from __future__ import annotations

import math
import numbers
import random
import warnings
from types import LambdaType
from typing import Any, Callable, Dict, List, Sequence, Tuple, Union, cast
from warnings import warn

import albucore
import cv2
import numpy as np
from albucore.functions import add_weighted, from_float, multiply, normalize, normalize_per_image, to_float
from albucore.utils import (
    MAX_VALUES_BY_DTYPE,
    NUM_MULTI_CHANNEL_DIMENSIONS,
    clip,
    get_num_channels,
    is_grayscale_image,
    is_rgb_image,
)
from pydantic import AfterValidator, BaseModel, Field, ValidationInfo, field_validator, model_validator
from scipy import special
from scipy.ndimage import gaussian_filter
from typing_extensions import Annotated, Literal, Self, TypedDict

from albumentations import random_utils
from albumentations.augmentations.blur.functional import blur
from albumentations.augmentations.blur.transforms import BlurInitSchema, process_blur_limit
from albumentations.augmentations.utils import check_range, non_rgb_error
from albumentations.core.pydantic import (
    InterpolationType,
    NonNegativeFloatRangeType,
    OnePlusFloatRangeType,
    OnePlusIntRangeType,
    ProbabilityType,
    SymmetricRangeType,
    ZeroOneRangeType,
    check_0plus,
    check_01,
    check_1plus,
    nondecreasing,
)
from albumentations.core.transforms_interface import (
    BaseTransformInitSchema,
    DualTransform,
    ImageOnlyTransform,
    Interpolation,
    NoOp,
)
from albumentations.core.types import (
    MAX_RAIN_ANGLE,
    MONO_CHANNEL_DIMENSIONS,
    NUM_RGB_CHANNELS,
    PAIR,
    ChromaticAberrationMode,
    ColorType,
    ImageMode,
    MorphologyMode,
    PlanckianJitterMode,
    RainMode,
    ScaleFloatType,
    ScaleIntType,
    SpatterMode,
    Targets,
)
from albumentations.core.utils import format_args, to_tuple

from . import functional as fmain

__all__ = [
    "Normalize",
    "RandomGamma",
    "RandomGridShuffle",
    "HueSaturationValue",
    "RGBShift",
    "GaussNoise",
    "CLAHE",
    "ChannelShuffle",
    "InvertImg",
    "ToGray",
    "ToRGB",
    "ToSepia",
    "ImageCompression",
    "ToFloat",
    "FromFloat",
    "RandomBrightnessContrast",
    "RandomSnow",
    "RandomGravel",
    "RandomRain",
    "RandomFog",
    "RandomSunFlare",
    "RandomShadow",
    "RandomToneCurve",
    "Lambda",
    "ISONoise",
    "Solarize",
    "Equalize",
    "Posterize",
    "Downscale",
    "MultiplicativeNoise",
    "FancyPCA",
    "ColorJitter",
    "Sharpen",
    "Emboss",
    "Superpixels",
    "TemplateTransform",
    "RingingOvershoot",
    "UnsharpMask",
    "PixelDropout",
    "Spatter",
    "ChromaticAberration",
    "Morphological",
    "PlanckianJitter",
]

NUM_BITS_ARRAY_LENGTH = 3
MAX_JPEG_QUALITY = 100
TWENTY = 20


class RandomGridShuffle(DualTransform):
    """Randomly shuffles the grid's cells on an image, mask, or keypoints,
    effectively rearranging patches within the image.
    This transformation divides the image into a grid and then permutes these grid cells based on a random mapping.


    Args:
        grid (tuple[int, int]): Size of the grid for splitting the image into cells. Each cell is shuffled randomly.
        p (float): Probability that the transform will be applied.

    Targets:
        image, mask, keypoints

    Image types:
        uint8, float32

    Examples:
        >>> import albumentations as A
        >>> transform = A.Compose([
            A.RandomGridShuffle(grid=(3, 3), p=1.0)
        ])
        >>> transformed = transform(image=my_image, mask=my_mask)
        >>> image, mask = transformed['image'], transformed['mask']
        # This will shuffle the 3x3 grid cells of `my_image` and `my_mask` randomly.
        # Mask and image are shuffled in a consistent way
    Note:
        This transform could be useful when only micro features are important for the model, and memorizing
        the global structure could be harmful. For example:
        - Identifying the type of cell phone used to take a picture based on micro artifacts generated by
        phone post-processing algorithms, rather than the semantic features of the photo.
        See more at https://ieeexplore.ieee.org/abstract/document/8622031
        - Identifying stress, glucose, hydration levels based on skin images.
    """

    class InitSchema(BaseTransformInitSchema):
        grid: Annotated[tuple[int, int], AfterValidator(check_1plus)] = (3, 3)

    _targets = (Targets.IMAGE, Targets.MASK, Targets.KEYPOINTS)

    def __init__(self, grid: tuple[int, int] = (3, 3), p: float = 0.5, always_apply: bool | None = None):
        super().__init__(p=p, always_apply=always_apply)
        self.grid = grid

    def apply(self, img: np.ndarray, tiles: np.ndarray, mapping: list[int], **params: Any) -> np.ndarray:
        return fmain.swap_tiles_on_image(img, tiles, mapping)

    def apply_to_mask(self, mask: np.ndarray, tiles: np.ndarray, mapping: list[int], **params: Any) -> np.ndarray:
        return fmain.swap_tiles_on_image(mask, tiles, mapping)

    def apply_to_keypoints(
        self,
        keypoints: np.ndarray,
        tiles: np.ndarray,
        mapping: np.ndarray,
        **params: Any,
    ) -> np.ndarray:
        return fmain.swap_tiles_on_keypoints(keypoints, tiles, mapping)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, np.ndarray]:
        height, width = params["shape"][:2]
        random_state = random_utils.get_random_state()
        original_tiles = fmain.split_uniform_grid(
            (height, width),
            self.grid,
            random_state=random_state,
        )
        shape_groups = fmain.create_shape_groups(original_tiles)
        mapping = fmain.shuffle_tiles_within_shape_groups(shape_groups, random_state=random_state)

        return {"tiles": original_tiles, "mapping": mapping}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return ("grid",)

    @property
    def targets(self) -> dict[str, Callable[..., Any]]:
        return {
            "image": self.apply,
            "mask": self.apply_to_mask,
            "masks": self.apply_to_masks,
            "keypoints": self.apply_to_keypoints,
        }


class Normalize(ImageOnlyTransform):
    """Applies various normalization techniques to an image. The specific normalization technique can be selected
        with the `normalization` parameter.

    Standard normalization is applied using the formula:
        `img = (img - mean * max_pixel_value) / (std * max_pixel_value)`.
        Other normalization techniques adjust the image based on global or per-channel statistics,
        or scale pixel values to a specified range.

    Args:
        mean (ColorType | None): Mean values for standard normalization.
            For "standard" normalization, the default values are ImageNet mean values: (0.485, 0.456, 0.406).
            For "inception" normalization, use mean values of (0.5, 0.5, 0.5).
        std (ColorType | None): Standard deviation values for standard normalization.
            For "standard" normalization, the default values are ImageNet standard deviation :(0.229, 0.224, 0.225).
            For "inception" normalization, use standard deviation values of (0.5, 0.5, 0.5).
        max_pixel_value (float | None): Maximum possible pixel value, used for scaling in standard normalization.
            Defaults to 255.0.
        normalization (Literal["standard", "image", "image_per_channel", "min_max", "min_max_per_channel", "inception"])
            Specifies the normalization technique to apply. Defaults to "standard".
            - "standard": Applies the formula `(img - mean * max_pixel_value) / (std * max_pixel_value)`.
                The default mean and std are based on ImageNet.
            - "image": Normalizes the whole image based on its global mean and standard deviation.
            - "image_per_channel": Normalizes the image per channel based on each channel's mean and standard deviation.
            - "min_max": Scales the image pixel values to a [0, 1] range based on the global
                minimum and maximum pixel values.
            - "min_max_per_channel": Scales each channel of the image pixel values to a [0, 1]
                range based on the per-channel minimum and maximum pixel values.

        p (float): Probability of applying the transform. Defaults to 1.0.

    Targets:
        image

    Image types:
        uint8, float32

    Note:
        - For "standard" normalization, `mean`, `std`, and `max_pixel_value` must be provided.
        - For other normalization types, these parameters are ignored.
        - For inception normalization, use mean values of (0.5, 0.5, 0.5).
        - For YOLO normalization, use mean values of (0.5, 0.5, 0.5) and std values of (0, 0, 0).
        - This transform is often used as a final step in image preprocessing pipelines to
          prepare images for neural network input.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> # Standard ImageNet normalization
        >>> transform = A.Normalize(
        ...     mean=(0.485, 0.456, 0.406),
        ...     std=(0.229, 0.224, 0.225),
        ...     max_pixel_value=255.0,
        ...     p=1.0
        ... )
        >>> normalized_image = transform(image=image)["image"]
        >>>
        >>> # Min-max normalization
        >>> transform_minmax = A.Normalize(normalization="min_max", p=1.0)
        >>> normalized_image_minmax = transform_minmax(image=image)["image"]

    References:
        - ImageNet mean and std: https://pytorch.org/vision/stable/models.html
        - Inception preprocessing: https://keras.io/api/applications/inceptionv3/
    """

    class InitSchema(BaseTransformInitSchema):
        mean: ColorType | None
        std: ColorType | None
        max_pixel_value: float | None
        normalization: Literal[
            "standard",
            "image",
            "image_per_channel",
            "min_max",
            "min_max_per_channel",
        ]

        @model_validator(mode="after")
        def validate_normalization(self) -> Self:
            if (
                self.mean is None
                or self.std is None
                or self.max_pixel_value is None
                and self.normalization == "standard"
            ):
                raise ValueError("mean, std, and max_pixel_value must be provided for standard normalization.")
            return self

    def __init__(
        self,
        mean: ColorType | None = (0.485, 0.456, 0.406),
        std: ColorType | None = (0.229, 0.224, 0.225),
        max_pixel_value: float | None = 255.0,
        normalization: Literal["standard", "image", "image_per_channel", "min_max", "min_max_per_channel"] = "standard",
        always_apply: bool | None = None,
        p: float = 1.0,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.mean = mean
        self.mean_np = np.array(mean, dtype=np.float32) * max_pixel_value
        self.std = std
        self.denominator = np.reciprocal(np.array(std, dtype=np.float32) * max_pixel_value)
        self.max_pixel_value = max_pixel_value
        self.normalization = normalization

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        if self.normalization == "standard":
            return normalize(
                img,
                self.mean_np,
                self.denominator,
            )
        return normalize_per_image(img, self.normalization)

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "mean", "std", "max_pixel_value", "normalization"


class ImageCompression(ImageOnlyTransform):
    """Decrease image quality by applying JPEG or WebP compression.

    This transform simulates the effect of saving an image with lower quality settings,
    which can introduce compression artifacts. It's useful for data augmentation and
    for testing model robustness against varying image qualities.

    Args:
        quality_range (tuple[int, int]): Range for the compression quality.
            The values should be in [1, 100] range, where:
            - 1 is the lowest quality (maximum compression)
            - 100 is the highest quality (minimum compression)
            Default: (99, 100)

        compression_type (Literal["jpeg", "webp"]): Type of compression to apply.
            - "jpeg": JPEG compression
            - "webp": WebP compression
            Default: "jpeg"

        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        Any

    Note:
        - This transform expects images with 1, 3, or 4 channels.
        - For JPEG compression, alpha channels (4th channel) will be ignored.
        - WebP compression supports transparency (4 channels).
        - The actual file is not saved to disk; the compression is simulated in memory.
        - Lower quality values result in smaller file sizes but may introduce visible artifacts.
        - This transform can be useful for:
          * Data augmentation to improve model robustness
          * Testing how models perform on images of varying quality
          * Simulating images transmitted over low-bandwidth connections

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.ImageCompression(quality_range=(50, 90), compression_type=0, p=1.0)
        >>> result = transform(image=image)
        >>> compressed_image = result["image"]

    References:
        - JPEG compression: https://en.wikipedia.org/wiki/JPEG
        - WebP compression: https://developers.google.com/speed/webp
    """

    class InitSchema(BaseTransformInitSchema):
        quality_range: Annotated[tuple[int, int], AfterValidator(check_1plus), AfterValidator(nondecreasing)]

        quality_lower: int | None = Field(
            ge=1,
            le=100,
        )
        quality_upper: int | None = Field(
            ge=1,
            le=100,
        )
        compression_type: Literal["jpeg", "webp"]

        @model_validator(mode="after")
        def validate_ranges(self) -> Self:
            # Update the quality_range based on the non-None values of quality_lower and quality_upper
            if self.quality_lower is not None or self.quality_upper is not None:
                if self.quality_lower is not None:
                    warn(
                        "`quality_lower` is deprecated. Use `quality_range` as tuple"
                        " (quality_lower, quality_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                if self.quality_upper is not None:
                    warn(
                        "`quality_upper` is deprecated. Use `quality_range` as tuple"
                        " (quality_lower, quality_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                lower = self.quality_lower if self.quality_lower is not None else self.quality_range[0]
                upper = self.quality_upper if self.quality_upper is not None else self.quality_range[1]
                self.quality_range = (lower, upper)
                # Clear the deprecated individual quality settings
                self.quality_lower = None
                self.quality_upper = None

            # Validate the quality_range
            if not (1 <= self.quality_range[0] <= MAX_JPEG_QUALITY and 1 <= self.quality_range[1] <= MAX_JPEG_QUALITY):
                raise ValueError(f"Quality range values should be within [1, {MAX_JPEG_QUALITY}] range.")

            return self

    def __init__(
        self,
        quality_lower: int | None = None,
        quality_upper: int | None = None,
        compression_type: Literal["jpeg", "webp"] = "jpeg",
        quality_range: tuple[int, int] = (99, 100),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.quality_range = quality_range
        self.compression_type = compression_type

    def apply(self, img: np.ndarray, quality: int, image_type: Literal[".jpg", ".webp"], **params: Any) -> np.ndarray:
        return fmain.image_compression(img, quality, image_type)

    def get_params(self) -> dict[str, int | str]:
        if self.compression_type == "jpeg":
            image_type = ".jpg"
        elif self.compression_type == "webp":
            image_type = ".webp"
        else:
            raise ValueError(f"Unknown image compression type: {self.compression_type}")

        return {
            "quality": random.randint(*self.quality_range),
            "image_type": image_type,
        }

    def get_transform_init_args(self) -> dict[str, Any]:
        return {
            "quality_range": self.quality_range,
            "compression_type": self.compression_type,
        }


class RandomSnow(ImageOnlyTransform):
    """Bleach out some pixel values imitating snow.

    Args:
        snow_point_range (tuple): tuple of bounds on the amount of snow i.e. (snow_point_lower, snow_point_upper).
            Both values should be in the (0, 1) range. Default: (0.1, 0.3).
        brightness_coeff (float): Coefficient applied to increase the brightness of pixels
            below the snow_point threshold. Larger values lead to more pronounced snow effects.
            Should be > 0. Default: 2.5.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

    """

    class InitSchema(BaseTransformInitSchema):
        snow_point_range: Annotated[tuple[float, float], AfterValidator(check_01), AfterValidator(nondecreasing)] = (
            Field(
                default=(0.1, 0.3),
                description="lower and upper bound on the amount of snow as tuple (snow_point_lower, snow_point_upper)",
            )
        )
        snow_point_lower: float | None = Field(
            default=None,
            description="Lower bound of the amount of snow",
            gt=0,
            lt=1,
        )
        snow_point_upper: float | None = Field(
            default=None,
            description="Upper bound of the amount of snow",
            gt=0,
            lt=1,
        )
        brightness_coeff: float = Field(default=2.5, description="Brightness coefficient, must be > 0", gt=0)

        @model_validator(mode="after")
        def validate_ranges(self) -> Self:
            if self.snow_point_lower is not None or self.snow_point_upper is not None:
                if self.snow_point_lower is not None:
                    warn(
                        "`snow_point_lower` deprecated. Use `snow_point_range` as tuple"
                        " (snow_point_lower, snow_point_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                if self.snow_point_upper is not None:
                    warn(
                        "`snow_point_upper` deprecated. Use `snow_point_range` as tuple"
                        "(snow_point_lower, snow_point_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                lower = self.snow_point_lower if self.snow_point_lower is not None else self.snow_point_range[0]
                upper = self.snow_point_upper if self.snow_point_upper is not None else self.snow_point_range[1]
                self.snow_point_range = (lower, upper)
                self.snow_point_lower = None
                self.snow_point_upper = None

            # Validate the snow_point_range
            if not (0 < self.snow_point_range[0] <= self.snow_point_range[1] < 1):
                raise ValueError("snow_point_range values should be increasing within (0, 1) range.")

            return self

    def __init__(
        self,
        snow_point_lower: float | None = None,
        snow_point_upper: float | None = None,
        brightness_coeff: float = 2.5,
        snow_point_range: tuple[float, float] = (0.1, 0.3),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p, always_apply)

        self.snow_point_range = snow_point_range
        self.brightness_coeff = brightness_coeff

    def apply(self, img: np.ndarray, snow_point: float, **params: Any) -> np.ndarray:
        return fmain.add_snow(img, snow_point, self.brightness_coeff)

    def get_params(self) -> dict[str, np.ndarray]:
        return {"snow_point": random.uniform(*self.snow_point_range)}

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return "snow_point_range", "brightness_coeff"


class RandomGravel(ImageOnlyTransform):
    """Add gravels.

    Args:
        gravel_roi: (top-left x, top-left y,
            bottom-right x, bottom right y). Should be in [0, 1] range
        number_of_patches: no. of gravel patches required

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

    """

    class InitSchema(BaseTransformInitSchema):
        gravel_roi: tuple[float, float, float, float] = Field(
            default=(0.1, 0.4, 0.9, 0.9),
            description="Region of interest for gravel placement",
        )
        number_of_patches: int = Field(default=2, description="Number of gravel patches", ge=1)

        @model_validator(mode="after")
        def validate_gravel_roi(self) -> Self:
            gravel_lower_x, gravel_lower_y, gravel_upper_x, gravel_upper_y = self.gravel_roi
            if not 0 <= gravel_lower_x < gravel_upper_x <= 1 or not 0 <= gravel_lower_y < gravel_upper_y <= 1:
                raise ValueError(f"Invalid gravel_roi. Got: {self.gravel_roi}.")
            return self

    def __init__(
        self,
        gravel_roi: tuple[float, float, float, float] = (0.1, 0.4, 0.9, 0.9),
        number_of_patches: int = 2,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p, always_apply)
        self.gravel_roi = gravel_roi
        self.number_of_patches = number_of_patches

    def generate_gravel_patch(self, rectangular_roi: tuple[int, int, int, int]) -> np.ndarray:
        x1, y1, x2, y2 = rectangular_roi
        area = abs((x2 - x1) * (y2 - y1))
        count = area // 10
        gravels = np.empty([count, 2], dtype=np.int64)
        gravels[:, 0] = random_utils.randint(x1, x2, count)
        gravels[:, 1] = random_utils.randint(y1, y2, count)
        return gravels

    def apply(self, img: np.ndarray, gravels_infos: list[Any], **params: Any) -> np.ndarray:
        if gravels_infos is None:
            gravels_infos = []
        return fmain.add_gravel(img, gravels_infos)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, np.ndarray]:
        height, width = params["shape"][:2]

        x_min, y_min, x_max, y_max = self.gravel_roi
        x_min = int(x_min * width)
        x_max = int(x_max * width)
        y_min = int(y_min * height)
        y_max = int(y_max * height)

        max_height = 200
        max_width = 30

        rectangular_rois = np.zeros([self.number_of_patches, 4], dtype=np.int64)
        xx1 = random_utils.randint(x_min + 1, x_max, self.number_of_patches)  # xmax
        xx2 = random_utils.randint(x_min, xx1)  # xmin
        yy1 = random_utils.randint(y_min + 1, y_max, self.number_of_patches)  # ymax
        yy2 = random_utils.randint(y_min, yy1)  # ymin

        rectangular_rois[:, 0] = xx2
        rectangular_rois[:, 1] = yy2
        rectangular_rois[:, 2] = [min(tup) for tup in zip(xx1, xx2 + max_height)]
        rectangular_rois[:, 3] = [min(tup) for tup in zip(yy1, yy2 + max_width)]

        minx = []
        maxx = []
        miny = []
        maxy = []
        val = []
        for roi in rectangular_rois:
            gravels = self.generate_gravel_patch(roi)
            x = gravels[:, 0]
            y = gravels[:, 1]
            r = random_utils.randint(1, 4, len(gravels))
            sat = random_utils.randint(0, 255, len(gravels))
            miny.append(np.maximum(y - r, 0))
            maxy.append(np.minimum(y + r, y))
            minx.append(np.maximum(x - r, 0))
            maxx.append(np.minimum(x + r, x))
            val.append(sat)

        return {
            "gravels_infos": np.stack(
                [
                    np.concatenate(miny),
                    np.concatenate(maxy),
                    np.concatenate(minx),
                    np.concatenate(maxx),
                    np.concatenate(val),
                ],
                1,
            ),
        }

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return "gravel_roi", "number_of_patches"


class RandomRain(ImageOnlyTransform):
    """Adds rain effects to an image.

    Args:
        slant_range (tuple[int, int]): tuple of type (slant_lower, slant_upper) representing the range for
            rain slant angle.
        drop_length (int): Length of the raindrops.
        drop_width (int): Width of the raindrops.
        drop_color (tuple[int, int, int]): Color of the rain drops in RGB format.
        blur_value (int): Blur value for simulating rain effect. Rainy views are blurry.
        brightness_coefficient (float): Coefficient to adjust the brightness of the image.
            Rainy days are usually shady. Should be in the range (0, 1].
        rain_type (Optional[str]): Type of rain to simulate. One of [None, "drizzle", "heavy", "torrential"].


    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

    """

    class InitSchema(BaseTransformInitSchema):
        slant_lower: int | None = Field(
            default=None,
            description="Lower bound for rain slant angle",
        )
        slant_upper: int | None = Field(
            default=None,
            description="Upper bound for rain slant angle",
        )
        slant_range: Annotated[tuple[float, float], AfterValidator(nondecreasing)] = Field(
            default=(-10, 10),
            description="tuple like (slant_lower, slant_upper) for rain slant angle",
        )
        drop_length: int = Field(default=20, description="Length of raindrops", ge=1)
        drop_width: int = Field(default=1, description="Width of raindrops", ge=1)
        drop_color: tuple[int, int, int] = Field(default=(200, 200, 200), description="Color of raindrops")
        blur_value: int = Field(default=7, description="Blur value for simulating rain effect", ge=1)
        brightness_coefficient: float = Field(
            default=0.7,
            description="Brightness coefficient for rainy effect",
            gt=0,
            le=1,
        )
        rain_type: RainMode | None = Field(default=None, description="Type of rain to simulate")

        @model_validator(mode="after")
        def validate_ranges(self) -> Self:
            if self.slant_lower is not None or self.slant_upper is not None:
                if self.slant_lower is not None:
                    warn(
                        "`slant_lower` deprecated. Use `slant_range` as tuple (slant_lower, slant_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                if self.slant_upper is not None:
                    warn(
                        "`slant_upper` deprecated. Use `slant_range` as tuple (slant_lower, slant_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                lower = self.slant_lower if self.slant_lower is not None else self.slant_range[0]
                upper = self.slant_upper if self.slant_upper is not None else self.slant_range[1]
                self.slant_range = (lower, upper)
                self.slant_lower = None
                self.slant_upper = None

            # Validate the slant_range
            if not (-MAX_RAIN_ANGLE <= self.slant_range[0] <= self.slant_range[1] <= MAX_RAIN_ANGLE):
                raise ValueError(
                    f"slant_range values should be increasing within [-{MAX_RAIN_ANGLE}, {MAX_RAIN_ANGLE}] range.",
                )
            return self

    def __init__(
        self,
        slant_lower: int | None = None,
        slant_upper: int | None = None,
        slant_range: tuple[int, int] = (-10, 10),
        drop_length: int = 20,
        drop_width: int = 1,
        drop_color: tuple[int, int, int] = (200, 200, 200),
        blur_value: int = 7,
        brightness_coefficient: float = 0.7,
        rain_type: RainMode | None = None,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.slant_range = slant_range
        self.drop_length = drop_length
        self.drop_width = drop_width
        self.drop_color = drop_color
        self.blur_value = blur_value
        self.brightness_coefficient = brightness_coefficient
        self.rain_type = rain_type

    def apply(
        self,
        img: np.ndarray,
        slant: int,
        drop_length: int,
        rain_drops: list[tuple[int, int]],
        **params: Any,
    ) -> np.ndarray:
        return fmain.add_rain(
            img,
            slant,
            drop_length,
            self.drop_width,
            self.drop_color,
            self.blur_value,
            self.brightness_coefficient,
            rain_drops,
        )

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        slant = int(random.uniform(*self.slant_range))

        height, width = params["shape"][:2]
        area = height * width

        if self.rain_type == "drizzle":
            num_drops = area // 770
            drop_length = 10
        elif self.rain_type == "heavy":
            num_drops = width * height // 600
            drop_length = 30
        elif self.rain_type == "torrential":
            num_drops = area // 500
            drop_length = 60
        else:
            drop_length = self.drop_length
            num_drops = area // 600

        rain_drops = []

        for _ in range(num_drops):  # If You want heavy rain, try increasing this
            x = random.randint(slant, width) if slant < 0 else random.randint(0, width - slant)

            y = random.randint(0, height - drop_length)

            rain_drops.append((x, y))

        return {"drop_length": drop_length, "slant": slant, "rain_drops": rain_drops}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return (
            "slant_range",
            "drop_length",
            "drop_width",
            "drop_color",
            "blur_value",
            "brightness_coefficient",
            "rain_type",
        )


class RandomFog(ImageOnlyTransform):
    """Simulates fog for the image.

    Args:
        fog_coef_range (tuple): tuple of bounds on the fog intensity coefficient (fog_coef_lower, fog_coef_upper).
            Default: (0.3, 1).
        alpha_coef (float): Transparency of the fog circles. Should be in [0, 1] range. Default: 0.08.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library
    """

    class InitSchema(BaseTransformInitSchema):
        fog_coef_lower: float | None = Field(
            default=None,
            description="Lower limit for fog intensity coefficient",
            ge=0,
            le=1,
        )
        fog_coef_upper: float | None = Field(
            default=None,
            description="Upper limit for fog intensity coefficient",
            ge=0,
            le=1,
        )
        fog_coef_range: Annotated[tuple[float, float], AfterValidator(check_01), AfterValidator(nondecreasing)] = (
            0.3,
            1,
        )

        alpha_coef: float = Field(default=0.08, description="Transparency of the fog circles", ge=0, le=1)

        @model_validator(mode="after")
        def validate_fog_coefficients(self) -> Self:
            if self.fog_coef_lower is not None:
                warn("`fog_coef_lower` is deprecated, use `fog_coef_range` instead.", DeprecationWarning, stacklevel=2)
            if self.fog_coef_upper is not None:
                warn("`fog_coef_upper` is deprecated, use `fog_coef_range` instead.", DeprecationWarning, stacklevel=2)

            lower = self.fog_coef_lower if self.fog_coef_lower is not None else self.fog_coef_range[0]
            upper = self.fog_coef_upper if self.fog_coef_upper is not None else self.fog_coef_range[1]
            self.fog_coef_range = (lower, upper)

            self.fog_coef_lower = None
            self.fog_coef_upper = None

            return self

    def __init__(
        self,
        fog_coef_lower: float | None = None,
        fog_coef_upper: float | None = None,
        alpha_coef: float = 0.08,
        fog_coef_range: tuple[float, float] = (0.3, 1),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.fog_coef_range = fog_coef_range
        self.alpha_coef = alpha_coef

    def apply(
        self,
        img: np.ndarray,
        fog_coef: np.ndarray,
        haze_list: list[tuple[int, int]],
        **params: Any,
    ) -> np.ndarray:
        return fmain.add_fog(img, fog_coef, self.alpha_coef, haze_list)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        fog_coef = random.uniform(*self.fog_coef_range)

        height, width = imshape = params["shape"][:2]

        hw = max(1, int(width // 3 * fog_coef))

        haze_list = []
        midx = width // 2 - 2 * hw
        midy = height // 2 - hw
        index = 1

        while midx > -hw or midy > -hw:
            for _ in range(hw // 10 * index):
                x = random.randint(midx, width - midx - hw)
                y = random.randint(midy, height - midy - hw)
                haze_list.append((x, y))

            midx -= 3 * hw * width // sum(imshape)
            midy -= 3 * hw * height // sum(imshape)
            index += 1

        return {"haze_list": haze_list, "fog_coef": fog_coef}

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return "fog_coef_range", "alpha_coef"


class RandomSunFlare(ImageOnlyTransform):
    """Simulates Sun Flare for the image

    Args:
        flare_roi (tuple[float, float, float, float]): Tuple specifying the region of the image where flare will
            appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1].
        src_radius (int): Radius of the source for the flare.
        src_color (tuple[int, int, int]): Color of the flare as an (R, G, B) tuple.
        angle_range (tuple[float, float]): tuple specifying the range of angles for the flare.
            Both ends of the range are in the [0, 1] interval.
        num_flare_circles_range (tuple[int, int]): tuple specifying the range for the number of flare circles.
        p (float): Probability of applying the transform.

    Targets:
        image

    Image types:
        uint8

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library
    """

    class InitSchema(BaseTransformInitSchema):
        flare_roi: tuple[float, float, float, float] = Field(
            default=(0, 0, 1, 0.5),
            description="Region of the image where flare will appear",
        )
        angle_lower: float | None = Field(default=None, description="Lower bound for the angle", ge=0, le=1)
        angle_upper: float | None = Field(default=None, description="Upper bound for the angle", ge=0, le=1)

        num_flare_circles_lower: int | None = Field(
            default=6,
            description="Lower limit for the number of flare circles",
            ge=0,
        )
        num_flare_circles_upper: int | None = Field(
            default=10,
            description="Upper limit for the number of flare circles",
            gt=0,
        )
        src_radius: int = Field(default=400, description="Source radius for the flare")
        src_color: tuple[int, ...] = Field(default=(255, 255, 255), description="Color of the flare")

        angle_range: Annotated[tuple[float, float], AfterValidator(check_01), AfterValidator(nondecreasing)] = Field(
            default=(0, 1),
            description="Angle range",
        )

        num_flare_circles_range: Annotated[
            tuple[int, int],
            AfterValidator(check_1plus),
            AfterValidator(nondecreasing),
        ] = Field(default=(6, 10), description="Number of flare circles range")

        @model_validator(mode="after")
        def validate_parameters(self) -> Self:
            flare_center_lower_x, flare_center_lower_y, flare_center_upper_x, flare_center_upper_y = self.flare_roi
            if (
                not 0 <= flare_center_lower_x < flare_center_upper_x <= 1
                or not 0 <= flare_center_lower_y < flare_center_upper_y <= 1
            ):
                raise ValueError(f"Invalid flare_roi. Got: {self.flare_roi}")

            if self.angle_lower is not None or self.angle_upper is not None:
                if self.angle_lower is not None:
                    warn(
                        "`angle_lower` deprecated. Use `angle_range` as tuple (angle_lower, angle_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                if self.angle_upper is not None:
                    warn(
                        "`angle_upper` deprecated. Use `angle_range` as tuple(angle_lower, angle_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                lower = self.angle_lower if self.angle_lower is not None else self.angle_range[0]
                upper = self.angle_upper if self.angle_upper is not None else self.angle_range[1]
                self.angle_range = (lower, upper)

            if self.num_flare_circles_lower is not None or self.num_flare_circles_upper is not None:
                if self.num_flare_circles_lower is not None:
                    warn(
                        "`num_flare_circles_lower` deprecated. Use `num_flare_circles_range` as tuple"
                        " (num_flare_circles_lower, num_flare_circles_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                if self.num_flare_circles_upper is not None:
                    warn(
                        "`num_flare_circles_upper` deprecated. Use `num_flare_circles_range` as tuple"
                        " (num_flare_circles_lower, num_flare_circles_upper) instead.",
                        DeprecationWarning,
                        stacklevel=2,
                    )
                lower = (
                    self.num_flare_circles_lower
                    if self.num_flare_circles_lower is not None
                    else self.num_flare_circles_range[0]
                )
                upper = (
                    self.num_flare_circles_upper
                    if self.num_flare_circles_upper is not None
                    else self.num_flare_circles_range[1]
                )
                self.num_flare_circles_range = (lower, upper)

            return self

    def __init__(
        self,
        flare_roi: tuple[float, float, float, float] = (0, 0, 1, 0.5),
        angle_lower: float | None = None,
        angle_upper: float | None = None,
        num_flare_circles_lower: int | None = None,
        num_flare_circles_upper: int | None = None,
        src_radius: int = 400,
        src_color: tuple[int, ...] = (255, 255, 255),
        angle_range: tuple[float, float] = (0, 1),
        num_flare_circles_range: tuple[int, int] = (6, 10),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)

        self.angle_range = angle_range
        self.num_flare_circles_range = num_flare_circles_range

        self.src_radius = src_radius
        self.src_color = src_color
        self.flare_roi = flare_roi

    def apply(
        self,
        img: np.ndarray,
        flare_center: tuple[float, float],
        circles: list[Any],
        **params: Any,
    ) -> np.ndarray:
        if circles is None:
            circles = []
        return fmain.add_sun_flare(
            img,
            flare_center,
            self.src_radius,
            self.src_color,
            circles,
        )

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        height, width = params["shape"][:2]

        angle = 2 * math.pi * random.uniform(*self.angle_range)

        (flare_center_lower_x, flare_center_lower_y, flare_center_upper_x, flare_center_upper_y) = self.flare_roi

        flare_center_x = random.uniform(flare_center_lower_x, flare_center_upper_x)
        flare_center_y = random.uniform(flare_center_lower_y, flare_center_upper_y)

        flare_center_x = int(width * flare_center_x)
        flare_center_y = int(height * flare_center_y)

        num_circles = random.randint(*self.num_flare_circles_range)

        circles = []

        x = []
        y = []

        def line(t: float) -> tuple[float, float]:
            return (flare_center_x + t * math.cos(angle), flare_center_y + t * math.sin(angle))

        for t_val in range(-flare_center_x, width - flare_center_x, 10):
            rand_x, rand_y = line(t_val)
            x.append(rand_x)
            y.append(rand_y)

        for _ in range(num_circles):
            alpha = random.uniform(0.05, 0.2)
            r = random.randint(0, len(x) - 1)
            rad = random.randint(1, max(height // 100 - 2, 2))

            r_color = random.randint(max(self.src_color[0] - 50, 0), self.src_color[0])
            g_color = random.randint(max(self.src_color[1] - 50, 0), self.src_color[1])
            b_color = random.randint(max(self.src_color[2] - 50, 0), self.src_color[2])

            circles += [
                (
                    alpha,
                    (int(x[r]), int(y[r])),
                    pow(rad, 3),
                    (r_color, g_color, b_color),
                ),
            ]

        return {
            "circles": circles,
            "flare_center": (flare_center_x, flare_center_y),
        }

    def get_transform_init_args(self) -> dict[str, Any]:
        return {
            "flare_roi": self.flare_roi,
            "angle_range": self.angle_range,
            "num_flare_circles_range": self.num_flare_circles_range,
            "src_radius": self.src_radius,
            "src_color": self.src_color,
        }


class RandomShadow(ImageOnlyTransform):
    """Simulates shadows for the image by reducing the brightness of the image in shadow regions.

    Args:
        shadow_roi (tuple): region of the image where shadows
            will appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1].
        num_shadows_limit (tuple): Lower and upper limits for the possible number of shadows.
            Default: (1, 2).
        shadow_dimension (int): number of edges in the shadow polygons. Default: 5.
        shadow_intensity_range (tuple): Range for the shadow intensity.
            Should be two float values between 0 and 1. Default: (0.5, 0.5).
        p (float): probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library
    """

    class InitSchema(BaseTransformInitSchema):
        shadow_roi: tuple[float, float, float, float] = Field(
            default=(0, 0.5, 1, 1),
            description="Region of the image where shadows will appear",
        )
        num_shadows_limit: Annotated[tuple[int, int], AfterValidator(check_1plus), AfterValidator(nondecreasing)] = (
            1,
            2,
        )
        num_shadows_lower: int | None = Field(
            default=None,
            description="Lower limit for the possible number of shadows",
        )
        num_shadows_upper: int | None = Field(
            default=None,
            description="Upper limit for the possible number of shadows",
        )
        shadow_dimension: int = Field(default=5, description="Number of edges in the shadow polygons", ge=1)

        shadow_intensity_range: Annotated[
            tuple[float, float],
            AfterValidator(check_01),
            AfterValidator(nondecreasing),
        ] = Field(
            default=(0.5, 0.5),
            description="Range for the shadow intensity",
        )

        @model_validator(mode="after")
        def validate_shadows(self) -> Self:
            if self.num_shadows_lower is not None:
                warn(
                    "`num_shadows_lower` is deprecated. Use `num_shadows_limit` instead.",
                    DeprecationWarning,
                    stacklevel=2,
                )

            if self.num_shadows_upper is not None:
                warn(
                    "`num_shadows_upper` is deprecated. Use `num_shadows_limit` instead.",
                    DeprecationWarning,
                    stacklevel=2,
                )

            if self.num_shadows_lower is not None or self.num_shadows_upper is not None:
                num_shadows_lower = (
                    self.num_shadows_lower if self.num_shadows_lower is not None else self.num_shadows_limit[0]
                )
                num_shadows_upper = (
                    self.num_shadows_upper if self.num_shadows_upper is not None else self.num_shadows_limit[1]
                )

                self.num_shadows_limit = (num_shadows_lower, num_shadows_upper)
                self.num_shadows_lower = None
                self.num_shadows_upper = None

            shadow_lower_x, shadow_lower_y, shadow_upper_x, shadow_upper_y = self.shadow_roi

            if not 0 <= shadow_lower_x <= shadow_upper_x <= 1 or not 0 <= shadow_lower_y <= shadow_upper_y <= 1:
                raise ValueError(f"Invalid shadow_roi. Got: {self.shadow_roi}")

            if isinstance(self.shadow_intensity_range, float):
                if not (0 <= self.shadow_intensity_range <= 1):
                    raise ValueError(
                        f"shadow_intensity_range value should be within [0, 1] range. "
                        f"Got: {self.shadow_intensity_range}",
                    )
            elif isinstance(self.shadow_intensity_range, tuple):
                if not (0 <= self.shadow_intensity_range[0] <= self.shadow_intensity_range[1] <= 1):
                    raise ValueError(
                        f"shadow_intensity_range values should be within [0, 1] range and increasing. "
                        f"Got: {self.shadow_intensity_range}",
                    )
            else:
                raise TypeError("shadow_intensity_range should be an float or a tuple of floats.")

            return self

    def __init__(
        self,
        shadow_roi: tuple[float, float, float, float] = (0, 0.5, 1, 1),
        num_shadows_limit: tuple[int, int] = (1, 2),
        num_shadows_lower: int | None = None,
        num_shadows_upper: int | None = None,
        shadow_dimension: int = 5,
        shadow_intensity_range: tuple[float, float] = (0.5, 0.5),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)

        self.shadow_roi = shadow_roi
        self.shadow_dimension = shadow_dimension
        self.num_shadows_limit = num_shadows_limit
        self.shadow_intensity_range = shadow_intensity_range

    def apply(
        self,
        img: np.ndarray,
        vertices_list: list[np.ndarray],
        intensities: np.ndarray,
        **params: Any,
    ) -> np.ndarray:
        return fmain.add_shadow(img, vertices_list, intensities)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, list[np.ndarray]]:
        height, width = params["shape"][:2]

        num_shadows = random.randint(self.num_shadows_limit[0], self.num_shadows_limit[1])

        x_min, y_min, x_max, y_max = self.shadow_roi

        x_min = int(x_min * width)
        x_max = int(x_max * width)
        y_min = int(y_min * height)
        y_max = int(y_max * height)

        vertices_list = [
            np.stack(
                [
                    random_utils.randint(x_min, x_max, size=5),
                    random_utils.randint(y_min, y_max, size=5),
                ],
                axis=1,
            )
            for _ in range(num_shadows)
        ]

        # Sample shadow intensity for each shadow
        intensities = random_utils.uniform(
            self.shadow_intensity_range[0],
            self.shadow_intensity_range[1],
            size=num_shadows,
        )

        return {"vertices_list": vertices_list, "intensities": intensities}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return (
            "shadow_roi",
            "num_shadows_limit",
            "shadow_dimension",
        )


class RandomToneCurve(ImageOnlyTransform):
    """Randomly change the relationship between bright and dark areas of the image by manipulating its tone curve.

    Args:
        scale (float): Standard deviation of the normal distribution used to sample random distances
            to move two control points that modify the image's curve. Values should be in range [0, 1]. Default: 0.1
        per_channel (bool): If `True`, the tone curve will be applied to each channel of the input image separately,
            which can lead to color distortion. Default: False.
        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        - "What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance"
          by Mahmoud Afifi and Michael S. Brown, ICCV 2019.
        - GitHub repository: https://github.com/mahmoudnafifi/WB_color_augmenter

    Example:
        >>> import numpy as np
        >>> from albumentations import RandomToneCurve
        >>> img = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = RandomToneCurve(scale=0.1, per_channel=True, p=1.0)
        >>> transformed_img = transform(image=img)['image']

    This transform applies a random tone curve to the input image by adjusting the relationship between bright and
    dark areas. When `per_channel` is set to True, each channel is adjusted separately, potentially causing color
    distortions. Otherwise, the same adjustment is applied to all channels, preserving the original color relationships.
    """

    class InitSchema(BaseTransformInitSchema):
        scale: float = Field(
            default=0.1,
            description="Standard deviation of the normal distribution used to sample random distances",
            ge=0,
            le=1,
        )
        per_channel: bool = Field(default=False, description="Apply the tone curve to each channel separately")

    def __init__(
        self,
        scale: float = 0.1,
        per_channel: bool = False,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.scale = scale
        self.per_channel = per_channel

    def apply(
        self,
        img: np.ndarray,
        low_y: float | np.ndarray,
        high_y: float | np.ndarray,
        **params: Any,
    ) -> np.ndarray:
        return fmain.move_tone_curve(img, low_y, high_y)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        image = data["image"] if "image" in data else data["images"][0]
        num_channels = get_num_channels(image)

        if self.per_channel and num_channels != 1:
            return {
                "low_y": np.clip(random_utils.normal(loc=0.25, scale=self.scale, size=(num_channels,)), 0, 1),
                "high_y": np.clip(random_utils.normal(loc=0.75, scale=self.scale, size=(num_channels,)), 0, 1),
            }
        # Same values for all channels
        low_y = np.clip(random_utils.normal(loc=0.25, scale=self.scale), 0, 1)
        high_y = np.clip(random_utils.normal(loc=0.75, scale=self.scale), 0, 1)

        return {"low_y": low_y, "high_y": high_y}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "scale", "per_channel"


class HueSaturationValue(ImageOnlyTransform):
    """Randomly change hue, saturation and value of the input image.

    This transform adjusts the HSV (Hue, Saturation, Value) channels of an input RGB image.
    It allows for independent control over each channel, providing a wide range of color
    and brightness modifications.

    Args:
        hue_shift_limit (float | tuple[float, float]): Range for changing hue.
            If a single float value is provided, the range will be (-hue_shift_limit, hue_shift_limit).
            Values should be in the range [-180, 180]. Default: (-20, 20).

        sat_shift_limit (float | tuple[float, float]): Range for changing saturation.
            If a single float value is provided, the range will be (-sat_shift_limit, sat_shift_limit).
            Values should be in the range [-255, 255]. Default: (-30, 30).

        val_shift_limit (float | tuple[float, float]): Range for changing value (brightness).
            If a single float value is provided, the range will be (-val_shift_limit, val_shift_limit).
            Values should be in the range [-255, 255]. Default: (-20, 20).

        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        3

    Note:
        - The transform first converts the input RGB image to the HSV color space.
        - Each channel (Hue, Saturation, Value) is adjusted independently.
        - Hue is circular, so it wraps around at 180 degrees.
        - For float32 images, the shift values are applied as percentages of the full range.
        - This transform is particularly useful for color augmentation and simulating
          different lighting conditions.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.HueSaturationValue(
        ...     hue_shift_limit=20,
        ...     sat_shift_limit=30,
        ...     val_shift_limit=20,
        ...     p=0.7
        ... )
        >>> result = transform(image=image)
        >>> augmented_image = result["image"]

    References:
        - HSV color space: https://en.wikipedia.org/wiki/HSL_and_HSV
    """

    class InitSchema(BaseTransformInitSchema):
        hue_shift_limit: SymmetricRangeType
        sat_shift_limit: SymmetricRangeType
        val_shift_limit: SymmetricRangeType

    def __init__(
        self,
        hue_shift_limit: ScaleFloatType = (-20, 20),
        sat_shift_limit: ScaleFloatType = (-30, 30),
        val_shift_limit: ScaleFloatType = (-20, 20),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.hue_shift_limit = cast(Tuple[float, float], hue_shift_limit)
        self.sat_shift_limit = cast(Tuple[float, float], sat_shift_limit)
        self.val_shift_limit = cast(Tuple[float, float], val_shift_limit)

    def apply(
        self,
        img: np.ndarray,
        hue_shift: int,
        sat_shift: int,
        val_shift: int,
        **params: Any,
    ) -> np.ndarray:
        if not is_rgb_image(img) and not is_grayscale_image(img):
            msg = "HueSaturationValue transformation expects 1-channel or 3-channel images."
            raise TypeError(msg)
        return fmain.shift_hsv(img, hue_shift, sat_shift, val_shift)

    def get_params(self) -> dict[str, float]:
        return {
            "hue_shift": random.uniform(*self.hue_shift_limit),
            "sat_shift": random.uniform(*self.sat_shift_limit),
            "val_shift": random.uniform(*self.val_shift_limit),
        }

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "hue_shift_limit", "sat_shift_limit", "val_shift_limit"


class Solarize(ImageOnlyTransform):
    """Invert all pixel values above a threshold.

    Args:
        threshold: range for solarizing threshold.
            If threshold is a single value, the range will be [1, threshold]. Default: 128.
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    """

    class InitSchema(BaseTransformInitSchema):
        threshold: OnePlusFloatRangeType = (128, 128)

    def __init__(self, threshold: ScaleIntType = (128, 128), p: float = 0.5, always_apply: bool | None = None):
        super().__init__(p=p, always_apply=always_apply)
        self.threshold = cast(Tuple[float, float], threshold)

    def apply(self, img: np.ndarray, threshold: int, **params: Any) -> np.ndarray:
        return fmain.solarize(img, threshold)

    def get_params(self) -> dict[str, float]:
        return {"threshold": random.uniform(self.threshold[0], self.threshold[1])}

    def get_transform_init_args_names(self) -> tuple[str]:
        return ("threshold",)


class Posterize(ImageOnlyTransform):
    """Reduce the number of bits for each color channel.

    Args:
        num_bits ((int, int) or int,
                  or list of ints [r, g, b],
                  or list of ints [[r1, r1], [g1, g2], [b1, b2]]): number of high bits.
            If num_bits is a single value, the range will be [num_bits, num_bits].
            Must be in range [0, 8]. Default: 4.
        p: probability of applying the transform. Default: 0.5.

    Targets:
    image

    Image types:
        uint8

    """

    class InitSchema(BaseTransformInitSchema):
        num_bits: Annotated[
            int | tuple[int, int] | tuple[int, int, int],
            Field(default=4, description="Number of high bits"),
        ]

        @field_validator("num_bits")
        @classmethod
        def validate_num_bits(cls, num_bits: Any) -> tuple[int, int] | list[tuple[int, int]]:
            if isinstance(num_bits, int):
                return cast(Tuple[int, int], to_tuple(num_bits, num_bits))
            if isinstance(num_bits, Sequence) and len(num_bits) == NUM_BITS_ARRAY_LENGTH:
                return [cast(Tuple[int, int], to_tuple(i, 0)) for i in num_bits]
            return cast(Tuple[int, int], to_tuple(num_bits, 0))

    def __init__(
        self,
        num_bits: int | tuple[int, int] | tuple[int, int, int] = 4,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.num_bits = cast(Union[Tuple[int, ...], List[Tuple[int, ...]]], num_bits)

    def apply(self, img: np.ndarray, num_bits: int, **params: Any) -> np.ndarray:
        return fmain.posterize(img, num_bits)

    def get_params(self) -> dict[str, Any]:
        if len(self.num_bits) == NUM_BITS_ARRAY_LENGTH:
            return {"num_bits": [random.randint(int(i[0]), int(i[1])) for i in self.num_bits]}  # type: ignore[index]
        num_bits = self.num_bits
        return {"num_bits": random.randint(int(num_bits[0]), int(num_bits[1]))}  # type: ignore[arg-type]

    def get_transform_init_args_names(self) -> tuple[str]:
        return ("num_bits",)


class Equalize(ImageOnlyTransform):
    """Equalize the image histogram.

    This transform applies histogram equalization to the input image. Histogram equalization
    is a method in image processing of contrast adjustment using the image's histogram.

    Args:
        mode (Literal['cv', 'pil']): Use OpenCV or Pillow equalization method.
            Default: 'cv'
        by_channels (bool): If True, use equalization by channels separately,
            else convert image to YCbCr representation and use equalization by `Y` channel.
            Default: True
        mask (np.ndarray, callable): If given, only the pixels selected by
            the mask are included in the analysis. Can be:
            - A 1-channel or 3-channel numpy array of the same size as the input image.
            - A callable (function) that generates a mask. The function should accept 'image'
              as its first argument, and can accept additional arguments specified in mask_params.
            Default: None
        mask_params (list[str]): Additional parameters to pass to the mask function.
            These parameters will be taken from the data dict passed to __call__.
            Default: ()
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Note:
        - When mode='cv', OpenCV's equalizeHist() function is used.
        - When mode='pil', Pillow's equalize() function is used.
        - The 'by_channels' parameter determines whether equalization is applied to each color channel
          independently (True) or to the luminance channel only (False).
        - If a mask is provided as a numpy array, it should have the same height and width as the input image.
        - If a mask is provided as a function, it allows for dynamic mask generation based on the input image
          and additional parameters. This is useful for scenarios where the mask depends on the image content
          or external data (e.g., bounding boxes, segmentation masks).

    Mask Function:
        When mask is a callable, it should have the following signature:
        mask_func(image, *args) -> np.ndarray

        - image: The input image (numpy array)
        - *args: Additional arguments as specified in mask_params

        The function should return a numpy array of the same height and width as the input image,
        where non-zero pixels indicate areas to be equalized.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>>
        >>> # Using a static mask
        >>> mask = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
        >>> transform = A.Equalize(mask=mask, p=1.0)
        >>> result = transform(image=image)
        >>>
        >>> # Using a dynamic mask function
        >>> def mask_func(image, bboxes):
        ...     mask = np.ones_like(image[:, :, 0], dtype=np.uint8)
        ...     for bbox in bboxes:
        ...         x1, y1, x2, y2 = map(int, bbox)
        ...         mask[y1:y2, x1:x2] = 0  # Exclude areas inside bounding boxes
        ...     return mask
        >>>
        >>> transform = A.Equalize(mask=mask_func, mask_params=['bboxes'], p=1.0)
        >>> bboxes = [(10, 10, 50, 50), (60, 60, 90, 90)]  # Example bounding boxes
        >>> result = transform(image=image, bboxes=bboxes)

    References:
        - OpenCV equalizeHist: https://docs.opencv.org/3.4/d6/dc7/group__imgproc__hist.html#ga7e54091f0c937d49bf84152a16f76d6e
        - Pillow ImageOps.equalize: https://pillow.readthedocs.io/en/stable/reference/ImageOps.html#PIL.ImageOps.equalize
        - Histogram Equalization: https://en.wikipedia.org/wiki/Histogram_equalization
    """

    class InitSchema(BaseTransformInitSchema):
        mode: ImageMode
        by_channels: bool
        mask: np.ndarray | Callable[..., Any] | None
        mask_params: Sequence[str]

    def __init__(
        self,
        mode: ImageMode = "cv",
        by_channels: bool = True,
        mask: np.ndarray | Callable[..., Any] | None = None,
        mask_params: Sequence[str] = (),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)

        self.mode = mode
        self.by_channels = by_channels
        self.mask = mask
        self.mask_params = mask_params

    def apply(self, img: np.ndarray, mask: np.ndarray, **params: Any) -> np.ndarray:
        return fmain.equalize(img, mode=self.mode, by_channels=self.by_channels, mask=mask)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        if not callable(self.mask):
            return {"mask": self.mask}

        mask_params = {"image": data["image"]}
        for key in self.mask_params:
            if key not in data:
                raise KeyError(f"Required parameter '{key}' for mask function is missing in data.")
            mask_params[key] = data[key]

        return {"mask": self.mask(**mask_params)}

    @property
    def targets_as_params(self) -> list[str]:
        return ["image", *list(self.mask_params)]

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "mode", "by_channels", "mask", "mask_params"


class RGBShift(ImageOnlyTransform):
    """Randomly shift values for each channel of the input RGB image.

    Args:
        r_shift_limit: range for changing values for the red channel. If r_shift_limit is a single
            int, the range will be (-r_shift_limit, r_shift_limit). Default: (-20, 20).
        g_shift_limit: range for changing values for the green channel. If g_shift_limit is a
            single int, the range  will be (-g_shift_limit, g_shift_limit). Default: (-20, 20).
        b_shift_limit: range for changing values for the blue channel. If b_shift_limit is a single
            int, the range will be (-b_shift_limit, b_shift_limit). Default: (-20, 20).
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    """

    class InitSchema(BaseTransformInitSchema):
        r_shift_limit: SymmetricRangeType = (-20, 20)
        g_shift_limit: SymmetricRangeType = (-20, 20)
        b_shift_limit: SymmetricRangeType = (-20, 20)

    def __init__(
        self,
        r_shift_limit: ScaleFloatType = (-20, 20),
        g_shift_limit: ScaleFloatType = (-20, 20),
        b_shift_limit: ScaleFloatType = (-20, 20),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.r_shift_limit = cast(Tuple[float, float], r_shift_limit)
        self.g_shift_limit = cast(Tuple[float, float], g_shift_limit)
        self.b_shift_limit = cast(Tuple[float, float], b_shift_limit)

    def apply(self, img: np.ndarray, shift: np.ndarray, **params: Any) -> np.ndarray:
        if not is_rgb_image(img):
            msg = "RGBShift transformation expects 3-channel images."
            raise TypeError(msg)

        return albucore.add_vector(img, shift)

    def get_params(self) -> dict[str, Any]:
        return {
            "shift": np.array(
                [
                    random.uniform(*self.r_shift_limit),
                    random.uniform(*self.g_shift_limit),
                    random.uniform(*self.b_shift_limit),
                ],
            ),
        }

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "r_shift_limit", "g_shift_limit", "b_shift_limit"


class RandomBrightnessContrast(ImageOnlyTransform):
    """Randomly change brightness and contrast of the input image.

    Args:
        brightness_limit: factor range for changing brightness.
            If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2).
        contrast_limit: factor range for changing contrast.
            If limit is a single float, the range will be (-limit, limit). Default: (-0.2, 0.2).
        brightness_by_max: If True adjust contrast by image dtype maximum,
            else adjust contrast by image mean.
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    """

    class InitSchema(BaseTransformInitSchema):
        brightness_limit: SymmetricRangeType = (-0.2, 0.2)
        contrast_limit: SymmetricRangeType = (-0.2, 0.2)
        brightness_by_max: bool = Field(default=True, description="Adjust brightness by image dtype maximum if True.")

    def __init__(
        self,
        brightness_limit: ScaleFloatType = (-0.2, 0.2),
        contrast_limit: ScaleFloatType = (-0.2, 0.2),
        brightness_by_max: bool = True,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.brightness_limit = cast(Tuple[float, float], brightness_limit)
        self.contrast_limit = cast(Tuple[float, float], contrast_limit)
        self.brightness_by_max = brightness_by_max

    def apply(self, img: np.ndarray, alpha: float, beta: float, **params: Any) -> np.ndarray:
        return fmain.brightness_contrast_adjust(img, alpha, beta, self.brightness_by_max)

    def get_params(self) -> dict[str, float]:
        return {
            "alpha": 1.0 + random.uniform(*self.contrast_limit),
            "beta": 0.0 + random.uniform(*self.brightness_limit),
        }

    def get_transform_init_args_names(self) -> tuple[str, str, str]:
        return ("brightness_limit", "contrast_limit", "brightness_by_max")


class GaussNoise(ImageOnlyTransform):
    """Apply Gaussian noise to the input image.

    Args:
        var_limit (tuple[float, float] | float): Variance range for noise. If var_limit is a single float value,
            the range will be (0, var_limit). Default: (10.0, 50.0).
        mean (float): Mean of the noise. Default: 0.
        per_channel (bool): If True, noise will be sampled for each channel independently.
            Otherwise, the noise will be sampled once for all channels. Default: True.
        noise_scale_factor (float): Scaling factor for noise generation. Value should be in the range (0, 1].
            When set to 1, noise is sampled for each pixel independently. If less, noise is sampled for a smaller size
            and resized to fit the shape of the image. Smaller values make the transform faster. Default: 1.0.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        Any

    Returns:
        numpy.ndarray: Image with applied Gaussian noise.

    Note:
        - The noise is generated in the same range as the input image.
        - For uint8 input images, the noise is generated in the range [0, 255].
        - For float32 input images, the noise is generated in the range [0, 1].
        - The resulting image is clipped to keep its values in the input range.
        - Setting per_channel=False is faster but applies the same noise to all channels.
        - The noise_scale_factor parameter allows for a trade-off between transform speed and noise granularity.

    Examples:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (224, 224, 3), dtype=np.uint8)
        >>>
        >>> # Apply Gaussian noise with default parameters
        >>> transform = A.GaussNoise(p=1.0)
        >>> noisy_image = transform(image=image)['image']
        >>>
        >>> # Apply Gaussian noise with custom variance range and mean
        >>> transform = A.GaussNoise(var_limit=(50.0, 100.0), mean=10, p=1.0)
        >>> noisy_image = transform(image=image)['image']
        >>>
        >>> # Apply the same noise to all channels
        >>> transform = A.GaussNoise(per_channel=False, p=1.0)
        >>> noisy_image = transform(image=image)['image']
        >>>
        >>> # Apply noise with reduced granularity for faster processing
        >>> transform = A.GaussNoise(noise_scale_factor=0.5, p=1.0)
        >>> noisy_image = transform(image=image)['image']

    """

    class InitSchema(BaseTransformInitSchema):
        var_limit: NonNegativeFloatRangeType
        mean: float
        per_channel: bool
        noise_scale_factor: float = Field(gt=0, le=1)

    def __init__(
        self,
        var_limit: ScaleFloatType = (10.0, 50.0),
        mean: float = 0,
        per_channel: bool = True,
        noise_scale_factor: float = 1,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.var_limit = cast(Tuple[float, float], var_limit)
        self.mean = mean
        self.per_channel = per_channel
        self.noise_scale_factor = noise_scale_factor

    def apply(self, img: np.ndarray, gauss: np.ndarray, **params: Any) -> np.ndarray:
        return fmain.add_noise(img, gauss)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, float]:
        image = data["image"] if "image" in data else data["images"][0]
        var = random.uniform(*self.var_limit)
        sigma = math.sqrt(var)

        if self.per_channel:
            target_shape = image.shape
            if self.noise_scale_factor == 1:
                gauss = random_utils.normal(self.mean, sigma, target_shape)
            else:
                gauss = fmain.generate_approx_gaussian_noise(target_shape, self.mean, sigma, self.noise_scale_factor)
        else:
            target_shape = image.shape[:2]
            if self.noise_scale_factor == 1:
                gauss = random_utils.normal(self.mean, sigma, target_shape)
            else:
                gauss = fmain.generate_approx_gaussian_noise(target_shape, self.mean, sigma, self.noise_scale_factor)

            if image.ndim > MONO_CHANNEL_DIMENSIONS:
                gauss = np.expand_dims(gauss, -1)

        return {"gauss": gauss}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "var_limit", "per_channel", "mean", "noise_scale_factor"


class ISONoise(ImageOnlyTransform):
    """Applies camera sensor noise to the input image, simulating high ISO settings.

    This transform adds random noise to an image, mimicking the effect of using high ISO settings
    in digital photography. It simulates two main components of ISO noise:
    1. Color noise: random shifts in color hue
    2. Luminance noise: random variations in pixel intensity

    Args:
        color_shift (tuple[float, float]): Range for changing color hue.
            Values should be in the range [0, 1], where 1 represents a full 360° hue rotation.
            Default: (0.01, 0.05)

        intensity (tuple[float, float]): Range for the noise intensity.
            Higher values increase the strength of both color and luminance noise.
            Default: (0.1, 0.5)

        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        3

    Note:
        - This transform only works with RGB images. It will raise a TypeError if applied to
          non-RGB images.
        - The color shift is applied in the HSV color space, affecting the hue channel.
        - Luminance noise is added to all channels independently.
        - This transform can be useful for data augmentation in low-light scenarios or when
          training models to be robust against noisy inputs.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.ISONoise(color_shift=(0.01, 0.05), intensity=(0.1, 0.5), p=0.5)
        >>> result = transform(image=image)
        >>> noisy_image = result["image"]

    References:
        - ISO noise in digital photography:
          https://en.wikipedia.org/wiki/Image_noise#In_digital_cameras
    """

    class InitSchema(BaseTransformInitSchema):
        color_shift: Annotated[tuple[float, float], AfterValidator(check_01), AfterValidator(nondecreasing)]
        intensity: Annotated[tuple[float, float], AfterValidator(check_0plus), AfterValidator(nondecreasing)]

    def __init__(
        self,
        color_shift: tuple[float, float] = (0.01, 0.05),
        intensity: tuple[float, float] = (0.1, 0.5),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.intensity = intensity
        self.color_shift = color_shift

    def apply(
        self,
        img: np.ndarray,
        color_shift: float,
        intensity: float,
        random_seed: int,
        **params: Any,
    ) -> np.ndarray:
        if not is_rgb_image(img):
            msg = "Image must be RGB"
            raise TypeError(msg)
        return fmain.iso_noise(img, color_shift, intensity, np.random.RandomState(random_seed))

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        return {
            "color_shift": random.uniform(*self.color_shift),
            "intensity": random.uniform(*self.intensity),
            "random_seed": random_utils.get_random_seed(),
        }

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return "intensity", "color_shift"


class CLAHE(ImageOnlyTransform):
    """Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input image.

    CLAHE is an advanced method of improving the contrast in an image. Unlike regular histogram
    equalization, which operates on the entire image, CLAHE operates on small regions (tiles)
    in the image. This results in a more balanced equalization, preventing over-amplification
    of contrast in areas with initially low contrast.

    Args:
        clip_limit (tuple[float, float] | float): Controls the contrast enhancement limit.
            - If a single float is provided, the range will be (1, clip_limit).
            - If a tuple of two floats is provided, it defines the range for random selection.
            Higher values allow for more contrast enhancement, but may also increase noise.
            Default: (1, 4)

        tile_grid_size (tuple[int, int]): Defines the number of tiles in the row and column directions.
            Format is (rows, columns). Smaller tile sizes can lead to more localized enhancements,
            while larger sizes give results closer to global histogram equalization.
            Default: (8, 8)

        p (float): Probability of applying the transform. Default: 0.5

    Notes:
        - Supports only RGB or grayscale images.
        - For color images, CLAHE is applied to the L channel in the LAB color space.
        - The clip limit determines the maximum slope of the cumulative histogram. A lower
          clip limit will result in more contrast limiting.
        - Tile grid size affects the adaptiveness of the method. More tiles increase local
          adaptiveness but can lead to an unnatural look if set too high.

    Targets:
        image

    Image types:
        uint8, float32

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.CLAHE(clip_limit=(1, 4), tile_grid_size=(8, 8), p=1.0)
        >>> result = transform(image=image)
        >>> clahe_image = result["image"]

    References:
        - https://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html
        - Zuiderveld, Karel. "Contrast Limited Adaptive Histogram Equalization."
          Graphic Gems IV. Academic Press Professional, Inc., 1994.
    """

    class InitSchema(BaseTransformInitSchema):
        clip_limit: OnePlusFloatRangeType
        tile_grid_size: Annotated[tuple[int, int], AfterValidator(check_1plus)]

    def __init__(
        self,
        clip_limit: ScaleFloatType = 4.0,
        tile_grid_size: tuple[int, int] = (8, 8),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.clip_limit = cast(Tuple[float, float], clip_limit)
        self.tile_grid_size = tile_grid_size

    def apply(self, img: np.ndarray, clip_limit: float, **params: Any) -> np.ndarray:
        if not is_rgb_image(img) and not is_grayscale_image(img):
            msg = "CLAHE transformation expects 1-channel or 3-channel images."
            raise TypeError(msg)

        return fmain.clahe(img, clip_limit, self.tile_grid_size)

    def get_params(self) -> dict[str, float]:
        return {"clip_limit": random.uniform(*self.clip_limit)}

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return ("clip_limit", "tile_grid_size")


class ChannelShuffle(ImageOnlyTransform):
    """Randomly rearrange channels of the image.

    Args:
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    """

    def apply(self, img: np.ndarray, channels_shuffled: tuple[int, ...], **params: Any) -> np.ndarray:
        return fmain.channel_shuffle(img, channels_shuffled)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        ch_arr = list(range(params["shape"][2]))
        ch_arr = random_utils.shuffle(ch_arr)
        return {"channels_shuffled": ch_arr}

    def get_transform_init_args_names(self) -> tuple[()]:
        return ()


class InvertImg(ImageOnlyTransform):
    """Invert the input image by subtracting pixel values from max values of the image types,
    i.e., 255 for uint8 and 1.0 for float32.

    Args:
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        Any

    """

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        return fmain.invert(img)

    def get_transform_init_args_names(self) -> tuple[()]:
        return ()


class RandomGamma(ImageOnlyTransform):
    """Applies random gamma correction to an image as a form of data augmentation.

    This class adjusts the luminance of an image by applying gamma correction with a randomly
    selected gamma value from a specified range. Gamma correction can simulate various lighting
    conditions, potentially enhancing model generalization.

    Attributes:
        gamma_limit (Union[int, tuple[int, int]]): The range for gamma adjustment. If `gamma_limit` is a single
            int, the range will be interpreted as (-gamma_limit, gamma_limit), defining how much
            to adjust the image's gamma. Default is (80, 120).
        always_apply: Depreciated. Use `p=1` instead.
        p (float): The probability that the transform will be applied. Default is 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
         https://en.wikipedia.org/wiki/Gamma_correction

    """

    class InitSchema(BaseTransformInitSchema):
        gamma_limit: OnePlusFloatRangeType = (80, 120)

    def __init__(
        self,
        gamma_limit: ScaleFloatType = (80, 120),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p, always_apply)
        self.gamma_limit = cast(Tuple[float, float], gamma_limit)

    def apply(self, img: np.ndarray, gamma: float, **params: Any) -> np.ndarray:
        return fmain.gamma_transform(img, gamma=gamma)

    def get_params(self) -> dict[str, float]:
        return {"gamma": random.uniform(self.gamma_limit[0], self.gamma_limit[1]) / 100.0}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return ("gamma_limit",)


class ToGray(ImageOnlyTransform):
    """Convert an image to grayscale and optionally replicate the grayscale channel.

    This transform first converts a color image to a single-channel grayscale image using various methods,
    then replicates the grayscale channel if num_output_channels is greater than 1.

    Args:
        num_output_channels (int): The number of channels in the output image. If greater than 1,
            the grayscale channel will be replicated. Default: 3.
        method (Literal["weighted_average", "from_lab", "desaturation", "average", "max", "pca"]):
            The method used for grayscale conversion:
            - "weighted_average": Uses a weighted sum of RGB channels (0.299R + 0.587G + 0.114B).
              Works only with 3-channel images. Provides realistic results based on human perception.
            - "from_lab": Extracts the L channel from the LAB color space.
              Works only with 3-channel images. Gives perceptually uniform results.
            - "desaturation": Averages the maximum and minimum values across channels.
              Works with any number of channels. Fast but may not preserve perceived brightness well.
            - "average": Simple average of all channels.
              Works with any number of channels. Fast but may not give realistic results.
            - "max": Takes the maximum value across all channels.
              Works with any number of channels. Tends to produce brighter results.
            - "pca": Applies Principal Component Analysis to reduce channels.
              Works with any number of channels. Can preserve more information but is computationally intensive.
        p (float): Probability of applying the transform. Default: 0.5.

    Raises:
        TypeError: If the input image doesn't have 3 channels for methods that require it.

    Note:
        - The transform first converts the input image to single-channel grayscale, then replicates
          this channel if num_output_channels > 1.
        - "weighted_average" and "from_lab" are typically used in image processing and computer vision
          applications where accurate representation of human perception is important.
        - "desaturation" and "average" are often used in simple image manipulation tools or when
          computational speed is a priority.
        - "max" method can be useful in scenarios where preserving bright features is important,
          such as in some medical imaging applications.
        - "pca" might be used in advanced image analysis tasks or when dealing with hyperspectral images.

    Image types:
        uint8, float32

    Returns:
        np.ndarray: Grayscale image with the specified number of channels.
    """

    class InitSchema(BaseTransformInitSchema):
        num_output_channels: int = Field(default=3, description="The number of output channels.", ge=1)
        method: Literal["weighted_average", "from_lab", "desaturation", "average", "max", "pca"]

    def __init__(
        self,
        num_output_channels: int = 3,
        method: Literal["weighted_average", "from_lab", "desaturation", "average", "max", "pca"] = "weighted_average",
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.num_output_channels = num_output_channels
        self.method = method

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        if is_grayscale_image(img):
            warnings.warn("The image is already gray.", stacklevel=2)
            return img

        num_channels = get_num_channels(img)

        if num_channels != NUM_RGB_CHANNELS and self.method not in {"desaturation", "average", "max", "pca"}:
            msg = "ToGray transformation expects 3-channel images."
            raise TypeError(msg)

        return fmain.to_gray(img, self.num_output_channels, self.method)

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "num_output_channels", "method"


class ToRGB(ImageOnlyTransform):
    """Convert the input grayscale image to RGB.

    Args:
        p: probability of applying the transform. Default: 1.

    Targets:
        image

    Image types:
        uint8, float32

    """

    def __init__(self, p: float = 1.0, always_apply: bool | None = None):
        super().__init__(p=p, always_apply=always_apply)

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        if is_rgb_image(img):
            warnings.warn("The image is already an RGB.", stacklevel=2)
            return np.ascontiguousarray(img)
        if not is_grayscale_image(img):
            msg = "ToRGB transformation expects 2-dim images or 3-dim with the last dimension equal to 1."
            raise TypeError(msg)

        return fmain.grayscale_to_multichannel(img, num_output_channels=3)

    def get_transform_init_args_names(self) -> tuple[()]:
        return ()


class ToSepia(ImageOnlyTransform):
    """Applies sepia filter to the input RGB image

    Args:
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    """

    def __init__(self, p: float = 0.5, always_apply: bool | None = None):
        super().__init__(p, always_apply)
        self.sepia_transformation_matrix = np.array(
            [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]],
        )

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        if not is_rgb_image(img):
            msg = "ToSepia transformation expects 3-channel images."
            raise TypeError(msg)
        return fmain.linear_transformation_rgb(img, self.sepia_transformation_matrix)

    def get_transform_init_args_names(self) -> tuple[()]:
        return ()


class ToFloat(ImageOnlyTransform):
    """Divide pixel values by `max_value` to get a float32 output array where all values lie in the range [0, 1.0].
    If `max_value` is None the transform will try to infer the maximum value by inspecting the data type of the input
    image.

    See Also:
        :class:`~albumentations.augmentations.transforms.FromFloat`

    Args:
        max_value: maximum possible input value. Default: None.
        p: probability of applying the transform. Default: 1.0.

    Targets:
        image

    Image types:
        any type

    """

    class InitSchema(BaseTransformInitSchema):
        max_value: float | None = Field(default=None, description="Maximum possible input value.")
        p: ProbabilityType = 1

    def __init__(self, max_value: float | None = None, p: float = 1.0, always_apply: bool | None = None):
        super().__init__(p, always_apply)
        self.max_value = max_value

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        return to_float(img, self.max_value)

    def get_transform_init_args_names(self) -> tuple[str]:
        return ("max_value",)


class FromFloat(ImageOnlyTransform):
    """Convert an image from floating point representation to the specified data type.

    This transform is designed to convert images from a normalized floating-point representation
    (typically with values in the range [0, 1]) to other data types, scaling the values appropriately.

    Args:
        dtype (str): The desired output data type. Supported types include 'uint8', 'uint16',
                     'uint32'. Default: 'uint8'.
        max_value (float | None): The maximum value for the output dtype. If None, the transform
                                  will attempt to infer the maximum value based on the dtype.
                                  Default: None.
        p (float): Probability of applying the transform. Default: 1.0.

    Targets:
        image

    Image types:
        float32, float64

    Note:
        - This is the inverse transform for ToFloat.
        - Input images are expected to be in floating point format with values in the range [0, 1].
        - For integer output types (uint8, uint16, uint32), the function will scale the values
          to the appropriate range (e.g., 0-255 for uint8).
        - For float output types (float32, float64), the values will remain in the [0, 1] range.
        - The transform uses the `from_float` function internally, which ensures output values
          are within the valid range for the specified dtype.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> transform = A.FromFloat(dtype='uint8', max_value=None, p=1.0)
        >>> image = np.random.rand(100, 100, 3).astype(np.float32)  # Float image in [0, 1] range
        >>> result = transform(image=image)
        >>> uint8_image = result['image']
        >>> assert uint8_image.dtype == np.uint8
        >>> assert uint8_image.min() >= 0 and uint8_image.max() <= 255

    """

    class InitSchema(BaseTransformInitSchema):
        dtype: Literal["uint8", "uint16", "float32", "float64"]
        max_value: float | None
        p: ProbabilityType = 1

    def __init__(
        self,
        dtype: Literal["uint8", "uint16", "float32", "float64"] = "uint8",
        max_value: float | None = None,
        always_apply: bool | None = None,
        p: float = 1.0,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.dtype = np.dtype(dtype)
        self.max_value = max_value

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        return from_float(img, self.dtype, self.max_value)

    def get_transform_init_args(self) -> dict[str, Any]:
        return {"dtype": self.dtype.name, "max_value": self.max_value}


class InterpolationDict(TypedDict):
    upscale: int
    downscale: int


class InterpolationPydantic(BaseModel):
    upscale: InterpolationType
    downscale: InterpolationType


class Downscale(ImageOnlyTransform):
    """Decrease image quality by downscaling and upscaling back.

    This transform simulates the effect of a low-resolution image by first downscaling
    the image to a lower resolution and then upscaling it back to its original size.
    This process introduces loss of detail and can be used to simulate low-quality
    images or to test the robustness of models to different image resolutions.

    Args:
        scale_range (tuple[float, float]): Range for the downscaling factor.
            Should be two float values between 0 and 1, where the first value is less than or equal to the second.
            The actual downscaling factor will be randomly chosen from this range for each image.
            Lower values result in more aggressive downscaling.
            Default: (0.25, 0.25)

        interpolation_pair (InterpolationDict): A dictionary specifying the interpolation methods to use for
            downscaling and upscaling. Should contain two keys:
            - 'downscale': Interpolation method for downscaling
            - 'upscale': Interpolation method for upscaling
            Values should be OpenCV interpolation flags (e.g., cv2.INTER_NEAREST, cv2.INTER_LINEAR, etc.)
            Default: {'downscale': cv2.INTER_NEAREST, 'upscale': cv2.INTER_NEAREST}

        p (float): Probability of applying the transform. Should be in the range [0, 1].
            Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Note:
        - The actual downscaling factor is randomly chosen for each image from the range
          specified in scale_range.
        - Using different interpolation methods for downscaling and upscaling can produce
          various effects. For example, using INTER_NEAREST for both can create a pixelated look,
          while using INTER_LINEAR or INTER_CUBIC can produce smoother results.
        - This transform can be useful for data augmentation, especially when training models
          that need to be robust to variations in image quality or resolution.

    Example:
        >>> import albumentations as A
        >>> import cv2
        >>> transform = A.Downscale(
        ...     scale_range=(0.5, 0.75),
        ...     interpolation_pair={'downscale': cv2.INTER_NEAREST, 'upscale': cv2.INTER_LINEAR},
        ...     p=0.5
        ... )
        >>> transformed = transform(image=image)
        >>> downscaled_image = transformed['image']
    """

    class InitSchema(BaseTransformInitSchema):
        scale_min: float | None
        scale_max: float | None

        interpolation: int | Interpolation | InterpolationDict | None = Field(
            default_factory=lambda: Interpolation(downscale=cv2.INTER_NEAREST, upscale=cv2.INTER_NEAREST),
        )
        interpolation_pair: InterpolationPydantic

        scale_range: Annotated[tuple[float, float], AfterValidator(check_01), AfterValidator(nondecreasing)]

        @model_validator(mode="after")
        def validate_params(self) -> Self:
            if self.scale_min is not None and self.scale_max is not None:
                warn(
                    "scale_min and scale_max are deprecated. Use scale_range instead.",
                    DeprecationWarning,
                    stacklevel=2,
                )

                self.scale_range = (self.scale_min, self.scale_max)
                self.scale_min = None
                self.scale_max = None

            if self.interpolation is not None:
                warn(
                    "Downscale.interpolation is deprecated. Use Downscale.interpolation_pair instead.",
                    DeprecationWarning,
                    stacklevel=2,
                )

                if isinstance(self.interpolation, dict):
                    self.interpolation_pair = InterpolationPydantic(**self.interpolation)
                elif isinstance(self.interpolation, int):
                    self.interpolation_pair = InterpolationPydantic(
                        upscale=self.interpolation,
                        downscale=self.interpolation,
                    )
                elif isinstance(self.interpolation, Interpolation):
                    self.interpolation_pair = InterpolationPydantic(
                        upscale=self.interpolation.upscale,
                        downscale=self.interpolation.downscale,
                    )
                self.interpolation = None

            return self

    def __init__(
        self,
        scale_min: float | None = None,
        scale_max: float | None = None,
        interpolation: int | Interpolation | InterpolationDict | None = None,
        scale_range: tuple[float, float] = (0.25, 0.25),
        interpolation_pair: InterpolationDict = InterpolationDict(
            {"upscale": cv2.INTER_NEAREST, "downscale": cv2.INTER_NEAREST},
        ),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.scale_range = scale_range
        self.interpolation_pair = interpolation_pair

    def apply(self, img: np.ndarray, scale: float, **params: Any) -> np.ndarray:
        return fmain.downscale(
            img,
            scale=scale,
            down_interpolation=self.interpolation_pair["downscale"],
            up_interpolation=self.interpolation_pair["upscale"],
        )

    def get_params(self) -> dict[str, Any]:
        return {"scale": random.uniform(*self.scale_range)}

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return "scale_range", "interpolation_pair"


class Lambda(NoOp):
    """A flexible transformation class for using user-defined transformation functions per targets.
    Function signature must include **kwargs to accept optional arguments like interpolation method, image size, etc:

    Args:
        image: Image transformation function.
        mask: Mask transformation function.
        keypoint: Keypoint transformation function.
        bbox: BBox transformation function.
        global_label: Global label transformation function.
        p: probability of applying the transform. Default: 1.0.

    Targets:
        image, mask, bboxes, keypoints, global_label

    Image types:
        Any

    """

    def __init__(
        self,
        image: Callable[..., Any] | None = None,
        mask: Callable[..., Any] | None = None,
        keypoints: Callable[..., Any] | None = None,
        bboxes: Callable[..., Any] | None = None,
        global_label: Callable[..., Any] | None = None,
        name: str | None = None,
        always_apply: bool | None = None,
        p: float = 1.0,
    ):
        super().__init__(p, always_apply)

        self.name = name
        self.custom_apply_fns = {
            target_name: fmain.noop for target_name in ("image", "mask", "keypoints", "bboxes", "global_label")
        }
        for target_name, custom_apply_fn in {
            "image": image,
            "mask": mask,
            "keypoints": keypoints,
            "bboxes": bboxes,
            "global_label": global_label,
        }.items():
            if custom_apply_fn is not None:
                if isinstance(custom_apply_fn, LambdaType) and custom_apply_fn.__name__ == "<lambda>":
                    warnings.warn(
                        "Using lambda is incompatible with multiprocessing. "
                        "Consider using regular functions or partial().",
                        stacklevel=2,
                    )

                self.custom_apply_fns[target_name] = custom_apply_fn

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        fn = self.custom_apply_fns["image"]
        return fn(img, **params)

    def apply_to_mask(self, mask: np.ndarray, **params: Any) -> np.ndarray:
        fn = self.custom_apply_fns["mask"]
        return fn(mask, **params)

    def apply_to_bboxes(self, bboxes: np.ndarray, **params: Any) -> np.ndarray:
        is_ndarray = True

        if not isinstance(bboxes, np.ndarray):
            is_ndarray = False
            bboxes = np.array(bboxes, dtype=np.float32)

        fn = self.custom_apply_fns["bboxes"]
        result = fn(bboxes, **params)

        if not is_ndarray:
            return result.tolist()

        return result

    def apply_to_keypoints(self, keypoints: np.ndarray, **params: Any) -> np.ndarray:
        is_ndarray = True
        if not isinstance(keypoints, np.ndarray):
            is_ndarray = False
            keypoints = np.array(keypoints, dtype=np.float32)

        fn = self.custom_apply_fns["keypoints"]
        result = fn(keypoints, **params)

        if not is_ndarray:
            return result.tolist()

        return result

    def apply_to_global_label(self, label: np.ndarray, **params: Any) -> np.ndarray:
        fn = self.custom_apply_fns["global_label"]
        return fn(label, **params)

    @classmethod
    def is_serializable(cls) -> bool:
        return False

    def to_dict_private(self) -> dict[str, Any]:
        if self.name is None:
            msg = (
                "To make a Lambda transform serializable you should provide the `name` argument, "
                "e.g. `Lambda(name='my_transform', image=<some func>, ...)`."
            )
            raise ValueError(msg)
        return {"__class_fullname__": self.get_class_fullname(), "__name__": self.name}

    def __repr__(self) -> str:
        state = {"name": self.name}
        state.update(self.custom_apply_fns.items())  # type: ignore[arg-type]
        state.update(self.get_base_init_args())
        return f"{self.__class__.__name__}({format_args(state)})"


class MultiplicativeNoise(ImageOnlyTransform):
    """Apply multiplicative noise to the input image.

    This transform multiplies each pixel in the image by a random value or array of values,
    effectively creating a noise pattern that scales with the image intensity.

    Args:
        multiplier (tuple[float, float]): The range for the random multiplier.
            Defines the range from which the multiplier is sampled.
            Default: (0.9, 1.1)

        per_channel (bool): If True, use a different random multiplier for each channel.
            If False, use the same multiplier for all channels.
            Setting this to False is slightly faster.
            Default: False

        elementwise (bool): If True, generates a unique multiplier for each pixel.
            If False, generates a single multiplier (or one per channel if per_channel=True).
            Default: False

        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        Any

    Note:
        - When elementwise=False and per_channel=False, a single multiplier is applied to the entire image.
        - When elementwise=False and per_channel=True, each channel gets a different multiplier.
        - When elementwise=True and per_channel=False, each pixel gets the same multiplier across all channels.
        - When elementwise=True and per_channel=True, each pixel in each channel gets a unique multiplier.
        - Setting per_channel=False is slightly faster, especially for larger images.
        - This transform can be used to simulate various lighting conditions or to create noise that
          scales with image intensity.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.MultiplicativeNoise(multiplier=(0.9, 1.1), per_channel=True, p=1.0)
        >>> result = transform(image=image)
        >>> noisy_image = result["image"]

    References:
        - Multiplicative noise: https://en.wikipedia.org/wiki/Multiplicative_noise
    """

    class InitSchema(BaseTransformInitSchema):
        multiplier: Annotated[tuple[float, float], AfterValidator(check_0plus), AfterValidator(nondecreasing)]
        per_channel: bool
        elementwise: bool

    def __init__(
        self,
        multiplier: ScaleFloatType = (0.9, 1.1),
        per_channel: bool = False,
        elementwise: bool = False,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.multiplier = cast(Tuple[float, float], multiplier)
        self.elementwise = elementwise
        self.per_channel = per_channel

    def apply(
        self,
        img: np.ndarray,
        multiplier: float | np.ndarray,
        **kwargs: Any,
    ) -> np.ndarray:
        return multiply(img, multiplier)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        img = data["image"] if "image" in data else data["images"][0]
        num_channels = get_num_channels(img)

        if self.elementwise:
            shape = img.shape if self.per_channel else (*img.shape[:2], 1)
        else:
            shape = (num_channels,) if self.per_channel else (1,)

        multiplier = random_utils.uniform(self.multiplier[0], self.multiplier[1], shape).astype(np.float32)

        if not self.per_channel and num_channels > 1:
            # Replicate the multiplier for all channels if not per_channel
            multiplier = np.repeat(multiplier, num_channels, axis=-1)

        if not self.elementwise and self.per_channel:
            # Reshape to broadcast correctly when not elementwise but per_channel
            multiplier = multiplier.reshape(1, 1, -1)

        if multiplier.shape != img.shape:
            multiplier = multiplier.squeeze()

        return {"multiplier": multiplier}

    def get_transform_init_args_names(self) -> tuple[str, str, str]:
        return "multiplier", "elementwise", "per_channel"


class FancyPCA(ImageOnlyTransform):
    """Apply Fancy PCA augmentation to the input image.

    This augmentation technique applies PCA (Principal Component Analysis) to the image's color channels,
    then adds multiples of the principal components to the image, with magnitudes proportional to the
    corresponding eigenvalues times a random variable drawn from a Gaussian with mean 0 and standard
    deviation 'alpha'.

    Args:
        alpha (float or tuple of float): Standard deviation of the Gaussian distribution used to generate
            random noise for each principal component. If a single float is provided, it will be used for
            all channels. If a tuple of two floats (min, max) is provided, the standard deviation will be
            uniformly sampled from this range for each run. Default: 0.1.
        always_apply (bool): If True, the transform will always be applied. Default: False.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        any

    Note:
        - This augmentation is particularly effective for RGB images but can work with any number of channels.
        - For grayscale images, it applies a simplified version of the augmentation.
        - The transform preserves the mean of the image while adjusting the color/intensity variation.
        - This implementation is based on the paper by Krizhevsky et al. and is similar to the one used
          in the original AlexNet paper.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.FancyPCA(alpha=0.1, p=1.0)
        >>> result = transform(image=image)
        >>> augmented_image = result["image"]

    References:
        - Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep
          convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
        - https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

    """

    class InitSchema(BaseTransformInitSchema):
        alpha: float = Field(ge=0)

    def __init__(self, alpha: float = 0.1, p: float = 0.5, always_apply: bool | None = None):
        super().__init__(p=p, always_apply=always_apply)
        self.alpha = alpha

    def apply(self, img: np.ndarray, alpha_vector: np.ndarray, **params: Any) -> np.ndarray:
        return fmain.fancy_pca(img, alpha_vector)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        shape = params["shape"]
        num_channels = shape[-1] if len(shape) == NUM_MULTI_CHANNEL_DIMENSIONS else 1
        alpha_vector = random_utils.normal(0, self.alpha, num_channels).astype(np.float32)
        return {"alpha_vector": alpha_vector}

    def get_transform_init_args_names(self) -> tuple[str]:
        return ("alpha",)


class ColorJitter(ImageOnlyTransform):
    """Randomly changes the brightness, contrast, saturation, and hue of an image.

    This transform is similar to torchvision's ColorJitter but with some differences due to the use of OpenCV
    instead of Pillow. The main differences are:
    1. OpenCV and Pillow use different formulas to convert images to HSV format.
    2. This implementation uses value saturation instead of uint8 overflow as in Pillow.

    These differences may result in slightly different output compared to torchvision's ColorJitter.

    Args:
        brightness (tuple[float, float] | float): How much to jitter brightness.
            If float:
                The brightness factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
            If tuple:
                The brightness factor is sampled from the range specified.
            Should be non-negative numbers.
            Default: (0.8, 1.2)

        contrast (tuple[float, float] | float): How much to jitter contrast.
            If float:
                The contrast factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
            If tuple:
                The contrast factor is sampled from the range specified.
            Should be non-negative numbers.
            Default: (0.8, 1.2)

        saturation (tuple[float, float] | float): How much to jitter saturation.
            If float:
                The saturation factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
            If tuple:
                The saturation factor is sampled from the range specified.
            Should be non-negative numbers.
            Default: (0.8, 1.2)

        hue (float or tuple of float (min, max)): How much to jitter hue.
            If float:
                The hue factor is chosen uniformly from [-hue, hue]. Should have 0 <= hue <= 0.5.
            If tuple:
                The hue factor is sampled from the range specified. Values should be in range [-0.5, 0.5].
            Default: (-0.5, 0.5)

         p (float): Probability of applying the transform. Should be in the range [0, 1].
            Default: 0.5


    Targets:
        image

    Image types:
        uint8, float32

    Note:
        - The order of application for these color transformations is random for each image.
        - The ranges for brightness, contrast, and saturation are applied as multiplicative factors.
        - The range for hue is applied as an additive factor.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, p=1.0)
        >>> result = transform(image=image)
        >>> jittered_image = result['image']

    References:
        - https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ColorJitter
        - https://docs.opencv.org/3.4/de/d25/imgproc_color_conversions.html
    """

    class InitSchema(BaseTransformInitSchema):
        brightness: ScaleFloatType
        contrast: ScaleFloatType
        saturation: ScaleFloatType
        hue: ScaleFloatType

        @field_validator("brightness", "contrast", "saturation", "hue")
        @classmethod
        def check_ranges(cls, value: ScaleFloatType, info: ValidationInfo) -> tuple[float, float]:
            if info.field_name == "hue":
                bounds = -0.5, 0.5
                bias = 0
                clip = False
            elif info.field_name in ["brightness", "contrast", "saturation"]:
                bounds = 0, float("inf")
                bias = 1
                clip = True

            if isinstance(value, numbers.Number):
                if value < 0:
                    raise ValueError(f"If {info.field_name} is a single number, it must be non negative.")
                left = bias - value
                if clip:
                    left = max(left, 0)
                value = (left, bias + value)
            elif isinstance(value, tuple) and len(value) == PAIR:
                check_range(value, *bounds, info.field_name)

            return cast(Tuple[float, float], value)

    def __init__(
        self,
        brightness: ScaleFloatType = (0.8, 1.2),
        contrast: ScaleFloatType = (0.8, 1.2),
        saturation: ScaleFloatType = (0.8, 1.2),
        hue: ScaleFloatType = (-0.5, 0.5),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)

        self.brightness = cast(Tuple[float, float], brightness)
        self.contrast = cast(Tuple[float, float], contrast)
        self.saturation = cast(Tuple[float, float], saturation)
        self.hue = cast(Tuple[float, float], hue)

        self.transforms = [
            fmain.adjust_brightness_torchvision,
            fmain.adjust_contrast_torchvision,
            fmain.adjust_saturation_torchvision,
            fmain.adjust_hue_torchvision,
        ]

    def get_params(self) -> dict[str, Any]:
        brightness = random.uniform(*self.brightness)
        contrast = random.uniform(*self.contrast)
        saturation = random.uniform(*self.saturation)
        hue = random.uniform(*self.hue)

        order = [0, 1, 2, 3]
        order = random_utils.shuffle(order)

        return {
            "brightness": brightness,
            "contrast": contrast,
            "saturation": saturation,
            "hue": hue,
            "order": order,
        }

    def apply(
        self,
        img: np.ndarray,
        brightness: float,
        contrast: float,
        saturation: float,
        hue: float,
        order: list[int],
        **params: Any,
    ) -> np.ndarray:
        if order is None:
            order = [0, 1, 2, 3]
        if not is_rgb_image(img) and not is_grayscale_image(img):
            msg = "ColorJitter transformation expects 1-channel or 3-channel images."
            raise TypeError(msg)
        color_transforms = [brightness, contrast, saturation, hue]
        for i in order:
            img = self.transforms[i](img, color_transforms[i])
        return img

    def get_transform_init_args_names(self) -> tuple[str, str, str, str]:
        return "brightness", "contrast", "saturation", "hue"


class Sharpen(ImageOnlyTransform):
    """Sharpen the input image and overlays the result with the original image.

    Args:
        alpha: range to choose the visibility of the sharpened image. At 0, only the original image is
            visible, at 1.0 only its sharpened version is visible. Default: (0.2, 0.5).
        lightness: range to choose the lightness of the sharpened image. Default: (0.5, 1.0).
        p: probability of applying the transform. Default: 0.5.

    Targets:
        image

    """

    class InitSchema(BaseTransformInitSchema):
        alpha: ZeroOneRangeType = (0.2, 0.5)
        lightness: NonNegativeFloatRangeType = (0.5, 1.0)

    def __init__(
        self,
        alpha: tuple[float, float] = (0.2, 0.5),
        lightness: tuple[float, float] = (0.5, 1.0),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.alpha = alpha
        self.lightness = lightness

    @staticmethod
    def __generate_sharpening_matrix(alpha_sample: np.ndarray, lightness_sample: np.ndarray) -> np.ndarray:
        matrix_nochange = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.float32)
        matrix_effect = np.array(
            [[-1, -1, -1], [-1, 8 + lightness_sample, -1], [-1, -1, -1]],
            dtype=np.float32,
        )

        return (1 - alpha_sample) * matrix_nochange + alpha_sample * matrix_effect

    def get_params(self) -> dict[str, np.ndarray]:
        alpha = random.uniform(*self.alpha)
        lightness = random.uniform(*self.lightness)
        sharpening_matrix = self.__generate_sharpening_matrix(alpha_sample=alpha, lightness_sample=lightness)
        return {"sharpening_matrix": sharpening_matrix}

    def apply(self, img: np.ndarray, sharpening_matrix: np.ndarray, **params: Any) -> np.ndarray:
        return fmain.convolve(img, sharpening_matrix)

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return ("alpha", "lightness")


class Emboss(ImageOnlyTransform):
    """Apply embossing effect to the input image.

    This transform creates an emboss effect by highlighting edges and creating a 3D-like texture
    in the image. It works by applying a specific convolution kernel to the image that emphasizes
    differences in adjacent pixel values.

    Args:
        alpha (tuple[float, float]): Range to choose the visibility of the embossed image.
            At 0, only the original image is visible, at 1.0 only its embossed version is visible.
            Values should be in the range [0, 1].
            Alpha will be randomly selected from this range for each image.
            Default: (0.2, 0.5)

        strength (tuple[float, float]): Range to choose the strength of the embossing effect.
            Higher values create a more pronounced 3D effect.
            Values should be non-negative.
            Strength will be randomly selected from this range for each image.
            Default: (0.2, 0.7)

        p (float): Probability of applying the transform. Should be in the range [0, 1].
            Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Note:
        - The emboss effect is created using a 3x3 convolution kernel.
        - The 'alpha' parameter controls the blend between the original image and the embossed version.
          A higher alpha value will result in a more pronounced emboss effect.
        - The 'strength' parameter affects the intensity of the embossing. Higher strength values
          will create more contrast in the embossed areas, resulting in a stronger 3D-like effect.
        - This transform can be useful for creating artistic effects or for data augmentation
          in tasks where edge information is important.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.Emboss(alpha=(0.2, 0.5), strength=(0.2, 0.7), p=0.5)
        >>> result = transform(image=image)
        >>> embossed_image = result['image']

    References:
        - https://en.wikipedia.org/wiki/Image_embossing
        - https://www.researchgate.net/publication/303412455_Application_of_Emboss_Filtering_in_Image_Processing
    """

    class InitSchema(BaseTransformInitSchema):
        alpha: Annotated[tuple[float, float], AfterValidator(check_01)]
        strength: Annotated[tuple[float, float], AfterValidator(check_0plus)]

    def __init__(
        self,
        alpha: tuple[float, float] = (0.2, 0.5),
        strength: tuple[float, float] = (0.2, 0.7),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.alpha = alpha
        self.strength = strength

    @staticmethod
    def __generate_emboss_matrix(alpha_sample: np.ndarray, strength_sample: np.ndarray) -> np.ndarray:
        matrix_nochange = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype=np.float32)
        matrix_effect = np.array(
            [
                [-1 - strength_sample, 0 - strength_sample, 0],
                [0 - strength_sample, 1, 0 + strength_sample],
                [0, 0 + strength_sample, 1 + strength_sample],
            ],
            dtype=np.float32,
        )
        return (1 - alpha_sample) * matrix_nochange + alpha_sample * matrix_effect

    def get_params(self) -> dict[str, np.ndarray]:
        alpha = random.uniform(*self.alpha)
        strength = random.uniform(*self.strength)
        emboss_matrix = self.__generate_emboss_matrix(alpha_sample=alpha, strength_sample=strength)
        return {"emboss_matrix": emboss_matrix}

    def apply(self, img: np.ndarray, emboss_matrix: np.ndarray, **params: Any) -> np.ndarray:
        return fmain.convolve(img, emboss_matrix)

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return ("alpha", "strength")


class Superpixels(ImageOnlyTransform):
    """Transform images partially/completely to their superpixel representation.
    This implementation uses skimage's version of the SLIC algorithm.

    Args:
        p_replace (float or tuple of float): Defines for any segment the probability that the pixels within that
            segment are replaced by their average color (otherwise, the pixels are not changed).

    Examples:
                * A probability of ``0.0`` would mean, that the pixels in no
                  segment are replaced by their average color (image is not
                  changed at all).
                * A probability of ``0.5`` would mean, that around half of all
                  segments are replaced by their average color.
                * A probability of ``1.0`` would mean, that all segments are
                  replaced by their average color (resulting in a Voronoi
                  image).
            Behavior based on chosen data types for this parameter:
                * If a ``float``, then that ``flat`` will always be used.
                * If ``tuple`` ``(a, b)``, then a random probability will be
                  sampled from the interval ``[a, b]`` per image.
        n_segments (tuple of int): Rough target number of how many superpixels to generate (the algorithm
            may deviate from this number). Lower value will lead to coarser superpixels.
            Higher values are computationally more intensive and will hence lead to a slowdown
            Then a value from the discrete interval ``[a..b]`` will be sampled per image.
            If input is a single integer, the range will be ``(1, n_segments)``.
            If interested in a fixed number of segments, use ``(n_segments, n_segments)``.
        max_size (int or None): Maximum image size at which the augmentation is performed.
            If the width or height of an image exceeds this value, it will be
            downscaled before the augmentation so that the longest side matches `max_size`.
            This is done to speed up the process. The final output image has the same size as the input image.
            Note that in case `p_replace` is below ``1.0``,
            the down-/upscaling will affect the not-replaced pixels too.
            Use ``None`` to apply no down-/upscaling.
        interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
            cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
            Default: cv2.INTER_LINEAR.
        p (float): probability of applying the transform. Default: 0.5.

    Targets:
        image

    """

    class InitSchema(BaseTransformInitSchema):
        p_replace: ZeroOneRangeType = (0, 0.1)
        n_segments: OnePlusIntRangeType = (100, 100)
        max_size: int | None = Field(default=128, ge=1, description="Maximum image size for the transformation.")
        interpolation: InterpolationType = cv2.INTER_LINEAR

    def __init__(
        self,
        p_replace: ScaleFloatType = (0, 0.1),
        n_segments: ScaleIntType = (100, 100),
        max_size: int | None = 128,
        interpolation: int = cv2.INTER_LINEAR,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.p_replace = cast(Tuple[float, float], p_replace)
        self.n_segments = cast(Tuple[int, int], n_segments)
        self.max_size = max_size
        self.interpolation = interpolation

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "p_replace", "n_segments", "max_size", "interpolation"

    def get_params(self) -> dict[str, Any]:
        n_segments = random.randint(self.n_segments[0], self.n_segments[1])
        p = random.uniform(*self.p_replace)
        return {"replace_samples": random_utils.random(n_segments) < p, "n_segments": n_segments}

    def apply(
        self,
        img: np.ndarray,
        replace_samples: Sequence[bool],
        n_segments: int,
        **kwargs: Any,
    ) -> np.ndarray:
        return fmain.superpixels(img, n_segments, replace_samples, self.max_size, self.interpolation)


class TemplateTransform(ImageOnlyTransform):
    """Apply blending of input image with specified templates
    Args:
        templates (numpy array or list of numpy arrays): Images as template for transform.
        img_weight: If single float weight will be sampled from (0, img_weight).
            If tuple of float img_weight will be in range `[img_weight[0], img_weight[1])`.
            If you want fixed weight, use (img_weight, img_weight)
            Default: (0.5, 0.5).
        template_weight: If single float weight will be sampled from (0, template_weight).
            If tuple of float template_weight will be in range `[template_weight[0], template_weight[1])`.
            If you want fixed weight, use (template_weight, template_weight)
            Default: (0.5, 0.5).
        template_transform: transformation object which could be applied to template,
            must produce template the same size as input image.
        name: (Optional) Name of transform, used only for deserialization.
        p: probability of applying the transform. Default: 0.5.
    Targets:
        image
    Image types:
        uint8, float32
    """

    class InitSchema(BaseTransformInitSchema):
        templates: np.ndarray | Sequence[np.ndarray] = Field(..., description="Images as template for transform.")
        img_weight: ZeroOneRangeType = (0.5, 0.5)
        template_weight: ZeroOneRangeType = (0.5, 0.5)
        template_transform: Callable[..., Any] | None = Field(
            default=None,
            description="Transformation object applied to template.",
        )
        name: str | None = Field(default=None, description="Name of transform, used only for deserialization.")

        @field_validator("templates")
        @classmethod
        def validate_templates(cls, v: np.ndarray | list[np.ndarray]) -> list[np.ndarray]:
            if isinstance(v, np.ndarray):
                return [v]
            if isinstance(v, list):
                if not all(isinstance(item, np.ndarray) for item in v):
                    msg = "All templates must be numpy arrays."
                    raise ValueError(msg)
                return v
            msg = "Templates must be a numpy array or a list of numpy arrays."
            raise TypeError(msg)

    def __init__(
        self,
        templates: np.ndarray | list[np.ndarray],
        img_weight: ScaleFloatType = (0.5, 0.5),
        template_weight: ScaleFloatType = (0.5, 0.5),
        template_transform: Callable[..., Any] | None = None,
        name: str | None = None,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.templates = templates
        self.img_weight = cast(Tuple[float, float], img_weight)
        self.template_weight = cast(Tuple[float, float], template_weight)
        self.template_transform = template_transform
        self.name = name

    def apply(
        self,
        img: np.ndarray,
        template: np.ndarray,
        img_weight: float,
        template_weight: float,
        **params: Any,
    ) -> np.ndarray:
        return add_weighted(img, img_weight, template, template_weight)

    def get_params(self) -> dict[str, float]:
        return {
            "img_weight": random.uniform(self.img_weight[0], self.img_weight[1]),
            "template_weight": random.uniform(self.template_weight[0], self.template_weight[1]),
        }

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        img = data["image"] if "image" in data else data["images"][0]
        template = random.choice(self.templates)

        if self.template_transform is not None:
            template = self.template_transform(image=template)["image"]

        if get_num_channels(template) not in [1, get_num_channels(img)]:
            msg = (
                "Template must be a single channel or "
                "has the same number of channels as input "
                f"image ({get_num_channels(img)}), got {get_num_channels(template)}"
            )
            raise ValueError(msg)

        if template.dtype != img.dtype:
            msg = "Image and template must be the same image type"
            raise ValueError(msg)

        if img.shape[:2] != template.shape[:2]:
            raise ValueError(f"Image and template must be the same size, got {img.shape[:2]} and {template.shape[:2]}")

        if get_num_channels(template) == 1 and get_num_channels(img) > 1:
            template = np.stack((template,) * get_num_channels(img), axis=-1)

        # in order to support grayscale image with dummy dim
        template = template.reshape(img.shape)

        return {"template": template}

    @classmethod
    def is_serializable(cls) -> bool:
        return False

    def to_dict_private(self) -> dict[str, Any]:
        if self.name is None:
            msg = (
                "To make a TemplateTransform serializable you should provide the `name` argument, "
                "e.g. `TemplateTransform(name='my_transform', ...)`."
            )
            raise ValueError(msg)
        return {"__class_fullname__": self.get_class_fullname(), "__name__": self.name}


class RingingOvershoot(ImageOnlyTransform):
    """Create ringing or overshoot artefacts by conlvolving image with 2D sinc filter.

    Args:
        blur_limit: maximum kernel size for sinc filter.
            Should be in range [3, inf). Default: (7, 15).
        cutoff: range to choose the cutoff frequency in radians.
            Should be in range (0, np.pi)
            Default: (np.pi / 4, np.pi / 2).
        p: probability of applying the transform. Default: 0.5.

    Reference:
        dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter
        https://arxiv.org/abs/2107.10833

    Targets:
        image

    """

    class InitSchema(BlurInitSchema):
        blur_limit: ScaleIntType = Field(default=(7, 15), description="Maximum kernel size for sinc filter.")
        cutoff: Annotated[tuple[float, float], nondecreasing]

        @field_validator("cutoff")
        @classmethod
        def check_cutoff(cls, v: tuple[float, float], info: ValidationInfo) -> tuple[float, float]:
            bounds = 0, np.pi
            check_range(v, *bounds, info.field_name)
            return v

    def __init__(
        self,
        blur_limit: ScaleIntType = (7, 15),
        cutoff: tuple[float, float] = (np.pi / 4, np.pi / 2),
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.blur_limit = cast(Tuple[int, int], blur_limit)
        self.cutoff = cutoff

    def get_params(self) -> dict[str, np.ndarray]:
        ksize = random.randrange(self.blur_limit[0], self.blur_limit[1] + 1, 2)
        if ksize % 2 == 0:
            raise ValueError(f"Kernel size must be odd. Got: {ksize}")

        cutoff = random.uniform(*self.cutoff)

        # From dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter
        with np.errstate(divide="ignore", invalid="ignore"):
            kernel = np.fromfunction(
                lambda x, y: cutoff
                * special.j1(cutoff * np.sqrt((x - (ksize - 1) / 2) ** 2 + (y - (ksize - 1) / 2) ** 2))
                / (2 * np.pi * np.sqrt((x - (ksize - 1) / 2) ** 2 + (y - (ksize - 1) / 2) ** 2)),
                [ksize, ksize],
            )
        kernel[(ksize - 1) // 2, (ksize - 1) // 2] = cutoff**2 / (4 * np.pi)

        # Normalize kernel
        kernel = kernel.astype(np.float32) / np.sum(kernel)

        return {"kernel": kernel}

    def apply(self, img: np.ndarray, kernel: int, **params: Any) -> np.ndarray:
        return fmain.convolve(img, kernel)

    def get_transform_init_args_names(self) -> tuple[str, str]:
        return ("blur_limit", "cutoff")


class UnsharpMask(ImageOnlyTransform):
    """Sharpen the input image using Unsharp Masking processing and overlays the result with the original image.

    Args:
        blur_limit: maximum Gaussian kernel size for blurring the input image.
            Must be zero or odd and in range [0, inf). If set to 0 it will be computed from sigma
            as `round(sigma * (3 if img.dtype == np.uint8 else 4) * 2 + 1) + 1`.
            If set single value `blur_limit` will be in range (0, blur_limit).
            Default: (3, 7).
        sigma_limit: Gaussian kernel standard deviation. Must be in range [0, inf).
            If set single value `sigma_limit` will be in range (0, sigma_limit).
            If set to 0 sigma will be computed as `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`. Default: 0.
        alpha: range to choose the visibility of the sharpened image.
            At 0, only the original image is visible, at 1.0 only its sharpened version is visible.
            Default: (0.2, 0.5).
        threshold: Value to limit sharpening only for areas with high pixel difference between original image
            and it's smoothed version. Higher threshold means less sharpening on flat areas.
            Must be in range [0, 255]. Default: 10.
        p: probability of applying the transform. Default: 0.5.

    Reference:
        arxiv.org/pdf/2107.10833.pdf

    Targets:
        image

    """

    class InitSchema(BaseTransformInitSchema):
        sigma_limit: NonNegativeFloatRangeType
        alpha: ZeroOneRangeType
        threshold: int = Field(default=10, ge=0, le=255, description="Threshold for limiting sharpening.")

        blur_limit: ScaleIntType

        @field_validator("blur_limit")
        @classmethod
        def process_blur(cls, value: ScaleIntType, info: ValidationInfo) -> tuple[int, int]:
            return process_blur_limit(value, info, min_value=3)

    def __init__(
        self,
        blur_limit: ScaleIntType = (3, 7),
        sigma_limit: ScaleFloatType = 0.0,
        alpha: ScaleFloatType = (0.2, 0.5),
        threshold: int = 10,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.blur_limit = cast(Tuple[int, int], blur_limit)
        self.sigma_limit = cast(Tuple[float, float], sigma_limit)
        self.alpha = cast(Tuple[float, float], alpha)
        self.threshold = threshold

    def get_params(self) -> dict[str, Any]:
        return {
            "ksize": random.randrange(self.blur_limit[0], self.blur_limit[1] + 1, 2),
            "sigma": random.uniform(*self.sigma_limit),
            "alpha": random.uniform(*self.alpha),
        }

    def apply(self, img: np.ndarray, ksize: int, sigma: int, alpha: float, **params: Any) -> np.ndarray:
        return fmain.unsharp_mask(img, ksize, sigma=sigma, alpha=alpha, threshold=self.threshold)

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "blur_limit", "sigma_limit", "alpha", "threshold"


class PixelDropout(DualTransform):
    """Set pixels to 0 with some probability.

    Args:
        dropout_prob (float): pixel drop probability. Default: 0.01
        per_channel (bool): if set to `True` drop mask will be sampled for each channel,
            otherwise the same mask will be sampled for all channels. Default: False
        drop_value (number or sequence of numbers or None): Value that will be set in dropped place.
            If set to None value will be sampled randomly, default ranges will be used:
                - uint8 - [0, 255]
                - uint16 - [0, 65535]
                - uint32 - [0, 4294967295]
                - float, double - [0, 1]
            Default: 0
        mask_drop_value (number or sequence of numbers or None): Value that will be set in dropped place in masks.
            If set to None masks will be unchanged. Default: 0
        p (float): probability of applying the transform. Default: 0.5.

    Targets:
        image, mask
    Image types:
        any

    """

    class InitSchema(BaseTransformInitSchema):
        dropout_prob: ProbabilityType = 0.01
        per_channel: bool = Field(default=False, description="Sample drop mask per channel.")
        drop_value: ScaleFloatType | None = Field(
            default=0,
            description="Value to set in dropped pixels. None for random sampling.",
        )
        mask_drop_value: ScaleFloatType | None = Field(
            default=None,
            description="Value to set in dropped pixels in masks. None to leave masks unchanged.",
        )

        @model_validator(mode="after")
        def validate_mask_drop_value(self) -> Self:
            if self.mask_drop_value is not None and self.per_channel:
                msg = "PixelDropout supports mask only with per_channel=False."
                raise ValueError(msg)
            return self

    _targets = (Targets.IMAGE, Targets.MASK)

    def __init__(
        self,
        dropout_prob: float = 0.01,
        per_channel: bool = False,
        drop_value: ScaleFloatType | None = 0,
        mask_drop_value: ScaleFloatType | None = None,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.dropout_prob = dropout_prob
        self.per_channel = per_channel
        self.drop_value = drop_value
        self.mask_drop_value = mask_drop_value

    def apply(
        self,
        img: np.ndarray,
        drop_mask: np.ndarray,
        drop_value: float | Sequence[float],
        **params: Any,
    ) -> np.ndarray:
        return fmain.pixel_dropout(img, drop_mask, drop_value)

    def apply_to_mask(self, mask: np.ndarray, drop_mask: np.ndarray, **params: Any) -> np.ndarray:
        if self.mask_drop_value is None:
            return mask

        if mask.ndim == MONO_CHANNEL_DIMENSIONS:
            drop_mask = np.squeeze(drop_mask)

        return fmain.pixel_dropout(mask, drop_mask, self.mask_drop_value)

    def apply_to_bboxes(self, bboxes: np.ndarray, **params: Any) -> np.ndarray:
        return bboxes

    def apply_to_keypoints(self, keypoints: np.ndarray, **params: Any) -> np.ndarray:
        return keypoints

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        img = data["image"] if "image" in data else data["images"][0]
        shape = img.shape if self.per_channel else img.shape[:2]

        rnd = np.random.RandomState(random.randint(0, 1 << 31))
        # Use choice to create boolean matrix, if we will use binomial after that we will need type conversion
        drop_mask = rnd.choice([True, False], shape, p=[self.dropout_prob, 1 - self.dropout_prob])

        drop_value: float | Sequence[float] | np.ndarray
        if drop_mask.ndim != img.ndim:
            drop_mask = np.expand_dims(drop_mask, -1)
        if self.drop_value is None:
            drop_shape = 1 if is_grayscale_image(img) else int(img.shape[-1])

            if img.dtype in (np.uint8, np.uint16, np.uint32):
                drop_value = rnd.randint(0, int(MAX_VALUES_BY_DTYPE[img.dtype]), drop_shape, img.dtype)
            elif img.dtype in [np.float32, np.double]:
                drop_value = rnd.uniform(0, 1, drop_shape).astype(img.dtype)
            else:
                raise ValueError(f"Unsupported dtype: {img.dtype}")
        else:
            drop_value = self.drop_value

        return {"drop_mask": drop_mask, "drop_value": drop_value}

    def get_transform_init_args_names(self) -> tuple[str, str, str, str]:
        return ("dropout_prob", "per_channel", "drop_value", "mask_drop_value")


class Spatter(ImageOnlyTransform):
    """Apply spatter transform. It simulates corruption which can occlude a lens in the form of rain or mud.

    Args:
        mean (float, or tuple of floats): Mean value of normal distribution for generating liquid layer.
            If single float mean will be sampled from `(0, mean)`
            If tuple of float mean will be sampled from range `(mean[0], mean[1])`.
            If you want constant value use (mean, mean).
            Default (0.65, 0.65)
        std (float, or tuple of floats): Standard deviation value of normal distribution for generating liquid layer.
            If single float the number will be sampled from `(0, std)`.
            If tuple of float std will be sampled from range `(std[0], std[1])`.
            If you want constant value use (std, std).
            Default: (0.3, 0.3).
        gauss_sigma (float, or tuple of floats): Sigma value for gaussian filtering of liquid layer.
            If single float the number will be sampled from `(0, gauss_sigma)`.
            If tuple of float gauss_sigma will be sampled from range `(gauss_sigma[0], gauss_sigma[1])`.
            If you want constant value use (gauss_sigma, gauss_sigma).
            Default: (2, 3).
        cutout_threshold (float, or tuple of floats): Threshold for filtering liqued layer
            (determines number of drops). If single float it will used as cutout_threshold.
            If single float the number will be sampled from `(0, cutout_threshold)`.
            If tuple of float cutout_threshold will be sampled from range `(cutout_threshold[0], cutout_threshold[1])`.
            If you want constant value use `(cutout_threshold, cutout_threshold)`.
            Default: (0.68, 0.68).
        intensity (float, or tuple of floats): Intensity of corruption.
            If single float the number will be sampled from `(0, intensity)`.
            If tuple of float intensity will be sampled from range `(intensity[0], intensity[1])`.
            If you want constant value use `(intensity, intensity)`.
            Default: (0.6, 0.6).
        mode (string, or list of strings): Type of corruption. Currently, supported options are 'rain' and 'mud'.
             If list is provided type of corruption will be sampled list. Default: ("rain").
        color (list of (r, g, b) or dict or None): Corruption elements color.
            If list uses provided list as color for specified mode.
            If dict uses provided color for specified mode. Color for each specified mode should be provided in dict.
            If None uses default colors (rain: (238, 238, 175), mud: (20, 42, 63)).
        p (float): probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        https://arxiv.org/abs/1903.12261
        https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

    """

    class InitSchema(BaseTransformInitSchema):
        mean: ZeroOneRangeType = (0.65, 0.65)
        std: ZeroOneRangeType = (0.3, 0.3)
        gauss_sigma: NonNegativeFloatRangeType = (2, 2)
        cutout_threshold: ZeroOneRangeType = (0.68, 0.68)
        intensity: ZeroOneRangeType = (0.6, 0.6)
        mode: SpatterMode | Sequence[SpatterMode] = Field(
            default="rain",
            description="Type of corruption ('rain', 'mud').",
        )
        color: Sequence[int] | dict[str, Sequence[int]] | None = None

        @field_validator("mode")
        @classmethod
        def check_mode(cls, mode: SpatterMode | Sequence[SpatterMode]) -> Sequence[SpatterMode]:
            if isinstance(mode, str):
                return [mode]
            return mode

        @model_validator(mode="after")
        def check_color(self) -> Self:
            if self.color is None:
                self.color = {"rain": [238, 238, 175], "mud": [20, 42, 63]}

            elif isinstance(self.color, (list, tuple)) and len(self.mode) == 1:
                if len(self.color) != NUM_RGB_CHANNELS:
                    msg = "Color must be a list of three integers for RGB format."
                    raise ValueError(msg)
                self.color = {self.mode[0]: self.color}
            elif isinstance(self.color, dict):
                result = {}
                for mode in self.mode:
                    if mode not in self.color:
                        raise ValueError(f"Color for mode {mode} is not specified.")
                    if len(self.color[mode]) != NUM_RGB_CHANNELS:
                        raise ValueError(f"Color for mode {mode} must be in RGB format.")
                    result[mode] = self.color[mode]
            else:
                msg = "Color must be a list of RGB values or a dict mapping mode to RGB values."
                raise ValueError(msg)
            return self

    def __init__(
        self,
        mean: ScaleFloatType = (0.65, 0.65),
        std: ScaleFloatType = (0.3, 0.3),
        gauss_sigma: ScaleFloatType = (2, 2),
        cutout_threshold: ScaleFloatType = (0.68, 0.68),
        intensity: ScaleFloatType = (0.6, 0.6),
        mode: SpatterMode | Sequence[SpatterMode] = "rain",
        color: Sequence[int] | dict[str, Sequence[int]] | None = None,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.mean = cast(Tuple[float, float], mean)
        self.std = cast(Tuple[float, float], std)
        self.gauss_sigma = cast(Tuple[float, float], gauss_sigma)
        self.cutout_threshold = cast(Tuple[float, float], cutout_threshold)
        self.intensity = cast(Tuple[float, float], intensity)
        self.mode = mode
        self.color = cast(Dict[str, Sequence[int]], color)

    def apply(
        self,
        img: np.ndarray,
        non_mud: np.ndarray,
        mud: np.ndarray,
        drops: np.ndarray,
        mode: SpatterMode,
        **params: dict[str, Any],
    ) -> np.ndarray:
        return fmain.spatter(img, non_mud, mud, drops, mode)

    def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, Any]:
        height, width = params["shape"][:2]

        mean = random.uniform(*self.mean)
        std = random.uniform(*self.std)
        cutout_threshold = random.uniform(*self.cutout_threshold)
        sigma = random.uniform(*self.gauss_sigma)
        mode = random.choice(self.mode)
        intensity = random.uniform(*self.intensity)
        color = np.array(self.color[mode]) / 255.0

        liquid_layer = random_utils.normal(size=(height, width), loc=mean, scale=std)
        liquid_layer = gaussian_filter(liquid_layer, sigma=sigma, mode="nearest")
        liquid_layer[liquid_layer < cutout_threshold] = 0

        if mode == "rain":
            liquid_layer = clip(liquid_layer * 255, np.uint8)
            dist = 255 - cv2.Canny(liquid_layer, 50, 150)
            dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5)
            _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC)
            dist = clip(blur(dist, 3), np.uint8)
            dist = fmain.equalize(dist)

            ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
            dist = fmain.convolve(dist, ker)
            dist = blur(dist, 3).astype(np.float32)

            m = liquid_layer * dist
            m *= 1 / np.max(m, axis=(0, 1))

            drops = m[:, :, None] * color * intensity
            mud = None
            non_mud = None
        else:
            m = np.where(liquid_layer > cutout_threshold, 1, 0)
            m = gaussian_filter(m.astype(np.float32), sigma=sigma, mode="nearest")
            m[m < 1.2 * cutout_threshold] = 0
            m = m[..., np.newaxis]

            mud = m * color
            non_mud = 1 - m
            drops = None

        return {
            "non_mud": non_mud,
            "mud": mud,
            "drops": drops,
            "mode": mode,
        }

    def get_transform_init_args_names(self) -> tuple[str, str, str, str, str, str, str]:
        return "mean", "std", "gauss_sigma", "intensity", "cutout_threshold", "mode", "color"


class ChromaticAberration(ImageOnlyTransform):
    """Add lateral chromatic aberration by distorting the red and blue channels of the input image.

    Chromatic aberration is an optical effect that occurs when a lens fails to focus all colors to the same point.
    This transform simulates this effect by applying different radial distortions to the red and blue channels
    of the image, while leaving the green channel unchanged.

    Args:
        primary_distortion_limit (tuple[float, float] | float): Range of the primary radial distortion coefficient.
            If a single float value is provided, the range
            will be (-primary_distortion_limit, primary_distortion_limit).
            This parameter controls the distortion in the center of the image:
            - Positive values result in pincushion distortion (edges bend inward)
            - Negative values result in barrel distortion (edges bend outward)
            Default: (-0.02, 0.02).

        secondary_distortion_limit (tuple[float, float] | float): Range of the secondary radial distortion coefficient.
            If a single float value is provided, the range
            will be (-secondary_distortion_limit, secondary_distortion_limit).
            This parameter controls the distortion in the corners of the image:
            - Positive values enhance pincushion distortion
            - Negative values enhance barrel distortion
            Default: (-0.05, 0.05).

        mode (Literal["green_purple", "red_blue", "random"]): Type of color fringing to apply. Options are:
            - 'green_purple': Distorts red and blue channels in opposite directions, creating green-purple fringing.
            - 'red_blue': Distorts red and blue channels in the same direction, creating red-blue fringing.
            - 'random': Randomly chooses between 'green_purple' and 'red_blue' modes for each application.
            Default: 'green_purple'.

        interpolation (InterpolationType): Flag specifying the interpolation algorithm. Should be one of:
            cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
            Default: cv2.INTER_LINEAR.

        p (float): Probability of applying the transform. Should be in the range [0, 1].
            Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Note:
        - This transform only affects RGB images. Grayscale images will raise an error.
        - The strength of the effect depends on both primary and secondary distortion limits.
        - Higher absolute values for distortion limits will result in more pronounced chromatic aberration.
        - The 'green_purple' mode tends to produce more noticeable effects than 'red_blue'.

    Example:
        >>> import albumentations as A
        >>> import cv2
        >>> transform = A.ChromaticAberration(
        ...     primary_distortion_limit=0.05,
        ...     secondary_distortion_limit=0.1,
        ...     mode='green_purple',
        ...     interpolation=cv2.INTER_LINEAR,
        ...     p=1.0
        ... )
        >>> transformed = transform(image=image)
        >>> aberrated_image = transformed['image']

    References:
        - https://en.wikipedia.org/wiki/Chromatic_aberration
        - https://www.researchgate.net/publication/320691320_Chromatic_Aberration_in_Digital_Images
    """

    class InitSchema(BaseTransformInitSchema):
        primary_distortion_limit: SymmetricRangeType
        secondary_distortion_limit: SymmetricRangeType
        mode: ChromaticAberrationMode
        interpolation: InterpolationType

    def __init__(
        self,
        primary_distortion_limit: ScaleFloatType = (-0.02, 0.02),
        secondary_distortion_limit: ScaleFloatType = (-0.05, 0.05),
        mode: ChromaticAberrationMode = "green_purple",
        interpolation: InterpolationType = cv2.INTER_LINEAR,
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p=p, always_apply=always_apply)
        self.primary_distortion_limit = cast(Tuple[float, float], primary_distortion_limit)
        self.secondary_distortion_limit = cast(Tuple[float, float], secondary_distortion_limit)
        self.mode = mode
        self.interpolation = interpolation

    def apply(
        self,
        img: np.ndarray,
        primary_distortion_red: float,
        secondary_distortion_red: float,
        primary_distortion_blue: float,
        secondary_distortion_blue: float,
        **params: Any,
    ) -> np.ndarray:
        non_rgb_error(img)
        return fmain.chromatic_aberration(
            img,
            primary_distortion_red,
            secondary_distortion_red,
            primary_distortion_blue,
            secondary_distortion_blue,
            self.interpolation,
        )

    def get_params(self) -> dict[str, float]:
        primary_distortion_red = random.uniform(*self.primary_distortion_limit)
        secondary_distortion_red = random.uniform(*self.secondary_distortion_limit)
        primary_distortion_blue = random.uniform(*self.primary_distortion_limit)
        secondary_distortion_blue = random.uniform(*self.secondary_distortion_limit)

        secondary_distortion_red = self._match_sign(primary_distortion_red, secondary_distortion_red)
        secondary_distortion_blue = self._match_sign(primary_distortion_blue, secondary_distortion_blue)

        if self.mode == "green_purple":
            # distortion coefficients of the red and blue channels have the same sign
            primary_distortion_blue = self._match_sign(primary_distortion_red, primary_distortion_blue)
            secondary_distortion_blue = self._match_sign(secondary_distortion_red, secondary_distortion_blue)
        if self.mode == "red_blue":
            # distortion coefficients of the red and blue channels have the opposite sign
            primary_distortion_blue = self._unmatch_sign(primary_distortion_red, primary_distortion_blue)
            secondary_distortion_blue = self._unmatch_sign(secondary_distortion_red, secondary_distortion_blue)

        return {
            "primary_distortion_red": primary_distortion_red,
            "secondary_distortion_red": secondary_distortion_red,
            "primary_distortion_blue": primary_distortion_blue,
            "secondary_distortion_blue": secondary_distortion_blue,
        }

    @staticmethod
    def _match_sign(a: float, b: float) -> float:
        # Match the sign of b to a
        if (a < 0 < b) or (a > 0 > b):
            return -b
        return b

    @staticmethod
    def _unmatch_sign(a: float, b: float) -> float:
        # Unmatch the sign of b to a
        if (a < 0 and b < 0) or (a > 0 and b > 0):
            return -b
        return b

    def get_transform_init_args_names(self) -> tuple[str, str, str, str]:
        return "primary_distortion_limit", "secondary_distortion_limit", "mode", "interpolation"


class Morphological(DualTransform):
    """Apply a morphological operation (dilation or erosion) to an image,
    with particular value for enhancing document scans.

    Morphological operations modify the structure of the image.
    Dilation expands the white (foreground) regions in a binary or grayscale image, while erosion shrinks them.
    These operations are beneficial in document processing, for example:
    - Dilation helps in closing up gaps within text or making thin lines thicker,
        enhancing legibility for OCR (Optical Character Recognition).
    - Erosion can remove small white noise and detach connected objects,
        making the structure of larger objects more pronounced.

    Args:
        scale (int or tuple/list of int): Specifies the size of the structuring element (kernel) used for the operation.
            - If an integer is provided, a square kernel of that size will be used.
            - If a tuple or list is provided, it should contain two integers representing the minimum
                and maximum sizes for the dilation kernel.
        operation (str, optional): The morphological operation to apply. Options are 'dilation' or 'erosion'.
            Default is 'dilation'.
        p (float, optional): The probability of applying this transformation. Default is 0.5.

    Targets:
        image, mask

    Image types:
        uint8, float32

    Reference:
        https://github.com/facebookresearch/nougat

    Example:
        >>> import albumentations as A
        >>> transform = A.Compose([
        >>>     A.Morphological(scale=(2, 3), operation='dilation', p=0.5)
        >>> ])
        >>> image = transform(image=image)["image"]
    """

    _targets = (Targets.IMAGE, Targets.MASK)

    class InitSchema(BaseTransformInitSchema):
        scale: OnePlusIntRangeType = (2, 3)
        operation: MorphologyMode = "dilation"

    def __init__(
        self,
        scale: ScaleIntType = (2, 3),
        operation: MorphologyMode = "dilation",
        always_apply: bool | None = None,
        p: float = 0.5,
    ):
        super().__init__(p, always_apply)
        self.scale = cast(Tuple[int, int], scale)
        self.operation = operation

    def apply(self, img: np.ndarray, kernel: tuple[int, int], **params: Any) -> np.ndarray:
        return fmain.morphology(img, kernel, self.operation)

    def apply_to_mask(self, mask: np.ndarray, kernel: tuple[int, int], **params: Any) -> np.ndarray:
        return fmain.morphology(mask, kernel, self.operation)

    def get_params(self) -> dict[str, float]:
        return {
            "kernel": cv2.getStructuringElement(cv2.MORPH_ELLIPSE, self.scale),
        }

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return ("scale", "operation")

    @property
    def targets(self) -> dict[str, Callable[..., Any]]:
        return {
            "image": self.apply,
            "mask": self.apply_to_mask,
            "masks": self.apply_to_masks,
        }


PLANKIAN_JITTER_CONST = {
    "MAX_TEMP": 15_000,
    "MIN_BLACKBODY_TEMP": 3_000,
    "MIN_CIED_TEMP": 4_000,
    "WHITE_TEMP": 6_000,
    "SAMPLING_TEMP_PROB": 0.4,
}


class PlanckianJitter(ImageOnlyTransform):
    r"""Randomly jitter the image illuminant along the Planckian locus.

    Physics-based color augmentation creates realistic variations in chromaticity, simulating illumination changes
    in a scene.

    Args:
        mode (Literal["blackbody", "cied"]): The mode of the transformation. `blackbody` simulates blackbody radiation,
            and `cied` uses the CIED illuminant series.
        temperature_limit (tuple[int, int]): Temperature range to sample from. For `blackbody` mode, the range should
            be within `[3000K, 15000K]`. For "cied" mode, the range should be within `[4000K, 15000K]`. Range should
            include white temperature `6000`
            Higher temperatures produce cooler (bluish) images. If not defined, it defaults to:
            - `[3000, 15000]` for `blackbody` mode
            - `[4000, 15000]` for `cied` mode
        p (float): Probability of applying the transform. Defaults to 0.5.
        sampling_method (Literal["uniform", "gaussian"]): Method to sample the temperature.
            "uniform" samples uniformly across the range, while "gaussian" samples from a Gaussian distribution.
        p (float): Probability of applying the transform. Defaults to 0.5.

    If `temperature_limit` is not defined, it defaults to:
        - `[3000, 15000]` for `blackbody` mode
        - `[4000, 15000]` for `cied` mode

    Targets:
        image

    Image types:
        uint8, float32

    References:
        - https://github.com/TheZino/PlanckianJitter
        - https://arxiv.org/pdf/2202.07993.pdf

    """

    class InitSchema(BaseTransformInitSchema):
        mode: PlanckianJitterMode = "blackbody"
        temperature_limit: Annotated[tuple[int, int], AfterValidator(nondecreasing)] | None = None
        sampling_method: Literal["uniform", "gaussian"] = "uniform"

        @model_validator(mode="after")
        def validate_temperature(self) -> Self:
            max_temp = int(PLANKIAN_JITTER_CONST["MAX_TEMP"])

            if self.temperature_limit is None:
                if self.mode == "blackbody":
                    self.temperature_limit = int(PLANKIAN_JITTER_CONST["MIN_BLACKBODY_TEMP"]), max_temp
                elif self.mode == "cied":
                    self.temperature_limit = int(PLANKIAN_JITTER_CONST["MIN_CIED_TEMP"]), max_temp
            else:
                if self.mode == "blackbody" and (
                    min(self.temperature_limit) < PLANKIAN_JITTER_CONST["MIN_BLACKBODY_TEMP"]
                    or max(self.temperature_limit) > max_temp
                ):
                    raise ValueError("Temperature limits for blackbody should be in [3000, 15000] range")
                if self.mode == "cied" and (
                    min(self.temperature_limit) < PLANKIAN_JITTER_CONST["MIN_CIED_TEMP"]
                    or max(self.temperature_limit) > max_temp
                ):
                    raise ValueError("Temperature limits for CIED should be in [4000, 15000] range")

                if not self.temperature_limit[0] <= PLANKIAN_JITTER_CONST["WHITE_TEMP"] <= self.temperature_limit[1]:
                    raise ValueError("White temperature should be within the temperature limits")

            return self

    def __init__(
        self,
        mode: PlanckianJitterMode = "blackbody",
        temperature_limit: tuple[int, int] | None = None,
        sampling_method: Literal["uniform", "gaussian"] = "uniform",
        always_apply: bool | None = None,
        p: float = 0.5,
    ) -> None:
        super().__init__(p=p, always_apply=always_apply)

        self.mode = mode
        self.temperature_limit = cast(Tuple[int, int], temperature_limit)
        self.sampling_method = sampling_method

    def apply(self, img: np.ndarray, temperature: int, **params: Any) -> np.ndarray:
        if not is_rgb_image(img):
            raise TypeError("PlanckianJitter transformation expects 3-channel images.")
        return fmain.planckian_jitter(img, temperature, mode=self.mode)

    def get_params(self) -> dict[str, Any]:
        sampling_prob_boundary = PLANKIAN_JITTER_CONST["SAMPLING_TEMP_PROB"]
        sampling_temp_boundary = PLANKIAN_JITTER_CONST["WHITE_TEMP"]

        if self.sampling_method == "uniform":
            # Split into 2 cases to avoid selecting cold temperatures (>6000) too often
            if random.random() < sampling_prob_boundary:
                temperature = (
                    random.uniform(
                        self.temperature_limit[0],
                        sampling_temp_boundary,
                    ),
                )
            else:
                temperature = (
                    random.uniform(
                        sampling_temp_boundary,
                        self.temperature_limit[1],
                    ),
                )
        elif self.sampling_method == "gaussian":
            # Sample values from asymmetric gaussian distribution
            if random.random() < sampling_prob_boundary:
                # Left side
                shift = np.abs(
                    random.gauss(
                        0,
                        np.abs(sampling_temp_boundary - self.temperature_limit[0]) / 3,
                    ),
                )
            else:
                # Right side
                shift = -np.abs(
                    random.gauss(
                        0,
                        np.abs(self.temperature_limit[1] - sampling_temp_boundary) / 3,
                    ),
                )

            temperature = sampling_temp_boundary - shift
        else:
            raise ValueError(f"Unknown sampling method: {self.sampling_method}")

        return {"temperature": int(np.clip(temperature, self.temperature_limit[0], self.temperature_limit[1]))}

    def get_transform_init_args_names(self) -> tuple[str, ...]:
        return "mode", "temperature_limit", "sampling_method"
