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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| import torch | |
| from ..utils import ext_loader | |
| ext_module = ext_loader.load_ext('_ext', ['contour_expand']) | |
| def contour_expand(kernel_mask, internal_kernel_label, min_kernel_area, | |
| kernel_num): | |
| """Expand kernel contours so that foreground pixels are assigned into | |
| instances. | |
| Arguments: | |
| kernel_mask (np.array or Tensor): The instance kernel mask with | |
| size hxw. | |
| internal_kernel_label (np.array or 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: | |
| label (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() | |
| else: | |
| label = ext_module.contour_expand(kernel_mask, internal_kernel_label, | |
| min_kernel_area, kernel_num) | |
| return label | |