文章目錄 1. 說明 2. 準備工作 3. 代碼 3.1 導入庫： 3.2 遍歷發票指定處理方式 3.3 發票識別相關函數 3.4 發票字段定位函數 3.6 識別記錄相關函數 3.6 識別結果校驗 3.7 文件預處理等其他函數 3.8 main主函數
1.1 以paddle識別引擎為基礎的增值稅發票識別程序，可批量識別和累積紙質發票和電子發票數據。已經生產環境中測試。
1.2 識別的源發票數據：- 文件夾中存放的用高速連續發票掃描儀批量掃描的JPG格式圖片- 文件夾中匯集的電子發票PDF格式文件
1.3 可選擇用識別引擎：快速-mb 平衡:sv 精細-pp (總體上，預識別用mb，精細用pd，速度和精確度比較好。
1.4 適配斷續工作，跳過已掃描的重復發票，邊識別邊存儲。
1.5 可裝在閑置低配置的win7老臺式，資源利用，識別速度視電腦配置差異大概2-3秒一張。
1.6 在實際生產環境中測試，如果紙質發票不清晰，綜合識別準確率大概85%-95%左右。如果數電發票比較多，識別準確率大概達到97%以上。
1.7 對于識別有誤或缺失的數據，在結果中提示錯誤并鏈接原發票文件，以便人工直接對照修改。
1.8 其他： - 公司名稱稅號可在代碼中預置設定好，位置在發票字段定位函數Loc_range_content_pandas。- 可自行預置對方公司名稱錯誤的更正，詳細可在Check_result函數中此處文字內容"字段修正：公司名錯別字"所在位置的字典修改。
2.1 準備工作發票電子文件夾：已用高速連續發票掃描儀掃描完紙質發票的圖片文件夾，和已匯集的電子發票PDF格式文件夾。
2.2 安裝好輔助程序 acrobat pro dc
2.3 語言環境 anaconda，python3.7(虛擬環境)
2.4 環境中安裝好所需要的庫(自行安裝好虛擬環境中所需的第三方庫)：imghdr, shutil, glob, pathlib, tkinter, cv2, numpy, paddlehub, pandas, psutil, openpyxl, paddleocr, pillow, pyzbar, ZipFile, pymupdf
import imghdr
import math
import os
import re
import shutil
from collections import OrderedDict
from datetime import datetime
from glob import glob
from pathlib import Path
from tkinter import filedialog
from tkinter import Tk
import cv2
import numpy as np
import paddlehub as hub
import pandas as pd
import psutil
from openpyxl import cell, load_workbook
from openpyxl. styles import Font, colors
from paddleocr import PaddleOCR, draw_ocr
from PIL import Image, ImageDraw, ImageEnhance, ImageFont
from pyzbar import pyzbar
from zipfile import ZipFile
import fitz
def walk_folder_ocr ( origin_pandas, duplicate_pandas, origin_folder_path, ** walk_folder_args) : ocr_engines = walk_folder_args[ 'ocr_engines' ] temp_folder_path = walk_folder_args[ 'temp_folder_path' ] prepare_engine = walk_folder_args[ 'engine_switch' ] result_pandas = origin_pandascnt_file = len ( { p. resolve( ) for p in Path( origin_folder_path) . glob( "*" ) if p. suffix in [ ".jpg" , ".pdf" ] } ) inv_dict = { } if not result_pandas. empty: for i, ( index, row) in enumerate ( result_pandas. iterrows( ) ) : if row[ '01票號' ] is np. NAN: continue if row[ '01票號' ] not in inv_dict: inv_dict[ row[ '01票號' ] ] = [ row[ 'file_path' ] ] else : inv_dict[ row[ '01票號' ] ] . append( row[ 'file_path' ] ) if not duplicate_pandas. empty: for i, ( index, row) in enumerate ( duplicate_pandas. iterrows( ) ) : if row[ '重復票號' ] is np. NAN: continue if row[ '重復票號' ] not in inv_dict: inv_dict[ row[ '重復票號' ] ] = [ row[ 'file_path' ] ] else : inv_dict[ row[ '重復票號' ] ] . append( row[ 'file_path' ] ) cnt_done = 0 cnt_duplicate = 0 if not origin_pandas. empty: cnt_done = len ( origin_pandas. loc[ origin_pandas[ 'file_path' ] . notnull( ) , : ] ) if not duplicate_pandas. empty: cnt_duplicate = len ( duplicate_pandas. loc[ duplicate_pandas[ 'file_path' ] . notnull( ) , : ] ) for file_name in os. listdir( origin_folder_path) : file_path = os. path. join( origin_folder_path, file_name) if os. path. isfile( file_path) : pr, nm, fr, ex = pathsplit( file_path) if ex not in [ '.pdf' , '.jpg' ] : continue inv_out_of_result_pandas = True inv_out_of_duplicate_pandas = True try : inv_out_of_result_pandas = result_pandas. loc[ result_pandas[ 'file_path' ] == file_path, : ] . emptyinv_out_of_duplicate_pandas = duplicate_pandas. loc[ duplicate_pandas[ 'file_path' ] == file_path, : ] . emptyexcept : pass if not ( inv_out_of_result_pandas and inv_out_of_duplicate_pandas) : continue result_series_orderdic = OrderedDict( ) err_info = '' if ex == '.pdf' : inv_code = '' pdf_trans_file_fr = frpdf_trans_file_ex = '.xlsx' pdf_trans_file_nm = pdf_trans_file_fr + pdf_trans_file_expdf_trans_folder_name = 'temp_pdf_trans_excel' pdf_trans_folder_path = os. path. join( temp_folder_path, pdf_trans_folder_name) if not os. path. exists( pdf_trans_folder_path) : os. mkdir( pdf_trans_folder_path) pdf_trans_file_path = os. path. join( pdf_trans_folder_path, pdf_trans_file_nm) if not os. path. exists( pdf_trans_file_path) : trans_type = '.xlsx' pdf_trans_file_path = Pdf_tans_to( file_path, pdf_trans_file_path, trans_type = trans_type, temp_pdf_trans_excel_out = True ) if os. path. exists( pdf_trans_file_path) : result_series_orderdic, err_info, inv_dict = Tele_inv_ocr( ocr_engines, result_series_orderdic, inv_dict, file_path, pdf_trans_file_path, err_info, engine_switch = precise_engine) if len ( result_series_orderdic) != 0 : if '01票號' in result_series_orderdic: inv_code = result_series_orderdic[ '01票號' ] [ 0 ] . values[ 0 ] if inv_code not in inv_dict: inv_dict[ inv_code] = [ file_path] else : if file_path not in inv_dict[ inv_code] : inv_dict[ inv_code] . append( file_path) if len ( inv_dict[ inv_code] ) > 1 : if duplicate_pandas. empty: duplicate_pandas = pd. DataFrame( data= { '重復票號' : [ inv_code] , 'file_path' : [ file_path] } ) else : duplicate_pandas = pd. concat( [ duplicate_pandas, pd. DataFrame( data= { '重復票號' : [ inv_code] , 'file_path' : [ file_path] } ) ] , ignore_index = True , axis = 0 ) Log_result_file( duplicate_pandas, result_file_path, duplicate_sheet_name) cnt_duplicate = cnt_duplicate + 1 print ( datetime. now( ) . strftime( "%H:%M:%S" ) , file_path, 'Skip. ' , '\n\t\tDuplicate:' , inv_code, inv_dict[ inv_code] [ 0 ] ) continue else : pdf_trans_file_ex = '.jpg' pdf_trans_file_nm = pdf_trans_file_fr + '.jpg' pdf_trans_folder_name = 'temp_pdf_trans_jpg' pdf_trans_folder_path = os. path. join( temp_folder_path, pdf_trans_folder_name) pdf_trans_jpg_file_path = os. path. join( pdf_trans_folder_path, pdf_trans_file_nm) pdf_trans_jpg_file_path = Pdf_tans_jpg( file_path, pdf_trans_jpg_file_path, temp_pdf_trans_jpg_out = True ) if len ( pdf_trans_jpg_file_path) > 0 : if os. path. exists( pdf_trans_jpg_file_path) : print ( '\n\nPDF轉成圖片識別：' , pdf_trans_jpg_file_path, '【此模塊待添加。】\n\n' ) elif str . lower( ex) == '.jpg' : known_dict = { } inv_code = '' temp_img_trans_excel_folder = os. path. join( temp_folder_path, 'temp_img_trans_excel' ) img_trans_xls_name = 'result_' + fr + '.xlsx' img_trans_xls_path = os. path. join( temp_img_trans_excel_folder, img_trans_xls_name) if os. path. exists( img_trans_xls_path) : origin_df = pd. read_excel( img_trans_xls_path, sheet_name= 0 , header= 0 , index_col= 0 , na_values= None , keep_default_na= False , dtype= object ) else : known_dict = Crop_known_from_qrcode( file_path) if len ( known_dict) > 0 : inv_code = known_dict[ '01票號' ] . values[ 0 ] if inv_code not in inv_dict: inv_dict[ inv_code] = [ file_path] else : if file_path not in inv_dict[ inv_code] : inv_dict[ inv_code] . append( file_path) if len ( inv_dict[ inv_code] ) > 1 : if duplicate_pandas. empty: duplicate_pandas = pd. DataFrame( data= { '重復票號' : [ inv_code] , 'file_path' : [ file_path] } ) else : duplicate_pandas = pd. concat( [ duplicate_pandas, pd. DataFrame( data= { '重復票號' : [ inv_code] , 'file_path' : [ file_path] } ) ] , ignore_index = True , axis = 0 ) Log_result_file( duplicate_pandas, result_file_path, duplicate_sheet_name) cnt_duplicate = cnt_duplicate + 1 print ( datetime. now( ) . strftime( "%H:%M:%S" ) , file_path, 'Skip. ' , '\n\t\tDuplicate:' , inv_code, inv_dict[ inv_code] [ 0 ] ) continue origin_df = Ocr_func( ocr_engines, img_path = file_path, temp_folder_path = temp_folder_path, range_title = '' , known_dict= known_dict, ocr_excel_out = ocr_excel_out, draw_result_out = draw_result_out, engine_switch= prepare_engine) if not origin_df. empty: result_series_orderdic, err_info = Loc_range_content_pandas( ocr_engines, origin_df, result_series_orderdic, err_info, known_dict, file_path, temp_folder_path, enhance = enhance, engine_switch= precise_engine) if len ( result_series_orderdic[ '01票號' ] ) > 0 : inv_code = result_series_orderdic[ '01票號' ] . values[ 0 ] if inv_code not in inv_dict: inv_dict[ inv_code] = [ file_path] else : if file_path not in inv_dict[ inv_code] : inv_dict[ inv_code] . append( file_path) if len ( inv_code) > 0 and inv_code in inv_dict and len ( inv_dict[ inv_code] ) > 1 : if duplicate_pandas. empty: duplicate_pandas = pd. DataFrame( data= { '重復票號' : [ inv_code] , 'file_path' : [ file_path] } ) else : duplicate_pandas = pd. concat( [ duplicate_pandas, pd. DataFrame( data= { '重復票號' : [ inv_code] , 'file_path' : [ file_path] } ) ] , ignore_index = True , axis = 0 ) Log_result_file( duplicate_pandas, result_file_path, duplicate_sheet_name) cnt_duplicate = cnt_duplicate + 1 print ( datetime. now( ) . strftime( "%H:%M:%S" ) , file_path, 'Skip. ' , '\n\t\tDuplicate:' , inv_code, inv_dict[ inv_code] [ 0 ] ) continue bind_df = pd. DataFrame( [ result_series_orderdic[ series_title] [ 0 ] if isinstance ( result_series_orderdic[ series_title] , list ) else result_series_orderdic[ series_title] for series_title in result_series_orderdic] ) . Tcolumns_list = [ '01票號' , '02代碼' , '03日期' , '04購方' , '05購方稅號' , '06品名' , '07單位' , '08數量' , '09單價' , '10稅前' , '11稅率' , '12稅額' , '13合計稅前' , '14合計稅額' , '15總額' , '16大寫' , '17銷方' , '18銷方稅號' ] if len ( bind_df) == 0 : bind_df = pd. DataFrame( columns = columns_list) result_df = bind_df. copy( ) result_df[ 'file_path' ] = '' if len ( result_df) == 0 : result_df = result_df. append( { 'file_path' : file_path} , ignore_index = True ) else : result_df[ 'file_path' ] . values[ 0 ] = file_path result_df[ 'err_info' ] = '' result_df. loc[ result_df. index[ 0 ] , 'err_info' ] = err_info result_df = Fill_na_result( result_df) if result_pandas. empty: result_pandas = result_dfelse : result_pandas = pd. concat( [ result_pandas, result_df] , ignore_index = True , axis = 0 ) result_pandas = Check_result( result_pandas) Log_result_file( result_pandas, result_file_path, result_sheet_name) Add_hyperlink( result_file_path, result_sheet_name) cnt_done = cnt_done + 1 print ( datetime. now( ) . strftime( "%H:%M:%S" ) , file_name, inv_code, 'done: ' + str ( cnt_done) + ' / ' + str ( cnt_file) ) return result_pandas, duplicate_pandas
def Ocr_func ( ocr_engines, img_path, temp_folder_path, range_title= '' , known_dict = { } , ocr_excel_out = True , draw_result_out = False , engine_switch = 0 ) - > object : p, n, fr, ex = pathsplit( img_path) temp_img_trans_excel_folder = os. path. join( temp_folder_path, 'temp_img_trans_excel' ) temp_draw_result_folder = os. path. join( temp_folder_path, 'temp_draw_result' ) if engine_switch == 0 : engine = 'mb' elif engine_switch == 1 : engine = 'pp' elif engine_switch == 2 : engine = 'sv' if range_title == '' : img_trans_xls_name = 'result(' + engine + ')_' + fr + '.xlsx' else : img_trans_xls_name = 'result(' + engine + ')_' + fr + '_' + range_title + '.xlsx' img_trans_xls_path = os. path. join( temp_img_trans_excel_folder, img_trans_xls_name) if not os. path. exists( temp_img_trans_excel_folder) : Create_clear_dir( temp_img_trans_excel_folder) if not os. path. exists( temp_draw_result_folder) : Create_clear_dir( temp_draw_result_folder) result = '' if engine_switch == 1 : paddleOcr = ocr_engines[ engine_switch] results = paddleOcr. ocr( img_path, cls= True ) df0 = pd. DataFrame( data= results, columns= [ 'pix' , 'result' ] ) df1 = pd. concat( [ pd. DataFrame( df0[ 'pix' ] . values. tolist( ) , columns= [ 'lu' , 'ru' , 'rd' , 'ld' ] ) , pd. DataFrame( df0[ 'result' ] . values. tolist( ) , columns= [ 'content' , 'trust' ] ) ] , axis= 1 ) title_list = [ 'lu' , 'ru' , 'rd' , 'ld' ] df = df1[ [ 'content' , 'trust' ] ] for i, title in enumerate ( title_list) : df = pd. concat( [ df, pd. DataFrame( df1[ title] . values. tolist( ) , columns= [ title + 'w' , title + 'h' ] ) ] , axis= 1 ) if ocr_excel_out == True : df. to_excel( img_trans_xls_path, index= False ) if draw_result_out == True : from PIL import Imageimage = Image. open ( img_path) . convert( 'RGB' ) boxes = [ line[ 0 ] for line in result] txts = [ line[ 1 ] [ 0 ] for line in result] scores = [ line[ 1 ] [ 1 ] for line in result] im_show = draw_ocr( image, boxes, txts, scores, font_path= './fonts/simfang.ttf' ) im_show = Image. fromarray( im_show) if range_title == '' : draw_result_name = 'draw_result_' + fr + exelse : draw_result_name = 'draw_result_' + fr + '_' + range_title + ex draw_result_path = os. path. join( temp_draw_result_folder, draw_result_name) im_show. save( draw_result_path) elif engine_switch == 0 or engine_switch == 2 : hubOcr = ocr_engines[ engine_switch] img = cv_imread( img_path) np_images = [ img]
hub_result = hubOcr. recognize_text( images= np_images, use_gpu= False , output_dir= temp_draw_result_folder, visualization= True , box_thresh= 0.5 , text_thresh= 0.5 ) results = hub_result[ 0 ] [ 'data' ] df = pd. DataFrame( ) column_list = [ 'content' , 'confdence' , 'luw' , 'luh' , 'ruw' , 'ruh' , 'rdw' , 'rdh' , 'ldw' , 'ldh' ] for infomation in results: content = infomation[ 'text' ] confidence = infomation[ 'confidence' ] box = infomation[ 'text_box_position' ] luw, luh, ruw, ruh = box[ 0 ] [ 0 ] , box[ 0 ] [ 1 ] , box[ 1 ] [ 0 ] , box[ 1 ] [ 1 ] rdw, rdh, ldw, ldh = box[ 2 ] [ 0 ] , box[ 2 ] [ 1 ] , box[ 3 ] [ 0 ] , box[ 3 ] [ 1 ] line = [ content, confidence, luw, luh, ruw, ruh, rdw, rdh, ldw, ldh] line_df = pd. DataFrame( data = line, index = column_list) . Tif df. empty: df = line_dfelse : df = pd. concat( [ df, line_df] , axis= 0 , ignore_index= True ) if ocr_excel_out == True : df. to_excel( img_trans_xls_path, index = False ) return df
def Crop_known_from_qrcode ( file_path) - > dict : known_dict = { } pr, nm, fr, ex = pathsplit( file_path) qrcode_folder_name = 'temp_crop_qrcode' qrcode_folder_path = os. path. join( temp_folder_path, qrcode_folder_name) if not os. path. exists( qrcode_folder_path) : Create_clear_dir( qrcode_folder_path) qrcode_file_name = 'qrcode_' + nmqrcode_file_path = os. path. join( qrcode_folder_path, qrcode_file_name) qrcode_image_crop = Crop_qrcode_image( file_path, qrcode_file_path) qrcode_result = '' if qrcode_image_crop == True : qrcode_result = qrcode_recongnize( qrcode_file_path) if len ( qrcode_result) > 0 : if len ( qrcode_result) > 20 : qrcode_list = qrcode_result. split( ',' ) for index, range_title in enumerate ( [ '02代碼' , '01票號' , '13合計稅前' , '04日期' ] ) : known_dict[ range_title] = pd. Series( data= qrcode_list[ index+ 2 ] , name = range_title) return known_dict
def Crop_qrcode_image ( origin_file_path, crop_file_path) : result = False img_inv = cv_imread( origin_file_path) img_crop = img_inv[ 100 : 400 , 50 : 350 ] img_magnify = cv2. resize( img_crop, ( 1200 , 1200 ) ) cv2. imencode( '.jpg' , img_magnify) [ 1 ] . tofile( crop_file_path) if os. path. exists( crop_file_path) : result = True return result
def qrcode_recongnize ( file_path, method = 'cv2' , drawframe = False , enhance= False ) : pr = os. path. split( file_path) [ 0 ] nm = os. path. split( file_path) [ 1 ] output_img_path = os. path. join( pr, 'draw_qrcode_' + nm) if method == 'cv2' : img = cv_imread( file_path) gray_img = cv2. cvtColor( img, cv2. COLOR_BGR2GRAY) barcodes = pyzbar. decode( gray_img) barcodeData = '' if len ( barcodes) > 0 : for barcode in barcodes: ( x, y, w, h) = barcode. rectcv2. rectangle( img, ( x, y) , ( x + w, y + h) , ( 255 , 255 , 0 ) , 2 ) barcodeData = barcode. data. decode( "utf-8" ) if len ( barcodeData) > 20 : if drawframe == True : from PIL import Image, ImageFont, ImageDrawbarcodeType = barcode. type img_PIL = Image. fromarray( cv2. cvtColor( img, cv2. COLOR_BGR2RGB) ) font = ImageFont. truetype( 'STFANGSO.TTF' , 25 ) fillColor = ( 0 , 255 , 0 ) position = ( x, y- 25 ) strl = barcodeDatadraw = ImageDraw. Draw( img_PIL) draw. text( position, strl, font= font, fill= fillColor) img_PIL. save( output_img_path, 'jpeg' ) break return barcodeDataelif method == 'pil' : from PIL import Image, ImageEnhanceimg = Image. open ( file_path) . convert( 'RGB' ) if enhance == True : img = ImageEnhance. Brightness( img) . enhance( 1.0 ) img = ImageEnhance. Sharpness( img) . enhance( 1.5 ) img = ImageEnhance. Contrast( img) . enhance( 2.0 ) img = img. convert( 'L' ) decoded = pyzbar. decode( img) result = decoded[ 0 ] [ 0 ] . decode( 'utf-8' ) return result
def Crop_ocr ( ocr_engines, result_series_orderdic, known_dict, img_inv, file_path, crop_folder_path, set_h_adjust, cond_list, enhance = False , engine_switch = 0 ) : pr, nm, fr, ex = pathsplit( file_path) range_title = cond_list[ 0 ] loc_method = cond_list[ 1 ] reg_type = cond_list[ 2 ] reg = cond_list[ 3 ] count_limit = cond_list[ 4 ] loc_dict = cond_list[ 5 ] chop_pix = loc_dict[ 'crop' ] [ min_w, max_w, min_h, max_h] = chop_pixadjust_ratio_dict = { '02代碼' : 1 , '03日期' : 1 , '10稅前' : 0.6 , '11稅率' : 0.7 , '12稅額' : 0.8 } if range_title in adjust_ratio_dict: adjust_ratio = adjust_ratio_dict[ range_title] min_h = min_h - int ( set_h_adjust * adjust_ratio) max_h = max_h - int ( set_h_adjust * adjust_ratio) crop_center_h = ( max_h - min_h) // 2 img_crop = img_inv[ min_h: max_h, min_w: max_w] enhance_title = [ '04購方' , '05購方稅號' , '06品名' , '07單位' , '16大寫' , '17銷方' , '18銷方稅號' ] if enhance == True : if range_title in enhance_title: img_pil = cv2_pil( img_crop) img_enhance = pil_enhance( img_pil) img_crop = pil_cv2( img_enhance) crop_file_name = 'crop_' + range_title + '_' + nmcrop_file_path = os. path. join( crop_folder_path, crop_file_name) cv2. imencode( '.jpg' , img_crop) [ 1 ] . tofile( crop_file_path) df = Ocr_func( ocr_engines, img_path = crop_file_path, temp_folder_path = crop_folder_path, range_title = range_title, known_dict= known_dict, ocr_excel_out = True , draw_result_out = True , engine_switch = engine_switch) get_h_adjust = 0 result_sr = pd. Series( name = range_title) if reg_type == 'extract' : cond_df = df[ 'content' ] . str . extract( reg) cond_df. loc[ : , [ 'luh' , 'ldh' ] ] = df. loc[ : , [ 'luh' , 'ldh' ] ] content_result = pd. notna( cond_df[ 0 ] ) if 'center_limit' in loc_dict: center_df = df center_df[ [ 'luw' , 'ruw' , 'luh' , 'ldh' ] ] . astype( int ) center_df[ 'center_w' ] = ( center_df[ 'luw' ] + center_df[ 'ruw' ] ) // 2 center_df[ 'center_h' ] = ( center_df[ 'luh' ] + center_df[ 'ldh' ] ) // 2 [ center_w_min, center_w_max, center_h_min, center_h_max] = loc_dict[ 'center_limit' ] cond_center = ( center_w_min <= center_df. loc[ : , 'center_w' ] ) & ( center_df. loc[ : , 'center_w' ] <= center_w_max) & \( center_h_min <= center_df. loc[ : , 'center_h' ] ) & ( center_df. loc[ : , 'center_h' ] <= center_h_max) content_result = content_result & cond_centertemp_df = df. loc[ cond_df[ content_result] . index, : ] if not temp_df. empty: temp_sr = temp_df. iloc[ : , 0 ] if range_title == '07單位' : list ( temp_sr. replace( to_replace = '[單|位|數|量]' , value= '' , regex= True ) . values[ 0 ] ) else : result_list = temp_sr. to_list( ) result_sr = pd. Series( data = result_list, name = range_title) if range_title == '01票號' : data_center_h = ( temp_df[ 'luh' ] . values[ 0 ] + temp_df[ 'ldh' ] . values[ 0 ] ) // 2 get_h_adjust = int ( crop_center_h - data_center_h) if reg_type == 'contains' : content_result = df[ 'content' ] . str . contains( reg) if 'center_limit' in loc_dict: center_df = df center_df[ [ 'luw' , 'ruw' , 'luh' , 'ldh' ] ] . astype( int ) center_df[ 'center_w' ] = ( center_df[ 'luw' ] + center_df[ 'ruw' ] ) // 2 center_df[ 'center_h' ] = ( center_df[ 'luh' ] + center_df[ 'ldh' ] ) // 2 [ center_w_min, center_w_max, center_h_min, center_h_max] = loc_dict[ 'center_limit' ] cond_center = ( center_w_min <= center_df. loc[ : , 'center_w' ] ) & ( center_df. loc[ : , 'center_w' ] <= center_w_max) & \( center_h_min <= center_df. loc[ : , 'center_h' ] ) & ( center_df. loc[ : , 'center_h' ] <= center_h_max) content_result = content_result & cond_centerif range_title == '07單位' : cond_special = ~ df[ 'content' ] . str . contains( '單\s*位|數\s*量' ) content_result = content_result & cond_specialcontent_df = df. loc[ content_result, : ] if range_title == '01票號' : data_center_h = ( content_df[ 'luh' ] . values[ 0 ] + content_df[ 'ldh' ] . values[ 0 ] ) // 2 get_h_adjust = int ( crop_center_h - data_center_h) temp_df = content_df. loc[ : , [ 'content' ] ] if not temp_df. empty: temp_sr = temp_df. iloc[ : , 0 ] if range_title == '07單位' : result_list = list ( temp_sr. replace( to_replace = '[單|位|數|量]' , value= '' , regex= True ) . values[ 0 ] ) else : result_list = temp_sr. to_list( ) result_sr = pd. Series( data = result_list, name = range_title) result_series_orderdic[ range_title] = result_srreturn result_series_orderdic, get_h_adjust
def Loc_jpg_content ( df, cond_list, order_dict) : range_title = cond_list[ 0 ] loc_method = cond_list[ 1 ] reg_type = cond_list[ 2 ] reg = cond_list[ 3 ] count_limit = cond_list[ 4 ] loc_dict = cond_list[ 5 ] w_min, w_max, h_min, h_max = loc_dict[ 'direct' ] [ 0 ] , loc_dict[ 'direct' ] [ 1 ] , loc_dict[ 'direct' ] [ 2 ] , loc_dict[ 'direct' ] [ 3 ] result_sr = pd. Series( name = range_title) loc_tuple = [ ] if reg_type == 'extract' : temp_df = df[ 'content' ] . str . extract( reg) if len ( temp_df) > 0 : temp_df[ [ 'center_w' , 'center_h' , 'luw' , 'ruw' , 'luh' , 'ldh' ] ] = df[ [ 'center_w' , 'center_h' , 'luw' , 'ruw' , 'luh' , 'ldh' ] ] content_result = ( temp_df. iloc[ : , 0 ] . str . len ( ) > 0 ) cond_loc= ( w_min <= temp_df. loc[ : , 'center_w' ] ) & ( temp_df. loc[ : , 'center_w' ] <= w_max) & \( h_min <= temp_df. loc[ : , 'center_h' ] ) & ( temp_df. loc[ : , 'center_h' ] <= h_max) cond_result = content_result & cond_loc temp_cond_pandas = temp_df. loc[ cond_result, : ] if not temp_cond_pandas. empty: result_sr = temp_cond_pandas. iloc[ : , 0 ] loc_tuple = temp_cond_pandas. loc[ : , [ 'luw' , 'luh' ] ] . values[ 0 ] if len ( result_sr) == 0 : if len ( loc_dict[ 'direct' ] ) >= 8 : w_min, w_max, h_min, h_max = loc_dict[ 'direct' ] [ 4 ] , loc_dict[ 'direct' ] [ 5 ] , loc_dict[ 'direct' ] [ 6 ] , loc_dict[ 'direct' ] [ 7 ] temp_df = df[ 'content' ] . str . extract( reg) temp_df[ [ 'center_w' , 'center_h' ] ] = df[ [ 'center_w' , 'center_h' ] ] content_result = ( temp_df. iloc[ : , 0 ] . str . len ( ) > 0 ) cond_loc= ( w_min <= temp_df. loc[ : , 'center_w' ] ) & ( temp_df. loc[ : , 'center_w' ] <= w_max) & \( h_min <= temp_df. loc[ : , 'center_h' ] ) & ( temp_df. loc[ : , 'center_h' ] <= h_max) cond_result = content_result & cond_loctemp_cond_pandas = temp_df. loc[ cond_result, : ] result_sr = temp_cond_pandas. iloc[ : , 0 ] loc_tuple = temp_cond_pandas. loc[ : , [ 'luw' , 'luh' ] ] . values[ 0 ] elif len ( result_sr) >= 1 and count_limit == '1' : temp_cond_pandas = temp_df. loc[ cond_result, : ] result_sr = temp_cond_pandas. iloc[ : , 0 ] . head( 1 ) loc_tuple = temp_cond_pandas. loc[ : , [ 'luw' , 'luh' ] ] . values[ 0 ] else : result_sr = temp_df. loc[ cond_result, 0 ] loc_tuple = temp_cond_pandas. loc[ : , [ 'luw' , 'luh' ] ] . values[ 0 ] elif reg_type == 'contains' : content_result = df[ 'content' ] . str . contains( reg) temp_df = df. loc[ content_result, : ] if len ( temp_df) > 0 : cond_loc = ( w_min <= temp_df. loc[ : , 'center_w' ] ) & ( temp_df. loc[ : , 'center_w' ] <= w_max) & \( h_min <= temp_df. loc[ : , 'center_h' ] ) & ( temp_df. loc[ : , 'center_h' ] <= h_max) cond_result = content_result & cond_loctemp_cond_pandas = temp_df. loc[ cond_result, : ] if not temp_cond_pandas. empty: result_sr = temp_cond_pandas. iloc[ : , 0 ] . head( 1 ) loc_tuple = temp_cond_pandas. loc[ : , [ 'luw' , 'luh' ] ] . values[ 0 ] else : if len ( loc_dict[ 'direct' ] ) >= 8 : w_min, w_max, h_min, h_max = loc_dict[ 'direct' ] [ 4 ] , loc_dict[ 'direct' ] [ 5 ] , loc_dict[ 'direct' ] [ 6 ] , loc_dict[ 'direct' ] [ 7 ] content_result = df[ 'content' ] . str . contains( reg) temp_df = df. loc[ content_result, : ] cond_loc = ( w_min <= temp_df. loc[ : , 'center_w' ] ) & ( temp_df. loc[ : , 'center_w' ] <= w_max) & \( h_min <= temp_df. loc[ : , 'center_h' ] ) & ( temp_df. loc[ : , 'center_h' ] <= h_max) cond_result = content_result & cond_loctemp_cond_pandas = temp_df. loc[ cond_result, : ] result_sr = temp_cond_pandas. iloc[ : , 0 ] loc_tuple = temp_cond_pandas. loc[ : , [ 'luw' , 'luh' ] ] . values[ 0 ] result_list = result_sr. to_list( ) order_dict[ range_title] = [ pd. Series( result_list, name= range_title) , loc_tuple] return order_dict
def Loc_tele_content ( df, known_dict, cond_list, order_dict) : range_title = cond_list[ 0 ] loc_method = cond_list[ 1 ] reg_type = cond_list[ 2 ] reg = cond_list[ 3 ] count_limit = cond_list[ 4 ] known_sr = pd. Series( name = range_title) result_sr = pd. Series( name = range_title) err_info = '' if range_title in known_dict: known_sr = pd. Series( data= known_dict[ range_title] , name = range_title) if reg_type == 'extract' : temp_cond_pandas = pd. DataFrame( ) for _, r in enumerate ( reg) : temp_df = df[ 'content' ] . str . extract( r) cond_result = temp_df. iloc[ : , 0 ] . str . len ( ) > 0 temp_cond_pandas = temp_df. loc[ temp_df. iloc[ : , 0 ] . str . len ( ) > 0 , : ] if len ( temp_cond_pandas) > 0 : break if len ( temp_cond_pandas) > 0 : if count_limit == '1' : result_sr = temp_cond_pandas. iloc[ : , 0 ] . head( 1 ) elif count_limit == '-1' : if len ( temp_cond_pandas) == 1 : result_sr = temp_cond_pandas. iloc[ : , 0 ] . head( 1 ) else : result_sr = temp_cond_pandas. iloc[ : , 0 ] . tail( - 1 ) else : result_sr = temp_df. loc[ cond_result, 0 ] result_sr = result_sr. replace( to_replace= '^\s|\s$' , value= '' , regex= True ) if range_title == '13合計稅前' : if len ( known_sr) > 0 : if len ( result_sr) == 0 : result_sr = known_srelse : result_value = result_sr. values[ 0 ] known_value = known_sr. values[ 0 ] if result_value == known_value: result_sr = known_sr. copy( ) elif range_title == '15總額' : if '13合計稅前' in known_dict: known_sr = pd. Series( data= known_dict[ '13合計稅前' ] , name = range_title) if len ( known_sr) > 0 : if len ( result_sr) > 0 : if result_sr. values[ 0 ] == known_sr. values[ 0 ] : result_sr = known_sr. copy( ) elif range_title == '06品名' : target_sr = result_sr. str . extractall( '([\u4e00-\u9fa5]+\s+[\u4e00-\u9fa5]+)' ) if len ( target_sr) > 0 : target_sr. index = list ( range ( len ( target_sr) ) ) replace_sr = target_sr. replace( '\s+' , '' , regex= True ) new_sr = result_sr. copy( ) for i in enumerate ( target_sr. index) : new_sr= new_sr. replace( target_sr. iloc[ i] , replace_sr. iloc[ i] , regex= True ) result_sr = new_sr. copy( ) data = result_sr. iloc[ 0 ] if data. count( ' ' ) > 0 : result_sr = pd. Series( data = data. split( ' ' ) , name= range_title) else : if len ( result_sr) == 0 and len ( known_sr) > 0 : result_sr = known_sr. copy( ) result_sr. name = range_titleresult_sr. index = list ( range ( len ( result_sr) ) ) order_dict[ range_title] = [ result_sr] return order_dict, err_infodef Get_known_from_from_xls_image ( origin_pdf_xls_path, paddle_ocr) : xls_file_path = origin_pdf_xls_pathpth_split = os. path. split( xls_file_path) pr = pth_split[ 0 ] nm = pth_split[ 1 ] nm_split = os. path. splitext( nm) fr = nm_split[ 0 ] ex = nm_split[ 1 ] unzip_path = os. path. join( pr, fr) sub_img_path = os. path. join( unzip_path, "xl\\media" ) result_title= [ 'content' ] result_df = pd. DataFrame( columns = result_title) known_dict = { } draw_result_out = True wb = load_workbook( xls_file_path) ws = wb[ 'Table 1' ] if not os. path. exists( unzip_path) : os. mkdir( unzip_path) if draw_result_out == True : draw_result_folder = os. path. join( unzip_path, 'draw_result' ) if not os. path. exists( draw_result_folder) : os. mkdir( draw_result_folder) with ZipFile( xls_file_path) as f: for file in f. namelist( ) : tempimg_path = '' if file . startswith( "xl/media" ) : f. extract( file , path= unzip_path) temp_img_name = os. path. split( file ) [ 1 ] temp_img_fr = os. path. splitext( temp_img_name) [ 0 ] ext = os. path. splitext( temp_img_name) [ 1 ] . lower( ) tempimg_path = os. path. join( unzip_path, file ) img = cv_imread( tempimg_path) ( h, w, _) = img. shapeif 80 <= max ( h, w) <= 200 and h == w: codedata = pyzbar. decode( img) if len ( codedata) > 0 : data_str = codedata[ 0 ] . data. decode( ) if len ( data_str) > 20 : data_list = data_str. split( ',' ) if len ( data_list) > 4 : known_dict[ '01票號' ] = data_list[ 3 ] , known_dict[ '02代碼' ] = data_list[ 2 ] , known_dict[ '03日期' ] = data_list[ 5 ] , known_dict[ '13合計稅前' ] = data_list[ 4 ] if h < 50 : enlarge = 4 img_new = new( img, enlarge) edge = 20 color = ( 255 , 255 , 255 ) img_large = cv2. copyMakeBorder( img_new, edge, edge, edge, edge, cv2. BORDER_CONSTANT, value= color) enlarge_img_folder = os. path. join( unzip_path, 'img_enlarge' ) if not os. path. exists( enlarge_img_folder) : os. mkdir( enlarge_img_folder) enlarge_img_path = os. path. join( enlarge_img_folder, 'enlarge_' + temp_img_name) cv2. imencode( ".jpg" , img_large) [ 1 ] . tofile( enlarge_img_path) result = paddle_ocr. ocr( img_large, cls= True ) if len ( result) > 0 : df = pd. DataFrame( data= [ result[ i] [ 1 ] [ 0 ] for i in range ( len ( result) ) ] , columns = result_title) result_df = Collect_df( result_df, df) if draw_result_out == True : from PIL import Imageimage = Image. open ( enlarge_img_path) . convert( 'RGB' ) boxes = [ line[ 0 ] for line in result] txts = [ line[ 1 ] [ 0 ] for line in result] scores = [ line[ 1 ] [ 1 ] for line in result] im_show = draw_ocr( image, boxes, txts, scores, font_path= './fonts/simfang.ttf' ) im_show = Image. fromarray( im_show) draw_result_name = 'draw_' + temp_img_namedraw_result_path = os. path. join( draw_result_folder, draw_result_name) im_show. save( draw_result_path) temp_df = result_df. loc[ : , 'content' ] . str . extract( '[￥￥]([.0-9]+)' ) temp_df. columns= [ 'content' ] amount_df = temp_df. loc[ temp_df[ 'content' ] . notna( ) , : ] if len ( amount_df) >= 3 : sqhj = float ( known_dict[ '13合計稅前' ] ) amount_df = amount_df. astype( float ) if sqhj > 1 : values = amount_df. loc[ amount_df[ 'content' ] != sqhj, 'content' ] . valuesknown_dict[ '15總額' ] = max ( values) known_dict[ '14合計稅額' ] = min ( values) temp_df = result_df. loc[ : , 'content' ] . str . extract( '^(91\S{16})$' ) temp_df. columns= [ 'content' ] tax_numbers_df = temp_df. loc[ temp_df[ 'content' ] . notna( ) , : ] if len ( tax_numbers_df) > 0 : our_number = '你公司的稅號' known_dict[ '05購方稅號' ] = our_numbervalues = tax_numbers_df. loc[ tax_numbers_df[ 'content' ] != our_number, 'content' ] . valuesif len ( values) > 0 : known_dict[ '18銷方稅號' ] = values[ 0 ] img_ocr_result_folder = os. path. join( unzip_path, 'result' ) if not os. path. exists( img_ocr_result_folder) : os. mkdir( img_ocr_result_folder) img_ocr_result_name = temp_img_fr + '.xlsx' img_ocr_result_path = os. path. join( img_ocr_result_folder, img_ocr_result_name) result_df. to_excel( img_ocr_result_path) return known_dict
def Pdf_tans_to ( file_path, pdf_trans_to_file_path, trans_type = '.xlsx' , temp_pdf_trans_excel_out = True ) : import winerrorfrom win32com. client. dynamic import ERRORS_BAD_CONTEXT, DispatchERRORS_BAD_CONTEXT. append( winerror. E_NOTIMPL) output_folder_path = os. path. split( pdf_trans_to_file_path) [ 0 ] if not os. path. exists( output_folder_path) : Create_clear_dir( output_folder_path) if trans_type == '.xlsx' : trans_engion = 'com.adobe.acrobat.xlsx' elif trans_type == '.txt' : trans_engion = 'com.adobe.acrobat.plain-text' else : trans_engion = 'com.adobe.acrobat.plain-text' try : AvDoc = Dispatch( "AcroExch.AVDoc" ) if AvDoc. Open( file_path, "" ) : pdDoc = AvDoc. GetPDDoc( ) jsObject = pdDoc. GetJSObject( ) jsObject. SaveAs( pdf_trans_to_file_path, trans_engion) except Exception as e: print ( str ( e) ) finally : AvDoc. Close( True ) jsObject = None pdDoc = None AvDoc = None if os. path. exists( pdf_trans_to_file_path) : return pdf_trans_to_file_pathelse : return None def Pdf_tans_jpg ( file_path, pdf_trans_jpg_file_path, temp_pdf_trans_jpg_out = True ) : output_folder_path = os. path. split( pdf_trans_jpg_file_path) [ 0 ] if not os. path. exists( output_folder_path) : Create_clear_dir( output_folder_path) doc = fitz. open ( file_path) pdf_name = os. path. splitext( file_path) [ 0 ] for pg in range ( doc. pageCount) : page = doc[ pg] rotate = int ( 0 ) zoom_x = 2.0 zoom_y = 2.0 trans = fitz. Matrix( zoom_x, zoom_y) . preRotate( rotate) pm = page. getPixmap( matrix= trans, alpha= False ) pm. writePNG( pdf_trans_jpg_file_path) if os. path. exists( pdf_trans_jpg_file_path) : return pdf_trans_jpg_file_pathelse : return None def pil_enhance ( img) : img = ImageEnhance. Brightness( img) . enhance( 1.0 ) img = ImageEnhance. Sharpness( img) . enhance( 1.5 ) img = ImageEnhance. Contrast( img) . enhance( 2.0 ) img_result = img. convert( 'L' ) return img_resultdef new ( img, enlarge) : img_new = np. zeros( ( img. shape[ 0 ] * enlarge, img. shape[ 1 ] * enlarge, img. shape[ 2 ] ) ) for i in range ( img. shape[ 0 ] ) : for j in range ( img. shape[ 1 ] ) : for m in range ( 4 ) : for n in range ( 4 ) : img_new[ 4 * i + m] [ 4 * j + n] = img[ i] [ j] return img_newdef Pil_make_border ( image, edge = 20 ) : iw, ih = image. size w, h = iw + edge, ih + edge target_size = ( w, h) nw = iwnh = ihimage = image. resize( ( nw, nh) , Image. BICUBIC) color= ( 255 , 255 , 255 ) new_image = Image. new( 'RGB' , target_size, color) new_image. paste( image, ( ( w - nw) // 2 , ( h - nh) // 2 ) ) return new_image
def Loc_range_content_pandas ( ocr_engines, df, result_series_orderdic, err_info, known_dict, file_path, temp_folder_path, enhance= False , engine_switch= 0 ) : user_name, user_code = '你的公司名稱' , '你公司的稅號' df[ 'content' ] . astype( str ) df[ 'center_w' ] = ( df. loc[ : , 'luw' ] + df. loc[ : , 'rdw' ] ) / 2 df[ 'center_h' ] = ( df. loc[ : , 'luh' ] + df. loc[ : , 'rdh' ] ) / 2 w_ratio = 1 h_ratio = 1 w_this_loc_tradtitle = 240 h_this_loc_tradtitle = 1170 min_w_zero_distance, max_w_zero_distance , min_h_zero_distance, max_h_zero_distance \= 521 , 1550 , - 33 , 98 min_w_zero = w_this_loc_tradtitle + w_ratio * min_w_zero_distancemax_w_zero = w_this_loc_tradtitle + w_ratio * max_w_zero_distancemin_h_zero = h_this_loc_tradtitle + h_ratio * min_h_zero_distancemax_h_zero = h_this_loc_tradtitle + h_ratio * max_h_zero_distanceloc_trad_range = [ min_w_zero, max_w_zero, min_h_zero, max_h_zero] cond_trad = [ '16大寫' , 'direct' , 'contains' , '[圓角分整零壹貳叁肆伍陸柒捌玖拾佰仟萬億]{2,}' , '1' , { 'direct' : loc_trad_range} ] known_dict = Loc_jpg_content( df, cond_trad, order_dict= known_dict) if len ( known_dict[ '16大寫' ] [ 1 ] ) > 0 : ( w_zero, h_zero) = known_dict[ '16大寫' ] [ 1 ] else : err_info = err_info + '識別失敗！未找到大寫金額內容。' w_zero = 750 h_zero = 1180 range_list = [ [ '01票號' , [ 'known' , 'crop' ] , 'extract' , '^\D*(\d{8})$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( 1430 ) ) , int ( w_zero + w_ratio * ( 1685 ) ) , int ( h_zero + h_ratio * ( - 990 ) ) , int ( h_zero + h_ratio * ( - 900 ) ) ] , 'known' : known_dict} ] , [ '02代碼' , [ 'known' , 'crop' ] , 'extract' , '([a-zA-Z0-9]{10})$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( - 475 ) ) , int ( w_zero + w_ratio * ( 80 ) ) , int ( h_zero + h_ratio * ( - 1100 ) ) , int ( h_zero + h_ratio * ( - 920 ) ) ] } ] , [ '03日期' , [ 'known' , 'crop' ] , 'extract' , '(\d{4}\s*年\s*\d{2}\s*月\s*\d{2}\s*日)$' , '1' , { 'direct' : [ int ( w_zero + w_ratio * ( 1100 ) ) , int ( w_zero + w_ratio * ( 1637 ) ) , int ( h_zero + h_ratio * ( - 925 ) ) , int ( h_zero + h_ratio * ( - 840 ) ) ] , 'crop' : [ int ( w_zero + w_ratio * ( 1300 ) ) , int ( w_zero + w_ratio * ( 1637 ) ) , int ( h_zero + h_ratio * ( - 925 ) ) , int ( h_zero + h_ratio * ( - 840 ) ) ] , } ] , [ '04購方' , [ 'crop' ] , 'extract' , '([\(\)（）\u4e00-\u9fa5]{8,30})' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( - 320 ) ) , int ( w_zero + w_ratio * ( 600 ) ) , int ( h_zero + h_ratio * ( - 800 ) ) , int ( h_zero + h_ratio * ( - 680 ) ) ] , } ] , [ '05購方稅號' , [ 'direct' ] , 'extract' , '([a-zA-Z0-9]{18})$' , '1' , { 'direct' : [ int ( w_zero + w_ratio * ( - 240 ) ) , int ( w_zero + w_ratio * ( 540 ) ) , int ( h_zero + h_ratio * ( - 800 ) ) , int ( h_zero + h_ratio * ( - 680 ) ) ] , 'crop' : [ int ( w_zero + w_ratio * ( - 320 ) ) , int ( w_zero + w_ratio * ( 600 ) ) , int ( h_zero + h_ratio * ( - 800 ) ) , int ( h_zero + h_ratio * ( - 680 ) ) ] , } ] , [ '06品名' , [ 'crop' ] , 'contains' , '^[\*冰水米\+]?(\S*[制品]\S*[\*冰水米\+]?\S+)$' , 'n' , { 'crop' : [ int ( w_zero + w_ratio * ( - 670 ) ) , int ( w_zero + w_ratio * ( 640 ) ) , int ( h_zero + h_ratio * ( - 560 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , 'center_limit' : [ 10 , 500 , 10 , 450 ] , } ] , [ '07單位' , [ 'crop' ] , 'contains' , '^\D{1,8}$' , 'n' , { 'crop' : [ int ( w_zero + w_ratio * ( - 670 ) ) , int ( w_zero + w_ratio * ( 640 ) ) , int ( h_zero + h_ratio * ( - 560 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , 'center_limit' : [ 820 , 1100 , 10 , 450 ] } ] , [ '08數量' , [ 'crop' ] , 'contains' , '^\d+$|^\d+\.\d+$' , 'n' , { 'crop' : [ int ( w_zero + w_ratio * ( 440 ) ) , int ( w_zero + w_ratio * ( 640 ) ) , int ( h_zero + h_ratio * ( - 510 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , } ] , [ '09單價' , [ 'crop' ] , 'contains' , '^[\.:：]?\d+[\.:：]?\s*\d*\s*$' , 'n' , { 'crop' : [ int ( w_zero + w_ratio * ( 635 ) ) , int ( w_zero + w_ratio * ( 890 ) ) , int ( h_zero + h_ratio * ( - 510 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , } ] , [ '10稅前' , [ 'crop' ] , 'contains' , '^\s*[+-]?(?:\d+|\d{1,3}(?:,\d{3})*)[\.:：]\s*\d{2}\s*$' , 'n' , { 'crop' : [ int ( w_zero + w_ratio * ( 980 ) ) , int ( w_zero + w_ratio * ( 1240 ) ) , int ( h_zero + h_ratio * ( - 510 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , } ] , [ '11稅率' , [ 'crop' ] , 'contains' , '^\d{1,2}\s*%$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( 1240 ) ) , int ( w_zero + w_ratio * ( 1350 ) ) , int ( h_zero + h_ratio * ( - 510 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , } ] , [ '12稅額' , [ 'crop' ] , 'contains' , '^\s*[+-]?(?:\d+|\d{1,3}(?:,\d{3}))[\.:：]?\s*\d{0,2}\s*\D*' , 'n' , { 'crop' : [ int ( w_zero + w_ratio * ( 1380 ) ) , int ( w_zero + w_ratio * ( 1700 ) ) , int ( h_zero + h_ratio * ( - 510 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) ] , } ] , [ '13合計稅前' , [ 'known' , 'crop' ] , 'contains' , '[￥￥]?s*[+-]?(?:\d+|\d{1,3}(?:,\d{3})*)[\.:：]\s*\d{2}\s*$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( 880 ) ) , int ( w_zero + w_ratio * ( 1235 ) ) , int ( h_zero + h_ratio * ( - 100 ) ) , int ( h_zero + h_ratio * ( - 10 ) ) ] , 'known' : known_dict} ] , [ '14合計稅額' , [ 'crop' ] , 'contains' , '[￥￥]?s*[+-]?(?:\d+|\d{1,3}(?:,\d{3})*)[\.:：]?\s*\d{0,2}\s*$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( 1300 ) ) , int ( w_zero + w_ratio * ( 1710 ) ) , int ( h_zero + h_ratio * ( - 110 ) ) , int ( h_zero + h_ratio * ( 0 ) ) ] , } ] , [ '15總額' , [ 'crop' ] , 'contains' , '[￥￥]?s*[+-]?(?:\d+|\d{1,3}(?:,\d{3})*)[\.:：]\s*\d{2}\s*$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( 1220 ) ) , int ( w_zero + w_ratio * ( 1700 ) ) , int ( h_zero + h_ratio * ( - 20 ) ) , int ( h_zero + h_ratio * ( 70 ) ) ] , } ] , [ '16大寫' , [ 'known' ] , known_dict] , [ '17銷方' , [ 'crop' ] , 'extract' , '([\(\)（）\u4e00-\u9fa5]{8,30}[辦|處|公|司|廠|社|部])$' , '1' , { 'crop' : [ int ( w_zero + w_ratio * ( - 280 ) ) , int ( w_zero + w_ratio * ( 540 ) ) , int ( h_zero + h_ratio * ( 60 ) ) , int ( h_zero + h_ratio * ( 165 ) ) ] , } ] , [ '18銷方稅號' , [ 'direct' ] , 'extract' , '([a-zA-Z0-9]{18})$' , '1' , { 'direct' : [ int ( w_zero + w_ratio * ( - 260 ) ) , int ( w_zero + w_ratio * ( 600 ) ) , int ( h_zero + h_ratio * ( 100 ) ) , int ( h_zero + h_ratio * ( 220 ) ) ] , 'crop' : [ int ( w_zero + w_ratio * ( - 320 ) ) , int ( w_zero + w_ratio * ( 600 ) ) , int ( h_zero + h_ratio * ( 100 ) ) , int ( h_zero + h_ratio * ( 220 ) ) ] , } ] ] img_inv = cv_imread( file_path) err_info = '' set_h_adjust = 0 for i, cond_list in enumerate ( range_list) : range_title = cond_list[ 0 ] loc_method = cond_list[ 1 ] result_series_orderdic[ range_title] = pd. Series( ) if 'known' in loc_method: if range_title in known_dict: known = True result_series_orderdic[ range_title] = known_dict[ range_title] if len ( result_series_orderdic[ range_title] ) > 0 : continue if 'crop' in loc_method: crop_folder_name = 'crop' crop_folder_path = os. path. join( temp_folder_path, crop_folder_name) if not os. path. exists( crop_folder_path) : Create_clear_dir( crop_folder_path) result_series_orderdic, get_h_adjust = Crop_ocr( ocr_engines, result_series_orderdic, known_dict, img_inv, file_path, crop_folder_path, set_h_adjust, cond_list, enhance, engine_switch = engine_switch) if range_title == '01票號' : if get_h_adjust > 5 : set_h_adjust = get_h_adjustif len ( result_series_orderdic[ range_title] ) > 0 : continue if 'direct' in loc_method: result_series_orderdic = Loc_jpg_content( df, cond_list, order_dict= result_series_orderdic) return result_series_orderdic, err_info
def Tele_inv_ocr ( ocr_engines, result_series_orderdic, inv_dict, file_path, excel_file_path, err_info, engine_switch = 0 ) : df_org = pd. read_excel( excel_file_path, sheet_name= 0 , header= None , index_col= None , na_values= '' , keep_default_na= True , dtype= object ) df_org = df_org. fillna( '' ) df_org = df_org. astype( str ) '去多空格' df_org = df_org. replace( to_replace = '\\n|\s+' , value= ' ' , regex= True ) df_org = df_org. replace( to_replace = '^\s+' , value= '' , regex= True ) df_new = pd. DataFrame( data= '' , index = df_org. index, columns= [ 'content' ] ) for i in df_org. columns: df_new[ 'content' ] = df_new[ 'content' ] + '|' + df_org[ i] df_new = df_new. replace( to_replace = '\|+' , value= '|' , regex= True ) df_new = df_new. replace( to_replace = '^\||\|+$' , value= '' , regex= True ) fp_mark = False if len ( df_new. loc[ df_new[ 'content' ] . str . contains( '發票' ) , : ] ) > 0 : fp_mark = True if fp_mark == False : err_info = 'inv character not found.' return result_series_orderdic, err_info, inv_dictknown_dict = { } known_dict = Get_known_from_from_xls_image( excel_file_path, ocr_engines[ engine_switch] ) range_list = [ [ '01票號' , [ 'direct' ] , 'extract' , [ '發票號碼[：|:]?\s*(\d+)' ] , '1' , ] , [ '02代碼' , [ 'direct' ] , 'extract' , [ '發票代碼[：|:]?\s*(\d+)' ] , '1' , ] , [ '03日期' , [ 'direct' ] , 'extract' , [ '(\d{4}\s*年\s*\d{2}\s*月\s*\d{2}\s*日)' ] , '1' , ] , [ '04購方' , [ 'direct' ] , 'extract' , [ '^購買方信息\|名稱：(.+?) 統一社會信用代碼/納稅人識別號：' , '名\s*稱：\s*(.+?)\s*納稅人識別號' ] , '1' ] , [ '05購方稅號' , [ 'direct' ] , 'extract' , [ '購買[\D]+納稅人識別號：[\|\s]*([0-9A-Z]{18?})' , '納稅人識別號：([a-zA-Z0-9]{18})' , ] , '1' ] , [ '06品名' , [ 'direct' ] , 'extract' , [ '^項目名稱\s*(.+)合\s*計\|' , '^項目名稱\s*(.+)合|' , ] , '1' ] , [ '07單位' , [ 'direct' ] , 'extract' , [ '^([\u4e00-\u9fa5]+)[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+$' , '\|單\s*([\u4e00-\u9fa5]+)\|位\|' , '\|?單\s*\|?\s*價\s*\|?\s*([\u4e00-\u9fa5]{1,3})\s*[.0-9]+' , '\|?單[\s\|]*價[\|\s]*([\u4e00-\u9fa5]{1,3})\s*[.0-9]+' , '\|?單[\s\|]*位[\|\s]*([\u4e00-\u9fa5]{1,3})[\|\s]*數[\|\s]*量[\|\s]*[.0-9]+[\|\s]*單[\|\s]*價[\|\s]*[.0-9]+' , ] , 'n' ] , [ '08數量' , [ 'direct' ] , 'extract' , [ '^[\u4e00-\u9fa5]+[\|\s]*([.0-9]+)[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+$' , '量\s*([.0-9]+)\s*\|單' , '\|?單[\s\|]*價[\|\s]*[\u4e00-\u9fa5]{1,3}\s*([.0-9]+)' , '量[\s\|]*單[\s\|]*價[\|\s]*([.0-9]+)\s+[.0-9]+' , '([.0-9]+)[\s\|]+[.0-9]+[\s\|]+[.0-9]+[\s\|]+[.0-9]+[\s\|]+[.0-9]+' ] , 'n' ] , [ '09單價' , [ 'direct' ] , 'extract' , [ '^[\u4e00-\u9fa5]+[\|\s]*[.0-9]+[\|\s]*([.0-9]+)[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+$' , '價\s*([.0-9]+)\s*\|金' , '\|?單[\s\|]*價[\|\s]*[\u4e00-\u9fa5]{1,3}\s*[.0-9]+[\|\s]+([.0-9]+)' , '量[\s\|]*單[\s\|]*價[\|\s]*[.0-9]+\s+([.0-9]+)' , '[.0-9]+[\s\|]+([.0-9]+)[\s\|]+[.0-9]+[\s\|]+[.0-9]+[\s\|]+[.0-9]+' ] , 'n' ] , [ '10稅前' , [ 'direct' ] , 'extract' , [ '^[\u4e00-\u9fa5]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*([.0-9]+)[\|\s]*[.0-9]+[\|\s]*[.0-9]+$' , '[率|\|]\s*([.0-9]+)\s+[0-9]{1,2}%[\||稅]' , '金\s*額\s*([.0-9]+)[\|\s]*稅率\s*[.0-9]+%[\|\s]*稅\s*額' , '[.0-9]+[\s\|]+[.0-9]+[\s\|]+([.0-9]+)[\s\|]+[.0-9]+[\s\|]+[.0-9]+' ] , 'n' ] , [ '11稅率' , [ 'direct' ] , 'extract' , [ '^[\u4e00-\u9fa5]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*([.0-9]+)[\|\s]*[.0-9]+$' , '[率|\|]\s*[.0-9]+\s+([0-9]{1,2}%)[\||稅]' , '金\s*額\s*[.0-9]+[\|\s]*稅\s*率\s*([.0-9]+%)[\|\s]*稅\s*額' , '[.0-9]+[\s\|]+[.0-9]+[\s\|]+[.0-9]+[\s\|]+([.0-9]+)[\s\|]+[.0-9]+' ] , '1' ] , [ '12稅額' , [ 'direct' ] , 'extract' , [ '^[\u4e00-\u9fa5]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*[.0-9]+[\|\s]*([.0-9]+)$' , '稅\s*[\|]?\s*額\s*[\|]?\s*([.0-9]+)' , '[.0-9]+[\s\|]+[.0-9]+[\s\|]+[.0-9]+[\s\|]+[.0-9]+[\s\|]+([.0-9]+)' ] , 'n' ] , [ '13合計稅前' , [ 'direct' ] , 'extract' , [ '[￥￥](-?\d+\.\d{0,2})[\|\s][￥￥]' , '^([.0-9]+)\|[.0-9]+$' ] , '1' ] , [ '14合計稅額' , [ 'direct' ] , 'extract' , [ '[￥￥]-?\d+\.\d+[\|\s][￥￥](-?\d+\.\d+)' , '^[.0-9]+\|([.0-9]+)$' ] , '1' ] , [ '15總額' , [ 'direct' ] , 'extract' , [ '（小寫）[￥￥](.+)' , '價稅合計[\|\s]*[零壹貳叁肆伍陸柒捌玖拾佰仟億角分圓整]{2,}[\|\s]*[￥￥]?([.0-9]+)$' ] , '1' ] , [ '16大寫' , [ 'direct' ] , 'extract' , [ '^價稅合計（大寫）\|(.+)\|（小寫）' , '價稅合計[\|\s]*([零壹貳叁肆伍陸柒捌玖拾佰仟億角分圓整]{2,})' ] , '1' ] , [ '17銷方' , [ 'direct' ] , 'extract' , [ '銷售方信息\|名稱：(.+?) 統一社會信用代碼' , '銷售方\s*\|\s*名\s*稱：\s*([\u4e00-\u9fa5]+)\s*納稅人識別號' ] , '1' ] , [ '18銷方稅號' , [ 'direct' ] , 'extract' , [ '銷售[\D]+納稅人識別號：[\|\s]*([0-9A-Z]{18})' , '納稅人識別號：([a-zA-Z0-9]{18})' ] , '-1' ] ] result_series_orderdic = OrderedDict( ) for i, cond_list in enumerate ( range_list) : result_series_orderdic, err_info = Loc_tele_content( df_new, known_dict, cond_list, result_series_orderdic) return result_series_orderdic, err_info, inv_dict
def Log_result_file ( result_pandas, result_file_path, result_sheet_name) : writer = pd. ExcelWriter( result_file_path, engine= 'openpyxl' , mode= 'a' , if_sheet_exists= 'replace' ) result_pandas. to_excel( writer, sheet_name= result_sheet_name, index= False ) writer. close( ) return True
def Add_hyperlink ( result_file_path, result_sheet_name) :
wb = load_workbook( result_file_path) wb. move_sheet( result_sheet_name, offset= - 1 ) ws = wb[ result_sheet_name] wb. _active_sheet_index = 0 rng = ws. iter_cols( min_row= 2 , max_row= ws. max_row, min_col= 19 , max_col= 20 ) for col in rng: for cell in col: txt = cell. valueif txt is None : continue if len ( txt) > 0 : if cell. column == 19 : pr, nm, fr, ex = pathsplit( txt) cell. hyperlink = '..\\' + nmcell. font = Font( color= colors. Color( index = 4 ) , italic= True ) else : cell. font = Font( color= colors. Color( index = 2 ) , italic= False ) wb. save( result_file_path) ws = None wb = None return True
def Collect_df ( collect_df, item_df) : if len ( item_df) == 0 : return collect_dfif collect_df. empty: collect_df = item_dfelse : test_set = { 0 , 1 } collect_df_col_set = set ( collect_df. columns) item_df_col_set = set ( item_df. columns) if len ( collect_df_col_set - item_df_col_set) > 0 : temp_collect_df = collect_df. copy( ) temp_collect_title_list = temp_collect_df. columns. to_list( ) temp_collect_title_df = pd. DataFrame( data = temp_collect_title_list) . Ttemp_collect_df. columns = list ( range ( len ( temp_collect_df. columns) ) ) collect_df = pd. concat( [ temp_collect_title_df, temp_collect_df] , ignore_index = True , axis = 0 ) temp_item_df = item_df. copy( ) temp_item_title_list = temp_item_df. columns. to_list( ) temp_item_title_df = pd. DataFrame( data = temp_item_title_list) . Ttemp_item_df. columns = list ( range ( len ( temp_item_df. columns) ) ) item_df = pd. concat( [ temp_item_title_df, temp_item_df] , ignore_index = True , axis = 0 ) collect_col_num = len ( temp_collect_title_list) item_df_col_num = len ( temp_item_title_list) max_col = max ( collect_col_num, item_df_col_num) collect_col_dif = max_col - collect_col_numitem_col_dif = max_col - item_df_col_numif collect_col_dif > 0 : for i in range ( collect_col_num, max_col + 1 ) : temp_collect_df[ i] = '' if item_col_dif > 0 : for i in range ( item_df_col_num, max_col + 1 ) : temp_item_df[ i] = '' collect_df = temp_collect_dfitem_df = temp_item_dfcollect_df = pd. concat( [ collect_df, item_df] , ignore_index = True , axis = 0 ) collect_df = reset_nature_index( collect_df) return collect_df
def Log_df_to_file ( df, save_path, sheet_name, keep_exists = True ) : writer = pd. ExcelWriter( save_path, engine= 'openpyxl' , mode= 'a' , if_sheet_exists= 'replace' ) pandas_write = pd. DataFrame( ) if not df. empty: if keep_exists == True : df_title = df. columns. to_list( ) df_non_title = dfdf_non_title. columns = list ( range ( len ( df_non_title. columns) ) ) pandas_write = pd. read_excel( save_path, sheet_name= sheet_name, index_col= 0 , header = 0 , keep_default_na= True , dtype= object ) pandas_write = Collect_df( pandas_write, df) else : pandas_write = dfif not pandas_write. empty: pandas_write. to_excel( writer, sheet_name= sheet_name) writer. close( ) return True def reset_nature_index ( df) : df. index = list ( range ( 1 , len ( df) + 1 ) ) return df
def Check_result ( result_pandas) : if len ( result_pandas) == 0 : return result_pandasedit_pandas = result_pandas. copy( ) edit_pandas = edit_pandas. fillna( '' ) edit_pandas = edit_pandas. astype( str ) temp_title_list = edit_pandas. columns. tolist( ) edit_pandas[ 'err_info' ] = '' pandas_title_list = edit_pandas. columns. tolist( ) inv_title_list = pandas_title_list[ 0 : - 2 ] detail_title_list = [ '06品名' , '07單位' , '08數量' , '09單價' , '10稅前' , '12稅額' ] num_title_list = [ '08數量' , '09單價' , '10稅前' , '11稅率' , '12稅額' , \'13合計稅前' , '14合計稅額' , '15總額' ] one_row_title_list = [ '01票號' , '02代碼' , '03日期' , '04購方' , '05購方稅號' , '13合計稅前' , '14合計稅額' , '15總額' , '16大寫' , '17銷方' , '18銷方稅號' ] one_row_title_list. sort( ) edit_pandas. loc[ : , num_title_list] = \edit_pandas. loc[ : , num_title_list] . replace( to_replace = '[￥￥%\s（）\(\)\u4e00-\u9fa5]' , value= '' , regex= True ) edit_pandas. loc[ : , num_title_list] = \edit_pandas. loc[ : , num_title_list] . replace( to_replace = '[:：]' , value= '.' , regex= True ) edit_pandas. loc[ : , '05購方稅號' ] = \edit_pandas. loc[ : , '05購方稅號' ] . replace( to_replace = '[:：]' , value= '' , regex= True ) edit_pandas. loc[ : , '06品名' ] = \edit_pandas. loc[ : , '06品名' ] . replace( to_replace = '^[米水冰]|[\+\*#]' , value= ' ' , regex= True ) edit_pandas. loc[ : , '06品名' ] = \edit_pandas. loc[ : , '06品名' ] . replace( to_replace = '^\s' , value= '' , regex= True ) comp_dict = { 'A有限公司' : 'AA有限公司' , } edit_pandas = edit_pandas. replace( { '17銷方' : comp_dict} ) replace_file = 'D:\\pyscripts\\發票修正.xlsx' if os. path. exists( replace_file) : replace_df = pd. read_excel( replace_file, sheet_name= 0 , header= 0 , keep_default_na= True , dtype= object ) if not replace_df. empty: replace_df = replace_df. fillna( '' ) edit_df_title_list = edit_pandas. columns. to_list( ) replace_df_title_list = replace_df. columns. to_list( ) for _, title in enumerate ( replace_df_title_list) : if title in edit_df_title_list: if not replace_df. loc[ replace_df[ title] != '' , : ] . empty: replace_title = title + '修正' if replace_title in replace_df_title_list: for _, row in enumerate ( replace_df[ [ title, replace_title] ] . iterrows( ) ) : str_origin = row[ 1 ] . values[ 0 ] str_replace = row[ 1 ] . values[ 1 ] edit_pandas[ title] = edit_pandas[ title] . replace( to_replace = str_origin, value= str_replace, regex= True ) row_start_index = edit_pandas. loc[ edit_pandas[ 'file_path' ] . str . len ( ) > 0 , 'file_path' ] . indexrow_start_list = row_start_index. to_list( ) temp_index = row_start_index - 1 temp_list = temp_index. to_list( ) row_end_list = temp_list[ 1 : ] row_pandas_last = edit_pandas. index[ - 1 ] row_end_list. append( row_pandas_last) rows_tuple = zip ( row_start_list, row_end_list) for i, ( row_start, row_end) in enumerate ( rows_tuple) : err_info = '' err_blank = '' err_code = '' err_product = '' err_num = '' this_inv_pandas = edit_pandas. iloc[ row_start: row_end+ 1 , : ] num_extract_reg = '((?:\d+|\d{0,3}(?:,\d{3})*)\.?\d{0,})\s*$' for _, num_title in enumerate ( num_title_list) : this_inv_pandas. loc[ : , num_title] = this_inv_pandas. loc[ : , num_title] . str . extract( num_extract_reg) this_inv_pandas. loc[ : , num_title_list] = this_inv_pandas. loc[ : , num_title_list] . replace( '^$' , '0' , regex= True ) this_inv_pandas. loc[ : , num_title_list] = this_inv_pandas. loc[ : , num_title_list] . astype( float ) if this_inv_pandas. loc[ : , '11稅率' ] . values[ 0 ] > 1 : this_inv_pandas. loc[ : , '11稅率' ] = this_inv_pandas. loc[ : , '11稅率' ] / 100 num_sum_pretax_amount = round ( sum ( this_inv_pandas[ '10稅前' ] . values) , 2 ) num_total_pretax_amount = this_inv_pandas[ '13合計稅前' ] . values[ 0 ] num_total_tax = this_inv_pandas[ '14合計稅額' ] . values[ 0 ] num_sum_detail_tax = round ( sum ( this_inv_pandas[ '12稅額' ] . values) , 2 ) num_total_amount= this_inv_pandas[ '15總額' ] . values[ 0 ] sum_total = num_total_pretax_amount + num_total_taxtitle_blank_list = [ ] err_inv_list = [ ] for _, title in enumerate ( detail_title_list) : cond1 = this_inv_pandas. loc[ : , title] == '' cond2 = this_inv_pandas. loc[ : , title] == 0 cond = cond1 | cond2count_blank = len ( this_inv_pandas. loc[ cond, : ] ) if count_blank > 0 : title_blank_list. append( title) if title == '06品名' : cond = this_inv_pandas. loc[ : , title] . str . contains( '品[\u4e00-\u9fa5]' ) product_wrong_df = this_inv_pandas. loc[ cond, '06品名' ] count_product_err = len ( product_wrong_df) if count_product_err > 0 : err_product = err_product + 'Check product name:' + ',' . join( product_wrong_df. to_list( ) ) + '.' if '品名' not in err_blank: if len ( this_inv_pandas. loc[ ~ this_inv_pandas[ '06品名' ] . str . contains( '[\u4e00-\u9fa5]\s[\u4e00-\u9fa5]' ) , : ] ) > 0 : err_product = err_product + '品名格式不符“類品+空格+品名”.' for _, title in enumerate ( one_row_title_list) : if title == '發票號碼' : temp_df = this_inv_pandas. loc[ this_inv_pandas[ 'file_path' ] != '' , '發票號碼' ] temp_df[ '發票號長度' ] = temp_df[ '發票號' ] . apply ( lambda x: len ( x) ) temp_check_df = temp_df. loc[ ~ ( ( temp_df[ '發票號長度' ] == 8 ) | ( temp_df[ '發票號長度' ] == 20 ) ) , : ] if len ( temp_check_df) > 0 : err_inv_list. append( 'Inv number lenth illegal' ) temp_check_df= temp_df. loc[ temp_df[ '發票號' ] . str . contains( '\D' ) , : ] if len ( temp_df) > 0 : err_inv_list. append( 'Inv number character illegal' ) cond1 = this_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , title] == '' cond2 = this_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , title] == 0 cond = cond1 | cond2if cond == True : if title == '02代碼' : if len ( this_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , '01票號' ] ) == 20 : continue if title == '15總額' : txt = this_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , '16大寫' ] if not txt == '' : trad = txt. split( '|' ) [ 0 ] repl_dict = { '參' : '叁' , '柴' : '柒' , '什' : '仟' } trad = repl_by_dict( trad, repl_dict) money = trad_to_int( trad) if not money == trad: money = float ( money) if money > 0 : this_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , title] = moneycontinue else : if num_total_pretax_amount > 0 and num_total_tax > 0 : this_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , title] = sum_totalcontinue if title == '16大寫' : continue title_blank_list. append( title) if '05購方稅號' not in title_blank_list: if this_inv_pandas[ '05購方稅號' ] . values[ 0 ] != user_code: err_code = '購方稅號[' + this_inv_pandas[ '05購方稅號' ] . values[ 0 ] + ']不是“' + user_code + '”。' if len ( title_blank_list) > 0 : title_blank_list. sort( ) err_blank = 'Null:[' + ',' . join( title_blank_list) + ']。' diff_pretax_amount = round ( num_total_pretax_amount - num_sum_pretax_amount, 2 ) if diff_pretax_amount != 0 : err_num = err_num + '稅前之和≠合計稅前[' + str ( num_total_pretax_amount) + ' - ' + str ( num_sum_pretax_amount) + ' = ' + str ( diff_pretax_amount) + ']。' sum_total_pretax_tax = round ( num_total_pretax_amount + num_total_tax, 2 ) diff_total = round ( num_total_amount - sum_total_pretax_tax, 2 ) diff_tax = round ( num_total_tax - num_sum_detail_tax, 2 ) if diff_total != 0 : err_num = err_num + '稅前合計與稅額合計之和≠發票總額[' + str ( sum_total_pretax_tax) + '≠' + str ( num_total_amount) + ']。' if diff_tax != 0 : err_num = err_num + '明細稅額之和≠14合計稅額:[' + str ( num_sum_detail_tax) + ' ≠ ' + str ( num_total_tax) + ']。' quantity_price_df = this_inv_pandas. loc[ : , [ '06品名' , '08數量' , '09單價' , '10稅前' ] ] quantity_price_df[ 'diff_quantity_price' ] = quantity_price_df[ '08數量' ] * quantity_price_df[ '09單價' ] - quantity_price_df[ '10稅前' ] round_quantity_price_df = quantity_price_df. loc[ : , 'diff_quantity_price' ] . astype( float ) . round ( 2 ) quantity_price_df[ 'diff_quantity_price' ] = round_quantity_price_dfdiff_quantity_price_df = quantity_price_df. loc[ quantity_price_df[ 'diff_quantity_price' ] != 0 , : ] if not diff_quantity_price_df. empty: str_temp_quantity = '，' . join( diff_quantity_price_df[ '08數量' ] . astype( str ) . tolist( ) ) str_temp_price = '，' . join( diff_quantity_price_df[ '09單價' ] . astype( str ) . tolist( ) ) str_temp_pretax_amount = '，' . join( diff_quantity_price_df[ '10稅前' ] . astype( str ) . tolist( ) ) str_temp_diff = '，' . join( diff_quantity_price_df[ 'diff_quantity_price' ] . astype( str ) . tolist( ) ) err_num = err_num + '量*價≠稅前,差異明細：[' + str_temp_quantity + ']×[' + str_temp_price + ']-[' + str_temp_pretax_amount + ']=[' + str_temp_diff + ']。' err_inv = '票號格式錯誤[' + ',' . join( err_inv_list) + ']。' if len ( err_inv_list) > 0 else '' err_info = err_inv + err_blank + err_code + err_product + err_numerr_before = this_inv_pandas. loc[ : , 'err_info' ] . values[ 0 ] err_info = err_before + err_infothis_inv_pandas. loc[ this_inv_pandas. index[ 0 ] , 'err_info' ] = err_infoedit_pandas. iloc[ row_start: row_end + 1 , : ] = this_inv_pandas result_pandas = edit_pandas return result_pandas
def cv2_pil ( img_cv) : pil_image = Image. fromarray( cv2. cvtColor( img_cv, cv2. COLOR_BGR2RGB) ) return pil_image
def cv_imread ( file_path) : cv_img = cv2. imdecode( np. fromfile( file_path, dtype= np. uint8) , cv2. IMREAD_COLOR) return cv_img
def trad_to_int ( money) : trad_dict = { '零' : 0 , '壹' : 1 , '貳' : 2 , '叁' : 3 , '肆' : 4 , '伍' : 5 , '陸' : 6 , '柒' : 7 , '捌' : 8 , '玖' : 9 , '拾' : 10 , '佰' : 100 , '仟' : 1000 , '萬' : 10000 , '億' : 100000000 , '角' : 0.1 , '分' : 0.01 } trad = re. search( r"[零壹貳叁肆伍陸柒捌玖拾佰仟億角分]+" , money) if trad is not None : num = 0 add = 0 sum = 0 for i in money: if i in [ '零' , '壹' , '貳' , '叁' , '肆' , '伍' , '陸' , '柒' , '捌' , '玖' ] : add = trad_dict[ i] sum = sum + addelif i in [ '拾' , '佰' , '仟' , '億' , '角' , '分' ] : num = add * trad_dict[ i] sum = sum - addsum = sum + numadd = numelif i == '萬' or i == '億' : sum = sum * trad_dict[ i] sum = str ( sum ) return sum else : return moneydef Fill_na_result ( result_df) : result_df. loc[ : , '03日期' ] = result_df. loc[ : , '03日期' ] . apply ( lambda x: delta_date( x) ) result_df. loc[ : , '11稅率' ] = result_df. loc[ : , '11稅率' ] . fillna( method= 'ffill' ) result_df. iloc[ : , 0 : 7 ] = result_df. iloc[ : , 0 : 7 ] . fillna( '' ) result_df. iloc[ : , 7 : 15 ] = result_df. iloc[ : , 7 : 15 ] . fillna( '0' ) result_df. iloc[ : , 15 : ] = result_df. iloc[ : , 15 : ] . fillna( '' ) result_df = result_df. fillna( '' ) return result_dfdef delta_date ( para) :
time = paraif isinstance ( para, int ) : time = pd. to_datetime( '1899-12-30' ) + pd. Timedelta( str ( int ( para) ) + 'days' ) time = time. strftime( "%Y-%m-%d" ) elif isinstance ( para, float ) : time = '' return time
def repl_by_dict ( my_str, repl_dict) : for ( k, v) in repl_dict. items( ) : my_str = my_str. replace( k, v) return my_str
def pathsplit ( f) - > tuple : parent = os. path. split( f) [ 0 ] fullname = os. path. split( f) [ 1 ] frontname = os. path. splitext( fullname) [ 0 ] extname = str . lower( os. path. splitext( f) [ 1 ] ) return ( parent, fullname, frontname, extname)
def Create_clear_dir ( folder_path) : if os. path. exists( folder_path) : for dirpath, dirnames, filenames in os. walk( folder_path) : for filepath in filenames: delFolderorFile( os. path. join( dirpath, filepath) ) else : os. mkdir( folder_path) if os. path. exists( folder_path) : return True else : return False
def delFolderorFile ( folder_path) : if not os. path. exists( folder_path) : return False if os. path. isfile( folder_path) : os. remove( folder_path) return for m in os. listdir( folder_path) : n = os. path. join( folder_path, m) if os. path. isdir( n) : delFolderorFile( n) else : os. unlink( n) os. rmdir( folder_path)
def cal_angle ( p1, p2) : """ px : (橫坐標，縱坐標)""" angle= math. atan2( p2[ 1 ] - p1[ 1 ] , p2[ 0 ] - p1[ 0 ] ) return angle * ( 180 / math. pi)
if __name__ == '__main__' : print ( '\n' , datetime. now( ) . strftime( "%H:%M:%S" ) , 'Program start running...\n' ) killexcel( ) user_name = '' user_code = '' reserve_template_before = True ocr_excel_out = True draw_result_out = True enhance = False acumulate_input = 'y' prepare_engine = 1 precise_engine = 1 root = Tk( ) print ( 'Please choose the images folder:' ) origin_folder_path = filedialog. askdirectory( ) if len ( origin_folder_path) > 0 : origin_folder_path = origin_folder_path. replace( '/' , '\\' ) print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'The images folder you chose：' , origin_folder_path) else : print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'No file chosen. \nQuit.' ) exit( ) root. destroy( ) result_folder_name = 'result' result_sheet_name = 'result' result_folder_path = os. path. join( origin_folder_path, result_folder_name) if not os. path. exists( result_folder_path) : Create_clear_dir( result_folder_path) result_file_name = 'result' + '.xlsx' result_file_path = os. path. join( result_folder_path, result_file_name) run_renew = True pr, nm, fr, ex = pathsplit( result_file_path) now = datetime. now( ) back_str = now. strftime( "%Y%m%d_%H%M%S" ) back_file_name = fr + '_' + back_str + exback_file_path = os. path. join( result_folder_path, back_file_name) origin_pandas = pd. DataFrame( ) t0 = datetime. now( ) if os. path. exists( result_file_path) : print ( datetime. now( ) . strftime( "%H:%M:%S" ) , f'Found previous result: { result_file_path} .' ) ocr_input = 'y' print ( '\nChoose please: \n"y" - run the orgnize engine. "n" - only check the result, do not run engine.\n' ) ocr_input = input ( 'Input(y/n):\n' ) origin_pandas = pd. DataFrame( ) if str . lower( ocr_input) == 'n' : shutil. copy( result_file_path, back_file_path) try : origin_pandas = pd. read_excel( result_file_path, sheet_name= result_sheet_name, header= 0 , keep_default_na= True , dtype= object ) except ValueError: origin_pandas = pd. read_excel( result_file_path, sheet_name= 0 , header= 0 , keep_default_na= True , dtype= object ) result_pandas = Check_result( origin_pandas) Log_result_file( result_pandas, result_file_path, result_sheet_name) Add_hyperlink( result_file_path, result_sheet_name) print ( '\n' ) print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'Done.《' , result_file_path, '》checked over.' ) exit( ) if ocr_input. lower( ) == 'y' : print ( '\nChoose run method: \n"y" - Run acumulated to the existed result. \n"n" - Run fresh and delete all existed results and template folders.\n' ) acumulate_input = input ( 'Input(y/n):\n' ) if acumulate_input. lower( ) == 'y' : reserve_template_before = 'y' shutil. copy( result_file_path, back_file_path) try : origin_pandas = pd. read_excel( result_file_path, sheet_name= result_sheet_name, header= 0 , keep_default_na= True , dtype= object ) except ValueError: origin_pandas = pd. read_excel( result_file_path, sheet_name= 0 , header= 0 , keep_default_na= True , dtype= object ) else : Create_clear_dir( result_folder_path) if not origin_pandas. empty: origin_pandas = Fill_na_result( origin_pandas) temp_folder_name = 'temp' temp_folder_path = os. path. join( origin_folder_path, temp_folder_name) if not ( reserve_template_before) or not ( os. path. exists( temp_folder_path) ) : Create_clear_dir( temp_folder_path) print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'Start the engine...' ) mobile_ocr = hub. Module( name= "chinese_ocr_db_crnn_mobile" ) paddle_ocr = PaddleOCR( enable_mkldnn= True , use_angle_cls= True , lang= 'ch' ) ocr_engines = [ mobile_ocr, paddle_ocr] print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'Engine start running...' ) result_pandas_orderdic = OrderedDict( ) duplicate_sheet_name = 'duplicate' duplicate_pandas = pd. DataFrame( ) try : duplicate_pandas = pd. read_excel( back_file_path, sheet_name= duplicate_sheet_name, header= 0 , keep_default_na= True , dtype= object ) except : pass if duplicate_pandas. empty: duplicate_pandas. to_excel( result_file_path, sheet_name= duplicate_sheet_name, index= False ) cnt_file = len ( { p. resolve( ) for p in Path( origin_folder_path) . glob( "*" ) if p. suffix in [ ".jpg" , ".pdf" ] } ) cnt_done_pre = 0 cnt_duplicate_pre = 0 if acumulate_input. lower( ) == 'y' : if not origin_pandas. empty: cnt_done_pre = len ( origin_pandas. loc[ origin_pandas[ 'file_path' ] . notnull( ) , : ] ) if not duplicate_pandas. empty: cnt_duplicate_pre = len ( duplicate_pandas. loc[ duplicate_pandas[ 'file_path' ] . notnull( ) , : ] ) inv_dict = { } walk_folder_args = { 'ocr_engines' : ocr_engines, 'temp_folder_path' : temp_folder_path, 'engine_switch' : prepare_engine} result_pandas, duplicate_pandas = walk_folder_ocr( origin_pandas, duplicate_pandas, origin_folder_path, ** walk_folder_args) print ( '\n' ) print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'Get the result.' ) cnt_done = 0 cnt_duplicate = 0 if not result_pandas. empty: cnt_done = len ( result_pandas. loc[ ( result_pandas[ 'file_path' ] != '' ) & ( result_pandas[ 'file_path' ] . notnull( ) ) , : ] ) - cnt_done_preif not duplicate_pandas. empty: cnt_duplicate = len ( duplicate_pandas. loc[ ( duplicate_pandas[ 'file_path' ] != '' ) & ( duplicate_pandas[ 'file_path' ] . notnull( ) ) , : ] ) - cnt_duplicate_preif not result_pandas. empty: print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'Checking result data...' ) result_pandas = Check_result( result_pandas) Log_result_file( result_pandas, result_file_path, result_sheet_name) print ( datetime. now( ) . strftime( "%H:%M:%S" ) , 'Result data check over.' ) Add_hyperlink( result_file_path, result_sheet_name) paddle_ocr = None server_ocr = None mobile_ocr = None ocr_engines = None print ( '\toriginal image path: ' + origin_folder_path) print ( '\toutput file path: ' + result_file_path) t1 = datetime. now( ) tx = t1 - t0 v = 0 try : v = round ( tx. total_seconds( ) / ( cnt_done + cnt_duplicate) , 2 ) except : pass print ( '\n' ) print ( t1, '\n Done. Time spent: ' , str ( tx) . split( '.' ) [ 0 ] , '. Files total: ' + str ( cnt_file) \+ '. Already done before start: ' + str ( cnt_done_pre) \+ '. Already find duplicate before start: ' + str ( cnt_duplicate_pre) \+ '. \n Files recognized this time total: ' + str ( cnt_done + cnt_duplicate) \+ ', valid: ' + str ( cnt_done) + ', duplicate: ' + str ( cnt_duplicate) \+ ', Everage: ' + str ( v) + ' s.\n' ) cnt_done_total = cnt_done_pre + cnt_donecnt_duplicate_total = cnt_duplicate_pre + cnt_duplicateif cnt_done_total != cnt_duplicate_total: print ( 'Warning: 有效發票數:' + str ( cnt_done_total) + ' 重復發票數：' + str ( cnt_duplicate_total) + ', 檢查是否有發票號碼錯誤。' )
-E卷-100分)
