deploying_3_3.py

# -*- coding: utf-8 -*-
"""Deploying 3.2

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1HEw0DdXhDcxtJN1pjs7bCnlhr-wXX3-m
"""

# pip install pymupdf

# pip install ezdxf

def normalize_vertices(vertices):
    """Sort vertices to ensure consistent order."""
    return tuple(sorted(tuple(v) for v in vertices))

def areas_are_similar(area1, area2, tolerance=0.2):
    """Check if two areas are within a given tolerance."""
    return abs(area1 - area2) <= tolerance


from ctypes import sizeof
# -*- coding: utf-8 -*-wj
"""Version to be deployed of 3.2 Calculating area/perimeter

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1XPeCoTBgWSNBYZ3aMKBteP4YG3w4bORs
"""

# pip install ezdxf[draw]

# pip install --upgrade ezdxf

# pip install pymupdf #==1.22.5

# pip install PyPDF2

# pip install ezdxf scipy

"""## Imports"""
import xml.etree.ElementTree as ET
from PyPDF2 import PdfReader, PdfWriter
from PyPDF2.generic import TextStringObject, NameObject, ArrayObject, FloatObject
from PyPDF2.generic import NameObject, TextStringObject, DictionaryObject, FloatObject, ArrayObject

from typing import NewType
from ctypes import sizeof

from PyPDF2 import PdfReader, PdfWriter
from PyPDF2.generic import NameObject, TextStringObject, FloatObject, DictionaryObject

import numpy as np
import cv2
from matplotlib import pyplot as plt
import math
from PIL import Image  , ImageDraw, ImageFont , ImageColor
import fitz
import ezdxf as ez
import sys
from ezdxf import units
# from google.colab.patches import cv2_imshow
from ezdxf.math import OCS, Matrix44, Vec3
import ezdxf
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from shapely.geometry import Point, Polygon as ShapelyPolygon
from ezdxf.math import Vec2
import random
import pandas as pd
# import google_sheet_Legend
import tsadropboxretrieval
from ezdxf import bbox
from math import sin, cos, radians
import google_sheet_Legend
from PyPDF2 import PdfReader
from io import BytesIO

from ezdxf.colors import aci2rgb
from collections import Counter

from math import isclose

from typing import List, Tuple, Any, Set
from shapely.geometry import Polygon, MultiPolygon, GeometryCollection
from shapely.ops import unary_union

import random
import string
import zlib
import base64
import datetime
import uuid
from xml.etree.ElementTree import Element, SubElement, tostring, ElementTree
from xml.dom.minidom import parseString

import colorsys
from xml.etree.ElementTree import Element, SubElement, tostring

import re

import ezdxf
from ezdxf.bbox import extents

def detect_scale_from_page(dxf_path, page_pixel_width, m_per_pixel_from_pdf):
    """
    Detects mm/px scale factor using the bounding box of the entire DXF content.
    """
    doc = ezdxf.readfile(dxf_path)

    #getting the bounding box from modelspace
    msp = doc.modelspace()
    bbox_msp = extents(msp, fast=True)

    if bbox_msp.has_data: #not empty
        min_x, min_y, max_x, max_y = bbox_msp.extmin.x, bbox_msp.extmin.y, bbox_msp.extmax.x, bbox_msp.extmax.y
    else:
        # Try paperspace as a fallback
        psp = doc.layout("Layout1")
        bbox_psp = extents(psp, fast=True)
        if not bbox_psp.has_data:
            raise ValueError("No bounding box data found in modelspace or paperspace.")
        min_x, min_y, max_x, max_y = bbox_psp.extmin.x, bbox_psp.extmin.y, bbox_psp.extmax.x, bbox_psp.extmax.y

    # DXF width
    dxf_width = max_x - min_x

    # PDF width in m
    pdf_metric_width = page_pixel_width * m_per_pixel_from_pdf

    # Correction factor
    correction_factor = dxf_width / pdf_metric_width
    # final_scale = mm_per_pixel_from_pdf * correction_factor

    return correction_factor


"""## Notes"""

#new approach to get width and height of dxf plan
'''
This portion is used to convert vertices read from dxf to pixels in order to accurately locate shapes in the image and pdf
  ratio :
  MeasuredMetric* PixelValue/ DxfMetric = MeasuredPixel
  PixelValue: get from pixel conversion code , second number in the bracker represents the perimeter
  DxfMetric: measured perimeter from foxit

  divide pixelvalue by dxfmetric, will give u a ratio , this is ur dxfratio


'''
AllhatchesCodes=  {
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,
'DiagonalBrick': '''<</Length 138
    /Type/Pattern/PatternType 1/PaintType 1/TilingType 1
    /Resources <<>>
    /Matrix [1 0 0 1 0 0]
    /BBox [0 0 18 18]
    /XStep 18/YStep 18>>stream
    {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w -1 -1 m 19.00001 19.00001 l 9 9 m 0 18 l -1 17 m 1 19.00001 l 17 -1 m 19.00001 1 l S
    endstream'''
,
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  /Matrix[1 0 0 1 0 0]
  /BBox[0 0 18 18]
  /XStep 18/YStep 18>>\nstream\n
  {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w -1 13.5 m 19.00001 13.5 l -1 4.5 m 19.00001 4.5 l S
  endstream'''
,
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  /Matrix[1 0 0 1 0 0]
  /BBox[0 0 18 18]
  /XStep 18/YStep 18>>\nstream\n
  {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w 4.5 19.00001 m 4.5 -1 l 13.5 19.00001 m 13.5 -1 l S
  endstream'''
,
'DiagonalDown':'''<</Length 133
  /Type/Pattern/PatternType 1/PaintType 1/TilingType 1
  /Resources<<>>/Matrix[1 0 0 1 0 0]
  /BBox[0 0 18 18]/XStep 18/YStep 18>>\nstream\n
  {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w -1 19.00001 m 19.00001 -1 l -1 1 m 1 -1 l 17 19.00001 m 19.00001 17 l S
  endstream'''
,
'DiagonalUp':'''<</Length 125
  /Type/Pattern/PatternType 1/PaintType 1/TilingType 1
  /Resources<<>>/Matrix[1 0 0 1 0 0]
  /BBox[0 0 18 18]/XStep 18/YStep 18>>\nstream\n
  {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w -1 17 m 1 19.00001 l -1 -1 m 19.00001 19.00001 l 17 -1 m 19.00001 1 l S
  endstream'''
,
'Grid':'''<</Length 163
    /Type/Pattern/PatternType 1/PaintType 1/TilingType 1
    /Resources<<>>/Matrix[1 0 0 1 0 0]
    /BBox[0 0 18 18]/XStep 18/YStep 18>>\nstream\n
    {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w 4.5 19.00001 m 4.5 -1 l 13.5 19.00001 m 13.5 -1 l -1 13.5 m 19.00001 13.5 l -1 4.5 m 19.00001 4.5 l S
    endstream'''
,
'Weave':'''<</Length 260
    /Type/Pattern/PatternType 1/PaintType 1/TilingType 1
    /Resources<<>>/Matrix[1 0 0 1 0 0]
    /BBox[0 0 18 18]/XStep 18/YStep 18>>\nstream\n
    {fillcolor} rg 0 0 18 18 re f {strokecolor} RG 1 w -1 19.00001 m 4.5 13.5 l -1 7.999999 m 10 19.00001 l 7.999999 19.00001 m 19.00001 7.999999 l 17 19.00001 m 19.00001 17 l -1 -1 m 13.5 13.5 l 4.5 4.5 m 10 -1 l 9 9 m 19.00001 -1 l 18 9 m 13.5 4.5 l S
    endstream'''
,
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,
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1.009572 5.928572 1.285714 5.928572 c h 1.285714 8.5 m 1.561857 8.5 1.785714 8.723858 1.785714 9 c 1.785714 9.276142 1.561857 9.5 1.285714 9.5 c 1.009572 9.5 0.7857141 9.276142 0.7857141 9 c 0.7857141 8.723858 1.009572 8.5 1.285714 8.5 c h 1.285714 11.07143 m 1.561857 11.07143 1.785714 11.29529 1.785714 11.57143 c 1.785714 11.84757 1.561857 12.07143 1.285714 12.07143 c 1.009572 12.07143 0.7857141 11.84757 0.7857141 11.57143 c 0.7857141 11.29529 1.009572 11.07143 1.285714 11.07143 c h 1.285714 13.64286 m 1.561857 13.64286 1.785714 13.86672 1.785714 14.14286 c 1.785714 14.419 1.561857 14.64286 1.285714 14.64286 c 1.009572 14.64286 0.7857141 14.419 0.7857141 14.14286 c 0.7857141 13.86672 1.009572 13.64286 1.285714 13.64286 c h 1.285714 16.21429 m 1.561857 16.21429 1.785714 16.43814 1.785714 16.71429 c 1.785714 16.99043 1.561857 17.21429 1.285714 17.21429 c 1.009572 17.21429 0.7857141 16.99043 0.7857141 16.71429 c 0.7857141 16.43814 1.009572 16.21429 1.285714 16.21429 c h 3.857143 0.7857141 m 4.133285 0.7857141 4.357142 1.009572 4.357142 1.285714 c 4.357142 1.561857 4.133285 1.785714 3.857143 1.785714 c 3.581 1.785714 3.357143 1.561857 3.357143 1.285714 c 3.357143 1.009572 3.581 0.7857141 3.857143 0.7857141 c h 3.857143 3.357143 m 4.133285 3.357143 4.357142 3.581 4.357142 3.857143 c 4.357142 4.133285 4.133285 4.357142 3.857143 4.357142 c 3.581 4.357142 3.357143 4.133285 3.357143 3.857143 c 3.357143 3.581 3.581 3.357143 3.857143 3.357143 c h 3.857143 5.928572 m 4.133285 5.928572 4.357142 6.15243 4.357142 6.428572 c 4.357142 6.704715 4.133285 6.928572 3.857143 6.928572 c 3.581 6.928572 3.357143 6.704715 3.357143 6.428572 c 3.357143 6.15243 3.581 5.928572 3.857143 5.928572 c h 3.857143 8.5 m 4.133285 8.5 4.357142 8.723858 4.357142 9 c 4.357142 9.276142 4.133285 9.5 3.857143 9.5 c 3.581 9.5 3.357143 9.276142 3.357143 9 c 3.357143 8.723858 3.581 8.5 3.857143 8.5 c h 3.857143 11.07143 m 4.133285 11.07143 4.357142 11.29529 4.357142 11.57143 c 4.357142 11.84757 4.133285 12.07143 3.857143 12.07143 c 3.581 12.07143 3.357143 11.84757 3.357143 11.57143 c 3.357143 11.29529 3.581 11.07143 3.857143 11.07143 c h 3.857143 13.64286 m 4.133285 13.64286 4.357142 13.86672 4.357142 14.14286 c 4.357142 14.419 4.133285 14.64286 3.857143 14.64286 c 3.581 14.64286 3.357143 14.419 3.357143 14.14286 c 3.357143 13.86672 3.581 13.64286 3.857143 13.64286 c h 3.857143 16.21429 m 4.133285 16.21429 4.357142 16.43814 4.357142 16.71429 c 4.357142 16.99043 4.133285 17.21429 3.857143 17.21429 c 3.581 17.21429 3.357143 16.99043 3.357143 16.71429 c 3.357143 16.43814 3.581 16.21429 3.857143 16.21429 c h 6.428572 0.7857141 m 6.704715 0.7857141 6.928572 1.009572 6.928572 1.285714 c 6.928572 1.561857 6.704715 1.785714 6.428572 1.785714 c 6.15243 1.785714 5.928572 1.561857 5.928572 1.285714 c 5.928572 1.009572 6.15243 0.7857141 6.428572 0.7857141 c h 6.428572 3.357143 m 6.704715 3.357143 6.928572 3.581 6.928572 3.857143 c 6.928572 4.133285 6.704715 4.357142 6.428572 4.357142 c 6.15243 4.357142 5.928572 4.133285 5.928572 3.857143 c 5.928572 3.581 6.15243 3.357143 6.428572 3.357143 c h 6.428572 5.928572 m 6.704715 5.928572 6.928572 6.15243 6.928572 6.428572 c 6.928572 6.704715 6.704715 6.928572 6.428572 6.928572 c 6.15243 6.928572 5.928572 6.704715 5.928572 6.428572 c 5.928572 6.15243 6.15243 5.928572 6.428572 5.928572 c h 6.428572 8.5 m 6.704715 8.5 6.928572 8.723858 6.928572 9 c 6.928572 9.276142 6.704715 9.5 6.428572 9.5 c 6.15243 9.5 5.928572 9.276142 5.928572 9 c 5.928572 8.723858 6.15243 8.5 6.428572 8.5 c h 6.428572 11.07143 m 6.704715 11.07143 6.928572 11.29529 6.928572 11.57143 c 6.928572 11.84757 6.704715 12.07143 6.428572 12.07143 c 6.15243 12.07143 5.928572 11.84757 5.928572 11.57143 c 5.928572 11.29529 6.15243 11.07143 6.428572 11.07143 c h 6.428572 13.64286 m 6.704715 13.64286 6.928572 13.86672 6.928572 14.14286 c 6.928572 14.419 6.704715 14.64286 6.428572 14.64286 c 6.15243 14.64286 5.928572 14.419 5.928572 14.14286 c 5.928572 13.86672 6.15243 13.64286 6.428572 13.64286 c h 6.428572 16.21429 m 6.704715 16.21429 6.928572 16.43814 6.928572 16.71429 c 6.928572 16.99043 6.704715 17.21429 6.428572 17.21429 c 6.15243 17.21429 5.928572 16.99043 5.928572 16.71429 c 5.928572 16.43814 6.15243 16.21429 6.428572 16.21429 c h 9 0.7857141 m 9.276142 0.7857141 9.5 1.009572 9.5 1.285714 c 9.5 1.561857 9.276142 1.785714 9 1.785714 c 8.723858 1.785714 8.5 1.561857 8.5 1.285714 c 8.5 1.009572 8.723858 0.7857141 9 0.7857141 c h 9 3.357143 m 9.276142 3.357143 9.5 3.581 9.5 3.857143 c 9.5 4.133285 9.276142 4.357142 9 4.357142 c 8.723858 4.357142 8.5 4.133285 8.5 3.857143 c 8.5 3.581 8.723858 3.357143 9 3.357143 c h 9 5.928572 m 9.276142 5.928572 9.5 6.15243 9.5 6.428572 c 9.5 6.704715 9.276142 6.928572 9 6.928572 c 8.723858 6.928572 8.5 6.704715 8.5 6.428572 c 8.5 6.15243 8.723858 5.928572 9 5.928572 c h 9 8.5 m 9.276142 8.5 9.5 8.723858 9.5 9 c 9.5 9.276142 9.276142 9.5 9 9.5 c 8.723858 9.5 8.5 9.276142 8.5 9 c 8.5 8.723858 8.723858 8.5 9 8.5 c h 9 11.07143 m 9.276142 11.07143 9.5
  11.29529 9.5 11.57143 c 9.5 11.84757 9.276142 12.07143 9 12.07143 c 8.723858 12.07143 8.5 11.84757 8.5 11.57143 c 8.5 11.29529 8.723858 11.07143 9 11.07143 c h 9 13.64286 m 9.276142 13.64286 9.5 13.86672 9.5 14.14286 c 9.5 14.419 9.276142 14.64286 9 14.64286 c 8.723858 14.64286 8.5 14.419 8.5 14.14286 c 8.5 13.86672 8.723858 13.64286 9 13.64286 c h 9 16.21429 m 9.276142 16.21429 9.5 16.43814 9.5 16.71429 c 9.5 16.99043 9.276142 17.21429 9 17.21429 c 8.723858 17.21429 8.5 16.99043 8.5 16.71429 c 8.5 16.43814 8.723858 16.21429 9 16.21429 c h 11.57143 0.7857141 m 11.84757 0.7857141 12.07143 1.009572 12.07143 1.285714 c 12.07143 1.561857 11.84757 1.785714 11.57143 1.785714 c 11.29529 1.785714 11.07143 1.561857 11.07143 1.285714 c 11.07143 1.009572 11.29529 0.7857141 11.57143 0.7857141 c h 11.57143 3.357143 m 11.84757 3.357143 12.07143 3.581 12.07143 3.857143 c 12.07143 4.133285 11.84757 4.357142 11.57143 4.357142 c 11.29529 4.357142 11.07143 4.133285 11.07143 3.857143 c 11.07143 3.581 11.29529 3.357143 11.57143 3.357143 c h 11.57143 5.928572 m 11.84757 5.928572 12.07143 6.15243 12.07143 6.428572 c 12.07143 6.704715 11.84757 6.928572 11.57143 6.928572 c 11.29529 6.928572 11.07143 6.704715 11.07143 6.428572 c 11.07143 6.15243 11.29529 5.928572 11.57143 5.928572 c h 11.57143 8.5 m 11.84757 8.5 12.07143 8.723858 12.07143 9 c 12.07143 9.276142 11.84757 9.5 11.57143 9.5 c 11.29529 9.5 11.07143 9.276142 11.07143 9 c 11.07143 8.723858 11.29529 8.5 11.57143 8.5 c h 11.57143 11.07143 m 11.84757 11.07143 12.07143 11.29529 12.07143 11.57143 c 12.07143 11.84757 11.84757 12.07143 11.57143 12.07143 c 11.29529 12.07143 11.07143 11.84757 11.07143 11.57143 c 11.07143 11.29529 11.29529 11.07143 11.57143 11.07143 c h 11.57143 13.64286 m 11.84757 13.64286 12.07143 13.86672 12.07143 14.14286 c 12.07143 14.419 11.84757 14.64286 11.57143 14.64286 c 11.29529 14.64286 11.07143 14.419 11.07143 14.14286 c 11.07143 13.86672 11.29529 13.64286 11.57143 13.64286 c h 11.57143 16.21429 m 11.84757 16.21429 12.07143 16.43814 12.07143 16.71429 c 12.07143 16.99043 11.84757 17.21429 11.57143 17.21429 c 11.29529 17.21429 11.07143 16.99043 11.07143 16.71429 c 11.07143 16.43814 11.29529 16.21429 11.57143 16.21429 c h 14.14286 0.7857141 m 14.419 0.7857141 14.64286 1.009572 14.64286 1.285714 c 14.64286 1.561857 14.419 1.785714 14.14286 1.785714 c 13.86672 1.785714 13.64286 1.561857 13.64286 1.285714 c 13.64286 1.009572 13.86672 0.7857141 14.14286 0.7857141 c h 14.14286 3.357143 m 14.419 3.357143 14.64286 3.581 14.64286 3.857143 c 14.64286 4.133285 14.419 4.357142 14.14286 4.357142 c 13.86672 4.357142 13.64286 4.133285 13.64286 3.857143 c 13.64286 3.581 13.86672 3.357143 14.14286 3.357143 c h 14.14286 5.928572 m 14.419 5.928572 14.64286 6.15243 14.64286 6.428572 c 14.64286 6.704715 14.419 6.928572 14.14286 6.928572 c 13.86672 6.928572 13.64286 6.704715 13.64286 6.428572 c 13.64286 6.15243 13.86672 5.928572 14.14286 5.928572 c h 14.14286 8.5 m 14.419 8.5 14.64286 8.723858 14.64286 9 c 14.64286 9.276142 14.419 9.5 14.14286 9.5 c 13.86672 9.5 13.64286 9.276142 13.64286 9 c 13.64286 8.723858 13.86672 8.5 14.14286 8.5 c h 14.14286 11.07143 m 14.419 11.07143 14.64286 11.29529 14.64286 11.57143 c 14.64286 11.84757 14.419 12.07143 14.14286 12.07143 c 13.86672 12.07143 13.64286 11.84757 13.64286 11.57143 c 13.64286 11.29529 13.86672 11.07143 14.14286 11.07143 c h 14.14286 13.64286 m 14.419 13.64286 14.64286 13.86672 14.64286 14.14286 c 14.64286 14.419 14.419 14.64286 14.14286 14.64286 c 13.86672 14.64286 13.64286 14.419 13.64286 14.14286 c 13.64286 13.86672 13.86672 13.64286 14.14286 13.64286 c h 14.14286 16.21429 m 14.419 16.21429 14.64286 16.43814 14.64286 16.71429 c 14.64286 16.99043 14.419 17.21429 14.14286 17.21429 c 13.86672 17.21429 13.64286 16.99043 13.64286 16.71429 c 13.64286 16.43814 13.86672 16.21429 14.14286 16.21429 c h 16.71429 0.7857141 m 16.99043 0.7857141 17.21429 1.009572 17.21429 1.285714 c 17.21429 1.561857 16.99043 1.785714 16.71429 1.785714 c 16.43814 1.785714 16.21429 1.561857 16.21429 1.285714 c 16.21429 1.009572 16.43814 0.7857141 16.71429 0.7857141 c h 16.71429 3.357143 m 16.99043 3.357143 17.21429 3.581 17.21429 3.857143 c 17.21429 4.133285 16.99043 4.357142 16.71429 4.357142 c 16.43814 4.357142 16.21429 4.133285 16.21429 3.857143 c 16.21429 3.581 16.43814 3.357143 16.71429 3.357143 c h 16.71429 5.928572 m 16.99043 5.928572 17.21429 6.15243 17.21429 6.428572 c 17.21429 6.704715 16.99043 6.928572 16.71429 6.928572 c 16.43814 6.928572 16.21429 6.704715 16.21429 6.428572 c 16.21429 6.15243 16.43814 5.928572 16.71429 5.928572 c h 16.71429 8.5 m 16.99043 8.5 17.21429 8.723858 17.21429 9 c 17.21429 9.276142 16.99043 9.5 16.71429 9.5 c 16.43814 9.5 16.21429 9.276142 16.21429 9 c 16.21429 8.723858 16.43814 8.5 16.71429 8.5 c h 16.71429 11.07143 m 16.99043 11.07143 17.21429 11.29529 17.21429 11.57143 c 17.21429 11.84757 16.99043 12.07143 16.71429 12.07143 c 16.43814 12.07143 16.21429 11.84757 16.21429 11.57143 c 16.21429 11.29529 16.43814 11.07143 16.71429 11.07143 c h 16.71429 13.64286 m 16.99043 13.64286 17.21429 13.86672 17.21429 14.14286 c 17.21429 14.419 16.99043 14.64286 16.71429 14.64286 c 16.43814 14.64286 16.21429 14.419 16.21429 14.14286 c 16.21429 13.86672 16.43814 13.64286 16.71429 13.64286 c h 16.71429 16.21429 m 16.99043 16.21429 17.21429 16.43814 17.21429 16.71429 c 17.21429 16.99043 16.99043 17.21429 16.71429 17.21429 c 16.43814 17.21429 16.21429 16.99043 16.21429 16.71429 c 16.21429 16.43814 16.43814 16.21429 16.71429 16.21429 c h
  {strokecolor} rg f
  endstream'''
}

HatchStyleTemplates={
  'Brick' :'/PatternName(Brick)', #BBObjPtr
  'DiagonalBrick':'/PatternName(Diagonal Brick)',
  'Horizontal':'/PatternName(Horizontal)',
  'Vertical':'/PatternName(Vertical)',
  'DiagonalDown':'/PatternName(Diagonal Down)',
  'DiagonalUp':'/PatternName(Diagonal Up)',
  'Grid':'/PatternName(Grid)',
  'Weave':'/PatternName(Weave)',
  '10Dots':'/PatternName(10% Dots)',
  '20Dots':'/PatternName(20% Dots)',
  '30Dots':'/PatternName(30% Dots)'
}

def calculate_bounding_rect(vertices):
    xs = [pt[0] for pt in vertices]
    ys = [pt[1] for pt in vertices]
    min_x = min(xs)
    max_x = max(xs)
    min_y = min(ys)
    max_y = max(ys)
    return [min_x, min_y, max_x, max_y]
def generate_annotation_xml_block(vertices, area_text, author, custom_data: dict, column_order: list, index: int,
                                   type_internal: str = 'Bluebeam.PDF.Annotations.AnnotationMeasureArea',
                                   subject: str = 'Area Measurement',
                                   label: str = '',opacity:str='',
                                   color:str='', linestyle:str='',
                                   hatchstyle:str='',hatchLinescolor:str='',
                                    bb_objptrMeas:str=''):
    now = datetime.datetime.utcnow()
    mod_date = now.strftime("D:%Y%m%d%H%M%S+00'00'")
    creation_date = now.isoformat() + 'Z'
    id_str = "fitz-" + uuid.uuid4().hex[:4].upper()

    vert_str = ' '.join([f'{x:.4f}' for point in vertices for x in point])
    ordered_column_values = [f'({custom_data.get(col, "")})' for col in column_order]
    bsi_column_data = ''.join(ordered_column_values)
    meastype=''
    if subject.startswith('Area'):
      meastype='129'
      polygonpolylineDimension='/PolygonDimension'
      polygonpolyline='/Polygon'
    elif subject.startswith('Perimeter'):
      meastype='130'
      polygonpolylineDimension='/PolyLineDimension'
      polygonpolyline='/PolyLine'
    rectvertices=calculate_bounding_rect(vertices)

    raw_text = f'''<<
/DS(font: Helvetica 12pt; text-align:center; line-height:13.8pt; color:#FF0000)
/Cap false
/AlignOnSegment true
/MeasurementTypes {meastype}
/SlopeType 1
/PitchRun 12
/IT
{polygonpolylineDimension}
/Vertices[{vert_str}]
/IC[{color}]
/Pattern/{hatchstyle}/PatternColor[{hatchLinescolor}]
/FillOpacity {opacity}
/T({author})
/CA {opacity}
/RC(<?xml version="1.0"?><body xmlns:xfa="http://www.xfa.org/schema/xfa-data/1.0/" xfa:contentType="text/html" xfa:APIVersion="BluebeamPDFRevu:2018" xfa:spec="2.2.0" style="font:Helvetica 12pt; text-align:center; line-height:13.8pt; color:#FF0000" xmlns="http://www.w3.org/1999/xhtml"><p>{area_text}</p></body>)
/Label({label})
/Subj({subject})
/Measure/BBObjPtr_{bb_objptrMeas}
/BSIColumnData[{bsi_column_data}]
/NM({id_str})
/Subtype/{polygonpolyline}
/Rect[{rectvertices[0]} {rectvertices[1]} {rectvertices[2]} {rectvertices[3]}]
/Contents({area_text})
/F 4
/C[{color}]
/BS{linestyle}
/M({mod_date})
>>'''.encode('utf-8')

    compressed = zlib.compress(raw_text)
    base64_raw = base64.b16encode(compressed).lower().decode()

    annotation = Element('Annotation')
    SubElement(annotation, 'Page').text = '1'
    SubElement(annotation, 'Contents').text = area_text
    SubElement(annotation, 'ModDate').text = creation_date
    SubElement(annotation, 'Color').text = '#B7B7E8'
    SubElement(annotation, 'Type').text = 'Polygon'
    SubElement(annotation, 'ID').text = id_str
    SubElement(annotation, 'TypeInternal').text = type_internal
    SubElement(annotation, 'Raw').text = base64_raw
    SubElement(annotation, 'Index').text = str(index)

    custom = SubElement(annotation, 'Custom')
    for key, value in custom_data.items():
        SubElement(custom, key).text = value

    SubElement(annotation, 'Subject').text = subject
    SubElement(annotation, 'CreationDate').text = creation_date
    SubElement(annotation, 'Author').text = author
    SubElement(annotation, 'Label').text = label

    return annotation

def generate_bb_objptr():
    return ''.join(random.choices(string.ascii_uppercase, k=16))

def compresslikeBBRaw(textToCompress):
  decompressedX = textToCompress.encode('utf-8')
  print(decompressedX)
  recompressedX = zlib.compress(decompressedX)
  print(recompressedX.hex())
  return recompressedX.hex()

def setBrickHatch(fillcolor,strokecolor):
  # resourceid='789cf30b0877f2f40cf30f758ff48e0a0df3040029f004fd'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['Brick'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['Brick'],compressedRaw, resourceid

def setDiagonalBrickHatch(fillcolor,strokecolor):
  # resourceid='789c0b0d8cf47274f60d0df28a740ef4f4f3020029ab04da'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['DiagonalBrick'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['DiagonalBrick'],compressedRaw,resourceid

def setHorizontalHatch(fillcolor,strokecolor):
  # resourceid='789cf3720b76f6f072f173f58cf071f209f00000273604a3'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['Horizontal'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['Horizontal'],compressedRaw,resourceid

def setVerticalHatch(fillcolor,strokecolor):
  # resourceid='789cf30d080ef4f609088b74740ff0890a7607002a1904f0'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['Vertical'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return  'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['Vertical'],compressedRaw,resourceid

def setDiagonalDownHatch(fillcolor,strokecolor):
  # resourceid='789cf3f28b74f477f7770b0c7675f68f74f60300288f04c3'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['DiagonalDown'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['DiagonalDown'],compressedRaw,resourceid

def setDiagonalUpHatch(fillcolor,strokecolor):
  # resourceid='789c0b8a70f30df4f70b09f40cf6f108757606002a2304dc'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['DiagonalUp'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return  'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['DiagonalUp'],compressedRaw,resourceid

def setGridHatch(fillcolor,strokecolor):
  # resourceid='789c730b71738e0a760cf3758972f370740a0300286b04ba'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['Grid'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return  'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['Grid'],compressedRaw,resourceid

def setWeaveHatch(fillcolor,strokecolor):
  # resourceid='789cf30af775f2f1f776720d8972740c8af40500285c04c6'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['Weave'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['Weave'],compressedRaw,resourceid

def set10DotsHatch(fillcolor,strokecolor):
  # resourceid='789cf3740f71f6770d0e8c0a0f76f50e0df00600291c04e4'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['10Dots'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['10Dots'],compressedRaw,resourceid

def set20DotsHatch(fillcolor,strokecolor):
  # resourceid='789c738f0cf70bf5f0f0770a0df471760df7000029b004d5'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['20Dots'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['20Dots'],compressedRaw,resourceid

def set30DotsHatch(fillcolor,strokecolor):
  # resourceid='789cf38c747789f4f68a8c0cf2f6f2f676f2070029b104dc'
  randombb_objptr=generate_bb_objptr()
  resourceid=compresslikeBBRaw(randombb_objptr)
  compressedRaw=compresslikeBBRaw(AllhatchesCodes['30Dots'].format(fillcolor=fillcolor, strokecolor=strokecolor))
  return 'BBObjPtr_'+randombb_objptr+HatchStyleTemplates['30Dots'],compressedRaw,resourceid

def save_multiple_annotations_bax(annotations, output_path, column_order,pdfWidth,pdfHeight):
    """
    annotations: list of dicts, each with:
      - vertices: list of [x, y]
      - text: str (label/tooltip)
      - author: str
      - custom_data: dict of custom field values
      - type_internal: str (e.g., Bluebeam.PDF.Annotations.AnnotationMeasurePerimeter)
      - subject: str (e.g., Perimeter Measurement)
    """
    globalhatches=[]
    scales=[]
    doc = Element('Document', Version='1')
    ########## Subelement1 - page ################
    page = SubElement(doc, 'Page', Index='0')
    SubElement(page, 'Label').text = '1'
    SubElement(page, 'Width').text = str(pdfWidth)
    SubElement(page, 'Height').text = str(pdfHeight)

    for i, ann in enumerate(annotations):

        bb_objptrMeas=generate_bb_objptr()
        resourceidComp=compresslikeBBRaw(bb_objptrMeas)
        scales.append(resourceidComp)

        hatchstyle_key = ann.get('hatchstyle')  # e.g., 'Brick'
        if hatchstyle_key not in globalhatches and hatchstyle_key:
          globalhatches.append([hatchstyle_key[2],hatchstyle_key[1]]) # id, raw
          hatchstyle=hatchstyle_key[0]
        else:
          hatchstyle='none'

        annotation_xml = generate_annotation_xml_block(
            vertices=ann['vertices'],
            area_text=ann['text'],
            author=ann['author'],
            custom_data=ann['custom_data'],
            column_order=column_order,
            index=i,
            bb_objptrMeas=bb_objptrMeas,
            type_internal=ann.get('type_internal', 'Bluebeam.PDF.Annotations.AnnotationMeasureArea'),
            subject=ann.get('subject', 'Area Measurement'),
            label=ann.get('label', 'label1'),
            opacity=ann.get('opacity', ''),
            color=ann.get('color', ''),
            linestyle=ann.get('linestyle', ''),
            hatchstyle=hatchstyle,
            hatchLinescolor=ann.get('hatchLinescolor',''),
        )
        page.append(annotation_xml)
    ################# Subelement 2 - Global resources############
    GlobalResources = SubElement(doc, 'GlobalResources')
    for hatch in globalhatches:
        Resource = SubElement(GlobalResources, 'Resource')
        SubElement(Resource, 'ID').text = hatch[0]
        SubElement(Resource, 'Raw').text = hatch[1]
    for scale in scales:
        Resource = SubElement(GlobalResources, 'Resource')
        SubElement(Resource, 'ID').text = scale
        SubElement(Resource, 'Raw').text = '789c85d04f0b82401005f0af3247bd34a35176b085503c55847f22a80e2a4b7858ad7537e8dba7951ea2a5e330efc783e7fb983eae1c373c6fb5e498e842f577bcc6d872a014b00407848d87e310dd6a5170193552e40a33abfb0540139ace5ccff316188243844962d98c9d3134b297eba2f4255626d1dee06d3e200a4149cd47989ae0c99dd32fb8ebe0a8f7265dea3fb5b9bc7095d5950a9aface655b3575bf070d8b30f604438f6873'
    bax_xml= tostring(doc, encoding="unicode", method="xml") #tostring(doc, encoding="utf-8", method="xml").decode("utf-8")
    return bax_xml
"""PDF to image"""

def pdftoimg(datadoc,pdf_content=0):
  if pdf_content:
     doc = fitz.open(stream=pdf_content, filetype="pdf")
  else:
     doc = fitz.open('pdf',datadoc)
  page=doc[0]
  pix = page.get_pixmap()  # render page to an image
  pl=Image.frombytes('RGB', [pix.width,pix.height],pix.samples)
  img=np.array(pl)
  img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
  return img


# Standard ISO paper sizes in inches
ISO_SIZES_INCHES = {
    "A0": (33.11, 46.81),
    "A1": (23.39, 33.11),
    "A2": (16.54, 23.39),
    "A3": (11.69, 16.54),
    "A4": (8.27, 11.69),
    "A5": (5.83, 8.27),
    "A6": (4.13, 5.83),
    "A7": (2.91, 4.13),
    "A8": (2.05, 2.91),
    "A9": (1.46, 2.05),
    "A10": (1.02, 1.46)
}

def get_paper_size_in_inches(width, height):
    """Find the closest matching paper size in inches."""
    for size, (w, h) in ISO_SIZES_INCHES.items():
        if (abs(w - width) < 0.1 and abs(h - height) < 0.1) or (abs(w - height) < 0.1 and abs(h - width) < 0.1):
            return size
    return "Unknown Size"

def analyze_pdf(datadoc,pdf_content=0):
    # Open the PDF file
    if pdf_content:
       pdf_document = fitz.open(stream=pdf_content, filetype="pdf")
    else:
      pdf_document = fitz.open('pdf',datadoc)

    # Iterate through pages and print their sizes
    for page_number in range(len(pdf_document)):
        page = pdf_document[page_number]
        rect = page.rect
        width_points, height_points = rect.width, rect.height

        # Convert points to inches
        width_inches, height_inches = width_points / 72, height_points / 72

        paper_size = get_paper_size_in_inches(width_inches, height_inches)

        print(f"Page {page_number + 1}: {width_inches:.2f} x {height_inches:.2f} inches ({paper_size})")

    pdf_document.close()
    return width_inches , height_inches , paper_size


def get_dxfSize(dxfpath):

  doc = ezdxf.readfile(dxfpath)
  msp = doc.modelspace()
  # Create a cache for bounding box calculations
  # Get the overall bounding box for all entities in the modelspace
  cache = bbox.Cache()
  overall_bbox = bbox.extents(msp, cache=cache)
  print("Overall Bounding Box:", overall_bbox)
  print(overall_bbox.extmin[0]+overall_bbox.extmax[0], overall_bbox.extmin[1]+overall_bbox.extmax[1])

  return overall_bbox.extmin[0]+overall_bbox.extmax[0], overall_bbox.extmin[1]+overall_bbox.extmax[1]



def switch_case(argument):
    switcher = {
      "A0": 1.27,
      "A1": 2.54,
      "A2": 5.08,
      "A3": 10.16,
      "A4": 20.32,
      "A5": 40.64,
      "A6": 81.28,
      "A7": 162.56,
      "A8": 325.12,
      "A9": 650.24,
      "A10": 1300.48
    }
    # Get the value from the dictionary; if not found, return a default value
    print("Final Ratio=",switcher.get(argument, 1))
    return switcher.get(argument, 1)




def RetriveRatio(datadoc,dxfpath,pdf_content=0):
  if pdf_content:
     width,height,paper_size = analyze_pdf (datadoc,pdf_content)
  else:
     width,height,paper_size = analyze_pdf (datadoc)
     

  if(width > height ):
    bigger=width
  else:
    bigger=height

  width_dxf,height_dxf = get_dxfSize(dxfpath)

  if(width_dxf > height_dxf ):
    bigger_dxf=width_dxf
  else:
    bigger_dxf=height_dxf

  if(0.2 < bigger_dxf/bigger < 1.2):
    print("bigger_dxf/bigger",bigger/bigger_dxf)
    argument = paper_size
    FinalRatio=switch_case(argument)
  else:
    FinalRatio=1
  return FinalRatio,width_dxf


"""Flips image
DXF origin is at the bottom left while img origin is top left
"""

def flip(img):
  height, width = img.shape[:2]

  # Define the rotation angle (clockwise)
  angle = 180

  # Calculate the rotation matrix
  rotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), angle, 1)

  # Rotate the image
  rotated_image = cv2.warpAffine(img, rotation_matrix, (width, height))
  flipped_horizontal = cv2.flip(rotated_image, 1)
  return flipped_horizontal



def aci_to_rgb(aci):
    aci_rgb_map = {
        0: (0, 0, 0),
        1: (255, 0, 0),
        2: (255, 255, 0),
        3: (0, 255, 0),
        4: (0, 255, 255),
        5: (0, 0, 255),
        6: (255, 0, 255),
        7: (255, 255, 255),
        8: (65, 65, 65),
        9: (128, 128, 128),
        10: (255, 0, 0),
        11: (255, 170, 170),
        12: (189, 0, 0),
        13: (189, 126, 126),
        14: (129, 0, 0),
        15: (129, 86, 86),
        16: (104, 0, 0),
        17: (104, 69, 69),
        18: (79, 0, 0),
        19: (79, 53, 53),
        20: (255, 63, 0),
        21: (255, 191, 170),
        22: (189, 46, 0),
        23: (189, 141, 126),
        24: (129, 31, 0),
        25: (129, 96, 86),
        26: (104, 25, 0),
        27: (104, 78, 69),
        28: (79, 19, 0),
        29: (79, 59, 53),
        30: (255, 127, 0),
        31: (255, 212, 170),
        32: (189, 94, 0),
        33: (189, 157, 126),
        34: (129, 64, 0),
        35: (129, 107, 86),
        36: (104, 52, 0),
        37: (104, 86, 69),
        38: (79, 39, 0),
        39: (79, 66, 53),
        40: (255, 191, 0),
        41: (255, 234, 170),
        42: (189, 141, 0),
        43: (189, 173, 126),
        44: (129, 96, 0),
        45: (129, 118, 86),
        46: (104, 78, 0),
        47: (104, 95, 69),
        48: (79, 59, 0),
        49: (79, 73, 53),
        50: (255, 255, 0),
        51: (255, 255, 170),
        52: (189, 189, 0),
        53: (189, 189, 126),
        54: (129, 129, 0),
        55: (129, 129, 86),
        56: (104, 104, 0),
        57: (104, 104, 69),
        58: (79, 79, 0),
        59: (79, 79, 53),
        60: (191, 255, 0),
        61: (234, 255, 170),
        62: (141, 189, 0),
        63: (173, 189, 126),
        64: (96, 129, 0),
        65: (118, 129, 86),
        66: (78, 104, 0),
        67: (95, 104, 69),
        68: (59, 79, 0),
        69: (73, 79, 53),
        70: (127, 255, 0),
        71: (212, 255, 170),
        72: (94, 189, 0),
        73: (157, 189, 126),
        74: (64, 129, 0),
        75: (107, 129, 86),
        76: (52, 104, 0),
        77: (86, 104, 69),
        78: (39, 79, 0),
        79: (66, 79, 53),
        80: (63, 255, 0),
        81: (191, 255, 170),
        82: (46, 189, 0),
        83: (141, 189, 126),
        84: (31, 129, 0),
        85: (96, 129, 86),
        86: (25, 104, 0),
        87: (78, 104, 69),
        88: (19, 79, 0),
        89: (59, 79, 53),
        90: (0, 255, 0),
        91: (170, 255, 170),
        92: (0, 189, 0),
        93: (126, 189, 126),
        94: (0, 129, 0),
        95: (86, 129, 86),
        96: (0, 104, 0),
        97: (69, 104, 69),
        98: (0, 79, 0),
        99: (53, 79, 53),
        100: (0, 255, 63),
        101: (170, 255, 191),
        102: (0, 189, 46),
        103: (126, 189, 141),
        104: (0, 129, 31),
        105: (86, 129, 96),
        106: (0, 104, 25),
        107: (69, 104, 78),
        108: (0, 79, 19),
        109: (53, 79, 59),
        110: (0, 255, 127),
        111: (170, 255, 212),
        112: (0, 189, 94),
        113: (126, 189, 157),
        114: (0, 129, 64),
        115: (86, 129, 107),
        116: (0, 104, 52),
        117: (69, 104, 86),
        118: (0, 79, 39),
        119: (53, 79, 66),
        120: (0, 255, 191),
        121: (170, 255, 234),
        122: (0, 189, 141),
        123: (126, 189, 173),
        124: (0, 129, 96),
        125: (86, 129, 118),
        126: (0, 104, 78),
        127: (69, 104, 95),
        128: (0, 79, 59),
        129: (53, 79, 73),
        130: (0, 255, 255),
        131: (170, 255, 255),
        132: (0, 189, 189),
        133: (126, 189, 189),
        134: (0, 129, 129),
        135: (86, 129, 129),
        136: (0, 104, 104),
        137: (69, 104, 104),
        138: (0, 79, 79),
        139: (53, 79, 79),
        140: (0, 191, 255),
        141: (170, 234, 255),
        142: (0, 141, 189),
        143: (126, 173, 189),
        144: (0, 96, 129),
        145: (86, 118, 129),
        146: (0, 78, 104),
        147: (69, 95, 104),
        148: (0, 59, 79),
        149: (53, 73, 79),
        150: (0, 127, 255),
        151: (170, 212, 255),
        152: (0, 94, 189),
        153: (126, 157, 189),
        154: (0, 64, 129),
        155: (86, 107, 129),
        156: (0, 52, 104),
        157: (69, 86, 104),
        158: (0, 39, 79),
        159: (53, 66, 79),
        160: (0, 63, 255),
        161: (170, 191, 255),
        162: (0, 46, 189),
        163: (126, 141, 189),
        164: (0, 31, 129),
        165: (86, 96, 129),
        166: (0, 25, 104),
        167: (69, 78, 104),
        168: (0, 19, 79),
        169: (53, 59, 79),
        170: (0, 0, 255),
        171: (170, 170, 255),
        172: (0, 0, 189),
        173: (126, 126, 189),
        174: (0, 0, 129),
        175: (86, 86, 129),
        176: (0, 0, 104),
        177: (69, 69, 104),
        178: (0, 0, 79),
        179: (53, 53, 79),
        180: (63, 0, 255),
        181: (191, 170, 255),
        182: (46, 0, 189),
        183: (141, 126, 189),
        184: (31, 0, 129),
        185: (96, 86, 129),
        186: (25, 0, 104),
        187: (78, 69, 104),
        188: (19, 0, 79),
        189: (59, 53, 79),
        190: (127, 0, 255),
        191: (212, 170, 255),
        192: (94, 0, 189),
        193: (157, 126, 189),
        194: (64, 0, 129),
        195: (107, 86, 129),
        196: (52, 0, 104),
        197: (86, 69, 104),
        198: (39, 0, 79),
        199: (66, 53, 79),
        200: (191, 0, 255),
        201: (234, 170, 255),
        202: (141, 0, 189),
        203: (173, 126, 189),
        204: (96, 0, 129),
        205: (118, 86, 129),
        206: (78, 0, 104),
        207: (95, 69, 104),
        208: (59, 0, 79),
        209: (73, 53, 79),
        210: (255, 0, 255),
        211: (255, 170, 255),
        212: (189, 0, 189),
        213: (189, 126, 189),
        214: (129, 0, 129),
        215: (129, 86, 129),
        216: (104, 0, 104),
        217: (104, 69, 104),
        218: (79, 0, 79),
        219: (79, 53, 79),
        220: (255, 0, 191),
        221: (255, 170, 234),
        222: (189, 0, 141),
        223: (189, 126, 173),
        224: (129, 0, 96),
        225: (129, 86, 118),
        226: (104, 0, 78),
        227: (104, 69, 95),
        228: (79, 0, 59),
        229: (79, 53, 73),
        230: (255, 0, 127),
        231: (255, 170, 212),
        232: (189, 0, 94),
        233: (189, 126, 157),
        234: (129, 0, 64),
        235: (129, 86, 107),
        236: (104, 0, 52),
        237: (104, 69, 86),
        238: (79, 0, 39),
        239: (79, 53, 66),
        240: (255, 0, 63),
        241: (255, 170, 191),
        242: (189, 0, 46),
        243: (189, 126, 141),
        244: (129, 0, 31),
        245: (129, 86, 96),
        246: (104, 0, 25),
        247: (104, 69, 78),
        248: (79, 0, 19),
        249: (79, 53, 59),
        250: (51, 51, 51),
        251: (80, 80, 80),
        252: (105, 105, 105),
        253: (130, 130, 130),
        254: (190, 190, 190),
        255: (255, 255, 255)
    }

    # Default to white if index is invalid or not found
    return aci_rgb_map.get(aci, (255, 255, 255))



def int_to_rgb(color_int):
    """Convert an integer to an (R, G, B) tuple."""
    r = (color_int >> 16) & 255
    g = (color_int >> 8) & 255
    b = color_int & 255
    return (r, g, b)



def get_hatch_color(entity):
    # Check if the entity has a "true color" set
    if entity.dxf.hasattr('true_color'):
      true_color = entity.dxf.true_color
      rgb_color = int_to_rgb(true_color)  # Convert integer to (R, G, B)
      print(f"True color detected (RGB): {rgb_color}")
      return rgb_color

    color_index = entity.dxf.color
    print("color_index = ", color_index)

    # Check if the color is set to ByLayer or ByBlock
    if color_index == 0:  # ByLayer color
        print("Color is ByLayer, checking layer color...")
        layer_name = entity.dxf.layer
        layer = entity.doc.layers.get(layer_name)

        if layer:  # Ensure layer exists
            layer_color_index = layer.dxf.color
            print(f"Layer '{layer_name}' Color Index = {layer_color_index}")
            return aci_to_rgb(layer_color_index)  # Use custom aci_to_rgb function
        else:
            print(f"Layer '{layer_name}' not found, defaulting to white.")
            return (255, 255, 255)  # Default to white if layer not found

    elif color_index == 256:  # ByBlock color
        print("Color is ByBlock, checking block color or defaulting to white.")
        block_color = (255, 255, 255)  # White as default

        # Check if the entity is inside a block reference and inherit its color
        if hasattr(entity, 'block'):  # Check if the entity belongs to a block
            block_ref = entity.block
            if block_ref.dxf.hasattr('color'):
                block_color = aci_to_rgb(block_ref.dxf.color)
                print(f"Block reference color found: {block_color}")
            else:
                print("Block has no color attribute, using default (white).")
        return block_color

    # Otherwise, convert the ACI color to RGB
    print(f"Entity Color Index = {color_index}")
    if 1 <= color_index <= 255:
        rgb_color = aci_to_rgb(color_index)  # Use custom aci_to_rgb function
        print(f"Converted RGB = {rgb_color}")
        return rgb_color

    # Default to white if color index is out of bounds or invalid
    print("Invalid or unhandled color index, defaulting to white.")
    return (255, 255, 255)

def calculate_distance(p1, p2):
    return math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)

def normalize_color(color):
    """Convert PDF color (range 0-1) to RGB (range 0-255)."""
    return tuple(min(max(round(c * 255), 0), 255) for c in color)

def color_close_enough(c1, c2, threshold=10):
    return all(abs(a - b) <= threshold for a, b in zip(c1, c2))
    
    
"""### Hatched areas"""
def get_hatched_areas(datadoc,filename,FinalRatio,rotationangle,SearchArray):

      doc = ezdxf.readfile(filename)
      doc.header['$MEASUREMENT'] = 1
      msp = doc.modelspace()
      trial=0
      hatched_areas = []
      threshold=0.001
      TextFound = 0
      j=0
      unique_shapes = []

      text_with_positions = []
      text_color_mapping = {}
      color_palette = [
          (255, 0, 0), (0, 0, 255), (0, 255, 255), (0, 64, 0), (255, 204, 0),
          (255, 128, 64), (255, 0, 128), (255, 128, 192), (128, 128, 255),
          (128, 64, 0), (0, 255, 0), (0, 200, 0), (255, 128, 255), (128, 0, 255),
          (0, 128, 192), (128, 0, 128), (128, 0, 0), (0, 128, 255), (149, 1, 70),
          (255, 182, 128), (222, 48, 71), (240, 0, 112), (255, 0, 255),
          (192, 46, 65), (0, 0, 128), (0, 128, 64), (255, 255, 0), (128, 0, 80),
          (255, 255, 128), (90, 255, 140), (255, 200, 20), (91, 16, 51),
          (90, 105, 138), (114, 10, 138), (36, 82, 78), (225, 105, 190),
          (108, 150, 170), (11, 35, 75), (42, 176, 170), (255, 176, 170),
          (209, 151, 15), (81, 27, 85), (226, 106, 122), (67, 119, 149),
          (159, 179, 140), (159, 179, 30), (255, 85, 198), (255, 27, 85),
          (188, 158, 8), (140, 188, 120), (59, 61, 52), (65, 81, 21),
          (212, 255, 174), (15, 164, 90), (41, 217, 245), (213, 23, 182),
          (11, 85, 169), (78, 153, 239), (0, 66, 141), (64, 98, 232),
          (140, 112, 255), (57, 33, 154), (194, 117, 252), (116, 92, 135),
          (74, 43, 98), (188, 13, 123), (129, 58, 91), (255, 128, 100),
          (171, 122, 145), (255, 98, 98), (222, 48, 77)
      ]

      import re

      Legendarray = []

      if(SearchArray):
        for i in range(len(SearchArray)):

          if (SearchArray[i][0] and SearchArray[i][1] and SearchArray[i][2]):
                  for text_entity in doc.modelspace().query('TEXT MTEXT'):
                      text = text_entity.text.strip() if hasattr(text_entity, 'text') else ""
                      # if (text.startswith("P") and len(text) == 3) or (text.startswith("I") and len(text) == 3):  # Filter for "Wall"
                      if(text.startswith(SearchArray[i][0]) and len(text)==int(SearchArray[i][2])):
                          position = text_entity.dxf.insert  # Extract text position
                          x, y = position.x, position.y

                          for text_entity in doc.modelspace().query('TEXT MTEXT'):
                              NBS = text_entity.text.strip() if hasattr(text_entity, 'text') else ""
                              if (NBS.startswith(SearchArray[i][1])):
                                  positionNBS = text_entity.dxf.insert  # Extract text position
                                  xNBS, yNBS = positionNBS.x, positionNBS.y

                                  if(x == xNBS or y == yNBS):
                                    textNBS=NBS
                                    break

                              else:
                                textNBS = None



                          nearest_hatch = None
                          min_distance = float('inf')  # Initialize with a very large value
                          detected_color = (255, 255, 255)  # Default to white

                          # Search for the nearest hatch
                          for hatch in doc.modelspace().query('HATCH'):  # Query only hatches
                              if hatch.paths:
                                  for path in hatch.paths:
                                      if path.type == 1:  # PolylinePath
                                          vertices = [v[:2] for v in path.vertices]
                                          # Calculate the centroid of the hatch
                                          centroid_x = sum(v[0] for v in vertices) / len(vertices)
                                          centroid_y = sum(v[1] for v in vertices) / len(vertices)
                                          centroid = (centroid_x, centroid_y)

                                          # Calculate the distance between the text and the hatch centroid
                                          distance = calculate_distance((x, y), centroid)

                                          # Update the nearest hatch if a closer one is found
                                          if distance < min_distance:
                                              min_distance = distance
                                              nearest_hatch = hatch

                                              # Get the color of this hatch
                                              current_color = get_hatch_color(hatch)
                                              if current_color != (255, 255, 255):  # Valid color found
                                                  detected_color = current_color
                                                  break  # Stop checking further paths for this hatch


                          # Append the detected result only once
                          Legendarray.append([text, textNBS, (x, y), detected_color])
                  print("text_with_positions=",text_with_positions)

          elif (SearchArray[i][0] and SearchArray[i][2]):
                    for text_entity in doc.modelspace().query('TEXT MTEXT'):
                      text = text_entity.text.strip() if hasattr(text_entity, 'text') else ""
                      # if (text.startswith("P") and len(text) == 3) or (text.startswith("I") and len(text) == 3):  # Filter for "Wall"
                      if(text.startswith(SearchArray[i][0]) and len(text)==int(SearchArray[i][2])):
                          position = text_entity.dxf.insert  # Extract text position
                          x, y = position.x, position.y
                          textNBS = None
                          nearest_hatch = None
                          min_distance = float('inf')  # Initialize with a very large value
                          detected_color = (255, 255, 255)  # Default to white

                          # Search for the nearest hatch
                          for hatch in doc.modelspace().query('HATCH'):  # Query only hatches
                              if hatch.paths:
                                  for path in hatch.paths:
                                      if path.type == 1:  # PolylinePath
                                          vertices = [v[:2] for v in path.vertices]
                                          # Calculate the centroid of the hatch
                                          centroid_x = sum(v[0] for v in vertices) / len(vertices)
                                          centroid_y = sum(v[1] for v in vertices) / len(vertices)
                                          centroid = (centroid_x, centroid_y)

                                          # Calculate the distance between the text and the hatch centroid
                                          distance = calculate_distance((x, y), centroid)

                                          # Update the nearest hatch if a closer one is found
                                          if distance < min_distance:
                                              min_distance = distance
                                              nearest_hatch = hatch

                                              # Get the color of this hatch
                                              current_color = get_hatch_color(hatch)
                                              if current_color != (255, 255, 255):  # Valid color found
                                                  detected_color = current_color
                                                  break  # Stop checking further paths for this hatch


                          # Append the detected result only once
                          Legendarray.append([text, textNBS, (x, y), detected_color])
                    print("text_with_positions=",text_with_positions)

          elif(SearchArray[i][0]):
            for text_entity in doc.modelspace().query('TEXT MTEXT'):
                      text = text_entity.text.strip() if hasattr(text_entity, 'text') else ""
                      # if (text.startswith("P") and len(text) == 3) or (text.startswith("I") and len(text) == 3):  # Filter for "Wall"
                      if(text.startswith(SearchArray[i][0])):
                          position = text_entity.dxf.insert  # Extract text position
                          x, y = position.x, position.y
                          textNBS = None
                          nearest_hatch = None
                          min_distance = float('inf')  # Initialize with a very large value
                          detected_color = (255, 255, 255)  # Default to white

                          # Search for the nearest hatch
                          for hatch in doc.modelspace().query('HATCH'):  # Query only hatches
                              if hatch.paths:
                                  for path in hatch.paths:
                                      if path.type == 1:  # PolylinePath
                                          vertices = [v[:2] for v in path.vertices]
                                          # Calculate the centroid of the hatch
                                          centroid_x = sum(v[0] for v in vertices) / len(vertices)
                                          centroid_y = sum(v[1] for v in vertices) / len(vertices)
                                          centroid = (centroid_x, centroid_y)

                                          # Calculate the distance between the text and the hatch centroid
                                          distance = calculate_distance((x, y), centroid)

                                          # Update the nearest hatch if a closer one is found
                                          if distance < min_distance:
                                              min_distance = distance
                                              nearest_hatch = hatch

                                              # Get the color of this hatch
                                              current_color = get_hatch_color(hatch)
                                              if current_color != (255, 255, 255):  # Valid color found
                                                  detected_color = current_color
                                                  break  # Stop checking further paths for this hatch


                          # Append the detected result only once
                          Legendarray.append([text, textNBS, (x, y), detected_color])
            print("text_with_positions=",Legendarray)








      grouped = {}
      for entry in Legendarray:
          key = entry[0]
          grouped.setdefault(key, []).append(entry)

      # Filter the groups: if any entry in a group has a non-None Text Nbs, keep only one of those
      filtered_results = []
      for key, entries in grouped.items():
          # Find the first entry with a valid textNBS (non-None)
          complete = next((entry for entry in entries if entry[1] is not None), None)
          if complete:
              filtered_results.append(complete)
          else:
              # If none are complete, you can choose to keep just one entry
              filtered_results.append(entries[0])

      Legendarray=filtered_results
    


      for entity in doc.modelspace().query('TEXT MTEXT'):
        if hasattr(entity, 'text'):  # Ensure the entity has text content
            text = entity.text
            if text.startswith('C') and (len(text) > 1 and (text[1].isdigit() or text[1].upper() == 'T' or text[1].upper() == 'L')):
                parts = text.split(' ')  # Split into two parts: before and after the first newline
                # print("Parts = ",parts[0])
                main_text = parts[0]  # Text before the first newline

                # Check if the main text starts with 'C' followed by a number or 'T'
                # if pattern.match(main_text):
                position = entity.dxf.insert


                # Check if the text already has a color assigned
                if main_text not in text_color_mapping:
                    # Assign a new color from the palette
                    color_index = len(text_color_mapping) % len(color_palette)
                    text_color_mapping[main_text] = color_palette[color_index]

                # Get the assigned color
                color = text_color_mapping[main_text]

                # Set the entity's true color
                # entity.dxf.true_color = rgb_to_true_color(color)

                # Append text, position, and color to the array
                text_with_positions.append([main_text, position, color])
                
      for entity in msp:
        if entity.dxftype() == 'HATCH':
            # print(f"Processing HATCH entity: {entity}")
            for path in entity.paths:
                vertices = []  # Reset vertices for each path

                if path.type == 1:
                    # Handle POLYLINE type HATCH
                    vertices = [(vertex[0] * FinalRatio, vertex[1] * FinalRatio) for vertex in path.vertices]

                    if len(vertices) > 3:
                        poly = ShapelyPolygon(vertices)
                        minx, miny, maxx, maxy = poly.bounds
                        width = maxx - minx
                        height = maxy - miny

                        if (poly.area > 0.9 and (height > 0.7 and width > 0.7)):
                            area1 = round(poly.area, 3)
                            perimeter = round(poly.length, 3)
                            normalized_vertices = normalize_vertices(vertices)

                            rgb_color = get_hatch_color(entity)
                            if(rgb_color == (255, 255, 255)):
                                if(len(text_with_positions)>0):

                                  for text, position, color in text_with_positions:
                                    text_position = Point(position[0], position[1])

                                    if poly.contains(text_position):
                                      rgb_color = color
                                      break

                            duplicate_found = False
                            for existing_vertices, existing_area in unique_shapes:
                                if normalized_vertices == existing_vertices and areas_are_similar(area1, existing_area):
                                    duplicate_found = True
                                    break

                            if not duplicate_found:
                                # rgb_color = get_hatch_color(entity)  # Assuming this function exists
                                unique_shapes.append((normalized_vertices, area1))
                                hatched_areas.append([vertices, area1, perimeter, rgb_color])

                elif path.type == 2:
                  # convert any spline edges to line edges (approximates splines)
                  # factor controls approximation density: control_points_count * factor
                  try:
                      path.spline_edges_to_line_edges(factor=8)  # increase factor for finer approx
                  except Exception:
                      # defensive: some versions / unexpected objects might raise — ignore and continue
                      pass

                  vert = []
                  # After conversion most edges will be LineEdge with .start and .end
                  for edge in path.edges:
                      # If edge has start/end attributes (LineEdge), use them
                      if hasattr(edge, "start") and hasattr(edge, "end"):
                          sx, sy = edge.start
                          ex, ey = edge.end
                          # append only start point; we'll append the end of the last edge too to close path
                          vert.append((sx * FinalRatio, sy * FinalRatio))
                      else:
                          # fallback: try control_points or fit_points if present (rare after conversion)
                          if hasattr(edge, "control_points") and edge.control_points:
                              for cp in edge.control_points:
                                  vert.append((cp[0] * FinalRatio, cp[1] * FinalRatio))
                          elif hasattr(edge, "fit_points") and edge.fit_points:
                              for fp in edge.fit_points:
                                  vert.append((fp[0] * FinalRatio, fp[1] * FinalRatio))
                          else:
                              # last resort: try to string-represent and skip if nothing usable
                              continue

                  # After loop, ensure polygon is closed: append last edge.end if available
                  if path.edges and hasattr(path.edges[-1], "end"):
                      ex, ey = path.edges[-1].end
                      vert.append((ex * FinalRatio, ey * FinalRatio))

                  # remove possible duplicate consecutive points (optional)
                  cleaned = []
                  for p in vert:
                      if not cleaned or (abs(cleaned[-1][0] - p[0]) > 1e-9 or abs(cleaned[-1][1] - p[1]) > 1e-9):
                          cleaned.append(p)
                  vert = cleaned

                  # validate and use Shapely as before
                  if len(vert) >= 3:
                      poly = ShapelyPolygon(vert)
                      minx, miny, maxx, maxy = poly.bounds
                      width = maxx - minx
                      height = maxy - miny

                      if (poly.area > 0.1 and (height > 0.1 and width > 0.1)):
                          area1 = round(poly.area, 3)
                          perimeter = round(poly.length, 3)
                          normalized_vertices = normalize_vertices(vert)
                          rgb_color = get_hatch_color(entity)
                          if rgb_color == (255, 255, 255) and len(text_with_positions) > 0:
                              for text, position, color in text_with_positions:
                                  text_position = Point(position[0], position[1])
                                  if poly.contains(text_position):
                                      rgb_color = color
                                      break

                          duplicate_found = False
                          for existing_vertices, existing_area in unique_shapes:
                              if normalized_vertices == existing_vertices and areas_are_similar(area1, existing_area):
                                  duplicate_found = True
                                  break

                          if not duplicate_found:
                              unique_shapes.append((normalized_vertices, area1))
                              hatched_areas.append([vert, area1, perimeter, rgb_color])

        elif entity.dxftype() == 'SOLID':
            vertices = [entity.dxf.vtx0 * (FinalRatio), entity.dxf.vtx1* (FinalRatio), entity.dxf.vtx2* (FinalRatio), entity.dxf.vtx3* (FinalRatio)]
            poly = ShapelyPolygon(vertices)
            minx, miny, maxx, maxy = poly.bounds

            # Calculate the width and height of the bounding box
            width = maxx - minx
            height = maxy - miny

            if (poly.area > 0.9 and (height > 0.7 and width > 0.7)):
              area1 = round(poly.area, 3)
              perimeter = round(poly.length, 3)
              normalized_vertices = normalize_vertices(vertices)

              duplicate_found = False
              for existing_vertices, existing_area in unique_shapes:
                  if normalized_vertices == existing_vertices or areas_are_similar(area1, existing_area):
                      duplicate_found = True
                      break

              if not duplicate_found:
                  rgb_color = get_hatch_color(entity)  # Assuming this function exists
                  unique_shapes.append((normalized_vertices, area1))
                  hatched_areas.append([vertices, area1, perimeter, rgb_color])




        elif entity.dxftype() == 'LWPOLYLINE':
          vertices = []
          lwpolyline = entity
          points = lwpolyline.get_points()
          flag = 0

          # Collect vertices and apply the FinalRatio
          for i in range(len(points)):
              vertices.append([points[i][0] * FinalRatio, points[i][1] * FinalRatio])

        #   # Ensure there are more than 3 vertices
          if len(vertices) > 3:
              # Check if the polyline is closed
              if vertices[0][0] == vertices[-1][0] or vertices[0][1] == vertices[-1][1]:
                  poly = ShapelyPolygon(vertices)
                  minx, miny, maxx, maxy = poly.bounds

                  # Calculate width and height of the bounding box
                  width = maxx - minx
                  height = maxy - miny

                  # Check area and size constraints
                  if (poly.area > 0.9 and (height > 0.7 and width > 0.7)):
                      area1 = round(poly.area, 3)
                      perimeter = round(poly.length, 3)

                      normalized_vertices = normalize_vertices(vertices)

                      duplicate_found = False
                      for existing_vertices, existing_area in unique_shapes:
                          if normalized_vertices == existing_vertices or areas_are_similar(area1, existing_area):
                              duplicate_found = True
                              break

                      if not duplicate_found:
                          rgb_color = get_hatch_color(entity)  # Assuming this function exists
                          unique_shapes.append((normalized_vertices, area1))
                          hatched_areas.append([vertices, area1, perimeter, rgb_color])



        elif entity.dxftype() == 'POLYLINE':

          # print("In POLYLINE")

          flag=0
          vertices = [(v.dxf.location.x * (FinalRatio), v.dxf.location.y * (FinalRatio)) for v in entity.vertices]
          # print('Vertices:', vertices)

          if(len(vertices)>3):

             if(vertices[0][0] == vertices[len(vertices)-1][0]  or vertices[0][1] == vertices[len(vertices)-1][1]):

               poly=ShapelyPolygon(vertices)
               minx, miny, maxx, maxy = poly.bounds

               # Calculate the width and height of the bounding box
               width = maxx - minx
               height = maxy - miny

               if (poly.area > 0.9 and (height > 0.7 and width > 0.7)):
                area1 = round(poly.area,3)
                perimeter = round (poly.length,3)
                normalized_vertices = normalize_vertices(vertices)

                duplicate_found = False
                for existing_vertices, existing_area in unique_shapes:
                    if normalized_vertices == existing_vertices or areas_are_similar(area1, existing_area):
                        duplicate_found = True
                        break

                if not duplicate_found:
                    rgb_color = get_hatch_color(entity)  # Assuming this function exists
                    unique_shapes.append((normalized_vertices, area1))
                    hatched_areas.append([vertices, area1, perimeter, rgb_color])


        elif entity.dxftype() == 'SPLINE':
          spline_entity = entity
          vertices = []
          control_points = spline_entity.control_points
          if(len(control_points)>3):
            for i in range(len(control_points)):
              vertices.append([control_points[i][0]* (FinalRatio),control_points[i][1]* (FinalRatio)])
            poly=ShapelyPolygon(vertices)

            minx, miny, maxx, maxy = poly.bounds

            # Calculate the width and height of the bounding box
            width = maxx - minx
            height = maxy - miny


            if (poly.area > 0.9 and (height > 0.7 and width > 0.7)):
                area1 = round(poly.area,3)
                perimeter = round (poly.length,3)
                normalized_vertices = normalize_vertices(vertices)

                duplicate_found = False
                for existing_vertices, existing_area in unique_shapes:
                    if normalized_vertices == existing_vertices or areas_are_similar(area1, existing_area):
                        duplicate_found = True
                        break

                if not duplicate_found:
                    rgb_color = get_hatch_color(entity)  # Assuming this function exists
                    unique_shapes.append((normalized_vertices, area1))
                    hatched_areas.append([vertices, area1, perimeter, rgb_color])

      sorted_data = sorted(hatched_areas, key=lambda x: x[1])
      return sorted_data,Legendarray

"""### Rotate polygon"""



def rotate_point(point, angle,pdfrotation,width,height, center_point=(0, 0)):
    """Rotates a point around center_point(origin by default)
    Angle is in degrees.
    Rotation is counter-clockwise
    """
    angle_rad = radians(angle % 360)
    # Shift the point so that center_point becomes the origin
    new_point = (point[0] - center_point[0], point[1] - center_point[1])
    new_point = (new_point[0] * cos(angle_rad) - new_point[1] * sin(angle_rad),
                 new_point[0] * sin(angle_rad) + new_point[1] * cos(angle_rad))
    # Reverse the shifting we have done
    if pdfrotation!=0:

      new_point = (new_point[0]+width  + center_point[0], new_point[1]  + center_point[1]) #pdfsize[2] is the same as +width
    else:

      new_point = (new_point[0] + center_point[0], new_point[1]+ height  + center_point[1]) # pdfsize[3] is the same as +height
    # new_point = (new_point[0] + center_point[0], new_point[1] + center_point[1])
    return new_point


def rotate_polygon(polygon, angle, pdfrotation,width,height,center_point=(0, 0)):
    """Rotates the given polygon which consists of corners represented as (x,y)
    around center_point (origin by default)
    Rotation is counter-clockwise
    Angle is in degrees
    """
    rotated_polygon = []
    for corner in polygon:
        rotated_corner = rotate_point(corner, angle,pdfrotation,width,height, center_point)
        rotated_polygon.append(rotated_corner)
    return rotated_polygon

#create a dataframe containing color , count(how many times is this object found in the plan), area of 1 of these shapes, total area
#perimeter, totat perimeter, length, total length
#import pandas as pd
#SimilarAreaDictionary= pd.DataFrame(columns=['Guess','Color','Occurences','Area','Total Area','Perimeter','Total Perimeter','Length','Total Length','R','G','B'])
#loop 3la hatched areas and count the occurences of each shape w create a table bl hagat di


def Create_DF(dxfpath,datadoc,hatched_areas,pdf_content=0):
    
  if pdf_content:
    FinalRatio,width_dxf= RetriveRatio(datadoc,dxfpath,pdf_content)
  else:
     FinalRatio,width_dxf= RetriveRatio(datadoc,dxfpath)

  # hatched_areas = get_hatched_areas(dxfpath,FinalRatio)
  # print('hatched_areas',hatched_areas)
  # hatched_areas=remove_duplicate_shapes(new_hatched_areas)

  # SimilarAreaDictionary= pd.DataFrame(columns=['Area', 'Total Area', 'Perimeter', 'Total Perimeter', 'Occurences', 'Color'])
  SimilarAreaDictionary= pd.DataFrame(columns=['Guess','Color','Occurences','Area','Total Area','Perimeter','Total Perimeter','Length','Total Length','Texts','Comments'])

  # colorRanges2=generate_color_array(30000)
  # colorRanges = [[255, 0, 0], [0, 0, 255], [0, 255, 255], [0, 64, 0], [255, 204, 0], [255, 128, 64], [255, 0, 128], [255, 128, 192], [128, 128, 255], [128, 64, 0],[0, 255, 0],[0, 200, 0],[255, 128, 255], [128, 0, 255], [0, 128, 192], [128, 0, 128],[128, 0, 0], [0, 128, 255], [149, 1, 70], [255, 182, 128], [222, 48, 71], [240, 0, 112], [255, 0, 255], [192, 46, 65], [0, 0, 128],[0, 128, 64],[255, 255, 0], [128, 0, 80], [255, 255, 128], [90, 255, 140],[255, 200, 20],[91, 16, 51], [90, 105, 138], [114, 10, 138], [36, 82, 78], [225, 105, 190], [108, 150, 170], [11, 35, 75], [42, 176, 170], [255, 176, 170], [209, 151, 15],[81, 27, 85], [226, 106, 122], [67, 119, 149], [159, 179, 140], [159, 179, 30],[255, 85, 198], [255, 27, 85], [188, 158, 8],[140, 188, 120], [59, 61, 52], [65, 81, 21], [212, 255, 174], [15, 164, 90],[41, 217, 245], [213, 23, 182], [11, 85, 169], [78, 153, 239], [0, 66, 141],[64, 98, 232], [140, 112, 255], [57, 33, 154], [194, 117, 252], [116, 92, 135], [74, 43, 98], [188, 13, 123], [129, 58, 91], [255, 128, 100], [171, 122, 145],  [255, 98, 98], [222, 48, 77]]
  # colorUsed=[]
    
  TotalArea=0
  TotalPerimeter=0
  for shape in hatched_areas:
      area = shape[1]  # area
      perimeter = shape[2]  # perimeter
      # if(i < len(colorRanges)):
      #   color = colorRanges[i]
      #   colorUsed.append(color)
      # else:
      #   color = colorRanges2[i]
      #   colorUsed.append(color)
      TotalArea = area
      TotalPerimeter = perimeter
      tol=0
      condition1 = (SimilarAreaDictionary['Area'] >= area - tol) & (SimilarAreaDictionary['Area'] <= area +tol)
      condition2 = (SimilarAreaDictionary['Perimeter'] >= perimeter -tol) & (SimilarAreaDictionary['Perimeter'] <= perimeter +tol)
      combined_condition = condition1 & condition2

      if any(combined_condition):
          index = np.where(combined_condition)[0][0]
          SimilarAreaDictionary.at[index, 'Occurences'] += 1
          SimilarAreaDictionary.at[index, 'Total Area'] = SimilarAreaDictionary.at[index, 'Total Area'] + area
          SimilarAreaDictionary.at[index, 'Total Perimeter'] = SimilarAreaDictionary.at[index, 'Total Perimeter'] + perimeter
      else:
          TotalArea=area
          TotalPerimeter=perimeter
          new_data = {'Area': area, 'Total Area': TotalArea ,'Perimeter': perimeter, 'Total Perimeter': TotalPerimeter, 'Occurences': 1, 'Color':shape[3],'Comments':''} #add color here and read color to insert in
          SimilarAreaDictionary = pd.concat([SimilarAreaDictionary, pd.DataFrame([new_data])], ignore_index=True)

  # print(SimilarAreaDictionary)
  return SimilarAreaDictionary
"""### Draw on Image and PDF"""
def color_close_enough(c1, c2, threshold=10):
    return all(abs(a - b) <= threshold for a, b in zip(c1, c2))



def group_vertices(raw):
    """Convert flat list [x1,y1,x2,y2,...] into [[x1,y1],[x2,y2],...]"""
    if not raw or len(raw) < 2:
        return []
    return [[raw[i], raw[i+1]] for i in range(0, len(raw), 2)]


def extract_measurement(obj):
    """Extract numeric measurement from annotation's /Contents field."""
    contents = obj.get("/Contents")
    if not contents:
        return None
    # Find first numeric occurrence
    text = str(contents)
    m = re.search(r"([0-9]*\.?[0-9]+)", text)
    return float(m.group(1)) if m else None

def adjustannotations(OutputPdfStage1,text_with_positions):
  input_pdf_path = OutputPdfStage1
  output_pdf_path = "Final-WallsAdjusted.pdf"
  annotations_data = []
  Trim=0

  # Load the input PDF
  pdf_bytes_io = BytesIO(OutputPdfStage1)

  reader = PdfReader(pdf_bytes_io)
  writer = PdfWriter()

  # Append all pages to the writer
  writer.append_pages_from_reader(reader)

  # Add metadata (optional)
  metadata = reader.metadata
  writer.add_metadata(metadata)

  for page_index, page in enumerate(writer.pages):
      if "/Annots" not in page:
            continue

      for annot in page["/Annots"]:
            obj = annot.get_object()
            subtype = obj.get("/Subtype")

            # Group vertices for metadata
            if subtype == "/Line":
                raw_vertices = obj.get("/L", [])
            else:
                raw_vertices = obj.get("/Vertices", [])
            vertices = group_vertices(raw_vertices)

            # Normalize color
            raw_color = obj.get("/C")
            try:
                annot_color = normalize_color(raw_color)
            except:
                annot_color = raw_color

            # Extract measurement from annotation content
            measurement = extract_measurement(obj)
            # Assign to area or perimeter based on subtype
            area = measurement if subtype == "/Polygon" else None
            perimeter = measurement if subtype in ["/Line", "/PolyLine"] else None

            # Match text and NBS
            matched_text = None
            matched_nbs = None
            if subtype in ["/Line", "/PolyLine", "/Polygon"] and raw_color:
                matched_entry = next(
                    ((t, n) for t, n, _, c in text_with_positions if color_close_enough(annot_color, c)),
                    (None, None)
                )
                matched_text, matched_nbs = matched_entry
                combined = ""
                if matched_text and matched_nbs:
                    combined = f"{matched_text} - {matched_nbs}"
                elif matched_text:
                    combined = matched_text
                elif matched_nbs:
                    combined = matched_nbs
                if combined:
                    obj.update({NameObject("/T"): TextStringObject(combined)})

            # Update annotation dictionaries for measurement type
            if subtype == "/PolyLine" and obj.get("/Subj", "") == "Perimeter Measurement":
                
                title = obj.get("/T", "")                 # read the /T field
                title_str = str(title).strip()            # cast to str and trim whitespace
                # print(title_str)
                if title_str == "Skirting Measurement":
                    # print("Skirting")
                    Trim=0
                elif title_str == "Trim Measurement":
                    # print("Trim")
                    Trim=1
                    
                obj.update({
                    NameObject("/Measure"): DictionaryObject({
                        NameObject("/Type"): NameObject("/Measure"),
                        NameObject("/L"): DictionaryObject({
                            NameObject("/G"): FloatObject(1),
                            NameObject("/U"): TextStringObject("m"),
                        }),
                    }),
                    NameObject("/IT"): NameObject("/LineDimension"),
                    NameObject("/Subj"): TextStringObject("Length Measurement"),
                })
            if subtype == "/Polygon" and obj.get("/Subj", "") == "Area Measurement":
                obj.update({
                    NameObject("/Measure"): DictionaryObject({
                        NameObject("/Type"): NameObject("/Measure"),
                        NameObject("/Area"): DictionaryObject({
                            NameObject("/G"): FloatObject(1),
                            NameObject("/U"): TextStringObject("sq m"),
                        }),
                    }),
                    NameObject("/IT"): NameObject("/Area_Annotation"),
                    NameObject("/Subj"): TextStringObject("Area Measurement"),
                })

            # Append metadata
            annotations_data.append([
                 vertices,
                 area,
                 perimeter,
                 annot_color,
                 matched_text,
                 matched_nbs,
                 Trim,
            ])



  output_pdf_io = BytesIO()
  writer.write(output_pdf_io)
  output_pdf_io.seek(0)

  print(f"Annotations updated and saved to {output_pdf_path}")
  return output_pdf_io.read() , annotations_data

  
def _order_points_monotonic(points, eps=1e-9):
    """
    Order a list of (x,y) points along their main axis (PCA),
    then refine ordering by greedy nearest-neighbor but never stepping
    backward along the principal-axis projection (monotonic).
    Returns ordered list of (x,y).
    """
    if not points:
        return []
    if len(points) <= 2:
        return list(points)

    pts = np.array(points, dtype=float)  # shape (n,2)
    mean = pts.mean(axis=0)
    centered = pts - mean

    # PCA via SVD
    try:
        _, _, vt = np.linalg.svd(centered, full_matrices=False)
        principal = vt[0]  # principal axis direction (unit-ish)
    except Exception:
        # fallback to simple x or y sort if SVD fails
        return sorted(points, key=lambda p: (p[0], p[1]))

    # projection scalars along principal axis
    proj = centered.dot(principal)  # length n array

    # sort indices by projection (ascending)
    idxs = list(np.argsort(proj))
    proj_sorted = proj[idxs]

    # greedy monotonic walk: start from first index (smallest projection)
    ordered = []
    remaining = set(idxs)
    curr_idx = idxs[0]
    ordered.append(tuple(pts[curr_idx].tolist()))
    remaining.remove(curr_idx)

    while remaining:
        # candidates that do not go backwards along projection
        cand = [i for i in remaining if proj[i] >= proj[curr_idx] - eps]
        if not cand:
            # if none, pick nearest among remaining (rare)
            cand = list(remaining)

        # choose candidate with minimum Euclidean distance to current point
        curr_pt = pts[curr_idx]
        best = min(cand, key=lambda i: np.hypot(*(pts[i] - curr_pt)))
        ordered.append(tuple(pts[best].tolist()))
        remaining.remove(best)
        curr_idx = best

    return ordered


def find_nearby_vertices(shapes, distance_threshold=0.5):
    """
    shapes: list structured as [ vertices_list, area, perimeter, color_tuple ]
            where vertices_list is [(x,y), ...]
    distance_threshold: maximum absolute difference in x and y to consider two vertices 'close'

    Returns:
        intersections: [ [ [(x,y), ...] ], ... ]
        grouped_shapes: list of grouped shapes (each group is a merged list of shape indices)
    """
    intersections = []
    grouped_shapes = []  # new array to collect shape groups
    used = set()

    n = len(shapes)
    for i in range(n):
        for j in range(i + 1, n):
            shape1 = shapes[i]
            shape2 = shapes[j]

            # Skip if same or invalid color
            if (
                shape1[3] == shape2[3] or
                shape1[3] in [(0,0,0), (255,255,255)] or
                shape2[3] in [(0,0,0), (255,255,255)]
            ):
                continue

            verts1 = list(shape1[0])
            verts2 = list(shape2[0])
            if len(verts1) > 1 and verts1[0] == verts1[-1]:
                verts1 = verts1[:-1]
            if len(verts2) > 1 and verts2[0] == verts2[-1]:
                verts2 = verts2[:-1]

            close_points = []
            for (x1, y1) in verts1:
                for (x2, y2) in verts2:
                    if abs(x1 - x2) <= distance_threshold and abs(y1 - y2) <= distance_threshold:
                        p = (float(x2), float(y2))
                        if p not in close_points:
                            close_points.append(p)

            if close_points:
                # Order + dedup (optional)
                ordered = _order_points_monotonic(close_points)
                dedup = []
                for pt in ordered:
                    if not dedup or (abs(pt[0]-dedup[-1][0])>1e-6 or abs(pt[1]-dedup[-1][1])>1e-6):
                        dedup.append(pt)

                if len(dedup) >= 2:
                    intersections.append([dedup])

                # ---- new: group these two shapes together ----
                group = {i, j}
                # merge if overlapping with existing groups
                merged = False
                for g in grouped_shapes:
                    if not group.isdisjoint(g):
                        g.update(group)
                        merged = True
                        break
                if not merged:
                    grouped_shapes.append(group)

    # Convert grouped indices into grouped shapes
    grouped_shapes_final = []
    for g in grouped_shapes:
        merged_verts = []
        colors = set()
        for idx in g:
            merged_verts.extend(shapes[idx][0])
            colors.add(shapes[idx][3])
        grouped_shapes_final.append([merged_verts, None, None, list(colors)])

    return intersections, grouped_shapes_final

def process_polygons(polygons, imgg, dxfratio, rotationangle, derotationMatrix, rotationOld, width, height):
    processed_shapes = []

    for polygon in polygons:
        cntPoints = []
        cntPoints1 = []
        shapeePerimeter = []
        shapeeArea = []
        Text_Detected = 0

        blackImgShapes = np.zeros(imgg.shape[:2], dtype="uint8")
        blackImgShapes = cv2.cvtColor(blackImgShapes, cv2.COLOR_GRAY2BGR)

        # Handle nested or flat structure
        vertices = polygon
        print("vertices = ", vertices)

        for vertex in vertices:
            for pt in vertex:
                x = pt[0] * dxfratio
                y = pt[1] * dxfratio
                if rotationangle == 0 and y < 0:
                    y = -y
                cntPoints.append([int(x), int(y)])
                cntPoints1.append([x, y])

        # Draw contour (for mask/image purposes)
        cv2.drawContours(blackImgShapes, [np.array(cntPoints)], -1, (255, 255, 255), thickness=-1)

        # Build perimeter points after derotation
        for px, py in cntPoints1:
            p = fitz.Point(px, py) * derotationMatrix
            shapeePerimeter.append([p[0], p[1]])

        # Apply flip and rotation to this polygon only
        shapeePerimeter = np.flip(np.array(shapeePerimeter), 1).tolist()
        shapeePerimeter = rotate_polygon(shapeePerimeter, rotationangle, rotationOld, width, height)

        # Append in [[polygon_points]] format
        processed_shapes.append(shapeePerimeter)

    return processed_shapes


def process_grouped_shapes(grouped_shapes, imgg, dxfratio, rotationangle, derotationMatrix, rotationOld, width, height):
    """
    Processes grouped shapes (merged polygons).

    grouped_shapes: list of [vertices_list, None, None, colors_list]
    Returns: list of [coords, area, perimeter, color]
    """
    processed_shapes = []

    for group in grouped_shapes:
        merged_vertices = group[0]   # all vertices from the group
        colors = group[3]            # list of colors (can pick the first or merge info)

        cntPoints = []
        cntPoints1 = []
        shapeePerimeter = []

        blackImgShapes = np.zeros(imgg.shape[:2], dtype="uint8")
        blackImgShapes = cv2.cvtColor(blackImgShapes, cv2.COLOR_GRAY2BGR)

        for pt in merged_vertices:
            x = pt[0] * dxfratio
            y = pt[1] * dxfratio
            if rotationangle == 0 and y < 0:
                y = -y
            cntPoints.append([int(x), int(y)])
            cntPoints1.append([x, y])

        if not cntPoints:
            continue

        # Draw contour for area/perimeter
        contour_np = np.array(cntPoints, dtype=np.int32)
        cv2.drawContours(blackImgShapes, [contour_np], -1, (255, 255, 255), thickness=-1)

        area = cv2.contourArea(contour_np)
        perimeter = cv2.arcLength(contour_np, True)

        # Apply derotation + transform
        for px, py in cntPoints1:
            p = fitz.Point(px, py) * derotationMatrix
            shapeePerimeter.append([p[0], p[1]])

        # Flip + rotate polygon
        shapeePerimeter = np.flip(np.array(shapeePerimeter), 1).tolist()
        shapeePerimeter = rotate_polygon(shapeePerimeter, rotationangle, rotationOld, width, height)

        # Pack result in full structure
        processed_shapes.append([shapeePerimeter, area, perimeter, colors[0] if colors else (0,0,0)])

    return processed_shapes

class DSU:
    def __init__(self, n: int):
        self.p = list(range(n))
        self.r = [0]*n
    def find(self, x: int) -> int:
        while self.p[x] != x:
            self.p[x] = self.p[self.p[x]]
            x = self.p[x]
        return x
    def union(self, a: int, b: int):
        ra, rb = self.find(a), self.find(b)
        if ra == rb: return
        if self.r[ra] < self.r[rb]:
            self.p[ra] = rb
        elif self.r[ra] > self.r[rb]:
            self.p[rb] = ra
        else:
            self.p[rb] = ra
            self.r[ra] += 1

# ---------- small helpers ----------
def _ensure_vertices_list(verts_any) -> List[Tuple[float, float]]:
    """Accept [(x,y), ...] or [[(x,y), ...]] and return a flat list of (x,y) floats."""
    if verts_any and isinstance(verts_any[0], (list, tuple)) and len(verts_any) == 1 \
       and verts_any[0] and isinstance(verts_any[0][0], (list, tuple)):
        verts_any = verts_any[0]
    return [(float(x), float(y)) for (x, y) in verts_any]

def _bbox(verts):
    xs = [p[0] for p in verts]; ys = [p[1] for p in verts]
    return (min(xs), min(ys), max(xs), max(ys))

def _boxes_manhattan_gap(b1, b2):
    minx1, miny1, maxx1, maxy1 = b1
    minx2, miny2, maxx2, maxy2 = b2
    dx = max(0.0, max(minx1 - maxx2, minx2 - maxx1))
    dy = max(0.0, max(miny1 - maxy2, miny2 - maxy1))
    return dx, dy

def _shapes_quick_close(verts1, verts2, axis_threshold):
    """Quick AABB gate then vertex-vertex proximity check (axis threshold)."""
    b1 = _bbox(verts1); b2 = _bbox(verts2)
    dx_box, dy_box = _boxes_manhattan_gap(b1, b2)
    if dx_box > axis_threshold or dy_box > axis_threshold:
        return False
    for (x1,y1) in verts1:
        for (x2,y2) in verts2:
            if abs(x1 - x2) <= axis_threshold and abs(y1 - y2) <= axis_threshold:
                return True
    return False

# --------------------------
# 1) Gather groups and produce unioned polygons
# --------------------------
def gather_and_union_groups(
    hatched_areas: List[Any],
    axis_threshold: float = 0.5,
    require_diff_color: bool = True,
    skip_colors: Set[Tuple[int,int,int]] = None,
) -> List[Tuple[List[Tuple[float,float]], float, float, Tuple[int,int,int]]]:
    """
    Group shapes that are close (chain merging), then UNITE the original polygons of each group.
    Returns list of (coords, area, perimeter, color) where coords is the outer boundary of each polygon
    produced by the union (for MultiPolygon each part is appended as a separate result).
    - hatched_areas items: [ vertices, area, perimeter, color ]
    """
    if skip_colors is None:
        skip_colors = set()

    # collect valid polygon geometries and metadata
    polys = []
    colors = []
    areas = []
    perims = []
    for item in hatched_areas:
        if not item or len(item) < 4:
            continue
        verts_any, area, perim, color = item[0], item[1], item[2], item[3]
        if color in skip_colors:
            continue
        verts = _ensure_vertices_list(verts_any)
        if len(verts) < 3:
            continue
        try:
            poly = Polygon(verts)
            if not poly.is_valid or poly.is_empty:
                # try fix by buffering 0
                poly = poly.buffer(0)
            if poly.is_empty:
                continue
            polys.append(poly)
            colors.append(tuple(color))
            areas.append(float(area) if isinstance(area, (int,float)) else poly.area)
            perims.append(float(perim) if isinstance(perim, (int,float)) else poly.length)
        except Exception:
            continue

    m = len(polys)
    if m == 0:
        return []

    # group indices using DSU with chain merging
    dsu = DSU(m)
    for i in range(m):
        for j in range(i+1, m):
            if require_diff_color and colors[i] == colors[j]:
                continue
            verts_i = list(polys[i].exterior.coords)
            verts_j = list(polys[j].exterior.coords)
            if _shapes_quick_close(verts_i, verts_j, axis_threshold):
                dsu.union(i, j)

    # collect components
    comp = {}
    for i in range(m):
        r = dsu.find(i)
        comp.setdefault(r, []).append(i)

    results = []
    for root, members in comp.items():
        group_polys = [polys[k] for k in members]
        try:
            unioned = unary_union(group_polys)
        except Exception:
            # fallback: union iteratively
            u = group_polys[0]
            for p in group_polys[1:]:
                u = u.union(p)
            unioned = u

        # unioned might be Polygon, MultiPolygon, GeometryCollection
        def _handle_polygon(polygon_geom, rep_color):
            coords = [(float(x), float(y)) for x,y in polygon_geom.exterior.coords]
            area = polygon_geom.area
            perim = polygon_geom.length
            results.append((coords, area, perim, rep_color))

        # choose representative color: first member's color
        rep_color = colors[members[0]] if members else (0,0,0)

        if isinstance(unioned, Polygon):
            _handle_polygon(unioned, rep_color)
        elif isinstance(unioned, MultiPolygon) or isinstance(unioned, GeometryCollection):
            # GeometryCollection may have polygons inside
            for geom in getattr(unioned, "geoms", []):
                if isinstance(geom, Polygon):
                    _handle_polygon(geom, rep_color)
        else:
            # unexpected geometry — skip
            continue

    return results

# --------------------------
# 2) process_grouped_shapes -> produce processed list ready for drawing
# --------------------------
def process_grouped_shapes(
    grouped_polys: List[Tuple[List[Tuple[float,float]], float, float, Tuple[int,int,int]]],
    imgg, dxfratio, rotationangle, derotationMatrix, rotationOld, width, height
) -> List[Tuple[List[Tuple[float,float]], float, float, Tuple[int,int,int]]]:
    """
    For each grouped polygon (coords, area, perim, color), scale, derotate, rotate and return
    processed polygon in same (coords, area, perim, color) format, coords as list of [x,y] floats.
    """
    processed = []
    for coords, area, perim, color in grouped_polys:
        if not coords:
            continue

        cntPoints = []
        cntPoints1 = []
        # convert/scaling
        for (x,y) in coords:
            xs = x * dxfratio
            ys = y * dxfratio
            if rotationangle == 0 and ys < 0:
                ys = -ys
            cntPoints.append([int(xs), int(ys)])
            cntPoints1.append([xs, ys])

        if not cntPoints1:
            continue

        # build derotated perimeter points
        shapeePerimeter = []
        for px, py in cntPoints1:
            p = fitz.Point(px, py) * derotationMatrix
            shapeePerimeter.append([p[0], p[1]])

        # flip and rotate (use your rotate_polygon)
        shapeePerimeter = np.flip(np.array(shapeePerimeter), 1).tolist()
        shapeePerimeter = rotate_polygon(shapeePerimeter, rotationangle, rotationOld, width, height)

        # ensure type and length
        final_coords = [(float(x), float(y)) for x,y in shapeePerimeter]
        if len(final_coords) < 3:
            continue

        processed.append((final_coords, area, perim, color))

    return processed

def ROI_boundingBoxCoor(img):
    # Threshold (invert: walls are white)
    imgGray= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(imgGray, 250, 255, cv2.THRESH_BINARY_INV)

    # Morphological cleanup
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
    walls = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

    # --- Connected components ---
    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(walls, connectivity=8)

    best_idx = None
    best_score = -1
    h, w = walls.shape
    cx_img, cy_img = w//2, h//2   # image center

    for i in range(1, num_labels):  # ignore background (0)
        x, y, bw, bh, area = stats[i]
        cx, cy = centroids[i]

        # Score = area minus distance penalty
        #prefer large area AND closeness to image center
        dist = np.hypot(cx - cx_img, cy - cy_img)
        score = area - 0.5 * dist #tune 0.5

        if score > best_score:
            best_score = score
            best_idx = i

    # --- Get bounding box of best region ---
    bbox = None
    if best_idx is not None:
        x, y, bw, bh, _ = stats[best_idx]
        margin = 50
        x = int(max(x - margin, 0))
        y = int(max(y - margin, 0))
        bw = int(min(bw + 2*margin, w-x))
        bh = int(min(bh + 2*margin, h-y))

        # Define bounding box as (x_min, y_min, x_max, y_max)
        bbox = (x, y, x+bw, y+bh)

        # Draw ROI
        imgcopy = img.copy()
        cv2.rectangle(imgcopy, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0,255,0), 2)

        mask = np.zeros_like(img)
        cv2.rectangle(mask, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255,255,255), -1)
        main_zone = cv2.bitwise_and(img, mask)

        return imgcopy, main_zone, bbox

    return img, img, bbox

def draw_bb_onPDF(doc,bbox):
    page = doc[0]
    x1, y1 = bbox[0],bbox[1]
    x2, y2 = bbox[2],bbox[3]

    p1 = fitz.Point(x1,y1)
    p2 = fitz.Point(x2,y2)

    p1=p1*page.derotation_matrix
    p2=p2*page.derotation_matrix

    rect = fitz.Rect(p1, p2).normalize()
    x0, y0, x1, y1 = rect.x0, rect.y0, rect.x1, rect.y1
    pdf_bbox=[x0, y0, x1, y1]

    page.draw_rect(rect) #for visualization only
    doc.save('kk.pdf') #ffor visualization only
    return pdf_bbox
   
def mainFunctionDrawImgPdf(datadoc,dxfpath, dxfratio,SearchArray,CorrectionRatio,points_Of_drawing_Canvas,pdfpath=0,pdfname=0,pdf_content=0):
  OutputPdfStage1='BB Trial.pdf'
  if pdf_content:
     FinalRatio,width_dxf= RetriveRatio(datadoc,dxfpath,pdf_content)
  else:
      FinalRatio,width_dxf= RetriveRatio(datadoc,dxfpath)
    

  # hatched_areas = get_hatched_areas(dxfpath,FinalRatio)
  # hatched_areas=remove_duplicate_shapes(new_hatched_areas)
  if pdf_content:
    img=pdftoimg(datadoc,pdf_content)
  else:
     img=pdftoimg(datadoc)
  flipped_horizontal=flip(img)
  allcnts = []
  imgg = flipped_horizontal
  # imgtransparent1=imgg.copy()
  if pdf_content:
    doc = fitz.open(stream=pdf_content, filetype="pdf")
  else:
    doc = fitz.open('pdf',datadoc)
  page2 = doc[0]
  rotationOld=page2.rotation
  derotationMatrix=page2.derotation_matrix
  pix=page2.get_pixmap()
  width=abs(page2.mediabox[2])+abs(page2.mediabox[0])
  height=abs(page2.mediabox[3])+abs(page2.mediabox[1])
  print('mediabox', width , height)

  Correction = CorrectionRatio / width_dxf
  print("Correction Factor = ",round(Correction/FinalRatio),1)
  dxfratio = dxfratio * round((Correction/FinalRatio),1)
  print("new omar dxfRatio = ",dxfratio)

  imgcopy, main_zone, bbox = ROI_boundingBoxCoor(img) #send here bgr img not gray
  pdf_bbox=draw_bb_onPDF(doc,bbox)
  bxmin, bymin, bxmax, bymax = pdf_bbox


#   print('olddxfratio',dxfratio)
#   correction_factor= detect_scale_from_page(dxfpath,width,dxfratio/1000)

#   factor=1
#   print('corr_factor',correction_factor)
#   if correction_factor <0.26: #if less than 0.25 then the dxf ratio is correeect, if greater then *2
#     factor=1
#     print('Ratio working: keep as it is')
#   else:
#     factor =2
#     print('Ratio was adjusted to be ur input ratio x2')

#   dxfratio=dxfratio*factor
#   print('new dxfratio', dxfratio)

  
  if page2.rotation!=0:

    rotationangle = page2.rotation
    page2.set_rotation(0)
    ratio =  pix.width/ img.shape[0]
  else:
    ratio =  pix.width/ img.shape[1]
    rotationangle = 270


  hatched_areas,Legendarray = get_hatched_areas(datadoc,dxfpath,FinalRatio,rotationangle,SearchArray)
    
  nearby,grouped_shapes_final=find_nearby_vertices(hatched_areas)
  processed_intersections = process_polygons(nearby, img, dxfratio, rotationangle, derotationMatrix,rotationOld,width,height)

  groups = gather_and_union_groups(hatched_areas, axis_threshold=0.5,
                                 require_diff_color=True,
                                 skip_colors={(0,0,0),(255,255,255)})

  processed_grouped_shapes = process_grouped_shapes(groups,
    imgg, dxfratio, rotationangle, derotationMatrix, rotationOld, width, height)
    
  allshapes=[]
  # Iterate through each polygon in metric units
  NewColors = []
  if pdf_content:
    SimilarAreaDictionary=Create_DF(dxfpath,datadoc,hatched_areas,pdf_content)
  else:
     SimilarAreaDictionary=Create_DF(dxfpath,datadoc,hatched_areas)
  i=0
  flagcolor = 0
  ColorCheck=[]
    
  XMLArea = []
  XMLPerimeter = []


  for polygon in hatched_areas:
      cntPoints = []
      cntPoints1 = []
      shapeePerimeter = []
      shapeeArea = []

      blackImgShapes = np.zeros(imgg.shape[:2], dtype="uint8")
      blackImgShapes= cv2.cvtColor(blackImgShapes, cv2.COLOR_GRAY2BGR)

      
      # Convert each vertex from metric to pixel coordinates
      for vertex in polygon[0]:
          x = (vertex[0]) *dxfratio
          y = (vertex[1]) *dxfratio
          if rotationangle==0:
            if y<0:
              y=y*-1
          cntPoints.append([int(x), int(y)])
          cntPoints1.append([x, y])

      cv2.drawContours(blackImgShapes, [np.array(cntPoints)], -1, ([255,255,255]), thickness=-1)
      x, y, w, h = cv2.boundingRect(np.array(cntPoints))
      firstpoint = 0
      for poi in np.array(cntPoints1):
          if firstpoint == 0:
            x2, y2 = poi
            p2 = fitz.Point(x2,y2)
            # p1 = fitz.Point(x1,y1)
            p2=p2*derotationMatrix
            shapeePerimeter.append([p2[0],p2[1]])
            firstpoint = 1
          else:
            x1, y1 = poi
            p1 = fitz.Point(x1,y1)
            # p1 = fitz.Point(x1,y1)
            p1=p1*derotationMatrix
            print("P1 = ",p1)
            shapeePerimeter.append([p1[0],p1[1]])

      shapeePerimeter.append([p2[0],p2[1]])
      shapeePerimeter=np.flip(shapeePerimeter,1)
      shapeePerimeter=rotate_polygon(shapeePerimeter,rotationangle,rotationOld,width,height)

      for poi in np.array(cntPoints1):
            x1, y1 = poi
            p1 = fitz.Point(x1,y1)
            # p1 = fitz.Point(x1,y1)
            p1=p1*derotationMatrix
            print("P1 = ",p1)
            shapeeArea.append([p1[0],p1[1]])

      shapeeArea.append([p2[0],p2[1]])
      shapeeArea=np.flip(shapeeArea,1)
      shapeeArea=rotate_polygon(shapeeArea,rotationangle,rotationOld,width,height)

      
      tol=0
      condition1 = (SimilarAreaDictionary['Area'] >= polygon[1] - tol) & (SimilarAreaDictionary['Area'] <= polygon[1] +tol)
      condition2 = (SimilarAreaDictionary['Perimeter'] >= polygon[2] -tol) & (SimilarAreaDictionary['Perimeter'] <= polygon[2] +tol)
      combined_condition = condition1 & condition2

      if any(combined_condition):
          flagcolor = 1
          index = np.where(combined_condition)[0][0]
          # print(SimilarAreaDictionary.at[index, 'Color'])
          NewColors=SimilarAreaDictionary.at[index, 'Color']
      else:
        flagcolor = 2
        NewColors=SimilarAreaDictionary.at[i, 'Color']

      if(int(NewColors[0])==255 and int(NewColors[1])==255 and int(NewColors[2])==255):
        WhiteImgFinal = cv2.bitwise_and(blackImgShapes,imgg)
        flipped=flip(WhiteImgFinal)
        

        imgslice = WhiteImgFinal[y:y+h, x:x+w]
        if(imgslice.shape[0] != 0 and imgslice.shape[1] != 0):
            flippedSlice=flip(imgslice)
            
    
        # Convert flippedSlice to PIL for color extraction
            flippedSlice_pil = Image.fromarray(flippedSlice)
    
            # Define patch size for color sampling (e.g., 10x10 pixels)
            patch_size = 100
            patch_colors = []
    
            # Loop through patches in the image
            for i in range(0, flippedSlice_pil.width, patch_size):
                for j in range(0, flippedSlice_pil.height, patch_size):
                    # Crop a patch from the original image
                    patch = flippedSlice_pil.crop((i, j, i + patch_size, j + patch_size))
                    patch_colors += patch.getcolors(patch_size * patch_size)
    
            # Calculate the dominant color from all patches
            max_count = 0
            dominant_color = None
            tolerance = 5
            black_threshold = 30  # Max RGB value for a color to be considered "black"
            white_threshold = 225  # Min RGB value for a color to be considered "white"
    
            for count, color in patch_colors:
                # Exclude colors within the black and white ranges
                if not (all(c <= black_threshold for c in color) or all(c >= white_threshold for c in color)):
                    # Update if the current color has a higher count than previous max
                    if count > max_count:
                        max_count = count
                        dominant_color = color
    
            
    
            # Append dominant color to ColorCheck and update NewColors
            if dominant_color is not None:
                ColorCheck.append(dominant_color)
    
                NewColors = None
    
                for color in ColorCheck:
                  # Check if the current color is within the tolerance
                  print("color = ",color)
                  print("dominant_color = ",dominant_color)
                  if (abs(color[0] - dominant_color[0]) < 20 and
                      abs(color[1] - dominant_color[1]) < 20 and
                      abs(color[2] - dominant_color[2]) < 20):
                      NewColors = (color[2], color[1], color[0])  # Set the new color
                      break
                  else:
                      # If no color in ColorCheck meets the tolerance, use the dominant color
                      NewColors = (dominant_color[2], dominant_color[1], dominant_color[0])
    
                if NewColors not in ColorCheck:
                  ColorCheck.append(NewColors)
    
                
                if flagcolor == 1:
                    SimilarAreaDictionary.at[index, 'Color'] = NewColors
                    # print(f"Updated Color at index {index} with {NewColors}.")
                elif flagcolor == 2:
                        SimilarAreaDictionary.at[i, 'Color'] = NewColors

            
      cv2.drawContours(imgg, [np.array(cntPoints)], -1, ([NewColors[2],NewColors[1],NewColors[0]]), thickness=-1)

      vertices_area = [
        [[float(x), float(y)] for x, y in shapeeArea],  # vertices
        str(polygon[1]),NewColors                                  # area
      ]
      XMLArea.append(vertices_area)

      vertices_Perimeter = [
        [[float(x), float(y)] for x, y in shapeePerimeter],  # vertices
        str(polygon[1]),NewColors                                  # area
      ]
      XMLPerimeter.append(vertices_Perimeter)

      discard = False
      if(points_Of_drawing_Canvas):
            Boundingpolygon = np.array(
                [(p.x, p.y) for p in points_Of_drawing_Canvas],
                dtype=np.float32
                )

            for x, y in shapeeArea:
                # Check if the point is outside the polygon
                result = cv2.pointPolygonTest(Boundingpolygon, (x, y), False)
                if result < 0:  # < 0 means point is outside
                    discard = True
                break
      else:
            for point in shapeeArea:
                if not (bxmin <= point[0] <= bxmax and bymin <= point[1] <= bymax):
                    discard = True
                    break

      if not discard:
                       
        annot11 = page2.add_polygon_annot( points=shapeeArea)  # 'Polygon'
        annot11.set_border(width=0.2)
        annot11.set_colors(stroke=(int(NewColors[0])/255,int(NewColors[1])/255,int(NewColors[2])/255), fill= (int(NewColors[0])/255,int(NewColors[1])/255,int(NewColors[2])/255) )
        annot11.set_info(content=str(polygon[1])+' sq m',subject='Area Measurement', title="ADR Team")
        annot11.set_opacity(0.8)
        # annot.set_line_ends(fitz.PDF_ANNOT_LE_DIAMOND, fitz.PDF_ANNOT_LE_CIRCLE)
        annot11.update()


      discard = False
      if(points_Of_drawing_Canvas):
            Boundingpolygon = np.array(
                [(p.x, p.y) for p in points_Of_drawing_Canvas],
                dtype=np.float32
                )

            for x, y in shapeePerimeter:
                # Check if the point is outside the polygon
                result = cv2.pointPolygonTest(Boundingpolygon, (x, y), False)
                if result < 0:  # < 0 means point is outside
                    discard = True
                break
      else:
            for point in shapeePerimeter:
                if not (bxmin <= point[0] <= bxmax and bymin <= point[1] <= bymax):
                    discard = True
                    break

      if not discard:
        annot12 = page2.add_polyline_annot( points=shapeePerimeter )  # 'Polygon'
        annot12.set_border(width=0.8)
        annot12.set_colors(stroke=(int(NewColors[0])/255,int(NewColors[1])/255,int(NewColors[2])/255))
        annot12.set_info(content=str(polygon[2])+' m',subject='Perimeter Measurement', title="ADR Team")
        annot12.set_opacity(0.8)
        # annot.set_line_ends(fitz.PDF_ANNOT_LE_DIAMOND, fitz.PDF_ANNOT_LE_CIRCLE)
        annot12.update()
      i += 1
  alpha = 0.8  # Transparency factor.
   

    
#   with open("3.2_custom_columns.xml", "w", encoding="utf-8") as f:
#         f.write(column_xml)

  page2.set_rotation(rotationOld)
  Correct_img=flip(imgg)

  image_new1 = cv2.addWeighted(Correct_img, alpha,  img, 1 - alpha, 0)
  SimilarAreaDictionary = SimilarAreaDictionary.fillna(' ')
    
  # Define white color to filter out
  white_color = (255, 255, 255)

  # Delete rows where 'Guess' equals white_color
  SimilarAreaDictionary = SimilarAreaDictionary[SimilarAreaDictionary['Color'] != white_color]

  # Reset the index to update row numbering
  SimilarAreaDictionary.reset_index(drop=True, inplace=True)

  grouped_df = SimilarAreaDictionary.groupby('Color').agg({
            'Guess':'first',
            'Occurences': 'sum',      # Sum of occurrences for each color
            'Area':'first',
            'Total Area': 'sum',           # Sum of areas for each color
            'Perimeter':'first',
            'Total Perimeter': 'sum',      # Sum of perimeters for each color
            'Length':'first',
            'Total Length':'first',
            'Texts':'first',
            'Comments':'first'
      
        }).reset_index()
    
  SimilarAreaDictionary = grouped_df
  # doc.save(OutputPdfStage1)
  modified_pdf_data = doc.tobytes()
  # OutputPdfStage2=adjustannotations(modified_pdf_data)
  OutputPdfStage2,annotations_data=adjustannotations(modified_pdf_data,Legendarray)
    
  allvertices = annotations_data
  hatchcolorR= '0'
  hatchcolorG= '1'
  hatchcolorB= '1'
  LinestyleTemplates = {
    'Solid': '<</W {w}/S/S/Type/Border>>',
    'Dashed1':'<</W {w}/S/D/D[2 2]/Type/Border>>',
    'Dashed2': '<</W {w}/S/D/D[3 3]/Type/Border>>',
    'Dashed3': '<</W {w}/S/D/D[4 4]/Type/Border>>',
    'Dashed4': '<</W {w}/S/D/D[4 3 2 3]/Type/Border>>',
    'Dashed5': '<</W {w}/S/D/D[4 3 16 3]/Type/Border>>',
    'Dashed6': '<</W {w}/S/D/D[8 4 4 4]/Type/Border>>'
}
    #/BS<</W 1/S/D/D[4 4]/Type/Border>>
    
  HatchFunctions = {
        'None':'',
        'Brick': setBrickHatch,
        'DiagonalBrick':setDiagonalBrickHatch,
        'Horizontal':setHorizontalHatch,
        'Vertical':setVerticalHatch,
        'DiagonalDown':setDiagonalDownHatch,
        'DiagonalUp':setDiagonalUpHatch,
        'Grid': setGridHatch,
        'Weave':setWeaveHatch,
        '10Dots':set10DotsHatch,
        '20Dots':set20DotsHatch,
        '30Dots':set30DotsHatch
    }

  #Area and perimeter numbers example
  area = 1
  perimeter = 1

  import colorsys

  annotations=[]
  for shapeinvertices in allvertices:
      # print("shapeinvertices[3] = ",shapeinvertices[3])
      
      if(shapeinvertices[3] == None):
          rn=0
          gn=0
          bn=0

      else:
          rn=shapeinvertices[3][0]-150/255
          gn=shapeinvertices[3][1]-150/255
          bn=shapeinvertices[3][2]-150/255
    
      h, s, v = colorsys.rgb_to_hsv(rn, gn, bn)
    
      # snap to full saturation, 50% brightness
      s2, v2 = 0.6, 0.9
    
      # back to RGB
      r2, g2, b2 = colorsys.hsv_to_rgb(h, s2, v2)
      # print(r2,g2,b2)
      R=str(r2)
      G=str(g2)
      B=str(b2)
      # print(R,G,B)
      r2, g2, b2 = colorsys.hsv_to_rgb(h, s2, v2)
      color_str = f"{r2:.3f} {g2:.3f} {b2:.3f}"
      hatch_color = f"{float(hatchcolorR):.0f} {float(hatchcolorG):.0f} {float(hatchcolorB):.0f}"
      # print(color_str,hatch_color)
      if(shapeinvertices[1] is not None):
    
        annotations.append(
            {
    
                'vertices': shapeinvertices[0], # [[x,y],[x1,y1],[....]] position of ur markup
                'text': str(shapeinvertices[1])+' sq m',
                'author': 'ADR',
                'custom_data': {'Legend': shapeinvertices[4],'NBS':shapeinvertices[5]},#identify custom colums here as( Column name: Text to add )
                'type_internal': 'Bluebeam.PDF.Annotations.AnnotationMeasureArea',
                'subject': 'Area Measurement',
                'label': 'label',#change label to whatever u want
                'opacity': '0.5',#opacity of ur shape fill
                'color': color_str,# normalized (RGB --> R/255 G/255 B/255)
                'linestyle': LinestyleTemplates['Solid'].format(w=0),
                'hatchstyle': HatchFunctions['DiagonalDown'](color_str, hatch_color),#this is the hatchstyle and the hatch color fill and the hatch line color,
                #if u want the hatch to be none (as in a solid color only) just pass HatchFunctions['None'] without any colors here
                'hatchLinescolor':hatch_color,
    
    
            }
        )
      elif(shapeinvertices[2] is not None):
          
        annotations.append(
          {

              'vertices': shapeinvertices[0], # [[x,y],[x1,y1],[....]] position of ur markup
              'text': str(shapeinvertices[2])+' m',
              'author': 'ADR',
              'custom_data': {'Legend': shapeinvertices[4],'NBS':shapeinvertices[5]},#identify custom colums here as( Column name: Text to add )
              'type_internal': 'Bluebeam.PDF.Annotations.AnnotationMeasurePerimeter',
              'subject': 'Perimeter Measurement',
              'label':'label2',
              'opacity': '0.7',#opacity of ur shape fill
              'color': R+ ' '+G + ' '+B,# normalized (RGB --> R/255 G/255 B/255)
              'linestyle': LinestyleTemplates['Solid'].format(w=2), # LineStyles as in BB ,this w is the linewidth
              'hatchstyle': '',
              'hatchLinescolor':'',

          }
      )


    
        



  column_order = ['Legend','NBS'] #specify here the custom columns in order
# print(bax_annotations)

#replace with ur pdf width and height variables
  pdfWidth=1684
  pdfHeight=2384


# save_multiple_annotations_bax(
#     bax_annotations, 'Area_Perimeter_OMAR_output.bax', column_order, pdfWidth, pdfHeight
# )
  bax_xml=save_multiple_annotations_bax(
    annotations, 'FF-Ceiling.bax', column_order, pdfWidth, pdfHeight
  )

  def generate_bluebeam_columns_raw(column_names):
    """
    Generate BluebeamUserDefinedColumns XML as raw string, without headers or extra fields.
    """
    root = Element("BluebeamUserDefinedColumns")

    for idx, name in enumerate(column_names):
        item = SubElement(root, "BSIColumnItem", Index=str(idx), Subtype="Text")
        SubElement(item, "Name").text = name
        SubElement(item, "DisplayOrder").text = str(idx)
        SubElement(item, "Deleted").text = "False"
        SubElement(item, "Multiline").text = "False"

    # Convert to string and decode raw bytes
    return tostring(root, encoding="unicode", method="xml")


  column_xml = generate_bluebeam_columns_raw(column_order)


  # with open("Adjusted_PDF.pdf", "wb") as f:
  #   f.write(OutputPdfStage2)
      
  # doc2 = fitz.open(stream=OutputPdfStage2, filetype="pdf")
  # doc2 = fitz.open(stream=OutputPdfStage2, filetype="pdf")
      
  doc2 =fitz.open('pdf',OutputPdfStage2)
  if pdf_content:
     gc,spreadsheet_service,spreadsheetId, spreadsheet_url , namepathArr=google_sheet_Legend.legendGoogleSheets(SimilarAreaDictionary , pdfname,pdfpath,pdf_content)  
  else:
    gc,spreadsheet_service,spreadsheetId, spreadsheet_url , namepathArr=google_sheet_Legend.legendGoogleSheets(SimilarAreaDictionary , pdfname,pdfpath)
  # dbxTeam=tsadropboxretrieval.ADR_Access_DropboxTeam('user')
  # md, res =dbxTeam.files_download(path= pdfpath+pdfname)
  # 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','color'])

  # for page in doc:
  for page in doc2:  
    # Iterate through annotations on the page
    for annot in page.annots():
        # Get the color of the annotation
        annot_color = annot.colors
        if annot_color is not None:
            # annot_color is a dictionary with 'stroke' and 'fill' keys
            stroke_color = annot_color.get('stroke')  # Border color
            fill_color = annot_color.get('fill')      # Fill color
            if fill_color:
              v='fill'
              # print('fill')
            if stroke_color:
              v='stroke'
            x,y,z=int(annot_color.get(v)[0]*255),int(annot_color.get(v)[1]*255),int(annot_color.get(v)[2]*255)
            list1.loc[len(list1)] =[annot.info['content'],annot.info['id'],annot.info['subject'],[x,y,z]]
  print('LISTTT',list1)
  return doc2,image_new1, SimilarAreaDictionary ,spreadsheetId, spreadsheet_url , namepathArr , list1,hatched_areas, bax_xml, column_xml

  # doc.save('Testing(2.7).pdf')
  # return doc,image_new1#, SimilarAreaDictionary ,spreadsheetId, spreadsheet_url , namepathArr , list1,hatched_areas

# datadoc='/content/3.3 - Ceiling finishes - Example 1 - Sheet 1.pdf' #pdf path here
# dxfpath='/content/3.3 - Ceiling finishes - Example 1 - Sheet 1.dxf'#dxfpath here
# dxfratio=28.3464527867108
# doc,image_new1=mainFunctionDrawImgPdf(datadoc,dxfpath, dxfratio)
# cv2_imshow(image_new1)