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SadhulaSaiKumar
2023-05-01 04:55:03 +00:00
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Electrol/app.py
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from flask import Flask, render_template, request, redirect, Response, send_file
import glob
from pdf2image import convert_from_path
from PyPDF2 import PdfReader
import pandas as pd
import csv
import os
import cv2
import numpy as np
from paddleocr import PaddleOCR, draw_ocr
import re
#ocr = PaddleOCR(use_angle_cls=True, lang='en', use_space_char=True, show_log=False,type='structure',image_enhance=True)
app = Flask(__name__)
@app.route('/pdftotext', methods=["POST"])
def pdftotext():
Dataset = request.get_json()
data = {'visiting': Dataset}
for i in data['visiting']:
import time
#time.sleep(1)
a = i
x = a['FileData']
# print(x)
y = a['FileName']
z = a['FileType']
# CreatedBy=a['CreatedBy']
name = y + '.' + z
print(name)
# print(y)
# image = y.split("/")
# filename=image[-1]
# print(x)
img_data = x.encode()
import base64
with open('./uploads/' + name, "wb") as fh:
fh.write(base64.decodebytes(img_data))
# pdf to images
images = convert_from_path('./uploads/' + name)
for i in range(len(images)):
images[i].save('./images/'+ str(i) +'.jpg', 'JPEG')
#count of a pdf files
reader = PdfReader('./uploads/' + name)
num = len(reader.pages)
print(num)
import pandas as pd
ocr = PaddleOCR(use_gpu=True,use_angle_cls=True, lang='en', use_space_char=True, show_log=False,type='structure',image_enhance=True)
dframe = pd.DataFrame({'Name': [], 'Father Name': [], 'Mother Name':[],'Husband Name':[],'Wife Name':[] , 'Others Name':[] ,'House Number': [],'Age': [], 'Gender': [],'Voter Number':[]})
# Assuming 'dframe' is the DataFrame with column names
with open(y+'.csv', 'a', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=dframe.columns)
writer.writeheader()
list_of_elements=[]
for i in range(num):
path='./images/'+ str(i)+'.jpg'
print(path)
img = cv2.imread(path)
if img.shape[0] > 1000:
img = cv2.resize(img, (int(img.shape[1]*0.7), int(img.shape[0]*0.7)))
result1 = ocr.ocr(img)
txts1 = [line[1][0] for line in result1[0]]
#print(txts1)
for i in txts1:
if 'Section' in i:
street = (i.split(':')[-1])
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 250, 300, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
import pandas as pd
min_size = 9000
max_size = 100000
# Sort contours based on y-coordinate and then x-coordinate
contours = sorted(contours, key=lambda contour: (cv2.boundingRect(contour)[1], cv2.boundingRect(contour)[0]))
# Extract text from each contour
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
box_size = w * h
if box_size >= min_size and box_size <= max_size:
# Draw bounding box around contour
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
# for contour in contours:
# x, y, w, h = cv2.boundingRect(contour)
# box_size = w * h
# if box_size >= min_size and box_size <= max_size:
# cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 1)
# cv2.putText(img, "Box", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
box = img[y:y + h, x:x + w]
#cv2_imshow(box)
result = ocr.ocr(box)
try:
txts = [line[1][0] for line in result[0]]
txts.remove('Photo is')
txts.remove('Available')
age = [re.findall(r"\d{2}", i)[0] for i in txts if "Age" in i and re.findall(r"\d{2}", i)]
voter_ID = [element for element in txts if
element.isalnum() and not element.isalpha() and not element.isdigit()]
all_data = [item.lower().replace('name', 'Name:').replace('number', 'Number:').replace("gender", "',Gender:'")
for item in txts]
all_data[0] = ":" + all_data[0]
all_data[1] = ":" + all_data[1]
for i in range(1, len(all_data)):
if ":" not in all_data[i]:
all_data[i - 1] += " " + all_data[i]
all_data[i] = ""
# Remove empty elements from the list
my_list = [element for element in all_data if element != ""]
# my_list = [all_data[i-1] + " " + all_data[i] if ":" not in all_data[i] else all_data[i] for i in range(0, len(all_data)) if all_data[i] != ""]
my_list = [my_list[i][1:] if my_list[i].startswith(":") else my_list[i] for i in range(len(my_list))]
my_list = [s.replace('::', ':') for s in my_list]
list_of_elements.append("Age: " + age[0])
list_of_elements.append("Voter Number: " + voter_ID[0])
for i in my_list:
if i.startswith("Name"):
person_name = i.split(":")[-1].upper()
list_of_elements.append("Name: " + person_name)
elif "father" in i:
fathername = i.split(":")[-1].upper()
list_of_elements.append("Father Name: " + fathername)
elif "husband" in i:
husband_name = i.split(":")[-1].upper()
list_of_elements.append("Husband Name: " + husband_name)
elif "mother" in i:
mother_name = i.split(":")[-1].upper()
list_of_elements.append("Mother Name: " + mother_name)
elif "Gender" in i:
gender = i.split(":")[-1].upper()
list_of_elements.append("Gender: " + gender)
elif "house" in i:
house = i.split(":")[-1].upper()
list_of_elements.append("House Number: " + house + ',' + street)
elif "wife" in i:
wife = i.split(":")[-1].upper()
list_of_elements.append("Wife Name: " + wife)
elif "other" in i:
other = i.split(":")[-1].upper()
list_of_elements.append("Others Name: " + other)
df = pd.DataFrame(list_of_elements)
#df=df.T
data = dict([val.split(': ') for val in df[0]])
list_of_elements.clear()
print(data)
writer.writerow(data)
csvfile.flush()
except (IndexError,ValueError):
pass
files = glob.glob('D:\\text_extraction\\uploads\\*')
for f in files:
os.remove(f)
files = glob.glob('D:\\text_extraction\\images\\*')
for f in files:
os.remove(f)
return 'done'
if __name__ == "__main__":
app.run(host='0.0.0.0',port=1112)