from functools import cached_property
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Union

import numpy as np
from PIL import Image
from skimage.util import img_as_float32

from ..base import MaskDataset

if TYPE_CHECKING:
    import albumentations


class TNBC(MaskDataset):
    """TNBC Nuclei Segmentation Dataset [1]_

    Parameters
    ----------
    root_dir : str
        Path to root directory
    output : {'both', 'image', 'mask'}, default: 'both'
        Change outputs. 'both' returns {'image': image, 'mask': mask}.
    transforms : albumentations.Compose, optional
        An instance of Compose (albumentations pkg) that defines
        augmentation in sequence.
    num_samples : int, optional
        Useful when ``transforms`` is set. Define the total length of the
        dataset. If it is set, it overwrites ``__len__``.
    grayscale : bool, default: False
        Convert images to grayscale
    grayscale_mode : {'cv2', 'equal', Sequence[float]}, default: 'cv2'
        How to convert to grayscale. If set to 'cv2', it follows opencv
        implementation. Else if set to 'equal', it sums up values along
        channel axis, then divides it by the number of expected channels.

    References
    ----------

    .. [1] Segmentation of Nuclei in Histopathology Images by Deep Regression of
       the Distance Map, https://ieeexplore.ieee.org/document/8438559

    See Also
    --------
    MaskDataset : Super class
    Dataset : Base class
    DatasetInterface : Interface
    """

    # Dataset's acronym
    acronym = 'TNBC'

    def __init__(
        self,
        # Interface requirement
        root_dir: str,
        *,
        output: str = 'both',
        transforms: Optional['albumentations.Compose'] = None,
        num_samples: Optional[int] = None,
        grayscale: bool = False,
        grayscale_mode: Union[str, Sequence[float]] = 'cv2',
        **kwargs
    ):
        # Interface and super-class arguments
        self._root_dir = root_dir
        self._output = output
        self._transforms = transforms
        self._num_samples = num_samples
        self._grayscale = grayscale
        self._grayscale_mode = grayscale_mode

    def get_image(self, p: Path) -> np.ndarray:
        img = Image.open(p)
        if img.mode == 'RGBA':
            img = img.convert(mode='RGB')
        return img_as_float32(np.asarray(img))

    def get_mask(self, p: Path) -> np.ndarray:
        mask = Image.open(p)
        return np.asarray(mask)

    @cached_property
    def file_list(self) -> List[Path]:
        # Call MaskDataset.root_dir
        root_dir = self.root_dir
        parent = 'Slide_*'
        file_list = sorted(root_dir.glob(f'{parent}/*.png'))
        return file_list

    @cached_property
    def anno_dict(self) -> Dict[int, Path]:
        """anno_dict[ind] = <file>"""
        root_dir = self.root_dir
        parent = 'GT_*'
        anno_dict = dict((k, v) for k, v in enumerate(
            sorted(root_dir.glob(f'{parent}/*.png'))
            ))
        return anno_dict
