haryana_mpi.py

#!/usr/bin/env python
# coding: utf-8


import traceback
sys.path.append('../')

import os
import pdf2image
from PIL import Image
import pytesseract
import re
import pandas as pd
import sys
from helper import *
import argparse
import multiprocessing
import time
from datetime import datetime
import shutil
from tempfile import mkstemp


if False:

	script_description = """ haryana parsing """

	parser = argparse.ArgumentParser(description=script_description,
									 formatter_class=argparse.ArgumentDefaultsHelpFormatter)

	parser.add_argument("data_path", help="data path of the states with append /")
	parser.add_argument("state_name", help="the exact state name of data with /")

	cli_args = parser.parse_args()

	DATA_PATH = cli_args.data_path
	STATE = cli_args.state_name

DATA_PATH = '/share/svasudevan2lab/parse_in_rolls/data/'
STATE = 'haryana/'

PARSE_DATA_PAGES = "/share/svasudevan2lab/parse_in_rolls/parseData/images/"+STATE
create_path(PARSE_DATA_PAGES)

PARSE_DATA_BLOCKS = "/share/svasudevan2lab/parse_in_rolls/parseData/blocks/"+STATE
create_path(PARSE_DATA_BLOCKS)

PARSE_DATA_CSVS = "/share/svasudevan2lab/parse_in_rolls/parseData/csvs/"+STATE
create_path(PARSE_DATA_CSVS)

COLUMNS = ["number","id", "elector_name", "father_or_husband_name", "relationship", "house_no", "age", "sex", "ac_name", "parl_constituency", "part_no", "year", "state", "filename", "main_town", "police_station", "mandal", "revenue_division", "district", "pin_code", "polling_station_name", "polling_station_address", "net_electors_male", "net_electors_female", "net_electors_third_gender", "net_electors_total","original_or_amendment"]

state_pdfs_path = DATA_PATH+STATE
state_pdfs_files = os.listdir(state_pdfs_path)

sort_nicely(state_pdfs_files)


def split_data(data):
	seps = [":",">","-","."]
	
	for s in seps:
		if s in data:
			break

	data = data.split(s)
	data = [ i for i in data if i.strip()!='']
	if len(data)>1:
		data = data[1].strip()
		return data
	else:
		data = ""


# In[5]:


def generate_poll_blocks_from_page(page_full_path,page_blocks_path,amend_page):
	
	img = Image.open(page_full_path)

	amend = False
	
	def generate(intial_width,a,b,gap):
		count = 0
		crop_width = 1243
		crop_height = 490

		for col in range(1,11):

			for row in range(1,4):
				c = a+crop_width
				d = b+crop_height
				area = (a, b, c, d)
				cropped_img = img.crop(area)
				
				count = count+1
				
				new_area = (900,100, 1300, 470)
				
				region = Image.new("RGB", (400, 370), (255, 255, 255))
				cropped_img.paste(region,new_area)
				
				cropped_img.save(page_blocks_path+str(count)+".jpg")
				cropped_img.close()

				a = c

			a = intial_width
			b = b+crop_height+gap
	
	page_type,intial_height = check_page_type(img,amend_page)
	
	if page_type == 1:
		intial_width = 150
		generate(intial_width,intial_width,intial_height,6)
		amend_page = False
	else:
		intial_width = 150
		generate(intial_width,intial_width,intial_height,40)
		amend_page = True
		
	return amend_page

def check_page_type(img,amend_page):
	
	return 1,435

	if amend_page:
		return 2,295
	
	a,b,c,d = 130, 280,800,155  # amend page check
	crop_img = crop_section(a,b,c,d,img)

	crop_temp_path = "temp.jpg"
	crop_img.save(crop_temp_path)
	crop_img.close()
	
	text = (pytesseract.image_to_string(crop_temp_path, config='--psm 6', lang='eng+hin')) #config='--psm 4' config='-c preserve_interword_spaces=1'
	text = text.split('\n')
	text = [ i for i in text if i!='' and i!='\x0c']
	
	if len(text)>0:
		for key in ['घटक','परिवर्धन']:
			for t in text:
				if key in t:
					return 2,460                  
			
	return 1,270
	


# In[6]:


def extract_name(name):
	
	row = name.split(":")
	if len(row)!=2:
		return ""
	else:
		return row[1].strip()


# In[7]:


def extract_vid(v_id):
	row = v_id.split("|")
	if len(row)>=2:
		number = re.findall(r'\d+', row[0].strip())
		if len(number)>0:
			return number[0],row[1]
		else:
			
			return "",row[1]
	
	if len(row)==1:
		return "", row[-1]
	elif len(row)>2:
		number = re.findall(r'\d+', row[-2].strip())
		
		if len(number)>0:
			return number[0],row[-1]
		else:
			return "",row[-1]
	else:
		return "",""


# In[8]:


def extract_house_no(house_no):
	row = house_no.split(":")
	if len(row)==2:
		house_no = re.findall(r'\d+', row[1].strip())
		if len(house_no)>0:
			return house_no[0]
		else:
			return ""
	else:
		house_no = re.findall(r'\d+', row[0].strip())
		if len(house_no)>0:
			return house_no[0]
		else:
			return ""


# In[34]:


def extract_age_gender(age_gender):
	
	age = re.findall(r'\d+', age_gender.strip())
	if len(age)>0:
		age = age[0]
	else:
		age = ""
	
	gender = ''
	
	if 'महिला' in age_gender.strip() or 'महिल' in age_gender.strip():
		gender = 'Female'
	elif 'पुरूष' in age_gender.strip() or 'पुरुष' in age_gender.strip():
		gender = "Male"
	else:
		gender = ""
	
	return age, gender


# In[35]:


def extract_rel_name(rel_name):
	row = rel_name.split(":")
	if len(row)!=2:
		return "",""
	else:
		rel_type = extract_rel_type(row[0].strip())
		
		return row[1].strip(),rel_type
	
def extract_rel_type(rel_type):
	line = rel_type
	if line.startswith("पति") :
		rel_type = 'husband'
	elif line.startswith("पिता") or 'ता' in line :
		rel_type = 'father'
	elif line.startswith("माता") :
		rel_type = 'mother'
	elif line.startswith("अन्य") :
		rel_type = 'other'
	else:
		rel_type = ""
	
	return rel_type 


# In[36]:


def extract_details_from_block(block):
	
	v_id = block[0]
	name = block[1]
	rel_name = block[2]
	house_no = block[3]
	age_gender = block[4]
	
	name = extract_name(name)
	rel_name,rel_type = extract_rel_name(rel_name)
	house_no = extract_house_no(house_no)
	age, gender = extract_age_gender(age_gender)
	number,voter_id = extract_vid(v_id)
	
	return [name,rel_name,rel_type,house_no,age,gender,voter_id,number]


# In[37]:


def extract_detail_section(text):
	
	keywords = ['शहर','थाना','राजस्व','अनुमंडल','जिला','पिन कोड']
	found_keywords = ["","","","","",""]
	for idx,keyword in enumerate(keywords):
		for t in text:
			if keyword in t:
				found_keywords[idx] = split_data(t)
				break
	return found_keywords



def extract_name_ac_parl(text):
	
	keywords = ['विधान','लोक']
	found_keywords = ["",""]
	
	for idx,key in enumerate(keywords):
		
		for t_idx, t in enumerate(text):
			if key in t:
				found_keywords[idx] = split_data(t)
				
	return found_keywords


# In[38]:


def extract_4_numbers(crop_stat_path):
	
	text = (pytesseract.image_to_string(crop_stat_path, config='--psm 6', lang='eng+hin')) #config='--psm 4' config='-c preserve_interword_spaces=1'

	text = re.findall(r'\d+', text)    
	if len(text)==4:
		if int(text[0]) + int(text[1]) == int(text[2]):
			net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = text[0],text[1],"0",text[2]
		elif int(text[0]) + int(text[1]) == int(text[3]):
			net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = text[0],text[1],"0",text[3]
		else:
			net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = text[0],text[1],text[2],text[3]
	elif len(text) == 3 and int(text[2])>=int(text[1]) and int(text[2])>=int(text[0]):
		net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = text[0],text[1],"0",text[2]
	elif len(text) == 2 and int(text[0])*2-100<int(text[1]):
		net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = text[0],int(text[1])-int(text[0]),"0",text[1]
	else:
		net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = "","","",""
	
	return net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total

def fetch_last_page_content(path,input_images_blocks_path):
	img = Image.open(path)
	crop_path = input_images_blocks_path+"page/"
	create_path(crop_path)
	
	a,b,c,d = 2494, 990, 1200, 130 # last page 1st
	crop_img = crop_section(a,b,c,d,img)

	crop_last_path = crop_path+"last.jpg"
	crop_img.save(crop_last_path)
	crop_img.close()
	
	a_1,b_1,c_1,d_1 = extract_4_numbers(crop_last_path)
	
	a,b,c,d = 2484, 1552, 1172, 120 # last page 2nd
	crop_img = crop_section(a,b,c,d,img)

	crop_last_path = crop_path+"last.jpg"
	crop_img.save(crop_last_path)
	crop_img.close()
	
	a_2,b_2,c_2,d_2 = extract_4_numbers(crop_last_path)
	
	a,b,c,d = 2484, 1766, 1160, 80 # last page 3nd
	crop_img = crop_section(a,b,c,d,img)

	crop_last_path = crop_path+"last.jpg"
	crop_img.save(crop_last_path)
	crop_img.close()
	
	a_3,b_3,c_3,d_3 = extract_4_numbers(crop_last_path)
	
	return a_1,b_1,c_1,d_1,a_2,b_2,c_2,d_2,a_3,b_3,c_3,d_3
	
	


# In[39]:


def arrange_columns(first_page_list,block_list,filename):
	
	year = 2018
	state = 'haryana'
	
	ac_name,parl_constituency,part_no,main_town,police_station,polling_station_name,polling_station_address,revenue_division,mandal,district,pin_code,net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total = first_page_list
	
	name,rel_name,rel_type,house_no,age,gender,voter_id,number = block_list
	
	final_list = [number,voter_id,name,rel_name,rel_type,house_no,age,gender,ac_name,
				 parl_constituency,part_no,year,state,filename,main_town,police_station,mandal,
				 revenue_division,district,pin_code,polling_station_name,polling_station_address,
				 net_electors_male,net_electors_female,net_electors_third_gender,net_electors_total]

	return final_list


# In[40]:


def extract_first_page_details(path,input_images_blocks_path):
	
	img = Image.open(path)
	
	a,b,c,d = 2080,5122,1680,130  # stats for male and female
	crop_img = crop_section(a,b,c,d,img)

	crop_path = input_images_blocks_path+"page/"
	create_path(crop_path)
	
	crop_stat_path = crop_path+"stat.jpg"
	crop_img.save(crop_stat_path)
	crop_img.close()

	a_n,b_n,c_n,d_n = extract_4_numbers(crop_stat_path)
	
	a,b,c,d = 2485,2775,1250,1101  # mandal block
	crop_img = crop_section(a,b,c,d,img)
	
	crop_det_path = crop_path+"det.jpg"
	crop_img.save(crop_det_path)
	crop_img.close()

	text = (pytesseract.image_to_string(crop_det_path, config='--psm 6', lang='eng+hin')) #config='--psm 4' config='-c preserve_interword_spaces=1'
	text = text.split('\n')
	text = [ i for i in text if i!='' and i!='\x0c']
	
	if len(text) == 9:
		main_town,revenue_division,police_station,mandal,district,pin_code = split_data(text[0]),split_data(text[1]),split_data(text[5]),split_data(text[6]),split_data(text[7]),split_data(text[8])
	else:
		main_town,police_station,revenue_division,mandal,district,pin_code = extract_detail_section(text)
		
	a,b,c,d = 2875,315,863,213 # part no
	crop_img = crop_section(a,b,c,d,img)
	
	crop_part_path = crop_path+"part.jpg"
	crop_img.save(crop_part_path)
	crop_img.close()

	text = (pytesseract.image_to_string(crop_part_path, config='--psm 6', lang='eng+hin')) #config='--psm 4' config='-c preserve_interword_spaces=1'
	text = re.findall(r'\d+', text)
		
	if len(text)>0:
		part_no = text[0]
	else:
		part_no = ""
		
	a,b,c,d = 410,4056,2253,514 # police name name and address
	crop_img = crop_section(a,b,c,d,img)
	
	crop_police_path = crop_path+"police.jpg"
	crop_img.save(crop_police_path)
	crop_img.close()

	text = (pytesseract.image_to_string(crop_police_path, config='--psm 6', lang='eng+hin')) #config='--psm 4' config='-c preserve_interword_spaces=1'
	text = text.split('\n')
	text = [ i for i in text if i!='' and i!='\x0c']

	if len(text) == 4:
		polling_station_name = text[0].split("की")[1]
		polling_station_address = text[2].split("केन्द्र का")[1]
	else:
		polling_station_name, polling_station_address = "",""        
	
	a,b,c,d = 400,290,2506,465 # ac name and parl
	crop_img = crop_section(a,b,c,d,img)
	
	crop_ac_path = crop_path+"ac.jpg"
	crop_img.save(crop_ac_path)
	crop_img.close()

	text = (pytesseract.image_to_string(crop_ac_path, config='--psm 6', lang='eng+hin')) #config='--psm 4' config='-c preserve_interword_spaces=1'
	text = text.split('\n')
	text = [ i for i in text if i!='' and i!='\x0c']

	if len(text) == 3:
		ac_name = text[0].split(",")[1]
		parl_constituency = text[2].split("स्थिति")[1]
	else:
		ac_name,parl_constituency = "",""
		

	
	return [ac_name,parl_constituency,part_no,main_town,police_station,polling_station_name,polling_station_address,revenue_division,mandal,district,pin_code,a_n,b_n,c_n,d_n]


def run_tesseract(path):
	text = (pytesseract.image_to_string(path, config='--psm 6', lang='eng+hin'))
	params_list = text.split('\n')
	new_params_list = [ i for i in params_list if i!='' and i!='\x0c']

	return new_params_list

def pdf_process(pdf_file_name):

	begin_time = time.time()
	
	print(pdf_file_name, datetime.now().strftime('%Y/%m/%d %H:%M:%S'))

	if not pdf_file_name.endswith(".PDF"):
		return pdf_file_name, 0

	try:
		#create images,blocks and csvs paths for each file
		pdf_file_name_without_ext = pdf_file_name.split('.PDF')[0]
		input_pdf_images_path = PARSE_DATA_PAGES+pdf_file_name_without_ext+"/"
		create_path(input_pdf_images_path)
		
		input_images_blocks_path = PARSE_DATA_BLOCKS+pdf_file_name_without_ext+"/"
		create_path(input_images_blocks_path)
		
		if os.path.exists(PARSE_DATA_CSVS+pdf_file_name_without_ext+".csv"):
			print(pdf_file_name_without_ext+".csv", "already exists")
			return pdf_file_name_without_ext, 0

		
		#convert pdf into bunch of images
		try:
			pdf_2_images_list = pdf_to_img(state_pdfs_path+pdf_file_name, input_pdf_images_path,dpi=500)
		except:
			print(pdf_file_name_without_ext+".csv", "problem generating images from this pdf, must be empty or corrupted")
			return pdf_file_name_without_ext, 0

		#sort pages for looping
		input_images = os.listdir(input_pdf_images_path)
		sort_nicely(input_images)
		
		#empty intial data
		df = pd.DataFrame(columns = COLUMNS)
		order_problem = []
		
		amend_page = False
		
		#for each page, parse the data
		for page in input_images:
		
			page_full_path = input_pdf_images_path+page
			
			#extract first page content
			if page == '1.jpg':
				first_page_list = extract_first_page_details(page_full_path,input_images_blocks_path)
				continue

			#ingnore 2nd page and last page
			if page == '2.jpg' or input_images[-1] == page:
				continue

			#loop from 3 page onwards
			if page.endswith('.jpg'):
				
				final_invidual_blocks = []
				blocks_path = input_images_blocks_path+"blocks/"
				create_path(blocks_path)

				page_idx = page.split(".jpg")[0] + "/"
				page_blocks_path = blocks_path+page_idx
				create_path(page_blocks_path)
					
				amend_page = generate_poll_blocks_from_page(page_full_path,page_blocks_path,amend_page)

				if amend_page:
					page_type = 'amendment'
				else:
					page_type = 'original'
					
				sorted_blocks = os.listdir(page_blocks_path)
				sort_nicely(sorted_blocks)
				
				for jpg_file in sorted_blocks:
					
					if jpg_file.endswith('.jpg') :

						new_params_list = run_tesseract(page_blocks_path+jpg_file)

						if len(new_params_list) !=5:
							order_problem.append((page, jpg_file,new_params_list))
						else:
							final_invidual_blocks.append(new_params_list)
			
			#put the data into dataframe
			for block in final_invidual_blocks:
				block_list = extract_details_from_block(block)
				name,rel_name,rel_type,house_no,age,gender,voter_id,number = block_list
				
				if name == "" and age == "" and gender=="":
					continue
							
				final_list = arrange_columns(first_page_list,block_list,pdf_file_name_without_ext)
				final_list.append(page_type)
				
				df_length = len(df)
				df.loc[df_length] = final_list
			
			print("page done : ",page)

		#save the dataframe(pdf) data into csv
		save_to_csv(df,PARSE_DATA_CSVS+pdf_file_name_without_ext+".csv")
		print("CSV saved")

	except Exception as e:
		print('ERROR:', e, pdf_file_name_without_ext)
		traceback.print_exc()
	finally:
		print("Clean up working files...")
		shutil.rmtree(input_pdf_images_path, ignore_errors=True)
		shutil.rmtree(input_images_blocks_path, ignore_errors=True)

	end_time = time.time()

	return pdf_file_name_without_ext, end_time - begin_time
   
if __name__ == '__main__':

	print('Tesseract Version:', pytesseract.get_tesseract_version())
	print('multiprocessing cpu_count:', multiprocessing.cpu_count())
	print('os cpu_count:', os.cpu_count())
	print('sched_getaffinity:', len(os.sched_getaffinity(0)))

	#a_pool = multiprocessing.Pool(multiprocessing.cpu_count())
	#results = a_pool.map(pdf_process, state_pdfs_files)

	with MPIPoolExecutor() as executor:
		results = executor.map(pdf_process, state_pdfs_files)
		for res in results:
			print(res)