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Source code for mmcv.ops.contour_expand

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union

import numpy as np
import torch

from ..utils import ext_loader

ext_module = ext_loader.load_ext('_ext', ['contour_expand'])


[docs]def contour_expand(kernel_mask: Union[np.array, torch.Tensor], internal_kernel_label: Union[np.array, torch.Tensor], min_kernel_area: int, kernel_num: int) -> list: """Expand kernel contours so that foreground pixels are assigned into instances. Args: kernel_mask (np.array or torch.Tensor): The instance kernel mask with size hxw. internal_kernel_label (np.array or torch.Tensor): The instance internal kernel label with size hxw. min_kernel_area (int): The minimum kernel area. kernel_num (int): The instance kernel number. Returns: list: The instance index map with size hxw. """ assert isinstance(kernel_mask, (torch.Tensor, np.ndarray)) assert isinstance(internal_kernel_label, (torch.Tensor, np.ndarray)) assert isinstance(min_kernel_area, int) assert isinstance(kernel_num, int) if isinstance(kernel_mask, np.ndarray): kernel_mask = torch.from_numpy(kernel_mask) if isinstance(internal_kernel_label, np.ndarray): internal_kernel_label = torch.from_numpy(internal_kernel_label) if torch.__version__ == 'parrots': if kernel_mask.shape[0] == 0 or internal_kernel_label.shape[0] == 0: label = [] else: label = ext_module.contour_expand( kernel_mask, internal_kernel_label, min_kernel_area=min_kernel_area, kernel_num=kernel_num) label = label.tolist() # type: ignore else: label = ext_module.contour_expand(kernel_mask, internal_kernel_label, min_kernel_area, kernel_num) return label
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