Source code for monai.transforms.croppad.dictionary

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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"""
A collection of dictionary-based wrappers around the "vanilla" transforms for crop and pad operations
defined in :py:class:`monai.transforms.croppad.array`.

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

import contextlib
from copy import deepcopy
from enum import Enum
from itertools import chain
from math import ceil, floor
from typing import Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union

import numpy as np

from monai.config import IndexSelection, KeysCollection
from monai.data.utils import get_random_patch, get_valid_patch_size
from monai.transforms.croppad.array import (
    BorderPad,
    BoundingRect,
    CenterSpatialCrop,
    CropForeground,
    DivisiblePad,
    ResizeWithPadOrCrop,
    SpatialCrop,
    SpatialPad,
)
from monai.transforms.inverse import InvertibleTransform
from monai.transforms.transform import MapTransform, Randomizable
from monai.transforms.utils import (
    allow_missing_keys_mode,
    generate_pos_neg_label_crop_centers,
    is_positive,
    map_binary_to_indices,
    weighted_patch_samples,
)
from monai.utils import ImageMetaKey as Key
from monai.utils import Method, NumpyPadMode, ensure_tuple, ensure_tuple_rep, fall_back_tuple
from monai.utils.enums import InverseKeys

__all__ = [
    "NumpyPadModeSequence",
    "SpatialPadd",
    "BorderPadd",
    "DivisiblePadd",
    "SpatialCropd",
    "CenterSpatialCropd",
    "CenterScaleCropd",
    "RandScaleCropd",
    "RandSpatialCropd",
    "RandSpatialCropSamplesd",
    "CropForegroundd",
    "RandWeightedCropd",
    "RandCropByPosNegLabeld",
    "ResizeWithPadOrCropd",
    "BoundingRectd",
    "SpatialPadD",
    "SpatialPadDict",
    "BorderPadD",
    "BorderPadDict",
    "DivisiblePadD",
    "DivisiblePadDict",
    "SpatialCropD",
    "SpatialCropDict",
    "CenterSpatialCropD",
    "CenterSpatialCropDict",
    "CenterScaleCropD",
    "CenterScaleCropDict",
    "RandScaleCropD",
    "RandScaleCropDict",
    "RandSpatialCropD",
    "RandSpatialCropDict",
    "RandSpatialCropSamplesD",
    "RandSpatialCropSamplesDict",
    "CropForegroundD",
    "CropForegroundDict",
    "RandWeightedCropD",
    "RandWeightedCropDict",
    "RandCropByPosNegLabelD",
    "RandCropByPosNegLabelDict",
    "ResizeWithPadOrCropD",
    "ResizeWithPadOrCropDict",
    "BoundingRectD",
    "BoundingRectDict",
]

NumpyPadModeSequence = Union[Sequence[Union[NumpyPadMode, str]], NumpyPadMode, str]


[docs]class SpatialPadd(MapTransform, InvertibleTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.SpatialPad`. Performs padding to the data, symmetric for all sides or all on one side for each dimension. """ def __init__( self, keys: KeysCollection, spatial_size: Union[Sequence[int], int], method: Union[Method, str] = Method.SYMMETRIC, mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` 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. 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 symmetric on every side or only pad at the end sides. Defaults to ``"symmetric"``. mode: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} 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 It also can be a sequence of string, each element corresponds to a key in ``keys``. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padder = SpatialPad(spatial_size, method)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key, m in self.key_iterator(d, self.mode): self.push_transform(d, key, extra_info={"mode": m.value if isinstance(m, Enum) else m}) d[key] = self.padder(d[key], mode=m) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = transform[InverseKeys.ORIG_SIZE] if self.padder.method == Method.SYMMETRIC: current_size = d[key].shape[1:] roi_center = [floor(i / 2) if r % 2 == 0 else (i - 1) // 2 for r, i in zip(orig_size, current_size)] else: roi_center = [floor(r / 2) if r % 2 == 0 else (r - 1) // 2 for r in orig_size] inverse_transform = SpatialCrop(roi_center, orig_size) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class BorderPadd(MapTransform, InvertibleTransform): """ Pad the input data by adding specified borders to every dimension. Dictionary-based wrapper of :py:class:`monai.transforms.BorderPad`. """ def __init__( self, keys: KeysCollection, spatial_border: Union[Sequence[int], int], mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` spatial_border: specified size for every spatial border. 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: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} 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 It also can be a sequence of string, each element corresponds to a key in ``keys``. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padder = BorderPad(spatial_border=spatial_border)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key, m in self.key_iterator(d, self.mode): self.push_transform(d, key, extra_info={"mode": m.value if isinstance(m, Enum) else m}) d[key] = self.padder(d[key], mode=m) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.array(transform[InverseKeys.ORIG_SIZE]) roi_start = np.array(self.padder.spatial_border) # Need to convert single value to [min1,min2,...] if roi_start.size == 1: roi_start = np.full((len(orig_size)), roi_start) # need to convert [min1,max1,min2,...] to [min1,min2,...] elif roi_start.size == 2 * orig_size.size: roi_start = roi_start[::2] roi_end = np.array(transform[InverseKeys.ORIG_SIZE]) + roi_start inverse_transform = SpatialCrop(roi_start=roi_start, roi_end=roi_end) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class DivisiblePadd(MapTransform, InvertibleTransform): """ Pad the input data, so that the spatial sizes are divisible by `k`. Dictionary-based wrapper of :py:class:`monai.transforms.DivisiblePad`. """ def __init__( self, keys: KeysCollection, k: Union[Sequence[int], int], mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` 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: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} 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 It also can be a sequence of string, each element corresponds to a key in ``keys``. allow_missing_keys: don't raise exception if key is missing. See also :py:class:`monai.transforms.SpatialPad` """ super().__init__(keys, allow_missing_keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padder = DivisiblePad(k=k)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key, m in self.key_iterator(d, self.mode): self.push_transform(d, key, extra_info={"mode": m.value if isinstance(m, Enum) else m}) d[key] = self.padder(d[key], mode=m) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.array(transform[InverseKeys.ORIG_SIZE]) current_size = np.array(d[key].shape[1:]) roi_start = np.floor((current_size - orig_size) / 2) roi_end = orig_size + roi_start inverse_transform = SpatialCrop(roi_start=roi_start, roi_end=roi_end) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class SpatialCropd(MapTransform, InvertibleTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.SpatialCrop`. 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 """ def __init__( self, keys: KeysCollection, roi_center: Optional[Sequence[int]] = None, roi_size: Optional[Sequence[int]] = None, roi_start: Optional[Sequence[int]] = None, roi_end: Optional[Sequence[int]] = None, roi_slices: Optional[Sequence[slice]] = None, allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` 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. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.cropper = SpatialCrop(roi_center, roi_size, roi_start, roi_end, roi_slices)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.key_iterator(d): self.push_transform(d, key) d[key] = self.cropper(d[key]) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.array(transform[InverseKeys.ORIG_SIZE]) current_size = np.array(d[key].shape[1:]) # get required pad to start and end pad_to_start = np.array([s.indices(o)[0] for s, o in zip(self.cropper.slices, orig_size)]) pad_to_end = orig_size - current_size - pad_to_start # interleave mins and maxes pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) inverse_transform = BorderPad(pad) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class CenterSpatialCropd(MapTransform, InvertibleTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.CenterSpatialCrop`. 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: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform roi_size: the 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]. allow_missing_keys: don't raise exception if key is missing. """ def __init__( self, keys: KeysCollection, roi_size: Union[Sequence[int], int], allow_missing_keys: bool = False ) -> None: super().__init__(keys, allow_missing_keys) self.cropper = CenterSpatialCrop(roi_size)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.key_iterator(d): orig_size = d[key].shape[1:] d[key] = self.cropper(d[key]) self.push_transform(d, key, orig_size=orig_size) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.array(transform[InverseKeys.ORIG_SIZE]) current_size = np.array(d[key].shape[1:]) pad_to_start = np.floor((orig_size - current_size) / 2).astype(int) # in each direction, if original size is even and current size is odd, += 1 pad_to_start[np.logical_and(orig_size % 2 == 0, current_size % 2 == 1)] += 1 pad_to_end = orig_size - current_size - pad_to_start pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) inverse_transform = BorderPad(pad) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class CenterScaleCropd(MapTransform, InvertibleTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.CenterScaleCrop`. Note: as using the same scaled ROI to crop, all the input data specified by `keys` should have the same spatial shape. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform 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. allow_missing_keys: don't raise exception if key is missing. """ def __init__( self, keys: KeysCollection, roi_scale: Union[Sequence[float], float], allow_missing_keys: bool = False ) -> None: super().__init__(keys, allow_missing_keys=allow_missing_keys) self.roi_scale = roi_scale
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) # use the spatial size of first image to scale, expect all images have the same spatial size img_size = data[self.keys[0]].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)] cropper = CenterSpatialCrop(roi_size) for key in self.key_iterator(d): self.push_transform(d, key, orig_size=img_size) d[key] = cropper(d[key]) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.array(transform[InverseKeys.ORIG_SIZE]) current_size = np.array(d[key].shape[1:]) pad_to_start = np.floor((orig_size - current_size) / 2).astype(int) # in each direction, if original size is even and current size is odd, += 1 pad_to_start[np.logical_and(orig_size % 2 == 0, current_size % 2 == 1)] += 1 pad_to_end = orig_size - current_size - pad_to_start pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) inverse_transform = BorderPad(pad) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class RandSpatialCropd(Randomizable, MapTransform, InvertibleTransform): """ Dictionary-based version :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 size to limit the randomly generated ROI. Suppose all the expected fields specified by `keys` have same shape. 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: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform 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_scale * image spatial size, max_roi_scale * image spatial size + 1)`. allow_missing_keys: don't raise exception if key is missing. """ def __init__( self, keys: KeysCollection, roi_size: Union[Sequence[int], int], max_roi_size: Optional[Union[Sequence[int], int]] = None, random_center: bool = True, random_size: bool = True, allow_missing_keys: bool = False, ) -> None: MapTransform.__init__(self, keys, allow_missing_keys) self.roi_size = roi_size self.max_roi_size = max_roi_size self.random_center = random_center self.random_size = random_size self._slices: Optional[Tuple[slice, ...]] = None self._size: Optional[Sequence[int]] = None
[docs] 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 = [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)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize(d[self.keys[0]].shape[1:]) # image shape from the first data key if self._size is None: raise AssertionError for key in self.key_iterator(d): if self.random_center: self.push_transform(d, key, {"slices": [(i.start, i.stop) for i in self._slices[1:]]}) # type: ignore d[key] = d[key][self._slices] else: self.push_transform(d, key) cropper = CenterSpatialCrop(self._size) d[key] = cropper(d[key]) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = transform[InverseKeys.ORIG_SIZE] random_center = self.random_center pad_to_start = np.empty((len(orig_size)), dtype=np.int32) pad_to_end = np.empty((len(orig_size)), dtype=np.int32) if random_center: for i, _slice in enumerate(transform[InverseKeys.EXTRA_INFO]["slices"]): pad_to_start[i] = _slice[0] pad_to_end[i] = orig_size[i] - _slice[1] else: current_size = d[key].shape[1:] for i, (o_s, c_s) in enumerate(zip(orig_size, current_size)): pad_to_start[i] = pad_to_end[i] = (o_s - c_s) / 2 if o_s % 2 == 0 and c_s % 2 == 1: pad_to_start[i] += 1 elif o_s % 2 == 1 and c_s % 2 == 0: pad_to_end[i] += 1 # interleave mins and maxes pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) inverse_transform = BorderPad(pad) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class RandScaleCropd(RandSpatialCropd): """ Dictionary-based version :py:class:`monai.transforms.RandScaleCrop`. 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. Suppose all the expected fields specified by `keys` have same shape. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform 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_size: 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)`. allow_missing_keys: don't raise exception if key is missing. """ def __init__( self, keys: KeysCollection, roi_scale: Union[Sequence[float], float], max_roi_scale: Optional[Union[Sequence[float], float]] = None, random_center: bool = True, random_size: bool = True, allow_missing_keys: bool = False, ) -> None: super().__init__( keys=keys, roi_size=-1, max_roi_size=None, random_center=random_center, random_size=random_size, allow_missing_keys=allow_missing_keys, ) MapTransform.__init__(self, keys, allow_missing_keys) self.roi_scale = roi_scale self.max_roi_scale = max_roi_scale
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: img_size = data[self.keys[0]].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__(data=data)
@contextlib.contextmanager def _nullcontext(x): """ This is just like contextlib.nullcontext but also works in Python 3.6. """ yield x
[docs]class RandSpatialCropSamplesd(Randomizable, MapTransform, InvertibleTransform): """ Dictionary-based version :py:class:`monai.transforms.RandSpatialCropSamples`. 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. Suppose all the expected fields specified by `keys` have same shape, and add `patch_index` to the corresponding meta data. It will return a list of dictionaries for all the 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: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform 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)`. meta_keys: explicitly indicate the key of the corresponding meta data dictionary. used to add `patch_index` to the meta dict. for example, for data with key `image`, the metadata by default is in `image_meta_dict`. the meta data is a dictionary object which contains: filename, original_shape, etc. it can be a sequence of string, map to the `keys`. if None, will try to construct meta_keys by `key_{meta_key_postfix}`. meta_key_postfix: if meta_keys is None, 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. used to add `patch_index` to the meta dict. allow_missing_keys: don't raise exception if key is missing. Raises: ValueError: When ``num_samples`` is nonpositive. """ def __init__( self, keys: KeysCollection, 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, meta_keys: Optional[KeysCollection] = None, meta_key_postfix: str = "meta_dict", allow_missing_keys: bool = False, ) -> None: MapTransform.__init__(self, keys, allow_missing_keys) if num_samples < 1: raise ValueError(f"num_samples must be positive, got {num_samples}.") self.num_samples = num_samples self.cropper = RandSpatialCropd(keys, roi_size, max_roi_size, random_center, random_size, allow_missing_keys) self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys) if len(self.keys) != len(self.meta_keys): raise ValueError("meta_keys should have the same length as keys.") self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))
[docs] def set_random_state( self, seed: Optional[int] = None, state: Optional[np.random.RandomState] = None ) -> "Randomizable": super().set_random_state(seed=seed, state=state) self.cropper.set_random_state(state=self.R) return self
[docs] def randomize(self, data: Optional[Any] = None) -> None: pass
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, np.ndarray]]: ret = [] for i in range(self.num_samples): d = dict(data) # deep copy all the unmodified data for key in set(data.keys()).difference(set(self.keys)): d[key] = deepcopy(data[key]) cropped = self.cropper(d) # self.cropper will have added RandSpatialCropd to the list. Change to RandSpatialCropSamplesd for key in self.key_iterator(cropped): cropped[str(key) + InverseKeys.KEY_SUFFIX][-1][InverseKeys.CLASS_NAME] = self.__class__.__name__ cropped[str(key) + InverseKeys.KEY_SUFFIX][-1][InverseKeys.ID] = id(self) # add `patch_index` to the meta data for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix): meta_key = meta_key or f"{key}_{meta_key_postfix}" if meta_key not in cropped: cropped[meta_key] = {} # type: ignore cropped[meta_key][Key.PATCH_INDEX] = i ret.append(cropped) return ret
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) # We changed the transform name from RandSpatialCropd to RandSpatialCropSamplesd # Need to revert that since we're calling RandSpatialCropd's inverse for key in self.key_iterator(d): d[key + InverseKeys.KEY_SUFFIX][-1][InverseKeys.CLASS_NAME] = self.cropper.__class__.__name__ d[key + InverseKeys.KEY_SUFFIX][-1][InverseKeys.ID] = id(self.cropper) context_manager = allow_missing_keys_mode if self.allow_missing_keys else _nullcontext with context_manager(self.cropper): return self.cropper.inverse(d)
[docs]class CropForegroundd(MapTransform, InvertibleTransform): """ Dictionary-based version :py:class:`monai.transforms.CropForeground`. Crop only the foreground object of the expected images. The typical usage is to help training and evaluation if the valid part is small in the whole medical image. The valid part can be determined by any field in the data with `source_key`, for example: - Select values > 0 in image field as the foreground and crop on all fields specified by `keys`. - Select label = 3 in label field as the foreground to crop on all fields specified by `keys`. - Select label > 0 in the third channel of a One-Hot label field as the foreground to crop all `keys` fields. Users can define arbitrary function to select expected foreground from the whole source image or specified channels. And it can also add margin to every dim of the bounding box of foreground object. """ def __init__( self, keys: KeysCollection, source_key: str, select_fn: Callable = is_positive, channel_indices: Optional[IndexSelection] = None, margin: Union[Sequence[int], int] = 0, k_divisible: Union[Sequence[int], int] = 1, mode: Union[NumpyPadMode, str] = NumpyPadMode.CONSTANT, start_coord_key: str = "foreground_start_coord", end_coord_key: str = "foreground_end_coord", allow_missing_keys: bool = False, ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` source_key: data source to generate the bounding box of foreground, can be image or label, etc. 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. 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: padding mode {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} 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 start_coord_key: key to record the start coordinate of spatial bounding box for foreground. end_coord_key: key to record the end coordinate of spatial bounding box for foreground. allow_missing_keys: don't raise exception if key is missing. """ super().__init__(keys, allow_missing_keys) self.source_key = source_key self.start_coord_key = start_coord_key self.end_coord_key = end_coord_key self.cropper = CropForeground( select_fn=select_fn, channel_indices=channel_indices, margin=margin, k_divisible=k_divisible, mode=mode, )
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) box_start, box_end = self.cropper.compute_bounding_box(img=d[self.source_key]) d[self.start_coord_key] = box_start d[self.end_coord_key] = box_end for key in self.key_iterator(d): self.push_transform(d, key, extra_info={"box_start": box_start, "box_end": box_end}) d[key] = self.cropper.crop_pad(d[key], box_start, box_end) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.asarray(transform[InverseKeys.ORIG_SIZE]) cur_size = np.asarray(d[key].shape[1:]) extra_info = transform[InverseKeys.EXTRA_INFO] box_start = np.asarray(extra_info["box_start"]) box_end = np.asarray(extra_info["box_end"]) # first crop the padding part roi_start = np.maximum(-box_start, 0) roi_end = cur_size - np.maximum(box_end - orig_size, 0) d[key] = SpatialCrop(roi_start=roi_start, roi_end=roi_end)(d[key]) # update bounding box to pad pad_to_start = np.maximum(box_start, 0) pad_to_end = orig_size - np.minimum(box_end, orig_size) # interleave mins and maxes pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) # second pad back the original size d[key] = BorderPad(pad)(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class RandWeightedCropd(Randomizable, MapTransform, InvertibleTransform): """ Samples a list of `num_samples` image patches according to the provided `weight_map`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` w_key: key for the weight map. The corresponding value will be used as the sampling weights, it should be a single-channel array in size, for example, `(1, spatial_dim_0, spatial_dim_1, ...)` 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. center_coord_key: if specified, the actual sampling location will be stored with the corresponding key. meta_keys: explicitly indicate the key of the corresponding meta data dictionary. used to add `patch_index` to the meta dict. for example, for data with key `image`, the metadata by default is in `image_meta_dict`. the meta data is a dictionary object which contains: filename, original_shape, etc. it can be a sequence of string, map to the `keys`. if None, will try to construct meta_keys by `key_{meta_key_postfix}`. meta_key_postfix: if meta_keys is None, 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. used to add `patch_index` to the meta dict. allow_missing_keys: don't raise exception if key is missing. See Also: :py:class:`monai.transforms.RandWeightedCrop` """ def __init__( self, keys: KeysCollection, w_key: str, spatial_size: Union[Sequence[int], int], num_samples: int = 1, center_coord_key: Optional[str] = None, meta_keys: Optional[KeysCollection] = None, meta_key_postfix: str = "meta_dict", allow_missing_keys: bool = False, ): MapTransform.__init__(self, keys, allow_missing_keys) self.spatial_size = ensure_tuple(spatial_size) self.w_key = w_key self.num_samples = int(num_samples) self.center_coord_key = center_coord_key self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys) if len(self.keys) != len(self.meta_keys): raise ValueError("meta_keys should have the same length as keys.") self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys)) self.centers: List[np.ndarray] = []
[docs] def randomize(self, weight_map: np.ndarray) -> None: self.centers = weighted_patch_samples( spatial_size=self.spatial_size, w=weight_map[0], n_samples=self.num_samples, r_state=self.R )
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, np.ndarray]]: d = dict(data) self.randomize(d[self.w_key]) _spatial_size = fall_back_tuple(self.spatial_size, d[self.w_key].shape[1:]) # initialize returned list with shallow copy to preserve key ordering results: List[Dict[Hashable, np.ndarray]] = [dict(data) for _ in range(self.num_samples)] # fill in the extra keys with unmodified data for i in range(self.num_samples): for key in set(data.keys()).difference(set(self.keys)): results[i][key] = deepcopy(data[key]) for key in self.key_iterator(d): img = d[key] if img.shape[1:] != d[self.w_key].shape[1:]: raise ValueError( f"data {key} and weight map {self.w_key} spatial shape mismatch: " f"{img.shape[1:]} vs {d[self.w_key].shape[1:]}." ) for i, center in enumerate(self.centers): cropper = SpatialCrop(roi_center=center, roi_size=_spatial_size) orig_size = img.shape[1:] results[i][key] = cropper(img) self.push_transform(results[i], key, extra_info={"center": center}, orig_size=orig_size) if self.center_coord_key: results[i][self.center_coord_key] = center # fill in the extra keys with unmodified data for i in range(self.num_samples): # add `patch_index` to the meta data for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix): meta_key = meta_key or f"{key}_{meta_key_postfix}" if meta_key not in results[i]: results[i][meta_key] = {} # type: ignore results[i][meta_key][Key.PATCH_INDEX] = i return results
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.asarray(transform[InverseKeys.ORIG_SIZE]) current_size = np.asarray(d[key].shape[1:]) center = transform[InverseKeys.EXTRA_INFO]["center"] cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size) # get required pad to start and end pad_to_start = np.array([s.indices(o)[0] for s, o in zip(cropper.slices, orig_size)]) pad_to_end = orig_size - current_size - pad_to_start # interleave mins and maxes pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) inverse_transform = BorderPad(pad) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class RandCropByPosNegLabeld(Randomizable, MapTransform, InvertibleTransform): """ Dictionary-based version :py:class:`monai.transforms.RandCropByPosNegLabel`. Crop random fixed sized regions with the center being a foreground or background voxel based on the Pos Neg Ratio. Suppose all the expected fields specified by `keys` have same shape, and add `patch_index` to the corresponding meta data. And will return a list of dictionaries for all the cropped images. 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 the expected size, and the cropped results of several images may not have exactly the same shape. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` label_key: name of key for label image, this will be used for finding foreground/background. 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 `data[label_key]` 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]. 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_key: if image_key is not None, use ``label == 0 & image > image_threshold`` to select the negative sample(background) center. so the crop center will only exist on valid image area. image_threshold: if enabled image_key, use ``image > image_threshold`` to determine the valid image content area. fg_indices_key: if provided pre-computed foreground indices of `label`, will ignore above `image_key` and `image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices_key` and `bg_indices_key` together, expect to be 1 dim array of spatial indices after flattening. a typical usage is to call `FgBgToIndicesd` transform first and cache the results. bg_indices_key: if provided pre-computed background indices of `label`, will ignore above `image_key` and `image_threshold`, and randomly select crop centers based on them, need to provide `fg_indices_key` and `bg_indices_key` together, expect to be 1 dim array of spatial indices after flattening. a typical usage is to call `FgBgToIndicesd` transform first and cache the results. meta_keys: explicitly indicate the key of the corresponding meta data dictionary. used to add `patch_index` to the meta dict. for example, for data with key `image`, the metadata by default is in `image_meta_dict`. the meta data is a dictionary object which contains: filename, original_shape, etc. it can be a sequence of string, map to the `keys`. if None, will try to construct meta_keys by `key_{meta_key_postfix}`. meta_key_postfix: if meta_keys is None, 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. used to add `patch_index` to the meta dict. allow_missing_keys: don't raise exception if key is missing. Raises: ValueError: When ``pos`` or ``neg`` are negative. ValueError: When ``pos=0`` and ``neg=0``. Incompatible values. """ def __init__( self, keys: KeysCollection, label_key: str, spatial_size: Union[Sequence[int], int], pos: float = 1.0, neg: float = 1.0, num_samples: int = 1, image_key: Optional[str] = None, image_threshold: float = 0.0, fg_indices_key: Optional[str] = None, bg_indices_key: Optional[str] = None, meta_keys: Optional[KeysCollection] = None, meta_key_postfix: str = "meta_dict", allow_missing_keys: bool = False, ) -> None: MapTransform.__init__(self, keys, allow_missing_keys) self.label_key = label_key self.spatial_size: Union[Tuple[int, ...], Sequence[int], int] = spatial_size 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_key = image_key self.image_threshold = image_threshold self.fg_indices_key = fg_indices_key self.bg_indices_key = bg_indices_key self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys) if len(self.keys) != len(self.meta_keys): raise ValueError("meta_keys should have the same length as keys.") self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys)) self.centers: Optional[List[List[np.ndarray]]] = None
[docs] def randomize( self, label: np.ndarray, fg_indices: Optional[np.ndarray] = None, bg_indices: Optional[np.ndarray] = None, image: Optional[np.ndarray] = 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: 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 )
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> List[Dict[Hashable, np.ndarray]]: d = dict(data) label = d[self.label_key] image = d[self.image_key] if self.image_key else None fg_indices = d.get(self.fg_indices_key) if self.fg_indices_key is not None else None bg_indices = d.get(self.bg_indices_key) if self.bg_indices_key is not None else None self.randomize(label, fg_indices, bg_indices, image) if not isinstance(self.spatial_size, tuple): raise AssertionError if self.centers is None: raise AssertionError # initialize returned list with shallow copy to preserve key ordering results: List[Dict[Hashable, np.ndarray]] = [dict(data) for _ in range(self.num_samples)] for i, center in enumerate(self.centers): # fill in the extra keys with unmodified data for key in set(data.keys()).difference(set(self.keys)): results[i][key] = deepcopy(data[key]) for key in self.key_iterator(d): img = d[key] cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size) # type: ignore orig_size = img.shape[1:] results[i][key] = cropper(img) self.push_transform(results[i], key, extra_info={"center": center}, orig_size=orig_size) # add `patch_index` to the meta data for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix): meta_key = meta_key or f"{key}_{meta_key_postfix}" if meta_key not in results[i]: results[i][meta_key] = {} # type: ignore results[i][meta_key][Key.PATCH_INDEX] = i return results
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.asarray(transform[InverseKeys.ORIG_SIZE]) current_size = np.asarray(d[key].shape[1:]) center = transform[InverseKeys.EXTRA_INFO]["center"] cropper = SpatialCrop(roi_center=tuple(center), roi_size=self.spatial_size) # type: ignore # get required pad to start and end pad_to_start = np.array([s.indices(o)[0] for s, o in zip(cropper.slices, orig_size)]) pad_to_end = orig_size - current_size - pad_to_start # interleave mins and maxes pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) inverse_transform = BorderPad(pad) # Apply inverse transform d[key] = inverse_transform(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class ResizeWithPadOrCropd(MapTransform, InvertibleTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.ResizeWithPadOrCrop`. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform spatial_size: the spatial size of output data after padding or crop. If has non-positive values, the corresponding size of input image will be used (no padding). mode: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``} One of the listed string values or a user supplied function for padding. Defaults to ``"constant"``. See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html It also can be a sequence of string, each element corresponds to a key in ``keys``. allow_missing_keys: don't raise exception if key is missing. """ def __init__( self, keys: KeysCollection, spatial_size: Union[Sequence[int], int], mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT, allow_missing_keys: bool = False, ) -> None: super().__init__(keys, allow_missing_keys) self.mode = ensure_tuple_rep(mode, len(self.keys)) self.padcropper = ResizeWithPadOrCrop(spatial_size=spatial_size)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key, m in self.key_iterator(d, self.mode): orig_size = d[key].shape[1:] d[key] = self.padcropper(d[key], mode=m) self.push_transform( d, key, orig_size=orig_size, extra_info={ "mode": m.value if isinstance(m, Enum) else m, }, ) return d
[docs] def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = deepcopy(dict(data)) for key in self.key_iterator(d): transform = self.get_most_recent_transform(d, key) # Create inverse transform orig_size = np.array(transform[InverseKeys.ORIG_SIZE]) current_size = np.array(d[key].shape[1:]) # Unfortunately, we can't just use ResizeWithPadOrCrop with original size because of odd/even rounding. # Instead, we first pad any smaller dimensions, and then we crop any larger dimensions. # First, do pad if np.any((orig_size - current_size) > 0): pad_to_start = np.floor((orig_size - current_size) / 2).astype(int) # in each direction, if original size is even and current size is odd, += 1 pad_to_start[np.logical_and(orig_size % 2 == 0, current_size % 2 == 1)] += 1 pad_to_start[pad_to_start < 0] = 0 pad_to_end = orig_size - current_size - pad_to_start pad_to_end[pad_to_end < 0] = 0 pad = list(chain(*zip(pad_to_start.tolist(), pad_to_end.tolist()))) d[key] = BorderPad(pad)(d[key]) # Next crop if np.any((orig_size - current_size) < 0): if self.padcropper.padder.method == Method.SYMMETRIC: roi_center = [floor(i / 2) if r % 2 == 0 else (i - 1) // 2 for r, i in zip(orig_size, current_size)] else: roi_center = [floor(r / 2) if r % 2 == 0 else (r - 1) // 2 for r in orig_size] d[key] = SpatialCrop(roi_center, orig_size)(d[key]) # Remove the applied transform self.pop_transform(d, key) return d
[docs]class BoundingRectd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.BoundingRect`. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform bbox_key_postfix: the output bounding box coordinates will be written to the value of `{key}_{bbox_key_postfix}`. select_fn: function to select expected foreground, default is to select values > 0. allow_missing_keys: don't raise exception if key is missing. """ def __init__( self, keys: KeysCollection, bbox_key_postfix: str = "bbox", select_fn: Callable = is_positive, allow_missing_keys: bool = False, ): super().__init__(keys, allow_missing_keys) self.bbox = BoundingRect(select_fn=select_fn) self.bbox_key_postfix = bbox_key_postfix
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: """ See also: :py:class:`monai.transforms.utils.generate_spatial_bounding_box`. """ d = dict(data) for key in self.key_iterator(d): bbox = self.bbox(d[key]) key_to_add = f"{key}_{self.bbox_key_postfix}" if key_to_add in d: raise KeyError(f"Bounding box data with key {key_to_add} already exists.") d[key_to_add] = bbox return d
SpatialPadD = SpatialPadDict = SpatialPadd BorderPadD = BorderPadDict = BorderPadd DivisiblePadD = DivisiblePadDict = DivisiblePadd SpatialCropD = SpatialCropDict = SpatialCropd CenterSpatialCropD = CenterSpatialCropDict = CenterSpatialCropd CenterScaleCropD = CenterScaleCropDict = CenterScaleCropd RandSpatialCropD = RandSpatialCropDict = RandSpatialCropd RandScaleCropD = RandScaleCropDict = RandScaleCropd RandSpatialCropSamplesD = RandSpatialCropSamplesDict = RandSpatialCropSamplesd CropForegroundD = CropForegroundDict = CropForegroundd RandWeightedCropD = RandWeightedCropDict = RandWeightedCropd RandCropByPosNegLabelD = RandCropByPosNegLabelDict = RandCropByPosNegLabeld ResizeWithPadOrCropD = ResizeWithPadOrCropDict = ResizeWithPadOrCropd BoundingRectD = BoundingRectDict = BoundingRectd