Delete pilecaps_adr.py

pilecaps_adr.py

@@ -1,1525 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Copy of XOR- ROI from plan-PileCaps-ADR.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/16RHtRae7VU_fqHMAjOUL4ET5slEFo3pf
-"""
-
-# pip install pdf-annotate
-
-# pip install pdf-annotate
-
-#pip install pdf2image
-
-#!pip install -q gradio
-
-#pip install pygsheets
-
-# !apt-get install poppler-utils
-
-import numpy as np 
-import cv2 
-#from google.colab.patches import cv2_imshow
-from matplotlib import pyplot as plt 
-#from pdf2image import convert_from_path
-
-import math
-
-import pandas as pd
-import random
-# import imutils
-# from imutils import contours 
-import colorsys
-from PIL import Image  , ImageDraw, ImageFont , ImageColor
-import numpy as np
-#import gradio as gr
-
-# from __future__ import print_function
-from googleapiclient.discovery import build 
-from google.oauth2 import service_account
-import pygsheets
-import re
-import pandas
-import fitz 
-import json
-import db
-import ast
-
-def detectCircles(imgOriginal ):
-  im=imgOriginal.copy()
-  imgGry1 = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
-  kernel=np.ones((3,3),np.uint8)
-  er1=cv2.erode(imgGry1,kernel, iterations=2)
- 
-  er1=cv2.dilate(er1,kernel, iterations=1)
-  gray_blurred = cv2.blur(er1, (3,3 ))
-  # Apply Hough transform on the blurred image.
-   # min distance between circles,  Upper threshold for the internal Canny edge detector.
-  detected_circles = cv2.HoughCircles( gray_blurred, cv2.HOUGH_GRADIENT, 1, 50, param1= 550,
-                    param2 =21, minRadius = 20, maxRadius = 40) #18 param2
-    
-  # Draw circles that are detected.
-  if detected_circles is not None:
-      # Convert the circle parameters a, b and r to integers.
-      detected_circles = np.uint16(np.around(detected_circles))
-      detected_circles = np.round(detected_circles[0, :]).astype("int")
-    #DRAW CIRCLES
-      for (x, y, r) in detected_circles:
-        cv2.circle(im, (x, y), r, (255, 255, 255), 5)
-  im=cv2.medianBlur(im,1)
-  print('circles')
-  # cv2_imshow(im)
-  return im
-
-def detectSmallCircles(img ):
-  #Remove tiny TOC points that interfere with shapes 
-  im=img.copy()
-  imgGry1 = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
-  kernel=np.ones((3,3),np.uint8)
-  er1=cv2.erode(imgGry1,kernel, iterations=1)
-  # Apply Hough transform on the blurred image.
-   # min distance between circles,  Upper threshold for the internal Canny edge detector.
-  detected_circles = cv2.HoughCircles( imgGry1, cv2.HOUGH_GRADIENT, 1, 60, param1 =550,
-                    param2 =13, minRadius = 1, maxRadius = 10) #18 param2
-    
-  # Draw circles that are detected.
-  if detected_circles is not None:
-      # Convert the circle parameters a, b and r to integers.
-      detected_circles = np.uint16(np.around(detected_circles))
-      detected_circles = np.round(detected_circles[0, :]).astype("int")
-    #DRAW CIRCLES
-      for (x, y, r) in detected_circles:
-        cv2.circle(im, (x, y), r+1, (255, 255, 255), -1)
-  # cv2_imshow(im)
-  return im
-# c=detectCircles(img)
-
-def DashedPreprocessing(imgOriginal,imgnoSmall):
-  h,w=imgOriginal.shape[0:2]
-  #remove the gray contours from the plan
-  imgBW=cv2.threshold(imgnoSmall, 180, 255, cv2.THRESH_BINARY)[1]
-
-  im_copy=imgBW.copy()
-  im_copy1=im_copy
-  kernel1 = np.ones((3,5),np.uint8)
-  kernel2 = np.ones((9,9),np.uint8)
-  kernel3= np.ones((3,3),np.uint8)
-  imgGray=cv2.cvtColor(imgBW,cv2.COLOR_BGR2GRAY)
-  imgBW1=cv2.threshold(imgGray, 200, 255, cv2.THRESH_BINARY_INV)[1]
-
-  img1=cv2.erode(imgBW1, kernel1, iterations=1)
-  img2=cv2.dilate(img1, kernel2, iterations=3)
-  img3 = cv2.bitwise_and(imgBW1,img2)
-  img3= cv2.bitwise_not(img3)
-  img4 = cv2.bitwise_and(imgBW1,imgBW1,mask=img3)
-  img4=cv2.blur(img4,(7,7))
-  if h > w :
-    max = h
-    min = w
-  else:
-    max = w
-    min = h
-  return img4, imgBW, max,min
-
-def removeDashedLines(img4, imgBW ,max,min):
-
-  imgLines= cv2.HoughLinesP(img4,1,np.pi/310,30,minLineLength=(max-min)//1.8,maxLineGap = 120)  #was w-h , gap=150 0.99
-#1 120
-
-  for i in range(len(imgLines)):
-      for x1,y1,x2,y2 in imgLines[i]:
-            cv2.line(imgBW,(x1,y1),(x2,y2),(0,255,0),2)
-
-  im_copy=imgBW.copy()
-  green=im_copy[:,:,1]
-  # cv2_imshow(im_copy)
-  return green
-
-def removeSmallDashes(imgOriginal,green):
-  smalldashes=green.copy()
-  smalldashes=cv2.bitwise_not(smalldashes)
-
-  kernel3= np.ones((3,3),np.uint8)
- 
-  img1=cv2.dilate(smalldashes, kernel3, iterations=2)
-  img2=cv2.erode(img1, kernel3, iterations=2)
-
-  smalldashes=cv2.medianBlur(img2,5)
-  smalldashes=cv2.medianBlur(smalldashes,7)
-  # cv2_imshow(smalldashes)
-  smalldashesOut=green.copy()
-  smalldashesOut=cv2.cvtColor(smalldashesOut,cv2.COLOR_GRAY2BGR)
-  imgLines= cv2.HoughLinesP(smalldashes,1,np.pi/150,27,minLineLength=10,maxLineGap = 70)  #was w-h , gap=150
-
-  imgCopy=imgOriginal.copy()
-  for i in range(len(imgLines)):
-      for x1,y1,x2,y2 in imgLines[i]:
-          cv2.line(smalldashesOut,(x1,y1),(x2,y2),(0,255,0),2)
-
-
-  smalldashesOut=smalldashesOut[:,:,1]
-  # cv2_imshow(smalldashesOut)
-  for i in range(len(imgLines)):
-      for x1,y1,x2,y2 in imgLines[i]:
-          cv2.line(imgCopy,(x1,y1),(x2,y2),(0,255,0),6)
-
-  imgCopy=imgCopy[:,:,1]
-  # cv2_imshow(imgCopy)
-  return imgCopy,smalldashesOut
-
-def euclidian_distance(point1, point2):
-    return sum([(point1[x] - point2[x]) ** 2 for x in range(len(point1))]) ** 0.5
-
-def removeDashedLinesSmall(img4, imgBW ,max,min):
-
-  imgBW=cv2.cvtColor(imgBW,cv2.COLOR_GRAY2BGR)
-
-  imgLines= cv2.HoughLinesP(img4,1,np.pi/100,20,minLineLength=(max-min)//2.2,maxLineGap = 70)  #2.1
-
-  for i in range(len(imgLines)):
-      for x1,y1,x2,y2 in imgLines[i]:
-           dist=euclidian_distance((x1,y1), (x2,y2))
-          #  if dist > 1300 and dist <1450:
-           if dist >= (max-min)//2.1 and dist < (max-min)//1.9: #1.4
-              cv2.line(imgBW,(x1,y1),(x2,y2),(0,255,0),3)
-
-  im_copy=imgBW.copy()
-  green=im_copy[:,:,1]
-  # cv2_imshow(im_copy)
-  return green
-
-def ConnectBeamLines(smalldashesOut):
-  green1=cv2.bitwise_not(smalldashesOut)
-  green2=smalldashesOut.copy()
-  green2=cv2.cvtColor(green2,cv2.COLOR_GRAY2BGR)
-  imgLines= cv2.HoughLinesP(green1,0.05,np.pi/250,10,minLineLength=25,maxLineGap = 20)  #was w-h , gap=150  #50
-  for i in range(len(imgLines)):
-      for x1,y1,x2,y2 in imgLines[i]:
-          cv2.line(green2,(x1,y1),(x2,y2),(0,0,0),1)
-
-  imgLines= cv2.HoughLinesP(green1,0.3,np.pi/360,10,minLineLength=25,maxLineGap = 20)  #try 180
-
-
-  for i in range(len(imgLines)):
-      for x1,y1,x2,y2 in imgLines[i]:
-          cv2.line(green2,(x1,y1),(x2,y2),(0,0,0),1)
-  # cv2_imshow(green2)
-  return green2
-
-def allpreSteps(imgOriginal):
-  noCircles=detectCircles(imgOriginal)
-  imgnoSmall=detectSmallCircles(noCircles )
-  img4,imgBW,max,min=DashedPreprocessing(imgOriginal,imgnoSmall)
-  green=removeDashedLines(img4,imgBW,max,min)
-  imgCopy,smalldashesOut=removeSmallDashes(imgOriginal,green)
-  noSmallDashes=removeDashedLinesSmall(img4, smalldashesOut ,max,min)
-  green2=ConnectBeamLines(noSmallDashes)
-  # cv2_imshow(green2)
-  return green2
-
-def ChangeBrightness(img,k):
-  imgdarker = 255 * (img/255)**k # k>1 darker , k <1 lighter
-  # cv2_imshow(imgdarker)
-  imgdarker = imgdarker.astype('uint8')
-  return imgdarker
-
-def preprocessold(img,number):
-  
-  # imcopy=detectCircles(img)
-  blurG = cv2.GaussianBlur(ChangeBrightness(img,6),(3,3),0)
- 
-  imgGry = cv2.cvtColor(blurG, cv2.COLOR_BGR2GRAY)
-
-  kernel=np.ones((3,3),np.uint8)
-
-  er1=cv2.dilate(imgGry,kernel, iterations=2) #thinning
-
-  er2=cv2.erode(er1,kernel, iterations=3) #thicken
-  er3=cv2.dilate(er2,kernel, iterations=4)
-
-  if number == 0:
-    ret3, thresh = cv2.threshold(er3, 200, 255, cv2.THRESH_BINARY_INV  + cv2.THRESH_OTSU)
-  else:
-    ret3, thresh = cv2.threshold(er3, 220, 255, cv2.THRESH_BINARY_INV) #`140 - 141
-  # cv2_imshow(thresh)
-  return thresh
-# preprocessold(img,0)
-
-def preprocess(imgOriginal,number,green2):
-  #first preprocessing ( old method - black img with white shapes)
-  img1=preprocessold(imgOriginal,number)
-  imgGry0 = cv2.cvtColor(imgOriginal , cv2.COLOR_BGR2GRAY)
-  
-  kernel=np.ones((3,3),np.uint8)
-
-  anding=cv2.bitwise_and(green2,green2,mask=img1)
-  anding = cv2.cvtColor(anding , cv2.COLOR_BGR2GRAY)
-
-  return anding
-
-"""# ROI (levels)
-## Detect regions with specific color and mask them
-"""
-
-def hexRGB(color):
-  color=color.lstrip('#')
-
-  color= tuple(int(color[i:i+2], 16) for i in (0, 2, 4)) #hex to rgb
-  color=np.array(color) #rgb to bgr 
-  return color
-def DetectColor(img,color=0):
-
-  imgCopy=img.copy()
-  imgCopy=cv2.cvtColor(imgCopy,cv2.COLOR_BGR2HSV)
-  tol=5 #tolerance
-  # color=hexRGB(color) 
-  h,s,v = cv2.cvtColor(np.uint8([[[color[2],color[1],color[0]]]]),cv2.COLOR_BGR2HSV)[0][0]
-
-  lower =np.array( [h- tol, 100, 100 ], dtype='uint8')
-  upper = np.array( [h + tol, 255, 255],dtype='uint8')
-
-  mask = cv2.inRange(imgCopy, lower , upper)
-
-  detectedColors = cv2.bitwise_and(imgCopy,imgCopy, mask= mask)   # Bitwise-AND mask and original image
-  
-  kernel=np.ones((3,3),np.uint8)
-  mask=cv2.dilate(mask,kernel, iterations=5)
-  mask=cv2.erode(mask,kernel, iterations=4)
-
-  detectedColors=cv2.dilate(detectedColors,kernel, iterations=5)
-  detectedColors=cv2.erode(detectedColors,kernel, iterations=4)
-
-  detectedColors=cv2.cvtColor(detectedColors,cv2.COLOR_HSV2BGR)
-  detectedColors=cv2.medianBlur(detectedColors,7)
-  # cv2_imshow(detectedColors)
-  
-  return mask, detectedColors, color
-
-    
-def detectAllColors(img,finalColorArray):
-  for i in range(len(finalColorArray)):
-    detectedColors= DetectColor(img,finalColorArray[i])[1]
-    if i == 0:
-      allcolorsImg=cv2.bitwise_or(detectedColors,detectedColors)
-    else:
-      allcolorsImg=cv2.bitwise_or(allcolorsImg,detectedColors)
-  allcolorsImg= cv2.medianBlur(allcolorsImg,7)
-  
-  return allcolorsImg
-
-def colorOrder(img,finalColorArray):
-  newimg=img.copy()
-  arraycolor=[]
-  allcolorsImg= detectAllColors(img,finalColorArray)
-  allcolorsImgG=  cv2.cvtColor(allcolorsImg, cv2.COLOR_BGR2GRAY)
-
-  ColoredContour, Coloredhierarchy = cv2.findContours(allcolorsImgG, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-  Coloredhierarchy=Coloredhierarchy[0]
-  for cnt in ColoredContour :
-    Blackmask = np.zeros(img.shape[:2], dtype="uint8")
-    cv2.drawContours(Blackmask,[cnt],0,(255,255,255),20) 
-    coloredand=cv2.bitwise_and(allcolorsImg,allcolorsImg,mask=Blackmask)
-    
-    for colors in finalColorArray:
-      getColor=DetectColor(coloredand,colors)[1]
-      
-      pil_image=Image.fromarray(getColor)
-      extrema = pil_image.convert("L").getextrema()
-      if extrema != (0, 0): # if image is not black --> has a colored mask within
-        arraycolor.append(colors)
-        break
-
-  res = []
-  [res.append(x) for x in arraycolor if x not in res]
-  
-  return res
-
-def getinnerColor(BlackmaskDetected,img,detectedColors,finalColorArray,num1,num2,flag,eachcolor):
-
-  countBlackMasks=0
-  xored=detectedColors
-
-  invertedmask=detectedColors
-
-  imgc=img.copy()
-  imgNewCopy=img.copy()
-  Blackmask = np.zeros(img.shape[:2], dtype="uint8")
-  for eachcolor in finalColorArray:
-    masked=DetectColor(detectedColors,eachcolor)[0]
-    pil_image=Image.fromarray(masked)
-    extrema = pil_image.convert("L").getextrema()
-    if extrema != (0, 0): # if image is not black --> has a colored mask within
-      cc=detectedColors.copy()
-      # cc1=detectedColorsB.copy()
-      ColoredContour, Coloredhierarchy = cv2.findContours(masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-      
-      for cnt in ColoredContour:
-
-        area1 = cv2.contourArea(cnt)
-        if (area1 > 1000 ): 
- 
-            x, y , width, height = cv2.boundingRect(cnt) 
-            # cv2.rectangle(cc, (x,y ), (x+width, y+height), (255,255,255), -1)
-            # cv2.rectangle(Blackmask, (x,y ), (x+width, y+height), 255, -1)
-            #to get rid of the edge of the inner reectangles
-            cv2.drawContours(cc,[cnt],0,(255,255,255), 3)  
-            cv2.drawContours(Blackmask,[cnt] ,0, (255,255,255), 3) 
-
-            cv2.drawContours(cc,[cnt],0,(255,255,255), -1) # (x-5,y-5 ), (x+width, y+height), 
-            cv2.drawContours(Blackmask,[cnt] ,0, (255,255,255), -1) #,(x,y ), (x+width, y+height)
-
-            cv2.drawContours(BlackmaskDetected,[cnt] ,0, (0,0,0), -1) #,(x,y ), (x+width, y+height)
-
-            invertedmask = cv2.bitwise_and(imgc,imgc, mask= Blackmask)
-            xored=cc
-             # masked b abyad
-      detectedColors=xored 
-
-    else: #black mask , no other levels are found  # to check law count == number of colors in array yb2a no more levels and break
-      countBlackMasks+=1
-
-  return xored,invertedmask , BlackmaskDetected
-    
-def allLevelsofColor(BlackmaskDetected,img,levelonly, invertedmask,color,finalColorArray):
-
-  # cc=levelonly.copy()
-  firstLevel=levelonly
-  firstLevel1=levelonly
-  print('in')
-  Blackmask = np.zeros(img.shape[:2], dtype="uint8")
-
-  masked,maskedColor,rgbcolor=DetectColor(invertedmask,color)
-  # color=hexRGB(color)
-  color=[color[0],color[1],color[2]]
-  
-  rgbcolor=[rgbcolor[0],rgbcolor[1],rgbcolor[2]]
-  print(rgbcolor,color)
-  pil_image=Image.fromarray(masked)
-  extrema = pil_image.convert("L").getextrema()
-  if extrema != (0, 0): # if image is not black --> has a colored mask within
-
-    if rgbcolor==color: #found level tany gowa b nfs el lon 
-      print('kkkkkkkk')
-      ColoredContour, Coloredhierarchy = cv2.findContours(masked, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)    
-      Coloredhierarchy=Coloredhierarchy[0]
-      for component in zip(ColoredContour,Coloredhierarchy):
-        cnt=component[0]
-        hier=component[1]
-        area1 = cv2.contourArea(cnt)
-        if (area1 > 1000 ): 
-          if hier[3]> -1:
-            cv2.drawContours(Blackmask,[cnt],0,(255,255,255), -1) 
-            cv2.drawContours(Blackmask,[cnt],0,(0,0,0), 20)  
-            cv2.drawContours(BlackmaskDetected,[cnt],0,(255,255,255), -1) 
-           
-            firstLevel=cv2.bitwise_and(invertedmask,invertedmask,mask=Blackmask)
-            ####remove black pixels and let them be all white 
-            # get (i, j) positions of all RGB pixels that are black (i.e. [0, 0, 0])
-            black_pixels = np.where(
-                (firstLevel[:, :, 0] == 0) & 
-                (firstLevel[:, :, 1] == 0) & 
-                (firstLevel[:, :, 2] == 0)
-            )
-
-            # set those pixels to white
-            firstLevel[black_pixels] = [255, 255, 255]
-            firstLevel1=cv2.bitwise_and(levelonly,firstLevel)
-            # cv2_imshow(firstLevel1)
-
-            # cv2_imshow(firstLevel1)
-            for othercolor in finalColorArray:
-              # othercolor2=hexRGB(othercolor)
-              othercolor2=[othercolor[0],othercolor[1],othercolor[2]]
-              print(othercolor2,color)
-              if othercolor2!=color:
-                print('anothre')
-                masked0=DetectColor(firstLevel,othercolor)[0] 
-                pil_image0=Image.fromarray(masked0)
-                extrema0 = pil_image0.convert("L").getextrema()
-                if extrema != (0, 0): # if image is not black --> has a colored mask within
-                  ColoredContour0, Coloredhierarchy0 = cv2.findContours(masked0, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    
-                  for cnt in ColoredContour0:
-                    area1 = cv2.contourArea(cnt)
-                    if (area1 > 1000 ): 
-                      cv2.drawContours(firstLevel1,[cnt],0,(255,255,255), -1)  
-                      cv2.drawContours(firstLevel1,[cnt],0,(255,255,255), 10)  
-                      cv2.drawContours(BlackmaskDetected,[cnt],0,(0,0,0), -1) 
-                      # cv2.drawContours(Blackmask,[cnt],0,(255,255,255), -1)  
-                      # cv2.drawContours(Blackmask,[cnt],0,(255,255,255), 10)  
-                      # cv2_imshow(firstLevel1)
-            # cv2_imshow(Blackmask)
-  return firstLevel1, BlackmaskDetected
-    
-def getColoredContour(mask,img,finalColorArray,num1,num2,flag,eachcolor):
-    print('uuuuuuuuummmmmmmmmmmmm')
-
-    ColoredContour, Coloredhierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    Coloredhierarchy=Coloredhierarchy[0]
-
-    imgc= img.copy()
-
-    detectedColors=np.zeros(img.shape[:2], dtype="uint8")
-    Blackmask = np.zeros(img.shape[:2], dtype="uint8")
-
-    for component in zip( ColoredContour, Coloredhierarchy):
-        cnt=component[0]
-        hier=component[1]
-        area1 = cv2.contourArea(cnt)
-        if (area1 > 3000 ): 
-      # cv2.drawContours(imgNewCopy, [cnt], 0,(255,255,255), 20)  #(x+20,y+20 ), (x+width-20, y+height-20),
-          if hier[3] >-1:
-
-            x, y , width, height = cv2.boundingRect(cnt) 
-            cv2.drawContours(Blackmask, [cnt], 0,(255,255,255), -1)  #(x+20,y+20 ), (x+width-20, y+height-20),
-            cv2.drawContours(Blackmask, [cnt], 0,(0,0,0), 10)  #(x+20,y+20 ), (x+width-20, y+height-20),
-    
-            detectedColors = cv2.bitwise_and(imgc,imgc, mask= Blackmask)
-            pil_image=Image.fromarray(detectedColors)
-            extrema = pil_image.convert("L").getextrema()
-            if extrema == (0, 0) :#and extremaB==(0,0): # if image is not black --> has a colored mask within
-              break
-    
-    levelOnly,invertedmask,BlackmaskDetected=getinnerColor(Blackmask,img,detectedColors,finalColorArray,num1,num2,flag,eachcolor) #mask inner levels b abyad 
-    firstLevel1, BlackmaskDetected1= allLevelsofColor(BlackmaskDetected,img,levelOnly, invertedmask,eachcolor,finalColorArray)
-    # cv2.imshow('kk',firstLevel1)
-    print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
-    return firstLevel1,invertedmask, BlackmaskDetected1
-
-"""#  contours"""
-
-def findContoursFullImage(green2,img,number,finalColorArray,num1,num2,flag,color=[0,0,0]):
-  if number == 0:
-    thresh=preprocess(img,number,green2)
-
-    contourss, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    return contourss
-  else:
-    mask, detectedColors, rgbcolor =DetectColor(img,color)
-    print(rgbcolor)
-    
-    pil_image=Image.fromarray(mask)
-    
-    extrema = pil_image.convert("L").getextrema()
-    if extrema != (0, 0): # if image is not black --> has a colored mask within
-      coloredregions,invertedmask,BlackmaskDetected1=getColoredContour(mask,img,finalColorArray,num1,num2,flag,color)
-      thresh=preprocess(coloredregions,number,green2)
-      x=cv2.bitwise_and(thresh,thresh,mask=BlackmaskDetected1)
-      contourss, hierarchy = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-      return contourss,rgbcolor ,invertedmask 
-
-    else:
-      print('ELSEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE')
-      thresh=preprocess(img,number,green2)
-
-      contourss, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-      hierarchy = hierarchy[0] 
-      return contourss,color ,mask
-
-
-
-def StraightenImage(contour,imgArea):
-    rect = cv2.minAreaRect(contour)
-    
-    (center, (width, height), angleR) = cv2.minAreaRect(contour)
-    
-    box = cv2.boxPoints(rect)
-    box = np.int0(box)
-
-    # get width and height of the detected rectangle
-    width = int(rect[1][0])
-    height = int(rect[1][1])
-
-    # src_pts = box.astype("float32")
-    # dst_pts = np.array([[0, height-1],
-    #                       [0, 0],
-    #                       [width-1, 0],
-    #                       [width-1, height-1]], dtype="float32")
-
-    #   # the perspective transformation matrix
-    # M = cv2.getPerspectiveTransform(src_pts, dst_pts)
-
-    # # directly warp the rotated rectangle to get the straightened rectangle
-
-    # warped = cv2.warpPerspective(imgArea, M, (width, height))
-    ##############  
-    return angleR,width,height
-
-def getAreasPerimeter(green2,img,number,num1,num2,flag,finalColorArray,color=[0,0,0]):
-  appended=[]
-  if number==0:
-    contourss=findContoursFullImage(green2,img,number,finalColorArray,num1,num2,flag,color)
-  else:
-    contourss=findContoursFullImage(green2,img,number,finalColorArray,num1,num2,flag,color)[0]
-
-  for contour in contourss:
-
-    area1 = cv2.contourArea(contour)
-    perimeter1 = cv2.arcLength(contour, True)
-    x, y , width, height = cv2.boundingRect(contour) 
-
-    angleR,widthR ,heightR= StraightenImage(contour,img)
-
-    if (angleR != 90.0 and angleR != -90.0 and angleR != 0.0 and angleR != -0.0 ): #inclined b ay degree 
-        width=widthR
-        height=heightR
-    if (area1 > 4000 ): #check perimeter kman fl condition  -- 2800
-      if num1!=0 and num2!=0:
-        #if flag=='area':
-          # addedMargin=area1+perimeter1*2
-          # areaa=round(addedMargin* (num1/(num2+perimeter1*2) ), 3) # true value of area of any shape/ area px value of same shape
-          areaa=round(area1*num1,3)
-          appended.append([areaa,width,height])
-
-        #else:
-          #perimeter=round(perimeter1*(num1/num2),3)
-          #appended.append([perimeter,width,height])
-
-  return appended
-
-
-def FillDictionary(green2,SimilarAreaDictionary,img,number,num1,num2,flag,finalColorArray,rgbcolor=[0,0,0],color=[0,0,0]):
-  #fills dictionary with key areas and number of occurences
-  print('wttttt')
-  areas_Perimeters=sorted(getAreasPerimeter(green2,img,number,num1,num2,flag,finalColorArray,color) )
-  
-  indices=[]
-  colorRanges=[[255,153,153],[51,255,51],[201,56,147],[255,0,0],[255,0,255],[0,102,204],[102,0,102],[153,0,76],[200,92,135],[52,161,99],[235,250,24],[40,30,170],[98,149,63],[100,30,179],[200,55,67],[150,80,200],[0,102,102],[250,28,191],[101,27,101],[230,150,76],[3,65,127],[114,39,39],[250,36,100],[180,30,40],[10,250,60],[140,30,253],[114,58,245],[47,255,255],[18,236,206],[225,105,29],[189,65,121],[206,204,48],[126,7,247],[3,168,251]]
-  print(colorRanges[0])
-  print(colorRanges[0][0],colorRanges[0][1], colorRanges[0][2])
-  colorsUsed=[]
-  for i in range(len(areas_Perimeters)):
-     
-      # colorRGB=hexRGB(color)
-      item1 = areas_Perimeters[i][0]
-      width1 = areas_Perimeters[i][1]
-      height1 = areas_Perimeters[i][2]
-      widthMin= width1-10
-      widthMax= width1+10
-      heightMin=height1-10
-      heightMax= height1+10
-      areaPerimeterMin= round(item1,1) - 0.3
-      areaPerimeterMax= round(item1,1) + 0.3
-      # print (areaMin, areaMax)
-      if color != [0,0,0]: #colored images 
-
-        mydata=[[rgbcolor[0],rgbcolor[1],rgbcolor[2] ],round(item1,1),width1,height1,1, 0,0,0,0,0,0,0] 
-        # mydata=[round(item1,1),width1,height1,0, 1,0,[rgbcolor[0],rgbcolor[1],rgbcolor[2] ],colorRanges[0][2],colorRanges[0][1],colorRanges[0][0]] 
-        # colorRanges.pop(0)
-      else:
-          # print('??')
-          
-          mydata=[' ', round(item1,1),width1,height1,1, 0,0,0,0,0,0,0] 
-
-      # if (( round(item1,1)  in SimilarAreaDictionary['Rounded'].values) or (areaMin in  SimilarAreaDictionary['Rounded'].values )or (areaMax in SimilarAreaDictionary['Rounded'].values )):
-      
-        # myindex= SimilarAreaDictionary.index[( SimilarAreaDictionary['Rounded']== round(item1,1) ) ].tolist()
-      myindex= SimilarAreaDictionary.index[((SimilarAreaDictionary['Rounded'] >=areaPerimeterMin) &(SimilarAreaDictionary['Rounded']<=areaPerimeterMax)  )].tolist()
-           # for i in myindex:
-      #   SimilarAreaDictionary['Rounded'].loc[i]
-      if color!= [0,0,0]: #leveled image
-
-        checkifColorExists=0 # to check whether this row was found or not( area and color )
-        for i in myindex: # loop on indices that were found --> rows containing this area to check its color and add occ.
-          if SimilarAreaDictionary['Color'].loc[i]==[rgbcolor[0],rgbcolor[1],rgbcolor[2]] and   ( SimilarAreaDictionary['Rounded'].loc[i] >= areaPerimeterMin and SimilarAreaDictionary['Rounded'].loc[i]  <= areaPerimeterMax) : 
-            if (SimilarAreaDictionary['Width'].loc[i] <=widthMax and SimilarAreaDictionary['Width'].loc[i] >= widthMin)  and (SimilarAreaDictionary['Height'].loc[i] <= heightMax  and SimilarAreaDictionary['Height'].loc[i] >= heightMin  ) or (SimilarAreaDictionary['Width'].loc[i] <=heightMax and SimilarAreaDictionary['Width'].loc[i] >= heightMin)  and (SimilarAreaDictionary['Height'].loc[i] <= widthMax  and SimilarAreaDictionary['Height'].loc[i] >= widthMin  ) :
-                checkifColorExists=1  #found and incremented
-                SimilarAreaDictionary['Occurences'].loc[i]+=1
-        if checkifColorExists==0: #couldnt find the color , doesnt exist so add it 
-          SimilarAreaDictionary.loc[len(SimilarAreaDictionary)] =mydata
-          
-      else: #full image
-        # print('here')
-#same code bs mgher color 
-        checkifColorExists=0
-        for i in myindex: #(SimilarAreaDictionary['Rounded'].loc[i] == round(item1,1) )  or  
-          if (   SimilarAreaDictionary['Rounded'].loc[i]  <= areaPerimeterMax and SimilarAreaDictionary['Rounded'].loc[i]  >= areaPerimeterMin) :
-            # print(SimilarAreaDictionary['Rounded'].loc[i]   ,'in rng if', areaMin,areaMax) 
-            if (SimilarAreaDictionary['Width'].loc[i] <=widthMax and SimilarAreaDictionary['Width'].loc[i] >= widthMin)  and (SimilarAreaDictionary['Height'].loc[i] <= heightMax  and SimilarAreaDictionary['Height'].loc[i] >= heightMin  ) or (SimilarAreaDictionary['Width'].loc[i] <=heightMax and SimilarAreaDictionary['Width'].loc[i] >= heightMin)  and (SimilarAreaDictionary['Height'].loc[i] <= widthMax  and SimilarAreaDictionary['Height'].loc[i] >= widthMin  ) :
-                  checkifColorExists=1  #found and incremented
-                  SimilarAreaDictionary['Occurences'].loc[i]+=1
-                  # SimilarAreaDictionary['R'].loc[i] =colorRanges[i][0]
-                  # SimilarAreaDictionary['G'].loc[i] =colorRanges[i][1]
-                  # SimilarAreaDictionary['B'].loc[i] = colorRanges[i][2]
-
-        # colorRanges.pop(0)
-
-        if checkifColorExists==0: #couldnt find the color , doesnt exist so add it 
-          SimilarAreaDictionary.loc[len(SimilarAreaDictionary)] =mydata
-  # s= SimilarAreaDictionary
-  for i in range(len(SimilarAreaDictionary)):
-      SimilarAreaDictionary['R'].loc[i] =colorRanges[i][0]
-      SimilarAreaDictionary['G'].loc[i] =colorRanges[i][1]
-      SimilarAreaDictionary['B'].loc[i] = colorRanges[i][2]
-      colorsUsed.append(colorRanges[i])
-
-
-  return SimilarAreaDictionary, colorsUsed , areas_Perimeters
-def drawAllContours(img,number,finalColorArray,ratioarea,ratioperim,flag , path,pdfpath):
-  green2=allpreSteps(img)
-  doc = fitz.open('dropbox_plans/1.0/'+path)
-  page = doc[0]    
-  page.set_rotation(0)
-  pix=page.get_pixmap()
-  ratio =  pix.width/ img.shape[1] 
- 
-  areasinImage=[]
-  totaldf=pd.DataFrame()
-  imgArea1= img.copy()
-  imgPerimeter1=img.copy()
-  imgtransparent1=img.copy()
-
-  Blackmask = np.zeros(img.shape[:2], dtype="uint8")
-
-  invertedmask=img
-  allpoints=[]
-
-  if number ==220:
-    # finalColorArray= colorOrder(img,finalColorArray)
-    # if flag== 'area':
-    #   SimilarAreaDictionary= pd.DataFrame(columns=['Color','Rounded','Width','Height','Area','Occurences','Total Area' , 'R','G','B']) #
-    # else:
-      # SimilarAreaDictionary= pd.DataFrame(columns=['Color','Rounded','Width','Height','Perimeter','Occurences','Total Perimeter' ,'R','G','B'])
-    SimilarAreaDictionary= pd.DataFrame(columns=['Color','Rounded','Width','Height','Occurences','Area','Total Area','Perimeter','Total Perimeter','R','G','B'])
-    firstcolor=finalColorArray[0]
-    # print(lastcolor)
-    counter=0
-    maskDone=img.copy()
-    for eachcolor in finalColorArray:
-
-      print(eachcolor)
-      if eachcolor==firstcolor: # 3shan a3rf el array of colors et3adet kam mara - to support embedded levels 
-        counter+=1
-
-      contourss,rgbcolor,invertedmask=findContoursFullImage(green2,maskDone,number,finalColorArray,ratioarea,ratioperim,flag,eachcolor) 
-      SimilarAreaDictionary, colorsUsed , areas_Perimeters= FillDictionary(green2,SimilarAreaDictionary,maskDone,number,ratioarea,ratioperim,flag,finalColorArray,rgbcolor,eachcolor)
-  
-      a = SimilarAreaDictionary.to_numpy()
-      
-      # for component in zip(contourss,hierarchy):
-      #     contour = component[0]
-      #     currentHierarchy = component[1]
-      for contour in contourss:
-          shape=[]
-          
-          # cv2_imshow(imgStraight)
-          area1 = cv2.contourArea(contour)
-          perimeter1 = cv2.arcLength(contour, True)  
-          if (area1 > 4000 ): #check perimeter kman fl condition  -- 2800
-            angleR,widthR ,heightR= StraightenImage(contour,imgArea1)
-            rect = cv2.minAreaRect(contour)
-    
-            (center, (width, height), angleR) = cv2.minAreaRect(contour)
-            
-            box = cv2.boxPoints(rect)
-            box = box.astype('int')
-            print(box)
-
-            x, y , width, height = cv2.boundingRect(contour) 
-            # cv2.drawContours(imgArea1,contours=[box], contourIdx=0 , color=(0, 0, 255), thickness=10) 
-            approx = cv2.approxPolyDP(contour, 0.005 * perimeter1, True)
-            for point in approx:
-                x1, y1 = point[0]
-                
-                shape.append([int(x1*ratio),int(y1*ratio)])
-                # shape= np.fliplr(shape)
-                
-                # cv2.circle(imgArea1, (x1, y1), 4, (0, 255, 0), -1)
-            allpoints.append(shape)
-            # print(x,y,width,height)
-            # print(allpoints)
-            print(shape)
-            if (angleR != 90.0 and angleR != -90.0 and angleR != 0.0 and angleR != -0.0 ): #inclined b ay degree 
-              width=widthR
-              height=heightR
-
-            widthMin= width-10
-            widthMax= width+10
-            heightMin=height-10
-            heightMax= height+10
-            if ratioarea !=0 and ratioperim!=0:
-              widthh=round(width*ratioperim,3)
-              heightt=round(height*ratioperim,3)
-              # if flag=='area':
-              areaa=round(area1* ratioarea, 3) # true value of area of any shape/ area px value of same shape
-              
-              # elif flag=='perimeter':
-              perimeterr=round(perimeter1* ratioperim, 3)
-            else:
-              areaa=area1
-              perimeterr=perimeter1
-
-            # if flag=='area':
-            areaPerimeterMin= round(areaa,1) - 0.3
-            areaPerimeterMax= round(areaa,1) + 0.3
-            # areaPerimeterMin= round(perimeterr,1) - 0.3
-            # areaPerimeterMax= round(perimeterr,1) + 0.3
-            masked=SimilarAreaDictionary.loc[SimilarAreaDictionary.index[((SimilarAreaDictionary['Rounded'] >=areaPerimeterMin) &(SimilarAreaDictionary['Rounded']<=areaPerimeterMax)  )]]
-            # masked=SimilarAreaDictionary.loc[SimilarAreaDictionary['Rounded'] ==round(areaa,1)]
-            # if (round(areaa,1) in masked['Rounded'].values ) :
-            passed=0
-            for i, row in masked.iterrows():
-              if passed ==0:
-                if SimilarAreaDictionary['Color'].loc[i] == [rgbcolor[0],rgbcolor[1],rgbcolor[2]]  and  (   SimilarAreaDictionary['Rounded'].loc[i]  <= areaPerimeterMax and SimilarAreaDictionary['Rounded'].loc[i]  >= areaPerimeterMin) :
-                    if (SimilarAreaDictionary['Width'].loc[i] <=widthMax and SimilarAreaDictionary['Width'].loc[i] >= widthMin)  and (SimilarAreaDictionary['Height'].loc[i] <= heightMax  and SimilarAreaDictionary['Height'].loc[i] >= heightMin  ) or (SimilarAreaDictionary['Width'].loc[i] <=heightMax and SimilarAreaDictionary['Width'].loc[i] >= heightMin)  and (SimilarAreaDictionary['Height'].loc[i] <= widthMax  and SimilarAreaDictionary['Height'].loc[i] >= widthMin  ) :
-                        SimilarAreaDictionary['Total Area'].loc[i]+=areaa
-                        SimilarAreaDictionary['Area'].loc[i]=areaa
-
-                        SimilarAreaDictionary['Total Perimeter'].loc[i]+=perimeterr
-                        SimilarAreaDictionary['Perimeter'].loc[i]=perimeterr                        
-                        passed=1
-                          # print(index)
-            # cv2.drawContours(imgArea1, [contour], 0, (int(rgbcolor[2]), int(rgbcolor[1]), int(rgbcolor[0])), -1)
-                        cv2.drawContours(imgArea1, [contour], 0, ( int(SimilarAreaDictionary['B'].loc[i]),  int(SimilarAreaDictionary['G'].loc[i]), int(SimilarAreaDictionary['R'].loc[i])), -1)
-                        annot = page.add_polygon_annot( points=shape )  # 'Polygon'
-                        annot.set_border(width=0.3, dashes=[2])
-                        annot.set_colors( fill=(  int(SimilarAreaDictionary['R'].loc[i])/255 ,  int(SimilarAreaDictionary['G'].loc[i])/255 ,  int(SimilarAreaDictionary['B'].loc[i])/255 ) ) 
-                        # annot.set_colors( fill=(1,0,1) )
-                        annot.set_opacity(0.5)
-                        annot.set_info(content='Area='+str(areaa)+' m2' +'\n \nPerimeter='+str(perimeterr)+' m',subject='ADR Team')#,title='uuum')
-                        # annot.set_line_ends(fitz.PDF_ANNOT_LE_DIAMOND, fitz.PDF_ANNOT_LE_CIRCLE)
-                        annot.update()
-            cv2.putText(imgtransparent1,'Area= '+str(area1) , (x+50,y-10)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-            # cv2.putText(imgtransparent1,'Width= '+str(width) , (x+50,y-10)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-            # cv2.putText(imgtransparent1,'Length= '+str(height) , (x+50,y-20)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-            areasinImage.append(areaa)
-
-            cv2.putText(imgPerimeter1,'Perimeter'+str(perimeterr), (x+50,y-10)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-
-  
-    for i,row in SimilarAreaDictionary.iterrows():
-      # print(row)
-      if row[5] not in areasinImage: # column of area 
-        SimilarAreaDictionary = SimilarAreaDictionary.drop(SimilarAreaDictionary.loc[SimilarAreaDictionary.index==i].index)
-
-    print(SimilarAreaDictionary)
-    # display(totaldf)
-  #########################
-  else:
-    
-    SimilarAreaDictionary= pd.DataFrame(columns=['Color','Rounded','Width','Height','Occurences','Area','Total Area','Perimeter','Total Perimeter','R','G','B'])  
-    contourss=findContoursFullImage(green2,img,number,finalColorArray,ratioarea,ratioperim,flag)
-    SimilarAreaDictionary,colorsUsed , areas_Perimeters= FillDictionary(green2,SimilarAreaDictionary,img,number,ratioarea,ratioperim,flag,finalColorArray)
-    # print('filled')
-    for contour in contourss:
-      # shape=[]
-      area1 = cv2.contourArea(contour)
-      perimeter1 = cv2.arcLength(contour, True)  
-      if (area1 >4000 ):
-        shape=[]
-        angleR,widthR ,heightR= StraightenImage(contour,imgArea1)
-        x, y , width, height = cv2.boundingRect(contour) 
-
-        approx = cv2.approxPolyDP(contour, 0.005 * perimeter1, True)
-        for point in approx:
-            x1, y1 = point[0]
-            shape.append([int(x1*ratio),int(y1*ratio)])
-        allpoints.append(shape)
-        if (angleR != 90.0 and angleR != -90.0 and angleR != 0.0 and angleR != -0.0 ): #inclined b ay degree 
-            width=widthR
-            height=heightR
-
-        widthMin= width-10 #5
-        widthMax= width+10
-        heightMin=height-10
-        heightMax= height+10
-
-        
-        if ratioarea !=0 and ratioperim!=0:
-          # if flag=='area':
-            # addedMargin=area1+perimeter1*2
-            # areaa=round(addedMargin* (num1/(num2+perimeter1*2) ), 3) # true value of area of any shape/ area px value of same shape
-          # elif flag=='perimeter':
-            areaa=round(area1* ratioarea, 3) # true value of area of any shape/ area px value of same shape
-            perimeterr=round(perimeter1* ratioperim, 3)
-        else:
-          areaa=area1
-          perimeterr=perimeter1
-        # if flag=='area': 
-        areaPerimeterMin= round(areaa,1) - 0.3
-        areaPerimeterMax= round(areaa,1) + 0.3
-        masked=SimilarAreaDictionary.loc[SimilarAreaDictionary.index[((SimilarAreaDictionary['Rounded'] >=areaPerimeterMin) & (SimilarAreaDictionary['Rounded']<=areaPerimeterMax)  )]]
-        passed=0
-        # if (round(areaa,1) in masked['Rounded'].values ) :
-
-        for i, row in masked.iterrows():
-          if passed ==0:
-            # if SimilarAreaDictionary['Rounded'].loc[i] == round(areaa,1) :
-            if (   SimilarAreaDictionary['Rounded'].loc[i]  <= areaPerimeterMax and SimilarAreaDictionary['Rounded'].loc[i]  >= areaPerimeterMin) :
-              if (SimilarAreaDictionary['Width'].loc[i] <=widthMax and SimilarAreaDictionary['Width'].loc[i] >= widthMin)  and (SimilarAreaDictionary['Height'].loc[i] <= heightMax  and SimilarAreaDictionary['Height'].loc[i] >= heightMin  ) or (SimilarAreaDictionary['Width'].loc[i] <=heightMax and SimilarAreaDictionary['Width'].loc[i] >= heightMin)  and (SimilarAreaDictionary['Height'].loc[i] <= widthMax  and SimilarAreaDictionary['Height'].loc[i] >= widthMin  ) :
-                    SimilarAreaDictionary['Total Area'].loc[i]+=areaa
-                    SimilarAreaDictionary['Area'].loc[i]=areaa
-
-                    SimilarAreaDictionary['Total Perimeter'].loc[i]+=perimeterr
-                    SimilarAreaDictionary['Perimeter'].loc[i]=perimeterr
-                    passed=1
-                    cv2.drawContours(imgArea1, [contour], 0, ( int(SimilarAreaDictionary['B'].loc[i]),  int(SimilarAreaDictionary['G'].loc[i]), int(SimilarAreaDictionary['R'].loc[i])), -1)
-                    
-                    annot = page.add_polygon_annot( points=shape )  # 'Polygon'
-                    annot.set_border(width=0.3, dashes=[2])
-                    annot.set_colors( fill=(  int(SimilarAreaDictionary['R'].loc[i])/255 ,  int(SimilarAreaDictionary['G'].loc[i])/255 ,  int(SimilarAreaDictionary['B'].loc[i])/255 ) ) 
-                    # annot.set_colors( fill=(1,0,1) )
-                    annot.set_opacity(0.5)
-                    annot.set_info(content='Area='+str(areaa)+' m2' +'\n \nPerimeter='+str(perimeterr)+' m',subject='ADR Team')#,title='uuum')
-                    # annot.set_line_ends(fitz.PDF_ANNOT_LE_DIAMOND, fitz.PDF_ANNOT_LE_CIRCLE)
-                    annot.update()
-        cv2.putText(imgtransparent1,'area= '+str(area1) + ' m', (x+50,y-10)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-        # cv2.putText(imgtransparent1,'Width= '+str(width) , (x+50,y-10)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-        # cv2.putText(imgtransparent1,'Length= '+str(height) , (x+50,y-40)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-        cv2.drawContours(imgArea1, [contour], 0, (0, 0, 255),2)
-        cv2.drawContours(imgPerimeter1, [contour], 0, (0, 0, 255), 4)
-        cv2.putText(imgPerimeter1,'Perimeter='+str(perimeterr), (x+30,y-30)  ,cv2.FONT_HERSHEY_SIMPLEX, 0.6, (50, 50, 255), 2)
-
-  
-  alpha = 0.4  # Transparency factor.
-  image_new1 = cv2.addWeighted(imgArea1, alpha,  imgtransparent1, 1 - alpha, 0)
-  
-  # SimilarAreaDictionary.drop(['Rounded', 'Width','Height','R','G','B'], axis=1, inplace=True)
-
-  print(SimilarAreaDictionary)
- 
-  # annotationsSave
-  # doc.save('k.pdf', deflate=True)
-  pdflink= db.dropbox_upload_file(doc=doc,pdfname=path,pdfpath=pdfpath)
-  # list1=pd.DataFrame(columns=['content', 'creationDate', 'id', 'modDate', 'name', 'subject', 'title'])
-  # doc1 = fitz.open('k.pdf')
-  # for page in doc1:
-  #   for annot in page.annots():
-  #       list1.loc[len(list1)] =annot.info
-  # print(list1)
-  dbx=db.dropbox_connect()
-  md, res =dbx.files_download(path= pdfpath+path)
-  data = res.content
-  doc=fitz.open("pdf", data)
-  # list1=pd.DataFrame(columns=['content', 'creationDate', 'id', 'modDate', 'name', 'subject', 'title'])
-  list1=pd.DataFrame(columns=['content',  'id',  'subject'])
-  for page in doc:
-      for annot in page.annots():
-          list1.loc[len(list1)] =annot.info
-
-  print(list1)
-  gc,spreadsheet_service,spreadsheetId, spreadsheet_url=legendGoogleSheets(SimilarAreaDictionary , path,colorsUsed)
-  return imgPerimeter1,image_new1,SimilarAreaDictionary, colorsUsed , spreadsheet_url , spreadsheetId , list1 , pdflink , areas_Perimeters
-
-######################################################
-
-def deletemarkups(list1, pdfpath , path):
-  '''list1 : original markup pdf
-     list2 : deleted markup pdf
-     deletedrows : deleted markups - difference betw both dfs
-
-  '''
-  myDict1=eval(list1)
-  list1=pd.DataFrame(myDict1)
- 
-  areastodelete = []
-  perimstodelete=[]
-  # list2=pd.DataFrame(columns=['content', 'creationDate', 'id', 'modDate', 'name', 'subject', 'title'])
-  # doc = fitz.open('k.pdf')
-  # for page in doc:
-  #     for annot in page.annots():
-  #       list2.loc[len(list2)] =annot.info
-  dbx=db.dropbox_connect()
-
-  md, res =dbx.files_download(path= pdfpath+path)
-  data = res.content
-  doc=fitz.open("pdf", data)
-  list2=pd.DataFrame(columns=['content',  'id',  'subject'])
-  #  list2=pd.DataFrame(columns=['content', 'creationDate', 'id', 'modDate', 'name', 'subject', 'title'])
-  for page in doc:
-      for annot in page.annots():
-        list2.loc[len(list2)] =annot.info
-  print(list1)
-  deletedrows=pd.concat([list1,list2]).drop_duplicates(keep=False)
-
-  print(deletedrows,len(deletedrows))
-  flag=0
-  if len(deletedrows)!=0:
-    flag=1
-    deletedrows=deletedrows[['content',  'id',  'subject']]
-    deletedrows = deletedrows.drop(deletedrows.index[deletedrows['content'].str.startswith('Scale')] )#, inplace=True)
-  else:
-    flag=0
-  return deletedrows
-    # return SimilarAreaDictionary
-def deletefromlegend(deletedrows,SimilarAreaDictionarycopy, areaPermArr):
-  items=[]
-
-  areaPermArr=ast.literal_eval(areaPermArr)
-
-  # print(type(areaPermArr))
-  myDict=eval(SimilarAreaDictionarycopy)
-  # print(type(myDict))
-  SimilarAreaDictionarycopy=pd.DataFrame(myDict)
-  strings=deletedrows['content']
- 
-  areastodelete = []
-  perimstodelete=[]
-
-
-  for item in strings:
-    items.append(str(item).split('\n \n'))
-
-  for i in range(len(items)):
-    # areastodelete.append(round(float(re.findall("\d+\.\d+", str(items[i][0]).split()[0])[0]),1) )
-    areastodelete.append(float(re.findall("\d+\.\d+", str(items[i][0]).split()[0])[0]))
-    perimstodelete.append(float(re.findall("\d+\.\d+", str(items[i][1]).split()[0])[0]) )
-
-
-  for i in range(len(areastodelete)):#item in areastodelete:
-    areamin=round(areastodelete[i],1)- 0.3
-    areamax=round(areastodelete[i],1)+ 0.3
-    perimmin=round(perimstodelete[i],1)- 0.3
-    perimmax=round(perimstodelete[i],1)+ 0.3
-    for p in range(len(areaPermArr)):
-      if areastodelete[i] in areaPermArr[p]:
-        area= areaPermArr[p][0]
-        width= areaPermArr[p][1]
-        height= areaPermArr[p][2]
-
-    widthMin= width -10
-    widthMax= width +10
-    heightMin = height-10
-    heightMax=height+10
-    print(width, widthMin ,height, heightMin)
-    found=SimilarAreaDictionarycopy.loc[SimilarAreaDictionarycopy.index[((SimilarAreaDictionarycopy['Rounded'] >=areamin) & (SimilarAreaDictionarycopy['Rounded']<=areamax) & (SimilarAreaDictionarycopy['Perimeter'] >=perimmin) & (SimilarAreaDictionarycopy['Perimeter']<=perimmax)   ) & ( ((SimilarAreaDictionarycopy['Width']>=widthMin) & (SimilarAreaDictionarycopy['Width']<=widthMax) & (SimilarAreaDictionarycopy['Height']>=heightMin) & (SimilarAreaDictionarycopy['Height']<=heightMax) ) | ((SimilarAreaDictionarycopy['Width']>=heightMin) & (SimilarAreaDictionarycopy['Width']<=heightMax) & (SimilarAreaDictionarycopy['Height']>=widthMin) & (SimilarAreaDictionarycopy['Height']<=widthMax) )) ]]
-    
-  #   if (   SimilarAreaDictionary['Rounded'].loc[i]  <= areaPerimeterMax and SimilarAreaDictionary['Rounded'].loc[i]  >= areaPerimeterMin) :
-  # if (SimilarAreaDictionary['Width'].loc[i] <=widthMax and SimilarAreaDictionary['Width'].loc[i] >= widthMin)  and (SimilarAreaDictionary['Height'].loc[i] <= heightMax  and SimilarAreaDictionary['Height'].loc[i] >= heightMin  ) or (SimilarAreaDictionary['Width'].loc[i] <=heightMax and SimilarAreaDictionary['Width'].loc[i] >= heightMin)  and (SimilarAreaDictionary['Height'].loc[i] <= widthMax  and SimilarAreaDictionary['Height'].loc[i] >= widthMin  ) :
-            
-    print(found.index.values)
-    if len(found.index.values ) >0:
-      occ=SimilarAreaDictionarycopy.loc[found.index.values[0],'Occurences']
-      if occ== 1: #drop row
-        print('occ=1')
-        # print(SimilarAreaDictionarycopy[(SimilarAreaDictionarycopy['Rounded'] == item ) ].index)
-        # SimilarAreaDictionarycopy.drop(SimilarAreaDictionarycopy['Occurences'] == item)
-        SimilarAreaDictionarycopy= SimilarAreaDictionarycopy.drop(found.index.values[0])
-
-      else: #occ minus 1 , total area - areavalue , total perim - perimvalue
-        print('occ>1')
-        idx=SimilarAreaDictionarycopy.index[((SimilarAreaDictionarycopy['Rounded'] >=areamin) & (SimilarAreaDictionarycopy['Rounded']<=areamax) & (SimilarAreaDictionarycopy['Perimeter'] >=perimmin) & (SimilarAreaDictionarycopy['Perimeter']<=perimmax)   ) & ( ((SimilarAreaDictionarycopy['Width']>=widthMin) & (SimilarAreaDictionarycopy['Width']<=widthMax) & (SimilarAreaDictionarycopy['Height']>=heightMin) & (SimilarAreaDictionarycopy['Height']<=heightMax) ) | ((SimilarAreaDictionarycopy['Width']>=heightMin) & (SimilarAreaDictionarycopy['Width']<=heightMax) & (SimilarAreaDictionarycopy['Height']>=widthMin) & (SimilarAreaDictionarycopy['Height']<=widthMax) )) ]
-        # SimilarAreaDictionary.loc[idx]['Total Area']
-        # for j in range(occ):
-        SimilarAreaDictionarycopy.loc[idx,'Total Area'] = SimilarAreaDictionarycopy.loc[idx,'Total Area'] - areastodelete[i]
-        SimilarAreaDictionarycopy.loc[idx,'Total Perimeter'] = SimilarAreaDictionarycopy.loc[idx,'Total Perimeter'] - perimstodelete[i]
-        SimilarAreaDictionarycopy.loc[idx,'Occurences'] = int(SimilarAreaDictionarycopy.loc[idx,'Occurences']) - 1
-
-  print(SimilarAreaDictionarycopy)
-  return SimilarAreaDictionarycopy
-#######################################################
-
-def getTitle(path):
-  planName= path.split("/")[-1].split('.')
-  LegendName='Legend of ' + str(planName[0]) + ' plan'
-  return LegendName
-
-def retrieveMCCol(gc):
-  ws=gc.open_by_key('1A8VtqLFhe2NXPxIjfAilbxF9xV2eSzZ-yZ9GP8_5jSo')
-  worksheet = ws.worksheet(0)
-  mcT_Names=worksheet.get_col(1)
-  newMcTNames=[]
-  for i in mcT_Names:
-    if i != '':
-      newMcTNames.append(i)
-  return newMcTNames
-
-
-def getdropdownValues(gc,spreadsheet_service,spreadsheetid):
-  dropdownValues=[]
-  ws=gc.open_by_key('1A8VtqLFhe2NXPxIjfAilbxF9xV2eSzZ-yZ9GP8_5jSo')  ## spreadsheet containing mc-t names 
- 
-  worksheet = ws.worksheet(0)
-  response = spreadsheet_service.spreadsheets().get(
-        spreadsheetId=spreadsheetid, fields='*',
-        ranges='A2:A60',includeGridData=True).execute()
-  r=list(response['sheets'][0]['data'][0]['rowData'][0]['values'][0])
-  print(r)
-  if 'dataValidation' in r:
-    print('yes')
-    colvals= response['sheets'][0]['data'][0]['rowData'][0]['values'][0]['dataValidation']
-    colvalsList=list(colvals.items())
-    print(colvalsList[0][1])
-    lengthVals=len(colvalsList[0][1]['values'])
-    for i in range(lengthVals):
-      dictVal=(colvalsList[0][1]['values'][i].values())
-      # val=[*dictVal]
-     
-      dropdownValues.append(*dictVal)
-    print(dropdownValues)
-  worksheet.update_col(index=1, values=dropdownValues)
-  return dropdownValues
-def authorizeLegend():
-  SCOPES = [
-  'https://www.googleapis.com/auth/spreadsheets',
-  'https://www.googleapis.com/auth/drive'
-  ]
-  credentials = service_account.Credentials.from_service_account_file('credentials.json', scopes=SCOPES)
-  spreadsheet_service = build('sheets', 'v4', credentials=credentials)
-  drive_service = build('drive', 'v3', credentials=credentials)
-  gc = pygsheets.authorize(custom_credentials=credentials, client_secret='credentials.json')
-  return spreadsheet_service,drive_service,gc
-
-def legendGoogleSheets(SimilarAreaDictionary,path , spreadsheetId=0,colorsUsed=[]):
-  # authorize uing json file 
-  # SimilarAreaDictionary.drop(['Rounded', 'Width','Height','R','G','B'], axis=1, inplace=True)
-  spreadsheet_service,drive_service,gc=authorizeLegend()
-  
-  print('colorsss ', colorsUsed)
-########
-  legendTitle='Legend of: ' +path
-  titles=gc.spreadsheet_titles()
-  ids=gc.spreadsheet_ids()
-  print(ids)
-#   # print(titles)
-  if spreadsheetId in  ids :
-    print('found sheet ', spreadsheetId)
-    ws=gc.open_by_key(spreadsheetId)
-    colorsUsed=[]
-    for i in range(len(SimilarAreaDictionary)):
-      colorsUsed.append([SimilarAreaDictionary['R'].iloc[i]  ,SimilarAreaDictionary['G'].iloc[i] , SimilarAreaDictionary['B'].iloc[i]] )
-  
-  if legendTitle in titles:
-    print('found sheet ', legendTitle)
-    ws=gc.open(str(legendTitle))
-    spreadsheetId=ws.id
-    colorsUsed=[]
-    for i in range(len(SimilarAreaDictionary)):
-      colorsUsed.append([SimilarAreaDictionary['R'].iloc[i]  ,SimilarAreaDictionary['G'].iloc[i] , SimilarAreaDictionary['B'].iloc[i]] )
- 
-  else:
-# ####### create new sheet
-    print('creating new sheeet')
-
-    spreadsheet_details = {
-      'properties': {
-          'title': 'Legend of: ' +path
-          }
-      }
-    sheet = spreadsheet_service.spreadsheets().create(body=spreadsheet_details,
-                                      fields='spreadsheetId').execute()
-                                          
-    spreadsheetId = sheet.get('spreadsheetId')
-    permission1 = {
-      'type': 'anyone',
-      'role': 'writer',
-      # 'emailAddress': 'marthe.adr@gmail.com'
-      }
-    # permission2 = {
-    #   'type': 'user',
-    #   'role': 'writer',
-    #   'emailAddress': 'marthe.adr@gmail.com',
-    #   'pendingOwner': True
-    
-    
-    # }
-
-
-    drive_service.permissions().create(fileId=spreadsheetId, body=permission1, supportsAllDrives=True ).execute()
-    # print('llliiiistt',drive_service.permissions().list(fileId=spreadsheetId))
-  ###################3
-  #open sheet 
-  # spreadsheetId='1dtDi_6-g3jkn6ePVlzM6PM3FE8wIHzyL2Rt4ksH59SE'
-    ws=gc.open_by_key(spreadsheetId)
-    
-  sheetId = '0'  # Please set sheet ID.
-  worksheet = ws.worksheet(0)
-  worksheet.title='Legend and data created'
-  worksheet.clear()
-  second_row_data=['Nr','m2','Total','m','Total']
-
-  top_header_format = [
-   
-    {'mergeCells': {
-        'mergeType': 'MERGE_ROWS',
-         'range': {
-             'sheetId': '0',
-             'startRowIndex': 1,
-             'endRowIndex': 2,
-             'startColumnIndex': 3,
-             'endColumnIndex':5
-            
-             
-         }
-    }},
-
-     {'mergeCells': {
-        'mergeType': 'MERGE_ROWS',
-         'range': {
-             'sheetId': '0',
-             'startRowIndex': 1,
-             'endRowIndex': 2,
-             'startColumnIndex': 5,
-             'endColumnIndex':7
-         }
-        
-    }},
-
-     {'mergeCells': {
-        'mergeType': 'MERGE_ROWS',
-         'range': {
-             'sheetId': '0',
-             'startRowIndex': 0,
-             'endRowIndex': 1,
-             'startColumnIndex': 0,
-             'endColumnIndex':7
-         }
-        
-    }}
-  ]
-  spreadsheet_service.spreadsheets().batchUpdate( spreadsheetId=spreadsheetId , body={'requests': top_header_format} ).execute()
-  worksheet.cell((1,1)).value='Legend and Data Created'
-  worksheet.cell((2,1)).value='Guess'
-  worksheet.cell((2,2)).value='Color'
-  worksheet.cell((2,3)).value='Count'
-  worksheet.cell((2,4)).value='Areas'
-  worksheet.cell((2,6)).value='Perimeter'
-  
-  worksheet.update_row(3,second_row_data,col_offset=2)
-
-  worksheet.update_col(3,list(SimilarAreaDictionary['Occurences']),row_offset=3)
-  worksheet.update_col(4,list(SimilarAreaDictionary['Area']),row_offset=3)
-  worksheet.update_col(5,list(SimilarAreaDictionary['Total Area']),row_offset=3)
-  worksheet.update_col(6,list(SimilarAreaDictionary['Perimeter']),row_offset=3)
-  worksheet.update_col(7,list(SimilarAreaDictionary['Total Perimeter']),row_offset=3)
-
-  rowsLen=len(SimilarAreaDictionary.values.tolist()) #kam row -- last row = rowsLen +1
-  lastcell=worksheet.cell((rowsLen+2,1)) #row,col
-  lastcellNotation=str(lastcell.address.label)
-  # worksheet.set_data_validation('A3',lastcellNotation, condition_type='ONE_OF_LIST', condition_values=['Ground Beam','Pile Cap'], showCustomUi=True)
-
-  #get lengths of df
-  columnsLen=len(SimilarAreaDictionary.columns.values.tolist()) #kam column -- last col = columnsLen+1 3shan base0
-  lastUsedCol=columnsLen+1
- 
-  worksheet.adjust_column_width(start=2,end=3)
-  worksheet.adjust_column_width(start=4,end=7,pixel_size=60)
-
-  colorsUsed=[]
-  for i in range(len(SimilarAreaDictionary)):
-    colorsUsed.append([SimilarAreaDictionary['R'].iloc[i]  ,SimilarAreaDictionary['G'].iloc[i] , SimilarAreaDictionary['B'].iloc[i]] )
-  
-  sheetId = '0'  # Please set sheet ID.
-  for i in range(len(colorsUsed)):
-
-    print(colorsUsed[i])
-    r,g,b=colorsUsed[i]
-    body = {
-      "requests": [
-        { 
-          "updateCells": {
-            "range": {
-              "sheetId": sheetId,
-              "startRowIndex": i+3,
-              # "endRowIndex":4 ,
-              "startColumnIndex":1,
-
-              # "endColumnIndex": 0
-            },
-
-            "rows": [
-              {
-                "values": [ 
-                  {
-                    "userEnteredFormat": {
-                      "backgroundColor": {
-                    
-                              "red": r/255,
-                              "green": g/255,
-                              "blue": b/255,
-                              "alpha": 0.4,
-                                   
-                      }
-                      
-                    }
-                  }
-                ]
-              }
-            ],
-            "fields": "userEnteredFormat.backgroundColor",
-        
-          }
-
-
-
-        }
-      ]
-    }
-    res =  spreadsheet_service.spreadsheets().batchUpdate(spreadsheetId=spreadsheetId, body=body).execute()
-  body2={
-  "requests": [
-    {
-      "updateBorders": {
-        "range": {
-          "sheetId": sheetId,
-          "startRowIndex": 0,
-          "endRowIndex": len(SimilarAreaDictionary)+3,
-          "startColumnIndex": 0,
-          "endColumnIndex": 7
-        },
-        "top": {
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "bottom": {
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "left":{
-          "style": "SOLID",
-          "width":2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "right":{
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "innerHorizontal":{
-          "style": "SOLID",
-          "width":2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "innerVertical": {
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-      }
-    }
-  ]
-}
-  spreadsheet_service.spreadsheets().batchUpdate(spreadsheetId=spreadsheetId, body=body2).execute()
-  
-  model_cell =worksheet.cell('A1')
-  model_cell.set_text_format('bold', True)
-  model_cell.set_horizontal_alignment( pygsheets.custom_types.HorizontalAlignment.CENTER )
-  pygsheets.DataRange('A2','G2', worksheet=worksheet).apply_format(model_cell)
-  model_cell.color  = (213/255,	219/255	,255/255)
-  spreadsheet_url = "https://docs.google.com/spreadsheets/d/%s" % spreadsheetId
-  print(spreadsheet_url)
-  drive_service.permissions().update(transferOwnership=True , fileId=spreadsheetId,permissionId='11OfoB4Z6wOVII8mYmbnCbbqTQs7rYA65') 
-  return gc,spreadsheet_service,spreadsheetId ,spreadsheet_url
-#######################
-
-def mapnametoLegend(McTName): 
-  print('aaaaaaaaaaaaaa')
-  print(McTName)
-  lastelement = McTName.pop()
-  print(lastelement[0])
-  # McTNameSplit= re.split(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./<>?]', McTName[0])
-  # namesSplit=x= re.split(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./<>?]', McTName[1])
- 
-  spreadsheet_service,drive_service,gc=authorizeLegend()
-  spreadsheet_key =str(lastelement[0])  # Please set the Spreadsheet ID.
- 
-  ws = gc.open_by_key(spreadsheet_key)
-  guessednamesfinal=getguessnames(gc,ws)
-  sheetnames=[]
-  unit=''
-  # ws.add_worksheet("Summary")  # Please set the new sheet name.
-  for i in ws._sheet_list:
-    print(i)
-    sheetnames.append(i.title)
-    print(i.index)
-  if 'XML Export Summary' in sheetnames:
-    worksheetS = ws.worksheet_by_title('XML Export Summary')
-  else:
-    ws.add_worksheet("XML Export Summary")  # Please set the new sheet name.
-  worksheetw = ws.worksheet(0) #legend
-  worksheetS = ws.worksheet_by_title('XML Export Summary')
-  summaryId= ws[1].id
-  worksheetS.clear()
-  countnames=0
-  row0=['MC_T Name','Qty','Unit']
-  worksheetS.update_row(1,row0)
-  
-  for i in range(len(McTName)):
-    allgbnames=''
-    item=''
-    print(McTName[i][0])
-    
-    firstpart= re.split(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./<>?]', McTName[i][0])
-    
-    # print(firstpart) #[ Ground Beams , m2 ]
-    # if (McTName[i][2]=='area'):
-    if firstpart[1]=='m2':
-      rowvalue=5# column 5
-      ar=0
-      unit='m2'
-    # if (McTName[i][2]=='perimeter'):
-    if firstpart[1]=='m':
-      rowvalue=7# column 7
-      ar=0    
-      unit='m'
-  # # print( worksheet.get_col(5, include_tailing_empty=False) )
-    
-    for m in McTName[i][1]:
-      print(m)
-      # if len(McTName[i][1])>2:
-      if m.startswith('g'):
-        allgbnames+= m +' +'
-        
-        print(m)
-        roww=worksheetw.find(m)
-        print(roww)
-        for j in range(len(roww)):
-          ar+=float(worksheetw.cell((roww[j].row ,rowvalue)).value) 
-          
-      elif m.startswith('p'):
-        allgbnames+= m +' + '
-        
-        print(m)
-        roww=worksheetw.find(m)
-        print(roww)
-        for j in range(len(roww)):
-          ar+=float(worksheetw.cell((roww[j].row ,rowvalue)).value) 
-      else:
-        item+=m + ' ,'
-        print(item)
-    
-    n= McTName[i][0] + ' ( '+ allgbnames[:-2] +' , ' + item[:-1]  + ' )  ' 
-    print(n)
-    
-   
-    # roww=worksheetw.find(allgbnames)
-    # for i in range (len(roww)):
-    #   ar+=float(worksheetw.cell((roww[i].row ,rowvalue)).value)
-    
-  #     print(ar)
-  #   # xx=str(x.address.label)
-    
-  # worksheetS.cell((1,1)).value='aaa'
-
-  # for count in range(len(name[1])):
-    # print(count)
-    rowi=[str(n),ar,firstpart[1]]
-    worksheetS.update_row(i+2,rowi)
-    # worksheetS.adjust_column_width(start=1,end=4)
-    worksheetS.adjust_column_width(start=1,end=1, pixel_size=350)
-    worksheetS.adjust_column_width(start=2,end=2, pixel_size=100)
-    worksheetS.adjust_column_width(start=3,end=3)
-    
-  xx=(worksheetS.cell( ( len(McTName) +1 ,3)) ).address.label
-  model_cell1 =worksheetS.cell('A2')
-  model_cell1.set_horizontal_alignment( pygsheets.custom_types.HorizontalAlignment.LEFT )
-  pygsheets.DataRange('A2', str(xx), worksheet=worksheetS).apply_format(model_cell1)
-
-
-  model_cell =worksheetS.cell('A1')
-  model_cell.set_text_format('bold', True)
-  model_cell.set_horizontal_alignment( pygsheets.custom_types.HorizontalAlignment.CENTER )
-  pygsheets.DataRange('A1','C1', worksheet=worksheetS).apply_format(model_cell)
-  
-  body2={
-  "requests": [
-    {
-      "updateBorders": {
-        "range": {
-          "sheetId": str(summaryId),
-          "startRowIndex": 0,
-          "endRowIndex":  len(McTName) +1 ,
-          "startColumnIndex": 0,
-          "endColumnIndex": 3
-        },
-        "top": {
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "bottom": {
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "left":{
-          "style": "SOLID",
-          "width":2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "right":{
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "innerHorizontal":{
-          "style": "SOLID",
-          "width":2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-        "innerVertical": {
-          "style": "SOLID",
-          "width": 2,
-          "color": {
-            "red": 0.0,
-            "green":0.0,
-            "blue":0.0
-          },
-        },
-      }
-    }
-  ]
-  }
-  spreadsheet_service.spreadsheets().batchUpdate(spreadsheetId=spreadsheet_key, body=body2).execute()
-  return summaryId,guessednamesfinal   
-
-  # print(x,xarea)
-def getguessnames(gc,ws):
-  guessednamesfinal=[]
-  worksheetw = ws.worksheet(0)
-  guessednames=worksheetw.get_col(1, returnas='matrix', include_tailing_empty=False)
-  print(guessednames[2:])
-  for item in guessednames[2:]:
-     if item not in guessednamesfinal:
-        guessednamesfinal.append(item)
-  print(guessednamesfinal)
-  return guessednamesfinal
-
-################################################################