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Duplicate from king007/TableTransformer2CSV

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Co-authored-by: king007 <king007@users.noreply.huggingface.co>

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  1. .gitattributes +31 -0
  2. README.md +13 -0
  3. app.py +510 -0
  4. packages.txt +6 -0
  5. requirements.txt +47 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ title: Image2Table
3
+ emoji: 🚀
4
+ colorFrom: indigo
5
+ colorTo: purple
6
+ sdk: streamlit
7
+ sdk_version: 1.10.0
8
+ app_file: app.py
9
+ pinned: false
10
+ duplicated_from: king007/TableTransformer2CSV
11
+ ---
12
+
13
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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1
+ import streamlit as st
2
+ from PIL import Image, ImageEnhance
3
+ import statistics
4
+ import os
5
+ import string
6
+ from collections import Counter
7
+ from itertools import tee, count
8
+ # import TDTSR
9
+ import pytesseract
10
+ from pytesseract import Output
11
+ import json
12
+ import pandas as pd
13
+ import matplotlib.pyplot as plt
14
+ import cv2
15
+ import numpy as np
16
+ # from transformers import TrOCRProcessor, VisionEncoderDecoderModel
17
+ # from cv2 import dnn_superres
18
+ from transformers import DetrFeatureExtractor
19
+ #from transformers import DetrForObjectDetection
20
+ from transformers import TableTransformerForObjectDetection
21
+ import torch
22
+ import asyncio
23
+ # pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
24
+
25
+
26
+ st.set_option('deprecation.showPyplotGlobalUse', False)
27
+ st.set_page_config(layout='wide')
28
+ st.title("Table Detection and Table Structure Recognition")
29
+ st.write("Implemented by MSFT team: https://github.com/microsoft/table-transformer")
30
+
31
+
32
+
33
+ def PIL_to_cv(pil_img):
34
+ return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
35
+
36
+ def cv_to_PIL(cv_img):
37
+ return Image.fromarray(cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB))
38
+
39
+
40
+ async def pytess(cell_pil_img):
41
+ return ' '.join(pytesseract.image_to_data(cell_pil_img, output_type=Output.DICT, config='-c tessedit_char_blacklist=œ˜â€œï¬â™Ã©œ¢!|”?«“¥ --psm 6 preserve_interword_spaces')['text']).strip()
42
+
43
+
44
+ # def super_res(pil_img):
45
+ # '''
46
+ # Useful for low-res docs
47
+ # '''
48
+ # requires opencv-contrib-python installed without the opencv-python
49
+ # sr = dnn_superres.DnnSuperResImpl_create()
50
+ # image = PIL_to_cv(pil_img)
51
+ # model_path = "/data/Salman/TRD/code/table-transformer/transformers/LapSRN_x2.pb"
52
+ # model_name = 'lapsrn'
53
+ # model_scale = 2
54
+ # sr.readModel(model_path)
55
+ # sr.setModel(model_name, model_scale)
56
+ # final_img = sr.upsample(image)
57
+ # final_img = cv_to_PIL(final_img)
58
+
59
+ # return final_img
60
+
61
+
62
+ def sharpen_image(pil_img):
63
+
64
+ img = PIL_to_cv(pil_img)
65
+ sharpen_kernel = np.array([[-1, -1, -1],
66
+ [-1, 9, -1],
67
+ [-1, -1, -1]])
68
+
69
+ sharpen = cv2.filter2D(img, -1, sharpen_kernel)
70
+ pil_img = cv_to_PIL(sharpen)
71
+ return pil_img
72
+
73
+
74
+ def uniquify(seq, suffs = count(1)):
75
+ """Make all the items unique by adding a suffix (1, 2, etc).
76
+ Credit: https://stackoverflow.com/questions/30650474/python-rename-duplicates-in-list-with-progressive-numbers-without-sorting-list
77
+ `seq` is mutable sequence of strings.
78
+ `suffs` is an optional alternative suffix iterable.
79
+ """
80
+ not_unique = [k for k,v in Counter(seq).items() if v>1]
81
+
82
+ suff_gens = dict(zip(not_unique, tee(suffs, len(not_unique))))
83
+ for idx,s in enumerate(seq):
84
+ try:
85
+ suffix = str(next(suff_gens[s]))
86
+ except KeyError:
87
+ continue
88
+ else:
89
+ seq[idx] += suffix
90
+
91
+ return seq
92
+
93
+ def binarizeBlur_image(pil_img):
94
+ image = PIL_to_cv(pil_img)
95
+ thresh = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY_INV)[1]
96
+
97
+ result = cv2.GaussianBlur(thresh, (5,5), 0)
98
+ result = 255 - result
99
+ return cv_to_PIL(result)
100
+
101
+
102
+
103
+ def td_postprocess(pil_img):
104
+ '''
105
+ Removes gray background from tables
106
+ '''
107
+ img = PIL_to_cv(pil_img)
108
+
109
+ hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
110
+ mask = cv2.inRange(hsv, (0, 0, 100), (255, 5, 255)) # (0, 0, 100), (255, 5, 255)
111
+ nzmask = cv2.inRange(hsv, (0, 0, 5), (255, 255, 255)) # (0, 0, 5), (255, 255, 255))
112
+ nzmask = cv2.erode(nzmask, np.ones((3,3))) # (3,3)
113
+ mask = mask & nzmask
114
+
115
+ new_img = img.copy()
116
+ new_img[np.where(mask)] = 255
117
+
118
+
119
+ return cv_to_PIL(new_img)
120
+
121
+ # def super_res(pil_img):
122
+ # # requires opencv-contrib-python installed without the opencv-python
123
+ # sr = dnn_superres.DnnSuperResImpl_create()
124
+ # image = PIL_to_cv(pil_img)
125
+ # model_path = "./LapSRN_x8.pb"
126
+ # model_name = model_path.split('/')[1].split('_')[0].lower()
127
+ # model_scale = int(model_path.split('/')[1].split('_')[1].split('.')[0][1])
128
+
129
+ # sr.readModel(model_path)
130
+ # sr.setModel(model_name, model_scale)
131
+ # final_img = sr.upsample(image)
132
+ # final_img = cv_to_PIL(final_img)
133
+
134
+ # return final_img
135
+
136
+ def table_detector(image, THRESHOLD_PROBA):
137
+ '''
138
+ Table detection using DEtect-object TRansformer pre-trained on 1 million tables
139
+
140
+ '''
141
+
142
+ feature_extractor = DetrFeatureExtractor(do_resize=True, size=800, max_size=800)
143
+ encoding = feature_extractor(image, return_tensors="pt")
144
+
145
+ model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection")
146
+
147
+ with torch.no_grad():
148
+ outputs = model(**encoding)
149
+
150
+ probas = outputs.logits.softmax(-1)[0, :, :-1]
151
+ keep = probas.max(-1).values > THRESHOLD_PROBA
152
+
153
+ target_sizes = torch.tensor(image.size[::-1]).unsqueeze(0)
154
+ postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
155
+ bboxes_scaled = postprocessed_outputs[0]['boxes'][keep]
156
+
157
+ return (model, probas[keep], bboxes_scaled)
158
+
159
+
160
+ def table_struct_recog(image, THRESHOLD_PROBA):
161
+ '''
162
+ Table structure recognition using DEtect-object TRansformer pre-trained on 1 million tables
163
+ '''
164
+
165
+ feature_extractor = DetrFeatureExtractor(do_resize=True, size=1000, max_size=1000)
166
+ encoding = feature_extractor(image, return_tensors="pt")
167
+
168
+ model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-structure-recognition")
169
+ with torch.no_grad():
170
+ outputs = model(**encoding)
171
+
172
+ probas = outputs.logits.softmax(-1)[0, :, :-1]
173
+ keep = probas.max(-1).values > THRESHOLD_PROBA
174
+
175
+ target_sizes = torch.tensor(image.size[::-1]).unsqueeze(0)
176
+ postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
177
+ bboxes_scaled = postprocessed_outputs[0]['boxes'][keep]
178
+
179
+ return (model, probas[keep], bboxes_scaled)
180
+
181
+
182
+
183
+
184
+
185
+ class TableExtractionPipeline():
186
+
187
+ colors = ["red", "blue", "green", "yellow", "orange", "violet"]
188
+
189
+ # colors = ["red", "blue", "green", "red", "red", "red"]
190
+
191
+ def add_padding(self, pil_img, top, right, bottom, left, color=(255,255,255)):
192
+ '''
193
+ Image padding as part of TSR pre-processing to prevent missing table edges
194
+ '''
195
+ width, height = pil_img.size
196
+ new_width = width + right + left
197
+ new_height = height + top + bottom
198
+ result = Image.new(pil_img.mode, (new_width, new_height), color)
199
+ result.paste(pil_img, (left, top))
200
+ return result
201
+
202
+ def plot_results_detection(self, c1, model, pil_img, prob, boxes, delta_xmin, delta_ymin, delta_xmax, delta_ymax):
203
+ '''
204
+ crop_tables and plot_results_detection must have same co-ord shifts because 1 only plots the other one updates co-ordinates
205
+ '''
206
+ # st.write('img_obj')
207
+ # st.write(pil_img)
208
+ plt.imshow(pil_img)
209
+ ax = plt.gca()
210
+
211
+ for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()):
212
+ cl = p.argmax()
213
+ xmin, ymin, xmax, ymax = xmin-delta_xmin, ymin-delta_ymin, xmax+delta_xmax, ymax+delta_ymax
214
+ ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,fill=False, color='red', linewidth=3))
215
+ text = f'{model.config.id2label[cl.item()]}: {p[cl]:0.2f}'
216
+ ax.text(xmin-20, ymin-50, text, fontsize=10,bbox=dict(facecolor='yellow', alpha=0.5))
217
+ plt.axis('off')
218
+ c1.pyplot()
219
+
220
+
221
+ def crop_tables(self, pil_img, prob, boxes, delta_xmin, delta_ymin, delta_xmax, delta_ymax):
222
+ '''
223
+ crop_tables and plot_results_detection must have same co-ord shifts because 1 only plots the other one updates co-ordinates
224
+ '''
225
+ cropped_img_list = []
226
+
227
+ for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()):
228
+
229
+ xmin, ymin, xmax, ymax = xmin-delta_xmin, ymin-delta_ymin, xmax+delta_xmax, ymax+delta_ymax
230
+ cropped_img = pil_img.crop((xmin, ymin, xmax, ymax))
231
+ cropped_img_list.append(cropped_img)
232
+
233
+
234
+ return cropped_img_list
235
+
236
+ def generate_structure(self, c2, model, pil_img, prob, boxes, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom):
237
+ '''
238
+ Co-ordinates are adjusted here by 3 'pixels'
239
+ To plot table pillow image and the TSR bounding boxes on the table
240
+ '''
241
+ # st.write('img_obj')
242
+ # st.write(pil_img)
243
+ plt.figure(figsize=(32,20))
244
+ plt.imshow(pil_img)
245
+ ax = plt.gca()
246
+ rows = {}
247
+ cols = {}
248
+ idx = 0
249
+
250
+
251
+ for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()):
252
+
253
+ xmin, ymin, xmax, ymax = xmin, ymin, xmax, ymax
254
+ cl = p.argmax()
255
+ class_text = model.config.id2label[cl.item()]
256
+ text = f'{class_text}: {p[cl]:0.2f}'
257
+ # or (class_text == 'table column')
258
+ if (class_text == 'table row') or (class_text =='table projected row header') or (class_text == 'table column'):
259
+ ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,fill=False, color=self.colors[cl.item()], linewidth=2))
260
+ ax.text(xmin-10, ymin-10, text, fontsize=5, bbox=dict(facecolor='yellow', alpha=0.5))
261
+
262
+ if class_text == 'table row':
263
+ rows['table row.'+str(idx)] = (xmin, ymin-expand_rowcol_bbox_top, xmax, ymax+expand_rowcol_bbox_bottom)
264
+ if class_text == 'table column':
265
+ cols['table column.'+str(idx)] = (xmin, ymin-expand_rowcol_bbox_top, xmax, ymax+expand_rowcol_bbox_bottom)
266
+
267
+ idx += 1
268
+
269
+
270
+ plt.axis('on')
271
+ c2.pyplot()
272
+ return rows, cols
273
+
274
+ def sort_table_featuresv2(self, rows:dict, cols:dict):
275
+ # Sometimes the header and first row overlap, and we need the header bbox not to have first row's bbox inside the headers bbox
276
+ rows_ = {table_feature : (xmin, ymin, xmax, ymax) for table_feature, (xmin, ymin, xmax, ymax) in sorted(rows.items(), key=lambda tup: tup[1][1])}
277
+ cols_ = {table_feature : (xmin, ymin, xmax, ymax) for table_feature, (xmin, ymin, xmax, ymax) in sorted(cols.items(), key=lambda tup: tup[1][0])}
278
+
279
+ return rows_, cols_
280
+
281
+ def individual_table_featuresv2(self, pil_img, rows:dict, cols:dict):
282
+
283
+ for k, v in rows.items():
284
+ xmin, ymin, xmax, ymax = v
285
+ cropped_img = pil_img.crop((xmin, ymin, xmax, ymax))
286
+ rows[k] = xmin, ymin, xmax, ymax, cropped_img
287
+
288
+ for k, v in cols.items():
289
+ xmin, ymin, xmax, ymax = v
290
+ cropped_img = pil_img.crop((xmin, ymin, xmax, ymax))
291
+ cols[k] = xmin, ymin, xmax, ymax, cropped_img
292
+
293
+ return rows, cols
294
+
295
+
296
+ def object_to_cellsv2(self, master_row:dict, cols:dict, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom, padd_left):
297
+ '''Removes redundant bbox for rows&columns and divides each row into cells from columns
298
+ Args:
299
+
300
+ Returns:
301
+
302
+
303
+ '''
304
+ cells_img = {}
305
+ header_idx = 0
306
+ row_idx = 0
307
+ previous_xmax_col = 0
308
+ new_cols = {}
309
+ new_master_row = {}
310
+ previous_ymin_row = 0
311
+ new_cols = cols
312
+ new_master_row = master_row
313
+ ## Below 2 for loops remove redundant bounding boxes ###
314
+ # for k_col, v_col in cols.items():
315
+ # xmin_col, _, xmax_col, _, col_img = v_col
316
+ # if (np.isclose(previous_xmax_col, xmax_col, atol=5)) or (xmin_col >= xmax_col):
317
+ # print('Found a column with double bbox')
318
+ # continue
319
+ # previous_xmax_col = xmax_col
320
+ # new_cols[k_col] = v_col
321
+
322
+ # for k_row, v_row in master_row.items():
323
+ # _, ymin_row, _, ymax_row, row_img = v_row
324
+ # if (np.isclose(previous_ymin_row, ymin_row, atol=5)) or (ymin_row >= ymax_row):
325
+ # print('Found a row with double bbox')
326
+ # continue
327
+ # previous_ymin_row = ymin_row
328
+ # new_master_row[k_row] = v_row
329
+ ######################################################
330
+ for k_row, v_row in new_master_row.items():
331
+
332
+ _, _, _, _, row_img = v_row
333
+ xmax, ymax = row_img.size
334
+ xa, ya, xb, yb = 0, 0, 0, ymax
335
+ row_img_list = []
336
+ # plt.imshow(row_img)
337
+ # st.pyplot()
338
+ for idx, kv in enumerate(new_cols.items()):
339
+ k_col, v_col = kv
340
+ xmin_col, _, xmax_col, _, col_img = v_col
341
+ xmin_col, xmax_col = xmin_col - padd_left - 10, xmax_col - padd_left
342
+ # plt.imshow(col_img)
343
+ # st.pyplot()
344
+ # xa + 3 : to remove borders on the left side of the cropped cell
345
+ # yb = 3: to remove row information from the above row of the cropped cell
346
+ # xb - 3: to remove borders on the right side of the cropped cell
347
+ xa = xmin_col
348
+ xb = xmax_col
349
+ if idx == 0:
350
+ xa = 0
351
+ if idx == len(new_cols)-1:
352
+ xb = xmax
353
+ xa, ya, xb, yb = xa, ya, xb, yb
354
+
355
+ row_img_cropped = row_img.crop((xa, ya, xb, yb))
356
+ row_img_list.append(row_img_cropped)
357
+
358
+ cells_img[k_row+'.'+str(row_idx)] = row_img_list
359
+ row_idx += 1
360
+
361
+ return cells_img, len(new_cols), len(new_master_row)-1
362
+
363
+ def clean_dataframe(self, df):
364
+ '''
365
+ Remove irrelevant symbols that appear with tesseractOCR
366
+ '''
367
+ # df.columns = [col.replace('|', '') for col in df.columns]
368
+
369
+ for col in df.columns:
370
+
371
+ df[col]=df[col].str.replace("'", '', regex=True)
372
+ df[col]=df[col].str.replace('"', '', regex=True)
373
+ df[col]=df[col].str.replace(']', '', regex=True)
374
+ df[col]=df[col].str.replace('[', '', regex=True)
375
+ df[col]=df[col].str.replace('{', '', regex=True)
376
+ df[col]=df[col].str.replace('}', '', regex=True)
377
+ return df
378
+
379
+ @st.cache
380
+ def convert_df(self, df):
381
+ return df.to_csv().encode('utf-8')
382
+
383
+
384
+ def create_dataframe(self, c3, cells_pytess_result:list, max_cols:int, max_rows:int):
385
+ '''Create dataframe using list of cell values of the table, also checks for valid header of dataframe
386
+ Args:
387
+ cells_pytess_result: list of strings, each element representing a cell in a table
388
+ max_cols, max_rows: number of columns and rows
389
+ Returns:
390
+ dataframe : final dataframe after all pre-processing
391
+ '''
392
+
393
+ headers = cells_pytess_result[:max_cols]
394
+ new_headers = uniquify(headers, (f' {x!s}' for x in string.ascii_lowercase))
395
+ counter = 0
396
+
397
+ cells_list = cells_pytess_result[max_cols:]
398
+ df = pd.DataFrame("", index=range(0, max_rows), columns=new_headers)
399
+
400
+ cell_idx = 0
401
+ for nrows in range(max_rows):
402
+ for ncols in range(max_cols):
403
+ df.iat[nrows, ncols] = str(cells_list[cell_idx])
404
+ cell_idx += 1
405
+
406
+ ## To check if there are duplicate headers if result of uniquify+col == col
407
+ ## This check removes headers when all headers are empty or if median of header word count is less than 6
408
+ for x, col in zip(string.ascii_lowercase, new_headers):
409
+ if f' {x!s}' == col:
410
+ counter += 1
411
+ header_char_count = [len(col) for col in new_headers]
412
+
413
+ # if (counter == len(new_headers)) or (statistics.median(header_char_count) < 6):
414
+ # st.write('woooot')
415
+ # df.columns = uniquify(df.iloc[0], (f' {x!s}' for x in string.ascii_lowercase))
416
+ # df = df.iloc[1:,:]
417
+
418
+ df = self.clean_dataframe(df)
419
+
420
+ c3.dataframe(df)
421
+ csv = self.convert_df(df)
422
+ c3.download_button("Download table", csv, "file.csv", "text/csv", key='download-csv')
423
+
424
+ return df
425
+
426
+
427
+
428
+
429
+
430
+
431
+ async def start_process(self, image_path:str, TD_THRESHOLD, TSR_THRESHOLD, padd_top, padd_left, padd_bottom, padd_right, delta_xmin, delta_ymin, delta_xmax, delta_ymax, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom):
432
+ '''
433
+ Initiates process of generating pandas dataframes from raw pdf-page images
434
+
435
+ '''
436
+ image = Image.open(image_path).convert("RGB")
437
+ model, probas, bboxes_scaled = table_detector(image, THRESHOLD_PROBA=TD_THRESHOLD)
438
+
439
+ if bboxes_scaled.nelement() == 0:
440
+ st.write('No table found in the pdf-page image')
441
+ return ''
442
+
443
+ # try:
444
+ # st.write('Document: '+image_path.split('/')[-1])
445
+ c1, c2, c3 = st.columns((1,1,1))
446
+
447
+ self.plot_results_detection(c1, model, image, probas, bboxes_scaled, delta_xmin, delta_ymin, delta_xmax, delta_ymax)
448
+ cropped_img_list = self.crop_tables(image, probas, bboxes_scaled, delta_xmin, delta_ymin, delta_xmax, delta_ymax)
449
+
450
+ for unpadded_table in cropped_img_list:
451
+
452
+ table = self.add_padding(unpadded_table, padd_top, padd_right, padd_bottom, padd_left)
453
+ # table = super_res(table)
454
+ # table = binarizeBlur_image(table)
455
+ # table = sharpen_image(table) # Test sharpen image next
456
+ # table = td_postprocess(table)
457
+
458
+ model, probas, bboxes_scaled = table_struct_recog(table, THRESHOLD_PROBA=TSR_THRESHOLD)
459
+ rows, cols = self.generate_structure(c2, model, table, probas, bboxes_scaled, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom)
460
+ # st.write(len(rows), len(cols))
461
+ rows, cols = self.sort_table_featuresv2(rows, cols)
462
+ master_row, cols = self.individual_table_featuresv2(table, rows, cols)
463
+
464
+ cells_img, max_cols, max_rows = self.object_to_cellsv2(master_row, cols, expand_rowcol_bbox_top, expand_rowcol_bbox_bottom, padd_left)
465
+
466
+ sequential_cell_img_list = []
467
+ for k, img_list in cells_img.items():
468
+ for img in img_list:
469
+ # img = super_res(img)
470
+ # img = sharpen_image(img) # Test sharpen image next
471
+ # img = binarizeBlur_image(img)
472
+ # img = self.add_padding(img, 10,10,10,10)
473
+ # plt.imshow(img)
474
+ # c3.pyplot()
475
+ sequential_cell_img_list.append(pytess(img))
476
+
477
+ cells_pytess_result = await asyncio.gather(*sequential_cell_img_list)
478
+
479
+
480
+ self.create_dataframe(c3, cells_pytess_result, max_cols, max_rows)
481
+ st.write('Errors in OCR is due to either quality of the image or performance of the OCR')
482
+ # except:
483
+ # st.write('Either incorrectly identified table or no table, to debug remove try/except')
484
+ # break
485
+ # break
486
+
487
+
488
+
489
+
490
+ if __name__ == "__main__":
491
+
492
+ img_name = st.file_uploader("Upload an image with table(s)")
493
+ st1, st2 = st.columns((1,1))
494
+ TD_th = st1.slider('Table detection threshold', 0.0, 1.0, 0.6)
495
+ TSR_th = st2.slider('Table structure recognition threshold', 0.0, 1.0, 0.8)
496
+
497
+ st1, st2, st3, st4 = st.columns((1,1,1,1))
498
+
499
+ padd_top = st1.slider('Padding top', 0, 200, 20)
500
+ padd_left = st2.slider('Padding left', 0, 200, 20)
501
+ padd_right = st3.slider('Padding right', 0, 200, 20)
502
+ padd_bottom = st4.slider('Padding bottom', 0, 200, 20)
503
+
504
+ te = TableExtractionPipeline()
505
+ # for img in image_list:
506
+ if img_name is not None:
507
+ asyncio.run(te.start_process(img_name, TD_THRESHOLD=TD_th , TSR_THRESHOLD=TSR_th , padd_top=padd_top, padd_left=padd_left, padd_bottom=padd_bottom, padd_right=padd_right, delta_xmin=0, delta_ymin=0, delta_xmax=0, delta_ymax=0, expand_rowcol_bbox_top=0, expand_rowcol_bbox_bottom=0))
508
+
509
+
510
+
packages.txt ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ ffmpeg
2
+ libsm6
3
+ libxext6
4
+ libgl1
5
+ tesseract-ocr-eng
6
+ python3-opencv
requirements.txt ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Cython==0.29.14
2
+ dask==2021.3.1
3
+ datasets==1.18.3
4
+ Flask==2.0.1
5
+ GitPython==3.1.26
6
+ imutils==0.5.4
7
+ multiprocess==0.70.12.2
8
+ numba==0.54.1
9
+ numexpr==2.7.3
10
+ numpy==1.20.3
11
+ oauthlib==3.1.0
12
+ opencv-contrib-python==4.6.0.66
13
+ openpyxl==3.0.7
14
+ Pillow==9.0.1
15
+ plotly==4.14.3
16
+ ply==3.11
17
+ protobuf==3.14.0
18
+ psutil==5.8.0
19
+ pyarrow==7.0.0
20
+ pydantic==1.7.3
21
+ pydeck==0.7.1
22
+ PyDictionary==2.0.1
23
+ pydot==1.4.2
24
+ pymongo==4.0.2
25
+ Pympler==1.0.1
26
+ PyMuPDF==1.20.2
27
+ pyperclip==1.8.2
28
+ pyppeteer==0.2.5
29
+ pyquery==1.4.3
30
+ pyreadline3==3.3
31
+ pytesseract==0.3.10
32
+ pytz-deprecation-shim==0.1.0.post0
33
+ PyWavelets==1.1.1
34
+ PyYAML==5.4.1
35
+ scipy==1.4.1
36
+ seaborn==0.11.1
37
+ sklearn==0.0
38
+ streamlit==1.5.1
39
+ timm==0.6.7
40
+ tokenizers==0.12.1
41
+ toml==0.10.2
42
+ toolz==0.11.1
43
+ torch==1.10.0
44
+ torchvision==0.11.1
45
+ git+https://github.com/huggingface/transformers.git
46
+ #-e git+https://github.com/nielsrogge/transformers.git@d34f7e6ffbb911d39465173ef2b35ba147ef58a9#egg=transformers
47
+ urllib3==1.26.7