基于Python的身份证验证识别和数据处理[Python基础]

  • Z时代
  • 2024-01-10
  • 分类：综合

python

根据GB11643-1999公民身份证号码是特征组合码，由十七位数字本体码和一位数字校验码组成，排列顺序从左至右依次为：

  1. 六位数字地址码

  2. 八位数字出生日期码

  3. 三位数字顺序码

  4. 一位数字校验码（数字10用罗马X表示）

校验系统：

     校验码采用ISO7064:1983，MOD11-2校验码系统（图为校验规则样例）

用身份证号的前17位的每一位号码字符值分别乘上对应的加权因子值，得到的结果求和后对11进行取余，最后的结果放到表2检验码字符值..换算关系表中得出最后的一位身份证号码

代码：

# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert BERT checkpoint."""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
    # Initialise PyTorch model
    config = BertConfig.from_json_file(bert_config_file)
    print("Building PyTorch model from configuration: {}".format(str(config)))
    model = BertForPreTraining(config)
    # Load weights from tf checkpoint
    load_tf_weights_in_bert(model, config, tf_checkpoint_path)
    # Save pytorch-model
    print("Save PyTorch model to {}".format(pytorch_dump_path))
    torch.save(model.state_dict(), pytorch_dump_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--bert_config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained BERT model. 
"
        "This specifies the model architecture.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

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