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import numpy as np
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import torch
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from ..utils import ext_loader
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ext_module = ext_loader.load_ext('_ext', ['contour_expand'])
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def contour_expand(kernel_mask, internal_kernel_label, min_kernel_area,
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kernel_num):
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"""Expand kernel contours so that foreground pixels are assigned into
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instances.
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Arguments:
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kernel_mask (np.array or Tensor): The instance kernel mask with
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size hxw.
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internal_kernel_label (np.array or Tensor): The instance internal
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kernel label with size hxw.
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min_kernel_area (int): The minimum kernel area.
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kernel_num (int): The instance kernel number.
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Returns:
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label (list): The instance index map with size hxw.
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"""
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assert isinstance(kernel_mask, (torch.Tensor, np.ndarray))
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assert isinstance(internal_kernel_label, (torch.Tensor, np.ndarray))
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assert isinstance(min_kernel_area, int)
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assert isinstance(kernel_num, int)
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if isinstance(kernel_mask, np.ndarray):
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kernel_mask = torch.from_numpy(kernel_mask)
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if isinstance(internal_kernel_label, np.ndarray):
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internal_kernel_label = torch.from_numpy(internal_kernel_label)
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if torch.__version__ == 'parrots':
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if kernel_mask.shape[0] == 0 or internal_kernel_label.shape[0] == 0:
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label = []
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else:
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label = ext_module.contour_expand(
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kernel_mask,
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internal_kernel_label,
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min_kernel_area=min_kernel_area,
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kernel_num=kernel_num)
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label = label.tolist()
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else:
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label = ext_module.contour_expand(kernel_mask, internal_kernel_label,
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min_kernel_area, kernel_num)
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return label
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