326 lines
16 KiB
Python
326 lines
16 KiB
Python
import camelot
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import re
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#from multiprocessing import Pool
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import os, time, random
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import json
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#from config import MILVUS_CLIENT,MYSQL_HOST,MYSQL_USER,MYSQL_PASSWORD,MYSQL_DB,MEASURE_COUNT
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from datetime import datetime
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# 读取PDF
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import PyPDF2
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# 分析PDF的layout，提取文本
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from pdfminer.high_level import extract_pages
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from pdfminer.layout import LTTextBoxHorizontal
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import pdfplumber
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import mysql.connector
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#import utils
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from pymilvus import MilvusClient
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#import llm_service
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#import db_service
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#import pdf_title
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import numpy as np
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#from multiprocessing import Process
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STR_PATTERN = '营业收入|净利润|变动比例|损益|现金流量净额|现金净流量|现金流|每股收益|总资产|资产总额|收益率|货币资金|应收账款|存货|固定资产|在建工程|商誉|短期借款|应付账款|合同负债|长期借款|营业成本|销售费用|管理费用|财务费用|研发费用|研发投入'
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#负责表内一旦出现某个字符，整个表丢弃
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PATTERN = '品牌类型|分门店|销售渠道|行业名称|产品名称|地区名称|子公司名称|业绩快报|调整情况说明|调整年初资产负债表|计入当期损益的政府补助|主要子公司|分部|母公司资产负债表|显示服务|渠道|商品类型|合同分类|会计政策变更|地区分类'
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#unit_pattern = re.compile(r'单位[：|:]?(百万元|千万元|亿元|万元|千元|元)')
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MUILT_PATTERN = '调整前'
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file_path = r"combined_v61.pdf"
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file_id = 1
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pages = '1-2'
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tables_range = {}
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# def get_table_range_test(file_path, file_id, pages, tables_range):
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# print('Run task %s (%s)...' % (f'解析表格{pages}', os.getpid()))
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# #(f'file_path: {file_path},file_id:{file_id},pages:{pages},tables_range:{tables_range}')
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# start = time.time()
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# import tempfile
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# temp_dir_path = "F:\\temp"
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# # 检查并创建临时文件夹
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# if not os.path.exists(temp_dir_path):
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# os.makedirs(temp_dir_path)
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# # 创建临时文件夹
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# temp_dir = tempfile.mkdtemp(prefix="camelot_temp_", dir=temp_dir_path)
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# # 设置全局临时文件夹路径
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# os.environ["TMP"] = temp_dir
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# os.environ["TEMP"] = temp_dir
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# # conn = mysql.connector.connect(
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# # host= MYSQL_HOST,
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# # user= MYSQL_USER,
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# # password= MYSQL_PASSWORD,
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# # database= MYSQL_DB
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# # )
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# # 创建一个cursor对象来执行SQL语句
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# #print(f'file_path的值是{file_path}')
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# #cursor = conn.cursor()
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# # try:
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# # tables = camelot.read_pdf(file_path, pages=pages, strip_text=' ,\n', copy_text=['h'])
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# # print('读取成功')
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# # except Exception as e:
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# # print(f'错误在{e}')
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# #print(f'file_path的值是{file_path}')
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# #file_path = "F:\\11_pdf\\688670-2023-nb-nb.pdf"
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# os.environ["GHOSTSCRIPT_BINARY"] = "gswin64c"
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# try:
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# # 确保 file_path 是正确的，并且文件是可访问的
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# if not os.path.exists(file_path):
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# print(f'文件路径不正确或文件不存在: {file_path}')
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# raise FileNotFoundError(f"文件不存在：{file_path}")
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# else:
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# pass#(f'file_path是存在的就是{file_path}')
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# # 读取 PDF 文件
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# #tables = camelot.read_pdf(file_path, pages=pages, strip_text=' ,\n')#, copy_text=['h']
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# #tables = camelot.read_pdf(file_path, pages=pages, flavor='lattice', strip_text=' ,\n', temp_dir=temp_dir)
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# tables = camelot.read_pdf(file_path, pages=pages, strip_text=' ,\n', copy_text=['h'], temp_dir=temp_dir)#line_scale=10,
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# print('读取成功')
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# print("检测到的表格数量：", tables.n)
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# except FileNotFoundError as fe:
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# print(fe)
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# except Exception as e:
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# print(f'处理PDF时出错: {e}')
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# for t in tables:
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# top = t._bbox[3]
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# buttom = t._bbox[1]
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# page_num = int(t.page)
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# table_index = int(t.order)
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# arr = np.array(t.data)
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# #recent_value = None
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# #这里开始对可能解析错误的值做判断：
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# for i, row in enumerate(arr):
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# if len(row) >= 4:
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# # first_value = row[0]
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# # if ("2023年度" in first_value or "2022年度" in first_value) and len(first_value) <= 12:
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# # recent_value = first_value
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# # if first_value == '' and recent_value:
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# # row[0] = recent_value
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# # 检查条件:第一列不为数字，第二列和第四列为空，第三列有三个小数点【三列的数字被识别到一起了】
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# if (not row[0].replace('.', '', 1).isdigit()) and (row[1] == '') and (len(row[2].split('.')) == 4 and len(row[2].rsplit('.', 1)[-1]) == 2) and (row[3] == ''):
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# split_values = row[2].split('.')
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# # 确保可以正确拆分成三个数值
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# if len(split_values) == 4:
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# new_value1 = f"{split_values[0]}.{split_values[1][:2]}"
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# new_value2 = f"{split_values[1][2:]}.{split_values[2][:2]}"
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# new_value3 = f"{split_values[2][2:]}.{split_values[3]}"
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# row[1] = new_value1
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# row[2] = new_value2
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# row[3] = new_value3
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# #检查条件：第一列不为数字，第二列第四列为空，第三列两个小数点，第五列两个小数点【两列的数字被识别到一起了】
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# if len(row) >= 5 and (not row[0].replace('.', '', 1).isdigit()) and (row[1] == '') and (len(row[2].split('.')) == 3) and (row[3] == '') and (len(row[4].split('.')) == 3) and len(row[2].rsplit('.', 1)[-1]) == 2 and len(row[4].rsplit('.', 1)[-1]) == 2:
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# split_value_3 = row[2].split('.')
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# split_value_5 = row[4].split('.')
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# if len(split_value_3) == 3:
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# new_value2 = f"{split_value_3[0]}.{split_value_3[1][:2]}"
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# new_value3 = f"{split_value_3[1][2:]}.{split_value_3[2]}"
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# if len(split_value_5) == 3:
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# new_value4 = f"{split_value_5[0]}.{split_value_5[1][:2]}"
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# new_value5 = f"{split_value_5[1][2:]}.{split_value_5[2]}"
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# row[1] = new_value2
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# row[2] = new_value3
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# row[3] = new_value4
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# row[4] = new_value5
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# #检查条件：第一列不为数字，第二列为空，第三列有两个小数点，第四列为正常数字【两列的数字被识别到一起了】
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# if len(row) >= 4 and (not row[0].replace('.', '', 1).isdigit()) and (row[1] == '') and (len(row[2].split('.')) == 3) and len(row[2].rsplit('.', 1)[-1]) == 2 and (row[3].replace('-', '', 1).replace('.', '', 1).isdigit()):
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# split_values = row[2].split('.')
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# if len(split_values) == 3:
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# new_value2 = f"{split_values[0]}.{split_values[1][:2]}"
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# new_value3 = f"{split_values[1][2:]}.{split_values[2]}"
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# row[1] = new_value2
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# row[2] = new_value3
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# #检查条件：第一列不位数字，后面有一列中的值存在“%”并且"%"不是结尾，就进行拆分
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# if not row[0].replace('.', '', 1).isdigit():
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# for i in range(1, len(row) - 1):
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# if row[i] == '' and '%' in row[i + 1] and len(row[i + 1].split('%')) == 2:
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# split_values = row[i + 1].split('%')
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# new_value1 = f"{split_values[0]}%"
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# new_value2 = f"{split_values[1]}"
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# row[i] = new_value1
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# row[i + 1] = new_value2
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# break
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# #检查条件：当一个列表中同时出现了2022年12月31日和2023年1月1日时【并且都只出现1次】，在2022年12月31日后面增加“调整前”字段
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# # if sum(1 for item in row if item.strip() == "2023年1月1日") == 1 and sum(1 for item in row if item.strip() == "2022年12月31日") == 1:
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# # for i, item in enumerate(row):
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# # stripped_item = item.strip() #去空格
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# # if stripped_item == "2022年12月31日":
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# # row[i] = stripped_item + '调整前'
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# new_data = arr.tolist()#用于后面保存到数据库中
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# rows, cols = arr.shape
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# if rows == 1 and cols == 1:
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# continue
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# arr_str = ''.join([''.join(map(str, row)) for row in arr])
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# #print(f'arr_str的值是 {arr_str}')
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# #过滤掉不包含需抽取指标表格的文本
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# matches = re.findall(STR_PATTERN, arr_str)
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# pattern = re.findall(PATTERN,arr_str)
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# muilt_pattern = re.findall(MUILT_PATTERN,arr_str)
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# if len(matches) > 0 and len(pattern) == 0 and len(muilt_pattern)<5:
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# if not tables_range.get(page_num):
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# tables_range[page_num] = []
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# tables_range[page_num].append({
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# 'top' : top,
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# 'buttom' : buttom,
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# 'table_index' : table_index,
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# 'page_num' : page_num,
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# })
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# print(f"tables_range的值是{tables_range}")
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# #(f'file_id是{file_id}')
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# # db_service.insert_pdf_parse_process({
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# # 'file_id': file_id,
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# # 'page_num' : page_num,
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# # 'page_count' : 100,
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# # 'type' : 'parse_table',
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# # 'content':{
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# # 'top' : top,
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# # 'buttom' : buttom,
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# # 'page_num' : page_num,
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# # 'table_index' : table_index,
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# # "type" : "table",
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# # "data" : new_data,
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# # 'sort_num' : page_num*1000 - top
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# # }},conn,cursor)
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# #get_text_content(file_path, file_id, tables_range, pages, conn, cursor)
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# # cursor.close()
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# # conn.close()
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# end = time.time()
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# print('Task %s runs %0.2f seconds.' % (f'解析表格{pages}', (end - start)))
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def get_table_range_test(file_path, file_id, pages, tables_range):
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print('Run task %s (%s)...' % (f'解析表格{pages}', os.getpid()))
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start = time.time()
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# conn = mysql.connector.connect(
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# host= MYSQL_HOST,
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# user= MYSQL_USER,
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# password= MYSQL_PASSWORD,
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# database= MYSQL_DB
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# )
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# 创建一个cursor对象来执行SQL语句
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#cursor = conn.cursor()
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#redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, db=6)
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tables = camelot.read_pdf(file_path, pages=pages, strip_text=' ,\n', copy_text=['h'])
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for t in tables:
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top = t._bbox[3]
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buttom = t._bbox[1]
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page_num = int(t.page)
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table_index = int(t.order)
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arr = np.array(t.data)
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#这里开始对可能解析错误的值做判断：
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for i, row in enumerate(arr):
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if len(row) >= 4:
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# 检查条件:第一列不为数字，第二列和第四列为空，第三列有三个小数点【三列的数字被识别到一起了】
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if (not row[0].replace('.', '', 1).isdigit()) and (row[1] == '') and (len(row[2].split('.')) == 4 and len(row[2].rsplit('.', 1)[-1]) == 2) and (row[3] == ''):
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split_values = row[2].split('.')
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# 确保可以正确拆分成三个数值
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if len(split_values) == 4:
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new_value1 = f"{split_values[0]}.{split_values[1][:2]}"
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new_value2 = f"{split_values[1][2:]}.{split_values[2][:2]}"
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new_value3 = f"{split_values[2][2:]}.{split_values[3]}"
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row[1] = new_value1
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row[2] = new_value2
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row[3] = new_value3
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#检查条件：第一列不为数字，第二列第四列为空，第三列两个小数点，第五列两个小数点【两列的数字被识别到一起了】
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if len(row) >= 5 and (not row[0].replace('.', '', 1).isdigit()) and (row[1] == '') and (len(row[2].split('.')) == 3) and (row[3] == '') and (len(row[4].split('.')) == 3) and len(row[2].rsplit('.', 1)[-1]) == 2 and len(row[4].rsplit('.', 1)[-1]) == 2:
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split_value_3 = row[2].split('.')
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split_value_5 = row[4].split('.')
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if len(split_value_3) == 3:
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new_value2 = f"{split_value_3[0]}.{split_value_3[1][:2]}"
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new_value3 = f"{split_value_3[1][2:]}.{split_value_3[2]}"
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if len(split_value_5) == 3:
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new_value4 = f"{split_value_5[0]}.{split_value_5[1][:2]}"
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new_value5 = f"{split_value_5[1][2:]}.{split_value_5[2]}"
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row[1] = new_value2
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row[2] = new_value3
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row[3] = new_value4
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row[4] = new_value5
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#检查条件：第一列不为数字，第二列为空，第三列有两个小数点，第四列为正常数字【两列的数字被识别到一起了】
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if len(row) >= 4 and (not row[0].replace('.', '', 1).isdigit()) and (row[1] == '') and (len(row[2].split('.')) == 3) and len(row[2].rsplit('.', 1)[-1]) == 2 and (row[3].replace('-', '', 1).replace('.', '', 1).isdigit()):
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split_values = row[2].split('.')
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if len(split_values) == 3:
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new_value2 = f"{split_values[0]}.{split_values[1][:2]}"
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new_value3 = f"{split_values[1][2:]}.{split_values[2]}"
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row[1] = new_value2
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row[2] = new_value3
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#检查条件：第一列不位数字，后面有一列中的值存在“%”并且"%"不是结尾，就进行拆分
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if not row[0].replace('.', '', 1).isdigit():
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for i in range(1, len(row) - 1):
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if row[i] == '' and '%' in row[i + 1] and len(row[i + 1].split('%')) == 2:
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split_values = row[i + 1].split('%')
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new_value1 = f"{split_values[0]}%"
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new_value2 = f"{split_values[1]}"
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row[i] = new_value1
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row[i + 1] = new_value2
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break
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new_data = arr.tolist()#用于后面保存到数据库中
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rows, cols = arr.shape
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if rows == 1 and cols == 1:
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continue
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arr_str = ''.join([''.join(map(str, row)) for row in arr])
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#过滤掉不包含需抽取指标表格的文本
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matches = re.findall(STR_PATTERN, arr_str)
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pattern = re.findall(PATTERN,arr_str)
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muilt_pattern = re.findall(MUILT_PATTERN,arr_str)
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if len(matches) > 0 and len(pattern) == 0 and len(muilt_pattern)<5:
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if not tables_range.get(page_num):
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tables_range[page_num] = []
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tables_range[page_num].append({
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'top' : top,
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'buttom' : buttom,
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'table_index' : table_index,
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'page_num' : page_num,
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})
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print(f"tables_range的值是{tables_range}")
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# db_service.insert_pdf_parse_process({
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# 'file_id': file_id,
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# 'page_num' : page_num,
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# 'page_count' : 100,
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# 'type' : 'parse_table',
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# 'content':{
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# 'top' : top,
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# 'buttom' : buttom,
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# 'page_num' : page_num,
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# 'table_index' : table_index,
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# "type" : "table",
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# "data" : new_data,
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# 'sort_num' : page_num*1000 - top
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# }},conn,cursor)
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# get_text_content(file_path, file_id, tables_range, pages, conn, cursor, redis_client)
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# cursor.close()
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# conn.close()
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# redis_client.close()
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end = time.time()
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print('Task %s runs %0.2f seconds.' % (f'解析表格{pages}', (end - start)))
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get_table_range_test(file_path, file_id, pages, tables_range)
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