# -*- coding: utf-8 -*-
"""(Deployment)2.1 counting columns.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1R2CszBuVN-Rugu8CyGQzqsdFw11E3eHN

## Libraries
"""

# from google.colab.patches import cv2_imshow
import cv2
import numpy as np
import pandas as pd

import statistics
from statistics import mode

from PIL import Image

# pip install PyPDF2

# pip install PyMuPDF

# pip install pip install PyMuPDF==1.19.0

import io

# !pip install pypdfium2
import pypdfium2 as pdfium

import fitz  # PyMuPDF

import pandas as pd
import pilecaps_adr
"""# Functions"""

def get_text_from_pdf(input_pdf_path):
    pdf_document = fitz.open('dropbox_plans/2.1/'+input_pdf_path)

    for page_num in range(pdf_document.page_count):
        page = pdf_document[page_num]
        text_instances = page.get_text("words")

        page.apply_redactions()
    return text_instances

def convert2img(path):
    pdf = pdfium.PdfDocument('dropbox_plans/2.1/'+path)
    page = pdf.get_page(0)
    pil_image = page.render().to_pil()
    pl1=np.array(pil_image)
    img = cv2.cvtColor(pl1, cv2.COLOR_RGB2BGR)
    return img

def segment(img):
  lowerRange1 = np.array([0, 9, 0])
  upperRange1 = np.array([81, 255, 255])
  hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
  mask2 = cv2.inRange(hsv, lowerRange1, upperRange1)
  imgResult3 = cv2.bitwise_and(img, img, mask=mask2)
  return imgResult3

def threshold(imgResult3):
  gaus = cv2.GaussianBlur(imgResult3, (3,3),9)
  gray2 = cv2.cvtColor(gaus, cv2.COLOR_BGR2GRAY)
  outsu2 = cv2.threshold(gray2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
  return outsu2

# Deleted the image drawing
def getColumnsPoints(outsu4):
  contours, hierarchy = cv2.findContours(image=outsu4, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_NONE)
  p = []
  for i, cnt in enumerate(contours):
    M = cv2.moments(cnt)
    if M['m00'] != 0.0:
      x1 = int(M['m10']/M['m00'])
      y1 = int(M['m01']/M['m00'])
    p.append((x1,y1))
  return p

def getTextsPoints(x):
  point_list = []
  for h in x:
    point_list.append((h[2],h[3]))
  return point_list

def distance(point1, point2):
    x1, y1 = point1
    x2, y2 = point2
    return np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)

def getNearestText(point_list, p):
  nearbyy = []
  dis = []
  for i in range(len(p)):
    nearest_point = min(point_list, key=lambda point: distance(point, p[i]))
    dist = distance(nearest_point, p[i])
    dis.append(dist)
    if dist < 44:
      nearbyy.append(nearest_point)
  return nearbyy

def getColumnsTypes(nearbyy, x):
  found_tuple = []
  # Loop through the list of tuples
  for i in range(len(nearbyy)):
    for tpl in x:
      if tpl[2] == nearbyy[i][0] and tpl[3] == nearbyy[i][1]:
        found_tuple.append(tpl[4])
  return found_tuple

def generate_legend(found_tuple):
  word_freq = {}
  for word in found_tuple:
    if word in word_freq:
        word_freq[word] += 1
    else:
        word_freq[word] = 1
  data = word_freq
  df = pd.DataFrame(data.items(), columns=['Column Type', 'Count'])
  return df

def mainfun(plan,pathtoplan):
  texts_from_pdf = get_text_from_pdf(plan)
  img = convert2img(plan)
  imgResult = segment(img)
  outsu = threshold(imgResult)
  column_points = getColumnsPoints(outsu)
  text_points = getTextsPoints(texts_from_pdf)
  nearby = getNearestText(text_points, column_points)
  columns_types = getColumnsTypes(nearby, texts_from_pdf)
  legend = generate_legend(columns_types)
  gc,spreadsheet_service,spreadsheetId ,spreadsheet_url  , namepathArr=pilecaps_adr.legendGoogleSheets(legend,path=plan,pdfpath=pathtoplan)
  return spreadsheet_url

"""# Call"""