# 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 "vanilla" transforms for intensity adjustment
https://github.com/Project-MONAI/MONAI/wiki/MONAI_Design
"""
from collections.abc import Iterable
from typing import Any, List, Optional, Sequence, Tuple, Union
from warnings import warn
import numpy as np
import torch
from monai.config import DtypeLike
from monai.networks.layers import GaussianFilter, HilbertTransform, SavitzkyGolayFilter
from monai.transforms.transform import RandomizableTransform, Transform
from monai.transforms.utils import rescale_array
from monai.utils import PT_BEFORE_1_7, InvalidPyTorchVersionError, dtype_torch_to_numpy, ensure_tuple_size
__all__ = [
"RandGaussianNoise",
"ShiftIntensity",
"RandShiftIntensity",
"StdShiftIntensity",
"RandStdShiftIntensity",
"RandBiasField",
"ScaleIntensity",
"RandScaleIntensity",
"NormalizeIntensity",
"ThresholdIntensity",
"ScaleIntensityRange",
"AdjustContrast",
"RandAdjustContrast",
"ScaleIntensityRangePercentiles",
"MaskIntensity",
"DetectEnvelope",
"SavitzkyGolaySmooth",
"GaussianSmooth",
"RandGaussianSmooth",
"GaussianSharpen",
"RandGaussianSharpen",
"RandHistogramShift",
]
[docs]class RandGaussianNoise(RandomizableTransform):
"""
Add Gaussian noise to image.
Args:
prob: Probability to add Gaussian noise.
mean: Mean or “centre” of the distribution.
std: Standard deviation (spread) of distribution.
"""
def __init__(self, prob: float = 0.1, mean: Union[Sequence[float], float] = 0.0, std: float = 0.1) -> None:
RandomizableTransform.__init__(self, prob)
self.mean = mean
self.std = std
self._noise = None
[docs] def randomize(self, im_shape: Sequence[int]) -> None:
super().randomize(None)
self._noise = self.R.normal(self.mean, self.R.uniform(0, self.std), size=im_shape)
[docs] def __call__(self, img: Union[torch.Tensor, np.ndarray]) -> Union[torch.Tensor, np.ndarray]:
"""
Apply the transform to `img`.
"""
self.randomize(img.shape)
if self._noise is None:
raise AssertionError
if not self._do_transform:
return img
dtype = dtype_torch_to_numpy(img.dtype) if isinstance(img, torch.Tensor) else img.dtype
return img + self._noise.astype(dtype)
[docs]class ShiftIntensity(Transform):
"""
Shift intensity uniformly for the entire image with specified `offset`.
Args:
offset: offset value to shift the intensity of image.
"""
def __init__(self, offset: float) -> None:
self.offset = offset
[docs] def __call__(self, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
return np.asarray((img + self.offset), dtype=img.dtype)
[docs]class RandShiftIntensity(RandomizableTransform):
"""
Randomly shift intensity with randomly picked offset.
"""
def __init__(self, offsets: Union[Tuple[float, float], float], prob: float = 0.1) -> None:
"""
Args:
offsets: offset range to randomly shift.
if single number, offset value is picked from (-offsets, offsets).
prob: probability of shift.
"""
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))
self._offset = self.offsets[0]
[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, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
self.randomize()
if not self._do_transform:
return img
shifter = ShiftIntensity(self._offset)
return shifter(img)
[docs]class StdShiftIntensity(Transform):
"""
Shift intensity for the image with a factor and the standard deviation of the image
by: ``v = v + factor * std(v)``.
This transform can focus on only non-zero values or the entire image,
and can also calculate the std on each channel separately.
Args:
factor: factor shift by ``v = v + factor * std(v)``.
nonzero: whether only count non-zero values.
channel_wise: if True, calculate on each channel separately. Please ensure
that the first dimension represents the channel of the image if True.
dtype: output data type, defaults to float32.
"""
def __init__(
self,
factor: float,
nonzero: bool = False,
channel_wise: bool = False,
dtype: DtypeLike = np.float32,
) -> None:
self.factor = factor
self.nonzero = nonzero
self.channel_wise = channel_wise
self.dtype = dtype
def _stdshift(self, img: np.ndarray) -> np.ndarray:
slices = (img != 0) if self.nonzero else np.ones(img.shape, dtype=bool)
if not np.any(slices):
return img
offset = self.factor * np.std(img[slices])
img[slices] = img[slices] + offset
return img
[docs] def __call__(self, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
img = img.astype(self.dtype)
if self.channel_wise:
for i, d in enumerate(img):
img[i] = self._stdshift(d)
else:
img = self._stdshift(img)
return img
[docs]class RandStdShiftIntensity(RandomizableTransform):
"""
Shift intensity for the image with a factor and the standard deviation of the image
by: ``v = v + factor * std(v)`` where the `factor` is randomly picked.
"""
def __init__(
self,
factors: Union[Tuple[float, float], float],
prob: float = 0.1,
nonzero: bool = False,
channel_wise: bool = False,
dtype: DtypeLike = np.float32,
) -> None:
"""
Args:
factors: if tuple, the randomly picked range is (min(factors), max(factors)).
If single number, the range is (-factors, factors).
prob: probability of std shift.
nonzero: whether only count non-zero values.
channel_wise: if True, calculate on each channel separately.
dtype: output data type, defaults to float32.
"""
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))
self.factor = self.factors[0]
self.nonzero = nonzero
self.channel_wise = channel_wise
self.dtype = dtype
[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, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
self.randomize()
if not self._do_transform:
return img
shifter = StdShiftIntensity(
factor=self.factor, nonzero=self.nonzero, channel_wise=self.channel_wise, dtype=self.dtype
)
return shifter(img)
[docs]class ScaleIntensity(Transform):
"""
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, minv: Optional[float] = 0.0, maxv: Optional[float] = 1.0, factor: Optional[float] = None
) -> None:
"""
Args:
minv: minimum value of output data.
maxv: maximum value of output data.
factor: factor scale by ``v = v * (1 + factor)``. In order to use
this parameter, please set `minv` and `maxv` into None.
"""
self.minv = minv
self.maxv = maxv
self.factor = factor
[docs] def __call__(self, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
Raises:
ValueError: When ``self.minv=None`` or ``self.maxv=None`` and ``self.factor=None``. Incompatible values.
"""
if self.minv is not None and self.maxv is not None:
return np.asarray(rescale_array(img, self.minv, self.maxv, img.dtype))
if self.factor is not None:
return np.asarray(img * (1 + self.factor), dtype=img.dtype)
raise ValueError("Incompatible values: minv=None or maxv=None and factor=None.")
[docs]class RandScaleIntensity(RandomizableTransform):
"""
Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
is randomly picked.
"""
def __init__(self, factors: Union[Tuple[float, float], float], prob: float = 0.1) -> None:
"""
Args:
factors: factor range to randomly scale by ``v = v * (1 + factor)``.
if single number, factor value is picked from (-factors, factors).
prob: probability of scale.
"""
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))
self.factor = self.factors[0]
[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, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
self.randomize()
if not self._do_transform:
return img
scaler = ScaleIntensity(minv=None, maxv=None, factor=self.factor)
return scaler(img)
[docs]class RandBiasField(RandomizableTransform):
"""
Random bias field augmentation for MR images.
The bias field is considered as a linear combination of smoothly varying basis (polynomial)
functions, as described in `Automated Model-Based Tissue Classification of MR Images of the Brain
<https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=811270>`_.
This implementation adapted from `NiftyNet
<https://github.com/NifTK/NiftyNet>`_.
Referred to `Longitudinal segmentation of age-related white matter hyperintensities
<https://www.sciencedirect.com/science/article/pii/S1361841517300257?via%3Dihub>`_.
Args:
degree: degree of freedom of the polynomials. The value should be no less than 1.
Defaults to 3.
coeff_range: range of the random coefficients. Defaults to (0.0, 0.1).
dtype: output data type, defaults to float32.
prob: probability to do random bias field.
"""
def __init__(
self,
degree: int = 3,
coeff_range: Tuple[float, float] = (0.0, 0.1),
dtype: DtypeLike = np.float32,
prob: float = 1.0,
) -> None:
RandomizableTransform.__init__(self, prob)
if degree < 1:
raise ValueError("degree should be no less than 1.")
self.degree = degree
self.coeff_range = coeff_range
self.dtype = dtype
def _generate_random_field(
self,
spatial_shape: Tuple[int, ...],
rank: int,
degree: int,
coeff: Tuple[int, ...],
):
"""
products of polynomials as bias field estimations
"""
coeff_mat = np.zeros((degree + 1,) * rank)
coords = [np.linspace(-1.0, 1.0, dim, dtype=np.float32) for dim in spatial_shape]
if rank == 2:
coeff_mat[np.tril_indices(degree + 1)] = coeff
field = np.polynomial.legendre.leggrid2d(coords[0], coords[1], coeff_mat)
elif rank == 3:
pts: List[List[int]] = [[0, 0, 0]]
for i in range(degree + 1):
for j in range(degree + 1 - i):
for k in range(degree + 1 - i - j):
pts.append([i, j, k])
if len(pts) > 1:
pts = pts[1:]
np_pts = np.stack(pts)
coeff_mat[np_pts[:, 0], np_pts[:, 1], np_pts[:, 2]] = coeff
field = np.polynomial.legendre.leggrid3d(coords[0], coords[1], coords[2], coeff_mat)
else:
raise NotImplementedError("only supoprts 2D or 3D fields")
return field
[docs] def randomize(self, data: np.ndarray) -> None:
super().randomize(None)
self.spatial_shape = data.shape[1:]
self.rank = len(self.spatial_shape)
n_coeff = int(np.prod([(self.degree + k) / k for k in range(1, self.rank + 1)]))
self._coeff = self.R.uniform(*self.coeff_range, n_coeff)
[docs] def __call__(self, img: np.ndarray):
"""
Apply the transform to `img`.
"""
self.randomize(data=img)
if not self._do_transform:
return img
num_channels = img.shape[0]
_bias_fields = np.stack(
[
self._generate_random_field(
spatial_shape=self.spatial_shape, rank=self.rank, degree=self.degree, coeff=self._coeff
)
for _ in range(num_channels)
],
axis=0,
)
return (img * _bias_fields).astype(self.dtype)
[docs]class NormalizeIntensity(Transform):
"""
Normalize input based on provided args, using calculated mean and std if not provided.
This transform can normalize only non-zero values or entire image, and can also calculate
mean and std on each channel separately.
When `channel_wise` is True, the first dimension of `subtrahend` and `divisor` should
be the number of image channels if they are not None.
Args:
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, defaults to float32.
"""
def __init__(
self,
subtrahend: Union[Sequence, np.ndarray, None] = None,
divisor: Union[Sequence, np.ndarray, None] = None,
nonzero: bool = False,
channel_wise: bool = False,
dtype: DtypeLike = np.float32,
) -> None:
self.subtrahend = subtrahend
self.divisor = divisor
self.nonzero = nonzero
self.channel_wise = channel_wise
self.dtype = dtype
def _normalize(self, img: np.ndarray, sub=None, div=None) -> np.ndarray:
slices = (img != 0) if self.nonzero else np.ones(img.shape, dtype=bool)
if not np.any(slices):
return img
_sub = sub if sub is not None else np.mean(img[slices])
if isinstance(_sub, np.ndarray):
_sub = _sub[slices]
_div = div if div is not None else np.std(img[slices])
if np.isscalar(_div):
if _div == 0.0:
_div = 1.0
elif isinstance(_div, np.ndarray):
_div = _div[slices]
_div[_div == 0.0] = 1.0
img[slices] = (img[slices] - _sub) / _div
return img
[docs] def __call__(self, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`, assuming `img` is a channel-first array if `self.channel_wise` is True,
"""
if self.channel_wise:
if self.subtrahend is not None and len(self.subtrahend) != len(img):
raise ValueError(f"img has {len(img)} channels, but subtrahend has {len(self.subtrahend)} components.")
if self.divisor is not None and len(self.divisor) != len(img):
raise ValueError(f"img has {len(img)} channels, but divisor has {len(self.divisor)} components.")
for i, d in enumerate(img):
img[i] = self._normalize(
d,
sub=self.subtrahend[i] if self.subtrahend is not None else None,
div=self.divisor[i] if self.divisor is not None else None,
)
else:
img = self._normalize(img, self.subtrahend, self.divisor)
return img.astype(self.dtype)
[docs]class ThresholdIntensity(Transform):
"""
Filter the intensity values of whole image to below threshold or above threshold.
And fill the remaining parts of the image to the `cval` value.
Args:
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, threshold: float, above: bool = True, cval: float = 0.0) -> None:
if not isinstance(threshold, (int, float)):
raise AssertionError("threshold must be a float or int number.")
self.threshold = threshold
self.above = above
self.cval = cval
[docs] def __call__(self, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
return np.asarray(
np.where(img > self.threshold if self.above else img < self.threshold, img, self.cval), dtype=img.dtype
)
[docs]class ScaleIntensityRange(Transform):
"""
Apply specific intensity scaling to the whole numpy array.
Scaling from [a_min, a_max] to [b_min, b_max] with clip option.
Args:
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, a_min: float, a_max: float, b_min: float, b_max: float, clip: bool = False) -> None:
self.a_min = a_min
self.a_max = a_max
self.b_min = b_min
self.b_max = b_max
self.clip = clip
[docs] def __call__(self, img: np.ndarray):
"""
Apply the transform to `img`.
"""
if self.a_max - self.a_min == 0.0:
warn("Divide by zero (a_min == a_max)", Warning)
return img - self.a_min + self.b_min
img = (img - self.a_min) / (self.a_max - self.a_min)
img = img * (self.b_max - self.b_min) + self.b_min
if self.clip:
img = np.asarray(np.clip(img, self.b_min, self.b_max))
return img
[docs]class AdjustContrast(Transform):
"""
Changes image intensity by gamma. Each pixel/voxel intensity is updated as::
x = ((x - min) / intensity_range) ^ gamma * intensity_range + min
Args:
gamma: gamma value to adjust the contrast as function.
"""
def __init__(self, gamma: float) -> None:
if not isinstance(gamma, (int, float)):
raise AssertionError("gamma must be a float or int number.")
self.gamma = gamma
[docs] def __call__(self, img: np.ndarray):
"""
Apply the transform to `img`.
"""
epsilon = 1e-7
img_min = img.min()
img_range = img.max() - img_min
return np.power(((img - img_min) / float(img_range + epsilon)), self.gamma) * img_range + img_min
[docs]class RandAdjustContrast(RandomizableTransform):
"""
Randomly changes image intensity by gamma. Each pixel/voxel intensity is updated as::
x = ((x - min) / intensity_range) ^ gamma * intensity_range + min
Args:
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, prob: float = 0.1, gamma: Union[Sequence[float], float] = (0.5, 4.5)) -> None:
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 = 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, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`.
"""
self.randomize()
if self.gamma_value is None:
raise AssertionError
if not self._do_transform:
return img
adjuster = AdjustContrast(self.gamma_value)
return adjuster(img)
[docs]class ScaleIntensityRangePercentiles(Transform):
"""
Apply range scaling to a numpy array based on the intensity distribution of the input.
By default this transform will scale from [lower_intensity_percentile, upper_intensity_percentile] to [b_min, b_max], where
{lower,upper}_intensity_percentile are the intensity values at the corresponding percentiles of ``img``.
The ``relative`` parameter can also be set to scale from [lower_intensity_percentile, upper_intensity_percentile] to the
lower and upper percentiles of the output range [b_min, b_max]
For example:
.. code-block:: python
:emphasize-lines: 11, 22
image = np.array(
[[[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5]]])
# Scale from lower and upper image intensity percentiles
# to output range [b_min, b_max]
scaler = ScaleIntensityRangePercentiles(10, 90, 0, 200, False, False)
print(scaler(image))
[[[0., 50., 100., 150., 200.],
[0., 50., 100., 150., 200.],
[0., 50., 100., 150., 200.],
[0., 50., 100., 150., 200.],
[0., 50., 100., 150., 200.],
[0., 50., 100., 150., 200.]]]
# Scale from lower and upper image intensity percentiles
# to lower and upper percentiles of the output range [b_min, b_max]
rel_scaler = ScaleIntensityRangePercentiles(10, 90, 0, 200, False, True)
print(rel_scaler(image))
[[[20., 60., 100., 140., 180.],
[20., 60., 100., 140., 180.],
[20., 60., 100., 140., 180.],
[20., 60., 100., 140., 180.],
[20., 60., 100., 140., 180.],
[20., 60., 100., 140., 180.]]]
Args:
lower: lower intensity percentile.
upper: upper intensity 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, lower: float, upper: float, b_min: float, b_max: float, clip: bool = False, relative: bool = False
) -> None:
if lower < 0.0 or lower > 100.0:
raise AssertionError("Percentiles must be in the range [0, 100]")
if upper < 0.0 or upper > 100.0:
raise AssertionError("Percentiles must be in the range [0, 100]")
self.lower = lower
self.upper = upper
self.b_min = b_min
self.b_max = b_max
self.clip = clip
self.relative = relative
[docs] def __call__(self, img: np.ndarray):
"""
Apply the transform to `img`.
"""
a_min = np.percentile(img, self.lower)
a_max = np.percentile(img, self.upper)
b_min = self.b_min
b_max = self.b_max
if self.relative:
b_min = ((self.b_max - self.b_min) * (self.lower / 100.0)) + self.b_min
b_max = ((self.b_max - self.b_min) * (self.upper / 100.0)) + self.b_min
scalar = ScaleIntensityRange(a_min=a_min, a_max=a_max, b_min=b_min, b_max=b_max, clip=False)
img = scalar(img)
if self.clip:
img = np.asarray(np.clip(img, self.b_min, self.b_max))
return img
[docs]class MaskIntensity(Transform):
"""
Mask the intensity values of input image with the specified mask data.
Mask data must have the same spatial size as the input image, and all
the intensity values of input image corresponding to `0` in the mask
data will be set to `0`, others will keep the original value.
Args:
mask_data: if `mask_data` is single channel, apply to every channel
of input image. if multiple channels, the number of channels must
match the input data. `mask_data` will be converted to `bool` values
by `mask_data > 0` before applying transform to input image.
"""
def __init__(self, mask_data: Optional[np.ndarray]) -> None:
self.mask_data = mask_data
[docs] def __call__(self, img: np.ndarray, mask_data: Optional[np.ndarray] = None) -> np.ndarray:
"""
Args:
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.
Raises:
- ValueError: When both ``mask_data`` and ``self.mask_data`` are None.
- ValueError: When ``mask_data`` and ``img`` channels differ and ``mask_data`` is not single channel.
"""
if self.mask_data is None and mask_data is None:
raise ValueError("Unknown mask_data.")
mask_data_ = np.array([[1]])
if self.mask_data is not None and mask_data is None:
mask_data_ = self.mask_data > 0
if mask_data is not None:
mask_data_ = mask_data > 0
mask_data_ = np.asarray(mask_data_)
if mask_data_.shape[0] != 1 and mask_data_.shape[0] != img.shape[0]:
raise ValueError(
"When mask_data is not single channel, mask_data channels must match img, "
f"got img={img.shape[0]} mask_data={mask_data_.shape[0]}."
)
return np.asarray(img * mask_data_)
[docs]class SavitzkyGolaySmooth(Transform):
"""
Smooth the input data along the given axis using a Savitzky-Golay filter.
Args:
window_length: Length of the filter window, must be a positive odd integer.
order: Order of the polynomial to fit to each window, must be less than ``window_length``.
axis: Optional axis along which to apply the filter kernel. Default 1 (first spatial dimension).
mode: Optional padding mode, passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'``
or ``'circular'``. Default: ``'zeros'``. See ``torch.nn.Conv1d()`` for more information.
"""
def __init__(self, window_length: int, order: int, axis: int = 1, mode: str = "zeros"):
if axis < 0:
raise ValueError("axis must be zero or positive.")
self.window_length = window_length
self.order = order
self.axis = axis
self.mode = mode
[docs] def __call__(self, img: np.ndarray):
"""
Args:
img: numpy.ndarray containing input data. Must be real and in shape [channels, spatial1, spatial2, ...].
Returns:
np.ndarray containing smoothed result.
"""
# add one to transform axis because a batch axis will be added at dimension 0
savgol_filter = SavitzkyGolayFilter(self.window_length, self.order, self.axis + 1, self.mode)
# convert to Tensor and add Batch axis expected by HilbertTransform
input_data = torch.as_tensor(np.ascontiguousarray(img)).unsqueeze(0)
return savgol_filter(input_data).squeeze(0).numpy()
[docs]class DetectEnvelope(Transform):
"""
Find the envelope of the input data along the requested axis using a Hilbert transform.
Requires PyTorch 1.7.0+ and the PyTorch FFT module (which is not included in NVIDIA PyTorch Release 20.10).
Args:
axis: Axis along which to detect the envelope. Default 1, i.e. the first spatial dimension.
N: FFT size. Default img.shape[axis]. Input will be zero-padded or truncated to this size along dimension
``axis``.
"""
def __init__(self, axis: int = 1, n: Union[int, None] = None) -> None:
if PT_BEFORE_1_7:
raise InvalidPyTorchVersionError("1.7.0", self.__class__.__name__)
if axis < 0:
raise ValueError("axis must be zero or positive.")
self.axis = axis
self.n = n
[docs] def __call__(self, img: np.ndarray):
"""
Args:
img: numpy.ndarray containing input data. Must be real and in shape [channels, spatial1, spatial2, ...].
Returns:
np.ndarray containing envelope of data in img along the specified axis.
"""
# add one to transform axis because a batch axis will be added at dimension 0
hilbert_transform = HilbertTransform(self.axis + 1, self.n)
# convert to Tensor and add Batch axis expected by HilbertTransform
input_data = torch.as_tensor(np.ascontiguousarray(img)).unsqueeze(0)
return np.abs(hilbert_transform(input_data).squeeze(0).numpy())
[docs]class GaussianSmooth(Transform):
"""
Apply Gaussian smooth to the input data based on specified `sigma` parameter.
A default value `sigma=1.0` is provided for reference.
Args:
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, sigma: Union[Sequence[float], float] = 1.0, approx: str = "erf") -> None:
self.sigma = sigma
self.approx = approx
[docs] def __call__(self, img: np.ndarray):
gaussian_filter = GaussianFilter(img.ndim - 1, self.sigma, approx=self.approx)
input_data = torch.as_tensor(np.ascontiguousarray(img), dtype=torch.float).unsqueeze(0)
return gaussian_filter(input_data).squeeze(0).detach().numpy()
[docs]class RandGaussianSmooth(RandomizableTransform):
"""
Apply Gaussian smooth to the input data based on randomly selected `sigma` parameters.
Args:
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.
prob: probability of Gaussian smooth.
approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace".
see also :py:meth:`monai.networks.layers.GaussianFilter`.
"""
def __init__(
self,
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),
prob: float = 0.1,
approx: str = "erf",
) -> None:
RandomizableTransform.__init__(self, prob)
self.sigma_x = sigma_x
self.sigma_y = sigma_y
self.sigma_z = sigma_z
self.approx = approx
self.x = self.sigma_x[0]
self.y = self.sigma_y[0]
self.z = self.sigma_z[0]
[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, img: np.ndarray):
self.randomize()
if not self._do_transform:
return img
sigma = ensure_tuple_size(tup=(self.x, self.y, self.z), dim=img.ndim - 1)
return GaussianSmooth(sigma=sigma, approx=self.approx)(img)
[docs]class GaussianSharpen(Transform):
"""
Sharpen images using the Gaussian Blur filter.
Referring to: http://scipy-lectures.org/advanced/image_processing/auto_examples/plot_sharpen.html.
The algorithm is shown as below
.. code-block:: python
blurred_f = gaussian_filter(img, sigma1)
filter_blurred_f = gaussian_filter(blurred_f, sigma2)
img = blurred_f + alpha * (blurred_f - filter_blurred_f)
A set of default values `sigma1=3.0`, `sigma2=1.0` and `alpha=30.0` is provide for reference.
Args:
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,
sigma1: Union[Sequence[float], float] = 3.0,
sigma2: Union[Sequence[float], float] = 1.0,
alpha: float = 30.0,
approx: str = "erf",
) -> None:
self.sigma1 = sigma1
self.sigma2 = sigma2
self.alpha = alpha
self.approx = approx
[docs] def __call__(self, img: np.ndarray):
gaussian_filter1 = GaussianFilter(img.ndim - 1, self.sigma1, approx=self.approx)
gaussian_filter2 = GaussianFilter(img.ndim - 1, self.sigma2, approx=self.approx)
input_data = torch.as_tensor(np.ascontiguousarray(img), dtype=torch.float).unsqueeze(0)
blurred_f = gaussian_filter1(input_data)
filter_blurred_f = gaussian_filter2(blurred_f)
return (blurred_f + self.alpha * (blurred_f - filter_blurred_f)).squeeze(0).detach().numpy()
[docs]class RandGaussianSharpen(RandomizableTransform):
"""
Sharpen images using the Gaussian Blur filter based on randomly selected `sigma1`, `sigma2` and `alpha`.
The algorithm is :py:class:`monai.transforms.GaussianSharpen`.
Args:
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,
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,
) -> None:
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, img: np.ndarray):
self.randomize()
if not self._do_transform:
return img
sigma1 = ensure_tuple_size(tup=(self.x1, self.y1, self.z1), dim=img.ndim - 1)
sigma2 = ensure_tuple_size(tup=(self.x2, self.y2, self.z2), dim=img.ndim - 1)
return GaussianSharpen(sigma1=sigma1, sigma2=sigma2, alpha=self.a, approx=self.approx)(img)
[docs]class RandHistogramShift(RandomizableTransform):
"""
Apply random nonlinear transform to the image's intensity histogram.
Args:
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, num_control_points: Union[Tuple[int, int], int] = 10, prob: float = 0.1) -> None:
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, img: np.ndarray) -> np.ndarray:
self.randomize()
if not self._do_transform:
return img
img_min, img_max = img.min(), img.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
return np.asarray(
np.interp(img, reference_control_points_scaled, floating_control_points_scaled), dtype=img.dtype
)