Spaces:
Sleeping
Sleeping
File size: 68,408 Bytes
49b7e5a c959e92 49b7e5a c959e92 49b7e5a c959e92 49b7e5a c959e92 49b7e5a e8ffdbe 49b7e5a e8ffdbe 49b7e5a e8ffdbe 49b7e5a e8ffdbe 49b7e5a e8ffdbe 49b7e5a e8ffdbe 49b7e5a e8ffdbe 49b7e5a a81188c c6fea03 a81188c 49b7e5a a81188c c6fea03 a81188c 49b7e5a c959e92 49b7e5a c959e92 49b7e5a 7e538e1 49b7e5a 7e538e1 49b7e5a a81188c 7e538e1 a81188c 7e538e1 a81188c 7e538e1 a81188c 7e538e1 a81188c 7e538e1 a81188c 7e538e1 a81188c 49b7e5a c959e92 49b7e5a c959e92 49b7e5a c959e92 49b7e5a c959e92 49b7e5a c959e92 49b7e5a 2e2005b f7df77e 49b7e5a 7e538e1 49b7e5a 7e538e1 49b7e5a 2e2005b 7e538e1 2e2005b 49b7e5a 0f54d21 49b7e5a 0f54d21 49b7e5a a0a8f94 c959e92 a81188c c959e92 49b7e5a 0f54d21 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 |
# -*- 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
|