File size: 22,643 Bytes
0538136 |
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 |
import json
import pandas as pd
def read_json(json_file):
with open(json_file, 'r', encoding='utf-8') as file:
return json.load(file)
def adjust_page_dimensions_and_bbox(modified_model_output_json, pdfminer_json):
for page_number, blocks in modified_model_output_json.items():
if page_number in pdfminer_json.keys():
if pdfminer_json[page_number]:
page_info = pdfminer_json[page_number][0]
page_width = page_info['page_width']
page_height = page_info['page_height']
for block in blocks:
original_width = block['page_img_width']
original_height = block['page_img_height']
width_scale = page_width / original_width
height_scale = page_height / original_height
block['page_img_width'] = page_width
block['page_img_height'] = page_height
block['bbox'] = [
block['bbox'][0] * width_scale,
block['bbox'][1] * height_scale,
block['bbox'][2] * width_scale,
block['bbox'][3] * height_scale
]
else:
print(f"Page {page_number} is empty.")
return modified_model_output_json
def convert_to_dataframe(extracted_df):
if isinstance(extracted_df, pd.DataFrame):
return extracted_df
elif isinstance(extracted_df, dict):
if all(isinstance(value, list) for value in extracted_df.values()):
return pd.DataFrame(extracted_df)
else:
return pd.DataFrame([extracted_df])
elif isinstance(extracted_df, list):
if all(isinstance(item, dict) for item in extracted_df):
return pd.DataFrame(extracted_df)
else:
return pd.DataFrame(extracted_df, columns=['Value'])
else:
return pd.DataFrame([extracted_df], columns=['Value'])
def calculate_centroid(bbox):
x1, y1, x2, y2 = bbox
x_center = (x1 + x2) / 2
y_center = (y1 + y2) / 2
return (x_center, y_center)
def is_within_radius(text_block_bbox, header_bbox, radius=50):
text_xmin, text_ymin, text_xmax, text_ymax = text_block_bbox
header_xmin, header_ymin, header_xmax, header_ymax = header_bbox
# Check for overlap between text_block_bbox and header_bbox
overlap_x = max(0, min(text_xmax, header_xmax) - max(text_xmin, header_xmin))
overlap_y = max(0, min(text_ymax, header_ymax) - max(text_ymin, header_ymin))
# If there is any overlap, return True
if overlap_x > 0 and overlap_y > 0:
return True
return False
def is_overlapped(text_block_bbox, header_bbox, threshold=0.20):
# Unpack bounding boxes
text_xmin, text_ymin, text_xmax, text_ymax = text_block_bbox
header_xmin, header_ymin, header_xmax, header_ymax = header_bbox
# Calculate overlap in the x and y directions
overlap_x = max(0, min(text_xmax, header_xmax) - max(text_xmin, header_xmin))
overlap_y = max(0, min(text_ymax, header_ymax) - max(text_ymin, header_ymin))
# Calculate the area of overlap
overlap_area = overlap_x * overlap_y
# Calculate the area of the text block and header
text_area = (text_xmax - text_xmin) * (text_ymax - text_ymin)
header_area = (header_xmax - header_xmin) * (header_ymax - header_ymin)
# Calculate the overlap ratio with respect to the smaller of the two areas
smaller_area = min(text_area, header_area)
overlap_ratio = overlap_area / smaller_area
# Check if the overlap ratio exceeds the threshold
if overlap_ratio > threshold:
return True
return False
def detect_header(text_block_bbox, adjusted_model_output_json, page_number ,next_header_index_in_model_udop):
text_centroid = calculate_centroid(text_block_bbox)
if str(page_number) in adjusted_model_output_json:
if next_header_index_in_model_udop is not None :
next_header_index_in_model_udop = int(next_header_index_in_model_udop)
header_block = adjusted_model_output_json[str(page_number)][next_header_index_in_model_udop]
if is_overlapped(text_block_bbox, header_block['bbox']):
return True
return False
def remove_header_from_start(first_row_text: str, first_row_header_text: str) -> str:
length_header_text = len(first_row_header_text)
return first_row_text[length_header_text:].strip()
def extract_last_header_index(all_blocks_with_indices):
last_header_index = -1
# Iterate through the list in reverse
for index in reversed(range(len(all_blocks_with_indices))):
block = all_blocks_with_indices[index]
# Check if the block is a Page-header or Section-header
if block['label_name'] in ['Page-header', 'Section-header']:
last_header_index = index
break
return last_header_index
def match_headers_with_text(adjusted_model_json, pdfminer_json):
matched_data = []
tree_format_matched_data = []
current_header = None
current_content = []
current_header_table_content = []
current_header_tree_structure = []
sorted_pages = sorted(adjusted_model_json.items(), key=lambda x: int(x[0]))
all_blocks_with_indices = []
for key, blocks in sorted_pages:
for index, block in enumerate(blocks):
if block['label_name'] in ['Page-header','Section-header','Table', "Portfolio-Company-Table"]:
block['used_model_index'] = index
all_blocks_with_indices.append(block)
for id,block in enumerate(all_blocks_with_indices):
if block['label_name'] in ['Page-header','Section-header']:
next_header_detect_flag = False
current_header_index_in_model = block['used_model_index']
current_header_bbox = block['bbox']
current_header_type = block['label_name']
current_header_centroid = calculate_centroid(block['bbox'])
current_header_page_number = block['pdf_page_id']
current_header_text = block['extracted_text'][0] if block['extracted_text'] else ""
current_header_page_width = block['page_img_width']
current_header_page_height = block['page_img_height']
current_header_page_block_id = block['page_block_id']
current_header_pdf_name = block['pdf_name']
content_source_pages = [] # Track pages where content is collected
new_start_index = id + 1
if new_start_index < len(all_blocks_with_indices):
for next_id ,next_block in enumerate(all_blocks_with_indices[new_start_index:], start = new_start_index):
if next_block['label_name'] in ['Page-header', 'Section-header']:
next_header_index_in_model_udop = next_block['used_model_index']
next_header_bbox = next_block['bbox']
next_header_centroid = calculate_centroid(next_block['bbox'])
next_header_page_number = next_block["pdf_page_id"]
next_header_text = next_block['extracted_text'][0] if next_block['extracted_text'] else ""
break
else:
next_header_bbox = None
next_header_centroid = None
next_header_page_number = None
next_header_index_in_model_udop = None
next_header_text = None
last_header_index = extract_last_header_index(all_blocks_with_indices)
if id == len(all_blocks_with_indices) - 1 or id == last_header_index:
next_header_bbox = None
next_header_centroid = None
next_header_page_number = None
next_header_index_in_model_udop = None
next_header_text = None
if current_header_text:
if current_header is not None:
current_content = []
current_header_table_content = []
current_header_tree_structure = []
current_header = {
"page_number": current_header_page_number,
"header_text": current_header_text,
"element_id": None,
"text_block_id": None
}
new_start_index = id + 1
for new_id,new_block in enumerate(all_blocks_with_indices[new_start_index:], start = new_start_index):
extracted_df_flag = False
next_block = new_block
if next_block and next_block['label_name'] in ['Page-header', 'Section-header']:
extracted_df_flag = False
break
# if next_block and next_block['label_name'] in ['Table']:
if next_block and next_block['label_name'] in ['Table', "Portfolio-Company-Table"]:
extracted_df_flag = True
extracted_df = next_block['extracted_text'][0]
if next_block["associated_table_header_info"] is not None:
extracted_df_table_header = next_block["associated_table_header_info"]['extracted_text'][0]
else:
extracted_df_table_header = None
extracted_df_new = convert_to_dataframe(extracted_df)
extracted_df_new_column_headers = extracted_df_new.columns.tolist()
extracted_df_markdown = extracted_df_new.to_csv(index=False)
table_metadata = { 'pdf_name': next_block['pdf_name'] ,
'table_page_id': next_block['pdf_page_id'],
'table_page_id_width' : next_block['page_img_width'],
'table_page_id_height': next_block['page_img_height'],
'table_bbox' : next_block['bbox']
}
table_header_pair = {
# 'label_name':'Table-header',
'label_name':next_block['label_name'],
'table_header': extracted_df_table_header,
'table_column_header' : extracted_df_new_column_headers,
'table_info': extracted_df_new,
'metadata' : table_metadata
}
tree_table_header_info = {
'label_name':'Table-header',
# 'label_name':next_block['label_name'],
'table_header_info': next_block["associated_table_header_info"],
'table_column_header' : extracted_df_new_column_headers,
'table_info': next_block
}
# current_header_table_content.append(extracted_df)
current_header_table_content.append(table_header_pair)
current_header_tree_structure.append(next_block)
last_pdf_page = int(list(pdfminer_json.keys())[-1])
first_append_flag = False
first_append_text = " "
for pdf_page_num in range(int(current_header_page_number), last_pdf_page + 1):
text_blocks = pdfminer_json.get(str(pdf_page_num), [])
start_index = 0
page_content_added = False # Track if content was added from this page
if current_header["element_id"] is None and current_header["text_block_id"] is None:
for index, text_block in enumerate(text_blocks):
if is_overlapped(text_block['bbox'],current_header_bbox):
current_header["element_id"] = text_block["element_id"]
current_header["text_block_id"] = text_block["text_block_id"]
start_index = index
first_append_flag = True
break
for next_header_index, text_block in enumerate(text_blocks[start_index:], start = start_index):
last_text_reached_flag = False
if first_append_flag:
first_row_text = text_block['text']
first_row_header_text = current_header_text
first_append_text = remove_header_from_start(first_row_text,first_row_header_text)
current_content.append(first_append_text)
page_content_added = True
first_append_flag = False
continue
if next_header_text is not None and pdf_page_num == int(next_header_page_number):
next_header_found_flag = False
if detect_header(text_block['bbox'], adjusted_model_json, next_header_page_number,next_header_index_in_model_udop):
next_header_found_flag = True
matched_data.append({
"page_number": current_header["page_number"],
"pdf_name" : current_header_pdf_name ,
"header": current_header["header_text"],
"label_name": current_header_type,
"content": " ".join(current_content),
"table_content" : current_header_table_content,
"all_source_pages": content_source_pages
})
tree_format_matched_data.append({
"header_page_number": current_header["page_number"],
"label_name":current_header_type,
'page_block_id' : current_header_page_block_id,
"header_bbox": current_header_bbox,
"header_page_width":current_header_page_width,
"header_page_height": current_header_page_height,
"header": current_header["header_text"],
"content": " ".join(current_content),
'tree_table_content' : current_header_tree_structure
})
current_content = []
current_table_content = []
current_header_tree_structure = []
next_header_detect_flag = True
break
if next_header_index == len(text_blocks) - 1:
last_text_block = text_block
if not next_header_found_flag and last_text_block:
matched_data.append({
"page_number": current_header["page_number"],
"pdf_name" : current_header_pdf_name ,
"header": current_header["header_text"],
"label_name": current_header_type,
"content": " ".join(current_content),
"table_content" : current_header_table_content,
"all_source_pages": content_source_pages
})
tree_format_matched_data.append({
"header_page_number": current_header["page_number"],
"label_name":currentHeaderType,
'page_block_id' : current_header_page_block_id,
"header_bbox": current_header_bbox,
"header_page_width":current_header_page_width,
"header_page_height": current_header_page_height,
"header": current_header["header_text"],
"content": " ".join(current_content),
'tree_table_content' : current_header_tree_structure
})
current_content = []
current_header_table_content = []
current_header_tree_structure = []
next_header_detect_flag = True
next_header_found_flag = True
break
current_content.append(text_block['text'])
page_content_added = True
if next_header_detect_flag:
break
# Add page number to source pages if content was added from this page
if page_content_added and pdf_page_num not in content_source_pages:
content_source_pages.append(pdf_page_num)
if next_header_detect_flag:
break
if next_header_text is None and next_header_page_number is None:
current_header = {
"page_number": current_header_page_number,
"header_text": current_header_text,
"element_id": None,
"text_block_id": None
}
for pdf_page_num in range(int(current_header_page_number), last_pdf_page + 1):
text_blocks = pdfminer_json.get(str(pdf_page_num), [])
start_index = 0
page_content_added = False # Track if content was added from this page
if current_header["element_id"] is None and current_header["text_block_id"] is None:
for index, text_block in enumerate(text_blocks):
if is_overlapped(text_block['bbox'],current_header_bbox):
current_header["element_id"] = text_block["element_id"]
current_header["text_block_id"] = text_block["text_block_id"]
start_index = index
first_append_flag = True
break
for no_header_index, text_block in enumerate(text_blocks[start_index:], start=start_index):
if first_append_flag:
first_row_text = text_block['text']
first_row_header_text = current_header_text
first_append_text = remove_header_from_start(first_row_text,first_row_header_text)
current_content.append(first_append_text)
page_content_added = True
first_append_flag = False
continue
# Add page number to source pages if content was added from this page
if page_content_added and pdf_page_num not in content_source_pages:
content_source_pages.append(pdf_page_num)
matched_data.append({
"page_number": current_header["page_number"],
"pdf_name" : current_header_pdf_name ,
"header": current_header["header_text"],
"label_name": current_header_type,
"content": " ".join(current_content),
"table_content" : current_header_table_content,
"all_source_pages": content_source_pages
})
tree_format_matched_data.append({
"header_page_number": current_header["page_number"],
"label_name": current_header_type,
'page_block_id' : current_header_page_block_id,
"header_bbox": current_header_bbox,
"header_page_width":current_header_page_width,
"header_page_height": current_header_page_height,
"header": current_header["header_text"],
"content": " ".join(current_content),
'tree_table_content' : current_header_tree_structure
})
return matched_data,tree_format_matched_data
def main_header_pipeline(modified_udop_json, pdfminer_json):
modified_udop_json = adjust_page_dimensions_and_bbox(modified_udop_json, pdfminer_json)
matched_data,tree_format_matched_data= match_headers_with_text(modified_udop_json, pdfminer_json)
df = pd.DataFrame(matched_data)
return df,tree_format_matched_data
|