Source code for monai.transforms.intensity.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.
# 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 dictionary-based wrappers around the "vanilla" transforms for intensity adjustment
defined in :py:class:`monai.transforms.intensity.array`.

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

from collections.abc import Iterable
from typing import Any, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union

import numpy as np
import torch

from monai.config import DtypeLike, KeysCollection
from monai.transforms.intensity.array import (
    AdjustContrast,
    GaussianSharpen,
    GaussianSmooth,
    MaskIntensity,
    NormalizeIntensity,
    ScaleIntensity,
    ScaleIntensityRange,
    ScaleIntensityRangePercentiles,
    ShiftIntensity,
    ThresholdIntensity,
)
from monai.transforms.transform import MapTransform, RandomizableTransform
from monai.utils import dtype_torch_to_numpy, ensure_tuple_rep, ensure_tuple_size

__all__ = [
    "RandGaussianNoised",
    "ShiftIntensityd",
    "RandShiftIntensityd",
    "ScaleIntensityd",
    "RandScaleIntensityd",
    "NormalizeIntensityd",
    "ThresholdIntensityd",
    "ScaleIntensityRanged",
    "AdjustContrastd",
    "RandAdjustContrastd",
    "ScaleIntensityRangePercentilesd",
    "MaskIntensityd",
    "GaussianSmoothd",
    "RandGaussianSmoothd",
    "GaussianSharpend",
    "RandGaussianSharpend",
    "RandHistogramShiftd",
    "RandGaussianNoiseD",
    "RandGaussianNoiseDict",
    "ShiftIntensityD",
    "ShiftIntensityDict",
    "RandShiftIntensityD",
    "RandShiftIntensityDict",
    "ScaleIntensityD",
    "ScaleIntensityDict",
    "RandScaleIntensityD",
    "RandScaleIntensityDict",
    "NormalizeIntensityD",
    "NormalizeIntensityDict",
    "ThresholdIntensityD",
    "ThresholdIntensityDict",
    "ScaleIntensityRangeD",
    "ScaleIntensityRangeDict",
    "AdjustContrastD",
    "AdjustContrastDict",
    "RandAdjustContrastD",
    "RandAdjustContrastDict",
    "ScaleIntensityRangePercentilesD",
    "ScaleIntensityRangePercentilesDict",
    "MaskIntensityD",
    "MaskIntensityDict",
    "GaussianSmoothD",
    "GaussianSmoothDict",
    "RandGaussianSmoothD",
    "RandGaussianSmoothDict",
    "GaussianSharpenD",
    "GaussianSharpenDict",
    "RandGaussianSharpenD",
    "RandGaussianSharpenDict",
    "RandHistogramShiftD",
    "RandHistogramShiftDict",
]


[docs]class RandGaussianNoised(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandGaussianNoise`. Add Gaussian noise to image. This transform assumes all the expected fields have same shape. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` prob: Probability to add Gaussian noise. mean: Mean or “centre” of the distribution. std: Standard deviation (spread) of distribution. """ def __init__( self, keys: KeysCollection, prob: float = 0.1, mean: Union[Sequence[float], float] = 0.0, std: float = 0.1 ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.mean = ensure_tuple_rep(mean, len(self.keys)) self.std = std self._noise: List[np.ndarray] = []
[docs] def randomize(self, im_shape: Sequence[int]) -> None: super().randomize(None) self._noise.clear() for m in self.mean: self._noise.append(self.R.normal(m, self.R.uniform(0, self.std), size=im_shape))
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) image_shape = d[self.keys[0]].shape # image shape from the first data key self.randomize(image_shape) if len(self._noise) != len(self.keys): raise AssertionError if not self._do_transform: return d for noise, key in zip(self._noise, self.keys): dtype = dtype_torch_to_numpy(d[key].dtype) if isinstance(d[key], torch.Tensor) else d[key].dtype d[key] = d[key] + noise.astype(dtype) return d
[docs]class ShiftIntensityd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.ShiftIntensity`. """ def __init__(self, keys: KeysCollection, offset: float) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` offset: offset value to shift the intensity of image. """ super().__init__(keys) self.shifter = ShiftIntensity(offset)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.shifter(d[key]) return d
[docs]class RandShiftIntensityd(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandShiftIntensity`. """ def __init__(self, keys: KeysCollection, offsets: Union[Tuple[float, float], float], prob: float = 0.1) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` offsets: offset range to randomly shift. if single number, offset value is picked from (-offsets, offsets). prob: probability of rotating. (Default 0.1, with 10% probability it returns a rotated array.) """ MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) if isinstance(offsets, (int, float)): self.offsets = (min(-offsets, offsets), max(-offsets, offsets)) else: if len(offsets) != 2: raise AssertionError("offsets should be a number or pair of numbers.") self.offsets = (min(offsets), max(offsets))
[docs] def randomize(self, data: Optional[Any] = None) -> None: self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1]) super().randomize(None)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() if not self._do_transform: return d shifter = ShiftIntensity(self._offset) for key in self.keys: d[key] = shifter(d[key]) return d
[docs]class ScaleIntensityd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.ScaleIntensity`. Scale the intensity of input image to the given value range (minv, maxv). If `minv` and `maxv` not provided, use `factor` to scale image by ``v = v * (1 + factor)``. """ def __init__( self, keys: KeysCollection, minv: float = 0.0, maxv: float = 1.0, factor: Optional[float] = None ) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` minv: minimum value of output data. maxv: maximum value of output data. factor: factor scale by ``v = v * (1 + factor)``. """ super().__init__(keys) self.scaler = ScaleIntensity(minv, maxv, factor)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.scaler(d[key]) return d
[docs]class RandScaleIntensityd(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandScaleIntensity`. """ def __init__(self, keys: KeysCollection, factors: Union[Tuple[float, float], float], prob: float = 0.1) -> None: """ Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` factors: factor range to randomly scale by ``v = v * (1 + factor)``. if single number, factor value is picked from (-factors, factors). prob: probability of rotating. (Default 0.1, with 10% probability it returns a rotated array.) """ MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) if isinstance(factors, (int, float)): self.factors = (min(-factors, factors), max(-factors, factors)) else: if len(factors) != 2: raise AssertionError("factors should be a number or pair of numbers.") self.factors = (min(factors), max(factors))
[docs] def randomize(self, data: Optional[Any] = None) -> None: self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1]) super().randomize(None)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() if not self._do_transform: return d scaler = ScaleIntensity(minv=None, maxv=None, factor=self.factor) for key in self.keys: d[key] = scaler(d[key]) return d
[docs]class NormalizeIntensityd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.NormalizeIntensity`. This transform can normalize only non-zero values or entire image, and can also calculate mean and std on each channel separately. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform subtrahend: the amount to subtract by (usually the mean) divisor: the amount to divide by (usually the standard deviation) nonzero: whether only normalize non-zero values. channel_wise: if using calculated mean and std, calculate on each channel separately or calculate on the entire image directly. dtype: output data type, defaut to float32. """ def __init__( self, keys: KeysCollection, subtrahend: Optional[np.ndarray] = None, divisor: Optional[np.ndarray] = None, nonzero: bool = False, channel_wise: bool = False, dtype: DtypeLike = np.float32, ) -> None: super().__init__(keys) self.normalizer = NormalizeIntensity(subtrahend, divisor, nonzero, channel_wise, dtype)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.normalizer(d[key]) return d
[docs]class ThresholdIntensityd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.ThresholdIntensity`. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform threshold: the threshold to filter intensity values. above: filter values above the threshold or below the threshold, default is True. cval: value to fill the remaining parts of the image, default is 0. """ def __init__(self, keys: KeysCollection, threshold: float, above: bool = True, cval: float = 0.0) -> None: super().__init__(keys) self.filter = ThresholdIntensity(threshold, above, cval)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.filter(d[key]) return d
[docs]class ScaleIntensityRanged(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.ScaleIntensityRange`. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform a_min: intensity original range min. a_max: intensity original range max. b_min: intensity target range min. b_max: intensity target range max. clip: whether to perform clip after scaling. """ def __init__( self, keys: KeysCollection, a_min: float, a_max: float, b_min: float, b_max: float, clip: bool = False ) -> None: super().__init__(keys) self.scaler = ScaleIntensityRange(a_min, a_max, b_min, b_max, clip)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.scaler(d[key]) return d
[docs]class AdjustContrastd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.AdjustContrast`. Changes image intensity by gamma. Each pixel/voxel intensity is updated as: `x = ((x - min) / intensity_range) ^ gamma * intensity_range + min` Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform gamma: gamma value to adjust the contrast as function. """ def __init__(self, keys: KeysCollection, gamma: float) -> None: super().__init__(keys) self.adjuster = AdjustContrast(gamma)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.adjuster(d[key]) return d
[docs]class RandAdjustContrastd(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandAdjustContrast`. Randomly changes image intensity by gamma. Each pixel/voxel intensity is updated as: `x = ((x - min) / intensity_range) ^ gamma * intensity_range + min` Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform prob: Probability of adjustment. gamma: Range of gamma values. If single number, value is picked from (0.5, gamma), default is (0.5, 4.5). """ def __init__( self, keys: KeysCollection, prob: float = 0.1, gamma: Union[Tuple[float, float], float] = (0.5, 4.5) ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) if isinstance(gamma, (int, float)): if gamma <= 0.5: raise AssertionError( "if gamma is single number, must greater than 0.5 and value is picked from (0.5, gamma)" ) self.gamma = (0.5, gamma) else: if len(gamma) != 2: raise AssertionError("gamma should be a number or pair of numbers.") self.gamma = (min(gamma), max(gamma)) self.gamma_value: Optional[float] = None
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) self.gamma_value = self.R.uniform(low=self.gamma[0], high=self.gamma[1])
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() if self.gamma_value is None: raise AssertionError if not self._do_transform: return d adjuster = AdjustContrast(self.gamma_value) for key in self.keys: d[key] = adjuster(d[key]) return d
[docs]class ScaleIntensityRangePercentilesd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.ScaleIntensityRangePercentiles`. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform lower: lower percentile. upper: upper percentile. b_min: intensity target range min. b_max: intensity target range max. clip: whether to perform clip after scaling. relative: whether to scale to the corresponding percentiles of [b_min, b_max] """ def __init__( self, keys: KeysCollection, lower: float, upper: float, b_min: float, b_max: float, clip: bool = False, relative: bool = False, ) -> None: super().__init__(keys) self.scaler = ScaleIntensityRangePercentiles(lower, upper, b_min, b_max, clip, relative)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.scaler(d[key]) return d
[docs]class MaskIntensityd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.MaskIntensity`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` mask_data: if mask data is single channel, apply to every channel of input image. if multiple channels, the channel number must match input data. mask_data will be converted to `bool` values by `mask_data > 0` before applying transform to input image. if None, will extract the mask data from input data based on `mask_key`. mask_key: the key to extract mask data from input dictionary, only works when `mask_data` is None. """ def __init__( self, keys: KeysCollection, mask_data: Optional[np.ndarray] = None, mask_key: Optional[str] = None, ) -> None: super().__init__(keys) self.converter = MaskIntensity(mask_data) self.mask_key = mask_key if mask_data is None else None
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.converter(d[key], d[self.mask_key]) if self.mask_key is not None else self.converter(d[key]) return d
[docs]class GaussianSmoothd(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSmooth`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` sigma: if a list of values, must match the count of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension. if only 1 value provided, use it for all spatial dimensions. approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace". see also :py:meth:`monai.networks.layers.GaussianFilter`. """ def __init__(self, keys: KeysCollection, sigma: Union[Sequence[float], float], approx: str = "erf") -> None: super().__init__(keys) self.converter = GaussianSmooth(sigma, approx=approx)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.converter(d[key]) return d
[docs]class RandGaussianSmoothd(RandomizableTransform, MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSmooth`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` sigma_x: randomly select sigma value for the first spatial dimension. sigma_y: randomly select sigma value for the second spatial dimension if have. sigma_z: randomly select sigma value for the third spatial dimension if have. approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace". see also :py:meth:`monai.networks.layers.GaussianFilter`. prob: probability of Gaussian smooth. """ def __init__( self, keys: KeysCollection, sigma_x: Tuple[float, float] = (0.25, 1.5), sigma_y: Tuple[float, float] = (0.25, 1.5), sigma_z: Tuple[float, float] = (0.25, 1.5), approx: str = "erf", prob: float = 0.1, ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.sigma_x = sigma_x self.sigma_y = sigma_y self.sigma_z = sigma_z self.approx = approx
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) self.x = self.R.uniform(low=self.sigma_x[0], high=self.sigma_x[1]) self.y = self.R.uniform(low=self.sigma_y[0], high=self.sigma_y[1]) self.z = self.R.uniform(low=self.sigma_z[0], high=self.sigma_z[1])
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() if not self._do_transform: return d for key in self.keys: sigma = ensure_tuple_size(tup=(self.x, self.y, self.z), dim=d[key].ndim - 1) d[key] = GaussianSmooth(sigma=sigma, approx=self.approx)(d[key]) return d
[docs]class GaussianSharpend(MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSharpen`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` sigma1: sigma parameter for the first gaussian kernel. if a list of values, must match the count of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension. if only 1 value provided, use it for all spatial dimensions. sigma2: sigma parameter for the second gaussian kernel. if a list of values, must match the count of spatial dimensions of input data, and apply every value in the list to 1 spatial dimension. if only 1 value provided, use it for all spatial dimensions. alpha: weight parameter to compute the final result. approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace". see also :py:meth:`monai.networks.layers.GaussianFilter`. """ def __init__( self, keys: KeysCollection, sigma1: Union[Sequence[float], float] = 3.0, sigma2: Union[Sequence[float], float] = 1.0, alpha: float = 30.0, approx: str = "erf", ) -> None: super().__init__(keys) self.converter = GaussianSharpen(sigma1, sigma2, alpha, approx=approx)
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) for key in self.keys: d[key] = self.converter(d[key]) return d
[docs]class RandGaussianSharpend(RandomizableTransform, MapTransform): """ Dictionary-based wrapper of :py:class:`monai.transforms.GaussianSharpen`. Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` sigma1_x: randomly select sigma value for the first spatial dimension of first gaussian kernel. sigma1_y: randomly select sigma value for the second spatial dimension(if have) of first gaussian kernel. sigma1_z: randomly select sigma value for the third spatial dimension(if have) of first gaussian kernel. sigma2_x: randomly select sigma value for the first spatial dimension of second gaussian kernel. if only 1 value `X` provided, it must be smaller than `sigma1_x` and randomly select from [X, sigma1_x]. sigma2_y: randomly select sigma value for the second spatial dimension(if have) of second gaussian kernel. if only 1 value `Y` provided, it must be smaller than `sigma1_y` and randomly select from [Y, sigma1_y]. sigma2_z: randomly select sigma value for the third spatial dimension(if have) of second gaussian kernel. if only 1 value `Z` provided, it must be smaller than `sigma1_z` and randomly select from [Z, sigma1_z]. alpha: randomly select weight parameter to compute the final result. approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace". see also :py:meth:`monai.networks.layers.GaussianFilter`. prob: probability of Gaussian sharpen. """ def __init__( self, keys: KeysCollection, sigma1_x: Tuple[float, float] = (0.5, 1.0), sigma1_y: Tuple[float, float] = (0.5, 1.0), sigma1_z: Tuple[float, float] = (0.5, 1.0), sigma2_x: Union[Tuple[float, float], float] = 0.5, sigma2_y: Union[Tuple[float, float], float] = 0.5, sigma2_z: Union[Tuple[float, float], float] = 0.5, alpha: Tuple[float, float] = (10.0, 30.0), approx: str = "erf", prob: float = 0.1, ): MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) self.sigma1_x = sigma1_x self.sigma1_y = sigma1_y self.sigma1_z = sigma1_z self.sigma2_x = sigma2_x self.sigma2_y = sigma2_y self.sigma2_z = sigma2_z self.alpha = alpha self.approx = approx
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) self.x1 = self.R.uniform(low=self.sigma1_x[0], high=self.sigma1_x[1]) self.y1 = self.R.uniform(low=self.sigma1_y[0], high=self.sigma1_y[1]) self.z1 = self.R.uniform(low=self.sigma1_z[0], high=self.sigma1_z[1]) sigma2_x = (self.sigma2_x, self.x1) if not isinstance(self.sigma2_x, Iterable) else self.sigma2_x sigma2_y = (self.sigma2_y, self.y1) if not isinstance(self.sigma2_y, Iterable) else self.sigma2_y sigma2_z = (self.sigma2_z, self.z1) if not isinstance(self.sigma2_z, Iterable) else self.sigma2_z self.x2 = self.R.uniform(low=sigma2_x[0], high=sigma2_x[1]) self.y2 = self.R.uniform(low=sigma2_y[0], high=sigma2_y[1]) self.z2 = self.R.uniform(low=sigma2_z[0], high=sigma2_z[1]) self.a = self.R.uniform(low=self.alpha[0], high=self.alpha[1])
[docs] def __call__(self, data): d = dict(data) self.randomize() if not self._do_transform: return d for key in self.keys: sigma1 = ensure_tuple_size(tup=(self.x1, self.y1, self.z1), dim=d[key].ndim - 1) sigma2 = ensure_tuple_size(tup=(self.x2, self.y2, self.z2), dim=d[key].ndim - 1) d[key] = GaussianSharpen(sigma1=sigma1, sigma2=sigma2, alpha=self.a, approx=self.approx)(d[key]) return d
[docs]class RandHistogramShiftd(RandomizableTransform, MapTransform): """ Dictionary-based version :py:class:`monai.transforms.RandHistogramShift`. Apply random nonlinear transform the the image's intensity histogram. Args: keys: keys of the corresponding items to be transformed. See also: monai.transforms.MapTransform num_control_points: number of control points governing the nonlinear intensity mapping. a smaller number of control points allows for larger intensity shifts. if two values provided, number of control points selecting from range (min_value, max_value). prob: probability of histogram shift. """ def __init__( self, keys: KeysCollection, num_control_points: Union[Tuple[int, int], int] = 10, prob: float = 0.1 ) -> None: MapTransform.__init__(self, keys) RandomizableTransform.__init__(self, prob) if isinstance(num_control_points, int): if num_control_points <= 2: raise AssertionError("num_control_points should be greater than or equal to 3") self.num_control_points = (num_control_points, num_control_points) else: if len(num_control_points) != 2: raise AssertionError("num_control points should be a number or a pair of numbers") if min(num_control_points) <= 2: raise AssertionError("num_control_points should be greater than or equal to 3") self.num_control_points = (min(num_control_points), max(num_control_points))
[docs] def randomize(self, data: Optional[Any] = None) -> None: super().randomize(None) num_control_point = self.R.randint(self.num_control_points[0], self.num_control_points[1] + 1) self.reference_control_points = np.linspace(0, 1, num_control_point) self.floating_control_points = np.copy(self.reference_control_points) for i in range(1, num_control_point - 1): self.floating_control_points[i] = self.R.uniform( self.floating_control_points[i - 1], self.floating_control_points[i + 1] )
[docs] def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]: d = dict(data) self.randomize() if not self._do_transform: return d for key in self.keys: img_min, img_max = d[key].min(), d[key].max() reference_control_points_scaled = self.reference_control_points * (img_max - img_min) + img_min floating_control_points_scaled = self.floating_control_points * (img_max - img_min) + img_min dtype = d[key].dtype d[key] = np.interp(d[key], reference_control_points_scaled, floating_control_points_scaled).astype(dtype) return d
RandGaussianNoiseD = RandGaussianNoiseDict = RandGaussianNoised ShiftIntensityD = ShiftIntensityDict = ShiftIntensityd RandShiftIntensityD = RandShiftIntensityDict = RandShiftIntensityd ScaleIntensityD = ScaleIntensityDict = ScaleIntensityd RandScaleIntensityD = RandScaleIntensityDict = RandScaleIntensityd NormalizeIntensityD = NormalizeIntensityDict = NormalizeIntensityd ThresholdIntensityD = ThresholdIntensityDict = ThresholdIntensityd ScaleIntensityRangeD = ScaleIntensityRangeDict = ScaleIntensityRanged AdjustContrastD = AdjustContrastDict = AdjustContrastd RandAdjustContrastD = RandAdjustContrastDict = RandAdjustContrastd ScaleIntensityRangePercentilesD = ScaleIntensityRangePercentilesDict = ScaleIntensityRangePercentilesd MaskIntensityD = MaskIntensityDict = MaskIntensityd GaussianSmoothD = GaussianSmoothDict = GaussianSmoothd RandGaussianSmoothD = RandGaussianSmoothDict = RandGaussianSmoothd GaussianSharpenD = GaussianSharpenDict = GaussianSharpend RandGaussianSharpenD = RandGaussianSharpenDict = RandGaussianSharpend RandHistogramShiftD = RandHistogramShiftDict = RandHistogramShiftd