# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

from __future__ import annotations

import math
import random
from copy import deepcopy
from typing import Any

import cv2
import numpy as np
import torch
from PIL import Image
from torch.nn import functional as F

from ultralytics.data.utils import polygons2masks, polygons2masks_overlap
from ultralytics.utils import LOGGER, IterableSimpleNamespace, colorstr, deprecation_warn
from ultralytics.utils.checks import check_version
from ultralytics.utils.instance import Instances
from ultralytics.utils.metrics import bbox_ioa
from ultralytics.utils.ops import segment2box, xywh2xyxy, xyxyxyxy2xywhr
from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13

DEFAULT_MEAN = (0.0, 0.0, 0.0)
DEFAULT_STD = (1.0, 1.0, 1.0)


class BaseTransform:
    """Base class for image transformations in the Ultralytics library.

    This class provides a unified interface for applying transformations to images, object instances, and semantic
    segmentation masks. Subclasses should override `apply_image`, `apply_instances`, and/or `apply_semantic` for simple
    transforms, or override `__call__` directly for complex transforms that need shared state between image and
    annotation modifications.

    Methods:
        get_params: Compute transformation parameters shared across image, instances, and semantic mask.
        apply_image: Apply transformation to the image in labels['img'].
        apply_instances: Apply transformation to object instances in labels['instances'].
        apply_semantic: Apply transformation to semantic mask in labels['semantic_mask'].
        __call__: Orchestrate the transformation pipeline.
    """

    def __call__(self, labels):
        """Apply transformation to labels dict.

        Args:
            labels (dict): Dictionary containing 'img', optionally 'instances' and 'semantic_mask'.

        Returns:
            (dict): Transformed labels dictionary.
        """
        params = self.get_params(labels)
        labels = self.apply_image(labels, params)
        labels = self.apply_instances(labels, params)
        labels = self.apply_semantic(labels, params)
        labels = self.apply_depth(labels, params)
        return labels

    def get_params(self, labels):
        """Compute and return transformation parameters.

        This method allows sharing random state or computed matrices (e.g. affine matrix, flip
        decision) between image, instances, and semantic mask transformations.

        Args:
            labels (dict): Input labels dictionary.

        Returns:
            (dict): Parameters to pass to apply_image, apply_instances, and apply_semantic.
        """
        return {}

    def apply_image(self, labels, params=None):
        """Apply transformation to image.

        Args:
            labels (dict): Dictionary containing 'img'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels dictionary.
        """
        return labels

    def apply_instances(self, labels, params=None):
        """Apply transformation to object instances.

        Args:
            labels (dict): Dictionary containing 'instances'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels dictionary.
        """
        return labels

    def apply_semantic(self, labels, params=None):
        """Apply transformation to semantic segmentation mask.

        Args:
            labels (dict): Dictionary containing 'semantic_mask'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels dictionary.
        """
        return labels

    def apply_depth(self, labels, params=None):
        """Apply transformation to depth map.

        Args:
            labels (dict): Dictionary containing 'depth'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels dictionary.
        """
        return labels


class Compose:
    """A class for composing multiple image transformations.

    Attributes:
        transforms (list[Callable]): A list of transformation functions to be applied sequentially.

    Methods:
        __call__: Apply a series of transformations to input data.
        append: Append a new transform to the existing list of transforms.
        insert: Insert a new transform at a specified index in the list of transforms.
        __getitem__: Retrieve a specific transform or a set of transforms using indexing.
        __setitem__: Set a specific transform or a set of transforms using indexing.
        tolist: Convert the list of transforms to a standard Python list.

    Examples:
        >>> transforms = [RandomFlip(), RandomPerspective(30)]
        >>> compose = Compose(transforms)
        >>> transformed_data = compose(data)
        >>> compose.append(CenterCrop((224, 224)))
        >>> compose.insert(0, RandomFlip())
    """

    def __init__(self, transforms):
        """Initialize the Compose object with a list of transforms.

        Args:
            transforms (list[Callable]): A list of callable transform objects to be applied sequentially.
        """
        self.transforms = transforms if isinstance(transforms, list) else [transforms]

    def __call__(self, data):
        """Apply a series of transformations to input data.

        This method sequentially applies each transformation in the Compose object's transforms to the input data.

        Args:
            data (Any): The input data to be transformed. This can be of any type, depending on the transformations in
                the list.

        Returns:
            (Any): The transformed data after applying all transformations in sequence.

        Examples:
            >>> transforms = [Transform1(), Transform2(), Transform3()]
            >>> compose = Compose(transforms)
            >>> transformed_data = compose(input_data)
        """
        for t in self.transforms:
            data = t(data)
        return data

    def append(self, transform):
        """Append a new transform to the existing list of transforms.

        Args:
            transform (BaseTransform): The transformation to be added to the composition.

        Examples:
            >>> compose = Compose([RandomFlip(), RandomPerspective()])
            >>> compose.append(RandomHSV())
        """
        self.transforms.append(transform)

    def insert(self, index, transform):
        """Insert a new transform at a specified index in the existing list of transforms.

        Args:
            index (int): The index at which to insert the new transform.
            transform (BaseTransform): The transform object to be inserted.

        Examples:
            >>> compose = Compose([Transform1(), Transform2()])
            >>> compose.insert(1, Transform3())
            >>> len(compose.transforms)
            3
        """
        self.transforms.insert(index, transform)

    def __getitem__(self, index: list | int) -> Compose:
        """Retrieve a specific transform or a set of transforms using indexing.

        Args:
            index (int | list[int]): Index or list of indices of the transforms to retrieve.

        Returns:
            (Compose | Any): A new Compose object if index is a list, or a single transform if index is an int.

        Raises:
            AssertionError: If the index is not of type int or list.

        Examples:
            >>> transforms = [RandomFlip(), RandomPerspective(10), RandomHSV(0.5, 0.5, 0.5)]
            >>> compose = Compose(transforms)
            >>> single_transform = compose[1]  # Returns the RandomPerspective transform directly
            >>> multiple_transforms = compose[[0, 1]]  # Returns a Compose object with RandomFlip and RandomPerspective
        """
        assert isinstance(index, (int, list)), f"The indices should be either list or int type but got {type(index)}"
        return Compose([self.transforms[i] for i in index]) if isinstance(index, list) else self.transforms[index]

    def __setitem__(self, index: list | int, value: list | int) -> None:
        """Set one or more transforms in the composition using indexing.

        Args:
            index (int | list[int]): Index or list of indices to set transforms at.
            value (Any | list[Any]): Transform or list of transforms to set at the specified index(es).

        Raises:
            AssertionError: If index type is invalid, value type doesn't match index type, or index is out of range.

        Examples:
            >>> compose = Compose([Transform1(), Transform2(), Transform3()])
            >>> compose[1] = NewTransform()  # Replace second transform
            >>> compose[[0, 1]] = [NewTransform1(), NewTransform2()]  # Replace first two transforms
        """
        assert isinstance(index, (int, list)), f"The indices should be either list or int type but got {type(index)}"
        if isinstance(index, list):
            assert isinstance(value, list), (
                f"The indices should be the same type as values, but got {type(index)} and {type(value)}"
            )
        if isinstance(index, int):
            index, value = [index], [value]
        for i, v in zip(index, value):
            assert i < len(self.transforms), f"list index {i} out of range {len(self.transforms)}."
            self.transforms[i] = v

    def tolist(self):
        """Convert the list of transforms to a standard Python list.

        Returns:
            (list): A list containing all the transform objects in the Compose instance.

        Examples:
            >>> transforms = [RandomFlip(), RandomPerspective(10), CenterCrop()]
            >>> compose = Compose(transforms)
            >>> transform_list = compose.tolist()
            >>> print(len(transform_list))
            3
        """
        return self.transforms

    def __repr__(self):
        """Return a string representation of the Compose object.

        Returns:
            (str): A string representation of the Compose object, including the list of transforms.

        Examples:
            >>> transforms = [RandomFlip(), RandomPerspective(degrees=10, translate=0.1, scale=0.1)]
            >>> compose = Compose(transforms)
            >>> "RandomFlip" in repr(compose) and "RandomPerspective" in repr(compose)
            True
        """
        return f"{self.__class__.__name__}({', '.join([f'{t}' for t in self.transforms])})"


class BaseMixTransform(BaseTransform):
    """Base class for mix transformations like Cutmix, MixUp and Mosaic.

    This class provides a foundation for implementing mix transformations on datasets. It handles the probability-based
    application of transforms and manages the mixing of multiple images and labels.

    Attributes:
        dataset (Any): The dataset object containing images and labels.
        pre_transform (Callable | None): Optional transform to apply before mixing.
        p (float): Probability of applying the mix transformation.

    Methods:
        __call__: Apply the mix transformation to the input labels.
        get_params: Prepare mixed labels and update text labels.
        get_indexes: Abstract method to get indexes of images to be mixed.
        _update_label_text: Update label text for mixed images.

    Examples:
        >>> class CustomMixTransform(BaseMixTransform):
        ...     def apply_image(self, labels, params=None):
        ...         # Implement custom image mixing here
        ...         return labels
        ...
        ...     def get_indexes(self):
        ...         return [random.randint(0, len(self.dataset) - 1) for _ in range(3)]
        >>> dataset = YourDataset()
        >>> transform = CustomMixTransform(dataset, p=0.5)
        >>> mixed_labels = transform(original_labels)
    """

    def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
        """Initialize the BaseMixTransform object for mix transformations like CutMix, MixUp and Mosaic.

        This class serves as a base for implementing mix transformations in image processing pipelines.

        Args:
            dataset (Any): The dataset object containing images and labels for mixing.
            pre_transform (Callable | None): Optional transform to apply before mixing.
            p (float): Probability of applying the mix transformation. Should be in the range [0.0, 1.0].
        """
        self.dataset = dataset
        self.pre_transform = pre_transform
        self.p = p

    def __call__(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Apply pre-processing transforms and cutmix/mixup/mosaic transforms to labels data.

        This method determines whether to apply the mix transform based on a probability factor. If applied, it selects
        additional images, applies pre-transforms if specified, and then performs the mix transform.

        Args:
            labels (dict[str, Any]): A dictionary containing label data for an image.

        Returns:
            (dict[str, Any]): The transformed labels dictionary, which may include mixed data from other images.

        Examples:
            >>> transform = BaseMixTransform(dataset, pre_transform=None, p=0.5)
            >>> result = transform({"image": img, "bboxes": boxes, "cls": classes})
        """
        if random.uniform(0, 1) > self.p:
            return labels

        params = self.get_params(labels)
        labels = self.apply_image(labels, params)
        labels = self.apply_instances(labels, params)
        labels = self.apply_semantic(labels, params)
        labels = self.apply_depth(labels, params)
        labels.pop("mix_labels", None)
        return labels

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Prepare mixed labels and update text labels.

        Args:
            labels (dict[str, Any]): A dictionary containing label data for an image.

        Returns:
            (dict[str, Any]): Parameters for apply_image, apply_instances, and apply_semantic.
        """
        # Get index of one or three other images
        indexes = self.get_indexes()
        if isinstance(indexes, int):
            indexes = [indexes]

        # Get images information will be used for Mosaic, CutMix or MixUp
        mix_labels = [self.dataset.get_image_and_label(i) for i in indexes]

        if self.pre_transform is not None:
            for i, data in enumerate(mix_labels):
                mix_labels[i] = self.pre_transform(data)
        labels["mix_labels"] = mix_labels

        # Update cls and texts
        self._update_label_text(labels)
        return {"mix_labels": mix_labels}

    def get_indexes(self):
        """Get a random index for mosaic augmentation.

        Returns:
            (int): A random index from the dataset.

        Examples:
            >>> transform = BaseMixTransform(dataset)
            >>> index = transform.get_indexes()
            >>> print(index)  # 7
        """
        return random.randint(0, len(self.dataset) - 1)

    @staticmethod
    def _update_label_text(labels: dict[str, Any]) -> dict[str, Any]:
        """Update label text and class IDs for mixed labels in image augmentation.

        This method processes the 'texts' and 'cls' fields of the input labels dictionary and any mixed labels, creating
        a unified set of text labels and updating class IDs accordingly.

        Args:
            labels (dict[str, Any]): A dictionary containing label information, including 'texts' and 'cls' fields, and
                optionally a 'mix_labels' field with additional label dictionaries.

        Returns:
            (dict[str, Any]): The updated labels dictionary with unified text labels and updated class IDs.

        Examples:
            >>> labels = {
            ...     "texts": [["cat"], ["dog"]],
            ...     "cls": torch.tensor([[0], [1]]),
            ...     "mix_labels": [{"texts": [["bird"], ["fish"]], "cls": torch.tensor([[0], [1]])}],
            ... }
            >>> updated_labels = BaseMixTransform._update_label_text(labels)
            >>> print(updated_labels["texts"])
            [['cat'], ['dog'], ['bird'], ['fish']]
            >>> print(updated_labels["cls"])
            tensor([[0],
                    [1]])
            >>> print(updated_labels["mix_labels"][0]["cls"])
            tensor([[2],
                    [3]])
        """
        if "texts" not in labels:
            return labels

        mix_texts = [*labels["texts"], *(item for x in labels["mix_labels"] for item in x["texts"])]
        mix_texts = [list(x) for x in dict.fromkeys(tuple(x) for x in mix_texts)]
        text2id = {tuple(text): i for i, text in enumerate(mix_texts)}

        for label in [labels] + labels["mix_labels"]:
            for i, cls in enumerate(label["cls"].squeeze(-1).tolist()):
                text = label["texts"][int(cls)]
                label["cls"][i] = text2id[tuple(text)]
            label["texts"] = mix_texts
        return labels


class Mosaic(BaseMixTransform):
    """Mosaic augmentation for image datasets.

    This class performs mosaic augmentation by combining multiple (4 or 9) images into a single mosaic image. The
    augmentation is applied to a dataset with a given probability.

    Attributes:
        dataset: The dataset on which the mosaic augmentation is applied.
        imgsz (int): Image size (height and width) after mosaic pipeline of a single image.
        p (float): Probability of applying the mosaic augmentation. Must be in the range 0-1.
        n (int): The grid size, either 4 (for 2x2) or 9 (for 3x3).
        border (tuple[int, int]): Border size for height and width.

    Methods:
        get_indexes: Return a list of random indexes from the dataset.
        get_params: Compute mosaic layout parameters.
        apply_image: Allocate canvas and paste images into mosaic.
        apply_instances: Concatenate and clip instances for mosaic.
        _update_labels: Update labels with padding.
        _cat_labels: Concatenate labels and clips mosaic border instances.

    Examples:
        >>> from ultralytics.data.augment import Mosaic
        >>> dataset = YourDataset(...)  # Your image dataset
        >>> mosaic_aug = Mosaic(dataset, imgsz=640, p=0.5, n=4)
        >>> augmented_labels = mosaic_aug(original_labels)
    """

    def __init__(self, dataset, imgsz: int = 640, p: float = 1.0, n: int = 4):
        """Initialize the Mosaic augmentation object.

        This class performs mosaic augmentation by combining multiple (4 or 9) images into a single mosaic image. The
        augmentation is applied to a dataset with a given probability.

        Args:
            dataset (Any): The dataset on which the mosaic augmentation is applied.
            imgsz (int): Image size (height and width) after mosaic pipeline of a single image.
            p (float): Probability of applying the mosaic augmentation. Must be in the range 0-1.
            n (int): The grid size, either 4 (for 2x2) or 9 (for 3x3).
        """
        assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."
        assert n in {4, 9}, "grid must be equal to 4 or 9."
        super().__init__(dataset=dataset, p=p)
        self.imgsz = imgsz
        self.border = (-imgsz // 2, -imgsz // 2)  # width, height
        self.n = n
        self.buffer_enabled = self.dataset.cache != "ram"

    def get_indexes(self):
        """Return a list of random indexes from the dataset for mosaic augmentation.

        This method selects random image indexes either from a buffer or from the entire dataset, depending on the
        'buffer_enabled' attribute. It is used to choose images for creating mosaic augmentations.

        Returns:
            (list[int]): A list of random image indexes. The length of the list is n-1, where n is the number of images
                used in the mosaic (either 3 or 8, depending on whether n is 4 or 9).

        Examples:
            >>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
            >>> indexes = mosaic.get_indexes()
            >>> print(len(indexes))  # Output: 3
        """
        if self.buffer_enabled:  # select images from buffer
            return random.choices(list(self.dataset.buffer), k=self.n - 1)
        else:  # select any images
            return [random.randint(0, len(self.dataset) - 1) for _ in range(self.n - 1)]

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute mosaic layout parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary.

        Returns:
            (dict[str, Any]): Parameters including 'layout' with per-patch geometry.
        """
        params = super().get_params(labels)
        assert labels.get("rect_shape") is None, "rect and mosaic are mutually exclusive."
        assert len(labels.get("mix_labels", [])), "There are no other images for mosaic augment."

        s = self.imgsz
        layout = []
        if self.n == 4:
            yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border)
            for i in range(4):
                labels_patch = labels if i == 0 else labels["mix_labels"][i - 1]
                img = labels_patch["img"]
                h, w = labels_patch.get("resized_shape", img.shape[:2])
                if i == 0:  # top left
                    x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc
                    x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h
                elif i == 1:  # top right
                    x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
                    x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
                elif i == 2:  # bottom left
                    x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
                    x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
                elif i == 3:  # bottom right
                    x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
                    x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
                padw = x1a - x1b
                padh = y1a - y1b
                layout.append(
                    {
                        "labels_patch": labels_patch,
                        "x1a": x1a,
                        "y1a": y1a,
                        "x2a": x2a,
                        "y2a": y2a,
                        "x1b": x1b,
                        "y1b": y1b,
                        "x2b": x2b,
                        "y2b": y2b,
                        "padw": padw,
                        "padh": padh,
                        "img_shape": (h, w),
                    }
                )
        elif self.n == 9:
            hp, wp = -1, -1
            h0, w0 = None, None
            for i in range(9):
                labels_patch = labels if i == 0 else labels["mix_labels"][i - 1]
                img = labels_patch["img"]
                h, w = labels_patch.get("resized_shape", img.shape[:2])
                if i == 0:  # center
                    c = s, s, s + w, s + h
                    h0, w0 = h, w
                elif i == 1:  # top
                    c = s, s - h, s + w, s
                elif i == 2:  # top right
                    c = s + wp, s - h, s + wp + w, s
                elif i == 3:  # right
                    c = s + w0, s, s + w0 + w, s + h
                elif i == 4:  # bottom right
                    c = s + w0, s + hp, s + w0 + w, s + hp + h
                elif i == 5:  # bottom
                    c = s + w0 - w, s + h0, s + w0, s + h0 + h
                elif i == 6:  # bottom left
                    c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
                elif i == 7:  # left
                    c = s - w, s + h0 - h, s, s + h0
                elif i == 8:  # top left
                    c = s - w, s + h0 - hp - h, s, s + h0 - hp
                padw, padh = c[:2]
                x1, y1, x2, y2 = (max(x, 0) for x in c)
                layout.append(
                    {
                        "labels_patch": labels_patch,
                        "x1": x1,
                        "y1": y1,
                        "x2": x2,
                        "y2": y2,
                        "padw": padw,
                        "padh": padh,
                        "img_shape": (h, w),
                    }
                )
                hp, wp = h, w
        params["layout"] = layout
        return params

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply mosaic augmentation to the image.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict | None): Parameters from get_params, including 'layout'.

        Returns:
            (dict): Updated labels with mosaic image.
        """
        layout = params["layout"]
        if self.n == 4:
            img4 = np.full((self.imgsz * 2, self.imgsz * 2, labels["img"].shape[2]), 114, dtype=np.uint8)
            for item in layout:
                labels_patch = item["labels_patch"]
                img = labels_patch["img"]
                x1a, y1a, x2a, y2a = item["x1a"], item["y1a"], item["x2a"], item["y2a"]
                x1b, y1b, x2b, y2b = item["x1b"], item["y1b"], item["x2b"], item["y2b"]
                img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]
            labels["img"] = img4
        elif self.n == 9:
            img9 = np.full((self.imgsz * 3, self.imgsz * 3, labels["img"].shape[2]), 114, dtype=np.uint8)
            for item in layout:
                labels_patch = item["labels_patch"]
                img = labels_patch["img"]
                x1, y1, x2, y2 = item["x1"], item["y1"], item["x2"], item["y2"]
                padw, padh = item["padw"], item["padh"]
                x1b, y1b = x1 - padw, y1 - padh
                x2b, y2b = x1b + (x2 - x1), y1b + (y2 - y1)
                img9[y1:y2, x1:x2] = img[y1b:y2b, x1b:x2b]
            labels["img"] = img9[-self.border[0] : self.border[0], -self.border[1] : self.border[1]]
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply mosaic augmentation to instances.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances' and 'cls'.
            params (dict | None): Parameters from get_params, including 'layout'.

        Returns:
            (dict): Updated labels with concatenated instances.
        """
        layout = params["layout"]
        mosaic_labels = []
        for item in layout:
            if self.n == 4:
                padw = item["padw"]
                padh = item["padh"]
            else:  # n == 9
                padw = item["padw"] + self.border[0]
                padh = item["padh"] + self.border[1]
            labels_patch = self._update_labels(item["labels_patch"], padw, padh, item.get("img_shape"))
            mosaic_labels.append(labels_patch)
        final_labels = self._cat_labels(mosaic_labels)
        labels.update(final_labels)
        return labels

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply mosaic augmentation to semantic mask.

        Args:
            labels (dict[str, Any]): Dictionary containing 'semantic_mask'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels with concatenated semantic mask.
        """
        if labels.get("semantic_mask") is None and all(
            m.get("semantic_mask") is None for m in labels.get("mix_labels", [])
        ):
            return labels

        layout = params["layout"]
        if self.n == 4:
            mask4 = np.full((self.imgsz * 2, self.imgsz * 2), 255, dtype=np.uint8)
            for item in layout:
                labels_patch = item["labels_patch"]
                mask = labels_patch.get("semantic_mask")
                if mask is None:
                    continue
                x1a, y1a, x2a, y2a = item["x1a"], item["y1a"], item["x2a"], item["y2a"]
                x1b, y1b, x2b, y2b = item["x1b"], item["y1b"], item["x2b"], item["y2b"]
                mask4[y1a:y2a, x1a:x2a] = mask[y1b:y2b, x1b:x2b]
            labels["semantic_mask"] = mask4
        elif self.n == 9:
            mask9 = np.full((self.imgsz * 3, self.imgsz * 3), 255, dtype=np.uint8)
            for item in layout:
                labels_patch = item["labels_patch"]
                mask = labels_patch.get("semantic_mask")
                if mask is None:
                    continue
                x1, y1, x2, y2 = item["x1"], item["y1"], item["x2"], item["y2"]
                padw, padh = item["padw"], item["padh"]
                x1b, y1b = x1 - padw, y1 - padh
                x2b, y2b = x1b + (x2 - x1), y1b + (y2 - y1)
                mask9[y1:y2, x1:x2] = mask[y1b:y2b, x1b:x2b]
            labels["semantic_mask"] = mask9[-self.border[0] : self.border[0], -self.border[1] : self.border[1]]
        return labels

    @staticmethod
    def _update_labels(labels, padw: int, padh: int, img_shape: tuple[int, int] | None = None) -> dict[str, Any]:
        """Update label coordinates with padding values.

        This method adjusts the bounding box coordinates of object instances in the labels by adding padding
        values. It also denormalizes the coordinates if they were previously normalized.

        Args:
            labels (dict[str, Any]): A dictionary containing image and instance information.
            padw (int): Padding width to be added to the x-coordinates.
            padh (int): Padding height to be added to the y-coordinates.
            img_shape (tuple[int, int] | None): Optional (h, w) of the original patch image. Needed because apply_image
                may overwrite labels["img"] with the mosaic canvas before apply_instances runs.

        Returns:
            (dict): Updated labels dictionary with adjusted instance coordinates.

        Examples:
            >>> labels = {"img": np.zeros((100, 100, 3)), "instances": Instances(...)}
            >>> padw, padh = 50, 50
            >>> updated_labels = Mosaic._update_labels(labels, padw, padh)
        """
        nh, nw = img_shape if img_shape is not None else labels["img"].shape[:2]
        labels["instances"].convert_bbox(format="xyxy")
        labels["instances"].denormalize(nw, nh)
        labels["instances"].add_padding(padw, padh)
        return labels

    def _cat_labels(self, mosaic_labels: list[dict[str, Any]]) -> dict[str, Any]:
        """Concatenate and process labels for mosaic augmentation.

        This method combines labels from multiple images used in mosaic augmentation, clips instances to the mosaic
        border, and removes zero-area boxes.

        Args:
            mosaic_labels (list[dict[str, Any]]): A list of label dictionaries for each image in the mosaic.

        Returns:
            (dict[str, Any]): A dictionary containing concatenated and processed labels for the mosaic image, including:
                - im_file (str): File path of the first image in the mosaic.
                - ori_shape (tuple[int, int]): Original shape of the first image.
                - resized_shape (tuple[int, int]): Shape of the mosaic image (imgsz * 2, imgsz * 2).
                - cls (np.ndarray): Concatenated class labels.
                - instances (Instances): Concatenated instance annotations.
                - texts (list[str], optional): Text labels if present in the original labels.

        Examples:
            >>> mosaic = Mosaic(dataset, imgsz=640)
            >>> mosaic_labels = [{"cls": np.array([0, 1]), "instances": Instances(...)} for _ in range(4)]
            >>> result = mosaic._cat_labels(mosaic_labels)
            >>> print(result.keys())
            dict_keys(['im_file', 'ori_shape', 'resized_shape', 'cls', 'instances'])
        """
        if not mosaic_labels:
            return {}
        cls = []
        instances = []
        imgsz = self.imgsz * 2  # mosaic imgsz
        for labels in mosaic_labels:
            cls.append(labels["cls"])
            instances.append(labels["instances"])
        # Final labels
        final_labels = {
            "im_file": mosaic_labels[0]["im_file"],
            "ori_shape": mosaic_labels[0]["ori_shape"],
            "resized_shape": (imgsz, imgsz),
            "cls": np.concatenate(cls, 0),
            "instances": Instances.concatenate(instances, axis=0),
        }
        final_labels["instances"].clip(imgsz, imgsz)
        good = final_labels["instances"].remove_zero_area_boxes()
        final_labels["cls"] = final_labels["cls"][good]
        if "texts" in mosaic_labels[0]:
            final_labels["texts"] = mosaic_labels[0]["texts"]
        return final_labels


class MixUp(BaseMixTransform):
    """Apply MixUp augmentation to image datasets.

    This class implements the MixUp augmentation technique as described in the paper [mixup: Beyond Empirical Risk
    Minimization](https://arxiv.org/abs/1710.09412). MixUp combines two images and their labels using a random weight.

    Attributes:
        dataset (Any): The dataset to which MixUp augmentation will be applied.
        pre_transform (Callable | None): Optional transform to apply before MixUp.
        p (float): Probability of applying MixUp augmentation.

    Methods:
        get_params: Compute MixUp parameters including blend ratio.
        apply_image: Blend images using MixUp.
        apply_instances: Concatenate instances for MixUp.

    Examples:
        >>> from ultralytics.data.augment import MixUp
        >>> dataset = YourDataset(...)  # Your image dataset
        >>> mixup = MixUp(dataset, p=0.5)
        >>> augmented_labels = mixup(original_labels)
    """

    def __init__(self, dataset, pre_transform=None, p: float = 0.0) -> None:
        """Initialize the MixUp augmentation object.

        MixUp is an image augmentation technique that combines two images by taking a weighted sum of their pixel values
        and labels. This implementation is designed for use with the Ultralytics YOLO framework.

        Args:
            dataset (Any): The dataset to which MixUp augmentation will be applied.
            pre_transform (Callable | None): Optional transform to apply to images before MixUp.
            p (float): Probability of applying MixUp augmentation to an image. Must be in the range [0, 1].
        """
        super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute MixUp parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary.

        Returns:
            (dict[str, Any]): Parameters including mix ratio 'r'.
        """
        params = super().get_params(labels)
        params["r"] = np.random.beta(32.0, 32.0)
        return params

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Blend images using MixUp.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict | None): Parameters from get_params, including 'r'.

        Returns:
            (dict): Updated labels with blended image.
        """
        r = params["r"]
        labels2 = labels["mix_labels"][0]
        labels["img"] = (labels["img"] * r + labels2["img"] * (1 - r)).astype(np.uint8)
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Concatenate instances for MixUp.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances' and 'cls'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels with concatenated instances.
        """
        labels2 = labels["mix_labels"][0]
        labels["instances"] = Instances.concatenate([labels["instances"], labels2["instances"]], axis=0)
        labels["cls"] = np.concatenate([labels["cls"], labels2["cls"]], 0)
        return labels

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply MixUp augmentation to semantic segmentation masks.

        Args:
            labels (dict[str, Any]): Primary image labels containing 'semantic_mask' and 'mix_labels'.
            params (dict[str, Any] | None): Parameters dict with key 'r' (mix ratio). Defaults to None.

        Returns:
            (dict[str, Any]): Updated labels with the semantic mask replaced by the mixed image's mask if r < 0.5.
        """
        if labels.get("semantic_mask") is None:
            return labels
        labels2 = labels["mix_labels"][0]
        if labels2.get("semantic_mask") is None:
            return labels
        r = params["r"]
        # Use mask from the image with higher weight to avoid fractional class indices
        if r < 0.5:
            labels["semantic_mask"] = labels2["semantic_mask"].copy()
        return labels


class CutMix(BaseMixTransform):
    """Apply CutMix augmentation to image datasets as described in the paper https://arxiv.org/abs/1905.04899.

    CutMix combines two images by replacing a random rectangular region of one image with the corresponding region from
    another image, and adjusts the labels proportionally to the area of the mixed region.

    Attributes:
        dataset (Any): The dataset to which CutMix augmentation will be applied.
        pre_transform (Callable | None): Optional transform to apply before CutMix.
        p (float): Probability of applying CutMix augmentation.
        beta (float): Beta distribution parameter for sampling the mixing ratio.
        num_areas (int): Number of areas to try to cut and mix.

    Methods:
        get_params: Compute CutMix parameters including cut area and filtered indexes.
        apply_image: Copy patch from secondary image into primary image.
        apply_instances: Clip and concatenate instances for CutMix.
        _rand_bbox: Generate random bounding box coordinates for the cut region.

    Examples:
        >>> from ultralytics.data.augment import CutMix
        >>> dataset = YourDataset(...)  # Your image dataset
        >>> cutmix = CutMix(dataset, p=0.5)
        >>> augmented_labels = cutmix(original_labels)
    """

    def __init__(self, dataset, pre_transform=None, p: float = 0.0, beta: float = 1.0, num_areas: int = 3) -> None:
        """Initialize the CutMix augmentation object.

        Args:
            dataset (Any): The dataset to which CutMix augmentation will be applied.
            pre_transform (Callable | None): Optional transform to apply before CutMix.
            p (float): Probability of applying CutMix augmentation.
            beta (float): Beta distribution parameter for sampling the mixing ratio.
            num_areas (int): Number of areas to try to cut and mix.
        """
        super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
        self.beta = beta
        self.num_areas = num_areas

    def _rand_bbox(self, width: int, height: int) -> tuple[int, int, int, int]:
        """Generate random bounding box coordinates for the cut region.

        Args:
            width (int): Width of the image.
            height (int): Height of the image.

        Returns:
            (tuple[int]): (x1, y1, x2, y2) coordinates of the bounding box.
        """
        # Sample mixing ratio from Beta distribution
        lam = np.random.beta(self.beta, self.beta)

        cut_ratio = np.sqrt(1.0 - lam)
        cut_w = int(width * cut_ratio)
        cut_h = int(height * cut_ratio)

        # Random center
        cx = np.random.randint(width)
        cy = np.random.randint(height)

        # Bounding box coordinates
        x1 = np.clip(cx - cut_w // 2, 0, width)
        y1 = np.clip(cy - cut_h // 2, 0, height)
        x2 = np.clip(cx + cut_w // 2, 0, width)
        y2 = np.clip(cy + cut_h // 2, 0, height)

        return x1, y1, x2, y2

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute CutMix parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary.

        Returns:
            (dict[str, Any]): Parameters including 'skip', 'area', and 'indexes2'.
        """
        params = super().get_params(labels)
        h, w = labels["img"].shape[:2]

        cut_areas = np.asarray([self._rand_bbox(w, h) for _ in range(self.num_areas)], dtype=np.float32)
        ioa1 = bbox_ioa(cut_areas, labels["instances"].bboxes)  # (self.num_areas, num_boxes)
        idx = np.nonzero(ioa1.sum(axis=1) <= 0)[0]
        if len(idx) == 0:
            params["skip"] = True
            return params

        labels2 = labels["mix_labels"][0]
        area = cut_areas[np.random.choice(idx)]  # randomly select one
        ioa2 = bbox_ioa(area[None], labels2["instances"].bboxes).squeeze(0)
        indexes2 = np.nonzero(ioa2 >= (0.01 if len(labels["instances"].segments) else 0.1))[0]
        if len(indexes2) == 0:
            params["skip"] = True
            return params

        params["area"] = area
        params["indexes2"] = indexes2
        params["w"] = w
        params["h"] = h
        return params

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply CutMix to the image.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels with mixed image.
        """
        if params.get("skip"):
            return labels
        x1, y1, x2, y2 = params["area"].astype(np.int32)
        labels2 = labels["mix_labels"][0]
        labels["img"][y1:y2, x1:x2] = labels2["img"][y1:y2, x1:x2]
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply CutMix to instances.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances' and 'cls'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels with mixed instances.
        """
        if params.get("skip"):
            return labels
        labels2 = labels["mix_labels"][0]
        w, h = params["w"], params["h"]
        area = params["area"]
        indexes2 = params["indexes2"]

        instances2 = labels2["instances"][indexes2]
        instances2.convert_bbox("xyxy")
        instances2.denormalize(w, h)

        x1, y1, x2, y2 = area.astype(np.int32)
        instances2.add_padding(-x1, -y1)
        instances2.clip(x2 - x1, y2 - y1)
        instances2.add_padding(x1, y1)

        labels["cls"] = np.concatenate([labels["cls"], labels2["cls"][indexes2]], axis=0)
        labels["instances"] = Instances.concatenate([labels["instances"], instances2], axis=0)
        return labels

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply CutMix augmentation to semantic segmentation masks.

        Args:
            labels (dict[str, Any]): Primary image labels containing 'semantic_mask' and 'mix_labels'.
            params (dict[str, Any] | None): Parameters dict with 'area' (bounding box coordinates) and 'skip' (bool
                flag). Defaults to None.

        Returns:
            (dict[str, Any]): Updated labels with the semantic mask region replaced by the mixed image's mask.
        """
        if params.get("skip"):
            return labels
        if labels.get("semantic_mask") is None:
            return labels
        x1, y1, x2, y2 = params["area"].astype(np.int32)
        labels2 = labels["mix_labels"][0]
        if labels2.get("semantic_mask") is not None:
            mask = labels["semantic_mask"].copy()
            mask[y1:y2, x1:x2] = labels2["semantic_mask"][y1:y2, x1:x2]
            labels["semantic_mask"] = mask
        return labels


class RandomPerspective(BaseTransform):
    """Implement random perspective and affine transformations on images and corresponding annotations.

    This class applies random rotations, translations, scaling, shearing, and perspective transformations to images and
    their associated bounding boxes, segments, and keypoints. It can be used as part of an augmentation pipeline for
    object detection and instance segmentation tasks.

    Attributes:
        degrees (float): Maximum absolute degree range for random rotations.
        translate (float): Maximum translation as a fraction of the image size.
        scale (float): Scaling factor range, e.g., scale=0.1 means 0.9-1.1.
        shear (float): Maximum shear angle in degrees.
        perspective (float): Perspective distortion factor.
        size (tuple[int, int] | None): Output size (width, height). If None, uses the input image size.

    Methods:
        get_params: Compute affine transformation matrix and related parameters.
        apply_image: Warp the image using the affine matrix.
        apply_instances: Transform bounding boxes, segments, and keypoints.
        apply_semantic: Placeholder for semantic segmentation mask transformation.
        apply_bboxes: Transform bounding boxes using the affine matrix.
        apply_segments: Transform segments and generate new bounding boxes.
        apply_keypoints: Transform keypoints using the affine matrix.
        box_candidates: Filter transformed bounding boxes based on size and aspect ratio.

    Examples:
        >>> transform = RandomPerspective(degrees=10, translate=0.1, scale=0.1, shear=10)
        >>> image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
        >>> labels = {"img": image, "cls": np.array([0, 1]), "instances": Instances(...)}
        >>> result = transform(labels)
        >>> transformed_image = result["img"]
        >>> transformed_instances = result["instances"]
    """

    def __init__(
        self,
        degrees: float = 0.0,
        translate: float = 0.1,
        scale: float | tuple[float, float] = 0.5,
        shear: float = 0.0,
        perspective: float = 0.0,
        size: tuple[int, int] | None = None,
    ):
        """Initialize RandomPerspective object with transformation parameters.

        This class implements random perspective and affine transformations on images and corresponding bounding boxes,
        segments, and keypoints. Transformations include rotation, translation, scaling, and shearing.

        Args:
            degrees (float): Degree range for random rotations.
            translate (float): Fraction of total width and height for random translation.
            scale (float | tuple[float, float]): Scaling factor interval. If float, e.g. 0.5 means resize between
                50%-150%. If tuple, interpreted as absolute (min, max) scale factors.
            shear (float): Shear intensity (angle in degrees).
            perspective (float): Perspective distortion factor.
            size (tuple[int, int] | None): Output size (width, height). If None, uses the input image size.
        """
        self.degrees = degrees
        self.translate = translate
        self.scale = scale
        self.shear = shear
        self.perspective = perspective
        self.size = size

    def _compute_affine_matrix(self, img: np.ndarray, size: tuple[int, int]) -> tuple[np.ndarray, float]:
        """Compute the affine transformation matrix without applying it.

        Args:
            img (np.ndarray): Input image used to determine center and dimensions.
            size (tuple[int, int]): Size of the output image (width, height) used for clipping translation transform.

        Returns:
            (M, scale): 3x3 transformation matrix and scale factor.
        """
        # Center
        C = np.eye(3, dtype=np.float32)
        C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
        C[1, 2] = -img.shape[0] / 2  # y translation (pixels)

        # Perspective
        P = np.eye(3, dtype=np.float32)
        P[2, 0] = random.uniform(-self.perspective, self.perspective)  # x perspective (about y)
        P[2, 1] = random.uniform(-self.perspective, self.perspective)  # y perspective (about x)

        # Rotation and Scale
        R = np.eye(3, dtype=np.float32)
        a = random.uniform(-self.degrees, self.degrees)
        if isinstance(self.scale, (tuple, list)):
            s = random.uniform(self.scale[0], self.scale[1])
        else:
            s = random.uniform(1 - self.scale, 1 + self.scale)
        R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)

        # Shear
        S = np.eye(3, dtype=np.float32)
        S[0, 1] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180)  # x shear (deg)
        S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180)  # y shear (deg)

        # Translation
        T = np.eye(3, dtype=np.float32)

        T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * size[0]  # x translation (pixels)
        T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * size[1]  # y translation (pixels)

        # Combined rotation matrix
        M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
        return M, s

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute affine transformation parameters shared across image and instances.

        Args:
            labels (dict[str, Any]): Input labels dictionary containing 'img'.

        Returns:
            (dict): Parameters including 'M' (affine matrix), 'scale', 'orig_shape', and 'size'.
        """
        img = labels["img"]
        if (rect_shape := labels.get("rect_shape")) is not None:  # rect has higher priority
            size = (int(rect_shape[1]), int(rect_shape[0]))  # rect mode batch shape (h, w) to (w, h)
        else:
            size = (img.shape[1], img.shape[0]) if self.size is None else self.size  # w, h
        orig_shape = img.shape[:2]
        M, scale = self._compute_affine_matrix(img, size)
        return {"M": M, "scale": scale, "orig_shape": orig_shape, "size": size}

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply affine warp to the image.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict | None): Parameters from get_params, including 'M' and 'size'.

        Returns:
            (dict): Updated labels with warped image and 'resized_shape'.
        """
        img = labels["img"]
        M = params["M"]
        size = params["size"]
        if (size[0] != img.shape[1] or size[1] != img.shape[0]) or (M != np.eye(3)).any():  # image changed
            # 4 values: cv2 tiles borderValue in blocks of 4, so a 3-tuple zeroes every 4th multispectral channel
            if self.perspective:
                img = cv2.warpPerspective(img, M, dsize=size, borderValue=(114, 114, 114, 114))
            else:  # affine
                img = cv2.warpAffine(img, M[:2], dsize=size, borderValue=(114, 114, 114, 114))
            if img.ndim == 2:
                img = img[..., None]
        labels["img"] = img
        labels["resized_shape"] = img.shape[:2]
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply affine transformation to object instances.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances' and 'cls'.
            params (dict | None): Parameters from get_params, including 'M' and 'scale'.

        Returns:
            (dict): Updated labels with transformed and filtered instances.
        """
        cls = labels["cls"]
        instances = labels.pop("instances")
        instances.convert_bbox(format="xyxy")
        instances.denormalize(*params["orig_shape"][::-1])

        M = params["M"]
        scale = params["scale"]

        bboxes = self.apply_bboxes(instances.bboxes, M)

        segments = instances.segments
        keypoints = instances.keypoints
        # Update bboxes if there are segments.
        if len(segments):
            bboxes, segments = self.apply_segments(segments, M, params["size"])

        if keypoints is not None:
            keypoints = self.apply_keypoints(keypoints, M, params["size"])
        new_instances = Instances(bboxes, segments, keypoints, bbox_format="xyxy", normalized=False)
        # Clip
        new_instances.clip(*params["size"])

        # Filter instances
        instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
        # Make the bboxes have the same scale with new_bboxes
        i = self.box_candidates(
            box1=instances.bboxes.T, box2=new_instances.bboxes.T, area_thr=0.01 if len(segments) else 0.10
        )
        labels["instances"] = new_instances[i]
        labels["cls"] = cls[i]
        return labels

    def apply_bboxes(self, bboxes: np.ndarray, M: np.ndarray) -> np.ndarray:
        """Apply affine transformation to bounding boxes.

        This function applies an affine transformation to a set of bounding boxes using the provided transformation
        matrix.

        Args:
            bboxes (np.ndarray): Bounding boxes in xyxy format with shape (N, 4), where N is the number of bounding
                boxes.
            M (np.ndarray): Affine transformation matrix with shape (3, 3).

        Returns:
            (np.ndarray): Transformed bounding boxes in xyxy format with shape (N, 4).

        Examples:
            >>> rp = RandomPerspective()
            >>> bboxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]], dtype=np.float32)
            >>> M = np.eye(3, dtype=np.float32)
            >>> transformed_bboxes = rp.apply_bboxes(bboxes, M)
        """
        n = len(bboxes)
        if n == 0:
            return bboxes

        xy = np.ones((n * 4, 3), dtype=bboxes.dtype)
        xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
        xy = xy @ M.T  # transform
        xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine

        # Create new boxes
        x = xy[:, [0, 2, 4, 6]]
        y = xy[:, [1, 3, 5, 7]]
        return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T

    def apply_segments(
        self, segments: np.ndarray, M: np.ndarray, size: tuple[int, int]
    ) -> tuple[np.ndarray, np.ndarray]:
        """Apply affine transformations to segments and generate new bounding boxes.

        This function applies affine transformations to input segments and generates new bounding boxes based on the
        transformed segments. It clips the transformed segments to fit within the new bounding boxes.

        Args:
            segments (np.ndarray): Input segments with shape (N, M, 2), where N is the number of segments and M is the
                number of points in each segment.
            M (np.ndarray): Affine transformation matrix with shape (3, 3).
            size (tuple[int, int]): Size of the output image (width, height) used for clipping the segments.

        Returns:
            bboxes (np.ndarray): New bounding boxes with shape (N, 4) in xyxy format.
            segments (np.ndarray): Transformed and clipped segments with shape (N, M, 2).

        Examples:
            >>> rp = RandomPerspective()
            >>> segments = np.random.rand(10, 500, 2)  # 10 segments with 500 points each
            >>> M = np.eye(3)  # Identity transformation matrix
            >>> new_bboxes, new_segments = rp.apply_segments(segments, M)
        """
        n, num = segments.shape[:2]
        if n == 0:
            return [], segments

        xy = np.ones((n * num, 3), dtype=segments.dtype)
        segments = segments.reshape(-1, 2)
        xy[:, :2] = segments
        xy = xy @ M.T  # transform
        xy = xy[:, :2] / xy[:, 2:3]
        segments = xy.reshape(n, -1, 2)
        bboxes = np.stack([segment2box(xy, size[0], size[1]) for xy in segments], 0)
        segments[..., 0] = segments[..., 0].clip(bboxes[:, 0:1], bboxes[:, 2:3])
        segments[..., 1] = segments[..., 1].clip(bboxes[:, 1:2], bboxes[:, 3:4])
        return bboxes, segments

    def apply_keypoints(self, keypoints: np.ndarray, M: np.ndarray, size: tuple[int, int]) -> np.ndarray:
        """Apply affine transformation to keypoints.

        This method transforms the input keypoints using the provided affine transformation matrix. It handles
        perspective rescaling if necessary and updates the visibility of keypoints that fall outside the image
        boundaries after transformation.

        Args:
            keypoints (np.ndarray): Array of keypoints with shape (N, K, 3), where N is the number of instances, K is
                the number of keypoints per instance, and 3 represents (x, y, visibility).
            M (np.ndarray): 3x3 affine transformation matrix.
            size (tuple[int, int]): Size of the output image (width, height) used to determine visibility of keypoints.

        Returns:
            (np.ndarray): Transformed keypoints array with the same shape as input (N, K, 3).

        Examples:
            >>> random_perspective = RandomPerspective()
            >>> keypoints = np.random.rand(5, 17, 3)  # 5 instances, 17 keypoints each
            >>> M = np.eye(3)  # Identity transformation
            >>> transformed_keypoints = random_perspective.apply_keypoints(keypoints, M)
        """
        n, nkpt = keypoints.shape[:2]
        if n == 0:
            return keypoints
        xy = np.ones((n * nkpt, 3), dtype=keypoints.dtype)
        visible = keypoints[..., 2].reshape(n * nkpt, 1)
        xy[:, :2] = keypoints[..., :2].reshape(n * nkpt, 2)
        xy = xy @ M.T  # transform
        xy = xy[:, :2] / xy[:, 2:3]  # perspective rescale or affine
        out_mask = (xy[:, 0] < 0) | (xy[:, 1] < 0) | (xy[:, 0] > size[0]) | (xy[:, 1] > size[1])
        visible[out_mask] = 0
        return np.concatenate([xy, visible], axis=-1).reshape(n, nkpt, 3)

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply affine transformation to semantic segmentation mask.

        Args:
            labels (dict[str, Any]): Dictionary containing 'semantic_mask'.
            params (dict | None): Parameters from get_params, including 'M' and 'size'.

        Returns:
            (dict): Updated labels with transformed semantic mask.
        """
        if "semantic_mask" not in labels or labels["semantic_mask"] is None:
            return labels
        mask = labels["semantic_mask"]
        M = params["M"]
        size = params["size"]
        if (size[0] != mask.shape[1] or size[1] != mask.shape[0]) or (M != np.eye(3)).any():
            if self.perspective:
                mask = cv2.warpPerspective(mask, M, dsize=size, flags=cv2.INTER_NEAREST, borderValue=255)
            else:
                mask = cv2.warpAffine(mask, M[:2], dsize=size, flags=cv2.INTER_NEAREST, borderValue=255)
        labels["semantic_mask"] = mask
        return labels

    def apply_depth(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply the same projective warp to metric depth maps.

        Depth values remain in meters; only their spatial support is warped. Nearest-neighbor interpolation avoids
        manufacturing spurious near-zero "valid" pixels when sparse or invalid regions border real depth.
        """
        depth = labels.get("depth")
        if depth is None:
            return labels

        M = params["M"]
        size = params["size"]
        if (size[0] != depth.shape[1] or size[1] != depth.shape[0]) or (M != np.eye(3)).any():
            if self.perspective:
                depth = cv2.warpPerspective(depth, M, dsize=size, flags=cv2.INTER_NEAREST, borderValue=0)
            else:
                depth = cv2.warpAffine(depth, M[:2], dsize=size, flags=cv2.INTER_NEAREST, borderValue=0)
        labels["depth"] = depth
        return labels

    @staticmethod
    def box_candidates(
        box1: np.ndarray,
        box2: np.ndarray,
        wh_thr: int = 2,
        ar_thr: int = 100,
        area_thr: float = 0.1,
        eps: float = 1e-16,
    ) -> np.ndarray:
        """Compute candidate boxes for further processing based on size and aspect ratio criteria.

        This method compares boxes before and after augmentation to determine if they meet specified thresholds for
        width, height, aspect ratio, and area. It's used to filter out boxes that have been overly distorted or reduced
        by the augmentation process.

        Args:
            box1 (np.ndarray): Original boxes before augmentation, shape (4, N) where N is the number of boxes. Format
                is [x1, y1, x2, y2] in absolute coordinates.
            box2 (np.ndarray): Augmented boxes after transformation, shape (4, N). Format is [x1, y1, x2, y2] in
                absolute coordinates.
            wh_thr (int): Width and height threshold in pixels. Boxes smaller than this in either dimension are
                rejected.
            ar_thr (int): Aspect ratio threshold. Boxes with an aspect ratio greater than this value are rejected.
            area_thr (float): Area ratio threshold. Boxes with an area ratio (new/old) less than this value are
                rejected.
            eps (float): Small epsilon value to prevent division by zero.

        Returns:
            (np.ndarray): Boolean array of shape (N,) indicating which boxes are candidates. True values correspond to
                boxes that meet all criteria.

        Examples:
            >>> random_perspective = RandomPerspective()
            >>> box1 = np.array([[0, 0, 100, 100], [0, 0, 50, 50]]).T
            >>> box2 = np.array([[10, 10, 90, 90], [5, 5, 45, 45]]).T
            >>> candidates = random_perspective.box_candidates(box1, box2)
            >>> print(candidates)
            [ True  True]
        """
        w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
        w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
        ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
        return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates


class RandomHSV(BaseTransform):
    """Randomly adjust the Hue, Saturation, and Value (HSV) channels of an image.

    This class applies random HSV augmentation to images within predefined limits set by hgain, sgain, and vgain.

    Attributes:
        hgain (float): Maximum variation for hue. Range is typically [0, 1].
        sgain (float): Maximum variation for saturation. Range is typically [0, 1].
        vgain (float): Maximum variation for value. Range is typically [0, 1].

    Methods:
        apply_image: Apply random HSV augmentation to an image.

    Examples:
        >>> import numpy as np
        >>> from ultralytics.data.augment import RandomHSV
        >>> augmenter = RandomHSV(hgain=0.5, sgain=0.5, vgain=0.5)
        >>> image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
        >>> labels = {"img": image}
        >>> labels = augmenter(labels)
        >>> augmented_image = labels["img"]
    """

    def __init__(self, hgain: float = 0.5, sgain: float = 0.5, vgain: float = 0.5) -> None:
        """Initialize the RandomHSV object for random HSV (Hue, Saturation, Value) augmentation.

        This class applies random adjustments to the HSV channels of an image within specified limits.

        Args:
            hgain (float): Maximum variation for hue. Should be in the range [0, 1].
            sgain (float): Maximum variation for saturation. Should be in the range [0, 1].
            vgain (float): Maximum variation for value. Should be in the range [0, 1].
        """
        self.hgain = hgain
        self.sgain = sgain
        self.vgain = vgain

    def apply_image(self, labels, params: dict[str, Any] | None = None):
        """Apply random HSV augmentation to an image within predefined limits.

        This method modifies the input image by randomly adjusting its Hue, Saturation, and Value (HSV) channels. The
        adjustments are made within the limits set by hgain, sgain, and vgain during initialization.

        Args:
            labels (dict[str, Any]): A dictionary containing image data and metadata. Must include an 'img' key with the
                image as a numpy array.
            params (dict[str, Any] | None): Unused parameters for API compatibility.

        Returns:
            (dict[str, Any]): The labels dictionary with the HSV-augmented image.

        Examples:
            >>> hsv_augmenter = RandomHSV(hgain=0.5, sgain=0.5, vgain=0.5)
            >>> labels = {"img": np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)}
            >>> labels = hsv_augmenter.apply_image(labels)
            >>> augmented_img = labels["img"]
        """
        img = labels["img"]
        if img.shape[-1] != 3:  # only apply to 3-channel (BGR) images
            return labels
        if self.hgain or self.sgain or self.vgain:
            dtype = img.dtype  # uint8

            r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain]  # random gains
            x = np.arange(0, 256, dtype=r.dtype)
            # lut_hue = ((x * (r[0] + 1)) % 180).astype(dtype)   # original hue implementation from ultralytics<=8.3.78
            lut_hue = ((x + r[0] * 180) % 180).astype(dtype)
            lut_sat = np.clip(x * (r[1] + 1), 0, 255).astype(dtype)
            lut_val = np.clip(x * (r[2] + 1), 0, 255).astype(dtype)
            lut_sat[0] = 0  # prevent pure white changing color, introduced in 8.3.79

            hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
            im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
            cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed
        return labels


class RandomFlip(BaseTransform):
    """Apply a random horizontal or vertical flip to an image with a given probability.

    This class performs random image flipping and updates corresponding instance annotations such as bounding boxes and
    keypoints.

    Attributes:
        p (float): Probability of applying the flip. Must be between 0 and 1.
        direction (str): Direction of flip, either 'horizontal' or 'vertical'.
        flip_idx (array-like): Index mapping for flipping keypoints, if applicable.

    Methods:
        __call__: Apply the random flip transformation to an image and its annotations.

    Examples:
        >>> transform = RandomFlip(p=0.5, direction="horizontal")
        >>> result = transform({"img": image, "instances": instances})
        >>> flipped_image = result["img"]
        >>> flipped_instances = result["instances"]
    """

    def __init__(self, p: float = 0.5, direction: str = "horizontal", flip_idx: list[int] | None = None) -> None:
        """Initialize the RandomFlip class with probability and direction.

        This class applies a random horizontal or vertical flip to an image with a given probability. It also updates
        any instances (bounding boxes, keypoints, etc.) accordingly.

        Args:
            p (float): The probability of applying the flip. Must be between 0 and 1.
            direction (str): The direction to apply the flip. Must be 'horizontal' or 'vertical'.
            flip_idx (list[int] | None): Index mapping for flipping keypoints, if any.

        Raises:
            AssertionError: If direction is not 'horizontal' or 'vertical', or if p is not between 0 and 1.
        """
        assert direction in {"horizontal", "vertical"}, f"Support direction `horizontal` or `vertical`, got {direction}"
        assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."

        self.p = p
        self.direction = direction
        self.flip_idx = flip_idx

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute random flip parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary containing 'img' and 'instances'.

        Returns:
            (dict): Parameters including 'flip' (bool), 'h', 'w', 'direction', and 'flip_idx'.
        """
        img = labels["img"]
        instances = labels["instances"]
        h, w = img.shape[:2]
        h = 1 if instances.normalized else h
        w = 1 if instances.normalized else w
        return {
            "flip": random.random() < self.p,
            "h": h,
            "w": w,
            "direction": self.direction,
            "flip_idx": self.flip_idx,
        }

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Apply flip to the image.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with flipped (or unchanged) image.
        """
        img = labels["img"]
        if params["flip"]:
            if params["direction"] == "vertical":
                img = np.flipud(img)
            elif params["direction"] == "horizontal":
                img = np.fliplr(img)
        labels["img"] = img
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Apply flip to object instances.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with flipped (or unchanged) instances.
        """
        instances = labels.pop("instances")
        instances.convert_bbox(format="xywh")
        if params["flip"]:
            if params["direction"] == "vertical":
                instances.flipud(params["h"])
            elif params["direction"] == "horizontal":
                instances.fliplr(params["w"])
            if params["flip_idx"] is not None and instances.keypoints is not None:
                instances.keypoints = np.ascontiguousarray(instances.keypoints[:, params["flip_idx"], :])
        labels["instances"] = instances
        return labels

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Apply flip to semantic segmentation mask.

        Args:
            labels (dict[str, Any]): Dictionary containing 'semantic_mask'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with flipped (or unchanged) semantic mask.
        """
        if "semantic_mask" not in labels or labels["semantic_mask"] is None:
            return labels
        if params["flip"]:
            if params["direction"] == "vertical":
                labels["semantic_mask"] = np.flipud(labels["semantic_mask"])
            elif params["direction"] == "horizontal":
                labels["semantic_mask"] = np.fliplr(labels["semantic_mask"])
        return labels

    def apply_depth(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Apply flip to the paired metric depth map.

        Args:
            labels (dict[str, Any]): Dictionary containing 'depth'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with flipped (or unchanged) depth map.
        """
        if labels.get("depth") is None:
            return labels
        if params["flip"]:
            if params["direction"] == "vertical":
                labels["depth"] = np.flipud(labels["depth"])
            elif params["direction"] == "horizontal":
                labels["depth"] = np.fliplr(labels["depth"])
        return labels


class LetterBox(BaseTransform):
    """Resize image and padding for detection, instance segmentation, pose.

    This class resizes and pads images to a specified shape while preserving aspect ratio. It also updates corresponding
    labels and bounding boxes.

    Attributes:
        new_shape (tuple): Target shape (height, width) for resizing.
        auto (bool): Whether to use minimum rectangle.
        scale_fill (bool): Whether to stretch the image to new_shape.
        scaleup (bool): Whether to allow scaling up. If False, only scale down.
        stride (int): Stride for rounding padding.
        center (bool): Whether to center the image or align to top-left.

    Methods:
        __call__: Resize and pad image, update labels and bounding boxes.

    Examples:
        >>> transform = LetterBox(new_shape=(640, 640))
        >>> result = transform(labels)
        >>> resized_img = result["img"]
        >>> updated_instances = result["instances"]
    """

    def __init__(
        self,
        new_shape: tuple[int, int] = (640, 640),
        auto: bool = False,
        scale_fill: bool = False,
        scaleup: bool = True,
        center: bool = True,
        stride: int = 32,
        padding_value: int = 114,
        interpolation: int = cv2.INTER_LINEAR,
    ):
        """Initialize LetterBox object for resizing and padding images.

        This class is designed to resize and pad images for object detection, instance segmentation, and pose estimation
        tasks. It supports various resizing modes including auto-sizing, scale-fill, and letterboxing.

        Args:
            new_shape (tuple[int, int]): Target size (height, width) for the resized image.
            auto (bool): If True, use minimum rectangle to resize. If False, use new_shape directly.
            scale_fill (bool): If True, stretch the image to new_shape without padding.
            scaleup (bool): If True, allow scaling up. If False, only scale down.
            center (bool): If True, center the placed image. If False, place image in top-left corner.
            stride (int): Stride of the model (e.g., 32 for YOLOv5).
            padding_value (int): Value for padding the image. Default is 114.
            interpolation (int): Interpolation method for resizing. Default is cv2.INTER_LINEAR.
        """
        self.new_shape = new_shape
        self.auto = auto
        self.scale_fill = scale_fill
        self.scaleup = scaleup
        self.stride = stride
        self.center = center  # Put the image in the middle or top-left
        self.padding_value = padding_value
        self.interpolation = interpolation

    def __call__(self, labels: dict[str, Any] | None = None, image: np.ndarray = None) -> dict[str, Any] | np.ndarray:
        """Resize and pad an image for object detection, instance segmentation, or pose estimation tasks.

        This method applies letterboxing to the input image, which involves resizing the image while maintaining its
        aspect ratio and adding padding to fit the new shape. It also updates any associated labels accordingly.

        Args:
            labels (dict[str, Any] | None): A dictionary containing image data and associated labels, or empty dict if
                None.
            image (np.ndarray | None): The input image as a numpy array. If None, the image is taken from 'labels'.

        Returns:
            (dict[str, Any] | np.ndarray): If 'labels' is provided, returns an updated dictionary with the resized and
                padded image, updated labels, and additional metadata. If 'labels' is empty, returns the resized and
                padded image only.

        Examples:
            >>> letterbox = LetterBox(new_shape=(640, 640))
            >>> result = letterbox(labels={"img": np.zeros((480, 640, 3)), "instances": Instances(...)})
            >>> resized_img = result["img"]
            >>> updated_instances = result["instances"]
        """
        if labels is None:
            labels = {}
        return_image_only = len(labels) == 0
        if image is not None:
            labels["img"] = image
        params = self.get_params(labels)
        labels = self.apply_image(labels, params)
        if not return_image_only:
            labels = self.apply_instances(labels, params)
        labels = self.apply_semantic(labels, params)
        if return_image_only:
            return labels["img"]
        return labels

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute letterboxing parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary containing 'img'.

        Returns:
            (dict): Parameters including 'orig_shape', 'new_shape', 'ratio', padding, and resize info.
        """
        img = labels["img"]
        shape = img.shape[:2]  # current shape [height, width]
        new_shape = labels.pop("rect_shape", self.new_shape)
        if isinstance(new_shape, int):
            new_shape = (new_shape, new_shape)

        # Scale ratio (new / old)
        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
        if not self.scaleup:  # only scale down, do not scale up (for better val mAP)
            r = min(r, 1.0)

        # Compute padding
        ratio = r, r  # width, height ratios
        new_unpad = round(shape[1] * r), round(shape[0] * r)
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
        if self.auto:  # minimum rectangle
            dw, dh = np.mod(dw, self.stride), np.mod(dh, self.stride)  # wh padding
        elif self.scale_fill:  # stretch
            dw, dh = 0.0, 0.0
            new_unpad = (new_shape[1], new_shape[0])
            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

        if self.center:
            dw /= 2  # divide padding into 2 sides
            dh /= 2

        top, bottom = round(dh - 0.1) if self.center else 0, round(dh + 0.1)
        left, right = round(dw - 0.1) if self.center else 0, round(dw + 0.1)

        return {
            "orig_shape": shape,
            "new_shape": new_shape,
            "ratio": ratio,
            "new_unpad": new_unpad,
            "top": top,
            "bottom": bottom,
            "left": left,
            "right": right,
        }

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Resize and pad the image.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with resized and padded image.
        """
        img = labels["img"]
        shape = img.shape[:2]
        new_unpad = params["new_unpad"]

        if shape[::-1] != new_unpad:  # resize
            img = cv2.resize(img, new_unpad, interpolation=self.interpolation)
            if img.ndim == 2:
                img = img[..., None]

        h, w, c = img.shape
        top, bottom = params["top"], params["bottom"]
        left, right = params["left"], params["right"]
        if c == 3:
            img = cv2.copyMakeBorder(
                img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(self.padding_value,) * 3
            )
        else:  # multispectral
            pad_img = np.full((h + top + bottom, w + left + right, c), fill_value=self.padding_value, dtype=img.dtype)
            pad_img[top : top + h, left : left + w] = img
            img = pad_img

        labels["img"] = img
        labels["resized_shape"] = params["new_shape"]
        return labels

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Apply letterboxing to semantic segmentation mask.

        Args:
            labels (dict[str, Any]): Dictionary containing 'semantic_mask'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with resized and padded semantic mask.
        """
        if "semantic_mask" not in labels or labels["semantic_mask"] is None:
            return labels
        mask = labels["semantic_mask"]
        shape = params["orig_shape"]
        new_unpad = params["new_unpad"]
        if shape[::-1] != new_unpad:
            mask = cv2.resize(mask, new_unpad, interpolation=cv2.INTER_NEAREST)
        top, bottom = params["top"], params["bottom"]
        left, right = params["left"], params["right"]
        mask = cv2.copyMakeBorder(mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255)
        labels["semantic_mask"] = mask
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Update instance coordinates after letterboxing.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with transformed instances.
        """
        if "instances" in labels:
            labels = self._update_labels(labels, params["ratio"], params["left"], params["top"], params["orig_shape"])
        if labels.get("ratio_pad"):
            labels["ratio_pad"] = (labels["ratio_pad"], (params["left"], params["top"]))  # for evaluation
        return labels

    @staticmethod
    def _update_labels(
        labels: dict[str, Any], ratio: tuple[float, float], padw: float, padh: float, orig_shape: tuple[int, int]
    ) -> dict[str, Any]:
        """Update labels after applying letterboxing to an image.

        This method modifies the bounding box coordinates of instances in the labels to account for resizing and padding
        applied during letterboxing.

        Args:
            labels (dict[str, Any]): A dictionary containing image labels and instances.
            ratio (tuple[float, float]): Scaling ratios (width, height) applied to the image.
            padw (float): Padding width added to the image.
            padh (float): Padding height added to the image.
            orig_shape (tuple[int, int]): Original image shape (height, width) before resizing.

        Returns:
            (dict[str, Any]): Updated labels dictionary with modified instance coordinates.

        Examples:
            >>> letterbox = LetterBox(new_shape=(640, 640))
            >>> labels = {"instances": Instances(...)}
            >>> ratio = (0.5, 0.5)
            >>> padw, padh = 10, 20
            >>> updated_labels = letterbox._update_labels(labels, ratio, padw, padh, (480, 640))
        """
        labels["instances"].convert_bbox(format="xyxy")
        labels["instances"].denormalize(*orig_shape[::-1])
        labels["instances"].scale(*ratio)
        labels["instances"].add_padding(padw, padh)
        return labels


class CopyPaste(BaseMixTransform):
    """CopyPaste class for applying Copy-Paste augmentation to image datasets.

    This class implements the Copy-Paste augmentation technique as described in the paper "Simple Copy-Paste is a Strong
    Data Augmentation Method for Instance Segmentation" (https://arxiv.org/abs/2012.07177). It combines objects from
    different images to create new training samples.

    Attributes:
        dataset (Any): The dataset to which Copy-Paste augmentation will be applied.
        pre_transform (Callable | None): Optional transform to apply before Copy-Paste.
        p (float): Probability of applying Copy-Paste augmentation.

    Methods:
        get_params: Compute CopyPaste parameters including selected instances and mask.
        apply_image: Draw contours and paste pixels for CopyPaste.
        apply_instances: Concatenate selected instances for CopyPaste.

    Examples:
        >>> from ultralytics.data.augment import CopyPaste
        >>> dataset = YourDataset(...)  # Your image dataset
        >>> copypaste = CopyPaste(dataset, p=0.5)
        >>> augmented_labels = copypaste(original_labels)
    """

    def __init__(self, dataset=None, pre_transform=None, p: float = 0.5, mode: str = "flip") -> None:
        """Initialize CopyPaste object with dataset, pre_transform, and probability of applying CopyPaste."""
        super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
        if mode not in ("flip", "mixup"):
            raise ValueError(f"Expected `mode` to be `flip` or `mixup`, but got {mode}.")
        self.mode = mode

    def __call__(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Apply Copy-Paste augmentation to an image and its labels."""
        if len(labels["instances"].segments) == 0 or self.p == 0:
            return labels
        if self.mode == "flip":
            params = self.get_params(labels)
            labels = self.apply_image(labels, params)
            labels = self.apply_instances(labels, params)
            labels = self.apply_semantic(labels, params)
            return labels
        return super().__call__(labels)

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute CopyPaste parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary.

        Returns:
            (dict[str, Any]): Parameters including 'instances2', 'selected', and 'im_new'.
        """
        params = {}
        if self.mode == "mixup":
            params = super().get_params(labels)
            labels2 = labels.get("mix_labels", [{}])[0]
        else:
            labels2 = {}

        h, w = labels["img"].shape[:2]
        instances = deepcopy(labels["instances"])
        instances.convert_bbox(format="xyxy")
        instances.denormalize(w, h)

        instances2 = deepcopy(labels2.get("instances")) if labels2 else None
        if instances2 is None:
            instances2 = deepcopy(instances)
            instances2.fliplr(w)

        ioa = bbox_ioa(instances2.bboxes, instances.bboxes)
        indexes = np.nonzero((ioa < 0.30).all(1))[0]
        n = len(indexes)
        sorted_idx = np.argsort(ioa.max(1)[indexes])
        indexes = indexes[sorted_idx]
        selected = indexes[: round(self.p * n)]

        im_new = np.zeros((h, w), np.uint8)

        params["instances"] = instances
        params["instances2"] = instances2
        params["selected"] = selected
        params["im_new"] = im_new
        params["labels2_cls"] = labels2.get("cls")
        params["labels2_img"] = labels2.get("img")
        return params

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply CopyPaste to the image.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels with pasted objects.
        """
        im = labels["img"].copy()

        instances2 = params["instances2"]
        selected = params["selected"]
        im_new = params["im_new"]

        for j in selected:
            cv2.drawContours(im_new, instances2.segments[[j]].astype(np.int32), -1, 1, cv2.FILLED)

        result = params.get("labels2_img")
        if result is None:
            result = cv2.flip(im, 1)
        if result.ndim == 2:
            result = result[..., None]

        i = im_new.astype(bool)
        im[i] = result[i]
        labels["img"] = im
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply CopyPaste to instances.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances' and 'cls'.
            params (dict | None): Parameters from get_params.

        Returns:
            (dict): Updated labels with concatenated instances.
        """
        instances = params["instances"]
        instances2 = params["instances2"]
        selected = params["selected"]
        cls = labels["cls"]
        labels2_cls = params.get("labels2_cls")

        for j in selected:
            cls = np.concatenate((cls, (labels2_cls if labels2_cls is not None else cls)[[j]]), axis=0)
            instances = Instances.concatenate((instances, instances2[[j]]), axis=0)

        labels["cls"] = cls
        labels["instances"] = instances
        return labels

    def apply_semantic(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Apply CopyPaste to semantic segmentation masks."""
        mask = labels.get("semantic_mask")
        if mask is None:
            return labels

        source = labels.get("mix_labels", [{}])[0].get("semantic_mask") if self.mode == "mixup" else cv2.flip(mask, 1)
        if source is None:
            return labels
        pasted = params["im_new"].astype(bool)
        mask = mask.copy()
        mask[pasted] = source[pasted]
        labels["semantic_mask"] = mask
        return labels


class Albumentations(BaseTransform):
    """Albumentations transformations for image augmentation.

    This class applies various image transformations using the Albumentations library. It includes operations such as
    Blur, Median Blur, conversion to grayscale, Contrast Limited Adaptive Histogram Equalization (CLAHE), random changes
    in brightness and contrast, RandomGamma, and image quality reduction through compression.

    Attributes:
        p (float): Probability of applying the transformations.
        transform (albumentations.Compose): Composed Albumentations transforms.
        contains_spatial (bool): Indicates if the transforms include spatial operations.

    Methods:
        __call__: Apply the Albumentations transformations to the input labels.

    Examples:
        >>> transform = Albumentations(p=0.5)
        >>> augmented_labels = transform(labels)

    Notes:
        - Requires Albumentations version 1.0.3 or higher.
        - Spatial transforms are handled differently to ensure bbox compatibility.
        - Some transforms are applied with very low probability (0.01) by default.
    """

    def __init__(self, p: float = 1.0, transforms: list | None = None) -> None:
        """Initialize the Albumentations transform object for YOLO bbox formatted parameters.

        This class applies various image augmentations using the Albumentations library, including Blur, Median Blur,
        conversion to grayscale, Contrast Limited Adaptive Histogram Equalization, random changes of brightness and
        contrast, RandomGamma, and image quality reduction through compression.

        Args:
            p (float): Probability of applying the augmentations. Must be between 0 and 1.
            transforms (list | None): List of custom Albumentations transforms. If None, uses default transforms.
        """
        self.p = p
        self.transform = None
        prefix = colorstr("albumentations: ")

        try:
            import os

            os.environ["NO_ALBUMENTATIONS_UPDATE"] = "1"  # suppress Albumentations upgrade message
            import albumentations as A

            check_version(A.__version__, "1.0.3", hard=True)  # version requirement

            # List of possible spatial transforms
            spatial_transforms = {
                "Affine",
                "BBoxSafeRandomCrop",
                "CenterCrop",
                "CoarseDropout",
                "Crop",
                "CropAndPad",
                "CropNonEmptyMaskIfExists",
                "D4",
                "ElasticTransform",
                "Flip",
                "GridDistortion",
                "GridDropout",
                "HorizontalFlip",
                "Lambda",
                "LongestMaxSize",
                "MaskDropout",
                "MixUp",
                "Morphological",
                "NoOp",
                "OpticalDistortion",
                "PadIfNeeded",
                "Perspective",
                "PiecewiseAffine",
                "PixelDropout",
                "RandomCrop",
                "RandomCropFromBorders",
                "RandomGridShuffle",
                "RandomResizedCrop",
                "RandomRotate90",
                "RandomScale",
                "RandomSizedBBoxSafeCrop",
                "RandomSizedCrop",
                "Resize",
                "Rotate",
                "SafeRotate",
                "ShiftScaleRotate",
                "SmallestMaxSize",
                "Transpose",
                "VerticalFlip",
                "XYMasking",
            }  # from https://albumentations.ai/docs/2-core-concepts/targets/

            # Transforms, use custom transforms if provided, otherwise use defaults
            T = (
                [
                    A.Blur(p=0.01),
                    A.MedianBlur(p=0.01),
                    A.ToGray(p=0.01),
                    A.CLAHE(p=0.01),
                    A.RandomBrightnessContrast(p=0.0),
                    A.RandomGamma(p=0.0),
                    A.ImageCompression(quality_range=(75, 100), p=0.0),
                ]
                if transforms is None
                else transforms
            )

            # Compose transforms
            self.contains_spatial = any(transform.__class__.__name__ in spatial_transforms for transform in T)
            self.transform = (
                A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
                if self.contains_spatial
                else A.Compose(T)
            )
            if hasattr(self.transform, "set_random_seed"):
                # Required for deterministic transforms in albumentations>=1.4.21
                self.transform.set_random_seed(torch.initial_seed())
            LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
        except ImportError:  # package not installed, skip
            pass
        except Exception as e:
            LOGGER.info(f"{prefix}{e}")

    def __call__(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Apply Albumentations transformations to input labels.

        This method applies a series of image augmentations using the Albumentations library. It can perform both
        spatial and non-spatial transformations on the input image and its corresponding labels.

        Args:
            labels (dict[str, Any]): A dictionary containing image data and annotations. Expected keys are:
                - 'img': np.ndarray representing the image
                - 'cls': np.ndarray of class labels
                - 'instances': object containing bounding boxes and other instance information
                - 'semantic_mask': optional np.ndarray of semantic class IDs

        Returns:
            (dict[str, Any]): The input dictionary with augmented image and updated annotations.

        Examples:
            >>> transform = Albumentations(p=0.5)
            >>> labels = {
            ...     "img": np.random.rand(640, 640, 3),
            ...     "cls": np.array([0, 1]),
            ...     "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]])),
            ... }
            >>> augmented = transform(labels)
            >>> assert augmented["img"].shape == (640, 640, 3)

        Notes:
            - The method applies transformations with probability self.p.
            - Spatial transforms update bounding boxes, while non-spatial transforms only modify the image.
            - Requires the Albumentations library to be installed.
        """
        if self.transform is None or random.random() > self.p:
            return labels

        im = labels["img"]
        if im.shape[2] != 3:  # Only apply Albumentation on 3-channel images
            return labels

        if self.contains_spatial:
            cls = labels["cls"]
            mask = labels.get("semantic_mask")
            if len(cls) or mask is not None:
                labels["instances"].convert_bbox("xywh")
                labels["instances"].normalize(*im.shape[:2][::-1])
                bboxes = labels["instances"].bboxes
                # TODO: add supports of segments and keypoints
                new = self.transform(
                    image=im, bboxes=bboxes, class_labels=cls, **({"mask": mask} if mask is not None else {})
                )
                if len(new["class_labels"]) > 0 or mask is not None:  # only box-only samples skip on losing all boxes
                    labels["img"] = new["image"]
                    labels["cls"] = np.array(new["class_labels"]).reshape(-1, 1)
                    bboxes = np.array(new["bboxes"], dtype=np.float32).reshape(-1, 4)
                    if mask is not None:
                        labels["semantic_mask"] = new["mask"]
                labels["instances"].update(bboxes=bboxes)
        else:
            labels["img"] = self.transform(image=labels["img"])["image"]  # transformed

        return labels


class Format(BaseTransform):
    """A class for formatting image annotations for object detection, instance segmentation, and pose estimation tasks.

    This class standardizes image and instance annotations to be used by the `collate_fn` in PyTorch DataLoader.

    Attributes:
        bbox_format (str): Format for bounding boxes. Options are 'xywh' or 'xyxy'.
        normalize (bool): Whether to normalize bounding boxes.
        return_mask (bool): Whether to return instance masks for segmentation.
        return_keypoint (bool): Whether to return keypoints for pose estimation.
        return_obb (bool): Whether to return oriented bounding boxes.
        mask_ratio (int): Downsample ratio for masks.
        mask_overlap (bool): Whether to overlap masks.
        batch_idx (bool): Whether to keep batch indexes.
        bgr (float): The probability to return BGR images.

    Methods:
        __call__: Format labels dictionary with image, classes, bounding boxes, and optionally masks and keypoints.
        _format_img: Convert image from Numpy array to PyTorch tensor.
        _format_segments: Convert polygon points to bitmap masks.

    Examples:
        >>> formatter = Format(bbox_format="xywh", normalize=True, return_mask=True)
        >>> formatted_labels = formatter(labels)
        >>> img = formatted_labels["img"]
        >>> bboxes = formatted_labels["bboxes"]
        >>> masks = formatted_labels["masks"]
    """

    def __init__(
        self,
        bbox_format: str = "xywh",
        normalize: bool = True,
        return_mask: bool = False,
        return_keypoint: bool = False,
        return_obb: bool = False,
        mask_ratio: int = 4,
        mask_overlap: bool = True,
        batch_idx: bool = True,
        bgr: float = 0.0,
    ):
        """Initialize the Format class with given parameters for image and instance annotation formatting.

        This class standardizes image and instance annotations for object detection, instance segmentation, and pose
        estimation tasks, preparing them for use in PyTorch DataLoader's `collate_fn`.

        Args:
            bbox_format (str): Format for bounding boxes. Options are 'xywh', 'xyxy', etc.
            normalize (bool): Whether to normalize bounding boxes to [0,1].
            return_mask (bool): If True, returns instance masks for segmentation tasks.
            return_keypoint (bool): If True, returns keypoints for pose estimation tasks.
            return_obb (bool): If True, returns oriented bounding boxes.
            mask_ratio (int): Downsample ratio for masks.
            mask_overlap (bool): If True, allows mask overlap.
            batch_idx (bool): If True, keeps batch indexes.
            bgr (float): Probability of returning BGR images instead of RGB.
        """
        self.bbox_format = bbox_format
        self.normalize = normalize
        self.return_mask = return_mask  # set False when training detection only
        self.return_keypoint = return_keypoint
        self.return_obb = return_obb
        self.mask_ratio = mask_ratio
        self.mask_overlap = mask_overlap
        self.batch_idx = batch_idx  # keep the batch indexes
        self.bgr = bgr

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute formatting parameters shared across image and instance formatting.

        Extracts image dimensions and pops instance annotations from labels, converting bounding box format
        and denormalizing coordinates for downstream tensor creation.

        Args:
            labels (dict[str, Any]): Input labels dictionary containing 'img', 'cls', and 'instances'.

        Returns:
            (dict[str, Any]): Parameters including 'h', 'w', 'cls', 'instances', and 'nl'.
        """
        img = labels.get("img")
        h, w = img.shape[:2] if img is not None else (0, 0)
        cls = labels.pop("cls", np.array([]))
        instances = labels.pop("instances", None)
        if instances is not None:
            instances.convert_bbox(format=self.bbox_format)
            instances.denormalize(w, h)
        return {"h": h, "w": w, "cls": cls, "instances": instances, "nl": len(instances) if instances else 0}

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Format image from Numpy array to PyTorch tensor.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img' as a numpy array.
            params (dict[str, Any] | None): Unused parameters for API compatibility.

        Returns:
            (dict[str, Any]): Updated labels with 'img' as a PyTorch tensor.
        """
        img = labels.pop("img", None)
        if img is not None:
            labels["img"] = self._format_img(img)
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Format instance annotations into PyTorch tensors.

        Converts class labels, bounding boxes, masks, and keypoints into tensors suitable for
        collation in PyTorch DataLoader.

        Args:
            labels (dict[str, Any]): Dictionary to populate with formatted tensors.
            params (dict[str, Any]): Parameters from get_params containing 'h', 'w', 'cls', 'instances', 'nl'.

        Returns:
            (dict[str, Any]): Updated labels with formatted instance tensors.
        """
        cls = params.get("cls", np.array([]))
        instances = params.get("instances")
        assert instances is not None, "instances are required for Format.apply_instances"
        h = params.get("h", 0)
        w = params.get("w", 0)
        nl = params.get("nl", 0)

        if self.return_mask:
            if self.mask_ratio > min(h, w):
                raise ValueError(
                    f"mask_ratio={self.mask_ratio} downsamples imgsz={(h, w)} masks to zero size; use mask_ratio <= {min(h, w)}"
                )
            if nl:
                masks, instances, cls = self._format_segments(instances, cls, w, h)
                masks = torch.from_numpy(masks)
                cls_tensor = torch.from_numpy(cls.squeeze(1))
                if not masks.shape[0] or not cls_tensor.numel():
                    sem_masks = torch.zeros(h // self.mask_ratio, w // self.mask_ratio)
                elif self.mask_overlap:
                    sem_masks = cls_tensor[masks[0].long() - 1]  # (H, W) from (1, H, W) instance indices
                else:
                    # Create sem_masks consistent with mask_overlap=True
                    sem_masks = (masks * cls_tensor[:, None, None]).max(0).values  # (H, W) from (N, H, W) binary
                    overlap = masks.sum(dim=0) > 1  # (H, W)
                    if overlap.any():
                        weights = masks.sum(axis=(1, 2))
                        weighted_masks = masks * weights[:, None, None]  # (N, H, W)
                        weighted_masks[masks == 0] = weights.max() + 1  # handle background
                        smallest_idx = weighted_masks.argmin(dim=0)  # (H, W)
                        sem_masks[overlap] = cls_tensor[smallest_idx[overlap]]
            else:
                masks = torch.zeros(1 if self.mask_overlap else nl, h // self.mask_ratio, w // self.mask_ratio)
                sem_masks = torch.zeros(h // self.mask_ratio, w // self.mask_ratio)
            labels["masks"] = masks
            labels["sem_masks"] = sem_masks.float()
        labels["cls"] = torch.from_numpy(cls) if nl else torch.zeros(nl, 1)
        labels["bboxes"] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
        if self.return_keypoint:
            labels["keypoints"] = (
                torch.empty(0, 3) if instances.keypoints is None else torch.from_numpy(instances.keypoints)
            )
            if self.normalize:
                labels["keypoints"][..., 0] /= w
                labels["keypoints"][..., 1] /= h
        if self.return_obb:
            labels["bboxes"] = (
                xyxyxyxy2xywhr(torch.from_numpy(instances.segments)) if len(instances.segments) else torch.zeros((0, 5))
            )
        # NOTE: need to normalize obb in xywhr format for width-height consistency
        if self.normalize:
            labels["bboxes"][:, [0, 2]] /= w
            labels["bboxes"][:, [1, 3]] /= h
        # Then we can use collate_fn
        if self.batch_idx:
            labels["batch_idx"] = torch.zeros(nl)
        return labels

    def _format_img(self, img: np.ndarray) -> torch.Tensor:
        """Format an image for YOLO from a Numpy array to a PyTorch tensor.

        This function performs the following operations:
        1. Ensures the image has 3 dimensions (adds a channel dimension if needed).
        2. Transposes the image from HWC to CHW format.
        3. Optionally reverses the color channels (e.g., BGR to RGB) based on the bgr probability.
        4. Converts the image to a contiguous array.
        5. Converts the Numpy array to a PyTorch tensor.

        Args:
            img (np.ndarray): Input image as a Numpy array with shape (H, W, C) or (H, W).

        Returns:
            (torch.Tensor): Formatted image as a PyTorch tensor with shape (C, H, W).

        Examples:
            >>> import numpy as np
            >>> img = np.random.rand(100, 100, 3)
            >>> formatted_img = self._format_img(img)
            >>> print(formatted_img.shape)
            torch.Size([3, 100, 100])
        """
        if len(img.shape) < 3:
            img = img[..., None]
        img = img.transpose(2, 0, 1)
        img = np.ascontiguousarray(img[::-1] if random.uniform(0, 1) > self.bgr and img.shape[0] == 3 else img)
        img = torch.from_numpy(img)
        return img

    def _format_segments(
        self, instances: Instances, cls: np.ndarray, w: int, h: int
    ) -> tuple[np.ndarray, Instances, np.ndarray]:
        """Convert polygon segments to bitmap masks.

        Args:
            instances (Instances): Object containing segment information.
            cls (np.ndarray): Class labels for each instance.
            w (int): Width of the image.
            h (int): Height of the image.

        Returns:
            masks (np.ndarray): Bitmap masks with shape (N, H, W) or (1, H, W) if mask_overlap is True.
            instances (Instances): Updated instances object with sorted segments if mask_overlap is True.
            cls (np.ndarray): Updated class labels, sorted if mask_overlap is True.

        Notes:
            - If self.mask_overlap is True, masks are overlapped and sorted by area.
            - If self.mask_overlap is False, each mask is represented separately.
            - Masks are downsampled according to self.mask_ratio.
        """
        segments = instances.segments
        if self.mask_overlap:
            masks, sorted_idx = polygons2masks_overlap((h, w), segments, downsample_ratio=self.mask_ratio)
            masks = masks[None]  # (640, 640) -> (1, 640, 640)
            instances = instances[sorted_idx]
            cls = cls[sorted_idx]
        else:
            masks = polygons2masks((h, w), segments, color=1, downsample_ratio=self.mask_ratio)

        return masks, instances, cls


class SemanticFormat(Format):
    """Format transform for semantic segmentation that converts images and masks to tensors.

    This transform handles the letterboxed semantic mask by resizing it to match the image dimensions and converts both
    to the appropriate tensor formats.
    """

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Format image and semantic mask for semantic segmentation.

        Args:
            labels (dict[str, Any]): Dictionary containing 'img' and 'semantic_mask'.
            params (dict[str, Any] | None): Unused parameters for API compatibility.

        Returns:
            (dict[str, Any]): Updated labels with 'img' and 'semantic_mask' as tensors.
        """
        img = labels.pop("img", None)
        if img is not None:
            labels["img"] = self._format_img(img)
        mask = labels.get("semantic_mask")
        if mask is not None:
            labels["semantic_mask"] = torch.from_numpy(mask.copy()).to(torch.int32)
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Remove instance-level keys not needed for semantic segmentation.

        Args:
            labels (dict[str, Any]): Dictionary to clean up.
            params (dict[str, Any] | None): Unused parameters for API compatibility.

        Returns:
            (dict[str, Any]): Updated labels with unused keys removed.
        """
        for k in ("cls", "instances", "resized_shape", "ori_shape", "ratio_pad"):
            labels.pop(k, None)
        return labels


class LoadVisualPrompt(BaseTransform):
    """Create visual prompts from bounding boxes or masks for model input."""

    def __init__(self, scale_factor: float = 1 / 8) -> None:
        """Initialize the LoadVisualPrompt with a scale factor.

        Args:
            scale_factor (float): Factor to scale the input image dimensions.
        """
        self.scale_factor = scale_factor

    @staticmethod
    def make_mask(boxes: torch.Tensor, h: int, w: int) -> torch.Tensor:
        """Create binary masks from bounding boxes.

        Args:
            boxes (torch.Tensor): Bounding boxes in xyxy format, shape: (N, 4).
            h (int): Height of the mask.
            w (int): Width of the mask.

        Returns:
            (torch.Tensor): Binary masks with shape (N, h, w).
        """
        x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1)  # x1 shape(n,1,1)
        r = torch.arange(w)[None, None, :]  # rows shape(1,1,w)
        c = torch.arange(h)[None, :, None]  # cols shape(1,h,1)

        return (r >= x1) * (r < x2) * (c >= y1) * (c < y2)

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute visual prompt parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary.

        Returns:
            (dict): Parameters including 'imgsz', 'bboxes', 'masks', and 'cls'.
        """
        imgsz = labels["img"].shape[1:]
        bboxes, masks = None, None
        if "bboxes" in labels:
            bboxes = labels["bboxes"]
            bboxes = xywh2xyxy(bboxes) * torch.tensor(imgsz)[[1, 0, 1, 0]]  # denormalize boxes
        elif "masks" in labels:
            masks = labels["masks"]

        cls = labels["cls"].squeeze(-1).to(torch.int)
        return {"imgsz": imgsz, "bboxes": bboxes, "masks": masks, "cls": cls}

    def apply_image(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Create visual prompts and add them to labels.

        Args:
            labels (dict[str, Any]): Dictionary containing image data and annotations.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with visual prompts added.
        """
        visuals = self.get_visuals(params["cls"], params["imgsz"], bboxes=params["bboxes"], masks=params["masks"])
        labels["visuals"] = visuals
        return labels

    def get_visuals(
        self,
        category: int | np.ndarray | torch.Tensor,
        shape: tuple[int, int],
        bboxes: np.ndarray | torch.Tensor = None,
        masks: np.ndarray | torch.Tensor = None,
    ) -> torch.Tensor:
        """Generate visual masks based on bounding boxes or masks.

        Args:
            category (int | np.ndarray | torch.Tensor): The category labels for the objects.
            shape (tuple[int, int]): The shape of the image (height, width).
            bboxes (np.ndarray | torch.Tensor, optional): Bounding boxes for the objects, xyxy format.
            masks (np.ndarray | torch.Tensor, optional): Masks for the objects.

        Returns:
            (torch.Tensor): A tensor containing the visual masks for each category.

        Raises:
            ValueError: If neither bboxes nor masks are provided.
        """
        masksz = (int(shape[0] * self.scale_factor), int(shape[1] * self.scale_factor))
        if bboxes is not None:
            if isinstance(bboxes, np.ndarray):
                bboxes = torch.from_numpy(bboxes)
            bboxes *= self.scale_factor
            masks = self.make_mask(bboxes, *masksz).float()
        elif masks is not None:
            if isinstance(masks, np.ndarray):
                masks = torch.from_numpy(masks)  # (N, H, W)
            masks = F.interpolate(masks.unsqueeze(1), masksz, mode="nearest").squeeze(1).float()
        else:
            raise ValueError("LoadVisualPrompt must have bboxes or masks in the label")
        if not isinstance(category, torch.Tensor):
            category = torch.tensor(category, dtype=torch.int)
        cls_unique, inverse_indices = torch.unique(category, sorted=True, return_inverse=True)
        # NOTE: `cls` indices from RandomLoadText should be continuous.
        # if len(cls_unique):
        #     assert len(cls_unique) == cls_unique[-1] + 1, (
        #         f"Expected a continuous range of class indices, but got {cls_unique}"
        #     )
        visuals = torch.zeros(cls_unique.shape[0], *masksz)
        for idx, mask in zip(inverse_indices, masks):
            visuals[idx] = torch.logical_or(visuals[idx], mask)
        return visuals


class RandomLoadText(BaseTransform):
    """Randomly sample positive and negative texts and update class indices accordingly.

    This class is responsible for sampling texts from a given set of class texts, including both positive (present in
    the image) and negative (not present in the image) samples. It updates the class indices to reflect the sampled
    texts and can optionally pad the text list to a fixed length.

    Attributes:
        prompt_format (str): Format string for text prompts.
        neg_samples (tuple[int, int]): Range for randomly sampling negative texts.
        max_samples (int): Maximum number of different text samples in one image.
        padding (bool): Whether to pad texts to max_samples.
        padding_value (list[str]): The text used for padding when padding is True.

    Methods:
        __call__: Process the input labels and return updated classes and texts.

    Examples:
        >>> loader = RandomLoadText(prompt_format="Object: {}", neg_samples=(5, 10), max_samples=20)
        >>> labels = {"cls": [0, 1, 2], "texts": [["cat"], ["dog"], ["bird"]], "instances": [...]}
        >>> updated_labels = loader(labels)
        >>> print(updated_labels["texts"])
        ['Object: cat', 'Object: dog', 'Object: bird', 'Object: elephant', 'Object: car']
    """

    def __init__(
        self,
        prompt_format: str = "{}",
        neg_samples: tuple[int, int] = (80, 80),
        max_samples: int = 80,
        padding: bool = False,
        padding_value: list[str] | None = None,
    ) -> None:
        """Initialize the RandomLoadText class for randomly sampling positive and negative texts.

        This class is designed to randomly sample positive texts and negative texts, and update the class indices
        accordingly to the number of samples. It can be used for text-based object detection tasks.

        Args:
            prompt_format (str): Format string for the prompt. The format string should contain a single pair of curly
                braces {} where the text will be inserted.
            neg_samples (tuple[int, int]): A range to randomly sample negative texts. The first integer specifies the
                minimum number of negative samples, and the second integer specifies the maximum.
            max_samples (int): The maximum number of different text samples in one image.
            padding (bool): Whether to pad texts to max_samples. If True, the number of texts will always be equal to
                max_samples.
            padding_value (list[str]): The padding text to use when padding is True.
        """
        self.prompt_format = prompt_format
        self.neg_samples = neg_samples
        self.max_samples = max_samples
        self.padding = padding
        self.padding_value = padding_value if padding_value is not None else [""]

    def get_params(self, labels: dict[str, Any]) -> dict[str, Any]:
        """Compute text sampling parameters.

        Args:
            labels (dict[str, Any]): Input labels dictionary containing 'texts', 'cls', and 'instances'.

        Returns:
            (dict): Parameters including 'valid_idx', 'new_cls', and 'texts'.
        """
        assert "texts" in labels, "No texts found in labels."
        class_texts = labels["texts"]
        num_classes = len(class_texts)
        cls = np.asarray(labels.pop("cls"), dtype=int)
        pos_labels = np.unique(cls).tolist()

        if len(pos_labels) > self.max_samples:
            pos_labels = random.sample(pos_labels, k=self.max_samples)

        neg_samples = min(min(num_classes, self.max_samples) - len(pos_labels), random.randint(*self.neg_samples))
        neg_labels = [i for i in range(num_classes) if i not in pos_labels]
        neg_labels = random.sample(neg_labels, k=neg_samples)

        sampled_labels = pos_labels + neg_labels
        # Randomness
        # random.shuffle(sampled_labels)

        label2ids = {label: i for i, label in enumerate(sampled_labels)}
        valid_idx = np.zeros(len(labels["instances"]), dtype=bool)
        new_cls = []
        for i, label in enumerate(cls.squeeze(-1).tolist()):
            if label not in label2ids:
                continue
            valid_idx[i] = True
            new_cls.append([label2ids[label]])

        # Randomly select one prompt when there's more than one prompts
        texts = []
        for label in sampled_labels:
            prompts = class_texts[label]
            assert len(prompts) > 0
            prompt = self.prompt_format.format(prompts[random.randrange(len(prompts))])
            texts.append(prompt)

        if self.padding:
            valid_labels = len(pos_labels) + len(neg_labels)
            num_padding = self.max_samples - valid_labels
            if num_padding > 0:
                texts += random.choices(self.padding_value, k=num_padding)

        assert len(texts) == self.max_samples

        return {"valid_idx": valid_idx, "new_cls": np.array(new_cls), "texts": texts}

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any]) -> dict[str, Any]:
        """Filter instances and update class labels based on sampled texts.

        Args:
            labels (dict[str, Any]): Dictionary containing 'instances' and 'cls'.
            params (dict): Parameters from get_params.

        Returns:
            (dict): Updated labels with filtered instances and new class/text entries.
        """
        labels["instances"] = labels["instances"][params["valid_idx"]]
        labels["cls"] = params["new_cls"]
        labels["texts"] = params["texts"]
        return labels


def v8_transforms(dataset, imgsz: int, hyp: IterableSimpleNamespace):
    """Apply a series of image transformations for training.

    This function creates a composition of image augmentation techniques to prepare images for YOLO training. It
    includes operations such as mosaic, copy-paste, random perspective, mixup, and various color adjustments.

    Args:
        dataset (Dataset): The dataset object containing image data and annotations.
        imgsz (int): The target image size for resizing.
        hyp (IterableSimpleNamespace): A namespace of hyperparameters controlling various aspects of the
            transformations.

    Returns:
        (Compose): A composition of image transformations to be applied to the dataset.

    Examples:
        >>> from ultralytics.cfg import DEFAULT_CFG
        >>> from ultralytics.data.dataset import YOLODataset
        >>> from ultralytics.utils import IterableSimpleNamespace
        >>> dataset = YOLODataset(img_path="path/to/images", data={"names": {0: "person"}}, imgsz=640)
        >>> hyp = IterableSimpleNamespace(
        ...     **{
        ...         **vars(DEFAULT_CFG),
        ...         "mosaic": 1.0,
        ...         "copy_paste": 0.5,
        ...         "degrees": 10.0,
        ...         "translate": 0.2,
        ...         "scale": 0.9,
        ...     }
        ... )
        >>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
        >>> augmented_data = transforms(dataset[0])

        >>> # With custom albumentations
        >>> import albumentations as A
        >>> augmentations = [A.Blur(p=0.01), A.CLAHE(p=0.01)]
        >>> hyp.augmentations = augmentations
        >>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
    """
    mosaic = Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic)
    affine = RandomPerspective(
        degrees=hyp.degrees,
        translate=hyp.translate,
        scale=hyp.scale,
        shear=hyp.shear,
        perspective=hyp.perspective,
        size=(imgsz, imgsz),
    )

    pre_transform = Compose([mosaic, affine])
    if hyp.copy_paste_mode == "flip":
        pre_transform.insert(1, CopyPaste(dataset, p=hyp.copy_paste, mode=hyp.copy_paste_mode))
    else:
        pre_transform.append(
            CopyPaste(
                dataset,
                pre_transform=Compose([Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic), affine]),
                p=hyp.copy_paste,
                mode=hyp.copy_paste_mode,
            )
        )
    flip_idx = dataset.data.get("flip_idx", [])  # for keypoints augmentation
    if getattr(dataset, "use_keypoints", False):
        kpt_shape = dataset.data.get("kpt_shape", None)
        if len(flip_idx) == 0 and (hyp.fliplr > 0.0 or hyp.flipud > 0.0):
            hyp.fliplr = hyp.flipud = 0.0  # both fliplr and flipud require flip_idx
            LOGGER.warning("No 'flip_idx' array defined in data.yaml, disabling 'fliplr' and 'flipud' augmentations.")
        elif flip_idx and (len(flip_idx) != kpt_shape[0]):
            raise ValueError(f"data.yaml flip_idx={flip_idx} length must be equal to kpt_shape[0]={kpt_shape[0]}")

    return Compose(
        [
            pre_transform,
            MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
            CutMix(dataset, pre_transform=pre_transform, p=hyp.cutmix),
            Albumentations(p=1.0, transforms=getattr(hyp, "augmentations", None)),
            RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
            RandomFlip(direction="vertical", p=hyp.flipud, flip_idx=flip_idx),
            RandomFlip(direction="horizontal", p=hyp.fliplr, flip_idx=flip_idx),
        ]
    )  # transforms


# Classification augmentations -----------------------------------------------------------------------------------------
def classify_transforms(
    size: tuple[int, int] | int = 224,
    mean: tuple[float, float, float] = DEFAULT_MEAN,
    std: tuple[float, float, float] = DEFAULT_STD,
    interpolation: str = "BILINEAR",
    crop_fraction: float | None = None,
):
    """Create a composition of image transforms for classification tasks.

    This function generates a sequence of torchvision transforms suitable for preprocessing images for classification
    models during evaluation or inference. The transforms include resizing, center cropping, conversion to tensor, and
    normalization.

    Args:
        size (tuple[int, int] | int): The target size for the transformed image. If an int, it defines the shortest
            edge. If a tuple, it defines (height, width).
        mean (tuple[float, float, float]): Mean values for each RGB channel used in normalization.
        std (tuple[float, float, float]): Standard deviation values for each RGB channel used in normalization.
        interpolation (str): Interpolation method of either 'NEAREST', 'BILINEAR' or 'BICUBIC'.
        crop_fraction (float | None): Deprecated, will be removed in a future version.

    Returns:
        (torchvision.transforms.Compose): A composition of torchvision transforms.

    Examples:
        >>> transforms = classify_transforms(size=224)
        >>> img = Image.open("path/to/image.jpg")
        >>> transformed_img = transforms(img)
    """
    import torchvision.transforms as T  # scope for faster 'import ultralytics'

    scale_size = size if isinstance(size, (tuple, list)) and len(size) == 2 else (size, size)

    if crop_fraction:
        deprecation_warn("crop_fraction")

    # Square target uses the scalar shortest-edge mode (preserves aspect); non-square resizes to the exact (h, w).
    resize = scale_size[0] if scale_size[0] == scale_size[1] else scale_size
    tfl = [
        T.Resize(resize, interpolation=getattr(T.InterpolationMode, interpolation)),
        T.CenterCrop(size),
        T.ToTensor(),
        T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
    ]
    return T.Compose(tfl)


# Classification training augmentations --------------------------------------------------------------------------------
def classify_augmentations(
    size: int = 224,
    mean: tuple[float, float, float] = DEFAULT_MEAN,
    std: tuple[float, float, float] = DEFAULT_STD,
    scale: tuple[float, float] | None = None,
    ratio: tuple[float, float] | None = None,
    hflip: float = 0.5,
    vflip: float = 0.0,
    auto_augment: str | None = None,
    hsv_h: float = 0.015,  # image HSV-Hue augmentation (fraction)
    hsv_s: float = 0.4,  # image HSV-Saturation augmentation (fraction)
    hsv_v: float = 0.4,  # image HSV-Value augmentation (fraction)
    force_color_jitter: bool = False,
    erasing: float = 0.0,
    interpolation: str = "BILINEAR",
):
    """Create a composition of image augmentation transforms for classification tasks.

    This function generates a set of image transformations suitable for training classification models. It includes
    options for resizing, flipping, color jittering, auto augmentation, and random erasing.

    Args:
        size (int): Target size for the image after transformations.
        mean (tuple[float, float, float]): Mean values for each RGB channel used in normalization.
        std (tuple[float, float, float]): Standard deviation values for each RGB channel used in normalization.
        scale (tuple[float, float] | None): Range of the proportion of the original image area to crop.
        ratio (tuple[float, float] | None): Range of aspect ratio for the cropped area.
        hflip (float): Probability of horizontal flip.
        vflip (float): Probability of vertical flip.
        auto_augment (str | None): Auto augmentation policy. Can be 'randaugment', 'augmix', 'autoaugment' or None.
        hsv_h (float): Image HSV-Hue augmentation factor.
        hsv_s (float): Image HSV-Saturation augmentation factor.
        hsv_v (float): Image HSV-Value augmentation factor.
        force_color_jitter (bool): Whether to apply color jitter even if auto augment is enabled.
        erasing (float): Probability of random erasing.
        interpolation (str): Interpolation method of either 'NEAREST', 'BILINEAR' or 'BICUBIC'.

    Returns:
        (torchvision.transforms.Compose): A composition of image augmentation transforms.

    Examples:
        >>> transforms = classify_augmentations(size=224, auto_augment="randaugment")
        >>> augmented_image = transforms(original_image)
    """
    # Transforms to apply if Albumentations not installed
    import torchvision.transforms as T  # scope for faster 'import ultralytics'

    if not isinstance(size, int):
        raise TypeError(f"classify_augmentations() size {size} must be integer, not (list, tuple)")
    scale = tuple(scale or (0.08, 1.0))  # default imagenet scale range
    ratio = tuple(ratio or (3.0 / 4.0, 4.0 / 3.0))  # default imagenet ratio range
    interpolation = getattr(T.InterpolationMode, interpolation)
    primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]
    if hflip > 0.0:
        primary_tfl.append(T.RandomHorizontalFlip(p=hflip))
    if vflip > 0.0:
        primary_tfl.append(T.RandomVerticalFlip(p=vflip))

    secondary_tfl = []
    disable_color_jitter = False
    if auto_augment:
        assert isinstance(auto_augment, str), f"Provided argument should be string, but got type {type(auto_augment)}"
        # color jitter is typically disabled if AA/RA on,
        # this allows override without breaking old hparm cfgs
        disable_color_jitter = not force_color_jitter

        if auto_augment == "randaugment":
            if TORCHVISION_0_11:
                secondary_tfl.append(T.RandAugment(interpolation=interpolation))
            else:
                LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.')

        elif auto_augment == "augmix":
            if TORCHVISION_0_13:
                secondary_tfl.append(T.AugMix(interpolation=interpolation))
            else:
                LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.')

        elif auto_augment == "autoaugment":
            if TORCHVISION_0_10:
                secondary_tfl.append(T.AutoAugment(interpolation=interpolation))
            else:
                LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.')

        else:
            raise ValueError(
                f'Invalid auto_augment policy: {auto_augment}. Should be one of "randaugment", '
                f'"augmix", "autoaugment" or None'
            )

    if not disable_color_jitter:
        secondary_tfl.append(T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h))

    final_tfl = [
        T.ToTensor(),
        T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
        T.RandomErasing(p=erasing, inplace=True),
    ]

    return T.Compose(primary_tfl + secondary_tfl + final_tfl)


class DepthFormat(Format):
    """Format transform for monocular depth estimation: image via Format, depth map resized and tensorized.

    Mirrors SemanticFormat: the base Format.apply_image converts the image (HWC BGR -> CHW RGB tensor), and the
    apply_depth hook (run after apply_image in BaseTransform.__call__) resizes the paired depth map to the letterboxed
    image size and emits it as a (1, H, W) float tensor.
    """

    def apply_depth(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Resize depth to the formatted image size (nearest) and emit a (1, H, W) float tensor.

        Args:
            labels (dict[str, Any]): Dictionary with 'img' (already a CHW tensor) and optionally 'depth'.
            params (dict[str, Any] | None): Unused parameters for API compatibility.

        Returns:
            (dict[str, Any]): Updated labels with 'depth' as a (1, H, W) float tensor.
        """
        depth = labels.get("depth")
        if depth is None or "img" not in labels:
            return labels
        _, h, w = labels["img"].shape
        if depth.shape[:2] != (h, w):
            depth = cv2.resize(depth, (w, h), interpolation=cv2.INTER_NEAREST)
        labels["depth"] = torch.from_numpy(np.ascontiguousarray(depth[None])).float()
        return labels

    def apply_instances(self, labels: dict[str, Any], params: dict[str, Any] | None = None) -> dict[str, Any]:
        """Remove instance-level keys not needed for depth estimation.

        Args:
            labels (dict[str, Any]): Dictionary to clean up.
            params (dict[str, Any] | None): Unused parameters for API compatibility.

        Returns:
            (dict[str, Any]): Updated labels with unused keys removed.
        """
        for k in ("cls", "instances", "resized_shape", "ori_shape", "ratio_pad"):
            labels.pop(k, None)
        return labels


# NOTE: keep this class for backward compatibility
class ClassifyLetterBox:
    """A class for resizing and padding images for classification tasks.

    This class is designed to be part of a transformation pipeline, e.g., T.Compose([LetterBox(size), ToTensor()]). It
    resizes and pads images to a specified size while maintaining the original aspect ratio.

    Attributes:
        h (int): Target height of the image.
        w (int): Target width of the image.
        auto (bool): If True, automatically calculates the short side using stride.
        stride (int): The stride value, used when 'auto' is True.

    Methods:
        __call__: Apply the letterbox transformation to an input image.

    Examples:
        >>> transform = ClassifyLetterBox(size=(640, 640), auto=False, stride=32)
        >>> img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
        >>> result = transform(img)
        >>> print(result.shape)
        (640, 640, 3)
    """

    def __init__(self, size: int | tuple[int, int] = (640, 640), auto: bool = False, stride: int = 32):
        """Initialize the ClassifyLetterBox object for image preprocessing.

        This class is designed to be part of a transformation pipeline for image classification tasks. It resizes and
        pads images to a specified size while maintaining the original aspect ratio.

        Args:
            size (int | tuple[int, int]): Target size for the letterboxed image. If an int, a square image of (size,
                size) is created. If a tuple, it should be (height, width).
            auto (bool): If True, automatically calculates the short side based on stride.
            stride (int): The stride value, used when 'auto' is True.
        """
        super().__init__()
        self.h, self.w = (size, size) if isinstance(size, int) else size
        self.auto = auto  # pass max size integer, automatically solve for short side using stride
        self.stride = stride  # used with auto

    def __call__(self, im: np.ndarray) -> np.ndarray:
        """Resize and pad an image using the letterbox method.

        This method resizes the input image to fit within the specified dimensions while maintaining its aspect ratio,
        then pads the resized image to match the target size.

        Args:
            im (np.ndarray): Input image as a numpy array with shape (H, W, C).

        Returns:
            (np.ndarray): Resized and padded image as a numpy array with shape (hs, ws, 3), where hs and ws are the
                target height and width respectively.

        Examples:
            >>> letterbox = ClassifyLetterBox(size=(640, 640))
            >>> image = np.random.randint(0, 255, (720, 1280, 3), dtype=np.uint8)
            >>> resized_image = letterbox(image)
            >>> print(resized_image.shape)
            (640, 640, 3)
        """
        imh, imw = im.shape[:2]
        r = min(self.h / imh, self.w / imw)  # ratio of new/old dimensions
        h, w = round(imh * r), round(imw * r)  # resized image dimensions

        # Calculate padding dimensions
        hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else (self.h, self.w)
        top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)

        # Create padded image
        im_out = np.full((hs, ws, 3), 114, dtype=im.dtype)
        im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
        return im_out


# NOTE: keep this class for backward compatibility
class CenterCrop:
    """Apply center cropping to images for classification tasks.

    This class performs center cropping on input images, resizing them to a specified size while maintaining the aspect
    ratio. It is designed to be part of a transformation pipeline, e.g., T.Compose([CenterCrop(size), ToTensor()]).

    Attributes:
        h (int): Target height of the cropped image.
        w (int): Target width of the cropped image.

    Methods:
        __call__: Apply the center crop transformation to an input image.

    Examples:
        >>> transform = CenterCrop(640)
        >>> image = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
        >>> cropped_image = transform(image)
        >>> print(cropped_image.shape)
        (640, 640, 3)
    """

    def __init__(self, size: int | tuple[int, int] = (640, 640)):
        """Initialize the CenterCrop object for image preprocessing.

        This class is designed to be part of a transformation pipeline, e.g., T.Compose([CenterCrop(size), ToTensor()]).
        It performs a center crop on input images to a specified size.

        Args:
            size (int | tuple[int, int]): The desired output size of the crop. If size is an int, a square crop (size,
                size) is made. If size is a sequence like (h, w), it is used as the output size.
        """
        super().__init__()
        self.h, self.w = (size, size) if isinstance(size, int) else size

    def __call__(self, im: Image.Image | np.ndarray) -> np.ndarray:
        """Apply center cropping to an input image.

        This method crops the largest centered square from the image and resizes it to the specified dimensions.

        Args:
            im (np.ndarray | PIL.Image.Image): The input image as a numpy array of shape (H, W, C) or a PIL Image
                object.

        Returns:
            (np.ndarray): The center-cropped and resized image as a numpy array of shape (self.h, self.w, C).

        Examples:
            >>> transform = CenterCrop(size=224)
            >>> image = np.random.randint(0, 255, (640, 480, 3), dtype=np.uint8)
            >>> cropped_image = transform(image)
            >>> assert cropped_image.shape == (224, 224, 3)
        """
        if isinstance(im, Image.Image):  # convert from PIL to numpy array if required
            im = np.asarray(im)
        imh, imw = im.shape[:2]
        m = min(imh, imw)  # min dimension
        top, left = (imh - m) // 2, (imw - m) // 2
        return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)


# NOTE: keep this class for backward compatibility
class ToTensor:
    """Convert an image from a numpy array to a PyTorch tensor.

    This class is designed to be part of a transformation pipeline, e.g., T.Compose([LetterBox(size), ToTensor()]).

    Attributes:
        half (bool): If True, converts the image to half precision (float16).

    Methods:
        __call__: Apply the tensor conversion to an input image.

    Examples:
        >>> transform = ToTensor(half=True)
        >>> img = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
        >>> tensor_img = transform(img)
        >>> print(tensor_img.shape, tensor_img.dtype)
        torch.Size([3, 640, 640]) torch.float16

    Notes:
        The input image is expected to be in BGR format with shape (H, W, C).
        The output tensor will be in BGR format with shape (C, H, W), normalized to [0, 1].
    """

    def __init__(self, half: bool = False):
        """Initialize the ToTensor object for converting images to PyTorch tensors.

        This class is designed to be used as part of a transformation pipeline for image preprocessing in the
        Ultralytics YOLO framework. It converts numpy arrays or PIL Images to PyTorch tensors, with an option for
        half-precision (float16) conversion.

        Args:
            half (bool): If True, converts the tensor to half precision (float16).
        """
        super().__init__()
        self.half = half

    def __call__(self, im: np.ndarray) -> torch.Tensor:
        """Transform an image from a numpy array to a PyTorch tensor.

        This method converts the input image from a numpy array to a PyTorch tensor, applying optional half-precision
        conversion and normalization. The image is transposed from HWC to CHW format.

        Args:
            im (np.ndarray): Input image as a numpy array with shape (H, W, C) in BGR order.

        Returns:
            (torch.Tensor): The transformed image as a PyTorch tensor in float32 or float16, normalized to [0, 1] with
                shape (C, H, W) in BGR order.

        Examples:
            >>> transform = ToTensor(half=True)
            >>> img = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
            >>> tensor_img = transform(img)
            >>> print(tensor_img.shape, tensor_img.dtype)
            torch.Size([3, 640, 640]) torch.float16
        """
        im = np.ascontiguousarray(im.transpose((2, 0, 1)))  # HWC to CHW -> contiguous
        im = torch.from_numpy(im)  # to torch
        im = im.half() if self.half else im.float()  # uint8 to fp16/32
        im /= 255.0  # 0-255 to 0.0-1.0
        return im
