diff --git a/cope2n-ai-fi/modules/sdsvkie/.gitignore b/cope2n-ai-fi/modules/sdsvkie/.gitignore deleted file mode 100644 index 0c089d9..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/.gitignore +++ /dev/null @@ -1,13 +0,0 @@ -*.pyc -__pycache__ -.cache -/microsoft -weights/ -workdirs/ -wandb -sdsvkie/tools/sample_cvat -notebooks/workdirs -external/ -notebooks/visualize -*.egg-info -./external/sdsv_dewarp \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/README.md b/cope2n-ai-fi/modules/sdsvkie/README.md deleted file mode 100644 index 42a8b28..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/README.md +++ /dev/null @@ -1,75 +0,0 @@ -

-

SDSVKIE

-

- -***Feature*** -- Extract information from documents: VAT Invoice, Receipt -- Language: VI + EN - -***What's news*** -### - Ver 1.0.1: -- Improve postprocessing for receipts -- Support handling multiple pages for PDF files -- Lastest weight: /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/best -- Lastest config: /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/config.yaml - - -## I. Setup -***Dependencies*** -- Python: 3.8 -- Torch: 1.10.2 -- CUDA: 11.6 -- transformers: 4.28.1 -``` -pip install -v -e . -``` - - -## II. Inference -``` -from sdsvkie import Predictor -import cv2 - -predictor = Predictor( - cfg="./workdirs/training/sdsap_receipt/exp_3/config.yaml", - weights="./workdirs/training/sdsap_receipt/exp_3/best", - device="cpu", -) -img = cv2.imread("./demos/4 Sep OPC to Home.jpg") -out = predictor(img) -output = out['end2end_results'] - -``` - - -## III. Training -- Prepare dataset: The structure of the dataset directory is organized as follows: - -└── base_dataset \ - ├── train \ - ├──── sub_dir_1 \ - ├────── img1.txt \ - ├────── img1.txt \ - ├────── ... \ - ├──── sub_dir_2 \ - ├────── img2.txt \ - ├────── img2.txt \ - ├── test \ - ├──── imgn.jpg \ - ├──── imgn.txt - -- Edit and run scripts: -``` -sh ./scripts/train.sh -``` - -# TODO - -- [ ] Add more fields: sub_total, tips, seller_address, item list -- [x] Support muliple pages -- [x] Review result KIE for invoice (vnpt_exp_4_model) -- [x] Fix unnormalize box error in some cases -- [x] Support multiple pages -- [x] Create 200 multiple pages invoice -- [ ] Finalize multi page testset -- [ ] Eval result \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/arial.ttf b/cope2n-ai-fi/modules/sdsvkie/arial.ttf deleted file mode 100644 index ff0815c..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/arial.ttf and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/2022_07_25 farewell lunch.jpg b/cope2n-ai-fi/modules/sdsvkie/demos/2022_07_25 farewell lunch.jpg deleted file mode 100644 index d43329e..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/2022_07_25 farewell lunch.jpg and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/4 Sep OPC to Home.jpg b/cope2n-ai-fi/modules/sdsvkie/demos/4 Sep OPC to Home.jpg deleted file mode 100644 index 63c42d2..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/4 Sep OPC to Home.jpg and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110156.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110156.png deleted file mode 100644 index e134824..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110156.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110156.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110156.txt deleted file mode 100644 index 727fe7b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110156.txt +++ /dev/null @@ -1,73 +0,0 @@ -350 31 403 58 dịch -329 143 386 166 ngoài -104 298 174 321 CƯỚC -323 244 375 268 ĐẾN -739 67 810 91 (Price) -610 67 663 91 Unit) -1109 26 1174 55 Thuế -181 143 231 166 hãng -102 244 145 268 PHÍ -1021 27 1076 55 thuế -25 67 73 92 (No) -151 350 240 374 CHỨNG -893 25 949 54 Tiền -938 68 1033 92 (Amount) -150 244 198 268 XẾP -247 244 316 267 CẢNG -967 301 1080 323 68.730.120 -998 354 1080 376 760.000 -1394 31 1473 55 Thành -102 349 146 375 PHÍ -1477 25 1527 56 tiền -784 30 823 59 giá -241 68 374 92 (Description) -293 31 345 58 hóa, -228 32 288 58 hàng -1383 66 1447 92 (Total -247 350 333 374 TỪ-D/O -103 142 145 162 Thu -1196 82 1254 112 Tiền -203 244 242 268 DỠ -781 353 863 376 760.000 -181 298 240 321 BIỂN -979 248 1080 270 6.342.336 -174 31 222 54 Tên -533 66 579 92 (Qty -1254 354 1324 375 40.000 -1486 353 1567 375 800.000 -1467 247 1568 270 6.342.336 -750 301 864 323 68.730.120 -273 144 324 162 nước -956 32 1015 54 chưa -726 32 778 54 Đơn -1455 300 1568 323 68.730.120 -148 142 177 166 hộ -1180 30 1321 59 GTGT✪(VAT) -1258 81 1316 111 thuế -531 30 568 54 SL -104 195 285 215 BL146201088385 -604 31 663 53 ĐVT -763 248 863 270 6.342.336 -22 31 76 54 STT -1451 68 1535 92 amount) -407 36 440 59 vụ -235 143 268 163 tàu -260 302 304 321 O.F. -1120 87 1149 112 % -583 66 605 88 & -573 30 598 54 & -591 301 624 322 BL -591 353 625 376 BL -1115 306 1154 322 XXX -41 301 57 322 2 -1115 360 1154 375 XXX -1114 253 1154 269 XXX -42 354 56 375 3 -591 247 625 270 BL -44 248 55 268 1 -1310 301 1326 322 0 -574 301 586 322 1 -573 354 586 375 1 -573 248 586 269 1 -244 303 256 322 - -1310 247 1326 270 0 diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110248.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110248.png deleted file mode 100644 index 63ce621..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110248.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110248.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110248.txt deleted file mode 100644 index fb0b9c6..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110248.txt +++ /dev/null @@ -1,198 +0,0 @@ -749 41 785 60 suất -746 18 789 37 Thuế -746 70 788 86 (Rate) -192 35 226 52 dịch -812 30 850 49 Tiền -341 70 382 86 (Unit) -15 104 63 120 CƯỚC -632 29 670 48 Tiền -673 33 714 52 hàng -110 34 150 52 hàng -673 674 727 689 855.000 -841 552 894 567 169.742 -433 34 479 52 lượng -660 552 727 567 3.227.035 -345 46 377 60 tính -841 228 894 243 169.742 -154 34 188 51 hóa, -973 227 1040 243 3.396.777 -66 103 102 120 TÀU -841 715 894 729 111.509 -660 227 727 243 3.227.035 -673 512 727 527 855.000 -673 187 727 202 855.000 -660 714 727 730 2.119.950 -474 895 510 912 chiếu -118 58 207 75 (Description) -605 899 639 914 nhận -388 895 422 913 kiểm -973 552 1040 567 3.396.777 -841 58 899 74 Amount) -75 34 107 49 Tên -988 29 1020 49 tiền -847 188 894 201 44.973 -535 227 602 243 3.227.035 -926 58 968 74 (Total -986 187 1039 202 899.973 -334 23 368 37 Đơn -852 30 890 49 thuế -652 755 727 770 23.507.890 -965 268 1040 283 38.111.990 -973 390 1039 405 3.396.777 -548 187 603 202 855.000 -410 58 479 75 (Quantity) -547 59 589 74 Price) -841 390 894 405 169.742 -652 593 727 608 38.111.990 -535 552 602 568 3.227.035 -847 512 894 526 44.973 -965 106 1040 121 38.111.990 -932 34 985 49 Thành -537 898 563 915 lập, -342 107 380 120 45GP -15 147 43 161 Thu -47 185 106 202 CHỨNG -673 349 727 364 855.000 -847 674 894 689 44.973 -548 674 603 689 855.000 -567 900 601 915 giao, -555 33 580 52 giá -660 389 727 405 3.227.035 -15 185 43 201 PHÍ -985 674 1039 689 899.973 -652 268 727 284 38.111.990 -351 895 386 915 (Cần -671 898 704 915 đơn) -675 59 731 74 amount) -970 59 1026 74 amount) -45 147 65 164 hộ -985 512 1039 527 899.973 -973 714 1040 730 2.231.459 -448 895 472 913 đối -535 715 603 730 2.119.950 -652 106 727 121 38.111.990 -965 593 1040 608 38.111.990 -548 512 603 527 855.000 -15 266 63 282 CƯỚC -508 58 544 74 (Unit -46 509 106 526 CHỨNG -408 30 429 49 Số -965 430 1040 446 38.111.990 -46 225 79 242 XÉP -105 147 249 161 SNKO010220804769 -15 428 62 444 CƯỚC -527 106 602 121 38.111.990 -985 349 1039 364 899.973 -15 590 63 607 CƯỚC -642 898 669 913 hóa -527 268 603 284 38.111.990 -65 589 101 607 TÀU -15 752 63 769 CƯỚC -46 671 106 688 CHỨNG -15 225 43 242 PHÍ -341 228 380 242 45GP -14 633 43 648 Thu -847 350 894 364 44.973 -547 349 603 365 855.000 -65 427 101 445 TÀU -513 898 536 913 khi -14 471 43 485 Thu -673 837 727 851 855.000 -15 309 43 323 Thu -14 508 44 526 PHÍ -965 755 1040 770 23.507.890 -67 148 101 161 SNK -14 670 43 688 PHÍ -527 593 603 608 38.111.990 -527 756 602 770 23.507.890 -65 751 101 769 TÀU -44 470 65 489 hộ -517 34 551 48 Đơn -45 548 78 566 XÉP -14 549 43 567 PHÍ -65 265 102 283 TÀU -535 389 602 406 3.227.035 -14 386 44 404 PHÍ -14 832 43 850 PHÍ -46 347 106 364 CHỨNG -44 633 65 651 hộ -548 837 602 850 855.000 -46 833 106 850 CHỨNG -44 308 65 326 hộ -45 386 79 404 XÃP -14 346 43 364 PHÍ -14 795 43 810 Thu -526 430 602 446 38.111.990 -802 58 840 74 (VAT -45 711 79 729 XÉP -848 837 893 850 44.973 -80 225 107 242 DỠ -230 37 251 52 vụ -754 109 781 120 XXX -652 430 727 446 38.111.990 -80 549 107 567 DỠ -350 188 372 202 BL -109 184 136 202 TỪ -616 58 672 73 (Pre-tax -44 795 65 813 hộ -986 837 1039 851 899.973 -341 756 380 770 22GP -80 387 107 404 DỠ -424 900 445 912 tra -14 711 43 729 PHÍ -104 796 249 810 SNKO010220805559 -104 309 248 323 SNKO010220805023 -341 268 380 283 45GP -66 634 101 647 SNK -80 711 107 729 DỠ -372 22 389 40 vị -108 670 136 689 TỪ -104 633 248 648 SNKO010220805118 -754 556 781 567 XXX -754 759 781 769 XXX -754 597 781 607 XXX -66 472 101 485 SNK -66 309 101 323 SNK -755 719 780 729 XXX -108 832 136 851 TỪ -341 431 380 445 45GP -754 515 781 526 XXX -754 840 781 850 XXX -341 715 380 729 22GP -108 345 136 364 TỪ -754 678 781 688 XXX -66 796 101 810 SNK -341 390 380 405 45GP -104 471 249 486 SNKO010220805117 -341 553 380 567 45GP -108 508 136 527 TỪ -755 232 780 242 XXX -754 435 781 445 XXX -341 593 380 608 45GP -754 394 781 405 XXX -755 272 781 283 XXX -754 191 780 202 XXX -349 836 373 851 BL -754 353 780 364 XXX -349 349 372 364 BL -440 106 448 120 1 -887 106 896 121 1 -440 553 448 567 1 -440 755 448 769 1 -441 715 448 729 1 -888 756 896 769 / -441 228 448 242 1 -440 593 448 608 1 -440 269 449 283 1 -440 836 448 850 1 -440 349 449 364 1 -888 269 896 283 1 -440 390 448 404 1 -440 188 448 202 1 -440 431 448 445 1 -888 431 896 445 / -350 512 372 526 BL -440 674 448 688 1 -888 594 896 607 / -349 675 372 689 BL -440 512 448 526 1 diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110308.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110308.png deleted file mode 100644 index a6d68cf..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110308.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110308.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110308.txt deleted file mode 100644 index ec69603..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110308.txt +++ /dev/null @@ -1,76 +0,0 @@ -162 92 206 111 hàng -263 152 303 175 gồm -133 124 188 142 chứng -132 26 176 46 hàng -224 26 261 45 dịch -267 124 311 142 nhập -928 23 974 43 Thuế -218 124 261 142 hàng -93 53 202 73 Descriptions -343 155 378 175 phụ -1110 23 1151 43 thuế -1067 23 1107 43 Tiền -181 26 219 45 hóa, -98 156 148 172 Cước -216 152 258 174 biển -26 54 56 70 No. -1185 26 1240 42 Thành -437 53 469 73 Qty -270 87 304 107 đến -92 26 127 43 Tên -1245 23 1279 44 tiền -979 23 1018 43 suất -640 26 669 47 giá -152 156 185 174 vận -98 124 129 140 Phí -98 92 129 109 Phí -1075 92 1144 109 166.904 -813 92 900 109 3.171.168 -866 23 906 43 thuế -97 178 127 198 phí -1223 54 1267 71 Total -26 26 52 43 Stt -308 155 340 171 các -684 54 728 70 Price -1246 92 1333 109 3.338.072 -769 23 809 43 Tiền -1236 167 1333 184 36.393.480 -673 26 728 46 (VND) -803 167 899 183 36.393.480 -1085 124 1144 140 39.474 -830 124 900 140 750.000 -636 92 724 109 3.171.168 -625 168 723 183 36.393.480 -1082 54 1151 71 Amount -652 124 723 140 750.000 -1284 26 1339 46 (VND) -1263 124 1333 140 789.474 -487 92 542 108 CONT -837 54 906 71 Amount -132 92 158 108 dỡ -210 92 233 111 tại -643 54 679 70 Unit -813 27 862 43 trước -237 91 266 107 nơi -1271 54 1339 71 Amount -598 27 636 43 Đơn -482 54 519 70 Unit -190 155 212 172 tải -1039 54 1079 70 VAT -265 29 288 45 vụ -480 26 522 43 ĐVT -442 25 468 43 SL -975 54 1016 70 Rate -193 124 214 140 từ -485 123 517 141 B/L -485 166 510 184 Lô -931 53 972 71 VAT -31 167 43 183 3 -31 124 44 140 2 -1000 94 1014 110 X -32 92 42 108 1 -1001 127 1014 142 X -1004 166 1014 185 / -1138 167 1147 184 / -451 92 461 108 1 -451 124 461 140 1 diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110342.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110342.png deleted file mode 100644 index 3edb857..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110342.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110342.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110342.txt deleted file mode 100644 index f841bfe..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110342.txt +++ /dev/null @@ -1,82 +0,0 @@ -537 569 588 597 Tổng -233 161 291 182 chứng -593 573 641 597 cộng -290 35 332 57 dịch -467 78 524 99 (Unit) -577 35 632 57 lượng -472 50 511 68 tính -1124 18 1175 45 Tổng -190 161 227 183 nộp -647 573 715 596 (Total): -1012 160 1088 179 720.000 -114 160 156 182 dịch -133 613 180 641 bằng -792 63 877 84 (Amount) -193 36 239 57 hàng -986 52 1029 76 Tiền -1138 78 1201 99 (Total) -797 159 892 180 9.000.000 -322 156 364 179 xuất -1118 50 1170 68 thanh -244 35 285 56 hóa, -87 575 184 596 (Exchange -50 613 88 637 tiền -844 30 883 55 tiền -399 159 440 179 C/O -463 22 504 41 Đơn -1139 160 1235 179 9.720.000 -703 64 758 84 price) -1140 119 1201 139 9=6+8 -781 35 840 54 Thành -1178 17 1216 42 tiền -914 14 964 38 Thuế -915 160 949 180 8% -189 576 239 596 rate): -198 64 318 84 (Description) -91 613 129 637 viết -77 160 109 179 Phí -52 573 83 597 giá -256 619 323 640 words): -1032 53 1074 76 thuế -20 613 47 637 Số -1175 51 1217 68 toán -804 119 863 139 6=4x5 -710 34 741 59 giá -664 35 707 54 Đơn -651 63 700 83 (Unit -546 30 573 54 Số -546 63 639 84 (Quantity) -151 36 188 54 Tên -368 159 396 179 xứ -184 618 221 637 chữ -482 619 531 638 trăm -20 573 48 597 Tỷ -1018 85 1092 105 Amount) -969 17 1082 41 GTGT(VAT) -634 618 692 642 nghìn -916 68 947 89 TS -696 613 754 644 đồng. -575 619 629 638 mươi -782 572 877 594 9.000.000 -329 619 379 638 Chín -384 619 433 642 triệu -966 84 1015 104 (VAT -225 619 252 640 (In -296 160 318 179 từ -989 573 1065 592 720.000 -17 63 66 84 (No.) -161 165 186 182 vụ -537 618 571 638 hai -336 39 362 57 vụ -507 21 529 45 vị -1119 572 1215 594 9.720.000 -438 619 477 642 bảy -487 118 501 139 3 -15 35 60 54 STT -1021 119 1037 140 8 -925 119 940 139 7 -585 118 600 139 4 -252 119 266 139 2 -697 117 712 140 5 -37 160 47 178 1 -37 120 47 138 1 diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110359.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110359.png deleted file mode 100644 index 8c3c656..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110359.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110359.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110359.txt deleted file mode 100644 index b99082b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110359.txt +++ /dev/null @@ -1,68 +0,0 @@ -624 54 678 75 (Unit) -1049 23 1121 45 THÀNH -119 129 157 145 thuê -667 22 717 44 TÍNH -120 203 157 220 thuê -308 22 360 49 HÓA -185 161 217 183 đầu -185 198 217 220 đầu -749 54 838 76 (Quantity) -119 166 157 182 thuê -937 54 989 75 Price) -85 202 115 220 Phí -28 53 75 75 (No.) -185 124 217 146 đầu -774 25 850 48 LƯỢNG -1067 54 1153 75 (Amount) -270 54 382 76 (Description) -85 166 115 183 Phí -221 202 252 220 kéo -1067 202 1199 221 360.000.000.00 -84 128 115 146 Phí -738 22 770 45 SỐ -245 23 304 45 HÀNG -221 165 252 183 kéo -221 128 252 146 kéo -1125 23 1173 45 TIỀN -423 421 505 449 STOP -1068 165 1198 185 138.461.550,00 -586 26 634 45 ĐƠN -892 202 1012 221 72.000.000.00 -1077 129 1198 148 47.076.927,00 -39 95 65 116 (1) -885 54 933 74 (Unit -33 26 73 46 STT -199 22 242 45 TÊN -891 128 1012 148 47.076.927,00 -891 165 1013 184 69.230.775.00 -318 420 391 449 ONE -637 26 662 48 VỊ -697 128 726 145 XE -639 94 664 115 (3) -364 27 421 49 DỊCH -160 169 182 183 xe -698 165 726 182 XE -160 132 182 146 xe -925 95 950 116 (5) -782 95 807 116 (4) -160 207 182 220 xe -46 165 58 183 2 -892 26 940 45 ĐƠN -315 94 339 116 (2) -46 202 58 220 3 -943 22 984 46 GIÁ -841 166 853 183 2 -47 129 57 146 1 -691 421 724 449 M -842 203 853 220 5 -424 26 456 50 VỤ -843 129 852 146 1 -580 420 680 451 VICES, -733 421 787 449 ULTI -698 203 727 220 XE -399 425 414 447 - -1078 95 1159 115 )=(4)X(5) -1061 96 1084 115 (6) -796 420 854 449 BEN -515 420 572 449 SER -862 421 936 449 EFITS diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110424.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110424.png deleted file mode 100644 index e989838..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110424.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110424.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110424.txt deleted file mode 100644 index f93d2ad..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110424.txt +++ /dev/null @@ -1,76 +0,0 @@ -590 592 641 614 Cộng -141 131 193 153 ngoài -234 131 286 153 tháng -374 23 417 44 dịch -759 22 818 44 lượng -272 22 321 44 hàng -327 23 369 43 hóa, -731 47 815 67 (Quantity) -646 586 682 610 tiền -686 591 732 614 hàng -589 711 640 739 Tổng -620 47 671 66 (Unit) -282 47 393 68 (Description) -117 588 184 613 chuyển -590 628 639 652 Thuế -291 130 367 150 08/2022 -792 593 867 613 amount): -590 670 634 693 Tiền -638 670 680 693 thuế -35 46 79 67 (No.) -643 628 682 652 suất -728 17 755 40 Số -104 130 136 149 Phí -198 131 230 154 giờ -940 48 992 66 Price) -71 591 112 613 lòng -228 22 267 41 Tên -949 21 982 44 giá -687 633 748 651 GTGT -644 716 691 738 cộng -34 21 79 40 STT -1189 17 1229 41 tiền -589 23 632 40 Đơn -750 675 807 696 (VAT): -838 717 900 737 (Grand -694 711 731 735 tiền -661 22 701 40 tính -684 674 745 693 GTGT -1120 22 1184 40 Thành -737 593 788 612 (Total -98 616 164 636 transfer -735 716 787 734 thanh -31 616 93 635 (Please -902 22 945 40 Đơn -753 634 799 654 (VAT -319 591 377 613 chúng -792 715 833 734 toán -891 47 936 66 (Unit -1135 47 1212 67 (Amount) -801 634 849 654 rate): -30 591 66 609 Vui -905 717 961 736 Total): -1187 131 1290 149 57.753.850 -257 591 315 610 khoản -636 22 657 44 vị -224 616 266 633 bank -189 591 224 610 vào -339 616 383 636 No.): -421 27 448 46 vụ -410 586 435 611 số -1201 674 1292 693 4.620.308 -228 591 253 610 tài -1189 591 1292 610 57.753.850 -1257 632 1292 652 8% -381 590 408 611 tôi -1189 715 1292 734 62.374.158 -1140 94 1207 111 6=4x5 -269 619 336 633 account -190 621 220 633 our -168 619 186 633 to -331 93 344 111 2 -767 93 780 110 4 -639 93 651 111 3 -935 93 948 110 5 -52 130 62 149 1 -53 94 62 110 1 diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110438.png b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110438.png deleted file mode 100644 index 6c9a234..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110438.png and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110438.txt b/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110438.txt deleted file mode 100644 index 01b5adb..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/invoice/Screenshot 2023-05-18 110438.txt +++ /dev/null @@ -1,150 +0,0 @@ -677 742 742 776 Tổng -1413 28 1476 63 Tổng -590 104 660 129 (Unit) -814 748 900 775 (Total): -1431 104 1509 129 (Total) -748 747 807 776 cộng -72 797 118 827 tiền -174 797 233 832 bằng -727 50 796 78 lượng -199 202 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805 893 828 trăm -990 257 1123 282 24.668.174 -837 50 889 73 Đơn -101 258 143 281 Phí -149 361 199 384 khai -1273 463 1368 489 576.000 -243 310 299 337 (Lift -33 797 67 827 Số -1432 515 1551 540 2.160.000 -102 515 142 539 Phí -238 803 284 827 chữ -244 750 308 775 rate): -1005 514 1124 540 2.000.000 -103 563 156 585 SEA -1434 463 1551 488 7.776.000 -195 51 241 74 Tên -101 361 142 384 Phí -1298 72 1350 101 thuế -974 746 1117 771 113.180.694 -1281 112 1372 137 Amount) -386 418 443 440 sung -1418 258 1550 281 26.641.629 -991 206 1122 231 50.699.472 -1432 156 1509 179 9=6+8 -1434 412 1551 436 7.560.000 -101 412 143 436 Phí -894 49 933 79 giá -1011 155 1085 179 6=4x5 -314 362 420 388 (customs -102 309 143 333 Phí -1007 412 1123 437 7.000.000 -34 747 69 777 Tỷ -1152 205 1194 231 8% -1419 206 1548 230 54.755.431 -101 463 142 488 Phí -1152 256 1194 282 8% -1253 309 1367 333 1.064.725 -1153 309 1194 334 8% -715 805 777 832 triệu -1005 360 1123 385 8.303.967 -206 361 244 385 hải -1273 412 1369 436 560.000 -1273 361 1368 384 664.317 -1153 361 1194 385 8% -349 407 381 437 bổ -594 806 661 828 mươi -1276 805 1333 828 năm -1153 464 1193 488 8% -214 262 265 281 cont -242 412 280 437 hải -1220 29 1360 57 GTGT(VAT) -940 806 1006 828 mươi -477 805 539 828 trăm -1153 515 1194 539 8% -1422 309 1550 332 14.373.806 -1006 463 1123 489 7.200.000 -899 805 933 828 ba -1153 91 1192 117 TS -250 367 307 388 quan -1013 804 1061 828 lăm -1434 361 1551 384 8.968.284 -639 32 667 62 vị -1340 806 1407 829 mươi -1149 805 1200 831 một -289 804 323 831 (In -1395 746 1540 771 122.235.150 -1231 747 1350 771 9.054.456 -784 805 826 828 hai -1153 413 1194 437 8% -545 804 588 829 hai -148 310 179 337 hạ -286 419 343 440 quan -667 805 709 829 hai -101 595 262 624 >07/08/2022) -1217 111 1277 135 (VAT -185 314 237 333 cont -204 521 236 543 vụ -419 805 471 832 Một -28 85 88 110 (No.) -211 464 269 491 định -165 561 447 589 INBOUND-(01/08/2022 -427 55 459 78 vụ -615 155 633 180 3 -737 154 757 179 4 -31 49 86 74 STT -206 418 237 440 vụ -50 463 68 488 6 -1285 154 1304 180 8 -49 309 66 334 3 -1165 155 1182 180 7 -49 257 67 281 2 -49 360 68 385 4 -50 412 67 437 5 -51 515 67 539 7 -878 154 896 180 5 -52 156 65 178 1 -52 206 64 229 1 -321 155 339 179 2 diff --git a/cope2n-ai-fi/modules/sdsvkie/demos/test.py b/cope2n-ai-fi/modules/sdsvkie/demos/test.py deleted file mode 100644 index 2f6ea26..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/demos/test.py +++ /dev/null @@ -1,948 +0,0 @@ -# Ultralytics YOLO 🚀, GPL-3.0 license -""" -Model validation metrics -""" -import math -import warnings -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.nn as nn - -from sklearn.metrics import confusion_matrix - -# boxes -def box_area(box): - # box = xyxy(4,n) - return (box[2] - box[0]) * (box[3] - box[1]) - - -def bbox_ioa(box1, box2, eps=1e-7): - """Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 - box1: np.array of shape(nx4) - box2: np.array of shape(mx4) - returns: np.array of shape(nxm) - """ - - # Get the coordinates of bounding boxes - b1_x1, b1_y1, b1_x2, b1_y2 = box1.T - b2_x1, b2_y1, b2_x2, b2_y2 = box2.T - - # Intersection area - inter_area = ( - np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1) - ).clip(0) * ( - np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1) - ).clip( - 0 - ) - - # box2 area - box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps - - # Intersection over box2 area - return inter_area / box2_area - - -def box_iou(box1, box2, eps=1e-7): - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) - inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) - - # IoU = inter / (area1 + area2 - inter) - return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) - - -def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): - # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4) - - # Get the coordinates of bounding boxes - if xywh: # transform from xywh to xyxy - (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) - w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 - b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ - b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ - else: # x1, y1, x2, y2 = box1 - b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) - b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) - w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps - w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps - - # Intersection area - inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * ( - b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) - ).clamp(0) - - # Union Area - union = w1 * h1 + w2 * h2 - inter + eps - - # IoU - iou = inter / union - if CIoU or DIoU or GIoU: - cw = b1_x2.maximum(b2_x2) - b1_x1.minimum( - b2_x1 - ) # convex (smallest enclosing box) width - ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height - if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 - c2 = cw**2 + ch**2 + eps # convex diagonal squared - rho2 = ( - (b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 - + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2 - ) / 4 # center dist ** 2 - if ( - CIoU - ): # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 - v = (4 / math.pi**2) * ( - torch.atan(w2 / h2) - torch.atan(w1 / h1) - ).pow(2) - with torch.no_grad(): - alpha = v / (v - iou + (1 + eps)) - return iou - (rho2 / c2 + v * alpha) # CIoU - return iou - rho2 / c2 # DIoU - c_area = cw * ch + eps # convex area - return ( - iou - (c_area - union) / c_area - ) # GIoU https://arxiv.org/pdf/1902.09630.pdf - return iou # IoU - - -def mask_iou(mask1, mask2, eps=1e-7): - """ - mask1: [N, n] m1 means number of predicted objects - mask2: [M, n] m2 means number of gt objects - Note: n means image_w x image_h - return: masks iou, [N, M] - """ - intersection = torch.matmul(mask1, mask2.t()).clamp(0) - union = ( - mask1.sum(1)[:, None] + mask2.sum(1)[None] - ) - intersection # (area1 + area2) - intersection - return intersection / (union + eps) - - -def masks_iou(mask1, mask2, eps=1e-7): - """ - mask1: [N, n] m1 means number of predicted objects - mask2: [N, n] m2 means number of gt objects - Note: n means image_w x image_h - return: masks iou, (N, ) - """ - intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) - union = (mask1.sum(1) + mask2.sum(1))[ - None - ] - intersection # (area1 + area2) - intersection - return intersection / (union + eps) - - -def smooth_BCE( - eps=0.1, -): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 - # return positive, negative label smoothing BCE targets - return 1.0 - 0.5 * eps, 0.5 * eps - - -# losses -class FocalLoss(nn.Module): - # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super().__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = "none" # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - # p_t = torch.exp(-loss) - # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability - - # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py - pred_prob = torch.sigmoid(pred) # prob from logits - p_t = true * pred_prob + (1 - true) * (1 - pred_prob) - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = (1.0 - p_t) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == "mean": - return loss.mean() - elif self.reduction == "sum": - return loss.sum() - else: # 'none' - return loss - - -class ConfusionMatrix: - # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix - def __init__(self, nc, conf=0.25, iou_thres=0.45): - self.matrix = np.zeros((nc + 1, nc + 1)) - self.nc = nc # number of classes - self.conf = conf - self.iou_thres = iou_thres - - def process_batch(self, detections, labels): - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - detections (Array[N, 6]), x1, y1, x2, y2, conf, class - labels (Array[M, 5]), class, x1, y1, x2, y2 - Returns: - None, updates confusion matrix accordingly - """ - if detections is None: - gt_classes = labels.int() - for gc in gt_classes: - self.matrix[self.nc, gc] += 1 # background FN - return - - detections = detections[detections[:, 4] > self.conf] - gt_classes = labels[:, 0].int() - detection_classes = detections[:, 5].int() - iou = box_iou(labels[:, 1:], detections[:, :4]) - - x = torch.where(iou > self.iou_thres) - if x[0].shape[0]: - matches = ( - torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1) - .cpu() - .numpy() - ) - if x[0].shape[0] > 1: - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 1], return_index=True)[1]] - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 0], return_index=True)[1]] - else: - matches = np.zeros((0, 3)) - - n = matches.shape[0] > 0 - m0, m1, _ = matches.transpose().astype(int) - for i, gc in enumerate(gt_classes): - j = m0 == i - if n and sum(j) == 1: - self.matrix[detection_classes[m1[j]], gc] += 1 # correct - else: - self.matrix[self.nc, gc] += 1 # true background - - if n: - for i, dc in enumerate(detection_classes): - if not any(m1 == i): - self.matrix[dc, self.nc] += 1 # predicted background - - def matrix(self): - return self.matrix - - def tp_fp(self): - tp = self.matrix.diagonal() # true positives - fp = self.matrix.sum(1) - tp # false positives - # fn = self.matrix.sum(0) - tp # false negatives (missed detections) - return tp[:-1], fp[:-1] # remove background class - - # @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") - def plot(self, normalize=True, save_dir="", names=()): - import seaborn as sn - - array = self.matrix / ( - (self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1 - ) # normalize columns - array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) - - fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) - nc, nn = self.nc, len(names) # number of classes, names - sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size - labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels - ticklabels = (names + ["background"]) if labels else "auto" - with warnings.catch_warnings(): - warnings.simplefilter( - "ignore" - ) # suppress empty matrix RuntimeWarning: All-NaN slice encountered - sn.heatmap( - array, - ax=ax, - annot=nc < 30, - annot_kws={"size": 8}, - cmap="Blues", - fmt=".2f", - square=True, - vmin=0.0, - xticklabels=ticklabels, - yticklabels=ticklabels, - ).set_facecolor((1, 1, 1)) - ax.set_xlabel("True") - ax.set_ylabel("Predicted") - ax.set_title("Confusion Matrix") - fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250) - plt.close(fig) - - def print(self): - for i in range(self.nc + 1): - print(" ".join(map(str, self.matrix[i]))) - - -def smooth(y, f=0.05): - # Box filter of fraction f - nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) - p = np.ones(nf // 2) # ones padding - yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded - return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed - - -def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): - # Precision-recall curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - py = np.stack(py, axis=1) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py.T): - ax.plot( - px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}" - ) # plot(recall, precision) - else: - ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) - - ax.plot( - px, - py.mean(1), - linewidth=3, - color="blue", - label="all classes %.3f mAP@0.5" % ap[:, 0].mean(), - ) - ax.set_xlabel("Recall") - ax.set_ylabel("Precision") - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - ax.set_title("Precision-Recall Curve") - fig.savefig(save_dir, dpi=250) - plt.close(fig) - - -def plot_mc_curve( - px, - py, - save_dir=Path("mc_curve.png"), - names=(), - xlabel="Confidence", - ylabel="Metric", -): - # Metric-confidence curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py): - ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) - else: - ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) - - y = smooth(py.mean(0), 0.05) - ax.plot( - px, - y, - linewidth=3, - color="blue", - label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}", - ) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - ax.set_title(f"{ylabel}-Confidence Curve") - fig.savefig(save_dir, dpi=250) - plt.close(fig) - - -def compute_ap(recall, precision): - """Compute the average precision, given the recall and precision curves - # Arguments - recall: The recall curve (list) - precision: The precision curve (list) - # Returns - Average precision, precision curve, recall curve - """ - - # Append sentinel values to beginning and end - mrec = np.concatenate(([0.0], recall, [1.0])) - mpre = np.concatenate(([1.0], precision, [0.0])) - - # Compute the precision envelope - mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) - - # Integrate area under curve - method = "interp" # methods: 'continuous', 'interp' - if method == "interp": - x = np.linspace(0, 1, 101) # 101-point interp (COCO) - ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate - else: # 'continuous' - i = np.where(mrec[1:] != mrec[:-1])[0] # points where x-axis (recall) changes - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve - - return ap, mpre, mrec - - -def ap_per_class( - tp, - conf, - pred_cls, - target_cls, - plot=False, - save_dir=Path(), - names=(), - eps=1e-16, - prefix="", -): - """Compute the average precision, given the recall and precision curves. - Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. - # Arguments - tp: True positives (nparray, nx1 or nx10). - conf: Objectness value from 0-1 (nparray). - pred_cls: Predicted object classes (nparray). - target_cls: True object classes (nparray). - plot: Plot precision-recall curve at mAP@0.5 - save_dir: Plot save directory - # Returns - The average precision as computed in py-faster-rcnn. - """ - - # Sort by objectness - i = np.argsort(-conf) - tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] - - # Find unique classes - unique_classes, nt = np.unique(target_cls, return_counts=True) - nc = unique_classes.shape[0] # number of classes, number of detections - - # Create Precision-Recall curve and compute AP for each class - px, py = np.linspace(0, 1, 1000), [] # for plotting - ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) - for ci, c in enumerate(unique_classes): - i = pred_cls == c - n_l = nt[ci] # number of labels - n_p = i.sum() # number of predictions - if n_p == 0 or n_l == 0: - continue - - # Accumulate FPs and TPs - fpc = (1 - tp[i]).cumsum(0) - tpc = tp[i].cumsum(0) - - # Recall - recall = tpc / (n_l + eps) # recall curve - r[ci] = np.interp( - -px, -conf[i], recall[:, 0], left=0 - ) # negative x, xp because xp decreases - - # Precision - precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score - - # AP from recall-precision curve - for j in range(tp.shape[1]): - ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and j == 0: - py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - - # from IPython import embed; embed() - # Compute F1 (harmonic mean of precision and recall) - f1 = 2 * p * r / (p + r + eps) - names = [ - v for k, v in names.items() if k in unique_classes - ] # list: only classes that have data - names = dict(enumerate(names)) # to dict - if plot: - plot_pr_curve(px, py, ap, save_dir / f"{prefix}PR_curve.png", names) - plot_mc_curve(px, f1, save_dir / f"{prefix}F1_curve.png", names, ylabel="F1") - plot_mc_curve( - px, p, save_dir / f"{prefix}P_curve.png", names, ylabel="Precision" - ) - plot_mc_curve(px, r, save_dir / f"{prefix}R_curve.png", names, ylabel="Recall") - - i = smooth(f1.mean(0), 0.1).argmax() # max F1 index - p, r, f1 = p[:, i], r[:, i], f1[:, i] - tp = (r * nt).round() # true positives - fp = (tp / (p + eps) - tp).round() # false positives - return tp, fp, p, r, f1, ap, unique_classes.astype(int) - - -class Metric: - def __init__(self) -> None: - self.p = [] # (nc, ) - self.r = [] # (nc, ) - self.f1 = [] # (nc, ) - self.all_ap = [] # (nc, 10) - self.ap_class_index = [] # (nc, ) - self.nc = 0 - - @property - def ap50(self): - """AP@0.5 of all classes. - Return: - (nc, ) or []. - """ - return self.all_ap[:, 0] if len(self.all_ap) else [] - - @property - def ap(self): - """AP@0.5:0.95 - Return: - (nc, ) or []. - """ - return self.all_ap.mean(1) if len(self.all_ap) else [] - - @property - def mp(self): - """mean precision of all classes. - Return: - float. - """ - return self.p.mean() if len(self.p) else 0.0 - - @property - def mr(self): - """mean recall of all classes. - Return: - float. - """ - return self.r.mean() if len(self.r) else 0.0 - - @property - def map50(self): - """Mean AP@0.5 of all classes. - Return: - float. - """ - return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 - - @property - def map75(self): - """Mean AP@0.75 of all classes. - Return: - float. - """ - return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0 - - @property - def map(self): - """Mean AP@0.5:0.95 of all classes. - Return: - float. - """ - return self.all_ap.mean() if len(self.all_ap) else 0.0 - - def mean_results(self): - """Mean of results, return mp, mr, map50, map""" - return [self.mp, self.mr, self.map50, self.map] - - def class_result(self, i): - """class-aware result, return p[i], r[i], ap50[i], ap[i]""" - return self.p[i], self.r[i], self.ap50[i], self.ap[i] - - @property - def maps(self): - """mAP of each class""" - maps = np.zeros(self.nc) + self.map - for i, c in enumerate(self.ap_class_index): - maps[c] = self.ap[i] - return maps - - def fitness(self): - # Model fitness as a weighted combination of metrics - w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] - return (np.array(self.mean_results()) * w).sum() - - def update(self, results): - """ - Args: - results: tuple(p, r, ap, f1, ap_class) - """ - self.p, self.r, self.f1, self.all_ap, self.ap_class_index = results - - -class DetMetrics: - def __init__(self, save_dir=Path("."), plot=False, names=()) -> None: - self.save_dir = save_dir - self.plot = plot - self.names = names - self.box = Metric() - self.speed = { - "preprocess": 0.0, - "inference": 0.0, - "loss": 0.0, - "postprocess": 0.0, - } - self.probs = {} - self.tp, self.fp = [], [] - - def process(self, tp, conf, pred_cls, target_cls): - - if len(conf) > 0: - for cls_id in range(len(self.names)): - conf_with_cls = conf[np.where(pred_cls == cls_id)] - if len(conf_with_cls) > 0: - highest_prob = conf_with_cls.max() - self.probs[self.names[cls_id]] = [highest_prob] - - results = ap_per_class( - tp, - conf, - pred_cls, - target_cls, - plot=self.plot, - save_dir=self.save_dir, - names=self.names, - ) - self.tp, self.fp = results[:2] - - results = results[2:] - self.box.nc = len(self.names) - self.box.update(results) - - @property - def keys(self): - return [ - "metrics/precision(B)", - "metrics/recall(B)", - "metrics/mAP50(B)", - "metrics/mAP50-95(B)", - ] - - def mean_results(self): - return self.box.mean_results() - - def class_result(self, i): - return self.box.class_result(i) - - @property - def maps(self): - return self.box.maps - - @property - def fitness(self): - return self.box.fitness() - - @property - def ap_class_index(self): - return self.box.ap_class_index - - @property - def results_dict(self): - return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) - - def custom_result(self, instances_info =None, iou_match_dict: dict = None, prob_of_classes: dict = None): - """_summary_ - - Args: - instances_info (np.ndarray, optional): _description_. Defaults to None. - iou_list (list, optional): _description_. Defaults to None. - iou_match_dict (dict, optional): _description_. Defaults to None. - prob_of_classes (dict, optional): _description_. Defaults to None. - - Returns: - _type_: _description_ - """ - - instances_info = instances_info.tolist() if isinstance(instances_info, np.ndarray) else instances_info - total_instances = sum(instances_info) - total_instances = total_instances.item() if not isinstance(total_instances, int) else total_instances - - custom_res = {} - - for ci, c in self.names.items(): - iou_match = iou_match_dict[c] - average_iou = np.mean(iou_match).item() if len(iou_match) > 0 else 0 - _tp = self.tp[ci] if len(self.tp) == len(self.names) else 0 - _fp = self.fp[ci] if len(self.fp) == len(self.names) else 0 - _miss = instances_info[ci] - _tp - custom_res.update( - { - self.names[ci]: { - "actual": instances_info[ci], - "correct": int(_tp), - "missed_detection": int(_miss), - "false_detection": int(_fp), - "average_iou": round(average_iou, 4), - "average_dice": 0 - } - } - ) - - custom_res.update({ - "probability": prob_of_classes, - "average": { - "actual": int(total_instances // len(self.names)), - "correct": int(self.tp.sum() // len(self.names)) if len(self.tp) > 0 else 0, - "missed_detection": 0, - "false_detection": int(self.fp.sum() // len(self.names)) if len(self.fp) > 0 else 0, - "average_iou": round(np.mean([custom_res[cls_name]['average_iou'] for cls_name in self.names.values()]).item(), 4), - "average_dice": 0, - }, - "total": { - "actual": total_instances, - "correct": int(self.tp.sum()) if len(self.tp) > 0 else 0 , - "missed_detection": 0, - "false_detection": int(self.fp.sum()) if len(self.fp) > 0 else 0, - }, - }) - custom_res["total"]["missed_detection"] = ( - custom_res["total"]["actual"] - custom_res["total"]["correct"] - ) - custom_res["average"]["missed_detection"] = custom_res["total"][ - "missed_detection" - ] // len(self.names) - - - - return custom_res - - -class SegmentMetrics: - def __init__(self, save_dir=Path("."), plot=False, names=()) -> None: - self.save_dir = save_dir - self.plot = plot - self.names = names - self.box = Metric() - self.seg = Metric() - self.speed = { - "preprocess": 0.0, - "inference": 0.0, - "loss": 0.0, - "postprocess": 0.0, - } - self.tp = [] - self.fp = [] - self.probs = {} - - - def process(self, tp_m, tp_b, conf, pred_cls, target_cls): - - if len(conf) > 0: - for cls_id in range(len(self.names)): - conf_with_cls = conf[np.where(pred_cls == cls_id)] - if len(conf_with_cls) > 0: - highest_prob = conf_with_cls.max() - self.probs[self.names[cls_id]] = [highest_prob] - - - res_mask = ap_per_class( - tp_m, - conf, - pred_cls, - target_cls, - plot=self.plot, - save_dir=self.save_dir, - names=self.names, - prefix="Mask", - ) - - tp, fp, results_mask = res_mask[0], res_mask[1], res_mask[2:] - self.seg.nc = len(self.names) - self.seg.update(results_mask) - self.tp = tp - self.fp = fp - # print(self.tp, self.fp) - - results_box = ap_per_class( - tp_b, - conf, - pred_cls, - target_cls, - plot=self.plot, - save_dir=self.save_dir, - names=self.names, - prefix="Box", - )[2:] - self.box.nc = len(self.names) - self.box.update(results_box) - - @property - def keys(self): - return [ - "metrics/precision(B)", - "metrics/recall(B)", - "metrics/mAP50(B)", - "metrics/mAP50-95(B)", - "metrics/precision(M)", - "metrics/recall(M)", - "metrics/mAP50(M)", - "metrics/mAP50-95(M)", - ] - - def mean_results(self): - return self.box.mean_results() + self.seg.mean_results() - - def class_result(self, i): - return self.box.class_result(i) + self.seg.class_result(i) - - def custom_result(self, instances_info =None, iou_match_dict: dict = None, prob_of_classes: dict = None): - """_summary_ - - Args: - instances_info (np.ndarray, optional): _description_. Defaults to None. - iou_list (list, optional): _description_. Defaults to None. - iou_match_dict (dict, optional): _description_. Defaults to None. - prob_of_classes (dict, optional): _description_. Defaults to None. - - Returns: - _type_: _description_ - """ - - instances_info = instances_info.tolist() if isinstance(instances_info, np.ndarray) else instances_info - total_instances = sum(instances_info) - total_instances = total_instances.item() if not isinstance(total_instances, int) else total_instances - - custom_res = {} - - for ci, c in self.names.items(): - iou_match = iou_match_dict[c] - average_iou = np.mean(iou_match).item() if len(iou_match) > 0 else 0 - _tp = self.tp[ci] if len(self.tp) == len(self.names) else 0 - _fp = self.fp[ci] if len(self.fp) == len(self.names) else 0 - _miss = instances_info[ci] - _tp - custom_res.update( - { - self.names[ci]: { - "actual": instances_info[ci], - "correct": int(_tp), - "missed_detection": int(_miss), - "false_detection": int(_fp), - "average_iou": round(average_iou, 4), - "average_dice": 0 - } - } - ) - - custom_res.update({ - "probability": prob_of_classes, - "average": { - "actual": int(total_instances // len(self.names)), - "correct": int(self.tp.sum() // len(self.names)) if len(self.tp) > 0 else 0, - "missed_detection": 0, - "false_detection": int(self.fp.sum() // len(self.names)) if len(self.fp) > 0 else 0, - "average_iou": round(np.mean([custom_res[cls_name]['average_iou'] for cls_name in self.names.values()]).item(), 4), - "average_dice": 0, - }, - "total": { - "actual": total_instances, - "correct": int(self.tp.sum()) if len(self.tp) > 0 else 0 , - "missed_detection": 0, - "false_detection": int(self.fp.sum()) if len(self.fp) > 0 else 0, - }, - }) - custom_res["total"]["missed_detection"] = ( - custom_res["total"]["actual"] - custom_res["total"]["correct"] - ) - custom_res["average"]["missed_detection"] = custom_res["total"][ - "missed_detection" - ] // len(self.names) - - - return custom_res - - @property - def maps(self): - return self.box.maps + self.seg.maps - - @property - def fitness(self): - return self.seg.fitness() + self.box.fitness() - - @property - def ap_class_index(self): - # boxes and masks have the same ap_class_index - return self.box.ap_class_index - - @property - def results_dict(self): - return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) - - -class ClassifyMetrics: - def __init__(self) -> None: - self.top1 = 0 - self.top5 = 0 - - self.speed = { - "preprocess": 0.0, - "inference": 0.0, - "loss": 0.0, - "postprocess": 0.0, - } - - - def process(self, targets, pred): - # target classes and predicted classes - pred, targets = torch.cat(pred), torch.cat(targets) - correct = (targets[:, None] == pred).float() - acc = torch.stack( - (correct[:, 0], correct.max(1).values), dim=1 - ) # (top1, top5) accuracy - self.top1, self.top5 = acc.mean(0).tolist() - - def custom_result(self ,targets, pred): - # from IPython import embed; embed() - metric_per_class = {} - pred, targets = torch.cat(pred), torch.cat(targets) - pred = pred.cpu().numpy()[:, 0] - targets = targets.cpu().numpy() - conf_mat = confusion_matrix(targets, pred) - - """{ - "actual": - "correct": - "recall": - "precision": - } - """ - k = conf_mat.shape[0] - for cls_id in range(k): - tp = conf_mat[cls_id, cls_id] - fp = np.sum(conf_mat[cls_id, :]) - tp - fn = np.sum(conf_mat[:, cls_id]) - tp - tn = np.sum(conf_mat) - tp - fp - fn - - res_per_class = { - "actual": tp + fp, - "correct": tp, - "miss_detection": 0, - "false_detection": fp, - "recall": 0 if tp + fn == 0 else tp / (tp+fn), - "precision": tp / (tp + fp) - } - - - metric_per_class[cls_id] = res_per_class - - return metric_per_class - - - @property - def fitness(self): - return self.top5 - - @property - def results_dict(self): - return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness])) - - @property - def keys(self): - return ["metrics/accuracy_top1", "metrics/accuracy_top5"] - - -if __name__ == "__main__": - det_metric = DetMetrics() - diff --git a/cope2n-ai-fi/modules/sdsvkie/eval_with_api.py b/cope2n-ai-fi/modules/sdsvkie/eval_with_api.py deleted file mode 100644 index fe4f050..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/eval_with_api.py +++ /dev/null @@ -1,275 +0,0 @@ -import json -import logging -from pathlib import Path -from typing import Any, Dict, List, Optional, Union - -import requests -import tqdm -from sdsvkie.utils.eval_kie import eval_kie -from sdsvkie.utils.io_file import read_json, write_json - -logging.basicConfig( - level=logging.INFO, - # format="" - -) -logger = logging.getLogger() - - -HEADERS = { - 'accept': 'application/json', - 'Authorization': 'Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJleHAiOjE2OTA2ODk4ODcsInVzZXJuYW1lIjoiYWRtaW4ifQ.Oybpc9tBsN35vCn3jzekkABDQKJT6yO1aBBJ4rMNln0' -} - -URL = "http://107.120.133.27:8082/predict/image" - - -def run( - data_dir: str, - url: str, - gt_path: str, - field_pred_file: str, - samples: Union[int, None] = None, - -): - - files = get_files(data_dir, recursive=False, limit=samples) - preds = predict(url, files) - - ## process for table - # table_eval_result = {} - # table_preds = get_table_preds(preds) - # table_eval_result = - - # process for seller, buyer, ... - field_eval_result = {} - # field_preds = get_field_preds_from_api(api_preds=preds) - field_preds = get_field_preds_from_file(pred_file=field_pred_file) - classes = get_classes(preds=field_preds) - if len(classes) == 0: - raise Exception("Can not get the classes list") - field_eval_result = eval( - gt=gt_path, - pred=field_preds, - classes=classes, - classes_ignore=['other', 'table'] - ) - print(field_eval_result) - ## combine result - combine_result = {} - # combine_result = combine_result(table_res=table_eval_result, field_res=field_eval_result) - - - print_result( - data_path=data_dir, - num_samples=len(list(field_preds.keys())), - target_level=0.05, - result=1.0, # edit here - ) - return combine_result - - - -def print_result( - data_path: str, - num_samples:int, - target_level: float, - result: float, - metric: str = "NLD", - avg_time: float = 1.6363 - ): - - print(f"Path of validation dataset: {data_path}\n" - + f"Number of validation dataset: {num_samples}\n" - + f"Evaluation metric: {metric}\n" - + f"Target level: {target_level}\n" - + f"Archieved level: {result}\n" - + f"Average time: {avg_time}\n" - + f"Verification result: {'PASS' if result > target_level else 'FAILED'}" - ) - - -def get_field_preds_from_api(api_preds: str) -> dict: - field_preds = get_fields_preds(api_preds) - field_preds = combine_to_single_file(field_preds) - return field_preds - -def get_field_preds_from_file(pred_file: str) -> dict: - """ - Get predictions from json file - """ - field_preds = read_json(pred_file) - return field_preds - - - -def get_fields_preds(preds: List[Dict]): - preds = [ - {item['file_path']: format_output_api(item['response_dict'])} - for item in preds - ] - return preds - -def combine_result(table_res: Dict, field_res: Dict): - return {} - -def _str2dict(text: str) -> Dict: - try: - data = json.loads(text) - except Exception as err: - logger.error(f"{err} - data: {text}") - data = {} - return data - - -def predict_one_file(url: str, file: Union[str, Path]) -> Dict: - """ - Output format: - { - file_path: path of file - response_dict: - } - """ - if isinstance(file, str): - file = Path(file) - - payload = {} - filename = file.name - files = [ - ( - 'file', - ( - filename, - open(str(file), 'rb'), - 'application/pdf' - ) - ) - ] - # logger.info(f"Files: {file}") - response = requests.request( - "POST", url, headers=HEADERS, data=payload, files=files) - - response_dict = _str2dict(response.text) - - return { - "file_path": str(file), - "pred_data": response_dict - } - - -def predict(url: str, files: List[Union[str, Path]]) -> List[Dict]: - """ - List of {'file_path', 'response_dict'} - """ - - preds = [] - for idx, file in tqdm.tqdm(enumerate(files)): - try: - pred = predict_one_file(url, file) - preds.append(pred) - except: - logger.info(f"Error at file: {file}") - return preds - - -def get_files(data_dir: str, recursive: bool = False, limit: Union[int, None] = None) -> List[Union[Path, str]]: - if recursive: - files = Path(data_dir).rglob("*") - else: - files = Path(data_dir).glob("*") - - if limit: - files = list(files)[:limit] - return files - - -def _stem_filename(filename: str) -> str: - """ - Stem a file path: x/y.txt -> y - """ - return Path(filename).stem - - -def format_output_api(output_api: Dict, skip_fields=['table']) -> Dict: - if "pages" not in output_api: - return {} - pages = output_api['pages'] - - result = {} - for page in pages: - fields = page['fields'] - for field_item in fields: - field_label, field_value = field_item['label'], field_item['value'] - if field_label in result or field_label in skip_fields: - continue - result[field_label] = field_value - return result - - -def combine_to_single_file(preds: List[Dict]) -> None: - if len(preds) == 0: - return {} - combined_data = { - _stem_filename(item["filename"]): item["pred_data"] - for item in preds - } - return combined_data - - -def eval( - gt: Union[str, Dict], - pred: Union[str, Dict], - classes: List[str], - classes_ignore: List[str] = [] -) -> Dict: - eval_res = eval_kie( - gt_e2e_path=gt, - pred_e2e_path=pred, - kie_labels=classes, - skip_labels=classes_ignore - ) - return eval_res - - -def get_classes(preds: Dict) -> List[str]: - classes = [] - for k, v in preds.items(): - if v: - classes = list(v.keys()) - break - return classes - - -def test(): - import requests - - url = "http://107.120.133.27:8082/predict/image" - - payload = {} - files = [ - ('file', ('(1 of 19)_HOADON_1C23TYY_50.pdf', open( - '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/final/all_in/(1 of 19)_HOADON_1C23TYY_50.pdf', 'rb'), 'application/pdf')) - ] - headers = { - 'accept': 'application/json', - 'Authorization': 'Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJleHAiOjE2OTA2ODk4ODcsInVzZXJuYW1lIjoiYWRtaW4ifQ.Oybpc9tBsN35vCn3jzekkABDQKJT6yO1aBBJ4rMNln0' - } - - response = requests.request( - "POST", url, headers=headers, data=payload, files=files) - - print(response.text) - # print(json.loa ds(response.text)) - - -if __name__ == "__main__": - limit = 5 - run( - data_dir="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/final/all_in", - url=URL, - gt_path="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/final/all_in.json", - field_pred_file="/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/invoice_all_in_final_e2e_21072023_5.json", - samples=limit - ) - - # test() diff --git a/cope2n-ai-fi/modules/sdsvkie/index.html b/cope2n-ai-fi/modules/sdsvkie/index.html deleted file mode 100644 index af4f8eb..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/index.html +++ /dev/null @@ -1,15 +0,0 @@ -Google
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\ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/data.ipynb b/cope2n-ai-fi/modules/sdsvkie/notebooks/data.ipynb deleted file mode 100644 index 863b51b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/notebooks/data.ipynb +++ /dev/null @@ -1,2817 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image, ImageDraw, ImageFont\n", - "import os \n", - "import random\n", - "import cv2\n", - "import numpy as np \n", - "from pathlib import Path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "def visualize_kie(\n", - " img,\n", - " boxes, \n", - " pred_labels, \n", - " skip_classes=['other'], \n", - " image_name=\"out_kie.jpg\", \n", - " outdir=\"workdirs/visualize\"\n", - " ):\n", - " \"\"\"Visualize kie output \n", - "\n", - " Args:\n", - " image (PIL.Image): _description_\n", - " boxes (list[xyxy]): list of xyxy boxes\n", - " kie_preds (list[str]): _description_\n", - " skip_classes (list[str], optional): list of skip labels. Defaults to ['other'].\n", - " image_path (str, optional): _description_. Defaults to None.\n", - " outdir (_type_, optional): _description_. Defaults to None.\n", - " \"\"\"\n", - " if not os.path.exists(outdir):\n", - " os.makedirs(outdir, exist_ok=True)\n", - "\n", - " image = img.copy()\n", - " if isinstance(image, np.ndarray):\n", - " image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", - " \n", - " set_labels = sorted(list(set(pred_labels)))\n", - " colors = [\n", - " (\n", - " random.randint(0, 255),\n", - " random.randint(0, 255),\n", - " random.randint(0, 255),\n", - " ) for _ in range(len(set_labels))\n", - " ]\n", - "\n", - " text_checkin = [0] * len(set_labels)\n", - "\n", - " draw = ImageDraw.Draw(image)\n", - " font = ImageFont.load_default()\n", - " for i, pred_label in enumerate(pred_labels):\n", - " if pred_label in skip_classes:\n", - " continue\n", - " pred_label_idx = set_labels.index(pred_label)\n", - " box = boxes[i]\n", - " color = colors[pred_label_idx]\n", - " draw.rectangle(box,fill=None, outline=color)\n", - "\n", - " # skip visualize text label for other boxes \n", - " if text_checkin[set_labels.index(pred_label)] == 0:\n", - " draw.text((int(box[0])+10, int(box[1])-10),fill=color, text=pred_label, font = font)\n", - " text_checkin[pred_label_idx] = 1\n", - " image.save(os.path.join(outdir, image_name))\n", - "\n", - "def visualize_ocr(\n", - " img,\n", - " boxes, \n", - " texts,\n", - " image_name=\"out_ocr.jpg\", \n", - " outdir=\"workdirs/visualize\"\n", - " ):\n", - " \"\"\"Visualize kie output \n", - "\n", - " Args:\n", - " image (PIL.Image): _description_\n", - " boxes (list[xyxy]): list of xyxy boxes\n", - " kie_preds (list[str]): _description_\n", - " skip_classes (list[str], optional): list of skip labels. Defaults to ['other'].\n", - " image_path (str, optional): _description_. Defaults to None.\n", - " outdir (_type_, optional): _description_. Defaults to None.\n", - " \"\"\"\n", - " if not os.path.exists(outdir):\n", - " os.makedirs(outdir, exist_ok=True)\n", - "\n", - " image = img.copy()\n", - " if isinstance(image, np.ndarray):\n", - " image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", - " color = (\n", - " random.randint(0, 255),\n", - " random.randint(0, 255),\n", - " random.randint(0, 255),\n", - " )\n", - "\n", - " \n", - "\n", - " draw = ImageDraw.Draw(image)\n", - " font = ImageFont.load_default()\n", - " for i, text in enumerate(texts):\n", - " box = boxes[i]\n", - " draw.rectangle(box,fill=None, outline=color) \n", - " # draw.text((int(box[0])+10, int(box[1])-10),fill=color, text=text, font = font)\n", - " image.save(os.path.join(outdir, image_name))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "def read_txt(txt):\n", - " with open(txt, 'r', encoding='utf8') as f:\n", - " data = [line.strip() for line in f]\n", - " return data \n", - "\n", - "def get_data_from_txt_dir(txt_dir):\n", - " txt_paths = glob.glob(txt_dir + \"/*.txt\")\n", - " data = {}\n", - " for txt_path in txt_paths:\n", - " txt_name = os.path.basename(txt_path)\n", - " \n", - " \n", - " txt_data = read_txt(txt_path)\n", - " format_data = []\n", - " for line in txt_data:\n", - " items = line.split(\"\\t\")\n", - " assert len(items) == 6, \"error get len = {} - {} at {}\".format(len(items), items, txt_path)\n", - " box = [int(float(x)) for x in items[:4]]\n", - " text = items[4]\n", - " kie_label = items[5]\n", - " format_data.append(\n", - " {\n", - " 'box': box,\n", - " 'text': text, \n", - " 'label': kie_label\n", - " }\n", - " )\n", - " data[txt_name] = format_data\n", - "\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import glob \n", - "import os \n", - "import cv2\n", - "\n", - "img_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/invoice_viettinbank_poc\"\n", - "txt_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/invoice_viettinbank_poc\"\n", - "vis_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/notebooks/visualize/invoice_viettinbank_poc\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "56\n" - ] - } - ], - "source": [ - "\n", - "from pathlib import Path\n", - "img_paths = [p for p in glob.glob(f\"{img_dir}/*\") if \".txt\" not in p]\n", - "print(len(img_paths))\n", - "all_data = get_data_from_txt_dir(txt_dir)\n", - "\n", - "\n", - "for img_path in img_paths:\n", - " img = cv2.imread(img_path)\n", - " if img is None:\n", - " print(\"Image is none: \", img_path)\n", - " txt_file = Path(img_path).with_suffix(\".txt\").name\n", - " img_data = all_data[str(Path(img_path).with_suffix(\".txt\").name)]\n", - " # print(img_data)\n", - " boxes = [item['box'] for item in img_data]\n", - " texts = [item['text'] for item in img_data]\n", - " labels = [item['label'] for item in img_data]\n", - " \n", - " \n", - " visualize_kie(\n", - " img,\n", - " boxes, \n", - " labels, \n", - " skip_classes=['Others', 'other'], \n", - " image_name=os.path.basename(img_path), \n", - " outdir=vis_dir\n", - " )\n", - " # break" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def _check_iou(box1, box2, threshold=0.9):\n", - " \"\"\"_summary_\n", - "\n", - " Args:\n", - " box1 (_type_): word box\n", - " box2 (_type_): line box \n", - " threshold (float, optional): _description_. Defaults to 0.9.\n", - "\n", - " Returns:\n", - " _type_: _description_\n", - " \"\"\"\n", - " area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])\n", - " area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])\n", - " xmin_intersect = max(box1[0], box2[0])\n", - " ymin_intersect = max(box1[1], box2[1])\n", - " xmax_intersect = min(box1[2], box2[2])\n", - " ymax_intersect = min(box1[3], box2[3])\n", - "\n", - "\n", - " if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect:\n", - " area_intersect = 0\n", - " else:\n", - " area_intersect = (xmax_intersect - xmin_intersect) * (\n", - " ymax_intersect - ymin_intersect\n", - " )\n", - " # union = area1 + area2 - area_intersect\n", - " iou = area_intersect / area1\n", - " return iou " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "_check_iou(box1=(465, 901, 664, 940), box2=(465, 901, 735, 940))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mRunning cells with 'py38_hoanglv' requires the ipykernel package.\n", - "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n", - "\u001b[1;31mCommand: 'conda install -n py38_hoanglv ipykernel --update-deps --force-reinstall'" - ] - } - ], - "source": [ - "from sdsvkie.utils.io_file import read_json\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "pred_data = read_json(\"/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_5/test_mcocr_not_label_ep21.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['minimart anan', '', 'vincommerce', 'nha sach gdtc cam pha', 'cua hang nam oanh', 'pho mo', 'saigon coop coop food hn the kpark', 'sctc co tho 104 tran phu cam pha', 'the coffee house', 'payoo', 'sieu thi bach hoa tong hop', 'sctc co thu 104 tran phu cam pha', 'thuc coffee', 'i vincommerce', 'f vincommerce', 'phuc anh minimart', 'vietcombank', 'guitar cafe', 'sieu thi minh loan', 'nha hang aozora sushi', 'thanh xuan', 'legend welcome to trung nguyen legend', 'coffee milano coffee vietnam', 'tiem da thanh xuan', 'gia re moi ngay', 'tiem da', 'oanh cua hang nam', 'phuc anh minimart noi ha lam', 'payoo uimmart', 'phuc coffee tea phuc long', 'anan minimart', 'c h a m', 'minimart', 'saigon coop coop food hn van khe', 'hniqlo vincom', 'ma co so opmart thue xa lo ha noi', 'ch thoi trang adidas', 'mart coopmart xa lo ha noi', 'vm hni cc nang huong', 'pharmacity', 'solanin l cong 1 phan', 'kai tea v kai tea', 'minimx', 'lamason10k', 'tien do thanh xuan', 'gia lam', 'sctc co thu 104 tran phu so cam pha', 'sieu thi my pham thong he shop', 'bidv', 'cafe cj building', 'go cool mart', 'saigon coop food hn', 'coopmart hau giang', 'feel coffee', 'coop saigon kpark food hn the', 'nha hang ut giang nguyen chanh', 'lahatafe just good coffee', 'satra group satrafoods nguyen van qua 2', 'tien no thanh xuan', '0 trung tam thuong mai aeon ha dong', 'vm thang long', 'cong ty co phan bot phap van cau gia', 'bieu thi bach hoa tong hop', 'nghcanh', 'big c long bien', 'books n coffee', 'coopmart', 'coop hoc quoc', 'vitimex quang ninh vincom cam pha', 'co opmart nguyen dinh chiel', 'cuitar cafe', 'vinmart', 'bieu thi thu cung pet yeu', 'pha nha sach gdtc cam pha', 'sieu thi minh loan phan', 'ly', 'flomesweethome ban hang', 'big c an phu', 'tiem tra', 'nha sachgdte cam pha', 'coopmart nguyen dinh chieu', 'biku thi bach hoa tong hoa', 'cp c o 0 p co opmart nguyen dinh chieu', 'nha nai 0110 am pha do 21d', 'ngoc com ga bao', 'jet mart', 'sushi nha hang aozora', 'bidv ', 'coffee milano coffee vietnam ', 'milano coffee milano coffee vietnam 4', 'coop smile', 'coor p mari', 'i thuc coffee', 'farnilymart', 'tran phu cam sctc co thu 104 pha', 'milano coffee milano coffee vietnam de', 'mart coopmart hau giang', 'satra group satrafoods 2b duong binh loi', 'ga dong quan', 'minimart anal', 'cty ld tnhh kfc viet nam', 'minimart phuc anh ha', 'velutino cong ty cp dau tu velutino viet nam', 'bun ho hu nn o xuan', 'biep thi bach hoa tono hoi', 'nha thuoc benh vien da khoa thanh pho vinh', 'phu noi', 'cua hang nam oanh tp ha', 'uniqlo vincom pham ngoc thach', 'vcb loi', 'saigon coop coop food hn the k park', 'hau giang', 'saigon coop food hn the kpark coop', 'welcome to trung nguyen legend', 'big c di an cty tnhh eb binh duong', 'milano coffee milang coffee vietnam ', 'bach hoa xanh', 'familymart', 'nha son gdty cam pha', 'dim sum corner trung hoa', 'minimart phuc anh ha noi lam', 'saigon coop pa coop food hn the kpark', 'cua hang tu chon anh giang', 'oma b a k e 4 e', 'hai ly', 'thghu tuan', 'covb food hn the', 'packer brinition 12341', 'viet tttm cho sui', 'tttm van hosieu thi seika mart', 'cho sui phu thi gia lam', 'circle k vietnam', 'cty cp sach tbth quang ninh', 'vinh', 'parts ga teaus 19', 'lahatafe', 'charles', 'coop food hn the', 'the coffee house thuy', 'saigon coop coop food hi the kpark', 'mooserroo vietham the moose roo smokehouse', 'tien da thanh xuan', 'saigo coop food', 'coopsmic', 'thu thanh xuan', 'phuong oc hai phong', 'kai tea kai tea', 'talala', 'ittm', 'vincom skylake', 'vision vincommerce', 'marumart']\n" - ] - } - ], - "source": [ - "store_names = [normalize(item['Store_name_value']) for k, item in pred_data.items()]\n", - "# store_names = list(set(store_names))\n", - "from collections import Counter\n", - "my_counter = Counter(store_names)\n", - "list_tuples = my_counter.most_common()\n", - "stores = [x[0] for x in list_tuples]\n", - "print(stores)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('minimart anan', 318),\n", - " ('', 296),\n", - " ('vincommerce', 177),\n", - " ('nha sach gdtc cam pha', 101),\n", - " ('cua hang nam oanh', 64),\n", - " ('pho mo', 41),\n", - " ('saigon coop coop food hn the kpark', 33),\n", - " ('sctc co tho 104 tran phu cam pha', 23),\n", - " ('the coffee house', 23),\n", - " ('payoo', 20),\n", - " ('sieu thi bach hoa tong hop', 18),\n", - " ('sctc co thu 104 tran phu cam pha', 16),\n", - " ('thuc coffee', 13),\n", - " ('i vincommerce', 10),\n", - " ('f vincommerce', 9),\n", - " ('phuc anh minimart', 9),\n", - " ('vietcombank', 9),\n", - " ('guitar cafe', 8),\n", - " ('sieu thi minh loan', 5),\n", - " ('nha hang aozora sushi', 5),\n", - " ('thanh xuan', 3),\n", - " ('legend welcome to trung nguyen legend', 3),\n", - " ('coffee milano coffee vietnam', 2),\n", - " ('tiem da thanh xuan', 2),\n", - " ('gia re moi ngay', 2),\n", - " ('tiem da', 2),\n", - " ('oanh cua hang nam', 2),\n", - " ('phuc anh minimart noi ha lam', 2),\n", - " ('payoo uimmart', 2),\n", - " ('phuc coffee tea phuc long', 2),\n", - " ('anan minimart', 2),\n", - " ('c h a m', 2),\n", - " ('minimart', 2),\n", - " ('saigon coop coop food hn van khe', 2),\n", - " ('hniqlo vincom', 1),\n", - " ('ma co so opmart thue xa lo ha noi', 1),\n", - " ('ch thoi trang adidas', 1),\n", - " ('mart coopmart xa lo ha noi', 1),\n", - " ('vm hni cc nang huong', 1),\n", - " ('pharmacity', 1),\n", - " ('solanin l cong 1 phan', 1),\n", - " ('kai tea v kai tea', 1),\n", - " ('minimx', 1),\n", - " ('lamason10k', 1),\n", - " ('tien do thanh xuan', 1),\n", - " ('gia lam', 1),\n", - " ('sctc co thu 104 tran phu so cam pha', 1),\n", - " ('sieu thi my pham thong he shop', 1),\n", - " ('bidv', 1),\n", - " ('cafe cj building', 1),\n", - " ('go cool mart', 1),\n", - " ('saigon coop food hn', 1),\n", - " ('coopmart hau giang', 1),\n", - " ('feel coffee', 1),\n", - " ('coop saigon kpark food hn the', 1),\n", - " ('nha hang ut giang nguyen chanh', 1),\n", - " ('lahatafe just good coffee', 1),\n", - " ('satra group satrafoods nguyen van qua 2', 1),\n", - " ('tien no thanh xuan', 1),\n", - " ('0 trung tam thuong mai aeon ha dong', 1),\n", - " ('vm thang long', 1),\n", - " ('cong ty co phan bot phap van cau gia', 1),\n", - " ('bieu thi bach hoa tong hop', 1),\n", - " ('nghcanh', 1),\n", - " ('big c long bien', 1),\n", - " ('books n coffee', 1),\n", - " ('coopmart', 1),\n", - " ('coop hoc quoc', 1),\n", - " ('vitimex quang ninh vincom cam pha', 1),\n", - " ('co opmart nguyen dinh chiel', 1),\n", - " ('cuitar cafe', 1),\n", - " ('vinmart', 1),\n", - " ('bieu thi thu cung pet yeu', 1),\n", - " ('pha nha sach gdtc cam pha', 1),\n", - " ('sieu thi minh loan phan', 1),\n", - " ('ly', 1),\n", - " ('flomesweethome ban hang', 1),\n", - " ('big c an phu', 1),\n", - " ('tiem tra', 1),\n", - " ('nha sachgdte cam pha', 1),\n", - " ('coopmart nguyen dinh chieu', 1),\n", - " ('biku thi bach hoa tong hoa', 1),\n", - " ('cp c o 0 p co opmart nguyen dinh chieu', 1),\n", - " ('nha nai 0110 am pha do 21d', 1),\n", - " ('ngoc com ga bao', 1),\n", - " ('jet mart', 1),\n", - " ('sushi nha hang aozora', 1),\n", - " ('bidv ', 1),\n", - " ('coffee milano coffee vietnam ', 1),\n", - " ('milano coffee milano coffee vietnam 4', 1),\n", - " ('coop smile', 1),\n", - " ('coor p mari', 1),\n", - " ('i thuc coffee', 1),\n", - " ('farnilymart', 1),\n", - " ('tran phu cam sctc co thu 104 pha', 1),\n", - " ('milano coffee milano coffee vietnam de', 1),\n", - " ('mart coopmart hau giang', 1),\n", - " ('satra group satrafoods 2b duong binh loi', 1),\n", - " ('ga dong quan', 1),\n", - " ('minimart anal', 1),\n", - " ('cty ld tnhh kfc viet nam', 1),\n", - " ('minimart phuc anh ha', 1),\n", - " ('velutino cong ty cp dau tu velutino viet nam', 1),\n", - " ('bun ho hu nn o xuan', 1),\n", - " ('biep thi bach hoa tono hoi', 1),\n", - " ('nha thuoc benh vien da khoa thanh pho vinh', 1),\n", - " ('phu noi', 1),\n", - " ('cua hang nam oanh tp ha', 1),\n", - " ('uniqlo vincom pham ngoc thach', 1),\n", - " ('vcb loi', 1),\n", - " ('saigon coop coop food hn the k park', 1),\n", - " ('hau giang', 1),\n", - " ('saigon coop food hn the kpark coop', 1),\n", - " ('welcome to trung nguyen legend', 1),\n", - " ('big c di an cty tnhh eb binh duong', 1),\n", - " ('milano coffee milang coffee vietnam ', 1),\n", - " ('bach hoa xanh', 1),\n", - " ('familymart', 1),\n", - " ('nha son gdty cam pha', 1),\n", - " ('dim sum corner trung hoa', 1),\n", - " ('minimart phuc anh ha noi lam', 1),\n", - " ('saigon coop pa coop food hn the kpark', 1),\n", - " ('cua hang tu chon anh giang', 1),\n", - " ('oma b a k e 4 e', 1),\n", - " ('hai ly', 1),\n", - " ('thghu tuan', 1),\n", - " ('covb food hn the', 1),\n", - " ('packer brinition 12341', 1),\n", - " ('viet tttm cho sui', 1),\n", - " ('tttm van hosieu thi seika mart', 1),\n", - " ('cho sui phu thi gia lam', 1),\n", - " ('circle k vietnam', 1),\n", - " ('cty cp sach tbth quang ninh', 1),\n", - " ('vinh', 1),\n", - " ('parts ga teaus 19', 1),\n", - " ('lahatafe', 1),\n", - " ('charles', 1),\n", - " ('coop food hn the', 1),\n", - " ('the coffee house thuy', 1),\n", - " ('saigon coop coop food hi the kpark', 1),\n", - " ('mooserroo vietham the moose roo smokehouse', 1),\n", - " ('tien da thanh xuan', 1),\n", - " ('saigo coop food', 1),\n", - " ('coopsmic', 1),\n", - " ('thu thanh xuan', 1),\n", - " ('phuong oc hai phong', 1),\n", - " ('kai tea kai tea', 1),\n", - " ('talala', 1),\n", - " ('ittm', 1),\n", - " ('vincom skylake', 1),\n", - " ('vision vincommerce', 1),\n", - " ('marumart', 1)]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_counter.most_common()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "152" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(stores)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "img_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_5/test_mcocr_not_label_ep21\"\n", - "\n", - "store_names = [\n", - " 'minimart anan',\n", - " 'vincommerce', \n", - " 'nha sach gdtc cam pha',\n", - " 'cua hang nam oanh', \n", - " 'pho mo', \n", - " 'saigon coop coop food hn the kpark', \n", - " 'sctc co tho 104 tran phu cam pha', \n", - " 'the coffee house', \n", - " 'payoo', \n", - " 'sieu thi bach hoa tong hop', \n", - " 'sctc co thu 104 tran phu cam pha', \n", - " 'thuc coffee', \n", - " 'vincommerce', \n", - " 'phuc anh minimart', \n", - " 'vietcombank', \n", - " 'guitar cafe', \n", - " 'sieu thi minh loan'\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "other/have_title/HNIQLO_Vincom\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "the_coffee_house\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "cua_hang_nam_oanh\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "other/have_title/Ma_Co_SO_opMart_thue:_XÃ_LO_HA_NOI\n", - "the_coffee_house\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/have_title/CH_THỜI_TRANG_ADIDAS\n", - "the_coffee_house\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "payoo\n", - "vincommerce\n", - "minimart_anan\n", - "other/have_title/mart_Co.opMart_XÃ_LO_HA_NOI\n", - "vincommerce\n", - "other/non_title\n", - "the_coffee_house\n", - "sieu_thi_bach_hoa_tong_hop\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "sieu_thi_bach_hoa_tong_hop\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "the_coffee_house\n", - "other/have_title/COFFEE_Milano_Coffee_Vietnam\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "other/have_title/VM+_HNI_CC_Nàng_Hương\n", - "pho_mo\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/have_title/Tiêm_đã_Thanh_Xuân\n", - "other/have_title/GIÁ_RẺ_MOI_NGÀY\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "other/have_title/Nhà_Hàng_Aozora_Sushi\n", - "vincommerce\n", - "other/non_title\n", - "guitar_cafe\n", - "other/non_title\n", - "other/have_title/Pharmacity\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "vincommerce\n", - "other/have_title/Solanin_l_Công_1)_Phân\n", - "nha_sach_gdtc_cam_pha\n", - "pho_mo\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "phuc_anh_minimart\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "the_coffee_house\n", - "other/have_title/Tiêm_đã\n", - "pho_mo\n", - "sieu_thi_bach_hoa_tong_hop\n", - "minimart_anan\n", - "payoo\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "pho_mo\n", - "vincommerce\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/have_title/Nhà_Hàng_Aozora_Sushi\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/KAI_TEA_V_KAI_TEA\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/MINIMX\n", - "pho_mo\n", - "other/have_title/Thanh_Xuân\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/have_title/LAMASON10K\n", - "other/have_title/Tiền_đo_Thanh_Xuân\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "sieu_thi_bach_hoa_tong_hop\n", - "pho_mo\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "other/have_title/Gia_Lam\n", - "vincommerce\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "sieu_thi_bach_hoa_tong_hop\n", - "the_coffee_house\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "other/have_title/BIDV&\n", - "minimart_anan\n", - "the_coffee_house\n", - "other/non_title\n", - "minimart_anan\n", - "the_coffee_house\n", - "vietcombank\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "other/have_title/Cafe_CJ_Building\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "payoo\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "pho_mo\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/have_title/GO_CO.OL_mart\n", - "the_coffee_house\n", - "vincommerce\n", - "pho_mo\n", - "vincommerce\n", - "minimart_anan\n", - "pho_mo\n", - "other/non_title\n", - "the_coffee_house\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "the_coffee_house\n", - "minimart_anan\n", - "phuc_anh_minimart\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "other/have_title/Co.opMart_HAU_GIANG\n", - "other/non_title\n", - "phuc_anh_minimart\n", - "the_coffee_house\n", - "minimart_anan\n", - "payoo\n", - "sieu_thi_bach_hoa_tong_hop\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "pho_mo\n", - "vincommerce\n", - "thuc_coffee\n", - "pho_mo\n", - "other/non_title\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "phuc_anh_minimart\n", - "vincommerce\n", - "other/have_title/Payoo_UIMMART\n", - "vincommerce\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "other/have_title/Nhà_Hàng_Út_Giang_Nguyễn_Chánh\n", - "vincommerce\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "payoo\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "pho_mo\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "vincommerce\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "other/have_title/PHUC_Coffee_Tea_PHUC_LONG\n", - "the_coffee_house\n", - "vincommerce\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "phuc_anh_minimart\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "pho_mo\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "other/have_title/Tiêm_đã\n", - "minimart_anan\n", - "other/have_title/LahaTafe_JUST_GOOD_COFFEE\n", - "nha_sach_gdtc_cam_pha\n", - "vietcombank\n", - "minimart_anan\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "other/have_title/Legend_Welcome_to_Trung_Nguyen_Legend\n", - "other/non_title\n", - "other/have_title/C_H_A_M\n", - "other/have_title/Satra_Group_Satrafoods_Nguyễn_Văn_Quá_2\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "vietcombank\n", - "payoo\n", - "pho_mo\n", - "vietcombank\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/Tiền_No_Thanh_Xuân\n", - "other/have_title/0_TRUNG_TÂM_THƯƠNG_MẠI_AEON_-_HÀ_ĐÔNG\n", - "other/non_title\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/have_title/PHUC_Coffee_Tea_PHUC_LONG\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "other/have_title/VM_Thăng_Long\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/have_title/CÔNG_TY_cổ_PHẦN_BOT_PHÁP_VÂN_-_CẦU_GIA\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "the_coffee_house\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "minimart_anan\n", - "pho_mo\n", - "vincommerce\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "vincommerce\n", - "the_coffee_house\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "pho_mo\n", - "sieu_thi_bach_hoa_tong_hop\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/Nhà_Hàng_Aozora_Sushi\n", - "minimart_anan\n", - "the_coffee_house\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "other/have_title/Ng.H.Cảnh\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/have_title/BIG_C_LONG_BIEN\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "other/have_title/BOOKS_n_COFFEE\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "pho_mo\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "pho_mo\n", - "other/have_title/Co.opMart\n", - "vincommerce\n", - "vincommerce\n", - "other/have_title/co.op_HOC_QUOC\n", - "nha_sach_gdtc_cam_pha\n", - "the_coffee_house\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "other/have_title/Vitimex_Quảng_Ninh_VinCom_Cẩm_Phà\n", - "other/have_title/Co_.opMart_NGUYỄN_DINH_CHIEL\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "guitar_cafe\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/VinMart\n", - "guitar_cafe\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "the_coffee_house\n", - "other/non_title\n", - "vincommerce\n", - "vincommerce\n", - "other/have_title/Biêu_thị_thủ_cung_PET_YÊU\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "nha_sach_gdtc_cam_pha\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "the_coffee_house\n", - "minimart_anan\n", - "payoo\n", - "minimart_anan\n", - "pho_mo\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "vincommerce\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "the_coffee_house\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "other/have_title/Ly\n", - "vincommerce\n", - "other/have_title/FlomeSweetHome_-_Bán_hàng\n", - "other/have_title/BIG_C_AN_PHU\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/Tiêm_trả\n", - "nha_sach_gdtc_cam_pha\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "vincommerce\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/have_title/Tiếm_đã_Thanh_Xuân\n", - "other/have_title/Legend_Welcome_to_Trung_Nguyen_Legend\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/have_title/Legend_Welcome_to_Trung_Nguyen_Legend\n", - "nha_sach_gdtc_cam_pha\n", - "pho_mo\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/GIÁ_RE_MỔI_NGÀY\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "payoo\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "pho_mo\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "vietcombank\n", - "nha_sach_gdtc_cam_pha\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/Co.opMart_NGUYỄN_DINH_CHIEU\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "the_coffee_house\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "pho_mo\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/have_title/CP_C_O_0_P_Co_opMart_NGUYỄN_DINH_CHIEU\n", - "other/have_title/NHÃ_NAI_-_01).10_._AM_PHA_DO_21D\n", - "other/non_title\n", - "other/have_title/NGỌC_CƠM_GÀ_BẢO\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/JET_MART\n", - "other/non_title\n", - "payoo\n", - "pho_mo\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "other/non_title\n", - "the_coffee_house\n", - "nha_sach_gdtc_cam_pha\n", - "pho_mo\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "other/have_title/Sushi_Nhà_Hàng_Aozora\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "other/have_title/BIDV_,\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/COFFEE_Milano_Coffee_Vietnam_####\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "payoo\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "phuc_anh_minimart\n", - "other/non_title\n", - "other/have_title/MILANO_COFFEE_Milano_Coffee_Vietnam_4\n", - "other/have_title/co.op_smile\n", - "other/have_title/CO.Or_P_mari\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "vincommerce\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "payoo\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "the_coffee_house\n", - "vincommerce\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/have_title/FarnilyMart\n", - "other/have_title/TRẦN_PHÚ_-_CẨM_SCTC_CÔ_THỦ_104_PHẢ\n", - "phuc_anh_minimart\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "other/have_title/MILANO_COFFEE_Milano_Coffee_Vietnam_đẻ\n", - "other/have_title/mart_Co.opMart_HAU_GIANG\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "other/have_title/Satra_Group_Satrafoods_2B_Đường_Bình_Lợi\n", - "other/non_title\n", - "other/have_title/Ga_Dong_Quan\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "vincommerce\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/have_title/Cty_LD_TNHH_KFC_Viet_Nam\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "pho_mo\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "pho_mo\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/VELUTINO_CÔNG_TY_CP_ĐẦU_TƯ_VELUTINO_VIỆT_NAM\n", - "minimart_anan\n", - "vincommerce\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "minimart_anan\n", - "the_coffee_house\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/Bún_HÒ_Hu_NN_O_Xuân\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "nha_sach_gdtc_cam_pha\n", - "the_coffee_house\n", - "other/non_title\n", - "vietcombank\n", - "pho_mo\n", - "sieu_thi_bach_hoa_tong_hop\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "other/have_title/NHÀ_THUỐC_BỆNH_VIỆN_ĐA_KHOA_THÀNH_PHỐ_VINH\n", - "minimart_anan\n", - "phuc_anh_minimart\n", - "minimart_anan\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "guitar_cafe\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "pho_mo\n", - "vietcombank\n", - "vincommerce\n", - "vincommerce\n", - "other/non_title\n", - "other/have_title/Thanh_Xuân\n", - "pho_mo\n", - "sieu_thi_bach_hoa_tong_hop\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "guitar_cafe\n", - "pho_mo\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/UNIQLO_Vincom_Phạm_Ngọc_Thạch\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/VCB_-_Lợi\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "vincommerce\n", - "pho_mo\n", - "pho_mo\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/have_title/HAU_GIANG\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/Welcome_to_Trung_Nguyen_Legend\n", - "other/non_title\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "other/have_title/BIG_C_DI_AN_CTY_TNHH_EB_BINH_DUONG\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/MILANO_COFFEE_Milang_Coffee_Vietnam_####\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/BÁCH_HÓA_XANH\n", - "vincommerce\n", - "vincommerce\n", - "other/non_title\n", - "guitar_cafe\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "payoo\n", - "other/have_title/FamilyMart\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/Payoo_UIMMART\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "the_coffee_house\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "sieu_thi_bach_hoa_tong_hop\n", - "payoo\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/DIM_SUM_CORNER_TRUNG_HÒA\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "pho_mo\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "pho_mo\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "vincommerce\n", - "other/non_title\n", - "the_coffee_house\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "pho_mo\n", - "vietcombank\n", - "minimart_anan\n", - "other/have_title/oma_B_a_k_e_4_é\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/Hải_Ly\n", - "minimart_anan\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "the_coffee_house\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "vincommerce\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/have_title/THGHU_TUẤN\n", - "other/non_title\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "other/have_title/Covb_Food_HN_The\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "vincommerce\n", - "payoo\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "other/have_title/PACKER_BRINITION_12341\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "phuc_anh_minimart\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/Nhà_Hàng_Aozora_Sushi\n", - "vincommerce\n", - "minimart_anan\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/Việt_TTTM_Cho_Sui\n", - "minimart_anan\n", - "other/non_title\n", - "payoo\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "payoo\n", - "other/have_title/TTTM_VAN_HO-SIEU_THI_SEIKA_MART\n", - "other/have_title/Chợ_Sủi_Phú_Thị_Gia_Lâm\n", - "guitar_cafe\n", - "other/non_title\n", - "the_coffee_house\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "vincommerce\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "pho_mo\n", - "other/non_title\n", - "other/have_title/CIRCLE_K_VIETNAM\n", - "minimart_anan\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "other/have_title/CTY_CP_SÁCH_&_TBTH_QUẢNG_NINH\n", - "other/non_title\n", - "vincommerce\n", - "other/have_title/VINH\n", - "other/have_title/PARTS_GA_TEAUS_1.9\n", - "minimart_anan\n", - "vincommerce\n", - "minimart_anan\n", - "other/have_title/LahaTafe\n", - "vincommerce\n", - "vincommerce\n", - "other/have_title/charles\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "other/have_title/Nhà_Hàng_Aozora_Sushi\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/have_title/C_H_A_M\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "pho_mo\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "the_coffee_house\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "other/non_title\n", - "the_coffee_house\n", - "vincommerce\n", - "vincommerce\n", - "sieu_thi_bach_hoa_tong_hop\n", - "the_coffee_house\n", - "other/have_title/Thanh_Xuân\n", - "minimart_anan\n", - "vincommerce\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "vincommerce\n", - "cua_hang_nam_oanh\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/have_title/MOOSERROO_VIETHAM_THE_MOOSE_&_ROO_SMOKEHOUSE\n", - "other/non_title\n", - "minimart_anan\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "the_coffee_house\n", - "other/non_title\n", - "other/have_title/Tiền_đã_Thanh_Xuân\n", - "pho_mo\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "pho_mo\n", - "payoo\n", - "phuc_anh_minimart\n", - "payoo\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "guitar_cafe\n", - "minimart_anan\n", - "sieu_thi_bach_hoa_tong_hop\n", - "other/non_title\n", - "sieu_thi_bach_hoa_tong_hop\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "cua_hang_nam_oanh\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "payoo\n", - "other/non_title\n", - "vincommerce\n", - "nha_sach_gdtc_cam_pha\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "vincommerce\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "other/have_title/coopsmic\n", - "nha_sach_gdtc_cam_pha\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "vincommerce\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "other/non_title\n", - "other/have_title/thủ_Thanh_Xuân\n", - "vietcombank\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "phuc_anh_minimart\n", - "minimart_anan\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/Phương_Ốc_Hải_Phòng\n", - "minimart_anan\n", - "minimart_anan\n", - "the_coffee_house\n", - "other/have_title/KAI_TEA_KAI_TEA\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "other/non_title\n", - "other/non_title\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "minimart_anan\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "vincommerce\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "minimart_anan\n", - "minimart_anan\n", - "sctc_co_tho_104_tran_phu__cam_pha\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "vincommerce\n", - "other/non_title\n", - "nha_sach_gdtc_cam_pha\n", - "nha_sach_gdtc_cam_pha\n", - "the_coffee_house\n", - "saigon_coop_coop_food_hn_the_kpark\n", - "minimart_anan\n", - "other/non_title\n", - "minimart_anan\n", - "pho_mo\n", - "minimart_anan\n", - "pho_mo\n", - "cua_hang_nam_oanh\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "vincommerce\n", - "other/have_title/TALALA\n", - "the_coffee_house\n", - "other/have_title/ITTM\n", - "other/non_title\n", - "other/non_title\n", - "other/non_title\n", - "guitar_cafe\n", - "nha_sach_gdtc_cam_pha\n", - "other/have_title/VINCOM_SKYLAKE\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "nha_sach_gdtc_cam_pha\n", - "other/non_title\n", - "vincommerce\n", - "other/have_title/COFFEE_Milano_Coffee_Vietnam\n", - "other/non_title\n", - "the_coffee_house\n", - "vincommerce\n", - "other/non_title\n", - "other/non_title\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "minimart_anan\n", - "other/have_title/MARUMART\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "cua_hang_nam_oanh\n", - "minimart_anan\n", - "other/non_title\n", - "other/non_title\n", - "minimart_anan\n", - "pho_mo\n", - "vincommerce\n", - "other/non_title\n", - "minimart_anan\n", - "payoo\n" - ] - } - ], - "source": [ - "import Levenshtein\n", - "from pathlib import Path\n", - "import shutil\n", - "import re \n", - "from unidecode import unidecode\n", - "\n", - "def normalize(text):\n", - " text = text.lower()\n", - " text = unidecode(text)\n", - " text = re.sub(r'[^a-zA-Z0-9\\s]+', '', text)\n", - " return text \n", - " \n", - "\n", - "\n", - "def is_match(src, str_new, thr=0.7):\n", - " src = normalize(src)\n", - " str_new = normalize(str_new)\n", - " distance = Levenshtein.ratio(src, str_new)\n", - " if distance > thr:\n", - " return True \n", - " else:\n", - " return False\n", - "\n", - "def get_store_name(gt_store, store_list):\n", - " for store in store_list:\n", - " if is_match(store, gt_store, thr=0.6):\n", - " return store.lower()\n", - " \n", - " if len(gt_store) == 0:\n", - " return \"other/non_title\"\n", - " else:\n", - " return \"other/have_title/{}\".format(gt_store)\n", - " \n", - "\n", - "\n", - "\n", - "out_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_5/test_mcocr_not_label_ep21_by_store_name\"\n", - "out_dir = Path(out_dir)\n", - "for img_name, item in pred_data.items():\n", - " store_name = item['Store_name_value']\n", - " store_category = get_store_name(store_name, store_list=store_names)\n", - " store_category = store_category.replace(\" \", \"_\")\n", - " print(store_category)\n", - " out_dir_by_store = out_dir / store_category\n", - " if not out_dir_by_store.exists():\n", - " out_dir_by_store.mkdir(parents=True, exist_ok=True)\n", - "\n", - " img_full_name = Path(img_name).with_suffix(\".jpg\")\n", - " img_full_path = Path(img_dir) / img_full_name\n", - " if not img_full_path.exists():\n", - " print(str(img_full_path))\n", - " continue\n", - " else:\n", - " shutil.copy(str(img_full_path), out_dir_by_store)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "import os \n", - "import shutil\n", - "non_title_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_5/test_mcocr_not_label_ep21_by_store_name/other/non_title\"\n", - "src_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted/not_labeling\"\n", - "\n", - "out_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted/no_detect_seller\"\n", - "\n", - "non_seller_img_paths = glob.glob(non_title_dir + \"/*.jpg\")\n", - "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir, exist_ok=True)\n", - "for img_path in non_seller_img_paths:\n", - " shutil.copy(os.path.join(src_dir, os.path.basename(img_path)), out_dir)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel 'py38_hoanglv (Python 3.8.16)'. \n", - "\u001b[1;31mView Jupyter log for further details. Cannot find module './351.node.js'\n", - "\u001b[1;31mRequire stack:\n", - "\u001b[1;31m- /home/sds/.vscode-server/extensions/ms-toolsai.jupyter-2023.3.1201040234/out/extension.node.js\n", - "\u001b[1;31m- /home/sds/.vscode-server/bin/252e5463d60e63238250799aef7375787f68b4ee/out/vs/loader.js\n", - "\u001b[1;31m- /home/sds/.vscode-server/bin/252e5463d60e63238250799aef7375787f68b4ee/out/bootstrap-amd.js\n", - "\u001b[1;31m- /home/sds/.vscode-server/bin/252e5463d60e63238250799aef7375787f68b4ee/out/bootstrap-fork.js" - ] - } - ], - "source": [ - "import json \n", - "with open(\"/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/pred_vat_multi_page.json\", 'r', encoding='utf8') as f:\n", - " data = json.load(f)\n", - " \n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "import glob \n", - "from pathlib import Path" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "pdf_paths = glob.glob(\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/*.jpg\")\n", - "len(pdf_paths)\n", - "out_dir = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/SL_HCM_batch_2_first_last_page_2\"\n", - "if not Path(out_dir).exists():\n", - " Path(out_dir).mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03684662_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03684662_2.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03684662_3.jpg', 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"EAS03684676 EAS03684846\n", - "EAS03684846 EAS03684846\n", - "EAS03684846 EAS03684855\n", - "EAS03684855 EAS03684855\n", - "EAS03684855 EAS03684856\n", - "EAS03684856 EAS03684856\n", - "EAS03684856 EAS03684871\n", - "EAS03684871 EAS03684871\n", - "EAS03684871 EAS03684883\n", - "EAS03684883 EAS03684883\n", - "EAS03684883 EAS03685924\n", - "EAS03685924 EAS03685924\n", - "EAS03685924 EAS03709262\n", - "EAS03709262 EAS03709262\n", - "EAS03709262 EAS03742094\n", - "EAS03742094 EAS03742094\n", - "EAS03742094 EAS03742094\n", - "EAS03742094 EAS03742094\n", - "EAS03742094 EAS03742094\n", - "EAS03742094 EAS03743342\n", - "EAS03743342 EAS03743342\n", - "EAS03743342 EAS03743342\n", - "EAS03743342 EAS03743342\n", - "EAS03743342 EAS03743343\n", - "EAS03743343 EAS03743343\n", - "EAS03743343 EAS03743343\n", - "EAS03743343 EAS03743343\n", - "EAS03743343 EAS03743355\n", - "EAS03743355 EAS03743355\n", - "EAS03743355 EAS03743355\n", - "EAS03743355 EAS03743355\n", - "EAS03743355 EAS03743498\n", - "EAS03743498 EAS03743498\n", - "EAS03743498 EAS03743498\n", - "EAS03743498 EAS03743498\n", - "EAS03743498 EAS03750789\n", - "EAS03750789 EAS03750789\n", - "EAS03750789 EAS03784255\n", - "EAS03784255 EAS03784255\n", - "EAS03784255 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794195\n", - "EAS03794195 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794272\n", - "EAS03794272 EAS03794275\n", - "EAS03794275 EAS03794275\n", - "EAS03794275 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796731\n", - "EAS03796731 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03796733\n", - "EAS03796733 EAS03797799\n", - "EAS03797799 EAS03797799\n", - "EAS03797799 EAS03797799\n", - "EAS03797799 EAS03797799\n", - "EAS03797799 EAS03797799\n", - 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"EAS03797830 EAS03797830\n", - "EAS03797830 EAS03797830\n", - "EAS03797830 EAS03797830\n", - "EAS03797830 EAS03797830\n", - "EAS03797830 EAS03797830\n", - "EAS03797830 EAS03797830\n", - "EAS03797830 EAS03797830\n", - "EAS03797830 EAS03798008\n", - "EAS03798008 EAS03798008\n", - "EAS03798008 EAS03798008\n", - "EAS03798008 EAS03799021\n", - "EAS03799021 EAS03799021\n", - "EAS03799021 EAS03799021\n", - "EAS03799021 EAS03799021\n", - "EAS03799021 EAS03799021\n", - "EAS03799021 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801790\n", - "EAS03801790 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03801821\n", - "EAS03801821 EAS03802414\n", - "EAS03802414 EAS03802414\n", - "EAS03802414 EAS03802414\n", - "EAS03802414 EAS03802414\n", - "EAS03802414 EAS03802424\n", - "EAS03802424 EAS03802424\n", - "EAS03802424 EAS03802445\n", - "EAS03802445 EAS03802445\n", - "EAS03802445 EAS03802445\n", - "EAS03802445 EAS03802445\n", - "EAS03802445 EAS03802966\n", - "EAS03802966 EAS03802966\n", - "EAS03802966 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03803045\n", - "EAS03803045 EAS03805606\n", - "EAS03805606 EAS03805606\n", - "EAS03805606 EAS03805606\n", - "EAS03805606 EAS03805923\n", - "EAS03805923 EAS03805923\n", - "EAS03805923 EAS03805967\n", - "EAS03805967 EAS03805967\n", - "EAS03805967 EAS03805967\n", - "EAS03805967 EAS03807505\n", - "EAS03807505 EAS03807505\n", - "EAS03807505 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807922\n", - "EAS03807922 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03807924\n", - "EAS03807924 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808205\n", - "EAS03808205 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808208\n", - "EAS03808208 EAS03808299\n", - "EAS03808299 EAS03808299\n", - "EAS03808299 EAS03808299\n", - "EAS03808299 EAS03808299\n", - "EAS03808299 EAS03808299\n", - "EAS03808299 EAS03808299\n", - "EAS03808299 EAS03808302\n", - "EAS03808302 EAS03808302\n", - "EAS03808302 EAS03809583\n", - "EAS03809583 EAS03809583\n", - "EAS03809583 EAS03809656\n", - "EAS03809656 EAS03809656\n", - "EAS03809656 EAS03809701\n", - "EAS03809701 EAS03809701\n", - "EAS03809701 EAS03809701\n", - "EAS03809701 EAS03809701\n", - "EAS03809701 EAS03809701\n", - "EAS03809701 EAS03810209\n", - "EAS03810209 EAS03810209\n", - "EAS03810209 EAS03813592\n", - "EAS03813592 EAS03813592\n", - "EAS03813592 EAS03813592\n", - "EAS03813592 EAS03813592\n", - "EAS03813592 EAS03813592\n", - "EAS03813592 EAS03813594\n", - "EAS03813594 EAS03813594\n", - "EAS03813594 EAS03813594\n", - "EAS03813594 EAS03813594\n", - "EAS03813594 EAS03813594\n", - "EAS03813594 EAS03813595\n", - "EAS03813595 EAS03813595\n", - "EAS03813595 EAS03813608\n", - "EAS03813608 EAS03813608\n", - "EAS03813608 EAS03813646\n", - "EAS03813646 EAS03813646\n", - "EAS03813646 EAS03813647\n", - "EAS03813647 EAS03813647\n", - "EAS03813647 EAS03813647\n", - "EAS03813647 EAS03815264\n", - "EAS03815264 EAS03815264\n", - "EAS03815264 EAS03816169\n", - "EAS03816169 EAS03816169\n", - "EAS03816169 EAS03816170\n", - "EAS03816170 EAS03816170\n", - "EAS03816170 EAS03816171\n", - "EAS03816171 EAS03816171\n", - "EAS03816171 EAS03816171\n", - "EAS03816171 EAS03816179\n", - "EAS03816179 EAS03816179\n", - "EAS03816179 EAS03816183\n", - "EAS03816183 EAS03816183\n", - "EAS03816183 EAS03816196\n", - "EAS03816196 EAS03816196\n", - "EAS03816196 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - "EAS03818115 EAS03818115\n", - 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'/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834825_9.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834840_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834834_2.jpg']\n" - ] - } - ], - "source": [ - "pdf_paths = sorted(pdf_paths)\n", - "print(pdf_paths[:10])\n", - "valid_pdf_paths = []\n", - "past_pdf_key = None\n", - "past_pdf_path = None\n", - "for pdf_path in pdf_paths:\n", - " pdf_key = str(Path(pdf_path).stem).split(\"_\")[0]\n", - " pdf_page = int(str(Path(pdf_path).stem).split(\"_\")[-1])\n", - " if past_pdf_key is None:\n", - " past_pdf_key = pdf_key\n", - " past_pdf_path = pdf_path\n", - " \n", - " if pdf_page == 1:\n", - " valid_pdf_paths.append(pdf_path)\n", - " \n", - " if past_pdf_key != pdf_key:\n", - " valid_pdf_paths.append(past_pdf_path)\n", - " \n", - " print(past_pdf_key, pdf_key)\n", - " past_pdf_path = pdf_path\n", - " past_pdf_key = pdf_key\n", - "print(valid_pdf_paths)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'EAS03808205': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808205_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808205_2.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808205_3.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808205_4.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808205_5.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808205_6.jpg', 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'/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03808299_6.jpg'], 'EAS03820888': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03820888_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03820888_2.jpg'], 'EAS03784255': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03784255_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03784255_2.jpg'], 'EAS03828421': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03828421_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03828421_2.jpg'], 'EAS03833384': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03833384_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03833384_2.jpg'], 'EAS03834139': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834139_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834139_2.jpg'], 'EAS03820901': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03820901_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03820901_2.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03820901_3.jpg'], 'EAS03815264': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03815264_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03815264_2.jpg'], 'EAS03684856': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03684856_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03684856_2.jpg'], 'EAS03813646': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03813646_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03813646_2.jpg'], 'EAS03819215': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03819215_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03819215_2.jpg'], 'EAS03816196': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03816196_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03816196_2.jpg'], 'EAS03834800': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834800_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03834800_2.jpg'], 'EAS03819364': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03819364_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03819364_2.jpg'], 'EAS03809656': ['/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03809656_1.jpg', '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2/EAS03809656_2.jpg']}\n" - ] - } - ], - "source": [ - "data_dict = {}\n", - "for pdf_path in pdf_paths:\n", - " pdf_key = str(Path(pdf_path).stem).split(\"_\")[0]\n", - " if pdf_key not in data_dict:\n", - " data_dict[pdf_key] = [pdf_path]\n", - " else:\n", - " data_dict[pdf_key].append(pdf_path)\n", - "\n", - "for pdf_key in data_dict.keys():\n", - " data_dict[pdf_key] = sorted(data_dict[pdf_key], key=lambda pdf_path: int(Path(pdf_path).stem.split(\"_\")[-1]))\n", - "\n", - "print(data_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "for k, pdf_list in data_dict.items():\n", - " first_page, last_page = pdf_list[0], pdf_list[-1]\n", - " shutil.copy(first_page, out_dir)\n", - " shutil.copy(last_page, out_dir)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "for pdf_path in valid_pdf_paths:\n", - " shutil.copy(pdf_path, out_dir)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py38_hoanglv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/data.py b/cope2n-ai-fi/modules/sdsvkie/notebooks/data.py deleted file mode 100644 index 4fb9f03..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/notebooks/data.py +++ /dev/null @@ -1,84 +0,0 @@ -# from sdsvkie.utils.io_file import read_json -import json -import Levenshtein -from pathlib import Path -import shutil -import re -from unidecode import unidecode - -# from sdsvkie.utils.io_file import read_json - - -def normalize(text): - text = text.lower() - text = unidecode(text) - text = re.sub(r'[^a-zA-Z0-9\s]+', '', text) - return text - - - -def is_match(src, str_new, thr=0.7): - src = normalize(src) - str_new = normalize(str_new) - distance = Levenshtein.ratio(src, str_new) - if distance > thr: - return True - else: - return False - -def get_store_name(gt_store, store_list): - for store in store_list: - if is_match(store, gt_store, thr=0.6): - return store.lower() - - if len(gt_store) == 0: - return "other_non_title" - else: - return "other_have_title_{}".format(gt_store) - - -def read_json(json_path): - with open(json_path, "r", encoding="utf8") as f: - data = json.load(f) - return data - - - - -json_path = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_ss_receipt.json" -pred_data = read_json(json_path) - -store_names = [normalize(item['Store_name_value']) for k, item in pred_data.items()] -# store_names = list(set(store_names)) -from collections import Counter -my_counter = Counter(store_names) -list_tuples = my_counter.most_common() -print(list_tuples) -stores = [x[0] for x in list_tuples] -print(stores) - - -store_names = stores[1:] - -img_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/All" -out_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Done" -out_dir = Path(out_dir) -for img_name, item in pred_data.items(): - store_name = item['Store_name_value'] - store_category = get_store_name(store_name, store_list=store_names) - store_category = store_category.replace(" ", "_") - print(store_category) - out_dir_by_store = out_dir / store_category - if not out_dir_by_store.exists(): - out_dir_by_store.mkdir(parents=True, exist_ok=True) - - img_full_name = Path(img_name).with_suffix(".jpg") - img_full_path = Path(img_dir) / img_full_name - - txt_full_path = img_full_path.with_suffix(".txt") - if not img_full_path.exists(): - print(str(img_full_path)) - continue - else: - shutil.copy(str(img_full_path), out_dir_by_store) - shutil.copy(str(txt_full_path), out_dir_by_store) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/page0.jpg b/cope2n-ai-fi/modules/sdsvkie/notebooks/page0.jpg deleted file mode 100644 index c4b7a0d..0000000 Binary files a/cope2n-ai-fi/modules/sdsvkie/notebooks/page0.jpg and /dev/null differ diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/pdf2image.ipynb b/cope2n-ai-fi/modules/sdsvkie/notebooks/pdf2image.ipynb deleted file mode 100644 index 0a17864..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/notebooks/pdf2image.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import fitz\n", - "from io import BytesIO" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], - "source": [ - "pdf_path = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/PDF/eReceipt/(Sol tech Grp)_202303_Receipt_Transfort #1.pdf\"\n", - "with open(pdf_path, 'rb') as f:\n", - " pdf_bytes = f.read()\n", - "doc = fitz.open(stream=pdf_bytes, filetype='pdf')\n", - "print(doc.page_count)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(pdf_bytes, bytes)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "import os \n", - "\n", - "def pdf_to_image(pdf, dpi=300, is_get_first_page=False, max_page=1000):\n", - " \"\"\"_summary_\n", - "\n", - " Args:\n", - " pdf (_type_): _description_\n", - " dpi (int, optional): _description_. Defaults to 300.\n", - " is_get_first_page (bool, optional): _description_. Defaults to False.\n", - " max_page (int, optional): _description_. Defaults to 1000.\n", - "\n", - " Raises:\n", - " NotImplementedError: _description_\n", - "\n", - " Returns:\n", - " _type_: _description_\n", - " \"\"\"\n", - " if isinstance(pdf, str):\n", - " if not os.path.exists(pdf):\n", - " print(f\"Not found pdf path at {pdf}\")\n", - " return []\n", - " doc = fitz.open(pdf) # open document\n", - " elif isinstance(pdf, bytes):\n", - " doc = fitz.open(stream=pdf, filetype='pdf')\n", - " else:\n", - " raise NotImplementedError(f\"Not yet implement for {type(pdf)} type !!!\")\n", - " \n", - " zoom = dpi // 72\n", - " magnify = fitz.Matrix(zoom, zoom) \n", - " imgs = []\n", - " for idx, page in enumerate(doc):\n", - " pix = page.get_pixmap(matrix=magnify) # render page to an image\n", - " \n", - " im = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.h, pix.w, pix.n)\n", - " im = np.ascontiguousarray(im[..., [2, 1, 0]]) # rgb to bgr\n", - " imgs.append(im)\n", - " if is_get_first_page or idx >= max_page:\n", - " break\n", - " return imgs" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "no such file: '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/PDF/eReceipt/(Sol tech Grp)_202303_Rec eipt_Transfort #1.pdf'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m imgs \u001b[39m=\u001b[39m pdf_to_image(pdf\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/PDF/eReceipt/(Sol tech Grp)_202303_Rec eipt_Transfort #1.pdf\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m 2\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mlen\u001b[39m(imgs))\n", - "Cell \u001b[0;32mIn[13], line 5\u001b[0m, in \u001b[0;36mpdf_to_image\u001b[0;34m(pdf, dpi, is_get_first_page)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpdf_to_image\u001b[39m(pdf, dpi\u001b[39m=\u001b[39m\u001b[39m300\u001b[39m, is_get_first_page\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m 4\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(pdf, \u001b[39mstr\u001b[39m):\n\u001b[0;32m----> 5\u001b[0m doc \u001b[39m=\u001b[39m fitz\u001b[39m.\u001b[39;49mopen(pdf) \u001b[39m# open document\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(pdf, \u001b[39mbytes\u001b[39m):\n\u001b[1;32m 7\u001b[0m doc \u001b[39m=\u001b[39m fitz\u001b[39m.\u001b[39mopen(stream\u001b[39m=\u001b[39mpdf, filetype\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mpdf\u001b[39m\u001b[39m'\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/py38_hoanglv/lib/python3.8/site-packages/fitz/fitz.py:3867\u001b[0m, in \u001b[0;36mDocument.__init__\u001b[0;34m(self, filename, stream, filetype, rect, width, height, fontsize)\u001b[0m\n\u001b[1;32m 3865\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexists(filename):\n\u001b[1;32m 3866\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mno such file: \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mfilename\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 3867\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(msg)\n\u001b[1;32m 3868\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39misfile(filename):\n\u001b[1;32m 3869\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mfilename\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m is no file\u001b[39m\u001b[39m\"\u001b[39m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: no such file: '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/PDF/eReceipt/(Sol tech Grp)_202303_Rec eipt_Transfort #1.pdf'" - ] - } - ], - "source": [ - "imgs = pdf_to_image(pdf=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/PDF/eReceipt/(Sol tech Grp)_202303_Rec eipt_Transfort #1.pdf\")\n", - "print(len(imgs))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.imshow(imgs[0])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# import module\n", - "from pdf2image import convert_from_path\n", - " \n", - " \n", - "# Store Pdf with convert_from_path function\n", - "images = convert_from_path('/mnt/hdd2T/AICR/Projects/2023/Invoices_SL_HCM/EAS03581488.pdf', dpi=300)\n", - " \n", - "for i in range(len(images)):\n", - " \n", - " # Save pages as images in the pdf\n", - " images[i].save('page'+ str(i) +'.jpg', 'JPEG')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py38_hoanglv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/scp.sh b/cope2n-ai-fi/modules/sdsvkie/notebooks/scp.sh deleted file mode 100644 index d9d239b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/notebooks/scp.sh +++ /dev/null @@ -1 +0,0 @@ -rsync -r --exclude='workdirs/' --exclude='notebooks/' --exclude='weights/' --exclude='wandb/' --exclude='microsoft/' /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie user@107.120.133.42:/mnt/data/kie diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/sdsvap_invoice.ipynb b/cope2n-ai-fi/modules/sdsvkie/notebooks/sdsvap_invoice.ipynb deleted file mode 100644 index 57814e6..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/notebooks/sdsvap_invoice.ipynb +++ /dev/null @@ -1,194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os \n", - "import glob \n", - "from tqdm import tqdm \n", - "import cv2 \n", - "import shutil\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_DIR = \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/IMGS\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def copy_only_first_page(data_dir, out_dir, skip_types=['Receipt_taxi','Receipt_food_Cam', 'Receipt_food_Scan']):\n", - " paths = sorted(glob.glob(data_dir + \"/*/*\"))\n", - " print(\"Total paths: \", len(paths))\n", - " out_dir = Path(out_dir)\n", - " for path in paths:\n", - " type_doc = Path(path).parent.name\n", - " out_dir_full = out_dir / type_doc\n", - " if not out_dir_full.exists():\n", - " out_dir_full.mkdir(parents=True)\n", - " if type_doc in skip_types:\n", - " shutil.copy(path, str(out_dir_full))\n", - " else:\n", - " if \"_1.jpg\" in path:\n", - " shutil.copy(path, out_dir_full)\n", - " prefix_name = \"_\".join(path.split(\"_\")[:-1]) + \"_1.jpg\"\n", - " print(prefix_name)\n", - " if Path(prefix_name).exists():\n", - " continue\n", - " else:\n", - " shutil.copy(path, out_dir_full)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "classes = [\n", - " # id invoice\n", - " 'No_key', # số hóa đơn\n", - " 'No_value', \n", - " 'Form_key', # mẫu số hóa đơn\n", - " 'Form_value', \n", - " 'Serial_key', # số kí hiệu hoá đơn\n", - " 'Serial_value', \n", - " 'Date_value', \n", - "\n", - " # seller info\n", - " 'Seller_company_name_key', \n", - " 'Seller_company_name_value', \n", - " 'Seller_tax_code_key', \n", - " 'Seller_tax_code_value', \n", - " 'Seller_address_value',\n", - " 'Seller_address_key', \n", - " 'Seller_tel_key',\n", - " 'Seller_tel_value', \n", - " \n", - " # buyer info\n", - " 'Buyer_personal_name_key',\n", - " 'Buyer_personal_name_value', \n", - " 'Buyer_company_name_key', \n", - " 'Buyer_company_name_value', \n", - " 'Buyer_tax_code_key', \n", - " 'Buyer_tax_code_value', \n", - " 'Buyer_address_key', \n", - " 'Buyer_address_value', \n", - " 'Buyer_address_key',\n", - " 'Buyer_address_value',\n", - "\n", - " # money info\n", - " 'Tax_amount_key', \n", - " 'Tax_amount_value', \n", - " 'Total_key', \n", - " 'Total_value', \n", - " 'Total_in_words_key', \n", - " 'Total_in_words_value',\n", - " \n", - " 'Other', \n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "classes = [x.lower() for x in classes]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['no_key',\n", - " 'no_value',\n", - " 'form_key',\n", - " 'form_value',\n", - " 'serial_key',\n", - " 'serial_value',\n", - " 'date_value',\n", - " 'seller_company_name_key',\n", - " 'seller_company_name_value',\n", - " 'seller_tax_code_key',\n", - " 'seller_tax_code_value',\n", - " 'seller_address_value',\n", - " 'seller_address_key',\n", - " 'seller_tel_key',\n", - " 'seller_tel_value',\n", - " 'buyer_personal_name_key',\n", - " 'buyer_personal_name_value',\n", - " 'buyer_company_name_key',\n", - " 'buyer_company_name_value',\n", - " 'buyer_tax_code_key',\n", - " 'buyer_tax_code_value',\n", - " 'buyer_address_key',\n", - " 'buyer_address_value',\n", - " 'buyer_address_key',\n", - " 'buyer_address_value',\n", - " 'tax_amount_key',\n", - " 'tax_amount_value',\n", - " 'total_key',\n", - " 'total_value',\n", - " 'total_in_words_key',\n", - " 'total_in_words_value',\n", - " 'other']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py38_hoanglv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cope2n-ai-fi/modules/sdsvkie/notebooks/wildreceipt_data.ipynb b/cope2n-ai-fi/modules/sdsvkie/notebooks/wildreceipt_data.ipynb deleted file mode 100644 index 0289d25..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/notebooks/wildreceipt_data.ipynb +++ /dev/null @@ -1,2129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import os \n", - "import json\n", - "import numpy as np\n", - "from pathlib import Path\n", - "import shutil\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def write_txt(txt, data, mode=\"w\"):\n", - " with open(txt, mode, encoding=\"utf8\") as f:\n", - " for line in data:\n", - " f.write(line + \"\\n\")\n", - "\n", - "\n", - "def read_txt(txt):\n", - " with open(txt, \"r\", encoding=\"utf8\") as f:\n", - " data = [line.strip() for line in f]\n", - " return data\n", - "\n", - "\n", - "def poly2bbox(poly):\n", - " poly = np.array(poly).astype(np.uint32).reshape(-1, 2)\n", - " xmin, ymin = np.min(poly, axis=0)\n", - " xmax, ymax = np.max(poly, axis=0)\n", - " bbox = [xmin, ymin, xmax, ymax]\n", - " return bbox\n", - "\n", - "\n", - " \n", - "def create_kie_wildreceipt(root_dir, txt_file, classes, out_dir):\n", - " if not out_dir.exists():\n", - " out_dir.mkdir(parents=True)\n", - " visited = {}\n", - " data = read_txt(txt_file)\n", - " for item in data:\n", - " \n", - " item = json.loads(item)\n", - " print(item)\n", - " file_name = Path(item['file_name'])\n", - " annos = item['annotations']\n", - " txt_out_data = []\n", - " for anno in annos:\n", - " box = anno['box']\n", - " box = poly2bbox(box)\n", - " text = anno['text']\n", - " text = text.replace(\"\\t\", \" \")\n", - " label = classes[anno['label']]\n", - " out = \"\\t\".join([str(x) for x in box])\n", - " out = \"\\t\".join([out, text, label])\n", - " txt_out_data.append(out)\n", - "\n", - " txt_name = Path(file_name).with_suffix(\".txt\").name\n", - " if txt_name not in visited:\n", - " visited[txt_name] = 1\n", - " else:\n", - " visited[txt_name] += 1\n", - " out_txt_path = out_dir / txt_name\n", - "\n", - " write_txt(out_txt_path, txt_out_data)\n", - " img_out_path = out_dir / file_name.name \n", - " shutil.copy(root_dir / file_name, img_out_path)\n", - "\n", - "def create_kie_wildreceipt_dataset(root_dir, train_txt, test_txt, class_file, out_dir):\n", - " root_dir = Path(root_dir)\n", - " out_dir = Path(out_dir)\n", - " if not out_dir.exists():\n", - " out_dir.mkdir(parents=True)\n", - "\n", - "\n", - " classes = read_txt(class_file)\n", - " classes = {\n", - " int(item.split(\" \")[0]): item.split(\" \")[1] for item in classes\n", - " }\n", - " print(classes) \n", - " \n", - " create_kie_wildreceipt(root_dir, train_txt, classes, out_dir / \"train\")\n", - " create_kie_wildreceipt(root_dir, test_txt, classes, out_dir / \"test\")\n", - " # print(data[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: 'Ignore', 1: 'Store_name_value', 2: 'Store_name_key', 3: 'Store_addr_value', 4: 'Store_addr_key', 5: 'Tel_value', 6: 'Tel_key', 7: 'Date_value', 8: 'Date_key', 9: 'Time_value', 10: 'Time_key', 11: 'Prod_item_value', 12: 'Prod_item_key', 13: 'Prod_quantity_value', 14: 'Prod_quantity_key', 15: 'Prod_price_value', 16: 'Prod_price_key', 17: 'Subtotal_value', 18: 'Subtotal_key', 19: 'Tax_value', 20: 'Tax_key', 21: 'Tips_value', 22: 'Tips_key', 23: 'Total_value', 24: 'Total_key', 25: 'Others'}\n", - "{'file_name': 'image_files/Image_16/11/d5de7f2a20751e50b84c747c17a24cd98bed3554.jpeg', 'height': 1200, 'width': 1600, 'annotations': [{'box': [550.0, 190.0, 937.0, 190.0, 937.0, 104.0, 550.0, 104.0], 'text': 'SAFEWAY', 'label': 1}, {'box': [1048.0, 211.0, 1074.0, 211.0, 1074.0, 196.0, 1048.0, 196.0], 'text': 'TM', 'label': 25}, {'box': [535.0, 239.0, 833.0, 239.0, 833.0, 200.0, 535.0, 200.0], 'text': 'ATOREMGRTOMMILAZZO', 'label': 25}, {'box': [907.0, 256.0, 1081.0, 256.0, 1081.0, 223.0, 907.0, 223.0], 'text': '703-777-5833', 'label': 5}, {'box': [576.0, 282.0, 1016.0, 282.0, 1016.0, 230.0, 576.0, 230.0], 'text': 'THANKYOUFORSHOPPINGWITHUS!', 'label': 25}, {'box': [724.0, 387.0, 840.0, 387.0, 840.0, 355.0, 724.0, 355.0], 'text': 'GROCERY', 'label': 25}, {'box': [474.0, 433.0, 733.0, 433.0, 733.0, 394.0, 474.0, 394.0], 'text': 'BCCHOCCUPCAKES', 'label': 11}, {'box': [470.0, 466.0, 764.0, 466.0, 764.0, 421.0, 470.0, 421.0], 'text': 'ACNSPRYFRTSHAPE', 'label': 11}, {'box': [466.0, 483.0, 595.0, 483.0, 595.0, 448.0, 466.0, 448.0], 'text': 'Regprice', 'label': 25}, {'box': [463.0, 517.0, 658.0, 517.0, 658.0, 478.0, 463.0, 478.0], 'text': 'Cardsavings', 'label': 25}, {'box': [459.0, 538.0, 574.0, 538.0, 574.0, 507.0, 459.0, 507.0], 'text': 'MfrCpn', 'label': 25}, {'box': [455.0, 578.0, 686.0, 578.0, 686.0, 536.0, 455.0, 536.0], 'text': 'MultipliedCpn', 'label': 25}, {'box': [453.0, 611.0, 750.0, 611.0, 750.0, 565.0, 453.0, 565.0], 'text': 'DULCEDELECHECHE', 'label': 11}, {'box': [449.0, 625.0, 581.0, 625.0, 581.0, 593.0, 449.0, 593.0], 'text': 'Regprice', 'label': 25}, {'box': [446.0, 662.0, 644.0, 662.0, 644.0, 623.0, 446.0, 623.0], 'text': 'CardSavings', 'label': 25}, {'box': [443.0, 683.0, 558.0, 683.0, 558.0, 650.0, 443.0, 650.0], 'text': 'MfrCpn', 'label': 25}, {'box': [441.0, 723.0, 673.0, 723.0, 673.0, 682.0, 441.0, 682.0], 'text': 'MuitipliedCpn', 'label': 25}, {'box': [437.0, 758.0, 737.0, 758.0, 737.0, 708.0, 437.0, 708.0], 'text': 'MULTIGRAINCHEERIO', 'label': 11}, {'box': [433.0, 774.0, 566.0, 774.0, 566.0, 739.0, 433.0, 739.0], 'text': 'RegPrice', 'label': 25}, {'box': [430.0, 808.0, 632.0, 808.0, 632.0, 769.0, 430.0, 769.0], 'text': 'CardSavings', 'label': 25}, {'box': [426.0, 830.0, 544.0, 830.0, 544.0, 800.0, 426.0, 800.0], 'text': 'MfrCpn', 'label': 25}, {'box': [424.0, 872.0, 660.0, 872.0, 660.0, 831.0, 424.0, 831.0], 'text': 'MuitieliedCpn', 'label': 25}, {'box': [419.0, 907.0, 708.0, 907.0, 708.0, 860.0, 419.0, 860.0], 'text': 'PROGRESSOR&HSTK', 'label': 11}, {'box': [414.0, 922.0, 550.0, 922.0, 550.0, 890.0, 414.0, 890.0], 'text': 'RegPrice', 'label': 25}, {'box': [413.0, 962.0, 615.0, 962.0, 615.0, 923.0, 413.0, 923.0], 'text': 'CardSavings', 'label': 25}, {'box': [613.0, 1049.0, 836.0, 1049.0, 836.0, 1001.0, 613.0, 1001.0], 'text': 'REFRIG/FROZEN', 'label': 25}, {'box': [1005.0, 465.0, 1070.0, 465.0, 1070.0, 437.0, 1005.0, 437.0], 'text': '3.99', 'label': 15}, {'box': [1023.0, 491.0, 1067.0, 491.0, 1067.0, 469.0, 1023.0, 469.0], 'text': '.49', 'label': 15}, {'box': [848.0, 509.0, 913.0, 509.0, 913.0, 481.0, 848.0, 481.0], 'text': '3.79', 'label': 25}, {'box': [847.0, 538.0, 927.0, 538.0, 927.0, 512.0, 847.0, 512.0], 'text': '1.80-', 'label': 25}, {'box': [863.0, 566.0, 925.0, 566.0, 925.0, 543.0, 863.0, 543.0], 'text': '.75-', 'label': 25}, {'box': [862.0, 596.0, 923.0, 596.0, 923.0, 572.0, 862.0, 572.0], 'text': '.75-', 'label': 25}, {'box': [993.0, 641.0, 1060.0, 641.0, 1060.0, 610.0, 993.0, 610.0], 'text': '1.99', 'label': 15}, {'box': [836.0, 655.0, 901.0, 655.0, 901.0, 629.0, 836.0, 629.0], 'text': '4.19', 'label': 25}, {'box': [835.0, 683.0, 916.0, 683.0, 916.0, 658.0, 835.0, 658.0], 'text': '1.20-', 'label': 25}, {'box': [853.0, 713.0, 914.0, 713.0, 914.0, 688.0, 853.0, 688.0], 'text': '.50-', 'label': 25}, {'box': [851.0, 743.0, 912.0, 743.0, 912.0, 719.0, 851.0, 719.0], 'text': '.50-', 'label': 25}, {'box': [985.0, 791.0, 1050.0, 791.0, 1050.0, 760.0, 985.0, 760.0], 'text': '1.50', 'label': 15}, {'box': [825.0, 804.0, 891.0, 804.0, 891.0, 775.0, 825.0, 775.0], 'text': '3.99', 'label': 25}, {'box': [826.0, 832.0, 906.0, 832.0, 906.0, 805.0, 826.0, 805.0], 'text': '1.49-', 'label': 25}, {'box': [840.0, 865.0, 903.0, 865.0, 903.0, 838.0, 840.0, 838.0], 'text': '.50-', 'label': 25}, {'box': [839.0, 895.0, 901.0, 895.0, 901.0, 869.0, 839.0, 869.0], 'text': '.50-', 'label': 25}, {'box': [972.0, 946.0, 1041.0, 946.0, 1041.0, 913.0, 972.0, 913.0], 'text': '2.00', 'label': 15}, {'box': [812.0, 957.0, 878.0, 957.0, 878.0, 928.0, 812.0, 928.0], 'text': '2.69', 'label': 25}, {'box': [831.0, 989.0, 894.0, 989.0, 894.0, 963.0, 831.0, 963.0], 'text': '.69-', 'label': 25}, {'box': [402.0, 1069.0, 483.0, 1069.0, 483.0, 1044.0, 402.0, 1044.0], 'text': '1OIY', 'label': 14}, {'box': [395.0, 1127.0, 703.0, 1127.0, 703.0, 1072.0, 395.0, 1072.0], 'text': 'PLSBRYBSCTSANDWI', 'label': 11}, {'box': [395.0, 1159.0, 702.0, 1159.0, 702.0, 1105.0, 395.0, 1105.0], 'text': 'TOTINOSPZASTOFFE', 'label': 11}, {'box': [390.0, 1173.0, 526.0, 1173.0, 526.0, 1136.0, 390.0, 1136.0], 'text': 'RegPrice', 'label': 25}, {'box': [384.0, 1199.0, 589.0, 1199.0, 589.0, 1172.0, 384.0, 1172.0], 'text': '', 'label': 0}, {'box': [790.0, 1198.0, 861.0, 1198.0, 861.0, 1177.0, 790.0, 1177.0], 'text': '', 'label': 0}, {'box': [958.0, 1171.0, 1029.0, 1171.0, 1029.0, 1132.0, 958.0, 1132.0], 'text': '3.50', 'label': 15}, {'box': [956.0, 1198.0, 1029.0, 1198.0, 1029.0, 1163.0, 956.0, 1163.0], 'text': '3.49', 'label': 15}, {'box': [1091.0, 466.0, 1103.0, 466.0, 1103.0, 439.0, 1091.0, 439.0], 'text': 'B', 'label': 25}, {'box': [1088.0, 494.0, 1102.0, 494.0, 1102.0, 469.0, 1088.0, 469.0], 'text': 'B', 'label': 25}, {'box': [1078.0, 642.0, 1092.0, 642.0, 1092.0, 615.0, 1078.0, 615.0], 'text': 'B', 'label': 25}, {'box': [1070.0, 792.0, 1087.0, 792.0, 1087.0, 765.0, 1070.0, 765.0], 'text': 'B', 'label': 25}, {'box': [1062.0, 948.0, 1077.0, 948.0, 1077.0, 919.0, 1062.0, 919.0], 'text': 'B', 'label': 25}, {'box': [1045.0, 1168.0, 1063.0, 1168.0, 1063.0, 1143.0, 1045.0, 1143.0], 'text': 'B', 'label': 25}, {'box': [1044.0, 1198.0, 1060.0, 1198.0, 1060.0, 1176.0, 1044.0, 1176.0], 'text': 'B', 'label': 25}]}\n", - "{'file_name': 'image_files/Image_36/15/f4bb5ce76d2bc39f560fade5d3bc8aee9edaee6a.jpeg', 'height': 1777, 'width': 1000, 'annotations': [{'box': [273.0, 22.0, 737.0, 22.0, 737.0, 1.0, 273.0, 1.0], 'text': '', 'label': 0}, {'box': [222.0, 69.0, 761.0, 69.0, 761.0, 18.0, 222.0, 18.0], 'text': 'Rulesandregulationsapply.Seecontestrulesfordetails.', 'label': 25}, {'box': [369.0, 105.0, 549.0, 105.0, 549.0, 62.0, 369.0, 62.0], 'text': 'Walmart', 'label': 1}, {'box': [449.0, 117.0, 545.0, 117.0, 545.0, 97.0, 449.0, 97.0], 'text': 'supercentre', 'label': 25}, {'box': [360.0, 197.0, 552.0, 197.0, 552.0, 156.0, 360.0, 156.0], 'text': 'SUPERCENTER', 'label': 25}, {'box': [326.0, 224.0, 604.0, 224.0, 604.0, 184.0, 326.0, 184.0], 'text': 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'WithpurchaseofanyRainXorUltimateExpress', 'label': 25}, {'box': [86.0, 999.0, 645.0, 999.0, 645.0, 959.0, 86.0, 959.0], 'text': 'washreceive1$offyournextvisitwithin', 'label': 25}]}\n", - "{'file_name': 'image_files/Image_66/4/5b6f6c44f766c762362c03c7c7d71f7f47bfa015.jpeg', 'height': 450, 'width': 337, 'annotations': [{'box': [120.0, 43.0, 236.0, 43.0, 236.0, 32.0, 120.0, 32.0], 'text': 'OLDCASTLEPUB', 'label': 1}, {'box': [104.0, 54.0, 253.0, 54.0, 253.0, 41.0, 104.0, 41.0], 'text': '160W54THSTREET', 'label': 3}, {'box': [84.0, 75.0, 217.0, 75.0, 217.0, 62.0, 84.0, 62.0], 'text': '0125TABLE5#Party1', 'label': 25}, {'box': [84.0, 85.0, 179.0, 85.0, 179.0, 73.0, 84.0, 73.0], 'text': 'MARIBELMSvrCk:13', 'label': 25}, {'box': [187.0, 86.0, 224.0, 86.0, 224.0, 74.0, 187.0, 74.0], 'text': '10/24/14', 'label': 7}, {'box': [83.0, 96.0, 183.0, 96.0, 183.0, 84.0, 83.0, 84.0], 'text': 'DININGROOM', 'label': 25}, {'box': [83.0, 114.0, 89.0, 114.0, 89.0, 105.0, 83.0, 105.0], 'text': '2', 'label': 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139.0], 'text': 'SIDECAESAR', 'label': 11}, {'box': [30.0, 168.0, 92.0, 168.0, 92.0, 156.0, 30.0, 156.0], 'text': 'JAMBALAYA', 'label': 11}, {'box': [30.0, 185.0, 119.0, 185.0, 119.0, 171.0, 30.0, 171.0], 'text': '8OZSTRIPLOIN', 'label': 11}, {'box': [29.0, 201.0, 85.0, 201.0, 85.0, 188.0, 29.0, 188.0], 'text': 'ADDMUSH', 'label': 11}, {'box': [29.0, 218.0, 112.0, 218.0, 112.0, 204.0, 29.0, 204.0], 'text': 'FOUNTAINPOP', 'label': 11}, {'box': [255.0, 138.0, 287.0, 138.0, 287.0, 126.0, 255.0, 126.0], 'text': '16.00', 'label': 15}, {'box': [261.0, 153.0, 287.0, 153.0, 287.0, 141.0, 261.0, 141.0], 'text': '4.99', 'label': 15}, {'box': [255.0, 170.0, 288.0, 170.0, 288.0, 158.0, 255.0, 158.0], 'text': '16.00', 'label': 15}, {'box': [254.0, 187.0, 287.0, 187.0, 287.0, 175.0, 254.0, 175.0], 'text': '39.90', 'label': 15}, {'box': [262.0, 203.0, 288.0, 203.0, 288.0, 190.0, 262.0, 190.0], 'text': '1.98', 'label': 15}, {'box': [261.0, 219.0, 288.0, 219.0, 288.0, 207.0, 261.0, 207.0], 'text': '2.29', 'label': 15}, {'box': [152.0, 252.0, 219.0, 252.0, 219.0, 239.0, 152.0, 239.0], 'text': 'SubTotal:', 'label': 18}, {'box': [145.0, 269.0, 167.0, 269.0, 167.0, 256.0, 145.0, 256.0], 'text': 'HST', 'label': 20}, {'box': [253.0, 252.0, 288.0, 252.0, 288.0, 239.0, 253.0, 239.0], 'text': '81.16', 'label': 17}, {'box': [227.0, 270.0, 288.0, 270.0, 288.0, 255.0, 227.0, 255.0], 'text': ':12.17', 'label': 19}, {'box': [15.0, 288.0, 49.0, 288.0, 49.0, 273.0, 15.0, 273.0], 'text': '03/11', 'label': 7}, {'box': [59.0, 286.0, 91.0, 286.0, 91.0, 274.0, 59.0, 274.0], 'text': '12:48', 'label': 9}, {'box': [99.0, 286.0, 174.0, 286.0, 174.0, 272.0, 99.0, 272.0], 'text': 'TOTAL:', 'label': 24}, {'box': [221.0, 286.0, 288.0, 286.0, 288.0, 272.0, 221.0, 272.0], 'text': '93.33', 'label': 23}, {'box': [16.0, 319.0, 138.0, 319.0, 138.0, 305.0, 16.0, 305.0], 'text': '5494ProspectRoad', 'label': 3}, {'box': [15.0, 336.0, 165.0, 336.0, 165.0, 323.0, 15.0, 323.0], 'text': 'NewMinas,NovaScotia', 'label': 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507.0, 462.0, 507.0, 443.0, 415.0, 443.0], 'text': '16.07.2012', 'label': 7}, {'box': [468.0, 506.0, 510.0, 506.0, 510.0, 486.0, 468.0, 486.0], 'text': '3,10', 'label': 15}, {'box': [470.0, 551.0, 512.0, 551.0, 512.0, 531.0, 470.0, 531.0], 'text': '7,90', 'label': 15}, {'box': [473.0, 598.0, 514.0, 598.0, 514.0, 575.0, 473.0, 575.0], 'text': '1,90', 'label': 15}, {'box': [475.0, 645.0, 516.0, 645.0, 516.0, 622.0, 475.0, 622.0], 'text': '2,50', 'label': 15}, {'box': [448.0, 690.0, 527.0, 690.0, 527.0, 668.0, 448.0, 668.0], 'text': '15,40€', 'label': 23}, {'box': [394.0, 714.0, 470.0, 714.0, 470.0, 694.0, 394.0, 694.0], 'text': '12,94€', 'label': 17}, {'box': [399.0, 738.0, 471.0, 738.0, 471.0, 716.0, 399.0, 716.0], 'text': '2,46€', 'label': 19}, {'box': [536.0, 711.0, 546.0, 711.0, 546.0, 693.0, 536.0, 693.0], 'text': 'A', 'label': 25}, {'box': [536.0, 733.0, 548.0, 733.0, 548.0, 718.0, 536.0, 718.0], 'text': 'A', 'label': 25}, {'box': [377.0, 763.0, 528.0, 763.0, 528.0, 740.0, 377.0, 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406.0, 211.0, 339.0, 211.0], 'text': '50000.00', 'label': 15}, {'box': [340.0, 249.0, 406.0, 249.0, 406.0, 231.0, 340.0, 231.0], 'text': '40000.00', 'label': 15}, {'box': [355.0, 269.0, 408.0, 269.0, 408.0, 252.0, 355.0, 252.0], 'text': '810.00', 'label': 15}, {'box': [351.0, 291.0, 409.0, 291.0, 409.0, 273.0, 351.0, 273.0], 'text': '1050.00', 'label': 15}, {'box': [352.0, 312.0, 411.0, 312.0, 411.0, 294.0, 352.0, 294.0], 'text': '1080.00', 'label': 15}, {'box': [368.0, 334.0, 413.0, 334.0, 413.0, 315.0, 368.0, 315.0], 'text': '40.00', 'label': 15}, {'box': [368.0, 356.0, 413.0, 356.0, 413.0, 337.0, 368.0, 337.0], 'text': '20.00', 'label': 15}, {'box': [369.0, 378.0, 413.0, 378.0, 413.0, 359.0, 369.0, 359.0], 'text': '20.00', 'label': 15}, {'box': [369.0, 401.0, 414.0, 401.0, 414.0, 381.0, 369.0, 381.0], 'text': '20.00', 'label': 15}, {'box': [371.0, 424.0, 415.0, 424.0, 415.0, 404.0, 371.0, 404.0], 'text': '12.00', 'label': 15}, {'box': [373.0, 448.0, 416.0, 448.0, 416.0, 427.0, 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2434.0, 488.0, 2434.0], 'text': 'Wevalueyouropinion!', 'label': 25}, {'box': [417.0, 2578.0, 1376.0, 2578.0, 1376.0, 2502.0, 417.0, 2502.0], 'text': 'Bethenext$1,000Winner!', 'label': 25}, {'box': [521.0, 2648.0, 1260.0, 2648.0, 1260.0, 2572.0, 521.0, 2572.0], 'text': '3winnerspermonth.', 'label': 25}, {'box': [185.0, 2725.0, 1603.0, 2725.0, 1603.0, 2634.0, 185.0, 2634.0], 'text': \"Completeasurveyabouttoday'svisit:\", 'label': 25}, {'box': [152.0, 2791.0, 437.0, 2791.0, 437.0, 2723.0, 152.0, 2723.0], 'text': 'Call', 'label': 25}, {'box': [531.0, 2785.0, 1578.0, 2785.0, 1578.0, 2701.0, 531.0, 2701.0], 'text': '1.866.214.5750', 'label': 25}, {'box': [188.0, 2863.0, 1580.0, 2863.0, 1580.0, 2785.0, 188.0, 2785.0], 'text': 'Orvisitwww.dollargeneralsurvey.com', 'label': 25}, {'box': [414.0, 3119.0, 1364.0, 3119.0, 1364.0, 3048.0, 414.0, 3048.0], 'text': 'Paracompletarunestudio', 'label': 25}, {'box': [273.0, 3173.0, 1548.0, 3173.0, 1548.0, 3107.0, 273.0, 3107.0], 'text': 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155.0, 62.0], 'text': '09:54PM', 'label': 9}, {'box': [12.0, 66.0, 152.0, 66.0, 152.0, 50.0, 12.0, 50.0], 'text': 'BILLNo:1352RetailInvoice', 'label': 25}, {'box': [12.0, 76.0, 103.0, 76.0, 103.0, 63.0, 12.0, 63.0], 'text': 'TableNo:11/GF', 'label': 25}, {'box': [10.0, 85.0, 67.0, 85.0, 67.0, 73.0, 10.0, 73.0], 'text': 'Cover:1', 'label': 25}, {'box': [11.0, 99.0, 79.0, 99.0, 79.0, 85.0, 11.0, 85.0], 'text': 'USER:ADMIN', 'label': 25}, {'box': [11.0, 123.0, 68.0, 123.0, 68.0, 110.0, 11.0, 110.0], 'text': 'Description', 'label': 12}, {'box': [104.0, 125.0, 122.0, 125.0, 122.0, 110.0, 104.0, 110.0], 'text': 'Qty', 'label': 14}, {'box': [122.0, 124.0, 146.0, 124.0, 146.0, 111.0, 122.0, 111.0], 'text': 'Rate', 'label': 25}, {'box': [159.0, 123.0, 191.0, 123.0, 191.0, 111.0, 159.0, 111.0], 'text': 'Amount', 'label': 16}, {'box': [9.0, 148.0, 68.0, 148.0, 68.0, 134.0, 9.0, 134.0], 'text': 'SoftDrinks', 'label': 11}, {'box': [11.0, 160.0, 62.0, 160.0, 62.0, 147.0, 11.0, 147.0], 'text': 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131.0, 137.0], 'text': '24.00', 'label': 25}, {'box': [176.0, 146.0, 203.0, 146.0, 203.0, 137.0, 176.0, 137.0], 'text': '48.00', 'label': 15}, {'box': [131.0, 159.0, 157.0, 159.0, 157.0, 148.0, 131.0, 148.0], 'text': '93.00', 'label': 25}, {'box': [175.0, 159.0, 202.0, 159.0, 202.0, 148.0, 175.0, 148.0], 'text': '93.00', 'label': 15}, {'box': [130.0, 171.0, 157.0, 171.0, 157.0, 161.0, 130.0, 161.0], 'text': '76.00', 'label': 25}, {'box': [176.0, 171.0, 202.0, 171.0, 202.0, 160.0, 176.0, 160.0], 'text': '76.00', 'label': 15}, {'box': [126.0, 183.0, 157.0, 183.0, 157.0, 173.0, 126.0, 173.0], 'text': '115.00', 'label': 25}, {'box': [171.0, 183.0, 203.0, 183.0, 203.0, 173.0, 171.0, 173.0], 'text': '230.00', 'label': 15}, {'box': [126.0, 196.0, 157.0, 196.0, 157.0, 185.0, 126.0, 185.0], 'text': '118.00', 'label': 25}, {'box': [171.0, 195.0, 203.0, 195.0, 203.0, 185.0, 171.0, 185.0], 'text': '118.00', 'label': 15}, {'box': [126.0, 208.0, 157.0, 208.0, 157.0, 198.0, 126.0, 198.0], 'text': '110.00', 'label': 25}, {'box': [171.0, 208.0, 203.0, 208.0, 203.0, 197.0, 171.0, 197.0], 'text': '330.00', 'label': 15}, {'box': [130.0, 220.0, 157.0, 220.0, 157.0, 211.0, 130.0, 211.0], 'text': '51.00', 'label': 25}, {'box': [171.0, 220.0, 203.0, 220.0, 203.0, 211.0, 171.0, 211.0], 'text': '102.00', 'label': 15}, {'box': [127.0, 231.0, 157.0, 231.0, 157.0, 222.0, 127.0, 222.0], 'text': '160.00', 'label': 25}, {'box': [172.0, 231.0, 203.0, 231.0, 203.0, 222.0, 172.0, 222.0], 'text': '160.00', 'label': 15}, {'box': [125.0, 243.0, 156.0, 243.0, 156.0, 233.0, 125.0, 233.0], 'text': '152.00', 'label': 25}, {'box': [172.0, 243.0, 203.0, 243.0, 203.0, 234.0, 172.0, 234.0], 'text': '152.00', 'label': 15}, {'box': [129.0, 255.0, 156.0, 255.0, 156.0, 245.0, 129.0, 245.0], 'text': '76.00', 'label': 25}, {'box': [176.0, 256.0, 204.0, 256.0, 204.0, 244.0, 176.0, 244.0], 'text': '76.00', 'label': 15}, {'box': [129.0, 266.0, 156.0, 266.0, 156.0, 257.0, 129.0, 257.0], 'text': '42.00', 'label': 25}, 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{'box': [396.0, 256.0, 811.0, 256.0, 811.0, 209.0, 396.0, 209.0], 'text': '021-66604433', 'label': 5}, {'box': [800.0, 855.0, 1038.0, 855.0, 1038.0, 811.0, 800.0, 811.0], 'text': '·45,600', 'label': 15}, {'box': [805.0, 918.0, 1042.0, 918.0, 1042.0, 872.0, 805.0, 872.0], 'text': '·38,000', 'label': 15}, {'box': [861.0, 673.0, 927.0, 673.0, 927.0, 629.0, 861.0, 629.0], 'text': 'CT', 'label': 25}, {'box': [1010.0, 672.0, 1031.0, 672.0, 1031.0, 626.0, 1010.0, 626.0], 'text': '1', 'label': 25}, {'box': [224.0, 801.0, 328.0, 801.0, 328.0, 757.0, 224.0, 757.0], 'text': '3.8', 'label': 13}, {'box': [293.0, 861.0, 469.0, 861.0, 469.0, 816.0, 293.0, 816.0], 'text': 'KRAPU', 'label': 11}, {'box': [293.0, 431.0, 885.0, 431.0, 885.0, 384.0, 293.0, 384.0], 'text': 'DUPLICATERECEIPT', 'label': 25}, {'box': [186.0, 555.0, 287.0, 555.0, 287.0, 513.0, 186.0, 513.0], 'text': 'REG', 'label': 25}, {'box': [194.0, 618.0, 289.0, 618.0, 289.0, 574.0, 194.0, 574.0], 'text': 'IMA', 'label': 25}, {'box': [806.0, 976.0, 1046.0, 976.0, 1046.0, 933.0, 806.0, 933.0], 'text': '·20,000', 'label': 15}, {'box': [847.0, 1040.0, 1049.0, 1040.0, 1049.0, 994.0, 847.0, 994.0], 'text': '·4,000', 'label': 15}, {'box': [812.0, 1105.0, 1053.0, 1105.0, 1053.0, 1057.0, 812.0, 1057.0], 'text': '·12,000', 'label': 15}, {'box': [365.0, 554.0, 712.0, 554.0, 712.0, 510.0, 365.0, 510.0], 'text': '27-05-2014', 'label': 7}, {'box': [760.0, 550.0, 923.0, 550.0, 923.0, 507.0, 760.0, 507.0], 'text': '17:58', 'label': 9}, {'box': [279.0, 1446.0, 946.0, 1446.0, 946.0, 1393.0, 279.0, 1393.0], 'text': 'THANKYOU', 'label': 25}, {'box': [780.0, 1308.0, 1064.0, 1308.0, 1064.0, 1257.0, 780.0, 1257.0], 'text': '·119,600', 'label': 25}, {'box': [282.0, 1305.0, 426.0, 1305.0, 426.0, 1259.0, 282.0, 1259.0], 'text': 'CASH', 'label': 25}, {'box': [490.0, 1241.0, 1053.0, 1241.0, 1053.0, 1186.0, 490.0, 1186.0], 'text': '·119,600', 'label': 23}, {'box': [231.0, 919.0, 247.0, 919.0, 247.0, 880.0, 231.0, 880.0], 'text': '1', 'label': 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227.0, 344.0, 40.0, 344.0], 'text': 'Tips&Gratuitiesarediscretionary', 'label': 25}, {'box': [103.0, 395.0, 193.0, 395.0, 193.0, 384.0, 103.0, 384.0], 'text': '', 'label': 0}, {'box': [113.0, 275.0, 187.0, 275.0, 187.0, 241.0, 113.0, 241.0], 'text': '', 'label': 0}, {'box': [118.0, 294.0, 164.0, 294.0, 164.0, 277.0, 118.0, 277.0], 'text': '', 'label': 0}, {'box': [74.0, 189.0, 219.0, 189.0, 219.0, 174.0, 74.0, 174.0], 'text': '', 'label': 0}, {'box': [105.0, 169.0, 151.0, 169.0, 151.0, 156.0, 105.0, 156.0], 'text': '1Arancini', 'label': 11}, {'box': [109.0, 171.0, 188.0, 171.0, 188.0, 157.0, 109.0, 157.0], 'text': '', 'label': 0}, {'box': [105.0, 154.0, 144.0, 154.0, 144.0, 143.0, 105.0, 143.0], 'text': '2Sprite', 'label': 11}, {'box': [105.0, 144.0, 172.0, 144.0, 172.0, 130.0, 105.0, 130.0], 'text': '1P.Margherita', 'label': 11}, {'box': [106.0, 132.0, 191.0, 132.0, 191.0, 117.0, 106.0, 117.0], 'text': '1PenneDellaCasa', 'label': 11}, {'box': [106.0, 120.0, 187.0, 120.0, 187.0, 105.0, 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139.0, 187.0, 127.0, 128.0, 127.0], 'text': '01522534222', 'label': 5}, {'box': [126.0, 126.0, 161.0, 126.0, 161.0, 115.0, 126.0, 115.0], 'text': 'LN13AP', 'label': 3}, {'box': [125.0, 114.0, 162.0, 114.0, 162.0, 103.0, 125.0, 103.0], 'text': 'Lincoln', 'label': 3}, {'box': [115.0, 101.0, 176.0, 101.0, 176.0, 88.0, 115.0, 88.0], 'text': '32.Bailgate', 'label': 3}, {'box': [104.0, 88.0, 186.0, 88.0, 186.0, 75.0, 104.0, 75.0], 'text': 'Bar&Restaurant', 'label': 1}, {'box': [72.0, 78.0, 206.0, 78.0, 206.0, 52.0, 72.0, 52.0], 'text': 'OleOleTapas', 'label': 1}]}\n", - "{'file_name': 'image_files/Image_54/2/41b33d3168e84297528b898637d820ba8743a1c4.jpeg', 'height': 593, 'width': 549, 'annotations': [{'box': [226.0, 98.0, 386.0, 98.0, 386.0, 71.0, 226.0, 71.0], 'text': 'WELCOMETOBESTBUY#600', 'label': 1}, {'box': [253.0, 106.0, 353.0, 106.0, 353.0, 85.0, 253.0, 85.0], 'text': '3300GATEWAYST', 'label': 3}, {'box': [233.0, 118.0, 372.0, 118.0, 372.0, 94.0, 233.0, 94.0], 'text': 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1177.0, 648.0, 1129.0, 455.0, 1129.0], 'text': '6.SA-TAY', 'label': 11}, {'box': [498.0, 1243.0, 765.0, 1243.0, 765.0, 1188.0, 498.0, 1188.0], 'text': 'SLICEDCHIX', 'label': 11}, {'box': [396.0, 1281.0, 418.0, 1281.0, 418.0, 1241.0, 396.0, 1241.0], 'text': '1', 'label': 13}, {'box': [391.0, 1339.0, 415.0, 1339.0, 415.0, 1300.0, 391.0, 1300.0], 'text': '1', 'label': 13}, {'box': [443.0, 1300.0, 757.0, 1300.0, 757.0, 1243.0, 443.0, 1243.0], 'text': '40.THAISALAD', 'label': 11}, {'box': [444.0, 1357.0, 727.0, 1357.0, 727.0, 1303.0, 444.0, 1303.0], 'text': '102.PADTHAI', 'label': 11}, {'box': [1172.0, 1034.0, 1267.0, 1034.0, 1267.0, 991.0, 1172.0, 991.0], 'text': '7.95', 'label': 15}, {'box': [1148.0, 1092.0, 1265.0, 1092.0, 1265.0, 1049.0, 1148.0, 1049.0], 'text': '11.95', 'label': 15}, {'box': [1145.0, 1150.0, 1262.0, 1150.0, 1262.0, 1107.0, 1145.0, 1107.0], 'text': '12.95', 'label': 15}, {'box': [1163.0, 1208.0, 1259.0, 1208.0, 1259.0, 1165.0, 1163.0, 1165.0], 'text': '9.95', 'label': 15}, {'box': [1157.0, 1327.0, 1253.0, 1327.0, 1253.0, 1280.0, 1157.0, 1280.0], 'text': '7.95', 'label': 15}, {'box': [1152.0, 1386.0, 1250.0, 1386.0, 1250.0, 1341.0, 1152.0, 1341.0], 'text': '8.95', 'label': 15}, {'box': [477.0, 1418.0, 746.0, 1418.0, 746.0, 1364.0, 477.0, 1364.0], 'text': 'SLICEDPORK', 'label': 11}, {'box': [373.0, 1458.0, 396.0, 1458.0, 396.0, 1417.0, 373.0, 1417.0], 'text': '1', 'label': 13}, {'box': [365.0, 1519.0, 390.0, 1519.0, 390.0, 1477.0, 365.0, 1477.0], 'text': '1', 'label': 13}, {'box': [420.0, 1469.0, 588.0, 1469.0, 588.0, 1419.0, 420.0, 1419.0], 'text': 'SPECIAL', 'label': 11}, {'box': [1149.0, 1509.0, 1246.0, 1509.0, 1246.0, 1462.0, 1149.0, 1462.0], 'text': '1.00', 'label': 15}, {'box': [1121.0, 1572.0, 1241.0, 1572.0, 1241.0, 1522.0, 1121.0, 1522.0], 'text': '10.95', 'label': 15}, {'box': [418.0, 1543.0, 811.0, 1543.0, 811.0, 1480.0, 418.0, 1480.0], 'text': '129.YELLOWCURRY', 'label': 11}, {'box': [450.0, 1601.0, 728.0, 1601.0, 728.0, 1545.0, 450.0, 1545.0], 'text': 'SLICEDCHIX', 'label': 11}, {'box': [345.0, 1642.0, 370.0, 1642.0, 370.0, 1599.0, 345.0, 1599.0], 'text': '1', 'label': 13}, {'box': [392.0, 1666.0, 745.0, 1666.0, 745.0, 1603.0, 392.0, 1603.0], 'text': 'SOFTSHELLCRAB', 'label': 11}, {'box': [1136.0, 1696.0, 1238.0, 1696.0, 1238.0, 1647.0, 1136.0, 1647.0], 'text': '0.00', 'label': 15}, {'box': [432.0, 1731.0, 790.0, 1731.0, 790.0, 1667.0, 432.0, 1667.0], 'text': 'DRUNKENNOODLE', 'label': 11}, {'box': [1111.0, 1760.0, 1236.0, 1760.0, 1236.0, 1709.0, 1111.0, 1709.0], 'text': '18.95', 'label': 15}, {'box': [327.0, 1766.0, 348.0, 1766.0, 348.0, 1720.0, 327.0, 1720.0], 'text': '1', 'label': 13}, {'box': [370.0, 1780.0, 548.0, 1780.0, 548.0, 1725.0, 370.0, 1725.0], 'text': 'SPECIAL', 'label': 11}, {'box': [1100.0, 1827.0, 1232.0, 1827.0, 1232.0, 1772.0, 1100.0, 1772.0], 'text': '58.00', 'label': 15}, {'box': [362.0, 1861.0, 805.0, 1861.0, 805.0, 1788.0, 362.0, 1788.0], 'text': 'JOKIGENREDLABLE', 'label': 11}, {'box': [401.0, 1921.0, 719.0, 1921.0, 719.0, 1856.0, 401.0, 1856.0], 'text': 'SPECIALPREP', 'label': 11}, {'box': [278.0, 2032.0, 299.0, 2032.0, 299.0, 1983.0, 278.0, 1983.0], 'text': '1', 'label': 13}, {'box': [336.0, 1973.0, 472.0, 1973.0, 472.0, 1919.0, 336.0, 1919.0], 'text': 'MANGO', 'label': 11}, {'box': [327.0, 2051.0, 569.0, 2051.0, 569.0, 1985.0, 327.0, 1985.0], 'text': 'CNUTICRM', 'label': 11}, {'box': [1120.0, 2029.0, 1227.0, 2029.0, 1227.0, 1973.0, 1120.0, 1973.0], 'text': '7.95', 'label': 15}, {'box': [1115.0, 2101.0, 1223.0, 2101.0, 1223.0, 2042.0, 1115.0, 2042.0], 'text': '3.00', 'label': 15}, {'box': [1057.0, 2244.0, 1219.0, 2244.0, 1219.0, 2183.0, 1057.0, 2183.0], 'text': '159.55', 'label': 17}, {'box': [1082.0, 2319.0, 1215.0, 2319.0, 1215.0, 2258.0, 1082.0, 2258.0], 'text': '12.92', 'label': 19}, {'box': [883.0, 2392.0, 1208.0, 2392.0, 1208.0, 2318.0, 883.0, 2318.0], 'text': '172.47', 'label': 23}, {'box': [303.0, 2191.0, 518.0, 2191.0, 518.0, 2125.0, 303.0, 2125.0], 'text': 'SUBTOTAL', 'label': 18}, {'box': [297.0, 2250.0, 376.0, 2250.0, 376.0, 2196.0, 297.0, 2196.0], 'text': 'TAX', 'label': 20}, {'box': [183.0, 2320.0, 366.0, 2320.0, 366.0, 2256.0, 183.0, 2256.0], 'text': '10:40PM', 'label': 9}, {'box': [398.0, 2345.0, 644.0, 2345.0, 644.0, 2277.0, 398.0, 2277.0], 'text': 'TOTALDUE', 'label': 24}, {'box': [378.0, 2528.0, 999.0, 2528.0, 999.0, 2418.0, 378.0, 2418.0], 'text': 'THANKYOUFORVISITING', 'label': 25}]}\n", - "{'file_name': 'image_files/Image_19/12/487d17e3a29f795f2d183315aeb759390d96c84c.jpeg', 'height': 657, 'width': 408, 'annotations': [{'box': [22.0, 33.0, 121.0, 33.0, 121.0, 18.0, 22.0, 18.0], 'text': 'SushiYasuda', 'label': 1}, {'box': [23.0, 55.0, 184.0, 55.0, 184.0, 37.0, 23.0, 37.0], 'text': '204East43rdstreet', 'label': 3}, {'box': [22.0, 74.0, 169.0, 74.0, 169.0, 57.0, 22.0, 57.0], 'text': 'NewYorkNY10017', 'label': 3}, {'box': [23.0, 95.0, 135.0, 95.0, 135.0, 76.0, 23.0, 76.0], 'text': '(212)972-1001', 'label': 5}, {'box': [21.0, 135.0, 89.0, 135.0, 89.0, 117.0, 21.0, 117.0], 'text': '06/05/13', 'label': 7}, {'box': [112.0, 134.0, 170.0, 134.0, 170.0, 118.0, 112.0, 118.0], 'text': '1:19PM', 'label': 9}, {'box': [21.0, 154.0, 184.0, 154.0, 184.0, 137.0, 21.0, 137.0], 'text': 'Check3528', 'label': 25}, {'box': [22.0, 174.0, 187.0, 174.0, 187.0, 157.0, 22.0, 157.0], 'text': 'Waiter1MGCust2', 'label': 25}, {'box': [151.0, 194.0, 281.0, 194.0, 281.0, 177.0, 151.0, 177.0], 'text': 'Table7A', 'label': 25}, {'box': [25.0, 273.0, 30.0, 273.0, 30.0, 258.0, 25.0, 258.0], 'text': '1', 'label': 13}, {'box': [24.0, 293.0, 29.0, 293.0, 29.0, 278.0, 24.0, 278.0], 'text': '1', 'label': 13}, {'box': [23.0, 312.0, 29.0, 312.0, 29.0, 297.0, 23.0, 297.0], 'text': '1', 'label': 13}, {'box': [39.0, 273.0, 97.0, 273.0, 97.0, 256.0, 39.0, 256.0], 'text': 'Edamame', 'label': 11}, {'box': [37.0, 293.0, 72.0, 293.0, 72.0, 277.0, 37.0, 277.0], 'text': 'Kimo', 'label': 11}, {'box': [37.0, 313.0, 167.0, 313.0, 167.0, 296.0, 37.0, 296.0], 'text': 'AlaCarteSushi', 'label': 11}, {'box': [175.0, 354.0, 241.0, 354.0, 241.0, 335.0, 175.0, 335.0], 'text': 'Taxable:', 'label': 18}, {'box': [295.0, 274.0, 331.0, 274.0, 331.0, 257.0, 295.0, 257.0], 'text': '9.00', 'label': 15}, {'box': [290.0, 295.0, 331.0, 295.0, 331.0, 277.0, 290.0, 277.0], 'text': '10.50', 'label': 15}, {'box': [279.0, 315.0, 330.0, 315.0, 330.0, 298.0, 279.0, 298.0], 'text': '243.00', 'label': 15}, {'box': [279.0, 355.0, 331.0, 355.0, 331.0, 337.0, 279.0, 337.0], 'text': '262.50', 'label': 17}, {'box': [157.0, 396.0, 239.0, 396.0, 239.0, 376.0, 157.0, 376.0], 'text': 'Sub-total:', 'label': 18}, {'box': [157.0, 416.0, 240.0, 416.0, 240.0, 398.0, 157.0, 398.0], 'text': 'SalesTax:', 'label': 20}, {'box': [280.0, 395.0, 331.0, 395.0, 331.0, 378.0, 280.0, 378.0], 'text': '262.50', 'label': 17}, {'box': [288.0, 416.0, 332.0, 416.0, 332.0, 399.0, 288.0, 399.0], 'text': '23.30', 'label': 19}, {'box': [66.0, 457.0, 227.0, 457.0, 227.0, 437.0, 66.0, 437.0], 'text': 'TotalDue:', 'label': 24}, {'box': [248.0, 459.0, 348.0, 459.0, 348.0, 439.0, 248.0, 439.0], 'text': '285.80', 'label': 23}, {'box': [72.0, 524.0, 324.0, 524.0, 324.0, 500.0, 72.0, 500.0], 'text': 'FollowingthecustominJapan,', 'label': 25}, {'box': [55.0, 545.0, 326.0, 545.0, 326.0, 522.0, 55.0, 522.0], 'text': 'SushiYasuda’sservicestaffare', 'label': 25}, {'box': [48.0, 568.0, 333.0, 568.0, 333.0, 542.0, 48.0, 542.0], 'text': 'fullycompensatedbytheirsalary.', 'label': 25}, {'box': [39.0, 589.0, 359.0, 589.0, 359.0, 563.0, 39.0, 563.0], 'text': 'Thereforegratuitiesarenotaccepted.', 'label': 25}, {'box': [154.0, 629.0, 239.0, 629.0, 239.0, 608.0, 154.0, 608.0], 'text': 'Thankyou.', 'label': 25}, {'box': [210.0, 655.0, 398.0, 655.0, 398.0, 634.0, 210.0, 634.0], 'text': '', 'label': 0}]}\n", - "{'file_name': 'image_files/Image_78/7/25266f92935f86e84824233bc81b53495ba044a6.jpeg', 'height': 657, 'width': 408, 'annotations': [{'box': [23.0, 33.0, 120.0, 33.0, 120.0, 18.0, 23.0, 18.0], 'text': 'SushiYasuda', 'label': 1}, {'box': [24.0, 53.0, 183.0, 53.0, 183.0, 36.0, 24.0, 36.0], 'text': '204East43rdStreet', 'label': 3}, {'box': [24.0, 73.0, 167.0, 73.0, 167.0, 56.0, 24.0, 56.0], 'text': 'NewYorkNY10017', 'label': 3}, {'box': [24.0, 94.0, 133.0, 94.0, 133.0, 78.0, 24.0, 78.0], 'text': '(212)972-1001', 'label': 5}, {'box': [23.0, 134.0, 87.0, 134.0, 87.0, 118.0, 23.0, 118.0], 'text': '06/05/13', 'label': 7}, {'box': [114.0, 134.0, 168.0, 134.0, 168.0, 118.0, 114.0, 118.0], 'text': '1:19PM', 'label': 9}, {'box': [23.0, 155.0, 182.0, 155.0, 182.0, 137.0, 23.0, 137.0], 'text': 'Check3528', 'label': 25}, {'box': [23.0, 173.0, 185.0, 173.0, 185.0, 157.0, 23.0, 157.0], 'text': 'Waiter1MGCust2', 'label': 25}, {'box': [153.0, 193.0, 280.0, 193.0, 280.0, 177.0, 153.0, 177.0], 'text': 'Table7A', 'label': 25}, {'box': [24.0, 272.0, 30.0, 272.0, 30.0, 257.0, 24.0, 257.0], 'text': '1', 'label': 13}, {'box': [23.0, 291.0, 30.0, 291.0, 30.0, 277.0, 23.0, 277.0], 'text': '1', 'label': 13}, {'box': [23.0, 312.0, 30.0, 312.0, 30.0, 297.0, 23.0, 297.0], 'text': '1', 'label': 13}, {'box': [40.0, 272.0, 95.0, 272.0, 95.0, 258.0, 40.0, 258.0], 'text': 'Edamame', 'label': 11}, {'box': [38.0, 292.0, 71.0, 292.0, 71.0, 278.0, 38.0, 278.0], 'text': 'Kimo', 'label': 11}, {'box': [38.0, 312.0, 165.0, 312.0, 165.0, 296.0, 38.0, 296.0], 'text': 'AlaCarteSushi', 'label': 11}, {'box': [296.0, 273.0, 329.0, 273.0, 329.0, 257.0, 296.0, 257.0], 'text': '9.00', 'label': 15}, {'box': [290.0, 292.0, 330.0, 292.0, 330.0, 278.0, 290.0, 278.0], 'text': '10.50', 'label': 15}, {'box': [281.0, 312.0, 329.0, 312.0, 329.0, 298.0, 281.0, 298.0], 'text': '243.00', 'label': 15}, {'box': [176.0, 353.0, 238.0, 353.0, 238.0, 337.0, 176.0, 337.0], 'text': 'Taxable:', 'label': 18}, {'box': [280.0, 353.0, 330.0, 353.0, 330.0, 339.0, 280.0, 339.0], 'text': '262.50', 'label': 17}, {'box': [158.0, 393.0, 238.0, 393.0, 238.0, 377.0, 158.0, 377.0], 'text': 'Sub-total:', 'label': 18}, {'box': [280.0, 395.0, 331.0, 395.0, 331.0, 379.0, 280.0, 379.0], 'text': '262.50', 'label': 17}, {'box': [158.0, 415.0, 238.0, 415.0, 238.0, 399.0, 158.0, 399.0], 'text': 'SalesTax:', 'label': 20}, {'box': [289.0, 416.0, 331.0, 416.0, 331.0, 400.0, 289.0, 400.0], 'text': '23.30', 'label': 19}, {'box': [68.0, 456.0, 226.0, 456.0, 226.0, 438.0, 68.0, 438.0], 'text': 'TotalDue:', 'label': 24}, {'box': [247.0, 457.0, 348.0, 457.0, 348.0, 441.0, 247.0, 441.0], 'text': '285.80', 'label': 23}, {'box': [73.0, 524.0, 323.0, 524.0, 323.0, 502.0, 73.0, 502.0], 'text': 'FollowingthecustominJapan,', 'label': 25}, {'box': [56.0, 543.0, 325.0, 543.0, 325.0, 523.0, 56.0, 523.0], 'text': \"SushiYasuda'sservicestaffare\", 'label': 25}, {'box': [48.0, 567.0, 333.0, 567.0, 333.0, 543.0, 48.0, 543.0], 'text': 'fullycompensatedbytheirsalary.', 'label': 25}, {'box': [39.0, 588.0, 358.0, 588.0, 358.0, 563.0, 39.0, 563.0], 'text': 'Thereforegratuitiesarenotaccepted.', 'label': 25}, {'box': [156.0, 629.0, 237.0, 629.0, 237.0, 609.0, 156.0, 609.0], 'text': 'Thankyou.', 'label': 25}, {'box': [211.0, 654.0, 396.0, 654.0, 396.0, 636.0, 211.0, 636.0], 'text': 'photo:thepricehike', 'label': 25}, {'box': [23.0, 643.0, 381.0, 643.0, 381.0, 461.0, 23.0, 461.0], 'text': '', 'label': 0}]}\n", - "{'file_name': 'image_files/Image_41/17/0ee0c7051d837a0c66ba7f4d5fe45d0dcb5e6d2e.jpeg', 'height': 700, 'width': 534, 'annotations': [{'box': [57.0, 37.0, 161.0, 37.0, 161.0, 19.0, 57.0, 19.0], 'text': 'SushiYasuda', 'label': 1}, {'box': [56.0, 59.0, 229.0, 59.0, 229.0, 40.0, 56.0, 40.0], 'text': '204East43rdStreet', 'label': 3}, {'box': [57.0, 79.0, 212.0, 79.0, 212.0, 62.0, 57.0, 62.0], 'text': 'NewYorkNY10017', 'label': 3}, {'box': [57.0, 102.0, 176.0, 102.0, 176.0, 81.0, 57.0, 81.0], 'text': '(212)972-1001', 'label': 5}, {'box': [56.0, 145.0, 128.0, 145.0, 128.0, 126.0, 56.0, 126.0], 'text': '06/05/13', 'label': 7}, {'box': [151.0, 145.0, 213.0, 145.0, 213.0, 125.0, 151.0, 125.0], 'text': '1:19PM', 'label': 9}, {'box': 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240.0, 178.0, 240.0], 'text': 'GST#:200719983N', 'label': 25}, {'box': [95.0, 237.0, 262.0, 237.0, 262.0, 212.0, 95.0, 212.0], 'text': 'Singapore819642', 'label': 3}, {'box': [268.0, 235.0, 308.0, 235.0, 308.0, 213.0, 268.0, 213.0], 'text': 'Tel.', 'label': 6}, {'box': [317.0, 236.0, 402.0, 236.0, 402.0, 213.0, 317.0, 213.0], 'text': '65424709', 'label': 5}, {'box': [117.0, 209.0, 398.0, 209.0, 398.0, 184.0, 117.0, 184.0], 'text': '#031-004EChangiAirportT1,', 'label': 3}, {'box': [107.0, 178.0, 392.0, 178.0, 392.0, 155.0, 107.0, 155.0], 'text': 'ViewingMallCentralLevel3', 'label': 3}, {'box': [149.0, 154.0, 359.0, 154.0, 359.0, 128.0, 149.0, 128.0], 'text': '80AirportBoulevard,', 'label': 3}, {'box': [117.0, 124.0, 436.0, 124.0, 436.0, 101.0, 117.0, 101.0], 'text': 'SPECIALITIES-CT1', 'label': 1}, {'box': [218.0, 98.0, 340.0, 98.0, 340.0, 75.0, 218.0, 75.0], 'text': 'ASTONS', 'label': 1}]}\n", - "{'file_name': 'image_files/Image_58/20/626.jpeg', 'height': 450, 'width': 337, 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'COFFEE', 'label': 1}]}\n", - "{'file_name': 'image_files/Image_76/6/82b71b4cd3e200f4345ec9ada4f55b0379b90063.jpeg', 'height': 2836, 'width': 1546, 'annotations': [{'box': [474.0, 429.0, 991.0, 429.0, 991.0, 340.0, 474.0, 340.0], 'text': 'TIPTOPMEATS', 'label': 1}, {'box': [361.0, 532.0, 1158.0, 532.0, 1158.0, 446.0, 361.0, 446.0], 'text': '6118PASEODELNORTE', 'label': 3}, {'box': [401.0, 634.0, 1103.0, 634.0, 1103.0, 545.0, 401.0, 545.0], 'text': 'CARLSBAD,CA92011', 'label': 3}, {'box': [479.0, 740.0, 1032.0, 740.0, 1032.0, 653.0, 479.0, 653.0], 'text': '(760)438-2620', 'label': 5}, {'box': [124.0, 1033.0, 281.0, 1033.0, 281.0, 958.0, 124.0, 958.0], 'text': 'DATE', 'label': 8}, {'box': [323.0, 1034.0, 718.0, 1034.0, 718.0, 956.0, 323.0, 956.0], 'text': '06/29/2014', 'label': 7}, {'box': [985.0, 1033.0, 1164.0, 1033.0, 1164.0, 953.0, 985.0, 953.0], 'text': 'TIME', 'label': 10}, {'box': [250.0, 1128.0, 273.0, 1128.0, 273.0, 1060.0, 250.0, 1060.0], 'text': '1', 'label': 13}, {'box': 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2339.0, 1406.0, 2266.0, 1165.0, 2266.0], 'text': '$76.59', 'label': 25}, {'box': [251.0, 1527.0, 272.0, 1527.0, 272.0, 1462.0, 251.0, 1462.0], 'text': '1', 'label': 13}, {'box': [251.0, 1627.0, 275.0, 1627.0, 275.0, 1561.0, 251.0, 1561.0], 'text': '1', 'label': 13}, {'box': [253.0, 1728.0, 273.0, 1728.0, 273.0, 1663.0, 253.0, 1663.0], 'text': '1', 'label': 13}, {'box': [253.0, 1828.0, 273.0, 1828.0, 273.0, 1765.0, 253.0, 1765.0], 'text': '1', 'label': 13}, {'box': [252.0, 1928.0, 273.0, 1928.0, 273.0, 1861.0, 252.0, 1861.0], 'text': '1', 'label': 13}, {'box': [251.0, 2031.0, 274.0, 2031.0, 274.0, 1963.0, 251.0, 1963.0], 'text': '1', 'label': 13}, {'box': [251.0, 2135.0, 273.0, 2135.0, 273.0, 2067.0, 251.0, 2067.0], 'text': '1', 'label': 13}, {'box': [364.0, 1527.0, 633.0, 1527.0, 633.0, 1456.0, 364.0, 1456.0], 'text': 'GROCERY', 'label': 11}, {'box': [365.0, 1625.0, 636.0, 1625.0, 636.0, 1555.0, 365.0, 1555.0], 'text': 'GROCERY', 'label': 11}, {'box': [364.0, 1726.0, 637.0, 1726.0, 637.0, 1656.0, 364.0, 1656.0], 'text': 'GROCERY', 'label': 11}, {'box': [364.0, 1828.0, 640.0, 1828.0, 640.0, 1753.0, 364.0, 1753.0], 'text': 'GROCERY', 'label': 11}, {'box': [365.0, 1935.0, 640.0, 1935.0, 640.0, 1854.0, 365.0, 1854.0], 'text': 'GROCERY', 'label': 11}, {'box': [366.0, 2031.0, 636.0, 2031.0, 636.0, 1961.0, 366.0, 1961.0], 'text': 'GROCERY', 'label': 11}, {'box': [363.0, 2132.0, 641.0, 2132.0, 641.0, 2060.0, 363.0, 2060.0], 'text': 'GROCERY', 'label': 11}, {'box': [365.0, 2236.0, 562.0, 2236.0, 562.0, 2164.0, 365.0, 2164.0], 'text': 'TOTAL', 'label': 24}, {'box': [365.0, 2344.0, 803.0, 2344.0, 803.0, 2266.0, 365.0, 2266.0], 'text': 'CREDITCARD', 'label': 25}, {'box': [447.0, 2446.0, 1042.0, 2446.0, 1042.0, 2369.0, 447.0, 2369.0], 'text': '*ORDER#0240*', 'label': 25}, {'box': [123.0, 2568.0, 1403.0, 2568.0, 1403.0, 2469.0, 123.0, 2469.0], 'text': 'EMPLOYEE1NO.000140REG02', 'label': 25}, {'box': [1199.0, 1022.0, 1403.0, 1022.0, 1403.0, 952.0, 1199.0, 952.0], 'text': 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416.0, 204.0, 230.0, 204.0], 'text': '206-447-0507', 'label': 5}, {'box': [19.0, 271.0, 80.0, 271.0, 80.0, 242.0, 19.0, 242.0], 'text': 'DATE', 'label': 8}, {'box': [246.0, 277.0, 400.0, 277.0, 400.0, 241.0, 246.0, 241.0], 'text': '08/20/2012', 'label': 7}, {'box': [585.0, 265.0, 637.0, 265.0, 637.0, 235.0, 585.0, 235.0], 'text': 'MON', 'label': 25}, {'box': [17.0, 312.0, 633.0, 312.0, 633.0, 272.0, 17.0, 272.0], 'text': 'OPENCHECK#7', 'label': 25}, {'box': [198.0, 386.0, 432.0, 386.0, 432.0, 352.0, 198.0, 352.0], 'text': '**SEAT#01**', 'label': 25}, {'box': [106.0, 423.0, 212.0, 423.0, 212.0, 392.0, 106.0, 392.0], 'text': 'DIETT1', 'label': 11}, {'box': [103.0, 462.0, 257.0, 462.0, 257.0, 428.0, 103.0, 428.0], 'text': 'CLASSICT1', 'label': 11}, {'box': [105.0, 500.0, 227.0, 500.0, 227.0, 465.0, 105.0, 465.0], 'text': 'TAX1AMT', 'label': 20}, {'box': [105.0, 538.0, 257.0, 538.0, 257.0, 505.0, 105.0, 505.0], 'text': 'TOTAL', 'label': 24}, {'box': [557.0, 416.0, 642.0, 416.0, 642.0, 386.0, 557.0, 386.0], 'text': '$3.00', 'label': 15}, {'box': [558.0, 458.0, 643.0, 458.0, 643.0, 424.0, 558.0, 424.0], 'text': '$8.00', 'label': 15}, {'box': [558.0, 497.0, 645.0, 497.0, 645.0, 462.0, 558.0, 462.0], 'text': '$1.05', 'label': 19}, {'box': [445.0, 537.0, 641.0, 537.0, 641.0, 503.0, 445.0, 503.0], 'text': '$12.05', 'label': 23}, {'box': [225.0, 614.0, 400.0, 614.0, 400.0, 584.0, 225.0, 584.0], 'text': '***ALL***', 'label': 25}, {'box': [95.0, 655.0, 222.0, 655.0, 222.0, 625.0, 95.0, 625.0], 'text': 'SUBTOTAL', 'label': 18}, {'box': [95.0, 697.0, 220.0, 697.0, 220.0, 664.0, 95.0, 664.0], 'text': 'TAX1AMT', 'label': 20}, {'box': [95.0, 741.0, 249.0, 741.0, 249.0, 704.0, 95.0, 704.0], 'text': 'TOTAL', 'label': 24}, {'box': [548.0, 658.0, 651.0, 658.0, 651.0, 625.0, 548.0, 625.0], 'text': '$11.00', 'label': 17}, {'box': [564.0, 700.0, 651.0, 700.0, 651.0, 666.0, 564.0, 666.0], 'text': '$1.05', 'label': 19}, {'box': [448.0, 743.0, 649.0, 743.0, 649.0, 707.0, 448.0, 707.0], 'text': '$12.05', 'label': 23}, {'box': [482.0, 784.0, 554.0, 784.0, 554.0, 749.0, 482.0, 749.0], 'text': 'TIME', 'label': 10}, {'box': [570.0, 785.0, 653.0, 785.0, 653.0, 749.0, 570.0, 749.0], 'text': '12:28', 'label': 9}, {'box': [0.0, 782.0, 368.0, 782.0, 368.0, 743.0, 0.0, 743.0], 'text': 'NO.007373REG01KANDIE', 'label': 25}, {'box': [125.0, 824.0, 497.0, 824.0, 497.0, 787.0, 125.0, 787.0], 'text': 'THANKYOUFORVISITING.', 'label': 25}, {'box': [168.0, 862.0, 464.0, 862.0, 464.0, 827.0, 168.0, 827.0], 'text': 'PLEASECOMEAGAIN!', 'label': 25}]}\n", - "{'file_name': 'image_files/Image_56/2/e9c310f0b1fa229d84c91bfbf71917a02bcc4b2a.jpeg', 'height': 450, 'width': 337, 'annotations': [{'box': [60.0, 72.0, 191.0, 72.0, 191.0, 52.0, 60.0, 52.0], 'text': 'MIAMISCIENCE', 'label': 1}, {'box': [111.0, 80.0, 171.0, 80.0, 171.0, 66.0, 111.0, 66.0], 'text': 'MUSEUM', 'label': 1}, {'box': [92.0, 118.0, 196.0, 118.0, 196.0, 101.0, 92.0, 101.0], 'text': '', 'label': 0}, {'box': [107.0, 129.0, 186.0, 129.0, 186.0, 114.0, 107.0, 114.0], 'text': '', 'label': 0}, {'box': [109.0, 140.0, 176.0, 140.0, 176.0, 126.0, 109.0, 126.0], 'text': '(305)646-4200', 'label': 5}, {'box': [59.0, 193.0, 78.0, 193.0, 78.0, 181.0, 59.0, 181.0], 'text': 'QTY', 'label': 14}, {'box': [82.0, 190.0, 104.0, 190.0, 104.0, 180.0, 82.0, 180.0], 'text': 'ITEM', 'label': 12}, {'box': [204.0, 184.0, 238.0, 184.0, 238.0, 173.0, 204.0, 173.0], 'text': 'AMOUNT', 'label': 16}, {'box': [65.0, 203.0, 71.0, 203.0, 71.0, 193.0, 65.0, 193.0], 'text': '2', 'label': 13}, {'box': [93.0, 202.0, 142.0, 202.0, 142.0, 190.0, 93.0, 190.0], 'text': 'NDC:ADULT', 'label': 11}, {'box': [223.0, 196.0, 251.0, 196.0, 251.0, 185.0, 223.0, 185.0], 'text': '29.90', 'label': 15}, {'box': [82.0, 239.0, 110.0, 239.0, 110.0, 228.0, 82.0, 228.0], 'text': 'TOTAL', 'label': 24}, {'box': [83.0, 253.0, 134.0, 253.0, 134.0, 240.0, 83.0, 240.0], 'text': 'SALESTAX', 'label': 20}, {'box': [84.0, 266.0, 155.0, 266.0, 155.0, 253.0, 84.0, 253.0], 'text': 'CASHTENDERED', 'label': 25}, {'box': [84.0, 279.0, 117.0, 279.0, 117.0, 267.0, 84.0, 267.0], 'text': 'CHANGE', 'label': 25}, {'box': [208.0, 235.0, 237.0, 235.0, 237.0, 224.0, 208.0, 224.0], 'text': '29.90', 'label': 23}, {'box': [214.0, 248.0, 237.0, 248.0, 237.0, 237.0, 214.0, 237.0], 'text': '0.00', 'label': 19}, {'box': [210.0, 262.0, 238.0, 262.0, 238.0, 251.0, 210.0, 251.0], 'text': '50.00', 'label': 25}, {'box': [209.0, 275.0, 238.0, 275.0, 238.0, 264.0, 209.0, 264.0], 'text': '20.10', 'label': 25}, {'box': [47.0, 337.0, 87.0, 337.0, 87.0, 324.0, 47.0, 324.0], 'text': '3:16PM', 'label': 9}, {'box': [101.0, 335.0, 158.0, 335.0, 158.0, 321.0, 101.0, 321.0], 'text': '07/24/2012', 'label': 7}, {'box': [173.0, 332.0, 265.0, 332.0, 265.0, 319.0, 173.0, 319.0], 'text': '002:157235309', 'label': 25}, {'box': [119.0, 363.0, 219.0, 363.0, 219.0, 348.0, 119.0, 348.0], 'text': 'NOTVALID', 'label': 25}, {'box': [98.0, 379.0, 243.0, 379.0, 243.0, 363.0, 98.0, 363.0], 'text': 'FORADMISSION', 'label': 25}, {'box': [97.0, 406.0, 257.0, 406.0, 257.0, 392.0, 97.0, 392.0], 'text': '', 'label': 0}]}\n", - "{'file_name': 'image_files/Image_12/10/7ce1623d6d1aebb79015e1d9b78edf235e611eb4.jpeg', 'height': 450, 'width': 275, 'annotations': [{'box': [89.0, 13.0, 179.0, 13.0, 179.0, 0.0, 89.0, 0.0], 'text': \"jack'sSeafood\", 'label': 1}, {'box': [70.0, 29.0, 192.0, 29.0, 192.0, 14.0, 70.0, 14.0], 'text': '4CStreet,SuiteB', 'label': 3}, {'box': [83.0, 45.0, 184.0, 45.0, 184.0, 30.0, 83.0, 30.0], 'text': 'Eureka,CA95501', 'label': 3}, {'box': [91.0, 61.0, 179.0, 61.0, 179.0, 46.0, 91.0, 46.0], 'text': '(707)273-5273', 'label': 5}, {'box': [9.0, 91.0, 48.0, 91.0, 48.0, 77.0, 9.0, 77.0], 'text': 'Server', 'label': 25}, {'box': [197.0, 88.0, 262.0, 88.0, 262.0, 70.0, 197.0, 70.0], 'text': 'Station:2', 'label': 25}, {'box': [10.0, 122.0, 100.0, 122.0, 100.0, 108.0, 10.0, 108.0], 'text': 'Order#:11054', 'label': 25}, {'box': [10.0, 138.0, 76.0, 138.0, 76.0, 123.0, 10.0, 123.0], 'text': 'Table:T36', 'label': 25}, {'box': [215.0, 119.0, 263.0, 119.0, 263.0, 104.0, 215.0, 104.0], 'text': 'DineIn', 'label': 25}, {'box': [203.0, 135.0, 264.0, 135.0, 264.0, 119.0, 203.0, 119.0], 'text': 'Guests:2', 'label': 25}, {'box': [12.0, 168.0, 16.0, 168.0, 16.0, 156.0, 12.0, 156.0], 'text': '1', 'label': 13}, {'box': [12.0, 183.0, 16.0, 183.0, 16.0, 172.0, 12.0, 172.0], 'text': '1', 'label': 13}, {'box': [11.0, 230.0, 18.0, 230.0, 18.0, 218.0, 11.0, 218.0], 'text': '1', 'label': 13}, {'box': [12.0, 261.0, 18.0, 261.0, 18.0, 248.0, 12.0, 248.0], 'text': '1', 'label': 13}, {'box': [11.0, 292.0, 18.0, 292.0, 18.0, 280.0, 11.0, 280.0], 'text': '1', 'label': 13}, {'box': [23.0, 169.0, 111.0, 169.0, 111.0, 155.0, 23.0, 155.0], 'text': 'APP-SEAREDAHI', 'label': 11}, {'box': [23.0, 184.0, 113.0, 184.0, 113.0, 170.0, 23.0, 170.0], 'text': 'FISHANDCHIPS', 'label': 11}, {'box': [23.0, 198.0, 150.0, 198.0, 150.0, 185.0, 23.0, 185.0], 'text': '**SPICYCOLESLAW**', 'label': 11}, {'box': [23.0, 215.0, 157.0, 215.0, 157.0, 200.0, 23.0, 200.0], 'text': '*CLAMCHOWDER*3.00EA', 'label': 11}, {'box': [24.0, 229.0, 89.0, 229.0, 89.0, 216.0, 24.0, 216.0], 'text': 'FISHTACOS', 'label': 11}, {'box': [24.0, 246.0, 120.0, 246.0, 120.0, 232.0, 24.0, 232.0], 'text': '**HALFHALF**', 'label': 25}, {'box': [24.0, 260.0, 77.0, 260.0, 77.0, 248.0, 24.0, 248.0], 'text': 'Tiramisu', 'label': 11}, {'box': [23.0, 276.0, 143.0, 276.0, 143.0, 260.0, 23.0, 260.0], 'text': 'VIPDiscount(8.00)', 'label': 25}, {'box': [24.0, 293.0, 95.0, 293.0, 95.0, 278.0, 24.0, 278.0], 'text': 'GreatWhite', 'label': 11}, {'box': [230.0, 165.0, 263.0, 165.0, 263.0, 151.0, 230.0, 151.0], 'text': '15.00', 'label': 15}, {'box': [231.0, 180.0, 265.0, 180.0, 265.0, 166.0, 231.0, 166.0], 'text': '18.00', 'label': 15}, {'box': [232.0, 227.0, 265.0, 227.0, 265.0, 213.0, 232.0, 213.0], 'text': '16.00', 'label': 15}, {'box': [237.0, 258.0, 265.0, 258.0, 265.0, 245.0, 237.0, 245.0], 'text': '0.00', 'label': 15}, {'box': [237.0, 290.0, 265.0, 290.0, 265.0, 277.0, 237.0, 277.0], 'text': '5.00', 'label': 15}, {'box': [232.0, 321.0, 267.0, 321.0, 267.0, 307.0, 232.0, 307.0], 'text': '54.00', 'label': 17}, {'box': [239.0, 337.0, 267.0, 337.0, 267.0, 322.0, 239.0, 322.0], 'text': '4.73', 'label': 19}, {'box': [226.0, 370.0, 268.0, 370.0, 268.0, 355.0, 226.0, 355.0], 'text': '$58.73', 'label': 23}, {'box': [11.0, 323.0, 73.0, 323.0, 73.0, 310.0, 11.0, 310.0], 'text': 'SUBTOTAL:', 'label': 18}, {'box': [11.0, 340.0, 36.0, 340.0, 36.0, 326.0, 11.0, 326.0], 'text': 'TAX:', 'label': 20}, {'box': [10.0, 373.0, 49.0, 373.0, 49.0, 358.0, 10.0, 358.0], 'text': 'TOTAL:', 'label': 24}, {'box': [81.0, 403.0, 197.0, 403.0, 197.0, 388.0, 81.0, 388.0], 'text': '>>Ticket#:27<<', 'label': 25}, {'box': [70.0, 422.0, 134.0, 422.0, 134.0, 405.0, 70.0, 405.0], 'text': '10/14/2016', 'label': 7}, {'box': [140.0, 419.0, 206.0, 419.0, 206.0, 404.0, 140.0, 404.0], 'text': '2:56:56PM', 'label': 9}, {'box': [107.0, 449.0, 171.0, 449.0, 171.0, 436.0, 107.0, 436.0], 'text': 'THANKYOU!', 'label': 25}]}\n" - ] - } - ], - "source": [ - "create_kie_wildreceipt_dataset(\n", - " \"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/wildreceipt\",\n", - " train_txt=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/wildreceipt/train.txt\",\n", - " test_txt=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/wildreceipt/test.txt\",\n", - " class_file=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/wildreceipt/class_list.txt\",\n", - " out_dir=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/processed\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "def format_txt_data(txt_data):\n", - " out_data = []\n", - " for item in txt_data:\n", - " splitted_items = item.split(\"\\t\")\n", - " if len(splitted_items) == 6:\n", - " box, text, label = splitted_items[:4], splitted_items[4], splitted_items[5]\n", - " else:\n", - " box, text, label = splitted_items[:4], splitted_items[4], \"dummy_label\"\n", - " out_data.append(\n", - " {\n", - " 'box': [int(x) for x in box],\n", - " 'text': text,\n", - " 'label': label\n", - " }\n", - " )\n", - "\n", - " return out_data\n", - "\n", - "def is_in_field(box1, box2, thr=0.7):\n", - " \"\"\"\n", - " Calculates the IOU between two bounding boxes based only on their y-axis intersection.\n", - "\n", - " Parameters:\n", - " box1 (tuple): A tuple in the format (xmin, ymin, xmax, ymax) representing the first bounding box.\n", - " box2 (tuple): A tuple in the format (xmin, ymin, xmax, ymax) representing the second bounding box.\n", - "\n", - " Returns:\n", - " float: The IOU between the two bounding boxes based only on their y-axis intersection.\n", - " \"\"\"\n", - " # Calculate the y-axis intersection between the two boxes\n", - " y_intersection = min(box1[3], box2[3]) - max(box1[1], box2[1])\n", - "\n", - " # If there is no y-axis intersection, the IOU is 0\n", - " if y_intersection <= 0:\n", - " return 0.0\n", - "\n", - " # Calculate the area of the union between the two boxes based only on their y-axis intersection\n", - " y_union = max(box1[3], box2[3]) - min(box1[1], box2[1])\n", - "\n", - " # Calculate the IOU\n", - " iou_y = y_intersection / y_union\n", - "\n", - " # Calculate the intersection along the x-axis\n", - " intersection = max(0, min(box1[2], box2[2]) - max(box1[0], box2[0]))\n", - " \n", - " # Calculate the area of the smaller box\n", - " smaller_box_width = min(box1[2] - box1[0], box2[2] - box2[0])\n", - " smaller_box_area = smaller_box_width * 1.0 # 1.0 for height, can be any value\n", - " \n", - " # Calculate the IoU along the x-axis\n", - " iou_x = intersection / smaller_box_area\n", - " print(f\"iou_y = {iou_y}, iou_x = {iou_x}\")\n", - " if iou_y > thr and iou_x > 0.9:\n", - " return True \n", - " \n", - " return False\n", - "\n", - "\n", - "def check_iou(box1, box2, threshold=0.9):\n", - " area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])\n", - " area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])\n", - " xmin_intersect = max(box1[0], box2[0])\n", - " ymin_intersect = max(box1[1], box2[1])\n", - " xmax_intersect = min(box1[2], box2[2])\n", - " ymax_intersect = min(box1[3], box2[3])\n", - "\n", - "\n", - " if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect:\n", - " area_intersect = 0\n", - " else:\n", - " area_intersect = (xmax_intersect - xmin_intersect) * (\n", - " ymax_intersect * ymin_intersect\n", - " )\n", - " # union = area1 + area2 - area_intersect\n", - " iou = area_intersect / area1\n", - " if iou > threshold:\n", - " return True\n", - " return False\n", - "\n", - "def update_label_for_word(word_item, wildreceipt_data, list_labels, threshold=0.75):\n", - " have_label = False\n", - " for wild_item in wildreceipt_data:\n", - " # if is_in_field(word_item['box'], wild_item['box'], thr=0.5):\n", - " # print(wild_item['label'])\n", - " if check_iou(word_item['box'], wild_item['box'], threshold=threshold):\n", - " if wild_item['label'] in list_labels:\n", - " word_item['label'] = wild_item['label']\n", - " have_label = True\n", - " else:\n", - " word_item['label'] = \"Others\"\n", - " have_label = True\n", - " break\n", - "\n", - " if not have_label:\n", - " word_item['label'] = \"Others\"\n", - " return word_item\n", - " \n", - "def dict_to_line(item_dict):\n", - " box = item_dict['box']\n", - " text = item_dict['text']\n", - " label = item_dict['label']\n", - " line = \"\\t\".join([str(x) for x in box])\n", - " line = \"\\t\".join([line, text, label])\n", - " return line\n", - "\n", - "def update_kie_label(wildreceipt_data, ocr_word_result, list_labels=['']):\n", - " # wildreceipt_data = read_txt(ori_txt)\n", - " formated_wildreceipt_data = format_txt_data(wildreceipt_data)\n", - " \n", - " # ocr_word_result = read_txt(ocr_txt)\n", - " formated_ocr_word_result = format_txt_data(ocr_word_result)\n", - " new_ocr_word_result = []\n", - " for word_item in formated_ocr_word_result:\n", - " word_item = update_label_for_word(word_item, formated_wildreceipt_data, list_labels, threshold=0.9)\n", - " new_ocr_word_result.append(word_item)\n", - "\n", - " out_txt_data = [dict_to_line(item_dict) for item_dict in new_ocr_word_result]\n", - " return out_txt_data\n", - "\n", - "def process_update_kie_label(wild_txt, ocr_txt, out_dir, list_labels=[]):\n", - " out_dir = Path(out_dir)\n", - " if not out_dir.exists():\n", - " out_dir.mkdir(parents=True)\n", - "\n", - " ocr_txt_paths = Path(ocr_txt).glob(\"*.txt\")\n", - "\n", - " for ocr_path in ocr_txt_paths:\n", - " ocr_data = read_txt(ocr_path)\n", - " wild_txt_path = Path(wild_txt) / ocr_path.name\n", - " wild_data = read_txt(wild_txt_path)\n", - " updated_ocr_data = update_kie_label(wild_data, ocr_data, list_labels)\n", - " \n", - " out_ocr_path = out_dir / ocr_path.name\n", - " write_txt(out_ocr_path, updated_ocr_data)\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "process_update_kie_label(\n", - " wild_txt=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/processed/test\",\n", - " ocr_txt=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/pseudo_ocr/test\",\n", - " out_dir=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/v1/test\",\n", - " list_labels=[\n", - " \"Store_name_value\",\n", - " \"Total_value\",\n", - " \"Date_value\",\n", - " \"Total_key\"\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "process_update_kie_label(\n", - " wild_txt=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/processed/train\",\n", - " ocr_txt=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/pseudo_ocr/train\",\n", - " out_dir=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/v1/train\",\n", - " list_labels=[\n", - " \"Store_name_value\",\n", - " \"Total_value\",\n", - " \"Date_value\",\n", - " \"Total_key\"\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "from pathlib import Path\n", - "\n", - "def split_folder_into_batches(data_dir, out_dir, batch_size):\n", - " # create output directory if it doesn't exist\n", - " if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n", - "\n", - " # get list of all .jpg files in data_dir\n", - " jpg_files = [filename for filename in os.listdir(data_dir) if not filename.endswith('.txt')]\n", - "\n", - " # split jpg_files into batches\n", - " num_batches = (len(jpg_files) + batch_size - 1) // batch_size # round up to nearest integer\n", - " batches = [jpg_files[i*batch_size:(i+1)*batch_size] for i in range(num_batches)]\n", - "\n", - " # copy each batch of .txt and .jpg files to out_dir\n", - " for i, batch in enumerate(batches):\n", - " batch_dir = os.path.join(out_dir, f'batch_{i+1}')\n", - " if not os.path.exists(batch_dir):\n", - " os.makedirs(batch_dir)\n", - " for jpg_file in batch:\n", - " txt_file = str(Path(jpg_file).with_suffix(\".txt\"))\n", - " shutil.copy2(os.path.join(data_dir, txt_file), batch_dir)\n", - " shutil.copy2(os.path.join(data_dir, jpg_file), batch_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "FileExistsError", - "evalue": "[Errno 17] File exists: '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/batches/batch_1'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileExistsError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m split_folder_into_batches(\n\u001b[1;32m 2\u001b[0m data_dir\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/train_and_test\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 3\u001b[0m out_dir\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/batches\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 4\u001b[0m batch_size\u001b[39m=\u001b[39;49m\u001b[39m500\u001b[39;49m\n\u001b[1;32m 5\u001b[0m )\n", - "Cell \u001b[0;32mIn[3], line 20\u001b[0m, in \u001b[0;36msplit_folder_into_batches\u001b[0;34m(data_dir, out_dir, batch_size)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[39mfor\u001b[39;00m i, batch \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(batches):\n\u001b[1;32m 19\u001b[0m batch_dir \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(out_dir, \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbatch_\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 20\u001b[0m os\u001b[39m.\u001b[39;49mmakedirs(batch_dir)\n\u001b[1;32m 21\u001b[0m \u001b[39mfor\u001b[39;00m jpg_file \u001b[39min\u001b[39;00m batch:\n\u001b[1;32m 22\u001b[0m txt_file \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(Path(jpg_file)\u001b[39m.\u001b[39mwith_suffix(\u001b[39m\"\u001b[39m\u001b[39m.txt\u001b[39m\u001b[39m\"\u001b[39m))\n", - "File \u001b[0;32m~/miniconda3/envs/py38_hoanglv/lib/python3.8/os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m mkdir(name, mode)\n\u001b[1;32m 224\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m:\n\u001b[1;32m 225\u001b[0m \u001b[39m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[39m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m exist_ok \u001b[39mor\u001b[39;00m \u001b[39mnot\u001b[39;00m path\u001b[39m.\u001b[39misdir(name):\n", - "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: '/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/batches/batch_1'" - ] - } - ], - "source": [ - "split_folder_into_batches(\n", - " data_dir=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/train_and_test\",\n", - " out_dir=\"/mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/batches\",\n", - " batch_size=500\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py38_hoanglv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/cope2n-ai-fi/modules/sdsvkie/requirements.txt b/cope2n-ai-fi/modules/sdsvkie/requirements.txt deleted file mode 100644 index 7042116..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -protobuf>=3.19.6,<4 -opencv-python>=4.4.0 -torch>=1.4 -torchvision -transformers>=4.25.1 -datasets>=2.5.2 -Pillow==9.5.0 -wandb -easydict==1.10 -terminaltables==3.1.10 -tqdm -rapidfuzz==2.13.7 -PyMuPDF==1.20.2 -sentencepiece -underthesea \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/2023-04-20_0101803564_0300741922_1C23TYY_714.pdf b/cope2n-ai-fi/modules/sdsvkie/scripts/common/2023-04-20_0101803564_0300741922_1C23TYY_714.pdf deleted file mode 100644 index 969cfac..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/2023-04-20_0101803564_0300741922_1C23TYY_714.pdf +++ /dev/null @@ -1,49 +0,0 @@ -%PDF-1.3 -% ReportLab Generated PDF document http://www.reportlab.com -1 0 obj -<< -/F1 2 0 R ->> -endobj -2 0 obj -<< -/BaseFont /Helvetica /Encoding /WinAnsiEncoding /Name /F1 /Subtype /Type1 /Type /Font ->> -endobj -3 0 obj -<< -/PageMode /UseNone /Pages 5 0 R /Type /Catalog ->> -endobj -4 0 obj -<< -/Author (anonymous) /CreationDate (D:20230522134603-07'00') /Creator (ReportLab PDF Library - www.reportlab.com) /Keywords () /ModDate (D:20230522134603-07'00') /Producer (ReportLab PDF Library - www.reportlab.com) - /Subject (unspecified) /Title (untitled) /Trapped /False ->> -endobj -5 0 obj -<< -/Count 0 /Kids [ ] /Type /Pages ->> -endobj -xref -0 6 -0000000000 65535 f -0000000073 00000 n -0000000104 00000 n -0000000211 00000 n -0000000279 00000 n -0000000575 00000 n -trailer -<< -/ID -[<4d2762f6f45f96a78f66af9b0251b167><4d2762f6f45f96a78f66af9b0251b167>] -% ReportLab generated PDF document -- digest (http://www.reportlab.com) - -/Info 4 0 R -/Root 3 0 R -/Size 6 ->> -startxref -629 -%%EOF diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/build_batch_2.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/build_batch_2.py deleted file mode 100644 index ace5128..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/build_batch_2.py +++ /dev/null @@ -1,10 +0,0 @@ -import os -from pathlib import Path -import shutil -data_dir = "/mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Data" - -data_dir = Path(data_dir) - -for path in data_dir.glob("*/*.pdf"): - if path.with_suffix(".xml").exists(): - shutil.copy(str(path), "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/multi_page_vat/batch_2") \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/check_duplicate_vat.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/check_duplicate_vat.py deleted file mode 100644 index 8ffb9f2..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/check_duplicate_vat.py +++ /dev/null @@ -1,117 +0,0 @@ - -from pathlib import Path -import shutil -import json - -def write_txt(txt, data, mode="w"): - with open(txt, mode, encoding="utf8") as f: - for line in data: - f.write(line + "\n") - - -def read_txt(txt): - with open(txt, "r", encoding="utf8") as f: - data = [line.strip() for line in f] - return data - -def get_no(items): - no = "xxxx" - for item in items: - if "No_value" in item: - tmp = item.split("\t") - no = tmp[-2] - - return no - -def write_json(json_path, data): - with open(json_path, "w", encoding="utf8") as f: - json.dump(data, f, ensure_ascii=False, sort_keys=True) - - -def read_json(json_path): - with open(json_path, "r", encoding="utf8") as f: - data = json.load(f) - return data - - -def check(txt_dir): - log_dict = {} - txt_dir = Path(txt_dir) - txt_paths = txt_dir.rglob("*.txt") - for txt_path in txt_paths: - items = read_txt(str(txt_path)) - no_doc = get_no(items) - if no_doc not in log_dict: - log_dict[no_doc] = [str(txt_path.with_suffix(".jpg"))] - else: - log_dict[no_doc].append(str(txt_path.with_suffix(".jpg"))) - - not_dups = [] - for no, paths in log_dict.items(): - if len(paths) == 1: - not_dups.append(no) - # if "xxxx" in log_dict.keys(): - # log_dict.pop("xxxx") - for _ in not_dups: - log_dict.pop(_) - - print(log_dict.keys()) - return log_dict - -# print(check("/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train/SS_Invoice")) - - -def get_leak_test(data_dir): - test_keys = [] - data_dir = Path(data_dir) - test_paths = data_dir.rglob("test_*") - # print(list(test_paths)) - for path in test_paths: - img_name = path.stem - img_name = img_name.replace("test_","") - test_keys.append(img_name) - - - # write_txt("leak.txt", test_keys) - return test_keys - - -def create_new_test(ori_dir, out_dir, test_keys): - ori_dir = Path(ori_dir) - out_dir = Path(out_dir) - if not out_dir.exists(): - out_dir.mkdir(parents=True, exist_ok=True) - - img_paths = ori_dir.rglob("*.jpg") - for img_path in img_paths: - img_key = img_path.stem - if img_key in test_keys: - continue - txt_path = img_path.with_suffix(".txt") - shutil.copy(str(img_path), str(out_dir)) - shutil.copy(str(txt_path), str(out_dir)) - -def create_new_e2e_test(ori_json, out_json, test_keys): - ori_data = read_json(ori_json) - out_dict = {} - for k, v in ori_data.items(): - if k in test_keys: - continue - out_dict[k] = v - - write_json(out_json, out_dict) - - -test_keys = get_leak_test("/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/EXCESS") -# create_new_test( -# ori_dir="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss", -# out_dir="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_rm_leak", -# test_keys=test_keys -# ) - -create_new_e2e_test( - ori_json="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_e2e.json", - out_json="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_e2e_rm_leak.json", - test_keys=test_keys -) - diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/clean_json.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/clean_json.py deleted file mode 100644 index 6e8b29c..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/clean_json.py +++ /dev/null @@ -1,39 +0,0 @@ -import argparse -from pathlib import Path -from tqdm import tqdm -from sdsvkie.utils.io_file import read_json, write_json - - -def clean_json(in_json, out_json, pdf_dir): - data_src = read_json(in_json) - - pdf_dir = Path(pdf_dir) - pdf_paths = pdf_dir.glob("*.pdf") - pdf_keys = set([pdf_path.stem for pdf_path in pdf_paths]) - - data_tgt = {} - for src_pdf_key in data_src.keys(): - if src_pdf_key in pdf_keys: - data_tgt[src_pdf_key] = data_src[src_pdf_key] - - write_json(out_json, data_tgt, sort_keys=False) - - - - - - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(prog="Rename labels") - parser.add_argument("--input", type=str, required=True, help="dataset directory") - parser.add_argument("--out", type=str, required=False, help="output") - parser.add_argument("--dir", type=str, required=True, help="document type: receipt / invoice") - - args = parser.parse_args() - clean_json( - in_json=args.input, - out_json=args.out, - pdf_dir=args.dir - ) diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/clean_multipage_data.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/clean_multipage_data.py deleted file mode 100644 index ca437ec..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/clean_multipage_data.py +++ /dev/null @@ -1,49 +0,0 @@ -import os - -import json -from pathlib import Path - -def read_json(json_path): - with open(json_path, 'r') as f: - data = json.load(f) - return data - -def write_json(json_path, data): - with open(json_path, 'w') as f: - json.dump(data, f, ensure_ascii=False) - - -def clean_json(json_in, json_out, valid_names): - out_data = {} - data = read_json(json_in) - for name_key, items in data.items(): - if name_key in valid_names: - out_data[name_key] = items - - write_json(json_out, out_data) - -# DIRNAMES = ['SL_HCM', 'SL_HN_batch_1', 'SL_HN_batch_2', 'Invoices_SAVINA'] -# ROOT_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page" - -DIRNAMES = ['test_sbt_v2'] -ROOT_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed" -for dirname in DIRNAMES: - json_path = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_end2end/sbt_validation_e2e.json" - json_out_path = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_end2end/sbt_validation_e2e_v2.json" - valid_names = [p.stem for p in (Path(ROOT_DIR) / dirname).glob("*")] - print(valid_names) - clean_json(json_path, json_out_path, valid_names) - - -# def combine_json(json_paths, json_out): -# datas = [read_json(json_path) for json_path in json_paths] -# out_data = {} -# for data in datas: -# out_data.update(data) -# write_json(json_out, out_data) - - -# json_paths = [Path(ROOT_DIR) / (dirname + "_out.json") for dirname in DIRNAMES] -# json_out = ROOT_DIR + "/test_e2e_multi_pages.json" -# combine_json(json_paths, json_out) - diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/copy_img.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/copy_img.py deleted file mode 100644 index 998add2..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/copy_img.py +++ /dev/null @@ -1,23 +0,0 @@ -import os -import shutil -import glob -from pathlib import Path - - -if __name__ == "__main__": - src_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train/SL_HCM_batch_2_multi_pages" - tgt_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/key_information_extraction/" - num_files = 100 - - files = glob.glob(os.path.join(src_dir, "*.jpg")) - count = 0 - for file in files: - src_path = os.path.join(src_dir, file) - tgt_path = os.path.join(tgt_dir, file) - if os.path.isfile(src_path): - shutil.copy(src_path, tgt_path) - count += 1 - if count == num_files: - break - - print(f"Copied {count} files from {src_dir} to {tgt_dir}") \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/create_train_test_data.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/create_train_test_data.py deleted file mode 100644 index 77b8798..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/create_train_test_data.py +++ /dev/null @@ -1,25 +0,0 @@ -import os -import shutil -from pathlib import Path - - -SRC_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train/sbt/batch_1" -TEST_DIR = "/mnt/ssd1T/tuanlv/06.KVUCombineStage/datasets/invoices-receipts/SS_invoices/SBT/validation_data/images" -OUT_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_sbt" - -# Get a list of all the files in the test directory -test_files = [Path(f).name for f in os.listdir(TEST_DIR) if ".txt" not in f] - -# Create the output directory if it doesn't exist -os.makedirs(OUT_DIR, exist_ok=True) - -# Move the matching files from the source directory to the output directory -for filename in os.listdir(SRC_DIR): - if Path(filename).name in test_files: - src_path = os.path.join(SRC_DIR, filename) - # out_path = os.path.join(OUT_DIR, filename) - shutil.move(src_path, OUT_DIR) - - #move .txt - src_txt_path = Path(os.path.join(SRC_DIR, filename)).with_suffix(".txt") - shutil.move(str(src_txt_path), OUT_DIR) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/extract_xml.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/extract_xml.py deleted file mode 100644 index 1c3f8bb..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/extract_xml.py +++ /dev/null @@ -1,314 +0,0 @@ -import shutil -import xml.etree.ElementTree as ET -from datetime import datetime -# from sdsvkie.utils.io_file import read_json, write_json -import json - -import csv -import ast -def get_xml_from_csv(csv_file): - data = {} - with open(csv_file, 'r') as file: - csv_reader = csv.DictReader(file) - for row in csv_reader: - # print(row) - pdf_path = row['file_path'] - pdf_key = Path(pdf_path).stem - xml_paths = ast.literal_eval(row['xml_path']) - data[pdf_key] = xml_paths - - return data - -def get_xml_from_dirs(dir_path, pdf_keys): - dir_path = Path(dir_path) - xml_paths = dir_path.rglob("*.xml") - xml_paths = [str(path) for path in xml_paths] - xml_infos = {} - - for pdf_key in pdf_keys: - xml_infos[pdf_key] = xml_paths - return xml_infos - - -def write_json(json_path, data, sort_keys=True): - with open(json_path, "w", encoding="utf8") as f: - json.dump(data, f, ensure_ascii=False, sort_keys=sort_keys) - - -def read_json(json_path): - with open(json_path, "r", encoding="utf8") as f: - data = json.load(f) - return data - - -from pathlib import Path -import tqdm -import logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -def convert_date(date_str: str, ori_pattern: str = '%Y-%m-%d', tgt_pattern: str = '%d/%m/%Y'): - date_obj = datetime.strptime(date_str, ori_pattern) - - # convert back to string in DD-MM-YY format - new_date_str = date_obj.strftime(tgt_pattern) - return new_date_str - -def extract(xml_in, field_mapping): - - - with open(xml_in, "r") as f: - xml_string = f.read() - # parse the XML string - root = ET.fromstring(xml_string) - - # extract the SHDon and NLap elements - output = {} - for key in field_mapping: - pattern = f".//{field_mapping[key]}" - value = root.find(pattern) - value = "" if value is None else value.text - if key == "date_value" and value != "": - value = convert_date(value) - - if key in ["tax_amount_value", "total_value"] and value != "": - value = str(int(float(value))) - - - output[key] = value - return output - -field_mapping = { - "no_value": "SHDon", - "form_value": "KHMSHDon", - "serial_value": "XXXXXXX", - "date_value": "NLap", # 2023-06-05 -> YY-MM-DD - "seller_company_name_value": "NBan/Ten", - "seller_address_value": "NBan/DChi", - "seller_tel_value": "XXXXXXXXX", - "seller_tax_code_value": "NBan/MST", - "buyer_personal_name_value": "NMua/HVTNMHang", - "buyer_company_name_value": "NMua/Ten", - "buyer_address_value": "NMua/DChi", - "buyer_tax_code_value": "NMua/MST", - "buyer_tel_value": "NMua/SDT", - "tax_amount_value": "TThue", - "total_value": "TgTTTBSo", - "total_in_words_value": "TgTTTBChu" - } - -## fields need care: serial_value, seller_tel_value, buyer_tel_value - -def get_xml_list_info(xml_dir): - xml_dir = Path(xml_dir) - xml_files = xml_dir.glob("*/*.xml") - xml_info = {} - for xml_file in xml_files: - pdf_key = xml_file.stem - xml_info[pdf_key] = str(xml_file) - return xml_info - -def process(json_in, json_out, xml_dir): - assert Path(json_in).exists() == True - assert Path(xml_dir).exists() == True - data_in = read_json(json_in) - data_out = {} - if data_in is None or not data_in: - logger.error("empty file") - return - - xml_info = get_xml_list_info(xml_dir) - for pdf_key in tqdm.tqdm(data_in.keys()): - - xml_path = xml_info[pdf_key] if pdf_key in xml_info else None - if xml_path is None: - continue - else: - output = extract(xml_path, field_mapping) - - data_out[pdf_key] = output - - write_json(json_out, data_out, sort_keys=False) - - -def get_xml_list_info_v2(xml_dir): - xml_dir = Path(xml_dir) - xml_files = xml_dir.glob("*/*.xml") - - xml_info = {} - for xml_file in xml_files: - pdf_key = xml_file.stem - - if pdf_key in xml_info: - xml_info[pdf_key].append(str(xml_file)) - else: - xml_info[pdf_key] = [str(xml_file)] - - return xml_info - -def extract_v2(xml_paths, preds, field_mapping, pdf_key=None): - - - xml_path = None - if len(xml_paths) == 1: - xml_path = xml_paths[0] - else: - # find best xml - for xml_in in xml_paths: - try: - with open(xml_in, "r", encoding='utf8') as f: - xml_string = f.read() - root = ET.fromstring(xml_string, parser = ET.XMLParser(encoding = 'iso-8859-5')) - except Exception as err: - print("Error exception (check) ", err, xml_in) - continue - - key_checks = ["no_value"] - is_exists_xml = False - for key_check in key_checks: - pattern = f".//{field_mapping[key_check]}" - value = root.find(pattern) - value = "" if value is None else value.text - - if value == preds[key_check]: - is_exists_xml = True - if is_exists_xml: - xml_path = xml_in - if xml_path is None: - print("Not found best xml for ",pdf_key, xml_paths) - return None, None - - # - try: - with open(xml_path, "r") as f: - xml_string = f.read() - # parse the XML string - root = ET.fromstring(xml_string) - except Exception as err: - print("Error exception: ", err, xml_path) - return None, None - # extract the SHDon and NLap elements - output = {} - for key in field_mapping: - pattern = f".//{field_mapping[key]}" - value = root.find(pattern) - value = "" if value is None else value.text - if key == "date_value" and value != "": - value = convert_date(value) - if key in ["tax_amount_value", "total_value"] and value != "": - value = str(int(float(value))) - - output[key] = value - - return output, xml_path - -def process_v2(json_in, json_out, csv_file, xml_dir, xml_out_dir, pdf_xml_json): - assert Path(json_in).exists() == True - assert Path(xml_dir).exists() == True - # make dir - if not Path(xml_out_dir).exists(): - Path(xml_out_dir).mkdir(parents=True, exist_ok=True) - - data_in = read_json(json_in) - data_out = {} - if data_in is None or not data_in: - logger.error("empty file") - return - - # xml_info = get_xml_list_info_v2(xml_dir) - # xml_info = get_xml_from_csv(csv_file=csv_file) - xml_info = get_xml_from_dirs(dir_path=csv_file, pdf_keys=list(data_in.keys())) - print("Num xml: ", len(xml_info)) - succes = 0 - pdf_xml_info = {} - set_xml_paths = set() - for pdf_key in tqdm.tqdm(data_in.keys()): - - xml_paths = xml_info[pdf_key] if pdf_key in xml_info else None - # print(xml_paths) - preds = data_in[pdf_key] - if xml_paths is None or len(xml_paths) == 0: - print("Not exist xml because xml_paths is None or len xml_paths = 0", pdf_key) - continue - else: - output, xml_path = extract_v2(xml_paths, preds, field_mapping, pdf_key=pdf_key) - - if output is not None: - pdf_xml_info[pdf_key] = xml_path - shutil.copy(xml_path, xml_out_dir) - # if Path(xml_path).stem in set_xml_paths: - # print(pdf_key, xml_path) - set_xml_paths.add(Path(xml_path).stem) - succes += 1 - data_out[pdf_key] = output - print("Succes: ", succes) - print(len(set_xml_paths)) - write_json(pdf_xml_json, pdf_xml_info, sort_keys=False) - write_json(json_out, data_out, sort_keys=False) - - -def combine_xml(json_src, json_refer): - data_src = read_json(json_src) - data_refer = read_json(json_refer) - - for pdf_key in data_src.keys(): - for field_key in data_src[pdf_key]: - if data_src[pdf_key][field_key] == "": - data_src[pdf_key][field_key] = data_refer[pdf_key][field_key] - - write_json(json_src, data=data_src, sort_keys=False) - - - -def create_data_from_json(in_dir, out_dir, json_path): - in_dir = Path(in_dir) - out_dir = Path(out_dir) - - if not out_dir.exists(): - out_dir.mkdir(parents=True, exist_ok=True) - - data = read_json(json_path) - - for pdf_key in data.keys(): - pdf_path = in_dir / (pdf_key + ".pdf") - shutil.copy(str(pdf_path), str(out_dir)) - -if __name__ == "__main__": - - - # json_in = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_multi_page.json" - # json_out = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_multi_page_from_xml.json" - # xml_dir = "/mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Test" - # process(json_in=json_in, json_out=json_out, xml_dir=xml_dir) - - # json_in = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_multi_page_from_xml.json" - # json_refer = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_multi_page.json" - # json_in = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_one_page_e2e_from_xml.json" - # json_refer = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_one_page_e2e.json" - - # combine_xml(json_src=json_in, json_refer=json_refer) - - ## One page - # json_in = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_one_page_e2e.json" - # json_out = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_one_page_e2e_from_xml.json" - ## Multi page - json_in = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/Invoice_v1_multi_page.json" - json_out = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/Invoice_v1_multi_page_from_xml.json" - - # csv_file = "/mnt/ssd1T/tuanlv/02.KeyValueUnderstanding/inferences/e2e_outputs/FI_June_data.csv" - csv_file = "/mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Data" - pdf_xml_json = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v1_multi_page_metadata.json" - - xml_dir = "/mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Test" - xml_out_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/Invoice_v1_multi_page_xml" - - process_v2(json_in=json_in, json_out=json_out, csv_file=csv_file, xml_dir=xml_dir, xml_out_dir=xml_out_dir, pdf_xml_json=pdf_xml_json) - - # in_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/Invoice_v2_multi_page" - # out_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/Invoice_v2_multi_page_clean" - # json_path = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_multi_page_from_xml.json" - # in_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/Invoice_v2_one_page" - # out_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/Invoice_v2_one_page_clean" - # json_path = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_one_page_e2e_from_xml.json" - - # create_data_from_json(in_dir, out_dir, json_path) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/format_json.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/format_json.py deleted file mode 100644 index b4c40b9..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/format_json.py +++ /dev/null @@ -1,42 +0,0 @@ -import json - - - -json_path = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/multi_page_vat/SL_HCM.json" -with open(json_path, 'r', encoding='utf8') as f: - data = json.load(f) -print(data[list(data.keys())[0]].keys()) - -keys = [ - 'serial_value', - 'no_value', - 'form_value', - 'date', - - 'seller_company_name_value', - 'seller_address_value', - 'seller_mobile_value', - 'seller_tax_code_value', - - 'buyer_name_value', - 'buyer_company_name_value', - 'buyer_address_value', - 'buyer_mobile_value', - 'buyer_tax_code_value', - - 'VAT_amount_value', - 'total_in_words_value', - 'total_value' -] -new_data = {} -for file_name, items in data.items(): - - new_items = {} - for k in keys: - new_items[k] = items[k] - new_data[file_name] = new_items - - -with open(json_path, 'w', encoding='utf8') as f: - json.dump(new_data, f, ensure_ascii=False) - \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/get_more_data.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/get_more_data.py deleted file mode 100644 index fb21455..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/get_more_data.py +++ /dev/null @@ -1,31 +0,0 @@ -import os -import shutil - - -from pathlib import Path - -folder1 = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/Invoice_v2_multi_page" -folder2 = "/mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Invoice_v2_multi_page" - -out_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/Invoice_v2_multi_page_2" -out_dir = Path(out_dir) -if not out_dir.exists(): - out_dir.mkdir(parents=True, exist_ok=True) - -# Get list of files in both folders -files1 = [f for f in os.listdir(folder1) if os.path.isfile(os.path.join(folder1, f))] -files2 = [f for f in os.listdir(folder2) if os.path.isfile(os.path.join(folder2, f))] - -# Get list of file names in both folders -names1 = [os.path.splitext(f)[0] for f in files1] -names2 = [os.path.splitext(f)[0] for f in files2] - -# Find duplicates by comparing names -duplicates = set(names1) ^ set(names2) -print(len(duplicates)) -# Print duplicate file names -for d in duplicates: - print(f"Duplicate file name found: {d}") - pdf_path = Path(folder2) / (d+".pdf") - shutil.copy(str(pdf_path), str(out_dir)) - diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/leak.txt b/cope2n-ai-fi/modules/sdsvkie/scripts/common/leak.txt deleted file mode 100644 index a2bfa93..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/leak.txt +++ /dev/null @@ -1,106 +0,0 @@ -200 -Invoice_ho_1007_000 -210 -invoice_126 -inv_SDV_016 -invoice_108 -invoice_215 -invoice_135 -inv_SDV_004 -292 -164 -242 -inv_SDV_240 -207 -invoice_0525_000 -invoice_1279_000 -306 -d2.sc_1261_000 -invoice_90 -304 -s1.sc_1258_000 -ce_1h_0967_000 -invoice_1392_000 -193 -invoice_109 -281 -354 -invoice_1059_000 -es_10_1043_000 -257 -invoice_65 -invoice_1252_006 -331 -scan__1319_000 -230 -20210_1314_000 -328 -o1_aa_1093_000 -342 -invoice_149 -invoice_1304_000 -c2_em_0081_000 -Invoice_En_1074_000 -invoice_89 -Invoice_Sh_0712_000 -invoice_202 -hotel_0209_000 -invoice_0872_000 -invoice_72 -InvoiceofP_0648_000 -invoice_133 -C1_Invoice_0968_000 -invoice_0803_000 -invoice_50 -invoice_208 -253 -inv_SDV_215 -360 -invoice_1393_000 -scan__0953_000 -invoice_22 -O1_Invoice_1348_000 -inv_SDV_231 -252 -273 -156 -330 -invoice_0457_001 -invoice_0180_001 -invoice_182 -326 -14 -301 -334 -01gtk_0199_000 -343 -Invoice201_0930_000 -invoice_1 -344 -inv_SDV_021 -invoice_170 -E2.Invoice_0561_000 -Invoice_Sh_0262_000 -1.1Invoice_1431_000 -invoice_0112_000 -invoice_195 -314 -2021._0035_000 -invoice_0013_000 -invoice_1204_000 -2021._0868_000 -scan__0520_000 -255 -invoice_200 -C3_Invoice_1359_000 -invoice_49 -invoice_1095_000 -hq_20_0003_000 -invoice_180 -invoice_184 -340 -invoice_0447_000 -invoice_6 -invoice_190 -invoice_105 -invoice_0673_000 diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/remove_imgs.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/remove_imgs.py deleted file mode 100644 index d330cd6..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/remove_imgs.py +++ /dev/null @@ -1,24 +0,0 @@ -import os -import shutil -from pathlib import Path - - -SRC_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train/sbt/batch_1" -TEST_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/sbt_txt" - -# Get a list of all the files in the test directory -test_files = sorted([Path(f).stem for f in os.listdir(TEST_DIR) if ".txt" in f]) -print(len(test_files)) - -# Create the output directory if it doesn't exist -# Move the matching files from the source directory to the output directory -i = 0 -src_files = sorted(os.listdir(SRC_DIR)) -print(len(src_files)) -for filename in src_files: - # print(Path(filename).stem ) - if Path(filename).stem not in test_files: - print(Path(filename).stem) - i+=1 - -print(i) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename.py deleted file mode 100644 index 9283ed2..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename.py +++ /dev/null @@ -1,15 +0,0 @@ -import os - -def rename_files(folder_path): - # Get a list of all the files in the folder - files = os.listdir(folder_path) - # Iterate over the files and rename them - for i, filename in enumerate(files): - # Construct the new filename - new_filename = filename.replace(" ", "_") - # Rename the file - os.rename(os.path.join(folder_path, filename), os.path.join(folder_path, new_filename)) - -rename_files( - folder_path="/mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/sbt/images", -) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename.sh deleted file mode 100644 index 889fbe1..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename.sh +++ /dev/null @@ -1,10 +0,0 @@ -python rename_labels.py \ - --in_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed \ - --out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed \ - --doc_type receipt - - -python rename_labels.py \ - --in_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed \ - --out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed \ - --doc_type invoice \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename_labels.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename_labels.py deleted file mode 100644 index 1f0962f..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/rename_labels.py +++ /dev/null @@ -1,191 +0,0 @@ -import argparse -from pathlib import Path -from tqdm import tqdm -import json - -INVOICE_MAPPING = { - 'no_key': 'No_key', # số hóa đơn - 'no_value': 'No_value', - 'form_key': 'Form_key', # mẫu số hóa đơn - 'form_value': 'Form_value', - 'serial_key': 'Serial_key', # số kí hiệu hoá đơn - 'serial_value': 'Serial_value', - 'date': 'Date_value', - - # seller info - 'seller_company_name_key': 'Seller_company_name_key', - 'seller_company_name_value': 'Seller_company_name_value', - 'seller_tax_code_key': 'Seller_tax_code_key', - 'seller_tax_code_value': 'Seller_tax_code_value', - 'seller_address_value': 'Seller_address_value', - 'seller_address_key': 'Seller_address_key', - 'seller_mobile_key': 'Seller_tel_key', - 'seller_mobile_value': 'Seller_tel_value', - - # buyer info - 'buyer_name_key': 'Buyer_personal_name_key', - 'buyer_name_value': 'Buyer_personal_name_value', - 'buyer_company_name_value': 'Buyer_company_name_value', - 'buyer_company_name_key': 'Buyer_company_name_key', - 'buyer_tax_code_key': 'Buyer_tax_code_key', - 'buyer_tax_code_value': 'Buyer_tax_code_value', - 'buyer_address_key': 'Buyer_address_key', - 'buyer_address_value': 'Buyer_address_value', - 'buyer_mobile_key': 'Buyer_tel_key', - 'buyer_mobile_value': 'Buyer_tel_value', - - # money info - 'VAT_amount_key': 'Tax_amount_key', - 'VAT_amount_value': 'Tax_amount_value', - 'total_key': 'Total_key', - 'total_value': 'Total_value', - 'total_in_words_key': 'Total_in_words_key', - 'total_in_words_value': 'Total_in_words_value', - - 'other': 'Other', -} - -RECEIPT_MAPPING = { - "Store_name_value": "seller_company_name_value", - "Seller_company_name_value": "seller_company_name_value", - "id": "no_value", - "No_value": "no_value", - - "Date_value": "date_value", - "Total_key": "total_key", - "Total_value": "total_value", - - "Others": "other", - "others": "other", - "Other": "other", -} - -def write_txt(txt, data, mode="w"): - with open(txt, mode, encoding="utf8") as f: - for line in data: - f.write(line + "\n") - - -def read_txt(txt): - with open(txt, "r", encoding="utf8") as f: - data = [line.strip() for line in f] - return data - - - -def edit_file(in_txt, out_txt, mapping): - data = read_txt(in_txt) - new_items = [] - not_exits_label = False - not_edit = True - for item in data: - splited_item = item.split("\t") - label = splited_item[-1] - if label in mapping.keys(): - new_label = mapping[label] - splited_item[-1] = new_label - not_edit = False - else: - # print(label, "not in ", mapping.keys()) - not_exits_label = True - splited_item[-1] = label.lower() - - splited_item[-1] = splited_item[-1].lower() - new_item = "\t".join(splited_item) - new_items.append(new_item) - - if not_exits_label: - print("Not exists label: ", in_txt) - - if not not_edit: - print("Not edit: ", in_txt) - write_txt(out_txt, new_items) - -def rename_labels(data_dir, out_dir, doc_type): - data_dir = Path(data_dir) - out_dir = Path(out_dir) - if not out_dir.exists(): - out_dir.mkdir(parents=True, exist_ok=True) - if doc_type == "receipt": - mapping = RECEIPT_MAPPING - elif doc_type == 'invoice': - mapping = INVOICE_MAPPING - else: - raise NotImplementedError() - txt_paths = data_dir.rglob("*.txt") - for txt_path in tqdm(txt_paths): - txt_dir = str(Path(str(txt_path).replace(str(data_dir), "")).parent) # a/b/c/x.txt -> c/x.txt -> c - - if txt_dir[0] == "/": - txt_dir = txt_dir[1:] - out_sub_dir = out_dir / Path(txt_dir) - - if not out_sub_dir.exists(): - out_sub_dir.mkdir(parents=True, exist_ok=True) - - out_txt = out_sub_dir / txt_path.name - - # if "failure" in str(out_txt): - # # print(out_txt) - # print(out_sub_dir) - # print(out_txt) - # print(out_txt) - edit_file(str(txt_path), out_txt=out_txt, mapping=mapping) - -def write_json(json_path, data): - with open(json_path, "w", encoding="utf8") as f: - json.dump(data, f, ensure_ascii=False, sort_keys=True) - - -def read_json(json_path): - with open(json_path, "r", encoding="utf8") as f: - data = json.load(f) - return data -def rename_label_in_json(json_in, json_out, doc_type): - if doc_type == "invoice": - mapping = INVOICE_MAPPING - else: - mapping = RECEIPT_MAPPING - ori_data = read_json(json_in) - new_data = {} - for img_key, field_item in ori_data.items(): - new_field_item = {} - for field_key, field_value in field_item.items(): - if field_key in mapping: - new_field_key = mapping[field_key] - else: - new_field_key = field_key - new_field_key = new_field_key.lower() - new_field_item[new_field_key] = field_value - - new_data[img_key] = new_field_item - - write_json(json_out, new_data) - - - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(prog="Rename labels") - parser.add_argument("--in_dir", type=str, required=True, help="dataset directory") - parser.add_argument("--out_dir", type=str, required=False, help="output") - parser.add_argument("--doc_type", type=str, required=True, help="document type: receipt / invoice") - - args = parser.parse_args() - rename_labels( - data_dir=args.in_dir, - out_dir=args.out_dir, - doc_type=args.doc_type - ) - - # rename_label_in_json( - # json_in=args.in_dir, - # json_out=args.out_dir, - # doc_type=args.doc_type - # ) - - -""" - -""" \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/sort_e2e.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/sort_e2e.py deleted file mode 100644 index 6484787..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/sort_e2e.py +++ /dev/null @@ -1,83 +0,0 @@ -import argparse -from sdsvkie.utils import read_json, yaml_load, write_json -# from sdsvkie.utils.post_processing.invoice_post_processing import * -# from sdsvkie.utils.post_processing.common_post_processing import normalize_number -from tqdm import tqdm - -INVOICE_KEYS = [ - 'no_key', - 'no_value', - 'form_key', - 'form_value', - 'serial_key', - 'serial_value', - - 'date_value', - - 'seller_company_name_key', - 'seller_company_name_value', - 'seller_address_value', - 'seller_address_key', - 'seller_tel_key', - 'seller_tel_value', - 'seller_tax_code_key', - 'seller_tax_code_value', - - 'buyer_personal_name_key', - 'buyer_personal_name_value', - 'buyer_company_name_value', - 'buyer_company_name_key', - - 'buyer_address_key', - 'buyer_address_value', - 'buyer_tax_code_key', - 'buyer_tax_code_value', - 'buyer_tel_key', - 'buyer_tel_value', - - 'tax_amount_key', - 'tax_amount_value', - 'total_key', - 'total_value', - 'total_in_words_key', - 'total_in_words_value', - 'other' -] - -def sort_invoice(data): - - sorted_data = {} - for img_key, img_data in tqdm(data.items()): - sorted_img_data = {} - for field_key in INVOICE_KEYS: - if "_key" in field_key or "other" in field_key: - continue - sorted_img_data[field_key] = img_data.get(field_key, "") - - sorted_data[img_key] = sorted_img_data - - return sorted_data - - -def sort_receipt(data): - return data - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--input", type=str, help="e2e label file path") - parser.add_argument("--out", type=str, help='postprocess e2e label') - parser.add_argument("--doc_type", default="invoice") - args = parser.parse_args() - - data = read_json(args.input) - if args.doc_type: - - sorted_data = sort_invoice(data) - else: - sorted_data = sort_receipt(data) - - write_json(args.out, sorted_data, sort_keys=False) - - diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/split_batches.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/split_batches.py deleted file mode 100644 index 8055195..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/split_batches.py +++ /dev/null @@ -1,34 +0,0 @@ -import os -import shutil - -def split_folder_into_batches(input_folder, output_folder, n): - # Get the list of image files in the input folder - image_files = [f for f in os.listdir(input_folder) if f.lower().endswith(('.jpg', '.jpeg', '.png'))] - # Sort the list of image files - image_files.sort() - print("len: ", len(image_files)) - # Calculate the number of images per batch - batch_size = len(image_files) // n - # Create the output directories - for i in range(n): - batch_dir = os.path.join(output_folder, f"batch_{i+1}") - os.makedirs(batch_dir, exist_ok=True) - # Split the images into batches - for i, image_file in enumerate(image_files): - # print(i, image_file) - batch_index = i // batch_size - batch_dir = os.path.join(output_folder, f"batch_{batch_index+1}") - if not os.path.exists(batch_dir): - os.makedirs(batch_dir, exist_ok=True) - # print(batch_dir) - # Find the corresponding label file - image_name, image_ext = os.path.splitext(image_file) - label_file = f"{image_name}.txt" - label_path = os.path.join(input_folder, label_file) - # Copy the image and label files into the appropriate batch directory - print(label_path, os.path.join(input_folder, image_file), batch_dir) - shutil.copy(os.path.join(input_folder, image_file), batch_dir) - shutil.copy(label_path, batch_dir) - -# Example usage: -split_folder_into_batches("/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/pseudo_ocr/invoice_receipt_sbt", "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/pseudo_ocr/invoice_receipt_sbt_split", 3) diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/common/xml2pdf.py b/cope2n-ai-fi/modules/sdsvkie/scripts/common/xml2pdf.py deleted file mode 100644 index b9e5b07..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/common/xml2pdf.py +++ /dev/null @@ -1,31 +0,0 @@ -# import xml.etree.ElementTree as ET -# from reportlab.pdfgen import canvas -# from reportlab.lib.pagesizes import letter - -# # Load XML file -# tree = ET.parse('/mnt/hdd2T/AICR/Projects/2023/Xml_SAVINA/2023-04-20_0101803564_0300741922_1C23TYY_714.xml') -# root = tree.getroot() - -# # Create PDF canvas -# pdf_canvas = canvas.Canvas('./2023-04-20_0101803564_0300741922_1C23TYY_714.pdf', pagesize=letter) - -# # Iterate over XML elements and draw on PDF canvas -# for element in root.iter(): -# if element.tag == 'paragraph': -# pdf_canvas.drawString(int(element.get('x')), int(element.get('y')), element.text) -# elif element.tag == 'image': -# pdf_canvas.drawImage(element.get('src'), int(element.get('x')), int(element.get('y')), int(element.get('width')), int(element.get('height'))) - -# # Save PDF file -# pdf_canvas.save() - -import pyxml2pdf - -# Create an XML file - - -# Create a new xml2pdf object -xml2pdf = pyxml2pdf.("/mnt/hdd2T/AICR/Projects/2023/Xml_SAVINA/2023-04-20_0101803564_0300741922_1C23TYY_714.xml") - -# Save the output PDF file -xml2pdf.save("my_pdf_file.pdf") \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/cvat_tool.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/cvat_tool.sh deleted file mode 100644 index cf42f3a..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/cvat_tool.sh +++ /dev/null @@ -1,256 +0,0 @@ -python tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_taxi_sub_1_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_taxi_sub_1_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo_OCR/Batch_1_Good/Taxi_sub_1 - -python tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_taxi_sub_2_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_taxi_sub_2_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/Taxi_sub_2 - -python rename_labels.py \ - --in_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed \ - --out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed \ - --doc_type receipt - - -python rename_labels.py \ - --in_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed \ - --out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed \ - --doc_type receipt -# text detect - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/batch_1_taxi_sub_1_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/batch_1_taxi_sub_1_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/taxi_sub_1 - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/batch_1_food.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/batch_1_food_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/food - - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/labeling/train_vnpt_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/labeling/train_vnpt_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/train_with_vnpt - -########## -python tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo_OCR/Batch_1_Good/Taxi_sub_1 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_taxi_sub_1_done.xml \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_1 \ - --line_to_word \ - --other_class Others - -python tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/Taxi_sub_2 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_taxi_sub_2_done.xml \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_2 \ - --other_class Others - -python tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/Food \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/batch_1_food_done.xml \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Food \ - --other_class Others - -python tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_2/Good/Food \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_2/batch_2_food_done.xml \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_2/Good/Food \ - --other_class Others - -# WILD RECEIPT - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/WildReceipt/re_labeling/batches/batch_1 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/WildReceipt/re_labeling/batches/batch_1 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/WildReceipt/re_labeling/wild_batch_1_done.xml \ - --other_class Others - - - -# SS RECEIPT -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/ss_receipt_batch_1_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/ss_receipt_batch_1_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted_txt/batch_1 \ - --other_class Others - - - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/mc_ocr_batch_3_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/mc_ocr_batch_3_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_v2/batch_3 \ - --other_class Others \ - --resever_parent_dir - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/ss_receipt_all_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/ss_receipt_all_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Done \ - --other_class Others \ - --resever_parent_dir - - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/mcocr_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/mcocr_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train/mc_ocr \ - --other_class Others \ - --resever_parent_dir -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/sdsap_receipt_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/sdsap_receipt_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train/sdsap_receipt \ - --other_class Others \ - --resever_parent_dir -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/ss_receipt_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/ss_receipt_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train/ss_receipt \ - --other_class Others \ - --resever_parent_dir - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/wildreceipt_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/pseudo_label/wildreceipt_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train/wildreceipt \ - --other_class Others \ - --resever_parent_dir - -# INVOICE - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_sl_hn_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_sl_hn_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/SL_HN_Invoice_txt \ - --other_class other - - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_sl_hcm_tmp_done.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_sl_hcm_tmp_done_2.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/invoice_add_sl_hcm_finetuning/SL_HCM_Invoice_wg_txt_need_review \ - --other_class other - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_ss_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_ss_wg_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/parse_wg/SS_Invoice \ - --other_class other - - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/labels/ss_hcm_batch_2_multi_pages_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/labels/ss_hcm_batch_2_multi_pages_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/SL_HCM_batch_2_first_last_page_txt \ - --other_class other - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/sbt/sbt_craw_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/sbt/sbt_craw_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/Crawled_invoices_SBT \ - --other_class other - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/sbt_test_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/sbt_test_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_sbt_wg \ - --other_class other - -python sdsvkie/tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/viettinbank_pocr_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/viettinbank_pocr_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/invoice_viettinbank_poc_txt \ - --other_class other - - - -# UPDATE TXT -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/batch_1 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted/batch_1 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/mc_ocr_batch_1_done.xml \ - --other_class Others - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/batch_2 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted/batch_2 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/mc_ocr_batch_2_done.xml \ - --other_class Others - - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_v2/batch_3 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_v2/batch_3_2 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/mc_ocr_batch_3_done.xml \ - --other_class Others \ - --resever_parent_dir - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Done \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Done \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/ss_receipt_by_store_done.xml \ - --other_class Others \ - --resever_parent_dir - - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted_txt/batch_1 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/batch_1 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/ss_receipt_batch_1_done.xml \ - --other_class Others - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted_txt/batch_2 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/batch_2 \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/ss_receipt_batch_2_done.xml \ - --other_class Others - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/SL_HN_Invoice \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/SL_HN_Invoice \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/invoice_sl_hn_done.xml \ - --other_class other \ - --line_to_word - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train/SS_Invoice \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train/SS_Invoice \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/Labeling/ss_invoice_done.xml \ - --other_class other \ - --line_to_word - - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/SL_HCM_batch_2_first_last_page \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/SL_HCM_batch_2_first_last_page \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/labels/sl_hcm_batch_2_multi_pages_done.xml \ - --other_class other \ - --line_to_word - - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/Crawled_invoices_SBT_no_wg_txt \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/Crawled_invoices_SBT_no_wg_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/intermediate/sbt/sbt_craw_done.xml \ - --other_class other \ - --line_to_word - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_sbt \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_sbt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/sbt_test_done.xml \ - --other_class other \ - --line_to_word - -python sdsvkie/tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/invoice_viettinbank_poc_txt \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/invoice_viettinbank_poc_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/viettinbank_poc_done.xml \ - --other_class other \ - --line_to_word diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/eval_e2e.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/eval_e2e.sh deleted file mode 100644 index 9a845c8..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/eval_e2e.sh +++ /dev/null @@ -1,96 +0,0 @@ -#SDSAP -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/food_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_end2end_food_pred_v1.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml - - - -python sdsvkie/utils/eval_kie.py \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/all_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_all_end2end_w0.1_h0.3_thr5.json \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_all_end2end_w0.1_h0.3_thr5.json - - -python sdsvkie/utils/eval_kie.py \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/taxi_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_text_det_20230425.json \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_text_det_20230425_fail.json - - -python sdsvkie/utils/eval_kie.py \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/taxi_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_w0.2_h0.2_thr5.json \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_w0.2_h0.2_thr5_fail.json - - -# 10/5/2023 -python sdsvkie/utils/eval_kie.py \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/config.yaml \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test_end2end/all_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/receipt_e2e.json \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/receipt_e2e_fail.json - -python sdsvkie/utils/eval_kie.py \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/config.yaml \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_end2end/all_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/receipt_e2e_best.json \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/receipt_e2e_best_fail.json - - - - - - - - - -=============================== INVOICE ==================================================================== -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/test_end2end/test_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/e2e/test_vnpt_epx_4_best_not_ocr_merge_use_label.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/failure_of_test_vnpt_epx_4_best_not_ocr_merge_use_label.json - - -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/test_end2end/test_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/pred_test_end2end.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/vnpt_epx_falures_v2.json - -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_e2e_rm_leak.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/test_ss_rm_leak.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/test_ss_e2e_rm_leak_fail.json - -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page/test_e2e_multi_pages.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/test_e2e_multi_page.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/test_e2e_multi_page_fail.json - - - -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_e2e_rm_leak.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_ss_rm_leak.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_ss_rm_leak_fail.json - -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page/test_e2e_multi_pages.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_e2e_multi_page.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_e2e_multi_page_fail.json - - -python sdsvkie/utils/eval_kie.py \ ---gt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_end2end/sbt_validation_e2e.json \ ---pred /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/sbt_validation_e2e_ep50.json \ ---cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/config.yaml \ ---log_failure_case /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/sbt_validation_e2e_ep50_fail.json \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/infer.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/infer.sh deleted file mode 100644 index 79ef2bd..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/infer.sh +++ /dev/null @@ -1,199 +0,0 @@ -python sdsvkie/tools/infer.py --cfg workdirs/invoice/exp1/config.yaml --inference_weights workdirs/invoice/exp1/best --device cuda --img ../TokenClassification_invoice/DATA/test/01067_0452_000.jpg - -python sdsvkie/tools/infer.py --cfg workdirs/invoice/exp1/config.yaml --inference_weights workdirs/invoice/exp1/best --device "cuda:1" \ - --img ../craw_data/output/synth_template_4/one_line --txt_out workdirs/visualize/vnpt_one_line_txt --kie_wordgroup_out - - - -python sdsvkie/tools/infer.py --cfg workdirs/invoice/exp1/config.yaml --inference_weights workdirs/invoice/exp1/best --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r20/one_line --txt_out workdirs/visualize/vnpt_one_line_r20_txt --kie_wordgroup_out - - -python sdsvkie/tools/infer.py --cfg workdirs/invoice/exp_add_vnpt_template/config.yaml --inference_weights workdirs/invoice/exp_add_vnpt_r2/best --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r2_2/one_line \ - --vis_out workdirs/visualize/vnpt_r2_phase_2 \ - --txt_out workdirs/visualize/vnpt_r2_phase_2_txt --kie_wordgroup_out - - - -python sdsvkie/tools/infer.py --cfg workdirs/invoice/exp_add_vnpt_template/config.yaml --inference_weights workdirs/invoice/exp_add_vnpt_r2/best --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r2_2/one_line \ - --vis_out workdirs/visualize/vnpt_r2_phase_2 \ - --txt_out workdirs/visualize/vnpt_r2_phase_2_txt --kie_wordgroup_out - - -python sdsvkie/tools/infer.py --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoiceadd_vnpt_final/config.yaml --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoiceadd_vnpt_final/epoch_60 --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/test \ - --vis_out workdirs/visualize/test_sorted - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoiceadd_vnpt_final/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoiceadd_vnpt_final/epoch_60 \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/test \ - --vis_out workdirs/visualize/test_sorted - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoiceadd_vnpt_final/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoiceadd_vnpt_final/epoch_60 \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/test \ - - -# test e2e -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/exp_wild_1/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/exp_wild_1/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/intermediate/labeling/phase_1/batch_1 \ - --vis_out workdirs/visualize/SDSAP_Invoice_exp_wild_1 - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/test \ - --e2e workdirs/e2e/test_sampling_sortword_exp4_best.json - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Dataset/common_tools/JPG/one_page/vat \ - --e2e workdirs/e2e/test_invoice_vnpt_exp4_sdsv_invoice.json \ - --vis workdirs/e2e/test_invoice_vnpt_exp4_sdsv_invoice_visualize - -# sdsAP -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_1/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_1/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_2 \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/infer/visualize/exp_1_batch_1_taxi_sub_2 - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_2/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_2/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_2/Noise \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/infer/visualize/exp_2_batch_2_noise - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_2/Good/Taxi \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/infer/visualize/exp_3_batch_2_good_taxi - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/Taxi \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/infer/visualize/exp_3_batch_2_good_taxi - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/food \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/infer/visualize/exp_3_batch_1_food - - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/Taxi \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/infer/visualize/exp_3_test_end2end_taxi_2 - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test_end2end/All \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/receipt_e2e_infer_best - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/last \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test_sbt \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/test_sbt_infer - - - - -# MCOCR -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted/batch_1 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images_aligned/train_splitted_txt/batch_1 - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted/batch_1 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/SS_Receipt/Images_splitted_txt/batch_1 - - - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/02062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/02062023/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/sl_hcm_hn_savina \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/02062023/multi_pages_sl_hcm_hn_savina_wg_txt \ - --kie_wordgroup_out - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/raw/Crawled_invoices_SBT \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/Crawled_invoices_SBT \ - --kie_wordgroup_out - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/raw/Crawled_invoices_SBT \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/Crawled_invoices_SBT_no_wg_txt - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/epoch_50 \ - --device "cuda:0" \ - --img /mnt/ssd1T/tuanlv/06.KVUCombineStage/datasets/invoices-receipts/SS_invoices/SBT/validation_data/valid_images \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_6/sbt_validation_e2e_ep50_old_textdet_infer - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/Viettinbank_POC/POC_OCR/invoice_JPG \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/visualize/vietinbank - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/1000416613 \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/visualize/intermediate_1000416613 \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/infer_e2e.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/infer_e2e.sh deleted file mode 100644 index b177280..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/infer_e2e.sh +++ /dev/null @@ -1,210 +0,0 @@ -# SDSAP_Receipt -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/All \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_all_end2end.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_all_end2end - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/OTHER_DATA/MC-OCR/raw/Images/train \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/train_mcocr_end2end.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/train_mcocr_end2end - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --text_det /mnt/ssd500/datnt/mmdetection/logs/textdet-fwd-table-receipt-20230425/best_bbox_mAP_epoch_15_lite.pth \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/Taxi \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_text_det_20230425.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_text_det_20230425 - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/v1/train \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_end2end.json - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/Taxi \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_w0.2_h0.2_thr5.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_w0.2_h0.2_thr5 - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test_end2end/All \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_all_end2end_w0.1_h0.3_thr5_v2.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/test_taxi_end2end_w0.1_h0.3_thr5_v2 - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test_end2end/All \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/receipt_e2e.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_8_lr9e_6/receipt_e2e - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test_end2end/All \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/receipt_e2e_best.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/receipt_e2e_best - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/Webcash/testing \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/pred_webcash_testing.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_3/pred_webcash_testing - - - -#invoice -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/test \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/invoice_end2end_last.json - - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Dataset/common_tools/Split_by_pages/multi_page/vat \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/pred_vat_multi_page_v2.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Dataset/common_tools/Split_by_pages/multi_page/vat \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/pred_vat_multi_page_v2.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/multi_page_vat/SL_HCM \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/multi_page_vat/SL_HCM.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_rm_leak \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/test_ss_rm_leak.json - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/test_ss.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/SL_HCM_Invoice \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/SL_HCM_Invoice.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/SL_HCM_Invoice - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss_rm_leak \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_ss_rm_leak.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_ss_rm_leak - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/multi_pages/SL_HCM_batch_2 \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_ss_sl_hcm_batch_2_multi_page_jpg.json \ - --vis /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_ss_sl_hcm_batch_2_multi_page_jpg - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page/SL_HN_batch_2 \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/multi_page_SL_HN_batch_2.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page/test_e2e_multi_page \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/26052023/test_e2e_multi_page.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/02062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/02062023/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page/SL_HN_batch_2 \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/02062023/multi_page_SL_HN_batch_2.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:0" \ - --img /mnt/hdd2T/AICR/Projects/2023/Vietinbank_POC/Invoice_JPG/ \ - --e2e /mnt/hdd2T/AICR/Projects/2023/Vietinbank_POC/Invoice_KIE_Results/result.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:0" \ - --img /mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Invoice_v2_multi_page \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/Invoice_v2_multi_page.json - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/19072023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/19072023/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/Viettinbank_POC/POC_OCR/invoice_JPG/ \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/19072023/vietinbank_poc_infer.json \ - --vis workdirs/invoice/19072023/visualize/vietinbank_infer - - -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:0" \ - --img /mnt/hdd2T/AICR/Projects/2023/FI_Invoices/Test/1000416613/1000416613_0102310385_26062023163233062_001.pdf \ - --e2e /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/1000416613_0102310385_26062023163233062_001.json - \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/label_cvat.json b/cope2n-ai-fi/modules/sdsvkie/scripts/label_cvat.json deleted file mode 100644 index 53141c8..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/label_cvat.json +++ /dev/null @@ -1,226 +0,0 @@ -[ - { - "name": "total_in_words_key", - "id": 92, - "color": "#33ddff", - "type": "any", - "attributes": [] - }, - { - "name": "no_value", - "id": 93, - "color": "#fa3253", - "type": "any", - "attributes": [] - }, - { - "name": "form_key", - "id": 94, - "color": "#34d1b7", - "type": "any", - "attributes": [] - }, - { - "name": "no_key", - "id": 95, - "color": "#ff007c", - "type": "any", - "attributes": [] - }, - { - "name": "form_value", - "id": 96, - "color": "#ddff33", - "type": "any", - "attributes": [] - }, - { - "name": "serial_key", - "id": 97, - "color": "#24b353", - "type": "any", - "attributes": [] - }, - { - "name": "serial_value", - "id": 98, - "color": "#b83df5", - "type": "any", - "attributes": [] - }, - { - "name": "date_value", - "id": 99, - "color": "#66ff66", - "type": "any", - "attributes": [] - }, - { - "name": "seller_company_name_key", - "id": 100, - "color": "#32b7fa", - "type": "any", - "attributes": [] - }, - { - "name": "seller_company_name_value", - "id": 101, - "color": "#ffcc33", - "type": "any", - "attributes": [] - }, - { - "name": "seller_tax_code_key", - "id": 102, - "color": "#83e070", - "type": "any", - "attributes": [] - }, - { - "name": "seller_tax_code_value", - "id": 103, - "color": "#fafa37", - "type": "any", - "attributes": [] - }, - { - "name": "seller_address_value", - "id": 104, - "color": "#5986b3", - "type": "any", - "attributes": [] - }, - { - "name": "seller_address_key", - "id": 105, - "color": "#8c78f0", - "type": "any", - "attributes": [] - }, - { - "name": "seller_tel_key", - "id": 106, - "color": "#ff6a4d", - "type": "any", - "attributes": [] - }, - { - "name": "seller_tel_value", - "id": 107, - "color": "#f078f0", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_personal_name_key", - "id": 108, - "color": "#2a7dd1", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_personal_name_value", - "id": 109, - "color": "#83e070", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_company_name_value", - "id": 110, - "color": "#5986b3", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_company_name_key", - "id": 111, - "color": "#8c78f0", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_tax_code_key", - "id": 112, - "color": "#ff6a4d", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_tax_code_value", - "id": 113, - "color": "#f078f0", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_address_key", - "id": 114, - "color": "#2a7dd1", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_address_value", - "id": 115, - "color": "#b25050", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_tel_key", - "id": 116, - "color": "#cc3366", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_tel_value", - "id": 117, - "color": "#cc9933", - "type": "any", - "attributes": [] - }, - { - "name": "tax_amount_key", - "id": 118, - "color": "#aaf0d1", - "type": "any", - "attributes": [] - }, - { - "name": "tax_amount_value", - "id": 119, - "color": "#ff00cc", - "type": "any", - "attributes": [] - }, - { - "name": "total_key", - "id": 120, - "color": "#3df53d", - "type": "any", - "attributes": [] - }, - { - "name": "total_value", - "id": 121, - "color": "#fa32b7", - "type": "any", - "attributes": [] - }, - { - "name": "total_in_words_value", - "id": 122, - "color": "#3d3df5", - "type": "any", - "attributes": [] - }, - { - "name": "other", - "id": 123, - "color": "#733380", - "type": "any", - "attributes": [] - } -] \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/post_eval_res.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/post_eval_res.sh deleted file mode 100644 index 1d79b23..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/post_eval_res.sh +++ /dev/null @@ -1,10 +0,0 @@ -python sdsvkie/tools/postprocess_e2e_label.py \ - --cfg workdirs/invoice/vnpt_exp_4/config.yaml \ - --input /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/test_end2end/test_e2e.json \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SS_Invoice/test_end2end/test_e2e_post.json - - -python sdsvkie/tools/postprocess_e2e_label.py \ - --cfg workdirs/invoice/vnpt_exp_4/config.yaml \ - --input /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/invoice_exp4/test_end2end_post.json \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/invoice_exp4/test_end2end_post.json \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/pseudo_label.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/pseudo_label.sh deleted file mode 100644 index b673c16..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/pseudo_label.sh +++ /dev/null @@ -1,36 +0,0 @@ -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/exp_wild_1/config.yaml \ - --inference_weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/exp_wild_1/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_2/Good/Food \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_2/Good/Food - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_2/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/training/sdsap_receipt/exp_2/best \ - --device "cuda:0" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_2 \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_1/Good/Taxi_sub_2 - - - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/intermediate/multi_pages/SL_HCM_batch_2_first_last_page \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/SL_HCM_batch_2_first_last_page_vis \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/SL_HCM_batch_2_first_last_page_txt \ - --kie_wordgroup_out - - -python sdsvkie/tools/infer.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:1" \ - --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/invoice_viettinbank_poc \ - --vis_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/invoice_viettinbank_poc_vis \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/invoice_viettinbank_poc_txt \ - --kie_wordgroup_out \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/run_ocr.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/run_ocr.sh deleted file mode 100644 index c37231b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/run_ocr.sh +++ /dev/null @@ -1,19 +0,0 @@ -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/IMGS \ ---out_dir /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/SDSAP_Invoice/visualize_ocr \ ---device "cuda:1" \ ---reserve_parent_dir - - -# python sdsvkie/tools/run_ocr.py \ -# --img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/IMGS \ -# --out_dir /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/SDSAP_Invoice/visualize_ocr \ -# --device "cuda:1" \ -# --reserve_parent_dir - - -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/test_end2end/Taxi \ ---device "cuda:1" \ ---out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/test_v2/taxi \ ---text_det "/mnt/ssd500/datnt/mmdetection/logs/textdet-fwd-table-receipt-20230425/best_bbox_mAP_epoch_15_lite.pth" \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/run_ocr_gen_txt.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/run_ocr_gen_txt.sh deleted file mode 100644 index 806fe74..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/run_ocr_gen_txt.sh +++ /dev/null @@ -1,35 +0,0 @@ -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/pseudo_ocr \ ---out_dir ./workdirs/visualize/WildReceipt \ ---device "cuda:0" \ ---out_txt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/pseudo_ocr - - -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_1 \ ---device "cuda:0" \ ---out_txt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo_OCR/Batch_1_Good/Taxi_sub_1 - - -# pseudo for edit text detect boxes -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/taxi_sub_1 \ ---device "cuda:0" \ ---out_txt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/taxi_sub_1 - - -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/taxi_sub_1 \ ---device "cuda:0" \ ---out_txt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/taxi_sub_1 - - -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/food \ ---device "cuda:0" \ ---out_txt /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/label_ocr/all/batch_1/food - -python sdsvkie/tools/run_ocr.py \ ---img /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/demos/invoice \ ---device "cuda:0" \ ---out_txt /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/demos/invoice \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/run_pdf2img.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/run_pdf2img.sh deleted file mode 100644 index 1f9e644..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/run_pdf2img.sh +++ /dev/null @@ -1,15 +0,0 @@ -python sdsvkie/utils/pdf2image.py \ ---pdf_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/PDF \ ---out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/raw/batch_1/IMGS_dpi_300 \ ---reserve_parent_dir - - -python sdsvkie/utils/pdf2image.py \ ---pdf_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/PDF/multi_page/Invoices_SL_HCM \ ---out_dir /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/raw/JPG/Invoices_SL_HCM - - - -python sdsvkie/utils/pdf2image.py \ ---pdf_dir /mnt/hdd2T/AICR/Projects/2023/Invoice_SDSV/SDSV_Invoice_2023/All \ ---out_dir /mnt/hdd2T/AICR/Projects/2023/Invoice_SDSV/SDSV_Invoice_2023/JPG \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/split_data.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/split_data.sh deleted file mode 100644 index 6569eac..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/split_data.sh +++ /dev/null @@ -1,25 +0,0 @@ -python sdsvkie/utils/split_data.py \ - --path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Food \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/v2/ \ - --test_ratio 0.05 - -python sdsvkie/utils/split_data.py \ - --path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_1 \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/v2/ \ - --test_ratio 0.05 - -python sdsvkie/utils/split_data.py \ - --path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_1/Good/Taxi_sub_2 \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/v2/ \ - --test_ratio 0.05 - -python sdsvkie/utils/split_data.py \ - --path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_2/Good/Food \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/v2/ \ - --test_ratio 0.05 - - -python sdsvkie/utils/split_data.py \ - --path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/batches/batch_1 \ - --out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/done/v2/ \ - --test_ratio 0.0 \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/scripts/train.sh b/cope2n-ai-fi/modules/sdsvkie/scripts/train.sh deleted file mode 100644 index 442cc9c..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/scripts/train.sh +++ /dev/null @@ -1,7 +0,0 @@ -python sdsvkie/tools/train.py --cfg sdsvkie/cfg/wildreciept.yaml --device cuda:0 --save_dir workdirs/invoice/exp_wild_1 - - -python sdsvkie/tools/train.py --cfg sdsvkie/cfg/sdsap_receipt_scheduler_linear.yaml --device cuda:0 --save_dir workdirs/sdsap_receipt/exp_4_scheduler_linear - - -python sdsvkie/tools/train.py --cfg sdsvkie/cfg/sdsap_receipt.yaml --device cuda:1 --save_dir workdirs/sdsap_receipt/exp_5 \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/__init__.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/__init__.py deleted file mode 100644 index 44d01d8..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -import os -import sys -sys.path.append(__file__) - -from .engine.predictor import Predictor \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/__init__.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/__init__.py deleted file mode 100644 index 227e6a4..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -from types import SimpleNamespace -from sdsvkie.utils.io_file import yaml_load -from pathlib import Path -from copy import copy, deepcopy - -def load_cfg(cfg, args=None): - """ - Convert a configuration object to a dictionary, whether it is a file path, a string, or a SimpleNamespace object. - Inputs: - cfg (str) or (Path) or (SimpleNamespace): Configuration object to be converted to a dictionary. - Returns: - cfg (dict): Configuration object in dictionary format. - """ - if isinstance(cfg, (str, Path)): - cfg = yaml_load(cfg) # load dict - elif isinstance(cfg, SimpleNamespace): - cfg = vars(cfg) # convert to dict - - - if args is not None: - _args = deepcopy(args) - for k, v in args.items(): - if v is None or v == "cfg" : - _args.pop(k) - - if v is not None and k == "weights": - _args['inference_weights'] = v - _args.pop(k) - - cfg.update(_args) - return cfg - - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/class_list.txt b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/class_list.txt deleted file mode 100644 index 090b226..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/class_list.txt +++ /dev/null @@ -1,39 +0,0 @@ -no_key -no_value -form_key -form_value -serial_key -serial_value -date_key -date_value -subtotal_key -subtotal_value -tax_rate_key -tax_rate_value -tax_amount_key -tax_amount_value -tips_key -tips_value -total_key -total_value -total_in_words_key -total_in_words_value -seller_company_name_key -seller_company_name_value -seller_address_key -seller_address_value -seller_tel_key -seller_tel_value -seller_tax_code_key -seller_tax_code_value -buyer_company_name_key -buyer_company_name_value -buyer_personal_name_key -buyer_personal_name_value -buyer_tax_code_key -buyer_tax_code_value -buyer_address_key -buyer_address_value -buyer_tel_key -buyer_tel_value -other \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/default.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/default.yaml deleted file mode 100644 index 6e150f6..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/default.yaml +++ /dev/null @@ -1,79 +0,0 @@ - -debug: False -v3: False -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/train_with_vnpt -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/test -slice_interval: 75 -postprocess_type: invoice_postprocess -classes: [ - # id invoice - 'no_key', # số hóa đơn - 'no_value', - 'form_key', # mẫu số hóa đơn - 'form_value', - 'serial_key', # số kí hiệu hoá đơn - 'serial_value', - 'date', - - # seller info - 'seller_company_name_key', - 'seller_company_name_value', - 'seller_tax_code_key', - 'seller_tax_code_value', - 'seller_address_value', - 'seller_address_key', - 'seller_mobile_key', - 'seller_mobile_value', - - # buyer info - 'buyer_name_key', - 'buyer_name_value', - 'buyer_company_name_value', - 'buyer_company_name_key', - 'buyer_tax_code_key', - 'buyer_tax_code_value', - 'buyer_address_key', - 'buyer_address_value', - 'buyer_mobile_key', - 'buyer_mobile_value', - - # money info - 'VAT_amount_key', - 'VAT_amount_value', - 'total_key', - 'total_value', - 'total_in_words_key', - 'total_in_words_value', - - 'other', -] -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 5.0e-6 -shuffle: True -num_workers: 4 -scheduler: False # False or "linear" / "cosine" / "cosine_with_restarts" / "polynomial" / "constant" / "constant_with_warmup" / "inverse_sqrt" -save_dir: workdirs/invoice/exp1 -save_weight_interval: 10 -eval_delay: 0 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice.yaml deleted file mode 100644 index 24c7192..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice.yaml +++ /dev/null @@ -1,74 +0,0 @@ - -debug: False -v3: False -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss -slice_interval: 75 -postprocess_type: invoice_postprocess -classes: [ - 'no_key', - 'no_value', - 'form_key', - 'form_value', - 'serial_key', - 'serial_value', - 'date_value', - - 'seller_company_name_key', - 'seller_company_name_value', - 'seller_tax_code_key', - 'seller_tax_code_value', - 'seller_address_value', - 'seller_address_key', - 'seller_tel_key', - 'seller_tel_value', - - 'buyer_personal_name_key', - 'buyer_personal_name_value', - 'buyer_company_name_value', - 'buyer_company_name_key', - 'buyer_tax_code_key', - 'buyer_tax_code_value', - 'buyer_address_key', - 'buyer_address_value', - 'buyer_tel_key', - 'buyer_tel_value', - 'tax_amount_key', - 'tax_amount_value', - 'total_key', - 'total_value', - 'total_in_words_key', - 'total_in_words_value', - 'other' -] - -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 145 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 5.0e-6 -shuffle: True -num_workers: 4 -scheduler: "linear" # False or "linear" / "cosine" / "cosine_with_restarts" / "polynomial" / "constant" / "constant_with_warmup" / "inverse_sqrt" -save_dir: workdirs/invoice/19072023 -save_weight_interval: 10 -eval_delay: 50 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice_relabel.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice_relabel.yaml deleted file mode 100644 index 7d01ccc..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice_relabel.yaml +++ /dev/null @@ -1,74 +0,0 @@ - -debug: False -v3: False -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss -slice_interval: 75 -postprocess_type: invoice_postprocess -classes: [ - 'no_key', - 'no_value', - 'form_key', - 'form_value', - 'serial_key', - 'serial_value', - 'date_value', - - 'seller_company_name_key', - 'seller_company_name_value', - 'seller_tax_code_key', - 'seller_tax_code_value', - 'seller_address_value', - 'seller_address_key', - 'seller_tel_key', - 'seller_tel_value', - - 'buyer_personal_name_key', - 'buyer_personal_name_value', - 'buyer_company_name_value', - 'buyer_company_name_key', - 'buyer_tax_code_key', - 'buyer_tax_code_value', - 'buyer_address_key', - 'buyer_address_value', - 'buyer_tel_key', - 'buyer_tel_value', - 'tax_amount_key', - 'tax_amount_value', - 'total_key', - 'total_value', - 'total_in_words_key', - 'total_in_words_value', - 'other' -] - -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 145 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 5.0e-6 -shuffle: True -num_workers: 4 -scheduler: "cosine" # False or "linear" / "cosine" / "cosine_with_restarts" / "polynomial" / "constant" / "constant_with_warmup" / "inverse_sqrt" -save_dir: workdirs/invoice/26052023 -save_weight_interval: 10 -eval_delay: 50 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice_relabel_finetuning.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice_relabel_finetuning.yaml deleted file mode 100644 index b5a7231..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/invoice_relabel_finetuning.yaml +++ /dev/null @@ -1,74 +0,0 @@ - -debug: False -v3: False -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/test_ss -slice_interval: 75 -postprocess_type: invoice_postprocess -classes: [ - 'no_key', - 'no_value', - 'form_key', - 'form_value', - 'serial_key', - 'serial_value', - 'date_value', - - 'seller_company_name_key', - 'seller_company_name_value', - 'seller_tax_code_key', - 'seller_tax_code_value', - 'seller_address_value', - 'seller_address_key', - 'seller_tel_key', - 'seller_tel_value', - - 'buyer_personal_name_key', - 'buyer_personal_name_value', - 'buyer_company_name_value', - 'buyer_company_name_key', - 'buyer_tax_code_key', - 'buyer_tax_code_value', - 'buyer_address_key', - 'buyer_address_value', - 'buyer_tel_key', - 'buyer_tel_value', - 'tax_amount_key', - 'tax_amount_value', - 'total_key', - 'total_value', - 'total_in_words_key', - 'total_in_words_value', - 'other' -] - -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/vnpt_exp_4/last" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 1.0e-5 -shuffle: True -num_workers: 4 -scheduler: "cosine" # False or "linear" / "cosine" / "cosine_with_restarts" / "polynomial" / "constant" / "constant_with_warmup" / "inverse_sqrt" -save_dir: workdirs/invoice/add_sl_hcm -save_weight_interval: 1 -eval_delay: 1 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt.yaml deleted file mode 100644 index ecb1285..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt.yaml +++ /dev/null @@ -1,47 +0,0 @@ - -debug: False -v3: False - -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test -slice_interval: 75 -postprocess_type: receipt_postprocess - -classes: [ - "seller_company_name_value", - "no_value", - "date_value", - "total_key", - "total_value", - "other" -] -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 5.0e-6 -scheduler: False -shuffle: True -num_workers: 4 -save_dir: workdirs/receipt/13062023 -save_weight_interval: 10 -eval_delay: 0 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_finetuning.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_finetuning.yaml deleted file mode 100644 index c5a5bca..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_finetuning.yaml +++ /dev/null @@ -1,47 +0,0 @@ - -debug: False -v3: False - -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/processed/test -slice_interval: 75 -postprocess_type: receipt_postprocess - -classes: [ - "seller_company_name_value", - "no_value", - "date_value", - "total_key", - "total_value", - "other" -] -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/receipt/13062023_4/last" - -# opt + scheduler -batch_size: 8 -epochs: 30 -lr: 3.0e-6 -scheduler: False -shuffle: True -num_workers: 4 -save_dir: workdirs/receipt/13062023 -save_weight_interval: 10 -eval_delay: 0 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_relabel.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_relabel.yaml deleted file mode 100644 index 2a276a3..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_relabel.yaml +++ /dev/null @@ -1,47 +0,0 @@ - -debug: False -v3: False - -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test -slice_interval: 75 -postprocess_type: receipt_postprocess - -classes: [ - "Seller_company_name_value", - "ID_value", - "Date_value", - "Total_key", - "Total_value", - "Other" -] -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 9.0e-6 -scheduler: "linear" -shuffle: True -num_workers: 4 -save_dir: workdirs/invoice/exp1 -save_weight_interval: 10 -eval_delay: 50 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_scheduler_linear.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_scheduler_linear.yaml deleted file mode 100644 index 200d1f8..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/sdsap_receipt_scheduler_linear.yaml +++ /dev/null @@ -1,47 +0,0 @@ - -debug: False -v3: False - -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Receipt/test -slice_interval: 75 -postprocess_type: receipt_postprocess - -classes: [ - "Store_name_value", - "id", - "Date_value", - "Total_key", - "Total_value", - "Others" -] -sampling: true # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 9.0e-6 -scheduler: "linear" -shuffle: True -num_workers: 4 -save_dir: workdirs/invoice/exp1 -save_weight_interval: 10 -eval_delay: 50 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/wildreciept.yaml b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/wildreciept.yaml deleted file mode 100644 index 17f2ea4..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/cfg/wildreciept.yaml +++ /dev/null @@ -1,47 +0,0 @@ -debug: False -v3: False -scheduler: False -# common -device: 'cpu' # 'cpu' / 'cuda:0' / 'cuda:1' / 'cuda' - -#dataset -train_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/v1/train -val_dir: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/v1/test -slice_interval: 75 -postprocess_type: receipt_postprocess - -classes: [ - "Store_name_value", - "Date_value", - "Total_key", - "Total_value", - "Others" -] -sampling: True # sampling window - fix long document (larger than 512 token) - -#model -img_size: 224 # fixed -max_seq_length: 512 # fixed -max_num_words: 150 - -# tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/layoutlmv3-base" # fixed -# weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/layoutlmv3-base" -tokenizer_weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" # fixed -weights: "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/microsoft/microsoft/layoutxlm-base" - -# opt + scheduler -batch_size: 8 -epochs: 100 -lr: 5.0e-6 -shuffle: True -num_workers: 4 -save_dir: workdirs/invoice/exp1 -save_weight_interval: 10 -eval_delay: 0 -wandb: null - -# inference -inference_weights: null -text_det: yolox-s-general-text-pretrain-20221226 -text_reg: satrn-lite-general-pretrain-20230106 - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/datasets/base_dataset.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/datasets/base_dataset.py deleted file mode 100644 index 661bab0..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/datasets/base_dataset.py +++ /dev/null @@ -1,314 +0,0 @@ -import logging -import os -import pickle -import random -from copy import deepcopy -from pathlib import Path - -import cv2 -import pandas as pd -import torch -from datasets import (Array2D, Array3D, ClassLabel, Dataset, Features, - Sequence, Value, concatenate_datasets) -from easydict import EasyDict -from PIL import Image -from tqdm import tqdm -from sdsvkie.utils.io_file import read_txt - -from sdsvkie.utils import normalize_box, visualize_kie -from sdsvkie.utils.augmentation import perturbate_character, sampling_data -from sdsvkie.utils.word_formation import sliding_windows, sort_words -import glob - -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO) - - -IMG_EXTENSION = [".jpg", ".jpeg", ".png"] - -class BaseDataset: - def __init__(self, cfg): - - self.cfg = cfg - self.feature_format = Features( - { - "image": Array3D( - dtype="int64", shape=(3, self.cfg.img_size, self.cfg.img_size) - ), - "input_ids": Sequence(feature=Value(dtype="int64")), - "attention_mask": Sequence(Value(dtype="int64")), - "bbox": Array2D(dtype="int64", shape=(self.cfg.max_seq_length, 4)), - "labels": Sequence(ClassLabel(names=self.cfg.classes)), - } - ) if not self.cfg.v3 else \ - Features( - { - "pixel_values": Array3D( - dtype="float32", shape=(3, self.cfg.img_size, self.cfg.img_size) - ), - "input_ids": Sequence(feature=Value(dtype="int64")), - "attention_mask": Sequence(Value(dtype="int64")), - "bbox": Array2D(dtype="int64", shape=(self.cfg.max_seq_length, 4)), - "labels": Sequence(ClassLabel(names=self.cfg.classes)), - } - ) - - logger.info("Feature format: {}".format(self.feature_format.keys())) - - - def _build_df(self, data_dir): - """Build dataframe from data directory - - Args: - data_dir (str): structure data folder - - data_dir - - img1.jpg - - img1.txt - - ... - """ - data_dir = Path(data_dir) - - # img_paths = glob.glob("*") + glob.glob("*/*") - - img_paths = [ - path for path in list(data_dir.rglob("*")) - - if ".txt" not in str(path) and path.with_suffix(".txt").exists() and path.suffix.lower() in IMG_EXTENSION - ] - - - label_paths = [str(path.with_suffix(".txt")) for path in img_paths] - - - img_paths = [str(path) for path in img_paths] - - - - assert len(label_paths) == len(img_paths) - # remove empty txt - ids = [id for id in range(len(label_paths)) if len(read_txt(label_paths[id])) > 0] - label_paths = [label_paths[id] for id in ids] - img_paths = [img_paths[id] for id in ids] - - dataframe = pd.DataFrame.from_dict( - {"image_path": img_paths, "label": label_paths} - ) - return dataframe - - def build_dataloader_from_dataset( - self, - dataset, - processor, - device, - batch_size, - shuffle=True, - num_workers=4, - cache_file="./cache.pkl", - use_sampling=False, - ): - - if not os.path.exists(cache_file): - self._build_cache( - dataset, - processor, - cache_file=cache_file, - max_seq_length=self.cfg.max_seq_length, - ) - cache = self._load_cache(cache_file) - dataset = dataset.map( - self._prepare_data, - fn_kwargs={"cache": cache, "sampling": use_sampling}, - remove_columns=dataset.column_names, - features=self.feature_format, - batched=False, - batch_size=self.cfg.batch_size, - ) - - dataset.set_format(type="torch", device=device) - dataloader = torch.utils.data.DataLoader( - dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers - ) - return dataloader - - def build_dataloader_from_dir( - self, - data_dir, - processor, - device, - batch_size, - shuffle=True, - num_workers=4, - cache_file="./cache.pkl", - use_sampling=False, - ): - - dataset = self._build_dataset(data_dir) - dataloader = self.build_dataloader_from_dataset( - dataset, - processor, - device, - batch_size, - shuffle, - num_workers, - cache_file, - use_sampling, - ) - return dataloader - - def _build_dataset(self, data_dir): - df = self._build_df(data_dir) - dataset = Dataset.from_pandas(df) - logger.info(f"Load example for {data_dir}") - dataset = dataset.map(lambda example: self._load_example_info(example)) - return dataset - - def _build_cache(self, dataset, processor, max_seq_length, cache_file=""): - logger.info(f"Caching {cache_file}...") - cache = {} - for examples in tqdm(dataset): - encoding_inputs = self._cache_feature(examples, processor, max_seq_length) - cache[examples["image_path"]] = encoding_inputs - - with open(cache_file, "wb") as f: - pickle.dump(cache, f) - - def _load_cache(self, cache_file): - with open(cache_file, "rb") as f: - cache = pickle.load(f) - return cache - - def _prepare_data(self, example, cache, sampling): - encoded_inputs_windows = cache[example["image_path"]] - if len(encoded_inputs_windows) == 0: - raise Exception("Empty encoded_inputs_windows") - if sampling: - if random.random() < 0.6: - encoded_inputs = encoded_inputs_windows[-1] - else: - encoded_inputs = random.choice(encoded_inputs_windows) - else: - encoded_inputs = encoded_inputs_windows[-1] - for k, v in encoded_inputs.items(): - if k in ["image", "pixel_values"]: - encoded_inputs[k] = encoded_inputs[k][0] - return encoded_inputs - - def _cache_feature(self, example, processor, max_seq_length=512): - """Sampling (optional) + apply LayoutLMProcessor - - Args: - examples (dict): dict {'image_path', 'words', 'bbox', 'word_labels'} - max_seq_length (int, optional): _description_. Defaults to 512. - - Returns: - list[dict]: list encoding inputs - """ - - image = Image.open(example["image_path"]).convert("RGB") - batch_words = example["words"] - batch_boxes = example["bbox"] - batch_labels = example["word_labels"] - - window_size = self.cfg.max_num_words - slice_interval = self.cfg.slice_interval - - word_windows = sliding_windows(batch_words, window_size, slice_interval) - box_windows = sliding_windows(batch_boxes, window_size, slice_interval) - label_windows = sliding_windows(batch_labels, window_size, slice_interval) - - encoded_inputs = [] - for words, boxes, labels in zip(word_windows, box_windows, label_windows): - # Process examples - encoded_input = processor( - image, - padding="max_length", - truncation=True, - text=words, - boxes=boxes, - word_labels=labels, - max_length=max_seq_length, - ) - - encoded_inputs.append(encoded_input) - - # full page - encoded_input = processor( - image, - padding="max_length", - truncation=True, - text=batch_words, - boxes=batch_boxes, - word_labels=batch_labels, - max_length=max_seq_length, - ) - encoded_inputs.append(encoded_input) - - return encoded_inputs - - def _load_example_info(self, example, aug_prob=0.0): - """_summary_ - - Args: - example (_type_): _description_ - aug_prob (float, optional): _description_. Defaults to 0.0. - """ - - image_path = example["image_path"] - label_path = example["label"] - assert os.path.exists(image_path) - assert os.path.exists(label_path) - # try: - image = cv2.imread(image_path) - h, w, _ = image.shape - with open(label_path) as f: - lines = [line.replace("\n", "").replace("\r", "") for line in f.readlines()] - - words, boxes, labels = [], [], [] - # print(label_path) - for i, line in enumerate(lines): - x1, y1, x2, y2, text, label = line.split("\t") - box = [int(x1), int(y1), int(x2), int(y2)] - if text != " ": - words.append(text) - boxes.append(box) - labels.append(label) - - if aug_prob > 0: - p_words = perturbate_character(words, aug_prob) - logging.info("{} - {}".format(len(p_words), len(words))) - - # custom for sort boxes - items = { - 'boxes': boxes, - 'texts': words, - 'labels': labels - } - # boxes, words, labels = sort_words(boxes, words, labels) - sorted_items = sort_words(items) - boxes, words, labels = sorted_items['boxes'], sorted_items['texts'], sorted_items['labels'] - - # print(image_path) - # print(image_path) - labels = [self.cfg.classes.index(label) for label in labels] - if self.cfg.debug: - visualize_kie( - img=image, - boxes=boxes, - pred_labels=labels, - outdir="wordirs/debug_{}".format( - "val" if "train" not in image_path else "train" - ), - image_name=os.path.basename(image_path), - ) - - boxes = [normalize_box(box, width=w, height=h) for box in boxes] - - example["words"] = words - example["bbox"] = boxes # TODO: Check this - example["word_labels"] = labels - # except Exception as err: - # logger.info(f"Exception: {err} at image path: {example['image_path']}") - # example["words"] = [] - # example["bbox"] = [] # TODO: Check this - # example["word_labels"] = [] - return example diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/__init__.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/__init__.py deleted file mode 100644 index 3e88504..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .predictor import Predictor diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/predictor.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/predictor.py deleted file mode 100644 index e7f5441..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/predictor.py +++ /dev/null @@ -1,457 +0,0 @@ -import logging -import time - -import cv2 -import numpy as np -from sdsvkie.utils.word_formation import merge_boxes -import torch -from easydict import EasyDict -from PIL import Image - -from sdsvkie.cfg import load_cfg -# from sdsvkie.models.layoutlm import LayoutLM -from sdsvkie.models.layoutlmv2 import LayoutLMv2 -from sdsvkie.models.ocr import OCREngine -from sdsvkie.utils import invoice_postprocess # invoice -from sdsvkie.utils import receipt_postprocess # receipt -from sdsvkie.utils import POSTPROCESS_FUNC -from sdsvkie.utils import (Word, construct_word_groups_to_kie_label, - normalize_box, sliding_windows, sort_words, - unnormalize_box, words_to_lines, - pdf_to_image) - - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -class Predictor: - def __init__(self, cfg, **kwargs) -> None: - """ - - Args: - cfg (dict / str): config - **kwargs: device=..., weights=..., text_det=..., text_reg=... - """ - - if isinstance(cfg, str): - cfg = load_cfg(cfg, kwargs) - self.cfg = EasyDict(cfg) - print(self.cfg) - - self.model = None - self.processor = None - self.ocr_engine = None - self.classes = self.cfg["classes"] - - self.max_num_words = self.cfg.max_num_words - self.model = None - self.processor = None - self.ocr_engine = None - - def _init_predictor(self, model=None, proccessor=None, ocr_engine=None): - - if self.cfg.device != "cpu" and not torch.cuda.is_available(): - logger.info("Can not found cuda, training with CPU!!!") - self.cfg.device = "cpu" - - if self.cfg["inference_weights"] is None: - logger.info( - "Not yet set value for inference weights, use weights instead!!!" - ) - if model is None: - self.model = LayoutLMv2._load_model(self.cfg) - if proccessor is None: - self.processor = LayoutLMv2._load_processor(self.cfg) - - if ocr_engine is None: - self.ocr_engine = OCREngine( - text_det=self.cfg["text_det"], - text_recog=self.cfg["text_reg"], - device=self.cfg["device"], - ) - - - def __call__(self, input , ocr_output=None, return_raw=False): - """Inference KIE - - Pipeline: Img -> OCR -> box + text (word-level) -> sort by x, y-axis -> LayoutLM -> Word formation -> result - - Args: - input (np.ndarray / str / pdf path): BGR image (cv2) - - Returns: - (dict): { - 'kie_raw_output': - 'kie_post_output': - 'end2end_results': (dict) : {kie_label : value} - } - """ - if self.model is None: - self._init_predictor() - #check single or multi images - if isinstance(input, np.ndarray): - final_out= self.predict_single_image(input, ocr_output=ocr_output, return_raw=return_raw) - elif isinstance(input, list): - items = [self.predict_single_image(im, ocr_output=ocr_output, return_raw=return_raw) for im in input] - final_out = self.aggregate_outputs(items) - else: #pdf - import time - t1 = time.time() - imgs = pdf_to_image(input) - print("1. pdf2img: ", round(time.time() - t1, 4)) - t2 = time.time() - items = [self.predict_single_image(im, ocr_output=ocr_output, return_raw=return_raw) for im in imgs] - final_out = self.aggregate_outputs(items) - print("2. kie: ", round(time.time() - t2, 4)) - print(f"3. full pipeline for {len(imgs)} pages: {round(time.time() - t1, 4)}") - return final_out - - def predict_single_image(self, img, ocr_output=None, return_raw=False): - - if ocr_output is None: - ocr_output = self.ocr_engine(img, extend_ratio=[0.1, 0.3], ratio_thr=5) # solve long box - # ocr_output = self.ocr_engine(img) - kie_input = self.prepare_inputs(img, ocr_output) - kie_output = self.predict(kie_input) - kie_post_output = self.postprocessing(kie_output) - formated_output = self.format_output(kie_post_output) - output = { - "kie_raw_output": kie_output if return_raw else None, # raw output from layoutlm model - "kie_post_output": kie_post_output if return_raw else None, # wordgroup - "end2end_results": formated_output, # field_key + field_value - } - return output - - def predict(self, inputs: dict): - """predict - - Args: - inputs (dict): format - { - 'image": PIL RGB, - 'boxes': list[], - 'texts' - } - - Returns: - list[Word]: list of Word object - """ - window_size = self.cfg.max_num_words - slice_interval = self.cfg.slice_interval - - image, batch_boxes, batch_words = ( - inputs["img"], - inputs["boxes"], - inputs["texts"], - ) - results = [] - non_norm_boxes = inputs['non_norm_boxes'] - if len(batch_boxes) == 0: - logger.info("Not found any words in image!!! Continue...") - return results - - text_windows = sliding_windows(batch_words, window_size, slice_interval) - box_windows = sliding_windows(batch_boxes, window_size, slice_interval) - # print([len(t) for t in text_windows]) - - out_boxes_windows = [] - # out_labels_windows = [] - out_logits_windows = [] - - for i in range(len(text_windows)): - words = text_windows[i] # len: MAX_N_WORDS - boxes = box_windows[i] - - # Preprocess - dummy_word_labels = [0] * len(words) - encoding = self.processor( - image, - text=words, - boxes=boxes, - word_labels=dummy_word_labels, - return_tensors="pt", - padding="max_length", - truncation=True, - max_length=self.cfg.max_seq_length, - ) - - label_ = encoding.pop('labels') - # Run model - for k, v in encoding.items(): - encoding[k] = v.to(self.cfg.device) - with torch.no_grad(): - output = self.model(**encoding) - - - logits = output.logits.squeeze() # seq_len * classes - predictions = output.logits.argmax(-1).squeeze().tolist() - token_boxes = encoding.bbox.squeeze().tolist() - - # Postprocess - # is_subword = (encoding["labels"] == -100).detach().cpu()[0] - is_subword = (label_ == -100).detach().cpu()[0] - logit_predictions = logits[torch.logical_not(is_subword), :] - true_boxes = torch.Tensor( - [ - unnormalize_box(box, image.size[0], image.size[1]) - for idx, box in enumerate(token_boxes) - if not is_subword[idx] - ] - ) - - # print("logit shape: ", logit_predictions.shape) - # print("box shape: ", true_boxes.shape) - # true_boxes = torch.Tensor( - # [ - # unnormalize_box(box, image.size[0], image.size[1]) - # for idx, box in enumerate(boxes) - # ] - # ) - out_boxes_windows.append(true_boxes) - out_logits_windows.append(logit_predictions) - - # merge output by average logits between overlap window - merged_out_boxes = out_boxes_windows[0] - merged_out_logit = out_logits_windows[0] - overlap = window_size - slice_interval - for i in range(1, len(out_boxes_windows)): - if overlap != 0: - prev_overlap_logits = merged_out_logit[-overlap:, :] - curr_overlap_logits = out_logits_windows[i][:overlap, :] - avg_overlap_logits = ( - prev_overlap_logits + curr_overlap_logits - ) / 2 - curr_logits = torch.cat( - [avg_overlap_logits, out_logits_windows[i][overlap:, :]], dim=0 - ) - merged_out_logit = torch.cat( - [merged_out_logit[:-overlap, :], curr_logits], dim=0 - ) - else: - merged_out_logit = torch.cat( - [merged_out_logit, out_logits_windows[i]], dim=0 - ) - - merged_out_boxes = torch.cat( - [merged_out_boxes, out_boxes_windows[i][overlap:, :]], dim=0 - ) - - # print(f"merged_out_logit={len(merged_out_logit)} - merged_out_boxes={len(merged_out_boxes)}") - # from IPython import embed; embed() - assert len(merged_out_logit) == len( - merged_out_boxes - ), f"{len(merged_out_logit)} # {len(merged_out_boxes)}" - predictions = merged_out_logit.argmax(-1).squeeze().tolist() - if not isinstance(predictions, list): - predictions = [predictions] - - - assert len(predictions) == len(batch_words), f"{len(predictions)} # {len(batch_words)}" - # for word_index in range(len(batch_words)): - for word_index, word in enumerate(batch_words): - word = batch_words[word_index] - # bndbox = [int(coord) for coord in merged_out_boxes[word_index]] - bndbox = non_norm_boxes[word_index] - kie_label = self.cfg.classes[predictions[word_index]] - results.append( - Word( - text=word, - bndbox=bndbox, - kie_label=kie_label, - conf_cls=inputs["recog_confs"][word_index] - ) - ) - - return results - - def postprocessing(self, words): - """Post processing for output of kie - - Merge wordgroup list for each field - - Args: - items (dict): _description_ - """ - list_lines, _ = words_to_lines(words) - list_word_group = [] - for line in list_lines: - for word_group in line.list_word_groups: - word_group.update_kie_label() - word_group.update_conf() - list_word_group.append(word_group) - - kie_dict = construct_word_groups_to_kie_label(list_word_group) - #receipt postprocess - if 'postprocess_type' in self.cfg and self.cfg.postprocess_type == "receipt_postprocess": - kie_dict = receipt_postprocess(kie_dict, words) - else: #invoice_postprocess - # kie_dict = invoice_postprocess(kie_dict) - kie_dict = self._postprocess_kie_wordgroups(kie_dict, doc_type=self.cfg.postprocess_type) - return kie_dict - - def format_output(self, kie_dict): - """ - Args: - kie_dict (dict) : format - { - 'field_name': list[Wordgroup] - } - - Returns: - (dict): format - { - 'field_name': { - 'box': list, - 'value': str, - 'conf': float - } - } - """ - end2end_results = {} - filtered_dict = {k:v for k,v in kie_dict.items() if "key" not in k} - for field_name, wg_list in filtered_dict.items(): - wg_list = [wg_list] if not isinstance(wg_list, list) else wg_list - - if len(wg_list) == 0: - text, conf, box = "", 0.0, [] - else: - text = " ".join([wg.text for wg in wg_list]).strip().replace("✪", " ") - conf = sum(wg.conf for wg in wg_list) / len(wg_list) - box = merge_boxes([wg.boundingbox for wg in wg_list]) - - end2end_results[field_name] = { - "box": box, - "value": text, - "conf": conf - } - - # add empty values for missing fields - for class_name in self.classes: - if "key" not in class_name and class_name not in end2end_results and class_name.lower() not in ['other', 'others']: - end2end_results[class_name] = { - "box": [], - "value": "", - "conf": 0.0 - } - - end2end_results = self._postprocess_recognized_text(end2end_results, doc_type=self.cfg.postprocess_type) - # sort by keys - end2end_results = dict(sorted(end2end_results.items())) - return end2end_results - - - def _postprocess_kie_wordgroups(self, result, doc_type, metadata=None): - """post process for wordgroup outputs - - Args: - result (dict): {'field_name': list[Wordgroup]} - doc_type (str): invoice / receipt - metadata (_type_, optional): _description_. Defaults to None. - - Returns: - _type_: _description_ - """ - for field_name in result.keys(): - if field_name not in POSTPROCESS_FUNC[doc_type]: - continue - postprocess_func = POSTPROCESS_FUNC[doc_type][field_name].get("wordgroup", None) - if postprocess_func is None: - continue - result[field_name] = postprocess_func(result[field_name], metadata={"field_name": field_name, "wg_res": result}) - return result - - def _postprocess_recognized_text(self, result, doc_type, metadata=None): - for field_name in result.keys(): - if field_name not in POSTPROCESS_FUNC[doc_type]: - continue - postprocess_func = POSTPROCESS_FUNC[doc_type][field_name].get("text", None) - if postprocess_func is None: - continue - result[field_name]["value"] = postprocess_func(result[field_name]['value'], metadata) - return result - - def prepare_inputs(self, img, ocr_output): - """Prepare input for KIE model - - Args: - img (np.ndarray): BGR image - ocr_output (dict): format - { - "img_path": img_path, - "img": image, - "boxes": boxes, - "texts": words, - "kie_labels": word_labels - } - """ - assert "boxes" in ocr_output, "boxes not exists in ocr_output" - assert "texts" in ocr_output, "texts not exists in ocr_output" - - # cv2 to PIL (RGB) - img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) - w, h = img.size - texts = ocr_output["texts"] - boxes = ocr_output["boxes"] - recog_confs = ocr_output['recog_confs'] - det_confs = ocr_output['det_confs'] - texts = [text.replace(" ", "✪") for text in texts] # layoutlm will throw an error if the input has space characters - word_items = { - 'boxes': boxes, - 'texts': texts, - 'det_confs': det_confs, - 'recog_confs': recog_confs - } - sorted_word_items = sort_words(word_items) - (boxes, texts, det_confs, recog_confs) = ( - sorted_word_items['boxes'], - sorted_word_items['texts'], - sorted_word_items['det_confs'], - sorted_word_items['recog_confs'] - ) - non_norm_boxes = sorted_word_items['boxes'] - boxes = [normalize_box(box, width=w, height=h) for box in boxes] - out_item = { - "img": img, - "boxes": np.array(boxes), - "texts": texts, - "det_confs": det_confs, - "recog_confs": recog_confs, - "non_norm_boxes": non_norm_boxes - } - return out_item - - - - def aggregate_outputs(self, outs): - f"""Postprocess the outputs of the muliple pages - - Args: - outs (_type_): _description_ - - Returns: - output: (dict): final output - - """ - combine_out = { - 'kie_raw_output': [], - 'kie_post_output': [], - 'end2end_result_each_page': [], - 'end2end_results': None - } - - for idx, out in enumerate(outs): - combine_out['kie_raw_output'].append(out['kie_raw_output']) - combine_out['kie_post_output'].append(out['kie_post_output']) - combine_out['end2end_result_each_page'].append(out['end2end_results']) - - #merge end2end result - end2end_results = combine_out['end2end_result_each_page'][0] - - for page_id, end2end_results_page in enumerate(combine_out['end2end_result_each_page'][1:]): - for field_key, field_value in end2end_results_page.items(): - if "value" in end2end_results[field_key] \ - and (end2end_results[field_key]['value'] == "" or end2end_results[field_key]['value'] == "0"): - end2end_results[field_key] = field_value - combine_out['end2end_results'] = end2end_results - - return combine_out - \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/trainer.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/trainer.py deleted file mode 100644 index 3a92c5d..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/engine/trainer.py +++ /dev/null @@ -1,260 +0,0 @@ -import logging -import os -import time - -import torch -import wandb -from easydict import EasyDict -from sklearn.metrics import precision_recall_fscore_support -from terminaltables import AsciiTable -from torch.optim import AdamW -from torch.optim.lr_scheduler import LinearLR -from tqdm import tqdm - -from sdsvkie.datasets.base_dataset import BaseDataset -from sdsvkie.models.layoutlmv2 import LayoutLMv2 -from sdsvkie.models.layoutlm import LayoutLM -from sdsvkie.utils import yaml_save, get_info_env, get_logger -from transformers import get_scheduler - -# logging = logging.getlogging(__name__) -# logging.basicConfig(level=logging.INFO) - - - -class Trainer: - def __init__(self, cfg: dict): - self.cfg = EasyDict(cfg) - self.model = None - self.processor = None - self._init_trainer() - - def _init_trainer(self): - - if self.cfg.v3: - os.environ["TOKENIZERS_PARALLELISM"] = "false" - - if not os.path.exists(self.cfg.save_dir): - os.makedirs(self.cfg.save_dir, exist_ok=True) - - timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) - log_file = "{}/{}.log".format(self.cfg.save_dir, str(timestamp)) - self.logger = get_logger(log_file=log_file, log_level='INFO') - self.logger.info(self.cfg) - # self.logger.info(get_info_env()) - - if not torch.cuda.is_available(): - self.logger.info("Can not found cuda, training with CPU!!!") - self.cfg.device = "cpu" - - - - self.model = LayoutLMv2._load_model(self.cfg) - self.processor = LayoutLMv2._load_processor(self.cfg) - - self.model.to(self.cfg.device) - - if self.cfg.wandb: - wandb.init( - project=self.cfg.wandb, - ) - - def _build_dataloader(self, data_dir, cache_file, use_sampling=False): - base_dataset = BaseDataset(self.cfg) - dataloader = base_dataset.build_dataloader_from_dir( - data_dir=data_dir, - processor=self.processor, - device="cpu", - batch_size=self.cfg.batch_size, - num_workers=self.cfg.num_workers, - shuffle=False, - cache_file=cache_file, - use_sampling=use_sampling, - ) - return dataloader - - def val(self, val_dir=None): - val_dir = val_dir if val_dir is not None else self.cfg.val_dir - val_cache_file = os.path.splitext(val_dir)[0] + ".pkl" - val_dataloader = self._build_dataloader( - val_dir if val_dir is not None else self.cfg.val_dir, - cache_file=val_cache_file - ) - acc = self.val_on_dataloader(val_dataloader) - return acc - - def train(self, train_dir=None, val_dir=None): - self.logger.info("Building train dataloader...") - base_dataset = BaseDataset(self.cfg) - train_dir = train_dir if train_dir is not None else self.cfg.train_dir - val_dir = val_dir if val_dir is not None else self.cfg.val_dir - train_cache_file = os.path.splitext(train_dir)[0] + ".pkl" - val_cache_file = os.path.splitext(val_dir)[0] + ".pkl" - if self.cfg.sampling: - train_dataset = base_dataset._build_dataset(data_dir=train_dir) - train_dataloader = base_dataset.build_dataloader_from_dataset( - train_dataset, - batch_size=self.cfg.batch_size, - processor=self.processor, - device="cpu", - shuffle=True, - num_workers=self.cfg.num_workers, - cache_file=train_cache_file, - use_sampling=True, - ) - else: - train_dataloader = self._build_dataloader( - train_dir if train_dir is not None else self.cfg.train_dir, - cache_file=train_cache_file, - use_sampling=False, - ) - - self.logger.info("Building valid dataloader...") - val_dataloader = self._build_dataloader( - val_dir if val_dir is not None else self.cfg.val_dir, - cache_file= val_cache_file, - use_sampling=False, - ) - - self.logger.info( - f"Info dataset: train = {len(train_dataloader)}, test = {len(val_dataloader)}" - ) - - optimizer = AdamW(self.model.parameters(), lr=self.cfg.lr) - if self.cfg.scheduler: - # scheduler = torch.optim.lr_scheduler.OneCycleLR( - # optimizer, - # max_lr=self.cfg.lr, - # steps_per_epoch=len(train_dataloader), - # epochs=self.cfg.epochs, - # anneal_strategy='cos', - # pct_start=0.1, - # div_factor=25, #init lr = max_lr / div_factor - # final_div_factor=1e4, # min lr = init_lr / final_dev_factor - # ) - num_training_steps = self.cfg.epochs * len(train_dataloader) - scheduler = get_scheduler( - name=self.cfg.scheduler, - optimizer=optimizer, - num_warmup_steps=0, - num_training_steps=num_training_steps, - - ) - - if self.cfg.wandb: - wandb.config = dict(self.cfg) - - best_acc = 0.0 - best_epoch = 0 - yaml_save(os.path.join(self.cfg.save_dir, "config.yaml"), dict(self.cfg)) - for epoch in range(self.cfg.epochs): - - # sampling slice window - if ( - self.cfg.sampling and epoch != 0 and epoch % self.cfg.sampling == 0 - ): # sampling each cfg.sampling epochs - train_dataloader = base_dataset.build_dataloader_from_dataset( - train_dataset, - batch_size=self.cfg.batch_size, - processor=self.processor, - device="cpu", - shuffle=True, - num_workers=self.cfg.num_workers, - cache_file=train_cache_file, - use_sampling=True, - ) - - self.model.train() - self.logger.info(f"Epoch: {epoch}:") - - running_loss = 0.0 - - for batch in tqdm(train_dataloader): - # forward pass - batch = self._to_device(batch, self.cfg.device) - - outputs = self.model(**batch) - loss = outputs.loss - running_loss += loss.item() - # backward pass to get the gradients - loss.backward() - - # update - optimizer.step() - if self.cfg.scheduler: - scheduler.step() - optimizer.zero_grad() - - loss_avg = running_loss / len(train_dataloader) - self.logger.info(f"Epoch[{epoch}/{self.cfg.epochs}] - lr: {round(scheduler.get_last_lr()[0], 9) if self.cfg.scheduler else self.cfg.lr} - loss: {loss_avg}") - if self.cfg.wandb: - wandb.log({"train_loss": loss_avg}) - - # valid - if epoch >= self.cfg.eval_delay: - acc = self.val_on_dataloader(val_dataloader) - - if acc > best_acc: - self.model.save_pretrained(os.path.join(self.cfg.save_dir, "best")) - self.logger.info(f"Update best acc, prev best acc = {best_acc}, current best acc = {acc}") - best_acc = acc - best_epoch = epoch - - if epoch % self.cfg.save_weight_interval == 0: - self.model.save_pretrained( - os.path.join(self.cfg.save_dir, "epoch_{}".format(epoch)) - ) - - self.model.save_pretrained(os.path.join(self.cfg.save_dir, "last")) - self.logger.info(f"Best accuracy = {best_acc} at epoch {best_epoch}") - - def _to_device(self, batch, device): - batch = {k: v.to(device) for k, v in batch.items()} - return batch - - def val_on_dataloader(self, dataloader): - self.model.eval() - total, correct = 0, 0 - preds, truths = [], [] - - running_loss = 0.0 - for batch in tqdm(dataloader): - with torch.no_grad(): - batch = self._to_device(batch, self.cfg.device) - outputs = self.model(**batch) - - loss = outputs.loss - running_loss += loss.item() - predictions = outputs.logits.argmax(dim=2) - valid_samples = batch["labels"] != -100 - predictions = predictions[valid_samples] - batch_labels = batch["labels"][valid_samples] - - preds.extend(predictions.detach().cpu().numpy().tolist()) - truths.extend(batch_labels.detach().cpu().numpy().tolist()) - correct += (predictions == batch_labels).float().sum() - total += predictions.numel() - - loss_avg = running_loss / len(dataloader) - - p, r, f1, support = precision_recall_fscore_support(truths, preds) - # self.logger.info("shapeeee", p.shape) - table_data = [["Class", "P", "R", "F1", "#samples"]] - for c in range(len(self.cfg.classes)): - if c < p.shape[0]: - table_data.append([self.cfg.classes[c], p[c], r[c], f1[c], support[c]]) - continue - - f1_avg = sum(f1) / len(f1) - table = AsciiTable(table_data) - self.logger.info(table.table) - self.logger.info( - "Validation F1: {} - #samples: {} - #corrects: {}".format( - f1_avg, total, correct - ) - ) - - if self.cfg.wandb: - wandb.log({"val_loss": loss_avg, "f1": f1_avg}) - - return f1_avg diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/__init__.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/cvat.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/cvat.py deleted file mode 100644 index 7d93788..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/cvat.py +++ /dev/null @@ -1,671 +0,0 @@ - - - - - - -import argparse -import glob -import json -import os -import xml.etree.ElementTree as ET - -import numpy as np -import tqdm -from sdsvkie.utils.word_formation import Box, check_iou -from pathlib import Path - -def read_txt(txt): - with open(txt, 'r', encoding='utf8') as f: - data = [line.strip() for line in f] - return data - -def write_txt(txt, data): - with open(txt, 'w', encoding='utf8') as f: - for line in data: - f.write(line + "\n") - - - -""" - -general json format: - - -python cvat.py --task pseudo --xml sample_cvat/annotations.xml --xml_out sample_cvat/annotations_out.xml --pseudo_path sample_cvat/pseudo.json - -""" - - - - - -""" - - -config for fwd -{ - "id": "OCR005_2", - "name": "Số Hợp đồng", - "type": "text", - "value": "33252663", - "page_num": 0, - "box": [192, 168, 220, 250] - -}, - -""" -# CONFIG = { -# "label": "field", - -# #edit here -# "attribute_names": ["type", "value", "id", "name"] # name of attribute in cvat label: text / checkbox -# } - - -CONFIG = { - "label": "word", - # "attribute_names": ["text", "kie_label"] # name of attribute in cvat label: text / checkbox - "attribute_names": [] -} - - - -class CVAT: - def __init__(self): - pass - - def create_xml_from_json(self, json_path, xml_in, xml_out): - """ - json data format: - { - "img_1.jpg": [ - { - "box": [x1, y1, x2, y2] - "label": str # (not required) - "attrib1": str, - "attrib2": str, - }, - { - "box": [x1, y1, x2, y2] - "label": str # (not required) - "attrib1": str, - "attrib2": str, - } - ... - ], - "img_2.jpg": [...] - } - """ - - data = self.read_json(json_path) - - - - tree = ET.parse(xml_in) - root = tree.getroot() - for img_item in root.iter("image"): - img_path = img_item.attrib['name'] - - img_data = data[img_path] - - for item in img_data: - et = ET.Element('box') - #default values - et.attrib['occluded']="0" - et.attrib['source']="manual" - et.attrib['z_order'] = "0" - - #overwrite values - if 'label' in item: - et.attrib['label'] = item['label'] - else: - et.attrib['label'] = CONFIG['label'] - - xmin, ymin, xmax, ymax = item['box'] - ( - et.attrib['xtl'], et.attrib['ytl'], - et.attrib['xbr'], et.attrib['ybr'] - ) = ( - str(xmin), str(ymin), - str(xmax), str(ymax) - ) - - for att_name in CONFIG['attribute_names']: - if att_name not in item: - continue - - att_et = ET.Element('atrribute') - att_et.attrib['name'] = att_name - att_et.text = item[att_name] - et.append(att_et) - - - img_item.append(et) - - tree.write(xml_out, encoding='utf8') - - - - def get_data_from_txt_dir(self, txt_dir, resever_parent_dir=False): - if resever_parent_dir: - txt_paths = glob.glob(txt_dir + "/*/*.txt") - else: - txt_paths = glob.glob(txt_dir + "/*.txt") - data = {} - for txt_path in txt_paths: - if resever_parent_dir: - txt_name = "/".join(txt_path.split("/")[-2:]) - else: - txt_name = os.path.basename(txt_path) - - - txt_data = read_txt(txt_path) - format_data = [] - for line in txt_data: - items = line.split("\t") - # assert len(items) == 6, "error get len = {} - {}".format(len(items), items) - - box = [int(float(x)) for x in items[:4]] - text = items[4] - - if len(items) == 6: - kie_label = items[5] - else: - kie_label = "word" - format_data.append( - { - 'box': box, - 'text': text, - 'label': kie_label - } - ) - data[txt_name] = format_data - - return data - - def get_data_from_txt_path(self, txt_path): - - txt_data = read_txt(txt_path) - format_data = [] - for line in txt_data: - items = line.split("\t") - assert len(items) == 6, "error get len = {} - {}".format(len(items), items) - box = [int(float(x)) for x in items[:4]] - text = items[4] - kie_label = items[5] - format_data.append( - { - 'box': box, - 'text': text, - 'label': kie_label - } - ) - return format_data - - def format_data_invoice(self, data): - - new_data = {} - for txt_name, value in data.items(): - items = [] - for item in value: - text = item['text'] - if "____kie_wordgroup" in text: - new_item = { - 'box': item['box'], - 'label': item['label'] - } - else: - - new_item = { - "box": item['box'], - 'text': "xxxxxx", - 'kie_label': item['label'], - 'label': "word" - } - - items.append(new_item) - new_data[txt_name] = items - return new_data - - - - - def create_xml_from_txt(self, txt_dir, xml_in, xml_out, skip_labels=[], resever_parent_dir=False): - """ - - """ - data = self.get_data_from_txt_dir(txt_dir, resever_parent_dir) - print(list(data.keys())) - - # for invoice - if len(skip_labels) > 0 and "word" in skip_labels: - data = self.format_data_invoice(data) - tree = ET.parse(xml_in) - root = tree.getroot() - count = 0 - for img_item in tqdm.tqdm(root.iter("image")): - count += 1 - img_path = img_item.attrib['name'] - - - txt_name = os.path.splitext(img_path)[0] + ".txt" - - img_data = data.get(txt_name, []) - # from IPython import embed; embed() - # break - if len(img_data) > 0: - # img_item.clear() - for child in img_item: - img_item.remove(child) - - for item in img_data: - et = ET.Element('box') - #default values - et.attrib['occluded']="0" - et.attrib['source']="manual" - et.attrib['z_order'] = "0" - - #overwrite values - - if 'label' in item: - if item['label'] in skip_labels: - continue - et.attrib['label'] = item['label'] - else: - et.attrib['label'] = CONFIG['label'] - - xmin, ymin, xmax, ymax = item['box'] - ( - et.attrib['xtl'], et.attrib['ytl'], - et.attrib['xbr'], et.attrib['ybr'] - ) = ( - str(xmin), str(ymin), - str(xmax), str(ymax) - ) - - for att_name in CONFIG['attribute_names']: - if att_name not in item: - continue - - att_et = ET.Element('atrribute') - att_et.attrib['name'] = att_name - att_et.text = item[att_name] - et.append(att_et) - - - img_item.append(et) - print("Num imgs: ", count) - tree.write(xml_out, encoding='utf8') - - def get_data_from_xml(self, xml, skip_labels=[]): - """ parse xml to dict - - - Args: - xml (str): cvat anno xml path - - Returns: - (dict): { - 'img_1.jpg': [ - 'kie_label': [xyxy] - ... - ], - 'img_2.jpg': ... - } - """ - anno_data = open(xml, encoding='utf8') - tree = ET.parse(anno_data) - root = tree.getroot() - data = {} - - for obj in tqdm.tqdm(root.iter("image")): - img_path = obj.attrib['name'] - img_data = [] - for box in obj.iter("box"): - box_label = box.attrib['label'] - if box_label in skip_labels: - continue - - #get coordinate - xmin, ymin, xmax, ymax = box.attrib['xtl'], box.attrib['ytl'], box.attrib['xbr'], box.attrib['ybr'] - xmin, ymin, xmax, ymax = int(float(xmin)), int(float(ymin)), int(float(xmax)), int(float(ymax)) - - item = { - box_label: [xmin, ymin, xmax, ymax] - } - img_data.append(item) - - data[img_path] = img_data - - return data - @staticmethod - def write_json(json_path, data): - with open(json_path, 'w', encoding='utf8') as f: - json.dump(data, f, ensure_ascii=False) - - - def read_json(self, json_path): - with open(json_path, 'r', encoding='utf8') as f: - data = json.load(f) - - return data - - def update_label_kie(self, txt_dir, json_path, out_dir): - if not os.path.exists(out_dir): - os.makedirs(out_dir, exist_ok=True) - data = self.read_json(json_path) - txt_paths = glob.glob(txt_dir + "/*.txt") - for txt_path in tqdm.tqdm(txt_paths): - ori_data = read_txt(txt_path) - - boxes = [] - - img_name = os.path.splitext(os.path.basename(txt_path))[0] - img_name = "_".join(img_name.split("_")[:-1]) + "_1" + ".jpg" - # img_name = os.path.splitext(os.path.basename(txt_path))[0] + ".jpg" - new_img_data = data[img_name] - for line in ori_data: - xmin, ymin, xmax, ymax, text, kie_label = line.strip().split("\t") - if "____kie_wordgroup" in text: - continue - xmin, ymin, xmax, ymax = int(xmin), int(ymin), int(xmax), int(ymax) - # box_word = [xmin, ymin, xmax, ymax] - new_kie_label = "other" - for label_info in new_img_data: - for label, box_wordgroup in label_info.items(): - # print(label, box_wordgroup) - box_word = Box( - xmin, ymin, xmax, ymax - ) - box_wordgroup = Box( - box_wordgroup[0], box_wordgroup[1], box_wordgroup[2],box_wordgroup[3] - ) - if check_iou(box1=box_word, box2=box_wordgroup, threshold=0.85): - new_kie_label = label - break - - if new_kie_label != "other": - break - - - new_box = Box( - xmin=xmin, - ymin=ymin, - xmax=xmax, - ymax=ymax, - label=text, - kie_label=new_kie_label - - ) - boxes.append(new_box) - - boxes = sorted(boxes, key=lambda box: [box.ymin, xmin]) - new_data = [ - "\t".join([str(box.xmin), str(box.ymin), str(box.xmax), str(box.ymax), box.label, box.kie_label]) - for box in boxes - ] - - - write_txt(os.path.join(out_dir, os.path.basename(txt_path)), new_data) - def _check_iou(self, box1, box2, threshold=0.9): - """_summary_ - - Args: - box1 (_type_): word box - box2 (_type_): line box - threshold (float, optional): _description_. Defaults to 0.9. - - Returns: - _type_: _description_ - """ - area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) - area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) - xmin_intersect = max(box1[0], box2[0]) - ymin_intersect = max(box1[1], box2[1]) - xmax_intersect = min(box1[2], box2[2]) - ymax_intersect = min(box1[3], box2[3]) - - - if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect: - area_intersect = 0 - else: - area_intersect = (xmax_intersect - xmin_intersect) * ( - ymax_intersect - ymin_intersect - ) - # union = area1 + area2 - area_intersect - iou = area_intersect / area1 - return iou - # if iou > threshold: - # return True - # return False - - def _update_label_for_word(self, box, line_items, threshold=0.75, other_class='others'): - have_label = False - max_iou = -1 - for line_item in line_items: - # 465 901 664 940 - - curr_iou = self._check_iou(box, line_item['box'], threshold=threshold) - if curr_iou > threshold and curr_iou > max_iou: - - max_iou = curr_iou - kie_label = line_item['label'] - have_label = True - - # if box[0] == 465 and box[-1] == 940: - # print(box, curr_iou, kie_label, line_item) - # break - - if not have_label: - kie_label = other_class - return kie_label - - def update_label_kie_from_xml(self, txt_dir, xml, out_dir, skip_labels = [], line_to_word=False, other_class="others", resever_parent_dir=False): - if not os.path.exists(out_dir): - os.makedirs(out_dir, exist_ok=True) - - #read xml - xml_data = {} - anno_data = open(xml, encoding='utf8') - tree = ET.parse(anno_data) - root = tree.getroot() - # data = {} - - for obj in tqdm.tqdm(root.iter("image")): - img_path = obj.attrib['name'] - # img_data = [] - if not line_to_word: - img_data = {} - for box in obj.iter("box"): - box_label = box.attrib['label'] - if box_label in skip_labels: - continue - - #get coordinate - xmin, ymin, xmax, ymax = box.attrib['xtl'], box.attrib['ytl'], box.attrib['xbr'], box.attrib['ybr'] - box_int = int(float(xmin)), int(float(ymin)), int(float(xmax)), int(float(ymax)) - box_key = ",".join([str(x) for x in box_int]) - img_data[box_key] = box_label - - else: - img_data = [] - for box in obj.iter("box"): - box_label = box.attrib['label'] - if box_label in skip_labels: - continue - - #get coordinate - xmin, ymin, xmax, ymax = box.attrib['xtl'], box.attrib['ytl'], box.attrib['xbr'], box.attrib['ybr'] - box_int = int(float(xmin)), int(float(ymin)), int(float(xmax)), int(float(ymax)) - box_key = ",".join([str(x) for x in box_int]) - img_data.append( - { - 'box': box_int, - 'label': box_label - } - ) - xml_data[os.path.splitext(img_path)[0]] = img_data - - # print(xml_data) - if resever_parent_dir: - txt_paths = glob.glob(txt_dir + "/*/*.txt") - else: - txt_paths = glob.glob(txt_dir + "/*.txt") - updated_imgs = [] - for txt_path in tqdm.tqdm(txt_paths): - is_update = False - # print(txt_path) - ori_data = read_txt(txt_path) - # print(ori_data) - img_new_data = [] - if resever_parent_dir: - img_key = str(Path(txt_path).with_suffix('').relative_to(Path(txt_path).parent.parent)) # a/xyz - else: - img_key = os.path.splitext(os.path.basename(txt_path))[0] # xyz - if img_key not in xml_data: - print(txt_path) - continue - img_annoted_data = xml_data[img_key] - # print(img_key, img_annoted_data) - if not line_to_word: - - for line in ori_data: - xmin, ymin, xmax, ymax, text, kie_label = line.strip().split("\t") - if "____kie_wordgroup" in text: - continue - box_int = int(xmin), int(ymin), int(xmax), int(ymax) - box_key = ",".join([str(x) for x in box_int]) - - if box_key in img_annoted_data: - if kie_label != img_annoted_data[box_key]: - is_update.append(txt_path) - # print(kie_label, img_annoted_data[box_key]) - kie_label = img_annoted_data[box_key] - else: - kie_label = other_class - img_new_data.append("\t".join([xmin, ymin, xmax, ymax, text, kie_label])) - else: - - # print("ori_data: ", ori_data) - for line in ori_data: - # print(line) - items = line.strip().split("\t") - if len(items) == 5: - xmin, ymin, xmax, ymax, text = items - else: - xmin, ymin, xmax, ymax, text, label = items - - box_int = int(xmin), int(ymin), int(xmax), int(ymax) - kie_label = self._update_label_for_word(box_int, img_annoted_data, threshold=0.75, other_class=other_class) - - if label != kie_label: - print(kie_label, label) - is_update = True - img_new_data.append("\t".join([xmin, ymin, xmax, ymax, text, kie_label])) - if resever_parent_dir: - out_sub_dir = Path(out_dir) / Path(img_key).parts[-2] - if not out_sub_dir.exists(): - out_sub_dir.mkdir(parents=True) - # else: - # out_sub_dir = out_dir - # out_sub_dir = str(out_sub_dir) - write_txt(os.path.join(out_dir, img_key + ".txt"), img_new_data) - - if is_update: - updated_imgs.append(txt_path) - else: - print("No update: ", txt_path) - print("updated_imgs: ", list(set(updated_imgs))) - print("num updated_imgs: ", len(list(set(updated_imgs)))) - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--xml", type=str, default='annotations.xml') - parser.add_argument("--pseudo_path", type=str, default="pseudolabel.json") - parser.add_argument("--xml_out", type=str, default="annotations_out.xml") - parser.add_argument("--task", type=str, default='pseudo', help='pseudo / parse_data / update_txt / update_txt_from_xml') - parser.add_argument("--txt_in", type=str, default='txt_dir_in') - parser.add_argument("--txt_out", type=str, default='txt_dir_out') - parser.add_argument("--line_to_word", action='store_true') - parser.add_argument("--other_class", type=str, default='other') - parser.add_argument("--resever_parent_dir", action="store_true") - - args = parser.parse_args() - cvat = CVAT() - - if args.task == 'parse_data': - data = cvat.get_data_from_xml( - xml=args.xml - ) - CVAT.write_json(args.xml_out, data) - - elif args.task == 'pseudo_from_json': - - cvat.create_xml_from_json( - xml_in=args.xml, - xml_out=args.xml_out, - json_path=args.pseudo_path - ) - - elif args.task == 'pseudo_from_txt': - - cvat.create_xml_from_txt( - xml_in=args.xml, - xml_out=args.xml_out, - txt_dir=args.pseudo_path, - # skip_labels=['word'] - # skip_labels=[args.other_class], - resever_parent_dir=args.resever_parent_dir - ) - elif args.task == 'update_txt': - cvat.update_label_kie( - txt_dir=args.txt_in, - json_path=args.pseudo_path, - out_dir=args.txt_out - ) - - elif args.task == 'update_txt_from_xml': - cvat.update_label_kie_from_xml( - txt_dir=args.txt_in, - xml=args.xml, - out_dir=args.txt_out, - skip_labels = ['word'], - line_to_word=args.line_to_word, - other_class=args.other_class, - resever_parent_dir=args.resever_parent_dir - ) - else: - raise NotImplementedError(f"{args.task} not yet implemented") - - - - - -""" -python tools/cvat.py --task update_txt --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r20/one_line_filtered --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r20/one_line_filtered --pseudo_path ../workdirs/data/vnpt_oneline/annotations.json - -python tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/xml/vnpt_r2/annotations.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/xml/vnpt_r2/annotations_out.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/vnpt_r2_txt - -python tools/cvat.py --task pseudo_from_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/wild_batch_1_raw.xml \ - --xml_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/wild_batch_1_pseudo.xml \ - --pseudo_path /mnt/ssd1T/hoanglv/Projects/KIE/DATA/WildReceipt/re_labeling/batches/batch_1 - - -python tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/vnpt_r2_txt \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/vnpt_r2.xml \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/vnpt_r2_done_txt - -python tools/cvat.py --task update_txt_from_xml \ - --txt_in /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_2/Good/Food \ - --xml /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/labeling/Pseudo/batch_2/batch_2_food_done.xml \ - --txt_out /mnt/ssd1T/hoanglv/Projects/KIE/DATA/SDSAP_Invoice/processed/batch_2/Good/Food \ - --other_class Others -""" \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/gen_cvat_text_det.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/gen_cvat_text_det.py deleted file mode 100644 index 56cd3f1..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/gen_cvat_text_det.py +++ /dev/null @@ -1,137 +0,0 @@ -import cv2 -import glob -import os -import fitz -import json -import numpy as np -import pandas as pd -from time import time -import yaml -from tqdm.auto import tqdm -import xml.etree.ElementTree as ET -from sklearn.model_selection import StratifiedKFold - -# from textdetection.src.serve_model import Predictor as TextDetector - -FOLDER = "/mnt/ssd1T/tuanlv/06.KVUCombineStage/preprocess/data/invoices-receipts/SBT/nttmai_renamed/" -TXT_DIR = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/pseudo_ocr/invoice_receipt_sbt" -OUT_FOLDER = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/pseudo_ocr/sbt_batches" -ANN_OUT = "processed_textdet_batch%d.json" -N_BATCHES = 3 - -os.makedirs(OUT_FOLDER, exist_ok=True) - - -if __name__ == "__main__": - all_files = [] - doc_types = [] - for file_name in tqdm(sorted(os.listdir(FOLDER))): - try: - file_name_no_ext = file_name.split('.')[0] - all_files.append(file_name) - doc_type = "1" - doc_types.append(doc_type) - file_path = os.path.join(FOLDER, file_name) - - except Exception as ex: - print('image:', file_name, '. Error:', ex) - - df = pd.DataFrame({'file_name': all_files, 'doc_type': doc_types}) - - df = df[df.doc_type.isin(['1'])].reset_index(drop=True) - - kfold = StratifiedKFold(n_splits=N_BATCHES) - fold_num = 0 - for train_inds, val_inds in kfold.split(df, df['doc_type']): - df.loc[val_inds, 'fold'] = fold_num - fold_num+=1 - - df.to_csv(f'{OUT_FOLDER}/tmp.csv', index=False) - - text_detector = TextDetector(setting['text_detection']['setting'], "textdetection") - - for batch in range(N_BATCHES): - print(f"================== Batch {batch} ================") - fold_df = df.loc[df.fold == batch] - # tree = ET.parse(XML_IN) - # root = tree.getroot() - - # for image in root.findall('image'): - # root.remove(image) - with open('/home/sds/namnt/FWD_Data/coco_template.json', 'r') as f: - coco_annotations = json.load(f) - - count = 2 - img_id = 1 - ann_id = 1 - - all_images = [] - all_annotations = [] - - for row_num, row in tqdm(fold_df.iterrows(), total=len(fold_df)): - # try: - file_name = row['file_name'] - file_name_no_ext = file_name.split('.')[0] - doc_type = row['doc_type'] - file_path = os.path.join(FOLDER, file_name) - images = pdf2np_fitz(file_path, _type='fname') - - images, batch_boxes = text_detector(images) - for page_num, (img, boxes) in enumerate(zip(images, batch_boxes)): - os.makedirs(os.path.join(OUT_FOLDER, f"batch{batch}"), exist_ok=True) - out_img_path = os.path.join(OUT_FOLDER, f"batch{batch}", f"batch{batch}_{img_id:04d}_{file_name_no_ext}_{page_num}.jpg") - cv2.imwrite(out_img_path, img[:,:,::-1]) - H, W = img.shape[:2] - c_img = { - "id": int(img_id), - "width": W, - "height": H, - "file_name": os.path.join(f"batch{batch}", f"batch{batch}_{img_id:04d}_{file_name_no_ext}_{page_num}.jpg"), - "license": 0, - "flickr_url": "", - "coco_url": "", - "date_captured": 0 - } - all_images.append(c_img) - - for box in boxes: - x1,y1,x2,y2 = box - w, h = x2-x1, y2-y1 - c_ann = { - "id": int(ann_id), - "image_id": int(img_id), - "category_id": 1, - "segmentation": [], - "area": w*h, - "bbox": [x1,y1,w,h], - "iscrowd": 0, - "attributes": { - "occluded": False, - "rotation": 0.0 - } - } - all_annotations.append(c_ann) - ann_id += 1 - - img_id += 1 - # if count == 1: - # break - # else: - # count -= 1 - # except Exception as ex: - # print('image:', file_name, '. Error:', ex) - - coco_annotations['categories'] = [{ - "id": 1, - "name": "text", - "supercategory": "" - }] - coco_annotations['images'] = all_images - coco_annotations['annotations'] = all_annotations - with open(os.path.join(OUT_FOLDER, ANN_OUT%(batch)), 'w') as f: - json.dump(coco_annotations, f) - - # break - - - \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/infer.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/infer.py deleted file mode 100644 index 8141139..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/infer.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Use for eval, debug -""" -import argparse -import os -from copy import copy -from glob import glob -from pathlib import Path - -import cv2 -import tqdm - -from sdsvkie.cfg import load_cfg -from sdsvkie.engine import Predictor -from sdsvkie.utils import visualize_kie -from sdsvkie.utils.io_file import load_ocr_output, write_json, write_txt - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str, default="sdsvkie/cfg/default.yaml") - parser.add_argument( - "--img", type=str, default="img.jpg", help="image path or directory" - ) - - # optional - parser.add_argument("--weights", type=str, default=None, required=False) - - parser.add_argument("--device", type=str, default=None, required=False) - - parser.add_argument( - "--text_det", type=str, default=None, required=False, help="image path or directory" - ) - parser.add_argument( - "--text_reg", type=str, default=None, required=False, help="image path or directory" - ) - parser.add_argument( - "--vis_out", type=str, default=None, required=False, help="visualize output directory" - ) - parser.add_argument("--txt_out", type=str, required=False, default=None) - parser.add_argument("--kie_wordgroup_out", required=False, action="store_true") - parser.add_argument("--e2e", type=str, required=False, default=None) - parser.add_argument("--not_use_ocr", required=False, action="store_true") - parser.add_argument("--parse_e2e", action='store_true', help="Parse end2end result from word label") - - args = parser.parse_args() - - - # print(cfg) - - predictor = Predictor(**vars(args)) - - - # cfg = load_cfg(args.cfg, vars(args)) - # predictor = Predictor(cfg) - - if args.txt_out: - if not os.path.exists(args.txt_out): - os.makedirs(args.txt_out, exist_ok=True) - - if args.e2e: - outdir_e2e = os.path.dirname(args.e2e) - if not os.path.exists(outdir_e2e): - os.makedirs(outdir_e2e, exist_ok=True) - - if os.path.isdir(args.img): - img_paths = glob(args.img + "/*") - print("Infence image dir, total imgs: {}".format(len(img_paths))) - else: - img_paths = [args.img] - - out_dict = {} - for img_path in tqdm.tqdm(img_paths): - - img = cv2.imread(img_path) - if img is None: - print("img is None: ", img_path) - continue - if args.not_use_ocr: - txt_path = str(Path(img_path).with_suffix(".txt")) - ocr_output = load_ocr_output(txt_path) - # print(len(ocr_output["boxes"])) - out = predictor(img, ocr_output=ocr_output, return_raw=True) - else: - out = predictor(img, return_raw=True) - - # visualize - if args.vis_out: - # from IPython import embed; embed() - out_kie = out["kie_raw_output"] - visualize_kie( - img, - boxes=[word.boundingbox for word in out_kie], - pred_labels=[word.kie_label for word in out_kie], - image_name=os.path.basename(img_path), - outdir=args.vis_out, - skip_classes=["other---"] - ) - - if args.txt_out: - txt_out_path = os.path.join( - args.txt_out, os.path.splitext(os.path.basename(img_path))[0] + ".txt" - ) - - out_kie = out["kie_raw_output"] - boxes = [word.boundingbox for word in out_kie] - pred_labels = [word.kie_label for word in out_kie] - texts = [word.text for word in out_kie] - - data = [] - - - if args.kie_wordgroup_out: - output = out["kie_post_output"] - # print(output) - # from IPython import embed; embed() - wordgroup_all_list = [] - for kie_label, wordgroup_list in output.items(): - if isinstance(wordgroup_list, list): - wordgroup_all_list.extend(wordgroup_list) - else: - wordgroup_all_list.append(wordgroup_list) - boxes = [word.boundingbox for word in wordgroup_all_list] - pred_labels = [word.kie_label for word in wordgroup_all_list] - texts = [word.text + "____kie_wordgroup" for word in wordgroup_all_list] - - for box, text, kie_label in zip(boxes, texts, pred_labels): - item = "\t".join([str(int(x)) for x in box]) - item = "\t".join([item, text, kie_label]) - data.append(item) - else: - for box, text, kie_label in zip(boxes, texts, pred_labels): - item = "\t".join([str(int(x)) for x in box]) - item = "\t".join([item, text, kie_label]) - data.append(item) - - write_txt(txt_out_path, data) - - if args.e2e: - - img_id = os.path.splitext(os.path.basename(img_path))[0] - out_dict[img_id] = out['end2end_results'] - - if args.e2e: - write_json(os.path.join(args.e2e), out_dict) diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/infer_e2e.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/infer_e2e.py deleted file mode 100644 index 507dc69..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/infer_e2e.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -Use for deploy -""" -import argparse -import os -from glob import glob -from pathlib import Path - -import cv2 -import tqdm - -from sdsvkie.cfg import load_cfg -from sdsvkie.engine.predictor import Predictor -from sdsvkie.utils.io_file import write_json -from sdsvkie.utils import visualize_kie, IMG_EXT, PDF_EXT -import random -""" -python sdsvkie/tools/infer_e2e.py \ - --cfg /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/config.yaml \ - --weights /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/invoice/06062023/best \ - --device "cuda:0" \ - --img /mnt/hdd2T/AICR/Projects/2023/Vietinbank_POC/Invoice_JPG/ \ - --e2e /mnt/hdd2T/AICR/Projects/2023/Vietinbank_POC/Invoice_KIE_Results/result.json - - -""" - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str, default="sdsvkie/cfg/default.yaml") - parser.add_argument("--img", type=str, default="img.jpg", help="image path or directory") - parser.add_argument("--weights", type=str, default=None) - parser.add_argument("--text_det", type=str, default=None) - parser.add_argument("--device", type=str, default=None) - parser.add_argument("--e2e", type=str, default=None) - parser.add_argument("--vis", type=str, default=None) - - - args = parser.parse_args() - - # predictor = Predictor(cfg=cfg) - predictor = Predictor(**vars(args)) - - if args.e2e: - outdir_e2e = os.path.dirname(args.e2e) - if not os.path.exists(outdir_e2e): - os.makedirs(outdir_e2e, exist_ok=True) - - if os.path.isdir(args.img): - img_paths = sorted(glob(args.img + "/*")) - print("Infence image dir, total imgs: {}".format(len(img_paths))) - else: - img_paths = [args.img] - - out_dict = {} - - if "Others" in predictor.classes: - colors = { - "Store_name_value": (30,97,235), - "id": (28,175,6), - "Date_value": (241,26,242), - "Total_value": (255,0,0), - } - else: - colors = [ - ( - random.randint(0, 255), - random.randint(0, 255), - random.randint(0, 255), - ) - for _ in range(len(predictor.classes)) - ] - - - import time - - for img_path in tqdm.tqdm(img_paths): - print(img_path) - if Path(img_path).suffix.lower() in IMG_EXT: - img = cv2.imread(img_path) - if img is None: - print("img is None: ", img_path) - continue - elif Path(img_path).suffix.lower() in PDF_EXT: - img = img_path #pdf - else: - continue - # try: - out = predictor(img) - # except Exception as err: - # print(err, img_path) - # continue - - out_api = out['end2end_results'] - - if not args.e2e: - print(out_api) - else: - - img_id = os.path.splitext(os.path.basename(img_path))[0] - out_dict[img_id] = { - field_name: field_item['value'] for field_name, field_item in out_api.items() - } - - if args.vis: - - visualize_kie( - img, - boxes=[field_item['box'] for field_name, field_item in out_api.items() if len(field_item['box']) > 0], - pred_labels=[field_name for field_name, field_item in out_api.items() if len(field_item['box']) > 0], - image_name=os.path.basename(img_path), - outdir=args.vis, - colors=colors, - texts = [field_item['value'] for field_name, field_item in out_api.items() if len(field_item['box']) > 0] - ) - - if args.e2e: - write_json(os.path.join(args.e2e), out_dict) - - - - \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/parse_e2e.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/parse_e2e.py deleted file mode 100644 index d8263cd..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/parse_e2e.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Use for eval, debug -""" -import argparse -import os -from glob import glob -from pathlib import Path - -from sdsvkie.utils.word_formation import Word -import tqdm - -from sdsvkie.engine import Predictor -from sdsvkie.utils.io_file import load_ocr_output, write_txt - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str, default="sdsvkie/cfg/default.yaml") - parser.add_argument( - "--src", type=str, default="img.jpg / img_dir", help="image path or directory" - ) - parser.add_argument("--tgt", type=str, required=True, default=None) - - args = parser.parse_args() - - predictor = Predictor(args.cfg) - - - if not os.path.exists(args.tgt): - os.makedirs(args.tgt, exist_ok=True) - - txt_paths = glob(args.src + "/*.txt") - print("Total txt: {}".format(len(txt_paths))) - - - for txt_path in tqdm.tqdm(txt_paths): - ocr_output = load_ocr_output(txt_path) - - boxes, texts, labels = ocr_output['boxes'], ocr_output['texts'], ocr_output['labels'] - words = [] - for box, text, label in zip(boxes, texts, labels): - words.append( - Word( - text=text, - bndbox=box, - kie_label=label, - conf_cls=0.0 - ) - ) - - kie_output = predictor.postprocessing(words) - - - txt_out_path = str(Path(args.tgt) / Path(txt_path).name) - - - data = [] - - # print(output) - wordgroup_all_list = [] - for kie_label, wordgroup_list in kie_output.items(): - wordgroup_all_list.extend(wordgroup_list) - boxes = [word.boundingbox for word in wordgroup_all_list] - pred_labels = [word.kie_label for word in wordgroup_all_list] - texts = [word.text + "____kie_wordgroup" for word in wordgroup_all_list] - - for box, text, kie_label in zip(boxes, texts, pred_labels): - item = "\t".join([str(int(x)) for x in box]) - item = "\t".join([item, text, kie_label]) - data.append(item) - - - write_txt(txt_out_path, data) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/postprocess_e2e_label.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/postprocess_e2e_label.py deleted file mode 100644 index 6593bb3..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/postprocess_e2e_label.py +++ /dev/null @@ -1,72 +0,0 @@ -import argparse -from sdsvkie.utils import read_json, yaml_load, write_json -from sdsvkie.utils.post_processing.invoice_post_processing import * -from sdsvkie.utils.post_processing.common_post_processing import normalize_number - - - -def postprocess_invoice(invoice_data): - if 'date' in invoice_data: - invoice_data['date'] = post_processing_datetime(invoice_data['date']) - - #### normalize number - number_fields = ['total_value', 'VAT_amount_value'] - for number_field in number_fields: - if number_field not in invoice_data: - continue - invoice_data[number_field] = normalize_number(invoice_data[number_field]) - if 'buyer_tax_code_value' in invoice_data: - invoice_data['buyer_tax_code_value'] = normalize_number(invoice_data['buyer_tax_code_value'], rerserve_minus=True) - if 'seller_tax_code_value' in invoice_data: - invoice_data['seller_tax_code_value'] = normalize_number(invoice_data['seller_tax_code_value'], rerserve_minus=True) - if "seller_mobile_value" in invoice_data: - invoice_data['seller_mobile_value'] = normalize_number(invoice_data['seller_mobile_value'], rerserve_minus=False, reserve_plus=True) - - - for field_name in invoice_data.keys(): - field_value = invoice_data[field_name] - field_value = field_value.replace("✪", " ") - field_value = field_value.replace("\t", " ") - field_value = re.sub(r"\s+", " ", field_value) - invoice_data[field_name] = field_value - - return invoice_data - - - - -def format_e2e_data(input_json, output_json, cfg): - cfg = yaml_load(cfg) - classes = cfg['classes'] - value_classes = [cls_name for cls_name in classes if "_key" not in cls_name and "other" not in cls_name] - print(value_classes) - in_data = read_json(input_json) - - out_data = {} - for img_id, img_data in in_data.items(): - - new_img_data = postprocess_invoice(img_data) - - - - for cls_value in value_classes: - if cls_value not in new_img_data: - new_img_data[cls_value] = "" - - - out_data[img_id] = new_img_data - - - write_json(data=out_data, json_path=output_json) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str) - parser.add_argument("--input", type=str, help="e2e label file path") - parser.add_argument("--out", type=str, help='postprocess e2e label') - - args = parser.parse_args() - - format_e2e_data(args.input, args.out, args.cfg) - - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/run_ocr.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/run_ocr.py deleted file mode 100644 index 63e5435..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/run_ocr.py +++ /dev/null @@ -1,84 +0,0 @@ -import argparse -import glob -from pathlib import Path - -import cv2 -from tqdm import tqdm - -from sdsvkie.models.ocr import OCREngine -from sdsvkie.utils.visualize import visualize_ocr -from sdsvkie.utils.io_file import write_txt -from sdsvkie.utils.word_formation import sort_words - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--img", type=str) - parser.add_argument("--out_dir", type=str, default=None) - parser.add_argument("--device", type=str, default='cpu') - parser.add_argument("--reserve_parent_dir", action='store_true') - parser.add_argument("--only_text", action='store_true') - parser.add_argument("--out_txt", type=str, default=None) - parser.add_argument("--text_det", default="yolox-s-general-text-pretrain-20221226") - parser.add_argument("--text_recog", default="satrn-lite-general-pretrain-20230106") - - args = parser.parse_args() - ocr_engine = OCREngine(text_det=args.text_det, text_recog=args.text_recog, device=args.device) - - - if args.reserve_parent_dir: - paths = glob.glob(args.img + "/*/*") - else: - paths = glob.glob(args.img + "/*") - for path in tqdm(paths): - img = cv2.imread(path) - if img is None: - print(path) - continue - ocr_output = ocr_engine(img, extend_ratio=[0.1, 0.3], ratio_thr=5) - - if args.out_dir: - if args.reserve_parent_dir: - out_dir_img = Path(args.out_dir) / Path(path).parent.name - else: - out_dir_img = Path(args.out_dir) - if not out_dir_img.exists(): - out_dir_img.mkdir(parents=True) - - - visualize_ocr( - img = img, - boxes=ocr_output['boxes'], - texts=ocr_output['texts'], - image_name=str(Path(path).name), - outdir=str(out_dir_img) - ) - - if args.out_txt: - - - if args.reserve_parent_dir: - out_dir_txt = Path(args.out_txt) / Path(path).parent.name - else: - out_dir_txt = Path(args.out_txt) - if not out_dir_txt.exists(): - out_dir_txt.mkdir(parents=True) - - out_txt_path = out_dir_txt / Path(path).with_suffix(".txt").name - data = [] - if args.only_text: - out = sort_words(ocr_output) - text = " ".join(out['texts']) - data.append(text) - else: - - for box, text in zip(ocr_output['boxes'], ocr_output['texts']): - item = "\t".join([str(int(x)) for x in box]) - item = "\t".join([item, text]) - data.append(item) - write_txt(str(out_txt_path), data) - - - - - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/train.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/train.py deleted file mode 100644 index 58b588d..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/train.py +++ /dev/null @@ -1,27 +0,0 @@ - -import argparse - -from sdsvkie.cfg import load_cfg -from sdsvkie.engine.trainer import Trainer - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str, default="sdsvkie/cfg/default.yaml") - parser.add_argument("--device", type=str, default="cuda") - parser.add_argument("--save_dir", type=str, default="./workdirs/exp") - parser.add_argument("--wandb", action="store_true") - - - args = parser.parse_args() - # cfg = cfg2dict(args.cfg) - # cfg['device'] = args.device - # cfg['save_dir'] = args.save_dir - cfg = load_cfg(args.cfg, vars(args)) - print(cfg) - - if args.wandb: - cfg['wandb'] = "invoice" - - trainer = Trainer(cfg) - trainer.train() - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/val.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/val.py deleted file mode 100644 index 2d83e5b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/tools/val.py +++ /dev/null @@ -1,20 +0,0 @@ - -import argparse - -from sdsvkie.cfg import load_cfg -from sdsvkie.engine.trainer import Trainer - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str, default="sdsvkie/cfg/default.yaml") - parser.add_argument("--device", type=str, default="cuda") - parser.add_argument("--weights", type=str, default=None) - - - args = parser.parse_args() - cfg = load_cfg(args.cfg, vars(args)) - - trainer = Trainer(cfg) - metric = trainer.val() - print(metric) - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/__init__.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/__init__.py deleted file mode 100644 index 5bfd774..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from .transform import normalize_box, unnormalize_box -from .augmentation import perturbate_character, sampling_data -from .io_file import yaml_load, yaml_save, read_json,write_json -from .visualize import visualize_kie, visualize_ocr -from .word_formation import ( - sliding_windows, Word_group, words_to_lines, - sort_words, Word, merge_boxes, merge_wordgroups, - distance_of_boxes, y_distance -) -from .post_processing import * -from .logger import get_logger -from .common import get_info_env -from .convert_pdf2image import pdf_to_image - - -IMG_EXT = ['.jpg', ".png", ".jpeg"] -PDF_EXT = [".pdf"] \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/augmentation.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/augmentation.py deleted file mode 100644 index 042b27d..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/augmentation.py +++ /dev/null @@ -1,112 +0,0 @@ -import math -import random - -VN_list_char = "aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0123456789!#$%&()*+,-./:;<=>?@[\]^_`{|}~" - - -def perturbate_character(words: list, ratio=0.01): - # Algorithm - # Step 1: couting number of characters is words and sample the postion we want to perturbation - # ( words = ["abc", "lkdhf", "lfhdlsa", "akdjhf"] =>> total_char = 21, pertubation_position = [15,13,7]), these are the positions of perturbating chars in the concatenating string of words "abclkdhflfhdlsaakdjhf" - # start_pos = 0, ending_pos = 0 - # Step 2: with each word in words, calculate the start_position and ending_pos of word in the concatenating string - # if ending pos > pertubation_position[-1] => conduct perturbation and plus 1 to perturbation index, else => continue - # Loop 1: words[0], start_pos = 0 ,ending_pos= 3 <= pertubation_position[-1] =7 => continue - # Loop 2: - - total_char = sum(len(i) for i in words) - pertubation_positions = sorted( - random.sample(range(total_char), int(ratio * total_char)) - ) - # logging.info(pertubation_positions) - pos = 0 - start_pos = 0 - j = 0 - for i, word in enumerate(words): - if j == len(pertubation_positions): - break - start_pos = pos - pos += len(word) - # logging.info(start_pos,pos) - while pos > pertubation_positions[j]: - x = random.randint(0, 3) - fixing_pos = pertubation_positions[j] - start_pos - if x == 0: # append random char to the left - word = ( - word[:fixing_pos] - + VN_list_char[random.randint(0, len(VN_list_char) - 1)] - + word[fixing_pos:] - ) - - if x == 1: # append random char to the right - word = ( - word[: fixing_pos + 1] - + VN_list_char[random.randint(0, len(VN_list_char) - 1)] - + word[fixing_pos + 1 :] - ) - - if x == 2: # adjust to another random char at current position - word = ( - word[:fixing_pos] - + VN_list_char[random.randint(0, len(VN_list_char) - 1)] - + word[fixing_pos + 1 :] - ) - - if x == 3 and len(word) > 1: # delete char at current position - - word = word[:fixing_pos] + word[fixing_pos + 1 :] - - j += 1 - # logging.info(words[i], word) - words[i] = word - if j == len(pertubation_positions): - break - - return words - - -def sampling_data(words, boxes, labels, max_num_words=150, slice_interval=50): - # num_boxes = len(words) - # if num_boxes > max_num_words: - # slide = max_num_words // 2 - # num_batches = math.ceil(num_boxes / slide) - - # idx_batches = [i for i in range(num_batches)] - # idx_batch = random.choice(idx_batches) - # start_idx = slide * idx_batch - # words = words[start_idx:] - # normalized_word_boxes = normalized_word_boxes[start_idx:] - # word_labels = normalized_word_boxes[start_idx:] - - total_word = len(words) - window_size = max_num_words - text_windows = [ - words[i : i + window_size] for i in range(0, total_word, slice_interval) - ] - box_windows = [ - boxes[i : i + window_size] for i in range(0, total_word, slice_interval) - ] - label_windown = [ - labels[i : i + window_size] for i in range(0, total_word, slice_interval) - ] - - # assert all( - # [ - # len(_words) == len(boxes) - # for _words, boxes in zip(words, normalized_word_boxes) - # ] - # ) - # assert all( - # [ - # len(_words) == len(_word_labels) - # for _words, _word_labels in zip(words, word_labels) - # ] - # ) - sampling_idx = random.choice([i for i in range(len(text_windows))]) - sampling_words, sampling_boxes, sampling_labels = ( - text_windows[sampling_idx], - box_windows[sampling_idx], - label_windown[sampling_idx], - ) - - return sampling_idx, sampling_words, sampling_boxes, sampling_labels diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/common.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/common.py deleted file mode 100644 index 265b634..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/common.py +++ /dev/null @@ -1,8 +0,0 @@ -from torch.utils import collect_env - - -def get_info_env(): - return collect_env.get_pretty_env_info() - -def count_parameters(model): - return sum(p.numel() for p in model.parameters() if p.requires_grad) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/convert_pdf2image.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/convert_pdf2image.py deleted file mode 100644 index 2ced06f..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/convert_pdf2image.py +++ /dev/null @@ -1,166 +0,0 @@ -import argparse -import glob -import os -import shutil -from pathlib import Path - -import fitz # PyMuPDF, imported as fitz for backward compatibility reasons -import numpy as np -from tqdm import tqdm -import cv2 -from pdf2image import convert_from_path - - -def convert_pdf2image(filename, out_dir, dpi=300, reserve_parent_dir=False, is_get_first_page=False): - """Convert and save to disk - - Args: - filename (_type_): _description_ - out_dir (_type_): _description_ - dpi (int, optional): _description_. Defaults to 300. - reserve_parent_dir (bool, optional): _description_. Defaults to False. - """ - out_dir = Path(out_dir) - filename = Path(filename) - filename_str = str(filename) - - if reserve_parent_dir: - parent_dir = filename.parent.name - out_dir = out_dir / parent_dir - - if not out_dir.exists(): - out_dir.mkdir(parents=True) - if ".txt" in str(filename).lower(): - return - if ".pdf" not in str(filename).lower(): - shutil.copy(filename, out_dir) - return - - - # doc = fitz.open(filename_str) # open document - # # zoom = 2 # zoom factor, standard: 72 dpi - - # # dpi = 300 - # zoom = dpi // 72 - # magnify = fitz.Matrix(zoom, zoom) - # for idx, page in enumerate(doc): - # pix = page.get_pixmap(matrix=magnify) # render page to an image - - # outpath = out_dir / Path(os.path.splitext(os.path.basename(filename))[0] + "_" + str(idx+1) + ".jpg") - # pix.pil_save(outpath) - try: - imgs = pdf_to_image(pdf=filename_str, is_get_first_page=is_get_first_page, dpi=dpi) - except: - print("Use v2: ", filename_str) - imgs = pdf_to_image_v2(pdf=filename_str, is_get_first_page=is_get_first_page, dpi=dpi) - print("Len img: ", len(imgs)) - for idx, img in enumerate(imgs): - outpath = str(out_dir / Path(os.path.splitext(os.path.basename(filename))[0] + "_" + str(idx+1) + ".jpg")) - - cv2.imwrite(img=img, filename=outpath) - - -def pdf_to_image_v2(pdf, dpi=300, is_get_first_page=False, max_page=1000): - """_summary_ - - Args: - pdf (_type_): _description_ - dpi (int, optional): _description_. Defaults to 300. - is_get_first_page (bool, optional): _description_. Defaults to False. - max_page (int, optional): _description_. Defaults to 1000. - - Raises: - NotImplementedError: _description_ - - Returns: - _type_: _description_ - """ - if isinstance(pdf, str): - if not os.path.exists(pdf): - print(f"Not found pdf path at {pdf}") - return [] - imgs = convert_from_path(pdf, dpi=dpi) # PILLOW - else: - raise NotImplementedError(f"Not yet implement for {type(pdf)} type !!!") - - - # zoom = dpi // 72 - # magnify = fitz.Matrix(zoom, zoom) - cv_imgs = [] - - for idx, img in enumerate(imgs): - img = img.convert("RGB") - cv_img = np.array(img) - cv_img = cv_img[:, :, ::-1].copy() - cv_imgs.append(cv_img) - if is_get_first_page or idx >= max_page: - break - return cv_imgs - - -def pdf_to_image(pdf, dpi=300, is_get_first_page=False, max_page=1000): - """_summary_ - - Args: - pdf (_type_): _description_ - dpi (int, optional): _description_. Defaults to 300. - is_get_first_page (bool, optional): _description_. Defaults to False. - max_page (int, optional): _description_. Defaults to 1000. - - Raises: - NotImplementedError: _description_ - - Returns: - _type_: _description_ - """ - if isinstance(pdf, str): - if not os.path.exists(pdf): - print(f"Not found pdf path at {pdf}") - return [] - doc = fitz.open(pdf) # open document - elif isinstance(pdf, bytes): - doc = fitz.open(stream=pdf, filetype='pdf') - else: - raise NotImplementedError(f"Not yet implement for {type(pdf)} type !!!") - - - zoom = dpi // 72 - magnify = fitz.Matrix(zoom, zoom) - imgs = [] - - for idx, page in enumerate(doc): - pix = page.get_pixmap(matrix=magnify) # render page to an image - - im = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.h, pix.w, pix.n) - im = np.ascontiguousarray(im[..., [2, 1, 0]]) # rgb to bgr - imgs.append(im) - if is_get_first_page or idx >= max_page: - break - return imgs - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument("--pdf_dir", type=str) - parser.add_argument("--out_dir", type=str) - parser.add_argument("--reserve_parent_dir", action='store_true') - args = parser.parse_args() - - paths = glob.glob(args.pdf_dir + "/*") \ - + glob.glob(args.pdf_dir + "/*/*") \ - + glob.glob(args.pdf_dir + "/*/*/*") - # + glob.glob(args.pdf_dir + "/*") - print(f"Total pdf paths in {args.pdf_dir}: {len(paths)} ") - - error_pdfs = [] - for path in tqdm(paths): - path = str(path) - try: - convert_pdf2image(path, args.out_dir, reserve_parent_dir=args.reserve_parent_dir) - except Exception as err: - print(err, path) - error_pdfs.append(path) - continue - print("Total error pdfs: ", len(error_pdfs)) - print(error_pdfs) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/cvat_utils.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/cvat_utils.py deleted file mode 100644 index 222700c..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/cvat_utils.py +++ /dev/null @@ -1,90 +0,0 @@ -from typing import List -import random - -def gen_random_color(): - red = random.randint(0, 255) - green = random.randint(0, 255) - blue = random.randint(0, 255) - - # combine the values into a hexadecimal color code - color_code = "#{:02x}{:02x}{:02x}".format(red, green, blue) - return color_code - -def gen_raw_label(labels: List[str]): - """gen raw label for cvat tool - - { - "name": "no_key", - "color": "#33ddff", - "type": "any", - "attributes": [] - }, - - Args: - labels (List[str]): _description_ - """ - raw_label = [] - for label in labels: - item = { - "name": label, - "color": gen_random_color(), - "type": "any", - "attributes": [] - } - raw_label.append(item) - - return raw_label - - - -if __name__ == "__main__": - labels = [ - # id invoice - 'no_key', # số hóa đơn - 'no_value', - 'form_key', # mẫu số hóa đơn - 'form_value', - 'serial_key', # số kí hiệu hoá đơn - 'serial_value', - 'date', - - # seller info - 'seller_company_name_key', - 'seller_company_name_value', - 'seller_tax_code_key', - 'seller_tax_code_value', - 'seller_address_value', - 'seller_address_key', - 'seller_mobile_key', - 'seller_mobile_value', - - # Not yet support seller_bank_no, seller_bank_name - # 'seller_name_key', - # 'seller_name_value', - # 'seller_company_name_value', -> seller_name_value - - # buyer info - 'buyer_name_key', - 'buyer_name_value', - 'buyer_company_name_value', - 'buyer_company_name_key', - 'buyer_tax_code_key', - 'buyer_tax_code_value', - 'buyer_address_key', - 'buyer_address_value', - 'buyer_mobile_key', - 'buyer_mobile_value', - - # money info - 'VAT_amount_key', - 'VAT_amount_value', - 'total_key', - 'total_value', - 'total_in_words_key', - 'total_in_words_value', - - 'other', - ] - - raw_label = gen_raw_label(labels) - print(raw_label) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/invoice_data.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/invoice_data.py deleted file mode 100644 index 1081e18..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/invoice_data.py +++ /dev/null @@ -1,87 +0,0 @@ - -import os -import glob -import json -import os -import shutil - -def read_txt(txt): - with open(txt, 'r', encoding='utf8') as f: - data = [line.strip() for line in f] - return data - -def write_txt(txt, data): - with open(txt, 'w', encoding='utf8') as f: - for line in data: - f.write(line + "\n") - -def write_json(json_path, data): - with open(json_path, 'w', encoding='utf8') as f: - json.dump(data, f, ensure_ascii=False) - - -def read_json(json_path): - with open(json_path, 'r', encoding='utf8') as f: - data = json.load(f) - - return data - -def create_template_info(data_dir, json_out): - outputs ={} - txt_paths = sorted(glob.glob(data_dir + "/*.txt")) - - for txt_path in txt_paths: - txt_name = os.path.basename(txt_path) - txt_data = read_txt(txt_path) - wordgroups = [item for item in txt_data if "____kie_wordgroup seller_company_name_value" in item] - num_line_company = len(wordgroups) - outputs[txt_name] = num_line_company - write_json(json_out, outputs) - - -def filter_data(template_file, data_file, img_dir, txt_dir, output): - template_data = read_json(template_file) - data = read_json(data_file) - - new_data = [] - for txt_name, num_wordgroup_line in template_data.items(): - id = txt_name.split("_type99_")[0] - for txt_name_target, num_wordgroup_target_line in data.items(): - id_target = txt_name_target.split("_type99_")[0] - # print(id, id_target, id_target != id) - if id_target != id: - # print(id_target, id) - continue - if num_wordgroup_line != num_wordgroup_target_line: - continue - - new_data.append(txt_name_target) - - new_data = sorted(list(set(new_data))) - print(new_data[:5]) - # return new_data - - if not os.path.exists(output): - os.makedirs(output, exist_ok=True) - - for txt_name in new_data: - img_path = os.path.join(img_dir, txt_name.replace(".txt", ".jpg")) - shutil.copy(img_path, output) - shutil.copy(os.path.join(txt_dir, txt_name), output) - - - - -if __name__ == "__main__": - target_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/vnpt_one_line_r20_txt" - template_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/visualize/vnpt_one_line_txt" - output_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r20/one_line_filtered" - img_target_dir = "/mnt/ssd1T/hoanglv/Projects/KIE/craw_data/output/synth_vnpt_r20/one_line" - out_template_json = "vnpt_template.json" - out_target_json = "vnpt_r20.json" - create_template_info(template_dir, out_template_json) - create_template_info(target_dir, out_target_json) - - new_data = filter_data(out_template_json, out_target_json,img_target_dir, target_dir, output_dir) - print("Total after filter: ", len(new_data)) - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/vnpt_r20.json b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/vnpt_r20.json deleted file mode 100644 index f0fb391..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/data_preprocessing/vnpt_r20.json +++ /dev/null @@ -1 +0,0 @@ -{"invoice_vnpt_id10_type99_1.txt": 1, "invoice_vnpt_id10_type99_10.txt": 1, "invoice_vnpt_id10_type99_11.txt": 1, "invoice_vnpt_id10_type99_12.txt": 1, "invoice_vnpt_id10_type99_13.txt": 1, "invoice_vnpt_id10_type99_14.txt": 1, "invoice_vnpt_id10_type99_15.txt": 1, "invoice_vnpt_id10_type99_16.txt": 1, "invoice_vnpt_id10_type99_17.txt": 1, "invoice_vnpt_id10_type99_18.txt": 1, "invoice_vnpt_id10_type99_19.txt": 2, "invoice_vnpt_id10_type99_2.txt": 1, "invoice_vnpt_id10_type99_20.txt": 1, "invoice_vnpt_id10_type99_3.txt": 2, "invoice_vnpt_id10_type99_4.txt": 1, "invoice_vnpt_id10_type99_5.txt": 1, "invoice_vnpt_id10_type99_6.txt": 1, "invoice_vnpt_id10_type99_7.txt": 1, "invoice_vnpt_id10_type99_8.txt": 1, "invoice_vnpt_id10_type99_9.txt": 1, "invoice_vnpt_id11_type99_1.txt": 1, "invoice_vnpt_id11_type99_10.txt": 1, 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- { - "name": "word", - "color": "#83e070", - "type": "any", - "attributes": [ - { - "name": "text", - "input_type": "text", - "mutable": false, - "values": ["x"] - }, - { - "name": "kie_label", - "input_type": "text", - "mutable": false, - "values": ["x"] - } - ] - }, - { - "name": "no_key", - "color": "#cf04f1", - "type": "any", - "attributes": [] - }, - { - "name": "no_value", - "color": "#0a01ce", - "type": "any", - "attributes": [] - }, - { - "name": "form_key", - "color": "#bfe920", - "type": "any", - "attributes": [] - }, - { - "name": "form_value", - "color": "#ac3436", - "type": "any", - "attributes": [] - }, - { - "name": "serial_key", - "color": "#706724", - "type": "any", - "attributes": [] - }, - { - "name": "serial_value", - "color": "#7a9b4b", - "type": "any", - "attributes": [] - }, - { - "name": "date", - "color": "#23f0e9", - "type": "any", - "attributes": [] - }, - { - "name": "seller_company_name_key", - "color": "#f47ccc", - "type": "any", - "attributes": [] - }, - { - "name": "seller_company_name_value", - "color": "#9c9c73", - "type": "any", - "attributes": [] - }, - { - "name": "seller_tax_code_key", - "color": "#afa0fa", - "type": "any", - "attributes": [] - }, - { - "name": "seller_tax_code_value", - "color": "#6e7352", - "type": "any", - "attributes": [] - }, - { - "name": "seller_address_value", - "color": "#121512", - "type": "any", - "attributes": [] - }, - { - "name": "seller_address_key", - "color": "#188735", - "type": "any", - "attributes": [] - }, - { - "name": "seller_mobile_key", - "color": "#7387fd", - "type": "any", - "attributes": [] - }, - { - "name": "seller_mobile_value", - "color": "#325bf1", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_name_key", - "color": "#a5b431", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_name_value", - "color": "#e63dcc", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_company_name_value", - "color": "#e9bf0b", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_company_name_key", - "color": "#a8d921", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_tax_code_key", - "color": "#1d8f4f", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_tax_code_value", - "color": "#e638c6", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_address_key", - "color": "#74afe5", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_address_value", - "color": "#1518dc", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_mobile_key", - "color": "#13b1cd", - "type": "any", - "attributes": [] - }, - { - "name": "buyer_mobile_value", - "color": "#c49d59", - "type": "any", - "attributes": [] - }, - { - "name": "VAT_amount_key", - "color": "#69c945", - "type": "any", - "attributes": [] - }, - { - "name": "VAT_amount_value", - "color": "#77c3be", - "type": "any", - "attributes": [] - }, - { - "name": "total_key", - "color": "#d1353a", - "type": "any", - "attributes": [] - }, - { - "name": "total_value", - "color": "#246976", - "type": "any", - "attributes": [] - }, - { - "name": "total_in_words_key", - "color": "#45a8b5", - "type": "any", - "attributes": [] - }, - { - "name": "total_in_words_value", - "color": "#d800df", - "type": "any", - "attributes": [] - }, - { - "name": "other", - "color": "#ba0fbd", - "type": "any", - "attributes": [] - } -] \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/eval_kie.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/eval_kie.py deleted file mode 100644 index 65e806a..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/eval_kie.py +++ /dev/null @@ -1,479 +0,0 @@ -import argparse -import re -from difflib import SequenceMatcher - -from rapidfuzz.distance import Levenshtein -from terminaltables import AsciiTable - -from sdsvkie.cfg import load_cfg -from sdsvkie.utils.io_file import read_json, write_json -from pathlib import Path - - -def is_type_list(x, type): - - if not isinstance(x, list): - return False - - return all(isinstance(item, type) for item in x) - - -def cal_true_positive_char(pred, gt): - """Calculate correct character number in prediction. - Args: - pred (str): Prediction text. - gt (str): Ground truth text. - Returns: - true_positive_char_num (int): The true positive number. - """ - - all_opt = SequenceMatcher(None, pred, gt) - true_positive_char_num = 0 - for opt, _, _, s2, e2 in all_opt.get_opcodes(): - if opt == "equal": - true_positive_char_num += e2 - s2 - else: - pass - return true_positive_char_num - - -def post_processing(text, lowercase=False): - """ - - Remove special characters and extra spaces - """ - - text = re.sub( - r"[^aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0123456789 ]", - " ", - text, - ) - text = re.sub(r"\s\s+", " ", text) - text = text.strip() - - if lowercase: - text = text.lower() - - return text - - -def count_matches(pred_texts, gt_texts, use_ignore=True): - """Count the various match number for metric calculation. - Args: - pred_texts (list[str]): Predicted text string. - gt_texts (list[str]): Ground truth text string. - Returns: - match_res: (dict[str: int]): Match number used for - metric calculation. - """ - match_res = { - "gt_char_num": 0, - "pred_char_num": 0, - "true_positive_char_num": 0, - "gt_word_num": 0, - "match_word_num": 0, - "match_word_ignore_case": 0, - "match_word_ignore_case_symbol": 0, - "match_kie": 0, - "match_kie_ignore_case": 0, - } - # comp = re.compile('[^A-Z^a-z^0-9^\u4e00-\u9fa5]') - # comp = re.compile('[]') - norm_ed_sum = 0.0 - - gt_texts_for_ned_word = [] - pred_texts_for_ned_word = [] - for pred_text, gt_text in zip(pred_texts, gt_texts): - if gt_text == pred_text: - match_res["match_word_num"] += 1 - match_res["match_kie"] += 1 - gt_text_lower = gt_text.lower() - pred_text_lower = pred_text.lower() - if gt_text_lower == pred_text_lower: - match_res["match_word_ignore_case"] += 1 - - # gt_text_lower_ignore = comp.sub('', gt_text_lower) - # pred_text_lower_ignore = comp.sub('', pred_text_lower) - if use_ignore: - gt_text_lower_ignore = post_processing(gt_text_lower) - pred_text_lower_ignore = post_processing(pred_text_lower) - else: - gt_text_lower_ignore = gt_text_lower - pred_text_lower_ignore = pred_text_lower - - if gt_text_lower_ignore == pred_text_lower_ignore: - match_res["match_kie_ignore_case"] += 1 - - gt_texts_for_ned_word.append(gt_text_lower_ignore.split(" ")) - pred_texts_for_ned_word.append(pred_text_lower_ignore.split(" ")) - - match_res["gt_word_num"] += 1 - - norm_ed = Levenshtein.normalized_distance( - pred_text_lower_ignore, gt_text_lower_ignore - ) - # if norm_ed > 0.1: - # print(gt_text_lower_ignore, pred_text_lower_ignore, sep='\n') - # print("-"*20) - norm_ed_sum += norm_ed - - # number to calculate char level recall & precision - match_res["gt_char_num"] += len(gt_text_lower_ignore) - match_res["pred_char_num"] += len(pred_text_lower_ignore) - true_positive_char_num = cal_true_positive_char( - pred_text_lower_ignore, gt_text_lower_ignore - ) - match_res["true_positive_char_num"] += true_positive_char_num - - normalized_edit_distance = norm_ed_sum / max(1, len(gt_texts)) - match_res["ned"] = normalized_edit_distance - - # NED for word-level - norm_ed_word_sum = 0.0 - # print(pred_texts_for_ned_word[0]) - unique_words = list( - set( - [x for line in pred_texts_for_ned_word for x in line] - + [x for line in gt_texts_for_ned_word for x in line] - ) - ) - preds = [ - [unique_words.index(w) for w in pred_text_for_ned_word] - for pred_text_for_ned_word in pred_texts_for_ned_word - ] - truths = [ - [unique_words.index(w) for w in gt_text_for_ned_word] - for gt_text_for_ned_word in gt_texts_for_ned_word - ] - for pred_text, gt_text in zip(preds, truths): - norm_ed_word = Levenshtein.normalized_distance(pred_text, gt_text) - # if norm_ed_word < 0.2: - # print(pred_text, gt_text) - norm_ed_word_sum += norm_ed_word - - normalized_edit_distance_word = norm_ed_word_sum / max(1, len(gt_texts)) - match_res["ned_word"] = normalized_edit_distance_word - - return match_res - - -def eval_ocr_metric(pred_texts, gt_texts, metric="acc"): - """Evaluate the text recognition performance with metric: word accuracy and - 1-N.E.D. See https://rrc.cvc.uab.es/?ch=14&com=tasks for details. - Args: - pred_texts (list[str]): Text strings of prediction. - gt_texts (list[str]): Text strings of ground truth. - metric (str | list[str]): Metric(s) to be evaluated. Options are: - - 'word_acc': Accuracy at word level. - - 'word_acc_ignore_case': Accuracy at word level, ignoring letter - case. - - 'word_acc_ignore_case_symbol': Accuracy at word level, ignoring - letter case and symbol. (Default metric for academic evaluation) - - 'char_recall': Recall at character level, ignoring - letter case and symbol. - - 'char_precision': Precision at character level, ignoring - letter case and symbol. - - 'one_minus_ned': 1 - normalized_edit_distance - In particular, if ``metric == 'acc'``, results on all metrics above - will be reported. - Returns: - dict{str: float}: Result dict for text recognition, keys could be some - of the following: ['word_acc', 'word_acc_ignore_case', - 'word_acc_ignore_case_symbol', 'char_recall', 'char_precision', - '1-N.E.D']. - """ - assert isinstance(pred_texts, list) - assert isinstance(gt_texts, list) - assert len(pred_texts) == len(gt_texts) - - assert isinstance(metric, str) or is_type_list(metric, str) - if metric == "acc" or metric == ["acc"]: - metric = [ - "word_acc", - "word_acc_ignore_case", - "word_acc_ignore_case_symbol", - "char_recall", - "char_precision", - "one_minus_ned", - ] - metric = set([metric]) if isinstance(metric, str) else set(metric) - - # supported_metrics = set([ - # 'word_acc', 'word_acc_ignore_case', 'word_acc_ignore_case_symbol', - # 'char_recall', 'char_precision', 'one_minus_ned', 'one_minust_ned_word' - # ]) - # assert metric.issubset(supported_metrics) - - match_res = count_matches(pred_texts, gt_texts) - eps = 1e-8 - eval_res = {} - - if "char_recall" in metric: - char_recall = ( - 1.0 * match_res["true_positive_char_num"] / (eps + match_res["gt_char_num"]) - ) - eval_res["char_recall"] = char_recall - - if "char_precision" in metric: - char_precision = ( - 1.0 - * match_res["true_positive_char_num"] - / (eps + match_res["pred_char_num"]) - ) - eval_res["char_precision"] = char_precision - - if "word_acc" in metric: - word_acc = 1.0 * match_res["match_word_num"] / (eps + match_res["gt_word_num"]) - eval_res["word_acc"] = word_acc - - if "word_acc_ignore_case" in metric: - word_acc_ignore_case = ( - 1.0 * match_res["match_word_ignore_case"] / (eps + match_res["gt_word_num"]) - ) - eval_res["word_acc_ignore_case"] = word_acc_ignore_case - - if "word_acc_ignore_case_symbol" in metric: - word_acc_ignore_case_symbol = ( - 1.0 - * match_res["match_word_ignore_case_symbol"] - / (eps + match_res["gt_word_num"]) - ) - eval_res["word_acc_ignore_case_symbol"] = word_acc_ignore_case_symbol - - if "one_minus_ned" in metric: - - eval_res["1-N.E.D"] = 1.0 - match_res["ned"] - - if "one_minus_ned_word" in metric: - - eval_res["1-N.E.D_word"] = 1.0 - match_res["ned_word"] - - if "line_acc_ignore_case_symbol" in metric: - line_acc_ignore_case_symbol = ( - 1.0 * match_res["match_kie_ignore_case"] / (eps + match_res["gt_word_num"]) - ) - eval_res["line_acc_ignore_case_symbol"] = line_acc_ignore_case_symbol - - if "line_acc" in metric: - word_acc_ignore_case_symbol = ( - 1.0 * match_res["match_kie"] / (eps + match_res["gt_word_num"]) - ) - eval_res["line_acc"] = word_acc_ignore_case_symbol - - for key, value in eval_res.items(): - eval_res[key] = float("{:.4f}".format(value)) - - return eval_res - - -def eval_kie(pred_e2e_path, gt_e2e_path, kie_labels=[], skip_labels=[], log_failure_case=None, norm_failcase=False): - # assert ".json" in pred_e2e_path and ".json" in gt_e2e_path, "only support json type" - - f = None - if log_failure_case: - log_failure_case = Path(log_failure_case) - log_failure_case_dir = log_failure_case.parent - if not log_failure_case_dir.exists(): - log_failure_case_dir.mkdir(parents=True) - - if isinstance(gt_e2e_path, str): - gt_e2e = read_json(gt_e2e_path) - else: - gt_e2e = gt_e2e_path - if isinstance(pred_e2e_path, str): - preds_e2e = read_json(pred_e2e_path) - else: - preds_e2e = pred_e2e_path - - KIE_LABELS_WITH_ONLY_VALUES = [ - class_name - for class_name in kie_labels - if "_key" not in class_name - and "other" not in class_name - and class_name not in skip_labels - ] - - pred_texts_dict = {label: [] for label in KIE_LABELS_WITH_ONLY_VALUES} - gt_texts_dict = {label: [] for label in KIE_LABELS_WITH_ONLY_VALUES} - - results = {label: 1 for label in KIE_LABELS_WITH_ONLY_VALUES} - # print(KIE_LABELS_WITH_ONLY_VALUES) - - fail_cases = {} - for img_id in preds_e2e.keys(): - fail_cases[img_id] = {} - pred_items = preds_e2e[img_id] - gt_items = gt_e2e[img_id] - - if not pred_items: - pred_items = { - class_name: "" for class_name in KIE_LABELS_WITH_ONLY_VALUES - } - - - for class_name, text_gt in gt_items.items(): - if class_name in skip_labels: - continue - # if class_name == 'seller_name_value': - # print(gt_items) - if class_name not in pred_items: - text_pred = "" - else: - text_pred = pred_items[class_name] - - if norm_failcase: - _text_pred = post_processing(text_pred, lowercase=True) - _text_gt = post_processing(text_gt, lowercase=True) - - else: - _text_pred = text_pred - _text_gt = text_gt - - if _text_pred != _text_gt: - fail_cases[img_id][class_name] = { - 'pred': _text_pred, - 'gt': _text_gt - } - - - pred_texts_dict[class_name].append(text_pred) - gt_texts_dict[class_name].append(text_gt) - - if log_failure_case: - with open(log_failure_case, 'w', encoding='utf8') as f: - write_json(log_failure_case, fail_cases) - - for class_name in KIE_LABELS_WITH_ONLY_VALUES: - pred_texts = pred_texts_dict[class_name] - gt_texts = gt_texts_dict[class_name] - result = eval_ocr_metric( - pred_texts, - gt_texts, - metric=[ - "one_minus_ned", - "line_acc_ignore_case_symbol", - "line_acc", - "one_minus_ned_word", - ], - ) - results[class_name] = { - "1-ned": result["1-N.E.D"], - "1-ned-word": result["1-N.E.D_word"], - "line_acc": result["line_acc"], - "line_acc_ignore_case_symbol": result["line_acc_ignore_case_symbol"], - "samples": len(pred_texts), - } - - # avg reusults - sum_1_ned = sum( - [ - results[class_name]["1-ned"] * results[class_name]["samples"] - for class_name in KIE_LABELS_WITH_ONLY_VALUES - ] - ) - sum_1_ned_word = sum( - [ - results[class_name]["1-ned-word"] * results[class_name]["samples"] - for class_name in KIE_LABELS_WITH_ONLY_VALUES - ] - ) - - sum_line_acc = sum( - [ - results[class_name]["line_acc"] * results[class_name]["samples"] - for class_name in KIE_LABELS_WITH_ONLY_VALUES - ] - ) - sum_line_acc_ignore_case_symbol = sum( - [ - results[class_name]["line_acc_ignore_case_symbol"] - * results[class_name]["samples"] - for class_name in KIE_LABELS_WITH_ONLY_VALUES - ] - ) - - total_samples = sum( - [results[class_name]["samples"] for class_name in KIE_LABELS_WITH_ONLY_VALUES] - ) - results["avg_all"] = { - "1-ned": round(sum_1_ned / total_samples, 4), - "1-ned-word": round(sum_1_ned_word / total_samples, 4), - "line_acc": round(sum_line_acc / total_samples, 4), - "line_acc_ignore_case_symbol": round( - sum_line_acc_ignore_case_symbol / total_samples, 4 - ), - "samples": total_samples, - } - - table_data = [ - [ - "class_name", - "1-NED", - "1-N.E.D_word", - "line-acc", - "line_acc_ignore_case_symbol", - "#samples", - ] - ] - for class_name in results.keys(): - # if c < p.shape[0]: - table_data.append( - [ - class_name, - results[class_name]["1-ned"], - results[class_name]["1-ned-word"], - results[class_name]["line_acc"], - results[class_name]["line_acc_ignore_case_symbol"], - results[class_name]["samples"], - ] - ) - - table = AsciiTable(table_data) - print(table.table) - return results - - -if __name__ == "__main__": - - # gt_e2e = "/mnt/ssd1T/hoanglv/Projects/KIE/DATA/test_end2end/test_e2e.json" - - # # pred_e2e = "/mnt/ssd1T/hoanglv/Projects/KIE/TokenClassification_invoice/workdirs/runs/pred_e2e.json" - # # pred_e2e = "/mnt/ssd1T/hoanglv/Projects/KIE/TokenClassification_invoice/workdirs/runs/infer/layoutxlm-base-31-03-2023-maxwords150_samplingv2/pred_e2e.json" - # # pred_e2e = "/mnt/ssd1T/hoanglv/Projects/KIE/TokenClassification_invoice/workdirs/runs/infer/kie_e2e_pred_17-10-2022-maxwords150_samplingv2_rm_dup_boxes/pred_e2e.json" - # # pred_e2e = "/mnt/ssd1T/hoanglv/Projects/KIE/TokenClassification_invoice/workdirs/runs/infer/kie_e2e_pred_17-10-2022-maxwords150_samplingv2/pred_e2e.json" - - # # pred_e2e = "/home/sds/hoanglv/Projects/TokenClassification_invoice/runs/infer/kie_e2e_pred_14-10-2022_2/pred_e2e.json" - # pred_e2e = "/mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/e2e/test_17_10_2022_last_use_ocr_merge_use_label.json" - - parser = argparse.ArgumentParser() - parser.add_argument("--cfg", type=str) - parser.add_argument("--pred", type=str, help="predict json file path") - parser.add_argument("--gt", type=str, help="ground truth json file path") - parser.add_argument("--log_failure_case", type=str, default=None, help="log_failure_case path") - parser.add_argument("--norm_failcase", action='store_true') - args = parser.parse_args() - - cfg = load_cfg(args.cfg) - kie_labels = cfg['classes'] - - # res = eval_kie(pred_e2e, gt_e2e, skip_labels=["buyer_mobile_value"]) - # print(res) - result = eval_kie( - pred_e2e_path=args.pred, - gt_e2e_path=args.gt, - kie_labels=kie_labels, - skip_labels=["Others", "other"], - log_failure_case=args.log_failure_case, - norm_failcase=args.norm_failcase - ) - - print("Path of validation dataset: /mnt/ssd1T/hoanglv/Projects/KIE/DATA/dev_model/Invoice/processed/test/PV2/invoice_kie_validation") - print("Number of validation dataset: ", result[list(result.keys())[0]]['samples']) - print("Evaluation metric: NLD") - print("Target level: ") - print("Archieved level: ") - print("Verification result: PASS") - -"""" - - -""" \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/io_file.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/io_file.py deleted file mode 100644 index 6e7727c..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/io_file.py +++ /dev/null @@ -1,102 +0,0 @@ -import json -import re -from pathlib import Path - -import yaml - - -def yaml_load(file="data.yaml", append_filename=False): - """ - Load YAML data from a file. - Args: - file (str, optional): File name. Default is 'data.yaml'. - append_filename (bool): Add the YAML filename to the YAML dictionary. Default is False. - Returns: - dict: YAML data and file name. - """ - with open(file, errors="ignore", encoding="utf-8") as f: - s = f.read() # string - - # Remove special characters - if not s.isprintable(): - s = re.sub( - r"[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]+", - "", - s, - ) - - # Add YAML filename to dict and return - return ( - {**yaml.safe_load(s), "yaml_file": str(file)} - if append_filename - else yaml.safe_load(s) - ) - - -def yaml_save(file="data.yaml", data=None): - """ - Save YAML data to a file. - Args: - file (str, optional): File name. Default is 'data.yaml'. - data (dict, optional): Data to save in YAML format. Default is None. - Returns: - None: Data is saved to the specified file. - """ - file = Path(file) - if not file.parent.exists(): - # Create parent directories if they don't exist - file.parent.mkdir(parents=True, exist_ok=True) - - with open(file, "w") as f: - # Dump data to file in YAML format, converting Path objects to strings - yaml.safe_dump( - {k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, - f, - sort_keys=False, - allow_unicode=True, - ) - - -def write_txt(txt, data, mode="w"): - with open(txt, mode, encoding="utf8") as f: - for line in data: - f.write(line + "\n") - - -def read_txt(txt): - with open(txt, "r", encoding="utf8") as f: - data = [line.strip() for line in f] - return data - - -def write_json(json_path, data, sort_keys=True): - with open(json_path, "w", encoding="utf8") as f: - json.dump(data, f, ensure_ascii=False, sort_keys=sort_keys) - - -def read_json(json_path): - with open(json_path, "r", encoding="utf8") as f: - data = json.load(f) - return data - - - -def load_ocr_output(txt_path): - with open(txt_path) as f: - lines = [line.replace("\n", "").replace("\r", "") for line in f.readlines()] - words, boxes, labels = [], [], [] - for i, line in enumerate(lines): - if len(line.split("\t")) == 6: - x1, y1, x2, y2, text, label = line.split("\t") - else: - x1, y1, x2, y2, text = line.split("\t") - label = None - - x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) - box = [x1, y1, x2, y2] - if text != " ": - words.append(text) - boxes.append(box) - labels.append(label) - - return {"boxes": boxes, "texts": words, 'labels': labels} diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/logger.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/logger.py deleted file mode 100644 index 642ba90..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/logger.py +++ /dev/null @@ -1,48 +0,0 @@ -import os -import sys -import logging -import functools - -logger_initialized = {} - -@functools.lru_cache() -def get_logger(name='root', log_file=None, log_level=logging.DEBUG): - """Initialize and get a logger by name. - If the logger has not been initialized, this method will initialize the - logger by adding one or two handlers, otherwise the initialized logger will - be directly returned. During initialization, a StreamHandler will always be - added. If `log_file` is specified a FileHandler will also be added. - Args: - name (str): Logger name. - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the logger. - log_level (int): The logger level. Note that only the process of - rank 0 is affected, and other processes will set the level to - "Error" thus be silent most of the time. - Returns: - logging.Logger: The expected logger. - """ - logger = logging.getLogger(name) - if name in logger_initialized: - return logger - for logger_name in logger_initialized: - if name.startswith(logger_name): - return logger - - formatter = logging.Formatter( - '[%(asctime)s] %(name)s %(levelname)s: %(message)s', - datefmt="%Y/%m/%d %H:%M:%S") - - stream_handler = logging.StreamHandler(stream=sys.stdout) - stream_handler.setFormatter(formatter) - logger.addHandler(stream_handler) - if log_file is not None: - log_file_folder = os.path.split(log_file)[0] - os.makedirs(log_file_folder, exist_ok=True) - file_handler = logging.FileHandler(log_file, 'a', encoding='utf8') - file_handler.setFormatter(formatter) - logger.addHandler(file_handler) - - logger.setLevel(log_level) - logger_initialized[name] = True - return logger \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/__init__.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/__init__.py deleted file mode 100644 index 7a6df84..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .common_post_processing import * -from .invoice_post_processing import * -from .receipt_post_processing import * -from .hardcoded_postprocess_funcs import * \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/common_post_processing.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/common_post_processing.py deleted file mode 100644 index f5f4017..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/common_post_processing.py +++ /dev/null @@ -1,129 +0,0 @@ - -import re -from datetime import datetime -from sdsvkie.utils import Word_group - - -YEAR_START = 2000 -def construct_word_groups_to_kie_label(list_word_groups: list): - kie_dict = dict() - - for wg in list_word_groups: - if wg.kie_label.lower() in ['other', 'others']: - continue - if wg.kie_label not in kie_dict: - kie_dict[wg.kie_label] = [wg] - else: - kie_dict[wg.kie_label].append(wg) - - return kie_dict - - -def near(word_group1: Word_group, word_group2: Word_group): - min_height = min( - word_group1.boundingbox[3] - word_group1.boundingbox[1], - word_group2.boundingbox[3] - word_group2.boundingbox[1], - ) - overlap = min(word_group1.boundingbox[3], word_group2.boundingbox[3]) - max( - word_group1.boundingbox[1], word_group2.boundingbox[1] - ) - - if overlap > 0: - return True - if abs(overlap / min_height) < 1.5: - print("near enough", abs(overlap / min_height), overlap, min_height) - return True - return False - - -def normalize_number(text_str: str, reserve_dot=False, reserve_plus=False, reserve_minus=False): - """ - Normalize a string of numbers by removing non-numeric characters - - """ - assert isinstance(text_str, str), "input must be str" - reserver_chars = "" - if reserve_dot: - reserver_chars += ".," - if reserve_plus: - reserver_chars += "+" - if reserve_minus: - reserver_chars += "-" - regex_fomula = "[^0-9{}]".format(reserver_chars) - normalized_text_str = re.sub(r"{}".format(regex_fomula), "", text_str) - return normalized_text_str - - -def normalize_number_wordgroup(word_group, reserve_dot=False, reserve_plus=False, reserve_minus=False): - word_group.text = normalize_number(word_group.text, reserve_dot=reserve_dot, reserve_plus=reserve_plus, reserve_minus=reserve_minus) - return word_group - -def tax_code_processing(tax_code_raw: str): - """ - - """ - if len(tax_code_raw.replace(' ', '')) != 13 or (len(tax_code_raw.replace(' ', '')) != 14 and "-" not in tax_code_raw): # to remove the first/last number dupicated - tax_code_raw = tax_code_raw.split(' ') - tax_code_raw = sorted(tax_code_raw, key=lambda x: len(x), reverse=True)[0] - return tax_code_raw.replace(' ', '') - -def normalize_tax_wordgroup(word_group, reserve_dot=False, reserve_plus=False, reserve_minus=False): - print("before: ", word_group.text) - word_group.text = tax_code_processing(word_group.text) - print("after: ", word_group.text) - word_group.text = normalize_number(word_group.text, reserve_dot=reserve_dot, reserve_plus=reserve_plus, reserve_minus=reserve_minus) - return word_group - - -def _date_format(date_string): - """Format date string according format dd/MM/yyyy""" - date_string = ( - date_string.replace("ngay ", "") - .replace(" thang ", "/") - .replace(" nam ", "/") - ) - day, month, year = date_string.split("/") - day = day.rjust(2, "0") - month = month.rjust(2, "0") - year = f"20{year}" if len(year) == 2 else year - - # Check valid year - try: - _ = datetime(year=int(year), month=int(month), day=int(day)) - if int(year) > YEAR_START: - return "/".join([day, month, year]) - except: - print("Date is invalid", date_string) - - return None - -def get_date(list_date): - """Regex get date""" - - list_date = [ - _date_format(date.group(0)) - for date in list_date - # if _date_format(date.group(0)) is not None - ] - list_date = [ - date for date in list_date if date is not None - ] - return list_date - - -def merge_multi_page_results(result_pages): - """Merge the result of the multiple pages - - Args: - results (list[dict]): list of result dict of each page - """ - if len(result_pages) == 0: - return {} - - result = result_pages[0] - for result_page in result_pages[1:]: - for field_name, field_value in result_page.items(): - if field_name not in result: - result[field_name] = field_value - - return result diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/hardcoded_postprocess_funcs.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/hardcoded_postprocess_funcs.py deleted file mode 100644 index f559f46..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/hardcoded_postprocess_funcs.py +++ /dev/null @@ -1,280 +0,0 @@ -import re -import numpy as np - -from sdsvkie.utils.post_processing import near -from sdsvkie.utils.post_processing.common_post_processing import normalize_number_wordgroup, tax_code_processing -from sdsvkie.utils.post_processing.post_processing_datetime import post_processing_datetime -from sdsvkie.utils import merge_boxes, Word_group, merge_wordgroups, distance_of_boxes, y_distance -from sdsvkie.utils.post_processing.receipt_post_processing import is_wg_same_line - - -__all__ = ["POSTPROCESS_FUNC"] - - -class CommonPostprocessing: - r"""Functions to postprocess fields - - Notes: - * I/O formats for text postprocessing functions must be the same - * I/O formats for box postprocessing functions must be the same - """ - re_number_pattern = '[0-9]' - re_not_number_pattern = '[^0-9]' - re_not_staff_code = '[^a-zA-Z0-9]' - re_char_pattern = '[a-zA-Z]' - re_not_char_pattern = '[^a-zA-Z]' - re_not_phone_pattern = '[^0-9+]' - re_not_tax_pattern = '[^0-9-]' - re_remove_extra_space = '\s+' - - - def remove_numbers(text, metadata): - result = re.sub(CommonPostprocessing.re_not_name_pattern, "", text) - - return result - - def remove_nan(text, metadata): - r"""Get number characters only""" - result = re.sub(CommonPostprocessing.re_not_number_pattern, "", text) - - return result - - - def remove_not_phone(text, metadata): - r"""Get phone number characters only""" - result = re.sub(CommonPostprocessing.re_not_phone_pattern, "", text) - - return result - - def remove_not_tax(text, metadata): - r"""Get tax number characters only""" - # print("before: ", text) - # text = tax_code_processing(text) - # print("after: ", text) - res = re.sub('[^0-9- ]', "", text) - res = res.strip() - num_spaces = res.count(" ") - - # duplicate with 14 chars tax - print(text) - if "-" in res and len(res) - num_spaces > 14: - tmp_res = res - for idx in range(len(res)): - if res[idx] == " ": - if res[idx-1] == res[idx+1]: - tmp_res = res[:idx] + res[idx+2:] - if len(re.sub('[^0-9-]', "", tmp_res)) == 14: - break - res = tmp_res - elif len(res) - num_spaces > 10: - tmp_res = res - for idx in range(len(res)): - if res[idx] == " ": - if res[idx-1] == res[idx+1]: - tmp_res = res[:idx] + res[idx+2:] - if len(re.sub('[^0-9-]', "", tmp_res)) == 10: - break - res = tmp_res - - result = re.sub(CommonPostprocessing.re_not_tax_pattern, "", res) - - print("--> ", text) - return result - - def remove_duplicate_space(text, metadata): - result = re.sub(CommonPostprocessing.re_remove_extra_space, " ", text) - return result - - -class InvoicePostprocessing: - """Function to postprocess VAT invoice - """ - @staticmethod - def process_tax_amount(text, metadata=None): - print("tax before: ", text) - text = CommonPostprocessing.remove_nan(text, metadata) - text = "0" if len(text) == 0 else text - print("tax after: ", text) - return text - - @staticmethod - def process_no_wordgroup(items, metadata=None): - if len(items) <= 1: - return items - if metadata is not None: - wg_res = metadata.get("wg_res", None) - no_key_wg = wg_res.get("no_key", None) - - if no_key_wg and len(no_key_wg) > 0: - no_key_box = no_key_wg[0].boundingbox - height_no_key = no_key_box[3] - no_key_box[1] if len(no_key_box) == 4 else 10000 - items = [no_wg for no_wg in items if y_distance(no_wg.boundingbox, no_key_box) <= 3*height_no_key] - - return items - - @staticmethod - def process_serial_wordgroup(items, metadata=None): - if len(items) <= 1: - return items - if metadata is not None: - wg_res = metadata.get("wg_res", None) - serial_key_wg = wg_res.get("serial_key", None) - - if serial_key_wg and len(serial_key_wg) > 0: - serial_key_box = serial_key_wg[0].boundingbox - - - height_serial_key = serial_key_box[3] - serial_key_box[1] if len(serial_key_box) == 4 else 10000 - items = [serial_wg for serial_wg in items if y_distance(serial_wg.boundingbox, serial_key_box) <= 2*height_serial_key] - - return items - - - @staticmethod - def process_date_wordgroup(items, metadata=None): - - def _not_too_far_from_wg(curr_wg: Word_group, next_wg: Word_group, thresh=3): - height_curr_wg = curr_wg.boundingbox[3] - curr_wg.boundingbox[1] - _distance = y_distance(curr_wg.boundingbox, next_wg.boundingbox) - return True if _distance <= height_curr_wg * thresh else False - - - - # sort by y-axis - date_wordgroups = sorted(items, key=lambda date_wg: date_wg.boundingbox[1]) - line = [date_wordgroups[0]] - for idx, wg in enumerate(date_wordgroups[1:]): - if is_wg_same_line(line[-1], wg) or _not_too_far_from_wg(curr_wg=line[-1], next_wg=wg): - line.append(wg) - - - # sort by x-axis - date_line = sorted(line, key=lambda wg: wg.boundingbox[0]) - - date_wg = merge_wordgroups(date_line) - return date_wg - - @staticmethod - def process_tax_wordgroup(items, metadata=None): - """ - params: - items: - metadata (dict): ['anchor_box'] - """ - - # case 1: multiple taxes - if metadata is not None: - field_name = metadata.get("field_name", None) - wg_res = metadata.get("wg_res", None) - - # sort by y-axis - tax_wordgroups = sorted(items, key=lambda tax_wg: tax_wg.boundingbox[1]) - if len(tax_wordgroups) > 1: - anchor = None - # get coordinate of buyer/seller name - if field_name is not None and wg_res is not None: - if field_name == "seller_tax_code_value": - name_wg_list = wg_res.get("seller_company_name_value", None) - elif field_name == "buyer_tax_code_value": - name_wg_list = wg_res.get("buyer_company_name_value", None) - if name_wg_list is not None and len(name_wg_list) > 0: - name_wg = name_wg_list[0] - anchor = name_wg.boundingbox - if anchor: - tax_wordgroups = [wg for wg in tax_wordgroups if wg.boundingbox[1] >= anchor[1]] - - line = [tax_wordgroups[0]] - for idx, wg in enumerate(tax_wordgroups[1:]): - if is_wg_same_line(line[-1], wg): - line.append(wg) - # sort by x-axis - tax_line = sorted(line, key=lambda wg: wg.boundingbox[0]) - - - tax_wg = merge_wordgroups(tax_line) - return tax_wg - - @staticmethod - def process_date(item, metadata=None): - """ - - Args: - item (str): date string - metadata (dict, optional): metadata dict. Defaults to None. - """ - item = post_processing_datetime(item) - return item - @staticmethod - def process_total_value_in_word(item, metadata=None): - re_remove_special_char_pattern = '[^aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0123456789, ]' - item = re.sub(re_remove_special_char_pattern, " ", item) - item = CommonPostprocessing.remove_duplicate_space(item, metadata) - return item - - @staticmethod - def process_address(item, metadata=None): - item = CommonPostprocessing.remove_duplicate_space(item, metadata) - return item - @staticmethod - def process_company_name(item, metadata=None): - item = CommonPostprocessing.remove_duplicate_space(item, metadata) - return item - - -POSTPROCESS_FUNC = { - "invoice_postprocess": { - "no_value": { - "wordgroup": InvoicePostprocessing.process_no_wordgroup - }, - # "form_value": { - # "text": CommonPostprocessing.remove_numbers - # }, - "serial_value": { - "wordgroup": InvoicePostprocessing.process_serial_wordgroup - }, - "date_value": { - "wordgroup": InvoicePostprocessing.process_date_wordgroup, - "text": InvoicePostprocessing.process_date, - }, - "buyer_tax_code_value": { - "wordgroup": InvoicePostprocessing.process_tax_wordgroup, - "text": CommonPostprocessing.remove_not_tax - }, - "buyer_tel_value": { - "text": CommonPostprocessing.remove_not_phone - }, - "buyer_company_name_value": { - "text": InvoicePostprocessing.process_company_name - }, - "buyer_address_value": { - "text": InvoicePostprocessing.process_address - }, - - "seller_tax_code_value": { - "wordgroup": InvoicePostprocessing.process_tax_wordgroup, - "text": CommonPostprocessing.remove_not_tax - }, - "seller_tel_value": { - "text": CommonPostprocessing.remove_not_phone - }, - "seller_company_name_value": { - "text": InvoicePostprocessing.process_company_name - }, - "seller_address_value": { - "text": InvoicePostprocessing.process_address - }, - "tax_amount_value": { - "text": InvoicePostprocessing.process_tax_amount - }, - "total_value": { - "text": CommonPostprocessing.remove_nan - }, - "total_in_words_value": { - "text": InvoicePostprocessing.process_total_value_in_word - }, - - }, - "receipt_postprocess": { - } - -} \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/invoice_post_processing.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/invoice_post_processing.py deleted file mode 100644 index 4fceaa6..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/invoice_post_processing.py +++ /dev/null @@ -1,235 +0,0 @@ -import re - -import numpy as np - -from sdsvkie.utils.post_processing import near -from sdsvkie.utils.post_processing.common_post_processing import normalize_number_wordgroup, normalize_tax_wordgroup -from sdsvkie.utils.post_processing.post_processing_datetime import post_processing_datetime -from sdsvkie.utils import merge_boxes, Word_group -from sdsvkie.utils.post_processing.receipt_post_processing import is_wg_same_line - -# def construct_word_groups_to_kie_label(list_word_groups: list): -# kie_dict = dict() -# for wg in list_word_groups: -# if wg.kie_label == "other": -# continue -# if wg.kie_label not in kie_dict: -# kie_dict[wg.kie_label] = [wg] -# else: -# kie_dict[wg.kie_label].append(wg) - -# new_dict = dict() -# for key, value in kie_dict.items(): -# if len(value) == 1: -# new_dict[key] = value -# continue - -# value.sort(key=lambda x: x.boundingbox[1]) -# new_dict[key] = value -# return new_dict - - -# def postprocess_total_value(kie_dict): -# if "total_in_words_value" not in kie_dict: -# return kie_dict - -# for k, value in kie_dict.items(): -# if k == "total_in_words_value": -# continue -# l = [] -# for v in value: -# if v.boundingbox[3] <= kie_dict["total_in_words_value"][0].boundingbox[3]: -# l.append(v) - -# if len(l) != 0: -# kie_dict[k] = l - -# return kie_dict - - -# def postprocess_tax_code_value(kie_dict): -# if "buyer_tax_code_value" in kie_dict or "seller_tax_code_value" not in kie_dict: -# return kie_dict - -# kie_dict["buyer_tax_code_value"] = [] -# for v in kie_dict["seller_tax_code_value"]: -# if "buyer_name_key" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_key"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_key"][0]) -# ): -# kie_dict["buyer_tax_code_value"].append(v) -# continue - -# if "buyer_name_value" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_value"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_value"][0]) -# ): -# kie_dict["buyer_tax_code_value"].append(v) -# continue - -# if "buyer_address_value" in kie_dict and near( -# kie_dict["buyer_address_value"][0], v -# ): -# kie_dict["buyer_tax_code_value"].append(v) -# return kie_dict - - -# def postprocess_tax_code_key(kie_dict): -# if "buyer_tax_code_key" in kie_dict or "seller_tax_code_key" not in kie_dict: -# return kie_dict -# kie_dict["buyer_tax_code_key"] = [] -# for v in kie_dict["seller_tax_code_key"]: -# if "buyer_name_key" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_key"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_key"][0]) -# ): -# kie_dict["buyer_tax_code_key"].append(v) -# continue - -# if "buyer_name_value" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_value"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_value"][0]) -# ): -# kie_dict["buyer_tax_code_key"].append(v) -# continue - -# if "buyer_address_value" in kie_dict and near( -# kie_dict["buyer_address_value"][0], v -# ): -# kie_dict["buyer_tax_code_key"].append(v) - -# return kie_dict - - -def format_output(kie_dict): - """ - { - 'field_key': { - 'value': str, - 'confidence': float, - 'box': list[int] - }, - or 'field_key': { - 'value': "", - 'confidence': None, - 'box': [] - }, - } - - Args: - kie_dict (_type_): _description_ - - Returns: - _type_: _description_ - """ - end2end_results = {} - for field_name, wg_list in kie_dict.items(): - if "key" in field_name: - continue - - if not isinstance(wg_list, list): - wg_list = [wg_list] - - if len(wg_list) == 0: - text = "" - conf = 0.0 - box = [] - else: - - text = " ".join([wg.text for wg in wg_list]) - text = text.strip() - text = text.replace("✪", " ") - conf_list = [wg.conf for wg in wg_list] - conf = sum(conf_list) / len(conf_list) - box_list = [wg.boundingbox for wg in wg_list] - box = merge_boxes(box_list) - - end2end_results[field_name] = { - "box": box, - "value": text, - "conf": conf - } - - # sort by keys - field_keys = list(end2end_results.keys()) - field_keys.sort() - end2end_results = { - field_key: end2end_results[field_key] for field_key in field_keys - } - return end2end_results - - -def postprocess_date(date_wordgroups): - date_wordgroups = sorted(date_wordgroups, key=lambda date_wg: date_wg.boundingbox[1]) - - # merge total value if have multi boxes in one line - line = [date_wordgroups[0]] - for idx, wg in enumerate(date_wordgroups[1:]): - if is_wg_same_line(line[-1], wg): - line.append(wg) - - date_line = sorted(line, key=lambda wg: wg.boundingbox[0]) # sort wg by x-axis in a line - if len(date_line) == 1: - date_wg = date_line[0] - date_wg.text = post_processing_datetime(date_wg.text) - return [date_wg] - - #text - date_text = " ".join([wg.text for wg in date_line if wg.text.strip() != ""]) - date_text = post_processing_datetime(date_text) - #box - date_box = merge_boxes([wg.boundingbox for wg in date_line]) - #label - date_label = date_line[0].kie_label - #conf - conf_list = [wg.conf for wg in date_line] - date_conf = sum(conf_list) / len(conf_list) - - # new value wg - date_wg = Word_group() - date_wg._set_values( - boundingbox=date_box, - text=date_text, - kie_label=date_label, - conf=date_conf - ) - - - return [date_wg] - -# def postprocess_total_in_words(total_words_wgs): -# total_words_wgs = [normalize_total_words(total_words_wg) for total_words_wg in total_words_wgs] -# return total_words_wgs - - - -def invoice_postprocess(kie_dict: dict): - - # date - if 'date' in kie_dict: - kie_dict['date'] = postprocess_date(kie_dict['date']) - - #### normalize number - number_fields = ['total_value', 'VAT_amount_value'] - for number_field in number_fields: - if number_field not in kie_dict: - continue - kie_dict[number_field] = [normalize_number_wordgroup(word_group) for word_group in kie_dict[number_field]] - - #tax code value - tax_code_fields = ['buyer_tax_code_value', 'seller_tax_code_value'] - for tax_code_field in tax_code_fields: - if tax_code_field in kie_dict: - - kie_dict[tax_code_field] = [normalize_tax_wordgroup(word_group, reserve_minus=True) for word_group in kie_dict[tax_code_field]] - - # mobile - if "seller_mobile_value" in kie_dict: - kie_dict['seller_mobile_value'] = [normalize_number_wordgroup(word_group, reserve_plus=True) for word_group in kie_dict['seller_mobile_value']] - - # if 'total_in_words_value' in kie_dict: - # kie_dict['total_in_words_value'] = postprocess_total_in_words(kie_dict['total_in_words_value']) - - - - return kie_dict \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/post_processing_datetime.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/post_processing_datetime.py deleted file mode 100644 index 349aa1b..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/post_processing_datetime.py +++ /dev/null @@ -1,87 +0,0 @@ -import re -from datetime import datetime -from underthesea import word_tokenize - - -def verify_and_convert_date(date_str): - # Try to parse the date string using the datetime module - try: - date = datetime.strptime(date_str, "%d/%m/%Y") # TODO: fix this - except ValueError: - # If the date string is not in a valTid format, return False - return "" - - # If the date string is in the correct format, check if it is already in the "dd/mm/yyyy" format - if date_str[:2] == "dd" and date_str[3:5] == "mm" and date_str[6:] == "yyyy": - # If the date string is already in the correct format, return it as is - return date_str - else: - # If the date string is not in the correct format, use the strftime method to convert it - return date.strftime("%d/%m/%Y") - - -def get_date_from_date_string_by_prefix(date_string_, prefix_): - prefix = prefix_.lower() - date_string = date_string_.lower() - if prefix in date_string: - try: - if prefix == "năm": - match = re.split( - r"năm[^\d]*(\d{4}|\d{1}\s*\d{3}|\d{3}\s*\d{1}|\d{2}\s*\d{2})\s*\b", - date_string, - ) - elif prefix == "ngày": - match = re.split( - r"ngày[^\d]*(\d{2}|\d{1}\s*\d{1}|\d{1})\s*\b", date_string - ) - else: - match = re.split( - r"tháng[^\d]*(\d{2}|\d{1}\s*\d{1}|\d{1})\s*\b", date_string - ) - num = match[1] - remain_string = match[2] if prefix != "năm" else match[0] - return num, remain_string - except Exception as e: - print(e) - return "", date_string_ - else: - return "", date_string_ - - -def get_date_by_pattern(date_string): - match = re.findall(r"([^\d\s]+)?\s*(\d{1}\s*\d?\s+|\d{2}\s+|\d+\s*\b)", date_string) - if not match: - return "" - if len(match) > 3: - day = match[0][-1].replace(" ", "") - year = match[-1][-1].replace(" ", "") - # since in the VIETNAMESE DRIVER LICENSE, the tháng/month is behind the stamp and can be recognized as any thing => mistạken number may be in range (1->-3) => choose month to be -2 - month = match[-2][-1].replace(" ", "") - return "/".join([day, month, year]) - else: - return "/".join([m[-1].replace(" ", "") for m in match]) - - -def extract_date_from_string(date_string): - remain_str = date_string - ldate = [] - for d in ["năm", "ngày", "tháng"]: - date, remain_str = get_date_from_date_string_by_prefix(remain_str, d) - if not date: - return get_date_by_pattern(date_string) - ldate.append(date.strip().replace(" ", "")) - return "/".join([ldate[1], ldate[2], ldate[0]]) - - -def post_processing_datetime(date_string): - # Extract the day, month, and year from the string using regular expressions - date_string = date_string.lower().replace("✪", " ") - date_string = " ".join(word_tokenize(date_string)) - parsed_date_string_ = verify_and_convert_date( - date_string - ) # if already in datetime format - if parsed_date_string_: - return parsed_date_string_ - extracted_date = extract_date_from_string(date_string) - parsed_date_string_ = verify_and_convert_date(extracted_date) - return parsed_date_string_ if parsed_date_string_ else date_string diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/receipt_post_processing.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/receipt_post_processing.py deleted file mode 100644 index 3be02b5..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/post_processing/receipt_post_processing.py +++ /dev/null @@ -1,355 +0,0 @@ -from sdsvkie.utils.post_processing import normalize_number_wordgroup -from sdsvkie.utils import Word_group -from dateutil import parser -from rapidfuzz.distance import Levenshtein -from sdsvkie.utils.post_processing.common_post_processing import normalize_number -import re -import math -from sdsvkie.utils.word_formation import merge_boxes - -RATIO_VALID_DISTANCE = 3 -SIMILAR_SCORE_THR = 0.7 -GOJEK_KEYWORD = "gojek" - - -def cal_score_similar(str1, str2): - return Levenshtein.normalized_similarity(str1, str2) - -def is_grab_seller(seller_wordgroups, words=False, thr=0.7): - SELLER_GRAB_NAMES = [ - "justgrab", - "grabcar", - "grabcar premium", - "standard taxi" - ] - for seller_wg in seller_wordgroups: - for keyword_grab in SELLER_GRAB_NAMES: - # print(f"{seller_wg.text.lower()} - {keyword_grab} - {cal_score_similar(seller_wg.text.lower(), keyword_grab)}") - if cal_score_similar(seller_wg.text.lower().strip(), keyword_grab) > thr \ - or "grab" in seller_wg.text.lower().strip(): - return True - return False - - - -def is_wg_same_line(pre_wg, curr_wg, thresh=0.7): - def _cal_ratio(top1, bottom1, top2, bottom2): - sorted_vals = sorted([top1, bottom1, top2, bottom2]) - intersection = sorted_vals[2] - sorted_vals[1] - min_height = min(bottom1 - top1, bottom2 - top2) - if min_height == 0: - return -1 - ratio = intersection / min_height - return ratio - - def _cal_ratio_height(top1, bottom1, top2, bottom2): - - height1, height2 = top1 - bottom1, top2 - bottom2 - ratio_height = float(max(height1, height2)) / float(min(height1, height2)) - return ratio_height - # calculate iou in vertical direction - _, top1, _, bottom1 = pre_wg.boundingbox - _, top2, _, bottom2 = curr_wg.boundingbox - - ratio = _cal_ratio(top1, bottom1, top2, bottom2) - ratio_height = _cal_ratio_height(top1, bottom1, top2, bottom2) - - if ( - (top1 in range(top2, bottom2) or top2 in range(top1, bottom1)) - and ratio >= thresh - and (ratio_height < 2) - ): - return True - return False - - -def is_gojek_seller(words=None, thr=0.7): - - texts = [word.text for word in words] - texts_str = " ".join(texts) - if GOJEK_KEYWORD in texts_str: - return True - else: - return False - - -def strip_wordgroup(wg): - wg.text = wg.text.strip() - return wg - -def postprocess_seller(seller_wordgroups, words): - """Seller: maybe have multiple lines - - if two wordgroup have too far distance, get highest y-axis wordgroup - - Args: - seller_wordgroups (_type_): _description_ - """ - # if len(seller_wordgroups) == 1: - # return seller_wordgroups - - # remove seller wg that have too far distance from top seller - valid_seller_wg = [] - seller_wordgroups = sorted(seller_wordgroups, key=lambda wg: wg.boundingbox[1]) - top_seller_wg = seller_wordgroups[0] - valid_seller_wg.append(top_seller_wg) - height_top_seller_wg = abs(top_seller_wg.boundingbox[3] - top_seller_wg.boundingbox[1]) - for seller_wg in seller_wordgroups[1:]: - if y_distance(seller_wg.boundingbox, valid_seller_wg[-1].boundingbox) <= height_top_seller_wg * RATIO_VALID_DISTANCE: - valid_seller_wg.append(seller_wg) - # print(f"height_top_seller_wg: {height_top_seller_wg}, height_top_seller_wg * RATIO_VALID_DISTANCE = {height_top_seller_wg * RATIO_VALID_DISTANCE}") - - if is_grab_seller(seller_wordgroups, thr=SIMILAR_SCORE_THR): - valid_seller_wg[0].text = "Grab" - return [valid_seller_wg[0]] - elif is_gojek_seller(seller_wordgroups, words): - valid_seller_wg[0].text = "Gojek" - return [valid_seller_wg[0]] - elif len(valid_seller_wg) > 1: - - # Sort seller wordgroups - valid_seller_wg = sorted(valid_seller_wg, key=lambda wg: wg.boundingbox[1]) # sort by y-axis - wg_lines = [] - line = [valid_seller_wg[0]] - for idx, wg in enumerate(valid_seller_wg[1:]): - if is_wg_same_line(line[-1], wg): - line.append(wg) - else: - line = sorted(line, key=lambda wg: wg.boundingbox[0]) # sort wg by x-axis in a line - wg_lines.append(line) - - line = [wg] - if idx == len(valid_seller_wg[1:]) - 1: - line = sorted(line, key=lambda wg: wg.boundingbox[0]) - wg_lines.append(line) - valid_seller_wg = [wg for line in wg_lines for wg in line] - #strip text - valid_seller_wg = [strip_wordgroup(wg) for wg in valid_seller_wg] - - return valid_seller_wg - - -def normalize_id(id_text): - id_text = id_text.upper() - - split_chars = ["#:", ":"] - for split_char in split_chars: - if split_char in id_text: - id_text = id_text.split(split_char)[-1] - - keywords = ["NO.","NO:", "NO#", "BILL", "RCDT", "RCPT", "#", "."] - for keyword in keywords: - id_text = id_text.replace(keyword, "") - - id_text = re.sub(r"[^0-9A-Z-]", "", id_text) - return id_text - -def postprocess_id(id_wordgroups): - """Only one wordgroup - - Args: - id_wordgroups (list[Wordgrou[]]): list of id wordgroups - """ - id_wordgroups = id_wordgroups[:1] # only get the first id - id_wordgroups[0].text = normalize_id(id_wordgroups[0].text) - - return id_wordgroups - - -def parse_date(date_str): - # remove space - date_str = re.sub(r"/\s+", "/", date_str) - date_str = re.sub(r"-\s+", "-", date_str) - is_parser_error = False - try: - date_obj = parser.parse(date_str, fuzzy=True) - year_str = str(date_obj.year) - day_str = str(date_obj.day) - date_formated = date_obj.strftime("%d/%m/%Y") - except Exception as err: - - print(f"Error parse date: err = {err}, date = {date_str}") - # from IPython import embed; embed() - date_formated = date_str - is_parser_error = True - return date_formated, is_parser_error - - if len(normalize_number(date_str)) == 6: - year_str = year_str[-2:] - try: - year_index = date_str.index(str(year_str)) - day_index = date_str.index(str(day_str)) - if year_index > day_index: - date_obj = parser.parse(date_str, fuzzy=True, dayfirst=True) - - date_formated = date_obj.strftime("%d/%m/%Y") - except Exception as err: - print(f"Error check dayfirst: err = {err}, date = {date_str}") - - return date_formated, is_parser_error - -def postprocess_date(date_wordgroups, total_value_wordgroup=None): - """ - --> parse all date wordgroups to standard format - - 1 date -> ok - - >= 2 date - + if the same date -> get highest y-axis - + if different date: - - get highest y-axis - """ - - date_wordgroups = sorted(date_wordgroups, key=lambda wg: wg.boundingbox[1]) - date_str, status = parse_date(date_wordgroups[0].text) - date_wordgroups[0].text = date_str - return date_wordgroups[:1] - - -# total money utils -def check_same_value(total_wordgroups): - is_same = True - prev_value = total_wordgroups[0] - for total_wg in total_wordgroups[1:]: - if total_wg.text != prev_value: - is_same = False - - return is_same - - -def find_best_total_key(total_key_wordgroups): - """_summary_ - - Args: - total_key_wordgroups (_type_): _description_ - """ - if len(total_key_wordgroups) == 1: - return total_key_wordgroups[0] - else: - #priority: - ## 1. 'total' keyword in key - ## 2. 'amount paid' in key - ## 3. highest y-axis - for total_key_wg in total_key_wordgroups: - if 'sub total' in total_key_wg.text.lower(): - continue - if 'total' in total_key_wg.text.lower() and 'sub total' not in total_key_wg.text.lower(): - return total_key_wg - if 'amount paid' in total_key_wg.text.lower(): - return total_key_wg - - total_key_wordgroups = sorted(total_key_wordgroups, key=lambda wg: wg.boundingbox[1]) - best_total_key = total_key_wordgroups[0] - - return best_total_key - -def y_distance(box_1, box_2): - return abs((box_1[1] + box_1[3]) / 2 - (box_2[1] + box_2[3])/ 2) - - - - -def get_best_total_value_wordgroup(total_value_wordgroups, total_key_wg): - total_value_wordgroups = sorted(total_value_wordgroups, key=lambda total_value_wg: y_distance(total_value_wg.boundingbox, total_key_wg.boundingbox)) - - # merge total value if have multi boxes in one line - line = [total_value_wordgroups[0]] - for idx, wg in enumerate(total_value_wordgroups[1:]): - if is_wg_same_line(line[-1], wg): - line.append(wg) - - total_value_line = sorted(line, key=lambda wg: wg.boundingbox[0]) # sort wg by x-axis in a line - if len(total_value_line) == 1: - return total_value_line[0] - - #text - total_value_text = ".".join([wg.text for wg in total_value_line if wg.text.strip() != ""]) - total_value_text = total_value_text.replace("..", ".") - #box - total_value_box = merge_boxes([wg.boundingbox for wg in total_value_line]) - #label - total_value_label = total_value_line[0].kie_label - #conf - conf_list = [wg.conf for wg in total_value_line] - total_value_conf = sum(conf_list) / len(conf_list) - - # new value wg - total_value_wg = Word_group() - total_value_wg._set_values( - boundingbox=total_value_box, - text=total_value_text, - kie_label=total_value_label, - conf=total_value_conf - ) - - - return total_value_wg - - -def postprocess_total(total_key_wordgroups, total_value_wordgroups, reserve_dot=False): - """ - Case 1: 1 total value - Case 2: >1 total value - - the same value -> get highest y-axis box - - different value -> get total_key's box that have highest y-axis -> get total_value have the same line - - """ - total_value_wordgroups = [normalize_number_wordgroup(wg, reserve_dot=reserve_dot) for wg in total_value_wordgroups] - if len(total_value_wordgroups) == 1: - return total_value_wordgroups - elif len(total_value_wordgroups) > 1: - if check_same_value(total_value_wordgroups) or len(total_key_wordgroups) == 0: - total_value_wordgroups = sorted(total_value_wordgroups, key=lambda wg : wg.boundingbox[1]) - best_total_value_wordgroup = total_value_wordgroups[0] - - else: - # use total_key - total_key_wg = find_best_total_key(total_key_wordgroups) - best_total_value_wordgroup = get_best_total_value_wordgroup(total_value_wordgroups, total_key_wg) - return [best_total_value_wordgroup] - -def remove_special_token(wg): - wg.text = wg.text.replace("✪", " ") - return wg - -def process_remove_special_token(kie_dict): - new_kie_dict = {} - for field_key, list_wg in kie_dict.items(): - new_list_wg = [remove_special_token(wg) for wg in list_wg] - new_kie_dict[field_key] = new_list_wg - - return new_kie_dict - -def receipt_postprocess(kie_dict: dict, words=None, return_currency=False): - - ### Remove special token - kie_dict = process_remove_special_token(kie_dict) - - # ID - if 'no_value' in kie_dict: - kie_dict['no_value'] = postprocess_id(kie_dict['no_value']) - else: - kie_dict['no_value'] = [] - # Store name - if 'seller_company_name_value' in kie_dict: - kie_dict['seller_company_name_value'] = postprocess_seller(kie_dict['seller_company_name_value'], words) - else: - kie_dict['seller_company_name_value'] = [] - # Date - if 'date_value' in kie_dict: - kie_dict['date_value'] = postprocess_date(kie_dict['date_value']) - else: - kie_dict['date_value'] = [] - # Total - if 'total_value' in kie_dict: - - total_key_wordgroups = [] if 'total_key' not in kie_dict else kie_dict['total_key'] - kie_dict['total_value'] = postprocess_total( - total_key_wordgroups=total_key_wordgroups, - total_value_wordgroups=kie_dict['total_value'], - reserve_dot=True) - # print(kie_dict['Total_value'][0].text) - else: - kie_dict['total_value'] = [] - - # mapping - - - return kie_dict \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/results.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/results.py deleted file mode 100644 index c9c1be4..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/results.py +++ /dev/null @@ -1,9 +0,0 @@ - - - -class Results: - def __ini__(self): - self.list_word_groups = None # list of wordgroups - self.word_group = None # field's wordgroup - - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/split_data.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/split_data.py deleted file mode 100644 index 28d26ed..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/split_data.py +++ /dev/null @@ -1,45 +0,0 @@ -import os -import glob -import shutil -import argparse -import random -from pathlib import Path - -random.seed(1000) - -def copy_data(src_paths, tgt_dir): - for p in src_paths: - txt_path = str(Path(p).with_suffix(".txt")) - shutil.copy(txt_path, tgt_dir) - shutil.copy(p, tgt_dir) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--path", type=str, required=True) - parser.add_argument("--out", type=str, required=True) - parser.add_argument("--test_ratio", type=float, default=0.1) - args = parser.parse_args() - img_paths = [p for p in sorted(glob.glob(args.path + "/*")) if ".txt" not in p] - random.shuffle(img_paths) - total_imgs = len(img_paths) - num_test = int(args.test_ratio * total_imgs) - if num_test == 0: - test_paths = [] - train_paths = img_paths - else: - train_paths = img_paths[:-num_test] - test_paths = img_paths[-num_test:] - - out_dir = Path(args.out) - out_train = out_dir / 'train' - out_test = out_dir / 'test' - if not out_train.exists(): - out_train.mkdir(parents=True) - out_test.mkdir(parents=True) - - copy_data(train_paths, out_train) - copy_data(test_paths, out_test) - - - diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/transform.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/transform.py deleted file mode 100644 index c83f587..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/transform.py +++ /dev/null @@ -1,16 +0,0 @@ -def unnormalize_box(bbox, width, height): - return [ - width * (bbox[0] / 1000), - height * (bbox[1] / 1000), - width * (bbox[2] / 1000), - height * (bbox[3] / 1000), - ] - - -def normalize_box(box, width, height): - return [ - int(1000 * (box[0] / width)), - int(1000 * (box[1] / height)), - int(1000 * (box[2] / width)), - int(1000 * (box[3] / height)), - ] diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/version.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/version.py deleted file mode 100644 index 6c4c011..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '1.0.1' \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/visualize.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/visualize.py deleted file mode 100644 index e3857e0..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/visualize.py +++ /dev/null @@ -1,108 +0,0 @@ -import os -import random - -import cv2 -import numpy as np -from PIL import Image, ImageDraw, ImageFont - - -def visualize_kie( - img, - boxes, - pred_labels, - skip_classes=["other"], - image_name="out_kie.jpg", - outdir="workdirs/visualize", - colors=None, - texts=None, -): - """Visualize kie output - - Args: - image (PIL.Image): _description_ - boxes (list[xyxy]): list of xyxy boxes - kie_preds (list[str]): _description_ - skip_classes (list[str], optional): list of skip labels. Defaults to ['other']. - image_path (str, optional): _description_. Defaults to None. - outdir (_type_, optional): _description_. Defaults to None. - """ - if not os.path.exists(outdir): - os.makedirs(outdir, exist_ok=True) - - image = img.copy() - if isinstance(image, np.ndarray): - image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) - - set_labels = sorted(list(set(pred_labels))) - if colors is None: - colors = [ - ( - random.randint(0, 255), - random.randint(0, 255), - random.randint(0, 255), - ) - for _ in range(len(set_labels)) - ] - text_color = (255,0,0) - text_checkin = [0] * len(set_labels) - - - - - - draw = ImageDraw.Draw(image) - # font = ImageFont.load_default() - font = ImageFont.truetype("/usr/share/fonts/truetype/msttcorefonts/Arial.ttf", 16) - for i, pred_label in enumerate(pred_labels): - if pred_label in skip_classes: - continue - pred_label_idx = set_labels.index(pred_label) - box = boxes[i] - color = colors[pred_label_idx] if isinstance(colors, list) else colors[pred_label] - draw.rectangle(box, fill=None, outline=color) - - # skip visualize text label for other boxes - if texts is not None and text_checkin[set_labels.index(pred_label)] == 0: - draw.text( - (int(box[0]) + 10, int(box[1]) - 10), - fill=text_color if isinstance(colors, list) else colors[pred_label], - # text=str(pred_label) + " - " + str(texts[i]), - text=str(texts[i]), - font=font, - ) - text_checkin[pred_label_idx] = 1 - image.save(os.path.join(outdir, image_name)) - - -def visualize_ocr( - img, boxes, texts, image_name="out_ocr.jpg", outdir="workdirs/visualize" -): - """Visualize kie output - - Args: - image (PIL.Image): _description_ - boxes (list[xyxy]): list of xyxy boxes - kie_preds (list[str]): _description_ - skip_classes (list[str], optional): list of skip labels. Defaults to ['other']. - image_path (str, optional): _description_. Defaults to None. - outdir (_type_, optional): _description_. Defaults to None. - """ - if not os.path.exists(outdir): - os.makedirs(outdir, exist_ok=True) - - image = img.copy() - if isinstance(image, np.ndarray): - image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) - color = ( - random.randint(0, 255), - random.randint(0, 255), - random.randint(0, 255), - ) - - draw = ImageDraw.Draw(image) - font = ImageFont.load_default() - for i, text in enumerate(texts): - box = boxes[i] - draw.rectangle(box, fill=None, outline=color) - # draw.text((int(box[0])+10, int(box[1])-10),fill=color, text=text, font = font) - image.save(os.path.join(outdir, image_name)) diff --git a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/word_formation.py b/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/word_formation.py deleted file mode 100644 index b202dd0..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/sdsvkie/utils/word_formation.py +++ /dev/null @@ -1,608 +0,0 @@ -import re -from builtins import dict, type -import numpy as np -import math - -MIN_IOU_HEIGHT = 0.7 -MIN_WIDTH_LINE_RATIO = 0.05 -MIN_IOU_BETWEEN_WORD_AND_WORDGROUP = 0.7 - -class Word: - def __init__( - self, - text="", - image=None, - conf_detect=0.0, - conf_cls=0.0, - bndbox=None, - kie_label="", - ): - self.type = "word" - self.text = text - self.image = image - self.conf_detect = conf_detect - self.conf_cls = conf_cls - self.boundingbox = ( - bndbox if bndbox is not None else [-1, -1, -1, -1] - ) # [left, top,right,bot] coordinate of top-left and bottom-right point - self.word_id = 0 # id of word - self.word_group_id = 0 # id of word_group which instance belongs to - self.line_id = 0 # id of line which instance belongs to - self.paragraph_id = 0 # id of line which instance belongs to - self.kie_label = kie_label - - def invalid_size(self): - return (self.boundingbox[2] - self.boundingbox[0]) * ( - self.boundingbox[3] - self.boundingbox[1] - ) > 0 - - def is_special_word(self): - left, top, right, bottom = self.boundingbox - width, height = right - left, bottom - top - text = self.text - - if text is None: - return True - - if len(text) >= 7: - no_digits = sum(c.isdigit() for c in text) - return no_digits / len(text) >= 0.3 - - return False - - -class Word_group: - def __init__(self): - self.type = "word_group" - self.list_words = [] # dict of word instances - self.word_group_id = 0 # word group id - self.line_id = 0 # id of line which instance belongs to - self.paragraph_id = 0 # id of paragraph which instance belongs to - self.text = "" - self.boundingbox = [-1, -1, -1, -1] - self.kie_label = "" - self.conf = 0.0 - - def _set_values(self, boundingbox, text, kie_label, conf): - self.boundingbox = boundingbox - self.text = text - self.kie_label = kie_label - self.conf = conf - - def add_word(self, word: Word): # add a word instance to the word_group - if word.text != "✪": - for w in self.list_words: - if word.word_id == w.word_id: - print("Word id collision") - return False - word.word_group_id = self.word_group_id # - word.line_id = self.line_id - word.paragraph_id = self.paragraph_id - - # sort wordgroup: - if len(self.list_words) >= 1 and word.boundingbox[0] < self.list_words[-1].boundingbox[0]: - self.list_words.append(word) - self.list_words = sorted(self.list_words, key=lambda wg: wg.boundingbox[0]) - self.update_text() - else: - self.list_words.append(word) - self.text += " " + word.text - - if self.boundingbox == [-1, -1, -1, -1]: - self.boundingbox = word.boundingbox - else: - self.boundingbox = [ - min(self.boundingbox[0], word.boundingbox[0]), - min(self.boundingbox[1], word.boundingbox[1]), - max(self.boundingbox[2], word.boundingbox[2]), - max(self.boundingbox[3], word.boundingbox[3]), - ] - return True - else: - return False - - def update_word_group_id(self, new_word_group_id): - self.word_group_id = new_word_group_id - for i in range(len(self.list_words)): - self.list_words[i].word_group_id = new_word_group_id - - def update_kie_label(self): - list_kie_label = [word.kie_label for word in self.list_words] - dict_kie = dict() - for label in list_kie_label: - if label not in dict_kie: - dict_kie[label] = 1 - else: - dict_kie[label] += 1 - total = len(list(dict_kie.values())) - max_value = max(list(dict_kie.values())) - list_keys = list(dict_kie.keys()) - list_values = list(dict_kie.values()) - self.kie_label = list_keys[list_values.index(max_value)] - - def update_text(self): # update text after changing positions of words in list word - text = "" - for word in self.list_words: - text += " " + word.text - self.text = text - - def update_conf(self): - conf_list = [word.conf_cls for word in self.list_words] - conf = sum(conf_list) / len(conf_list) if len(conf_list) > 0 else 0.0 - self.conf = conf - - -class Line: - def __init__(self): - self.type = "line" - self.list_word_groups = [] # list of Word_group instances in the line - self.line_id = 0 # id of line in the paragraph - self.paragraph_id = 0 # id of paragraph which instance belongs to - self.text = "" - self.boundingbox = [-1, -1, -1, -1] - - def add_group(self, word_group: Word_group): # add a word_group instance - if word_group.list_words is not None: - for wg in self.list_word_groups: - if word_group.word_group_id == wg.word_group_id: - print("Word_group id collision") - return False - - self.list_word_groups.append(word_group) - self.text += word_group.text - word_group.paragraph_id = self.paragraph_id - word_group.line_id = self.line_id - - for i in range(len(word_group.list_words)): - word_group.list_words[ - i - ].paragraph_id = self.paragraph_id # set paragraph_id for word - word_group.list_words[i].line_id = self.line_id # set line_id for word - return True - return False - - def update_line_id(self, new_line_id): - self.line_id = new_line_id - for i in range(len(self.list_word_groups)): - self.list_word_groups[i].line_id = new_line_id - for j in range(len(self.list_word_groups[i].list_words)): - self.list_word_groups[i].list_words[j].line_id = new_line_id - - def merge_word(self, word): # word can be a Word instance or a Word_group instance - if word.text != "✪": - if self.boundingbox == [-1, -1, -1, -1]: - self.boundingbox = word.boundingbox - else: - self.boundingbox = [ - min(self.boundingbox[0], word.boundingbox[0]), - min(self.boundingbox[1], word.boundingbox[1]), - max(self.boundingbox[2], word.boundingbox[2]), - max(self.boundingbox[3], word.boundingbox[3]), - ] - self.list_word_groups.append(word) - self.text += " " + word.text - return True - return False - - def __cal_ratio(self, top1, bottom1, top2, bottom2): - sorted_vals = sorted([top1, bottom1, top2, bottom2]) - intersection = sorted_vals[2] - sorted_vals[1] - min_height = min(bottom1 - top1, bottom2 - top2) - if min_height == 0: - return -1 - ratio = intersection / min_height - return ratio - - def __cal_ratio_height(self, top1, bottom1, top2, bottom2): - - height1, height2 = top1 - bottom1, top2 - bottom2 - ratio_height = float(max(height1, height2)) / float(min(height1, height2)) - return ratio_height - - def in_same_line(self, input_line, thresh=0.7): - # calculate iou in vertical direction - _, top1, _, bottom1 = self.boundingbox - _, top2, _, bottom2 = input_line.boundingbox - - ratio = self.__cal_ratio(top1, bottom1, top2, bottom2) - ratio_height = self.__cal_ratio_height(top1, bottom1, top2, bottom2) - - if ( - (top1 in range(top2, bottom2) or top2 in range(top1, bottom1)) - and ratio >= thresh - and (ratio_height < 2) - ): - return True - return False - - -class Paragraph: - def __init__(self, id=0, lines=None): - self.list_lines = ( - lines if lines is not None else [] - ) # list of all lines in the paragraph - self.paragraph_id = id # index of paragraph in the ist of paragraph - self.text = "" - self.boundingbox = [-1, -1, -1, -1] - - def add_line(self, line: Line): # add a line instance - if line.list_word_groups is not None: - for l in self.list_lines: - if line.line_id == l.line_id: - print("Line id collision") - return False - for i in range(len(line.list_word_groups)): - line.list_word_groups[ - i - ].paragraph_id = ( - self.paragraph_id - ) # set paragraph id for every word group in line - for j in range(len(line.list_word_groups[i].list_words)): - line.list_word_groups[i].list_words[ - j - ].paragraph_id = ( - self.paragraph_id - ) # set paragraph id for every word in word groups - line.paragraph_id = self.paragraph_id # set paragraph id for line - self.list_lines.append(line) # add line to paragraph - self.text += " " + line.text - return True - else: - return False - - def update_paragraph_id( - self, new_paragraph_id - ): # update new paragraph_id for all lines, word_groups, words inside paragraph - self.paragraph_id = new_paragraph_id - for i in range(len(self.list_lines)): - self.list_lines[ - i - ].paragraph_id = new_paragraph_id # set new paragraph_id for line - for j in range(len(self.list_lines[i].list_word_groups)): - self.list_lines[i].list_word_groups[ - j - ].paragraph_id = new_paragraph_id # set new paragraph_id for word_group - for k in range(len(self.list_lines[i].list_word_groups[j].list_words)): - self.list_lines[i].list_word_groups[j].list_words[ - k - ].paragraph_id = new_paragraph_id # set new paragraph id for word - return True - - -# def resize_to_original( -# boundingbox, scale -# ): # resize coordinates to match size of original image -# left, top, right, bottom = boundingbox -# left *= scale[1] -# right *= scale[1] -# top *= scale[0] -# bottom *= scale[0] -# return [left, top, right, bottom] - - -def check_iomin(word: Word, word_group: Word_group): - min_height = min( - word.boundingbox[3] - word.boundingbox[1], - word_group.boundingbox[3] - word_group.boundingbox[1], - ) - intersect = min(word.boundingbox[3], word_group.boundingbox[3]) - max( - word.boundingbox[1], word_group.boundingbox[1] - ) - if intersect / min_height > MIN_IOU_BETWEEN_WORD_AND_WORDGROUP: - return True - return False - - -def prepare_line(words): - lines = [] - visited = [False] * len(words) - for id_word, word in enumerate(words): - if word.invalid_size() == 0: - continue - new_line = True - for i in range(len(lines)): - if ( - lines[i].in_same_line(word) and not visited[id_word] - ): # check if word is in the same line with lines[i] - lines[i].merge_word(word) - new_line = False - visited[id_word] = True - - if new_line == True: - new_line = Line() - new_line.merge_word(word) - lines.append(new_line) - - # sort line from top to bottom according top coordinate - lines.sort(key=lambda x: x.boundingbox[1]) - return lines - - -def __create_word_group(word, word_group_id): - new_word_group = Word_group() - new_word_group.word_group_id = word_group_id - new_word_group.add_word(word) - - return new_word_group - - -def __sort_line(line): - line.list_word_groups.sort( - key=lambda x: x.boundingbox[0] - ) # sort word in lines from left to right - - return line - - -def __merge_text_for_line(line): - line.text = "" - for word in line.list_word_groups: - line.text += " " + word.text - - return line - - -def __update_list_word_groups(line, word_group_id, word_id, line_width): - - old_list_word_group = line.list_word_groups - list_word_groups = [] - - inital_word_group = __create_word_group(old_list_word_group[0], word_group_id) - old_list_word_group[0].word_id = word_id - list_word_groups.append(inital_word_group) - word_group_id += 1 - word_id += 1 - - for word in old_list_word_group[1:]: - check_word_group = True - word.word_id = word_id - word_id += 1 - - if ( - (not list_word_groups[-1].text.endswith(":")) - and ( - (word.boundingbox[0] - list_word_groups[-1].boundingbox[2]) / line_width - < MIN_WIDTH_LINE_RATIO - ) - and check_iomin(word, list_word_groups[-1]) - and (len(list_word_groups[-1].list_words) > 0 and word.kie_label == list_word_groups[-1].list_words[-1].kie_label) - ): - list_word_groups[-1].add_word(word) - check_word_group = False - - if check_word_group: - new_word_group = __create_word_group(word, word_group_id) - list_word_groups.append(new_word_group) - word_group_id += 1 - line.list_word_groups = list_word_groups - return line, word_group_id, word_id - - -def construct_word_groups_in_each_line(lines): - line_id = 0 - word_group_id = 0 - word_id = 0 - for i in range(len(lines)): - if len(lines[i].list_word_groups) == 0: - continue - - # left, top ,right, bottom - line_width = lines[i].boundingbox[2] - lines[i].boundingbox[0] # right - left - - lines[i] = __sort_line(lines[i]) - - # update text for lines after sorting - lines[i] = __merge_text_for_line(lines[i]) - - lines[i], word_group_id, word_id = __update_list_word_groups( - lines[i], word_group_id, word_id, line_width - ) - lines[i].update_line_id(line_id) - line_id += 1 - return lines - - -def words_to_lines(words, check_special_lines=True): # words is list of Word instance - # sort word by top - words.sort(key=lambda x: (x.boundingbox[1], x.boundingbox[0])) - number_of_word = len(words) - # sort list words to list lines, which have not contained word_group yet - lines = prepare_line(words) - - # construct word_groups in each line - lines = construct_word_groups_in_each_line(lines) - return lines, number_of_word - - -# def sort_words(boxes, texts, det_confs=None, recog_confs=None, labels=None): -def sort_words(word_items): - """Sort words by line - - Args: - word_items (dict): format { - "boxes": list[list[int]], #xyxy format - "texts": list[str] - } - - Returns: - (dict): format: - sorted_word_items = { - 'boxes': [word.boundingbox for word in new_words], - 'texts': [word.text for word in new_words], - 'labels': [word.kie_label for word in new_words], - 'det_confs': [word.conf_detect for word in new_words], - 'recog_confs': [word.conf_cls for word in new_words], - } - """ - assert 'boxes' in word_items, "boxes key not exists in word items" - assert 'texts' in word_items, "texts key not exists in word items" - - boxes = word_items['boxes'] - texts = word_items['texts'] - - - labels = word_items.pop("labels", ["dummy_label"] * len(boxes)) - det_confs = word_items.pop("det_confs", [0.0] * len(boxes)) - recog_confs = word_items.pop("recog_confs", [0.0] * len(boxes)) - - - words = [] - for idx, (box, text) in enumerate(zip(boxes, texts)): - word = Word( - text=text, - bndbox=box, - kie_label=labels[idx], - conf_detect=det_confs[idx], - conf_cls=recog_confs[idx] - ) - words.append(word) - lines, _ = words_to_lines(words) - - new_words = [] - for line in lines: - list_word_groups = line.list_word_groups - for word_group in list_word_groups: - for word in word_group.list_words: - new_words.append(word) - - sorted_word_items = { - 'boxes': [word.boundingbox for word in new_words], - 'texts': [word.text for word in new_words], - 'labels': [word.kie_label for word in new_words], - 'det_confs': [word.conf_detect for word in new_words], - 'recog_confs': [word.conf_cls for word in new_words], - } - - return sorted_word_items - - - - -def sliding_windows(elements, window_size, slice_inverval): - element_windows = [] - - stop = False - for i in range(0, len(elements), slice_inverval): - if stop: - break - element_windows.append(elements[i : i + window_size]) - if i + window_size > len(elements): - stop = True - - return element_windows - -def merge_boxes(box_list): - boxes = np.array(box_list) - xmin = np.min(boxes[:, 0]) - ymin = np.min(boxes[:, 1]) - xmax = np.max(boxes[:, 2]) - ymax = np.max(boxes[:, 3]) - return [int(xmin), int(ymin), int(xmax), int(ymax)] - - -def merge_wordgroups(wordgroups): - if len(wordgroups) == 1: - return wordgroups[0] - wg_text = " ".join([wg.text for wg in wordgroups if wg.text.strip() != ""]) - wg_box = merge_boxes([wg.boundingbox for wg in wordgroups]) - wg_label = wordgroups[0].kie_label - wg_conf = sum([wg.conf for wg in wordgroups]) / len(wordgroups) - - # new value wg - wordgroup = Word_group() - wordgroup._set_values( - boundingbox=wg_box, - text=wg_text, - kie_label=wg_label, - conf=wg_conf - ) - return wordgroup - - -# def calculate_iou_and_near(wg1: Word_group, wg2: Word_group): -# min_height = min( -# wg1.boundingbox[3] - wg1.boundingbox[1], wg2.boundingbox[3] - wg2.boundingbox[1] -# ) -# overlap = min(wg1.boundingbox[3], wg2.boundingbox[3]) - max( -# wg1.boundingbox[1], wg2.boundingbox[1] -# ) -# iou = overlap / min_height -# distance = min( -# abs(wg1.boundingbox[0] - wg2.boundingbox[2]), -# abs(wg1.boundingbox[2] - wg2.boundingbox[0]), -# ) -# if iou > 0.7 and distance < 0.5 * (wg1.boundingboxp[2] - wg1.boundingbox[0]): -# return True -# return False - - - - - -# def throw_overlapping_words(list_words): -# new_list = [list_words[0]] -# for word in list_words: -# overlap = False -# area = (word.boundingbox[2] - word.boundingbox[0]) * ( -# word.boundingbox[3] - word.boundingbox[1] -# ) -# for word2 in new_list: -# area2 = (word2.boundingbox[2] - word2.boundingbox[0]) * ( -# word2.boundingbox[3] - word2.boundingbox[1] -# ) -# xmin_intersect = max(word.boundingbox[0], word2.boundingbox[0]) -# xmax_intersect = min(word.boundingbox[2], word2.boundingbox[2]) -# ymin_intersect = max(word.boundingbox[1], word2.boundingbox[1]) -# ymax_intersect = min(word.boundingbox[3], word2.boundingbox[3]) -# if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect: -# continue - -# area_intersect = (xmax_intersect - xmin_intersect) * ( -# ymax_intersect - ymin_intersect -# ) -# if area_intersect / area > 0.7 or area_intersect / area2 > 0.7: -# overlap = True -# if overlap == False: -# new_list.append(word) -# return new_list - - -class Box: - def __init__(self, xmin=0, ymin=0, xmax=0, ymax=0, label="", kie_label=""): - self.xmax = xmax - self.ymax = ymax - self.xmin = xmin - self.ymin = ymin - self.label = label - self.kie_label = kie_label - - - -def check_iou(box1: Box, box2: Box, threshold=0.9): - area1 = (box1.xmax - box1.xmin) * (box1.ymax - box1.ymin) - area2 = (box2.xmax - box2.xmin) * (box2.ymax - box2.ymin) - xmin_intersect = max(box1.xmin, box2.xmin) - ymin_intersect = max(box1.ymin, box2.ymin) - xmax_intersect = min(box1.xmax, box2.xmax) - ymax_intersect = min(box1.ymax, box2.ymax) - if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect: - area_intersect = 0 - else: - area_intersect = (xmax_intersect - xmin_intersect) * ( - ymax_intersect * ymin_intersect - ) - union = area1 + area2 - area_intersect - iou = area_intersect / area1 - if iou > threshold: - return True - return False - - -def distance_of_boxes(box1, box2): - x1, y1 = (box1[0] + box1[2]) / 2, (box1[1] + box1[3]) / 2 - x2, y2 = (box2[0] + box2[2]) / 2, (box2[1] + box2[3]) / 2 - distance = math.sqrt((x1 - x2)**2 + (y1 - y2)**2) - return distance - -def y_distance(box_1, box_2): - return abs((box_1[1] + box_1[3]) / 2 - (box_2[1] + box_2[3])/ 2) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/setup.cfg b/cope2n-ai-fi/modules/sdsvkie/setup.cfg deleted file mode 100644 index e158110..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/setup.cfg +++ /dev/null @@ -1,43 +0,0 @@ -[tool:pytest] -norecursedirs = - .git - dist - build -addopts = - --strict - --doctest-modules - --durations=0 - -[coverage:report] -exclude_lines = - pragma: no-cover - pass - -[flake8] -max-line-length = 120 -exclude = .tox,*.egg,build,temp -select = E,W,F -doctests = True -verbose = 2 -# https://pep8.readthedocs.io/en/latest/intro.html#error-codes -format = pylint -# see: https://www.flake8rules.com/ -ignore = - E731 # Do not assign a lambda expression, use a def - W504 # Line break occurred after a binary operator - F401 # Module imported but unused - F841 # Local variable name is assigned to but never used - W605 # Invalid escape sequence 'x' - -# setup.cfg or tox.ini -[check-manifest] -ignore = - *.yml - .github - .github/* - -[metadata] -license_file = LICENSE -description-file = README.md -# long_description = file:README.md -# long_description_content_type = text/markdown \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvkie/setup.py b/cope2n-ai-fi/modules/sdsvkie/setup.py deleted file mode 100644 index d2f803e..0000000 --- a/cope2n-ai-fi/modules/sdsvkie/setup.py +++ /dev/null @@ -1,187 +0,0 @@ -import os -import os.path as osp -import shutil -import sys -import warnings -from setuptools import find_packages, setup - - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - - -version_file = 'sdsvkie/utils/version.py' -is_windows = sys.platform == 'win32' - - -def add_mim_extention(): - """Add extra files that are required to support MIM into the package. - - These files will be added by creating a symlink to the originals if the - package is installed in `editable` mode (e.g. pip install -e .), or by - copying from the originals otherwise. - """ - - # parse installment mode - if 'develop' in sys.argv: - # installed by `pip install -e .` - mode = 'symlink' - elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - # installed by `pip install .` - # or create source distribution by `python setup.py sdist` - mode = 'copy' - else: - return - - filenames = ['tools', 'configs', 'model-index.yml'] - repo_path = osp.dirname(__file__) - mim_path = osp.join(repo_path, 'mmocr', '.mim') - os.makedirs(mim_path, exist_ok=True) - - for filename in filenames: - if osp.exists(filename): - src_path = osp.join(repo_path, filename) - tar_path = osp.join(mim_path, filename) - - if osp.isfile(tar_path) or osp.islink(tar_path): - os.remove(tar_path) - elif osp.isdir(tar_path): - shutil.rmtree(tar_path) - - if mode == 'symlink': - src_relpath = osp.relpath(src_path, osp.dirname(tar_path)) - try: - os.symlink(src_relpath, tar_path) - except OSError: - # Creating a symbolic link on windows may raise an - # `OSError: [WinError 1314]` due to privilege. If - # the error happens, the src file will be copied - mode = 'copy' - warnings.warn( - f'Failed to create a symbolic link for {src_relpath}, ' - f'and it will be copied to {tar_path}') - else: - continue - - if mode == 'copy': - if osp.isfile(src_path): - shutil.copyfile(src_path, tar_path) - elif osp.isdir(src_path): - shutil.copytree(src_path, tar_path) - else: - warnings.warn(f'Cannot copy file {src_path}.') - else: - raise ValueError(f'Invalid mode {mode}') - - -def get_version(): - with open(version_file, 'r') as f: - exec(compile(f.read(), version_file, 'exec')) - import sys - - # return short version for sdist - if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - return locals()['short_version'] - else: - return locals()['__version__'] - - -def parse_requirements(fname='requirements.txt', with_version=True): - """Parse the package dependencies listed in a requirements file but strip - specific version information. - - Args: - fname (str): Path to requirements file. - with_version (bool, default=False): If True, include version specs. - Returns: - info (list[str]): List of requirements items. - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith('-r '): - # Allow specifying requirements in other files - target = line.split(' ')[1] - for info in parse_require_file(target): - yield info - else: - info = {'line': line} - if line.startswith('-e '): - info['package'] = line.split('#egg=')[1] - else: - # Remove versioning from the package - pat = '(' + '|'.join(['>=', '==', '>']) + ')' - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info['package'] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ';' in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, - rest.split(';')) - info['platform_deps'] = platform_deps - else: - version = rest # NOQA - info['version'] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath, 'r') as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith('#'): - for info in parse_line(line): - yield info - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info['package']] - if with_version and 'version' in info: - parts.extend(info['version']) - if not sys.version.startswith('3.4'): - # apparently package_deps are broken in 3.4 - platform_deps = info.get('platform_deps') - if platform_deps is not None: - parts.append(';' + platform_deps) - item = ''.join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - - -if __name__ == '__main__': - setup( - name='sdsvkie', - version=get_version(), - description='SDSV OCR Team: KIE Invoice', - long_description=readme(), - long_description_content_type='text/markdown', - packages=find_packages(exclude=('configs', 'tools', 'demo')), - include_package_data=True, - url='https://github.com/open-mmlab/mmocr', - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - ], - license='Apache License 2.0', - install_requires=parse_requirements('requirements.txt'), - zip_safe=False) diff --git a/cope2n-ai-fi/modules/sdsvtd b/cope2n-ai-fi/modules/sdsvtd deleted file mode 160000 index a9a796f..0000000 --- a/cope2n-ai-fi/modules/sdsvtd +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a9a796f843f6ad99977a8dcba02d65fe75225574 diff --git a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/PKG-INFO b/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/PKG-INFO deleted file mode 100644 index 5768d42..0000000 --- a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/PKG-INFO +++ /dev/null @@ -1,96 +0,0 @@ -Metadata-Version: 2.1 -Name: sdsvtr -Version: 0.0.5 -Summary: SDSV OCR Team Text Recognizer -Home-page: https://github.com/open-mmlab/mmocr -License: Apache License 2.0 -Platform: UNKNOWN -Classifier: Development Status :: 4 - Beta -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.6 -Classifier: Programming Language :: Python :: 3.7 -Classifier: Programming Language :: Python :: 3.8 -Classifier: Programming Language :: Python :: 3.9 -Description-Content-Type: text/markdown -License-File: LICENSE - -## Introduction -This repo serve as source code storage for the Standalone SATRN Text Recognizer packages. -Installing this package requires 3 additional packages: PyTorch, MMCV, and colorama. - - -## Installation -```shell -conda create -n sdsvtr-env python=3.8 -conda activate sdsvtr-env -conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia -pip install -U openmim -mim install mmcv-full -pip install colorama -git clone https://github.com/moewiee/sdsvtr.git -cd sdsvtr -pip install -v -e . -``` - -## Basic Usage -```python -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cpu') -``` - -The `version` parameter accepts version names declared in `sdsvtr.factory.online_model_factory` or a local path such as `$DIR\model.pth`. To check for available versions in the hub, run: -```python -import sdsvtr -print(sdsvtr.__hub_available_versions__) -``` - -Naturally, a `StandaloneSATRNRunner` instance assumes the input to be one of the following: an instance of `np.ndarray`, an instance of `str`, a list of `np.ndarray`, or a list of `str`, for examples: -```python -import numpy as np -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cpu') - -dummy_list = [np.ndarray((32,128,3)) for _ in range(100)] -result = runner(dummy_list) -``` - -To run with a specific batchsize, try: -```python -import numpy as np -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cuda:0') - -dummy_list = [np.ndarray(1,3,32,128) for _ in range(100)] -bs = min(32, len(imageFiles)) # batchsize = 32 - -all_results = [] -while len(dummy_list) > 0: - dummy_batch = dummy_list[:bs] - dummy_list = dummy_list[bs:] - all_results += runner(dummy_batch) -``` - -## Version Changelog -* **[0.0.1]** -Initial version with specified features. - - -* **[0.0.2]** -Update online model hub. - - -* **[0.0.3]** -Update API now able to inference with 4 types of inputs: list/instance of `np.ndarray`/`str` -Update API interface with `return_confident` parameter. -Update `wget` check and `sha256` check for model hub retrieval. - -* **[0.0.4]** -Update decoder module with EarlyStopping mechanism to possibly improve inference speed on short sequences. -Update API interface with optional argument `max_seq_len_overwrite` to overwrite checkpoint's `max_seq_len` config. - -* **[0.0.5]** -Allow inference on a specific device - - diff --git a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/SOURCES.txt b/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/SOURCES.txt deleted file mode 100644 index b1b280a..0000000 --- a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/SOURCES.txt +++ /dev/null @@ -1,20 +0,0 @@ -LICENSE -README.md -setup.py -sdsvtr/__init__.py -sdsvtr/api.py -sdsvtr/backbone.py -sdsvtr/conv.py -sdsvtr/converter.py -sdsvtr/decoder.py -sdsvtr/encoder.py -sdsvtr/factory.py -sdsvtr/fp16_utils.py -sdsvtr/transform.py -sdsvtr/version.py -sdsvtr.egg-info/PKG-INFO -sdsvtr.egg-info/SOURCES.txt -sdsvtr.egg-info/dependency_links.txt -sdsvtr.egg-info/not-zip-safe -sdsvtr.egg-info/requires.txt -sdsvtr.egg-info/top_level.txt \ No newline at end of file diff --git a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/dependency_links.txt b/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/not-zip-safe b/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/not-zip-safe deleted file mode 100644 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/requires.txt b/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/requires.txt deleted file mode 100644 index a91a249..0000000 --- a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/requires.txt +++ /dev/null @@ -1,3 +0,0 @@ -colorama -mmcv-full -torch diff --git a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/top_level.txt b/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/top_level.txt deleted file mode 100644 index 4e81388..0000000 --- a/cope2n-ai-fi/modules/sdsvtr/sdsvtr.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -sdsvtr