ROBOT_IMAGE_PROCESS_OCR_MODEL_TRAIN_v2.0.1/code_ocr/train_data.py

import os
import shutil
from batch_process import batch_save_txt
from params import *
from remove_data import copytree
from split_train_val import split_train_val
from data_2_test import data_to_test
import time
import shutil
from train import multigpu_train
from train import save_pb_model
# from test import eval_file_200, score_the_ocr_model






def del_file(path_data):
    if os.path.exists(path_data):
        for i in os.listdir(path_data) :
            file_data = path_data + "/" + i
            if os.path.isfile(file_data) == True:
                os.remove(file_data)
            else:
                del_file(file_data)

del_file(path_data) # 删除data文件夹下已存在文件


copytree(old_file_path,new_file_path) # 将 field_data现场数据复制到data/total_data下

split_train_val(dataset_path,train_set_save_path,val_set_save_path) # 划分训练集和测试集



# 生成对应的txt文件
rootdir = os.path.join(dir, 'train')
batch_save_txt(rootdir)

rootdir = os.path.join(dir, 'val')
batch_save_txt(rootdir)


data_to_test() # 将测试集数据转换为测试所需格式


multigpu_train.MultigpuTrain().main() # 训练
multigpu_train.SavePb().save_pb(argv=None) # 保存pb模式









# del multigpu_train
# for key in list(globals().keys()):
#     if not key.startswith("__"):
#         globals().pop(key)




# eval_file_200.run(1)
#
# score_the_ocr_model.score_ocr_model()