Source code for mmrotate.core.visualization.palette
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
[docs]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 == 'dota':
from mmrotate.datasets import DOTADataset
dataset_palette = DOTADataset.PALETTE
elif palette == 'sar':
from mmrotate.datasets import SARDataset
dataset_palette = SARDataset.PALETTE
elif palette == 'hrsc':
from mmrotate.datasets import HRSCDataset
dataset_palette = HRSCDataset.PALETTE
elif palette == 'hrsc_classwise':
from mmrotate.datasets import HRSCDataset
dataset_palette = HRSCDataset.CLASSWISE_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