-
Notifications
You must be signed in to change notification settings - Fork 3.5k
/
Copy pathpdf_parser.py
1174 lines (1065 loc) · 45.8 KB
/
pdf_parser.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import random
import xgboost as xgb
from io import BytesIO
import torch
import re
import pdfplumber
import logging
from PIL import Image, ImageDraw
import numpy as np
from timeit import default_timer as timer
from pypdf import PdfReader as pdf2_read
from api.utils.file_utils import get_project_base_directory
from deepdoc.vision import OCR, Recognizer, LayoutRecognizer, TableStructureRecognizer
from rag.nlp import rag_tokenizer
from copy import deepcopy
from huggingface_hub import snapshot_download
logging.getLogger("pdfminer").setLevel(logging.WARNING)
class RAGFlowPdfParser:
def __init__(self):
self.ocr = OCR()
if hasattr(self, "model_speciess"):
self.layouter = LayoutRecognizer("layout." + self.model_speciess)
else:
self.layouter = LayoutRecognizer("layout")
self.tbl_det = TableStructureRecognizer()
self.updown_cnt_mdl = xgb.Booster()
if torch.cuda.is_available():
self.updown_cnt_mdl.set_param({"device": "cuda"})
try:
model_dir = os.path.join(
get_project_base_directory(),
"rag/res/deepdoc")
self.updown_cnt_mdl.load_model(os.path.join(
model_dir, "updown_concat_xgb.model"))
except Exception as e:
model_dir = snapshot_download(
repo_id="InfiniFlow/text_concat_xgb_v1.0",
local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
local_dir_use_symlinks=False)
self.updown_cnt_mdl.load_model(os.path.join(
model_dir, "updown_concat_xgb.model"))
self.page_from = 0
"""
If you have trouble downloading HuggingFace models, -_^ this might help!!
For Linux:
export HF_ENDPOINT=https://hf-mirror.com
For Windows:
Good luck
^_-
"""
def __char_width(self, c):
return (c["x1"] - c["x0"]) // max(len(c["text"]), 1)
def __height(self, c):
return c["bottom"] - c["top"]
def _x_dis(self, a, b):
return min(abs(a["x1"] - b["x0"]), abs(a["x0"] - b["x1"]),
abs(a["x0"] + a["x1"] - b["x0"] - b["x1"]) / 2)
def _y_dis(
self, a, b):
return (
b["top"] + b["bottom"] - a["top"] - a["bottom"]) / 2
def _match_proj(self, b):
proj_patt = [
r"第[零一二三四五六七八九十百]+章",
r"第[零一二三四五六七八九十百]+[条节]",
r"[零一二三四五六七八九十百]+[、是 ]",
r"[\(（][零一二三四五六七八九十百]+[）\)]",
r"[\(（][0-9]+[）\)]",
r"[0-9]+(、|\.[ ]|）|\.[^0-9./a-zA-Z_%><-]{4,})",
r"[0-9]+\.[0-9.]+(、|\.[ ])",
r"[⚫•➢①② ]",
]
return any([re.match(p, b["text"]) for p in proj_patt])
def _updown_concat_features(self, up, down):
w = max(self.__char_width(up), self.__char_width(down))
h = max(self.__height(up), self.__height(down))
y_dis = self._y_dis(up, down)
LEN = 6
tks_down = rag_tokenizer.tokenize(down["text"][:LEN]).split(" ")
tks_up = rag_tokenizer.tokenize(up["text"][-LEN:]).split(" ")
tks_all = up["text"][-LEN:].strip() \
+ (" " if re.match(r"[a-zA-Z0-9]+",
up["text"][-1] + down["text"][0]) else "") \
+ down["text"][:LEN].strip()
tks_all = rag_tokenizer.tokenize(tks_all).split(" ")
fea = [
up.get("R", -1) == down.get("R", -1),
y_dis / h,
down["page_number"] - up["page_number"],
up["layout_type"] == down["layout_type"],
up["layout_type"] == "text",
down["layout_type"] == "text",
up["layout_type"] == "table",
down["layout_type"] == "table",
True if re.search(
r"([。？！；!?;+)）]|[a-z]\.)$",
up["text"]) else False,
True if re.search(r"[，：‘“、0-9（+-]$", up["text"]) else False,
True if re.search(
r"(^.?[/,?;:\]，。；：’”？！》】）-])",
down["text"]) else False,
True if re.match(r"[\(（][^\(\)（）]+[）\)]$", up["text"]) else False,
True if re.search(r"[，,][^。.]+$", up["text"]) else False,
True if re.search(r"[，,][^。.]+$", up["text"]) else False,
True if re.search(r"[\(（][^\)）]+$", up["text"])
and re.search(r"[\)）]", down["text"]) else False,
self._match_proj(down),
True if re.match(r"[A-Z]", down["text"]) else False,
True if re.match(r"[A-Z]", up["text"][-1]) else False,
True if re.match(r"[a-z0-9]", up["text"][-1]) else False,
True if re.match(r"[0-9.%,-]+$", down["text"]) else False,
up["text"].strip()[-2:] == down["text"].strip()[-2:] if len(up["text"].strip()
) > 1 and len(
down["text"].strip()) > 1 else False,
up["x0"] > down["x1"],
abs(self.__height(up) - self.__height(down)) / min(self.__height(up),
self.__height(down)),
self._x_dis(up, down) / max(w, 0.000001),
(len(up["text"]) - len(down["text"])) /
max(len(up["text"]), len(down["text"])),
len(tks_all) - len(tks_up) - len(tks_down),
len(tks_down) - len(tks_up),
tks_down[-1] == tks_up[-1],
max(down["in_row"], up["in_row"]),
abs(down["in_row"] - up["in_row"]),
len(tks_down) == 1 and rag_tokenizer.tag(tks_down[0]).find("n") >= 0,
len(tks_up) == 1 and rag_tokenizer.tag(tks_up[0]).find("n") >= 0
]
return fea
@staticmethod
def sort_X_by_page(arr, threashold):
# sort using y1 first and then x1
arr = sorted(arr, key=lambda r: (r["page_number"], r["x0"], r["top"]))
for i in range(len(arr) - 1):
for j in range(i, -1, -1):
# restore the order using th
if abs(arr[j + 1]["x0"] - arr[j]["x0"]) < threashold \
and arr[j + 1]["top"] < arr[j]["top"] \
and arr[j + 1]["page_number"] == arr[j]["page_number"]:
tmp = arr[j]
arr[j] = arr[j + 1]
arr[j + 1] = tmp
return arr
def _has_color(self, o):
if o.get("ncs", "") == "DeviceGray":
if o["stroking_color"] and o["stroking_color"][0] == 1 and o["non_stroking_color"] and \
o["non_stroking_color"][0] == 1:
if re.match(r"[a-zT_\[\]\(\)-]+", o.get("text", "")):
return False
return True
def _table_transformer_job(self, ZM):
logging.info("Table processing...")
imgs, pos = [], []
tbcnt = [0]
MARGIN = 10
self.tb_cpns = []
assert len(self.page_layout) == len(self.page_images)
for p, tbls in enumerate(self.page_layout): # for page
tbls = [f for f in tbls if f["type"] == "table"]
tbcnt.append(len(tbls))
if not tbls:
continue
for tb in tbls: # for table
left, top, right, bott = tb["x0"] - MARGIN, tb["top"] - MARGIN, \
tb["x1"] + MARGIN, tb["bottom"] + MARGIN
left *= ZM
top *= ZM
right *= ZM
bott *= ZM
pos.append((left, top))
imgs.append(self.page_images[p].crop((left, top, right, bott)))
assert len(self.page_images) == len(tbcnt) - 1
if not imgs:
return
recos = self.tbl_det(imgs)
tbcnt = np.cumsum(tbcnt)
for i in range(len(tbcnt) - 1): # for page
pg = []
for j, tb_items in enumerate(
recos[tbcnt[i]: tbcnt[i + 1]]): # for table
poss = pos[tbcnt[i]: tbcnt[i + 1]]
for it in tb_items: # for table components
it["x0"] = (it["x0"] + poss[j][0])
it["x1"] = (it["x1"] + poss[j][0])
it["top"] = (it["top"] + poss[j][1])
it["bottom"] = (it["bottom"] + poss[j][1])
for n in ["x0", "x1", "top", "bottom"]:
it[n] /= ZM
it["top"] += self.page_cum_height[i]
it["bottom"] += self.page_cum_height[i]
it["pn"] = i
it["layoutno"] = j
pg.append(it)
self.tb_cpns.extend(pg)
def gather(kwd, fzy=10, ption=0.6):
eles = Recognizer.sort_Y_firstly(
[r for r in self.tb_cpns if re.match(kwd, r["label"])], fzy)
eles = Recognizer.layouts_cleanup(self.boxes, eles, 5, ption)
return Recognizer.sort_Y_firstly(eles, 0)
# add R,H,C,SP tag to boxes within table layout
headers = gather(r".*header$")
rows = gather(r".* (row|header)")
spans = gather(r".*spanning")
clmns = sorted([r for r in self.tb_cpns if re.match(
r"table column$", r["label"])], key=lambda x: (x["pn"], x["layoutno"], x["x0"]))
clmns = Recognizer.layouts_cleanup(self.boxes, clmns, 5, 0.5)
for b in self.boxes:
if b.get("layout_type", "") != "table":
continue
ii = Recognizer.find_overlapped_with_threashold(b, rows, thr=0.3)
if ii is not None:
b["R"] = ii
b["R_top"] = rows[ii]["top"]
b["R_bott"] = rows[ii]["bottom"]
ii = Recognizer.find_overlapped_with_threashold(
b, headers, thr=0.3)
if ii is not None:
b["H_top"] = headers[ii]["top"]
b["H_bott"] = headers[ii]["bottom"]
b["H_left"] = headers[ii]["x0"]
b["H_right"] = headers[ii]["x1"]
b["H"] = ii
ii = Recognizer.find_horizontally_tightest_fit(b, clmns)
if ii is not None:
b["C"] = ii
b["C_left"] = clmns[ii]["x0"]
b["C_right"] = clmns[ii]["x1"]
ii = Recognizer.find_overlapped_with_threashold(b, spans, thr=0.3)
if ii is not None:
b["H_top"] = spans[ii]["top"]
b["H_bott"] = spans[ii]["bottom"]
b["H_left"] = spans[ii]["x0"]
b["H_right"] = spans[ii]["x1"]
b["SP"] = ii
def __ocr(self, pagenum, img, chars, ZM=3):
bxs = self.ocr.detect(np.array(img))
if not bxs:
self.boxes.append([])
return
bxs = [(line[0], line[1][0]) for line in bxs]
bxs = Recognizer.sort_Y_firstly(
[{"x0": b[0][0] / ZM, "x1": b[1][0] / ZM,
"top": b[0][1] / ZM, "text": "", "txt": t,
"bottom": b[-1][1] / ZM,
"page_number": pagenum} for b, t in bxs if b[0][0] <= b[1][0] and b[0][1] <= b[-1][1]],
self.mean_height[-1] / 3
)
# merge chars in the same rect
for c in Recognizer.sort_Y_firstly(
chars, self.mean_height[pagenum - 1] // 4):
ii = Recognizer.find_overlapped(c, bxs)
if ii is None:
self.lefted_chars.append(c)
continue
ch = c["bottom"] - c["top"]
bh = bxs[ii]["bottom"] - bxs[ii]["top"]
if abs(ch - bh) / max(ch, bh) >= 0.7 and c["text"] != ' ':
self.lefted_chars.append(c)
continue
if c["text"] == " " and bxs[ii]["text"]:
if re.match(r"[0-9a-zA-Zа-яА-Я,.?;:!%%]", bxs[ii]["text"][-1]):
bxs[ii]["text"] += " "
else:
bxs[ii]["text"] += c["text"]
for b in bxs:
if not b["text"]:
left, right, top, bott = b["x0"] * ZM, b["x1"] * \
ZM, b["top"] * ZM, b["bottom"] * ZM
b["text"] = self.ocr.recognize(np.array(img),
np.array([[left, top], [right, top], [right, bott], [left, bott]],
dtype=np.float32))
del b["txt"]
bxs = [b for b in bxs if b["text"]]
if self.mean_height[-1] == 0:
self.mean_height[-1] = np.median([b["bottom"] - b["top"]
for b in bxs])
self.boxes.append(bxs)
def _layouts_rec(self, ZM, drop=True):
assert len(self.page_images) == len(self.boxes)
self.boxes, self.page_layout = self.layouter(
self.page_images, self.boxes, ZM, drop=drop)
# cumlative Y
for i in range(len(self.boxes)):
self.boxes[i]["top"] += \
self.page_cum_height[self.boxes[i]["page_number"] - 1]
self.boxes[i]["bottom"] += \
self.page_cum_height[self.boxes[i]["page_number"] - 1]
def _text_merge(self):
# merge adjusted boxes
bxs = self.boxes
def end_with(b, txt):
txt = txt.strip()
tt = b.get("text", "").strip()
return tt and tt.find(txt) == len(tt) - len(txt)
def start_with(b, txts):
tt = b.get("text", "").strip()
return tt and any([tt.find(t.strip()) == 0 for t in txts])
# horizontally merge adjacent box with the same layout
i = 0
while i < len(bxs) - 1:
b = bxs[i]
b_ = bxs[i + 1]
if b.get("layoutno", "0") != b_.get("layoutno", "1") or b.get("layout_type", "") in ["table", "figure",
"equation"]:
i += 1
continue
if abs(self._y_dis(b, b_)
) < self.mean_height[bxs[i]["page_number"] - 1] / 3:
# merge
bxs[i]["x1"] = b_["x1"]
bxs[i]["top"] = (b["top"] + b_["top"]) / 2
bxs[i]["bottom"] = (b["bottom"] + b_["bottom"]) / 2
bxs[i]["text"] += b_["text"]
bxs.pop(i + 1)
continue
i += 1
continue
dis_thr = 1
dis = b["x1"] - b_["x0"]
if b.get("layout_type", "") != "text" or b_.get(
"layout_type", "") != "text":
if end_with(b, "，") or start_with(b_, "（，"):
dis_thr = -8
else:
i += 1
continue
if abs(self._y_dis(b, b_)) < self.mean_height[bxs[i]["page_number"] - 1] / 5 \
and dis >= dis_thr and b["x1"] < b_["x1"]:
# merge
bxs[i]["x1"] = b_["x1"]
bxs[i]["top"] = (b["top"] + b_["top"]) / 2
bxs[i]["bottom"] = (b["bottom"] + b_["bottom"]) / 2
bxs[i]["text"] += b_["text"]
bxs.pop(i + 1)
continue
i += 1
self.boxes = bxs
def _naive_vertical_merge(self):
bxs = Recognizer.sort_Y_firstly(
self.boxes, np.median(
self.mean_height) / 3)
i = 0
while i + 1 < len(bxs):
b = bxs[i]
b_ = bxs[i + 1]
if b["page_number"] < b_["page_number"] and re.match(
r"[0-9 •一—-]+$", b["text"]):
bxs.pop(i)
continue
if not b["text"].strip():
bxs.pop(i)
continue
concatting_feats = [
b["text"].strip()[-1] in ",;:'\"，、‘“；：-",
len(b["text"].strip()) > 1 and b["text"].strip(
)[-2] in ",;:'\"，‘“、；：",
b_["text"].strip() and b_["text"].strip()[0] in "。；？！?”）),，、：",
]
# features for not concating
feats = [
b.get("layoutno", 0) != b_.get("layoutno", 0),
b["text"].strip()[-1] in "。？！?",
self.is_english and b["text"].strip()[-1] in ".!?",
b["page_number"] == b_["page_number"] and b_["top"] -
b["bottom"] > self.mean_height[b["page_number"] - 1] * 1.5,
b["page_number"] < b_["page_number"] and abs(
b["x0"] - b_["x0"]) > self.mean_width[b["page_number"] - 1] * 4,
]
# split features
detach_feats = [b["x1"] < b_["x0"],
b["x0"] > b_["x1"]]
if (any(feats) and not any(concatting_feats)) or any(detach_feats):
print(
b["text"],
b_["text"],
any(feats),
any(concatting_feats),
any(detach_feats))
i += 1
continue
# merge up and down
b["bottom"] = b_["bottom"]
b["text"] += b_["text"]
b["x0"] = min(b["x0"], b_["x0"])
b["x1"] = max(b["x1"], b_["x1"])
bxs.pop(i + 1)
self.boxes = bxs
def _concat_downward(self, concat_between_pages=True):
# count boxes in the same row as a feature
for i in range(len(self.boxes)):
mh = self.mean_height[self.boxes[i]["page_number"] - 1]
self.boxes[i]["in_row"] = 0
j = max(0, i - 12)
while j < min(i + 12, len(self.boxes)):
if j == i:
j += 1
continue
ydis = self._y_dis(self.boxes[i], self.boxes[j]) / mh
if abs(ydis) < 1:
self.boxes[i]["in_row"] += 1
elif ydis > 0:
break
j += 1
# concat between rows
boxes = deepcopy(self.boxes)
blocks = []
while boxes:
chunks = []
def dfs(up, dp):
chunks.append(up)
i = dp
while i < min(dp + 12, len(boxes)):
ydis = self._y_dis(up, boxes[i])
smpg = up["page_number"] == boxes[i]["page_number"]
mh = self.mean_height[up["page_number"] - 1]
mw = self.mean_width[up["page_number"] - 1]
if smpg and ydis > mh * 4:
break
if not smpg and ydis > mh * 16:
break
down = boxes[i]
if not concat_between_pages and down["page_number"] > up["page_number"]:
break
if up.get("R", "") != down.get(
"R", "") and up["text"][-1] != "，":
i += 1
continue
if re.match(r"[0-9]{2,3}/[0-9]{3}$", up["text"]) \
or re.match(r"[0-9]{2,3}/[0-9]{3}$", down["text"]) \
or not down["text"].strip():
i += 1
continue
if not down["text"].strip():
i += 1
continue
if up["x1"] < down["x0"] - 10 * \
mw or up["x0"] > down["x1"] + 10 * mw:
i += 1
continue
if i - dp < 5 and up.get("layout_type") == "text":
if up.get("layoutno", "1") == down.get(
"layoutno", "2"):
dfs(down, i + 1)
boxes.pop(i)
return
i += 1
continue
fea = self._updown_concat_features(up, down)
if self.updown_cnt_mdl.predict(
xgb.DMatrix([fea]))[0] <= 0.5:
i += 1
continue
dfs(down, i + 1)
boxes.pop(i)
return
dfs(boxes[0], 1)
boxes.pop(0)
if chunks:
blocks.append(chunks)
# concat within each block
boxes = []
for b in blocks:
if len(b) == 1:
boxes.append(b[0])
continue
t = b[0]
for c in b[1:]:
t["text"] = t["text"].strip()
c["text"] = c["text"].strip()
if not c["text"]:
continue
if t["text"] and re.match(
r"[0-9\.a-zA-Z]+$", t["text"][-1] + c["text"][-1]):
t["text"] += " "
t["text"] += c["text"]
t["x0"] = min(t["x0"], c["x0"])
t["x1"] = max(t["x1"], c["x1"])
t["page_number"] = min(t["page_number"], c["page_number"])
t["bottom"] = c["bottom"]
if not t["layout_type"] \
and c["layout_type"]:
t["layout_type"] = c["layout_type"]
boxes.append(t)
self.boxes = Recognizer.sort_Y_firstly(boxes, 0)
def _filter_forpages(self):
if not self.boxes:
return
findit = False
i = 0
while i < len(self.boxes):
if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$",
re.sub(r"( | |\u3000)+", "", self.boxes[i]["text"].lower())):
i += 1
continue
findit = True
eng = re.match(
r"[0-9a-zA-Z :'.-]{5,}",
self.boxes[i]["text"].strip())
self.boxes.pop(i)
if i >= len(self.boxes):
break
prefix = self.boxes[i]["text"].strip()[:3] if not eng else " ".join(
self.boxes[i]["text"].strip().split(" ")[:2])
while not prefix:
self.boxes.pop(i)
if i >= len(self.boxes):
break
prefix = self.boxes[i]["text"].strip()[:3] if not eng else " ".join(
self.boxes[i]["text"].strip().split(" ")[:2])
self.boxes.pop(i)
if i >= len(self.boxes) or not prefix:
break
for j in range(i, min(i + 128, len(self.boxes))):
if not re.match(prefix, self.boxes[j]["text"]):
continue
for k in range(i, j):
self.boxes.pop(i)
break
if findit:
return
page_dirty = [0] * len(self.page_images)
for b in self.boxes:
if re.search(r"(··|··|··)", b["text"]):
page_dirty[b["page_number"] - 1] += 1
page_dirty = set([i + 1 for i, t in enumerate(page_dirty) if t > 3])
if not page_dirty:
return
i = 0
while i < len(self.boxes):
if self.boxes[i]["page_number"] in page_dirty:
self.boxes.pop(i)
continue
i += 1
def _merge_with_same_bullet(self):
i = 0
while i + 1 < len(self.boxes):
b = self.boxes[i]
b_ = self.boxes[i + 1]
if not b["text"].strip():
self.boxes.pop(i)
continue
if not b_["text"].strip():
self.boxes.pop(i + 1)
continue
if b["text"].strip()[0] != b_["text"].strip()[0] \
or b["text"].strip()[0].lower() in set("qwertyuopasdfghjklzxcvbnm") \
or rag_tokenizer.is_chinese(b["text"].strip()[0]) \
or b["top"] > b_["bottom"]:
i += 1
continue
b_["text"] = b["text"] + "\n" + b_["text"]
b_["x0"] = min(b["x0"], b_["x0"])
b_["x1"] = max(b["x1"], b_["x1"])
b_["top"] = b["top"]
self.boxes.pop(i)
def _extract_table_figure(self, need_image, ZM,
return_html, need_position):
tables = {}
figures = {}
# extract figure and table boxes
i = 0
lst_lout_no = ""
nomerge_lout_no = []
while i < len(self.boxes):
if "layoutno" not in self.boxes[i]:
i += 1
continue
lout_no = str(self.boxes[i]["page_number"]) + \
"-" + str(self.boxes[i]["layoutno"])
if TableStructureRecognizer.is_caption(self.boxes[i]) or self.boxes[i]["layout_type"] in ["table caption",
"title",
"figure caption",
"reference"]:
nomerge_lout_no.append(lst_lout_no)
if self.boxes[i]["layout_type"] == "table":
if re.match(r"(数据|资料|图表)*来源[:： ]", self.boxes[i]["text"]):
self.boxes.pop(i)
continue
if lout_no not in tables:
tables[lout_no] = []
tables[lout_no].append(self.boxes[i])
self.boxes.pop(i)
lst_lout_no = lout_no
continue
if need_image and self.boxes[i]["layout_type"] == "figure":
if re.match(r"(数据|资料|图表)*来源[:： ]", self.boxes[i]["text"]):
self.boxes.pop(i)
continue
if lout_no not in figures:
figures[lout_no] = []
figures[lout_no].append(self.boxes[i])
self.boxes.pop(i)
lst_lout_no = lout_no
continue
i += 1
# merge table on different pages
nomerge_lout_no = set(nomerge_lout_no)
tbls = sorted([(k, bxs) for k, bxs in tables.items()],
key=lambda x: (x[1][0]["top"], x[1][0]["x0"]))
i = len(tbls) - 1
while i - 1 >= 0:
k0, bxs0 = tbls[i - 1]
k, bxs = tbls[i]
i -= 1
if k0 in nomerge_lout_no:
continue
if bxs[0]["page_number"] == bxs0[0]["page_number"]:
continue
if bxs[0]["page_number"] - bxs0[0]["page_number"] > 1:
continue
mh = self.mean_height[bxs[0]["page_number"] - 1]
if self._y_dis(bxs0[-1], bxs[0]) > mh * 23:
continue
tables[k0].extend(tables[k])
del tables[k]
def x_overlapped(a, b):
return not any([a["x1"] < b["x0"], a["x0"] > b["x1"]])
# find captions and pop out
i = 0
while i < len(self.boxes):
c = self.boxes[i]
# mh = self.mean_height[c["page_number"]-1]
if not TableStructureRecognizer.is_caption(c):
i += 1
continue
# find the nearest layouts
def nearest(tbls):
nonlocal c
mink = ""
minv = 1000000000
for k, bxs in tbls.items():
for b in bxs:
if b.get("layout_type", "").find("caption") >= 0:
continue
y_dis = self._y_dis(c, b)
x_dis = self._x_dis(
c, b) if not x_overlapped(
c, b) else 0
dis = y_dis * y_dis + x_dis * x_dis
if dis < minv:
mink = k
minv = dis
return mink, minv
tk, tv = nearest(tables)
fk, fv = nearest(figures)
# if min(tv, fv) > 2000:
# i += 1
# continue
if tv < fv and tk:
tables[tk].insert(0, c)
logging.debug(
"TABLE:" +
self.boxes[i]["text"] +
"; Cap: " +
tk)
elif fk:
figures[fk].insert(0, c)
logging.debug(
"FIGURE:" +
self.boxes[i]["text"] +
"; Cap: " +
tk)
self.boxes.pop(i)
res = []
positions = []
def cropout(bxs, ltype, poss):
nonlocal ZM
pn = set([b["page_number"] - 1 for b in bxs])
if len(pn) < 2:
pn = list(pn)[0]
ht = self.page_cum_height[pn]
b = {
"x0": np.min([b["x0"] for b in bxs]),
"top": np.min([b["top"] for b in bxs]) - ht,
"x1": np.max([b["x1"] for b in bxs]),
"bottom": np.max([b["bottom"] for b in bxs]) - ht
}
louts = [l for l in self.page_layout[pn] if l["type"] == ltype]
ii = Recognizer.find_overlapped(b, louts, naive=True)
if ii is not None:
b = louts[ii]
else:
logging.warn(
f"Missing layout match: {pn + 1},%s" %
(bxs[0].get(
"layoutno", "")))
left, top, right, bott = b["x0"], b["top"], b["x1"], b["bottom"]
if right < left: right = left + 1
poss.append((pn + self.page_from, left, right, top, bott))
return self.page_images[pn] \
.crop((left * ZM, top * ZM,
right * ZM, bott * ZM))
pn = {}
for b in bxs:
p = b["page_number"] - 1
if p not in pn:
pn[p] = []
pn[p].append(b)
pn = sorted(pn.items(), key=lambda x: x[0])
imgs = [cropout(arr, ltype, poss) for p, arr in pn]
pic = Image.new("RGB",
(int(np.max([i.size[0] for i in imgs])),
int(np.sum([m.size[1] for m in imgs]))),
(245, 245, 245))
height = 0
for img in imgs:
pic.paste(img, (0, int(height)))
height += img.size[1]
return pic
# crop figure out and add caption
for k, bxs in figures.items():
txt = "\n".join([b["text"] for b in bxs])
if not txt:
continue
poss = []
res.append(
(cropout(
bxs,
"figure", poss),
[txt]))
positions.append(poss)
for k, bxs in tables.items():
if not bxs:
continue
bxs = Recognizer.sort_Y_firstly(bxs, np.mean(
[(b["bottom"] - b["top"]) / 2 for b in bxs]))
poss = []
res.append((cropout(bxs, "table", poss),
self.tbl_det.construct_table(bxs, html=return_html, is_english=self.is_english)))
positions.append(poss)
assert len(positions) == len(res)
if need_position:
return list(zip(res, positions))
return res
def proj_match(self, line):
if len(line) <= 2:
return
if re.match(r"[0-9 ().,%%+/-]+$", line):
return False
for p, j in [
(r"第[零一二三四五六七八九十百]+章", 1),
(r"第[零一二三四五六七八九十百]+[条节]", 2),
(r"[零一二三四五六七八九十百]+[、 ]", 3),
(r"[\(（][零一二三四五六七八九十百]+[）\)]", 4),
(r"[0-9]+(、|\.[ ]|\.[^0-9])", 5),
(r"[0-9]+\.[0-9]+(、|[. ]|[^0-9])", 6),
(r"[0-9]+\.[0-9]+\.[0-9]+(、|[ ]|[^0-9])", 7),
(r"[0-9]+\.[0-9]+\.[0-9]+\.[0-9]+(、|[ ]|[^0-9])", 8),
(r".{,48}[：:?？]$", 9),
(r"[0-9]+）", 10),
(r"[\(（][0-9]+[）\)]", 11),
(r"[零一二三四五六七八九十百]+是", 12),
(r"[⚫•➢✓]", 12)
]:
if re.match(p, line):
return j
return
def _line_tag(self, bx, ZM):
pn = [bx["page_number"]]
top = bx["top"] - self.page_cum_height[pn[0] - 1]
bott = bx["bottom"] - self.page_cum_height[pn[0] - 1]
page_images_cnt = len(self.page_images)
if pn[-1] - 1 >= page_images_cnt: return ""
while bott * ZM > self.page_images[pn[-1] - 1].size[1]:
bott -= self.page_images[pn[-1] - 1].size[1] / ZM
pn.append(pn[-1] + 1)
if pn[-1] - 1 >= page_images_cnt:
return ""
return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \
.format("-".join([str(p) for p in pn]),
bx["x0"], bx["x1"], top, bott)
def __filterout_scraps(self, boxes, ZM):
def width(b):
return b["x1"] - b["x0"]
def height(b):
return b["bottom"] - b["top"]
def usefull(b):
if b.get("layout_type"):
return True
if width(
b) > self.page_images[b["page_number"] - 1].size[0] / ZM / 3:
return True
if b["bottom"] - b["top"] > self.mean_height[b["page_number"] - 1]:
return True
return False
res = []
while boxes:
lines = []
widths = []
pw = self.page_images[boxes[0]["page_number"] - 1].size[0] / ZM
mh = self.mean_height[boxes[0]["page_number"] - 1]
mj = self.proj_match(
boxes[0]["text"]) or boxes[0].get(
"layout_type",
"") == "title"
def dfs(line, st):
nonlocal mh, pw, lines, widths
lines.append(line)
widths.append(width(line))
width_mean = np.mean(widths)
mmj = self.proj_match(
line["text"]) or line.get(
"layout_type",
"") == "title"
for i in range(st + 1, min(st + 20, len(boxes))):
if (boxes[i]["page_number"] - line["page_number"]) > 0:
break
if not mmj and self._y_dis(
line, boxes[i]) >= 3 * mh and height(line) < 1.5 * mh:
break
if not usefull(boxes[i]):
continue
if mmj or \
(self._x_dis(boxes[i], line) < pw / 10): \
# and abs(width(boxes[i])-width_mean)/max(width(boxes[i]),width_mean)<0.5):
# concat following
dfs(boxes[i], i)
boxes.pop(i)
break
try:
if usefull(boxes[0]):
dfs(boxes[0], 0)
else:
logging.debug("WASTE: " + boxes[0]["text"])
except Exception as e:
pass
boxes.pop(0)
mw = np.mean(widths)
if mj or mw / pw >= 0.35 or mw > 200:
res.append(
"\n".join([c["text"] + self._line_tag(c, ZM) for c in lines]))
else:
logging.debug("REMOVED: " +
"<<".join([c["text"] for c in lines]))
return "\n\n".join(res)
@staticmethod
def total_page_number(fnm, binary=None):
try:
pdf = pdfplumber.open(
fnm) if not binary else pdfplumber.open(BytesIO(binary))
return len(pdf.pages)
except Exception as e:
logging.error(str(e))
def __images__(self, fnm, zoomin=3, page_from=0,
page_to=299, callback=None):
self.lefted_chars = []
self.mean_height = []
self.mean_width = []
self.boxes = []
self.garbages = {}
self.page_cum_height = [0]
self.page_layout = []
self.page_from = page_from
st = timer()
try:
self.pdf = pdfplumber.open(fnm) if isinstance(
fnm, str) else pdfplumber.open(BytesIO(fnm))
self.page_images = [p.to_image(resolution=72 * zoomin).annotated for i, p in
enumerate(self.pdf.pages[page_from:page_to])]
self.page_chars = [[{**c, 'top': c['top'], 'bottom': c['bottom']} for c in page.dedupe_chars().chars if self._has_color(c)] for page in
self.pdf.pages[page_from:page_to]]
self.total_page = len(self.pdf.pages)
except Exception as e:
logging.error(str(e))
self.outlines = []
try:
self.pdf = pdf2_read(fnm if isinstance(fnm, str) else BytesIO(fnm))
outlines = self.pdf.outline
def dfs(arr, depth):
for a in arr:
if isinstance(a, dict):
self.outlines.append((a["/Title"], depth))
continue
dfs(a, depth + 1)
dfs(outlines, 0)
except Exception as e:
logging.warning(f"Outlines exception: {e}")
if not self.outlines:
logging.warning(f"Miss outlines")
logging.info("Images converted.")
self.is_english = [re.search(r"[a-zA-Z0-9,/¸;:'\[\]\(\)!@#$%^&*\"?<>._-]{30,}", "".join(
random.choices([c["text"] for c in self.page_chars[i]], k=min(100, len(self.page_chars[i]))))) for i in
range(len(self.page_chars))]
if sum([1 if e else 0 for e in self.is_english]) > len(
self.page_images) / 2:
self.is_english = True
else:
self.is_english = False
st = timer()
for i, img in enumerate(self.page_images):
chars = self.page_chars[i] if not self.is_english else []
self.mean_height.append(
np.median(sorted([c["height"] for c in chars])) if chars else 0
)
self.mean_width.append(
np.median(sorted([c["width"] for c in chars])) if chars else 8
)
self.page_cum_height.append(img.size[1] / zoomin)
j = 0
while j + 1 < len(chars):