) -> NdarrayOrTensor:
"""
Args:
select_labels: labels to generate mask from. for 1 channel label, the `select_labels`
is the expected label values, like: [1, 2, 3]. for One-Hot format label, the
`select_labels` is the expected channel indices.
merge_channels: whether to use `np.any()` to merge the result on channel dim. if yes,
will return a single channel mask with binary data.
"""
img = convert_to_tensor(img, track_meta=get_track_meta())
if select_labels is None:
select_labels = self.select_labels
else:
select_labels = ensure_tuple(select_labels)
if img.shape[0] > 1:
data = img[[*select_labels]]
else:
where: Callable = np.where if isinstance(img, np.ndarray) else torch.where # type: ignore
if isinstance(img, np.ndarray) or is_module_ver_at_least(torch, (1, 8, 0)):
data = where(in1d(img, select_labels), True, False).reshape(img.shape)
# pre pytorch 1.8.0, need to use 1/0 instead of True/False
else:
data = where(
in1d(img, select_labels), torch.tensor(1, device=img.device), torch.tensor(0, device=img.device)
).reshape(img.shape)
if merge_channels or self.merge_channels:
if isinstance(img, np.ndarray) or is_module_ver_at_least(torch, (1, 8, 0)):
return data.any(0)[None]
# pre pytorch 1.8.0 compatibility
return data.to(torch.uint8).any(0)[None].to(bool) # type: ignore
return data
[docs]
class FgBgToIndices(Transform, MultiSampleTrait):
"""
Compute foreground and background of the input label data, return the indices.
If no output_shape specified, output data will be 1 dim indices after flattening.
This transform can help pre-compute foreground and background regions for other transforms.
A typical usage is to randomly select foreground and background to crop.
The main logic is based on :py:class:`monai.transforms.utils.map_binary_to_indices`.
Args:
image_threshold: if enabled `image` at runtime, use ``image > image_threshold`` to
determine the valid image content area and select background only in this area.
output_shape: expected shape of output indices. if not None, unravel indices to specified shape.
"""
backend = [TransformBackends.NUMPY, TransformBackends.TORCH]
def __init__(self, image_threshold: float = 0.0, output_shape: Sequence[int] | None = None) -> None:
self.image_threshold = image_threshold
self.output_shape = output_shape
[docs]
def __call__(
self, label: NdarrayOrTensor, image: NdarrayOrTensor | None = None, output_shape: Sequence[int] | None = None
) -> tuple[NdarrayOrTensor, NdarrayOrTensor]:
"""
Args:
label: input data to compute foreground and background indices.
image: if image is not None, use ``label = 0 & image > image_threshold``
to define background. so the output items will not map to all the voxels in the label.
output_shape: expected shape of output indices. if None, use `self.output_shape` instead.
"""
if output_shape is None:
output_shape = self.output_shape
fg_indices, bg_indices = map_binary_to_indices(label, image, self.image_threshold)
if output_shape is not None:
fg_indices = unravel_indices(fg_indices, output_shape)
bg_indices = unravel_indices(bg_indices, output_shape)
return fg_indices, bg_indices
[docs]
class ClassesToIndices(Transform, MultiSampleTrait):
backend = [TransformBackends.NUMPY, TransformBackends.TORCH]
[docs]
def __init__(
self,
num_classes: int | None = None,
image_threshold: float = 0.0,
output_shape: Sequence[int] | None = None,
max_samples_per_class: int | None = None,
) -> None:
"""
Compute indices of every class of the input label data, return a list of indices.
If no output_shape specified, output data will be 1 dim indices after flattening.
This transform can help pre-compute indices of the class regions for other transforms.
A typical usage is to randomly select indices of classes to crop.
The main logic is based on :py:class:`monai.transforms.utils.map_classes_to_indices`.
Args:
num_classes: number of classes for argmax label, not necessary for One-Hot label.
image_threshold: if enabled `image` at runtime, use ``image > image_threshold`` to
determine the valid image content area and select only the indices of classes in this area.
output_shape: expected shape of output indices. if not None, unravel indices to specified shape.
max_samples_per_class: maximum length of indices to sample in each class to reduce memory consumption.
Default is None, no subsampling.
"""
self.num_classes = num_classes
self.image_threshold = image_threshold
self.output_shape = output_shape
self.max_samples_per_class = max_samples_per_class
[docs]
def __call__(
self, label: NdarrayOrTensor, image: NdarrayOrTensor | None = None, output_shape: Sequence[int] | None = None
) -> list[NdarrayOrTensor]:
"""
Args:
label: input data to compute the indices of every class.
image: if image is not None, use ``image > image_threshold`` to define valid region, and only select
the indices within the valid region.
output_shape: expected shape of output indices. if None, use `self.output_shape` instead.
"""
if output_shape is None:
output_shape = self.output_shape
indices: list[NdarrayOrTensor]
indices = map_classes_to_indices(
label, self.num_classes, image, self.image_threshold, self.max_samples_per_class
)
if output_shape is not None:
indices = [unravel_indices(cls_indices, output_shape) for cls_indices in indices]
return indices
[docs]
class ConvertToMultiChannelBasedOnBratsClasses(Transform):
"""
Convert labels to multi channels based on brats18 classes:
label 1 is the necrotic and non-enhancing tumor core
label 2 is the peritumoral edema
label 4 is the GD-enhancing tumor
The possible classes are TC (Tumor core), WT (Whole tumor)
and ET (Enhancing tumor).
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
# if img has channel dim, squeeze it
if img.ndim == 4 and img.shape[0] == 1:
img = img.squeeze(0)
result = [(img == 1) | (img == 4), (img == 1) | (img == 4) | (img == 2), img == 4]
# merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
# label 4 is ET
return torch.stack(result, dim=0) if isinstance(img, torch.Tensor) else np.stack(result, axis=0)
[docs]
class AddExtremePointsChannel(Randomizable, Transform):
"""
Add extreme points of label to the image as a new channel. This transform generates extreme
point from label and applies a gaussian filter. The pixel values in points image are rescaled
to range [rescale_min, rescale_max] and added as a new channel to input image. The algorithm is
described in Roth et al., Going to Extremes: Weakly Supervised Medical Image Segmentation
https://arxiv.org/abs/2009.11988.
This transform only supports single channel labels (1, spatial_dim1, [spatial_dim2, ...]). The
background ``index`` is ignored when calculating extreme points.
Args:
background: Class index of background label, defaults to 0.
pert: Random perturbation amount to add to the points, defaults to 0.0.
Raises:
ValueError: When no label image provided.
ValueError: When label image is not single channel.
"""
backend = [TransformBackends.TORCH]
def __init__(self, background: int = 0, pert: float = 0.0) -> None:
self._background = background
self._pert = pert
self._points: list[tuple[int, ...]] = []
[docs]
def randomize(self, label: NdarrayOrTensor) -> None:
self._points = get_extreme_points(label, rand_state=self.R, background=self._background, pert=self._pert)
[docs]
def __call__(
self,
img: NdarrayOrTensor,
label: NdarrayOrTensor | None = None,
sigma: Sequence[float] | float | Sequence[torch.Tensor] | torch.Tensor = 3.0,
rescale_min: float = -1.0,
rescale_max: float = 1.0,
) -> NdarrayOrTensor:
"""
Args:
img: the image that we want to add new channel to.
label: label image to get extreme points from. Shape must be
(1, spatial_dim1, [, spatial_dim2, ...]). Doesn't support one-hot labels.
sigma: if a list of values, must match the count of spatial dimensions of input data,
and apply every value in the list to 1 spatial dimension. if only 1 value provided,
use it for all spatial dimensions.
rescale_min: minimum value of output data.
rescale_max: maximum value of output data.
"""
if label is None:
raise ValueError("This transform requires a label array!")
if label.shape[0] != 1:
raise ValueError("Only supports single channel labels!")
# Generate extreme points
self.randomize(label[0, :])
points_image = extreme_points_to_image(
points=self._points, label=label, sigma=sigma, rescale_min=rescale_min, rescale_max=rescale_max
)
points_image, *_ = convert_to_dst_type(points_image, img) # type: ignore
return concatenate((img, points_image), axis=0)
[docs]
class TorchVision:
"""
This is a wrapper transform for PyTorch TorchVision transform based on the specified transform name and args.
As most of the TorchVision transforms only work for PIL image and PyTorch Tensor, this transform expects input
data to be PyTorch Tensor, users can easily call `ToTensor` transform to convert a Numpy array to Tensor.
"""
backend = [TransformBackends.TORCH]
[docs]
def __init__(self, name: str, *args, **kwargs) -> None:
"""
Args:
name: The transform name in TorchVision package.
args: parameters for the TorchVision transform.
kwargs: parameters for the TorchVision transform.
"""
super().__init__()
self.name = name
transform, _ = optional_import("torchvision.transforms", "0.8.0", min_version, name=name)
self.trans = transform(*args, **kwargs)
[docs]
def __call__(self, img: NdarrayOrTensor):
"""
Args:
img: PyTorch Tensor data for the TorchVision transform.
"""
img_t, *_ = convert_data_type(img, torch.Tensor)
out = self.trans(img_t)
out, *_ = convert_to_dst_type(src=out, dst=img)
return out
[docs]
class MapLabelValue:
"""
Utility to map label values to another set of values.
For example, map [3, 2, 1] to [0, 1, 2], [1, 2, 3] -> [0.5, 1.5, 2.5], ["label3", "label2", "label1"] -> [0, 1, 2],
[3.5, 2.5, 1.5] -> ["label0", "label1", "label2"], etc.
The label data must be numpy array or array-like data and the output data will be numpy array.
"""
backend = [TransformBackends.NUMPY, TransformBackends.TORCH]
[docs]
def __init__(self, orig_labels: Sequence, target_labels: Sequence, dtype: DtypeLike = np.float32) -> None:
"""
Args:
orig_labels: original labels that map to others.
target_labels: expected label values, 1: 1 map to the `orig_labels`.
dtype: convert the output data to dtype, default to float32.
if dtype is from PyTorch, the transform will use the pytorch backend, else with numpy backend.
"""
if len(orig_labels) != len(target_labels):
raise ValueError("orig_labels and target_labels must have the same length.")
self.orig_labels = orig_labels
self.target_labels = target_labels
self.pair = tuple((o, t) for o, t in zip(self.orig_labels, self.target_labels) if o != t)
type_dtype = type(dtype)
if getattr(type_dtype, "__module__", "") == "torch":
self.use_numpy = False
self.dtype = get_equivalent_dtype(dtype, data_type=torch.Tensor)
else:
self.use_numpy = True
self.dtype = get_equivalent_dtype(dtype, data_type=np.ndarray)
[docs]
def __call__(self, img: NdarrayOrTensor):
if self.use_numpy:
img_np, *_ = convert_data_type(img, np.ndarray)
_out_shape = img_np.shape
img_flat = img_np.flatten()
try:
out_flat = img_flat.astype(self.dtype)
except ValueError:
# can't copy unchanged labels as the expected dtype is not supported, must map all the label values
out_flat = np.zeros(shape=img_flat.shape, dtype=self.dtype)
for o, t in self.pair:
out_flat[img_flat == o] = t
out_t = out_flat.reshape(_out_shape)
else:
img_t, *_ = convert_data_type(img, torch.Tensor)
out_t = img_t.detach().clone().to(self.dtype) # type: ignore
for o, t in self.pair:
out_t[img_t == o] = t
out, *_ = convert_to_dst_type(src=out_t, dst=img, dtype=self.dtype)
return out
[docs]
class IntensityStats(Transform):
"""
Compute statistics for the intensity values of input image and store into the metadata dictionary.
For example: if `ops=[lambda x: np.mean(x), "max"]` and `key_prefix="orig"`, may generate below stats:
`{"orig_custom_0": 1.5, "orig_max": 3.0}`.
Args:
ops: expected operations to compute statistics for the intensity.
if a string, will map to the predefined operations, supported: ["mean", "median", "max", "min", "std"]
mapping to `np.nanmean`, `np.nanmedian`, `np.nanmax`, `np.nanmin`, `np.nanstd`.
if a callable function, will execute the function on input image.
key_prefix: the prefix to combine with `ops` name to generate the key to store the results in the
metadata dictionary. if some `ops` are callable functions, will use "{key_prefix}_custom_{index}"
as the key, where index counts from 0.
channel_wise: whether to compute statistics for every channel of input image separately.
if True, return a list of values for every operation, default to False.
"""
backend = [TransformBackends.NUMPY]
def __init__(self, ops: Sequence[str | Callable], key_prefix: str, channel_wise: bool = False) -> None:
self.ops = ensure_tuple(ops)
self.key_prefix = key_prefix
self.channel_wise = channel_wise
[docs]
def __call__(
self, img: NdarrayOrTensor, meta_data: dict | None = None, mask: np.ndarray | None = None
) -> tuple[NdarrayOrTensor, dict]:
"""
Compute statistics for the intensity of input image.
Args:
img: input image to compute intensity stats.
meta_data: metadata dictionary to store the statistics data, if None, will create an empty dictionary.
mask: if not None, mask the image to extract only the interested area to compute statistics.
mask must have the same shape as input `img`.
"""
img_np, *_ = convert_data_type(img, np.ndarray)
if meta_data is None:
meta_data = {}
if mask is not None:
if mask.shape != img_np.shape:
raise ValueError(f"mask must have the same shape as input `img`, got {mask.shape} and {img_np.shape}.")
if mask.dtype != bool:
raise TypeError(f"mask must be bool array, got type {mask.dtype}.")
img_np = img_np[mask]
supported_ops = {
"mean": np.nanmean,
"median": np.nanmedian,
"max": np.nanmax,
"min": np.nanmin,
"std": np.nanstd,
}
def _compute(op: Callable, data: np.ndarray):
if self.channel_wise:
return [op(c) for c in data]
return op(data)
custom_index = 0
for o in self.ops:
if isinstance(o, str):
o = look_up_option(o, supported_ops.keys())
meta_data[self.key_prefix + "_" + o] = _compute(supported_ops[o], img_np) # type: ignore
elif callable(o):
meta_data[self.key_prefix + "_custom_" + str(custom_index)] = _compute(o, img_np)
custom_index += 1
else:
raise ValueError("ops must be key string for predefined operations or callable function.")
return img, meta_data
[docs]
class ToDevice(Transform):
"""
Move PyTorch Tensor to the specified device.
It can help cache data into GPU and execute following logic on GPU directly.
Note:
If moving data to GPU device in the multi-processing workers of DataLoader, may got below CUDA error:
"RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing,
you must use the 'spawn' start method."
So usually suggest to set `num_workers=0` in the `DataLoader` or `ThreadDataLoader`.
"""
backend = [TransformBackends.TORCH]
[docs]
def __init__(self, device: torch.device | str, **kwargs) -> None:
"""
Args:
device: target device to move the Tensor, for example: "cuda:1".
kwargs: other args for the PyTorch `Tensor.to()` API, for more details:
https://pytorch.org/docs/stable/generated/torch.Tensor.to.html.
"""
self.device = device
self.kwargs = kwargs
[docs]
def __call__(self, img: torch.Tensor):
if not isinstance(img, torch.Tensor):
raise ValueError("img must be PyTorch Tensor, consider converting img by `EnsureType` transform first.")
return img.to(self.device, **self.kwargs)
[docs]
class CuCIM(Transform):
"""
Wrap a non-randomized cuCIM transform, defined based on the transform name and args.
For randomized transforms use :py:class:`monai.transforms.RandCuCIM`.
Args:
name: the transform name in CuCIM package
args: parameters for the CuCIM transform
kwargs: parameters for the CuCIM transform
Note:
CuCIM transform only work with CuPy arrays, so this transform expects input data to be `cupy.ndarray`.
Users can call `ToCuPy` transform to convert a numpy array or torch tensor to cupy array.
"""
def __init__(self, name: str, *args, **kwargs) -> None:
super().__init__()
self.name = name
self.transform, _ = optional_import("cucim.core.operations.expose.transform", name=name)
self.args = args
self.kwargs = kwargs
[docs]
def __call__(self, data):
"""
Args:
data: a CuPy array (`cupy.ndarray`) for the cuCIM transform
Returns:
`cupy.ndarray`
"""
return self.transform(data, *self.args, **self.kwargs)
[docs]
class RandCuCIM(CuCIM, RandomizableTrait):
"""
Wrap a randomized cuCIM transform, defined based on the transform name and args
For deterministic non-randomized transforms use :py:class:`monai.transforms.CuCIM`.
Args:
name: the transform name in CuCIM package.
args: parameters for the CuCIM transform.
kwargs: parameters for the CuCIM transform.
Note:
- CuCIM transform only work with CuPy arrays, so this transform expects input data to be `cupy.ndarray`.
Users can call `ToCuPy` transform to convert a numpy array or torch tensor to cupy array.
- If the random factor of the underlying cuCIM transform is not derived from `self.R`,
the results may not be deterministic. See Also: :py:class:`monai.transforms.Randomizable`.
"""
def __init__(self, name: str, *args, **kwargs) -> None:
CuCIM.__init__(self, name, *args, **kwargs)
[docs]
class AddCoordinateChannels(Transform):
"""
Appends additional channels encoding coordinates of the input. Useful when e.g. training using patch-based sampling,
to allow feeding of the patch's location into the network.
This can be seen as a input-only version of CoordConv:
Liu, R. et al. An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution, NeurIPS 2018.
Args:
spatial_dims: the spatial dimensions that are to have their coordinates encoded in a channel and
appended to the input image. E.g., `(0, 1, 2)` represents `H, W, D` dims and append three channels
to the input image, encoding the coordinates of the input's three spatial dimensions.
"""
backend = [TransformBackends.NUMPY]
def __init__(self, spatial_dims: Sequence[int]) -> None:
self.spatial_dims = spatial_dims
[docs]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Args:
img: data to be transformed, assuming `img` is channel first.
"""
if max(self.spatial_dims) > img.ndim - 2 or min(self.spatial_dims) < 0:
raise ValueError(f"`spatial_dims` values must be within [0, {img.ndim - 2}]")
spatial_size = img.shape[1:]
coord_channels = np.array(np.meshgrid(*tuple(np.linspace(-0.5, 0.5, s) for s in spatial_size), indexing="ij"))
coord_channels, *_ = convert_to_dst_type(coord_channels, img) # type: ignore
coord_channels = coord_channels[list(self.spatial_dims)]
return concatenate((img, coord_channels), axis=0)
[docs]
class ImageFilter(Transform):
"""
Applies a convolution filter to the input image.
Args:
filter:
A string specifying the filter, a custom filter as ``torch.Tenor`` or ``np.ndarray`` or a ``nn.Module``.
Available options for string are: ``mean``, ``laplace``, ``elliptical``, ``sobel``, ``sharpen``, ``median``, ``gauss``
See below for short explanations on every filter.
filter_size:
A single integer value specifying the size of the quadratic or cubic filter.
Computational complexity scales to the power of 2 (2D filter) or 3 (3D filter), which
should be considered when choosing filter size.
kwargs:
Additional arguments passed to filter function, required by ``sobel`` and ``gauss``.
See below for details.
Raises:
ValueError: When ``filter_size`` is not an uneven integer
ValueError: When ``filter`` is an array and ``ndim`` is not in [1,2,3]
ValueError: When ``filter`` is an array and any dimension has an even shape
NotImplementedError: When ``filter`` is a string and not in ``self.supported_filters``
KeyError: When necessary ``kwargs`` are not passed to a filter that requires additional arguments.
**Mean Filtering:** ``filter='mean'``
Mean filtering can smooth edges and remove aliasing artifacts in an segmentation image.
See also py:func:`monai.networks.layers.simplelayers.MeanFilter`
Example 2D filter (5 x 5)::
[[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]]
If smoothing labels with this filter, ensure they are in one-hot format.
**Outline Detection:** ``filter='laplace'``
Laplacian filtering for outline detection in images. Can be used to transform labels to contours.
See also py:func:`monai.networks.layers.simplelayers.LaplaceFilter`
Example 2D filter (5x5)::
[[-1., -1., -1., -1., -1.],
[-1., -1., -1., -1., -1.],
[-1., -1., 24., -1., -1.],
[-1., -1., -1., -1., -1.],
[-1., -1., -1., -1., -1.]]
**Dilation:** ``filter='elliptical'``
An elliptical filter can be used to dilate labels or label-contours.
Example 2D filter (5x5)::
[[0., 0., 1., 0., 0.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[0., 0., 1., 0., 0.]]
**Edge Detection:** ``filter='sobel'``
This filter allows for additional arguments passed as ``kwargs`` during initialization.
See also py:func:`monai.transforms.post.SobelGradients`
*kwargs*
* ``spatial_axes``: the axes that define the direction of the gradient to be calculated.
It calculates the gradient along each of the provide axis.
By default it calculate the gradient for all spatial axes.
* ``normalize_kernels``: if normalize the Sobel kernel to provide proper gradients. Defaults to True.
* ``normalize_gradients``: if normalize the output gradient to 0 and 1. Defaults to False.
* ``padding_mode``: the padding mode of the image when convolving with Sobel kernels. Defaults to ``"reflect"``.
Acceptable values are ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
See ``torch.nn.Conv1d()`` for more information.
* ``dtype``: kernel data type (torch.dtype). Defaults to ``torch.float32``.
**Sharpening:** ``filter='sharpen'``
Sharpen an image with a 2D or 3D filter.
Example 2D filter (5x5)::
[[ 0., 0., -1., 0., 0.],
[-1., -1., -1., -1., -1.],
[-1., -1., 17., -1., -1.],
[-1., -1., -1., -1., -1.],
[ 0., 0., -1., 0., 0.]]
**Gaussian Smooth:** ``filter='gauss'``
Blur/smooth an image with 2D or 3D gaussian filter.
This filter requires additional arguments passed as ``kwargs`` during initialization.
See also py:func:`monai.networks.layers.simplelayers.GaussianFilter`
*kwargs*
* ``sigma``: std. could be a single value, or spatial_dims number of values.
* ``truncated``: spreads how many stds.
* ``approx``: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace".
**Median Filter:** ``filter='median'``
Blur an image with 2D or 3D median filter to remove noise.
Useful in image preprocessing to improve results of later processing.
See also py:func:`monai.networks.layers.simplelayers.MedianFilter`
**Savitzky Golay Filter:** ``filter = 'savitzky_golay'``
Convolve a Tensor along a particular axis with a Savitzky-Golay kernel.
This filter requires additional arguments passed as ``kwargs`` during initialization.
See also py:func:`monai.networks.layers.simplelayers.SavitzkyGolayFilter`
*kwargs*
* ``order``: Order of the polynomial to fit to each window, must be less than ``window_length``.
* ``axis``: (optional): Axis along which to apply the filter kernel. Default 2 (first spatial dimension).
* ``mode``: (string, optional): padding mode passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'`` or
``'circular'``. Default: ``'zeros'``. See torch.nn.Conv1d() for more information.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
supported_filters = sorted(
["mean", "laplace", "elliptical", "sobel", "sharpen", "median", "gauss", "savitzky_golay"]
)
def __init__(self, filter: str | NdarrayOrTensor | nn.Module, filter_size: int | None = None, **kwargs) -> None:
self._check_filter_format(filter, filter_size)
self._check_kwargs_are_present(filter, **kwargs)
self.filter = filter
self.filter_size = filter_size
self.additional_args_for_filter = kwargs
[docs]
def __call__(self, img: NdarrayOrTensor, meta_dict: dict | None = None) -> NdarrayOrTensor:
"""
Args:
img: torch tensor data to apply filter to with shape: [channels, height, width[, depth]]
meta_dict: An optional dictionary with metadata
Returns:
A MetaTensor with the same shape as `img` and identical metadata
"""
if isinstance(img, MetaTensor):
meta_dict = img.meta
img_, prev_type, device = convert_data_type(img, torch.Tensor)
ndim = img_.ndim - 1 # assumes channel first format
if isinstance(self.filter, str):
self.filter = self._get_filter_from_string(self.filter, self.filter_size, ndim) # type: ignore
elif isinstance(self.filter, (torch.Tensor, np.ndarray)):
self.filter = ApplyFilter(self.filter)
img_ = self._apply_filter(img_)
if meta_dict:
img_ = MetaTensor(img_, meta=meta_dict)
else:
img_, *_ = convert_data_type(img_, prev_type, device)
return img_
def _check_all_values_uneven(self, x: tuple) -> None:
for value in x:
if value % 2 == 0:
raise ValueError(f"Only uneven filters are supported, but filter size is {x}")
def _check_filter_format(self, filter: str | NdarrayOrTensor | nn.Module, filter_size: int | None = None) -> None:
if isinstance(filter, str):
if not filter_size:
raise ValueError("`filter_size` must be specified when specifying filters by string.")
if filter_size % 2 == 0:
raise ValueError("`filter_size` should be a single uneven integer.")
if filter not in self.supported_filters:
raise NotImplementedError(f"{filter}. Supported filters are {self.supported_filters}.")
elif isinstance(filter, (torch.Tensor, np.ndarray)):
if filter.ndim not in [1, 2, 3]:
raise ValueError("Only 1D, 2D, and 3D filters are supported.")
self._check_all_values_uneven(filter.shape)
elif not isinstance(filter, (nn.Module, Transform)):
raise TypeError(
f"{type(filter)} is not supported."
"Supported types are `class 'str'`, `class 'torch.Tensor'`, `class 'np.ndarray'`, "
"`class 'torch.nn.modules.module.Module'`, `class 'monai.transforms.Transform'`"
)
def _check_kwargs_are_present(self, filter: str | NdarrayOrTensor | nn.Module, **kwargs: Any) -> None:
"""
Perform sanity checks on the kwargs if the filter contains the required keys.
If the filter is ``gauss``, kwargs should contain ``sigma``.
If the filter is ``savitzky_golay``, kwargs should contain ``order``.
Args:
filter: A string specifying the filter, a custom filter as ``torch.Tenor`` or ``np.ndarray`` or a ``nn.Module``.
kwargs: additional arguments defining the filter.
Raises:
KeyError if the filter doesn't contain the requirement key.
"""
if not isinstance(filter, str):
return
if filter == "gauss" and "sigma" not in kwargs.keys():
raise KeyError("`filter='gauss', requires the additional keyword argument `sigma`")
if filter == "savitzky_golay" and "order" not in kwargs.keys():
raise KeyError("`filter='savitzky_golay', requires the additional keyword argument `order`")
def _get_filter_from_string(self, filter: str, size: int, ndim: int) -> nn.Module | Callable:
if filter == "mean":
return MeanFilter(ndim, size)
elif filter == "laplace":
return LaplaceFilter(ndim, size)
elif filter == "elliptical":
return EllipticalFilter(ndim, size)
elif filter == "sobel":
from monai.transforms.post.array import SobelGradients # cannot import on top because of circular imports
allowed_keys = SobelGradients.__init__.__annotations__.keys()
kwargs = {k: v for k, v in self.additional_args_for_filter.items() if k in allowed_keys}
return SobelGradients(size, **kwargs)
elif filter == "sharpen":
return SharpenFilter(ndim, size)
elif filter == "gauss":
allowed_keys = GaussianFilter.__init__.__annotations__.keys()
kwargs = {k: v for k, v in self.additional_args_for_filter.items() if k in allowed_keys}
return GaussianFilter(ndim, **kwargs)
elif filter == "median":
return partial(median_filter, kernel_size=size, spatial_dims=ndim)
elif filter == "savitzky_golay":
allowed_keys = SavitzkyGolayFilter.__init__.__annotations__.keys()
kwargs = {k: v for k, v in self.additional_args_for_filter.items() if k in allowed_keys}
return SavitzkyGolayFilter(size, **kwargs)
else:
raise NotImplementedError(f"Filter {filter} not implemented")
def _apply_filter(self, img: torch.Tensor) -> torch.Tensor:
if isinstance(self.filter, Transform):
img = self.filter(img)
else:
img = self.filter(img.unsqueeze(0)) # type: ignore
img = img[0] # add and remove batch dim
return img
[docs]
class RandImageFilter(RandomizableTransform):
"""
Randomly apply a convolutional filter to the input data.
Args:
filter:
A string specifying the filter or a custom filter as `torch.Tenor` or `np.ndarray`.
Available options are: `mean`, `laplace`, `elliptical`, `gaussian``
See below for short explanations on every filter.
filter_size:
A single integer value specifying the size of the quadratic or cubic filter.
Computational complexity scales to the power of 2 (2D filter) or 3 (3D filter), which
should be considered when choosing filter size.
prob:
Probability the transform is applied to the data
"""
backend = ImageFilter.backend
def __init__(
self, filter: str | NdarrayOrTensor, filter_size: int | None = None, prob: float = 0.1, **kwargs
) -> None:
super().__init__(prob)
self.filter = ImageFilter(filter, filter_size, **kwargs)
[docs]
def __call__(self, img: NdarrayOrTensor, meta_dict: Mapping | None = None) -> NdarrayOrTensor:
"""
Args:
img: torch tensor data to apply filter to with shape: [channels, height, width[, depth]]
meta_dict: An optional dictionary with metadata
kwargs: optional arguments required by specific filters. E.g. `sigma`if filter is `gauss`.
see py:func:`monai.transforms.utility.array.ImageFilter` for more details
Returns:
A MetaTensor with the same shape as `img` and identical metadata
"""
self.randomize(None)
if self._do_transform:
img = self.filter(img)
return img