File size: 102,759 Bytes
e9c4101 5b4b5fb a770956 eea5c07 5b4b5fb a680619 ebf9010 641ff3e e9c4101 8652429 641ff3e 84c83c0 8652429 339a165 ebf9010 1d772de 8652429 34addbf f0f9378 e2aae24 8652429 1d772de e3365ed e2aae24 f0f9378 34addbf eea5c07 e3365ed eea5c07 9504619 eea5c07 a03496e 8652429 34addbf 8652429 7aa4d5f 8652429 34addbf 8652429 34addbf 8652429 eea5c07 f0f9378 eea5c07 1d772de eea5c07 f0f9378 8235bbb 68a91f4 eea5c07 8652429 eea5c07 f0f9378 eea5c07 1d772de eea5c07 f0f9378 8235bbb 68a91f4 eea5c07 8652429 eea5c07 8652429 1d772de f0f9378 8652429 542c252 8652429 eea5c07 8652429 eea5c07 8652429 eea5c07 8652429 eea5c07 2e71433 8652429 2e71433 eea5c07 8652429 eea5c07 34addbf 8235bbb 01c88c0 eea5c07 0f18146 8235bbb eea5c07 e5dfae7 6ea0852 e5dfae7 a770956 6ea0852 84c83c0 eea5c07 e2aae24 68a91f4 e2aae24 01c88c0 eea5c07 e5dfae7 eea5c07 a770956 e5dfae7 eea5c07 84c83c0 eea5c07 7810536 eea5c07 8c33828 7810536 e2aae24 7810536 eea5c07 8235bbb 7810536 e2aae24 5b4b5fb 8652429 e2aae24 7810536 8652429 339a165 a770956 f0f9378 8235bbb e2aae24 1d772de eea5c07 e2aae24 eea5c07 a770956 eea5c07 8235bbb eea5c07 8235bbb f0f9378 e2aae24 f0f9378 8235bbb e2aae24 1d772de eea5c07 8235bbb eea5c07 04d80a1 eea5c07 6ea0852 339a165 ec98119 339a165 eea5c07 7810536 f0f9378 a770956 e9c4101 84c83c0 eea5c07 a770956 7810536 eea5c07 f0f9378 6ea0852 e2aae24 a770956 e2aae24 a770956 1d772de a770956 1d772de a03496e 1d772de a770956 1d772de a770956 e2aae24 eea5c07 f0f9378 eea5c07 f0f9378 2e71433 34addbf 0f18146 8652429 6ea0852 8235bbb 6ea0852 8652429 0f18146 6ea0852 8652429 eea5c07 04d80a1 eea5c07 6ea0852 8235bbb ebf9010 a03496e ebf9010 ec98119 ebf9010 ec98119 ebf9010 ec98119 ebf9010 ec98119 ebf9010 ec98119 a03496e ec98119 ebf9010 ec98119 ebf9010 ec98119 ebf9010 a03496e ebf9010 a03496e ebf9010 a03496e ebf9010 a03496e ebf9010 6ea0852 ebf9010 a03496e ebf9010 a03496e ebf9010 a03496e ec98119 a03496e ec98119 ebf9010 a03496e 339a165 15026f7 339a165 23f8ca3 ebf9010 a03496e ebf9010 a03496e ebf9010 339a165 ebf9010 15026f7 ebf9010 23f8ca3 339a165 a03496e ebf9010 23f8ca3 339a165 ebf9010 a03496e 339a165 ebf9010 15026f7 ebf9010 a03496e 23f8ca3 15026f7 ebf9010 339a165 ebf9010 339a165 ebf9010 ec98119 a03496e ec98119 ebf9010 a03496e 339a165 ebf9010 a770956 339a165 a770956 23f8ca3 1d772de a770956 339a165 ebf9010 a770956 339a165 ebf9010 339a165 8652429 a770956 84c83c0 a770956 e9c4101 a770956 ec98119 a770956 ec98119 a770956 ec98119 8652429 a770956 8652429 a770956 8652429 a748df6 8652429 a748df6 8652429 a770956 8652429 ebf9010 8652429 a770956 8652429 a770956 8652429 e9c4101 8652429 e9c4101 a770956 ebf9010 e9c4101 a770956 6ea0852 ebf9010 e9c4101 a770956 e9c4101 eea5c07 a770956 f0f9378 e2aae24 f0f9378 8235bbb e3365ed f0f9378 a770956 8235bbb f0f9378 eea5c07 641ff3e eea5c07 f0f9378 eea5c07 e2aae24 eea5c07 f0f9378 8235bbb e2aae24 e3365ed eea5c07 a770956 8235bbb eea5c07 f0f9378 641ff3e e9c4101 e3365ed 8235bbb 641ff3e e3365ed 1d772de e3365ed 1d772de e3365ed 1d772de e3365ed e2aae24 a770956 e2aae24 a770956 eea5c07 339a165 ebf9010 2807627 ebf9010 641ff3e ebf9010 bc4bdbd 641ff3e bc4bdbd 641ff3e eea5c07 12224f5 e9c4101 542c252 eea5c07 e2aae24 bc4bdbd f0c28d7 a770956 f0c28d7 a770956 f0c28d7 eea5c07 e9c4101 8652429 eea5c07 ebf9010 eea5c07 f0c28d7 eea5c07 5b4b5fb bc4bdbd 5b4b5fb bc4bdbd e3365ed bc4bdbd eea5c07 5b4b5fb ebf9010 eea5c07 bc4bdbd eea5c07 bc4bdbd 6ea0852 bc4bdbd 6ea0852 8235bbb bc4bdbd e9c4101 e2aae24 6ea0852 84c83c0 e9c4101 6ea0852 84c83c0 8652429 6ea0852 e9c4101 e2aae24 6ea0852 e9c4101 f0c28d7 9504619 a770956 8652429 eea5c07 f0c28d7 eea5c07 e9c4101 f0c28d7 eea5c07 f0c28d7 eea5c07 f0c28d7 eea5c07 f0c28d7 a770956 f0c28d7 eea5c07 f0c28d7 eea5c07 f0c28d7 a770956 f0c28d7 e9c4101 eea5c07 8652429 e9c4101 ebf9010 f0f9378 8235bbb ebf9010 f0f9378 e2aae24 ebf9010 f0f9378 c71d0c1 f0f9378 ebf9010 f0f9378 eea5c07 e2aae24 8652429 84c83c0 8652429 12224f5 e9c4101 84c83c0 eea5c07 e9c4101 339a165 ebf9010 339a165 ebf9010 339a165 12224f5 ebf9010 339a165 1d772de 339a165 1d772de 12224f5 ebf9010 a03496e ebf9010 a03496e 84c83c0 ebf9010 84c83c0 eea5c07 59ff822 a03496e 59ff822 eea5c07 f0c28d7 eea5c07 a770956 eea5c07 339a165 ebf9010 641ff3e 59ff822 a03496e 59ff822 6ea0852 eea5c07 5b4b5fb eea5c07 6ea0852 f0c28d7 a770956 f0c28d7 eea5c07 a770956 ebf9010 339a165 641ff3e 93ac94f 339a165 eea5c07 339a165 84c83c0 339a165 84c83c0 eea5c07 84c83c0 339a165 84c83c0 eea5c07 84c83c0 339a165 84c83c0 eea5c07 84c83c0 eea5c07 84c83c0 339a165 eea5c07 339a165 eea5c07 339a165 84c83c0 a770956 e9c4101 8652429 e9c4101 ebf9010 a770956 ebf9010 a748df6 ebf9010 a770956 a748df6 84c83c0 a748df6 a770956 84c83c0 a770956 a748df6 8652429 a748df6 84c83c0 e9c4101 a770956 a748df6 a770956 a748df6 84c83c0 a748df6 a770956 ebf9010 a770956 eea5c07 a770956 84c83c0 a770956 ebf9010 a748df6 a770956 a748df6 84c83c0 93ac94f a770956 ebf9010 93ac94f ebf9010 93ac94f ebf9010 93ac94f ebf9010 a03496e ebf9010 a03496e ebf9010 93ac94f 339a165 93ac94f a03496e ebf9010 93ac94f ebf9010 93ac94f a03496e 93ac94f eea5c07 f0f9378 eea5c07 e2aae24 eea5c07 f0f9378 8235bbb e2aae24 e3365ed eea5c07 f0f9378 eea5c07 f0f9378 641ff3e eea5c07 93ac94f eea5c07 f0f9378 eea5c07 f0f9378 e2aae24 e3365ed eea5c07 8235bbb eea5c07 e2aae24 e3365ed 1d772de e3365ed 1d772de e3365ed e2aae24 1d772de eea5c07 12224f5 339a165 ebf9010 bc4bdbd ebf9010 bc4bdbd ebf9010 eea5c07 5b4b5fb eea5c07 bc4bdbd eea5c07 ebf9010 eea5c07 ebf9010 a03496e ebf9010 a03496e ebf9010 e2aae24 eea5c07 ebf9010 eea5c07 ebf9010 eea5c07 ebf9010 542c252 f0f9378 542c252 f0f9378 542c252 1d772de 59ff822 542c252 59ff822 a03496e 542c252 ec98119 59ff822 ec98119 542c252 ebf9010 542c252 eea5c07 ebf9010 eea5c07 ebf9010 5b4b5fb ebf9010 59ff822 a03496e 59ff822 a03496e 59ff822 a03496e e9c4101 ebf9010 a03496e e9c4101 a03496e 59ff822 eea5c07 1d772de a03496e 93ac94f eea5c07 f0c28d7 ebf9010 eea5c07 12224f5 ebf9010 eea5c07 a03496e 339a165 ebf9010 eea5c07 59ff822 339a165 eea5c07 5b4b5fb 8235bbb eea5c07 59ff822 eea5c07 8235bbb eea5c07 339a165 8235bbb |
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 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 |
import time
import re
import json
import io
import os
import boto3
import copy
from tqdm import tqdm
from PIL import Image, ImageChops, ImageFile, ImageDraw
ImageFile.LOAD_TRUNCATED_IMAGES = True
from typing import List, Dict, Tuple
import pandas as pd
#from presidio_image_redactor.entities import ImageRecognizerResult
from pdfminer.high_level import extract_pages
from pdfminer.layout import LTTextContainer, LTChar, LTTextLine, LTTextLineHorizontal, LTAnno
from pikepdf import Pdf, Dictionary, Name
import pymupdf
from pymupdf import Rect
from fitz import Page
import gradio as gr
from gradio import Progress
from collections import defaultdict # For efficient grouping
from presidio_analyzer import RecognizerResult
from tools.aws_functions import RUN_AWS_FUNCTIONS
from tools.custom_image_analyser_engine import CustomImageAnalyzerEngine, OCRResult, combine_ocr_results, CustomImageRecognizerResult
from tools.file_conversion import process_file, image_dpi, convert_review_json_to_pandas_df, redact_whole_pymupdf_page, redact_single_box, convert_pymupdf_to_image_coords
from tools.load_spacy_model_custom_recognisers import nlp_analyser, score_threshold, custom_entities, custom_recogniser, custom_word_list_recogniser
from tools.helper_functions import get_file_path_end, output_folder, clean_unicode_text, get_or_create_env_var, tesseract_ocr_option, text_ocr_option, textract_option, local_pii_detector, aws_pii_detector
from tools.file_conversion import process_file, is_pdf, is_pdf_or_image
from tools.aws_textract import analyse_page_with_textract, json_to_ocrresult
from tools.presidio_analyzer_custom import recognizer_result_from_dict
# Number of pages to loop through before breaking. Currently set very high, as functions are breaking on time metrics (e.g. every 105 seconds), rather than on number of pages redacted.
page_break_value = get_or_create_env_var('page_break_value', '50000')
print(f'The value of page_break_value is {page_break_value}')
max_time_value = get_or_create_env_var('max_time_value', '999999')
print(f'The value of max_time_value is {max_time_value}')
def bounding_boxes_overlap(box1, box2):
"""Check if two bounding boxes overlap."""
return (box1[0] < box2[2] and box2[0] < box1[2] and
box1[1] < box2[3] and box2[1] < box1[3])
def sum_numbers_before_seconds(string:str):
"""Extracts numbers that precede the word 'seconds' from a string and adds them up.
Args:
string: The input string.
Returns:
The sum of all numbers before 'seconds' in the string.
"""
# Extract numbers before 'seconds' using regular expression
numbers = re.findall(r'(\d+\.\d+)?\s*seconds', string)
# Extract the numbers from the matches
numbers = [float(num.split()[0]) for num in numbers]
# Sum up the extracted numbers
sum_of_numbers = round(sum(numbers),1)
return sum_of_numbers
def choose_and_run_redactor(file_paths:List[str],
prepared_pdf_file_paths:List[str],
prepared_pdf_image_paths:List[str],
language:str,
chosen_redact_entities:List[str],
chosen_redact_comprehend_entities:List[str],
in_redact_method:str,
in_allow_list:List[List[str]]=None,
custom_recogniser_word_list:List[str]=None,
redact_whole_page_list:List[str]=None,
latest_file_completed:int=0,
out_message:list=[],
out_file_paths:list=[],
log_files_output_paths:list=[],
first_loop_state:bool=False,
page_min:int=0,
page_max:int=999,
estimated_time_taken_state:float=0.0,
handwrite_signature_checkbox:List[str]=["Redact all identified handwriting", "Redact all identified signatures"],
all_request_metadata_str:str = "",
annotations_all_pages:dict={},
all_line_level_ocr_results_df=[],
all_decision_process_table=[],
pymupdf_doc=[],
current_loop_page:int=0,
page_break_return:bool=False,
pii_identification_method:str="Local",
comprehend_query_number:int=0,
output_folder:str=output_folder,
progress=gr.Progress(track_tqdm=True)):
'''
This function orchestrates the redaction process based on the specified method and parameters. It takes the following inputs:
- file_paths (List[str]): A list of paths to the files to be redacted.
- prepared_pdf_file_paths (List[str]): A list of paths to the PDF files prepared for redaction.
- prepared_pdf_image_paths (List[str]): A list of paths to the PDF files converted to images for redaction.
- language (str): The language of the text in the files.
- chosen_redact_entities (List[str]): A list of entity types to redact from the files using the local model (spacy) with Microsoft Presidio.
- chosen_redact_comprehend_entities (List[str]): A list of entity types to redact from files, chosen from the official list from AWS Comprehend service
- in_redact_method (str): The method to use for redaction.
- in_allow_list (List[List[str]], optional): A list of allowed terms for redaction. Defaults to None.
- custom_recogniser_word_list (List[List[str]], optional): A list of allowed terms for redaction. Defaults to None.
- redact_whole_page_list (List[List[str]], optional): A list of allowed terms for redaction. Defaults to None.
- latest_file_completed (int, optional): The index of the last completed file. Defaults to 0.
- out_message (list, optional): A list to store output messages. Defaults to an empty list.
- out_file_paths (list, optional): A list to store paths to the output files. Defaults to an empty list.
- log_files_output_paths (list, optional): A list to store paths to the log files. Defaults to an empty list.
- first_loop_state (bool, optional): A flag indicating if this is the first iteration. Defaults to False.
- page_min (int, optional): The minimum page number to start redaction from. Defaults to 0.
- page_max (int, optional): The maximum page number to end redaction at. Defaults to 999.
- estimated_time_taken_state (float, optional): The estimated time taken for the redaction process. Defaults to 0.0.
- handwrite_signature_checkbox (List[str], optional): A list of options for redacting handwriting and signatures. Defaults to ["Redact all identified handwriting", "Redact all identified signatures"].
- all_request_metadata_str (str, optional): A string containing all request metadata. Defaults to an empty string.
- annotations_all_pages (dict, optional): A dictionary containing all image annotations. Defaults to an empty dictionary.
- all_line_level_ocr_results_df (optional): A DataFrame containing all line-level OCR results. Defaults to an empty DataFrame.
- all_decision_process_table (optional): A DataFrame containing all decision process tables. Defaults to an empty DataFrame.
- pymupdf_doc (optional): A list containing the PDF document object. Defaults to an empty list.
- current_loop_page (int, optional): The current page being processed in the loop. Defaults to 0.
- page_break_return (bool, optional): A flag indicating if the function should return after a page break. Defaults to False.
- pii_identification_method (str, optional): The method to redact personal information. Either 'Local' (spacy model), or 'AWS Comprehend' (AWS Comprehend API).
- comprehend_query_number (int, optional): A counter tracking the number of queries to AWS Comprehend.
- output_folder (str, optional): Output folder for results.
- progress (gr.Progress, optional): A progress tracker for the redaction process. Defaults to a Progress object with track_tqdm set to True.
The function returns a redacted document along with processing logs.
'''
combined_out_message = ""
tic = time.perf_counter()
all_request_metadata = all_request_metadata_str.split('\n') if all_request_metadata_str else []
if isinstance(custom_recogniser_word_list, pd.DataFrame):
custom_recogniser_word_list = custom_recogniser_word_list.iloc[:,0].tolist()
# Sort the strings in order from the longest string to the shortest
custom_recogniser_word_list = sorted(custom_recogniser_word_list, key=len, reverse=True)
if isinstance(redact_whole_page_list, pd.DataFrame):
redact_whole_page_list = redact_whole_page_list.iloc[:,0].tolist()
# If this is the first time around, set variables to 0/blank
if first_loop_state==True:
#print("First_loop_state is True")
latest_file_completed = 0
current_loop_page = 0
out_file_paths = []
estimate_total_processing_time = 0
estimated_time_taken_state = 0
# If not the first time around, and the current page loop has been set to a huge number (been through all pages), reset current page to 0
elif (first_loop_state == False) & (current_loop_page == 999):
current_loop_page = 0
if not out_file_paths:
out_file_paths = []
latest_file_completed = int(latest_file_completed)
number_of_pages = len(prepared_pdf_image_paths)
if isinstance(file_paths,str):
number_of_files = 1
else:
number_of_files = len(file_paths)
# If we have already redacted the last file, return the input out_message and file list to the relevant components
if latest_file_completed >= number_of_files:
print("Completed last file")
# Set to a very high number so as not to mix up with subsequent file processing by the user
# latest_file_completed = 99
current_loop_page = 0
if isinstance(out_message, list):
combined_out_message = '\n'.join(out_message)
else:
combined_out_message = out_message
estimate_total_processing_time = sum_numbers_before_seconds(combined_out_message)
print("Estimated total processing time:", str(estimate_total_processing_time))
return combined_out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
# If we have reached the last page, return message
if current_loop_page >= number_of_pages:
print("current_loop_page:", current_loop_page, "is equal to or greater than number of pages in document:", number_of_pages)
# Set to a very high number so as not to mix up with subsequent file processing by the user
current_loop_page = 999
combined_out_message = out_message
return combined_out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = False, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
# Create allow list
# If string, assume file path
if isinstance(in_allow_list, str):
in_allow_list = pd.read_csv(in_allow_list)
if not in_allow_list.empty:
in_allow_list_flat = in_allow_list.iloc[:,0].tolist()
#print("In allow list:", in_allow_list_flat)
else:
in_allow_list_flat = []
# Try to connect to AWS services only if RUN_AWS_FUNCTIONS environmental variable is 1
if pii_identification_method == "AWS Comprehend":
print("Trying to connect to AWS Comprehend service")
if RUN_AWS_FUNCTIONS == "1":
comprehend_client = boto3.client('comprehend')
else:
comprehend_client = ""
out_message = "Cannot connect to AWS Comprehend service. Please choose another PII identification method."
print(out_message)
return out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page, precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
else:
comprehend_client = ""
if in_redact_method == textract_option:
print("Trying to connect to AWS Comprehend service")
if RUN_AWS_FUNCTIONS == "1":
textract_client = boto3.client('textract')
else:
textract_client = ""
out_message = "Cannot connect to AWS Textract. Please choose another text extraction method."
print(out_message)
return out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page, precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
else:
textract_client = ""
# Check if output_folder exists, create it if it doesn't
if not os.path.exists(output_folder):
os.makedirs(output_folder)
progress(0.5, desc="Redacting file")
if isinstance(file_paths, str):
file_paths_list = [os.path.abspath(file_paths)]
file_paths_loop = file_paths_list
elif isinstance(file_paths, dict):
file_paths = file_paths["name"]
file_paths_list = [os.path.abspath(file_paths)]
file_paths_loop = file_paths_list
else:
file_paths_list = file_paths
file_paths_loop = [file_paths_list[int(latest_file_completed)]]
# print("file_paths_list in choose_redactor function:", file_paths_list)
for file in file_paths_loop:
if isinstance(file, str):
file_path = file
else:
file_path = file.name
if file_path:
file_path_without_ext = get_file_path_end(file_path)
print("Redacting file:", file_path_without_ext)
is_a_pdf = is_pdf(file_path) == True
if is_a_pdf == False:
# If user has not submitted a pdf, assume it's an image
print("File is not a pdf, assuming that image analysis needs to be used.")
in_redact_method = tesseract_ocr_option
else:
out_message = "No file selected"
print(out_message)
return combined_out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
if in_redact_method == tesseract_ocr_option or in_redact_method == textract_option:
#Analyse and redact image-based pdf or image
if is_pdf_or_image(file_path) == False:
out_message = "Please upload a PDF file or image file (JPG, PNG) for image analysis."
return out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page, precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
print("Redacting file " + file_path_without_ext + " as an image-based file")
pymupdf_doc,all_decision_process_table,log_files_output_paths,new_request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number = redact_image_pdf(file_path,
prepared_pdf_image_paths,
language,
chosen_redact_entities,
chosen_redact_comprehend_entities,
in_allow_list_flat,
is_a_pdf,
page_min,
page_max,
in_redact_method,
handwrite_signature_checkbox,
"",
current_loop_page,
page_break_return,
prepared_pdf_image_paths,
annotations_all_pages,
all_line_level_ocr_results_df,
all_decision_process_table,
pymupdf_doc,
pii_identification_method,
comprehend_query_number,
comprehend_client,
textract_client,
custom_recogniser_word_list,
redact_whole_page_list)
# Save Textract request metadata (if exists)
if new_request_metadata:
print("Request metadata:", new_request_metadata)
all_request_metadata.append(new_request_metadata)
elif in_redact_method == text_ocr_option:
#log_files_output_paths = []
if is_pdf(file_path) == False:
out_message = "Please upload a PDF file for text analysis. If you have an image, select 'Image analysis'."
return out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
# Analyse text-based pdf
print('Redacting file as text-based PDF')
pymupdf_doc, all_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number = redact_text_pdf(file_path,
prepared_pdf_image_paths,language,
chosen_redact_entities,
chosen_redact_comprehend_entities,
in_allow_list_flat,
page_min,
page_max,
text_ocr_option,
current_loop_page,
page_break_return,
annotations_all_pages,
all_line_level_ocr_results_df,
all_decision_process_table,
pymupdf_doc,
pii_identification_method,
comprehend_query_number,
comprehend_client,
custom_recogniser_word_list,
redact_whole_page_list)
else:
out_message = "No redaction method selected"
print(out_message)
return out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
# If at last page, save to file
if current_loop_page >= number_of_pages:
print("Current page loop:", current_loop_page, "is greater or equal to number of pages:", number_of_pages)
latest_file_completed += 1
current_loop_page = 999
if latest_file_completed != len(file_paths_list):
print("Completed file number:", str(latest_file_completed), "there are more files to do")
# Save file
if is_pdf(file_path) == False:
out_image_file_path = output_folder + file_path_without_ext + "_redacted_as_pdf.pdf"
pymupdf_doc[0].save(out_image_file_path, "PDF" ,resolution=image_dpi, save_all=False)#, append_images=pymupdf_doc[:1])
else:
out_image_file_path = output_folder + file_path_without_ext + "_redacted.pdf"
pymupdf_doc.save(out_image_file_path)
out_file_paths.append(out_image_file_path)
#if log_files_output_paths:
# log_files_output_paths.extend(log_files_output_paths)
logs_output_file_name = out_image_file_path + "_decision_process_output.csv"
all_decision_process_table.to_csv(logs_output_file_name, index = None, encoding="utf-8")
log_files_output_paths.append(logs_output_file_name)
all_text_output_file_name = out_image_file_path + "_ocr_output.csv"
all_line_level_ocr_results_df.to_csv(all_text_output_file_name, index = None, encoding="utf-8")
out_file_paths.append(all_text_output_file_name)
# Save the gradio_annotation_boxes to a JSON file
try:
print("Saving annotations to JSON")
out_annotation_file_path = out_image_file_path + '_review_file.json'
with open(out_annotation_file_path, 'w') as f:
json.dump(annotations_all_pages, f)
log_files_output_paths.append(out_annotation_file_path)
#print("Saving annotations to CSV")
# Convert json to csv and also save this
#print("annotations_all_pages:", annotations_all_pages)
review_df = convert_review_json_to_pandas_df(annotations_all_pages, all_decision_process_table)
out_review_file_file_path = out_image_file_path + '_review_file.csv'
review_df.to_csv(out_review_file_file_path, index=None)
out_file_paths.append(out_review_file_file_path)
print("Saved review file to csv")
except Exception as e:
print("Could not save annotations to json file:", e)
# Make a combined message for the file
if isinstance(out_message, list):
combined_out_message = '\n'.join(out_message) # Ensure out_message is a list of strings
else: combined_out_message = out_message
toc = time.perf_counter()
time_taken = toc - tic
estimated_time_taken_state = estimated_time_taken_state + time_taken
out_time_message = f" Redacted in {estimated_time_taken_state:0.1f} seconds."
combined_out_message = combined_out_message + " " + out_time_message # Ensure this is a single string
estimate_total_processing_time = sum_numbers_before_seconds(combined_out_message)
print("Estimated total processing time:", str(estimate_total_processing_time))
else:
toc = time.perf_counter()
time_taken = toc - tic
estimated_time_taken_state = estimated_time_taken_state + time_taken
# If textract requests made, write to logging file
if all_request_metadata:
all_request_metadata_str = '\n'.join(all_request_metadata).strip()
all_request_metadata_file_path = output_folder + file_path_without_ext + "_textract_request_metadata.txt"
with open(all_request_metadata_file_path, "w") as f:
f.write(all_request_metadata_str)
# Add the request metadata to the log outputs if not there already
if all_request_metadata_file_path not in log_files_output_paths:
log_files_output_paths.append(all_request_metadata_file_path)
if combined_out_message: out_message = combined_out_message
#print("\nout_message at choose_and_run_redactor end is:", out_message)
# Ensure no duplicated output files
log_files_output_paths = list(set(log_files_output_paths))
out_file_paths = list(set(out_file_paths))
return out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page, precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_decision_process_table, comprehend_query_number
def convert_pikepdf_coords_to_pymupdf(pymupdf_page, pikepdf_bbox, type="pikepdf_annot"):
'''
Convert annotations from pikepdf to pymupdf format, handling the mediabox larger than rect.
'''
# Use cropbox if available, otherwise use mediabox
reference_box = pymupdf_page.rect
mediabox = pymupdf_page.mediabox
reference_box_height = reference_box.height
reference_box_width = reference_box.width
# Convert PyMuPDF coordinates back to PDF coordinates (bottom-left origin)
media_height = mediabox.height
media_width = mediabox.width
media_reference_y_diff = media_height - reference_box_height
media_reference_x_diff = media_width - reference_box_width
y_diff_ratio = media_reference_y_diff / reference_box_height
x_diff_ratio = media_reference_x_diff / reference_box_width
# Extract the annotation rectangle field
if type=="pikepdf_annot":
rect_field = pikepdf_bbox["/Rect"]
else:
rect_field = pikepdf_bbox
rect_coordinates = [float(coord) for coord in rect_field] # Convert to floats
# Unpack coordinates
x1, y1, x2, y2 = rect_coordinates
new_x1 = x1 - (media_reference_x_diff * x_diff_ratio)
new_y1 = media_height - y2 - (media_reference_y_diff * y_diff_ratio)
new_x2 = x2 - (media_reference_x_diff * x_diff_ratio)
new_y2 = media_height - y1 - (media_reference_y_diff * y_diff_ratio)
return new_x1, new_y1, new_x2, new_y2
def convert_pikepdf_to_image_coords(pymupdf_page, annot, image:Image, type="pikepdf_annot"):
'''
Convert annotations from pikepdf coordinates to image coordinates.
'''
# Get the dimensions of the page in points with pymupdf
rect_height = pymupdf_page.rect.height
rect_width = pymupdf_page.rect.width
# Get the dimensions of the image
image_page_width, image_page_height = image.size
# Calculate scaling factors between pymupdf and PIL image
scale_width = image_page_width / rect_width
scale_height = image_page_height / rect_height
# Extract the /Rect field
if type=="pikepdf_annot":
rect_field = annot["/Rect"]
else:
rect_field = annot
# Convert the extracted /Rect field to a list of floats
rect_coordinates = [float(coord) for coord in rect_field]
# Convert the Y-coordinates (flip using the image height)
x1, y1, x2, y2 = rect_coordinates
x1_image = x1 * scale_width
new_y1_image = image_page_height - (y2 * scale_height) # Flip Y0 (since it starts from bottom)
x2_image = x2 * scale_width
new_y2_image = image_page_height - (y1 * scale_height) # Flip Y1
return x1_image, new_y1_image, x2_image, new_y2_image
def convert_pikepdf_decision_output_to_image_coords(pymupdf_page, pikepdf_decision_ouput_data:List, image):
if isinstance(image, str):
image_path = image
image = Image.open(image_path)
# Loop through each item in the data
for item in pikepdf_decision_ouput_data:
# Extract the bounding box
bounding_box = item['boundingBox']
# Create a pikepdf_bbox dictionary to match the expected input
pikepdf_bbox = {"/Rect": bounding_box}
# Call the conversion function
new_x1, new_y1, new_x2, new_y2 = convert_pikepdf_to_image_coords(pymupdf_page, pikepdf_bbox, image, type="pikepdf_annot")
# Update the original object with the new bounding box values
item['boundingBox'] = [new_x1, new_y1, new_x2, new_y2]
return pikepdf_decision_ouput_data
def convert_image_coords_to_pymupdf(pymupdf_page, annot, image:Image, type="image_recognizer"):
'''
Converts an image with redaction coordinates from a CustomImageRecognizerResult or pikepdf object with image coordinates to pymupdf coordinates.
'''
rect_height = pymupdf_page.rect.height
rect_width = pymupdf_page.rect.width
image_page_width, image_page_height = image.size
# Calculate scaling factors between PIL image and pymupdf
scale_width = rect_width / image_page_width
scale_height = rect_height / image_page_height
# Calculate scaled coordinates
if type == "image_recognizer":
x1 = (annot.left * scale_width)# + page_x_adjust
new_y1 = (annot.top * scale_height)# - page_y_adjust # Flip Y0 (since it starts from bottom)
x2 = ((annot.left + annot.width) * scale_width)# + page_x_adjust # Calculate x1
new_y2 = ((annot.top + annot.height) * scale_height)# - page_y_adjust # Calculate y1 correctly
# Else assume it is a pikepdf derived object
else:
rect_field = annot["/Rect"]
rect_coordinates = [float(coord) for coord in rect_field] # Convert to floats
# Unpack coordinates
x1, y1, x2, y2 = rect_coordinates
#print("scale_width:", scale_width)
#print("scale_height:", scale_height)
x1 = (x1* scale_width)# + page_x_adjust
new_y1 = ((y2 + (y1 - y2))* scale_height)# - page_y_adjust # Calculate y1 correctly
x2 = ((x1 + (x2 - x1)) * scale_width)# + page_x_adjust # Calculate x1
new_y2 = (y2 * scale_height)# - page_y_adjust # Flip Y0 (since it starts from bottom)
return x1, new_y1, x2, new_y2
def convert_gradio_annotation_coords_to_pymupdf(pymupdf_page:Page, annot:dict, image:Image):
'''
Converts an image with redaction coordinates from a gradio annotation component to pymupdf coordinates.
'''
rect_height = pymupdf_page.rect.height
rect_width = pymupdf_page.rect.width
image_page_width, image_page_height = image.size
# Calculate scaling factors between PIL image and pymupdf
scale_width = rect_width / image_page_width
scale_height = rect_height / image_page_height
# Calculate scaled coordinates
x1 = (annot["xmin"] * scale_width)# + page_x_adjust
new_y1 = (annot["ymin"] * scale_height)# - page_y_adjust # Flip Y0 (since it starts from bottom)
x2 = ((annot["xmax"]) * scale_width)# + page_x_adjust # Calculate x1
new_y2 = ((annot["ymax"]) * scale_height)# - page_y_adjust # Calculate y1 correctly
return x1, new_y1, x2, new_y2
def move_page_info(file_path: str) -> str:
# Split the string at '.png'
base, extension = file_path.rsplit('.pdf', 1)
# Extract the page info
page_info = base.split('page ')[1].split(' of')[0] # Get the page number
new_base = base.replace(f'page {page_info} of ', '') # Remove the page info from the original position
# Construct the new file path
new_file_path = f"{new_base}_page_{page_info}.png"
return new_file_path
def redact_page_with_pymupdf(page:Page, page_annotations:dict, image=None, custom_colours:bool=False, redact_whole_page:bool=False, convert_coords:bool=True):
mediabox_height = page.mediabox[3] - page.mediabox[1]
mediabox_width = page.mediabox[2] - page.mediabox[0]
rect_height = page.rect.height
rect_width = page.rect.width
pymupdf_x1 = None
pymupdf_x2 = None
out_annotation_boxes = {}
all_image_annotation_boxes = []
image_path = ""
if isinstance(image, Image.Image):
image_path = move_page_info(str(page))
image.save(image_path)
elif isinstance(image, str):
image_path = image
image = Image.open(image_path)
# Check if this is an object used in the Gradio Annotation component
if isinstance (page_annotations, dict):
page_annotations = page_annotations["boxes"]
for annot in page_annotations:
# Check if an Image recogniser result, or a Gradio annotation object
if (isinstance(annot, CustomImageRecognizerResult)) | isinstance(annot, dict):
img_annotation_box = {}
# Should already be in correct format if img_annotator_box is an input
if isinstance(annot, dict):
img_annotation_box = annot
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_gradio_annotation_coords_to_pymupdf(page, annot, image)
x1 = pymupdf_x1
x2 = pymupdf_x2
# if hasattr(annot, 'text') and annot.text:
# img_annotation_box["text"] = annot.text
# else:
# img_annotation_box["text"] = ""
# Else should be CustomImageRecognizerResult
else:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_image_coords_to_pymupdf(page, annot, image)
x1 = pymupdf_x1
x2 = pymupdf_x2
img_annotation_box["xmin"] = annot.left
img_annotation_box["ymin"] = annot.top
img_annotation_box["xmax"] = annot.left + annot.width
img_annotation_box["ymax"] = annot.top + annot.height
img_annotation_box["color"] = (0,0,0)
try:
img_annotation_box["label"] = annot.entity_type
except:
img_annotation_box["label"] = "Redaction"
# if hasattr(annot, 'text') and annot.text:
# img_annotation_box["text"] = annot.text
# else:
# img_annotation_box["text"] = ""
rect = Rect(x1, pymupdf_y1, x2, pymupdf_y2) # Create the PyMuPDF Rect
# Else it should be a pikepdf annotation object
else:
if convert_coords == True:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_pikepdf_coords_to_pymupdf(page, annot)
else:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_image_coords_to_pymupdf(page, annot, image, type="pikepdf_image_coords")
x1 = pymupdf_x1
x2 = pymupdf_x2
rect = Rect(x1, pymupdf_y1, x2, pymupdf_y2)
img_annotation_box = {}
if image:
img_width, img_height = image.size
print("annot:", annot)
x1, image_y1, x2, image_y2 = convert_pymupdf_to_image_coords(page, x1, pymupdf_y1, x2, pymupdf_y2, image)
img_annotation_box["xmin"] = x1 #* (img_width / rect_width) # Use adjusted x1
img_annotation_box["ymin"] = image_y1 #* (img_width / rect_width) # Use adjusted y1
img_annotation_box["xmax"] = x2# * (img_height / rect_height) # Use adjusted x2
img_annotation_box["ymax"] = image_y2 #* (img_height / rect_height) # Use adjusted y2
img_annotation_box["color"] = (0, 0, 0)
if isinstance(annot, Dictionary):
img_annotation_box["label"] = str(annot["/T"])
else:
img_annotation_box["label"] = "REDACTION"
# if hasattr(annot, 'text') and annot.text:
# img_annotation_box["text"] = annot.text
# else:
# img_annotation_box["text"] = ""
# Convert to a PyMuPDF Rect object
#rect = Rect(rect_coordinates)
all_image_annotation_boxes.append(img_annotation_box)
redact_single_box(page, rect, img_annotation_box, custom_colours)
# If whole page is to be redacted, do that here
if redact_whole_page == True:
whole_page_img_annotation_box = redact_whole_pymupdf_page(rect_height, rect_width, image, page, custom_colours, border = 5)
all_image_annotation_boxes.append(whole_page_img_annotation_box)
out_annotation_boxes = {
"image": image_path, #Image.open(image_path), #image_path,
"boxes": all_image_annotation_boxes
}
page.apply_redactions(images=0, graphics=0)
page.clean_contents()
return page, out_annotation_boxes
def merge_img_bboxes(bboxes, combined_results: Dict, signature_recogniser_results=[], handwriting_recogniser_results=[], handwrite_signature_checkbox: List[str]=["Redact all identified handwriting", "Redact all identified signatures"], horizontal_threshold:int=50, vertical_threshold:int=12):
all_bboxes = []
merged_bboxes = []
grouped_bboxes = defaultdict(list)
# Deep copy original bounding boxes to retain them
original_bboxes = copy.deepcopy(bboxes)
# Process signature and handwriting results
if signature_recogniser_results or handwriting_recogniser_results:
if "Redact all identified handwriting" in handwrite_signature_checkbox:
merged_bboxes.extend(copy.deepcopy(handwriting_recogniser_results))
if "Redact all identified signatures" in handwrite_signature_checkbox:
merged_bboxes.extend(copy.deepcopy(signature_recogniser_results))
# Reconstruct bounding boxes for substrings of interest
reconstructed_bboxes = []
for bbox in bboxes:
bbox_box = (bbox.left, bbox.top, bbox.left + bbox.width, bbox.top + bbox.height)
for line_text, line_info in combined_results.items():
line_box = line_info['bounding_box']
if bounding_boxes_overlap(bbox_box, line_box):
if bbox.text in line_text:
start_char = line_text.index(bbox.text)
end_char = start_char + len(bbox.text)
relevant_words = []
current_char = 0
for word in line_info['words']:
word_end = current_char + len(word['text'])
if current_char <= start_char < word_end or current_char < end_char <= word_end or (start_char <= current_char and word_end <= end_char):
relevant_words.append(word)
if word_end >= end_char:
break
current_char = word_end
if not word['text'].endswith(' '):
current_char += 1 # +1 for space if the word doesn't already end with a space
if relevant_words:
left = min(word['bounding_box'][0] for word in relevant_words)
top = min(word['bounding_box'][1] for word in relevant_words)
right = max(word['bounding_box'][2] for word in relevant_words)
bottom = max(word['bounding_box'][3] for word in relevant_words)
combined_text = " ".join(word['text'] for word in relevant_words)
reconstructed_bbox = CustomImageRecognizerResult(
bbox.entity_type,
bbox.start,
bbox.end,
bbox.score,
left,
top,
right - left, # width
bottom - top, # height,
combined_text
)
#reconstructed_bboxes.append(bbox) # Add original bbox
reconstructed_bboxes.append(reconstructed_bbox) # Add merged bbox
break
else:
reconstructed_bboxes.append(bbox)
# Group reconstructed bboxes by approximate vertical proximity
for box in reconstructed_bboxes:
grouped_bboxes[round(box.top / vertical_threshold)].append(box)
# Merge within each group
for _, group in grouped_bboxes.items():
group.sort(key=lambda box: box.left)
merged_box = group[0]
for next_box in group[1:]:
if next_box.left - (merged_box.left + merged_box.width) <= horizontal_threshold:
new_text = merged_box.text + " " + next_box.text
new_entity_type = merged_box.entity_type + " - " + next_box.entity_type
new_left = min(merged_box.left, next_box.left)
new_top = min(merged_box.top, next_box.top)
new_width = max(merged_box.left + merged_box.width, next_box.left + next_box.width) - new_left
new_height = max(merged_box.top + merged_box.height, next_box.top + next_box.height) - new_top
merged_box = CustomImageRecognizerResult(
new_entity_type, merged_box.start, merged_box.end, merged_box.score, new_left, new_top, new_width, new_height, new_text
)
else:
merged_bboxes.append(merged_box)
merged_box = next_box
merged_bboxes.append(merged_box)
all_bboxes.extend(original_bboxes)
#all_bboxes.extend(reconstructed_bboxes)
all_bboxes.extend(merged_bboxes)
# Return the unique original and merged bounding boxes
unique_bboxes = list({(bbox.left, bbox.top, bbox.width, bbox.height): bbox for bbox in all_bboxes}.values())
return unique_bboxes
def redact_image_pdf(file_path:str,
prepared_pdf_file_paths:List[str],
language:str,
chosen_redact_entities:List[str],
chosen_redact_comprehend_entities:List[str],
allow_list:List[str]=None,
is_a_pdf:bool=True,
page_min:int=0,
page_max:int=999,
analysis_type:str=tesseract_ocr_option,
handwrite_signature_checkbox:List[str]=["Redact all identified handwriting", "Redact all identified signatures"],
request_metadata:str="", current_loop_page:int=0,
page_break_return:bool=False,
images=[],
annotations_all_pages:List=[],
all_line_level_ocr_results_df = pd.DataFrame(),
all_decision_process_table = pd.DataFrame(),
pymupdf_doc = [],
pii_identification_method:str="Local",
comprehend_query_number:int=0,
comprehend_client:str="",
textract_client:str="",
custom_recogniser_word_list:List[str]=[],
redact_whole_page_list:List[str]=[],
page_break_val:int=int(page_break_value),
log_files_output_paths:List=[],
max_time:int=int(max_time_value),
progress=Progress(track_tqdm=True)):
'''
This function redacts sensitive information from a PDF document. It takes the following parameters:
- file_path (str): The path to the PDF file to be redacted.
- prepared_pdf_file_paths (List[str]): A list of paths to the PDF file pages converted to images.
- language (str): The language of the text in the PDF.
- chosen_redact_entities (List[str]): A list of entity types to redact from the PDF.
- chosen_redact_comprehend_entities (List[str]): A list of entity types to redact from the list allowed by the AWS Comprehend service.
- allow_list (List[str], optional): A list of entity types to allow in the PDF. Defaults to None.
- is_a_pdf (bool, optional): Indicates if the input file is a PDF. Defaults to True.
- page_min (int, optional): The minimum page number to start redaction from. Defaults to 0.
- page_max (int, optional): The maximum page number to end redaction at. Defaults to 999.
- analysis_type (str, optional): The type of analysis to perform on the PDF. Defaults to tesseract_ocr_option.
- handwrite_signature_checkbox (List[str], optional): A list of options for redacting handwriting and signatures. Defaults to ["Redact all identified handwriting", "Redact all identified signatures"].
- request_metadata (str, optional): Metadata related to the redaction request. Defaults to an empty string.
- page_break_return (bool, optional): Indicates if the function should return after a page break. Defaults to False.
- images (list, optional): List of image objects for each PDF page.
- annotations_all_pages (List, optional): List of annotations on all pages that is used by the gradio_image_annotation object.
- all_line_level_ocr_results_df (pd.DataFrame(), optional): All line level OCR results for the document as a Pandas dataframe,
- all_decision_process_table (pd.DataFrame(), optional): All redaction decisions for document as a Pandas dataframe.
- pymupdf_doc (List, optional): The document as a PyMupdf object.
- pii_identification_method (str, optional): The method to redact personal information. Either 'Local' (spacy model), or 'AWS Comprehend' (AWS Comprehend API).
- comprehend_query_number (int, optional): A counter tracking the number of queries to AWS Comprehend.
- comprehend_client (optional): A connection to the AWS Comprehend service via the boto3 package.
- textract_client (optional): A connection to the AWS Textract service via the boto3 package.
- custom_recogniser_word_list (optional): A list of custom words that the user has chosen specifically to redact.
- redact_whole_page_list (optional, List[str]): A list of pages to fully redact.
- page_break_val (int, optional): The value at which to trigger a page break. Defaults to 3.
- log_files_output_paths (List, optional): List of file paths used for saving redaction process logging results.
- max_time (int, optional): The maximum amount of time (s) that the function should be running before it breaks. To avoid timeout errors with some APIs.
- progress (Progress, optional): A progress tracker for the redaction process. Defaults to a Progress object with track_tqdm set to True.
The function returns a fully or partially-redacted PDF document.
'''
file_name = get_file_path_end(file_path)
fill = (0, 0, 0) # Fill colour for redactions
comprehend_query_number_new = 0
# Update custom word list analyser object with any new words that have been added to the custom deny list
#print("custom_recogniser_word_list:", custom_recogniser_word_list)
if custom_recogniser_word_list:
nlp_analyser.registry.remove_recognizer("CUSTOM")
new_custom_recogniser = custom_word_list_recogniser(custom_recogniser_word_list)
#print("new_custom_recogniser:", new_custom_recogniser)
nlp_analyser.registry.add_recognizer(new_custom_recogniser)
# List all elements currently in the nlp_analyser registry
#print("Current recognizers in nlp_analyser registry:")
for recognizer_name in nlp_analyser.registry.recognizers:
print(recognizer_name)
image_analyser = CustomImageAnalyzerEngine(nlp_analyser)
if pii_identification_method == "AWS Comprehend" and comprehend_client == "":
print("Connection to AWS Comprehend service unsuccessful.")
return pymupdf_doc, all_decision_process_table, log_files_output_paths, request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number
if analysis_type == textract_option and textract_client == "":
print("Connection to AWS Textract service unsuccessful.")
return pymupdf_doc, all_decision_process_table, log_files_output_paths, request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number
tic = time.perf_counter()
if not prepared_pdf_file_paths:
out_message = "PDF does not exist as images. Converting pages to image"
print(out_message)
prepared_pdf_file_paths = process_file(file_path)
number_of_pages = len(prepared_pdf_file_paths)
print("Number of pages:", str(number_of_pages))
# Check that page_min and page_max are within expected ranges
if page_max > number_of_pages or page_max == 0:
page_max = number_of_pages
if page_min <= 0: page_min = 0
else: page_min = page_min - 1
print("Page range:", str(page_min + 1), "to", str(page_max))
#print("Current_loop_page:", current_loop_page)
if analysis_type == tesseract_ocr_option: ocr_results_file_path = output_folder + "ocr_results_" + file_name + "_pages_" + str(page_min + 1) + "_" + str(page_max) + ".csv"
elif analysis_type == textract_option: ocr_results_file_path = output_folder + "ocr_results_" + file_name + "_pages_" + str(page_min + 1) + "_" + str(page_max) + "_textract.csv"
# If running Textract, check if file already exists. If it does, load in existing data
# Import results from json and convert
if analysis_type == textract_option:
json_file_path = output_folder + file_name + "_textract.json"
log_files_output_paths.append(json_file_path)
if not os.path.exists(json_file_path):
no_textract_file = True
print("No existing Textract results file found.")
existing_data = {}
#text_blocks, new_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
#log_files_output_paths.append(json_file_path)
#request_metadata = request_metadata + "\n" + new_request_metadata
#wrapped_text_blocks = {"pages":[text_blocks]}
else:
# Open the file and load the JSON data
no_textract_file = False
print("Found existing Textract json results file.")
with open(json_file_path, 'r') as json_file:
existing_data = json.load(json_file)
###
if current_loop_page == 0: page_loop_start = 0
else: page_loop_start = current_loop_page
progress_bar = tqdm(range(page_loop_start, number_of_pages), unit="pages remaining", desc="Redacting pages")
for page_no in progress_bar:
handwriting_or_signature_boxes = []
signature_recogniser_results = []
handwriting_recogniser_results = []
page_break_return = False
reported_page_number = str(page_no + 1)
#print("Redacting page:", reported_page_number)
# Assuming prepared_pdf_file_paths[page_no] is a PIL image object
try:
image = prepared_pdf_file_paths[page_no]#.copy()
#print("image:", image)
except Exception as e:
print("Could not redact page:", reported_page_number, "due to:", e)
continue
image_annotations = {"image": image, "boxes": []}
pymupdf_page = pymupdf_doc.load_page(page_no)
if page_no >= page_min and page_no < page_max:
#print("Image is in range of pages to redact")
if isinstance(image, str):
#print("image is a file path")
image = Image.open(image)
# Need image size to convert textract OCR outputs to the correct sizes
page_width, page_height = image.size
# Possibility to use different languages
if language == 'en': ocr_lang = 'eng'
else: ocr_lang = language
# Step 1: Perform OCR. Either with Tesseract, or with AWS Textract
if analysis_type == tesseract_ocr_option:
word_level_ocr_results = image_analyser.perform_ocr(image)
# Combine OCR results
line_level_ocr_results, line_level_ocr_results_with_children = combine_ocr_results(word_level_ocr_results)
# Import results from json and convert
if analysis_type == textract_option:
# Convert the image to bytes using an in-memory buffer
image_buffer = io.BytesIO()
image.save(image_buffer, format='PNG') # Save as PNG, or adjust format if needed
pdf_page_as_bytes = image_buffer.getvalue()
if not existing_data:
text_blocks, new_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
log_files_output_paths.append(json_file_path)
request_metadata = request_metadata + "\n" + new_request_metadata
existing_data = {"pages":[text_blocks]}
else:
# Check if the current reported_page_number exists in the loaded JSON
page_exists = any(page['page_no'] == reported_page_number for page in existing_data.get("pages", []))
if not page_exists: # If the page does not exist, analyze again
print(f"Page number {reported_page_number} not found in existing Textract data. Analysing.")
text_blocks, new_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
# Check if "pages" key exists, if not, initialize it as an empty list
if "pages" not in existing_data:
existing_data["pages"] = []
# Append the new page data
existing_data["pages"].append(text_blocks)
request_metadata = request_metadata + "\n" + new_request_metadata
else:
# If the page exists, retrieve the data
text_blocks = next(page['data'] for page in existing_data["pages"] if page['page_no'] == reported_page_number)
# if not os.path.exists(json_file_path):
# text_blocks, new_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
# log_files_output_paths.append(json_file_path)
# request_metadata = request_metadata + "\n" + new_request_metadata
# existing_data = {"pages":[text_blocks]}
# else:
# # Open the file and load the JSON data
# print("Found existing Textract json results file.")
# with open(json_file_path, 'r') as json_file:
# existing_data = json.load(json_file)
# # Check if the current reported_page_number exists in the loaded JSON
# page_exists = any(page['page_no'] == reported_page_number for page in existing_data.get("pages", []))
# if not page_exists: # If the page does not exist, analyze again
# print(f"Page number {reported_page_number} not found in existing data. Analyzing again.")
# text_blocks, new_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
# # Check if "pages" key exists, if not, initialize it as an empty list
# if "pages" not in existing_data:
# existing_data["pages"] = []
# # Append the new page data
# existing_data["pages"].append(text_blocks)
# # Write the updated existing_data back to the JSON file
# with open(json_file_path, 'w') as json_file:
# json.dump(existing_data, json_file, indent=4) # indent=4 makes the JSON file pretty-printed
# log_files_output_paths.append(json_file_path)
# request_metadata = request_metadata + "\n" + new_request_metadata
# else:
# # If the page exists, retrieve the data
# text_blocks = next(page['data'] for page in existing_data["pages"] if page['page_no'] == reported_page_number)
line_level_ocr_results, handwriting_or_signature_boxes, signature_recogniser_results, handwriting_recogniser_results, line_level_ocr_results_with_children = json_to_ocrresult(text_blocks, page_width, page_height, reported_page_number)
# Step 2: Analyze text and identify PII
if chosen_redact_entities:
redaction_bboxes, comprehend_query_number_new = image_analyser.analyze_text(
line_level_ocr_results,
line_level_ocr_results_with_children,
chosen_redact_comprehend_entities = chosen_redact_comprehend_entities,
pii_identification_method = pii_identification_method,
comprehend_client=comprehend_client,
language=language,
entities=chosen_redact_entities,
allow_list=allow_list,
score_threshold=score_threshold
)
comprehend_query_number = comprehend_query_number + comprehend_query_number_new
else:
redaction_bboxes = []
if analysis_type == tesseract_ocr_option: interim_results_file_path = output_folder + "interim_analyser_bboxes_" + file_name + "_pages_" + str(page_min + 1) + "_" + str(page_max) + ".txt"
elif analysis_type == textract_option: interim_results_file_path = output_folder + "interim_analyser_bboxes_" + file_name + "_pages_" + str(page_min + 1) + "_" + str(page_max) + "_textract.txt"
# Save decision making process
bboxes_str = str(redaction_bboxes)
with open(interim_results_file_path, "w") as f:
f.write(bboxes_str)
# Merge close bounding boxes
merged_redaction_bboxes = merge_img_bboxes(redaction_bboxes, line_level_ocr_results_with_children, signature_recogniser_results, handwriting_recogniser_results, handwrite_signature_checkbox)
# 3. Draw the merged boxes
if is_pdf(file_path) == False:
draw = ImageDraw.Draw(image)
all_image_annotations_boxes = []
for box in merged_redaction_bboxes:
print("box:", box)
x0 = box.left
y0 = box.top
x1 = x0 + box.width
y1 = y0 + box.height
try:
label = box.entity_type
except:
label = "Redaction"
# Directly append the dictionary with the required keys
all_image_annotations_boxes.append({
"xmin": x0,
"ymin": y0,
"xmax": x1,
"ymax": y1,
"label": label,
"color": (0, 0, 0)
})
draw.rectangle([x0, y0, x1, y1], fill=fill) # Adjusted to use a list for rectangle
image_annotations = {"image": file_path, "boxes": all_image_annotations_boxes}
## Apply annotations with pymupdf
else:
#print("redact_whole_page_list:", redact_whole_page_list)
if redact_whole_page_list:
if current_loop_page in redact_whole_page_list: redact_whole_page = True
else: redact_whole_page = False
else: redact_whole_page = False
pymupdf_page, image_annotations = redact_page_with_pymupdf(pymupdf_page, merged_redaction_bboxes, image, redact_whole_page=redact_whole_page)
# Convert decision process to table
decision_process_table = pd.DataFrame([{
'text': result.text,
'xmin': result.left,
'ymin': result.top,
'xmax': result.left + result.width,
'ymax': result.top + result.height,
'label': result.entity_type,
'start': result.start,
'end': result.end,
'score': result.score,
'page': reported_page_number
} for result in merged_redaction_bboxes]) #'left': result.left,
#'top': result.top,
#'width': result.width,
#'height': result.height,
all_decision_process_table = pd.concat([all_decision_process_table, decision_process_table])
# Convert to DataFrame and add to ongoing logging table
line_level_ocr_results_df = pd.DataFrame([{
'page': reported_page_number,
'text': result.text,
'left': result.left,
'top': result.top,
'width': result.width,
'height': result.height
} for result in line_level_ocr_results])
all_line_level_ocr_results_df = pd.concat([all_line_level_ocr_results_df, line_level_ocr_results_df])
toc = time.perf_counter()
time_taken = toc - tic
#print("toc - tic:", time_taken)
# Break if time taken is greater than max_time seconds
if time_taken > max_time:
print("Processing for", max_time, "seconds, breaking loop.")
page_break_return = True
progress.close(_tqdm=progress_bar)
tqdm._instances.clear()
if is_pdf(file_path) == False:
images.append(image)
pymupdf_doc = images
# Check if the image already exists in annotations_all_pages
#print("annotations_all_pages:", annotations_all_pages)
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(image_annotations)
if analysis_type == textract_option:
# Write the updated existing textract data back to the JSON file
with open(json_file_path, 'w') as json_file:
json.dump(existing_data, json_file, indent=4) # indent=4 makes the JSON file pretty-printed
current_loop_page += 1
return pymupdf_doc, all_decision_process_table, log_files_output_paths, request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number
if is_pdf(file_path) == False:
images.append(image)
pymupdf_doc = images
# Check if the image already exists in annotations_all_pages
#print("annotations_all_pages:", annotations_all_pages)
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(image_annotations)
current_loop_page += 1
# Break if new page is a multiple of chosen page_break_val
if current_loop_page % page_break_val == 0:
page_break_return = True
progress.close(_tqdm=progress_bar)
tqdm._instances.clear()
if analysis_type == textract_option:
# Write the updated existing textract data back to the JSON file
with open(json_file_path, 'w') as json_file:
json.dump(existing_data, json_file, indent=4) # indent=4 makes the JSON file pretty-printed
return pymupdf_doc, all_decision_process_table, log_files_output_paths, request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number
if analysis_type == textract_option:
# Write the updated existing textract data back to the JSON file
with open(json_file_path, 'w') as json_file:
json.dump(existing_data, json_file, indent=4) # indent=4 makes the JSON file pretty-printed
return pymupdf_doc, all_decision_process_table, log_files_output_paths, request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number
###
# PIKEPDF TEXT PDF REDACTION
###
def get_text_container_characters(text_container:LTTextContainer):
if isinstance(text_container, LTTextContainer):
characters = [char
for line in text_container
if isinstance(line, LTTextLine) or isinstance(line, LTTextLineHorizontal)
for char in line]
return characters
return []
def create_text_bounding_boxes_from_characters(char_objects:List[LTChar]) -> Tuple[List[OCRResult], List[LTChar]]:
'''
Create an OCRResult object based on a list of pdfminer LTChar objects.
'''
line_level_results_out = []
line_level_characters_out = []
#all_line_level_characters_out = []
character_objects_out = [] # New list to store character objects
# Initialize variables
full_text = ""
added_text = ""
overall_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')] # [x0, y0, x1, y1]
word_bboxes = []
# Iterate through the character objects
current_word = ""
current_word_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')] # [x0, y0, x1, y1]
for char in char_objects:
character_objects_out.append(char) # Collect character objects
if isinstance(char, LTAnno):
# Handle space separately by finalizing the word
full_text += char.get_text() # Adds space or newline
if current_word: # Only finalize if there is a current word
word_bboxes.append((current_word, current_word_bbox))
current_word = ""
current_word_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')] # Reset for next word
# Check for line break (assuming a new line is indicated by a specific character)
if '\n' in char.get_text():
#print("char_anno:", char)
# Finalize the current line
if current_word:
word_bboxes.append((current_word, current_word_bbox))
# Create an OCRResult for the current line
line_level_results_out.append(OCRResult(full_text, round(overall_bbox[0], 2), round(overall_bbox[1], 2), round(overall_bbox[2] - overall_bbox[0], 2), round(overall_bbox[3] - overall_bbox[1], 2)))
line_level_characters_out.append(character_objects_out)
# Reset for the next line
character_objects_out = []
full_text = ""
overall_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')]
current_word = ""
current_word_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')]
continue
# Concatenate text for LTChar
#full_text += char.get_text()
#added_text = re.sub(r'[^\x00-\x7F]+', ' ', char.get_text())
added_text = char.get_text()
if re.search(r'[^\x00-\x7F]', added_text): # Matches any non-ASCII character
#added_text.encode('latin1', errors='replace').decode('utf-8')
added_text = clean_unicode_text(added_text)
full_text += added_text # Adds space or newline, removing
# Update overall bounding box
x0, y0, x1, y1 = char.bbox
overall_bbox[0] = min(overall_bbox[0], x0) # x0
overall_bbox[1] = min(overall_bbox[1], y0) # y0
overall_bbox[2] = max(overall_bbox[2], x1) # x1
overall_bbox[3] = max(overall_bbox[3], y1) # y1
# Update current word
#current_word += char.get_text()
current_word += added_text
# Update current word bounding box
current_word_bbox[0] = min(current_word_bbox[0], x0) # x0
current_word_bbox[1] = min(current_word_bbox[1], y0) # y0
current_word_bbox[2] = max(current_word_bbox[2], x1) # x1
current_word_bbox[3] = max(current_word_bbox[3], y1) # y1
# Finalize the last word if any
if current_word:
word_bboxes.append((current_word, current_word_bbox))
if full_text:
#print("full_text before:", full_text)
if re.search(r'[^\x00-\x7F]', full_text): # Matches any non-ASCII character
# Convert special characters to a human-readable format
#full_text = full_text.encode('latin1', errors='replace').decode('utf-8')
full_text = clean_unicode_text(full_text)
#print("full_text:", full_text)
line_level_results_out.append(OCRResult(full_text, round(overall_bbox[0],2), round(overall_bbox[1], 2), round(overall_bbox[2]-overall_bbox[0],2), round(overall_bbox[3]-overall_bbox[1],2)))
#line_level_characters_out = character_objects_out
return line_level_results_out, line_level_characters_out # Return both results and character objects
def merge_text_bounding_boxes(analyser_results, characters: List[LTChar], combine_pixel_dist: int = 20, vertical_padding: int = 0):
'''
Merge identified bounding boxes containing PII that are very close to one another
'''
analysed_bounding_boxes = []
original_bounding_boxes = [] # List to hold original bounding boxes
if len(analyser_results) > 0 and len(characters) > 0:
# Extract bounding box coordinates for sorting
bounding_boxes = []
for result in analyser_results:
#print("Result:", result)
char_boxes = [char.bbox for char in characters[result.start:result.end] if isinstance(char, LTChar)]
char_text = [char._text for char in characters[result.start:result.end] if isinstance(char, LTChar)]
if char_boxes:
# Calculate the bounding box that encompasses all characters
left = min(box[0] for box in char_boxes)
bottom = min(box[1] for box in char_boxes)
right = max(box[2] for box in char_boxes)
top = max(box[3] for box in char_boxes) + vertical_padding
bbox = [left, bottom, right, top]
bounding_boxes.append((bottom, left, result, bbox, char_text)) # (y, x, result, bbox, text)
# Store original bounding boxes
original_bounding_boxes.append({"text": "".join(char_text), "boundingBox": bbox, "result": copy.deepcopy(result)})
#print("Original bounding boxes:", original_bounding_boxes)
# Sort the results by y-coordinate and then by x-coordinate
bounding_boxes.sort()
merged_bounding_boxes = []
current_box = None
current_y = None
current_result = None
current_text = []
for y, x, result, next_box, text in bounding_boxes:
if current_y is None or current_box is None:
# Initialize the first bounding box
current_box = next_box
current_y = next_box[1]
current_result = result
current_text = list(text)
else:
vertical_diff_bboxes = abs(next_box[1] - current_y)
horizontal_diff_bboxes = abs(next_box[0] - current_box[2])
if vertical_diff_bboxes <= 5 and horizontal_diff_bboxes <= combine_pixel_dist:
# Merge bounding boxes
#print("Merging boxes")
merged_box = current_box.copy()
merged_result = current_result
merged_text = current_text.copy()
#print("current_box_max_x:", current_box[2])
#print("char_max_x:", next_box[2])
merged_box[2] = next_box[2] # Extend horizontally
merged_box[3] = max(current_box[3], next_box[3]) # Adjust the top
merged_result.end = max(current_result.end, result.end) # Extend text range
try:
merged_result.entity_type = current_result.entity_type + " - " + result.entity_type
except Exception as e:
print("Unable to combine result entity types:", e)
if current_text:
merged_text.append(" ") # Add space between texts
merged_text.extend(text)
merged_bounding_boxes.append({
"text": "".join(merged_text),
"boundingBox": merged_box,
"result": merged_result
})
else:
# Save the current merged box before starting a new one
# merged_bounding_boxes.append({
# "text": "".join(current_text),
# "boundingBox": current_box,
# "result": current_result
# })
# Start a new bounding box
current_box = next_box
current_y = next_box[1]
current_result = result
current_text = list(text)
# Handle the last box
# if current_box is not None:
# merged_bounding_boxes.append({
# "text": "".join(current_text),
# "boundingBox": current_box,
# "result": current_result
# })
# Combine original and merged bounding boxes
analysed_bounding_boxes.extend(original_bounding_boxes)
analysed_bounding_boxes.extend(merged_bounding_boxes)
#print("Analysed bounding boxes:", analysed_bounding_boxes)
return analysed_bounding_boxes
def create_text_redaction_process_results(analyser_results, analysed_bounding_boxes, page_num):
decision_process_table = pd.DataFrame()
if len(analyser_results) > 0:
# Create summary df of annotations to be made
analysed_bounding_boxes_df_new = pd.DataFrame(analysed_bounding_boxes)
# Remove brackets and split the string into four separate columns
#print("analysed_bounding_boxes_df_new:", analysed_bounding_boxes_df_new['boundingBox'])
# analysed_bounding_boxes_df_new[['xmin', 'ymin', 'xmax', 'ymax']] = analysed_bounding_boxes_df_new['boundingBox'].str.strip('[]').str.split(',', expand=True)
# Split the boundingBox list into four separate columns
analysed_bounding_boxes_df_new[['xmin', 'ymin', 'xmax', 'ymax']] = analysed_bounding_boxes_df_new['boundingBox'].apply(pd.Series)
# Convert the new columns to integers (if needed)
analysed_bounding_boxes_df_new[['xmin', 'ymin', 'xmax', 'ymax']] = analysed_bounding_boxes_df_new[['xmin', 'ymin', 'xmax', 'ymax']].astype(float)
analysed_bounding_boxes_df_text = analysed_bounding_boxes_df_new['result'].astype(str).str.split(",",expand=True).replace(".*: ", "", regex=True)
analysed_bounding_boxes_df_text.columns = ["label", "start", "end", "score"]
analysed_bounding_boxes_df_new = pd.concat([analysed_bounding_boxes_df_new, analysed_bounding_boxes_df_text], axis = 1)
analysed_bounding_boxes_df_new['page'] = page_num + 1
decision_process_table = pd.concat([decision_process_table, analysed_bounding_boxes_df_new], axis = 0).drop('result', axis=1)
#print('\n\ndecision_process_table:\n\n', decision_process_table)
return decision_process_table
def create_pikepdf_annotations_for_bounding_boxes(analysed_bounding_boxes):
pikepdf_annotations_on_page = []
for analysed_bounding_box in analysed_bounding_boxes:
bounding_box = analysed_bounding_box["boundingBox"]
annotation = Dictionary(
Type=Name.Annot,
Subtype=Name.Square, #Name.Highlight,
QuadPoints=[bounding_box[0], bounding_box[3], bounding_box[2], bounding_box[3],
bounding_box[0], bounding_box[1], bounding_box[2], bounding_box[1]],
Rect=[bounding_box[0], bounding_box[1], bounding_box[2], bounding_box[3]],
C=[0, 0, 0],
IC=[0, 0, 0],
CA=1, # Transparency
T=analysed_bounding_box["result"].entity_type,
BS=Dictionary(
W=0, # Border width: 1 point
S=Name.S # Border style: solid
)
)
pikepdf_annotations_on_page.append(annotation)
return pikepdf_annotations_on_page
def redact_text_pdf(
filename: str, # Path to the PDF file to be redacted
prepared_pdf_image_path: str, # Path to the prepared PDF image for redaction
language: str, # Language of the PDF content
chosen_redact_entities: List[str], # List of entities to be redacted
chosen_redact_comprehend_entities: List[str],
allow_list: List[str] = None, # Optional list of allowed entities
page_min: int = 0, # Minimum page number to start redaction
page_max: int = 999, # Maximum page number to end redaction
analysis_type: str = text_ocr_option, # Type of analysis to perform
current_loop_page: int = 0, # Current page being processed in the loop
page_break_return: bool = False, # Flag to indicate if a page break should be returned
annotations_all_pages: List = [], # List of annotations across all pages
all_line_level_ocr_results_df: pd.DataFrame = pd.DataFrame(), # DataFrame for OCR results
all_decision_process_table: pd.DataFrame = pd.DataFrame(), # DataFrame for decision process table
pymupdf_doc: List = [], # List of PyMuPDF documents
pii_identification_method: str = "Local",
comprehend_query_number:int = 0,
comprehend_client="",
custom_recogniser_word_list:List[str]=[],
redact_whole_page_list:List[str]=[],
page_break_val: int = int(page_break_value), # Value for page break
max_time: int = int(max_time_value),
progress: Progress = Progress(track_tqdm=True) # Progress tracking object
):
'''
Redact chosen entities from a PDF that is made up of multiple pages that are not images.
Input Variables:
- filename: Path to the PDF file to be redacted
- prepared_pdf_image_path: Path to the prepared PDF image for redaction
- language: Language of the PDF content
- chosen_redact_entities: List of entities to be redacted
- chosen_redact_comprehend_entities: List of entities to be redacted for AWS Comprehend
- allow_list: Optional list of allowed entities
- page_min: Minimum page number to start redaction
- page_max: Maximum page number to end redaction
- analysis_type: Type of analysis to perform
- current_loop_page: Current page being processed in the loop
- page_break_return: Flag to indicate if a page break should be returned
- annotations_all_pages: List of annotations across all pages
- all_line_level_ocr_results_df: DataFrame for OCR results
- all_decision_process_table: DataFrame for decision process table
- pymupdf_doc: List of PyMuPDF documents
- pii_identification_method (str, optional): The method to redact personal information. Either 'Local' (spacy model), or 'AWS Comprehend' (AWS Comprehend API).
- comprehend_query_number (int, optional): A counter tracking the number of queries to AWS Comprehend.
- comprehend_client (optional): A connection to the AWS Comprehend service via the boto3 package.
- custom_recogniser_word_list (optional, List[str]): A list of custom words that the user has chosen specifically to redact.
- redact_whole_page_list (optional, List[str]): A list of pages to fully redact.
- page_break_val: Value for page break
- max_time (int, optional): The maximum amount of time (s) that the function should be running before it breaks. To avoid timeout errors with some APIs.
- progress: Progress tracking object
'''
if pii_identification_method == "AWS Comprehend" and comprehend_client == "":
print("Connection to AWS Comprehend service not found.")
return pymupdf_doc, all_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number
# Update custom word list analyser object with any new words that have been added to the custom deny list
#print("custom_recogniser_word_list:", custom_recogniser_word_list)
if custom_recogniser_word_list:
nlp_analyser.registry.remove_recognizer("CUSTOM")
new_custom_recogniser = custom_word_list_recogniser(custom_recogniser_word_list)
#print("new_custom_recogniser:", new_custom_recogniser)
nlp_analyser.registry.add_recognizer(new_custom_recogniser)
# List all elements currently in the nlp_analyser registry
#print("Current recognizers in nlp_analyser registry:")
#for recognizer_name in nlp_analyser.registry.recognizers:
# print(recognizer_name)
#print("Custom recogniser:", nlp_analyser.registry.)
tic = time.perf_counter()
# Open with Pikepdf to get text lines
pikepdf_pdf = Pdf.open(filename)
number_of_pages = len(pikepdf_pdf.pages)
# Check that page_min and page_max are within expected ranges
if page_max > number_of_pages or page_max == 0:
page_max = number_of_pages
if page_min <= 0: page_min = 0
else: page_min = page_min - 1
print("Page range is",str(page_min + 1), "to", str(page_max))
print("Current_loop_page:", current_loop_page)
if current_loop_page == 0: page_loop_start = 0
else: page_loop_start = current_loop_page
progress_bar = tqdm(range(current_loop_page, number_of_pages), unit="pages remaining", desc="Redacting pages")
#for page_no in range(0, number_of_pages):
for page_no in progress_bar:
reported_page_number = str(page_no + 1)
print("Redacting page:", reported_page_number)
# Assuming prepared_pdf_file_paths[page_no] is a PIL image object
try:
image = prepared_pdf_image_path[page_no]#.copy()
#print("image:", image)
except Exception as e:
print("Could not redact page:", reported_page_number, "due to:")
print(e)
continue
image_annotations = {"image": image, "boxes": []}
pymupdf_page = pymupdf_doc.load_page(page_no)
if page_min <= page_no < page_max:
if isinstance(image, str):
image_path = image
image = Image.open(image_path)
for page_layout in extract_pages(filename, page_numbers = [page_no], maxpages=1):
page_analyser_results = []
page_analysed_bounding_boxes = []
characters = []
pikepdf_annotations_on_page = []
decision_process_table_on_page = pd.DataFrame()
page_text_outputs = pd.DataFrame()
if analysis_type == text_ocr_option:
for n, text_container in enumerate(page_layout):
text_container_analyser_results = []
text_container_analysed_bounding_boxes = []
characters = []
if isinstance(text_container, LTTextContainer) or isinstance(text_container, LTAnno):
characters = get_text_container_characters(text_container)
# Create dataframe for all the text on the page
line_level_text_results_list, line_characters = create_text_bounding_boxes_from_characters(characters)
# Create page_text_outputs (OCR format outputs)
if line_level_text_results_list:
# Convert to DataFrame and add to ongoing logging table
line_level_text_results_df = pd.DataFrame([{
'page': page_no + 1,
'text': result.text,
'left': result.left,
'top': result.top,
'width': result.width,
'height': result.height
} for result in line_level_text_results_list])
page_text_outputs = pd.concat([page_text_outputs, line_level_text_results_df])
# Initialize batching variables
current_batch = ""
current_batch_mapping = [] # List of (start_pos, line_index, OCRResult) tuples
all_text_line_results = [] # Store results for all lines
# First pass: collect all lines into batches
for i, text_line in enumerate(line_level_text_results_list):
if chosen_redact_entities:
if pii_identification_method == "Local":
#print("chosen_redact_entities:", chosen_redact_entities)
# Process immediately for local analysis
text_line_analyser_result = nlp_analyser.analyze(
text=text_line.text,
language=language,
entities=chosen_redact_entities,
score_threshold=score_threshold,
return_decision_process=True,
allow_list=allow_list
)
all_text_line_results.append((i, text_line_analyser_result))
elif pii_identification_method == "AWS Comprehend":
# First use the local Spacy model to pick up custom entities that AWS Comprehend can't search for.
custom_redact_entities = [entity for entity in chosen_redact_comprehend_entities if entity in custom_entities]
text_line_analyser_result = nlp_analyser.analyze(
text=text_line.text,
language=language,
entities=custom_redact_entities,
score_threshold=score_threshold,
return_decision_process=True,
allow_list=allow_list
)
all_text_line_results.append((i, text_line_analyser_result))
if len(text_line.text) >= 3:
# Add separator between lines
if current_batch:
current_batch += " | "
start_pos = len(current_batch)
current_batch += text_line.text
current_batch_mapping.append((start_pos, i, text_line))
# Process batch if approaching 300 characters or last line
if len(current_batch) >= 200 or i == len(line_level_text_results_list) - 1:
print("length of text for Comprehend:", len(current_batch))
try:
response = comprehend_client.detect_pii_entities(
Text=current_batch,
LanguageCode=language
)
except Exception as e:
print(e)
time.sleep(3)
response = comprehend_client.detect_pii_entities(
Text=current_batch,
LanguageCode=language
)
comprehend_query_number += 1
# Process response and map back to original lines
if response and "Entities" in response:
for entity in response["Entities"]:
entity_start = entity["BeginOffset"]
entity_end = entity["EndOffset"]
# Find which line this entity belongs to
for batch_start, line_idx, original_line in current_batch_mapping:
batch_end = batch_start + len(original_line.text)
# Check if entity belongs to this line
if batch_start <= entity_start < batch_end:
# Adjust offsets relative to original line
relative_start = entity_start - batch_start
relative_end = min(entity_end - batch_start, len(original_line.text))
result_text = original_line.text[relative_start:relative_end]
if result_text not in allow_list:
if entity.get("Type") in chosen_redact_comprehend_entities:
# Create adjusted entity
adjusted_entity = entity.copy()
adjusted_entity["BeginOffset"] = relative_start
adjusted_entity["EndOffset"] = relative_end
recogniser_entity = recognizer_result_from_dict(adjusted_entity)
# Add to results for this line
existing_results = next((results for idx, results in all_text_line_results if idx == line_idx), [])
if not existing_results:
all_text_line_results.append((line_idx, [recogniser_entity]))
else:
existing_results.append(recogniser_entity)
# Reset batch
current_batch = ""
current_batch_mapping = []
# Second pass: process results for each line
for i, text_line in enumerate(line_level_text_results_list):
text_line_analyser_result = []
text_line_bounding_boxes = []
# Get results for this line
line_results = next((results for idx, results in all_text_line_results if idx == i), [])
if line_results:
text_line_analyser_result = line_results
#print("Analysed text container, now merging bounding boxes")
# Merge bounding boxes if very close together
text_line_bounding_boxes = merge_text_bounding_boxes(text_line_analyser_result, line_characters[i])
#print("merged bounding boxes")
text_container_analyser_results.extend(text_line_analyser_result)
text_container_analysed_bounding_boxes.extend(text_line_bounding_boxes)
#print("text_container_analyser_results:", text_container_analyser_results)
page_analyser_results.extend(text_container_analyser_results) # Add this line
page_analysed_bounding_boxes.extend(text_line_bounding_boxes) # Add this line
#print("page_analyser_results:", page_analyser_results)
#print("page_analysed_bounding_boxes:", page_analysed_bounding_boxes)
#print("image:", image)
page_analysed_bounding_boxes = convert_pikepdf_decision_output_to_image_coords(pymupdf_page, page_analysed_bounding_boxes, image)
#print("page_analysed_bounding_boxes_out_converted:", page_analysed_bounding_boxes)
# Annotate redactions on page
pikepdf_annotations_on_page = create_pikepdf_annotations_for_bounding_boxes(page_analysed_bounding_boxes)
#print("pikepdf_annotations_on_page:", pikepdf_annotations_on_page)
# Make pymupdf page redactions
#print("redact_whole_page_list:", redact_whole_page_list)
if redact_whole_page_list:
if current_loop_page in redact_whole_page_list: redact_whole_page = True
else: redact_whole_page = False
else: redact_whole_page = False
pymupdf_page, image_annotations = redact_page_with_pymupdf(pymupdf_page, pikepdf_annotations_on_page, image, redact_whole_page=redact_whole_page, convert_coords=False)
#print("image_annotations:", image_annotations)
#print("Did redact_page_with_pymupdf function")
reported_page_no = page_no + 1
print("For page number:", reported_page_no, "there are", len(image_annotations["boxes"]), "annotations")
# Write logs
# Create decision process table
decision_process_table_on_page = create_text_redaction_process_results(page_analyser_results, page_analysed_bounding_boxes, current_loop_page)
if not decision_process_table_on_page.empty:
all_decision_process_table = pd.concat([all_decision_process_table, decision_process_table_on_page])
#print("all_decision_process_table:", all_decision_process_table)
if not page_text_outputs.empty:
page_text_outputs = page_text_outputs.sort_values(["top", "left"], ascending=[False, False]).reset_index(drop=True)
all_line_level_ocr_results_df = pd.concat([all_line_level_ocr_results_df, page_text_outputs])
toc = time.perf_counter()
time_taken = toc - tic
#print("toc - tic:", time_taken)
# Break if time taken is greater than max_time seconds
if time_taken > max_time:
print("Processing for", max_time, "seconds, breaking.")
page_break_return = True
progress.close(_tqdm=progress_bar)
tqdm._instances.clear()
# Check if the image already exists in annotations_all_pages
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(image_annotations)
current_loop_page += 1
return pymupdf_doc, all_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number
# Check if the image already exists in annotations_all_pages
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(image_annotations)
current_loop_page += 1
# Break if new page is a multiple of 10
if current_loop_page % page_break_val == 0:
page_break_return = True
progress.close(_tqdm=progress_bar)
return pymupdf_doc, all_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number
return pymupdf_doc, all_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number |