# -*- coding: utf-8 -*- """2.7 Code to be deployed 21.02.2025 Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1RWSQn0GW_KXoHkJLcbYzLAGGyc0tiDWl """ """## Imports""" 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 print(ezdxf.__version__) 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 # from ezdxf.tools import rgb from ezdxf.colors import aci2rgb # from ezdxf.math import rgb_from_color from collections import Counter 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, NumberObject from typing import NewType from ctypes import sizeof from io import BytesIO 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 # -*- 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 """ """## 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 ''' """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) print("IMAGE") # cv2_imshow(img) return img,pix # 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 """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): """Extract hatch color with detailed debugging.""" if not entity: # print("No entity provided for color extraction.") return (255, 255, 255) # Check for true color 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 # Check for color index color_index = entity.dxf.color # print(f"Entity color index: {color_index}") if 1 <= color_index <= 255: rgb_color = aci_to_rgb(color_index) # Convert ACI to RGB # print(f"Converted ACI to RGB: {rgb_color}") return rgb_color # Handle ByLayer or ByBlock if color_index == 0: # ByLayer layer_name = entity.dxf.layer layer = entity.doc.layers.get(layer_name) # print(f"ByLayer detected for layer '{layer_name}'.") if layer: layer_color_index = layer.dxf.color # print(layer_color_index) rgb_color = aci_to_rgb(layer_color_index) # print(f"Layer '{layer_name}' color index {layer_color_index} converted to RGB: {rgb_color}") return rgb_color else: # print(f"Layer '{layer_name}' not found. Defaulting to white.") return (255, 255, 255) # Default # print("Unhandled color case. Defaulting to white.") return (255, 255, 255) def point_in_rectangle(point, rect_coords): x, y = point (x1, y1), (x2, y2) = rect_coords return x1 <= x <= x2 and y1 <= y <= y2 from math import sqrt def euclidean_distance(point1, point2): x1, y1 = point1 x2, y2 = point2 return sqrt((x2 - x1)**2 + (y2 - y1)**2) def compute_hatch_centroid(hatch): x_coords = [] y_coords = [] for path in hatch.paths: if path.PATH_TYPE == "PolylinePath": for vertex in path.vertices: x_coords.append(vertex[0]) y_coords.append(vertex[1]) elif path.PATH_TYPE == "EdgePath": for edge in path.edges: if hasattr(edge, "start"): x_coords.append(edge.start[0]) y_coords.append(edge.start[1]) if hasattr(edge, "end"): x_coords.append(edge.end[0]) y_coords.append(edge.end[1]) if x_coords and y_coords: return (sum(x_coords) / len(x_coords), sum(y_coords) / len(y_coords)) return None """### Hatched areas""" def get_hatched_areas(datadoc,filename,FinalRatio,rotationangle,SearchArray): print("SearchArray = ",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 text_with_positions = [] # SearchArray=[["","Wall Type","",""],["","","",""]] # print("SearchArray=",len(SearchArray)) # print("SearchArray=",len(SearchArray[0])) # print("SearchArray=",SearchArray[0][0]) 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 text_with_positions.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 text_with_positions.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 text_with_positions.append([text, textNBS, (x, y), detected_color]) print("text_with_positions=",text_with_positions) grouped = {} for entry in text_with_positions: 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]) text_with_positions=filtered_results for entity in msp: if entity.dxftype() == 'HATCH': cntPoints=[] for path in entity.paths: # path_type = path.type # # Resolve the path type to its name # path_type_name = BoundaryPathType(path_type).name # print(f"Encountered path type: {path_type_name}") vertices = [] # Reset vertices for each path # print(str(path.type)) if str(path.type) == 'BoundaryPathType.POLYLINE' or path.type == 1: # if path.type == 2: # Polyline path # Handle POLYLINE type HATCH vertices = [(vertex[0] * FinalRatio, vertex[1] * FinalRatio) for vertex in path.vertices] # print("Hatch Vertices = ",vertices) if len(vertices) > 3: poly = ShapelyPolygon(vertices) minx, miny, maxx, maxy = poly.bounds width = maxx - minx height = maxy - miny if (poly.area > 0 and (height > 0.2 or width > 0.2)): length = height if(width > length): length = width area1 = round(poly.area, 3) perimeter = round(poly.length, 3) # print("Vertices = ",vertices) normalized_vertices = normalize_vertices(vertices) rgb_color = get_hatch_color(entity) # print("rgb_color = ",rgb_color) # 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)) if length > 0.6: hatched_areas.append([vertices, area1, length, rgb_color]) elif str(path.type) == 'BoundaryPathType.EDGE' or path.type == 2: # elif path.type == 2: # Edge path # Handle EDGE type HATCH vert = [] for edge in path.edges: x, y = edge.start x1, y1 = edge.end vert.append((x * FinalRatio, y * FinalRatio)) vert.append((x1 * FinalRatio, y1 * FinalRatio)) poly = ShapelyPolygon(vert) minx, miny, maxx, maxy = poly.bounds width = maxx - minx height = maxy - miny if (poly.area > 0 and (height > 0.2 or width > 0.2)): length = height if(width > length): length = width area1 = round(poly.area, 3) perimeter = round(poly.length, 3) normalized_vertices = normalize_vertices(vert) rgb_color = get_hatch_color(entity) # print("rgb_color = ",rgb_color) # 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)) if length > 0.6: hatched_areas.append([vert, area1, length, rgb_color]) else: print(f"Encountered path type: {path.type}") 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 and (height > 0 and width > 0)): 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 and (height > 0 and width > 0)): 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': 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 and (height > 0 and width > 0)): 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 and (height > 0 and width > 0)): 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,text_with_positions """### 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= RetriveRatio(datadoc,dxfpath,pdf_content) else: FinalRatio= RetriveRatio(datadoc,dxfpath) # hatched_areas = get_hatched_areas(datadoc,dxfpath,FinalRatio) # 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, 'Area'] * SimilarAreaDictionary.at[index, 'Occurences'] SimilarAreaDictionary.at[index, 'Total Perimeter'] = SimilarAreaDictionary.at[index, 'Perimeter'] * SimilarAreaDictionary.at[index, 'Occurences'] else: TotalArea=area TotalPerimeter=perimeter # print("Shape[3]",shape[3]) 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""" # from sklearn.cluster import KMeans def color_distance(color1, color2): print("color1 = ",color1) print("color2 = ",color2) print("abs(color1[0] - color2[0]) = ",abs(color1[0] - color2[0])) print("abs(color1[1] - color2[1]) = ",abs(color1[1] - color2[1])) print("abs(color1[2] - color2[2]) = ",abs(color1[2] - color2[2])) if(abs(color1[0] - color2[0]) < 20 and abs(color1[1] - color2[1]) < 20 and abs(color1[2] - color2[2]) < 20): return 1 else: return 100 # return np.sqrt(sum((a - b) ** 2 for a, b in zip(color1, color2))) # Unify colors within a distance threshold def unify_colors(df, threshold=20): # Convert colors to tuple if they are not already in tuple format df['Color'] = df['Color'].apply(lambda x: tuple(x) if isinstance(x, list) else x) # Iterate through the DataFrame and compare each color with the next one for i in range(len(df) - 1): # We don't need to compare the last color with anything current_color = df.at[i, 'Color'] next_color = df.at[i + 1, 'Color'] # If the distance between current color and the next color is smaller than the threshold if color_distance(current_color, next_color) <= threshold: # Make both the same color (unify them to the current color) df.at[i + 1, 'Color'] = current_color # Change the next color to the current color return df 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)) def adjustannotations(OutputPdfStage1,text_with_positions): input_pdf_path = OutputPdfStage1 output_pdf_path = "Final-WallsAdjusted.pdf" # 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" in page: annotations = page["/Annots"] for annot_index, annot in enumerate(annotations): obj = annot.get_object() # print("obj", obj) # print(obj.get("/IT")) if obj.get("/Subtype") == "/Line": # print("AWL ANNOT IF") # Check the /IT value to differentiate annotations # if "/Contents" in obj and "m" in obj["/Contents"]: if "/Subj" in obj and "Perimeter Measurement" in obj["/Subj"]: # print("Tany IF") obj.update({ NameObject("/Measure"): DictionaryObject({ NameObject("/Type"): NameObject("/Measure"), NameObject("/L"): DictionaryObject({ NameObject("/G"): FloatObject(1), NameObject("/U"): TextStringObject("m"), # Unit of measurement for area }), }), NameObject("/IT"): NameObject("/LineDimension"), # Use more distinctive name NameObject("/Subj"): TextStringObject("Length Measurement"), # Intent explicitly for Area }) # print(obj) if obj.get("/Subtype") in ["/Line", "/PolyLine"] and "/C" in obj: # Normalize and match the color annot_color = normalize_color(obj["/C"]) matched_entry = next( ((text, NBS) for text,NBS, _, color in text_with_positions if color_close_enough(annot_color, color)), (None, None) ) # print("matched_entry = ",matched_entry) matched_text, matched_nbs = matched_entry combined_text = "" if matched_text and matched_nbs: combined_text = f"{matched_text} - {matched_nbs}" elif matched_text: combined_text = matched_text elif matched_nbs: combined_text = matched_nbs obj.update({ NameObject("/T"): TextStringObject(combined_text), # Custom text for "Comment" column }) 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() def distance(rect1, rect2): """Calculate the Euclidean distance between two annotation centers.""" x1, y1 = (float(rect1[0]) + float(rect1[2])) / 2, (float(rect1[1]) + float(rect1[3])) / 2 x2, y2 = (float(rect2[0]) + float(rect2[2])) / 2, (float(rect2[1]) + float(rect2[3])) / 2 return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) def remove_duplicate_annotations(pdf_path, threshold): """Remove one of the duplicate annotations if they are close and have the same color.""" input_pdf_path = pdf_path output_pdf_path = "Filtered-Walls.pdf" # Load the input PDF pdf_bytes_io = BytesIO(pdf_path) 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 in range(len(reader.pages)): page = reader.pages[page_index] if "/Annots" in page: annotations = page["/Annots"] annots_data = [] to_delete = set() # Extract annotation positions and colors # for annot_index, annot_ref in enumerate(annotations): # annot = annot_ref.get_object() # if "/Rect" in annot and "/C" in annot: # rect = annot["/Rect"] # if isinstance(rect, ArrayObject): # Ensure rect is a list # rect = list(rect) # color = normalize_color(annot["/C"]) # annots_data.append((annot_index, rect, color)) for i, annot_ref in enumerate(annotations): annot = annot_ref.get_object() rect = annot.get("/Rect") color = annot.get("/C") if rect and color and isinstance(rect, ArrayObject) and len(rect) == 4: norm_color = normalize_color(color) annots_data.append((i, list(rect), norm_color)) for i, (idx1, rect1, color1) in enumerate(annots_data): if idx1 in to_delete: continue for j in range(i + 1, len(annots_data)): idx2, rect2, color2 = annots_data[j] if idx2 in to_delete: continue if color_close_enough(color1, color2) and distance(rect1, rect2) < threshold: to_delete.add(idx2) # Keep only non-duplicates new_annots = [annotations[i] for i in range(len(annotations)) if i not in to_delete] page[NameObject("/Annots")] = ArrayObject(new_annots) # Compare distances and mark duplicates # for i, (idx1, rect1, color1) in enumerate(annots_data): # if idx1 in to_delete: # continue # for j, (idx2, rect2, color2) in enumerate(annots_data[i+1:], start=i+1): # if idx2 in to_delete: # continue # if color1 == color2 and distance(rect1, rect2) < threshold: # to_delete.add(idx2) # Mark second annotation for deletion # # Remove duplicates # new_annotations = [annotations[i] for i in range(len(annotations)) if i not in to_delete] # page[NameObject("/Annots")] = ArrayObject(new_annotations) writer.add_page(page) output_pdf_io = BytesIO() writer.write(output_pdf_io) output_pdf_io.seek(0) return output_pdf_io.read() def calculate_distance(p1, p2): return math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2) def mainFunctionDrawImgPdf(datadoc,dxfpath, dxfratio,SearchArray,Thickness,pdfpath=0,pdfname=0,pdf_content=0): OutputPdfStage1='BB Trial.pdf' if pdf_content: FinalRatio= RetriveRatio(datadoc,dxfpath,pdf_content) else: FinalRatio= RetriveRatio(datadoc,dxfpath) # hatched_areas = get_hatched_areas(datadoc,dxfpath,FinalRatio) # hatched_areas=remove_duplicate_shapes(new_hatched_areas) if pdf_content: img,pix2=pdftoimg(datadoc,pdf_content) else: img,pix2=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 # print("Derotation Matrix = ",derotationMatrix) 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) 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,text_with_positions = get_hatched_areas(datadoc,dxfpath,FinalRatio,rotationangle,SearchArray) 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 ColorCounter = 0 ColorCheck=[] deleterows = [] # def color_distance(color1, color2): # return np.sqrt(sum((a - b) ** 2 for a, b in zip(color1, color2))) color_margin = 2 # Define margin threshold for polygon in hatched_areas: cntPoints = [] cntPoints1 = [] shapeePerimeter = [] shapeeArea = [] Text_Detected = 0 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 # print("combined_condition = ",combined_condition) 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'] # flagcolor = 2 # cv2.drawContours(imgg, [np.array(cntPoints)], -1, (NewColors), thickness=2) # print("new color = ",NewColors) # print("New Colors = ",NewColors) # if img is not None or img.shape[0] != 0 or img.shape[1] != 0: if(int(NewColors[0])==255 and int(NewColors[1])==255 and int(NewColors[2])==255): WhiteImgFinal = cv2.bitwise_and(blackImgShapes,imgg) # print("length = ",WhiteImgFinal.shape[0]) # print("width = ",WhiteImgFinal.shape[1]) flipped=flip(WhiteImgFinal) # print("Flipped") # cv2_imshow(flipped) imgslice = WhiteImgFinal[y:y+h, x:x+w] # print("length slice = ",imgslice.shape[0]) # print("width slice = ",imgslice.shape[1]) if(imgslice.shape[0] != 0 and imgslice.shape[1] != 0): flippedSlice=flip(imgslice) # print("Sliced & Flipped") # cv2_imshow(flippedSlice) # 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 # print("Dominant Color =", dominant_color) # Append dominant color to ColorCheck and update NewColors if dominant_color is not None: ColorCheck.append(dominant_color) NewColors = None # Initialize NewColors 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]) # break # Avoid appending `dominant_color` again unnecessarily 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 # print("New Colors = ",NewColors) cv2.drawContours(imgg, [np.array(cntPoints)], -1, ([NewColors[2],NewColors[1],NewColors[0]]), thickness=3) start_point1 = shapeePerimeter[0] end_point1 = shapeePerimeter[1] start_point2 = shapeePerimeter[0] end_point2 = shapeePerimeter[-2] distance1 = calculate_distance(start_point1, end_point1) distance2 = calculate_distance(start_point2, end_point2) # Divide the shapePerimeter into two halves half_index = len(shapeePerimeter) // 2 half1 = shapeePerimeter[1:half_index+1] half2 = shapeePerimeter[half_index:] # half1 = shapeePerimeter[1:half_index] # half2 = shapeePerimeter[half_index:-1] # Calculate distances for the halves if len(half1) >= 2: half1_distance = sum(calculate_distance(half1[i], half1[i + 1]) for i in range(len(half1) - 1)) else: half1_distance = 0 if len(half2) >= 2: half2_distance = sum(calculate_distance(half2[i], half2[i + 1]) for i in range(len(half2) - 1)) else: half2_distance = 0 max_distance = max(distance1, distance2, half1_distance) if max_distance == distance1: # Draw the line annotation for distance1 chosen_start = start_point1 chosen_end = end_point1 # annot12 = page2.add_line_annot(chosen_start, chosen_end) points=[] points.append(chosen_start) points.append(chosen_end) annot12 = page2.add_polyline_annot(points) elif max_distance == distance2: # Draw the line annotation for distance2 chosen_start = start_point2 chosen_end = end_point2 # annot12 = page2.add_line_annot(chosen_start, chosen_end) points=[] points.append(chosen_start) points.append(chosen_end) # annot12 = page2.add_polyline_annot(points) points=[] points.append(chosen_start) points.append(chosen_end) annot12 = page2.add_polyline_annot(points) elif max_distance == half1_distance: # annot12 = page2.add_polyline_annot(half1) max_pair_distance = 0.0 max_pair_start = None max_pair_end = None # 2. Loop through each consecutive pair in half1 for i in range(len(half1) - 1): p_current = half1[i] p_next = half1[i + 1] # 3. Compute distance between these two points dist = calculate_distance(p_current, p_next) # 4. Update max if this distance is greater if dist > max_pair_distance: max_pair_distance = dist max_pair_start = p_current max_pair_end = p_next # 5. After the loop, max_pair_start and max_pair_end represent # the two consecutive points with the greatest separation. if max_pair_start is not None and max_pair_end is not None: # 6. Draw the line annotation using these two points # annot12 = page2.add_line_annot(max_pair_start, max_pair_end) points=[] points.append(max_pair_start) points.append(max_pair_end) annot12 = page2.add_polyline_annot(points) # print(f"Drew line annotation between {max_pair_start} and {max_pair_end}") else: # This case only occurs if half1 has fewer than 2 points print("Not enough points in half1 to compute a line.") 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_info(subject='Perimeter Measurement',content=str(polygon[2])+' m') annot12.set_opacity(0.8) annot12.update() i += 1 alpha = 0.8 # Transparency factor. 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': 'sum', # Sum of lengths for each color 'Texts': 'first', # Keep the first occurrence of 'Texts' 'Comments': 'first' # Keep the first occurrence of 'Comments' }).reset_index() # doc.save(OutputPdfStage1) # OutputPdfStage2=adjustannotations(OutputPdfStage1,text_with_positions) modified_pdf_data = doc.tobytes() OutputPdfStage2=adjustannotations(modified_pdf_data,text_with_positions) if (Thickness): threshold = math.ceil(float(Thickness) * float(dxfratio) ) print(threshold) OutputPdfStage3 = remove_duplicate_annotations(OutputPdfStage2,threshold) else: OutputPdfStage3 = remove_duplicate_annotations(OutputPdfStage2,threshold=10) if pdf_content: latestimg,pix=pdftoimg(OutputPdfStage3,pdf_content) else: latestimg,pix=pdftoimg(OutputPdfStage3) doc2 =fitz.open('pdf',OutputPdfStage3) if pdf_content: gc,spreadsheet_service,spreadsheetId, spreadsheet_url , namepathArr=google_sheet_Legend.legendGoogleSheets(grouped_df , pdfname,pdfpath,pdf_content) else: gc,spreadsheet_service,spreadsheetId, spreadsheet_url , namepathArr=google_sheet_Legend.legendGoogleSheets(grouped_df , pdfname,pdfpath) 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,latestimg, SimilarAreaDictionary ,spreadsheetId, spreadsheet_url , namepathArr , list1,hatched_areas