def demo_flask(image_file):
grocery = Grocery('NameIdAdd_NLP')
model_name = grocery.name
text_converter = None
tgm = GroceryTextModel(text_converter, model_name)
tgm.load(model_name)
grocery.model = tgm
t = time.time()
result_dir = './result'
image = np.array(Image.open(image_file).convert('RGB'))
result, image_framed = ocr_whole.model(image)
output_file = os.path.join(result_dir, image_file.split('/')[-1])
Image.fromarray(image_framed).save(output_file)
name_total = ''
id_total = ''
for key in result:
string1 = result[key][1]
if len(string1) <= 8:
continue
string2 = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——！，。？、~@#￥%……&*{}[]+", "", string1)
no_digit = len(list(filter(str.isdigit, string2.encode('gbk'))))
no_alpha = len(list(filter(is_alphabet, string2)))
if len(set('法定代表人') & set(string2)) >= 2 or len(set('经营范围') & set(string2)) >= 2 or '资本' in string2 or '类型' in string2 or len(set('年月日') & set(string2)) >= 2 or len(set('登记机关') & set(string2)) >= 2 or '电话' in string2:
predict_result = 'others'
elif len(set('经营场所') & set(string2)) >= 3 or '住所' in string2 or len(set('营业场所') & set(string2)) >= 3:
predict_result = 'company-address'
elif len(set('统一社会信用代码') & set(string2)) >= 2 or ((no_digit+no_alpha) / len(string2) > 0.5 and no_digit > 8):
predict_result = 'company-id'
elif '名称' in string2:
predict_result = 'company-name'
else:
predict_result = grocery.predict(string2)
if str(predict_result) == 'company-name':
name_total += string1
break
elif str(predict_result) == 'company-id':
id_total += string1
else:
continue
id_total = re.sub(r'\W', '', id_total)
name_total = stupid_revise(name_total)
print("Mission complete, it took {:.3f}s".format(time.time() - t))
print('\nRecongition Result:\n')
print(id_total)
print(name_total)
return output_file, id_total, name_total
"""
import re
import pandas as pd
from tgrocery import Grocery
from tgrocery.classifier import *
extra_addrs_dir = 'addrs_libs/full_address1.csv'
extra_lib = pd.read_csv(extra_addrs_dir, encoding='utf-8')
provinces = extra_lib[extra_lib['level'] == 1].loc[:, 'Name']
cities = extra_lib[extra_lib['level'] == 2].loc[:, 'Name']
grocery = Grocery('NameIdAdd_NLP')
model_name = grocery.name
text_converter = None
tgm = GroceryTextModel(text_converter, model_name)
tgm.load(model_name)
grocery.model = tgm
class Found(Exception):
pass
def is_alphabet(uchar):
"""判断一个unicode是否是英文字母"""
if (u'\u0041' <= uchar <= u'\u005a') or (u'\u0061' <= uchar <= u'\u007a'):
return True
else:
return False
def preprocess_ocr(result):
def demo_flask(image_file):
grocery = Grocery('Addrss_NLP')
model_name=grocery.name
text_converter=None
if (os.path.exists(model_name)):
tgM=GroceryTextModel(text_converter,model_name)
tgM.load(model_name)
grocery.model=tgM
print('load!!!!!')
else:
add_file = open('pkl_data/address1.pkl', 'rb')
other_file = open('pkl_data/others1.pkl', 'rb')
add_list = pickle.load(add_file)
other_list = pickle.load(other_file)
add_file .close()
other_file .close()
grocery = Grocery('Addrss_NLP')
add_list.extend(other_list)
grocery.train(add_list)
print (grocery.get_load_status())
grocery.save()
# print('train!!!!!!!!')
addrline = []
t = time.time()
result_dir = '/data/share/nginx/html/bbox'
image = np.array(Image.open(image_file).convert('RGB'))
result, image_framed = ocr_whole.model(image)
output_file = os.path.join(result_dir, image_file.split('/')[-1])
Image.fromarray(image_framed).save(output_file)
ret_total = ''
for key in result:
string1 = result[key][1]
# print("predict line text :",string1)
string2 = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——！，。？、~@#￥%……&*{}[]+", "", string1)
no_digit = len(list(filter(str.isdigit, string2)))
no_alpha = len(list(filter(is_alphabet, string2)))
if '注册' in string2 or '洼册' in string2 or '洼·册' in string2 or '洼.册' in string2 or '汪·册' in string2 or len(set('登记机关') & set(string2)) >= 3 or '电话' in string2 or ((no_digit / len(string2) > 0.7 and no_digit > 5)):
predict_result='others'
elif no_alpha>5 or len(set('经营范围化学品') & set(string2)) >= 3 or len(set('年月日') & set(string2)) >= 2:
predict_result='others'
else:
predict_result = grocery.predict(string2)
if (str(predict_result) == 'address'):
string1 = string1.replace('《', '(')
string1 = string1.replace('》', ')')
string1 = string1.replace('(', '（')
string1 = string1.replace(')', '）')
string1 = string1.replace('（（','（')
if ((not ret_total) or len(string1) > len(ret_total)):
ret_total = ''
ret_total += string1
else:
ret_total += string1
if '）' in ret_total:
if '（' not in ret_total:
ret_total = ret_total.replace('C', '（')
ret_total = re.sub(r'（(\w)住所(.*)', '', ret_total)
ret_total = re.sub(r'（(\w)住房(.*)', '', ret_total)
ret_total = re.sub(r'（不作为(.*)', '', ret_total)
ret_total = re.sub(r'（有效期(.*)', '', ret_total)
ret_total = re.sub(r'（仅限(.*)', '', ret_total)
ret_total = re.sub(r'（临时经营(.*)', '', ret_total)
ret_total = re.sub(r'（仅限办公(.*)', '', ret_total)
ret_total = re.sub(r'（经营场所(.*)', '', ret_total)
ret_total = re.sub(r"^[经]*[营]*[场/住]*[所]*", "", ret_total)
ret_total = stupid_revise(ret_total)
print("Mission complete, it took {:.3f}s".format(time.time() - t))
print('\nRecongition Result:\n')
print(ret_total)
return output_file,ret_total
