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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import cv2
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def prepro_mask(mask: np.ndarray):
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mask_shape = mask.shape
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if len(mask_shape) != 2:
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mask = mask[..., 0]
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mask = mask.astype("uint8")
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mask = cv2.medianBlur(mask, 5)
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class_num = len(np.unique(mask))
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if class_num != 2:
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_, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY |
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cv2.THRESH_OTSU)
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mask = np.clip(mask, 0, 1).astype("uint8") # 0-255 / 0-1 -> 0-1
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return mask
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def calc_distance(p1: np.ndarray, p2: np.ndarray) -> float:
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return float(np.sqrt(np.sum(np.power((p1[0] - p2[0]), 2))))
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