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68 lines
2.1 KiB
68 lines
2.1 KiB
# Copyright 2020 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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import argparse
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from scipy import io
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###############################################
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# load testdata
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# testdata.mat structure
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# test[:][0] : image name
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# test[:][1] : label
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# test[:][2] : 50 lexicon
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# test[:][3] : 1000 lexicon
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##############################################
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def init_args():
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parser = argparse.ArgumentParser('')
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parser.add_argument('-m', '--mat_file', type=str, default='testdata.mat',
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help='Directory where character dictionaries for the dataset were stored')
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parser.add_argument('-o', '--output_dir', type=str, default='./processed',
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help='Directory where ord map dictionaries for the dataset were stored')
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parser.add_argument('-a', '--output_annotation', type=str, default='./annotation.txt',
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help='Directory where ord map dictionaries for the dataset were stored')
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return parser.parse_args()
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def mat_to_list(mat_file):
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ann_ori = io.loadmat(mat_file)
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testdata = ann_ori['testdata'][0]
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ann_output = []
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for elem in testdata:
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img_name = elem[0]
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label = elem[1]
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ann = img_name+',' +label
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ann_output.append(ann)
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return ann_output
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def convert():
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args = init_args()
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ann_list = mat_to_list(args.mat_file)
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ann_file = args.output_annotation
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with open(ann_file, 'w') as f:
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for line in ann_list:
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txt = line + '\n'
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f.write(txt)
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if __name__ == "__main__":
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convert()
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