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array.py
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# 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 crop and pad operations
https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design
"""
from itertools import chain
from math import ceil
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch.nn.functional import pad as pad_pt
from monai.config import IndexSelection
from monai.config.type_definitions import NdarrayOrTensor
from monai.data.utils import get_random_patch, get_valid_patch_size
from monai.transforms.transform import Randomizable, Transform
from monai.transforms.utils import (
compute_divisible_spatial_size,
convert_pad_mode,
generate_label_classes_crop_centers,
generate_pos_neg_label_crop_centers,
generate_spatial_bounding_box,
is_positive,
map_binary_to_indices,
map_classes_to_indices,
weighted_patch_samples,
)
from monai.transforms.utils_pytorch_numpy_unification import floor_divide, maximum
from monai.utils import (
Method,
NumpyPadMode,
PytorchPadMode,
ensure_tuple,
ensure_tuple_rep,
fall_back_tuple,
look_up_option,
)
from monai.utils.enums import TransformBackends
from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
__all__ = [
"Pad",
"SpatialPad",
"BorderPad",
"DivisiblePad",
"SpatialCrop",
"CenterSpatialCrop",
"CenterScaleCrop",
"RandSpatialCrop",
"RandScaleCrop",
"RandSpatialCropSamples",
"CropForeground",
"RandWeightedCrop",
"RandCropByPosNegLabel",
"RandCropByLabelClasses",
"ResizeWithPadOrCrop",
"BoundingRect",
]
class Pad(Transform):
"""
Perform padding for a given an amount of padding in each dimension.
If input is `torch.Tensor`, `torch.nn.functional.pad` will be used, otherwise, `np.pad` will be used.
Args:
to_pad: the amount to be padded in each dimension [(low_H, high_H), (low_W, high_W), ...].
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to ``"constant"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
kwargs: other arguments for the `np.pad` or `torch.pad` function.
note that `np.pad` treats channel dimension as the first dimension.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(
self,
to_pad: List[Tuple[int, int]],
mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,
**kwargs,
) -> None:
self.to_pad = to_pad
self.mode = mode
self.kwargs = kwargs
@staticmethod
def _np_pad(img: np.ndarray, all_pad_width, mode, **kwargs) -> np.ndarray:
return np.pad(img, all_pad_width, mode=mode, **kwargs) # type: ignore
@staticmethod
def _pt_pad(img: torch.Tensor, all_pad_width, mode, **kwargs) -> torch.Tensor:
pt_pad_width = [val for sublist in all_pad_width[1:] for val in sublist[::-1]][::-1]
# torch.pad expects `[B, C, H, W, [D]]` shape
return pad_pt(img.unsqueeze(0), pt_pad_width, mode=mode, **kwargs).squeeze(0)
def __call__(
self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None
) -> NdarrayOrTensor:
"""
Args:
img: data to be transformed, assuming `img` is channel-first and
padding doesn't apply to the channel dim.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"`` or ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to `self.mode`.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
"""
if not np.asarray(self.to_pad).any():
# all zeros, skip padding
return img
mode = convert_pad_mode(dst=img, mode=mode or self.mode).value
pad = self._pt_pad if isinstance(img, torch.Tensor) else self._np_pad
return pad(img, self.to_pad, mode, **self.kwargs) # type: ignore
class SpatialPad(Transform):
"""
Performs padding to the data, symmetric for all sides or all on one side for each dimension.
If input is `torch.Tensor` and mode is `constant`, `torch.nn.functional.pad` will be used.
Otherwise, `np.pad` will be used (input converted to `np.ndarray` if necessary).
Uses np.pad so in practice, a mode needs to be provided. See numpy.lib.arraypad.pad
for additional details.
Args:
spatial_size: the spatial size of output data after padding, if a dimension of the input
data size is bigger than the pad size, will not pad that dimension.
If its components have non-positive values, the corresponding size of input image will be used
(no padding). for example: if the spatial size of input data is [30, 30, 30] and
`spatial_size=[32, 25, -1]`, the spatial size of output data will be [32, 30, 30].
method: {``"symmetric"``, ``"end"``}
Pad image symmetrically on every side or only pad at the end sides. Defaults to ``"symmetric"``.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to ``"constant"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
kwargs: other arguments for the `np.pad` or `torch.pad` function.
note that `np.pad` treats channel dimension as the first dimension.
"""
backend = Pad.backend
def __init__(
self,
spatial_size: Union[Sequence[int], int],
method: Union[Method, str] = Method.SYMMETRIC,
mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,
**kwargs,
) -> None:
self.spatial_size = spatial_size
self.method: Method = look_up_option(method, Method)
self.mode = mode
self.kwargs = kwargs
def _determine_data_pad_width(self, data_shape: Sequence[int]) -> List[Tuple[int, int]]:
spatial_size = fall_back_tuple(self.spatial_size, data_shape)
if self.method == Method.SYMMETRIC:
pad_width = []
for i, sp_i in enumerate(spatial_size):
width = max(sp_i - data_shape[i], 0)
pad_width.append((width // 2, width - (width // 2)))
return pad_width
return [(0, max(sp_i - data_shape[i], 0)) for i, sp_i in enumerate(spatial_size)]
def __call__(
self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None
) -> NdarrayOrTensor:
"""
Args:
img: data to be transformed, assuming `img` is channel-first and
padding doesn't apply to the channel dim.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to `self.mode`.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
"""
data_pad_width = self._determine_data_pad_width(img.shape[1:])
all_pad_width = [(0, 0)] + data_pad_width
if not np.asarray(all_pad_width).any():
# all zeros, skip padding
return img
padder = Pad(all_pad_width, mode or self.mode, **self.kwargs)
return padder(img)
class BorderPad(Transform):
"""
Pad the input data by adding specified borders to every dimension.
Args:
spatial_border: specified size for every spatial border. Any -ve values will be set to 0. It can be 3 shapes:
- single int number, pad all the borders with the same size.
- length equals the length of image shape, pad every spatial dimension separately.
for example, image shape(CHW) is [1, 4, 4], spatial_border is [2, 1],
pad every border of H dim with 2, pad every border of W dim with 1, result shape is [1, 8, 6].
- length equals 2 x (length of image shape), pad every border of every dimension separately.
for example, image shape(CHW) is [1, 4, 4], spatial_border is [1, 2, 3, 4], pad top of H dim with 1,
pad bottom of H dim with 2, pad left of W dim with 3, pad right of W dim with 4.
the result shape is [1, 7, 11].
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to ``"constant"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
kwargs: other arguments for the `np.pad` or `torch.pad` function.
note that `np.pad` treats channel dimension as the first dimension.
"""
backend = Pad.backend
def __init__(
self,
spatial_border: Union[Sequence[int], int],
mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,
**kwargs,
) -> None:
self.spatial_border = spatial_border
self.mode = mode
self.kwargs = kwargs
def __call__(
self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None
) -> NdarrayOrTensor:
"""
Args:
img: data to be transformed, assuming `img` is channel-first and
padding doesn't apply to the channel dim.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to `self.mode`.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
Raises:
ValueError: When ``self.spatial_border`` does not contain ints.
ValueError: When ``self.spatial_border`` length is not one of
[1, len(spatial_shape), 2*len(spatial_shape)].
"""
spatial_shape = img.shape[1:]
spatial_border = ensure_tuple(self.spatial_border)
if not all(isinstance(b, int) for b in spatial_border):
raise ValueError(f"self.spatial_border must contain only ints, got {spatial_border}.")
spatial_border = tuple(max(0, b) for b in spatial_border)
if len(spatial_border) == 1:
data_pad_width = [(spatial_border[0], spatial_border[0]) for _ in spatial_shape]
elif len(spatial_border) == len(spatial_shape):
data_pad_width = [(sp, sp) for sp in spatial_border[: len(spatial_shape)]]
elif len(spatial_border) == len(spatial_shape) * 2:
data_pad_width = [(spatial_border[2 * i], spatial_border[2 * i + 1]) for i in range(len(spatial_shape))]
else:
raise ValueError(
f"Unsupported spatial_border length: {len(spatial_border)}, available options are "
f"[1, len(spatial_shape)={len(spatial_shape)}, 2*len(spatial_shape)={2*len(spatial_shape)}]."
)
all_pad_width = [(0, 0)] + data_pad_width
padder = Pad(all_pad_width, mode or self.mode, **self.kwargs)
return padder(img)
class DivisiblePad(Transform):
"""
Pad the input data, so that the spatial sizes are divisible by `k`.
"""
backend = SpatialPad.backend
def __init__(
self,
k: Union[Sequence[int], int],
mode: Union[NumpyPadMode, PytorchPadMode, str] = NumpyPadMode.CONSTANT,
method: Union[Method, str] = Method.SYMMETRIC,
**kwargs,
) -> None:
"""
Args:
k: the target k for each spatial dimension.
if `k` is negative or 0, the original size is preserved.
if `k` is an int, the same `k` be applied to all the input spatial dimensions.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to ``"constant"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
method: {``"symmetric"``, ``"end"``}
Pad image symmetrically on every side or only pad at the end sides. Defaults to ``"symmetric"``.
kwargs: other arguments for the `np.pad` or `torch.pad` function.
note that `np.pad` treats channel dimension as the first dimension.
See also :py:class:`monai.transforms.SpatialPad`
"""
self.k = k
self.mode: NumpyPadMode = NumpyPadMode(mode)
self.method: Method = Method(method)
self.kwargs = kwargs
def __call__(
self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None
) -> NdarrayOrTensor:
"""
Args:
img: data to be transformed, assuming `img` is channel-first
and padding doesn't apply to the channel dim.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to `self.mode`.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
"""
new_size = compute_divisible_spatial_size(spatial_shape=img.shape[1:], k=self.k)
spatial_pad = SpatialPad(spatial_size=new_size, method=self.method, mode=mode or self.mode, **self.kwargs)
return spatial_pad(img)
class SpatialCrop(Transform):
"""
General purpose cropper to produce sub-volume region of interest (ROI).
If a dimension of the expected ROI size is bigger than the input image size, will not crop that dimension.
So the cropped result may be smaller than the expected ROI, and the cropped results of several images may
not have exactly the same shape.
It can support to crop ND spatial (channel-first) data.
The cropped region can be parameterised in various ways:
- a list of slices for each spatial dimension (allows for use of -ve indexing and `None`)
- a spatial center and size
- the start and end coordinates of the ROI
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(
self,
roi_center: Union[Sequence[int], NdarrayOrTensor, None] = None,
roi_size: Union[Sequence[int], NdarrayOrTensor, None] = None,
roi_start: Union[Sequence[int], NdarrayOrTensor, None] = None,
roi_end: Union[Sequence[int], NdarrayOrTensor, None] = None,
roi_slices: Optional[Sequence[slice]] = None,
) -> None:
"""
Args:
roi_center: voxel coordinates for center of the crop ROI.
roi_size: size of the crop ROI, if a dimension of ROI size is bigger than image size,
will not crop that dimension of the image.
roi_start: voxel coordinates for start of the crop ROI.
roi_end: voxel coordinates for end of the crop ROI, if a coordinate is out of image,
use the end coordinate of image.
roi_slices: list of slices for each of the spatial dimensions.
"""
roi_start_torch: torch.Tensor
if roi_slices:
if not all(s.step is None or s.step == 1 for s in roi_slices):
raise ValueError("Only slice steps of 1/None are currently supported")
self.slices = list(roi_slices)
else:
if roi_center is not None and roi_size is not None:
roi_center, *_ = convert_data_type(
data=roi_center, output_type=torch.Tensor, dtype=torch.int16, wrap_sequence=True
)
roi_size, *_ = convert_to_dst_type(src=roi_size, dst=roi_center, wrap_sequence=True)
_zeros = torch.zeros_like(roi_center)
roi_start_torch = maximum(roi_center - floor_divide(roi_size, 2), _zeros) # type: ignore
roi_end_torch = maximum(roi_start_torch + roi_size, roi_start_torch)
else:
if roi_start is None or roi_end is None:
raise ValueError("Please specify either roi_center, roi_size or roi_start, roi_end.")
roi_start_torch, *_ = convert_data_type(
data=roi_start, output_type=torch.Tensor, dtype=torch.int16, wrap_sequence=True
)
roi_start_torch = maximum(roi_start_torch, torch.zeros_like(roi_start_torch)) # type: ignore
roi_end_torch, *_ = convert_to_dst_type(src=roi_end, dst=roi_start_torch, wrap_sequence=True)
roi_end_torch = maximum(roi_end_torch, roi_start_torch)
# convert to slices (accounting for 1d)
if roi_start_torch.numel() == 1:
self.slices = [slice(int(roi_start_torch.item()), int(roi_end_torch.item()))]
else:
self.slices = [slice(int(s), int(e)) for s, e in zip(roi_start_torch.tolist(), roi_end_torch.tolist())]
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't apply to the channel dim.
"""
sd = min(len(self.slices), len(img.shape[1:])) # spatial dims
slices = [slice(None)] + self.slices[:sd]
return img[tuple(slices)]
class CenterSpatialCrop(Transform):
"""
Crop at the center of image with specified ROI size.
If a dimension of the expected ROI size is bigger than the input image size, will not crop that dimension.
So the cropped result may be smaller than the expected ROI, and the cropped results of several images may
not have exactly the same shape.
Args:
roi_size: the spatial size of the crop region e.g. [224,224,128]
if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.
If its components have non-positive values, the corresponding size of input image will be used.
for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,
the spatial size of output data will be [32, 40, 40].
"""
backend = SpatialCrop.backend
def __init__(self, roi_size: Union[Sequence[int], int]) -> None:
self.roi_size = roi_size
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't apply to the channel dim.
"""
roi_size = fall_back_tuple(self.roi_size, img.shape[1:])
center = [i // 2 for i in img.shape[1:]]
cropper = SpatialCrop(roi_center=center, roi_size=roi_size)
return cropper(img)
class CenterScaleCrop(Transform):
"""
Crop at the center of image with specified scale of ROI size.
Args:
roi_scale: specifies the expected scale of image size to crop. e.g. [0.3, 0.4, 0.5] or a number for all dims.
If its components have non-positive values, will use `1.0` instead, which means the input image size.
"""
backend = CenterSpatialCrop.backend
def __init__(self, roi_scale: Union[Sequence[float], float]):
self.roi_scale = roi_scale
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
img_size = img.shape[1:]
ndim = len(img_size)
roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]
sp_crop = CenterSpatialCrop(roi_size=roi_size)
return sp_crop(img=img)
class RandSpatialCrop(Randomizable, Transform):
"""
Crop image with random size or specific size ROI. It can crop at a random position as center
or at the image center. And allows to set the minimum and maximum size to limit the randomly generated ROI.
Note: even `random_size=False`, if a dimension of the expected ROI size is bigger than the input image size,
will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped results
of several images may not have exactly the same shape.
Args:
roi_size: if `random_size` is True, it specifies the minimum crop region.
if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]
if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.
If its components have non-positive values, the corresponding size of input image will be used.
for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,
the spatial size of output data will be [32, 40, 40].
max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`
can specify the max crop region size. if None, defaults to the input image size.
if its components have non-positive values, the corresponding size of input image will be used.
random_center: crop at random position as center or the image center.
random_size: crop with random size or specific size ROI.
if True, the actual size is sampled from `randint(roi_size, max_roi_size + 1)`.
"""
backend = CenterSpatialCrop.backend
def __init__(
self,
roi_size: Union[Sequence[int], int],
max_roi_size: Optional[Union[Sequence[int], int]] = None,
random_center: bool = True,
random_size: bool = True,
) -> None:
self.roi_size = roi_size
self.max_roi_size = max_roi_size
self.random_center = random_center
self.random_size = random_size
self._size: Optional[Sequence[int]] = None
self._slices: Optional[Tuple[slice, ...]] = None
def randomize(self, img_size: Sequence[int]) -> None:
self._size = fall_back_tuple(self.roi_size, img_size)
if self.random_size:
max_size = img_size if self.max_roi_size is None else fall_back_tuple(self.max_roi_size, img_size)
if any(i > j for i, j in zip(self._size, max_size)):
raise ValueError(f"min ROI size: {self._size} is bigger than max ROI size: {max_size}.")
self._size = tuple(self.R.randint(low=self._size[i], high=max_size[i] + 1) for i in range(len(img_size)))
if self.random_center:
valid_size = get_valid_patch_size(img_size, self._size)
self._slices = (slice(None),) + get_random_patch(img_size, valid_size, self.R)
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't apply to the channel dim.
"""
self.randomize(img.shape[1:])
if self._size is None:
raise RuntimeError("self._size not specified.")
if self.random_center:
return img[self._slices]
cropper = CenterSpatialCrop(self._size)
return cropper(img)
class RandScaleCrop(RandSpatialCrop):
"""
Subclass of :py:class:`monai.transforms.RandSpatialCrop`. Crop image with
random size or specific size ROI. It can crop at a random position as
center or at the image center. And allows to set the minimum and maximum
scale of image size to limit the randomly generated ROI.
Args:
roi_scale: if `random_size` is True, it specifies the minimum crop size: `roi_scale * image spatial size`.
if `random_size` is False, it specifies the expected scale of image size to crop. e.g. [0.3, 0.4, 0.5].
If its components have non-positive values, will use `1.0` instead, which means the input image size.
max_roi_scale: if `random_size` is True and `roi_scale` specifies the min crop region size, `max_roi_scale`
can specify the max crop region size: `max_roi_scale * image spatial size`.
if None, defaults to the input image size. if its components have non-positive values,
will use `1.0` instead, which means the input image size.
random_center: crop at random position as center or the image center.
random_size: crop with random size or specified size ROI by `roi_scale * image spatial size`.
if True, the actual size is sampled from
`randint(roi_scale * image spatial size, max_roi_scale * image spatial size + 1)`.
"""
def __init__(
self,
roi_scale: Union[Sequence[float], float],
max_roi_scale: Optional[Union[Sequence[float], float]] = None,
random_center: bool = True,
random_size: bool = True,
) -> None:
super().__init__(roi_size=-1, max_roi_size=None, random_center=random_center, random_size=random_size)
self.roi_scale = roi_scale
self.max_roi_scale = max_roi_scale
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't apply to the channel dim.
"""
img_size = img.shape[1:]
ndim = len(img_size)
self.roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.roi_scale, ndim), img_size)]
if self.max_roi_scale is not None:
self.max_roi_size = [ceil(r * s) for r, s in zip(ensure_tuple_rep(self.max_roi_scale, ndim), img_size)]
else:
self.max_roi_size = None
return super().__call__(img=img)
class RandSpatialCropSamples(Randomizable, Transform):
"""
Crop image with random size or specific size ROI to generate a list of N samples.
It can crop at a random position as center or at the image center. And allows to set
the minimum size to limit the randomly generated ROI.
It will return a list of cropped images.
Note: even `random_size=False`, if a dimension of the expected ROI size is bigger than the input image size,
will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped
results of several images may not have exactly the same shape.
Args:
roi_size: if `random_size` is True, it specifies the minimum crop region.
if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]
if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.
If its components have non-positive values, the corresponding size of input image will be used.
for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,
the spatial size of output data will be [32, 40, 40].
num_samples: number of samples (crop regions) to take in the returned list.
max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`
can specify the max crop region size. if None, defaults to the input image size.
if its components have non-positive values, the corresponding size of input image will be used.
random_center: crop at random position as center or the image center.
random_size: crop with random size or specific size ROI.
The actual size is sampled from `randint(roi_size, img_size)`.
Raises:
ValueError: When ``num_samples`` is nonpositive.
"""
backend = RandSpatialCrop.backend
def __init__(
self,
roi_size: Union[Sequence[int], int],
num_samples: int,
max_roi_size: Optional[Union[Sequence[int], int]] = None,
random_center: bool = True,
random_size: bool = True,
) -> None:
if num_samples < 1:
raise ValueError(f"num_samples must be positive, got {num_samples}.")
self.num_samples = num_samples
self.cropper = RandSpatialCrop(roi_size, max_roi_size, random_center, random_size)
def set_random_state(
self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None
) -> "RandSpatialCropSamples":
super().set_random_state(seed, state)
self.cropper.set_random_state(seed, state)
return self
def randomize(self, data: Optional[Any] = None) -> None:
pass
def __call__(self, img: NdarrayOrTensor) -> List[NdarrayOrTensor]:
"""
Apply the transform to `img`, assuming `img` is channel-first and
cropping doesn't change the channel dim.
"""
return [self.cropper(img) for _ in range(self.num_samples)]
class CropForeground(Transform):
"""
Crop an image using a bounding box. The bounding box is generated by selecting foreground using select_fn
at channels channel_indices. margin is added in each spatial dimension of the bounding box.
The typical usage is to help training and evaluation if the valid part is small in the whole medical image.
Users can define arbitrary function to select expected foreground from the whole image or specified channels.
And it can also add margin to every dim of the bounding box of foreground object.
For example:
.. code-block:: python
image = np.array(
[[[0, 0, 0, 0, 0],
[0, 1, 2, 1, 0],
[0, 1, 3, 2, 0],
[0, 1, 2, 1, 0],
[0, 0, 0, 0, 0]]]) # 1x5x5, single channel 5x5 image
def threshold_at_one(x):
# threshold at 1
return x > 1
cropper = CropForeground(select_fn=threshold_at_one, margin=0)
print(cropper(image))
[[[2, 1],
[3, 2],
[2, 1]]]
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(
self,
select_fn: Callable = is_positive,
channel_indices: Optional[IndexSelection] = None,
margin: Union[Sequence[int], int] = 0,
return_coords: bool = False,
k_divisible: Union[Sequence[int], int] = 1,
mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = NumpyPadMode.CONSTANT,
**np_kwargs,
) -> None:
"""
Args:
select_fn: function to select expected foreground, default is to select values > 0.
channel_indices: if defined, select foreground only on the specified channels
of image. if None, select foreground on the whole image.
margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
return_coords: whether return the coordinates of spatial bounding box for foreground.
k_divisible: make each spatial dimension to be divisible by k, default to 1.
if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions.
mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
One of the listed string values or a user supplied function. Defaults to ``"constant"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
np_kwargs: other args for `np.pad` API, note that `np.pad` treats channel dimension as the first dimension.
more details: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
"""
self.select_fn = select_fn
self.channel_indices = ensure_tuple(channel_indices) if channel_indices is not None else None
self.margin = margin
self.return_coords = return_coords
self.k_divisible = k_divisible
self.mode: NumpyPadMode = look_up_option(mode, NumpyPadMode)
self.np_kwargs = np_kwargs
def compute_bounding_box(self, img: NdarrayOrTensor):
"""
Compute the start points and end points of bounding box to crop.
And adjust bounding box coords to be divisible by `k`.
"""
box_start, box_end = generate_spatial_bounding_box(img, self.select_fn, self.channel_indices, self.margin)
box_start_, *_ = convert_data_type(box_start, output_type=np.ndarray, dtype=np.int16, wrap_sequence=True)
box_end_, *_ = convert_data_type(box_end, output_type=np.ndarray, dtype=np.int16, wrap_sequence=True)
orig_spatial_size = box_end_ - box_start_
# make the spatial size divisible by `k`
spatial_size = np.asarray(compute_divisible_spatial_size(orig_spatial_size.tolist(), k=self.k_divisible))
# update box_start and box_end
box_start_ = box_start_ - np.floor_divide(np.asarray(spatial_size) - orig_spatial_size, 2)
box_end_ = box_start_ + spatial_size
return box_start_, box_end_
def crop_pad(
self,
img: NdarrayOrTensor,
box_start: np.ndarray,
box_end: np.ndarray,
mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = None,
):
"""
Crop and pad based on the bounding box.
"""
cropped = SpatialCrop(roi_start=box_start, roi_end=box_end)(img)
pad_to_start = np.maximum(-box_start, 0)
pad_to_end = np.maximum(box_end - np.asarray(img.shape[1:]), 0)
pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist())))
return BorderPad(spatial_border=pad, mode=mode or self.mode, **self.np_kwargs)(cropped)
def __call__(self, img: NdarrayOrTensor, mode: Optional[Union[NumpyPadMode, str]] = None):
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't change the channel dim.
"""
box_start, box_end = self.compute_bounding_box(img)
cropped = self.crop_pad(img, box_start, box_end, mode)
if self.return_coords:
return cropped, box_start, box_end
return cropped
class RandWeightedCrop(Randomizable, Transform):
"""
Samples a list of `num_samples` image patches according to the provided `weight_map`.
Args:
spatial_size: the spatial size of the image patch e.g. [224, 224, 128].
If its components have non-positive values, the corresponding size of `img` will be used.
num_samples: number of samples (image patches) to take in the returned list.
weight_map: weight map used to generate patch samples. The weights must be non-negative.
Each element denotes a sampling weight of the spatial location. 0 indicates no sampling.
It should be a single-channel array in shape, for example, `(1, spatial_dim_0, spatial_dim_1, ...)`.
"""
backend = SpatialCrop.backend
def __init__(
self,
spatial_size: Union[Sequence[int], int],
num_samples: int = 1,
weight_map: Optional[NdarrayOrTensor] = None,
):
self.spatial_size = ensure_tuple(spatial_size)
self.num_samples = int(num_samples)
self.weight_map = weight_map
self.centers: List[np.ndarray] = []
def randomize(self, weight_map: NdarrayOrTensor) -> None:
self.centers = weighted_patch_samples(
spatial_size=self.spatial_size, w=weight_map[0], n_samples=self.num_samples, r_state=self.R
) # using only the first channel as weight map
def __call__(self, img: NdarrayOrTensor, weight_map: Optional[NdarrayOrTensor] = None) -> List[NdarrayOrTensor]:
"""
Args:
img: input image to sample patches from. assuming `img` is a channel-first array.
weight_map: weight map used to generate patch samples. The weights must be non-negative.
Each element denotes a sampling weight of the spatial location. 0 indicates no sampling.
It should be a single-channel array in shape, for example, `(1, spatial_dim_0, spatial_dim_1, ...)`
Returns:
A list of image patches
"""
if weight_map is None:
weight_map = self.weight_map
if weight_map is None:
raise ValueError("weight map must be provided for weighted patch sampling.")
if img.shape[1:] != weight_map.shape[1:]:
raise ValueError(f"image and weight map spatial shape mismatch: {img.shape[1:]} vs {weight_map.shape[1:]}.")
self.randomize(weight_map)
_spatial_size = fall_back_tuple(self.spatial_size, weight_map.shape[1:])
results: List[NdarrayOrTensor] = []
for center in self.centers:
cropper = SpatialCrop(roi_center=center, roi_size=_spatial_size)
results.append(cropper(img))
return results
class RandCropByPosNegLabel(Randomizable, Transform):
"""
Crop random fixed sized regions with the center being a foreground or background voxel
based on the Pos Neg Ratio.
And will return a list of arrays for all the cropped images.
For example, crop two (3 x 3) arrays from (5 x 5) array with pos/neg=1::
[[[0, 0, 0, 0, 0],
[0, 1, 2, 1, 0], [[0, 1, 2], [[2, 1, 0],
[0, 1, 3, 0, 0], --> [0, 1, 3], [3, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0]] [0, 0, 0]]
[0, 0, 0, 0, 0]]]
If a dimension of the expected spatial size is bigger than the input image size,
will not crop that dimension. So the cropped result may be smaller than expected size, and the cropped
results of several images may not have exactly same shape.
Args:
spatial_size: the spatial size of the crop region e.g. [224, 224, 128].
if a dimension of ROI size is bigger than image size, will not crop that dimension of the image.
if its components have non-positive values, the corresponding size of `label` will be used.
for example: if the spatial size of input data is [40, 40, 40] and `spatial_size=[32, 64, -1]`,
the spatial size of output data will be [32, 40, 40].
label: the label image that is used for finding foreground/background, if None, must set at
`self.__call__`. Non-zero indicates foreground, zero indicates background.
pos: used with `neg` together to calculate the ratio ``pos / (pos + neg)`` for the probability
to pick a foreground voxel as a center rather than a background voxel.
neg: used with `pos` together to calculate the ratio ``pos / (pos + neg)`` for the probability
to pick a foreground voxel as a center rather than a background voxel.
num_samples: number of samples (crop regions) to take in each list.
image: optional image data to help select valid area, can be same as `img` or another image array.
if not None, use ``label == 0 & image > image_threshold`` to select the negative
sample (background) center. So the crop center will only come from the valid image areas.
image_threshold: if enabled `image`, use ``image > image_threshold`` to determine
the valid image content areas.
fg_indices: if provided pre-computed foreground indices of `label`, will ignore above `image` and
`image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices`
and `bg_indices` together, expect to be 1 dim array of spatial indices after flattening.
a typical usage is to call `FgBgToIndices` transform first and cache the results.
bg_indices: if provided pre-computed background indices of `label`, will ignore above `image` and
`image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices`
and `bg_indices` together, expect to be 1 dim array of spatial indices after flattening.
a typical usage is to call `FgBgToIndices` transform first and cache the results.
allow_smaller: if `False`, an exception will be raised if the image is smaller than
the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
match the cropped size (i.e., no cropping in that dimension).
Raises:
ValueError: When ``pos`` or ``neg`` are negative.
ValueError: When ``pos=0`` and ``neg=0``. Incompatible values.
"""
backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
def __init__(
self,
spatial_size: Union[Sequence[int], int],
label: Optional[NdarrayOrTensor] = None,
pos: float = 1.0,
neg: float = 1.0,
num_samples: int = 1,
image: Optional[NdarrayOrTensor] = None,
image_threshold: float = 0.0,
fg_indices: Optional[NdarrayOrTensor] = None,
bg_indices: Optional[NdarrayOrTensor] = None,
allow_smaller: bool = False,
) -> None:
self.spatial_size = ensure_tuple(spatial_size)
self.label = label
if pos < 0 or neg < 0:
raise ValueError(f"pos and neg must be nonnegative, got pos={pos} neg={neg}.")
if pos + neg == 0:
raise ValueError("Incompatible values: pos=0 and neg=0.")
self.pos_ratio = pos / (pos + neg)
self.num_samples = num_samples
self.image = image
self.image_threshold = image_threshold
self.centers: Optional[List[List[int]]] = None
self.fg_indices = fg_indices
self.bg_indices = bg_indices
self.allow_smaller = allow_smaller
def randomize(
self,
label: NdarrayOrTensor,
fg_indices: Optional[NdarrayOrTensor] = None,
bg_indices: Optional[NdarrayOrTensor] = None,
image: Optional[NdarrayOrTensor] = None,
) -> None:
self.spatial_size = fall_back_tuple(self.spatial_size, default=label.shape[1:])
if fg_indices is None or bg_indices is None:
if self.fg_indices is not None and self.bg_indices is not None:
fg_indices_ = self.fg_indices
bg_indices_ = self.bg_indices
else:
fg_indices_, bg_indices_ = map_binary_to_indices(label, image, self.image_threshold)
else:
fg_indices_ = fg_indices
bg_indices_ = bg_indices
self.centers = generate_pos_neg_label_crop_centers(
self.spatial_size,
self.num_samples,
self.pos_ratio,
label.shape[1:],
fg_indices_,
bg_indices_,
self.R,
self.allow_smaller,
)
def __call__(
self,
img: NdarrayOrTensor,
label: Optional[NdarrayOrTensor] = None,
image: Optional[NdarrayOrTensor] = None,
fg_indices: Optional[NdarrayOrTensor] = None,
bg_indices: Optional[NdarrayOrTensor] = None,
) -> List[NdarrayOrTensor]:
"""
Args:
img: input data to crop samples from based on the pos/neg ratio of `label` and `image`.
Assumes `img` is a channel-first array.
label: the label image that is used for finding foreground/background, if None, use `self.label`.
image: optional image data to help select valid area, can be same as `img` or another image array.
use ``label == 0 & image > image_threshold`` to select the negative sample(background) center.
so the crop center will only exist on valid image area. if None, use `self.image`.
fg_indices: foreground indices to randomly select crop centers,
need to provide `fg_indices` and `bg_indices` together.
bg_indices: background indices to randomly select crop centers,
need to provide `fg_indices` and `bg_indices` together.
"""
if label is None:
label = self.label
if label is None:
raise ValueError("label should be provided.")
if image is None:
image = self.image
self.randomize(label, fg_indices, bg_indices, image)
results: List[NdarrayOrTensor] = []
if self.centers is not None:
for center in self.centers:
cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)
results.append(cropper(img))
return results
class RandCropByLabelClasses(Randomizable, Transform):
"""
Crop random fixed sized regions with the center being a class based on the specified ratios of every class.
The label data can be One-Hot format array or Argmax data. And will return a list of arrays for all the
cropped images. For example, crop two (3 x 3) arrays from (5 x 5) array with `ratios=[1, 2, 3, 1]`::
image = np.array([
[[0.0, 0.3, 0.4, 0.2, 0.0],
[0.0, 0.1, 0.2, 0.1, 0.4],
[0.0, 0.3, 0.5, 0.2, 0.0],
[0.1, 0.2, 0.1, 0.1, 0.0],
[0.0, 0.1, 0.2, 0.1, 0.0]]
])
label = np.array([
[[0, 0, 0, 0, 0],
[0, 1, 2, 1, 0],
[0, 1, 3, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
])