SegMamba/monai/transforms/intensity/array.py

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A collection of "vanilla" transforms for intensity adjustment.
"""

from __future__ import annotations

from abc import abstractmethod
from collections.abc import Callable, Iterable, Sequence
from functools import partial
from typing import Any
from warnings import warn

import numpy as np
import torch

from monai.config import DtypeLike
from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
from monai.data.meta_obj import get_track_meta
from monai.data.ultrasound_confidence_map import UltrasoundConfidenceMap
from monai.data.utils import get_random_patch, get_valid_patch_size
from monai.networks.layers import GaussianFilter, HilbertTransform, MedianFilter, SavitzkyGolayFilter
from monai.transforms.transform import RandomizableTransform, Transform
from monai.transforms.utils import Fourier, equalize_hist, is_positive, rescale_array, soft_clip
from monai.transforms.utils_pytorch_numpy_unification import clip, percentile, where
from monai.utils.enums import TransformBackends
from monai.utils.misc import ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple
from monai.utils.module import min_version, optional_import
from monai.utils.type_conversion import convert_data_type, convert_to_dst_type, convert_to_tensor, get_equivalent_dtype

skimage, _ = optional_import("skimage", "0.19.0", min_version)

__all__ = [
    "RandGaussianNoise",
    "RandRicianNoise",
    "ShiftIntensity",
    "RandShiftIntensity",
    "StdShiftIntensity",
    "RandStdShiftIntensity",
    "RandBiasField",
    "ScaleIntensity",
    "RandScaleIntensity",
    "ScaleIntensityFixedMean",
    "RandScaleIntensityFixedMean",
    "NormalizeIntensity",
    "ThresholdIntensity",
    "ScaleIntensityRange",
    "ClipIntensityPercentiles",
    "AdjustContrast",
    "RandAdjustContrast",
    "ScaleIntensityRangePercentiles",
    "MaskIntensity",
    "DetectEnvelope",
    "SavitzkyGolaySmooth",
    "MedianSmooth",
    "GaussianSmooth",
    "RandGaussianSmooth",
    "GaussianSharpen",
    "RandGaussianSharpen",
    "RandHistogramShift",
    "GibbsNoise",
    "RandGibbsNoise",
    "KSpaceSpikeNoise",
    "RandKSpaceSpikeNoise",
    "RandCoarseTransform",
    "RandCoarseDropout",
    "RandCoarseShuffle",
    "HistogramNormalize",
    "IntensityRemap",
    "RandIntensityRemap",
    "ForegroundMask",
    "ComputeHoVerMaps",
    "UltrasoundConfidenceMapTransform",
]


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.
        dtype: output data type, if None, same as input image. defaults to float32.
        sample_std: If True, sample the spread of the Gaussian distribution uniformly from 0 to std.

    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        prob: float = 0.1,
        mean: float = 0.0,
        std: float = 0.1,
        dtype: DtypeLike = np.float32,
        sample_std: bool = True,
    ) -> None:
        RandomizableTransform.__init__(self, prob)
        self.mean = mean
        self.std = std
        self.dtype = dtype
        self.noise: np.ndarray | None = None
        self.sample_std = sample_std

    def randomize(self, img: NdarrayOrTensor, mean: float | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        std = self.R.uniform(0, self.std) if self.sample_std else self.std
        noise = self.R.normal(self.mean if mean is None else mean, std, size=img.shape)
        # noise is float64 array, convert to the output dtype to save memory
        self.noise, *_ = convert_data_type(noise, dtype=self.dtype)

    def __call__(self, img: NdarrayOrTensor, mean: float | None = None, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize(img=img, mean=self.mean if mean is None else mean)

        if not self._do_transform:
            return img

        if self.noise is None:
            raise RuntimeError("please call the `randomize()` function first.")
        img, *_ = convert_data_type(img, dtype=self.dtype)
        noise, *_ = convert_to_dst_type(self.noise, img)
        return img + noise


class RandRicianNoise(RandomizableTransform):
    """
    Add Rician noise to image.
    Rician noise in MRI is the result of performing a magnitude operation on complex
    data with Gaussian noise of the same variance in both channels, as described in
    `Noise in Magnitude Magnetic Resonance Images <https://doi.org/10.1002/cmr.a.20124>`_.
    This transform is adapted from `DIPY <https://github.com/dipy/dipy>`_.
    See also: `The rician distribution of noisy mri data <https://doi.org/10.1002/mrm.1910340618>`_.

    Args:
        prob: Probability to add Rician noise.
        mean: Mean or "centre" of the Gaussian distributions sampled to make up
            the Rician noise.
        std: Standard deviation (spread) of the Gaussian distributions sampled
            to make up the Rician noise.
        channel_wise: If True, treats each channel of the image separately.
        relative: If True, the spread of the sampled Gaussian distributions will
            be std times the standard deviation of the image or channel's intensity
            histogram.
        sample_std: If True, sample the spread of the Gaussian distributions
            uniformly from 0 to std.
        dtype: output data type, if None, same as input image. defaults to float32.

    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        prob: float = 0.1,
        mean: Sequence[float] | float = 0.0,
        std: Sequence[float] | float = 1.0,
        channel_wise: bool = False,
        relative: bool = False,
        sample_std: bool = True,
        dtype: DtypeLike = np.float32,
    ) -> None:
        RandomizableTransform.__init__(self, prob)
        self.prob = prob
        self.mean = mean
        self.std = std
        self.channel_wise = channel_wise
        self.relative = relative
        self.sample_std = sample_std
        self.dtype = dtype
        self._noise1: NdarrayOrTensor
        self._noise2: NdarrayOrTensor

    def _add_noise(self, img: NdarrayOrTensor, mean: float, std: float):
        dtype_np = get_equivalent_dtype(img.dtype, np.ndarray)
        im_shape = img.shape
        _std = self.R.uniform(0, std) if self.sample_std else std
        self._noise1 = self.R.normal(mean, _std, size=im_shape).astype(dtype_np, copy=False)
        self._noise2 = self.R.normal(mean, _std, size=im_shape).astype(dtype_np, copy=False)
        if isinstance(img, torch.Tensor):
            n1 = torch.tensor(self._noise1, device=img.device)
            n2 = torch.tensor(self._noise2, device=img.device)
            return torch.sqrt((img + n1) ** 2 + n2**2)

        return np.sqrt((img + self._noise1) ** 2 + self._noise2**2)

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta(), dtype=self.dtype)
        if randomize:
            super().randomize(None)

        if not self._do_transform:
            return img

        if self.channel_wise:
            _mean = ensure_tuple_rep(self.mean, len(img))
            _std = ensure_tuple_rep(self.std, len(img))
            for i, d in enumerate(img):
                img[i] = self._add_noise(d, mean=_mean[i], std=_std[i] * d.std() if self.relative else _std[i])
        else:
            if not isinstance(self.mean, (int, float)):
                raise RuntimeError(f"If channel_wise is False, mean must be a float or int, got {type(self.mean)}.")
            if not isinstance(self.std, (int, float)):
                raise RuntimeError(f"If channel_wise is False, std must be a float or int, got {type(self.std)}.")
            std = self.std * img.std().item() if self.relative else self.std
            if not isinstance(std, (int, float)):
                raise RuntimeError(f"std must be a float or int number, got {type(std)}.")
            img = self._add_noise(img, mean=self.mean, std=std)
        return img


class ShiftIntensity(Transform):
    """
    Shift intensity uniformly for the entire image with specified `offset`.

    Args:
        offset: offset value to shift the intensity of image.
        safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
            E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, offset: float, safe: bool = False) -> None:
        self.offset = offset
        self.safe = safe

    def __call__(self, img: NdarrayOrTensor, offset: float | None = None) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """

        img = convert_to_tensor(img, track_meta=get_track_meta())
        offset = self.offset if offset is None else offset
        out = img + offset
        out, *_ = convert_data_type(data=out, dtype=img.dtype, safe=self.safe)

        return out


class RandShiftIntensity(RandomizableTransform):
    """
    Randomly shift intensity with randomly picked offset.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self, offsets: tuple[float, float] | float, safe: bool = False, prob: float = 0.1, channel_wise: bool = False
    ) -> None:
        """
        Args:
            offsets: offset range to randomly shift.
                if single number, offset value is picked from (-offsets, offsets).
            safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
                E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
            prob: probability of shift.
            channel_wise: if True, shift intensity on each channel separately. For each channel, a random offset will be chosen.
                Please ensure that the first dimension represents the channel of the image if True.
        """
        RandomizableTransform.__init__(self, prob)
        if isinstance(offsets, (int, float)):
            self.offsets = (min(-offsets, offsets), max(-offsets, offsets))
        elif len(offsets) != 2:
            raise ValueError(f"offsets should be a number or pair of numbers, got {offsets}.")
        else:
            self.offsets = (min(offsets), max(offsets))
        self._offset = self.offsets[0]
        self.channel_wise = channel_wise
        self._shifter = ShiftIntensity(self._offset, safe)

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        if self.channel_wise:
            self._offset = [self.R.uniform(low=self.offsets[0], high=self.offsets[1]) for _ in range(data.shape[0])]  # type: ignore
        else:
            self._offset = self.R.uniform(low=self.offsets[0], high=self.offsets[1])

    def __call__(self, img: NdarrayOrTensor, factor: float | None = None, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.

        Args:
            img: input image to shift intensity.
            factor: a factor to multiply the random offset, then shift.
                can be some image specific value at runtime, like: max(img), etc.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize(img)

        if not self._do_transform:
            return img

        ret: NdarrayOrTensor
        if self.channel_wise:
            out = []
            for i, d in enumerate(img):
                out_channel = self._shifter(d, self._offset[i] if factor is None else self._offset[i] * factor)  # type: ignore
                out.append(out_channel)
            ret = torch.stack(out)  # type: ignore
        else:
            ret = self._shifter(img, self._offset if factor is None else self._offset * factor)
        return ret


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, if None, same as input image. defaults to float32.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    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: NdarrayOrTensor) -> NdarrayOrTensor:
        ones: Callable
        std: Callable
        if isinstance(img, torch.Tensor):
            ones = torch.ones
            std = partial(torch.std, unbiased=False)
        else:
            ones = np.ones
            std = np.std

        slices = (img != 0) if self.nonzero else ones(img.shape, dtype=bool)
        if slices.any():
            offset = self.factor * std(img[slices])
            img[slices] = img[slices] + offset
        return img

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta(), dtype=self.dtype)
        if self.channel_wise:
            for i, d in enumerate(img):
                img[i] = self._stdshift(d)  # type: ignore
        else:
            img = self._stdshift(img)
        return img


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.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        factors: 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, if None, same as input image. defaults to float32.

        """
        RandomizableTransform.__init__(self, prob)
        if isinstance(factors, (int, float)):
            self.factors = (min(-factors, factors), max(-factors, factors))
        elif len(factors) != 2:
            raise ValueError(f"factors should be a number or pair of numbers, got {factors}.")
        else:
            self.factors = (min(factors), max(factors))
        self.factor = self.factors[0]
        self.nonzero = nonzero
        self.channel_wise = channel_wise
        self.dtype = dtype

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta(), dtype=self.dtype)
        if randomize:
            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=img)


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)``.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        minv: float | None = 0.0,
        maxv: float | None = 1.0,
        factor: float | None = None,
        channel_wise: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> 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 both `minv` and `maxv` into None.
            channel_wise: if True, scale on each channel separately. Please ensure
                that the first dimension represents the channel of the image if True.
            dtype: output data type, if None, same as input image. defaults to float32.
        """
        self.minv = minv
        self.maxv = maxv
        self.factor = factor
        self.channel_wise = channel_wise
        self.dtype = dtype

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.

        Raises:
            ValueError: When ``self.minv=None`` or ``self.maxv=None`` and ``self.factor=None``. Incompatible values.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t = convert_to_tensor(img, track_meta=False)
        ret: NdarrayOrTensor
        if self.minv is not None or self.maxv is not None:
            if self.channel_wise:
                out = [rescale_array(d, self.minv, self.maxv, dtype=self.dtype) for d in img_t]
                ret = torch.stack(out)  # type: ignore
            else:
                ret = rescale_array(img_t, self.minv, self.maxv, dtype=self.dtype)
        else:
            ret = (img_t * (1 + self.factor)) if self.factor is not None else img_t
        ret = convert_to_dst_type(ret, dst=img, dtype=self.dtype or img_t.dtype)[0]
        return ret


class ScaleIntensityFixedMean(Transform):
    """
    Scale the intensity of input image by ``v = v * (1 + factor)``, then shift the output so that the output image has the
    same mean as the input.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        factor: float = 0,
        preserve_range: bool = False,
        fixed_mean: bool = True,
        channel_wise: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None:
        """
        Args:
            factor: factor scale by ``v = v * (1 + factor)``.
            preserve_range: clips the output array/tensor to the range of the input array/tensor
            fixed_mean: subtract the mean intensity before scaling with `factor`, then add the same value after scaling
                to ensure that the output has the same mean as the input.
            channel_wise: if True, scale on each channel separately. `preserve_range` and `fixed_mean` are also applied
                on each channel separately if `channel_wise` is True. Please ensure that the first dimension represents the
                channel of the image if True.
            dtype: output data type, if None, same as input image. defaults to float32.
        """
        self.factor = factor
        self.preserve_range = preserve_range
        self.fixed_mean = fixed_mean
        self.channel_wise = channel_wise
        self.dtype = dtype

    def __call__(self, img: NdarrayOrTensor, factor=None) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        Args:
            img: the input tensor/array
            factor: factor scale by ``v = v * (1 + factor)``

        """

        factor = factor if factor is not None else self.factor

        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t = convert_to_tensor(img, track_meta=False)
        ret: NdarrayOrTensor
        if self.channel_wise:
            out = []
            for d in img_t:
                if self.preserve_range:
                    clip_min = d.min()
                    clip_max = d.max()

                if self.fixed_mean:
                    mn = d.mean()
                    d = d - mn

                out_channel = d * (1 + factor)

                if self.fixed_mean:
                    out_channel = out_channel + mn

                if self.preserve_range:
                    out_channel = clip(out_channel, clip_min, clip_max)

                out.append(out_channel)
            ret = torch.stack(out)
        else:
            if self.preserve_range:
                clip_min = img_t.min()
                clip_max = img_t.max()

            if self.fixed_mean:
                mn = img_t.mean()
                img_t = img_t - mn

            ret = img_t * (1 + factor)

            if self.fixed_mean:
                ret = ret + mn

            if self.preserve_range:
                ret = clip(ret, clip_min, clip_max)

        ret = convert_to_dst_type(ret, dst=img, dtype=self.dtype or img_t.dtype)[0]
        return ret


class RandScaleIntensityFixedMean(RandomizableTransform):
    """
    Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
    is randomly picked. Subtract the mean intensity before scaling with `factor`, then add the same value after scaling
    to ensure that the output has the same mean as the input.
    """

    backend = ScaleIntensityFixedMean.backend

    def __init__(
        self,
        prob: float = 0.1,
        factors: Sequence[float] | float = 0,
        fixed_mean: bool = True,
        preserve_range: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None:
        """
        Args:
            factors: factor range to randomly scale by ``v = v * (1 + factor)``.
                if single number, factor value is picked from (-factors, factors).
            preserve_range: clips the output array/tensor to the range of the input array/tensor
            fixed_mean: subtract the mean intensity before scaling with `factor`, then add the same value after scaling
                to ensure that the output has the same mean as the input.
            channel_wise: if True, scale on each channel separately. `preserve_range` and `fixed_mean` are also applied
            on each channel separately if `channel_wise` is True. Please ensure that the first dimension represents the
            channel of the image if True.
            dtype: output data type, if None, same as input image. defaults to float32.

        """
        RandomizableTransform.__init__(self, prob)
        if isinstance(factors, (int, float)):
            self.factors = (min(-factors, factors), max(-factors, factors))
        elif len(factors) != 2:
            raise ValueError("factors should be a number or pair of numbers.")
        else:
            self.factors = (min(factors), max(factors))
        self.factor = self.factors[0]
        self.fixed_mean = fixed_mean
        self.preserve_range = preserve_range
        self.dtype = dtype

        self.scaler = ScaleIntensityFixedMean(
            factor=self.factor, fixed_mean=self.fixed_mean, preserve_range=self.preserve_range, dtype=self.dtype
        )

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize()

        if not self._do_transform:
            return convert_data_type(img, dtype=self.dtype)[0]

        return self.scaler(img, self.factor)


class RandScaleIntensity(RandomizableTransform):
    """
    Randomly scale the intensity of input image by ``v = v * (1 + factor)`` where the `factor`
    is randomly picked.
    """

    backend = ScaleIntensity.backend

    def __init__(
        self,
        factors: tuple[float, float] | float,
        prob: float = 0.1,
        channel_wise: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> 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.
            channel_wise: if True, scale on each channel separately. Please ensure
                that the first dimension represents the channel of the image if True.
            dtype: output data type, if None, same as input image. defaults to float32.

        """
        RandomizableTransform.__init__(self, prob)
        if isinstance(factors, (int, float)):
            self.factors = (min(-factors, factors), max(-factors, factors))
        elif len(factors) != 2:
            raise ValueError(f"factors should be a number or pair of numbers, got {factors}.")
        else:
            self.factors = (min(factors), max(factors))
        self.factor = self.factors[0]
        self.channel_wise = channel_wise
        self.dtype = dtype

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        if self.channel_wise:
            self.factor = [self.R.uniform(low=self.factors[0], high=self.factors[1]) for _ in range(data.shape[0])]  # type: ignore
        else:
            self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize(img)

        if not self._do_transform:
            return convert_data_type(img, dtype=self.dtype)[0]

        ret: NdarrayOrTensor
        if self.channel_wise:
            out = []
            for i, d in enumerate(img):
                out_channel = ScaleIntensity(minv=None, maxv=None, factor=self.factor[i], dtype=self.dtype)(d)  # type: ignore
                out.append(out_channel)
            ret = torch.stack(out)  # type: ignore
        else:
            ret = ScaleIntensity(minv=None, maxv=None, factor=self.factor, dtype=self.dtype)(img)
        return ret


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, if None, same as input image. defaults to float32.
        prob: probability to do random bias field.

    """

    backend = [TransformBackends.NUMPY]

    def __init__(
        self,
        degree: int = 3,
        coeff_range: tuple[float, float] = (0.0, 0.1),
        dtype: DtypeLike = np.float32,
        prob: float = 0.1,
    ) -> None:
        RandomizableTransform.__init__(self, prob)
        if degree < 1:
            raise ValueError(f"degree should be no less than 1, got {degree}.")
        self.degree = degree
        self.coeff_range = coeff_range
        self.dtype = dtype

        self._coeff = [1.0]

    def _generate_random_field(self, spatial_shape: Sequence[int], degree: int, coeff: Sequence[float]):
        """
        products of polynomials as bias field estimations
        """
        rank = len(spatial_shape)
        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
            return np.polynomial.legendre.leggrid2d(coords[0], coords[1], coeff_mat)
        if 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
            return np.polynomial.legendre.leggrid3d(coords[0], coords[1], coords[2], coeff_mat)
        raise NotImplementedError("only supports 2D or 3D fields")

    def randomize(self, img_size: Sequence[int]) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        n_coeff = int(np.prod([(self.degree + k) / k for k in range(1, len(img_size) + 1)]))
        self._coeff = self.R.uniform(*self.coeff_range, n_coeff).tolist()

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize(img_size=img.shape[1:])

        if not self._do_transform:
            return img

        num_channels, *spatial_shape = img.shape
        _bias_fields = np.stack(
            [
                self._generate_random_field(spatial_shape=spatial_shape, degree=self.degree, coeff=self._coeff)
                for _ in range(num_channels)
            ],
            axis=0,
        )
        img_np, *_ = convert_data_type(img, np.ndarray)
        out: NdarrayOrTensor = img_np * np.exp(_bias_fields)
        out, *_ = convert_to_dst_type(src=out, dst=img, dtype=self.dtype or img.dtype)
        return out


class NormalizeIntensity(Transform):
    """
    Normalize input based on the `subtrahend` and `divisor`: `(img - subtrahend) / divisor`.
    Use calculated mean or std value of the input image if no `subtrahend` or `divisor` 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 True, calculate on each channel separately, otherwise, calculate on
            the entire image directly. default to False.
        dtype: output data type, if None, same as input image. defaults to float32.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        subtrahend: Sequence | NdarrayOrTensor | None = None,
        divisor: Sequence | NdarrayOrTensor | 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

    @staticmethod
    def _mean(x):
        if isinstance(x, np.ndarray):
            return np.mean(x)
        x = torch.mean(x.float())
        return x.item() if x.numel() == 1 else x

    @staticmethod
    def _std(x):
        if isinstance(x, np.ndarray):
            return np.std(x)
        x = torch.std(x.float(), unbiased=False)
        return x.item() if x.numel() == 1 else x

    def _normalize(self, img: NdarrayOrTensor, sub=None, div=None) -> NdarrayOrTensor:
        img, *_ = convert_data_type(img, dtype=torch.float32)

        if self.nonzero:
            slices = img != 0
            masked_img = img[slices]
            if not slices.any():
                return img
        else:
            slices = None
            masked_img = img

        _sub = sub if sub is not None else self._mean(masked_img)
        if isinstance(_sub, (torch.Tensor, np.ndarray)):
            _sub, *_ = convert_to_dst_type(_sub, img)
            if slices is not None:
                _sub = _sub[slices]

        _div = div if div is not None else self._std(masked_img)
        if np.isscalar(_div):
            if _div == 0.0:
                _div = 1.0
        elif isinstance(_div, (torch.Tensor, np.ndarray)):
            _div, *_ = convert_to_dst_type(_div, img)
            if slices is not None:
                _div = _div[slices]
            _div[_div == 0.0] = 1.0

        if slices is not None:
            img[slices] = (masked_img - _sub) / _div
        else:
            img = (img - _sub) / _div
        return img

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`, assuming `img` is a channel-first array if `self.channel_wise` is True,
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        dtype = self.dtype or img.dtype
        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(  # type: ignore
                    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)

        out = convert_to_dst_type(img, img, dtype=dtype)[0]
        return out


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.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, threshold: float, above: bool = True, cval: float = 0.0) -> None:
        if not isinstance(threshold, (int, float)):
            raise ValueError(f"threshold must be a float or int number, got {type(threshold)} {threshold}.")
        self.threshold = threshold
        self.above = above
        self.cval = cval

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        mask = img > self.threshold if self.above else img < self.threshold
        res = where(mask, img, self.cval)
        res, *_ = convert_data_type(res, dtype=img.dtype)
        return res


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.

    When `b_min` or `b_max` are `None`, `scaled_array * (b_max - b_min) + b_min` will be skipped.
    If `clip=True`, when `b_min`/`b_max` is None, the clipping is not performed on the corresponding edge.

    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.
        dtype: output data type, if None, same as input image. defaults to float32.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        a_min: float,
        a_max: float,
        b_min: float | None = None,
        b_max: float | None = None,
        clip: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None:
        self.a_min = a_min
        self.a_max = a_max
        self.b_min = b_min
        self.b_max = b_max
        self.clip = clip
        self.dtype = dtype

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        dtype = self.dtype or img.dtype
        if self.a_max - self.a_min == 0.0:
            warn("Divide by zero (a_min == a_max)", Warning)
            if self.b_min is None:
                return img - self.a_min
            return img - self.a_min + self.b_min

        img = (img - self.a_min) / (self.a_max - self.a_min)
        if (self.b_min is not None) and (self.b_max is not None):
            img = img * (self.b_max - self.b_min) + self.b_min
        if self.clip:
            img = clip(img, self.b_min, self.b_max)
        ret: NdarrayOrTensor = convert_data_type(img, dtype=dtype)[0]

        return ret


class ClipIntensityPercentiles(Transform):
    """
    Apply clip based on the intensity distribution of input image.
    If `sharpness_factor` is provided, the intensity values will be soft clipped according to
    f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv))
    From https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291

    Soft clipping preserves the order of the values and maintains the gradient everywhere.
    For example:

    .. code-block:: python
        :emphasize-lines: 11, 22

        image = torch.Tensor(
            [[[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]]])

        # Hard clipping from lower and upper image intensity percentiles
        hard_clipper = ClipIntensityPercentiles(30, 70)
        print(hard_clipper(image))
        metatensor([[[2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.],
                [2., 2., 3., 4., 4.]]])


        # Soft clipping from lower and upper image intensity percentiles
        soft_clipper = ClipIntensityPercentiles(30, 70, 10.)
        print(soft_clipper(image))
        metatensor([[[2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000],
         [2.0000, 2.0693, 3.0000, 3.9307, 4.0000]]])

    See Also:

        - :py:class:`monai.transforms.ScaleIntensityRangePercentiles`
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        lower: float | None,
        upper: float | None,
        sharpness_factor: float | None = None,
        channel_wise: bool = False,
        return_clipping_values: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None:
        """
        Args:
            lower: lower intensity percentile. In the case of hard clipping, None will have the same effect as 0 by
                not clipping the lowest input values. However, in the case of soft clipping, None and zero will have
                two different effects: None will not apply clipping to low values, whereas zero will still transform
                the lower values according to the soft clipping transformation. Please check for more details:
                https://medium.com/life-at-hopper/clip-it-clip-it-good-1f1bf711b291.
            upper: upper intensity percentile.  The same as for lower, but this time with the highest values. If we
                are looking to perform soft clipping, if None then there will be no effect on this side whereas if set
                to 100, the values will be passed via the corresponding clipping equation.
            sharpness_factor: if not None, the intensity values will be soft clipped according to
                f(x) = x + (1/sharpness_factor)*softplus(- c(x - minv)) - (1/sharpness_factor)*softplus(c(x - maxv)).
                defaults to None.
            channel_wise: if True, compute intensity percentile and normalize every channel separately.
                default to False.
            return_clipping_values: whether to return the calculated percentiles in tensor meta information.
                If soft clipping and requested percentile is None, return None as the corresponding clipping
                values in meta information. Clipping values are stored in a list with each element corresponding
                to a channel if channel_wise is set to True. defaults to False.
            dtype: output data type, if None, same as input image. defaults to float32.
        """
        if lower is None and upper is None:
            raise ValueError("lower or upper percentiles must be provided")
        if lower is not None and (lower < 0.0 or lower > 100.0):
            raise ValueError("Percentiles must be in the range [0, 100]")
        if upper is not None and (upper < 0.0 or upper > 100.0):
            raise ValueError("Percentiles must be in the range [0, 100]")
        if upper is not None and lower is not None and upper < lower:
            raise ValueError("upper must be greater than or equal to lower")
        if sharpness_factor is not None and sharpness_factor <= 0:
            raise ValueError("sharpness_factor must be greater than 0")

        self.lower = lower
        self.upper = upper
        self.sharpness_factor = sharpness_factor
        self.channel_wise = channel_wise
        if return_clipping_values:
            self.clipping_values: list[tuple[float | None, float | None]] = []
        self.return_clipping_values = return_clipping_values
        self.dtype = dtype

    def _clip(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        if self.sharpness_factor is not None:
            lower_percentile = percentile(img, self.lower) if self.lower is not None else None
            upper_percentile = percentile(img, self.upper) if self.upper is not None else None
            img = soft_clip(img, self.sharpness_factor, lower_percentile, upper_percentile, self.dtype)
        else:
            lower_percentile = percentile(img, self.lower) if self.lower is not None else percentile(img, 0)
            upper_percentile = percentile(img, self.upper) if self.upper is not None else percentile(img, 100)
            img = clip(img, lower_percentile, upper_percentile)

        if self.return_clipping_values:
            self.clipping_values.append(
                (
                    (
                        lower_percentile
                        if lower_percentile is None
                        else lower_percentile.item() if hasattr(lower_percentile, "item") else lower_percentile
                    ),
                    (
                        upper_percentile
                        if upper_percentile is None
                        else upper_percentile.item() if hasattr(upper_percentile, "item") else upper_percentile
                    ),
                )
            )
        img = convert_to_tensor(img, track_meta=False)
        return img

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t = convert_to_tensor(img, track_meta=False)
        if self.channel_wise:
            img_t = torch.stack([self._clip(img=d) for d in img_t])  # type: ignore
        else:
            img_t = self._clip(img=img_t)

        img = convert_to_dst_type(img_t, dst=img)[0]
        if self.return_clipping_values:
            img.meta["clipping_values"] = self.clipping_values  # type: ignore

        return img


class AdjustContrast(Transform):
    """
    Changes image intensity with gamma transform. 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.
        invert_image: whether to invert the image before applying gamma augmentation. If True, multiply all intensity
            values with -1 before the gamma transform and again after the gamma transform. This behaviour is mimicked
            from `nnU-Net <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
            function.
        retain_stats: if True, applies a scaling factor and an offset to all intensity values after gamma transform to
            ensure that the output intensity distribution has the same mean and standard deviation as the intensity
            distribution of the input. This behaviour is mimicked from `nnU-Net
            <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
            function.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, gamma: float, invert_image: bool = False, retain_stats: bool = False) -> None:
        if not isinstance(gamma, (int, float)):
            raise ValueError(f"gamma must be a float or int number, got {type(gamma)} {gamma}.")
        self.gamma = gamma
        self.invert_image = invert_image
        self.retain_stats = retain_stats

    def __call__(self, img: NdarrayOrTensor, gamma=None) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        gamma: gamma value to adjust the contrast as function.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        gamma = gamma if gamma is not None else self.gamma

        if self.invert_image:
            img = -img

        if self.retain_stats:
            mn = img.mean()
            sd = img.std()

        epsilon = 1e-7
        img_min = img.min()
        img_range = img.max() - img_min
        ret: NdarrayOrTensor = ((img - img_min) / float(img_range + epsilon)) ** gamma * img_range + img_min

        if self.retain_stats:
            # zero mean and normalize
            ret = ret - ret.mean()
            ret = ret / (ret.std() + 1e-8)
            # restore old mean and standard deviation
            ret = sd * ret + mn

        if self.invert_image:
            ret = -ret

        return ret


class RandAdjustContrast(RandomizableTransform):
    """
    Randomly changes image intensity with gamma transform. 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).
        invert_image: whether to invert the image before applying gamma augmentation. If True, multiply all intensity
            values with -1 before the gamma transform and again after the gamma transform. This behaviour is mimicked
            from `nnU-Net <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
            function.
        retain_stats: if True, applies a scaling factor and an offset to all intensity values after gamma transform to
            ensure that the output intensity distribution has the same mean and standard deviation as the intensity
            distribution of the input. This behaviour is mimicked from `nnU-Net
            <https://www.nature.com/articles/s41592-020-01008-z>`_, specifically `this
            <https://github.com/MIC-DKFZ/batchgenerators/blob/7fb802b28b045b21346b197735d64f12fbb070aa/batchgenerators/augmentations/color_augmentations.py#L107>`_
            function.
    """

    backend = AdjustContrast.backend

    def __init__(
        self,
        prob: float = 0.1,
        gamma: Sequence[float] | float = (0.5, 4.5),
        invert_image: bool = False,
        retain_stats: bool = False,
    ) -> None:
        RandomizableTransform.__init__(self, prob)

        if isinstance(gamma, (int, float)):
            if gamma <= 0.5:
                raise ValueError(
                    f"if gamma is a number, must greater than 0.5 and value is picked from (0.5, gamma), got {gamma}"
                )
            self.gamma = (0.5, gamma)
        elif len(gamma) != 2:
            raise ValueError("gamma should be a number or pair of numbers.")
        else:
            self.gamma = (min(gamma), max(gamma))

        self.gamma_value: float = 1.0
        self.invert_image: bool = invert_image
        self.retain_stats: bool = retain_stats

        self.adjust_contrast = AdjustContrast(
            self.gamma_value, invert_image=self.invert_image, retain_stats=self.retain_stats
        )

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        self.gamma_value = self.R.uniform(low=self.gamma[0], high=self.gamma[1])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize()

        if not self._do_transform:
            return img

        if self.gamma_value is None:
            raise RuntimeError("gamma_value is not set, please call `randomize` function first.")

        return self.adjust_contrast(img, self.gamma_value)


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 = torch.Tensor(
            [[[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))
        metatensor([[[  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))
        metatensor([[[ 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.]]])

    See Also:

        - :py:class:`monai.transforms.ScaleIntensityRange`

    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].
        channel_wise: if True, compute intensity percentile and normalize every channel separately.
            default to False.
        dtype: output data type, if None, same as input image. defaults to float32.
    """

    backend = ScaleIntensityRange.backend

    def __init__(
        self,
        lower: float,
        upper: float,
        b_min: float | None,
        b_max: float | None,
        clip: bool = False,
        relative: bool = False,
        channel_wise: bool = False,
        dtype: DtypeLike = np.float32,
    ) -> None:
        if lower < 0.0 or lower > 100.0:
            raise ValueError("Percentiles must be in the range [0, 100]")
        if upper < 0.0 or upper > 100.0:
            raise ValueError("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
        self.channel_wise = channel_wise
        self.dtype = dtype

    def _normalize(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        a_min: float = percentile(img, self.lower)  # type: ignore
        a_max: float = percentile(img, self.upper)  # type: ignore
        b_min = self.b_min
        b_max = self.b_max

        if self.relative:
            if (self.b_min is None) or (self.b_max is None):
                raise ValueError("If it is relative, b_min and b_max should not be None.")
            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=self.clip, dtype=self.dtype
        )
        img = scalar(img)
        img = convert_to_tensor(img, track_meta=False)
        return img

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Apply the transform to `img`.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t = convert_to_tensor(img, track_meta=False)
        if self.channel_wise:
            img_t = torch.stack([self._normalize(img=d) for d in img_t])  # type: ignore
        else:
            img_t = self._normalize(img=img_t)

        return convert_to_dst_type(img_t, dst=img)[0]


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 the selected values
    in the mask data will keep the original value, others will be set to `0`.

    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. the intensity values of input image corresponding
            to the selected values in the mask data will keep the original value,
            others will be set to `0`. if None, must specify the `mask_data` at runtime.
        select_fn: function to select valid values of the `mask_data`, default is
            to select `values > 0`.

    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, mask_data: NdarrayOrTensor | None = None, select_fn: Callable = is_positive) -> None:
        self.mask_data = mask_data
        self.select_fn = select_fn

    def __call__(self, img: NdarrayOrTensor, mask_data: NdarrayOrTensor | None = None) -> NdarrayOrTensor:
        """
        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.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        mask_data = self.mask_data if mask_data is None else mask_data
        if mask_data is None:
            raise ValueError("must provide the mask_data when initializing the transform or at runtime.")

        mask_data_, *_ = convert_to_dst_type(src=mask_data, dst=img)

        mask_data_ = self.select_fn(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 channels={img.shape[0]} mask_data channels={mask_data_.shape[0]}."
            )

        return convert_to_dst_type(img * mask_data_, dst=img)[0]


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.
    """

    backend = [TransformBackends.TORCH]

    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
        self.img_t: torch.Tensor = torch.tensor(0.0)

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Args:
            img: array containing input data. Must be real and in shape [channels, spatial1, spatial2, ...].

        Returns:
            array containing smoothed result.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        self.img_t = convert_to_tensor(img, track_meta=False)

        # 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
        smoothed = savgol_filter(self.img_t.unsqueeze(0)).squeeze(0)
        out, *_ = convert_to_dst_type(smoothed, dst=img)

        return out


class DetectEnvelope(Transform):
    """
    Find the envelope of the input data along the requested axis using a Hilbert transform.

    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``.

    """

    backend = [TransformBackends.TORCH]

    def __init__(self, axis: int = 1, n: int | None = None) -> None:
        if axis < 0:
            raise ValueError("axis must be zero or positive.")

        self.axis = axis
        self.n = n

    def __call__(self, img: NdarrayOrTensor):
        """

        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.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t, *_ = convert_data_type(img, torch.Tensor)
        # 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
        out = hilbert_transform(img_t.unsqueeze(0)).squeeze(0).abs()
        out, *_ = convert_to_dst_type(src=out, dst=img)

        return out


class MedianSmooth(Transform):
    """
    Apply median filter to the input data based on specified `radius` parameter.
    A default value `radius=1` is provided for reference.

    See also: :py:func:`monai.networks.layers.median_filter`

    Args:
        radius: 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.
    """

    backend = [TransformBackends.TORCH]

    def __init__(self, radius: Sequence[int] | int = 1) -> None:
        self.radius = radius

    def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
        spatial_dims = img_t.ndim - 1
        r = ensure_tuple_rep(self.radius, spatial_dims)
        median_filter_instance = MedianFilter(r, spatial_dims=spatial_dims)
        out_t: torch.Tensor = median_filter_instance(img_t)
        out, *_ = convert_to_dst_type(out_t, dst=img, dtype=out_t.dtype)
        return out


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`.

    """

    backend = [TransformBackends.TORCH]

    def __init__(self, sigma: Sequence[float] | float = 1.0, approx: str = "erf") -> None:
        self.sigma = sigma
        self.approx = approx

    def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
        sigma: Sequence[torch.Tensor] | torch.Tensor
        if isinstance(self.sigma, Sequence):
            sigma = [torch.as_tensor(s, device=img_t.device) for s in self.sigma]
        else:
            sigma = torch.as_tensor(self.sigma, device=img_t.device)
        gaussian_filter = GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx)
        out_t: torch.Tensor = gaussian_filter(img_t.unsqueeze(0)).squeeze(0)
        out, *_ = convert_to_dst_type(out_t, dst=img, dtype=out_t.dtype)

        return out


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`.

    """

    backend = GaussianSmooth.backend

    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]

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return 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])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize()

        if not self._do_transform:
            return img

        sigma = ensure_tuple_size(vals=(self.x, self.y, self.z), dim=img.ndim - 1)
        return GaussianSmooth(sigma=sigma, approx=self.approx)(img)


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`.

    """

    backend = [TransformBackends.TORCH]

    def __init__(
        self,
        sigma1: Sequence[float] | float = 3.0,
        sigma2: 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

    def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float32)

        gf1, gf2 = (
            GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx).to(img_t.device)
            for sigma in (self.sigma1, self.sigma2)
        )
        blurred_f = gf1(img_t.unsqueeze(0))
        filter_blurred_f = gf2(blurred_f)
        out_t: torch.Tensor = (blurred_f + self.alpha * (blurred_f - filter_blurred_f)).squeeze(0)
        out, *_ = convert_to_dst_type(out_t, dst=img, dtype=out_t.dtype)
        return out


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.

    """

    backend = GaussianSharpen.backend

    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: tuple[float, float] | float = 0.5,
        sigma2_y: tuple[float, float] | float = 0.5,
        sigma2_z: 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
        self.x1: float | None = None
        self.y1: float | None = None
        self.z1: float | None = None
        self.x2: float | None = None
        self.y2: float | None = None
        self.z2: float | None = None
        self.a: float | None = None

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return 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])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize()

        if not self._do_transform:
            return img

        if self.x2 is None or self.y2 is None or self.z2 is None or self.a is None:
            raise RuntimeError("please call the `randomize()` function first.")
        sigma1 = ensure_tuple_size(vals=(self.x1, self.y1, self.z1), dim=img.ndim - 1)
        sigma2 = ensure_tuple_size(vals=(self.x2, self.y2, self.z2), dim=img.ndim - 1)
        return GaussianSharpen(sigma1=sigma1, sigma2=sigma2, alpha=self.a, approx=self.approx)(img)


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.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, num_control_points: 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 ValueError("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 ValueError("num_control points should be a number or a pair of numbers")
            if min(num_control_points) <= 2:
                raise ValueError("num_control_points should be greater than or equal to 3")
            self.num_control_points = (min(num_control_points), max(num_control_points))
        self.reference_control_points: NdarrayOrTensor
        self.floating_control_points: NdarrayOrTensor

    def interp(self, x: NdarrayOrTensor, xp: NdarrayOrTensor, fp: NdarrayOrTensor) -> NdarrayOrTensor:
        ns = torch if isinstance(x, torch.Tensor) else np
        if isinstance(x, np.ndarray):
            # approx 2x faster than code below for ndarray
            return np.interp(x, xp, fp)

        m = (fp[1:] - fp[:-1]) / (xp[1:] - xp[:-1])
        b = fp[:-1] - (m * xp[:-1])

        indices = ns.searchsorted(xp.reshape(-1), x.reshape(-1)) - 1
        indices = ns.clip(indices, 0, len(m) - 1)

        f = (m[indices] * x.reshape(-1) + b[indices]).reshape(x.shape)
        f[x < xp[0]] = fp[0]
        f[x > xp[-1]] = fp[-1]
        return f

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return 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]
            )

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize()

        if not self._do_transform:
            return img

        if self.reference_control_points is None or self.floating_control_points is None:
            raise RuntimeError("please call the `randomize()` function first.")
        img_t = convert_to_tensor(img, track_meta=False)
        img_min, img_max = img_t.min(), img_t.max()
        if img_min == img_max:
            warn(
                f"The image's intensity is a single value {img_min}. "
                "The original image is simply returned, no histogram shift is done."
            )
            return img
        xp, *_ = convert_to_dst_type(self.reference_control_points, dst=img_t)
        yp, *_ = convert_to_dst_type(self.floating_control_points, dst=img_t)
        reference_control_points_scaled = xp * (img_max - img_min) + img_min
        floating_control_points_scaled = yp * (img_max - img_min) + img_min
        img_t = self.interp(img_t, reference_control_points_scaled, floating_control_points_scaled)
        return convert_to_dst_type(img_t, dst=img)[0]


class GibbsNoise(Transform, Fourier):
    """
    The transform applies Gibbs noise to 2D/3D MRI images. Gibbs artifacts
    are one of the common type of type artifacts appearing in MRI scans.

    The transform is applied to all the channels in the data.

    For general information on Gibbs artifacts, please refer to:

    `An Image-based Approach to Understanding the Physics of MR Artifacts
    <https://pubs.rsna.org/doi/full/10.1148/rg.313105115>`_.

    `The AAPM/RSNA Physics Tutorial for Residents
    <https://pubs.rsna.org/doi/full/10.1148/radiographics.22.4.g02jl14949>`_

    Args:
        alpha: Parametrizes the intensity of the Gibbs noise filter applied. Takes
            values in the interval [0,1] with alpha = 0 acting as the identity mapping.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, alpha: float = 0.1) -> None:
        if alpha > 1 or alpha < 0:
            raise ValueError("alpha must take values in the interval [0, 1].")
        self.alpha = alpha

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_t = convert_to_tensor(img, track_meta=False)
        n_dims = len(img_t.shape[1:])

        # FT
        k = self.shift_fourier(img_t, n_dims)
        # build and apply mask
        k = self._apply_mask(k)
        # map back
        out = self.inv_shift_fourier(k, n_dims)
        img, *_ = convert_to_dst_type(out, dst=img, dtype=out.dtype)

        return img

    def _apply_mask(self, k: NdarrayOrTensor) -> NdarrayOrTensor:
        """Builds and applies a mask on the spatial dimensions.

        Args:
            k: k-space version of the image.
        Returns:
            masked version of the k-space image.
        """
        shape = k.shape[1:]

        # compute masking radius and center
        r = (1 - self.alpha) * np.max(shape) * np.sqrt(2) / 2.0
        center = (np.array(shape) - 1) / 2

        # gives list w/ len==self.dim. Each dim gives coordinate in that dimension
        coords = np.ogrid[tuple(slice(0, i) for i in shape)]

        # need to subtract center coord and then square for Euc distance
        coords_from_center_sq = [(coord - c) ** 2 for coord, c in zip(coords, center)]
        dist_from_center = np.sqrt(sum(coords_from_center_sq))
        mask = dist_from_center <= r

        # add channel dimension into mask
        mask = np.repeat(mask[None], k.shape[0], axis=0)

        if isinstance(k, torch.Tensor):
            mask, *_ = convert_data_type(mask, torch.Tensor, device=k.device)

        # apply binary mask
        k_masked: NdarrayOrTensor
        k_masked = k * mask
        return k_masked


class RandGibbsNoise(RandomizableTransform):
    """
    Naturalistic image augmentation via Gibbs artifacts. The transform
    randomly applies Gibbs noise to 2D/3D MRI images. Gibbs artifacts
    are one of the common type of type artifacts appearing in MRI scans.

    The transform is applied to all the channels in the data.

    For general information on Gibbs artifacts, please refer to:
    https://pubs.rsna.org/doi/full/10.1148/rg.313105115
    https://pubs.rsna.org/doi/full/10.1148/radiographics.22.4.g02jl14949


    Args:
        prob (float): probability of applying the transform.
        alpha (float, Sequence(float)): Parametrizes the intensity of the Gibbs noise filter applied. Takes
            values in the interval [0,1] with alpha = 0 acting as the identity mapping.
            If a length-2 list is given as [a,b] then the value of alpha will be
            sampled uniformly from the interval [a,b]. 0 <= a <= b <= 1.
            If a float is given, then the value of alpha will be sampled uniformly from the interval [0, alpha].
    """

    backend = GibbsNoise.backend

    def __init__(self, prob: float = 0.1, alpha: float | Sequence[float] = (0.0, 1.0)) -> None:
        if isinstance(alpha, float):
            alpha = (0, alpha)
        alpha = ensure_tuple(alpha)
        if len(alpha) != 2:
            raise ValueError("alpha length must be 2.")
        if alpha[1] > 1 or alpha[0] < 0:
            raise ValueError("alpha must take values in the interval [0, 1]")
        if alpha[0] > alpha[1]:
            raise ValueError("When alpha = [a,b] we need a < b.")

        self.alpha = alpha
        self.sampled_alpha = -1.0  # stores last alpha sampled by randomize()

        RandomizableTransform.__init__(self, prob=prob)

    def randomize(self, data: Any) -> None:
        """
        (1) Set random variable to apply the transform.
        (2) Get alpha from uniform distribution.
        """
        super().randomize(None)
        if not self._do_transform:
            return None
        self.sampled_alpha = self.R.uniform(self.alpha[0], self.alpha[1])

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True):
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            # randomize application and possibly alpha
            self.randomize(None)

        if not self._do_transform:
            return img

        return GibbsNoise(self.sampled_alpha)(img)


class KSpaceSpikeNoise(Transform, Fourier):
    """
    Apply localized spikes in `k`-space at the given locations and intensities.
    Spike (Herringbone) artifact is a type of data acquisition artifact which
    may occur during MRI scans.

    For general information on spike artifacts, please refer to:

    `AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging
    <https://pubmed.ncbi.nlm.nih.gov/16009826>`_.

    `Body MRI artifacts in clinical practice: A physicist's and radiologist's
    perspective <https://doi.org/10.1002/jmri.24288>`_.

    Args:
        loc: spatial location for the spikes. For
            images with 3D spatial dimensions, the user can provide (C, X, Y, Z)
            to fix which channel C is affected, or (X, Y, Z) to place the same
            spike in all channels. For 2D cases, the user can provide (C, X, Y)
            or (X, Y).
        k_intensity: value for the log-intensity of the
            `k`-space version of the image. If one location is passed to ``loc`` or the
            channel is not specified, then this argument should receive a float. If
            ``loc`` is given a sequence of locations, then this argument should
            receive a sequence of intensities. This value should be tested as it is
            data-dependent. The default values are the 2.5 the mean of the
            log-intensity for each channel.

    Example:
        When working with 4D data, ``KSpaceSpikeNoise(loc = ((3,60,64,32), (64,60,32)), k_intensity = (13,14))``
        will place a spike at `[3, 60, 64, 32]` with `log-intensity = 13`, and
        one spike per channel located respectively at `[: , 64, 60, 32]`
        with `log-intensity = 14`.
    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(self, loc: tuple | Sequence[tuple], k_intensity: Sequence[float] | float | None = None):
        self.loc = ensure_tuple(loc)
        self.k_intensity = k_intensity

        # assert one-to-one relationship between factors and locations
        if isinstance(k_intensity, Sequence):
            if not isinstance(loc[0], Sequence):
                raise ValueError(
                    "If a sequence is passed to k_intensity, then a sequence of locations must be passed to loc"
                )
            if len(k_intensity) != len(loc):
                raise ValueError("There must be one intensity_factor value for each tuple of indices in loc.")
        if isinstance(self.loc[0], Sequence) and k_intensity is not None and not isinstance(self.k_intensity, Sequence):
            raise ValueError("There must be one intensity_factor value for each tuple of indices in loc.")

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Args:
            img: image with dimensions (C, H, W) or (C, H, W, D)
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        # checking that tuples in loc are consistent with img size
        self._check_indices(img)

        if len(img.shape) < 3:
            raise RuntimeError("Image needs a channel direction.")
        if isinstance(self.loc[0], int) and len(img.shape) == 4 and len(self.loc) == 2:
            raise RuntimeError("Input images of dimension 4 need location tuple to be length 3 or 4")
        if isinstance(self.loc[0], Sequence) and len(img.shape) == 4 and min(map(len, self.loc)) == 2:
            raise RuntimeError("Input images of dimension 4 need location tuple to be length 3 or 4")

        n_dims = len(img.shape[1:])

        # FT
        k = self.shift_fourier(img, n_dims)
        lib = np if isinstance(k, np.ndarray) else torch
        log_abs = lib.log(lib.abs(k) + 1e-10)
        phase = lib.angle(k)

        k_intensity = self.k_intensity
        # default log intensity
        if k_intensity is None:
            k_intensity = tuple(lib.mean(log_abs, axis=tuple(range(-n_dims, 0))) * 2.5)

        # highlight
        if isinstance(self.loc[0], Sequence):
            for idx, val in zip(self.loc, ensure_tuple(k_intensity)):
                self._set_spike(log_abs, idx, val)
        else:
            self._set_spike(log_abs, self.loc, k_intensity)
        # map back
        k = lib.exp(log_abs) * lib.exp(1j * phase)
        img, *_ = convert_to_dst_type(self.inv_shift_fourier(k, n_dims), dst=img)

        return img

    def _check_indices(self, img) -> None:
        """Helper method to check consistency of self.loc and input image.

        Raises assertion error if any index in loc is out of bounds."""

        loc = list(self.loc)
        if not isinstance(loc[0], Sequence):
            loc = [loc]
        for i in range(len(loc)):
            if len(loc[i]) < len(img.shape):
                loc[i] = [0] + list(loc[i])

        for i in range(len(img.shape)):
            if img.shape[i] <= max(x[i] for x in loc):
                raise ValueError(
                    f"The index value at position {i} of one of the tuples in loc = {self.loc} is out of bounds for current image."
                )

    def _set_spike(self, k: NdarrayOrTensor, idx: tuple, val: Sequence[float] | float):
        """
        Helper function to introduce a given intensity at given location.

        Args:
            k: intensity array to alter.
            idx: index of location where to apply change.
            val: value of intensity to write in.
        """
        if len(k.shape) == len(idx):
            k[idx] = val[idx[0]] if isinstance(val, Sequence) else val
        elif len(k.shape) == 4 and len(idx) == 3:
            k[:, idx[0], idx[1], idx[2]] = val  # type: ignore
        elif len(k.shape) == 3 and len(idx) == 2:
            k[:, idx[0], idx[1]] = val  # type: ignore


class RandKSpaceSpikeNoise(RandomizableTransform, Fourier):
    """
    Naturalistic data augmentation via spike artifacts. The transform applies
    localized spikes in `k`-space, and it is the random version of
    :py:class:`monai.transforms.KSpaceSpikeNoise`.

    Spike (Herringbone) artifact is a type of data acquisition artifact which
    may occur during MRI scans. For general information on spike artifacts,
    please refer to:

    `AAPM/RSNA physics tutorial for residents: fundamental physics of MR imaging
    <https://pubmed.ncbi.nlm.nih.gov/16009826>`_.

    `Body MRI artifacts in clinical practice: A physicist's and radiologist's
    perspective <https://doi.org/10.1002/jmri.24288>`_.

    Args:
        prob: probability of applying the transform, either on all
            channels at once, or channel-wise if ``channel_wise = True``.
        intensity_range: pass a tuple (a, b) to sample the log-intensity from the interval (a, b)
            uniformly for all channels. Or pass sequence of intervals
            ((a0, b0), (a1, b1), ...) to sample for each respective channel.
            In the second case, the number of 2-tuples must match the number of channels.
            Default ranges is `(0.95x, 1.10x)` where `x` is the mean
            log-intensity for each channel.
        channel_wise: treat each channel independently. True by
            default.

    Example:
        To apply `k`-space spikes randomly with probability 0.5, and
        log-intensity sampled from the interval [11, 12] for each channel
        independently, one uses
        ``RandKSpaceSpikeNoise(prob=0.5, intensity_range=(11, 12), channel_wise=True)``
    """

    backend = KSpaceSpikeNoise.backend

    def __init__(
        self,
        prob: float = 0.1,
        intensity_range: Sequence[Sequence[float] | float] | None = None,
        channel_wise: bool = True,
    ):
        self.intensity_range = intensity_range
        self.channel_wise = channel_wise
        self.sampled_k_intensity: list = []
        self.sampled_locs: list[tuple] = []

        if intensity_range is not None and isinstance(intensity_range[0], Sequence) and not channel_wise:
            raise ValueError("When channel_wise = False, intensity_range should be a 2-tuple (low, high) or None.")

        super().__init__(prob)

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True):
        """
        Apply transform to `img`. Assumes data is in channel-first form.

        Args:
            img: image with dimensions (C, H, W) or (C, H, W, D)
        """

        if (
            self.intensity_range is not None
            and isinstance(self.intensity_range[0], Sequence)
            and len(self.intensity_range) != img.shape[0]
        ):
            raise RuntimeError(
                "If intensity_range is a sequence of sequences, then there must be one (low, high) tuple for each channel."
            )
        img = convert_to_tensor(img, track_meta=get_track_meta())
        self.sampled_k_intensity = []
        self.sampled_locs = []

        if randomize:
            intensity_range = self._make_sequence(img)
            self.randomize(img, intensity_range)

        if not self._do_transform:
            return img

        return KSpaceSpikeNoise(self.sampled_locs, self.sampled_k_intensity)(img)

    def randomize(self, img: NdarrayOrTensor, intensity_range: Sequence[Sequence[float]]) -> None:  # type: ignore
        """
        Helper method to sample both the location and intensity of the spikes.
        When not working channel wise (channel_wise=False) it use the random
        variable ``self._do_transform`` to decide whether to sample a location
        and intensity.

        When working channel wise, the method randomly samples a location and
        intensity for each channel depending on ``self._do_transform``.
        """
        super().randomize(None)
        if not self._do_transform:
            return None
        if self.channel_wise:
            # randomizing per channel
            for i, chan in enumerate(img):
                self.sampled_locs.append((i,) + tuple(self.R.randint(0, k) for k in chan.shape))
                self.sampled_k_intensity.append(self.R.uniform(intensity_range[i][0], intensity_range[i][1]))
        else:
            # working with all channels together
            spatial = tuple(self.R.randint(0, k) for k in img.shape[1:])
            self.sampled_locs = [(i,) + spatial for i in range(img.shape[0])]
            if isinstance(intensity_range[0], Sequence):
                self.sampled_k_intensity = [self.R.uniform(p[0], p[1]) for p in intensity_range]
            else:
                self.sampled_k_intensity = [self.R.uniform(intensity_range[0], intensity_range[1])] * len(img)

    def _make_sequence(self, x: NdarrayOrTensor) -> Sequence[Sequence[float]]:
        """
        Formats the sequence of intensities ranges to Sequence[Sequence[float]].
        """
        if self.intensity_range is None:
            # set default range if one not provided
            return self._set_default_range(x)

        if not isinstance(self.intensity_range[0], Sequence):
            return (ensure_tuple(self.intensity_range),) * x.shape[0]
        return ensure_tuple(self.intensity_range)

    def _set_default_range(self, img: NdarrayOrTensor) -> Sequence[Sequence[float]]:
        """
        Sets default intensity ranges to be sampled.

        Args:
            img: image to transform.
        """
        n_dims = len(img.shape[1:])

        k = self.shift_fourier(img, n_dims)
        mod = torch if isinstance(k, torch.Tensor) else np
        log_abs = mod.log(mod.absolute(k) + 1e-10)
        shifted_means = mod.mean(log_abs, tuple(range(-n_dims, 0))) * 2.5
        if isinstance(shifted_means, torch.Tensor):
            shifted_means = shifted_means.to("cpu")
        return tuple((i * 0.95, i * 1.1) for i in shifted_means)


class RandCoarseTransform(RandomizableTransform):
    """
    Randomly select coarse regions in the image, then execute transform operations for the regions.
    It's the base class of all kinds of region transforms.
    Refer to papers: https://arxiv.org/abs/1708.04552

    Args:
        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to
            randomly select the expected number of regions.
        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg
            as the minimum spatial size to randomly select size for every region.
            if some components of the `spatial_size` are non-positive values, the transform will use the
            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        max_holes: if not None, define the maximum number to randomly select the expected number of regions.
        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.
            if some components of the `max_spatial_size` are non-positive values, the transform will use the
            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        prob: probability of applying the transform.

    """

    backend = [TransformBackends.NUMPY]

    def __init__(
        self,
        holes: int,
        spatial_size: Sequence[int] | int,
        max_holes: int | None = None,
        max_spatial_size: Sequence[int] | int | None = None,
        prob: float = 0.1,
    ) -> None:
        RandomizableTransform.__init__(self, prob)
        if holes < 1:
            raise ValueError("number of holes must be greater than 0.")
        self.holes = holes
        self.spatial_size = spatial_size
        self.max_holes = max_holes
        self.max_spatial_size = max_spatial_size
        self.hole_coords: list = []

    def randomize(self, img_size: Sequence[int]) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        size = fall_back_tuple(self.spatial_size, img_size)
        self.hole_coords = []  # clear previously computed coords
        num_holes = self.holes if self.max_holes is None else self.R.randint(self.holes, self.max_holes + 1)
        for _ in range(num_holes):
            if self.max_spatial_size is not None:
                max_size = fall_back_tuple(self.max_spatial_size, img_size)
                size = tuple(self.R.randint(low=size[i], high=max_size[i] + 1) for i in range(len(img_size)))
            valid_size = get_valid_patch_size(img_size, size)
            self.hole_coords.append((slice(None),) + get_random_patch(img_size, valid_size, self.R))

    @abstractmethod
    def _transform_holes(self, img: np.ndarray) -> np.ndarray:
        """
        Transform the randomly selected `self.hole_coords` in input images.

        """
        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")

    def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if randomize:
            self.randomize(img.shape[1:])

        if not self._do_transform:
            return img

        img_np, *_ = convert_data_type(img, np.ndarray)
        out = self._transform_holes(img=img_np)
        ret, *_ = convert_to_dst_type(src=out, dst=img)
        return ret


class RandCoarseDropout(RandCoarseTransform):
    """
    Randomly coarse dropout regions in the image, then fill in the rectangular regions with specified value.
    Or keep the rectangular regions and fill in the other areas with specified value.
    Refer to papers: https://arxiv.org/abs/1708.04552, https://arxiv.org/pdf/1604.07379
    And other implementation: https://albumentations.ai/docs/api_reference/augmentations/transforms/
    #albumentations.augmentations.transforms.CoarseDropout.

    Args:
        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to
            randomly select the expected number of regions.
        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg
            as the minimum spatial size to randomly select size for every region.
            if some components of the `spatial_size` are non-positive values, the transform will use the
            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        dropout_holes: if `True`, dropout the regions of holes and fill value, if `False`, keep the holes and
            dropout the outside and fill value. default to `True`.
        fill_value: target value to fill the dropout regions, if providing a number, will use it as constant
            value to fill all the regions. if providing a tuple for the `min` and `max`, will randomly select
            value for every pixel / voxel from the range `[min, max)`. if None, will compute the `min` and `max`
            value of input image then randomly select value to fill, default to None.
        max_holes: if not None, define the maximum number to randomly select the expected number of regions.
        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.
            if some components of the `max_spatial_size` are non-positive values, the transform will use the
            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        prob: probability of applying the transform.

    """

    def __init__(
        self,
        holes: int,
        spatial_size: Sequence[int] | int,
        dropout_holes: bool = True,
        fill_value: tuple[float, float] | float | None = None,
        max_holes: int | None = None,
        max_spatial_size: Sequence[int] | int | None = None,
        prob: float = 0.1,
    ) -> None:
        super().__init__(
            holes=holes, spatial_size=spatial_size, max_holes=max_holes, max_spatial_size=max_spatial_size, prob=prob
        )
        self.dropout_holes = dropout_holes
        if isinstance(fill_value, (tuple, list)):
            if len(fill_value) != 2:
                raise ValueError("fill value should contain 2 numbers if providing the `min` and `max`.")
        self.fill_value = fill_value

    def _transform_holes(self, img: np.ndarray):
        """
        Fill the randomly selected `self.hole_coords` in input images.
        Please note that we usually only use `self.R` in `randomize()` method, here is a special case.

        """
        fill_value = (img.min(), img.max()) if self.fill_value is None else self.fill_value

        if self.dropout_holes:
            for h in self.hole_coords:
                if isinstance(fill_value, (tuple, list)):
                    img[h] = self.R.uniform(fill_value[0], fill_value[1], size=img[h].shape)
                else:
                    img[h] = fill_value
            ret = img
        else:
            if isinstance(fill_value, (tuple, list)):
                ret = self.R.uniform(fill_value[0], fill_value[1], size=img.shape).astype(img.dtype, copy=False)
            else:
                ret = np.full_like(img, fill_value)
            for h in self.hole_coords:
                ret[h] = img[h]
        return ret


class RandCoarseShuffle(RandCoarseTransform):
    """
    Randomly select regions in the image, then shuffle the pixels within every region.
    It shuffles every channel separately.
    Refer to paper:
    Kang, Guoliang, et al. "Patchshuffle regularization." arXiv preprint arXiv:1707.07103 (2017).
    https://arxiv.org/abs/1707.07103

    Args:
        holes: number of regions to dropout, if `max_holes` is not None, use this arg as the minimum number to
            randomly select the expected number of regions.
        spatial_size: spatial size of the regions to dropout, if `max_spatial_size` is not None, use this arg
            as the minimum spatial size to randomly select size for every region.
            if some components of the `spatial_size` are non-positive values, the transform will use the
            corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        max_holes: if not None, define the maximum number to randomly select the expected number of regions.
        max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.
            if some components of the `max_spatial_size` are non-positive values, the transform will use the
            corresponding components of input img size. For example, `max_spatial_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        prob: probability of applying the transform.

    """

    def _transform_holes(self, img: np.ndarray):
        """
        Shuffle the content of randomly selected `self.hole_coords` in input images.
        Please note that we usually only use `self.R` in `randomize()` method, here is a special case.

        """
        for h in self.hole_coords:
            # shuffle every channel separately
            for i, c in enumerate(img[h]):
                patch_channel = c.flatten()
                self.R.shuffle(patch_channel)
                img[h][i] = patch_channel.reshape(c.shape)
        return img


class HistogramNormalize(Transform):
    """
    Apply the histogram normalization to input image.
    Refer to: https://github.com/facebookresearch/CovidPrognosis/blob/master/covidprognosis/data/transforms.py#L83.

    Args:
        num_bins: number of the bins to use in histogram, default to `256`. for more details:
            https://numpy.org/doc/stable/reference/generated/numpy.histogram.html.
        min: the min value to normalize input image, default to `0`.
        max: the max value to normalize input image, default to `255`.
        mask: if provided, must be ndarray of bools or 0s and 1s, and same shape as `image`.
            only points at which `mask==True` are used for the equalization.
            can also provide the mask along with img at runtime.
        dtype: data type of the output, if None, same as input image. default to `float32`.

    """

    backend = [TransformBackends.NUMPY]

    def __init__(
        self,
        num_bins: int = 256,
        min: int = 0,
        max: int = 255,
        mask: NdarrayOrTensor | None = None,
        dtype: DtypeLike = np.float32,
    ) -> None:
        self.num_bins = num_bins
        self.min = min
        self.max = max
        self.mask = mask
        self.dtype = dtype

    def __call__(self, img: NdarrayOrTensor, mask: NdarrayOrTensor | None = None) -> NdarrayOrTensor:
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_np, *_ = convert_data_type(img, np.ndarray)
        mask = mask if mask is not None else self.mask
        mask_np: np.ndarray | None = None
        if mask is not None:
            mask_np, *_ = convert_data_type(mask, np.ndarray)

        ret = equalize_hist(img=img_np, mask=mask_np, num_bins=self.num_bins, min=self.min, max=self.max)
        out, *_ = convert_to_dst_type(src=ret, dst=img, dtype=self.dtype or img.dtype)

        return out


class IntensityRemap(RandomizableTransform):
    """
    Transform for intensity remapping of images. The intensity at each
    pixel is replaced by a new values coming from an intensity remappping
    curve.

    The remapping curve is created by uniformly sampling values from the
    possible intensities for the input image and then adding a linear
    component. The curve is the rescaled to the input image intensity range.

    Intended to be used as a means to data augmentation via:
    :py:class:`monai.transforms.RandIntensityRemap`.

    Implementation is described in the work:
    `Intensity augmentation for domain transfer of whole breast segmentation
    in MRI <https://ieeexplore.ieee.org/abstract/document/9166708>`_.

    Args:
        kernel_size: window size for averaging operation for the remapping
            curve.
        slope: slope of the linear component. Easiest to leave default value
            and tune the kernel_size parameter instead.
    """

    def __init__(self, kernel_size: int = 30, slope: float = 0.7):
        super().__init__()

        self.kernel_size = kernel_size
        self.slope = slope

    def __call__(self, img: torch.Tensor) -> torch.Tensor:
        """
        Args:
            img: image to remap.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_ = convert_to_tensor(img, track_meta=False)
        # sample noise
        vals_to_sample = torch.unique(img_).tolist()
        noise = torch.from_numpy(self.R.choice(vals_to_sample, len(vals_to_sample) - 1 + self.kernel_size))
        # smooth
        noise = torch.nn.AvgPool1d(self.kernel_size, stride=1)(noise.unsqueeze(0)).squeeze()
        # add linear component
        grid = torch.arange(len(noise)) / len(noise)
        noise += self.slope * grid
        # rescale
        noise = (noise - noise.min()) / (noise.max() - noise.min()) * img_.max() + img_.min()

        # intensity remapping function
        index_img = torch.bucketize(img_, torch.tensor(vals_to_sample))
        img, *_ = convert_to_dst_type(noise[index_img], dst=img)

        return img


class RandIntensityRemap(RandomizableTransform):
    """
    Transform for intensity remapping of images. The intensity at each
    pixel is replaced by a new values coming from an intensity remappping
    curve.

    The remapping curve is created by uniformly sampling values from the
    possible intensities for the input image and then adding a linear
    component. The curve is the rescaled to the input image intensity range.

    Implementation is described in the work:
    `Intensity augmentation for domain transfer of whole breast segmentation
    in MRI <https://ieeexplore.ieee.org/abstract/document/9166708>`_.

    Args:
        prob: probability of applying the transform.
        kernel_size: window size for averaging operation for the remapping
            curve.
        slope: slope of the linear component. Easiest to leave default value
            and tune the kernel_size parameter instead.
        channel_wise: set to True to treat each channel independently.
    """

    def __init__(self, prob: float = 0.1, kernel_size: int = 30, slope: float = 0.7, channel_wise: bool = True):
        RandomizableTransform.__init__(self, prob=prob)
        self.kernel_size = kernel_size
        self.slope = slope
        self.channel_wise = channel_wise

    def __call__(self, img: torch.Tensor) -> torch.Tensor:
        """
        Args:
            img: image to remap.
        """
        super().randomize(None)
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if self._do_transform:
            if self.channel_wise:
                img = torch.stack(
                    [
                        IntensityRemap(self.kernel_size, self.R.choice([-self.slope, self.slope]))(img[i])
                        for i in range(len(img))
                    ]
                )
            else:
                img = IntensityRemap(self.kernel_size, self.R.choice([-self.slope, self.slope]))(img)

        return img


class ForegroundMask(Transform):
    """
    Creates a binary mask that defines the foreground based on thresholds in RGB or HSV color space.
    This transform receives an RGB (or grayscale) image where by default it is assumed that the foreground has
    low values (dark) while the background has high values (white). Otherwise, set `invert` argument to `True`.

    Args:
        threshold: an int or a float number that defines the threshold that values less than that are foreground.
            It also can be a callable that receives each dimension of the image and calculate the threshold,
            or a string that defines such callable from `skimage.filter.threshold_...`. For the list of available
            threshold functions, please refer to https://scikit-image.org/docs/stable/api/skimage.filters.html
            Moreover, a dictionary can be passed that defines such thresholds for each channel, like
            {"R": 100, "G": "otsu", "B": skimage.filter.threshold_mean}
        hsv_threshold: similar to threshold but HSV color space ("H", "S", and "V").
            Unlike RBG, in HSV, value greater than `hsv_threshold` are considered foreground.
        invert: invert the intensity range of the input image, so that the dtype maximum is now the dtype minimum,
            and vice-versa.

    """

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        threshold: dict | Callable | str | float | int = "otsu",
        hsv_threshold: dict | Callable | str | float | int | None = None,
        invert: bool = False,
    ) -> None:
        self.thresholds: dict[str, Callable | float] = {}
        if threshold is not None:
            if isinstance(threshold, dict):
                for mode, th in threshold.items():
                    self._set_threshold(th, mode.upper())
            else:
                self._set_threshold(threshold, "R")
                self._set_threshold(threshold, "G")
                self._set_threshold(threshold, "B")
        if hsv_threshold is not None:
            if isinstance(hsv_threshold, dict):
                for mode, th in hsv_threshold.items():
                    self._set_threshold(th, mode.upper())
            else:
                self._set_threshold(hsv_threshold, "H")
                self._set_threshold(hsv_threshold, "S")
                self._set_threshold(hsv_threshold, "V")

        self.thresholds = {k: v for k, v in self.thresholds.items() if v is not None}
        if self.thresholds.keys().isdisjoint(set("RGBHSV")):
            raise ValueError(
                f"Threshold for at least one channel of RGB or HSV needs to be set. {self.thresholds} is provided."
            )
        self.invert = invert

    def _set_threshold(self, threshold, mode):
        if callable(threshold):
            self.thresholds[mode] = threshold
        elif isinstance(threshold, str):
            self.thresholds[mode] = getattr(skimage.filters, "threshold_" + threshold.lower())
        elif isinstance(threshold, (float, int)):
            self.thresholds[mode] = float(threshold)
        else:
            raise ValueError(
                f"`threshold` should be either a callable, string, or float number, {type(threshold)} was given."
            )

    def _get_threshold(self, image, mode):
        threshold = self.thresholds.get(mode)
        if callable(threshold):
            return threshold(image)
        return threshold

    def __call__(self, image: NdarrayOrTensor):
        image = convert_to_tensor(image, track_meta=get_track_meta())
        img_rgb, *_ = convert_data_type(image, np.ndarray)
        if self.invert:
            img_rgb = skimage.util.invert(img_rgb)
        foregrounds = []
        if not self.thresholds.keys().isdisjoint(set("RGB")):
            rgb_foreground = np.zeros_like(img_rgb[:1])
            for img, mode in zip(img_rgb, "RGB"):
                threshold = self._get_threshold(img, mode)
                if threshold:
                    rgb_foreground = np.logical_or(rgb_foreground, img <= threshold)
            foregrounds.append(rgb_foreground)
        if not self.thresholds.keys().isdisjoint(set("HSV")):
            img_hsv = skimage.color.rgb2hsv(img_rgb, channel_axis=0)
            hsv_foreground = np.zeros_like(img_rgb[:1])
            for img, mode in zip(img_hsv, "HSV"):
                threshold = self._get_threshold(img, mode)
                if threshold:
                    hsv_foreground = np.logical_or(hsv_foreground, img > threshold)
            foregrounds.append(hsv_foreground)

        mask = np.stack(foregrounds).all(axis=0)
        return convert_to_dst_type(src=mask, dst=image)[0]


class ComputeHoVerMaps(Transform):
    """Compute horizontal and vertical maps from an instance mask
    It generates normalized horizontal and vertical distances to the center of mass of each region.
    Input data with the size of [1xHxW[xD]], which channel dim will temporarily removed for calculating coordinates.

    Args:
        dtype: the data type of output Tensor. Defaults to `"float32"`.

    Return:
        A torch.Tensor with the size of [2xHxW[xD]], which is stack horizontal and vertical maps

    """

    def __init__(self, dtype: DtypeLike = "float32") -> None:
        super().__init__()
        self.dtype = dtype

    def __call__(self, mask: NdarrayOrTensor):
        instance_mask = convert_data_type(mask, np.ndarray)[0]

        h_map = instance_mask.astype(self.dtype, copy=True)
        v_map = instance_mask.astype(self.dtype, copy=True)
        instance_mask = instance_mask.squeeze(0)  # remove channel dim

        for region in skimage.measure.regionprops(instance_mask):
            v_dist = region.coords[:, 0] - region.centroid[0]
            h_dist = region.coords[:, 1] - region.centroid[1]

            h_dist[h_dist < 0] /= -np.amin(h_dist)
            h_dist[h_dist > 0] /= np.amax(h_dist)

            v_dist[v_dist < 0] /= -np.amin(v_dist)
            v_dist[v_dist > 0] /= np.amax(v_dist)

            h_map[h_map == region.label] = h_dist
            v_map[v_map == region.label] = v_dist

        hv_maps = convert_to_tensor(np.concatenate([h_map, v_map]), track_meta=get_track_meta())
        return hv_maps


class UltrasoundConfidenceMapTransform(Transform):
    """Compute confidence map from an ultrasound image.
    This transform uses the method introduced by Karamalis et al. in https://doi.org/10.1016/j.media.2012.07.005.
    It generates a confidence map by setting source and sink points in the image and computing the probability
    for random walks to reach the source for each pixel.

    Args:
        alpha (float, optional): Alpha parameter. Defaults to 2.0.
        beta (float, optional): Beta parameter. Defaults to 90.0.
        gamma (float, optional): Gamma parameter. Defaults to 0.05.
        mode (str, optional): 'RF' or 'B' mode data. Defaults to 'B'.
        sink_mode (str, optional): Sink mode. Defaults to 'all'. If 'mask' is selected, a mask must be when
            calling the transform. Can be one of 'all', 'mid', 'min', 'mask'.
    """

    def __init__(self, alpha: float = 2.0, beta: float = 90.0, gamma: float = 0.05, mode="B", sink_mode="all") -> None:
        self.alpha = alpha
        self.beta = beta
        self.gamma = gamma
        self.mode = mode
        self.sink_mode = sink_mode

        if self.mode not in ["B", "RF"]:
            raise ValueError(f"Unknown mode: {self.mode}. Supported modes are 'B' and 'RF'.")

        if self.sink_mode not in ["all", "mid", "min", "mask"]:
            raise ValueError(
                f"Unknown sink mode: {self.sink_mode}. Supported modes are 'all', 'mid', 'min' and 'mask'."
            )

        self._compute_conf_map = UltrasoundConfidenceMap(self.alpha, self.beta, self.gamma, self.mode, self.sink_mode)

    def __call__(self, img: NdarrayOrTensor, mask: NdarrayOrTensor | None = None) -> NdarrayOrTensor:
        """Compute confidence map from an ultrasound image.

        Args:
            img (ndarray or Tensor): Ultrasound image of shape [1, H, W] or [1, D, H, W]. If the image has channels,
                they will be averaged before computing the confidence map.
            mask (ndarray or Tensor, optional): Mask of shape [1, H, W]. Defaults to None. Must be
                provided when sink mode is 'mask'. The non-zero values of the mask are used as sink points.

        Returns:
            ndarray or Tensor: Confidence map of shape [1, H, W].
        """

        if self.sink_mode == "mask" and mask is None:
            raise ValueError("A mask must be provided when sink mode is 'mask'.")

        if img.shape[0] != 1:
            raise ValueError("The correct shape of the image is [1, H, W] or [1, D, H, W].")

        _img = convert_to_tensor(img, track_meta=get_track_meta())
        img_np, *_ = convert_data_type(_img, np.ndarray)
        img_np = img_np[0]  # Remove the first dimension

        mask_np = None
        if mask is not None:
            mask = convert_to_tensor(mask, dtype=torch.bool, track_meta=get_track_meta())
            mask_np, *_ = convert_data_type(mask, np.ndarray)
            mask_np = mask_np[0]  # Remove the first dimension

        # If the image is RGB, convert it to grayscale
        if len(img_np.shape) == 3:
            img_np = np.mean(img_np, axis=0)

        if mask_np is not None and mask_np.shape != img_np.shape:
            raise ValueError("The mask must have the same shape as the image.")

        # Compute confidence map
        conf_map: NdarrayOrTensor = self._compute_conf_map(img_np, mask_np)

        if type(img) is torch.Tensor:
            conf_map = torch.from_numpy(conf_map)

        return conf_map