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convest.py
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convest.py
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import cv2
import numpy as np
import pytesseract
import os
import datetime
import base64
# C:\Program Files\Tesseract-OCR\tesseract.exe
# poppler_path=r'D:\downloads\poppler-0.68.0_x86\poppler-0.68.0\bin'
poppler_path=r'D:\downloads\poppler-0.68.0_x86\poppler-0.68.0\bin'
pytesseract.pytesseract.tesseract_cmd = r"C:\Users\madhu\AppData\Local\Programs\Tesseract-OCR\tesseract.exe"
# Mongodb
from pymongo import MongoClient
# output_file = r'D:\projects\pytesseract\images' ## give images folder path
output_file = r'D:\downloads\img'
folder_path = r'D:\downloads\pdf' ## give pdf's folder path
# creation of MongoClient
client = MongoClient()
he = []
import urllib.parse
Username = 'devops_admin'
Password = 'Devops1234'
username = urllib.parse.quote_plus(Username)
password = urllib.parse.quote_plus(Password)
# Connect with the portnumber and host
client = MongoClient('mongodb://localhost:27017/')
# client = MongoClient('mongodb://%s:%s@13.234.70.44:27017' % (username,password))
# Access database
mydatabase = client['Data_conversion']
# Access collection of the database
mycollection = mydatabase['test']
mycollection2 = mydatabase['testimage']
myimage = mydatabase['test2']
# dictionary to be added in the database
rec = {
'title': 'MongoDB and Python',
'description': 'MongoDB is no SQL database',
'tags': ['mongodb', 'database', 'NoSQL'],
'viewers': 104
}
# Time kosam
dt = datetime.datetime.now()
print(dt)
lis = ["",""]
count = 0
def imgetobase(file):
global count
f =f"sample{count}.png"
import boto3
b_name = "ivin-pro-data-conversion"
s3 = boto3.client("s3")
b_res = s3.list_buckets()
# for i in b_res['Buckets']:
# print(i)
cv2.imshow('Resized', file)
img = cv2.imwrite(f, file)
cv2.waitKey(3)
count=1+count
with open(f,'rb') as img:
# s3.upload_fileobj(img,b_name,f)
return f
# def pagetobase(image):
# image = open(image, 'rb')
# image_read = image.read()
# image_64_encode = base64.encodebytes(image_read) #encodestring also works aswell as decodestring
# # print('This is the image in base64: ' + str(image_64_encode))
# return str(image_64_encode)
def page(image):
text = pytesseract.pytesseract.image_to_string(image)
# print(text)
text.replace('Photo', "")
# print(text)
f = open('text.txt', 'w')
f.write(text + "\n")
f.close()
fi = open('text.txt', 'r')
x = fi.readlines()
for i in x:
if "Assembly" in i:
lis.append(i)
elif "Section" in i:
lis.append(i)
fi.close()
# print(lis)
c = 1
def covert(x, y, w, h, im2, img_file):
# print(h)
# try:
width = int(w / 3)
if h >= 400 and h < 1000:
# print(h,w)
width = int(w / 3)
# print(width,'--------')
f = open('test.txt', 'a')
rect = cv2.rectangle(im2, (x, y), (x + width, y + h), (0, 255, 0), 5)
if x == 0 and y == 0:
pass
else:
cropped = im2[y:y + h, x:x + width]
# print(cropped)
# print(w, '----------')
img = cv2.resize(rect, (1020, 750))
cv2.imshow('d', cropped)
cv2.waitKey(3)
# count=count+1
b = imgetobase(cropped)
text = pytesseract.image_to_string(cropped)
f = open('text.txt', 'a')
f.write(text)
f.close()
# cv2.destroyAllWindows()
# print('data base error')
rec =mycollection2.insert_one({
'Voter_file_tracker': img_file,
"Voter_Image": b,})
rec = mycollection.insert_one({
'Voter_file_tracker': img_file,
"votere_slip":b,
"Assembly Name": lis[0],
"Section": lis[1],
'details': text,
'Created on': dt})
# except:
# print('convert function exception')
def covert2(x, y, w, h, im2, img_file):
if h >= 400 and h < 1000 and w > 400:
# print(h,w)
width2 = int(w / 3)
# print(width,'--------')
f = open('test.txt', 'a')
rect = cv2.rectangle(im2, (x, y), (x + width2, y + h), (0, 255, 255), 3)
# print(h,width)
if x == 0 and y == 0:
pass
else:
cropped = im2[y:y + h, x:x + width2]
b = imgetobase(cropped)
# print(w,'----------')
img = cv2.resize(rect, (1020, 750))
cv2.imshow('d', cropped)
cv2.waitKey(3)
# cv2.destroyWindow()
text = pytesseract.image_to_string(cropped)
f = open('text.txt', 'a')
f.write(text)
f.close()
# cv2.destroyAllWindows()
# if len(lis)>1:
rec = mycollection2.insert_one({
'Voter_file_tracker': img_file,
"Voter_Image": b, })
rec = mycollection.insert_one({
'Voter_file_tracker': img_file,
"votere_slip":b,
"Assembly Name": lis[0],
"Section": lis[1],
'details': text,
'Created on': dt})
def covert3(x, y, w, h, im2, img_file):
if h >= 400 and h < 1000 and w > 400:
# print(h,w)
width1 = int(w / 3)
# print(width,'--------')
f = open('test.txt', 'a')
rect = cv2.rectangle(im2, (x, y), (x + width1, y + h), (255, 255, 255), 3)
# print(h,width)
if x == 0 and y == 0:
pass
else:
cropped = im2[y:y + h, x:x + width1]
# #print(w,'----------')
img = cv2.resize(rect, (1020, 750))
cv2.imshow('d', cropped)
cv2.waitKey(3)
cv2.imwrite("cropped.png", im2)
b = imgetobase(cropped)
# cv2.destroyWindow()
text = pytesseract.image_to_string(cropped)
f = open('text.txt', 'a')
f.write(text)
f.close()
rec = mycollection2.insert_one({
'Voter_file_tracker': img_file,
"Voter_Image": b, })
rec = mycollection.insert_one({
'Voter_file_tracker': img_file,
"votere_slip":b,
"Assembly Name": lis[0],
"Section": lis[1],
'details': text,
'Created on': dt})
def ima(x, y, w, h, im2, img_file):
# #print(x,y)
im2 = cv2.imread(im2)
cv2.putText(im2, 'Rectangle', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# rect = cv2.rectangle(im2, (x, y), (x + w, y + h), (0, 255, 0), 5)
# img = cv2.resize(rect, (1020, 750))
# cv2.imshow('d', img)
#print(x, y)
covert(x, y, w, h, im2, img_file)
W = int(w / 3)
covert(x + W, y, w, h, im2, img_file)
covert(x + W + W, y, w, h, im2, img_file)
area = []
value = []
# def img_detect(img_path, img_file):
# lis.clear()
# page(img_path)
#
def img_detect(img_path, img_file):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 50, 255, 0)
contours, hierarchy = cv2.findContours(thresh, 1, 2)
for cnt in contours:
x1, y1 = cnt[0][0]
approx = cv2.approxPolyDP(cnt, 0.01 * cv2.arcLength(cnt, True), True)
if len(approx) == 4:
x, y, w, h = cv2.boundingRect(cnt)
ratio = float(w) / h
if ratio >= 0.9 and ratio <= 1.1:
pass
else:
area.append((h * w))
value.append((h, w, x, y))
he.append(h)
for i in range(len(area)):
# if value[i][1]>=0 and value[i][1]>100:
xa = value[i][2]
ya = value[i][3]
l = value[i][0]
w = value[i][1]
# cv2.rectangle(img, (xa, ya), (xa + w, ya + l), (0, 255, 0), 2)
ima(x=xa, y=ya, w=w, h=l, im2=img_path, img_file=img_file)
import os
def image_upload(image_file):
print("count of images :",len(os.listdir(folder_path)))
l = os.listdir(image_file)
path = image_file
for k in range(len(l)):
print(k, 'started')
area.clear()
value.clear()
he.clear()
img_detect(img_path=f"{path}\{l[k]}", img_file=l[k])
# os.remove(f"{path}\{l[k]}")
print(k, 'done')
def converting_pdftoimg(pdf_path):
from pdf2image import convert_from_path
# print(pdf_path)
images = convert_from_path(pdf_path, 500, poppler_path=poppler_path)
name = pdf_path.split("\\")
print(name)
for i, image in enumerate(images):
print(i, 'is pdf converting into images')
fname = f'{name[-1]}' + str(i) + '.png'
# print(fname)
image.save(f"{output_file}\{fname}", "PNG")
l = os.listdir(folder_path)
print(l)
# for i in range(len(l)):
# print(l[i])
# converting_pdftoimg(pdf_path=f'{folder_path}\{l[i]}')
# yesy()
image_upload(f'{output_file}')
# dt = datetime.datetime.now()