Source code for monai.transforms.spatial.dictionary

# Copyright 2020 - 2021 MONAI Consortium
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"""
A collection of dictionary-based wrappers around the "vanilla" transforms for spatial operations
defined in :py:class:`monai.transforms.spatial.array`.

Class names are ended with 'd' to denote dictionary-based transforms.
"""

from typing import Any, Dict, Hashable, Mapping, Optional, Sequence, Tuple, Union

import numpy as np
import torch

from monai.config import DtypeLike, KeysCollection
from monai.networks.layers.simplelayers import GaussianFilter
from monai.transforms.croppad.array import CenterSpatialCrop
from monai.transforms.spatial.array import (
    Flip,
    Orientation,
    Rand2DElastic,
    Rand3DElastic,
    RandAffine,
    Resize,
    Rotate,
    Rotate90,
    Spacing,
    Zoom,
)
from monai.transforms.transform import MapTransform, RandomizableTransform
from monai.transforms.utils import create_grid
from monai.utils import (
    GridSampleMode,
    GridSamplePadMode,
    InterpolateMode,
    NumpyPadMode,
    ensure_tuple,
    ensure_tuple_rep,
    fall_back_tuple,
)

__all__ = [
    "Spacingd",
    "Orientationd",
    "Rotate90d",
    "RandRotate90d",
    "Resized",
    "RandAffined",
    "Rand2DElasticd",
    "Rand3DElasticd",
    "Flipd",
    "RandFlipd",
    "Rotated",
    "RandRotated",
    "Zoomd",
    "RandZoomd",
    "SpacingD",
    "SpacingDict",
    "OrientationD",
    "OrientationDict",
    "Rotate90D",
    "Rotate90Dict",
    "RandRotate90D",
    "RandRotate90Dict",
    "ResizeD",
    "ResizeDict",
    "RandAffineD",
    "RandAffineDict",
    "Rand2DElasticD",
    "Rand2DElasticDict",
    "Rand3DElasticD",
    "Rand3DElasticDict",
    "FlipD",
    "FlipDict",
    "RandFlipD",
    "RandFlipDict",
    "RotateD",
    "RotateDict",
    "RandRotateD",
    "RandRotateDict",
    "ZoomD",
    "ZoomDict",
    "RandZoomD",
    "RandZoomDict",
]

GridSampleModeSequence = Union[Sequence[Union[GridSampleMode, str]], GridSampleMode, str]
GridSamplePadModeSequence = Union[Sequence[Union[GridSamplePadMode, str]], GridSamplePadMode, str]
InterpolateModeSequence = Union[Sequence[Union[InterpolateMode, str]], InterpolateMode, str]
NumpyPadModeSequence = Union[Sequence[Union[NumpyPadMode, str]], NumpyPadMode, str]


[docs]class Spacingd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Spacing`. This transform assumes the ``data`` dictionary has a key for the input data's metadata and contains `affine` field. The key is formed by ``key_{meta_key_postfix}``. After resampling the input array, this transform will write the new affine to the `affine` field of metadata which is formed by ``key_{meta_key_postfix}``. see also: :py:class:`monai.transforms.Spacing` """ def __init__( self, keys: KeysCollection, pixdim: Sequence[float], diagonal: bool = False, mode: GridSampleModeSequence = GridSampleMode.BILINEAR, padding_mode: GridSamplePadModeSequence = GridSamplePadMode.BORDER, align_corners: Union[Sequence[bool], bool] = False, dtype: Optional[Union[Sequence[DtypeLike], DtypeLike]] = np.float64, meta_key_postfix: str = "meta_dict", ) -> None: """ Args: pixdim: output voxel spacing. diagonal: whether to resample the input to have a diagonal affine matrix. If True, the input data is resampled to the following affine:: np.diag((pixdim_0, pixdim_1, pixdim_2, 1)) This effectively resets the volume to the world coordinate system (RAS+ in nibabel). The original orientation, rotation, shearing are not preserved. If False, the axes orientation, orthogonal rotation and translations components from the original affine will be preserved in the target affine. This option will not flip/swap axes against the original ones. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. align_corners: Geometrically, we consider the pixels of the input as squares rather than points. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of bool, each element corresponds to a key in ``keys``. dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. It also can be a sequence of dtypes, each element corresponds to a key in ``keys``. meta_key_postfix: use `key_{postfix}` to to fetch the meta data according to the key data, default is `meta_dict`, the meta data is a dictionary object. For example, to handle key `image`, read/write affine matrices from the metadata `image_meta_dict` dictionary's `affine` field. Raises: TypeError: When ``meta_key_postfix`` is not a ``str``. """ super().__init__(keys) self.spacing_transform = Spacing(pixdim, diagonal=diagonal) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.dtype = ensure_tuple_rep(dtype, len(self.keys)) if not isinstance(meta_key_postfix, str): raise TypeError(f"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.") self.meta_key_postfix = meta_key_postfix
[docs] def __call__( self, data: Mapping[Union[Hashable, str], Dict[str, np.ndarray]] ) -> Dict[Union[Hashable, str], Union[np.ndarray, Dict[str, np.ndarray]]]: d: Dict = dict(data) for idx, key in enumerate(self.keys): meta_data = d[f"{key}_{self.meta_key_postfix}"] # resample array of each corresponding key # using affine fetched from d[affine_key] d[key], _, new_affine = self.spacing_transform( data_array=np.asarray(d[key]), affine=meta_data["affine"], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], dtype=self.dtype[idx], ) # set the 'affine' key meta_data["affine"] = new_affine return d
[docs]class Orientationd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Orientation`. This transform assumes the ``data`` dictionary has a key for the input data's metadata and contains `affine` field. The key is formed by ``key_{meta_key_postfix}``. After reorienting the input array, this transform will write the new affine to the `affine` field of metadata which is formed by ``key_{meta_key_postfix}``. """ def __init__( self, keys: KeysCollection, axcodes: Optional[str] = None, as_closest_canonical: bool = False, labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip("LPI", "RAS")), meta_key_postfix: str = "meta_dict", ) -> None: """ Args: axcodes: N elements sequence for spatial ND input's orientation. e.g. axcodes='RAS' represents 3D orientation: (Left, Right), (Posterior, Anterior), (Inferior, Superior). default orientation labels options are: 'L' and 'R' for the first dimension, 'P' and 'A' for the second, 'I' and 'S' for the third. as_closest_canonical: if True, load the image as closest to canonical axis format. labels: optional, None or sequence of (2,) sequences (2,) sequences are labels for (beginning, end) of output axis. Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``. meta_key_postfix: use `key_{postfix}` to to fetch the meta data according to the key data, default is `meta_dict`, the meta data is a dictionary object. For example, to handle key `image`, read/write affine matrices from the metadata `image_meta_dict` dictionary's `affine` field. Raises: TypeError: When ``meta_key_postfix`` is not a ``str``. See Also: `nibabel.orientations.ornt2axcodes`. """ super().__init__(keys) self.ornt_transform = Orientation(axcodes=axcodes, as_closest_canonical=as_closest_canonical, labels=labels) if not isinstance(meta_key_postfix, str): raise TypeError(f"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.") self.meta_key_postfix = meta_key_postfix
[docs] def __call__( self, data: Mapping[Union[Hashable, str], Dict[str, np.ndarray]] ) -> Dict[Union[Hashable, str], Union[np.ndarray, Dict[str, np.ndarray]]]: d: Dict = dict(data) for key in self.keys: meta_data = d[f"{key}_{self.meta_key_postfix}"] d[key], _, new_affine = self.ornt_transform(d[key], affine=meta_data["affine"]) meta_data["affine"] = new_affine return d
[docs]class Rotate90d(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Rotate90`. """ def __init__(self, keys: KeysCollection, k: int = 1, spatial_axes: Tuple[int, int] = (0, 1)) -> None: """ Args: k: number of times to rotate by 90 degrees. spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes. Default: (0, 1), this is the first two axis in spatial dimensions. """ super().__init__(keys) self.rotator = Rotate90(k, spatial_axes)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.rotator(d[key]) return d
[docs]class RandRotate90d(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandRotate90`. With probability `prob`, input arrays are rotated by 90 degrees in the plane specified by `spatial_axes`. """ def __init__( self, keys: KeysCollection, prob: float = 0.1, max_k: int = 3, spatial_axes: Tuple[int, int] = (0, 1), ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` prob: probability of rotating. (Default 0.1, with 10% probability it returns a rotated array.) max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`. (Default 3) spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes. Default: (0, 1), this is the first two axis in spatial dimensions. """ MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.max_k = max_k self.spatial_axes = spatial_axes self._rand_k = 0
[docs] def randomize(self, data: Optional[Any] = None) -> None: self._rand_k = self.R.randint(self.max_k) + 1 super().randomize(None)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Mapping[Hashable, np.ndarray]: self.randomize() d = dict(data) rotator = Rotate90(self._rand_k, self.spatial_axes) for key in self.keys: if self._do_transform: d[key] = rotator(d[key]) return d
[docs]class Resized(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Resize`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` spatial_size: expected shape of spatial dimensions after resize operation. if the components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``"area"``. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of string, each element corresponds to a key in ``keys``. align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of bool or None, each element corresponds to a key in ``keys``. """ def __init__( self, keys: KeysCollection, spatial_size: Union[Sequence[int], int], mode: InterpolateModeSequence = InterpolateMode.AREA, align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None, ) -> None: super().__init__(keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.resizer = Resize(spatial_size=spatial_size)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for idx, key in enumerate(self.keys): d[key] = self.resizer(d[key], mode=self.mode[idx], align_corners=self.align_corners[idx]) return d
[docs]class RandAffined(RandomizableTransform, MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.RandAffine`. """ def __init__( self, keys: KeysCollection, spatial_size: Optional[Union[Sequence[int], int]] = None, prob: float = 0.1, rotate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, shear_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, translate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, scale_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, mode: GridSampleModeSequence = GridSampleMode.BILINEAR, padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION, as_tensor_output: bool = True, device: Optional[torch.device] = None, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. spatial_size: output image spatial size. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if the components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. prob: probability of returning a randomized affine grid. defaults to 0.1, with 10% chance returns a randomized grid. rotate_range: angle range in radians. If element `i` is iterable, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear_range with format matching `rotate_range`. translate_range: translate_range with format matching `rotate_range`. scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1). mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. as_tensor_output: the computation is implemented using pytorch tensors, this option specifies whether to convert it back to numpy arrays. device: device on which the tensor will be allocated. See also: - :py:class:`monai.transforms.compose.MapTransform` - :py:class:`RandAffineGrid` for the random affine parameters configurations. """ MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.rand_affine = RandAffine( prob=1.0, # because probability handled in this class rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, spatial_size=spatial_size, as_tensor_output=as_tensor_output, device=device, ) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
[docs] def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "RandAffined": self.rand_affine.set_random_state(seed, state) super().set_random_state(seed, state) return self
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) self.rand_affine.randomize()
[docs] def __call__( self, data: Mapping[Hashable, Union[np.ndarray, torch.Tensor]] ) -> Dict[Hashable, Union[np.ndarray, torch.Tensor]]: d = dict(data) self.randomize() sp_size = fall_back_tuple(self.rand_affine.spatial_size, data[self.keys[0]].shape[1:]) if self._do_transform: grid = self.rand_affine.rand_affine_grid(spatial_size=sp_size) else: grid = create_grid(spatial_size=sp_size) for idx, key in enumerate(self.keys): d[key] = self.rand_affine.resampler(d[key], grid, mode=self.mode[idx], padding_mode=self.padding_mode[idx]) return d
[docs]class Rand2DElasticd(RandomizableTransform, MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Rand2DElastic`. """ def __init__( self, keys: KeysCollection, spacing: Union[Tuple[float, float], float], magnitude_range: Tuple[float, float], spatial_size: Optional[Union[Tuple[int, int], int]] = None, prob: float = 0.1, rotate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, shear_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, translate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, scale_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, mode: GridSampleModeSequence = GridSampleMode.BILINEAR, padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION, as_tensor_output: bool = False, device: Optional[torch.device] = None, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. spacing: distance in between the control points. magnitude_range: 2 int numbers, the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``. spatial_size: specifying output image spatial size [h, w]. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if the components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted to `(32, 64)` if the second spatial dimension size of img is `64`. prob: probability of returning a randomized affine grid. defaults to 0.1, with 10% chance returns a randomized grid, otherwise returns a ``spatial_size`` centered area extracted from the input image. rotate_range: angle range in radians. If element `i` is iterable, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear_range with format matching `rotate_range`. translate_range: translate_range with format matching `rotate_range`. scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1). mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. as_tensor_output: the computation is implemented using pytorch tensors, this option specifies whether to convert it back to numpy arrays. device: device on which the tensor will be allocated. See also: - :py:class:`RandAffineGrid` for the random affine parameters configurations. - :py:class:`Affine` for the affine transformation parameters configurations. """ MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.rand_2d_elastic = Rand2DElastic( spacing=spacing, magnitude_range=magnitude_range, prob=1.0, # because probability controlled by this class rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, spatial_size=spatial_size, as_tensor_output=as_tensor_output, device=device, ) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
[docs] def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Rand2DElasticd": self.rand_2d_elastic.set_random_state(seed, state) super().set_random_state(seed, state) return self
[docs] def randomize(self, spatial_size: Sequence[int]) -> None: super().randomize(None) self.rand_2d_elastic.randomize(spatial_size)
[docs] def __call__( self, data: Mapping[Hashable, Union[np.ndarray, torch.Tensor]] ) -> Dict[Hashable, Union[np.ndarray, torch.Tensor]]: d = dict(data) sp_size = fall_back_tuple(self.rand_2d_elastic.spatial_size, data[self.keys[0]].shape[1:]) self.randomize(spatial_size=sp_size) if self._do_transform: grid = self.rand_2d_elastic.deform_grid(spatial_size=sp_size) grid = self.rand_2d_elastic.rand_affine_grid(grid=grid) grid = torch.nn.functional.interpolate( # type: ignore recompute_scale_factor=True, input=grid.unsqueeze(0), scale_factor=ensure_tuple_rep(self.rand_2d_elastic.deform_grid.spacing, 2), mode=InterpolateMode.BICUBIC.value, align_corners=False, ) grid = CenterSpatialCrop(roi_size=sp_size)(grid[0]) else: grid = create_grid(spatial_size=sp_size) for idx, key in enumerate(self.keys): d[key] = self.rand_2d_elastic.resampler( d[key], grid, mode=self.mode[idx], padding_mode=self.padding_mode[idx] ) return d
[docs]class Rand3DElasticd(RandomizableTransform, MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Rand3DElastic`. """ def __init__( self, keys: KeysCollection, sigma_range: Tuple[float, float], magnitude_range: Tuple[float, float], spatial_size: Optional[Union[Tuple[int, int, int], int]] = None, prob: float = 0.1, rotate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, shear_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, translate_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, scale_range: Optional[Union[Sequence[Union[Tuple[float, float], float]], float]] = None, mode: GridSampleModeSequence = GridSampleMode.BILINEAR, padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION, as_tensor_output: bool = False, device: Optional[torch.device] = None, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. sigma_range: a Gaussian kernel with standard deviation sampled from ``uniform[sigma_range[0], sigma_range[1])`` will be used to smooth the random offset grid. magnitude_range: the random offsets on the grid will be generated from ``uniform[magnitude[0], magnitude[1])``. spatial_size: specifying output image spatial size [h, w, d]. if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1, the transform will use the spatial size of `img`. if the components of the `spatial_size` are non-positive values, the transform will use the corresponding components of img size. For example, `spatial_size=(32, 32, -1)` will be adapted to `(32, 32, 64)` if the third spatial dimension size of img is `64`. prob: probability of returning a randomized affine grid. defaults to 0.1, with 10% chance returns a randomized grid, otherwise returns a ``spatial_size`` centered area extracted from the input image. rotate_range: angle range in radians. If element `i` is iterable, then `uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0 and nothing for the remaining dimensions. shear_range: shear_range with format matching `rotate_range`. translate_range: translate_range with format matching `rotate_range`. scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result. This allows 0 to correspond to no change (i.e., a scaling of 1). mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"reflection"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. as_tensor_output: the computation is implemented using pytorch tensors, this option specifies whether to convert it back to numpy arrays. device: device on which the tensor will be allocated. See also: - :py:class:`RandAffineGrid` for the random affine parameters configurations. - :py:class:`Affine` for the affine transformation parameters configurations. """ MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.rand_3d_elastic = Rand3DElastic( sigma_range=sigma_range, magnitude_range=magnitude_range, prob=1.0, # because probability controlled by this class rotate_range=rotate_range, shear_range=shear_range, translate_range=translate_range, scale_range=scale_range, spatial_size=spatial_size, as_tensor_output=as_tensor_output, device=device, ) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
[docs] def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Rand3DElasticd": self.rand_3d_elastic.set_random_state(seed, state) super().set_random_state(seed, state) return self
[docs] def randomize(self, grid_size: Sequence[int]) -> None: super().randomize(None) self.rand_3d_elastic.randomize(grid_size)
[docs] def __call__( self, data: Mapping[Hashable, Union[np.ndarray, torch.Tensor]] ) -> Dict[Hashable, Union[np.ndarray, torch.Tensor]]: d = dict(data) sp_size = fall_back_tuple(self.rand_3d_elastic.spatial_size, data[self.keys[0]].shape[1:]) self.randomize(grid_size=sp_size) grid = create_grid(spatial_size=sp_size) if self._do_transform: device = self.rand_3d_elastic.device grid = torch.tensor(grid).to(device) gaussian = GaussianFilter(spatial_dims=3, sigma=self.rand_3d_elastic.sigma, truncated=3.0).to(device) offset = torch.tensor(self.rand_3d_elastic.rand_offset, device=device).unsqueeze(0) grid[:3] += gaussian(offset)[0] * self.rand_3d_elastic.magnitude grid = self.rand_3d_elastic.rand_affine_grid(grid=grid) for idx, key in enumerate(self.keys): d[key] = self.rand_3d_elastic.resampler( d[key], grid, mode=self.mode[idx], padding_mode=self.padding_mode[idx] ) return d
[docs]class Flipd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Flip`. See `numpy.flip` for additional details. https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html Args: keys: Keys to pick data for transformation. spatial_axis: Spatial axes along which to flip over. Default is None. """ def __init__(self, keys: KeysCollection, spatial_axis: Optional[Union[Sequence[int], int]] = None) -> None: super().__init__(keys) self.flipper = Flip(spatial_axis=spatial_axis)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.flipper(d[key]) return d
[docs]class RandFlipd(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandFlip`. See `numpy.flip` for additional details. https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html Args: keys: Keys to pick data for transformation. prob: Probability of flipping. spatial_axis: Spatial axes along which to flip over. Default is None. """ def __init__( self, keys: KeysCollection, prob: float = 0.1, spatial_axis: Optional[Union[Sequence[int], int]] = None, ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.spatial_axis = spatial_axis self.flipper = Flip(spatial_axis=spatial_axis)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: self.randomize(None) d = dict(data) for key in self.keys: if self._do_transform: d[key] = self.flipper(d[key]) return d
[docs]class Rotated(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Rotate`. Args: keys: Keys to pick data for transformation. angle: Rotation angle(s) in radians. keep_size: If it is False, the output shape is adapted so that the input array is contained completely in the output. If it is True, the output shape is the same as the input. Default is True. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. align_corners: Defaults to False. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of bool, each element corresponds to a key in ``keys``. dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. It also can be a sequence of dtype or None, each element corresponds to a key in ``keys``. """ def __init__( self, keys: KeysCollection, angle: Union[Sequence[float], float], keep_size: bool = True, mode: GridSampleModeSequence = GridSampleMode.BILINEAR, padding_mode: GridSamplePadModeSequence = GridSamplePadMode.BORDER, align_corners: Union[Sequence[bool], bool] = False, dtype: Union[Sequence[DtypeLike], DtypeLike] = np.float64, ) -> None: super().__init__(keys) self.rotator = Rotate(angle=angle, keep_size=keep_size) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.dtype = ensure_tuple_rep(dtype, len(self.keys))
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for idx, key in enumerate(self.keys): d[key] = self.rotator( d[key], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], dtype=self.dtype[idx], ) return d
[docs]class RandRotated(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandRotate` Randomly rotates the input arrays. Args: keys: Keys to pick data for transformation. range_x: Range of rotation angle in radians in the plane defined by the first and second axes. If single number, angle is uniformly sampled from (-range_x, range_x). range_y: Range of rotation angle in radians in the plane defined by the first and third axes. If single number, angle is uniformly sampled from (-range_y, range_y). range_z: Range of rotation angle in radians in the plane defined by the second and third axes. If single number, angle is uniformly sampled from (-range_z, range_z). prob: Probability of rotation. keep_size: If it is False, the output shape is adapted so that the input array is contained completely in the output. If it is True, the output shape is the same as the input. Default is True. mode: {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. Defaults to ``"bilinear"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. Defaults to ``"border"``. See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample It also can be a sequence of string, each element corresponds to a key in ``keys``. align_corners: Defaults to False. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of bool, each element corresponds to a key in ``keys``. dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. If None, use the data type of input data. To be compatible with other modules, the output data type is always ``np.float32``. It also can be a sequence of dtype or None, each element corresponds to a key in ``keys``. """ def __init__( self, keys: KeysCollection, range_x: Union[Tuple[float, float], float] = 0.0, range_y: Union[Tuple[float, float], float] = 0.0, range_z: Union[Tuple[float, float], float] = 0.0, prob: float = 0.1, keep_size: bool = True, mode: GridSampleModeSequence = GridSampleMode.BILINEAR, padding_mode: GridSamplePadModeSequence = GridSamplePadMode.BORDER, align_corners: Union[Sequence[bool], bool] = False, dtype: Union[Sequence[DtypeLike], DtypeLike] = np.float64, ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.range_x = ensure_tuple(range_x) if len(self.range_x) == 1: self.range_x = tuple(sorted([-self.range_x[0], self.range_x[0]])) self.range_y = ensure_tuple(range_y) if len(self.range_y) == 1: self.range_y = tuple(sorted([-self.range_y[0], self.range_y[0]])) self.range_z = ensure_tuple(range_z) if len(self.range_z) == 1: self.range_z = tuple(sorted([-self.range_z[0], self.range_z[0]])) self.keep_size = keep_size self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.dtype = ensure_tuple_rep(dtype, len(self.keys)) self.x = 0.0 self.y = 0.0 self.z = 0.0
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) self.x = self.R.uniform(low=self.range_x[0], high=self.range_x[1]) self.y = self.R.uniform(low=self.range_y[0], high=self.range_y[1]) self.z = self.R.uniform(low=self.range_z[0], high=self.range_z[1])
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: self.randomize() d = dict(data) if not self._do_transform: return d rotator = Rotate( angle=self.x if d[self.keys[0]].ndim == 3 else (self.x, self.y, self.z), keep_size=self.keep_size, ) for idx, key in enumerate(self.keys): d[key] = rotator( d[key], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], dtype=self.dtype[idx], ) return d
[docs]class Zoomd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.Zoom`. Args: keys: Keys to pick data for transformation. zoom: The zoom factor along the spatial axes. If a float, zoom is the same for each spatial axis. If a sequence, zoom should contain one value for each spatial axis. mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``"area"``. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"constant"``, ``"edge``", ``"linear_ramp``", ``"maximum``", ``"mean``", `"median``", ``"minimum``", `"reflect``", ``"symmetric``", ``"wrap``", ``"empty``", ``"<function>``"} The mode to pad data after zooming. See also: https://numpy.org/doc/stable/reference/generated/numpy.pad.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of bool or None, each element corresponds to a key in ``keys``. keep_size: Should keep original size (pad if needed), default is True. """ def __init__( self, keys: KeysCollection, zoom: Union[Sequence[float], float], mode: InterpolateModeSequence = InterpolateMode.AREA, padding_mode: NumpyPadModeSequence = NumpyPadMode.EDGE, align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None, keep_size: bool = True, ) -> None: super().__init__(keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.zoomer = Zoom(zoom=zoom, keep_size=keep_size)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for idx, key in enumerate(self.keys): d[key] = self.zoomer( d[key], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], ) return d
[docs]class RandZoomd(RandomizableTransform, MapTransform): """ Dict-based version :py:class:`monai.transforms.RandZoom`. Args: keys: Keys to pick data for transformation. prob: Probability of zooming. min_zoom: Min zoom factor. Can be float or sequence same size as image. If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims to keep the original spatial shape ratio. If a sequence, min_zoom should contain one value for each spatial axis. If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio. max_zoom: Max zoom factor. Can be float or sequence same size as image. If a float, select a random factor from `[min_zoom, max_zoom]` then apply to all spatial dims to keep the original spatial shape ratio. If a sequence, max_zoom should contain one value for each spatial axis. If 2 values provided for 3D data, use the first value for both H & W dims to keep the same zoom ratio. mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. Defaults to ``"area"``. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of string, each element corresponds to a key in ``keys``. padding_mode: {``"constant"``, ``"edge``", ``"linear_ramp``", ``"maximum``", ``"mean``", `"median``", ``"minimum``", `"reflect``", ``"symmetric``", ``"wrap``", ``"empty``", ``"<function>``"} The mode to pad data after zooming. See also: https://numpy.org/doc/stable/reference/generated/numpy.pad.html align_corners: This only has an effect when mode is 'linear', 'bilinear', 'bicubic' or 'trilinear'. Default: None. See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate It also can be a sequence of bool or None, each element corresponds to a key in ``keys``. keep_size: Should keep original size (pad if needed), default is True. """ def __init__( self, keys: KeysCollection, prob: float = 0.1, min_zoom: Union[Sequence[float], float] = 0.9, max_zoom: Union[Sequence[float], float] = 1.1, mode: InterpolateModeSequence = InterpolateMode.AREA, padding_mode: NumpyPadModeSequence = NumpyPadMode.EDGE, align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None, keep_size: bool = True, ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.min_zoom = ensure_tuple(min_zoom) self.max_zoom = ensure_tuple(max_zoom) if len(self.min_zoom) != len(self.max_zoom): raise AssertionError("min_zoom and max_zoom must have same length.") self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys)) self.align_corners = ensure_tuple_rep(align_corners, len(self.keys)) self.keep_size = keep_size self._zoom: Sequence[float] = [1.0]
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) self._zoom = [self.R.uniform(l, h) for l, h in zip(self.min_zoom, self.max_zoom)]
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: # match the spatial dim of first item self.randomize() d = dict(data) if not self._do_transform: return d img_dims = data[self.keys[0]].ndim if len(self._zoom) == 1: # to keep the spatial shape ratio, use same random zoom factor for all dims self._zoom = ensure_tuple_rep(self._zoom[0], img_dims - 1) elif len(self._zoom) == 2 and img_dims > 3: # if 2 zoom factors provided for 3D data, use the first factor for H and W dims, second factor for D dim self._zoom = ensure_tuple_rep(self._zoom[0], img_dims - 2) + ensure_tuple(self._zoom[-1]) zoomer = Zoom(self._zoom, keep_size=self.keep_size) for idx, key in enumerate(self.keys): d[key] = zoomer( d[key], mode=self.mode[idx], padding_mode=self.padding_mode[idx], align_corners=self.align_corners[idx], ) return d
SpacingD = SpacingDict = Spacingd OrientationD = OrientationDict = Orientationd Rotate90D = Rotate90Dict = Rotate90d RandRotate90D = RandRotate90Dict = RandRotate90d ResizeD = ResizeDict = Resized RandAffineD = RandAffineDict = RandAffined Rand2DElasticD = Rand2DElasticDict = Rand2DElasticd Rand3DElasticD = Rand3DElasticDict = Rand3DElasticd FlipD = FlipDict = Flipd RandFlipD = RandFlipDict = RandFlipd RotateD = RotateDict = Rotated RandRotateD = RandRotateDict = RandRotated ZoomD = ZoomDict = Zoomd RandZoomD = RandZoomDict = RandZoomd