# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np


def palette_val(palette):
    """Convert palette to matplotlib palette.

    Args:
        palette List[tuple]: A list of color tuples.

    Returns:
        List[tuple[float]]: A list of RGB matplotlib color tuples.
    """
    new_palette = []
    for color in palette:
        color = [c / 255 for c in color]
        new_palette.append(tuple(color))
    return new_palette


def get_palette(palette, num_classes):
    """Get palette from various inputs.

    Args:
        palette (list[tuple] | str | tuple | :obj:`Color`): palette inputs.
        num_classes (int): the number of classes.

    Returns:
        list[tuple[int]]: A list of color tuples.
    """
    assert isinstance(num_classes, int)

    if isinstance(palette, list):
        dataset_palette = palette
    elif isinstance(palette, tuple):
        dataset_palette = [palette] * num_classes
    elif palette == 'random' or palette is None:
        state = np.random.get_state()
        # random color
        np.random.seed(42)
        palette = np.random.randint(0, 256, size=(num_classes, 3))
        np.random.set_state(state)
        dataset_palette = [tuple(c) for c in palette]
    elif palette == 'coco':
        from mmdet.datasets import CocoDataset, CocoPanopticDataset
        dataset_palette = CocoDataset.PALETTE
        if len(dataset_palette) < num_classes:
            dataset_palette = CocoPanopticDataset.PALETTE
    elif palette == 'citys':
        from mmdet.datasets import CityscapesDataset
        dataset_palette = CityscapesDataset.PALETTE
    elif palette == 'voc':
        from mmdet.datasets import VOCDataset
        dataset_palette = VOCDataset.PALETTE
    elif mmcv.is_str(palette):
        dataset_palette = [mmcv.color_val(palette)[::-1]] * num_classes
    else:
        raise TypeError(f'Invalid type for palette: {type(palette)}')

    assert len(dataset_palette) >= num_classes, \
        'The length of palette should not be less than `num_classes`.'
    return dataset_palette

# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np

import mmdet


def palette_val(palette):
    """Convert palette to matplotlib palette.

    Args:
        palette List[tuple]: A list of color tuples.

    Returns:
        List[tuple[float]]: A list of RGB matplotlib color tuples.
    """
    new_palette = []
    for color in palette:
        color = [c / 255 for c in color]
        new_palette.append(tuple(color))
    return new_palette


def get_palette(palette, num_classes=None):
    """Get palette from various inputs.

    Args:
        palette (list[tuple]/str/tuple/:obj:`Color`): palette inputs

    Returns:
        list[tuple[int]]: A list of color tuples.
    """
    if isinstance(palette, list):
        return palette
    elif isinstance(palette, tuple):
        assert isinstance(num_classes, int)
        return [palette] * num_classes
    elif palette == 'coco':
        return mmdet.datasets.CocoDataset.PALETTE
    elif palette == 'voc':
        return mmdet.datasets.VOCDataset.PALETTE
    elif palette == 'citys':
        return mmdet.datasets.CityscapesDataset.PALETTE
    elif palette == 'random' or palette is None:
        assert isinstance(num_classes, int)
        state = np.random.get_state()
        # random color
        np.random.seed(42)
        palette = np.random.randint(0, 256, size=(num_classes, 3))
        np.random.set_state(state)
        return [tuple(c) for c in palette]
    elif mmcv.is_str(palette):
        assert isinstance(num_classes, int)
        return [mmcv.color_val(palette)[::-1]] * num_classes
    else:
        raise TypeError(f'Invalid type for palette: {type(palette)}')
