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104 lines
4.0 KiB
104 lines
4.0 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""unet 310 infer preprocess dataset"""
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import argparse
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import os
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import numpy as np
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import cv2
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from src.data_loader import create_dataset
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from src.config import cfg_unet
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def preprocess_dataset(data_dir, result_path, cross_valid_ind=1, cfg=None):
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_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=cfg['crop'],
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img_size=cfg['img_size'])
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for i, data in enumerate(valid_dataset):
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file_name = "ISBI_test_bs_1_" + str(i) + ".bin"
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file_path = result_path + file_name
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data[0].asnumpy().tofile(file_path)
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class CellNucleiDataset:
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"""
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Cell nuclei dataset preprocess class.
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"""
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def __init__(self, data_dir, repeat, result_path, is_train=False, split=0.8):
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self.data_dir = data_dir
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self.img_ids = sorted(next(os.walk(self.data_dir))[1])
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self.train_ids = self.img_ids[:int(len(self.img_ids) * split)] * repeat
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np.random.shuffle(self.train_ids)
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self.val_ids = self.img_ids[int(len(self.img_ids) * split):]
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self.is_train = is_train
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self.result_path = result_path
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self._preprocess_dataset()
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def _preprocess_dataset(self):
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for img_id in self.val_ids:
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path = os.path.join(self.data_dir, img_id)
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img = cv2.imread(os.path.join(path, "images", img_id + ".png"))
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if len(img.shape) == 2:
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img = np.expand_dims(img, axis=-1)
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img = np.concatenate([img, img, img], axis=-1)
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mask = []
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for mask_file in next(os.walk(os.path.join(path, "masks")))[2]:
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mask_ = cv2.imread(os.path.join(path, "masks", mask_file), cv2.IMREAD_GRAYSCALE)
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mask.append(mask_)
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mask = np.max(mask, axis=0)
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cv2.imwrite(os.path.join(self.result_path, img_id + ".png"), img)
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def _read_img_mask(self, img_id):
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path = os.path.join(self.data_dir, img_id)
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img = cv2.imread(os.path.join(path, "image.png"))
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mask = cv2.imread(os.path.join(path, "mask.png"), cv2.IMREAD_GRAYSCALE)
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return img, mask
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def __getitem__(self, index):
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if self.is_train:
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return self._read_img_mask(self.train_ids[index])
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return self._read_img_mask(self.val_ids[index])
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@property
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def column_names(self):
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column_names = ['image', 'mask']
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return column_names
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def __len__(self):
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if self.is_train:
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return len(self.train_ids)
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return len(self.val_ids)
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def get_args():
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parser = argparse.ArgumentParser(description='Preprocess the UNet dataset ',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
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help='data directory')
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parser.add_argument('-p', '--result_path', dest='result_path', type=str, default='./preprocess_Result/',
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help='result path')
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return parser.parse_args()
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if __name__ == '__main__':
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args = get_args()
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if 'dataset' in cfg_unet and cfg_unet['dataset'] == "Cell_nuclei":
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cell_dataset = CellNucleiDataset(args.data_url, 1, args.result_path, False, 0.8)
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else:
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preprocess_dataset(data_dir=args.data_url, cross_valid_ind=cfg_unet['cross_valid_ind'], cfg=cfg_unet,
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result_path=args.result_path)
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