Merge pull request #1099 from myhloli/dev

refactor(magic_pdf): remove unused functions and simplify code
This commit is contained in:
Xiaomeng Zhao
2024-11-26 17:53:07 +08:00
committed by GitHub
50 changed files with 0 additions and 15896 deletions
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import math
from loguru import logger
from magic_pdf.config.ocr_content_type import ContentType
from magic_pdf.libs.boxbase import (find_bottom_nearest_text_bbox,
find_top_nearest_text_bbox)
from magic_pdf.libs.commons import join_path
TYPE_INLINE_EQUATION = ContentType.InlineEquation
TYPE_INTERLINE_EQUATION = ContentType.InterlineEquation
UNI_FORMAT_TEXT_TYPE = ['text', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6']
@DeprecationWarning
def mk_nlp_markdown_1(para_dict: dict):
"""对排序后的bboxes拼接内容."""
content_lst = []
for _, page_info in para_dict.items():
para_blocks = page_info.get('para_blocks')
if not para_blocks:
continue
for block in para_blocks:
item = block['paras']
for _, p in item.items():
para_text = p['para_text']
is_title = p['is_para_title']
title_level = p['para_title_level']
md_title_prefix = '#' * title_level
if is_title:
content_lst.append(f'{md_title_prefix} {para_text}')
else:
content_lst.append(para_text)
content_text = '\n\n'.join(content_lst)
return content_text
# 找到目标字符串在段落中的索引
def __find_index(paragraph, target):
index = paragraph.find(target)
if index != -1:
return index
else:
return None
def __insert_string(paragraph, target, position):
new_paragraph = paragraph[:position] + target + paragraph[position:]
return new_paragraph
def __insert_after(content, image_content, target):
"""在content中找到target,将image_content插入到target后面."""
index = content.find(target)
if index != -1:
content = (
content[: index + len(target)]
+ '\n\n'
+ image_content
+ '\n\n'
+ content[index + len(target) :]
)
else:
logger.error(
f"Can't find the location of image {image_content} in the markdown file, search target is {target}"
)
return content
def __insert_before(content, image_content, target):
"""在content中找到target,将image_content插入到target前面."""
index = content.find(target)
if index != -1:
content = content[:index] + '\n\n' + image_content + '\n\n' + content[index:]
else:
logger.error(
f"Can't find the location of image {image_content} in the markdown file, search target is {target}"
)
return content
@DeprecationWarning
def mk_mm_markdown_1(para_dict: dict):
"""拼装多模态markdown."""
content_lst = []
for _, page_info in para_dict.items():
page_lst = [] # 一个page内的段落列表
para_blocks = page_info.get('para_blocks')
pymu_raw_blocks = page_info.get('preproc_blocks')
all_page_images = []
all_page_images.extend(page_info.get('images', []))
all_page_images.extend(page_info.get('image_backup', []))
all_page_images.extend(page_info.get('tables', []))
all_page_images.extend(page_info.get('table_backup', []))
if not para_blocks or not pymu_raw_blocks: # 只有图片的拼接的场景
for img in all_page_images:
page_lst.append(f"![]({img['image_path']})") # TODO 图片顺序
page_md = '\n\n'.join(page_lst)
else:
for block in para_blocks:
item = block['paras']
for _, p in item.items():
para_text = p['para_text']
is_title = p['is_para_title']
title_level = p['para_title_level']
md_title_prefix = '#' * title_level
if is_title:
page_lst.append(f'{md_title_prefix} {para_text}')
else:
page_lst.append(para_text)
"""拼装成一个页面的文本"""
page_md = '\n\n'.join(page_lst)
"""插入图片"""
for img in all_page_images:
imgbox = img['bbox']
img_content = f"![]({img['image_path']})"
# 先看在哪个block内
for block in pymu_raw_blocks:
bbox = block['bbox']
if (
bbox[0] - 1 <= imgbox[0] < bbox[2] + 1
and bbox[1] - 1 <= imgbox[1] < bbox[3] + 1
): # 确定在block内
for l in block['lines']: # noqa: E741
line_box = l['bbox']
if (
line_box[0] - 1 <= imgbox[0] < line_box[2] + 1
and line_box[1] - 1 <= imgbox[1] < line_box[3] + 1
): # 在line内的,插入line前面
line_txt = ''.join([s['text'] for s in l['spans']])
page_md = __insert_before(
page_md, img_content, line_txt
)
break
break
else: # 在行与行之间
# 找到图片x0,y0与line的x0,y0最近的line
min_distance = 100000
min_line = None
for l in block['lines']: # noqa: E741
line_box = l['bbox']
distance = math.sqrt(
(line_box[0] - imgbox[0]) ** 2
+ (line_box[1] - imgbox[1]) ** 2
)
if distance < min_distance:
min_distance = distance
min_line = l
if min_line:
line_txt = ''.join(
[s['text'] for s in min_line['spans']]
)
img_h = imgbox[3] - imgbox[1]
if min_distance < img_h: # 文字在图片前面
page_md = __insert_after(
page_md, img_content, line_txt
)
else:
page_md = __insert_before(
page_md, img_content, line_txt
)
else:
logger.error(
f"Can't find the location of image {img['image_path']} in the markdown file #1"
)
else: # 应当在两个block之间
# 找到上方最近的block,如果上方没有就找大下方最近的block
top_txt_block = find_top_nearest_text_bbox(pymu_raw_blocks, imgbox)
if top_txt_block:
line_txt = ''.join(
[s['text'] for s in top_txt_block['lines'][-1]['spans']]
)
page_md = __insert_after(page_md, img_content, line_txt)
else:
bottom_txt_block = find_bottom_nearest_text_bbox(
pymu_raw_blocks, imgbox
)
if bottom_txt_block:
line_txt = ''.join(
[
s['text']
for s in bottom_txt_block['lines'][0]['spans']
]
)
page_md = __insert_before(page_md, img_content, line_txt)
else:
logger.error(
f"Can't find the location of image {img['image_path']} in the markdown file #2"
)
content_lst.append(page_md)
"""拼装成全部页面的文本"""
content_text = '\n\n'.join(content_lst)
return content_text
def __insert_after_para(text, type, element, content_list):
"""在content_list中找到text,将image_path作为一个新的node插入到text后面."""
for i, c in enumerate(content_list):
content_type = c.get('type')
if content_type in UNI_FORMAT_TEXT_TYPE and text in c.get('text', ''):
if type == 'image':
content_node = {
'type': 'image',
'img_path': element.get('image_path'),
'img_alt': '',
'img_title': '',
'img_caption': '',
}
elif type == 'table':
content_node = {
'type': 'table',
'img_path': element.get('image_path'),
'table_latex': element.get('text'),
'table_title': '',
'table_caption': '',
'table_quality': element.get('quality'),
}
content_list.insert(i + 1, content_node)
break
else:
logger.error(
f"Can't find the location of image {element.get('image_path')} in the markdown file, search target is {text}"
)
def __insert_before_para(text, type, element, content_list):
"""在content_list中找到text,将image_path作为一个新的node插入到text前面."""
for i, c in enumerate(content_list):
content_type = c.get('type')
if content_type in UNI_FORMAT_TEXT_TYPE and text in c.get('text', ''):
if type == 'image':
content_node = {
'type': 'image',
'img_path': element.get('image_path'),
'img_alt': '',
'img_title': '',
'img_caption': '',
}
elif type == 'table':
content_node = {
'type': 'table',
'img_path': element.get('image_path'),
'table_latex': element.get('text'),
'table_title': '',
'table_caption': '',
'table_quality': element.get('quality'),
}
content_list.insert(i, content_node)
break
else:
logger.error(
f"Can't find the location of image {element.get('image_path')} in the markdown file, search target is {text}"
)
def mk_universal_format(pdf_info_list: list, img_buket_path):
"""构造统一格式 https://aicarrier.feishu.cn/wiki/FqmMwcH69iIdCWkkyjvcDwNUnTY."""
content_lst = []
for page_info in pdf_info_list:
page_lst = [] # 一个page内的段落列表
para_blocks = page_info.get('para_blocks')
pymu_raw_blocks = page_info.get('preproc_blocks')
all_page_images = []
all_page_images.extend(page_info.get('images', []))
all_page_images.extend(page_info.get('image_backup', []))
# all_page_images.extend(page_info.get("tables",[]))
# all_page_images.extend(page_info.get("table_backup",[]) )
all_page_tables = []
all_page_tables.extend(page_info.get('tables', []))
if not para_blocks or not pymu_raw_blocks: # 只有图片的拼接的场景
for img in all_page_images:
content_node = {
'type': 'image',
'img_path': join_path(img_buket_path, img['image_path']),
'img_alt': '',
'img_title': '',
'img_caption': '',
}
page_lst.append(content_node) # TODO 图片顺序
for table in all_page_tables:
content_node = {
'type': 'table',
'img_path': join_path(img_buket_path, table['image_path']),
'table_latex': table.get('text'),
'table_title': '',
'table_caption': '',
'table_quality': table.get('quality'),
}
page_lst.append(content_node) # TODO 图片顺序
else:
for block in para_blocks:
item = block['paras']
for _, p in item.items():
font_type = p[
'para_font_type'
] # 对于文本来说,要么是普通文本,要么是个行间公式
if font_type == TYPE_INTERLINE_EQUATION:
content_node = {'type': 'equation', 'latex': p['para_text']}
page_lst.append(content_node)
else:
para_text = p['para_text']
is_title = p['is_para_title']
title_level = p['para_title_level']
if is_title:
content_node = {
'type': f'h{title_level}',
'text': para_text,
}
page_lst.append(content_node)
else:
content_node = {'type': 'text', 'text': para_text}
page_lst.append(content_node)
content_lst.extend(page_lst)
"""插入图片"""
for img in all_page_images:
insert_img_or_table('image', img, pymu_raw_blocks, content_lst)
"""插入表格"""
for table in all_page_tables:
insert_img_or_table('table', table, pymu_raw_blocks, content_lst)
# end for
return content_lst
def insert_img_or_table(type, element, pymu_raw_blocks, content_lst):
element_bbox = element['bbox']
# 先看在哪个block内
for block in pymu_raw_blocks:
bbox = block['bbox']
if (
bbox[0] - 1 <= element_bbox[0] < bbox[2] + 1
and bbox[1] - 1 <= element_bbox[1] < bbox[3] + 1
): # 确定在这个大的block内,然后进入逐行比较距离
for l in block['lines']: # noqa: E741
line_box = l['bbox']
if (
line_box[0] - 1 <= element_bbox[0] < line_box[2] + 1
and line_box[1] - 1 <= element_bbox[1] < line_box[3] + 1
): # 在line内的,插入line前面
line_txt = ''.join([s['text'] for s in l['spans']])
__insert_before_para(line_txt, type, element, content_lst)
break
break
else: # 在行与行之间
# 找到图片x0,y0与line的x0,y0最近的line
min_distance = 100000
min_line = None
for l in block['lines']: # noqa: E741
line_box = l['bbox']
distance = math.sqrt(
(line_box[0] - element_bbox[0]) ** 2
+ (line_box[1] - element_bbox[1]) ** 2
)
if distance < min_distance:
min_distance = distance
min_line = l
if min_line:
line_txt = ''.join([s['text'] for s in min_line['spans']])
img_h = element_bbox[3] - element_bbox[1]
if min_distance < img_h: # 文字在图片前面
__insert_after_para(line_txt, type, element, content_lst)
else:
__insert_before_para(line_txt, type, element, content_lst)
break
else:
logger.error(
f"Can't find the location of image {element.get('image_path')} in the markdown file #1"
)
else: # 应当在两个block之间
# 找到上方最近的block,如果上方没有就找大下方最近的block
top_txt_block = find_top_nearest_text_bbox(pymu_raw_blocks, element_bbox)
if top_txt_block:
line_txt = ''.join([s['text'] for s in top_txt_block['lines'][-1]['spans']])
__insert_after_para(line_txt, type, element, content_lst)
else:
bottom_txt_block = find_bottom_nearest_text_bbox(
pymu_raw_blocks, element_bbox
)
if bottom_txt_block:
line_txt = ''.join(
[s['text'] for s in bottom_txt_block['lines'][0]['spans']]
)
__insert_before_para(line_txt, type, element, content_lst)
else: # TODO ,图片可能独占一列,这种情况上下是没有图片的
logger.error(
f"Can't find the location of image {element.get('image_path')} in the markdown file #2"
)
def mk_mm_markdown(content_list):
"""基于同一格式的内容列表,构造markdown,含图片."""
content_md = []
for c in content_list:
content_type = c.get('type')
if content_type == 'text':
content_md.append(c.get('text'))
elif content_type == 'equation':
content = c.get('latex')
if content.startswith('$$') and content.endswith('$$'):
content_md.append(content)
else:
content_md.append(f"\n$$\n{c.get('latex')}\n$$\n")
elif content_type in UNI_FORMAT_TEXT_TYPE:
content_md.append(f"{'#'*int(content_type[1])} {c.get('text')}")
elif content_type == 'image':
content_md.append(f"![]({c.get('img_path')})")
return '\n\n'.join(content_md)
def mk_nlp_markdown(content_list):
"""基于同一格式的内容列表,构造markdown,不含图片."""
content_md = []
for c in content_list:
content_type = c.get('type')
if content_type == 'text':
content_md.append(c.get('text'))
elif content_type == 'equation':
content_md.append(f"$$\n{c.get('latex')}\n$$")
elif content_type == 'table':
content_md.append(f"$$$\n{c.get('table_latex')}\n$$$")
elif content_type in UNI_FORMAT_TEXT_TYPE:
content_md.append(f"{'#'*int(content_type[1])} {c.get('text')}")
return '\n\n'.join(content_md)
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# 定义这里的bbox是一个list [x0, y0, x1, y1, block_content, idx_x, idx_y, content_type, ext_x0, ext_y0, ext_x1, ext_y1], 初始时候idx_x, idx_y都是None
# 其中x0, y0代表左上角坐标,x1, y1代表右下角坐标,坐标原点在左上角。
from magic_pdf.layout.layout_spiler_recog import get_spilter_of_page
from magic_pdf.libs.boxbase import _is_in, _is_in_or_part_overlap, _is_vertical_full_overlap
from magic_pdf.libs.commons import mymax
X0_IDX = 0
Y0_IDX = 1
X1_IDX = 2
Y1_IDX = 3
CONTENT_IDX = 4
IDX_X = 5
IDX_Y = 6
CONTENT_TYPE_IDX = 7
X0_EXT_IDX = 8
Y0_EXT_IDX = 9
X1_EXT_IDX = 10
Y1_EXT_IDX = 11
def prepare_bboxes_for_layout_split(image_info, image_backup_info, table_info, inline_eq_info, interline_eq_info, text_raw_blocks: dict, page_boundry, page):
"""
text_raw_blocks:结构参考test/assets/papre/pymu_textblocks.json
把bbox重新组装成一个list,每个元素[x0, y0, x1, y1, block_content, idx_x, idx_y, content_type, ext_x0, ext_y0, ext_x1, ext_y1], 初始时候idx_x, idx_y都是None. 对于图片、公式来说,block_content是图片的地址, 对于段落来说,block_content是pymupdf里的block结构
"""
all_bboxes = []
for image in image_info:
box = image['bbox']
# 由于没有实现横向的栏切分,因此在这里先过滤掉一些小的图片。这些图片有可能影响layout,造成没有横向栏切分的情况下,layout切分不准确。例如 scihub_76500000/libgen.scimag76570000-76570999.zip_10.1186/s13287-019-1355-1
# 把长宽都小于50的去掉
if abs(box[0]-box[2]) < 50 and abs(box[1]-box[3]) < 50:
continue
all_bboxes.append([box[0], box[1], box[2], box[3], None, None, None, 'image', None, None, None, None])
for table in table_info:
box = table['bbox']
all_bboxes.append([box[0], box[1], box[2], box[3], None, None, None, 'table', None, None, None, None])
"""由于公式与段落混合,因此公式不再参与layout划分,无需加入all_bboxes"""
# 加入文本block
text_block_temp = []
for block in text_raw_blocks:
bbox = block['bbox']
text_block_temp.append([bbox[0], bbox[1], bbox[2], bbox[3], None, None, None, 'text', None, None, None, None])
text_block_new = resolve_bbox_overlap_for_layout_det(text_block_temp)
text_block_new = filter_lines_bbox(text_block_new) # 去掉线条bbox,有可能让layout探测陷入无限循环
"""找出会影响layout的色块、横向分割线"""
spilter_bboxes = get_spilter_of_page(page, [b['bbox'] for b in image_info]+[b['bbox'] for b in image_backup_info], [b['bbox'] for b in table_info], )
# 还要去掉存在于spilter_bboxes里的text_block
if len(spilter_bboxes) > 0:
text_block_new = [box for box in text_block_new if not any([_is_in_or_part_overlap(box[:4], spilter_bbox) for spilter_bbox in spilter_bboxes])]
for bbox in text_block_new:
all_bboxes.append([bbox[0], bbox[1], bbox[2], bbox[3], None, None, None, 'text', None, None, None, None])
for bbox in spilter_bboxes:
all_bboxes.append([bbox[0], bbox[1], bbox[2], bbox[3], None, None, None, 'spilter', None, None, None, None])
return all_bboxes
def resolve_bbox_overlap_for_layout_det(bboxes:list):
"""
1. 去掉bbox互相包含的,去掉被包含的
2. 上下方向上如果有重叠,就扩大大box范围,直到覆盖小box
"""
def _is_in_other_bbox(i:int):
"""
判断i个box是否被其他box有所包含
"""
for j in range(0, len(bboxes)):
if j!=i and _is_in(bboxes[i][:4], bboxes[j][:4]):
return True
# elif j!=i and _is_bottom_full_overlap(bboxes[i][:4], bboxes[j][:4]):
# return True
return False
# 首先去掉被包含的bbox
new_bbox_1 = []
for i in range(0, len(bboxes)):
if not _is_in_other_bbox(i):
new_bbox_1.append(bboxes[i])
# 其次扩展大的box
new_box = []
new_bbox_2 = []
len_1 = len(new_bbox_2)
while True:
merged_idx = []
for i in range(0, len(new_bbox_1)):
if i in merged_idx:
continue
for j in range(i+1, len(new_bbox_1)):
if j in merged_idx:
continue
bx1 = new_bbox_1[i]
bx2 = new_bbox_1[j]
if i!=j and _is_vertical_full_overlap(bx1[:4], bx2[:4]):
merged_box = min([bx1[0], bx2[0]]), min([bx1[1], bx2[1]]), max([bx1[2], bx2[2]]), max([bx1[3], bx2[3]])
new_bbox_2.append(merged_box)
merged_idx.append(i)
merged_idx.append(j)
for i in range(0, len(new_bbox_1)): # 没有合并的加入进来
if i not in merged_idx:
new_bbox_2.append(new_bbox_1[i])
if len(new_bbox_2)==0 or len_1==len(new_bbox_2):
break
else:
len_1 = len(new_bbox_2)
new_box = new_bbox_2
new_bbox_1, new_bbox_2 = new_bbox_2, []
return new_box
def filter_lines_bbox(bboxes: list):
"""
过滤掉bbox为空的行
"""
new_box = []
for box in bboxes:
x0, y0, x1, y1 = box[0], box[1], box[2], box[3]
if abs(x0-x1)<=1 or abs(y0-y1)<=1:
continue
else:
new_box.append(box)
return new_box
################################################################################
# 第一种排序算法
# 以下是基于延长线遮挡做的一个算法
#
################################################################################
def find_all_left_bbox(this_bbox, all_bboxes) -> list:
"""
寻找this_bbox左边的所有bbox
"""
left_boxes = [box for box in all_bboxes if box[X1_IDX] <= this_bbox[X0_IDX]]
return left_boxes
def find_all_top_bbox(this_bbox, all_bboxes) -> list:
"""
寻找this_bbox上面的所有bbox
"""
top_boxes = [box for box in all_bboxes if box[Y1_IDX] <= this_bbox[Y0_IDX]]
return top_boxes
def get_and_set_idx_x(this_bbox, all_bboxes) -> int:
"""
寻找this_bbox在all_bboxes中的遮挡深度 idx_x
"""
if this_bbox[IDX_X] is not None:
return this_bbox[IDX_X]
else:
all_left_bboxes = find_all_left_bbox(this_bbox, all_bboxes)
if len(all_left_bboxes) == 0:
this_bbox[IDX_X] = 0
else:
all_left_bboxes_idx = [get_and_set_idx_x(bbox, all_bboxes) for bbox in all_left_bboxes]
max_idx_x = mymax(all_left_bboxes_idx)
this_bbox[IDX_X] = max_idx_x + 1
return this_bbox[IDX_X]
def get_and_set_idx_y(this_bbox, all_bboxes) -> int:
"""
寻找this_bbox在all_bboxes中y方向的遮挡深度 idx_y
"""
if this_bbox[IDX_Y] is not None:
return this_bbox[IDX_Y]
else:
all_top_bboxes = find_all_top_bbox(this_bbox, all_bboxes)
if len(all_top_bboxes) == 0:
this_bbox[IDX_Y] = 0
else:
all_top_bboxes_idx = [get_and_set_idx_y(bbox, all_bboxes) for bbox in all_top_bboxes]
max_idx_y = mymax(all_top_bboxes_idx)
this_bbox[IDX_Y] = max_idx_y + 1
return this_bbox[IDX_Y]
def bbox_sort(all_bboxes: list):
"""
排序
"""
all_bboxes_idx_x = [get_and_set_idx_x(bbox, all_bboxes) for bbox in all_bboxes]
all_bboxes_idx_y = [get_and_set_idx_y(bbox, all_bboxes) for bbox in all_bboxes]
all_bboxes_idx = [(idx_x, idx_y) for idx_x, idx_y in zip(all_bboxes_idx_x, all_bboxes_idx_y)]
all_bboxes_idx = [idx_x_y[0] * 100000 + idx_x_y[1] for idx_x_y in all_bboxes_idx] # 变换成一个点,保证能够先X,X相同时按Y排序
all_bboxes_idx = list(zip(all_bboxes_idx, all_bboxes))
all_bboxes_idx.sort(key=lambda x: x[0])
sorted_bboxes = [bbox for idx, bbox in all_bboxes_idx]
return sorted_bboxes
################################################################################
# 第二种排序算法
# 下面的算法在计算idx_x和idx_y的时候不考虑延长线,而只考虑实际的长或者宽被遮挡的情况
#
################################################################################
def find_left_nearest_bbox(this_bbox, all_bboxes) -> list:
"""
在all_bboxes里找到所有右侧高度和this_bbox有重叠的bbox
"""
left_boxes = [box for box in all_bboxes if box[X1_IDX] <= this_bbox[X0_IDX] and any([
box[Y0_IDX] < this_bbox[Y0_IDX] < box[Y1_IDX], box[Y0_IDX] < this_bbox[Y1_IDX] < box[Y1_IDX],
this_bbox[Y0_IDX] < box[Y0_IDX] < this_bbox[Y1_IDX], this_bbox[Y0_IDX] < box[Y1_IDX] < this_bbox[Y1_IDX],
box[Y0_IDX]==this_bbox[Y0_IDX] and box[Y1_IDX]==this_bbox[Y1_IDX]])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个
if len(left_boxes) > 0:
left_boxes.sort(key=lambda x: x[X1_IDX], reverse=True)
left_boxes = [left_boxes[0]]
else:
left_boxes = []
return left_boxes
def get_and_set_idx_x_2(this_bbox, all_bboxes):
"""
寻找this_bbox在all_bboxes中的被直接遮挡的深度 idx_x
这个遮挡深度不考虑延长线,而是被实际的长或者宽遮挡的情况
"""
if this_bbox[IDX_X] is not None:
return this_bbox[IDX_X]
else:
left_nearest_bbox = find_left_nearest_bbox(this_bbox, all_bboxes)
if len(left_nearest_bbox) == 0:
this_bbox[IDX_X] = 0
else:
left_idx_x = get_and_set_idx_x_2(left_nearest_bbox[0], all_bboxes)
this_bbox[IDX_X] = left_idx_x + 1
return this_bbox[IDX_X]
def find_top_nearest_bbox(this_bbox, all_bboxes) -> list:
"""
在all_bboxes里找到所有下侧宽度和this_bbox有重叠的bbox
"""
top_boxes = [box for box in all_bboxes if box[Y1_IDX] <= this_bbox[Y0_IDX] and any([
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个
if len(top_boxes) > 0:
top_boxes.sort(key=lambda x: x[Y1_IDX], reverse=True)
top_boxes = [top_boxes[0]]
else:
top_boxes = []
return top_boxes
def get_and_set_idx_y_2(this_bbox, all_bboxes):
"""
寻找this_bbox在all_bboxes中的被直接遮挡的深度 idx_y
这个遮挡深度不考虑延长线,而是被实际的长或者宽遮挡的情况
"""
if this_bbox[IDX_Y] is not None:
return this_bbox[IDX_Y]
else:
top_nearest_bbox = find_top_nearest_bbox(this_bbox, all_bboxes)
if len(top_nearest_bbox) == 0:
this_bbox[IDX_Y] = 0
else:
top_idx_y = get_and_set_idx_y_2(top_nearest_bbox[0], all_bboxes)
this_bbox[IDX_Y] = top_idx_y + 1
return this_bbox[IDX_Y]
def paper_bbox_sort(all_bboxes: list, page_width, page_height):
all_bboxes_idx_x = [get_and_set_idx_x_2(bbox, all_bboxes) for bbox in all_bboxes]
all_bboxes_idx_y = [get_and_set_idx_y_2(bbox, all_bboxes) for bbox in all_bboxes]
all_bboxes_idx = [(idx_x, idx_y) for idx_x, idx_y in zip(all_bboxes_idx_x, all_bboxes_idx_y)]
all_bboxes_idx = [idx_x_y[0] * 100000 + idx_x_y[1] for idx_x_y in all_bboxes_idx] # 变换成一个点,保证能够先X,X相同时按Y排序
all_bboxes_idx = list(zip(all_bboxes_idx, all_bboxes))
all_bboxes_idx.sort(key=lambda x: x[0])
sorted_bboxes = [bbox for idx, bbox in all_bboxes_idx]
return sorted_bboxes
################################################################################
"""
第三种排序算法, 假设page的最左侧为X0,最右侧为X1,最上侧为Y0,最下侧为Y1
这个排序算法在第二种算法基础上增加对bbox的预处理步骤。预处理思路如下:
1. 首先在水平方向上对bbox进行扩展。扩展方法是:
- 对每个bbox,找到其左边最近的bbox(也就是y方向有重叠),然后将其左边界扩展到左边最近bbox的右边界(x1+1),这里加1是为了避免重叠。如果没有左边的bbox,那么就将其左边界扩展到page的最左侧X0。
- 对每个bbox,找到其右边最近的bbox(也就是y方向有重叠),然后将其右边界扩展到右边最近bbox的左边界(x0-1),这里减1是为了避免重叠。如果没有右边的bbox,那么就将其右边界扩展到page的最右侧X1。
- 经过上面2个步骤,bbox扩展到了水平方向的最大范围。[左最近bbox.x1+1, 右最近bbox.x0-1]
2. 合并所有的连续水平方向的bbox, 合并方法是:
- 对bbox进行y方向排序,然后从上到下遍历所有bbox,如果当前bbox和下一个bbox的x0, x1等于X0, X1,那么就合并这两个bbox。
3. 然后在垂直方向上对bbox进行扩展。扩展方法是:
- 首先从page上切割掉合并后的水平bbox, 得到几个新的block
针对每个block
- x0: 扎到位于左侧x=x0延长线的左侧所有的bboxes, 找到最大的x1,让x0=x1+1。如果没有,则x0=X0
- x1: 找到位于右侧x=x1延长线右侧所有的bboxes, 找到最小的x0, 让x1=x0-1。如果没有,则x1=X1
随后在垂直方向上合并所有的连续的block,方法如下:
- 对block进行x方向排序,然后从左到右遍历所有block,如果当前block和下一个block的x0, x1相等,那么就合并这两个block。
如果垂直切分后所有小bbox都被分配到了一个block, 那么分割就完成了。这些合并后的block打上标签'GOOD_LAYOUT’
如果在某个垂直方向上无法被完全分割到一个block,那么就将这个block打上标签'BAD_LAYOUT'。
至此完成,一个页面的预处理,天然的block要么属于'GOOD_LAYOUT',要么属于'BAD_LAYOUT'。针对含有'BAD_LAYOUT'的页面,可以先按照自上而下,自左到右进行天然排序,也可以先过滤掉这种书籍。
(完成条件下次加强:进行水平方向切分,把混乱的layout部分尽可能切割出去)
"""
################################################################################
def find_left_neighbor_bboxes(this_bbox, all_bboxes) -> list:
"""
在all_bboxes里找到所有右侧高度和this_bbox有重叠的bbox
这里使用扩展之后的bbox
"""
left_boxes = [box for box in all_bboxes if box[X1_EXT_IDX] <= this_bbox[X0_EXT_IDX] and any([
box[Y0_EXT_IDX] < this_bbox[Y0_EXT_IDX] < box[Y1_EXT_IDX], box[Y0_EXT_IDX] < this_bbox[Y1_EXT_IDX] < box[Y1_EXT_IDX],
this_bbox[Y0_EXT_IDX] < box[Y0_EXT_IDX] < this_bbox[Y1_EXT_IDX], this_bbox[Y0_EXT_IDX] < box[Y1_EXT_IDX] < this_bbox[Y1_EXT_IDX],
box[Y0_EXT_IDX]==this_bbox[Y0_EXT_IDX] and box[Y1_EXT_IDX]==this_bbox[Y1_EXT_IDX]])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个
if len(left_boxes) > 0:
left_boxes.sort(key=lambda x: x[X1_EXT_IDX], reverse=True)
left_boxes = left_boxes
else:
left_boxes = []
return left_boxes
def find_top_neighbor_bboxes(this_bbox, all_bboxes) -> list:
"""
在all_bboxes里找到所有下侧宽度和this_bbox有重叠的bbox
这里使用扩展之后的bbox
"""
top_boxes = [box for box in all_bboxes if box[Y1_EXT_IDX] <= this_bbox[Y0_EXT_IDX] and any([
box[X0_EXT_IDX] < this_bbox[X0_EXT_IDX] < box[X1_EXT_IDX], box[X0_EXT_IDX] < this_bbox[X1_EXT_IDX] < box[X1_EXT_IDX],
this_bbox[X0_EXT_IDX] < box[X0_EXT_IDX] < this_bbox[X1_EXT_IDX], this_bbox[X0_EXT_IDX] < box[X1_EXT_IDX] < this_bbox[X1_EXT_IDX],
box[X0_EXT_IDX]==this_bbox[X0_EXT_IDX] and box[X1_EXT_IDX]==this_bbox[X1_EXT_IDX]])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个
if len(top_boxes) > 0:
top_boxes.sort(key=lambda x: x[Y1_EXT_IDX], reverse=True)
top_boxes = top_boxes
else:
top_boxes = []
return top_boxes
def get_and_set_idx_x_2_ext(this_bbox, all_bboxes):
"""
寻找this_bbox在all_bboxes中的被直接遮挡的深度 idx_x
这个遮挡深度不考虑延长线,而是被实际的长或者宽遮挡的情况
"""
if this_bbox[IDX_X] is not None:
return this_bbox[IDX_X]
else:
left_nearest_bbox = find_left_neighbor_bboxes(this_bbox, all_bboxes)
if len(left_nearest_bbox) == 0:
this_bbox[IDX_X] = 0
else:
left_idx_x = [get_and_set_idx_x_2(b, all_bboxes) for b in left_nearest_bbox]
this_bbox[IDX_X] = mymax(left_idx_x) + 1
return this_bbox[IDX_X]
def get_and_set_idx_y_2_ext(this_bbox, all_bboxes):
"""
寻找this_bbox在all_bboxes中的被直接遮挡的深度 idx_y
这个遮挡深度不考虑延长线,而是被实际的长或者宽遮挡的情况
"""
if this_bbox[IDX_Y] is not None:
return this_bbox[IDX_Y]
else:
top_nearest_bbox = find_top_neighbor_bboxes(this_bbox, all_bboxes)
if len(top_nearest_bbox) == 0:
this_bbox[IDX_Y] = 0
else:
top_idx_y = [get_and_set_idx_y_2_ext(b, all_bboxes) for b in top_nearest_bbox]
this_bbox[IDX_Y] = mymax(top_idx_y) + 1
return this_bbox[IDX_Y]
def _paper_bbox_sort_ext(all_bboxes: list):
all_bboxes_idx_x = [get_and_set_idx_x_2_ext(bbox, all_bboxes) for bbox in all_bboxes]
all_bboxes_idx_y = [get_and_set_idx_y_2_ext(bbox, all_bboxes) for bbox in all_bboxes]
all_bboxes_idx = [(idx_x, idx_y) for idx_x, idx_y in zip(all_bboxes_idx_x, all_bboxes_idx_y)]
all_bboxes_idx = [idx_x_y[0] * 100000 + idx_x_y[1] for idx_x_y in all_bboxes_idx] # 变换成一个点,保证能够先X,X相同时按Y排序
all_bboxes_idx = list(zip(all_bboxes_idx, all_bboxes))
all_bboxes_idx.sort(key=lambda x: x[0])
sorted_bboxes = [bbox for idx, bbox in all_bboxes_idx]
return sorted_bboxes
# ===============================================================================================
def find_left_bbox_ext_line(this_bbox, all_bboxes) -> list:
"""
寻找this_bbox左边的所有bbox, 使用延长线
"""
left_boxes = [box for box in all_bboxes if box[X1_IDX] <= this_bbox[X0_IDX]]
if len(left_boxes):
left_boxes.sort(key=lambda x: x[X1_IDX], reverse=True)
left_boxes = left_boxes[0]
else:
left_boxes = None
return left_boxes
def find_right_bbox_ext_line(this_bbox, all_bboxes) -> list:
"""
寻找this_bbox右边的所有bbox, 使用延长线
"""
right_boxes = [box for box in all_bboxes if box[X0_IDX] >= this_bbox[X1_IDX]]
if len(right_boxes):
right_boxes.sort(key=lambda x: x[X0_IDX])
right_boxes = right_boxes[0]
else:
right_boxes = None
return right_boxes
# =============================================================================================
def find_left_nearest_bbox_direct(this_bbox, all_bboxes) -> list:
"""
在all_bboxes里找到所有右侧高度和this_bbox有重叠的bbox, 不用延长线并且不能像
"""
left_boxes = [box for box in all_bboxes if box[X1_IDX] <= this_bbox[X0_IDX] and any([
box[Y0_IDX] < this_bbox[Y0_IDX] < box[Y1_IDX], box[Y0_IDX] < this_bbox[Y1_IDX] < box[Y1_IDX],
this_bbox[Y0_IDX] < box[Y0_IDX] < this_bbox[Y1_IDX], this_bbox[Y0_IDX] < box[Y1_IDX] < this_bbox[Y1_IDX],
box[Y0_IDX]==this_bbox[Y0_IDX] and box[Y1_IDX]==this_bbox[Y1_IDX]])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个——x1最大的那个
if len(left_boxes) > 0:
left_boxes.sort(key=lambda x: x[X1_EXT_IDX] if x[X1_EXT_IDX] else x[X1_IDX], reverse=True)
left_boxes = left_boxes[0]
else:
left_boxes = None
return left_boxes
def find_right_nearst_bbox_direct(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox右侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
right_bboxes = [box for box in all_bboxes if box[X0_IDX] >= this_bbox[X1_IDX] and any([
this_bbox[Y0_IDX] < box[Y0_IDX] < this_bbox[Y1_IDX], this_bbox[Y0_IDX] < box[Y1_IDX] < this_bbox[Y1_IDX],
box[Y0_IDX] < this_bbox[Y0_IDX] < box[Y1_IDX], box[Y0_IDX] < this_bbox[Y1_IDX] < box[Y1_IDX],
box[Y0_IDX]==this_bbox[Y0_IDX] and box[Y1_IDX]==this_bbox[Y1_IDX]])]
if len(right_bboxes)>0:
right_bboxes.sort(key=lambda x: x[X0_EXT_IDX] if x[X0_EXT_IDX] else x[X0_IDX])
right_bboxes = right_bboxes[0]
else:
right_bboxes = None
return right_bboxes
def reset_idx_x_y(all_boxes:list)->list:
for box in all_boxes:
box[IDX_X] = None
box[IDX_Y] = None
return all_boxes
# ===================================================================================================
def find_top_nearest_bbox_direct(this_bbox, bboxes_collection) -> list:
"""
找到在this_bbox上方且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
top_bboxes = [box for box in bboxes_collection if box[Y1_IDX] <= this_bbox[Y0_IDX] and any([
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
# 然后再过滤一下,找到上方距离this_bbox最近的那个
if len(top_bboxes) > 0:
top_bboxes.sort(key=lambda x: x[Y1_IDX], reverse=True)
top_bboxes = top_bboxes[0]
else:
top_bboxes = None
return top_bboxes
def find_bottom_nearest_bbox_direct(this_bbox, bboxes_collection) -> list:
"""
找到在this_bbox下方且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
bottom_bboxes = [box for box in bboxes_collection if box[Y0_IDX] >= this_bbox[Y1_IDX] and any([
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个
if len(bottom_bboxes) > 0:
bottom_bboxes.sort(key=lambda x: x[Y0_IDX])
bottom_bboxes = bottom_bboxes[0]
else:
bottom_bboxes = None
return bottom_bboxes
def find_boundry_bboxes(bboxes:list) -> tuple:
"""
找到bboxes的边界——找到所有bbox里最小的(x0, y0), 最大的(x1, y1)
"""
x0, y0, x1, y1 = bboxes[0][X0_IDX], bboxes[0][Y0_IDX], bboxes[0][X1_IDX], bboxes[0][Y1_IDX]
for box in bboxes:
x0 = min(box[X0_IDX], x0)
y0 = min(box[Y0_IDX], y0)
x1 = max(box[X1_IDX], x1)
y1 = max(box[Y1_IDX], y1)
return x0, y0, x1, y1
def extend_bbox_vertical(bboxes:list, boundry_x0, boundry_y0, boundry_x1, boundry_y1) -> list:
"""
在垂直方向上扩展能够直接垂直打通的bbox,也就是那些上下都没有其他box的bbox
"""
for box in bboxes:
top_nearest_bbox = find_top_nearest_bbox_direct(box, bboxes)
bottom_nearest_bbox = find_bottom_nearest_bbox_direct(box, bboxes)
if top_nearest_bbox is None and bottom_nearest_bbox is None: # 独占一列
box[X0_EXT_IDX] = box[X0_IDX]
box[Y0_EXT_IDX] = boundry_y0
box[X1_EXT_IDX] = box[X1_IDX]
box[Y1_EXT_IDX] = boundry_y1
# else:
# if top_nearest_bbox is None:
# box[Y0_EXT_IDX] = boundry_y0
# else:
# box[Y0_EXT_IDX] = top_nearest_bbox[Y1_IDX] + 1
# if bottom_nearest_bbox is None:
# box[Y1_EXT_IDX] = boundry_y1
# else:
# box[Y1_EXT_IDX] = bottom_nearest_bbox[Y0_IDX] - 1
# box[X0_EXT_IDX] = box[X0_IDX]
# box[X1_EXT_IDX] = box[X1_IDX]
return bboxes
# ===================================================================================================
def paper_bbox_sort_v2(all_bboxes: list, page_width:int, page_height:int):
"""
增加预处理行为的排序:
return:
[
{
"layout_bbox": [x0, y0, x1, y1],
"layout_label":"GOOD_LAYOUT/BAD_LAYOUT",
"content_bboxes": [] #每个元素都是[x0, y0, x1, y1, block_content, idx_x, idx_y, content_type, ext_x0, ext_y0, ext_x1, ext_y1], 并且顺序就是阅读顺序
}
]
"""
sorted_layouts = [] # 最后的返回结果
page_x0, page_y0, page_x1, page_y1 = 1, 1, page_width-1, page_height-1
all_bboxes = paper_bbox_sort(all_bboxes) # 大致拍下序
# 首先在水平方向上扩展独占一行的bbox
for bbox in all_bboxes:
left_nearest_bbox = find_left_nearest_bbox_direct(bbox, all_bboxes) # 非扩展线
right_nearest_bbox = find_right_nearst_bbox_direct(bbox, all_bboxes)
if left_nearest_bbox is None and right_nearest_bbox is None: # 独占一行
bbox[X0_EXT_IDX] = page_x0
bbox[Y0_EXT_IDX] = bbox[Y0_IDX]
bbox[X1_EXT_IDX] = page_x1
bbox[Y1_EXT_IDX] = bbox[Y1_IDX]
# 此时独占一行的被成功扩展到指定的边界上,这个时候利用边界条件合并连续的bbox,成为一个group
if len(all_bboxes)==1:
return [{"layout_bbox": [page_x0, page_y0, page_x1, page_y1], "layout_label":"GOOD_LAYOUT", "content_bboxes": all_bboxes}]
if len(all_bboxes)==0:
return []
"""
然后合并所有连续水平方向的bbox.
"""
all_bboxes.sort(key=lambda x: x[Y0_IDX])
h_bboxes = []
h_bbox_group = []
v_boxes = []
for bbox in all_bboxes:
if bbox[X0_IDX] == page_x0 and bbox[X1_IDX] == page_x1:
h_bbox_group.append(bbox)
else:
if len(h_bbox_group)>0:
h_bboxes.append(h_bbox_group)
h_bbox_group = []
# 最后一个group
if len(h_bbox_group)>0:
h_bboxes.append(h_bbox_group)
"""
现在h_bboxes里面是所有的group了,每个group都是一个list
对h_bboxes里的每个group进行计算放回到sorted_layouts里
"""
for gp in h_bboxes:
gp.sort(key=lambda x: x[Y0_IDX])
block_info = {"layout_label":"GOOD_LAYOUT", "content_bboxes": gp}
# 然后计算这个group的layout_bbox,也就是最小的x0,y0, 最大的x1,y1
x0, y0, x1, y1 = gp[0][X0_EXT_IDX], gp[0][Y0_EXT_IDX], gp[-1][X1_EXT_IDX], gp[-1][Y1_EXT_IDX]
block_info["layout_bbox"] = [x0, y0, x1, y1]
sorted_layouts.append(block_info)
# 接下来利用这些连续的水平bbox的layout_bbox的y0, y1,从水平上切分开其余的为几个部分
h_split_lines = [page_y0]
for gp in h_bboxes:
layout_bbox = gp['layout_bbox']
y0, y1 = layout_bbox[1], layout_bbox[3]
h_split_lines.append(y0)
h_split_lines.append(y1)
h_split_lines.append(page_y1)
unsplited_bboxes = []
for i in range(0, len(h_split_lines), 2):
start_y0, start_y1 = h_split_lines[i:i+2]
# 然后找出[start_y0, start_y1]之间的其他bbox,这些组成一个未分割板块
bboxes_in_block = [bbox for bbox in all_bboxes if bbox[Y0_IDX]>=start_y0 and bbox[Y1_IDX]<=start_y1]
unsplited_bboxes.append(bboxes_in_block)
# ================== 至此,水平方向的 已经切分排序完毕====================================
"""
接下来针对每个非水平的部分切分垂直方向的
此时,只剩下了无法被完全水平打通的bbox了。对这些box,优先进行垂直扩展,然后进行垂直切分.
分3步:
1. 先把能完全垂直打通的隔离出去当做一个layout
2. 其余的先垂直切分
3. 垂直切分之后的部分再尝试水平切分
4. 剩下的不能被切分的各个部分当成一个layout
"""
# 对每部分进行垂直切分
for bboxes_in_block in unsplited_bboxes:
# 首先对这个block的bbox进行垂直方向上的扩展
boundry_x0, boundry_y0, boundry_x1, boundry_y1 = find_boundry_bboxes(bboxes_in_block)
# 进行垂直方向上的扩展
extended_vertical_bboxes = extend_bbox_vertical(bboxes_in_block, boundry_x0, boundry_y0, boundry_x1, boundry_y1)
# 然后对这个block进行垂直方向上的切分
extend_bbox_vertical.sort(key=lambda x: x[X0_IDX]) # x方向上从小到大,代表了从左到右读取
v_boxes_group = []
for bbox in extended_vertical_bboxes:
if bbox[Y0_IDX]==boundry_y0 and bbox[Y1_IDX]==boundry_y1:
v_boxes_group.append(bbox)
else:
if len(v_boxes_group)>0:
v_boxes.append(v_boxes_group)
v_boxes_group = []
if len(v_boxes_group)>0:
v_boxes.append(v_boxes_group)
# 把连续的垂直部分加入到sorted_layouts里。注意这个时候已经是连续的垂直部分了,因为上面已经做了
for gp in v_boxes:
gp.sort(key=lambda x: x[X0_IDX])
block_info = {"layout_label":"GOOD_LAYOUT", "content_bboxes": gp}
# 然后计算这个group的layout_bbox,也就是最小的x0,y0, 最大的x1,y1
x0, y0, x1, y1 = gp[0][X0_EXT_IDX], gp[0][Y0_EXT_IDX], gp[-1][X1_EXT_IDX], gp[-1][Y1_EXT_IDX]
block_info["layout_bbox"] = [x0, y0, x1, y1]
sorted_layouts.append(block_info)
# 在垂直方向上,划分子块,也就是用贯通的垂直线进行切分。这些被切分出来的块,极大可能是可被垂直切分的,如果不能完全的垂直切分,那么尝试水平切分。都不能的则当成一个layout
v_split_lines = [boundry_x0]
for gp in v_boxes:
layout_bbox = gp['layout_bbox']
x0, x1 = layout_bbox[0], layout_bbox[2]
v_split_lines.append(x0)
v_split_lines.append(x1)
v_split_lines.append(boundry_x1)
reset_idx_x_y(all_bboxes)
all_boxes = _paper_bbox_sort_ext(all_bboxes)
return all_boxes
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from magic_pdf.layout.bbox_sort import X0_EXT_IDX, X0_IDX, X1_EXT_IDX, X1_IDX, Y0_IDX, Y1_EXT_IDX, Y1_IDX
from magic_pdf.libs.boxbase import _is_bottom_full_overlap, _left_intersect, _right_intersect
def find_all_left_bbox_direct(this_bbox, all_bboxes) -> list:
"""
在all_bboxes里找到所有右侧垂直方向上和this_bbox有重叠的bbox, 不用延长线
并且要考虑两个box左右相交的情况,如果相交了,那么右侧的box就不算最左侧。
"""
left_boxes = [box for box in all_bboxes if box[X1_IDX] <= this_bbox[X0_IDX]
and any([
box[Y0_IDX] < this_bbox[Y0_IDX] < box[Y1_IDX], box[Y0_IDX] < this_bbox[Y1_IDX] < box[Y1_IDX],
this_bbox[Y0_IDX] < box[Y0_IDX] < this_bbox[Y1_IDX], this_bbox[Y0_IDX] < box[Y1_IDX] < this_bbox[Y1_IDX],
box[Y0_IDX]==this_bbox[Y0_IDX] and box[Y1_IDX]==this_bbox[Y1_IDX]]) or _left_intersect(box[:4], this_bbox[:4])]
# 然后再过滤一下,找到水平上距离this_bbox最近的那个——x1最大的那个
if len(left_boxes) > 0:
left_boxes.sort(key=lambda x: x[X1_EXT_IDX] if x[X1_EXT_IDX] else x[X1_IDX], reverse=True)
left_boxes = left_boxes[0]
else:
left_boxes = None
return left_boxes
def find_all_right_bbox_direct(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox右侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
right_bboxes = [box for box in all_bboxes if box[X0_IDX] >= this_bbox[X1_IDX]
and any([
this_bbox[Y0_IDX] < box[Y0_IDX] < this_bbox[Y1_IDX], this_bbox[Y0_IDX] < box[Y1_IDX] < this_bbox[Y1_IDX],
box[Y0_IDX] < this_bbox[Y0_IDX] < box[Y1_IDX], box[Y0_IDX] < this_bbox[Y1_IDX] < box[Y1_IDX],
box[Y0_IDX]==this_bbox[Y0_IDX] and box[Y1_IDX]==this_bbox[Y1_IDX]]) or _right_intersect(this_bbox[:4], box[:4])]
if len(right_bboxes)>0:
right_bboxes.sort(key=lambda x: x[X0_EXT_IDX] if x[X0_EXT_IDX] else x[X0_IDX])
right_bboxes = right_bboxes[0]
else:
right_bboxes = None
return right_bboxes
def find_all_top_bbox_direct(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox上侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
top_bboxes = [box for box in all_bboxes if box[Y1_IDX] <= this_bbox[Y0_IDX] and any([
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
if len(top_bboxes)>0:
top_bboxes.sort(key=lambda x: x[Y1_EXT_IDX] if x[Y1_EXT_IDX] else x[Y1_IDX], reverse=True)
top_bboxes = top_bboxes[0]
else:
top_bboxes = None
return top_bboxes
def find_all_bottom_bbox_direct(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox下侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
bottom_bboxes = [box for box in all_bboxes if box[Y0_IDX] >= this_bbox[Y1_IDX] and any([
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
if len(bottom_bboxes)>0:
bottom_bboxes.sort(key=lambda x: x[Y0_IDX])
bottom_bboxes = bottom_bboxes[0]
else:
bottom_bboxes = None
return bottom_bboxes
# ===================================================================================================================
def find_bottom_bbox_direct_from_right_edge(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox下侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
bottom_bboxes = [box for box in all_bboxes if box[Y0_IDX] >= this_bbox[Y1_IDX] and any([
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
if len(bottom_bboxes)>0:
# y0最小, X1最大的那个,也就是box上边缘最靠近this_bbox的那个,并且还最靠右
bottom_bboxes.sort(key=lambda x: x[Y0_IDX])
bottom_bboxes = [box for box in bottom_bboxes if box[Y0_IDX]==bottom_bboxes[0][Y0_IDX]]
# 然后再y1相同的情况下,找到x1最大的那个
bottom_bboxes.sort(key=lambda x: x[X1_IDX], reverse=True)
bottom_bboxes = bottom_bboxes[0]
else:
bottom_bboxes = None
return bottom_bboxes
def find_bottom_bbox_direct_from_left_edge(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox下侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
bottom_bboxes = [box for box in all_bboxes if box[Y0_IDX] >= this_bbox[Y1_IDX] and any([
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
if len(bottom_bboxes)>0:
# y0最小, X0最小的那个
bottom_bboxes.sort(key=lambda x: x[Y0_IDX])
bottom_bboxes = [box for box in bottom_bboxes if box[Y0_IDX]==bottom_bboxes[0][Y0_IDX]]
# 然后再y0相同的情况下,找到x0最小的那个
bottom_bboxes.sort(key=lambda x: x[X0_IDX])
bottom_bboxes = bottom_bboxes[0]
else:
bottom_bboxes = None
return bottom_bboxes
def find_top_bbox_direct_from_left_edge(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox上侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
top_bboxes = [box for box in all_bboxes if box[Y1_IDX] <= this_bbox[Y0_IDX] and any([
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
if len(top_bboxes)>0:
# y1最大, X0最小的那个
top_bboxes.sort(key=lambda x: x[Y1_IDX], reverse=True)
top_bboxes = [box for box in top_bboxes if box[Y1_IDX]==top_bboxes[0][Y1_IDX]]
# 然后再y1相同的情况下,找到x0最小的那个
top_bboxes.sort(key=lambda x: x[X0_IDX])
top_bboxes = top_bboxes[0]
else:
top_bboxes = None
return top_bboxes
def find_top_bbox_direct_from_right_edge(this_bbox, all_bboxes) -> list:
"""
找到在this_bbox上侧且距离this_bbox距离最近的bbox.必须是直接遮挡的那种
"""
top_bboxes = [box for box in all_bboxes if box[Y1_IDX] <= this_bbox[Y0_IDX] and any([
box[X0_IDX] < this_bbox[X0_IDX] < box[X1_IDX], box[X0_IDX] < this_bbox[X1_IDX] < box[X1_IDX],
this_bbox[X0_IDX] < box[X0_IDX] < this_bbox[X1_IDX], this_bbox[X0_IDX] < box[X1_IDX] < this_bbox[X1_IDX],
box[X0_IDX]==this_bbox[X0_IDX] and box[X1_IDX]==this_bbox[X1_IDX]])]
if len(top_bboxes)>0:
# y1最大, X1最大的那个
top_bboxes.sort(key=lambda x: x[Y1_IDX], reverse=True)
top_bboxes = [box for box in top_bboxes if box[Y1_IDX]==top_bboxes[0][Y1_IDX]]
# 然后再y1相同的情况下,找到x1最大的那个
top_bboxes.sort(key=lambda x: x[X1_IDX], reverse=True)
top_bboxes = top_bboxes[0]
else:
top_bboxes = None
return top_bboxes
# ===================================================================================================================
def get_left_edge_bboxes(all_bboxes) -> list:
"""
返回最左边的bbox
"""
left_bboxes = [box for box in all_bboxes if find_all_left_bbox_direct(box, all_bboxes) is None]
return left_bboxes
def get_right_edge_bboxes(all_bboxes) -> list:
"""
返回最右边的bbox
"""
right_bboxes = [box for box in all_bboxes if find_all_right_bbox_direct(box, all_bboxes) is None]
return right_bboxes
def fix_vertical_bbox_pos(bboxes:list):
"""
检查这批bbox在垂直方向是否有轻微的重叠,如果重叠了,就把重叠的bbox往下移动一点
在x方向上必须一个包含或者被包含,或者完全重叠,不能只有部分重叠
"""
bboxes.sort(key=lambda x: x[Y0_IDX]) # 从上向下排列
for i in range(0, len(bboxes)):
for j in range(i+1, len(bboxes)):
if _is_bottom_full_overlap(bboxes[i][:4], bboxes[j][:4]):
# 如果两个bbox有部分重叠,那么就把下面的bbox往下移动一点
bboxes[j][Y0_IDX] = bboxes[i][Y1_IDX] + 2 # 2是个经验值
break
return bboxes
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"""对pdf上的box进行layout识别,并对内部组成的box进行排序."""
from loguru import logger
from magic_pdf.layout.bbox_sort import (CONTENT_IDX, CONTENT_TYPE_IDX,
X0_EXT_IDX, X0_IDX, X1_EXT_IDX, X1_IDX,
Y0_EXT_IDX, Y0_IDX, Y1_EXT_IDX, Y1_IDX,
paper_bbox_sort)
from magic_pdf.layout.layout_det_utils import (
find_all_bottom_bbox_direct, find_all_left_bbox_direct,
find_all_right_bbox_direct, find_all_top_bbox_direct,
find_bottom_bbox_direct_from_left_edge,
find_bottom_bbox_direct_from_right_edge,
find_top_bbox_direct_from_left_edge, find_top_bbox_direct_from_right_edge,
get_left_edge_bboxes, get_right_edge_bboxes)
from magic_pdf.libs.boxbase import get_bbox_in_boundary
LAYOUT_V = 'V'
LAYOUT_H = 'H'
LAYOUT_UNPROC = 'U'
LAYOUT_BAD = 'B'
def _is_single_line_text(bbox):
"""检查bbox里面的文字是否只有一行."""
return True # TODO
box_type = bbox[CONTENT_TYPE_IDX]
if box_type != 'text':
return False
paras = bbox[CONTENT_IDX]['paras']
text_content = ''
for para_id, para in paras.items(): # 拼装内部的段落文本
is_title = para['is_title']
if is_title != 0:
text_content += f"## {para['text']}"
else:
text_content += para['text']
text_content += '\n\n'
return bbox[CONTENT_TYPE_IDX] == 'text' and len(text_content.split('\n\n')) <= 1
def _horizontal_split(bboxes: list, boundary: tuple, avg_font_size=20) -> list:
"""
对bboxes进行水平切割
方法是:找到左侧和右侧都没有被直接遮挡的box,然后进行扩展,之后进行切割
return:
返回几个大的Layout区域 [[x0, y0, x1, y1, "h|u|v"], ], h代表水平,u代表未探测的,v代表垂直布局
"""
sorted_layout_blocks = [] # 这是要最终返回的值
bound_x0, bound_y0, bound_x1, bound_y1 = boundary
all_bboxes = get_bbox_in_boundary(bboxes, boundary)
# all_bboxes = paper_bbox_sort(all_bboxes, abs(bound_x1-bound_x0), abs(bound_y1-bound_x0)) # 大致拍下序, 这个是基于直接遮挡的。
"""
首先在水平方向上扩展独占一行的bbox
"""
last_h_split_line_y1 = bound_y0 # 记录下上次的水平分割线
for i, bbox in enumerate(all_bboxes):
left_nearest_bbox = find_all_left_bbox_direct(bbox, all_bboxes) # 非扩展线
right_nearest_bbox = find_all_right_bbox_direct(bbox, all_bboxes)
if left_nearest_bbox is None and right_nearest_bbox is None: # 独占一行
"""
然而,如果只是孤立的一行文字,那么就还要满足以下几个条件才可以:
1. bbox和中心线相交。或者
2. 上方或者下方也存在同类水平的独占一行的bbox。 或者
3. TODO 加强条件:这个bbox上方和下方是同一列column,那么就不能算作独占一行
"""
# 先检查这个bbox里是否只包含一行文字
# is_single_line = _is_single_line_text(bbox)
"""
这里有个点需要注意,当页面内容不是居中的时候,第一次调用传递的是page的boundary,这个时候mid_x就不是中心线了.
所以这里计算出最紧致的boundary,然后再计算mid_x
"""
boundary_real_x0, boundary_real_x1 = min(
[bbox[X0_IDX] for bbox in all_bboxes]
), max([bbox[X1_IDX] for bbox in all_bboxes])
mid_x = (boundary_real_x0 + boundary_real_x1) / 2
# 检查这个box是否内容在中心线有交
# 必须跨过去2个字符的宽度
is_cross_boundary_mid_line = (
min(mid_x - bbox[X0_IDX], bbox[X1_IDX] - mid_x) > avg_font_size * 2
)
"""
检查条件2
"""
is_belong_to_col = False
"""
检查是否能被上方col吸收,方法是:
1. 上方非空且不是独占一行的,并且
2. 从上个水平分割的最大y=y1开始到当前bbox,最左侧的bbox的[min_x0, max_x1],能够覆盖当前box的[x0, x1]
"""
"""
以迭代的方式向上找,查找范围是[bound_x0, last_h_sp, bound_x1, bbox[Y0_IDX]]
"""
# 先确定上方的y0, y0
b_y0, b_y1 = last_h_split_line_y1, bbox[Y0_IDX]
# 然后从box开始逐个向上找到所有与box在x上有交集的box
box_to_check = [bound_x0, b_y0, bound_x1, b_y1]
bbox_in_bound_check = get_bbox_in_boundary(all_bboxes, box_to_check)
bboxes_on_top = []
virtual_box = bbox
while True:
b_on_top = find_all_top_bbox_direct(virtual_box, bbox_in_bound_check)
if b_on_top is not None:
bboxes_on_top.append(b_on_top)
virtual_box = [
min([virtual_box[X0_IDX], b_on_top[X0_IDX]]),
min(virtual_box[Y0_IDX], b_on_top[Y0_IDX]),
max([virtual_box[X1_IDX], b_on_top[X1_IDX]]),
b_y1,
]
else:
break
# 随后确定这些box的最小x0, 最大x1
if len(bboxes_on_top) > 0 and len(bboxes_on_top) != len(
bbox_in_bound_check
): # virtual_box可能会膨胀到占满整个区域,这实际上就不能属于一个col了。
min_x0, max_x1 = virtual_box[X0_IDX], virtual_box[X1_IDX]
# 然后采用一种比较粗糙的方法,看min_x0,max_x1是否与位于[bound_x0, last_h_sp, bound_x1, bbox[Y0_IDX]]之间的box有相交
if not any(
[
b[X0_IDX] <= min_x0 - 1 <= b[X1_IDX]
or b[X0_IDX] <= max_x1 + 1 <= b[X1_IDX]
for b in bbox_in_bound_check
]
):
# 其上,下都不能被扩展成行,暂时只检查一下上方 TODO
top_nearest_bbox = find_all_top_bbox_direct(bbox, bboxes)
bottom_nearest_bbox = find_all_bottom_bbox_direct(bbox, bboxes)
if not any(
[
top_nearest_bbox is not None
and (
find_all_left_bbox_direct(top_nearest_bbox, bboxes)
is None
and find_all_right_bbox_direct(top_nearest_bbox, bboxes)
is None
),
bottom_nearest_bbox is not None
and (
find_all_left_bbox_direct(bottom_nearest_bbox, bboxes)
is None
and find_all_right_bbox_direct(
bottom_nearest_bbox, bboxes
)
is None
),
top_nearest_bbox is None or bottom_nearest_bbox is None,
]
):
is_belong_to_col = True
# 检查是否能被下方col吸收 TODO
"""
这里为什么没有is_cross_boundary_mid_line的条件呢?
确实有些杂志左右两栏宽度不是对称的。
"""
if not is_belong_to_col or is_cross_boundary_mid_line:
bbox[X0_EXT_IDX] = bound_x0
bbox[Y0_EXT_IDX] = bbox[Y0_IDX]
bbox[X1_EXT_IDX] = bound_x1
bbox[Y1_EXT_IDX] = bbox[Y1_IDX]
last_h_split_line_y1 = bbox[Y1_IDX] # 更新这条线
else:
continue
"""
此时独占一行的被成功扩展到指定的边界上,这个时候利用边界条件合并连续的bbox,成为一个group
然后合并所有连续水平方向的bbox.
"""
all_bboxes.sort(key=lambda x: x[Y0_IDX])
h_bboxes = []
h_bbox_group = []
for bbox in all_bboxes:
if bbox[X0_EXT_IDX] == bound_x0 and bbox[X1_EXT_IDX] == bound_x1:
h_bbox_group.append(bbox)
else:
if len(h_bbox_group) > 0:
h_bboxes.append(h_bbox_group)
h_bbox_group = []
# 最后一个group
if len(h_bbox_group) > 0:
h_bboxes.append(h_bbox_group)
"""
现在h_bboxes里面是所有的group了,每个group都是一个list
对h_bboxes里的每个group进行计算放回到sorted_layouts里
"""
h_layouts = []
for gp in h_bboxes:
gp.sort(key=lambda x: x[Y0_IDX])
# 然后计算这个group的layout_bbox,也就是最小的x0,y0, 最大的x1,y1
x0, y0, x1, y1 = (
gp[0][X0_EXT_IDX],
gp[0][Y0_EXT_IDX],
gp[-1][X1_EXT_IDX],
gp[-1][Y1_EXT_IDX],
)
h_layouts.append([x0, y0, x1, y1, LAYOUT_H]) # 水平的布局
"""
接下来利用这些连续的水平bbox的layout_bbox的y0, y1,从水平上切分开其余的为几个部分
"""
h_split_lines = [bound_y0]
for gp in h_bboxes: # gp是一个list[bbox_list]
y0, y1 = gp[0][1], gp[-1][3]
h_split_lines.append(y0)
h_split_lines.append(y1)
h_split_lines.append(bound_y1)
unsplited_bboxes = []
for i in range(0, len(h_split_lines), 2):
start_y0, start_y1 = h_split_lines[i : i + 2]
# 然后找出[start_y0, start_y1]之间的其他bbox,这些组成一个未分割板块
bboxes_in_block = [
bbox
for bbox in all_bboxes
if bbox[Y0_IDX] >= start_y0 and bbox[Y1_IDX] <= start_y1
]
unsplited_bboxes.append(bboxes_in_block)
# 接着把未处理的加入到h_layouts里
for bboxes_in_block in unsplited_bboxes:
if len(bboxes_in_block) == 0:
continue
x0, y0, x1, y1 = (
bound_x0,
min([bbox[Y0_IDX] for bbox in bboxes_in_block]),
bound_x1,
max([bbox[Y1_IDX] for bbox in bboxes_in_block]),
)
h_layouts.append([x0, y0, x1, y1, LAYOUT_UNPROC])
h_layouts.sort(key=lambda x: x[1]) # 按照y0排序, 也就是从上到下的顺序
"""
转换成如下格式返回
"""
for layout in h_layouts:
sorted_layout_blocks.append(
{
'layout_bbox': layout[:4],
'layout_label': layout[4],
'sub_layout': [],
}
)
return sorted_layout_blocks
###############################################################################################
#
# 垂直方向的处理
#
#
###############################################################################################
def _vertical_align_split_v1(bboxes: list, boundary: tuple) -> list:
"""
计算垂直方向上的对齐, 并分割bboxes成layout。负责对一列多行的进行列维度分割。
如果不能完全分割,剩余部分作为layout_lable为u的layout返回
-----------------------
| | |
| | |
| | |
| | |
-------------------------
此函数会将:以上布局将会切分出来2列
"""
sorted_layout_blocks = [] # 这是要最终返回的值
new_boundary = [boundary[0], boundary[1], boundary[2], boundary[3]]
v_blocks = []
"""
先从左到右切分
"""
while True:
all_bboxes = get_bbox_in_boundary(bboxes, new_boundary)
left_edge_bboxes = get_left_edge_bboxes(all_bboxes)
if len(left_edge_bboxes) == 0:
break
right_split_line_x1 = max([bbox[X1_IDX] for bbox in left_edge_bboxes]) + 1
# 然后检查这条线能不与其他bbox的左边界相交或者重合
if any(
[bbox[X0_IDX] <= right_split_line_x1 <= bbox[X1_IDX] for bbox in all_bboxes]
):
# 垂直切分线与某些box发生相交,说明无法完全垂直方向切分。
break
else: # 说明成功分割出一列
# 找到左侧边界最靠左的bbox作为layout的x0
layout_x0 = min(
[bbox[X0_IDX] for bbox in left_edge_bboxes]
) # 这里主要是为了画出来有一定间距
v_blocks.append(
[
layout_x0,
new_boundary[1],
right_split_line_x1,
new_boundary[3],
LAYOUT_V,
]
)
new_boundary[0] = right_split_line_x1 # 更新边界
"""
再从右到左切, 此时如果还是无法完全切分,那么剩余部分作为layout_lable为u的layout返回
"""
unsplited_block = []
while True:
all_bboxes = get_bbox_in_boundary(bboxes, new_boundary)
right_edge_bboxes = get_right_edge_bboxes(all_bboxes)
if len(right_edge_bboxes) == 0:
break
left_split_line_x0 = min([bbox[X0_IDX] for bbox in right_edge_bboxes]) - 1
# 然后检查这条线能不与其他bbox的左边界相交或者重合
if any(
[bbox[X0_IDX] <= left_split_line_x0 <= bbox[X1_IDX] for bbox in all_bboxes]
):
# 这里是余下的
unsplited_block.append(
[
new_boundary[0],
new_boundary[1],
new_boundary[2],
new_boundary[3],
LAYOUT_UNPROC,
]
)
break
else:
# 找到右侧边界最靠右的bbox作为layout的x1
layout_x1 = max([bbox[X1_IDX] for bbox in right_edge_bboxes])
v_blocks.append(
[
left_split_line_x0,
new_boundary[1],
layout_x1,
new_boundary[3],
LAYOUT_V,
]
)
new_boundary[2] = left_split_line_x0 # 更新右边界
"""
最后拼装成layout格式返回
"""
for block in v_blocks:
sorted_layout_blocks.append(
{
'layout_bbox': block[:4],
'layout_label': block[4],
'sub_layout': [],
}
)
for block in unsplited_block:
sorted_layout_blocks.append(
{
'layout_bbox': block[:4],
'layout_label': block[4],
'sub_layout': [],
}
)
# 按照x0排序
sorted_layout_blocks.sort(key=lambda x: x['layout_bbox'][0])
return sorted_layout_blocks
def _vertical_align_split_v2(bboxes: list, boundary: tuple) -> list:
"""改进的
_vertical_align_split算法,原算法会因为第二列的box由于左侧没有遮挡被认为是左侧的一部分,导致整个layout多列被识别为一列。
利用从左上角的box开始向下看的方法,不断扩展w_x0, w_x1,直到不能继续向下扩展,或者到达边界下边界。"""
sorted_layout_blocks = [] # 这是要最终返回的值
new_boundary = [boundary[0], boundary[1], boundary[2], boundary[3]]
bad_boxes = [] # 被割中的box
v_blocks = []
while True:
all_bboxes = get_bbox_in_boundary(bboxes, new_boundary)
if len(all_bboxes) == 0:
break
left_top_box = min(
all_bboxes, key=lambda x: (x[X0_IDX], x[Y0_IDX])
) # 这里应该加强,检查一下必须是在第一列的 TODO
start_box = [
left_top_box[X0_IDX],
left_top_box[Y0_IDX],
left_top_box[X1_IDX],
left_top_box[Y1_IDX],
]
w_x0, w_x1 = left_top_box[X0_IDX], left_top_box[X1_IDX]
"""
然后沿着这个box线向下找最近的那个box, 然后扩展w_x0, w_x1
扩展之后,宽度会增加,随后用x=w_x1来检测在边界内是否有box与相交,如果相交,那么就说明不能再扩展了。
当不能扩展的时候就要看是否到达下边界:
1. 达到,那么更新左边界继续分下一个列
2. 没有达到,那么此时开始从右侧切分进入下面的循环里
"""
while left_top_box is not None: # 向下去找
virtual_box = [w_x0, left_top_box[Y0_IDX], w_x1, left_top_box[Y1_IDX]]
left_top_box = find_bottom_bbox_direct_from_left_edge(
virtual_box, all_bboxes
)
if left_top_box:
w_x0, w_x1 = min(virtual_box[X0_IDX], left_top_box[X0_IDX]), max(
[virtual_box[X1_IDX], left_top_box[X1_IDX]]
)
# 万一这个初始的box在column中间,那么还要向上看
start_box = [
w_x0,
start_box[Y0_IDX],
w_x1,
start_box[Y1_IDX],
] # 扩展一下宽度更鲁棒
left_top_box = find_top_bbox_direct_from_left_edge(start_box, all_bboxes)
while left_top_box is not None: # 向上去找
virtual_box = [w_x0, left_top_box[Y0_IDX], w_x1, left_top_box[Y1_IDX]]
left_top_box = find_top_bbox_direct_from_left_edge(virtual_box, all_bboxes)
if left_top_box:
w_x0, w_x1 = min(virtual_box[X0_IDX], left_top_box[X0_IDX]), max(
[virtual_box[X1_IDX], left_top_box[X1_IDX]]
)
# 检查相交
if any([bbox[X0_IDX] <= w_x1 + 1 <= bbox[X1_IDX] for bbox in all_bboxes]):
for b in all_bboxes:
if b[X0_IDX] <= w_x1 + 1 <= b[X1_IDX]:
bad_boxes.append([b[X0_IDX], b[Y0_IDX], b[X1_IDX], b[Y1_IDX]])
break
else: # 说明成功分割出一列
v_blocks.append([w_x0, new_boundary[1], w_x1, new_boundary[3], LAYOUT_V])
new_boundary[0] = w_x1 # 更新边界
"""
接着开始从右上角的box扫描
"""
w_x0, w_x1 = 0, 0
unsplited_block = []
while True:
all_bboxes = get_bbox_in_boundary(bboxes, new_boundary)
if len(all_bboxes) == 0:
break
# 先找到X1最大的
bbox_list_sorted = sorted(
all_bboxes, key=lambda bbox: bbox[X1_IDX], reverse=True
)
# Then, find the boxes with the smallest Y0 value
bigest_x1 = bbox_list_sorted[0][X1_IDX]
boxes_with_bigest_x1 = [
bbox for bbox in bbox_list_sorted if bbox[X1_IDX] == bigest_x1
] # 也就是最靠右的那些
right_top_box = min(
boxes_with_bigest_x1, key=lambda bbox: bbox[Y0_IDX]
) # y0最小的那个
start_box = [
right_top_box[X0_IDX],
right_top_box[Y0_IDX],
right_top_box[X1_IDX],
right_top_box[Y1_IDX],
]
w_x0, w_x1 = right_top_box[X0_IDX], right_top_box[X1_IDX]
while right_top_box is not None:
virtual_box = [w_x0, right_top_box[Y0_IDX], w_x1, right_top_box[Y1_IDX]]
right_top_box = find_bottom_bbox_direct_from_right_edge(
virtual_box, all_bboxes
)
if right_top_box:
w_x0, w_x1 = min([w_x0, right_top_box[X0_IDX]]), max(
[w_x1, right_top_box[X1_IDX]]
)
# 在向上扫描
start_box = [
w_x0,
start_box[Y0_IDX],
w_x1,
start_box[Y1_IDX],
] # 扩展一下宽度更鲁棒
right_top_box = find_top_bbox_direct_from_right_edge(start_box, all_bboxes)
while right_top_box is not None:
virtual_box = [w_x0, right_top_box[Y0_IDX], w_x1, right_top_box[Y1_IDX]]
right_top_box = find_top_bbox_direct_from_right_edge(
virtual_box, all_bboxes
)
if right_top_box:
w_x0, w_x1 = min([w_x0, right_top_box[X0_IDX]]), max(
[w_x1, right_top_box[X1_IDX]]
)
# 检查是否与其他box相交, 垂直切分线与某些box发生相交,说明无法完全垂直方向切分。
if any([bbox[X0_IDX] <= w_x0 - 1 <= bbox[X1_IDX] for bbox in all_bboxes]):
unsplited_block.append(
[
new_boundary[0],
new_boundary[1],
new_boundary[2],
new_boundary[3],
LAYOUT_UNPROC,
]
)
for b in all_bboxes:
if b[X0_IDX] <= w_x0 - 1 <= b[X1_IDX]:
bad_boxes.append([b[X0_IDX], b[Y0_IDX], b[X1_IDX], b[Y1_IDX]])
break
else: # 说明成功分割出一列
v_blocks.append([w_x0, new_boundary[1], w_x1, new_boundary[3], LAYOUT_V])
new_boundary[2] = w_x0
"""转换数据结构"""
for block in v_blocks:
sorted_layout_blocks.append(
{
'layout_bbox': block[:4],
'layout_label': block[4],
'sub_layout': [],
}
)
for block in unsplited_block:
sorted_layout_blocks.append(
{
'layout_bbox': block[:4],
'layout_label': block[4],
'sub_layout': [],
'bad_boxes': bad_boxes, # 记录下来,这个box是被割中的
}
)
# 按照x0排序
sorted_layout_blocks.sort(key=lambda x: x['layout_bbox'][0])
return sorted_layout_blocks
def _try_horizontal_mult_column_split(bboxes: list, boundary: tuple) -> list:
"""
尝试水平切分,如果切分不动,那就当一个BAD_LAYOUT返回
------------------
| | |
------------------
| | | | <- 这里是此函数要切分的场景
------------------
| | |
| | |
"""
pass
def _vertical_split(bboxes: list, boundary: tuple) -> list:
"""
从垂直方向进行切割,分block
这个版本里,如果垂直切分不动,那就当一个BAD_LAYOUT返回
--------------------------
| | |
| | |
| |
这种列是此函数要切分的 -> | |
| |
| | |
| | |
-------------------------
"""
sorted_layout_blocks = [] # 这是要最终返回的值
bound_x0, bound_y0, bound_x1, bound_y1 = boundary
all_bboxes = get_bbox_in_boundary(bboxes, boundary)
"""
all_bboxes = fix_vertical_bbox_pos(all_bboxes) # 垂直方向解覆盖
all_bboxes = fix_hor_bbox_pos(all_bboxes) # 水平解覆盖
这两行代码目前先不执行,因为公式检测,表格检测还不是很成熟,导致非常多的textblock参与了运算,时间消耗太大。
这两行代码的作用是:
如果遇到互相重叠的bbox, 那么会把面积较小的box进行压缩,从而避免重叠。对布局切分来说带来正反馈。
"""
# all_bboxes = paper_bbox_sort(all_bboxes, abs(bound_x1-bound_x0), abs(bound_y1-bound_x0)) # 大致拍下序, 这个是基于直接遮挡的。
"""
首先在垂直方向上扩展独占一行的bbox
"""
for bbox in all_bboxes:
top_nearest_bbox = find_all_top_bbox_direct(bbox, all_bboxes) # 非扩展线
bottom_nearest_bbox = find_all_bottom_bbox_direct(bbox, all_bboxes)
if (
top_nearest_bbox is None
and bottom_nearest_bbox is None
and not any(
[
b[X0_IDX] < bbox[X1_IDX] < b[X1_IDX]
or b[X0_IDX] < bbox[X0_IDX] < b[X1_IDX]
for b in all_bboxes
]
)
): # 独占一列, 且不和其他重叠
bbox[X0_EXT_IDX] = bbox[X0_IDX]
bbox[Y0_EXT_IDX] = bound_y0
bbox[X1_EXT_IDX] = bbox[X1_IDX]
bbox[Y1_EXT_IDX] = bound_y1
"""
此时独占一列的被成功扩展到指定的边界上,这个时候利用边界条件合并连续的bbox,成为一个group
然后合并所有连续垂直方向的bbox.
"""
all_bboxes.sort(key=lambda x: x[X0_IDX])
# fix: 这里水平方向的列不要合并成一个行,因为需要保证返回给下游的最小block,总是可以无脑从上到下阅读文字。
v_bboxes = []
for box in all_bboxes:
if box[Y0_EXT_IDX] == bound_y0 and box[Y1_EXT_IDX] == bound_y1:
v_bboxes.append(box)
"""
现在v_bboxes里面是所有的group了,每个group都是一个list
对v_bboxes里的每个group进行计算放回到sorted_layouts里
"""
v_layouts = []
for vbox in v_bboxes:
# gp.sort(key=lambda x: x[X0_IDX])
# 然后计算这个group的layout_bbox,也就是最小的x0,y0, 最大的x1,y1
x0, y0, x1, y1 = (
vbox[X0_EXT_IDX],
vbox[Y0_EXT_IDX],
vbox[X1_EXT_IDX],
vbox[Y1_EXT_IDX],
)
v_layouts.append([x0, y0, x1, y1, LAYOUT_V]) # 垂直的布局
"""
接下来利用这些连续的垂直bbox的layout_bbox的x0, x1,从垂直上切分开其余的为几个部分
"""
v_split_lines = [bound_x0]
for gp in v_bboxes:
x0, x1 = gp[X0_IDX], gp[X1_IDX]
v_split_lines.append(x0)
v_split_lines.append(x1)
v_split_lines.append(bound_x1)
unsplited_bboxes = []
for i in range(0, len(v_split_lines), 2):
start_x0, start_x1 = v_split_lines[i : i + 2]
# 然后找出[start_x0, start_x1]之间的其他bbox,这些组成一个未分割板块
bboxes_in_block = [
bbox
for bbox in all_bboxes
if bbox[X0_IDX] >= start_x0 and bbox[X1_IDX] <= start_x1
]
unsplited_bboxes.append(bboxes_in_block)
# 接着把未处理的加入到v_layouts里
for bboxes_in_block in unsplited_bboxes:
if len(bboxes_in_block) == 0:
continue
x0, y0, x1, y1 = (
min([bbox[X0_IDX] for bbox in bboxes_in_block]),
bound_y0,
max([bbox[X1_IDX] for bbox in bboxes_in_block]),
bound_y1,
)
v_layouts.append(
[x0, y0, x1, y1, LAYOUT_UNPROC]
) # 说明这篇区域未能够分析出可靠的版面
v_layouts.sort(key=lambda x: x[0]) # 按照x0排序, 也就是从左到右的顺序
for layout in v_layouts:
sorted_layout_blocks.append(
{
'layout_bbox': layout[:4],
'layout_label': layout[4],
'sub_layout': [],
}
)
"""
至此,垂直方向切成了2种类型,其一是独占一列的,其二是未处理的。
下面对这些未处理的进行垂直方向切分,这个切分要切出来类似“吕”这种类型的垂直方向的布局
"""
for i, layout in enumerate(sorted_layout_blocks):
if layout['layout_label'] == LAYOUT_UNPROC:
x0, y0, x1, y1 = layout['layout_bbox']
v_split_layouts = _vertical_align_split_v2(bboxes, [x0, y0, x1, y1])
sorted_layout_blocks[i] = {
'layout_bbox': [x0, y0, x1, y1],
'layout_label': LAYOUT_H,
'sub_layout': v_split_layouts,
}
layout['layout_label'] = LAYOUT_H # 被垂线切分成了水平布局
return sorted_layout_blocks
def split_layout(bboxes: list, boundary: tuple, page_num: int) -> list:
"""
把bboxes切割成layout
return:
[
{
"layout_bbox": [x0,y0,x1,y1],
"layout_label":"u|v|h|b", 未处理|垂直|水平|BAD_LAYOUT
"sub_layout":[] #每个元素都是[
x0,y0,
x1,y1,
block_content,
idx_x,idx_y,
content_type,
ext_x0,ext_y0,
ext_x1,ext_y1
], 并且顺序就是阅读顺序
}
]
example:
[
{
"layout_bbox": [0, 0, 100, 100],
"layout_label":"u|v|h|b",
"sub_layout":[
]
},
{
"layout_bbox": [0, 0, 100, 100],
"layout_label":"u|v|h|b",
"sub_layout":[
{
"layout_bbox": [0, 0, 100, 100],
"layout_label":"u|v|h|b",
"content_bboxes":[
[],
[],
[]
]
},
{
"layout_bbox": [0, 0, 100, 100],
"layout_label":"u|v|h|b",
"sub_layout":[
]
}
}
]
"""
sorted_layouts = [] # 最终返回的结果
boundary_x0, boundary_y0, boundary_x1, boundary_y1 = boundary
if len(bboxes) <= 1:
return [
{
'layout_bbox': [boundary_x0, boundary_y0, boundary_x1, boundary_y1],
'layout_label': LAYOUT_V,
'sub_layout': [],
}
]
"""
接下来按照先水平后垂直的顺序进行切分
"""
bboxes = paper_bbox_sort(
bboxes, boundary_x1 - boundary_x0, boundary_y1 - boundary_y0
)
sorted_layouts = _horizontal_split(bboxes, boundary) # 通过水平分割出来的layout
for i, layout in enumerate(sorted_layouts):
x0, y0, x1, y1 = layout['layout_bbox']
layout_type = layout['layout_label']
if layout_type == LAYOUT_UNPROC: # 说明是非独占单行的,这些需要垂直切分
v_split_layouts = _vertical_split(bboxes, [x0, y0, x1, y1])
"""
最后这里有个逻辑问题:如果这个函数只分离出来了一个column layout,那么这个layout分割肯定超出了算法能力范围。因为我们假定的是传进来的
box已经把行全部剥离了,所以这里必须十多个列才可以。如果只剥离出来一个layout,并且是多个box,那么就说明这个layout是无法分割的,标记为LAYOUT_UNPROC
"""
layout_label = LAYOUT_V
if len(v_split_layouts) == 1:
if len(v_split_layouts[0]['sub_layout']) == 0:
layout_label = LAYOUT_UNPROC
# logger.warning(f"WARNING: pageno={page_num}, 无法分割的layout: ", v_split_layouts)
"""
组合起来最终的layout
"""
sorted_layouts[i] = {
'layout_bbox': [x0, y0, x1, y1],
'layout_label': layout_label,
'sub_layout': v_split_layouts,
}
layout['layout_label'] = LAYOUT_H
"""
水平和垂直方向都切分完毕了。此时还有一些未处理的,这些未处理的可能是因为水平和垂直方向都无法切分。
这些最后调用_try_horizontal_mult_block_split做一次水平多个block的联合切分,如果也不能切分最终就当做BAD_LAYOUT返回
"""
# TODO
return sorted_layouts
def get_bboxes_layout(all_boxes: list, boundary: tuple, page_id: int):
"""
对利用layout排序之后的box,进行排序
return:
[
{
"layout_bbox": [x0, y0, x1, y1],
"layout_label":"u|v|h|b", 未处理|垂直|水平|BAD_LAYOUT
},
]
"""
def _preorder_traversal(layout):
"""对sorted_layouts的叶子节点,也就是len(sub_layout)==0的节点进行排序。排序按照前序遍历的顺序,也就是从上到
下,从左到右的顺序."""
sorted_layout_blocks = []
for layout in layout:
sub_layout = layout['sub_layout']
if len(sub_layout) == 0:
sorted_layout_blocks.append(layout)
else:
s = _preorder_traversal(sub_layout)
sorted_layout_blocks.extend(s)
return sorted_layout_blocks
# -------------------------------------------------------------------------------------------------------------------------
sorted_layouts = split_layout(
all_boxes, boundary, page_id
) # 先切分成layout,得到一个Tree
total_sorted_layout_blocks = _preorder_traversal(sorted_layouts)
return total_sorted_layout_blocks, sorted_layouts
def get_columns_cnt_of_layout(layout_tree):
"""获取一个layout的宽度."""
max_width_list = [0] # 初始化一个元素,防止max,min函数报错
for items in layout_tree: # 针对每一层(横切)计算列数,横着的算一列
layout_type = items['layout_label']
sub_layouts = items['sub_layout']
if len(sub_layouts) == 0:
max_width_list.append(1)
else:
if layout_type == LAYOUT_H:
max_width_list.append(1)
else:
width = 0
for sub_layout in sub_layouts:
if len(sub_layout['sub_layout']) == 0:
width += 1
else:
for lay in sub_layout['sub_layout']:
width += get_columns_cnt_of_layout([lay])
max_width_list.append(width)
return max(max_width_list)
def sort_with_layout(bboxes: list, page_width, page_height) -> (list, list):
"""输入是一个bbox的list.
获取到输入之后,先进行layout切分,然后对这些bbox进行排序。返回排序后的bboxes
"""
new_bboxes = []
for box in bboxes:
# new_bboxes.append([box[0], box[1], box[2], box[3], None, None, None, 'text', None, None, None, None])
new_bboxes.append(
[
box[0],
box[1],
box[2],
box[3],
None,
None,
None,
'text',
None,
None,
None,
None,
box[4],
]
)
layout_bboxes, _ = get_bboxes_layout(
new_bboxes, tuple([0, 0, page_width, page_height]), 0
)
if any([lay['layout_label'] == LAYOUT_UNPROC for lay in layout_bboxes]):
logger.warning('drop this pdf, reason: 复杂版面')
return None, None
sorted_bboxes = []
# 利用layout bbox每次框定一些box,然后排序
for layout in layout_bboxes:
lbox = layout['layout_bbox']
bbox_in_layout = get_bbox_in_boundary(new_bboxes, lbox)
sorted_bbox = paper_bbox_sort(
bbox_in_layout, lbox[2] - lbox[0], lbox[3] - lbox[1]
)
sorted_bboxes.extend(sorted_bbox)
return sorted_bboxes, layout_bboxes
def sort_text_block(text_block, layout_bboxes):
"""对一页的text_block进行排序."""
sorted_text_bbox = []
all_text_bbox = []
# 做一个box=>text的映射
box_to_text = {}
for blk in text_block:
box = blk['bbox']
box_to_text[(box[0], box[1], box[2], box[3])] = blk
all_text_bbox.append(box)
# text_blocks_to_sort = []
# for box in box_to_text.keys():
# text_blocks_to_sort.append([box[0], box[1], box[2], box[3], None, None, None, 'text', None, None, None, None])
# 按照layout_bboxes的顺序,对text_block进行排序
for layout in layout_bboxes:
layout_box = layout['layout_bbox']
text_bbox_in_layout = get_bbox_in_boundary(
all_text_bbox,
[
layout_box[0] - 1,
layout_box[1] - 1,
layout_box[2] + 1,
layout_box[3] + 1,
],
)
# sorted_bbox = paper_bbox_sort(text_bbox_in_layout, layout_box[2]-layout_box[0], layout_box[3]-layout_box[1])
text_bbox_in_layout.sort(
key=lambda x: x[1]
) # 一个layout内部的box,按照y0自上而下排序
# sorted_bbox = [[b] for b in text_blocks_to_sort]
for sb in text_bbox_in_layout:
sorted_text_bbox.append(box_to_text[(sb[0], sb[1], sb[2], sb[3])])
return sorted_text_bbox
-101
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@@ -1,101 +0,0 @@
"""
找到能分割布局的水平的横线、色块
"""
import os
from magic_pdf.libs.commons import fitz
from magic_pdf.libs.boxbase import _is_in_or_part_overlap
def __rect_filter_by_width(rect, page_w, page_h):
mid_x = page_w/2
if rect[0]< mid_x < rect[2]:
return True
return False
def __rect_filter_by_pos(rect, image_bboxes, table_bboxes):
"""
不能出现在table和image的位置
"""
for box in image_bboxes:
if _is_in_or_part_overlap(rect, box):
return False
for box in table_bboxes:
if _is_in_or_part_overlap(rect, box):
return False
return True
def __debug_show_page(page, bboxes1: list,bboxes2: list,bboxes3: list,):
save_path = "./tmp/debug.pdf"
if os.path.exists(save_path):
# 删除已经存在的文件
os.remove(save_path)
# 创建一个新的空白 PDF 文件
doc = fitz.open('')
width = page.rect.width
height = page.rect.height
new_page = doc.new_page(width=width, height=height)
shape = new_page.new_shape()
for bbox in bboxes1:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=fitz.pdfcolor['blue'], fill_opacity=0.2)
shape.finish()
shape.commit()
for bbox in bboxes2:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=fitz.pdfcolor['yellow'], fill_opacity=0.2)
shape.finish()
shape.commit()
for bbox in bboxes3:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=None)
shape.finish()
shape.commit()
parent_dir = os.path.dirname(save_path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
doc.save(save_path)
doc.close()
def get_spilter_of_page(page, image_bboxes, table_bboxes):
"""
获取到色块和横线
"""
cdrawings = page.get_cdrawings()
spilter_bbox = []
for block in cdrawings:
if 'fill' in block:
fill = block['fill']
if 'fill' in block and block['fill'] and block['fill']!=(1.0,1.0,1.0):
rect = block['rect']
if __rect_filter_by_width(rect, page.rect.width, page.rect.height) and __rect_filter_by_pos(rect, image_bboxes, table_bboxes):
spilter_bbox.append(list(rect))
"""过滤、修正一下这些box。因为有时候会有一些矩形,高度为0或者为负数,造成layout计算无限循环。如果是负高度或者0高度,统一修正为高度为1"""
for box in spilter_bbox:
if box[3]-box[1] <= 0:
box[3] = box[1] + 1
#__debug_show_page(page, spilter_bbox, [], [])
return spilter_bbox
-336
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@@ -1,336 +0,0 @@
"""
This is an advanced PyMuPDF utility for detecting multi-column pages.
It can be used in a shell script, or its main function can be imported and
invoked as descript below.
Features
---------
- Identify text belonging to (a variable number of) columns on the page.
- Text with different background color is handled separately, allowing for
easier treatment of side remarks, comment boxes, etc.
- Uses text block detection capability to identify text blocks and
uses the block bboxes as primary structuring principle.
- Supports ignoring footers via a footer margin parameter.
- Returns re-created text boundary boxes (integer coordinates), sorted ascending
by the top, then by the left coordinates.
Restrictions
-------------
- Only supporting horizontal, left-to-right text
- Returns a list of text boundary boxes - not the text itself. The caller is
expected to extract text from within the returned boxes.
- Text written above images is ignored altogether (option).
- This utility works as expected in most cases. The following situation cannot
be handled correctly:
* overlapping (non-disjoint) text blocks
* image captions are not recognized and are handled like normal text
Usage
------
- As a CLI shell command use
python multi_column.py input.pdf footer_margin
Where footer margin is the height of the bottom stripe to ignore on each page.
This code is intended to be modified according to your need.
- Use in a Python script as follows:
----------------------------------------------------------------------------------
from multi_column import column_boxes
# for each page execute
bboxes = column_boxes(page, footer_margin=50, no_image_text=True)
# bboxes is a list of fitz.IRect objects, that are sort ascending by their y0,
# then x0 coordinates. Their text content can be extracted by all PyMuPDF
# get_text() variants, like for instance the following:
for rect in bboxes:
print(page.get_text(clip=rect, sort=True))
----------------------------------------------------------------------------------
"""
import sys
from magic_pdf.libs.commons import fitz
def column_boxes(page, footer_margin=50, header_margin=50, no_image_text=True):
"""Determine bboxes which wrap a column."""
paths = page.get_drawings()
bboxes = []
# path rectangles
path_rects = []
# image bboxes
img_bboxes = []
# bboxes of non-horizontal text
# avoid when expanding horizontal text boxes
vert_bboxes = []
# compute relevant page area
clip = +page.rect
clip.y1 -= footer_margin # Remove footer area
clip.y0 += header_margin # Remove header area
def can_extend(temp, bb, bboxlist):
"""Determines whether rectangle 'temp' can be extended by 'bb'
without intersecting any of the rectangles contained in 'bboxlist'.
Items of bboxlist may be None if they have been removed.
Returns:
True if 'temp' has no intersections with items of 'bboxlist'.
"""
for b in bboxlist:
if not intersects_bboxes(temp, vert_bboxes) and (
b == None or b == bb or (temp & b).is_empty
):
continue
return False
return True
def in_bbox(bb, bboxes):
"""Return 1-based number if a bbox contains bb, else return 0."""
for i, bbox in enumerate(bboxes):
if bb in bbox:
return i + 1
return 0
def intersects_bboxes(bb, bboxes):
"""Return True if a bbox intersects bb, else return False."""
for bbox in bboxes:
if not (bb & bbox).is_empty:
return True
return False
def extend_right(bboxes, width, path_bboxes, vert_bboxes, img_bboxes):
"""Extend a bbox to the right page border.
Whenever there is no text to the right of a bbox, enlarge it up
to the right page border.
Args:
bboxes: (list[IRect]) bboxes to check
width: (int) page width
path_bboxes: (list[IRect]) bboxes with a background color
vert_bboxes: (list[IRect]) bboxes with vertical text
img_bboxes: (list[IRect]) bboxes of images
Returns:
Potentially modified bboxes.
"""
for i, bb in enumerate(bboxes):
# do not extend text with background color
if in_bbox(bb, path_bboxes):
continue
# do not extend text in images
if in_bbox(bb, img_bboxes):
continue
# temp extends bb to the right page border
temp = +bb
temp.x1 = width
# do not cut through colored background or images
if intersects_bboxes(temp, path_bboxes + vert_bboxes + img_bboxes):
continue
# also, do not intersect other text bboxes
check = can_extend(temp, bb, bboxes)
if check:
bboxes[i] = temp # replace with enlarged bbox
return [b for b in bboxes if b != None]
def clean_nblocks(nblocks):
"""Do some elementary cleaning."""
# 1. remove any duplicate blocks.
blen = len(nblocks)
if blen < 2:
return nblocks
start = blen - 1
for i in range(start, -1, -1):
bb1 = nblocks[i]
bb0 = nblocks[i - 1]
if bb0 == bb1:
del nblocks[i]
# 2. repair sequence in special cases:
# consecutive bboxes with almost same bottom value are sorted ascending
# by x-coordinate.
y1 = nblocks[0].y1 # first bottom coordinate
i0 = 0 # its index
i1 = -1 # index of last bbox with same bottom
# Iterate over bboxes, identifying segments with approx. same bottom value.
# Replace every segment by its sorted version.
for i in range(1, len(nblocks)):
b1 = nblocks[i]
if abs(b1.y1 - y1) > 10: # different bottom
if i1 > i0: # segment length > 1? Sort it!
nblocks[i0 : i1 + 1] = sorted(
nblocks[i0 : i1 + 1], key=lambda b: b.x0
)
y1 = b1.y1 # store new bottom value
i0 = i # store its start index
i1 = i # store current index
if i1 > i0: # segment waiting to be sorted
nblocks[i0 : i1 + 1] = sorted(nblocks[i0 : i1 + 1], key=lambda b: b.x0)
return nblocks
# extract vector graphics
for p in paths:
path_rects.append(p["rect"].irect)
path_bboxes = path_rects
# sort path bboxes by ascending top, then left coordinates
path_bboxes.sort(key=lambda b: (b.y0, b.x0))
# bboxes of images on page, no need to sort them
for item in page.get_images():
img_bboxes.extend(page.get_image_rects(item[0]))
# blocks of text on page
blocks = page.get_text(
"dict",
flags=fitz.TEXTFLAGS_TEXT,
clip=clip,
)["blocks"]
# Make block rectangles, ignoring non-horizontal text
for b in blocks:
bbox = fitz.IRect(b["bbox"]) # bbox of the block
# ignore text written upon images
if no_image_text and in_bbox(bbox, img_bboxes):
continue
# confirm first line to be horizontal
line0 = b["lines"][0] # get first line
if line0["dir"] != (1, 0): # only accept horizontal text
vert_bboxes.append(bbox)
continue
srect = fitz.EMPTY_IRECT()
for line in b["lines"]:
lbbox = fitz.IRect(line["bbox"])
text = "".join([s["text"].strip() for s in line["spans"]])
if len(text) > 1:
srect |= lbbox
bbox = +srect
if not bbox.is_empty:
bboxes.append(bbox)
# Sort text bboxes by ascending background, top, then left coordinates
bboxes.sort(key=lambda k: (in_bbox(k, path_bboxes), k.y0, k.x0))
# Extend bboxes to the right where possible
bboxes = extend_right(
bboxes, int(page.rect.width), path_bboxes, vert_bboxes, img_bboxes
)
# immediately return of no text found
if bboxes == []:
return []
# --------------------------------------------------------------------
# Join bboxes to establish some column structure
# --------------------------------------------------------------------
# the final block bboxes on page
nblocks = [bboxes[0]] # pre-fill with first bbox
bboxes = bboxes[1:] # remaining old bboxes
for i, bb in enumerate(bboxes): # iterate old bboxes
check = False # indicates unwanted joins
# check if bb can extend one of the new blocks
for j in range(len(nblocks)):
nbb = nblocks[j] # a new block
# never join across columns
if bb == None or nbb.x1 < bb.x0 or bb.x1 < nbb.x0:
continue
# never join across different background colors
if in_bbox(nbb, path_bboxes) != in_bbox(bb, path_bboxes):
continue
temp = bb | nbb # temporary extension of new block
check = can_extend(temp, nbb, nblocks)
if check == True:
break
if not check: # bb cannot be used to extend any of the new bboxes
nblocks.append(bb) # so add it to the list
j = len(nblocks) - 1 # index of it
temp = nblocks[j] # new bbox added
# check if some remaining bbox is contained in temp
check = can_extend(temp, bb, bboxes)
if check == False:
nblocks.append(bb)
else:
nblocks[j] = temp
bboxes[i] = None
# do some elementary cleaning
nblocks = clean_nblocks(nblocks)
# return identified text bboxes
return nblocks
if __name__ == "__main__":
"""Only for debugging purposes, currently.
Draw red borders around the returned text bboxes and insert
the bbox number.
Then save the file under the name "input-blocks.pdf".
"""
# get the file name
filename = sys.argv[1]
# check if footer margin is given
if len(sys.argv) > 2:
footer_margin = int(sys.argv[2])
else: # use default vaue
footer_margin = 50
# check if header margin is given
if len(sys.argv) > 3:
header_margin = int(sys.argv[3])
else: # use default vaue
header_margin = 50
# open document
doc = fitz.open(filename)
# iterate over the pages
for page in doc:
# remove any geometry issues
page.wrap_contents()
# get the text bboxes
bboxes = column_boxes(page, footer_margin=footer_margin, header_margin=header_margin)
# prepare a canvas to draw rectangles and text
shape = page.new_shape()
# iterate over the bboxes
for i, rect in enumerate(bboxes):
shape.draw_rect(rect) # draw a border
# write sequence number
shape.insert_text(rect.tl + (5, 15), str(i), color=fitz.pdfcolor["red"])
# finish drawing / text with color red
shape.finish(color=fitz.pdfcolor["red"])
shape.commit() # store to the page
# save document with text bboxes
doc.ez_save(filename.replace(".pdf", "-blocks.pdf"))
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import os
import csv
import json
import pandas as pd
from pandas import DataFrame as df
from matplotlib import pyplot as plt
from termcolor import cprint
"""
Execute this script in the following way:
1. Make sure there are pdf_dic.json files under the directory code-clean/tmp/unittest/md/, such as the following:
code-clean/tmp/unittest/md/scihub/scihub_00500000/libgen.scimag00527000-00527999.zip_10.1002/app.25178/pdf_dic.json
2. Under the directory code-clean, execute the following command:
$ python -m libs.calc_span_stats
"""
def print_green_on_red(text):
cprint(text, "green", "on_red", attrs=["bold"], end="\n\n")
def print_green(text):
print()
cprint(text, "green", attrs=["bold"], end="\n\n")
def print_red(text):
print()
cprint(text, "red", attrs=["bold"], end="\n\n")
def safe_get(dict_obj, key, default):
val = dict_obj.get(key)
if val is None:
return default
else:
return val
class SpanStatsCalc:
"""Calculate statistics of span."""
def draw_charts(self, span_stats: pd.DataFrame, fig_num: int, save_path: str):
"""Draw multiple figures in one figure."""
# make a canvas
fig = plt.figure(fig_num, figsize=(20, 20))
pass
def calc_stats_per_dict(self, pdf_dict) -> pd.DataFrame:
"""Calculate statistics per pdf_dict."""
span_stats = pd.DataFrame()
span_stats = []
span_id = 0
for page_id, blocks in pdf_dict.items():
if page_id.startswith("page_"):
if "para_blocks" in blocks.keys():
for para_block in blocks["para_blocks"]:
for line in para_block["lines"]:
for span in line["spans"]:
span_text = safe_get(span, "text", "")
span_font_name = safe_get(span, "font", "")
span_font_size = safe_get(span, "size", 0)
span_font_color = safe_get(span, "color", "")
span_font_flags = safe_get(span, "flags", 0)
span_font_flags_decoded = safe_get(span, "decomposed_flags", {})
span_is_super_script = safe_get(span_font_flags_decoded, "is_superscript", False)
span_is_italic = safe_get(span_font_flags_decoded, "is_italic", False)
span_is_serifed = safe_get(span_font_flags_decoded, "is_serifed", False)
span_is_sans_serifed = safe_get(span_font_flags_decoded, "is_sans_serifed", False)
span_is_monospaced = safe_get(span_font_flags_decoded, "is_monospaced", False)
span_is_proportional = safe_get(span_font_flags_decoded, "is_proportional", False)
span_is_bold = safe_get(span_font_flags_decoded, "is_bold", False)
span_stats.append(
{
"span_id": span_id, # id of span
"page_id": page_id, # page number of pdf
"span_text": span_text, # text of span
"span_font_name": span_font_name, # font name of span
"span_font_size": span_font_size, # font size of span
"span_font_color": span_font_color, # font color of span
"span_font_flags": span_font_flags, # font flags of span
"span_is_superscript": int(
span_is_super_script
), # indicate whether the span is super script or not
"span_is_italic": int(span_is_italic), # indicate whether the span is italic or not
"span_is_serifed": int(span_is_serifed), # indicate whether the span is serifed or not
"span_is_sans_serifed": int(
span_is_sans_serifed
), # indicate whether the span is sans serifed or not
"span_is_monospaced": int(
span_is_monospaced
), # indicate whether the span is monospaced or not
"span_is_proportional": int(
span_is_proportional
), # indicate whether the span is proportional or not
"span_is_bold": int(span_is_bold), # indicate whether the span is bold or not
}
)
span_id += 1
span_stats = pd.DataFrame(span_stats)
# print(span_stats)
return span_stats
def __find_pdf_dic_files(
jf_name="pdf_dic.json",
base_code_name="code-clean",
tgt_base_dir_name="tmp",
unittest_dir_name="unittest",
md_dir_name="md",
book_names=[
"scihub",
], # other possible values: "zlib", "arxiv" and so on
):
pdf_dict_files = []
curr_dir = os.path.dirname(__file__)
for i in range(len(curr_dir)):
if curr_dir[i : i + len(base_code_name)] == base_code_name:
base_code_dir_name = curr_dir[: i + len(base_code_name)]
for book_name in book_names:
search_dir_relative_name = os.path.join(tgt_base_dir_name, unittest_dir_name, md_dir_name, book_name)
if os.path.exists(base_code_dir_name):
search_dir_name = os.path.join(base_code_dir_name, search_dir_relative_name)
for root, dirs, files in os.walk(search_dir_name):
for file in files:
if file == jf_name:
pdf_dict_files.append(os.path.join(root, file))
break
return pdf_dict_files
def combine_span_texts(group_df, span_stats):
combined_span_texts = []
for _, row in group_df.iterrows():
curr_span_id = row.name
curr_span_text = row["span_text"]
pre_span_id = curr_span_id - 1
pre_span_text = span_stats.at[pre_span_id, "span_text"] if pre_span_id in span_stats.index else ""
next_span_id = curr_span_id + 1
next_span_text = span_stats.at[next_span_id, "span_text"] if next_span_id in span_stats.index else ""
# pointer_sign is a right arrow if the span is superscript, otherwise it is a down arrow
pointer_sign = "→ → → "
combined_text = "\n".join([pointer_sign + pre_span_text, pointer_sign + curr_span_text, pointer_sign + next_span_text])
combined_span_texts.append(combined_text)
return "\n\n".join(combined_span_texts)
# pd.set_option("display.max_colwidth", None) # 设置为 None 来显示完整的文本
pd.set_option("display.max_rows", None) # 设置为 None 来显示更多的行
def main():
pdf_dict_files = __find_pdf_dic_files()
# print(pdf_dict_files)
span_stats_calc = SpanStatsCalc()
for pdf_dict_file in pdf_dict_files:
print("-" * 100)
print_green_on_red(f"Processing {pdf_dict_file}")
with open(pdf_dict_file, "r", encoding="utf-8") as f:
pdf_dict = json.load(f)
raw_df = span_stats_calc.calc_stats_per_dict(pdf_dict)
save_path = pdf_dict_file.replace("pdf_dic.json", "span_stats_raw.csv")
raw_df.to_csv(save_path, index=False)
filtered_df = raw_df[raw_df["span_is_superscript"] == 1]
if filtered_df.empty:
print("No superscript span found!")
continue
filtered_grouped_df = filtered_df.groupby(["span_font_name", "span_font_size", "span_font_color"])
combined_span_texts = filtered_grouped_df.apply(combine_span_texts, span_stats=raw_df) # type: ignore
final_df = filtered_grouped_df.size().reset_index(name="count")
final_df["span_texts"] = combined_span_texts.reset_index(level=[0, 1, 2], drop=True)
print(final_df)
final_df["span_texts"] = final_df["span_texts"].apply(lambda x: x.replace("\n", "\r\n"))
save_path = pdf_dict_file.replace("pdf_dic.json", "span_stats_final.csv")
# 使用 UTF-8 编码并添加 BOM,确保所有字段被双引号包围
final_df.to_csv(save_path, index=False, encoding="utf-8-sig", quoting=csv.QUOTE_ALL)
# 创建一个 2x2 的图表布局
fig, axs = plt.subplots(2, 2, figsize=(15, 10))
# 按照 span_font_name 分类作图
final_df.groupby("span_font_name")["count"].sum().plot(kind="bar", ax=axs[0, 0], title="By Font Name")
# 按照 span_font_size 分类作图
final_df.groupby("span_font_size")["count"].sum().plot(kind="bar", ax=axs[0, 1], title="By Font Size")
# 按照 span_font_color 分类作图
final_df.groupby("span_font_color")["count"].sum().plot(kind="bar", ax=axs[1, 0], title="By Font Color")
# 按照 span_font_name、span_font_size 和 span_font_color 共同分类作图
grouped = final_df.groupby(["span_font_name", "span_font_size", "span_font_color"])
grouped["count"].sum().unstack().plot(kind="bar", ax=axs[1, 1], title="Combined Grouping")
# 调整布局
plt.tight_layout()
# 显示图表
# plt.show()
# 保存图表到 PNG 文件
save_path = pdf_dict_file.replace("pdf_dic.json", "span_stats_combined.png")
plt.savefig(save_path)
# 清除画布
plt.clf()
if __name__ == "__main__":
main()
@@ -1,21 +0,0 @@
from collections import Counter
from magic_pdf.libs.language import detect_lang
def get_language_from_model(model_list: list):
language_lst = []
for ocr_page_info in model_list:
page_text = ""
layout_dets = ocr_page_info["layout_dets"]
for layout_det in layout_dets:
category_id = layout_det["category_id"]
allow_category_id_list = [15]
if category_id in allow_category_id_list:
page_text += layout_det["text"]
page_language = detect_lang(page_text)
language_lst.append(page_language)
# 统计text_language_list中每种语言的个数
count_dict = Counter(language_lst)
# 输出text_language_list中出现的次数最多的语言
language = max(count_dict, key=count_dict.get)
return language
-203
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@@ -1,203 +0,0 @@
import re
from os import path
from collections import Counter
from loguru import logger
# from langdetect import detect
import spacy
import en_core_web_sm
import zh_core_web_sm
from magic_pdf.libs.language import detect_lang
class NLPModels:
"""
How to upload local models to s3:
- config aws cli:
doc\SETUP-CLI.md
doc\setup_cli.sh
app\config\__init__.py
- $ cd {local_dir_storing_models}
- $ ls models
en_core_web_sm-3.7.1/
zh_core_web_sm-3.7.0/
- $ aws s3 sync models/ s3://llm-infra/models --profile=p_project_norm
- $ aws s3 --profile=p_project_norm ls s3://llm-infra/models/
PRE en_core_web_sm-3.7.1/
PRE zh_core_web_sm-3.7.0/
"""
def __init__(self):
# if OS is windows, set "TMP_DIR" to "D:/tmp"
home_dir = path.expanduser("~")
self.default_local_path = path.join(home_dir, ".nlp_models")
self.default_shared_path = "/share/pdf_processor/nlp_models"
self.default_hdfs_path = "hdfs://pdf_processor/nlp_models"
self.default_s3_path = "s3://llm-infra/models"
self.nlp_models = self.nlp_models = {
"en_core_web_sm": {
"type": "spacy",
"version": "3.7.1",
},
"en_core_web_md": {
"type": "spacy",
"version": "3.7.1",
},
"en_core_web_lg": {
"type": "spacy",
"version": "3.7.1",
},
"zh_core_web_sm": {
"type": "spacy",
"version": "3.7.0",
},
"zh_core_web_md": {
"type": "spacy",
"version": "3.7.0",
},
"zh_core_web_lg": {
"type": "spacy",
"version": "3.7.0",
},
}
self.en_core_web_sm_model = en_core_web_sm.load()
self.zh_core_web_sm_model = zh_core_web_sm.load()
def load_model(self, model_name, model_type, model_version):
if (
model_name in self.nlp_models
and self.nlp_models[model_name]["type"] == model_type
and self.nlp_models[model_name]["version"] == model_version
):
return spacy.load(model_name) if spacy.util.is_package(model_name) else None
else:
logger.error(f"Unsupported model name or version: {model_name} {model_version}")
return None
def detect_language(self, text, use_langdetect=False):
if len(text) == 0:
return None
if use_langdetect:
# print("use_langdetect")
# print(detect_lang(text))
# return detect_lang(text)
if detect_lang(text) == "zh":
return "zh"
else:
return "en"
if not use_langdetect:
en_count = len(re.findall(r"[a-zA-Z]", text))
cn_count = len(re.findall(r"[\u4e00-\u9fff]", text))
if en_count > cn_count:
return "en"
if cn_count > en_count:
return "zh"
def detect_entity_catgr_using_nlp(self, text, threshold=0.5):
"""
Detect entity categories using NLP models and return the most frequent entity types.
Parameters
----------
text : str
Text to be processed.
Returns
-------
str
The most frequent entity type.
"""
lang = self.detect_language(text, use_langdetect=True)
if lang == "en":
nlp_model = self.en_core_web_sm_model
elif lang == "zh":
nlp_model = self.zh_core_web_sm_model
else:
# logger.error(f"Unsupported language: {lang}")
return {}
# Splitting text into smaller parts
text_parts = re.split(r"[,;,;、\s & |]+", text)
text_parts = [part for part in text_parts if not re.match(r"[\d\W]+", part)] # Remove non-words
text_combined = " ".join(text_parts)
try:
doc = nlp_model(text_combined)
entity_counts = Counter([ent.label_ for ent in doc.ents])
word_counts_in_entities = Counter()
for ent in doc.ents:
word_counts_in_entities[ent.label_] += len(ent.text.split())
total_words_in_entities = sum(word_counts_in_entities.values())
total_words = len([token for token in doc if not token.is_punct])
if total_words_in_entities == 0 or total_words == 0:
return None
entity_percentage = total_words_in_entities / total_words
if entity_percentage < 0.5:
return None
most_common_entity, word_count = word_counts_in_entities.most_common(1)[0]
entity_percentage = word_count / total_words_in_entities
if entity_percentage >= threshold:
return most_common_entity
else:
return None
except Exception as e:
logger.error(f"Error in entity detection: {e}")
return None
def __main__():
nlpModel = NLPModels()
test_strings = [
"张三",
"张三, 李四,王五; 赵六",
"John Doe",
"Jane Smith",
"Lee, John",
"John Doe, Jane Smith; Alice Johnson,Bob Lee",
"孙七, Michael Jordan;赵八",
"David Smith Michael O'Connor; Kevin ßáçøñ",
"李雷·韩梅梅, 张三·李四",
"Charles Robert Darwin, Isaac Newton",
"莱昂纳多·迪卡普里奥, 杰克·吉伦哈尔",
"John Doe, Jane Smith; Alice Johnson",
"张三, 李四,王五; 赵六",
"Lei Wang, Jia Li, and Xiaojun Chen, LINKE YANG OU, and YUAN ZHANG",
"Rachel Mills & William Barry & Susanne B. Haga",
"Claire Chabut* and Jean-François Bussières",
"1 Department of Chemistry, Northeastern University, Shenyang 110004, China 2 State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China",
"Changchun",
"china",
"Rongjun Song, 1,2 Baoyan Zhang, 1 Baotong Huang, 2 Tao Tang 2",
"Synergistic Effect of Supported Nickel Catalyst with Intumescent Flame-Retardants on Flame Retardancy and Thermal Stability of Polypropylene",
"Synergistic Effect of Supported Nickel Catalyst with",
"Intumescent Flame-Retardants on Flame Retardancy",
"and Thermal Stability of Polypropylene",
]
for test in test_strings:
print()
print(f"Original String: {test}")
result = nlpModel.detect_entity_catgr_using_nlp(test)
print(f"Detected entities: {result}")
if __name__ == "__main__":
__main__()
-33
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@@ -1,33 +0,0 @@
import math
def __inc_dict_val(mp, key, val_inc:int):
if mp.get(key):
mp[key] = mp[key] + val_inc
else:
mp[key] = val_inc
def get_text_block_base_info(block):
"""
获取这个文本块里的字体的颜色、字号、字体
按照正文字数最多的返回
"""
counter = {}
for line in block['lines']:
for span in line['spans']:
color = span['color']
size = round(span['size'], 2)
font = span['font']
txt_len = len(span['text'])
__inc_dict_val(counter, (color, size, font), txt_len)
c, s, ft = max(counter, key=counter.get)
return c, s, ft
-308
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@@ -1,308 +0,0 @@
from magic_pdf.libs.commons import fitz
import os
def draw_bbox_on_page(raw_pdf_doc: fitz.Document, paras_dict:dict, save_path: str):
"""
在page上画出bbox,保存到save_path
"""
# 检查文件是否存在
is_new_pdf = False
if os.path.exists(save_path):
# 打开现有的 PDF 文件
doc = fitz.open(save_path)
else:
# 创建一个新的空白 PDF 文件
is_new_pdf = True
doc = fitz.open('')
color_map = {
'image': fitz.pdfcolor["yellow"],
'text': fitz.pdfcolor['blue'],
"table": fitz.pdfcolor['green']
}
for k, v in paras_dict.items():
page_idx = v['page_idx']
width = raw_pdf_doc[page_idx].rect.width
height = raw_pdf_doc[page_idx].rect.height
new_page = doc.new_page(width=width, height=height)
shape = new_page.new_shape()
for order, block in enumerate(v['preproc_blocks']):
rect = fitz.Rect(block['bbox'])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=color_map['text'], fill_opacity=0.2)
shape.finish()
shape.commit()
for img in v['images']:
# 原始box画上去
rect = fitz.Rect(img['bbox'])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=fitz.pdfcolor['yellow'])
shape.finish()
shape.commit()
for img in v['image_backup']:
# 原始box画上去
rect = fitz.Rect(img['bbox'])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['yellow'], fill=None)
shape.finish()
shape.commit()
for tb in v['droped_text_block']:
# 原始box画上去
rect = fitz.Rect(tb['bbox'])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=fitz.pdfcolor['black'], fill_opacity=0.4)
shape.finish()
shape.commit()
# TODO table
for tb in v['tables']:
rect = fitz.Rect(tb['bbox'])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=fitz.pdfcolor['green'], fill_opacity=0.2)
shape.finish()
shape.commit()
parent_dir = os.path.dirname(save_path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
if is_new_pdf:
doc.save(save_path)
else:
doc.saveIncr()
doc.close()
def debug_show_bbox(raw_pdf_doc: fitz.Document, page_idx: int, bboxes: list, droped_bboxes:list, expect_drop_bboxes:list, save_path: str, expected_page_id:int):
"""
以覆盖的方式写个临时的pdf,用于debug
"""
if page_idx!=expected_page_id:
return
if os.path.exists(save_path):
# 删除已经存在的文件
os.remove(save_path)
# 创建一个新的空白 PDF 文件
doc = fitz.open('')
width = raw_pdf_doc[page_idx].rect.width
height = raw_pdf_doc[page_idx].rect.height
new_page = doc.new_page(width=width, height=height)
shape = new_page.new_shape()
for bbox in bboxes:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=fitz.pdfcolor['blue'], fill_opacity=0.2)
shape.finish()
shape.commit()
for bbox in droped_bboxes:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=fitz.pdfcolor['yellow'], fill_opacity=0.2)
shape.finish()
shape.commit()
for bbox in expect_drop_bboxes:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=None)
shape.finish()
shape.commit()
# shape.insert_textbox(fitz.Rect(200, 0, 600, 20), f"total bboxes: {len(bboxes)}", fontname="helv", fontsize=12,
# color=(0, 0, 0))
# shape.finish(color=fitz.pdfcolor['black'])
# shape.commit()
parent_dir = os.path.dirname(save_path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
doc.save(save_path)
doc.close()
def debug_show_page(page, bboxes1: list,bboxes2: list,bboxes3: list,):
save_path = "./tmp/debug.pdf"
if os.path.exists(save_path):
# 删除已经存在的文件
os.remove(save_path)
# 创建一个新的空白 PDF 文件
doc = fitz.open('')
width = page.rect.width
height = page.rect.height
new_page = doc.new_page(width=width, height=height)
shape = new_page.new_shape()
for bbox in bboxes1:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=fitz.pdfcolor['blue'], fill_opacity=0.2)
shape.finish()
shape.commit()
for bbox in bboxes2:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=None, fill=fitz.pdfcolor['yellow'], fill_opacity=0.2)
shape.finish()
shape.commit()
for bbox in bboxes3:
# 原始box画上去
rect = fitz.Rect(*bbox[0:4])
shape = new_page.new_shape()
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=None)
shape.finish()
shape.commit()
parent_dir = os.path.dirname(save_path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
doc.save(save_path)
doc.close()
def draw_layout_bbox_on_page(raw_pdf_doc: fitz.Document, paras_dict:dict, header, footer, pdf_path: str):
"""
在page上画出bbox,保存到save_path
"""
# 检查文件是否存在
is_new_pdf = False
if os.path.exists(pdf_path):
# 打开现有的 PDF 文件
doc = fitz.open(pdf_path)
else:
# 创建一个新的空白 PDF 文件
is_new_pdf = True
doc = fitz.open('')
for k, v in paras_dict.items():
page_idx = v['page_idx']
layouts = v['layout_bboxes']
page = doc[page_idx]
shape = page.new_shape()
for order, layout in enumerate(layouts):
border_offset = 1
rect_box = layout['layout_bbox']
layout_label = layout['layout_label']
fill_color = fitz.pdfcolor['pink'] if layout_label=='U' else None
rect_box = [rect_box[0]+1, rect_box[1]-border_offset, rect_box[2]-1, rect_box[3]+border_offset]
rect = fitz.Rect(*rect_box)
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=fill_color, fill_opacity=0.4)
"""
draw order text on layout box
"""
font_size = 10
shape.insert_text((rect_box[0] + 1, rect_box[1] + font_size), f"{order}", fontsize=font_size, color=(0, 0, 0))
"""画上footer header"""
if header:
shape.draw_rect(fitz.Rect(header))
shape.finish(color=None, fill=fitz.pdfcolor['black'], fill_opacity=0.2)
if footer:
shape.draw_rect(fitz.Rect(footer))
shape.finish(color=None, fill=fitz.pdfcolor['black'], fill_opacity=0.2)
shape.commit()
if is_new_pdf:
doc.save(pdf_path)
else:
doc.saveIncr()
doc.close()
@DeprecationWarning
def draw_layout_on_page(raw_pdf_doc: fitz.Document, page_idx: int, page_layout: list, pdf_path: str):
"""
把layout的box用红色边框花在pdf_path的page_idx上
"""
def draw(shape, layout, fill_color=fitz.pdfcolor['pink']):
border_offset = 1
rect_box = layout['layout_bbox']
layout_label = layout['layout_label']
sub_layout = layout['sub_layout']
if len(sub_layout)==0:
fill_color = fill_color if layout_label=='U' else None
rect_box = [rect_box[0]+1, rect_box[1]-border_offset, rect_box[2]-1, rect_box[3]+border_offset]
rect = fitz.Rect(*rect_box)
shape.draw_rect(rect)
shape.finish(color=fitz.pdfcolor['red'], fill=fill_color, fill_opacity=0.2)
# if layout_label=='U':
# bad_boxes = layout.get("bad_boxes", [])
# for bad_box in bad_boxes:
# rect = fitz.Rect(*bad_box)
# shape.draw_rect(rect)
# shape.finish(color=fitz.pdfcolor['red'], fill=fitz.pdfcolor['red'], fill_opacity=0.2)
# else:
# rect = fitz.Rect(*rect_box)
# shape.draw_rect(rect)
# shape.finish(color=fitz.pdfcolor['blue'])
for sub_layout in sub_layout:
draw(shape, sub_layout)
shape.commit()
# 检查文件是否存在
is_new_pdf = False
if os.path.exists(pdf_path):
# 打开现有的 PDF 文件
doc = fitz.open(pdf_path)
else:
# 创建一个新的空白 PDF 文件
is_new_pdf = True
doc = fitz.open('')
page = doc[page_idx]
shape = page.new_shape()
for order, layout in enumerate(page_layout):
draw(shape, layout, fitz.pdfcolor['yellow'])
# shape.insert_textbox(fitz.Rect(200, 0, 600, 20), f"total bboxes: {len(layout)}", fontname="helv", fontsize=12,
# color=(0, 0, 0))
# shape.finish(color=fitz.pdfcolor['black'])
# shape.commit()
parent_dir = os.path.dirname(pdf_path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
if is_new_pdf:
doc.save(pdf_path)
else:
doc.saveIncr()
doc.close()
@@ -1,562 +0,0 @@
import os
import unicodedata
from magic_pdf.para.commons import *
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
class BlockContinuationProcessor:
"""
This class is used to process the blocks to detect block continuations.
"""
def __init__(self) -> None:
pass
def __is_similar_font_type(self, font_type1, font_type2, prefix_length_ratio=0.3):
"""
This function checks if the two font types are similar.
Definition of similar font types: the two font types have a common prefix,
and the length of the common prefix is at least a certain ratio of the length of the shorter font type.
Parameters
----------
font_type1 : str
font type 1
font_type2 : str
font type 2
prefix_length_ratio : float
minimum ratio of the common prefix length to the length of the shorter font type
Returns
-------
bool
True if the two font types are similar, False otherwise.
"""
if isinstance(font_type1, list):
font_type1 = font_type1[0] if font_type1 else ""
if isinstance(font_type2, list):
font_type2 = font_type2[0] if font_type2 else ""
if font_type1 == font_type2:
return True
# Find the length of the common prefix
common_prefix_length = len(os.path.commonprefix([font_type1, font_type2]))
# Calculate the minimum prefix length based on the ratio
min_prefix_length = int(min(len(font_type1), len(font_type2)) * prefix_length_ratio)
return common_prefix_length >= min_prefix_length
def __is_same_block_font(self, block1, block2):
"""
This function compares the font of block1 and block2
Parameters
----------
block1 : dict
block1
block2 : dict
block2
Returns
-------
is_same : bool
True if block1 and block2 have the same font, else False
"""
block_1_font_type = safe_get(block1, "block_font_type", "")
block_1_font_size = safe_get(block1, "block_font_size", 0)
block_1_avg_char_width = safe_get(block1, "avg_char_width", 0)
block_2_font_type = safe_get(block2, "block_font_type", "")
block_2_font_size = safe_get(block2, "block_font_size", 0)
block_2_avg_char_width = safe_get(block2, "avg_char_width", 0)
if isinstance(block_1_font_size, list):
block_1_font_size = block_1_font_size[0] if block_1_font_size else 0
if isinstance(block_2_font_size, list):
block_2_font_size = block_2_font_size[0] if block_2_font_size else 0
block_1_text = safe_get(block1, "text", "")
block_2_text = safe_get(block2, "text", "")
if block_1_avg_char_width == 0 or block_2_avg_char_width == 0:
return False
if not block_1_text or not block_2_text:
return False
else:
text_len_ratio = len(block_2_text) / len(block_1_text)
if text_len_ratio < 0.2:
avg_char_width_condition = (
abs(block_1_avg_char_width - block_2_avg_char_width) / min(block_1_avg_char_width, block_2_avg_char_width)
< 0.5
)
else:
avg_char_width_condition = (
abs(block_1_avg_char_width - block_2_avg_char_width) / min(block_1_avg_char_width, block_2_avg_char_width)
< 0.2
)
block_font_size_condtion = abs(block_1_font_size - block_2_font_size) < 1
return (
self.__is_similar_font_type(block_1_font_type, block_2_font_type)
and avg_char_width_condition
and block_font_size_condtion
)
def _is_alphabet_char(self, char):
if (char >= "\u0041" and char <= "\u005a") or (char >= "\u0061" and char <= "\u007a"):
return True
else:
return False
def _is_chinese_char(self, char):
if char >= "\u4e00" and char <= "\u9fa5":
return True
else:
return False
def _is_other_letter_char(self, char):
try:
cat = unicodedata.category(char)
if cat == "Lu" or cat == "Ll":
return not self._is_alphabet_char(char) and not self._is_chinese_char(char)
except TypeError:
print("The input to the function must be a single character.")
return False
def _is_year(self, s: str):
try:
number = int(s)
return 1900 <= number <= 2099
except ValueError:
return False
def __is_para_font_consistent(self, para_1, para_2):
"""
This function compares the font of para1 and para2
Parameters
----------
para1 : dict
para1
para2 : dict
para2
Returns
-------
is_same : bool
True if para1 and para2 have the same font, else False
"""
if para_1 is None or para_2 is None:
return False
para_1_font_type = safe_get(para_1, "para_font_type", "")
para_1_font_size = safe_get(para_1, "para_font_size", 0)
para_1_font_color = safe_get(para_1, "para_font_color", "")
para_2_font_type = safe_get(para_2, "para_font_type", "")
para_2_font_size = safe_get(para_2, "para_font_size", 0)
para_2_font_color = safe_get(para_2, "para_font_color", "")
if isinstance(para_1_font_type, list): # get the most common font type
para_1_font_type = max(set(para_1_font_type), key=para_1_font_type.count)
if isinstance(para_2_font_type, list):
para_2_font_type = max(set(para_2_font_type), key=para_2_font_type.count)
if isinstance(para_1_font_size, list): # compute average font type
para_1_font_size = sum(para_1_font_size) / len(para_1_font_size)
if isinstance(para_2_font_size, list): # compute average font type
para_2_font_size = sum(para_2_font_size) / len(para_2_font_size)
return (
self.__is_similar_font_type(para_1_font_type, para_2_font_type)
and abs(para_1_font_size - para_2_font_size) < 1.5
# and para_font_color1 == para_font_color2
)
def _is_para_puncs_consistent(self, para_1, para_2):
"""
This function determines whether para1 and para2 are originally from the same paragraph by checking the puncs of para1(former) and para2(latter)
Parameters
----------
para1 : dict
para1
para2 : dict
para2
Returns
-------
is_same : bool
True if para1 and para2 are from the same paragraph by using the puncs, else False
"""
para_1_text = safe_get(para_1, "para_text", "").strip()
para_2_text = safe_get(para_2, "para_text", "").strip()
para_1_bboxes = safe_get(para_1, "para_bbox", [])
para_1_font_sizes = safe_get(para_1, "para_font_size", 0)
para_2_bboxes = safe_get(para_2, "para_bbox", [])
para_2_font_sizes = safe_get(para_2, "para_font_size", 0)
# print_yellow(" Features of determine puncs_consistent:")
# print(f" para_1_text: {para_1_text}")
# print(f" para_2_text: {para_2_text}")
# print(f" para_1_bboxes: {para_1_bboxes}")
# print(f" para_2_bboxes: {para_2_bboxes}")
# print(f" para_1_font_sizes: {para_1_font_sizes}")
# print(f" para_2_font_sizes: {para_2_font_sizes}")
if is_nested_list(para_1_bboxes):
x0_1, y0_1, x1_1, y1_1 = para_1_bboxes[-1]
else:
x0_1, y0_1, x1_1, y1_1 = para_1_bboxes
if is_nested_list(para_2_bboxes):
x0_2, y0_2, x1_2, y1_2 = para_2_bboxes[0]
para_2_font_sizes = para_2_font_sizes[0] # type: ignore
else:
x0_2, y0_2, x1_2, y1_2 = para_2_bboxes
right_align_threshold = 0.5 * (para_1_font_sizes + para_2_font_sizes) * 0.8
are_two_paras_right_aligned = abs(x1_1 - x1_2) < right_align_threshold
left_indent_threshold = 0.5 * (para_1_font_sizes + para_2_font_sizes) * 0.8
is_para1_left_indent_than_papa2 = x0_1 - x0_2 > left_indent_threshold
is_para2_left_indent_than_papa1 = x0_2 - x0_1 > left_indent_threshold
# Check if either para_text1 or para_text2 is empty
if not para_1_text or not para_2_text:
return False
# Define the end puncs for a sentence to end and hyphen
end_puncs = [".", "?", "!", "。", "?", "!", "…"]
hyphen = ["-", "—"]
# Check if para_text1 ends with either hyphen or non-end punctuation or spaces
para_1_end_with_hyphen = para_1_text and para_1_text[-1] in hyphen
para_1_end_with_end_punc = para_1_text and para_1_text[-1] in end_puncs
para_1_end_with_space = para_1_text and para_1_text[-1] == " "
para_1_not_end_with_end_punc = para_1_text and para_1_text[-1] not in end_puncs
# print_yellow(f" para_1_end_with_hyphen: {para_1_end_with_hyphen}")
# print_yellow(f" para_1_end_with_end_punc: {para_1_end_with_end_punc}")
# print_yellow(f" para_1_not_end_with_end_punc: {para_1_not_end_with_end_punc}")
# print_yellow(f" para_1_end_with_space: {para_1_end_with_space}")
if para_1_end_with_hyphen: # If para_text1 ends with hyphen
# print_red(f"para_1 is end with hyphen.")
para_2_is_consistent = para_2_text and (
para_2_text[0] in hyphen
or (self._is_alphabet_char(para_2_text[0]) and para_2_text[0].islower())
or (self._is_chinese_char(para_2_text[0]))
or (self._is_other_letter_char(para_2_text[0]))
)
if para_2_is_consistent:
# print(f"para_2 is consistent.\n")
return True
else:
# print(f"para_2 is not consistent.\n")
pass
elif para_1_end_with_end_punc: # If para_text1 ends with ending punctuations
# print_red(f"para_1 is end with end_punc.")
para_2_is_consistent = (
para_2_text
and (
para_2_text[0] == " "
or (self._is_alphabet_char(para_2_text[0]) and para_2_text[0].isupper())
or (self._is_chinese_char(para_2_text[0]))
or (self._is_other_letter_char(para_2_text[0]))
)
and not is_para2_left_indent_than_papa1
)
if para_2_is_consistent:
# print(f"para_2 is consistent.\n")
return True
else:
# print(f"para_2 is not consistent.\n")
pass
elif para_1_not_end_with_end_punc: # If para_text1 is not end with ending punctuations
# print_red(f"para_1 is NOT end with end_punc.")
para_2_is_consistent = para_2_text and (
para_2_text[0] == " "
or (self._is_alphabet_char(para_2_text[0]) and para_2_text[0].islower())
or (self._is_alphabet_char(para_2_text[0]))
or (self._is_year(para_2_text[0:4]))
or (are_two_paras_right_aligned or is_para1_left_indent_than_papa2)
or (self._is_chinese_char(para_2_text[0]))
or (self._is_other_letter_char(para_2_text[0]))
)
if para_2_is_consistent:
# print(f"para_2 is consistent.\n")
return True
else:
# print(f"para_2 is not consistent.\n")
pass
elif para_1_end_with_space: # If para_text1 ends with space
# print_red(f"para_1 is end with space.")
para_2_is_consistent = para_2_text and (
para_2_text[0] == " "
or (self._is_alphabet_char(para_2_text[0]) and para_2_text[0].islower())
or (self._is_chinese_char(para_2_text[0]))
or (self._is_other_letter_char(para_2_text[0]))
)
if para_2_is_consistent:
# print(f"para_2 is consistent.\n")
return True
else:
pass
# print(f"para_2 is not consistent.\n")
return False
def _is_block_consistent(self, block1, block2):
"""
This function determines whether block1 and block2 are originally from the same block
Parameters
----------
block1 : dict
block1s
block2 : dict
block2
Returns
-------
is_same : bool
True if block1 and block2 are from the same block, else False
"""
return self.__is_same_block_font(block1, block2)
def _is_para_continued(self, para1, para2):
"""
This function determines whether para1 and para2 are originally from the same paragraph
Parameters
----------
para1 : dict
para1
para2 : dict
para2
Returns
-------
is_same : bool
True if para1 and para2 are from the same paragraph, else False
"""
is_para_font_consistent = self.__is_para_font_consistent(para1, para2)
is_para_puncs_consistent = self._is_para_puncs_consistent(para1, para2)
return is_para_font_consistent and is_para_puncs_consistent
def _are_boundaries_of_block_consistent(self, block1, block2):
"""
This function checks if the boundaries of block1 and block2 are consistent
Parameters
----------
block1 : dict
block1
block2 : dict
block2
Returns
-------
is_consistent : bool
True if the boundaries of block1 and block2 are consistent, else False
"""
last_line_of_block1 = block1["lines"][-1]
first_line_of_block2 = block2["lines"][0]
spans_of_last_line_of_block1 = last_line_of_block1["spans"]
spans_of_first_line_of_block2 = first_line_of_block2["spans"]
font_type_of_last_line_of_block1 = spans_of_last_line_of_block1[0]["font"].lower()
font_size_of_last_line_of_block1 = spans_of_last_line_of_block1[0]["size"]
font_color_of_last_line_of_block1 = spans_of_last_line_of_block1[0]["color"]
font_flags_of_last_line_of_block1 = spans_of_last_line_of_block1[0]["flags"]
font_type_of_first_line_of_block2 = spans_of_first_line_of_block2[0]["font"].lower()
font_size_of_first_line_of_block2 = spans_of_first_line_of_block2[0]["size"]
font_color_of_first_line_of_block2 = spans_of_first_line_of_block2[0]["color"]
font_flags_of_first_line_of_block2 = spans_of_first_line_of_block2[0]["flags"]
return (
self.__is_similar_font_type(font_type_of_last_line_of_block1, font_type_of_first_line_of_block2)
and abs(font_size_of_last_line_of_block1 - font_size_of_first_line_of_block2) < 1
# and font_color_of_last_line_of_block1 == font_color_of_first_line_of_block2
and font_flags_of_last_line_of_block1 == font_flags_of_first_line_of_block2
)
def _get_last_paragraph(self, block):
"""
Retrieves the last paragraph from a block.
Parameters
----------
block : dict
The block from which to retrieve the paragraph.
Returns
-------
dict
The last paragraph of the block.
"""
if block["paras"]:
last_para_key = list(block["paras"].keys())[-1]
return block["paras"][last_para_key]
else:
return None
def _get_first_paragraph(self, block):
"""
Retrieves the first paragraph from a block.
Parameters
----------
block : dict
The block from which to retrieve the paragraph.
Returns
-------
dict
The first paragraph of the block.
"""
if block["paras"]:
first_para_key = list(block["paras"].keys())[0]
return block["paras"][first_para_key]
else:
return None
def should_merge_next_para(self, curr_para, next_para):
if self._is_para_continued(curr_para, next_para):
return True
else:
return False
def batch_tag_paras(self, pdf_dict):
the_last_page_id = len(pdf_dict) - 1
for curr_page_idx, (curr_page_id, curr_page_content) in enumerate(pdf_dict.items()):
if curr_page_id.startswith("page_") and curr_page_content.get("para_blocks", []):
para_blocks_of_curr_page = curr_page_content["para_blocks"]
next_page_idx = curr_page_idx + 1
next_page_id = f"page_{next_page_idx}"
next_page_content = pdf_dict.get(next_page_id, {})
for i, current_block in enumerate(para_blocks_of_curr_page):
for para_id, curr_para in current_block["paras"].items():
curr_para["curr_para_location"] = [
curr_page_idx,
current_block["block_id"],
int(para_id.split("_")[-1]),
]
curr_para["next_para_location"] = None # 默认设置为None
curr_para["merge_next_para"] = False # 默认设置为False
next_block = para_blocks_of_curr_page[i + 1] if i < len(para_blocks_of_curr_page) - 1 else None
if next_block:
curr_block_last_para_key = list(current_block["paras"].keys())[-1]
curr_blk_last_para = current_block["paras"][curr_block_last_para_key]
next_block_first_para_key = list(next_block["paras"].keys())[0]
next_blk_first_para = next_block["paras"][next_block_first_para_key]
if self.should_merge_next_para(curr_blk_last_para, next_blk_first_para):
curr_blk_last_para["next_para_location"] = [
curr_page_idx,
next_block["block_id"],
int(next_block_first_para_key.split("_")[-1]),
]
curr_blk_last_para["merge_next_para"] = True
else:
# Handle the case where the next block is in a different page
curr_block_last_para_key = list(current_block["paras"].keys())[-1]
curr_blk_last_para = current_block["paras"][curr_block_last_para_key]
while not next_page_content.get("para_blocks", []) and next_page_idx <= the_last_page_id:
next_page_idx += 1
next_page_id = f"page_{next_page_idx}"
next_page_content = pdf_dict.get(next_page_id, {})
if next_page_content.get("para_blocks", []):
next_blk_first_para_key = list(next_page_content["para_blocks"][0]["paras"].keys())[0]
next_blk_first_para = next_page_content["para_blocks"][0]["paras"][next_blk_first_para_key]
if self.should_merge_next_para(curr_blk_last_para, next_blk_first_para):
curr_blk_last_para["next_para_location"] = [
next_page_idx,
next_page_content["para_blocks"][0]["block_id"],
int(next_blk_first_para_key.split("_")[-1]),
]
curr_blk_last_para["merge_next_para"] = True
return pdf_dict
def find_block_by_id(self, para_blocks, block_id):
for block in para_blocks:
if block.get("block_id") == block_id:
return block
return None
def batch_merge_paras(self, pdf_dict):
for page_id, page_content in pdf_dict.items():
if page_id.startswith("page_") and page_content.get("para_blocks", []):
para_blocks_of_page = page_content["para_blocks"]
for i in range(len(para_blocks_of_page)):
current_block = para_blocks_of_page[i]
paras = current_block["paras"]
for para_id, curr_para in list(paras.items()):
# 跳过标题段落
if curr_para.get("is_para_title"):
continue
while curr_para.get("merge_next_para"):
next_para_location = curr_para.get("next_para_location")
if not next_para_location:
break
next_page_idx, next_block_id, next_para_id = next_para_location
next_page_id = f"page_{next_page_idx}"
next_page_content = pdf_dict.get(next_page_id)
if not next_page_content:
break
next_block = self.find_block_by_id(next_page_content.get("para_blocks", []), next_block_id)
if not next_block:
break
next_para = next_block["paras"].get(f"para_{next_para_id}")
if not next_para or next_para.get("is_para_title"):
break
# 合并段落文本
curr_para_text = curr_para.get("para_text", "")
next_para_text = next_para.get("para_text", "")
curr_para["para_text"] = curr_para_text + " " + next_para_text
# 更新 next_para_location
curr_para["next_para_location"] = next_para.get("next_para_location")
# 将下一个段落文本置为空,表示已被合并
next_para["para_text"] = ""
# 更新 merge_next_para 标记
curr_para["merge_next_para"] = next_para.get("merge_next_para", False)
return pdf_dict
@@ -1,480 +0,0 @@
from magic_pdf.para.commons import *
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
class BlockTerminationProcessor:
def __init__(self) -> None:
pass
def _is_consistent_lines(
self,
curr_line,
prev_line,
next_line,
consistent_direction, # 0 for prev, 1 for next, 2 for both
):
"""
This function checks if the line is consistent with its neighbors
Parameters
----------
curr_line : dict
current line
prev_line : dict
previous line
next_line : dict
next line
consistent_direction : int
0 for prev, 1 for next, 2 for both
Returns
-------
bool
True if the line is consistent with its neighbors, False otherwise.
"""
curr_line_font_size = curr_line["spans"][0]["size"]
curr_line_font_type = curr_line["spans"][0]["font"].lower()
if consistent_direction == 0:
if prev_line:
prev_line_font_size = prev_line["spans"][0]["size"]
prev_line_font_type = prev_line["spans"][0]["font"].lower()
return curr_line_font_size == prev_line_font_size and curr_line_font_type == prev_line_font_type
else:
return False
elif consistent_direction == 1:
if next_line:
next_line_font_size = next_line["spans"][0]["size"]
next_line_font_type = next_line["spans"][0]["font"].lower()
return curr_line_font_size == next_line_font_size and curr_line_font_type == next_line_font_type
else:
return False
elif consistent_direction == 2:
if prev_line and next_line:
prev_line_font_size = prev_line["spans"][0]["size"]
prev_line_font_type = prev_line["spans"][0]["font"].lower()
next_line_font_size = next_line["spans"][0]["size"]
next_line_font_type = next_line["spans"][0]["font"].lower()
return (curr_line_font_size == prev_line_font_size and curr_line_font_type == prev_line_font_type) and (
curr_line_font_size == next_line_font_size and curr_line_font_type == next_line_font_type
)
else:
return False
else:
return False
def _is_regular_line(self, curr_line_bbox, prev_line_bbox, next_line_bbox, avg_char_width, X0, X1, avg_line_height):
"""
This function checks if the line is a regular line
Parameters
----------
curr_line_bbox : list
bbox of the current line
prev_line_bbox : list
bbox of the previous line
next_line_bbox : list
bbox of the next line
avg_char_width : float
average of char widths
X0 : float
median of x0 values, which represents the left average boundary of the page
X1 : float
median of x1 values, which represents the right average boundary of the page
avg_line_height : float
average of line heights
Returns
-------
bool
True if the line is a regular line, False otherwise.
"""
horizontal_ratio = 0.5
vertical_ratio = 0.5
horizontal_thres = horizontal_ratio * avg_char_width
vertical_thres = vertical_ratio * avg_line_height
x0, y0, x1, y1 = curr_line_bbox
x0_near_X0 = abs(x0 - X0) < horizontal_thres
x1_near_X1 = abs(x1 - X1) < horizontal_thres
prev_line_is_end_of_para = prev_line_bbox and (abs(prev_line_bbox[2] - X1) > avg_char_width)
sufficient_spacing_above = False
if prev_line_bbox:
vertical_spacing_above = y1 - prev_line_bbox[3]
sufficient_spacing_above = vertical_spacing_above > vertical_thres
sufficient_spacing_below = False
if next_line_bbox:
vertical_spacing_below = next_line_bbox[1] - y0
sufficient_spacing_below = vertical_spacing_below > vertical_thres
return (
(sufficient_spacing_above or sufficient_spacing_below)
or (not x0_near_X0 and not x1_near_X1)
or prev_line_is_end_of_para
)
def _is_possible_start_of_para(self, curr_line, prev_line, next_line, X0, X1, avg_char_width, avg_font_size):
"""
This function checks if the line is a possible start of a paragraph
Parameters
----------
curr_line : dict
current line
prev_line : dict
previous line
next_line : dict
next line
X0 : float
median of x0 values, which represents the left average boundary of the page
X1 : float
median of x1 values, which represents the right average boundary of the page
avg_char_width : float
average of char widths
avg_line_height : float
average of line heights
Returns
-------
bool
True if the line is a possible start of a paragraph, False otherwise.
"""
start_confidence = 0.5 # Initial confidence of the line being a start of a paragraph
decision_path = [] # Record the decision path
curr_line_bbox = curr_line["bbox"]
prev_line_bbox = prev_line["bbox"] if prev_line else None
next_line_bbox = next_line["bbox"] if next_line else None
indent_ratio = 1
vertical_ratio = 1.5
vertical_thres = vertical_ratio * avg_font_size
left_horizontal_ratio = 0.5
left_horizontal_thres = left_horizontal_ratio * avg_char_width
right_horizontal_ratio = 2.5
right_horizontal_thres = right_horizontal_ratio * avg_char_width
x0, y0, x1, y1 = curr_line_bbox
indent_condition = x0 > X0 + indent_ratio * avg_char_width
if indent_condition:
start_confidence += 0.2
decision_path.append("indent_condition_met")
x0_near_X0 = abs(x0 - X0) < left_horizontal_thres
if x0_near_X0:
start_confidence += 0.1
decision_path.append("x0_near_X0")
x1_near_X1 = abs(x1 - X1) < right_horizontal_thres
if x1_near_X1:
start_confidence += 0.1
decision_path.append("x1_near_X1")
if prev_line is None:
prev_line_is_end_of_para = True
start_confidence += 0.2
decision_path.append("no_prev_line")
else:
prev_line_is_end_of_para, _, _ = self._is_possible_end_of_para(prev_line, next_line, X0, X1, avg_char_width)
if prev_line_is_end_of_para:
start_confidence += 0.1
decision_path.append("prev_line_is_end_of_para")
sufficient_spacing_above = False
if prev_line_bbox:
vertical_spacing_above = y1 - prev_line_bbox[3]
sufficient_spacing_above = vertical_spacing_above > vertical_thres
if sufficient_spacing_above:
start_confidence += 0.2
decision_path.append("sufficient_spacing_above")
sufficient_spacing_below = False
if next_line_bbox:
vertical_spacing_below = next_line_bbox[1] - y0
sufficient_spacing_below = vertical_spacing_below > vertical_thres
if sufficient_spacing_below:
start_confidence += 0.2
decision_path.append("sufficient_spacing_below")
is_regular_line = self._is_regular_line(
curr_line_bbox, prev_line_bbox, next_line_bbox, avg_char_width, X0, X1, avg_font_size
)
if is_regular_line:
start_confidence += 0.1
decision_path.append("is_regular_line")
is_start_of_para = (
(sufficient_spacing_above or sufficient_spacing_below)
or (indent_condition)
or (not indent_condition and x0_near_X0 and x1_near_X1 and not is_regular_line)
or prev_line_is_end_of_para
)
return (is_start_of_para, start_confidence, decision_path)
def _is_possible_end_of_para(self, curr_line, next_line, X0, X1, avg_char_width):
"""
This function checks if the line is a possible end of a paragraph
Parameters
----------
curr_line : dict
current line
next_line : dict
next line
X0 : float
median of x0 values, which represents the left average boundary of the page
X1 : float
median of x1 values, which represents the right average boundary of the page
avg_char_width : float
average of char widths
Returns
-------
bool
True if the line is a possible end of a paragraph, False otherwise.
"""
end_confidence = 0.5 # Initial confidence of the line being a end of a paragraph
decision_path = [] # Record the decision path
curr_line_bbox = curr_line["bbox"]
next_line_bbox = next_line["bbox"] if next_line else None
left_horizontal_ratio = 0.5
right_horizontal_ratio = 0.5
x0, _, x1, y1 = curr_line_bbox
next_x0, next_y0, _, _ = next_line_bbox if next_line_bbox else (0, 0, 0, 0)
x0_near_X0 = abs(x0 - X0) < left_horizontal_ratio * avg_char_width
if x0_near_X0:
end_confidence += 0.1
decision_path.append("x0_near_X0")
x1_smaller_than_X1 = x1 < X1 - right_horizontal_ratio * avg_char_width
if x1_smaller_than_X1:
end_confidence += 0.1
decision_path.append("x1_smaller_than_X1")
next_line_is_start_of_para = (
next_line_bbox
and (next_x0 > X0 + left_horizontal_ratio * avg_char_width)
and (not is_line_left_aligned_from_neighbors(curr_line_bbox, None, next_line_bbox, avg_char_width, direction=1))
)
if next_line_is_start_of_para:
end_confidence += 0.2
decision_path.append("next_line_is_start_of_para")
is_line_left_aligned_from_neighbors_bool = is_line_left_aligned_from_neighbors(
curr_line_bbox, None, next_line_bbox, avg_char_width
)
if is_line_left_aligned_from_neighbors_bool:
end_confidence += 0.1
decision_path.append("line_is_left_aligned_from_neighbors")
is_line_right_aligned_from_neighbors_bool = is_line_right_aligned_from_neighbors(
curr_line_bbox, None, next_line_bbox, avg_char_width
)
if not is_line_right_aligned_from_neighbors_bool:
end_confidence += 0.1
decision_path.append("line_is_not_right_aligned_from_neighbors")
is_end_of_para = end_with_punctuation(curr_line["text"]) and (
(x0_near_X0 and x1_smaller_than_X1)
or (is_line_left_aligned_from_neighbors_bool and not is_line_right_aligned_from_neighbors_bool)
)
return (is_end_of_para, end_confidence, decision_path)
def _cut_paras_per_block(
self,
block,
):
"""
Processes a raw block from PyMuPDF and returns the processed block.
Parameters
----------
raw_block : dict
A raw block from pymupdf.
Returns
-------
processed_block : dict
"""
def _construct_para(lines, is_block_title, para_title_level):
"""
Construct a paragraph from given lines.
"""
font_sizes = [span["size"] for line in lines for span in line["spans"]]
avg_font_size = sum(font_sizes) / len(font_sizes) if font_sizes else 0
font_colors = [span["color"] for line in lines for span in line["spans"]]
most_common_font_color = max(set(font_colors), key=font_colors.count) if font_colors else None
# font_types = [span["font"] for line in lines for span in line["spans"]]
# most_common_font_type = max(set(font_types), key=font_types.count) if font_types else None
font_type_lengths = {}
for line in lines:
for span in line["spans"]:
font_type = span["font"]
bbox_width = span["bbox"][2] - span["bbox"][0]
if font_type in font_type_lengths:
font_type_lengths[font_type] += bbox_width
else:
font_type_lengths[font_type] = bbox_width
# get the font type with the longest bbox width
most_common_font_type = max(font_type_lengths, key=font_type_lengths.get) if font_type_lengths else None # type: ignore
para_bbox = calculate_para_bbox(lines)
para_text = " ".join(line["text"] for line in lines)
return {
"para_bbox": para_bbox,
"para_text": para_text,
"para_font_type": most_common_font_type,
"para_font_size": avg_font_size,
"para_font_color": most_common_font_color,
"is_para_title": is_block_title,
"para_title_level": para_title_level,
}
block_bbox = block["bbox"]
block_text = block["text"]
block_lines = block["lines"]
X0 = safe_get(block, "X0", 0)
X1 = safe_get(block, "X1", 0)
avg_char_width = safe_get(block, "avg_char_width", 0)
avg_char_height = safe_get(block, "avg_char_height", 0)
avg_font_size = safe_get(block, "avg_font_size", 0)
is_block_title = safe_get(block, "is_block_title", False)
para_title_level = safe_get(block, "block_title_level", 0)
# Segment into paragraphs
para_ranges = []
in_paragraph = False
start_idx_of_para = None
# Create the processed paragraphs
processed_paras = {}
para_bboxes = []
end_idx_of_para = 0
for line_index, line in enumerate(block_lines):
curr_line = line
prev_line = block_lines[line_index - 1] if line_index > 0 else None
next_line = block_lines[line_index + 1] if line_index < len(block_lines) - 1 else None
"""
Start processing paragraphs.
"""
# Check if the line is the start of a paragraph
is_start_of_para, start_confidence, decision_path = self._is_possible_start_of_para(
curr_line, prev_line, next_line, X0, X1, avg_char_width, avg_font_size
)
if not in_paragraph and is_start_of_para:
in_paragraph = True
start_idx_of_para = line_index
# print_green(">>> Start of a paragraph")
# print(" curr_line_text: ", curr_line["text"])
# print(" start_confidence: ", start_confidence)
# print(" decision_path: ", decision_path)
# Check if the line is the end of a paragraph
is_end_of_para, end_confidence, decision_path = self._is_possible_end_of_para(
curr_line, next_line, X0, X1, avg_char_width
)
if in_paragraph and (is_end_of_para or not next_line):
para_ranges.append((start_idx_of_para, line_index))
start_idx_of_para = None
in_paragraph = False
# print_red(">>> End of a paragraph")
# print(" curr_line_text: ", curr_line["text"])
# print(" end_confidence: ", end_confidence)
# print(" decision_path: ", decision_path)
# Add the last paragraph if it is not added
if in_paragraph and start_idx_of_para is not None:
para_ranges.append((start_idx_of_para, len(block_lines) - 1))
# Process the matched paragraphs
for para_index, (start_idx, end_idx) in enumerate(para_ranges):
matched_lines = block_lines[start_idx : end_idx + 1]
para_properties = _construct_para(matched_lines, is_block_title, para_title_level)
para_key = f"para_{len(processed_paras)}"
processed_paras[para_key] = para_properties
para_bboxes.append(para_properties["para_bbox"])
end_idx_of_para = end_idx + 1
# Deal with the remaining lines
if end_idx_of_para < len(block_lines):
unmatched_lines = block_lines[end_idx_of_para:]
unmatched_properties = _construct_para(unmatched_lines, is_block_title, para_title_level)
unmatched_key = f"para_{len(processed_paras)}"
processed_paras[unmatched_key] = unmatched_properties
para_bboxes.append(unmatched_properties["para_bbox"])
block["paras"] = processed_paras
return block
def batch_process_blocks(self, pdf_dict):
"""
Parses the blocks of all pages.
Parameters
----------
pdf_dict : dict
PDF dictionary.
filter_blocks : list
List of bounding boxes to filter.
Returns
-------
result_dict : dict
Result dictionary.
"""
num_paras = 0
for page_id, page in pdf_dict.items():
if page_id.startswith("page_"):
para_blocks = []
if "para_blocks" in page.keys():
input_blocks = page["para_blocks"]
for input_block in input_blocks:
new_block = self._cut_paras_per_block(input_block)
para_blocks.append(new_block)
num_paras += len(new_block["paras"])
page["para_blocks"] = para_blocks
pdf_dict["statistics"]["num_paras"] = num_paras
return pdf_dict
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import sys
from magic_pdf.libs.commons import fitz
from termcolor import cprint
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
def open_pdf(pdf_path):
try:
pdf_document = fitz.open(pdf_path) # type: ignore
return pdf_document
except Exception as e:
print(f"无法打开PDF文件:{pdf_path}。原因是:{e}")
raise e
def print_green_on_red(text):
cprint(text, "green", "on_red", attrs=["bold"], end="\n\n")
def print_green(text):
print()
cprint(text, "green", attrs=["bold"], end="\n\n")
def print_red(text):
print()
cprint(text, "red", attrs=["bold"], end="\n\n")
def print_yellow(text):
print()
cprint(text, "yellow", attrs=["bold"], end="\n\n")
def safe_get(dict_obj, key, default):
val = dict_obj.get(key)
if val is None:
return default
else:
return val
def is_bbox_overlap(bbox1, bbox2):
"""
This function checks if bbox1 and bbox2 overlap or not
Parameters
----------
bbox1 : list
bbox1
bbox2 : list
bbox2
Returns
-------
bool
True if bbox1 and bbox2 overlap, else False
"""
x0_1, y0_1, x1_1, y1_1 = bbox1
x0_2, y0_2, x1_2, y1_2 = bbox2
if x0_1 > x1_2 or x0_2 > x1_1:
return False
if y0_1 > y1_2 or y0_2 > y1_1:
return False
return True
def is_in_bbox(bbox1, bbox2):
"""
This function checks if bbox1 is in bbox2
Parameters
----------
bbox1 : list
bbox1
bbox2 : list
bbox2
Returns
-------
bool
True if bbox1 is in bbox2, else False
"""
x0_1, y0_1, x1_1, y1_1 = bbox1
x0_2, y0_2, x1_2, y1_2 = bbox2
if x0_1 >= x0_2 and y0_1 >= y0_2 and x1_1 <= x1_2 and y1_1 <= y1_2:
return True
else:
return False
def calculate_para_bbox(lines):
"""
This function calculates the minimum bbox of the paragraph
Parameters
----------
lines : list
lines
Returns
-------
para_bbox : list
bbox of the paragraph
"""
x0 = min(line["bbox"][0] for line in lines)
y0 = min(line["bbox"][1] for line in lines)
x1 = max(line["bbox"][2] for line in lines)
y1 = max(line["bbox"][3] for line in lines)
return [x0, y0, x1, y1]
def is_line_right_aligned_from_neighbors(curr_line_bbox, prev_line_bbox, next_line_bbox, avg_char_width, direction=2):
"""
This function checks if the line is right aligned from its neighbors
Parameters
----------
curr_line_bbox : list
bbox of the current line
prev_line_bbox : list
bbox of the previous line
next_line_bbox : list
bbox of the next line
avg_char_width : float
average of char widths
direction : int
0 for prev, 1 for next, 2 for both
Returns
-------
bool
True if the line is right aligned from its neighbors, False otherwise.
"""
horizontal_ratio = 0.5
horizontal_thres = horizontal_ratio * avg_char_width
_, _, x1, _ = curr_line_bbox
_, _, prev_x1, _ = prev_line_bbox if prev_line_bbox else (0, 0, 0, 0)
_, _, next_x1, _ = next_line_bbox if next_line_bbox else (0, 0, 0, 0)
if direction == 0:
return abs(x1 - prev_x1) < horizontal_thres
elif direction == 1:
return abs(x1 - next_x1) < horizontal_thres
elif direction == 2:
return abs(x1 - prev_x1) < horizontal_thres and abs(x1 - next_x1) < horizontal_thres
else:
return False
def is_line_left_aligned_from_neighbors(curr_line_bbox, prev_line_bbox, next_line_bbox, avg_char_width, direction=2):
"""
This function checks if the line is left aligned from its neighbors
Parameters
----------
curr_line_bbox : list
bbox of the current line
prev_line_bbox : list
bbox of the previous line
next_line_bbox : list
bbox of the next line
avg_char_width : float
average of char widths
direction : int
0 for prev, 1 for next, 2 for both
Returns
-------
bool
True if the line is left aligned from its neighbors, False otherwise.
"""
horizontal_ratio = 0.5
horizontal_thres = horizontal_ratio * avg_char_width
x0, _, _, _ = curr_line_bbox
prev_x0, _, _, _ = prev_line_bbox if prev_line_bbox else (0, 0, 0, 0)
next_x0, _, _, _ = next_line_bbox if next_line_bbox else (0, 0, 0, 0)
if direction == 0:
return abs(x0 - prev_x0) < horizontal_thres
elif direction == 1:
return abs(x0 - next_x0) < horizontal_thres
elif direction == 2:
return abs(x0 - prev_x0) < horizontal_thres and abs(x0 - next_x0) < horizontal_thres
else:
return False
def end_with_punctuation(line_text):
"""
This function checks if the line ends with punctuation marks
"""
english_end_puncs = [".", "?", "!"]
chinese_end_puncs = ["。", "?", "!"]
end_puncs = english_end_puncs + chinese_end_puncs
last_non_space_char = None
for ch in line_text[::-1]:
if not ch.isspace():
last_non_space_char = ch
break
if last_non_space_char is None:
return False
return last_non_space_char in end_puncs
def is_nested_list(lst):
if isinstance(lst, list):
return any(isinstance(sub, list) for sub in lst)
return False
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@@ -1,246 +0,0 @@
import math
from collections import defaultdict
from magic_pdf.para.commons import *
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
class HeaderFooterProcessor:
def __init__(self) -> None:
pass
def get_most_common_bboxes(self, bboxes, page_height, position="top", threshold=0.25, num_bboxes=3, min_frequency=2):
"""
This function gets the most common bboxes from the bboxes
Parameters
----------
bboxes : list
bboxes
page_height : float
height of the page
position : str, optional
"top" or "bottom", by default "top"
threshold : float, optional
threshold, by default 0.25
num_bboxes : int, optional
number of bboxes to return, by default 3
min_frequency : int, optional
minimum frequency of the bbox, by default 2
Returns
-------
common_bboxes : list
common bboxes
"""
# Filter bbox by position
if position == "top":
filtered_bboxes = [bbox for bbox in bboxes if bbox[1] < page_height * threshold]
else:
filtered_bboxes = [bbox for bbox in bboxes if bbox[3] > page_height * (1 - threshold)]
# Find the most common bbox
bbox_count = defaultdict(int)
for bbox in filtered_bboxes:
bbox_count[tuple(bbox)] += 1
# Get the most frequently occurring bbox, but only consider it when the frequency exceeds min_frequency
common_bboxes = [
bbox for bbox, count in sorted(bbox_count.items(), key=lambda item: item[1], reverse=True) if count >= min_frequency
][:num_bboxes]
return common_bboxes
def detect_footer_header(self, result_dict, similarity_threshold=0.5):
"""
This function detects the header and footer of the document.
Parameters
----------
result_dict : dict
result dictionary
Returns
-------
result_dict : dict
result dictionary
"""
def compare_bbox_with_list(bbox, bbox_list, tolerance=1):
return any(all(abs(a - b) < tolerance for a, b in zip(bbox, common_bbox)) for common_bbox in bbox_list)
def is_single_line_block(block):
# Determine based on the width and height of the block
block_width = block["X1"] - block["X0"]
block_height = block["bbox"][3] - block["bbox"][1]
# If the height of the block is close to the average character height and the width is large, it is considered a single line
return block_height <= block["avg_char_height"] * 3 and block_width > block["avg_char_width"] * 3
# Traverse all blocks in the document
single_preproc_blocks = 0
total_blocks = 0
single_preproc_blocks = 0
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_key, block in blocks.items():
if block_key.startswith("block_"):
total_blocks += 1
if is_single_line_block(block):
single_preproc_blocks += 1
# If there are no blocks, skip the header and footer detection
if total_blocks == 0:
print("No blocks found. Skipping header/footer detection.")
return result_dict
# If most of the blocks are single-line, skip the header and footer detection
if single_preproc_blocks / total_blocks > 0.5: # 50% of the blocks are single-line
return result_dict
# Collect the bounding boxes of all blocks
all_bboxes = []
all_texts = []
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_key, block in blocks.items():
if block_key.startswith("block_"):
all_bboxes.append(block["bbox"])
# Get the height of the page
page_height = max(bbox[3] for bbox in all_bboxes)
# Get the most common bbox lists for headers and footers
common_header_bboxes = self.get_most_common_bboxes(all_bboxes, page_height, position="top") if all_bboxes else []
common_footer_bboxes = self.get_most_common_bboxes(all_bboxes, page_height, position="bottom") if all_bboxes else []
# Detect and mark headers and footers
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_key, block in blocks.items():
if block_key.startswith("block_"):
bbox = block["bbox"]
text = block["text"]
is_header = compare_bbox_with_list(bbox, common_header_bboxes)
is_footer = compare_bbox_with_list(bbox, common_footer_bboxes)
block["is_header"] = int(is_header)
block["is_footer"] = int(is_footer)
return result_dict
class NonHorizontalTextProcessor:
def __init__(self) -> None:
pass
def detect_non_horizontal_texts(self, result_dict):
"""
This function detects watermarks and vertical margin notes in the document.
Watermarks are identified by finding blocks with the same coordinates and frequently occurring identical texts across multiple pages.
If these conditions are met, the blocks are highly likely to be watermarks, as opposed to headers or footers, which can change from page to page.
If the direction of these blocks is not horizontal, they are definitely considered to be watermarks.
Vertical margin notes are identified by finding blocks with the same coordinates and frequently occurring identical texts across multiple pages.
If these conditions are met, the blocks are highly likely to be vertical margin notes, which typically appear on the left and right sides of the page.
If the direction of these blocks is vertical, they are definitely considered to be vertical margin notes.
Parameters
----------
result_dict : dict
The result dictionary.
Returns
-------
result_dict : dict
The updated result dictionary.
"""
# Dictionary to store information about potential watermarks
potential_watermarks = {}
potential_margin_notes = {}
for page_id, page_content in result_dict.items():
if page_id.startswith("page_"):
for block_id, block_data in page_content.items():
if block_id.startswith("block_"):
if "dir" in block_data:
coordinates_text = (block_data["bbox"], block_data["text"]) # Tuple of coordinates and text
angle = math.atan2(block_data["dir"][1], block_data["dir"][0])
angle = abs(math.degrees(angle))
if angle > 5 and angle < 85: # Check if direction is watermarks
if coordinates_text in potential_watermarks:
potential_watermarks[coordinates_text] += 1
else:
potential_watermarks[coordinates_text] = 1
if angle > 85 and angle < 105: # Check if direction is vertical
if coordinates_text in potential_margin_notes:
potential_margin_notes[coordinates_text] += 1 # Increment count
else:
potential_margin_notes[coordinates_text] = 1 # Initialize count
# Identify watermarks by finding entries with counts higher than a threshold (e.g., appearing on more than half of the pages)
watermark_threshold = len(result_dict) // 2
watermarks = {k: v for k, v in potential_watermarks.items() if v > watermark_threshold}
# Identify margin notes by finding entries with counts higher than a threshold (e.g., appearing on more than half of the pages)
margin_note_threshold = len(result_dict) // 2
margin_notes = {k: v for k, v in potential_margin_notes.items() if v > margin_note_threshold}
# Add watermark information to the result dictionary
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_id, block_data in blocks.items():
coordinates_text = (block_data["bbox"], block_data["text"])
if coordinates_text in watermarks:
block_data["is_watermark"] = 1
else:
block_data["is_watermark"] = 0
if coordinates_text in margin_notes:
block_data["is_vertical_margin_note"] = 1
else:
block_data["is_vertical_margin_note"] = 0
return result_dict
class NoiseRemover:
def __init__(self) -> None:
pass
def skip_data_noises(self, result_dict):
"""
This function skips the data noises, including overlap blocks, header, footer, watermark, vertical margin note, title
"""
filtered_result_dict = {}
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
filtered_blocks = {}
for block_id, block in blocks.items():
if block_id.startswith("block_"):
if any(
block.get(key, 0)
for key in [
"is_overlap",
"is_header",
"is_footer",
"is_watermark",
"is_vertical_margin_note",
"is_block_title",
]
):
continue
filtered_blocks[block_id] = block
if filtered_blocks:
filtered_result_dict[page_id] = filtered_blocks
return filtered_result_dict
-121
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@@ -1,121 +0,0 @@
from magic_pdf.libs.commons import fitz
from magic_pdf.para.commons import *
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
class DrawAnnos:
"""
This class draws annotations on the pdf file
----------------------------------------
Color Code
----------------------------------------
Red: (1, 0, 0)
Green: (0, 1, 0)
Blue: (0, 0, 1)
Yellow: (1, 1, 0) - mix of red and green
Cyan: (0, 1, 1) - mix of green and blue
Magenta: (1, 0, 1) - mix of red and blue
White: (1, 1, 1) - red, green and blue full intensity
Black: (0, 0, 0) - no color component whatsoever
Gray: (0.5, 0.5, 0.5) - equal and medium intensity of red, green and blue color components
Orange: (1, 0.65, 0) - maximum intensity of red, medium intensity of green, no blue component
"""
def __init__(self) -> None:
pass
def __is_nested_list(self, lst):
"""
This function returns True if the given list is a nested list of any degree.
"""
if isinstance(lst, list):
return any(self.__is_nested_list(i) for i in lst) or any(isinstance(i, list) for i in lst)
return False
def __valid_rect(self, bbox):
# Ensure that the rectangle is not empty or invalid
if isinstance(bbox[0], list):
return False # It's a nested list, hence it can't be valid rect
else:
return bbox[0] < bbox[2] and bbox[1] < bbox[3]
def __draw_nested_boxes(self, page, nested_bbox, color=(0, 1, 1)):
"""
This function draws the nested boxes
Parameters
----------
page : fitz.Page
page
nested_bbox : list
nested bbox
color : tuple
color, by default (0, 1, 1) # draw with cyan color for combined paragraph
"""
if self.__is_nested_list(nested_bbox): # If it's a nested list
for bbox in nested_bbox:
self.__draw_nested_boxes(page, bbox, color) # Recursively call the function
elif self.__valid_rect(nested_bbox): # If valid rectangle
para_rect = fitz.Rect(nested_bbox)
para_anno = page.add_rect_annot(para_rect)
para_anno.set_colors(stroke=color) # draw with cyan color for combined paragraph
para_anno.set_border(width=1)
para_anno.update()
def draw_annos(self, input_pdf_path, pdf_dic, output_pdf_path):
pdf_doc = open_pdf(input_pdf_path)
if pdf_dic is None:
pdf_dic = {}
if output_pdf_path is None:
output_pdf_path = input_pdf_path.replace(".pdf", "_anno.pdf")
for page_id, page in enumerate(pdf_doc): # type: ignore
page_key = f"page_{page_id}"
for ele_key, ele_data in pdf_dic[page_key].items():
if ele_key == "para_blocks":
para_blocks = ele_data
for para_block in para_blocks:
if "paras" in para_block.keys():
paras = para_block["paras"]
for para_key, para_content in paras.items():
para_bbox = para_content["para_bbox"]
# print(f"para_bbox: {para_bbox}")
# print(f"is a nested list: {self.__is_nested_list(para_bbox)}")
if self.__is_nested_list(para_bbox) and len(para_bbox) > 1:
color = (0, 1, 1)
self.__draw_nested_boxes(
page, para_bbox, color
) # draw with cyan color for combined paragraph
else:
if self.__valid_rect(para_bbox):
para_rect = fitz.Rect(para_bbox)
para_anno = page.add_rect_annot(para_rect)
para_anno.set_colors(stroke=(0, 1, 0)) # draw with green color for normal paragraph
para_anno.set_border(width=0.5)
para_anno.update()
is_para_title = para_content["is_para_title"]
if is_para_title:
if self.__is_nested_list(para_content["para_bbox"]) and len(para_content["para_bbox"]) > 1:
color = (0, 0, 1)
self.__draw_nested_boxes(
page, para_content["para_bbox"], color
) # draw with cyan color for combined title
else:
if self.__valid_rect(para_content["para_bbox"]):
para_rect = fitz.Rect(para_content["para_bbox"])
if self.__valid_rect(para_content["para_bbox"]):
para_anno = page.add_rect_annot(para_rect)
para_anno.set_colors(stroke=(0, 0, 1)) # draw with blue color for normal title
para_anno.set_border(width=0.5)
para_anno.update()
pdf_doc.save(output_pdf_path)
pdf_doc.close()
-198
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@@ -1,198 +0,0 @@
class DenseSingleLineBlockException(Exception):
"""
This class defines the exception type for dense single line-block.
"""
def __init__(self, message="DenseSingleLineBlockException"):
self.message = message
super().__init__(self.message)
def __str__(self):
return f"{self.message}"
def __repr__(self):
return f"{self.message}"
class TitleDetectionException(Exception):
"""
This class defines the exception type for title detection.
"""
def __init__(self, message="TitleDetectionException"):
self.message = message
super().__init__(self.message)
def __str__(self):
return f"{self.message}"
def __repr__(self):
return f"{self.message}"
class TitleLevelException(Exception):
"""
This class defines the exception type for title level.
"""
def __init__(self, message="TitleLevelException"):
self.message = message
super().__init__(self.message)
def __str__(self):
return f"{self.message}"
def __repr__(self):
return f"{self.message}"
class ParaSplitException(Exception):
"""
This class defines the exception type for paragraph splitting.
"""
def __init__(self, message="ParaSplitException"):
self.message = message
super().__init__(self.message)
def __str__(self):
return f"{self.message}"
def __repr__(self):
return f"{self.message}"
class ParaMergeException(Exception):
"""
This class defines the exception type for paragraph merging.
"""
def __init__(self, message="ParaMergeException"):
self.message = message
super().__init__(self.message)
def __str__(self):
return f"{self.message}"
def __repr__(self):
return f"{self.message}"
class DiscardByException:
"""
This class discards pdf files by exception
"""
def __init__(self) -> None:
pass
def discard_by_single_line_block(self, pdf_dic, exception: DenseSingleLineBlockException):
"""
This function discards pdf files by single line block exception
Parameters
----------
pdf_dic : dict
pdf dictionary
exception : str
exception message
Returns
-------
error_message : str
"""
exception_page_nums = 0
page_num = 0
for page_id, page in pdf_dic.items():
if page_id.startswith("page_"):
page_num += 1
if "preproc_blocks" in page.keys():
preproc_blocks = page["preproc_blocks"]
all_single_line_blocks = []
for block in preproc_blocks:
if len(block["lines"]) == 1:
all_single_line_blocks.append(block)
if len(preproc_blocks) > 0 and len(all_single_line_blocks) / len(preproc_blocks) > 0.9:
exception_page_nums += 1
if page_num == 0:
return None
if exception_page_nums / page_num > 0.1: # Low ratio means basically, whenever this is the case, it is discarded
return exception.message
return None
def discard_by_title_detection(self, pdf_dic, exception: TitleDetectionException):
"""
This function discards pdf files by title detection exception
Parameters
----------
pdf_dic : dict
pdf dictionary
exception : str
exception message
Returns
-------
error_message : str
"""
# return exception.message
return None
def discard_by_title_level(self, pdf_dic, exception: TitleLevelException):
"""
This function discards pdf files by title level exception
Parameters
----------
pdf_dic : dict
pdf dictionary
exception : str
exception message
Returns
-------
error_message : str
"""
# return exception.message
return None
def discard_by_split_para(self, pdf_dic, exception: ParaSplitException):
"""
This function discards pdf files by split para exception
Parameters
----------
pdf_dic : dict
pdf dictionary
exception : str
exception message
Returns
-------
error_message : str
"""
# return exception.message
return None
def discard_by_merge_para(self, pdf_dic, exception: ParaMergeException):
"""
This function discards pdf files by merge para exception
Parameters
----------
pdf_dic : dict
pdf dictionary
exception : str
exception message
Returns
-------
error_message : str
"""
# return exception.message
return None
@@ -1,40 +0,0 @@
import math
from magic_pdf.para.commons import *
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
class LayoutFilterProcessor:
def __init__(self) -> None:
pass
def batch_process_blocks(self, pdf_dict):
for page_id, blocks in pdf_dict.items():
if page_id.startswith("page_"):
if "layout_bboxes" in blocks.keys() and "para_blocks" in blocks.keys():
layout_bbox_objs = blocks["layout_bboxes"]
if layout_bbox_objs is None:
continue
layout_bboxes = [bbox_obj["layout_bbox"] for bbox_obj in layout_bbox_objs]
# Use math.ceil function to enlarge each value of x0, y0, x1, y1 of each layout_bbox
layout_bboxes = [
[math.ceil(x0), math.ceil(y0), math.ceil(x1), math.ceil(y1)] for x0, y0, x1, y1 in layout_bboxes
]
para_blocks = blocks["para_blocks"]
if para_blocks is None:
continue
for lb_bbox in layout_bboxes:
for i, para_block in enumerate(para_blocks):
para_bbox = para_block["bbox"]
para_blocks[i]["in_layout"] = 0
if is_in_bbox(para_bbox, lb_bbox):
para_blocks[i]["in_layout"] = 1
blocks["para_blocks"] = para_blocks
return pdf_dict
-807
View File
@@ -1,807 +0,0 @@
import numpy as np
from loguru import logger
from sklearn.cluster import DBSCAN
from magic_pdf.config.ocr_content_type import ContentType
from magic_pdf.libs.boxbase import \
_is_in_or_part_overlap_with_area_ratio as is_in_layout
LINE_STOP_FLAG = ['.', '!', '?', '。', '!', '?', ':', ':', ')', ')', ';']
INLINE_EQUATION = ContentType.InlineEquation
INTERLINE_EQUATION = ContentType.InterlineEquation
TEXT = ContentType.Text
def __get_span_text(span):
c = span.get('content', '')
if len(c) == 0:
c = span.get('image_path', '')
return c
def __detect_list_lines(lines, new_layout_bboxes, lang):
"""探测是否包含了列表,并且把列表的行分开.
这样的段落特点是,顶格字母大写/数字,紧跟着几行缩进的。缩进的行首字母含小写的。
"""
def find_repeating_patterns(lst):
indices = []
ones_indices = []
i = 0
while i < len(lst) - 1: # 确保余下元素至少有2个
if lst[i] == 1 and lst[i + 1] in [2, 3]: # 额外检查以防止连续出现的1
start = i
ones_in_this_interval = [i]
i += 1
while i < len(lst) and lst[i] in [2, 3]:
i += 1
# 验证下一个序列是否符合条件
if (
i < len(lst) - 1
and lst[i] == 1
and lst[i + 1] in [2, 3]
and lst[i - 1] in [2, 3]
):
while i < len(lst) and lst[i] in [1, 2, 3]:
if lst[i] == 1:
ones_in_this_interval.append(i)
i += 1
indices.append((start, i - 1))
ones_indices.append(ones_in_this_interval)
else:
i += 1
else:
i += 1
return indices, ones_indices
"""===================="""
def split_indices(slen, index_array):
result = []
last_end = 0
for start, end in sorted(index_array):
if start > last_end:
# 前一个区间结束到下一个区间开始之间的部分标记为"text"
result.append(('text', last_end, start - 1))
# 区间内标记为"list"
result.append(('list', start, end))
last_end = end + 1
if last_end < slen:
# 如果最后一个区间结束后还有剩余的字符串,将其标记为"text"
result.append(('text', last_end, slen - 1))
return result
"""===================="""
if lang != 'en':
return lines, None
else:
total_lines = len(lines)
line_fea_encode = []
"""
对每一行进行特征编码,编码规则如下:
1. 如果行顶格,且大写字母开头或者数字开头,编码为1
2. 如果顶格,其他非大写开头编码为4
3. 如果非顶格,首字符大写,编码为2
4. 如果非顶格,首字符非大写编码为3
"""
for l in lines: # noqa: E741
first_char = __get_span_text(l['spans'][0])[0]
layout_left = __find_layout_bbox_by_line(l['bbox'], new_layout_bboxes)[0]
if l['bbox'][0] == layout_left:
if first_char.isupper() or first_char.isdigit():
line_fea_encode.append(1)
else:
line_fea_encode.append(4)
else:
if first_char.isupper():
line_fea_encode.append(2)
else:
line_fea_encode.append(3)
# 然后根据编码进行分段, 选出来 1,2,3连续出现至少2次的行,认为是列表。
list_indice, list_start_idx = find_repeating_patterns(line_fea_encode)
if len(list_indice) > 0:
logger.info(f'发现了列表,列表行数:{list_indice}, {list_start_idx}')
# TODO check一下这个特列表里缩进的行左侧是不是对齐的。
for start, end in list_indice:
for i in range(start, end + 1):
if i > 0:
if line_fea_encode[i] == 4:
logger.info(f'列表行的第{i}行不是顶格的')
break
else:
logger.info(f'列表行的第{start}到第{end}行是列表')
return split_indices(total_lines, list_indice), list_start_idx
def __valign_lines(blocks, layout_bboxes):
"""在一个layoutbox内对齐行的左侧和右侧。 扫描行的左侧和右侧,如果x0,
x1差距不超过一个阈值,就强行对齐到所处layout的左右两侧(和layout有一段距离)。
3是个经验值,TODO,计算得来,可以设置为1.5个正文字符。"""
min_distance = 3
min_sample = 2
new_layout_bboxes = []
for layout_box in layout_bboxes:
blocks_in_layoutbox = [
b for b in blocks if is_in_layout(b['bbox'], layout_box['layout_bbox'])
]
if len(blocks_in_layoutbox) == 0:
continue
x0_lst = np.array(
[
[line['bbox'][0], 0]
for block in blocks_in_layoutbox
for line in block['lines']
]
)
x1_lst = np.array(
[
[line['bbox'][2], 0]
for block in blocks_in_layoutbox
for line in block['lines']
]
)
x0_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x0_lst)
x1_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x1_lst)
x0_uniq_label = np.unique(x0_clusters.labels_)
x1_uniq_label = np.unique(x1_clusters.labels_)
x0_2_new_val = {} # 存储旧值对应的新值映射
x1_2_new_val = {}
for label in x0_uniq_label:
if label == -1:
continue
x0_index_of_label = np.where(x0_clusters.labels_ == label)
x0_raw_val = x0_lst[x0_index_of_label][:, 0]
x0_new_val = np.min(x0_lst[x0_index_of_label][:, 0])
x0_2_new_val.update({idx: x0_new_val for idx in x0_raw_val})
for label in x1_uniq_label:
if label == -1:
continue
x1_index_of_label = np.where(x1_clusters.labels_ == label)
x1_raw_val = x1_lst[x1_index_of_label][:, 0]
x1_new_val = np.max(x1_lst[x1_index_of_label][:, 0])
x1_2_new_val.update({idx: x1_new_val for idx in x1_raw_val})
for block in blocks_in_layoutbox:
for line in block['lines']:
x0, x1 = line['bbox'][0], line['bbox'][2]
if x0 in x0_2_new_val:
line['bbox'][0] = int(x0_2_new_val[x0])
if x1 in x1_2_new_val:
line['bbox'][2] = int(x1_2_new_val[x1])
# 其余对不齐的保持不动
# 由于修改了block里的line长度,现在需要重新计算block的bbox
for block in blocks_in_layoutbox:
block['bbox'] = [
min([line['bbox'][0] for line in block['lines']]),
min([line['bbox'][1] for line in block['lines']]),
max([line['bbox'][2] for line in block['lines']]),
max([line['bbox'][3] for line in block['lines']]),
]
"""新计算layout的bbox,因为block的bbox变了。"""
layout_x0 = min([block['bbox'][0] for block in blocks_in_layoutbox])
layout_y0 = min([block['bbox'][1] for block in blocks_in_layoutbox])
layout_x1 = max([block['bbox'][2] for block in blocks_in_layoutbox])
layout_y1 = max([block['bbox'][3] for block in blocks_in_layoutbox])
new_layout_bboxes.append([layout_x0, layout_y0, layout_x1, layout_y1])
return new_layout_bboxes
def __align_text_in_layout(blocks, layout_bboxes):
"""由于ocr出来的line,有时候会在前后有一段空白,这个时候需要对文本进行对齐,超出的部分被layout左右侧截断。"""
for layout in layout_bboxes:
lb = layout['layout_bbox']
blocks_in_layoutbox = [b for b in blocks if is_in_layout(b['bbox'], lb)]
if len(blocks_in_layoutbox) == 0:
continue
for block in blocks_in_layoutbox:
for line in block['lines']:
x0, x1 = line['bbox'][0], line['bbox'][2]
if x0 < lb[0]:
line['bbox'][0] = lb[0]
if x1 > lb[2]:
line['bbox'][2] = lb[2]
def __common_pre_proc(blocks, layout_bboxes):
"""不分语言的,对文本进行预处理."""
# __add_line_period(blocks, layout_bboxes)
__align_text_in_layout(blocks, layout_bboxes)
aligned_layout_bboxes = __valign_lines(blocks, layout_bboxes)
return aligned_layout_bboxes
def __pre_proc_zh_blocks(blocks, layout_bboxes):
"""对中文文本进行分段预处理."""
pass
def __pre_proc_en_blocks(blocks, layout_bboxes):
"""对英文文本进行分段预处理."""
pass
def __group_line_by_layout(blocks, layout_bboxes, lang='en'):
"""每个layout内的行进行聚合."""
# 因为只是一个block一行目前, 一个block就是一个段落
lines_group = []
for lyout in layout_bboxes:
lines = [
line
for block in blocks
if is_in_layout(block['bbox'], lyout['layout_bbox'])
for line in block['lines']
]
lines_group.append(lines)
return lines_group
def __split_para_in_layoutbox(lines_group, new_layout_bbox, lang='en', char_avg_len=10):
"""
lines_group 进行行分段——layout内部进行分段。lines_group内每个元素是一个Layoutbox内的所有行。
1. 先计算每个group的左右边界。
2. 然后根据行末尾特征进行分段。
末尾特征:以句号等结束符结尾。并且距离右侧边界有一定距离。
且下一行开头不留空白。
"""
list_info = [] # 这个layout最后是不是列表,记录每一个layout里是不是列表开头,列表结尾
layout_paras = []
right_tail_distance = 1.5 * char_avg_len
for lines in lines_group:
paras = []
total_lines = len(lines)
if total_lines == 0:
continue # 0行无需处理
if total_lines == 1: # 1行无法分段。
layout_paras.append([lines])
list_info.append([False, False])
continue
"""在进入到真正的分段之前,要对文字块从统计维度进行对齐方式的探测,
对齐方式分为以下:
1. 左对齐的文本块(特点是左侧顶格,或者左侧不顶格但是右侧顶格的行数大于非顶格的行数,顶格的首字母有大写也有小写)
1) 右侧对齐的行,单独成一段
2) 中间对齐的行,按照字体/行高聚合成一段
2. 左对齐的列表块(其特点是左侧顶格的行数小于等于非顶格的行数,非定格首字母会有小写,顶格90%是大写。并且左侧顶格行数大于1,大于1是为了这种模式连续出现才能称之为列表)
这样的文本块,顶格的为一个段落开头,紧随其后非顶格的行属于这个段落。
"""
text_segments, list_start_line = __detect_list_lines(
lines, new_layout_bbox, lang
)
"""根据list_range,把lines分成几个部分
"""
layout_right = __find_layout_bbox_by_line(lines[0]['bbox'], new_layout_bbox)[2]
layout_left = __find_layout_bbox_by_line(lines[0]['bbox'], new_layout_bbox)[0]
para = [] # 元素是line
layout_list_info = [
False,
False,
] # 这个layout最后是不是列表,记录每一个layout里是不是列表开头,列表结尾
for content_type, start, end in text_segments:
if content_type == 'list':
for i, line in enumerate(lines[start : end + 1]):
line_x0 = line['bbox'][0]
if line_x0 == layout_left: # 列表开头
if len(para) > 0:
paras.append(para)
para = []
para.append(line)
else:
para.append(line)
if len(para) > 0:
paras.append(para)
para = []
if start == 0:
layout_list_info[0] = True
if end == total_lines - 1:
layout_list_info[1] = True
else: # 是普通文本
for i, line in enumerate(lines[start : end + 1]):
# 如果i有下一行,那么就要根据下一行位置综合判断是否要分段。如果i之后没有行,那么只需要判断i行自己的结尾特征。
cur_line_type = line['spans'][-1]['type']
next_line = lines[i + 1] if i < total_lines - 1 else None
if cur_line_type in [TEXT, INLINE_EQUATION]:
if line['bbox'][2] < layout_right - right_tail_distance:
para.append(line)
paras.append(para)
para = []
elif (
line['bbox'][2] >= layout_right - right_tail_distance
and next_line
and next_line['bbox'][0] == layout_left
): # 现在这行到了行尾沾满,下一行存在且顶格。
para.append(line)
else:
para.append(line)
paras.append(para)
para = []
else: # 其他,图片、表格、行间公式,各自占一段
if len(para) > 0: # 先把之前的段落加入到结果中
paras.append(para)
para = []
paras.append(
[line]
) # 再把当前行加入到结果中。当前行为行间公式、图、表等。
para = []
if len(para) > 0:
paras.append(para)
para = []
list_info.append(layout_list_info)
layout_paras.append(paras)
paras = []
return layout_paras, list_info
def __connect_list_inter_layout(
layout_paras, new_layout_bbox, layout_list_info, page_num, lang
):
"""如果上个layout的最后一个段落是列表,下一个layout的第一个段落也是列表,那么将他们连接起来。 TODO
因为没有区分列表和段落,所以这个方法暂时不实现。
根据layout_list_info判断是不是列表。,下个layout的第一个段如果不是列表,那么看他们是否有几行都有相同的缩进。"""
if (
len(layout_paras) == 0 or len(layout_list_info) == 0
): # 0的时候最后的return 会出错
return layout_paras, [False, False]
for i in range(1, len(layout_paras)):
pre_layout_list_info = layout_list_info[i - 1]
next_layout_list_info = layout_list_info[i]
pre_last_para = layout_paras[i - 1][-1]
next_paras = layout_paras[i]
if (
pre_layout_list_info[1] and not next_layout_list_info[0]
): # 前一个是列表结尾,后一个是非列表开头,此时检测是否有相同的缩进
logger.info(f'连接page {page_num} 内的list')
# 向layout_paras[i] 寻找开头具有相同缩进的连续的行
may_list_lines = []
for j in range(len(next_paras)):
line = next_paras[j]
if len(line) == 1: # 只可能是一行,多行情况再需要分析了
if (
line[0]['bbox'][0]
> __find_layout_bbox_by_line(line[0]['bbox'], new_layout_bbox)[
0
]
):
may_list_lines.append(line[0])
else:
break
else:
break
# 如果这些行的缩进是相等的,那么连到上一个layout的最后一个段落上。
if (
len(may_list_lines) > 0
and len(set([x['bbox'][0] for x in may_list_lines])) == 1
):
pre_last_para.extend(may_list_lines)
layout_paras[i] = layout_paras[i][len(may_list_lines) :]
return layout_paras, [
layout_list_info[0][0],
layout_list_info[-1][1],
] # 同时还返回了这个页面级别的开头、结尾是不是列表的信息
def __connect_list_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
pre_page_list_info,
next_page_list_info,
page_num,
lang,
):
"""如果上个layout的最后一个段落是列表,下一个layout的第一个段落也是列表,那么将他们连接起来。 TODO
因为没有区分列表和段落,所以这个方法暂时不实现。
根据layout_list_info判断是不是列表。,下个layout的第一个段如果不是列表,那么看他们是否有几行都有相同的缩进。"""
if (
len(pre_page_paras) == 0 or len(next_page_paras) == 0
): # 0的时候最后的return 会出错
return False
if (
pre_page_list_info[1] and not next_page_list_info[0]
): # 前一个是列表结尾,后一个是非列表开头,此时检测是否有相同的缩进
logger.info(f'连接page {page_num} 内的list')
# 向layout_paras[i] 寻找开头具有相同缩进的连续的行
may_list_lines = []
for j in range(len(next_page_paras[0])):
line = next_page_paras[0][j]
if len(line) == 1: # 只可能是一行,多行情况再需要分析了
if (
line[0]['bbox'][0]
> __find_layout_bbox_by_line(
line[0]['bbox'], next_page_layout_bbox
)[0]
):
may_list_lines.append(line[0])
else:
break
else:
break
# 如果这些行的缩进是相等的,那么连到上一个layout的最后一个段落上。
if (
len(may_list_lines) > 0
and len(set([x['bbox'][0] for x in may_list_lines])) == 1
):
pre_page_paras[-1].append(may_list_lines)
next_page_paras[0] = next_page_paras[0][len(may_list_lines) :]
return True
return False
def __find_layout_bbox_by_line(line_bbox, layout_bboxes):
"""根据line找到所在的layout."""
for layout in layout_bboxes:
if is_in_layout(line_bbox, layout):
return layout
return None
def __connect_para_inter_layoutbox(layout_paras, new_layout_bbox, lang):
"""
layout之间进行分段。
主要是计算前一个layOut的最后一行和后一个layout的第一行是否可以连接。
连接的条件需要同时满足:
1. 上一个layout的最后一行沾满整个行。并且没有结尾符号。
2. 下一行开头不留空白。
"""
connected_layout_paras = []
if len(layout_paras) == 0:
return connected_layout_paras
connected_layout_paras.append(layout_paras[0])
for i in range(1, len(layout_paras)):
try:
if (
len(layout_paras[i]) == 0 or len(layout_paras[i - 1]) == 0
): # TODO 考虑连接问题,
continue
pre_last_line = layout_paras[i - 1][-1][-1]
next_first_line = layout_paras[i][0][0]
except Exception:
logger.error(f'page layout {i} has no line')
continue
pre_last_line_text = ''.join(
[__get_span_text(span) for span in pre_last_line['spans']]
)
pre_last_line_type = pre_last_line['spans'][-1]['type']
next_first_line_text = ''.join(
[__get_span_text(span) for span in next_first_line['spans']]
)
next_first_line_type = next_first_line['spans'][0]['type']
if pre_last_line_type not in [
TEXT,
INLINE_EQUATION,
] or next_first_line_type not in [TEXT, INLINE_EQUATION]:
connected_layout_paras.append(layout_paras[i])
continue
pre_x2_max = __find_layout_bbox_by_line(pre_last_line['bbox'], new_layout_bbox)[
2
]
next_x0_min = __find_layout_bbox_by_line(
next_first_line['bbox'], new_layout_bbox
)[0]
pre_last_line_text = pre_last_line_text.strip()
next_first_line_text = next_first_line_text.strip()
if (
pre_last_line['bbox'][2] == pre_x2_max
and pre_last_line_text[-1] not in LINE_STOP_FLAG
and next_first_line['bbox'][0] == next_x0_min
): # 前面一行沾满了整个行,并且没有结尾符号.下一行没有空白开头。
"""连接段落条件成立,将前一个layout的段落和后一个layout的段落连接。"""
connected_layout_paras[-1][-1].extend(layout_paras[i][0])
layout_paras[i].pop(
0
) # 删除后一个layout的第一个段落, 因为他已经被合并到前一个layout的最后一个段落了。
if len(layout_paras[i]) == 0:
layout_paras.pop(i)
else:
connected_layout_paras.append(layout_paras[i])
else:
"""连接段落条件不成立,将前一个layout的段落加入到结果中。"""
connected_layout_paras.append(layout_paras[i])
return connected_layout_paras
def __connect_para_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
page_num,
lang,
):
"""
连接起来相邻两个页面的段落——前一个页面最后一个段落和后一个页面的第一个段落。
是否可以连接的条件:
1. 前一个页面的最后一个段落最后一行沾满整个行。并且没有结尾符号。
2. 后一个页面的第一个段落第一行没有空白开头。
"""
# 有的页面可能压根没有文字
if (
len(pre_page_paras) == 0
or len(next_page_paras) == 0
or len(pre_page_paras[0]) == 0
or len(next_page_paras[0]) == 0
): # TODO [[]]为什么出现在pre_page_paras里?
return False
pre_last_para = pre_page_paras[-1][-1]
next_first_para = next_page_paras[0][0]
pre_last_line = pre_last_para[-1]
next_first_line = next_first_para[0]
pre_last_line_text = ''.join(
[__get_span_text(span) for span in pre_last_line['spans']]
)
pre_last_line_type = pre_last_line['spans'][-1]['type']
next_first_line_text = ''.join(
[__get_span_text(span) for span in next_first_line['spans']]
)
next_first_line_type = next_first_line['spans'][0]['type']
if pre_last_line_type not in [
TEXT,
INLINE_EQUATION,
] or next_first_line_type not in [
TEXT,
INLINE_EQUATION,
]: # TODO,真的要做好,要考虑跨table, image, 行间的情况
# 不是文本,不连接
return False
pre_x2_max = __find_layout_bbox_by_line(
pre_last_line['bbox'], pre_page_layout_bbox
)[2]
next_x0_min = __find_layout_bbox_by_line(
next_first_line['bbox'], next_page_layout_bbox
)[0]
pre_last_line_text = pre_last_line_text.strip()
next_first_line_text = next_first_line_text.strip()
if (
pre_last_line['bbox'][2] == pre_x2_max
and pre_last_line_text[-1] not in LINE_STOP_FLAG
and next_first_line['bbox'][0] == next_x0_min
): # 前面一行沾满了整个行,并且没有结尾符号.下一行没有空白开头。
"""连接段落条件成立,将前一个layout的段落和后一个layout的段落连接。"""
pre_last_para.extend(next_first_para)
next_page_paras[0].pop(
0
) # 删除后一个页面的第一个段落, 因为他已经被合并到前一个页面的最后一个段落了。
return True
else:
return False
def find_consecutive_true_regions(input_array):
start_index = None # 连续True区域的起始索引
regions = [] # 用于保存所有连续True区域的起始和结束索引
for i in range(len(input_array)):
# 如果我们找到了一个True值,并且当前并没有在连续True区域中
if input_array[i] and start_index is None:
start_index = i # 记录连续True区域的起始索引
# 如果我们找到了一个False值,并且当前在连续True区域中
elif not input_array[i] and start_index is not None:
# 如果连续True区域长度大于1,那么将其添加到结果列表中
if i - start_index > 1:
regions.append((start_index, i - 1))
start_index = None # 重置起始索引
# 如果最后一个元素是True,那么需要将最后一个连续True区域加入到结果列表中
if start_index is not None and len(input_array) - start_index > 1:
regions.append((start_index, len(input_array) - 1))
return regions
def __connect_middle_align_text(
page_paras, new_layout_bbox, page_num, lang, debug_mode
):
"""
找出来中间对齐的连续单行文本,如果连续行高度相同,那么合并为一个段落。
一个line居中的条件是:
1. 水平中心点跨越layout的中心点。
2. 左右两侧都有空白
"""
for layout_i, layout_para in enumerate(page_paras):
layout_box = new_layout_bbox[layout_i]
single_line_paras_tag = []
for i in range(len(layout_para)):
single_line_paras_tag.append(
len(layout_para[i]) == 1
and layout_para[i][0]['spans'][0]['type'] == TEXT
)
"""找出来连续的单行文本,如果连续行高度相同,那么合并为一个段落。"""
consecutive_single_line_indices = find_consecutive_true_regions(
single_line_paras_tag
)
if len(consecutive_single_line_indices) > 0:
index_offset = 0
"""检查这些行是否是高度相同的,居中的"""
for start, end in consecutive_single_line_indices:
start += index_offset
end += index_offset
line_hi = np.array(
[
line[0]['bbox'][3] - line[0]['bbox'][1]
for line in layout_para[start : end + 1]
]
)
first_line_text = ''.join(
[__get_span_text(span) for span in layout_para[start][0]['spans']]
)
if 'Table' in first_line_text or 'Figure' in first_line_text:
pass
if debug_mode:
logger.debug(line_hi.std())
if line_hi.std() < 2:
"""行高度相同,那么判断是否居中."""
all_left_x0 = [
line[0]['bbox'][0] for line in layout_para[start : end + 1]
]
all_right_x1 = [
line[0]['bbox'][2] for line in layout_para[start : end + 1]
]
layout_center = (layout_box[0] + layout_box[2]) / 2
if (
all(
[
x0 < layout_center < x1
for x0, x1 in zip(all_left_x0, all_right_x1)
]
)
and not all([x0 == layout_box[0] for x0 in all_left_x0])
and not all([x1 == layout_box[2] for x1 in all_right_x1])
):
merge_para = [l[0] for l in layout_para[start : end + 1]] # noqa: E741
para_text = ''.join(
[
__get_span_text(span)
for line in merge_para
for span in line['spans']
]
)
if debug_mode:
logger.debug(para_text)
layout_para[start : end + 1] = [merge_para]
index_offset -= end - start
return
def __merge_signle_list_text(page_paras, new_layout_bbox, page_num, lang):
"""找出来连续的单行文本,如果首行顶格,接下来的几个单行段落缩进对齐,那么合并为一个段落。"""
pass
def __do_split_page(blocks, layout_bboxes, new_layout_bbox, page_num, lang):
"""根据line和layout情况进行分段 先实现一个根据行末尾特征分段的简单方法。"""
"""
算法思路:
1. 扫描layout里每一行,找出来行尾距离layout有边界有一定距离的行。
2. 从上述行中找到末尾是句号等可作为断行标志的行。
3. 参照上述行尾特征进行分段。
4. 图、表,目前独占一行,不考虑分段。
"""
if page_num == 343:
pass
lines_group = __group_line_by_layout(blocks, layout_bboxes, lang) # block内分段
layout_paras, layout_list_info = __split_para_in_layoutbox(
lines_group, new_layout_bbox, lang
) # layout内分段
layout_paras2, page_list_info = __connect_list_inter_layout(
layout_paras, new_layout_bbox, layout_list_info, page_num, lang
) # layout之间连接列表段落
connected_layout_paras = __connect_para_inter_layoutbox(
layout_paras2, new_layout_bbox, lang
) # layout间链接段落
return connected_layout_paras, page_list_info
def para_split(pdf_info_dict, debug_mode, lang='en'):
"""根据line和layout情况进行分段."""
new_layout_of_pages = [] # 数组的数组,每个元素是一个页面的layoutS
all_page_list_info = [] # 保存每个页面开头和结尾是否是列表
for page_num, page in pdf_info_dict.items():
blocks = page['preproc_blocks']
layout_bboxes = page['layout_bboxes']
new_layout_bbox = __common_pre_proc(blocks, layout_bboxes)
new_layout_of_pages.append(new_layout_bbox)
splited_blocks, page_list_info = __do_split_page(
blocks, layout_bboxes, new_layout_bbox, page_num, lang
)
all_page_list_info.append(page_list_info)
page['para_blocks'] = splited_blocks
"""连接页面与页面之间的可能合并的段落"""
pdf_infos = list(pdf_info_dict.values())
for page_num, page in enumerate(pdf_info_dict.values()):
if page_num == 0:
continue
pre_page_paras = pdf_infos[page_num - 1]['para_blocks']
next_page_paras = pdf_infos[page_num]['para_blocks']
pre_page_layout_bbox = new_layout_of_pages[page_num - 1]
next_page_layout_bbox = new_layout_of_pages[page_num]
is_conn = __connect_para_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
page_num,
lang,
)
if debug_mode:
if is_conn:
logger.info(f'连接了第{page_num-1}页和第{page_num}页的段落')
is_list_conn = __connect_list_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
all_page_list_info[page_num - 1],
all_page_list_info[page_num],
page_num,
lang,
)
if debug_mode:
if is_list_conn:
logger.info(f'连接了第{page_num-1}页和第{page_num}页的列表段落')
"""接下来可能会漏掉一些特别的一些可以合并的内容,对他们进行段落连接
1. 正文中有时出现一个行顶格,接下来几行缩进的情况。
2. 居中的一些连续单行,如果高度相同,那么可能是一个段落。
"""
for page_num, page in enumerate(pdf_info_dict.values()):
page_paras = page['para_blocks']
new_layout_bbox = new_layout_of_pages[page_num]
__connect_middle_align_text(
page_paras, new_layout_bbox, page_num, lang, debug_mode=debug_mode
)
__merge_signle_list_text(page_paras, new_layout_bbox, page_num, lang)
-959
View File
@@ -1,959 +0,0 @@
import copy
import re
import numpy as np
from loguru import logger
from sklearn.cluster import DBSCAN
from magic_pdf.config.constants import * # noqa: F403
from magic_pdf.config.ocr_content_type import BlockType, ContentType
from magic_pdf.libs.boxbase import \
_is_in_or_part_overlap_with_area_ratio as is_in_layout
LINE_STOP_FLAG = ['.', '!', '?', '。', '!', '?', ':', ':', ')', ')', ';']
INLINE_EQUATION = ContentType.InlineEquation
INTERLINE_EQUATION = ContentType.InterlineEquation
TEXT = ContentType.Text
debug_able = False
def __get_span_text(span):
c = span.get('content', '')
if len(c) == 0:
c = span.get('image_path', '')
return c
def __detect_list_lines(lines, new_layout_bboxes, lang):
global debug_able
"""
探测是否包含了列表,并且把列表的行分开.
这样的段落特点是,顶格字母大写/数字,紧跟着几行缩进的。缩进的行首字母含小写的。
"""
def find_repeating_patterns2(lst):
indices = []
ones_indices = []
i = 0
while i < len(lst): # Loop through the entire list
if (
lst[i] == 1
): # If we encounter a '1', we might be at the start of a pattern
start = i
ones_in_this_interval = [i]
i += 1
# Traverse elements that are 1, 2 or 3, until we encounter something else
while i < len(lst) and lst[i] in [1, 2, 3]:
if lst[i] == 1:
ones_in_this_interval.append(i)
i += 1
if len(ones_in_this_interval) > 1 or (
start < len(lst) - 1
and ones_in_this_interval
and lst[start + 1] in [2, 3]
):
indices.append((start, i - 1))
ones_indices.append(ones_in_this_interval)
else:
i += 1
return indices, ones_indices
def find_repeating_patterns(lst):
indices = []
ones_indices = []
i = 0
while i < len(lst) - 1: # 确保余下元素至少有2个
if lst[i] == 1 and lst[i + 1] in [2, 3]: # 额外检查以防止连续出现的1
start = i
ones_in_this_interval = [i]
i += 1
while i < len(lst) and lst[i] in [2, 3]:
i += 1
# 验证下一个序列是否符合条件
if (
i < len(lst) - 1
and lst[i] == 1
and lst[i + 1] in [2, 3]
and lst[i - 1] in [2, 3]
):
while i < len(lst) and lst[i] in [1, 2, 3]:
if lst[i] == 1:
ones_in_this_interval.append(i)
i += 1
indices.append((start, i - 1))
ones_indices.append(ones_in_this_interval)
else:
i += 1
else:
i += 1
return indices, ones_indices
"""===================="""
def split_indices(slen, index_array):
result = []
last_end = 0
for start, end in sorted(index_array):
if start > last_end:
# 前一个区间结束到下一个区间开始之间的部分标记为"text"
result.append(('text', last_end, start - 1))
# 区间内标记为"list"
result.append(('list', start, end))
last_end = end + 1
if last_end < slen:
# 如果最后一个区间结束后还有剩余的字符串,将其标记为"text"
result.append(('text', last_end, slen - 1))
return result
"""===================="""
if lang != 'en':
return lines, None
total_lines = len(lines)
line_fea_encode = []
"""
对每一行进行特征编码,编码规则如下:
1. 如果行顶格,且大写字母开头或者数字开头,编码为1
2. 如果顶格,其他非大写开头编码为4
3. 如果非顶格,首字符大写,编码为2
4. 如果非顶格,首字符非大写编码为3
"""
if len(lines) > 0:
x_map_tag_dict, min_x_tag = cluster_line_x(lines)
for l in lines: # noqa: E741
span_text = __get_span_text(l['spans'][0])
if not span_text:
line_fea_encode.append(0)
continue
first_char = span_text[0]
layout = __find_layout_bbox_by_line(l['bbox'], new_layout_bboxes)
if not layout:
line_fea_encode.append(0)
else:
#
if x_map_tag_dict[round(l['bbox'][0])] == min_x_tag:
# if first_char.isupper() or first_char.isdigit() or not first_char.isalnum():
if not first_char.isalnum() or if_match_reference_list(span_text):
line_fea_encode.append(1)
else:
line_fea_encode.append(4)
else:
if first_char.isupper():
line_fea_encode.append(2)
else:
line_fea_encode.append(3)
# 然后根据编码进行分段, 选出来 1,2,3连续出现至少2次的行,认为是列表。
list_indice, list_start_idx = find_repeating_patterns2(line_fea_encode)
if len(list_indice) > 0:
if debug_able:
logger.info(f'发现了列表,列表行数:{list_indice}, {list_start_idx}')
# TODO check一下这个特列表里缩进的行左侧是不是对齐的。
for start, end in list_indice:
for i in range(start, end + 1):
if i > 0:
if line_fea_encode[i] == 4:
if debug_able:
logger.info(f'列表行的第{i}行不是顶格的')
break
else:
if debug_able:
logger.info(f'列表行的第{start}到第{end}行是列表')
return split_indices(total_lines, list_indice), list_start_idx
def cluster_line_x(lines: list) -> dict:
"""对一个block内所有lines的bbox的x0聚类."""
min_distance = 5
min_sample = 1
x0_lst = np.array([[round(line['bbox'][0]), 0] for line in lines])
x0_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x0_lst)
x0_uniq_label = np.unique(x0_clusters.labels_)
# x1_lst = np.array([[line['bbox'][2], 0] for line in lines])
x0_2_new_val = {} # 存储旧值对应的新值映射
min_x0 = round(lines[0]['bbox'][0])
for label in x0_uniq_label:
if label == -1:
continue
x0_index_of_label = np.where(x0_clusters.labels_ == label)
x0_raw_val = x0_lst[x0_index_of_label][:, 0]
x0_new_val = np.min(x0_lst[x0_index_of_label][:, 0])
x0_2_new_val.update(
{round(raw_val): round(x0_new_val) for raw_val in x0_raw_val}
)
if x0_new_val < min_x0:
min_x0 = x0_new_val
return x0_2_new_val, min_x0
def if_match_reference_list(text: str) -> bool:
pattern = re.compile(r'^\d+\..*')
if pattern.match(text):
return True
else:
return False
def __valign_lines(blocks, layout_bboxes):
"""在一个layoutbox内对齐行的左侧和右侧。 扫描行的左侧和右侧,如果x0,
x1差距不超过一个阈值,就强行对齐到所处layout的左右两侧(和layout有一段距离)。
3是个经验值,TODO,计算得来,可以设置为1.5个正文字符。"""
min_distance = 3
min_sample = 2
new_layout_bboxes = []
# add bbox_fs for para split calculation
for block in blocks:
block['bbox_fs'] = copy.deepcopy(block['bbox'])
for layout_box in layout_bboxes:
blocks_in_layoutbox = [
b
for b in blocks
if b['type'] == BlockType.Text
and is_in_layout(b['bbox'], layout_box['layout_bbox'])
]
if len(blocks_in_layoutbox) == 0 or len(blocks_in_layoutbox[0]['lines']) == 0:
new_layout_bboxes.append(layout_box['layout_bbox'])
continue
x0_lst = np.array(
[
[line['bbox'][0], 0]
for block in blocks_in_layoutbox
for line in block['lines']
]
)
x1_lst = np.array(
[
[line['bbox'][2], 0]
for block in blocks_in_layoutbox
for line in block['lines']
]
)
x0_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x0_lst)
x1_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x1_lst)
x0_uniq_label = np.unique(x0_clusters.labels_)
x1_uniq_label = np.unique(x1_clusters.labels_)
x0_2_new_val = {} # 存储旧值对应的新值映射
x1_2_new_val = {}
for label in x0_uniq_label:
if label == -1:
continue
x0_index_of_label = np.where(x0_clusters.labels_ == label)
x0_raw_val = x0_lst[x0_index_of_label][:, 0]
x0_new_val = np.min(x0_lst[x0_index_of_label][:, 0])
x0_2_new_val.update({idx: x0_new_val for idx in x0_raw_val})
for label in x1_uniq_label:
if label == -1:
continue
x1_index_of_label = np.where(x1_clusters.labels_ == label)
x1_raw_val = x1_lst[x1_index_of_label][:, 0]
x1_new_val = np.max(x1_lst[x1_index_of_label][:, 0])
x1_2_new_val.update({idx: x1_new_val for idx in x1_raw_val})
for block in blocks_in_layoutbox:
for line in block['lines']:
x0, x1 = line['bbox'][0], line['bbox'][2]
if x0 in x0_2_new_val:
line['bbox'][0] = int(x0_2_new_val[x0])
if x1 in x1_2_new_val:
line['bbox'][2] = int(x1_2_new_val[x1])
# 其余对不齐的保持不动
# 由于修改了block里的line长度,现在需要重新计算block的bbox
for block in blocks_in_layoutbox:
if len(block['lines']) > 0:
block['bbox_fs'] = [
min([line['bbox'][0] for line in block['lines']]),
min([line['bbox'][1] for line in block['lines']]),
max([line['bbox'][2] for line in block['lines']]),
max([line['bbox'][3] for line in block['lines']]),
]
"""新计算layout的bbox,因为block的bbox变了。"""
layout_x0 = min([block['bbox_fs'][0] for block in blocks_in_layoutbox])
layout_y0 = min([block['bbox_fs'][1] for block in blocks_in_layoutbox])
layout_x1 = max([block['bbox_fs'][2] for block in blocks_in_layoutbox])
layout_y1 = max([block['bbox_fs'][3] for block in blocks_in_layoutbox])
new_layout_bboxes.append([layout_x0, layout_y0, layout_x1, layout_y1])
return new_layout_bboxes
def __align_text_in_layout(blocks, layout_bboxes):
"""由于ocr出来的line,有时候会在前后有一段空白,这个时候需要对文本进行对齐,超出的部分被layout左右侧截断。"""
for layout in layout_bboxes:
lb = layout['layout_bbox']
blocks_in_layoutbox = [
block
for block in blocks
if block['type'] == BlockType.Text and is_in_layout(block['bbox'], lb)
]
if len(blocks_in_layoutbox) == 0:
continue
for block in blocks_in_layoutbox:
for line in block.get('lines', []):
x0, x1 = line['bbox'][0], line['bbox'][2]
if x0 < lb[0]:
line['bbox'][0] = lb[0]
if x1 > lb[2]:
line['bbox'][2] = lb[2]
def __common_pre_proc(blocks, layout_bboxes):
"""不分语言的,对文本进行预处理."""
# __add_line_period(blocks, layout_bboxes)
__align_text_in_layout(blocks, layout_bboxes)
aligned_layout_bboxes = __valign_lines(blocks, layout_bboxes)
return aligned_layout_bboxes
def __pre_proc_zh_blocks(blocks, layout_bboxes):
"""对中文文本进行分段预处理."""
pass
def __pre_proc_en_blocks(blocks, layout_bboxes):
"""对英文文本进行分段预处理."""
pass
def __group_line_by_layout(blocks, layout_bboxes):
"""每个layout内的行进行聚合."""
# 因为只是一个block一行目前, 一个block就是一个段落
blocks_group = []
for lyout in layout_bboxes:
blocks_in_layout = [
block
for block in blocks
if is_in_layout(block.get('bbox_fs', None), lyout['layout_bbox'])
]
blocks_group.append(blocks_in_layout)
return blocks_group
def __split_para_in_layoutbox(blocks_group, new_layout_bbox, lang='en'):
"""
lines_group 进行行分段——layout内部进行分段。lines_group内每个元素是一个Layoutbox内的所有行。
1. 先计算每个group的左右边界。
2. 然后根据行末尾特征进行分段。
末尾特征:以句号等结束符结尾。并且距离右侧边界有一定距离。
且下一行开头不留空白。
"""
list_info = [] # 这个layout最后是不是列表,记录每一个layout里是不是列表开头,列表结尾
for blocks in blocks_group:
is_start_list = None
is_end_list = None
if len(blocks) == 0:
list_info.append([False, False])
continue
if blocks[0]['type'] != BlockType.Text and blocks[-1]['type'] != BlockType.Text:
list_info.append([False, False])
continue
if blocks[0]['type'] != BlockType.Text:
is_start_list = False
if blocks[-1]['type'] != BlockType.Text:
is_end_list = False
lines = [
line
for block in blocks
if block['type'] == BlockType.Text
for line in block['lines']
]
total_lines = len(lines)
if total_lines == 1 or total_lines == 0:
list_info.append([False, False])
continue
"""在进入到真正的分段之前,要对文字块从统计维度进行对齐方式的探测,
对齐方式分为以下:
1. 左对齐的文本块(特点是左侧顶格,或者左侧不顶格但是右侧顶格的行数大于非顶格的行数,顶格的首字母有大写也有小写)
1) 右侧对齐的行,单独成一段
2) 中间对齐的行,按照字体/行高聚合成一段
2. 左对齐的列表块(其特点是左侧顶格的行数小于等于非顶格的行数,非定格首字母会有小写,顶格90%是大写。并且左侧顶格行数大于1,大于1是为了这种模式连续出现才能称之为列表)
这样的文本块,顶格的为一个段落开头,紧随其后非顶格的行属于这个段落。
"""
text_segments, list_start_line = __detect_list_lines(
lines, new_layout_bbox, lang
)
"""根据list_range,把lines分成几个部分
"""
for list_start in list_start_line:
if len(list_start) > 1:
for i in range(0, len(list_start)):
index = list_start[i] - 1
if index >= 0:
if 'content' in lines[index]['spans'][-1] and lines[index][
'spans'
][-1].get('type', '') not in [
ContentType.InlineEquation,
ContentType.InterlineEquation,
]:
lines[index]['spans'][-1]['content'] += '\n\n'
layout_list_info = [
False,
False,
] # 这个layout最后是不是列表,记录每一个layout里是不是列表开头,列表结尾
for content_type, start, end in text_segments:
if content_type == 'list':
if start == 0 and is_start_list is None:
layout_list_info[0] = True
if end == total_lines - 1 and is_end_list is None:
layout_list_info[1] = True
list_info.append(layout_list_info)
return list_info
def __split_para_lines(lines: list, text_blocks: list) -> list:
text_paras = []
other_paras = []
text_lines = []
for line in lines:
spans_types = [span['type'] for span in line]
if ContentType.Table in spans_types:
other_paras.append([line])
continue
if ContentType.Image in spans_types:
other_paras.append([line])
continue
if ContentType.InterlineEquation in spans_types:
other_paras.append([line])
continue
text_lines.append(line)
for block in text_blocks:
block_bbox = block['bbox']
para = []
for line in text_lines:
bbox = line['bbox']
if is_in_layout(bbox, block_bbox):
para.append(line)
if len(para) > 0:
text_paras.append(para)
paras = other_paras.extend(text_paras)
paras_sorted = sorted(paras, key=lambda x: x[0]['bbox'][1])
return paras_sorted
def __connect_list_inter_layout(
blocks_group, new_layout_bbox, layout_list_info, page_num, lang
):
global debug_able
"""
如果上个layout的最后一个段落是列表,下一个layout的第一个段落也是列表,那么将他们连接起来。 TODO 因为没有区分列表和段落,所以这个方法暂时不实现。
根据layout_list_info判断是不是列表。,下个layout的第一个段如果不是列表,那么看他们是否有几行都有相同的缩进。
"""
if len(blocks_group) == 0 or len(blocks_group) == 0: # 0的时候最后的return 会出错
return blocks_group, [False, False]
for i in range(1, len(blocks_group)):
if len(blocks_group[i]) == 0 or len(blocks_group[i - 1]) == 0:
continue
pre_layout_list_info = layout_list_info[i - 1]
next_layout_list_info = layout_list_info[i]
pre_last_para = blocks_group[i - 1][-1].get('lines', [])
next_paras = blocks_group[i]
next_first_para = next_paras[0]
if (
pre_layout_list_info[1]
and not next_layout_list_info[0]
and next_first_para['type'] == BlockType.Text
): # 前一个是列表结尾,后一个是非列表开头,此时检测是否有相同的缩进
if debug_able:
logger.info(f'连接page {page_num} 内的list')
# 向layout_paras[i] 寻找开头具有相同缩进的连续的行
may_list_lines = []
lines = next_first_para.get('lines', [])
for line in lines:
if (
line['bbox'][0]
> __find_layout_bbox_by_line(line['bbox'], new_layout_bbox)[0]
):
may_list_lines.append(line)
else:
break
# 如果这些行的缩进是相等的,那么连到上一个layout的最后一个段落上。
if (
len(may_list_lines) > 0
and len(set([x['bbox'][0] for x in may_list_lines])) == 1
):
pre_last_para.extend(may_list_lines)
next_first_para['lines'] = next_first_para['lines'][
len(may_list_lines) :
]
return blocks_group, [
layout_list_info[0][0],
layout_list_info[-1][1],
] # 同时还返回了这个页面级别的开头、结尾是不是列表的信息
def __connect_list_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
pre_page_list_info,
next_page_list_info,
page_num,
lang,
):
"""如果上个layout的最后一个段落是列表,下一个layout的第一个段落也是列表,那么将他们连接起来。 TODO
因为没有区分列表和段落,所以这个方法暂时不实现。
根据layout_list_info判断是不是列表。,下个layout的第一个段如果不是列表,那么看他们是否有几行都有相同的缩进。"""
if (
len(pre_page_paras) == 0 or len(next_page_paras) == 0
): # 0的时候最后的return 会出错
return False
if len(pre_page_paras[-1]) == 0 or len(next_page_paras[0]) == 0:
return False
if (
pre_page_paras[-1][-1]['type'] != BlockType.Text
or next_page_paras[0][0]['type'] != BlockType.Text
):
return False
if (
pre_page_list_info[1] and not next_page_list_info[0]
): # 前一个是列表结尾,后一个是非列表开头,此时检测是否有相同的缩进
if debug_able:
logger.info(f'连接page {page_num} 内的list')
# 向layout_paras[i] 寻找开头具有相同缩进的连续的行
may_list_lines = []
next_page_first_para = next_page_paras[0][0]
if next_page_first_para['type'] == BlockType.Text:
lines = next_page_first_para['lines']
for line in lines:
if (
line['bbox'][0]
> __find_layout_bbox_by_line(line['bbox'], next_page_layout_bbox)[0]
):
may_list_lines.append(line)
else:
break
# 如果这些行的缩进是相等的,那么连到上一个layout的最后一个段落上。
if (
len(may_list_lines) > 0
and len(set([x['bbox'][0] for x in may_list_lines])) == 1
):
# pre_page_paras[-1].append(may_list_lines)
# 下一页合并到上一页最后一段,打一个cross_page的标签
for line in may_list_lines:
for span in line['spans']:
span[CROSS_PAGE] = True # noqa: F405
pre_page_paras[-1][-1]['lines'].extend(may_list_lines)
next_page_first_para['lines'] = next_page_first_para['lines'][
len(may_list_lines) :
]
return True
return False
def __find_layout_bbox_by_line(line_bbox, layout_bboxes):
"""根据line找到所在的layout."""
for layout in layout_bboxes:
if is_in_layout(line_bbox, layout):
return layout
return None
def __connect_para_inter_layoutbox(blocks_group, new_layout_bbox):
"""
layout之间进行分段。
主要是计算前一个layOut的最后一行和后一个layout的第一行是否可以连接。
连接的条件需要同时满足:
1. 上一个layout的最后一行沾满整个行。并且没有结尾符号。
2. 下一行开头不留空白。
"""
connected_layout_blocks = []
if len(blocks_group) == 0:
return connected_layout_blocks
connected_layout_blocks.append(blocks_group[0])
for i in range(1, len(blocks_group)):
try:
if len(blocks_group[i]) == 0:
continue
if len(blocks_group[i - 1]) == 0: # TODO 考虑连接问题,
connected_layout_blocks.append(blocks_group[i])
continue
# text类型的段才需要考虑layout间的合并
if (
blocks_group[i - 1][-1]['type'] != BlockType.Text
or blocks_group[i][0]['type'] != BlockType.Text
):
connected_layout_blocks.append(blocks_group[i])
continue
if (
len(blocks_group[i - 1][-1]['lines']) == 0
or len(blocks_group[i][0]['lines']) == 0
):
connected_layout_blocks.append(blocks_group[i])
continue
pre_last_line = blocks_group[i - 1][-1]['lines'][-1]
next_first_line = blocks_group[i][0]['lines'][0]
except Exception:
logger.error(f'page layout {i} has no line')
continue
pre_last_line_text = ''.join(
[__get_span_text(span) for span in pre_last_line['spans']]
)
pre_last_line_type = pre_last_line['spans'][-1]['type']
next_first_line_text = ''.join(
[__get_span_text(span) for span in next_first_line['spans']]
)
next_first_line_type = next_first_line['spans'][0]['type']
if pre_last_line_type not in [
TEXT,
INLINE_EQUATION,
] or next_first_line_type not in [TEXT, INLINE_EQUATION]:
connected_layout_blocks.append(blocks_group[i])
continue
pre_layout = __find_layout_bbox_by_line(pre_last_line['bbox'], new_layout_bbox)
next_layout = __find_layout_bbox_by_line(
next_first_line['bbox'], new_layout_bbox
)
pre_x2_max = pre_layout[2] if pre_layout else -1
next_x0_min = next_layout[0] if next_layout else -1
pre_last_line_text = pre_last_line_text.strip()
next_first_line_text = next_first_line_text.strip()
if (
pre_last_line['bbox'][2] == pre_x2_max
and pre_last_line_text
and pre_last_line_text[-1] not in LINE_STOP_FLAG
and next_first_line['bbox'][0] == next_x0_min
): # 前面一行沾满了整个行,并且没有结尾符号.下一行没有空白开头。
"""连接段落条件成立,将前一个layout的段落和后一个layout的段落连接。"""
connected_layout_blocks[-1][-1]['lines'].extend(blocks_group[i][0]['lines'])
blocks_group[i][0][
'lines'
] = [] # 删除后一个layout第一个段落中的lines,因为他已经被合并到前一个layout的最后一个段落了
blocks_group[i][0][LINES_DELETED] = True # noqa: F405
# if len(layout_paras[i]) == 0:
# layout_paras.pop(i)
# else:
# connected_layout_paras.append(layout_paras[i])
connected_layout_blocks.append(blocks_group[i])
else:
"""连接段落条件不成立,将前一个layout的段落加入到结果中。"""
connected_layout_blocks.append(blocks_group[i])
return connected_layout_blocks
def __connect_para_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
page_num,
lang,
):
"""
连接起来相邻两个页面的段落——前一个页面最后一个段落和后一个页面的第一个段落。
是否可以连接的条件:
1. 前一个页面的最后一个段落最后一行沾满整个行。并且没有结尾符号。
2. 后一个页面的第一个段落第一行没有空白开头。
"""
# 有的页面可能压根没有文字
if (
len(pre_page_paras) == 0
or len(next_page_paras) == 0
or len(pre_page_paras[0]) == 0
or len(next_page_paras[0]) == 0
): # TODO [[]]为什么出现在pre_page_paras里?
return False
pre_last_block = pre_page_paras[-1][-1]
next_first_block = next_page_paras[0][0]
if (
pre_last_block['type'] != BlockType.Text
or next_first_block['type'] != BlockType.Text
):
return False
if len(pre_last_block['lines']) == 0 or len(next_first_block['lines']) == 0:
return False
pre_last_para = pre_last_block['lines']
next_first_para = next_first_block['lines']
pre_last_line = pre_last_para[-1]
next_first_line = next_first_para[0]
pre_last_line_text = ''.join(
[__get_span_text(span) for span in pre_last_line['spans']]
)
pre_last_line_type = pre_last_line['spans'][-1]['type']
next_first_line_text = ''.join(
[__get_span_text(span) for span in next_first_line['spans']]
)
next_first_line_type = next_first_line['spans'][0]['type']
if pre_last_line_type not in [
TEXT,
INLINE_EQUATION,
] or next_first_line_type not in [
TEXT,
INLINE_EQUATION,
]: # TODO,真的要做好,要考虑跨table, image, 行间的情况
# 不是文本,不连接
return False
pre_x2_max_bbox = __find_layout_bbox_by_line(
pre_last_line['bbox'], pre_page_layout_bbox
)
if not pre_x2_max_bbox:
return False
next_x0_min_bbox = __find_layout_bbox_by_line(
next_first_line['bbox'], next_page_layout_bbox
)
if not next_x0_min_bbox:
return False
pre_x2_max = pre_x2_max_bbox[2]
next_x0_min = next_x0_min_bbox[0]
pre_last_line_text = pre_last_line_text.strip()
next_first_line_text = next_first_line_text.strip()
if (
pre_last_line['bbox'][2] == pre_x2_max
and pre_last_line_text[-1] not in LINE_STOP_FLAG
and next_first_line['bbox'][0] == next_x0_min
): # 前面一行沾满了整个行,并且没有结尾符号.下一行没有空白开头。
"""连接段落条件成立,将前一个layout的段落和后一个layout的段落连接。"""
# 下一页合并到上一页最后一段,打一个cross_page的标签
for line in next_first_para:
for span in line['spans']:
span[CROSS_PAGE] = True # noqa: F405
pre_last_para.extend(next_first_para)
# next_page_paras[0].pop(0) # 删除后一个页面的第一个段落, 因为他已经被合并到前一个页面的最后一个段落了。
next_page_paras[0][0]['lines'] = []
next_page_paras[0][0][LINES_DELETED] = True # noqa: F405
return True
else:
return False
def find_consecutive_true_regions(input_array):
start_index = None # 连续True区域的起始索引
regions = [] # 用于保存所有连续True区域的起始和结束索引
for i in range(len(input_array)):
# 如果我们找到了一个True值,并且当前并没有在连续True区域中
if input_array[i] and start_index is None:
start_index = i # 记录连续True区域的起始索引
# 如果我们找到了一个False值,并且当前在连续True区域中
elif not input_array[i] and start_index is not None:
# 如果连续True区域长度大于1,那么将其添加到结果列表中
if i - start_index > 1:
regions.append((start_index, i - 1))
start_index = None # 重置起始索引
# 如果最后一个元素是True,那么需要将最后一个连续True区域加入到结果列表中
if start_index is not None and len(input_array) - start_index > 1:
regions.append((start_index, len(input_array) - 1))
return regions
def __connect_middle_align_text(page_paras, new_layout_bbox, page_num, lang):
global debug_able
"""
找出来中间对齐的连续单行文本,如果连续行高度相同,那么合并为一个段落。
一个line居中的条件是:
1. 水平中心点跨越layout的中心点。
2. 左右两侧都有空白
"""
for layout_i, layout_para in enumerate(page_paras):
layout_box = new_layout_bbox[layout_i]
single_line_paras_tag = []
for i in range(len(layout_para)):
# single_line_paras_tag.append(len(layout_para[i]) == 1 and layout_para[i][0]['spans'][0]['type'] == TEXT)
single_line_paras_tag.append(
layout_para[i]['type'] == BlockType.Text
and len(layout_para[i]['lines']) == 1
)
"""找出来连续的单行文本,如果连续行高度相同,那么合并为一个段落。"""
consecutive_single_line_indices = find_consecutive_true_regions(
single_line_paras_tag
)
if len(consecutive_single_line_indices) > 0:
"""检查这些行是否是高度相同的,居中的."""
for start, end in consecutive_single_line_indices:
# start += index_offset
# end += index_offset
line_hi = np.array(
[
block['lines'][0]['bbox'][3] - block['lines'][0]['bbox'][1]
for block in layout_para[start : end + 1]
]
)
first_line_text = ''.join(
[
__get_span_text(span)
for span in layout_para[start]['lines'][0]['spans']
]
)
if 'Table' in first_line_text or 'Figure' in first_line_text:
pass
if debug_able:
logger.info(line_hi.std())
if line_hi.std() < 2:
"""行高度相同,那么判断是否居中."""
all_left_x0 = [
block['lines'][0]['bbox'][0]
for block in layout_para[start : end + 1]
]
all_right_x1 = [
block['lines'][0]['bbox'][2]
for block in layout_para[start : end + 1]
]
layout_center = (layout_box[0] + layout_box[2]) / 2
if (
all(
[
x0 < layout_center < x1
for x0, x1 in zip(all_left_x0, all_right_x1)
]
)
and not all([x0 == layout_box[0] for x0 in all_left_x0])
and not all([x1 == layout_box[2] for x1 in all_right_x1])
):
merge_para = [
block['lines'][0] for block in layout_para[start : end + 1]
]
para_text = ''.join(
[
__get_span_text(span)
for line in merge_para
for span in line['spans']
]
)
if debug_able:
logger.info(para_text)
layout_para[start]['lines'] = merge_para
for i_para in range(start + 1, end + 1):
layout_para[i_para]['lines'] = []
layout_para[i_para][LINES_DELETED] = True # noqa: F405
# layout_para[start:end + 1] = [merge_para]
# index_offset -= end - start
return
def __merge_signle_list_text(page_paras, new_layout_bbox, page_num, lang):
"""找出来连续的单行文本,如果首行顶格,接下来的几个单行段落缩进对齐,那么合并为一个段落。"""
pass
def __do_split_page(blocks, layout_bboxes, new_layout_bbox, page_num, lang):
"""根据line和layout情况进行分段 先实现一个根据行末尾特征分段的简单方法。"""
"""
算法思路:
1. 扫描layout里每一行,找出来行尾距离layout有边界有一定距离的行。
2. 从上述行中找到末尾是句号等可作为断行标志的行。
3. 参照上述行尾特征进行分段。
4. 图、表,目前独占一行,不考虑分段。
"""
blocks_group = __group_line_by_layout(blocks, layout_bboxes) # block内分段
layout_list_info = __split_para_in_layoutbox(
blocks_group, new_layout_bbox, lang
) # layout内分段
blocks_group, page_list_info = __connect_list_inter_layout(
blocks_group, new_layout_bbox, layout_list_info, page_num, lang
) # layout之间连接列表段落
connected_layout_blocks = __connect_para_inter_layoutbox(
blocks_group, new_layout_bbox
) # layout间链接段落
return connected_layout_blocks, page_list_info
def para_split(pdf_info_dict, debug_mode, lang='en'):
global debug_able
debug_able = debug_mode
new_layout_of_pages = [] # 数组的数组,每个元素是一个页面的layoutS
all_page_list_info = [] # 保存每个页面开头和结尾是否是列表
for page_num, page in pdf_info_dict.items():
blocks = copy.deepcopy(page['preproc_blocks'])
layout_bboxes = page['layout_bboxes']
new_layout_bbox = __common_pre_proc(blocks, layout_bboxes)
new_layout_of_pages.append(new_layout_bbox)
splited_blocks, page_list_info = __do_split_page(
blocks, layout_bboxes, new_layout_bbox, page_num, lang
)
all_page_list_info.append(page_list_info)
page['para_blocks'] = splited_blocks
"""连接页面与页面之间的可能合并的段落"""
pdf_infos = list(pdf_info_dict.values())
for page_num, page in enumerate(pdf_info_dict.values()):
if page_num == 0:
continue
pre_page_paras = pdf_infos[page_num - 1]['para_blocks']
next_page_paras = pdf_infos[page_num]['para_blocks']
pre_page_layout_bbox = new_layout_of_pages[page_num - 1]
next_page_layout_bbox = new_layout_of_pages[page_num]
is_conn = __connect_para_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
page_num,
lang,
)
if debug_able:
if is_conn:
logger.info(f'连接了第{page_num - 1}页和第{page_num}页的段落')
is_list_conn = __connect_list_inter_page(
pre_page_paras,
next_page_paras,
pre_page_layout_bbox,
next_page_layout_bbox,
all_page_list_info[page_num - 1],
all_page_list_info[page_num],
page_num,
lang,
)
if debug_able:
if is_list_conn:
logger.info(f'连接了第{page_num - 1}页和第{page_num}页的列表段落')
"""接下来可能会漏掉一些特别的一些可以合并的内容,对他们进行段落连接
1. 正文中有时出现一个行顶格,接下来几行缩进的情况。
2. 居中的一些连续单行,如果高度相同,那么可能是一个段落。
"""
for page_num, page in enumerate(pdf_info_dict.values()):
page_paras = page['para_blocks']
new_layout_bbox = new_layout_of_pages[page_num]
__connect_middle_align_text(page_paras, new_layout_bbox, page_num, lang)
__merge_signle_list_text(page_paras, new_layout_bbox, page_num, lang)
# layout展平
for page_num, page in enumerate(pdf_info_dict.values()):
page_paras = page['para_blocks']
page_blocks = [block for layout in page_paras for block in layout]
page['para_blocks'] = page_blocks
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@@ -1,207 +0,0 @@
class RawBlockProcessor:
def __init__(self) -> None:
self.y_tolerance = 2
self.pdf_dic = {}
def __span_flags_decomposer(self, span_flags):
"""
Make font flags human readable.
Parameters
----------
self : object
The instance of the class.
span_flags : int
span flags
Returns
-------
l : dict
decomposed flags
"""
l = {
"is_superscript": False,
"is_italic": False,
"is_serifed": False,
"is_sans_serifed": False,
"is_monospaced": False,
"is_proportional": False,
"is_bold": False,
}
if span_flags & 2**0:
l["is_superscript"] = True # 表示上标
if span_flags & 2**1:
l["is_italic"] = True # 表示斜体
if span_flags & 2**2:
l["is_serifed"] = True # 表示衬线字体
else:
l["is_sans_serifed"] = True # 表示非衬线字体
if span_flags & 2**3:
l["is_monospaced"] = True # 表示等宽字体
else:
l["is_proportional"] = True # 表示比例字体
if span_flags & 2**4:
l["is_bold"] = True # 表示粗体
return l
def __make_new_lines(self, raw_lines):
"""
This function makes new lines.
Parameters
----------
self : object
The instance of the class.
raw_lines : list
raw lines
Returns
-------
new_lines : list
new lines
"""
new_lines = []
new_line = None
for raw_line in raw_lines:
raw_line_bbox = raw_line["bbox"]
raw_line_spans = raw_line["spans"]
raw_line_text = "".join([span["text"] for span in raw_line_spans])
raw_line_dir = raw_line.get("dir", None)
decomposed_line_spans = []
for span in raw_line_spans:
raw_flags = span["flags"]
decomposed_flags = self.__span_flags_decomposer(raw_flags)
span["decomposed_flags"] = decomposed_flags
decomposed_line_spans.append(span)
if new_line is None:
new_line = {
"bbox": raw_line_bbox,
"text": raw_line_text,
"dir": raw_line_dir if raw_line_dir else (0, 0),
"spans": decomposed_line_spans,
}
else:
if (
abs(raw_line_bbox[1] - new_line["bbox"][1]) <= self.y_tolerance
and abs(raw_line_bbox[3] - new_line["bbox"][3]) <= self.y_tolerance
):
new_line["bbox"] = (
min(new_line["bbox"][0], raw_line_bbox[0]), # left
new_line["bbox"][1], # top
max(new_line["bbox"][2], raw_line_bbox[2]), # right
raw_line_bbox[3], # bottom
)
new_line["text"] += " " + raw_line_text
new_line["spans"].extend(raw_line_spans)
new_line["dir"] = (
new_line["dir"][0] + raw_line_dir[0],
new_line["dir"][1] + raw_line_dir[1],
)
else:
new_lines.append(new_line)
new_line = {
"bbox": raw_line_bbox,
"text": raw_line_text,
"dir": raw_line_dir if raw_line_dir else (0, 0),
"spans": raw_line_spans,
}
if new_line:
new_lines.append(new_line)
return new_lines
def __make_new_block(self, raw_block):
"""
This function makes a new block.
Parameters
----------
self : object
The instance of the class.
----------
raw_block : dict
a raw block
Returns
-------
new_block : dict
Schema of new_block:
{
"block_id": "block_1",
"bbox": [0, 0, 100, 100],
"text": "This is a block.",
"lines": [
{
"bbox": [0, 0, 100, 100],
"text": "This is a line.",
"spans": [
{
"text": "This is a span.",
"font": "Times New Roman",
"size": 12,
"color": "#000000",
}
],
}
],
}
"""
new_block = {}
block_id = raw_block["number"]
block_bbox = raw_block["bbox"]
block_text = " ".join(span["text"] for line in raw_block["lines"] for span in line["spans"])
raw_lines = raw_block["lines"]
block_lines = self.__make_new_lines(raw_lines)
new_block["block_id"] = block_id
new_block["bbox"] = block_bbox
new_block["text"] = block_text
new_block["lines"] = block_lines
return new_block
def batch_process_blocks(self, pdf_dic):
"""
This function processes the blocks in batch.
Parameters
----------
self : object
The instance of the class.
----------
blocks : list
Input block is a list of raw blocks. Schema can refer to the value of key ""preproc_blocks", demo file is app/pdf_toolbox/tests/preproc_2_parasplit_example.json.
Returns
-------
result_dict : dict
result dictionary
"""
for page_id, blocks in pdf_dic.items():
if page_id.startswith("page_"):
para_blocks = []
if "preproc_blocks" in blocks.keys():
input_blocks = blocks["preproc_blocks"]
for raw_block in input_blocks:
new_block = self.__make_new_block(raw_block)
para_blocks.append(new_block)
blocks["para_blocks"] = para_blocks
return pdf_dic
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@@ -1,268 +0,0 @@
from collections import Counter
import numpy as np
from magic_pdf.para.commons import *
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
class BlockStatisticsCalculator:
def __init__(self) -> None:
pass
def __calc_stats_of_new_lines(self, new_lines):
"""
This function calculates the paragraph metrics
Parameters
----------
combined_lines : list
combined lines
Returns
-------
X0 : float
Median of x0 values, which represents the left average boundary of the block
X1 : float
Median of x1 values, which represents the right average boundary of the block
avg_char_width : float
Average of char widths, which represents the average char width of the block
avg_char_height : float
Average of line heights, which represents the average line height of the block
"""
x0_values = []
x1_values = []
char_widths = []
char_heights = []
block_font_types = []
block_font_sizes = []
block_directions = []
if len(new_lines) > 0:
for i, line in enumerate(new_lines):
line_bbox = line["bbox"]
line_text = line["text"]
line_spans = line["spans"]
num_chars = len([ch for ch in line_text if not ch.isspace()])
x0_values.append(line_bbox[0])
x1_values.append(line_bbox[2])
if num_chars > 0:
char_width = (line_bbox[2] - line_bbox[0]) / num_chars
char_widths.append(char_width)
for span in line_spans:
block_font_types.append(span["font"])
block_font_sizes.append(span["size"])
if "dir" in line:
block_directions.append(line["dir"])
# line_font_types = [span["font"] for span in line_spans]
char_heights = [span["size"] for span in line_spans]
X0 = np.median(x0_values) if x0_values else 0
X1 = np.median(x1_values) if x1_values else 0
avg_char_width = sum(char_widths) / len(char_widths) if char_widths else 0
avg_char_height = sum(char_heights) / len(char_heights) if char_heights else 0
# max_freq_font_type = max(set(block_font_types), key=block_font_types.count) if block_font_types else None
max_span_length = 0
max_span_font_type = None
for line in new_lines:
line_spans = line["spans"]
for span in line_spans:
span_length = span["bbox"][2] - span["bbox"][0]
if span_length > max_span_length:
max_span_length = span_length
max_span_font_type = span["font"]
max_freq_font_type = max_span_font_type
avg_font_size = sum(block_font_sizes) / len(block_font_sizes) if block_font_sizes else None
avg_dir_horizontal = sum([dir[0] for dir in block_directions]) / len(block_directions) if block_directions else 0
avg_dir_vertical = sum([dir[1] for dir in block_directions]) / len(block_directions) if block_directions else 0
median_font_size = float(np.median(block_font_sizes)) if block_font_sizes else None
return (
X0,
X1,
avg_char_width,
avg_char_height,
max_freq_font_type,
avg_font_size,
(avg_dir_horizontal, avg_dir_vertical),
median_font_size,
)
def __make_new_block(self, input_block):
new_block = {}
raw_lines = input_block["lines"]
stats = self.__calc_stats_of_new_lines(raw_lines)
block_id = input_block["block_id"]
block_bbox = input_block["bbox"]
block_text = input_block["text"]
block_lines = raw_lines
block_avg_left_boundary = stats[0]
block_avg_right_boundary = stats[1]
block_avg_char_width = stats[2]
block_avg_char_height = stats[3]
block_font_type = stats[4]
block_font_size = stats[5]
block_direction = stats[6]
block_median_font_size = stats[7]
new_block["block_id"] = block_id
new_block["bbox"] = block_bbox
new_block["text"] = block_text
new_block["dir"] = block_direction
new_block["X0"] = block_avg_left_boundary
new_block["X1"] = block_avg_right_boundary
new_block["avg_char_width"] = block_avg_char_width
new_block["avg_char_height"] = block_avg_char_height
new_block["block_font_type"] = block_font_type
new_block["block_font_size"] = block_font_size
new_block["lines"] = block_lines
new_block["median_font_size"] = block_median_font_size
return new_block
def batch_process_blocks(self, pdf_dic):
"""
This function processes the blocks in batch.
Parameters
----------
self : object
The instance of the class.
----------
blocks : list
Input block is a list of raw blocks. Schema can refer to the value of key ""preproc_blocks", demo file is app/pdf_toolbox/tests/preproc_2_parasplit_example.json
Returns
-------
result_dict : dict
result dictionary
"""
for page_id, blocks in pdf_dic.items():
if page_id.startswith("page_"):
para_blocks = []
if "para_blocks" in blocks.keys():
input_blocks = blocks["para_blocks"]
for input_block in input_blocks:
new_block = self.__make_new_block(input_block)
para_blocks.append(new_block)
blocks["para_blocks"] = para_blocks
return pdf_dic
class DocStatisticsCalculator:
def __init__(self) -> None:
pass
def calc_stats_of_doc(self, pdf_dict):
"""
This function computes the statistics of the document
Parameters
----------
result_dict : dict
result dictionary
Returns
-------
statistics : dict
statistics of the document
"""
total_text_length = 0
total_num_blocks = 0
for page_id, blocks in pdf_dict.items():
if page_id.startswith("page_"):
if "para_blocks" in blocks.keys():
para_blocks = blocks["para_blocks"]
for para_block in para_blocks:
total_text_length += len(para_block["text"])
total_num_blocks += 1
avg_text_length = total_text_length / total_num_blocks if total_num_blocks else 0
font_list = []
for page_id, blocks in pdf_dict.items():
if page_id.startswith("page_"):
if "para_blocks" in blocks.keys():
input_blocks = blocks["para_blocks"]
for input_block in input_blocks:
block_text_length = len(input_block.get("text", ""))
if block_text_length < avg_text_length * 0.5:
continue
block_font_type = safe_get(input_block, "block_font_type", "")
block_font_size = safe_get(input_block, "block_font_size", 0)
font_list.append((block_font_type, block_font_size))
font_counter = Counter(font_list)
most_common_font = font_counter.most_common(1)[0] if font_list else (("", 0), 0)
second_most_common_font = font_counter.most_common(2)[1] if len(font_counter) > 1 else (("", 0), 0)
statistics = {
"num_pages": 0,
"num_blocks": 0,
"num_paras": 0,
"num_titles": 0,
"num_header_blocks": 0,
"num_footer_blocks": 0,
"num_watermark_blocks": 0,
"num_vertical_margin_note_blocks": 0,
"most_common_font_type": most_common_font[0][0],
"most_common_font_size": most_common_font[0][1],
"number_of_most_common_font": most_common_font[1],
"second_most_common_font_type": second_most_common_font[0][0],
"second_most_common_font_size": second_most_common_font[0][1],
"number_of_second_most_common_font": second_most_common_font[1],
"avg_text_length": avg_text_length,
}
for page_id, blocks in pdf_dict.items():
if page_id.startswith("page_"):
blocks = pdf_dict[page_id]["para_blocks"]
statistics["num_pages"] += 1
for block_id, block_data in enumerate(blocks):
statistics["num_blocks"] += 1
if "paras" in block_data.keys():
statistics["num_paras"] += len(block_data["paras"])
for line in block_data["lines"]:
if line.get("is_title", 0):
statistics["num_titles"] += 1
if block_data.get("is_header", 0):
statistics["num_header_blocks"] += 1
if block_data.get("is_footer", 0):
statistics["num_footer_blocks"] += 1
if block_data.get("is_watermark", 0):
statistics["num_watermark_blocks"] += 1
if block_data.get("is_vertical_margin_note", 0):
statistics["num_vertical_margin_note_blocks"] += 1
pdf_dict["statistics"] = statistics
return pdf_dict
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from loguru import logger
from magic_pdf.config.drop_reason import DropReason
from magic_pdf.layout.layout_sort import get_columns_cnt_of_layout
def __is_pseudo_single_column(page_info) -> bool:
"""判断一个页面是否伪单列。
Args:
page_info (dict): 页面信息字典,包括'_layout_tree'和'preproc_blocks'。
Returns:
Tuple[bool, Optional[str]]: 如果页面伪单列返回(True, extra_info),否则返回(False, None)。
"""
layout_tree = page_info['_layout_tree']
layout_column_width = get_columns_cnt_of_layout(layout_tree)
if layout_column_width == 1:
text_blocks = page_info['preproc_blocks']
# 遍历每一个text_block
for text_block in text_blocks:
lines = text_block['lines']
num_lines = len(lines)
num_satisfying_lines = 0
for i in range(num_lines - 1):
current_line = lines[i]
next_line = lines[i + 1]
# 获取当前line和下一个line的bbox属性
current_bbox = current_line['bbox']
next_bbox = next_line['bbox']
# 检查是否满足条件
if next_bbox[0] > current_bbox[2] or next_bbox[2] < current_bbox[0]:
num_satisfying_lines += 1
# 如果有一半以上的line满足条件,就drop
# print("num_satisfying_lines:", num_satisfying_lines, "num_lines:", num_lines)
if num_lines > 20:
radio = num_satisfying_lines / num_lines
if radio >= 0.5:
extra_info = f'{{num_lines: {num_lines}, num_satisfying_lines: {num_satisfying_lines}}}'
block_text = []
for line in lines:
if line['spans']:
for span in line['spans']:
block_text.append(span['text'])
logger.warning(f'pseudo_single_column block_text: {block_text}')
return True, extra_info
return False, None
def pdf_post_filter(page_info) -> tuple:
"""return:(True|False, err_msg) True, 如果pdf符合要求 False, 如果pdf不符合要求."""
bool_is_pseudo_single_column, extra_info = __is_pseudo_single_column(page_info)
if bool_is_pseudo_single_column:
return False, {'_need_drop': True, '_drop_reason': DropReason.PSEUDO_SINGLE_COLUMN, 'extra_info': extra_info}
return True, None
@@ -1,153 +0,0 @@
from magic_pdf.libs.boxbase import _is_in, _is_in_or_part_overlap
import collections # 统计库
def is_below(bbox1, bbox2):
# 如果block1的上边y坐标大于block2的下边y坐标,那么block1在block2下面
return bbox1[1] > bbox2[3]
def merge_bboxes(bboxes):
# 找出所有blocks的最小x0,最大y1,最大x1,最小y0,这就是合并后的bbox
x0 = min(bbox[0] for bbox in bboxes)
y0 = min(bbox[1] for bbox in bboxes)
x1 = max(bbox[2] for bbox in bboxes)
y1 = max(bbox[3] for bbox in bboxes)
return [x0, y0, x1, y1]
def merge_footnote_blocks(page_info, main_text_font):
page_info['merged_bboxes'] = []
for layout in page_info['layout_bboxes']:
# 找出layout中的所有footnote blocks和preproc_blocks
footnote_bboxes = [block for block in page_info['footnote_bboxes_tmp'] if _is_in(block, layout['layout_bbox'])]
# 如果没有footnote_blocks,就跳过这个layout
if not footnote_bboxes:
continue
preproc_blocks = [block for block in page_info['preproc_blocks'] if _is_in(block['bbox'], layout['layout_bbox'])]
# preproc_bboxes = [block['bbox'] for block in preproc_blocks]
font_names = collections.Counter()
if len(preproc_blocks) > 0:
# 存储每一行的文本块大小的列表
line_sizes = []
# 存储每个文本块的平均行大小
block_sizes = []
for block in preproc_blocks:
block_line_sizes = []
block_fonts = collections.Counter()
for line in block['lines']:
# 提取每个span的size属性,并计算行大小
span_sizes = [span['size'] for span in line['spans'] if 'size' in span]
if span_sizes:
line_size = sum(span_sizes) / len(span_sizes)
line_sizes.append(line_size)
block_line_sizes.append(line_size)
span_font = [(span['font'], len(span['text'])) for span in line['spans'] if
'font' in span and len(span['text']) > 0]
if span_font:
# # todo main_text_font应该用基于字数最多的字体而不是span级别的统计
# font_names.append(font_name for font_name in span_font)
# block_fonts.append(font_name for font_name in span_font)
for font, count in span_font:
# font_names.extend([font] * count)
# block_fonts.extend([font] * count)
font_names[font] += count
block_fonts[font] += count
if block_line_sizes:
# 计算文本块的平均行大小
block_size = sum(block_line_sizes) / len(block_line_sizes)
block_font = block_fonts.most_common(1)[0][0]
block_sizes.append((block, block_size, block_font))
# 计算main_text_size
# main_text_font = font_names.most_common(1)[0][0]
main_text_size = collections.Counter(line_sizes).most_common(1)[0][0]
else:
continue
need_merge_bboxes = []
# 任何一个下面有正文block的footnote bbox都是假footnote
for footnote_bbox in footnote_bboxes:
# 检测footnote下面是否有正文block(正文block需满足,block平均size大于等于main_text_size,且block行数大于等于5)
main_text_bboxes_below = [block['bbox'] for block, size, block_font in block_sizes if
is_below(block['bbox'], footnote_bbox) and
sum([size >= main_text_size,
len(block['lines']) >= 5,
block_font == main_text_font])
>= 2]
# 如果main_text_bboxes_below不为空,说明footnote下面有正文block,这个footnote不成立,跳过
if len(main_text_bboxes_below) > 0:
continue
else:
# 否则,说明footnote下面没有正文block,这个footnote成立,添加到待merge的footnote_bboxes中
need_merge_bboxes.append(footnote_bbox)
if len(need_merge_bboxes) == 0:
continue
# 找出最靠上的footnote block
top_footnote_bbox = min(need_merge_bboxes, key=lambda bbox: bbox[1])
# 找出所有在top_footnote_block下面的preproc_blocks,并确保这些preproc_blocks的平均行大小小于main_text_size
bboxes_below = [block['bbox'] for block, size, block_font in block_sizes if is_below(block['bbox'], top_footnote_bbox)]
# # 找出所有在top_footnote_block下面的preproc_blocks
# bboxes_below = [bbox for bbox in preproc_bboxes if is_below(bbox, top_footnote_bbox)]
# 合并top_footnote_block和blocks_below
merged_bbox = merge_bboxes([top_footnote_bbox] + bboxes_below)
# 添加到新的footnote_bboxes_tmp中
page_info['merged_bboxes'].append(merged_bbox)
return page_info
def remove_footnote_blocks(page_info):
if page_info.get('merged_bboxes'):
# 从文字中去掉footnote
remain_text_blocks, removed_footnote_text_blocks = remove_footnote_text(page_info['preproc_blocks'], page_info['merged_bboxes'])
# 从图片中去掉footnote
image_blocks, removed_footnote_imgs_blocks = remove_footnote_image(page_info['images'], page_info['merged_bboxes'])
# 更新page_info
page_info['preproc_blocks'] = remain_text_blocks
page_info['images'] = image_blocks
page_info['droped_text_block'].extend(removed_footnote_text_blocks)
page_info['droped_image_block'].extend(removed_footnote_imgs_blocks)
# 删除footnote_bboxes_tmp和merged_bboxes
del page_info['merged_bboxes']
del page_info['footnote_bboxes_tmp']
return page_info
def remove_footnote_text(raw_text_block, footnote_bboxes):
"""
:param raw_text_block: str类型,是当前页的文本内容
:param footnoteBboxes: list类型,是当前页的脚注bbox
"""
footnote_text_blocks = []
for block in raw_text_block:
text_bbox = block['bbox']
# TODO 更严谨点在line级别做
if any([_is_in_or_part_overlap(text_bbox, footnote_bbox) for footnote_bbox in footnote_bboxes]):
# if any([text_bbox[3]>=footnote_bbox[1] for footnote_bbox in footnote_bboxes]):
block['tag'] = 'footnote'
footnote_text_blocks.append(block)
# raw_text_block.remove(block)
# 移除,不能再内部移除,否则会出错
for block in footnote_text_blocks:
raw_text_block.remove(block)
return raw_text_block, footnote_text_blocks
def remove_footnote_image(image_blocks, footnote_bboxes):
"""
:param image_bboxes: list类型,是当前页的图片bbox(结构体)
:param footnoteBboxes: list类型,是当前页的脚注bbox
"""
footnote_imgs_blocks = []
for image_block in image_blocks:
if any([_is_in(image_block['bbox'], footnote_bbox) for footnote_bbox in footnote_bboxes]):
footnote_imgs_blocks.append(image_block)
for footnote_imgs_block in footnote_imgs_blocks:
image_blocks.remove(footnote_imgs_block)
return image_blocks, footnote_imgs_blocks
@@ -1,161 +0,0 @@
"""
去掉正文的引文引用marker
https://aicarrier.feishu.cn/wiki/YLOPwo1PGiwFRdkwmyhcZmr0n3d
"""
import re
# from magic_pdf.libs.nlp_utils import NLPModels
# __NLP_MODEL = NLPModels()
def check_1(spans, cur_span_i):
"""寻找前一个char,如果是句号,逗号,那么就是角标"""
if cur_span_i==0:
return False # 不是角标
pre_span = spans[cur_span_i-1]
pre_char = pre_span['chars'][-1]['c']
if pre_char in ['。', ',', '.', ',']:
return True
return False
# def check_2(spans, cur_span_i):
# """检查前面一个span的最后一个单词,如果长度大于5,全都是字母,并且不含大写,就是角标"""
# pattern = r'\b[A-Z]\.\s[A-Z][a-z]*\b' # 形如A. Bcde, L. Bcde, 人名的缩写
#
# if cur_span_i==0 and len(spans)>1:
# next_span = spans[cur_span_i+1]
# next_txt = "".join([c['c'] for c in next_span['chars']])
# result = __NLP_MODEL.detect_entity_catgr_using_nlp(next_txt)
# if result in ["PERSON", "GPE", "ORG"]:
# return True
#
# if re.findall(pattern, next_txt):
# return True
#
# return False # 不是角标
# elif cur_span_i==0 and len(spans)==1: # 角标占用了整行?谨慎删除
# return False
#
# # 如果这个span是最后一个span,
# if cur_span_i==len(spans)-1:
# pre_span = spans[cur_span_i-1]
# pre_txt = "".join([c['c'] for c in pre_span['chars']])
# pre_word = pre_txt.split(' ')[-1]
# result = __NLP_MODEL.detect_entity_catgr_using_nlp(pre_txt)
# if result in ["PERSON", "GPE", "ORG"]:
# return True
#
# if re.findall(pattern, pre_txt):
# return True
#
# return len(pre_word) > 5 and pre_word.isalpha() and pre_word.islower()
# else: # 既不是第一个span,也不是最后一个span,那么此时检查一下这个角标距离前后哪个单词更近就属于谁的角标
# pre_span = spans[cur_span_i-1]
# next_span = spans[cur_span_i+1]
# cur_span = spans[cur_span_i]
# # 找到前一个和后一个span里的距离最近的单词
# pre_distance = 10000 # 一个很大的数
# next_distance = 10000 # 一个很大的数
# for c in pre_span['chars'][::-1]:
# if c['c'].isalpha():
# pre_distance = cur_span['bbox'][0] - c['bbox'][2]
# break
# for c in next_span['chars']:
# if c['c'].isalpha():
# next_distance = c['bbox'][0] - cur_span['bbox'][2]
# break
#
# if pre_distance<next_distance:
# belong_to_span = pre_span
# else:
# belong_to_span = next_span
#
# txt = "".join([c['c'] for c in belong_to_span['chars']])
# pre_word = txt.split(' ')[-1]
# result = __NLP_MODEL.detect_entity_catgr_using_nlp(txt)
# if result in ["PERSON", "GPE", "ORG"]:
# return True
#
# if re.findall(pattern, txt):
# return True
#
# return len(pre_word) > 5 and pre_word.isalpha() and pre_word.islower()
def check_3(spans, cur_span_i):
"""上标里有[], 有*, 有-, 有逗号"""
# 如[2-3],[22]
# 如 2,3,4
cur_span_txt = ''.join(c['c'] for c in spans[cur_span_i]['chars']).strip()
bad_char = ['[', ']', '*', ',']
if any([c in cur_span_txt for c in bad_char]) and any(character.isdigit() for character in cur_span_txt):
return True
# 如2-3, a-b
patterns = [r'\d+-\d+', r'[a-zA-Z]-[a-zA-Z]', r'[a-zA-Z],[a-zA-Z]']
for pattern in patterns:
match = re.match(pattern, cur_span_txt)
if match is not None:
return True
return False
def remove_citation_marker(with_char_text_blcoks):
for blk in with_char_text_blcoks:
for line in blk['lines']:
# 如果span里的个数少于2个,那只能忽略,角标不可能自己独占一行
if len(line['spans'])<=1:
continue
# 找到高度最高的span作为位置比较的基准
max_hi_span = line['spans'][0]['bbox']
min_font_sz = 10000 # line里最小的字体
max_font_sz = 0 # line里最大的字体
for s in line['spans']:
if max_hi_span[3]-max_hi_span[1]<s['bbox'][3]-s['bbox'][1]:
max_hi_span = s['bbox']
if min_font_sz>s['size']:
min_font_sz = s['size']
if max_font_sz<s['size']:
max_font_sz = s['size']
base_span_mid_y = (max_hi_span[3]+max_hi_span[1])/2
span_to_del = []
for i, span in enumerate(line['spans']):
span_hi = span['bbox'][3]-span['bbox'][1]
span_mid_y = (span['bbox'][3]+span['bbox'][1])/2
span_font_sz = span['size']
if max_font_sz-span_font_sz<1: # 先以字体过滤正文,如果是正文就不再继续判断了
continue
# 对被除数为0的情况进行过滤
if span_hi==0 or min_font_sz==0:
continue
if (base_span_mid_y-span_mid_y)/span_hi>0.2 or (base_span_mid_y-span_mid_y>0 and abs(span_font_sz-min_font_sz)/min_font_sz<0.1):
"""
1. 它的前一个char如果是句号或者逗号的话,那么肯定是角标而不是公式
2. 如果这个角标的前面是一个单词(长度大于5)而不是任何大写或小写的短字母的话 应该也是角标
3. 上标里有数字和逗号或者数字+星号的组合,方括号,一般肯定就是角标了
4. 这个角标属于前文还是后文要根据距离来判断,如果距离前面的文本太近,那么就是前面的角标,否则就是后面的角标
"""
if (check_1(line['spans'], i) or
# check_2(line['spans'], i) or
check_3(line['spans'], i)
):
"""删除掉这个角标:删除这个span, 同时还要更新line的text"""
span_to_del.append(span)
if len(span_to_del)>0:
for span in span_to_del:
line['spans'].remove(span)
line['text'] = ''.join([c['c'] for s in line['spans'] for c in s['chars']])
return with_char_text_blcoks
-134
View File
@@ -1,134 +0,0 @@
from magic_pdf.libs.boxbase import _is_in, calculate_overlap_area_2_minbox_area_ratio # 正则
from magic_pdf.libs.commons import fitz # pyMuPDF库
def __solve_contain_bboxs(all_bbox_list: list):
"""将两个公式的bbox做判断是否有包含关系,若有的话则删掉较小的bbox"""
dump_list = []
for i in range(len(all_bbox_list)):
for j in range(i + 1, len(all_bbox_list)):
# 获取当前两个值
bbox1 = all_bbox_list[i][:4]
bbox2 = all_bbox_list[j][:4]
# 删掉较小的框
if _is_in(bbox1, bbox2):
dump_list.append(all_bbox_list[i])
elif _is_in(bbox2, bbox1):
dump_list.append(all_bbox_list[j])
else:
ratio = calculate_overlap_area_2_minbox_area_ratio(bbox1, bbox2)
if ratio > 0.7:
s1 = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
s2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
if s2 > s1:
dump_list.append(all_bbox_list[i])
else:
dump_list.append(all_bbox_list[i])
# 遍历需要删除的列表中的每个元素
for item in dump_list:
while item in all_bbox_list:
all_bbox_list.remove(item)
return all_bbox_list
def parse_equations(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
DPI = 72 # use this resolution
pix = page.get_pixmap(dpi=DPI)
pageL = 0
pageR = int(pix.w)
pageU = 0
pageD = int(pix.h)
#--------- 通过json_from_DocXchain来获取 table ---------#
equationEmbedding_from_DocXChain_bboxs = []
equationIsolated_from_DocXChain_bboxs = []
xf_json = json_from_DocXchain_obj
width_from_json = xf_json['page_info']['width']
height_from_json = xf_json['page_info']['height']
LR_scaleRatio = width_from_json / (pageR - pageL)
UD_scaleRatio = height_from_json / (pageD - pageU)
for xf in xf_json['layout_dets']:
# {0: 'title', 1: 'figure', 2: 'plain text', 3: 'header', 4: 'page number', 5: 'footnote', 6: 'footer', 7: 'table', 8: 'table caption', 9: 'figure caption', 10: 'equation', 11: 'full column', 12: 'sub column'}
L = xf['poly'][0] / LR_scaleRatio
U = xf['poly'][1] / UD_scaleRatio
R = xf['poly'][2] / LR_scaleRatio
D = xf['poly'][5] / UD_scaleRatio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
# equation
img_suffix = f"{page_ID}_{int(L)}_{int(U)}_{int(R)}_{int(D)}"
if xf['category_id'] == 13 and xf['score'] >= 0.3:
latex_text = xf.get("latex", "EmptyInlineEquationResult")
debugable_latex_text = f"{latex_text}|{img_suffix}"
equationEmbedding_from_DocXChain_bboxs.append((L, U, R, D, latex_text))
if xf['category_id'] == 14 and xf['score'] >= 0.3:
latex_text = xf.get("latex", "EmptyInterlineEquationResult")
debugable_latex_text = f"{latex_text}|{img_suffix}"
equationIsolated_from_DocXChain_bboxs.append((L, U, R, D, latex_text))
#---------------------------------------- 排序,编号,保存 -----------------------------------------#
equationIsolated_from_DocXChain_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
equationIsolated_from_DocXChain_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
equationEmbedding_from_DocXChain_names = []
equationEmbedding_ID = 0
equationIsolated_from_DocXChain_names = []
equationIsolated_ID = 0
for L, U, R, D, _ in equationEmbedding_from_DocXChain_bboxs:
if not(L < R and U < D):
continue
try:
# cur_equation = page.get_pixmap(clip=(L,U,R,D))
new_equation_name = "equationEmbedding_{}_{}.png".format(page_ID, equationEmbedding_ID) # 公式name
# cur_equation.save(res_dir_path + '/' + new_equation_name) # 把公式存出在新建的文件夹,并命名
equationEmbedding_from_DocXChain_names.append(new_equation_name) # 把公式的名字存在list中,方便在md中插入引用
equationEmbedding_ID += 1
except:
pass
for L, U, R, D, _ in equationIsolated_from_DocXChain_bboxs:
if not(L < R and U < D):
continue
try:
# cur_equation = page.get_pixmap(clip=(L,U,R,D))
new_equation_name = "equationEmbedding_{}_{}.png".format(page_ID, equationIsolated_ID) # 公式name
# cur_equation.save(res_dir_path + '/' + new_equation_name) # 把公式存出在新建的文件夹,并命名
equationIsolated_from_DocXChain_names.append(new_equation_name) # 把公式的名字存在list中,方便在md中插入引用
equationIsolated_ID += 1
except:
pass
equationEmbedding_from_DocXChain_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
equationIsolated_from_DocXChain_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
"""根据pdf可视区域,调整bbox的坐标"""
cropbox = page.cropbox
if cropbox[0]!=page.rect[0] or cropbox[1]!=page.rect[1]:
for eq_box in equationEmbedding_from_DocXChain_bboxs:
eq_box = [eq_box[0]+cropbox[0], eq_box[1]+cropbox[1], eq_box[2]+cropbox[0], eq_box[3]+cropbox[1], eq_box[4]]
for eq_box in equationIsolated_from_DocXChain_bboxs:
eq_box = [eq_box[0]+cropbox[0], eq_box[1]+cropbox[1], eq_box[2]+cropbox[0], eq_box[3]+cropbox[1], eq_box[4]]
deduped_embedding_eq_bboxes = __solve_contain_bboxs(equationEmbedding_from_DocXChain_bboxs)
return deduped_embedding_eq_bboxes, equationIsolated_from_DocXChain_bboxs
@@ -1,64 +0,0 @@
from magic_pdf.libs.commons import fitz # pyMuPDF库
from magic_pdf.libs.coordinate_transform import get_scale_ratio
def parse_footers(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
#--------- 通过json_from_DocXchain来获取 footer ---------#
footer_bbox_from_DocXChain = []
xf_json = json_from_DocXchain_obj
horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(xf_json, page)
# {0: 'title', # 标题
# 1: 'figure', # 图片
# 2: 'plain text', # 文本
# 3: 'header', # 页眉
# 4: 'page number', # 页码
# 5: 'footnote', # 脚注
# 6: 'footer', # 页脚
# 7: 'table', # 表格
# 8: 'table caption', # 表格描述
# 9: 'figure caption', # 图片描述
# 10: 'equation', # 公式
# 11: 'full column', # 单栏
# 12: 'sub column', # 多栏
# 13: 'embedding', # 嵌入公式
# 14: 'isolated'} # 单行公式
for xf in xf_json['layout_dets']:
L = xf['poly'][0] / horizontal_scale_ratio
U = xf['poly'][1] / vertical_scale_ratio
R = xf['poly'][2] / horizontal_scale_ratio
D = xf['poly'][5] / vertical_scale_ratio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if xf['category_id'] == 6 and xf['score'] >= 0.3:
footer_bbox_from_DocXChain.append((L, U, R, D))
footer_final_names = []
footer_final_bboxs = []
footer_ID = 0
for L, U, R, D in footer_bbox_from_DocXChain:
# cur_footer = page.get_pixmap(clip=(L,U,R,D))
new_footer_name = "footer_{}_{}.png".format(page_ID, footer_ID) # 脚注name
# cur_footer.save(res_dir_path + '/' + new_footer_name) # 把页脚存储在新建的文件夹,并命名
footer_final_names.append(new_footer_name) # 把脚注的名字存在list中
footer_final_bboxs.append((L, U, R, D))
footer_ID += 1
footer_final_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_footer_bboxs = footer_final_bboxs
return curPage_all_footer_bboxs
@@ -1,284 +0,0 @@
from collections import defaultdict
from magic_pdf.libs.boxbase import calculate_iou
def compare_bbox_with_list(bbox, bbox_list, tolerance=1):
return any(all(abs(a - b) < tolerance for a, b in zip(bbox, common_bbox)) for common_bbox in bbox_list)
def is_single_line_block(block):
# Determine based on the width and height of the block
block_width = block["X1"] - block["X0"]
block_height = block["bbox"][3] - block["bbox"][1]
# If the height of the block is close to the average character height and the width is large, it is considered a single line
return block_height <= block["avg_char_height"] * 3 and block_width > block["avg_char_width"] * 3
def get_most_common_bboxes(bboxes, page_height, position="top", threshold=0.25, num_bboxes=3, min_frequency=2):
"""
This function gets the most common bboxes from the bboxes
Parameters
----------
bboxes : list
bboxes
page_height : float
height of the page
position : str, optional
"top" or "bottom", by default "top"
threshold : float, optional
threshold, by default 0.25
num_bboxes : int, optional
number of bboxes to return, by default 3
min_frequency : int, optional
minimum frequency of the bbox, by default 2
Returns
-------
common_bboxes : list
common bboxes
"""
# Filter bbox by position
if position == "top":
filtered_bboxes = [bbox for bbox in bboxes if bbox[1] < page_height * threshold]
else:
filtered_bboxes = [bbox for bbox in bboxes if bbox[3] > page_height * (1 - threshold)]
# Find the most common bbox
bbox_count = defaultdict(int)
for bbox in filtered_bboxes:
bbox_count[tuple(bbox)] += 1
# Get the most frequently occurring bbox, but only consider it when the frequency exceeds min_frequency
common_bboxes = [
bbox for bbox, count in sorted(bbox_count.items(), key=lambda item: item[1], reverse=True) if count >= min_frequency
][:num_bboxes]
return common_bboxes
def detect_footer_header2(result_dict, similarity_threshold=0.5):
"""
This function detects the header and footer of the document.
Parameters
----------
result_dict : dict
result dictionary
Returns
-------
result_dict : dict
result dictionary
"""
# Traverse all blocks in the document
single_line_blocks = 0
total_blocks = 0
single_line_blocks = 0
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_key, block in blocks.items():
if block_key.startswith("block_"):
total_blocks += 1
if is_single_line_block(block):
single_line_blocks += 1
# If there are no blocks, skip the header and footer detection
if total_blocks == 0:
print("No blocks found. Skipping header/footer detection.")
return result_dict
# If most of the blocks are single-line, skip the header and footer detection
if single_line_blocks / total_blocks > 0.5: # 50% of the blocks are single-line
# print("Skipping header/footer detection for text-dense document.")
return result_dict
# Collect the bounding boxes of all blocks
all_bboxes = []
all_texts = []
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_key, block in blocks.items():
if block_key.startswith("block_"):
all_bboxes.append(block["bbox"])
# Get the height of the page
page_height = max(bbox[3] for bbox in all_bboxes)
# Get the most common bbox lists for headers and footers
common_header_bboxes = get_most_common_bboxes(all_bboxes, page_height, position="top") if all_bboxes else []
common_footer_bboxes = get_most_common_bboxes(all_bboxes, page_height, position="bottom") if all_bboxes else []
# Detect and mark headers and footers
for page_id, blocks in result_dict.items():
if page_id.startswith("page_"):
for block_key, block in blocks.items():
if block_key.startswith("block_"):
bbox = block["bbox"]
text = block["text"]
is_header = compare_bbox_with_list(bbox, common_header_bboxes)
is_footer = compare_bbox_with_list(bbox, common_footer_bboxes)
block["is_header"] = int(is_header)
block["is_footer"] = int(is_footer)
return result_dict
def __get_page_size(page_sizes:list):
"""
页面大小可能不一样
"""
w = sum([w for w,h in page_sizes])/len(page_sizes)
h = sum([h for w,h in page_sizes])/len(page_sizes)
return w, h
def __calculate_iou(bbox1, bbox2):
iou = calculate_iou(bbox1, bbox2)
return iou
def __is_same_pos(box1, box2, iou_threshold):
iou = __calculate_iou(box1, box2)
return iou >= iou_threshold
def get_most_common_bbox(bboxes:list, page_size:list, page_cnt:int, page_range_threshold=0.2, iou_threshold=0.9):
"""
common bbox必须大于page_cnt的1/3
"""
min_occurance_cnt = max(3, page_cnt//4)
header_det_bbox = []
footer_det_bbox = []
hdr_same_pos_group = []
btn_same_pos_group = []
page_w, page_h = __get_page_size(page_size)
top_y, bottom_y = page_w*page_range_threshold, page_h*(1-page_range_threshold)
top_bbox = [b for b in bboxes if b[3]<top_y]
bottom_bbox = [b for b in bboxes if b[1]>bottom_y]
# 然后开始排序,寻找最经常出现的bbox, 寻找的时候如果IOU>iou_threshold就算是一个
for i in range(0, len(top_bbox)):
hdr_same_pos_group.append([top_bbox[i]])
for j in range(i+1, len(top_bbox)):
if __is_same_pos(top_bbox[i], top_bbox[j], iou_threshold):
#header_det_bbox = [min(top_bbox[i][0], top_bbox[j][0]), min(top_bbox[i][1], top_bbox[j][1]), max(top_bbox[i][2], top_bbox[j][2]), max(top_bbox[i][3],top_bbox[j][3])]
hdr_same_pos_group[i].append(top_bbox[j])
for i in range(0, len(bottom_bbox)):
btn_same_pos_group.append([bottom_bbox[i]])
for j in range(i+1, len(bottom_bbox)):
if __is_same_pos(bottom_bbox[i], bottom_bbox[j], iou_threshold):
#footer_det_bbox = [min(bottom_bbox[i][0], bottom_bbox[j][0]), min(bottom_bbox[i][1], bottom_bbox[j][1]), max(bottom_bbox[i][2], bottom_bbox[j][2]), max(bottom_bbox[i][3],bottom_bbox[j][3])]
btn_same_pos_group[i].append(bottom_bbox[j])
# 然后看下每一组的bbox,是否符合大于page_cnt一定比例
hdr_same_pos_group = [g for g in hdr_same_pos_group if len(g)>=min_occurance_cnt]
btn_same_pos_group = [g for g in btn_same_pos_group if len(g)>=min_occurance_cnt]
# 平铺2个list[list]
hdr_same_pos_group = [bbox for g in hdr_same_pos_group for bbox in g]
btn_same_pos_group = [bbox for g in btn_same_pos_group for bbox in g]
# 寻找hdr_same_pos_group中的box[3]最大值,btn_same_pos_group中的box[1]最小值
hdr_same_pos_group.sort(key=lambda b:b[3])
btn_same_pos_group.sort(key=lambda b:b[1])
hdr_y = hdr_same_pos_group[-1][3] if hdr_same_pos_group else 0
btn_y = btn_same_pos_group[0][1] if btn_same_pos_group else page_h
header_det_bbox = [0, 0, page_w, hdr_y]
footer_det_bbox = [0, btn_y, page_w, page_h]
# logger.warning(f"header: {header_det_bbox}, footer: {footer_det_bbox}")
return header_det_bbox, footer_det_bbox, page_w, page_h
def drop_footer_header(pdf_info_dict:dict):
"""
启用规则探测,在全局的视角上通过统计的方法。
"""
header = []
footer = []
all_text_bboxes = [blk['bbox'] for _, val in pdf_info_dict.items() for blk in val['preproc_blocks']]
image_bboxes = [img['bbox'] for _, val in pdf_info_dict.items() for img in val['images']] + [img['bbox'] for _, val in pdf_info_dict.items() for img in val['image_backup']]
page_size = [val['page_size'] for _, val in pdf_info_dict.items()]
page_cnt = len(pdf_info_dict.keys()) # 一共多少页
header, footer, page_w, page_h = get_most_common_bbox(all_text_bboxes+image_bboxes, page_size, page_cnt)
""""
把范围扩展到页面水平的整个方向上
"""
if header:
header = [0, 0, page_w, header[3]+1]
if footer:
footer = [0, footer[1]-1, page_w, page_h]
# 找到footer, header范围之后,针对每一页pdf,从text、图片中删除这些范围内的内容
# 移除text block
for _, page_info in pdf_info_dict.items():
header_text_blk = []
footer_text_blk = []
for blk in page_info['preproc_blocks']:
blk_bbox = blk['bbox']
if header and blk_bbox[3]<=header[3]:
blk['tag'] = "header"
header_text_blk.append(blk)
elif footer and blk_bbox[1]>=footer[1]:
blk['tag'] = "footer"
footer_text_blk.append(blk)
# 放入text_block_droped中
page_info['droped_text_block'].extend(header_text_blk)
page_info['droped_text_block'].extend(footer_text_blk)
for blk in header_text_blk:
page_info['preproc_blocks'].remove(blk)
for blk in footer_text_blk:
page_info['preproc_blocks'].remove(blk)
"""接下来把footer、header上的图片也删除掉。图片包括正常的和backup的"""
header_image = []
footer_image = []
for image_info in page_info['images']:
img_bbox = image_info['bbox']
if header and img_bbox[3]<=header[3]:
image_info['tag'] = "header"
header_image.append(image_info)
elif footer and img_bbox[1]>=footer[1]:
image_info['tag'] = "footer"
footer_image.append(image_info)
page_info['droped_image_block'].extend(header_image)
page_info['droped_image_block'].extend(footer_image)
for img in header_image:
page_info['images'].remove(img)
for img in footer_image:
page_info['images'].remove(img)
"""接下来吧backup的图片也删除掉"""
header_image = []
footer_image = []
for image_info in page_info['image_backup']:
img_bbox = image_info['bbox']
if header and img_bbox[3]<=header[3]:
image_info['tag'] = "header"
header_image.append(image_info)
elif footer and img_bbox[1]>=footer[1]:
image_info['tag'] = "footer"
footer_image.append(image_info)
page_info['droped_image_block'].extend(header_image)
page_info['droped_image_block'].extend(footer_image)
for img in header_image:
page_info['image_backup'].remove(img)
for img in footer_image:
page_info['image_backup'].remove(img)
return header, footer
-170
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@@ -1,170 +0,0 @@
from collections import Counter
from magic_pdf.libs.commons import fitz # pyMuPDF库
from magic_pdf.libs.coordinate_transform import get_scale_ratio
def parse_footnotes_by_model(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict, md_bookname_save_path=None, debug_mode=False):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
#--------- 通过json_from_DocXchain来获取 footnote ---------#
footnote_bbox_from_DocXChain = []
xf_json = json_from_DocXchain_obj
horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(xf_json, page)
# {0: 'title', # 标题
# 1: 'figure', # 图片
# 2: 'plain text', # 文本
# 3: 'header', # 页眉
# 4: 'page number', # 页码
# 5: 'footnote', # 脚注
# 6: 'footer', # 页脚
# 7: 'table', # 表格
# 8: 'table caption', # 表格描述
# 9: 'figure caption', # 图片描述
# 10: 'equation', # 公式
# 11: 'full column', # 单栏
# 12: 'sub column', # 多栏
# 13: 'embedding', # 嵌入公式
# 14: 'isolated'} # 单行公式
for xf in xf_json['layout_dets']:
L = xf['poly'][0] / horizontal_scale_ratio
U = xf['poly'][1] / vertical_scale_ratio
R = xf['poly'][2] / horizontal_scale_ratio
D = xf['poly'][5] / vertical_scale_ratio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
# if xf['category_id'] == 5 and xf['score'] >= 0.3:
if xf['category_id'] == 5 and xf['score'] >= 0.43: # 新的footnote阈值
footnote_bbox_from_DocXChain.append((L, U, R, D))
footnote_final_names = []
footnote_final_bboxs = []
footnote_ID = 0
for L, U, R, D in footnote_bbox_from_DocXChain:
if debug_mode:
# cur_footnote = page.get_pixmap(clip=(L,U,R,D))
new_footnote_name = "footnote_{}_{}.png".format(page_ID, footnote_ID) # 脚注name
# cur_footnote.save(md_bookname_save_path + '/' + new_footnote_name) # 把脚注存储在新建的文件夹,并命名
footnote_final_names.append(new_footnote_name) # 把脚注的名字存在list中
footnote_final_bboxs.append((L, U, R, D))
footnote_ID += 1
footnote_final_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_footnote_bboxs = footnote_final_bboxs
return curPage_all_footnote_bboxs
def need_remove(block):
if 'lines' in block and len(block['lines']) > 0:
# block中只有一行,且该行文本全是大写字母,或字体为粗体bold关键词,SB关键词,把这个block捞回来
if len(block['lines']) == 1:
if 'spans' in block['lines'][0] and len(block['lines'][0]['spans']) == 1:
font_keywords = ['SB', 'bold', 'Bold']
if block['lines'][0]['spans'][0]['text'].isupper() or any(keyword in block['lines'][0]['spans'][0]['font'] for keyword in font_keywords):
return True
for line in block['lines']:
if 'spans' in line and len(line['spans']) > 0:
for span in line['spans']:
# 检测"keyword"是否在span中,忽略大小写
if "keyword" in span['text'].lower():
return True
return False
def parse_footnotes_by_rule(remain_text_blocks, page_height, page_id, main_text_font):
"""
根据给定的文本块、页高和页码,解析出符合规则的脚注文本块,并返回其边界框。
Args:
remain_text_blocks (list): 包含所有待处理的文本块的列表。
page_height (float): 页面的高度。
page_id (int): 页面的ID。
Returns:
list: 符合规则的脚注文本块的边界框列表。
"""
# if page_id > 20:
if page_id > 2: # 为保证精确度,先只筛选前3页
return []
else:
# 存储每一行的文本块大小的列表
line_sizes = []
# 存储每个文本块的平均行大小
block_sizes = []
# 存储每一行的字体信息
# font_names = []
font_names = Counter()
if len(remain_text_blocks) > 0:
for block in remain_text_blocks:
block_line_sizes = []
# block_fonts = []
block_fonts = Counter()
for line in block['lines']:
# 提取每个span的size属性,并计算行大小
span_sizes = [span['size'] for span in line['spans'] if 'size' in span]
if span_sizes:
line_size = sum(span_sizes) / len(span_sizes)
line_sizes.append(line_size)
block_line_sizes.append(line_size)
span_font = [(span['font'], len(span['text'])) for span in line['spans'] if 'font' in span and len(span['text']) > 0]
if span_font:
# main_text_font应该用基于字数最多的字体而不是span级别的统计
# font_names.append(font_name for font_name in span_font)
# block_fonts.append(font_name for font_name in span_font)
for font, count in span_font:
# font_names.extend([font] * count)
# block_fonts.extend([font] * count)
font_names[font] += count
block_fonts[font] += count
if block_line_sizes:
# 计算文本块的平均行大小
block_size = sum(block_line_sizes) / len(block_line_sizes)
# block_font = collections.Counter(block_fonts).most_common(1)[0][0]
block_font = block_fonts.most_common(1)[0][0]
block_sizes.append((block, block_size, block_font))
# 计算main_text_size
main_text_size = Counter(line_sizes).most_common(1)[0][0]
# 计算main_text_font
# main_text_font = collections.Counter(font_names).most_common(1)[0][0]
# main_text_font = font_names.most_common(1)[0][0]
# 删除一些可能被误识别为脚注的文本块
block_sizes = [(block, block_size, block_font) for block, block_size, block_font in block_sizes if not need_remove(block)]
# 检测footnote_block 并返回 footnote_bboxes
# footnote_bboxes = [block['bbox'] for block, block_size, block_font in block_sizes if
# block['bbox'][1] > page_height * 0.6 and block_size < main_text_size
# and (len(block['lines']) < 5 or block_font != main_text_font)]
# and len(block['lines']) < 5]
footnote_bboxes = [block['bbox'] for block, block_size, block_font in block_sizes if
block['bbox'][1] > page_height * 0.6 and
# 较为严格的规则
block_size < main_text_size and
(len(block['lines']) < 5 or
block_font != main_text_font)]
# 较为宽松的规则
# sum([block_size < main_text_size,
# len(block['lines']) < 5,
# block_font != main_text_font])
# >= 2]
return footnote_bboxes
else:
return []
-64
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@@ -1,64 +0,0 @@
from magic_pdf.libs.commons import fitz # pyMuPDF库
from magic_pdf.libs.coordinate_transform import get_scale_ratio
def parse_headers(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
#--------- 通过json_from_DocXchain来获取 header ---------#
header_bbox_from_DocXChain = []
xf_json = json_from_DocXchain_obj
horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(xf_json, page)
# {0: 'title', # 标题
# 1: 'figure', # 图片
# 2: 'plain text', # 文本
# 3: 'header', # 页眉
# 4: 'page number', # 页码
# 5: 'footnote', # 脚注
# 6: 'footer', # 页脚
# 7: 'table', # 表格
# 8: 'table caption', # 表格描述
# 9: 'figure caption', # 图片描述
# 10: 'equation', # 公式
# 11: 'full column', # 单栏
# 12: 'sub column', # 多栏
# 13: 'embedding', # 嵌入公式
# 14: 'isolated'} # 单行公式
for xf in xf_json['layout_dets']:
L = xf['poly'][0] / horizontal_scale_ratio
U = xf['poly'][1] / vertical_scale_ratio
R = xf['poly'][2] / horizontal_scale_ratio
D = xf['poly'][5] / vertical_scale_ratio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if xf['category_id'] == 3 and xf['score'] >= 0.3:
header_bbox_from_DocXChain.append((L, U, R, D))
header_final_names = []
header_final_bboxs = []
header_ID = 0
for L, U, R, D in header_bbox_from_DocXChain:
# cur_header = page.get_pixmap(clip=(L,U,R,D))
new_header_name = "header_{}_{}.png".format(page_ID, header_ID) # 页眉name
# cur_header.save(res_dir_path + '/' + new_header_name) # 把页眉存储在新建的文件夹,并命名
header_final_names.append(new_header_name) # 把页面的名字存在list中
header_final_bboxs.append((L, U, R, D))
header_ID += 1
header_final_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_header_bboxs = header_final_bboxs
return curPage_all_header_bboxs
-647
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@@ -1,647 +0,0 @@
import collections # 统计库
import re
from magic_pdf.libs.commons import fitz # pyMuPDF库
#--------------------------------------- Tool Functions --------------------------------------#
# 正则化,输入文本,输出只保留a-z,A-Z,0-9
def remove_special_chars(s: str) -> str:
pattern = r"[^a-zA-Z0-9]"
res = re.sub(pattern, "", s)
return res
def check_rect1_sameWith_rect2(L1: float, U1: float, R1: float, D1: float, L2: float, U2: float, R2: float, D2: float) -> bool:
# 判断rect1和rect2是否一模一样
return L1 == L2 and U1 == U2 and R1 == R2 and D1 == D2
def check_rect1_contains_rect2(L1: float, U1: float, R1: float, D1: float, L2: float, U2: float, R2: float, D2: float) -> bool:
# 判断rect1包含了rect2
return (L1 <= L2 <= R2 <= R1) and (U1 <= U2 <= D2 <= D1)
def check_rect1_overlaps_rect2(L1: float, U1: float, R1: float, D1: float, L2: float, U2: float, R2: float, D2: float) -> bool:
# 判断rect1与rect2是否存在重叠(只有一条边重叠,也算重叠)
return max(L1, L2) <= min(R1, R2) and max(U1, U2) <= min(D1, D2)
def calculate_overlapRatio_between_rect1_and_rect2(L1: float, U1: float, R1: float, D1: float, L2: float, U2: float, R2: float, D2: float) -> (float, float):
# 计算两个rect,重叠面积各占2个rect面积的比例
if min(R1, R2) < max(L1, L2) or min(D1, D2) < max(U1, U2):
return 0, 0
square_1 = (R1 - L1) * (D1 - U1)
square_2 = (R2 - L2) * (D2 - U2)
if square_1 == 0 or square_2 == 0:
return 0, 0
square_overlap = (min(R1, R2) - max(L1, L2)) * (min(D1, D2) - max(U1, U2))
return square_overlap / square_1, square_overlap / square_2
def calculate_overlapRatio_between_line1_and_line2(L1: float, R1: float, L2: float, R2: float) -> (float, float):
# 计算两个line,重叠区间各占2个line长度的比例
if max(L1, L2) > min(R1, R2):
return 0, 0
if L1 == R1 or L2 == R2:
return 0, 0
overlap_line = min(R1, R2) - max(L1, L2)
return overlap_line / (R1 - L1), overlap_line / (R2 - L2)
# 判断rect其实是一条line
def check_rect_isLine(L: float, U: float, R: float, D: float) -> bool:
width = R - L
height = D - U
if width <= 3 or height <= 3:
return True
if width / height >= 30 or height / width >= 30:
return True
def parse_images(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict, junk_img_bojids=[]):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
#### 通过fitz获取page信息
## 超越边界
DPI = 72 # use this resolution
pix = page.get_pixmap(dpi=DPI)
pageL = 0
pageR = int(pix.w)
pageU = 0
pageD = int(pix.h)
#----------------- 保存每一个文本块的LURD ------------------#
textLine_blocks = []
blocks = page.get_text(
"dict",
flags=fitz.TEXTFLAGS_TEXT,
#clip=clip,
)["blocks"]
for i in range(len(blocks)):
bbox = blocks[i]['bbox']
# print(bbox)
for tt in blocks[i]['lines']:
# 当前line
cur_line_bbox = None # 当前line,最右侧的section的bbox
for xf in tt['spans']:
L, U, R, D = xf['bbox']
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
textLine_blocks.append((L, U, R, D))
textLine_blocks.sort(key = lambda LURD: (LURD[1], LURD[0]))
#---------------------------------------------- 保存img --------------------------------------------------#
raw_imgs = page.get_images() # 获取所有的图片
imgs = []
img_names = [] # 保存图片的名字,方便在md中插入引用
img_bboxs = [] # 保存图片的location信息。
img_visited = [] # 记忆化,记录该图片是否在md中已经插入过了
img_ID = 0
## 获取、保存每张img的location信息(x1, y1, x2, y2, UL, DR坐标)
for i in range(len(raw_imgs)):
# 如果图片在junklist中则跳过
if raw_imgs[i][0] in junk_img_bojids:
continue
else:
try:
tt = page.get_image_rects(raw_imgs[i][0], transform = True)
rec = tt[0][0]
L, U, R, D = int(rec[0]), int(rec[1]), int(rec[2]), int(rec[3])
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if not(pageL <= L < R <= pageR and pageU <= U < D <= pageD):
continue
if pageL == L and R == pageR:
continue
if pageU == U and D == pageD:
continue
# pix1 = page.get_Pixmap(clip=(L,U,R,D))
new_img_name = "{}_{}.png".format(page_ID, i) # 图片name
# pix1.save(res_dir_path + '/' + new_img_name) # 把图片存出在新建的文件夹,并命名
img_names.append(new_img_name)
img_bboxs.append((L, U, R, D))
img_visited.append(False)
imgs.append(raw_imgs[i])
except:
continue
#-------- 如果img之间有重叠。说明获取的img大小有问题,位置也不一定对。就扔掉--------#
imgs_ok = [True for _ in range(len(imgs))]
for i in range(len(imgs)):
L1, U1, R1, D1 = img_bboxs[i]
for j in range(i + 1, len(imgs)):
L2, U2, R2, D2 = img_bboxs[j]
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
s1 = abs(R1 - L1) * abs(D1 - U1)
s2 = abs(R2 - L2) * abs(D2 - U2)
if ratio_1 > 0 and ratio_2 > 0:
if ratio_1 == 1 and ratio_2 > 0.8:
imgs_ok[i] = False
elif ratio_1 > 0.8 and ratio_2 == 1:
imgs_ok[j] = False
elif s1 > 20000 and s2 > 20000 and ratio_1 > 0.4 and ratio_2 > 0.4:
imgs_ok[i] = False
imgs_ok[j] = False
elif s1 / s2 > 5 and ratio_2 > 0.5:
imgs_ok[j] = False
elif s2 / s1 > 5 and ratio_1 > 0.5:
imgs_ok[i] = False
imgs = [imgs[i] for i in range(len(imgs)) if imgs_ok[i] == True]
img_names = [img_names[i] for i in range(len(imgs)) if imgs_ok[i] == True]
img_bboxs = [img_bboxs[i] for i in range(len(imgs)) if imgs_ok[i] == True]
img_visited = [img_visited[i] for i in range(len(imgs)) if imgs_ok[i] == True]
#*******************************************************************************#
#---------------------------------------- 通过fitz提取svg的信息 -----------------------------------------#
#
svgs = page.get_drawings()
#------------ preprocess, check一些大框,看是否是合理的 ----------#
## 去重。有时候会遇到rect1和rect2是完全一样的情形。
svg_rect_visited = set()
available_svgIdx = []
for i in range(len(svgs)):
L, U, R, D = svgs[i]['rect'].irect
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
tt = (L, U, R, D)
if tt not in svg_rect_visited:
svg_rect_visited.add(tt)
available_svgIdx.append(i)
svgs = [svgs[i] for i in available_svgIdx] # 去重后,有效的svgs
svg_childs = [[] for _ in range(len(svgs))]
svg_parents = [[] for _ in range(len(svgs))]
svg_overlaps = [[] for _ in range(len(svgs))] #svg_overlaps[i]是一个list,存的是与svg_i有重叠的svg的index。e.g., svg_overlaps[0] = [1, 2, 7, 9]
svg_visited = [False for _ in range(len(svgs))]
svg_exceedPage = [0 for _ in range(len(svgs))] # 是否超越边界(artbox),很大,但一般是一个svg的底。
for i in range(len(svgs)):
L, U, R, D = svgs[i]['rect'].irect
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L, U, R, D, pageL, pageU, pageR, pageD)
if (pageL + 20 < L <= R < pageR - 20) and (pageU + 20 < U <= D < pageD - 20):
if ratio_2 >= 0.7:
svg_exceedPage[i] += 4
else:
if L <= pageL:
svg_exceedPage[i] += 1
if pageR <= R:
svg_exceedPage[i] += 1
if U <= pageU:
svg_exceedPage[i] += 1
if pageD <= D:
svg_exceedPage[i] += 1
#### 如果有≥2个的超边界的框,就不要手写规则判断svg了。很难写对。
if len([x for x in svg_exceedPage if x >= 1]) >= 2:
svgs = []
svg_childs = []
svg_parents = []
svg_overlaps = []
svg_visited = []
svg_exceedPage = []
#---------------------------- build graph ----------------------------#
for i, p in enumerate(svgs):
L1, U1, R1, D1 = svgs[i]["rect"].irect
for j in range(len(svgs)):
if i == j:
continue
L2, U2, R2, D2 = svgs[j]["rect"].irect
## 包含
if check_rect1_contains_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
svg_childs[i].append(j)
svg_parents[j].append(i)
else:
## 交叉
if check_rect1_overlaps_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
svg_overlaps[i].append(j)
#---------------- 确定最终的svg。连通块儿的外围 -------------------#
eps_ERROR = 5 # 给识别出的svg,四周留白(为了防止pyMuPDF的rect不准)
svg_ID = 0
svg_final_names = []
svg_final_bboxs = []
svg_final_visited = [] # 为下面,text识别左准备。作用同img_visited
svg_idxs = [i for i in range(len(svgs))]
svg_idxs.sort(key = lambda i: -(svgs[i]['rect'].irect[2] - svgs[i]['rect'].irect[0]) * (svgs[i]['rect'].irect[3] - svgs[i]['rect'].irect[1])) # 按照面积,从大到小排序
for i in svg_idxs:
if svg_visited[i] == True:
continue
svg_visited[i] = True
L, U, R, D = svgs[i]['rect'].irect
width = R - L
height = D - U
if check_rect_isLine(L, U, R, D) == True:
svg_visited[i] = False
continue
# if i == 4:
# print(i, L, U, R, D)
# print(svg_parents[i])
cur_block_element_cnt = 0 # 当前要判定为svg的区域中,有多少elements,最外围的最大svg框除外。
if len(svg_parents[i]) == 0:
## 是个普通框的情形
cur_block_element_cnt += len(svg_childs[i])
if svg_exceedPage[i] == 0:
## 误差。可能已经包含在某个框里面了
neglect_flag = False
for pL, pU, pR, pD in svg_final_bboxs:
if pL <= L <= R <= pR and pU <= U <= D <= pD:
neglect_flag = True
break
if neglect_flag == True:
continue
## 搜索连通域, bfs+记忆化
q = collections.deque()
for j in svg_overlaps[i]:
q.append(j)
while q:
j = q.popleft()
svg_visited[j] = True
L2, U2, R2, D2 = svgs[j]['rect'].irect
# width2 = R2 - L2
# height2 = D2 - U2
# if width2 <= 2 or height2 <= 2 or (height2 / width2) >= 30 or (width2 / height2) >= 30:
# continue
L = min(L, L2)
R = max(R, R2)
U = min(U, U2)
D = max(D, D2)
cur_block_element_cnt += 1
cur_block_element_cnt += len(svg_childs[j])
for k in svg_overlaps[j]:
if svg_visited[k] == False and svg_exceedPage[k] == 0:
svg_visited[k] = True
q.append(k)
elif svg_exceedPage[i] <= 2:
## 误差。可能已经包含在某个svg_final_bbox框里面了
neglect_flag = False
for sL, sU, sR, sD in svg_final_bboxs:
if sL <= L <= R <= sR and sU <= U <= D <= sD:
neglect_flag = True
break
if neglect_flag == True:
continue
L, U, R, D = pageR, pageD, pageL, pageU
## 所有孩子元素的最大边界
for j in svg_childs[i]:
if svg_visited[j] == True:
continue
if svg_exceedPage[j] >= 1:
continue
svg_visited[j] = True #### 这个位置考虑一下
L2, U2, R2, D2 = svgs[j]['rect'].irect
L = min(L, L2)
R = max(R, R2)
U = min(U, U2)
D = max(D, D2)
cur_block_element_cnt += 1
# 如果是条line,就不用保存了
if check_rect_isLine(L, U, R, D) == True:
continue
# 如果当前的svg,连2个elements都没有,就不用保存了
if cur_block_element_cnt < 3:
continue
## 当前svg,框住了多少文本框。如果框多了,可能就是错了
contain_textLineBlock_cnt = 0
for L2, U2, R2, D2 in textLine_blocks:
if check_rect1_contains_rect2(L, U, R, D, L2, U2, R2, D2) == True:
contain_textLineBlock_cnt += 1
if contain_textLineBlock_cnt >= 10:
continue
# L -= eps_ERROR * 2
# U -= eps_ERROR
# R += eps_ERROR * 2
# D += eps_ERROR
# # cur_svg = page.get_pixmap(matrix=fitz.Identity, dpi=None, colorspace=fitz.csRGB, clip=(U,L,R,D), alpha=False, annots=True)
# cur_svg = page.get_pixmap(clip=(L,U,R,D))
new_svg_name = "svg_{}_{}.png".format(page_ID, svg_ID) # 图片name
# cur_svg.save(res_dir_path + '/' + new_svg_name) # 把图片存出在新建的文件夹,并命名
svg_final_names.append(new_svg_name) # 把图片的名字存在list中,方便在md中插入引用
svg_final_bboxs.append((L, U, R, D))
svg_final_visited.append(False)
svg_ID += 1
## 识别出的svg,可能有 包含,相邻的情形。需要进一步合并
svg_idxs = [i for i in range(len(svg_final_bboxs))]
svg_idxs.sort(key = lambda i: (svg_final_bboxs[i][1], svg_final_bboxs[i][0])) # (U, L)
svg_final_names_2 = []
svg_final_bboxs_2 = []
svg_final_visited_2 = [] # 为下面,text识别左准备。作用同img_visited
svg_ID_2 = 0
for i in range(len(svg_final_bboxs)):
L1, U1, R1, D1 = svg_final_bboxs[i]
for j in range(i + 1, len(svg_final_bboxs)):
L2, U2, R2, D2 = svg_final_bboxs[j]
# 如果 rect1包含了rect2
if check_rect1_contains_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
svg_final_visited[j] = True
continue
# 水平并列
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(U1, D1, U2, D2)
if ratio_1 >= 0.7 and ratio_2 >= 0.7:
if abs(L2 - R1) >= 20:
continue
LL = min(L1, L2)
UU = min(U1, U2)
RR = max(R1, R2)
DD = max(D1, D2)
svg_final_bboxs[i] = (LL, UU, RR, DD)
svg_final_visited[j] = True
continue
# 竖直并列
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R2, L2, R2)
if ratio_1 >= 0.7 and ratio_2 >= 0.7:
if abs(U2 - D1) >= 20:
continue
LL = min(L1, L2)
UU = min(U1, U2)
RR = max(R1, R2)
DD = max(D1, D2)
svg_final_bboxs[i] = (LL, UU, RR, DD)
svg_final_visited[j] = True
for i in range(len(svg_final_bboxs)):
if svg_final_visited[i] == False:
L, U, R, D = svg_final_bboxs[i]
svg_final_bboxs_2.append((L, U, R, D))
L -= eps_ERROR * 2
U -= eps_ERROR
R += eps_ERROR * 2
D += eps_ERROR
# cur_svg = page.get_pixmap(clip=(L,U,R,D))
new_svg_name = "svg_{}_{}.png".format(page_ID, svg_ID_2) # 图片name
# cur_svg.save(res_dir_path + '/' + new_svg_name) # 把图片存出在新建的文件夹,并命名
svg_final_names_2.append(new_svg_name) # 把图片的名字存在list中,方便在md中插入引用
svg_final_bboxs_2.append((L, U, R, D))
svg_final_visited_2.append(False)
svg_ID_2 += 1
## svg收尾。识别为drawing,但是在上面没有拼成一张图的。
# 有收尾才comprehensive
# xxxx
# xxxx
# xxxx
# xxxx
#--------- 通过json_from_DocXchain来获取,figure, table, equation的bbox ---------#
figure_bbox_from_DocXChain = []
figure_from_DocXChain_visited = [] # 记忆化
figure_bbox_from_DocXChain_overlappedRatio = []
figure_only_from_DocXChain_bboxs = [] # 存储
figure_only_from_DocXChain_names = []
figure_only_from_DocXChain_visited = []
figure_only_ID = 0
xf_json = json_from_DocXchain_obj
width_from_json = xf_json['page_info']['width']
height_from_json = xf_json['page_info']['height']
LR_scaleRatio = width_from_json / (pageR - pageL)
UD_scaleRatio = height_from_json / (pageD - pageU)
for xf in xf_json['layout_dets']:
# {0: 'title', 1: 'figure', 2: 'plain text', 3: 'header', 4: 'page number', 5: 'footnote', 6: 'footer', 7: 'table', 8: 'table caption', 9: 'figure caption', 10: 'equation', 11: 'full column', 12: 'sub column'}
L = xf['poly'][0] / LR_scaleRatio
U = xf['poly'][1] / UD_scaleRatio
R = xf['poly'][2] / LR_scaleRatio
D = xf['poly'][5] / UD_scaleRatio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
# figure
if xf["category_id"] == 1 and xf['score'] >= 0.3:
figure_bbox_from_DocXChain.append((L, U, R, D))
figure_from_DocXChain_visited.append(False)
figure_bbox_from_DocXChain_overlappedRatio.append(0.0)
#---------------------- 比对上面识别出来的img,svg 与DocXChain给的figure -----------------------#
## 比对imgs
for i, b1 in enumerate(figure_bbox_from_DocXChain):
# print('--------- DocXChain的图片', b1)
L1, U1, R1, D1 = b1
for b2 in img_bboxs:
# print('-------- igms得到的图', b2)
L2, U2, R2, D2 = b2
s1 = abs(R1 - L1) * abs(D1 - U1)
s2 = abs(R2 - L2) * abs(D2 - U2)
# 相同
if check_rect1_sameWith_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
figure_from_DocXChain_visited[i] = True
# 包含
elif check_rect1_contains_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
if s2 / s1 > 0.8:
figure_from_DocXChain_visited[i] = True
elif check_rect1_contains_rect2(L2, U2, R2, D2, L1, U1, R1, D1) == True:
if s1 / s2 > 0.8:
figure_from_DocXChain_visited[i] = True
else:
# 重叠了相当一部分
# print('进入第3部分')
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
if (ratio_1 >= 0.6 and ratio_2 >= 0.6) or (ratio_1 >= 0.8 and s1/s2>0.8) or (ratio_2 >= 0.8 and s2/s1>0.8):
figure_from_DocXChain_visited[i] = True
else:
figure_bbox_from_DocXChain_overlappedRatio[i] += ratio_1
# print('图片的重叠率是{}'.format(ratio_1))
## 比对svgs
svg_final_bboxs_2_badIdxs = []
for i, b1 in enumerate(figure_bbox_from_DocXChain):
L1, U1, R1, D1 = b1
for j, b2 in enumerate(svg_final_bboxs_2):
L2, U2, R2, D2 = b2
s1 = abs(R1 - L1) * abs(D1 - U1)
s2 = abs(R2 - L2) * abs(D2 - U2)
# 相同
if check_rect1_sameWith_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
figure_from_DocXChain_visited[i] = True
# 包含
elif check_rect1_contains_rect2(L1, U1, R1, D1, L2, U2, R2, D2) == True:
figure_from_DocXChain_visited[i] = True
elif check_rect1_contains_rect2(L2, U2, R2, D2, L1, U1, R1, D1) == True:
if s1 / s2 > 0.7:
figure_from_DocXChain_visited[i] = True
else:
svg_final_bboxs_2_badIdxs.append(j) # svg丢弃。用DocXChain的结果。
else:
# 重叠了相当一部分
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
if (ratio_1 >= 0.5 and ratio_2 >= 0.5) or (min(ratio_1, ratio_2) >= 0.4 and max(ratio_1, ratio_2) >= 0.6):
figure_from_DocXChain_visited[i] = True
else:
figure_bbox_from_DocXChain_overlappedRatio[i] += ratio_1
# 丢掉错误的svg
svg_final_bboxs_2 = [svg_final_bboxs_2[i] for i in range(len(svg_final_bboxs_2)) if i not in set(svg_final_bboxs_2_badIdxs)]
for i in range(len(figure_from_DocXChain_visited)):
if figure_bbox_from_DocXChain_overlappedRatio[i] >= 0.7:
figure_from_DocXChain_visited[i] = True
# DocXChain识别出来的figure,但是没被保存的。
for i in range(len(figure_from_DocXChain_visited)):
if figure_from_DocXChain_visited[i] == False:
figure_from_DocXChain_visited[i] = True
cur_bbox = figure_bbox_from_DocXChain[i]
# cur_figure = page.get_pixmap(clip=cur_bbox)
new_figure_name = "figure_only_{}_{}.png".format(page_ID, figure_only_ID) # 图片name
# cur_figure.save(res_dir_path + '/' + new_figure_name) # 把图片存出在新建的文件夹,并命名
figure_only_from_DocXChain_names.append(new_figure_name) # 把图片的名字存在list中,方便在md中插入引用
figure_only_from_DocXChain_bboxs.append(cur_bbox)
figure_only_from_DocXChain_visited.append(False)
figure_only_ID += 1
img_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
svg_final_bboxs_2.sort(key = lambda LURD: (LURD[1], LURD[0]))
figure_only_from_DocXChain_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_fig_bboxs = img_bboxs + svg_final_bboxs + figure_only_from_DocXChain_bboxs
#--------------------------- 最后统一去重 -----------------------------------#
curPage_all_fig_bboxs.sort(key = lambda LURD: ( (LURD[2]-LURD[0])*(LURD[3]-LURD[1]) , LURD[0], LURD[1]) )
#### 先考虑包含关系的小块
final_duplicate = set()
for i in range(len(curPage_all_fig_bboxs)):
L1, U1, R1, D1 = curPage_all_fig_bboxs[i]
for j in range(len(curPage_all_fig_bboxs)):
if i == j:
continue
L2, U2, R2, D2 = curPage_all_fig_bboxs[j]
s1 = abs(R1 - L1) * abs(D1 - U1)
s2 = abs(R2 - L2) * abs(D2 - U2)
if check_rect1_contains_rect2(L2, U2, R2, D2, L1, U1, R1, D1) == True:
final_duplicate.add((L1, U1, R1, D1))
else:
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
if ratio_1 >= 0.8 and ratio_2 <= 0.6:
final_duplicate.add((L1, U1, R1, D1))
curPage_all_fig_bboxs = [LURD for LURD in curPage_all_fig_bboxs if LURD not in final_duplicate]
#### 再考虑重叠关系的块
final_duplicate = set()
final_synthetic_bboxs = []
for i in range(len(curPage_all_fig_bboxs)):
L1, U1, R1, D1 = curPage_all_fig_bboxs[i]
for j in range(len(curPage_all_fig_bboxs)):
if i == j:
continue
L2, U2, R2, D2 = curPage_all_fig_bboxs[j]
s1 = abs(R1 - L1) * abs(D1 - U1)
s2 = abs(R2 - L2) * abs(D2 - U2)
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
union_ok = False
if (ratio_1 >= 0.8 and ratio_2 <= 0.6) or (ratio_1 > 0.6 and ratio_2 > 0.6):
union_ok = True
if (ratio_1 > 0.2 and s2 / s1 > 5):
union_ok = True
if (L1 <= (L2+R2)/2 <= R1) and (U1 <= (U2+D2)/2 <= D1):
union_ok = True
if (L2 <= (L1+R1)/2 <= R2) and (U2 <= (U1+D1)/2 <= D2):
union_ok = True
if union_ok == True:
final_duplicate.add((L1, U1, R1, D1))
final_duplicate.add((L2, U2, R2, D2))
L3, U3, R3, D3 = min(L1, L2), min(U1, U2), max(R1, R2), max(D1, D2)
final_synthetic_bboxs.append((L3, U3, R3, D3))
# print('---------- curPage_all_fig_bboxs ---------')
# print(curPage_all_fig_bboxs)
curPage_all_fig_bboxs = [b for b in curPage_all_fig_bboxs if b not in final_duplicate]
final_synthetic_bboxs = list(set(final_synthetic_bboxs))
## 再再考虑重叠关系。极端情况下会迭代式地2进1
new_images = []
droped_img_idx = []
image_bboxes = [[b[0], b[1], b[2], b[3]] for b in final_synthetic_bboxs]
for i in range(0, len(image_bboxes)):
for j in range(i+1, len(image_bboxes)):
if j not in droped_img_idx:
L2, U2, R2, D2 = image_bboxes[j]
s1 = abs(R1 - L1) * abs(D1 - U1)
s2 = abs(R2 - L2) * abs(D2 - U2)
ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
union_ok = False
if (ratio_1 >= 0.8 and ratio_2 <= 0.6) or (ratio_1 > 0.6 and ratio_2 > 0.6):
union_ok = True
if (ratio_1 > 0.2 and s2 / s1 > 5):
union_ok = True
if (L1 <= (L2+R2)/2 <= R1) and (U1 <= (U2+D2)/2 <= D1):
union_ok = True
if (L2 <= (L1+R1)/2 <= R2) and (U2 <= (U1+D1)/2 <= D2):
union_ok = True
if union_ok == True:
# 合并
image_bboxes[i][0], image_bboxes[i][1],image_bboxes[i][2],image_bboxes[i][3] = min(image_bboxes[i][0], image_bboxes[j][0]), min(image_bboxes[i][1], image_bboxes[j][1]), max(image_bboxes[i][2], image_bboxes[j][2]), max(image_bboxes[i][3], image_bboxes[j][3])
droped_img_idx.append(j)
for i in range(0, len(image_bboxes)):
if i not in droped_img_idx:
new_images.append(image_bboxes[i])
# find_union_FLAG = True
# while find_union_FLAG == True:
# find_union_FLAG = False
# final_duplicate = set()
# tmp = []
# for i in range(len(final_synthetic_bboxs)):
# L1, U1, R1, D1 = final_synthetic_bboxs[i]
# for j in range(len(final_synthetic_bboxs)):
# if i == j:
# continue
# L2, U2, R2, D2 = final_synthetic_bboxs[j]
# s1 = abs(R1 - L1) * abs(D1 - U1)
# s2 = abs(R2 - L2) * abs(D2 - U2)
# ratio_1, ratio_2 = calculate_overlapRatio_between_rect1_and_rect2(L1, U1, R1, D1, L2, U2, R2, D2)
# union_ok = False
# if (ratio_1 >= 0.8 and ratio_2 <= 0.6) or (ratio_1 > 0.6 and ratio_2 > 0.6):
# union_ok = True
# if (ratio_1 > 0.2 and s2 / s1 > 5):
# union_ok = True
# if (L1 <= (L2+R2)/2 <= R1) and (U1 <= (U2+D2)/2 <= D1):
# union_ok = True
# if (L2 <= (L1+R1)/2 <= R2) and (U2 <= (U1+D1)/2 <= D2):
# union_ok = True
# if union_ok == True:
# find_union_FLAG = True
# final_duplicate.add((L1, U1, R1, D1))
# final_duplicate.add((L2, U2, R2, D2))
# L3, U3, R3, D3 = min(L1, L2), min(U1, U2), max(R1, R2), max(D1, D2)
# tmp.append((L3, U3, R3, D3))
# if find_union_FLAG == True:
# tmp = list(set(tmp))
# final_synthetic_bboxs = tmp[:]
# curPage_all_fig_bboxs += final_synthetic_bboxs
# print('--------- final synthetic')
# print(final_synthetic_bboxs)
#**************************************************************************#
images1 = [[img[0], img[1], img[2], img[3]] for img in curPage_all_fig_bboxs]
images = images1 + new_images
return images
@@ -1,64 +0,0 @@
from magic_pdf.libs.commons import fitz # pyMuPDF库
from magic_pdf.libs.coordinate_transform import get_scale_ratio
def parse_pageNos(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
#--------- 通过json_from_DocXchain来获取 pageNo ---------#
pageNo_bbox_from_DocXChain = []
xf_json = json_from_DocXchain_obj
horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(xf_json, page)
# {0: 'title', # 标题
# 1: 'figure', # 图片
# 2: 'plain text', # 文本
# 3: 'header', # 页眉
# 4: 'page number', # 页码
# 5: 'footnote', # 脚注
# 6: 'footer', # 页脚
# 7: 'table', # 表格
# 8: 'table caption', # 表格描述
# 9: 'figure caption', # 图片描述
# 10: 'equation', # 公式
# 11: 'full column', # 单栏
# 12: 'sub column', # 多栏
# 13: 'embedding', # 嵌入公式
# 14: 'isolated'} # 单行公式
for xf in xf_json['layout_dets']:
L = xf['poly'][0] / horizontal_scale_ratio
U = xf['poly'][1] / vertical_scale_ratio
R = xf['poly'][2] / horizontal_scale_ratio
D = xf['poly'][5] / vertical_scale_ratio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if xf['category_id'] == 4 and xf['score'] >= 0.3:
pageNo_bbox_from_DocXChain.append((L, U, R, D))
pageNo_final_names = []
pageNo_final_bboxs = []
pageNo_ID = 0
for L, U, R, D in pageNo_bbox_from_DocXChain:
# cur_pageNo = page.get_pixmap(clip=(L,U,R,D))
new_pageNo_name = "pageNo_{}_{}.png".format(page_ID, pageNo_ID) # 页码name
# cur_pageNo.save(res_dir_path + '/' + new_pageNo_name) # 把页码存储在新建的文件夹,并命名
pageNo_final_names.append(new_pageNo_name) # 把页码的名字存在list中
pageNo_final_bboxs.append((L, U, R, D))
pageNo_ID += 1
pageNo_final_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_pageNo_bboxs = pageNo_final_bboxs
return curPage_all_pageNo_bboxs
-62
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@@ -1,62 +0,0 @@
from magic_pdf.libs.commons import fitz # pyMuPDF库
def parse_tables(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict):
"""
:param page_ID: int类型,当前page在当前pdf文档中是第page_D页。
:param page :fitz读取的当前页的内容
:param res_dir_path: str类型,是每一个pdf文档,在当前.py文件的目录下生成一个与pdf文档同名的文件夹,res_dir_path就是文件夹的dir
:param json_from_DocXchain_obj: dict类型,把pdf文档送入DocXChain模型中后,提取bbox,结果保存到pdf文档同名文件夹下的 page_ID.json文件中了。json_from_DocXchain_obj就是打开后的dict
"""
DPI = 72 # use this resolution
pix = page.get_pixmap(dpi=DPI)
pageL = 0
pageR = int(pix.w)
pageU = 0
pageD = int(pix.h)
#--------- 通过json_from_DocXchain来获取 table ---------#
table_bbox_from_DocXChain = []
xf_json = json_from_DocXchain_obj
width_from_json = xf_json['page_info']['width']
height_from_json = xf_json['page_info']['height']
LR_scaleRatio = width_from_json / (pageR - pageL)
UD_scaleRatio = height_from_json / (pageD - pageU)
for xf in xf_json['layout_dets']:
# {0: 'title', 1: 'figure', 2: 'plain text', 3: 'header', 4: 'page number', 5: 'footnote', 6: 'footer', 7: 'table', 8: 'table caption', 9: 'figure caption', 10: 'equation', 11: 'full column', 12: 'sub column'}
# 13: 'embedding', # 嵌入公式
# 14: 'isolated'} # 单行公式
L = xf['poly'][0] / LR_scaleRatio
U = xf['poly'][1] / UD_scaleRatio
R = xf['poly'][2] / LR_scaleRatio
D = xf['poly'][5] / UD_scaleRatio
# L += pageL # 有的页面,artBox偏移了。不在(0,0)
# R += pageL
# U += pageU
# D += pageU
L, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if xf['category_id'] == 7 and xf['score'] >= 0.3:
table_bbox_from_DocXChain.append((L, U, R, D))
table_final_names = []
table_final_bboxs = []
table_ID = 0
for L, U, R, D in table_bbox_from_DocXChain:
# cur_table = page.get_pixmap(clip=(L,U,R,D))
new_table_name = "table_{}_{}.png".format(page_ID, table_ID) # 表格name
# cur_table.save(res_dir_path + '/' + new_table_name) # 把表格存出在新建的文件夹,并命名
table_final_names.append(new_table_name) # 把表格的名字存在list中,方便在md中插入引用
table_final_bboxs.append((L, U, R, D))
table_ID += 1
table_final_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_table_bboxs = table_final_bboxs
return curPage_all_table_bboxs
-550
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@@ -1,550 +0,0 @@
"""对pymupdf返回的结构里的公式进行替换,替换为模型识别的公式结果."""
import json
import os
from pathlib import Path
from loguru import logger
from magic_pdf.config.ocr_content_type import ContentType
from magic_pdf.libs.commons import fitz
TYPE_INLINE_EQUATION = ContentType.InlineEquation
TYPE_INTERLINE_EQUATION = ContentType.InterlineEquation
def combine_chars_to_pymudict(block_dict, char_dict):
"""把block级别的pymupdf 结构里加入char结构."""
# 因为block_dict 被裁剪过,因此先把他和char_dict文字块对齐,才能进行补充
char_map = {tuple(item['bbox']): item for item in char_dict}
for i in range(len(block_dict)): # block
block = block_dict[i]
key = block['bbox']
char_dict_item = char_map[tuple(key)]
char_dict_map = {tuple(item['bbox']): item for item in char_dict_item['lines']}
for j in range(len(block['lines'])):
lines = block['lines'][j]
with_char_lines = char_dict_map[lines['bbox']]
for k in range(len(lines['spans'])):
spans = lines['spans'][k]
try:
chars = with_char_lines['spans'][k]['chars']
except Exception:
logger.error(char_dict[i]['lines'][j])
spans['chars'] = chars
return block_dict
def calculate_overlap_area_2_minbox_area_ratio(bbox1, min_bbox):
"""计算box1和box2的重叠面积占最小面积的box的比例."""
# Determine the coordinates of the intersection rectangle
x_left = max(bbox1[0], min_bbox[0])
y_top = max(bbox1[1], min_bbox[1])
x_right = min(bbox1[2], min_bbox[2])
y_bottom = min(bbox1[3], min_bbox[3])
if x_right < x_left or y_bottom < y_top:
return 0.0
# The area of overlap area
intersection_area = (x_right - x_left) * (y_bottom - y_top)
min_box_area = (min_bbox[3] - min_bbox[1]) * (min_bbox[2] - min_bbox[0])
if min_box_area == 0:
return 0
else:
return intersection_area / min_box_area
def _is_xin(bbox1, bbox2):
area1 = abs(bbox1[2] - bbox1[0]) * abs(bbox1[3] - bbox1[1])
area2 = abs(bbox2[2] - bbox2[0]) * abs(bbox2[3] - bbox2[1])
if area1 < area2:
ratio = calculate_overlap_area_2_minbox_area_ratio(bbox2, bbox1)
else:
ratio = calculate_overlap_area_2_minbox_area_ratio(bbox1, bbox2)
return ratio > 0.6
def remove_text_block_in_interline_equation_bbox(interline_bboxes, text_blocks):
"""消除掉整个块都在行间公式块内部的文本块."""
for eq_bbox in interline_bboxes:
removed_txt_blk = []
for text_blk in text_blocks:
text_bbox = text_blk['bbox']
if (
calculate_overlap_area_2_minbox_area_ratio(eq_bbox['bbox'], text_bbox)
>= 0.7
):
removed_txt_blk.append(text_blk)
for blk in removed_txt_blk:
text_blocks.remove(blk)
return text_blocks
def _is_in_or_part_overlap(box1, box2) -> bool:
"""两个bbox是否有部分重叠或者包含."""
if box1 is None or box2 is None:
return False
x0_1, y0_1, x1_1, y1_1 = box1
x0_2, y0_2, x1_2, y1_2 = box2
return not (
x1_1 < x0_2 # box1在box2的左边
or x0_1 > x1_2 # box1在box2的右边
or y1_1 < y0_2 # box1在box2的上边
or y0_1 > y1_2
) # box1在box2的下边
def remove_text_block_overlap_interline_equation_bbox(
interline_eq_bboxes, pymu_block_list
):
"""消除掉行行内公式有部分重叠的文本块的内容。 同时重新计算消除重叠之后文本块的大小."""
deleted_block = []
for text_block in pymu_block_list:
deleted_line = []
for line in text_block['lines']:
deleted_span = []
for span in line['spans']:
deleted_chars = []
for char in span['chars']:
if any(
[
(
calculate_overlap_area_2_minbox_area_ratio(
eq_bbox['bbox'], char['bbox']
)
> 0.5
)
for eq_bbox in interline_eq_bboxes
]
):
deleted_chars.append(char)
# 检查span里没有char则删除这个span
for char in deleted_chars:
span['chars'].remove(char)
# 重新计算这个span的大小
if len(span['chars']) == 0: # 删除这个span
deleted_span.append(span)
else:
span['bbox'] = (
min([b['bbox'][0] for b in span['chars']]),
min([b['bbox'][1] for b in span['chars']]),
max([b['bbox'][2] for b in span['chars']]),
max([b['bbox'][3] for b in span['chars']]),
)
# 检查这个span
for span in deleted_span:
line['spans'].remove(span)
if len(line['spans']) == 0: # 删除这个line
deleted_line.append(line)
else:
line['bbox'] = (
min([b['bbox'][0] for b in line['spans']]),
min([b['bbox'][1] for b in line['spans']]),
max([b['bbox'][2] for b in line['spans']]),
max([b['bbox'][3] for b in line['spans']]),
)
# 检查这个block是否可以删除
for line in deleted_line:
text_block['lines'].remove(line)
if len(text_block['lines']) == 0: # 删除block
deleted_block.append(text_block)
else:
text_block['bbox'] = (
min([b['bbox'][0] for b in text_block['lines']]),
min([b['bbox'][1] for b in text_block['lines']]),
max([b['bbox'][2] for b in text_block['lines']]),
max([b['bbox'][3] for b in text_block['lines']]),
)
# 检查text block删除
for block in deleted_block:
pymu_block_list.remove(block)
if len(pymu_block_list) == 0:
return []
return pymu_block_list
def insert_interline_equations_textblock(interline_eq_bboxes, pymu_block_list):
"""在行间公式对应的地方插上一个伪造的block."""
for eq in interline_eq_bboxes:
bbox = eq['bbox']
latex_content = eq['latex']
text_block = {
'number': len(pymu_block_list),
'type': 0,
'bbox': bbox,
'lines': [
{
'spans': [
{
'size': 9.962599754333496,
'type': TYPE_INTERLINE_EQUATION,
'flags': 4,
'font': TYPE_INTERLINE_EQUATION,
'color': 0,
'ascender': 0.9409999847412109,
'descender': -0.3050000071525574,
'latex': latex_content,
'origin': [bbox[0], bbox[1]],
'bbox': bbox,
}
],
'wmode': 0,
'dir': [1.0, 0.0],
'bbox': bbox,
}
],
}
pymu_block_list.append(text_block)
def x_overlap_ratio(box1, box2):
a, _, c, _ = box1
e, _, g, _ = box2
# 计算重叠宽度
overlap_x = max(min(c, g) - max(a, e), 0)
# 计算box1的宽度
width1 = g - e
# 计算重叠比例
overlap_ratio = overlap_x / width1 if width1 != 0 else 0
return overlap_ratio
def __is_x_dir_overlap(bbox1, bbox2):
return not (bbox1[2] < bbox2[0] or bbox1[0] > bbox2[2])
def __y_overlap_ratio(box1, box2):
""""""
_, b, _, d = box1
_, f, _, h = box2
# 计算重叠高度
overlap_y = max(min(d, h) - max(b, f), 0)
# 计算box1的高度
height1 = d - b
# 计算重叠比例
overlap_ratio = overlap_y / height1 if height1 != 0 else 0
return overlap_ratio
def replace_line_v2(eqinfo, line):
"""扫描这一行所有的和公式框X方向重叠的char,然后计算char的左、右x0, x1,位于这个区间内的span删除掉。
最后与这个x0,x1有相交的span0, span1内部进行分割。"""
first_overlap_span = -1
first_overlap_span_idx = -1
last_overlap_span = -1
delete_chars = []
for i in range(0, len(line['spans'])):
if 'chars' not in line['spans'][i]:
continue
if line['spans'][i].get('_type', None) is not None:
continue # 忽略,因为已经是插入的伪造span公式了
for char in line['spans'][i]['chars']:
if __is_x_dir_overlap(eqinfo['bbox'], char['bbox']):
line_txt = ''
for span in line['spans']:
span_txt = '<span>'
for ch in span['chars']:
span_txt = span_txt + ch['c']
span_txt = span_txt + '</span>'
line_txt = line_txt + span_txt
if first_overlap_span_idx == -1:
first_overlap_span = line['spans'][i]
first_overlap_span_idx = i
last_overlap_span = line['spans'][i]
delete_chars.append(char)
# 第一个和最后一个char要进行检查,到底属于公式多还是属于正常span多
if len(delete_chars) > 0:
ch0_bbox = delete_chars[0]['bbox']
if x_overlap_ratio(eqinfo['bbox'], ch0_bbox) < 0.51:
delete_chars.remove(delete_chars[0])
if len(delete_chars) > 0:
ch0_bbox = delete_chars[-1]['bbox']
if x_overlap_ratio(eqinfo['bbox'], ch0_bbox) < 0.51:
delete_chars.remove(delete_chars[-1])
# 计算x方向上被删除区间内的char的真实x0, x1
if len(delete_chars):
x0, x1 = (
min([b['bbox'][0] for b in delete_chars]),
max([b['bbox'][2] for b in delete_chars]),
)
else:
# logger.debug(f"行内公式替换没有发生,尝试下一行匹配, eqinfo={eqinfo}")
return False
# 删除位于x0, x1这两个中间的span
delete_span = []
for span in line['spans']:
span_box = span['bbox']
if x0 <= span_box[0] and span_box[2] <= x1:
delete_span.append(span)
for span in delete_span:
line['spans'].remove(span)
equation_span = {
'size': 9.962599754333496,
'type': TYPE_INLINE_EQUATION,
'flags': 4,
'font': TYPE_INLINE_EQUATION,
'color': 0,
'ascender': 0.9409999847412109,
'descender': -0.3050000071525574,
'latex': '',
'origin': [337.1410153102337, 216.0205245153934],
'bbox': eqinfo['bbox'],
}
# equation_span = line['spans'][0].copy()
equation_span['latex'] = eqinfo['latex']
equation_span['bbox'] = [x0, equation_span['bbox'][1], x1, equation_span['bbox'][3]]
equation_span['origin'] = [equation_span['bbox'][0], equation_span['bbox'][1]]
equation_span['chars'] = delete_chars
equation_span['type'] = TYPE_INLINE_EQUATION
equation_span['_eq_bbox'] = eqinfo['bbox']
line['spans'].insert(first_overlap_span_idx + 1, equation_span) # 放入公式
# logger.info(f"==>text is 【{line_txt}】, equation is 【{eqinfo['latex_text']}】")
# 第一个、和最后一个有overlap的span进行分割,然后插入对应的位置
first_span_chars = [
char
for char in first_overlap_span['chars']
if (char['bbox'][2] + char['bbox'][0]) / 2 < x0
]
tail_span_chars = [
char
for char in last_overlap_span['chars']
if (char['bbox'][0] + char['bbox'][2]) / 2 > x1
]
if len(first_span_chars) > 0:
first_overlap_span['chars'] = first_span_chars
first_overlap_span['text'] = ''.join([char['c'] for char in first_span_chars])
first_overlap_span['bbox'] = (
first_overlap_span['bbox'][0],
first_overlap_span['bbox'][1],
max([chr['bbox'][2] for chr in first_span_chars]),
first_overlap_span['bbox'][3],
)
# first_overlap_span['_type'] = "first"
else:
# 删掉
if first_overlap_span not in delete_span:
line['spans'].remove(first_overlap_span)
if len(tail_span_chars) > 0:
min_of_tail_span_x0 = min([chr['bbox'][0] for chr in tail_span_chars])
min_of_tail_span_y0 = min([chr['bbox'][1] for chr in tail_span_chars])
max_of_tail_span_x1 = max([chr['bbox'][2] for chr in tail_span_chars])
max_of_tail_span_y1 = max([chr['bbox'][3] for chr in tail_span_chars])
if last_overlap_span == first_overlap_span: # 这个时候应该插入一个新的
tail_span_txt = ''.join([char['c'] for char in tail_span_chars]) # noqa: F841
last_span_to_insert = last_overlap_span.copy()
last_span_to_insert['chars'] = tail_span_chars
last_span_to_insert['text'] = ''.join(
[char['c'] for char in tail_span_chars]
)
if equation_span['bbox'][2] >= last_overlap_span['bbox'][2]:
last_span_to_insert['bbox'] = (
min_of_tail_span_x0,
min_of_tail_span_y0,
max_of_tail_span_x1,
max_of_tail_span_y1,
)
else:
last_span_to_insert['bbox'] = (
min([chr['bbox'][0] for chr in tail_span_chars]),
last_overlap_span['bbox'][1],
last_overlap_span['bbox'][2],
last_overlap_span['bbox'][3],
)
# 插入到公式对象之后
equation_idx = line['spans'].index(equation_span)
line['spans'].insert(equation_idx + 1, last_span_to_insert) # 放入公式
else: # 直接修改原来的span
last_overlap_span['chars'] = tail_span_chars
last_overlap_span['text'] = ''.join([char['c'] for char in tail_span_chars])
last_overlap_span['bbox'] = (
min([chr['bbox'][0] for chr in tail_span_chars]),
last_overlap_span['bbox'][1],
last_overlap_span['bbox'][2],
last_overlap_span['bbox'][3],
)
else:
# 删掉
if (
last_overlap_span not in delete_span
and last_overlap_span != first_overlap_span
):
line['spans'].remove(last_overlap_span)
remain_txt = ''
for span in line['spans']:
span_txt = '<span>'
for char in span['chars']:
span_txt = span_txt + char['c']
span_txt = span_txt + '</span>'
remain_txt = remain_txt + span_txt
# logger.info(f"<== succ replace, text is 【{remain_txt}】, equation is 【{eqinfo['latex_text']}】")
return True
def replace_eq_blk(eqinfo, text_block):
"""替换行内公式."""
for line in text_block['lines']:
line_bbox = line['bbox']
if (
_is_xin(eqinfo['bbox'], line_bbox)
or __y_overlap_ratio(eqinfo['bbox'], line_bbox) > 0.6
): # 定位到行, 使用y方向重合率是因为有的时候,一个行的宽度会小于公式位置宽度:行很高,公式很窄,
replace_succ = replace_line_v2(eqinfo, line)
if not replace_succ: # 有的时候,一个pdf的line高度从API里会计算的有问题,因此在行内span级别会替换不成功,这就需要继续重试下一行
continue
else:
break
else:
return False
return True
def replace_inline_equations(inline_equation_bboxes, raw_text_blocks):
"""替换行内公式."""
for eqinfo in inline_equation_bboxes:
eqbox = eqinfo['bbox']
for blk in raw_text_blocks:
if _is_xin(eqbox, blk['bbox']):
if not replace_eq_blk(eqinfo, blk):
logger.warning(f'行内公式没有替换成功:{eqinfo} ')
else:
break
return raw_text_blocks
def remove_chars_in_text_blocks(text_blocks):
"""删除text_blocks里的char."""
for blk in text_blocks:
for line in blk['lines']:
for span in line['spans']:
_ = span.pop('chars', 'no such key')
return text_blocks
def replace_equations_in_textblock(
raw_text_blocks, inline_equation_bboxes, interline_equation_bboxes
):
"""替换行间和和行内公式为latex."""
raw_text_blocks = remove_text_block_in_interline_equation_bbox(
interline_equation_bboxes, raw_text_blocks
) # 消除重叠:第一步,在公式内部的
raw_text_blocks = remove_text_block_overlap_interline_equation_bbox(
interline_equation_bboxes, raw_text_blocks
) # 消重,第二步,和公式覆盖的
insert_interline_equations_textblock(interline_equation_bboxes, raw_text_blocks)
raw_text_blocks = replace_inline_equations(inline_equation_bboxes, raw_text_blocks)
return raw_text_blocks
def draw_block_on_pdf_with_txt_replace_eq_bbox(json_path, pdf_path):
""""""
new_pdf = f'{Path(pdf_path).parent}/{Path(pdf_path).stem}.step3-消除行内公式text_block.pdf'
with open(json_path, 'r', encoding='utf-8') as f:
obj = json.loads(f.read())
if os.path.exists(new_pdf):
os.remove(new_pdf)
new_doc = fitz.open('')
doc = fitz.open(pdf_path) # noqa: F841
new_doc = fitz.open(pdf_path)
for i in range(len(new_doc)):
page = new_doc[i]
inline_equation_bboxes = obj[f'page_{i}']['inline_equations']
interline_equation_bboxes = obj[f'page_{i}']['interline_equations']
raw_text_blocks = obj[f'page_{i}']['preproc_blocks']
raw_text_blocks = remove_text_block_in_interline_equation_bbox(
interline_equation_bboxes, raw_text_blocks
) # 消除重叠:第一步,在公式内部的
raw_text_blocks = remove_text_block_overlap_interline_equation_bbox(
interline_equation_bboxes, raw_text_blocks
) # 消重,第二步,和公式覆盖的
insert_interline_equations_textblock(interline_equation_bboxes, raw_text_blocks)
raw_text_blocks = replace_inline_equations(
inline_equation_bboxes, raw_text_blocks
)
# 为了检验公式是否重复,把每一行里,含有公式的span背景改成黄色的
color_map = [fitz.pdfcolor['blue'], fitz.pdfcolor['green']] # noqa: F841
j = 0 # noqa: F841
for blk in raw_text_blocks:
for i, line in enumerate(blk['lines']):
# line_box = line['bbox']
# shape = page.new_shape()
# shape.draw_rect(line_box)
# shape.finish(color=fitz.pdfcolor['red'], fill=color_map[j%2], fill_opacity=0.3)
# shape.commit()
# j = j+1
for i, span in enumerate(line['spans']):
shape_page = page.new_shape()
span_type = span.get('_type')
color = fitz.pdfcolor['blue']
if span_type == 'first':
color = fitz.pdfcolor['blue']
elif span_type == 'tail':
color = fitz.pdfcolor['green']
elif span_type == TYPE_INLINE_EQUATION:
color = fitz.pdfcolor['black']
else:
color = None
b = span['bbox']
shape_page.draw_rect(b)
shape_page.finish(color=None, fill=color, fill_opacity=0.3)
shape_page.commit()
new_doc.save(new_pdf)
logger.info(f'save ok {new_pdf}')
final_json = json.dumps(obj, ensure_ascii=False, indent=2)
with open('equations_test/final_json.json', 'w') as f:
f.write(final_json)
return new_pdf
if __name__ == '__main__':
# draw_block_on_pdf_with_txt_replace_eq_bbox(new_json_path, equation_color_pdf)
pass
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import re
from magic_pdf.libs.boxbase import _is_in_or_part_overlap, _is_part_overlap, find_bottom_nearest_text_bbox, find_left_nearest_text_bbox, find_right_nearest_text_bbox, find_top_nearest_text_bbox
from magic_pdf.libs.textbase import get_text_block_base_info
def fix_image_vertical(image_bboxes:list, text_blocks:list):
"""
修正图片的位置
如果图片与文字block发生一定重叠(也就是图片切到了一部分文字),那么减少图片边缘,让文字和图片不再重叠。
只对垂直方向进行。
"""
for image_bbox in image_bboxes:
for text_block in text_blocks:
text_bbox = text_block["bbox"]
if _is_part_overlap(text_bbox, image_bbox) and any([text_bbox[0]>=image_bbox[0] and text_bbox[2]<=image_bbox[2], text_bbox[0]<=image_bbox[0] and text_bbox[2]>=image_bbox[2]]):
if text_bbox[1] < image_bbox[1]:#在图片上方
image_bbox[1] = text_bbox[3]+1
elif text_bbox[3]>image_bbox[3]:#在图片下方
image_bbox[3] = text_bbox[1]-1
return image_bboxes
def __merge_if_common_edge(bbox1, bbox2):
x_min_1, y_min_1, x_max_1, y_max_1 = bbox1
x_min_2, y_min_2, x_max_2, y_max_2 = bbox2
# 检查是否有公共的水平边
if y_min_1 == y_min_2 or y_max_1 == y_max_2:
# 确保一个框的x范围在另一个框的x范围内
if max(x_min_1, x_min_2) <= min(x_max_1, x_max_2):
return [min(x_min_1, x_min_2), min(y_min_1, y_min_2), max(x_max_1, x_max_2), max(y_max_1, y_max_2)]
# 检查是否有公共的垂直边
if x_min_1 == x_min_2 or x_max_1 == x_max_2:
# 确保一个框的y范围在另一个框的y范围内
if max(y_min_1, y_min_2) <= min(y_max_1, y_max_2):
return [min(x_min_1, x_min_2), min(y_min_1, y_min_2), max(x_max_1, x_max_2), max(y_max_1, y_max_2)]
# 如果没有公共边
return None
def fix_seperated_image(image_bboxes:list):
"""
如果2个图片有一个边重叠,那么合并2个图片
"""
new_images = []
droped_img_idx = []
for i in range(0, len(image_bboxes)):
for j in range(i+1, len(image_bboxes)):
new_img = __merge_if_common_edge(image_bboxes[i], image_bboxes[j])
if new_img is not None:
new_images.append(new_img)
droped_img_idx.append(i)
droped_img_idx.append(j)
break
for i in range(0, len(image_bboxes)):
if i not in droped_img_idx:
new_images.append(image_bboxes[i])
return new_images
def __check_img_title_pattern(text):
"""
检查文本段是否是表格的标题
"""
patterns = [r"^(fig|figure).*", r"^(scheme).*"]
text = text.strip()
for pattern in patterns:
match = re.match(pattern, text, re.IGNORECASE)
if match:
return True
return False
def __get_fig_caption_text(text_block):
txt = " ".join(span['text'] for line in text_block['lines'] for span in line['spans'])
line_cnt = len(text_block['lines'])
txt = txt.replace("Ž . ", '')
return txt, line_cnt
def __find_and_extend_bottom_caption(text_block, pymu_blocks, image_box):
"""
继续向下方寻找和图片caption字号,字体,颜色一样的文字框,合并入caption。
text_block是已经找到的图片catpion(这个caption可能不全,多行被划分到多个pymu block里了)
"""
combined_image_caption_text_block = list(text_block.copy()['bbox'])
base_font_color, base_font_size, base_font_type = get_text_block_base_info(text_block)
while True:
tb_add = find_bottom_nearest_text_bbox(pymu_blocks, combined_image_caption_text_block)
if not tb_add:
break
tb_font_color, tb_font_size, tb_font_type = get_text_block_base_info(tb_add)
if tb_font_color==base_font_color and tb_font_size==base_font_size and tb_font_type==base_font_type:
combined_image_caption_text_block[0] = min(combined_image_caption_text_block[0], tb_add['bbox'][0])
combined_image_caption_text_block[2] = max(combined_image_caption_text_block[2], tb_add['bbox'][2])
combined_image_caption_text_block[3] = tb_add['bbox'][3]
else:
break
image_box[0] = min(image_box[0], combined_image_caption_text_block[0])
image_box[1] = min(image_box[1], combined_image_caption_text_block[1])
image_box[2] = max(image_box[2], combined_image_caption_text_block[2])
image_box[3] = max(image_box[3], combined_image_caption_text_block[3])
text_block['_image_caption'] = True
def include_img_title(pymu_blocks, image_bboxes: list):
"""
向上方和下方寻找符合图片title的文本block,合并到图片里
如果图片上下都有fig的情况怎么办?寻找标题距离最近的那个。
---
增加对左侧和右侧图片标题的寻找
"""
for tb in image_bboxes:
# 优先找下方的
max_find_cnt = 3 # 向上,向下最多找3个就停止
temp_box = tb.copy()
while max_find_cnt>0:
text_block_btn = find_bottom_nearest_text_bbox(pymu_blocks, temp_box)
if text_block_btn:
txt, line_cnt = __get_fig_caption_text(text_block_btn)
if len(txt.strip())>0:
if not __check_img_title_pattern(txt) and max_find_cnt>0 and line_cnt<3: # 设置line_cnt<=2目的是为了跳过子标题,或者有时候图片下方文字没有被图片识别模型放入图片里
max_find_cnt = max_find_cnt - 1
temp_box[3] = text_block_btn['bbox'][3]
continue
else:
break
else:
temp_box[3] = text_block_btn['bbox'][3] # 宽度不变,扩大
max_find_cnt = max_find_cnt - 1
else:
break
max_find_cnt = 3 # 向上,向下最多找3个就停止
temp_box = tb.copy()
while max_find_cnt>0:
text_block_top = find_top_nearest_text_bbox(pymu_blocks, temp_box)
if text_block_top:
txt, line_cnt = __get_fig_caption_text(text_block_top)
if len(txt.strip())>0:
if not __check_img_title_pattern(txt) and max_find_cnt>0 and line_cnt <3:
max_find_cnt = max_find_cnt - 1
temp_box[1] = text_block_top['bbox'][1]
continue
else:
break
else:
b = text_block_top['bbox']
temp_box[1] = b[1] # 宽度不变,扩大
max_find_cnt = max_find_cnt - 1
else:
break
if text_block_btn and text_block_top and text_block_btn.get("_image_caption", False) is False and text_block_top.get("_image_caption", False) is False :
btn_text, _ = __get_fig_caption_text(text_block_btn)
top_text, _ = __get_fig_caption_text(text_block_top)
if __check_img_title_pattern(btn_text) and __check_img_title_pattern(top_text):
# 取距离图片最近的
btn_text_distance = text_block_btn['bbox'][1] - tb[3]
top_text_distance = tb[1] - text_block_top['bbox'][3]
if btn_text_distance<top_text_distance: # caption在下方
__find_and_extend_bottom_caption(text_block_btn, pymu_blocks, tb)
else:
text_block = text_block_top
tb[0] = min(tb[0], text_block['bbox'][0])
tb[1] = min(tb[1], text_block['bbox'][1])
tb[2] = max(tb[2], text_block['bbox'][2])
tb[3] = max(tb[3], text_block['bbox'][3])
text_block_btn['_image_caption'] = True
continue
text_block = text_block_btn # find_bottom_nearest_text_bbox(pymu_blocks, tb)
if text_block and text_block.get("_image_caption", False) is False:
first_text_line, _ = __get_fig_caption_text(text_block)
if __check_img_title_pattern(first_text_line):
# 发现特征之后,继续向相同方向寻找(想同颜色,想同大小,想同字体)的textblock
__find_and_extend_bottom_caption(text_block, pymu_blocks, tb)
continue
text_block = text_block_top # find_top_nearest_text_bbox(pymu_blocks, tb)
if text_block and text_block.get("_image_caption", False) is False:
first_text_line, _ = __get_fig_caption_text(text_block)
if __check_img_title_pattern(first_text_line):
tb[0] = min(tb[0], text_block['bbox'][0])
tb[1] = min(tb[1], text_block['bbox'][1])
tb[2] = max(tb[2], text_block['bbox'][2])
tb[3] = max(tb[3], text_block['bbox'][3])
text_block['_image_caption'] = True
continue
"""向左、向右寻找,暂时只寻找一次"""
left_text_block = find_left_nearest_text_bbox(pymu_blocks, tb)
if left_text_block and left_text_block.get("_image_caption", False) is False:
first_text_line, _ = __get_fig_caption_text(left_text_block)
if __check_img_title_pattern(first_text_line):
tb[0] = min(tb[0], left_text_block['bbox'][0])
tb[1] = min(tb[1], left_text_block['bbox'][1])
tb[2] = max(tb[2], left_text_block['bbox'][2])
tb[3] = max(tb[3], left_text_block['bbox'][3])
left_text_block['_image_caption'] = True
continue
right_text_block = find_right_nearest_text_bbox(pymu_blocks, tb)
if right_text_block and right_text_block.get("_image_caption", False) is False:
first_text_line, _ = __get_fig_caption_text(right_text_block)
if __check_img_title_pattern(first_text_line):
tb[0] = min(tb[0], right_text_block['bbox'][0])
tb[1] = min(tb[1], right_text_block['bbox'][1])
tb[2] = max(tb[2], right_text_block['bbox'][2])
tb[3] = max(tb[3], right_text_block['bbox'][3])
right_text_block['_image_caption'] = True
continue
return image_bboxes
def combine_images(image_bboxes:list):
"""
合并图片,如果图片有重叠,那么合并
"""
new_images = []
droped_img_idx = []
for i in range(0, len(image_bboxes)):
for j in range(i+1, len(image_bboxes)):
if j not in droped_img_idx and _is_in_or_part_overlap(image_bboxes[i], image_bboxes[j]):
# 合并
image_bboxes[i][0], image_bboxes[i][1],image_bboxes[i][2],image_bboxes[i][3] = min(image_bboxes[i][0], image_bboxes[j][0]), min(image_bboxes[i][1], image_bboxes[j][1]), max(image_bboxes[i][2], image_bboxes[j][2]), max(image_bboxes[i][3], image_bboxes[j][3])
droped_img_idx.append(j)
for i in range(0, len(image_bboxes)):
if i not in droped_img_idx:
new_images.append(image_bboxes[i])
return new_images
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from magic_pdf.libs.commons import fitz # pyMuPDF库
import re
from magic_pdf.libs.boxbase import _is_in_or_part_overlap, _is_part_overlap, find_bottom_nearest_text_bbox, find_left_nearest_text_bbox, find_right_nearest_text_bbox, find_top_nearest_text_bbox # json
## version 2
def get_merged_line(page):
"""
这个函数是为了从pymuPDF中提取出的矢量里筛出水平的横线,并且将断开的线段进行了合并。
:param page :fitz读取的当前页的内容
"""
drawings_bbox = []
drawings_line = []
drawings = page.get_drawings() # 提取所有的矢量
for p in drawings:
drawings_bbox.append(p["rect"].irect) # (L, U, R, D)
lines = []
for L, U, R, D in drawings_bbox:
if abs(D - U) <= 3: # 筛出水平的横线
lines.append((L, U, R, D))
U_groups = []
visited = [False for _ in range(len(lines))]
for i, (L1, U1, R1, D1) in enumerate(lines):
if visited[i] == True:
continue
tmp_g = [(L1, U1, R1, D1)]
for j, (L2, U2, R2, D2) in enumerate(lines):
if i == j:
continue
if visited[j] == True:
continue
if max(U1, D1, U2, D2) - min(U1, D1, U2, D2) <= 5: # 把高度一致的线放进一个group
tmp_g.append((L2, U2, R2, D2))
visited[j] = True
U_groups.append(tmp_g)
res = []
for group in U_groups:
group.sort(key = lambda LURD: (LURD[0], LURD[2]))
LL, UU, RR, DD = group[0]
for i, (L1, U1, R1, D1) in enumerate(group):
if (L1 - RR) >= 5:
cur_line = (LL, UU, RR, DD)
res.append(cur_line)
LL = L1
else:
RR = max(RR, R1)
cur_line = (LL, UU, RR, DD)
res.append(cur_line)
return res
def fix_tables(page: fitz.Page, table_bboxes: list, include_table_title: bool, scan_line_num: int):
"""
:param page :fitz读取的当前页的内容
:param table_bboxes: list类型,每一个元素是一个元祖 (L, U, R, D)
:param include_table_title: 是否将表格的标题也圈进来
:param scan_line_num: 在与表格框临近的上下几个文本框里扫描搜索标题
"""
drawings_lines = get_merged_line(page)
fix_table_bboxes = []
for table in table_bboxes:
(L, U, R, D) = table
fix_table_L = []
fix_table_U = []
fix_table_R = []
fix_table_D = []
width = R - L
width_range = width * 0.1 # 只看距离表格整体宽度10%之内偏差的线
height = D - U
height_range = height * 0.1 # 只看距离表格整体高度10%之内偏差的线
for line in drawings_lines:
if (L - width_range) <= line[0] <= (L + width_range) and (R - width_range) <= line[2] <= (R + width_range): # 相近的宽度
if (U - height_range) < line[1] < (U + height_range): # 上边界,在一定的高度范围内
fix_table_U.append(line[1])
fix_table_L.append(line[0])
fix_table_R.append(line[2])
elif (D - height_range) < line[1] < (D + height_range): # 下边界,在一定的高度范围内
fix_table_D.append(line[1])
fix_table_L.append(line[0])
fix_table_R.append(line[2])
if fix_table_U:
U = min(fix_table_U)
if fix_table_D:
D = max(fix_table_D)
if fix_table_L:
L = min(fix_table_L)
if fix_table_R:
R = max(fix_table_R)
if include_table_title: # 需要将表格标题包括
text_blocks = page.get_text("dict", flags=fitz.TEXTFLAGS_TEXT)["blocks"] # 所有的text的block
incolumn_text_blocks = [block for block in text_blocks if not ((block['bbox'][0] < L and block['bbox'][2] < L) or (block['bbox'][0] > R and block['bbox'][2] > R))] # 将与表格完全没有任何遮挡的文字筛除掉(比如另一栏的文字)
upper_text_blocks = [block for block in incolumn_text_blocks if (U - block['bbox'][3]) > 0] # 将在表格线以上的text block筛选出来
sorted_filtered_text_blocks = sorted(upper_text_blocks, key=lambda x: (U - x['bbox'][3], x['bbox'][0])) # 按照text block的下边界距离表格上边界的距离升序排序,如果是同一个高度,则先左再右
for idx in range(scan_line_num):
if idx+1 <= len(sorted_filtered_text_blocks):
line_temp = sorted_filtered_text_blocks[idx]['lines']
if line_temp:
text = line_temp[0]['spans'][0]['text'] # 提取出第一个span里的text内容
check_en = re.match('Table', text) # 检查是否有Table开头的(英文)
check_ch = re.match('表', text) # 检查是否有Table开头的(中文)
if check_en or check_ch:
if sorted_filtered_text_blocks[idx]['bbox'][1] < D: # 以防出现负的bbox
U = sorted_filtered_text_blocks[idx]['bbox'][1]
fix_table_bboxes.append([L-2, U-2, R+2, D+2])
return fix_table_bboxes
def __check_table_title_pattern(text):
"""
检查文本段是否是表格的标题
"""
patterns = [r'^table\s\d+']
for pattern in patterns:
match = re.match(pattern, text, re.IGNORECASE)
if match:
return True
else:
return False
def fix_table_text_block(pymu_blocks, table_bboxes: list):
"""
调整table, 如果table和上下的text block有相交区域,则将table的上下边界调整到text block的上下边界
例如 tmp/unittest/unittest_pdf/纯2列_ViLT_6_文字 表格.pdf
"""
for tb in table_bboxes:
(L, U, R, D) = tb
for block in pymu_blocks:
if _is_in_or_part_overlap((L, U, R, D), block['bbox']):
txt = " ".join(span['text'] for line in block['lines'] for span in line['spans'])
if not __check_table_title_pattern(txt) and block.get("_table", False) is False: # 如果是table的title,那么不调整。因为下一步会统一调整,如果这里进行了调整,后面的调整会造成调整到其他table的title上(在连续出现2个table的情况下)。
tb[0] = min(tb[0], block['bbox'][0])
tb[1] = min(tb[1], block['bbox'][1])
tb[2] = max(tb[2], block['bbox'][2])
tb[3] = max(tb[3], block['bbox'][3])
block['_table'] = True # 占位,防止其他table再次占用
"""如果是个table的title,但是有部分重叠,那么修正这个title,使得和table不重叠"""
if _is_part_overlap(tb, block['bbox']) and __check_table_title_pattern(txt):
block['bbox'] = list(block['bbox'])
if block['bbox'][3] > U:
block['bbox'][3] = U-1
if block['bbox'][1] < D:
block['bbox'][1] = D+1
return table_bboxes
def __get_table_caption_text(text_block):
txt = " ".join(span['text'] for line in text_block['lines'] for span in line['spans'])
line_cnt = len(text_block['lines'])
txt = txt.replace("Ž . ", '')
return txt, line_cnt
def include_table_title(pymu_blocks, table_bboxes: list):
"""
把表格的title也包含进来,扩展到table_bbox上
"""
for tb in table_bboxes:
max_find_cnt = 3 # 上上最多找3次
temp_box = tb.copy()
while max_find_cnt>0:
text_block_top = find_top_nearest_text_bbox(pymu_blocks, temp_box)
if text_block_top:
txt, line_cnt = __get_table_caption_text(text_block_top)
if len(txt.strip())>0:
if not __check_table_title_pattern(txt) and max_find_cnt>0 and line_cnt<3:
max_find_cnt = max_find_cnt -1
temp_box[1] = text_block_top['bbox'][1]
continue
else:
break
else:
temp_box[1] = text_block_top['bbox'][1] # 宽度不变,扩大
max_find_cnt = max_find_cnt - 1
else:
break
max_find_cnt = 3 # 向下找
temp_box = tb.copy()
while max_find_cnt>0:
text_block_bottom = find_bottom_nearest_text_bbox(pymu_blocks, temp_box)
if text_block_bottom:
txt, line_cnt = __get_table_caption_text(text_block_bottom)
if len(txt.strip())>0:
if not __check_table_title_pattern(txt) and max_find_cnt>0 and line_cnt<3:
max_find_cnt = max_find_cnt - 1
temp_box[3] = text_block_bottom['bbox'][3]
continue
else:
break
else:
temp_box[3] = text_block_bottom['bbox'][3]
max_find_cnt = max_find_cnt - 1
else:
break
if text_block_top and text_block_bottom and text_block_top.get("_table_caption", False) is False and text_block_bottom.get("_table_caption", False) is False :
btn_text, _ = __get_table_caption_text(text_block_bottom)
top_text, _ = __get_table_caption_text(text_block_top)
if __check_table_title_pattern(btn_text) and __check_table_title_pattern(top_text): # 上下都有一个tbale的caption
# 取距离最近的
btn_text_distance = text_block_bottom['bbox'][1] - tb[3]
top_text_distance = tb[1] - text_block_top['bbox'][3]
text_block = text_block_bottom if btn_text_distance<top_text_distance else text_block_top
tb[0] = min(tb[0], text_block['bbox'][0])
tb[1] = min(tb[1], text_block['bbox'][1])
tb[2] = max(tb[2], text_block['bbox'][2])
tb[3] = max(tb[3], text_block['bbox'][3])
text_block_bottom['_table_caption'] = True
continue
# 如果以上条件都不满足,那么就向下找
text_block = text_block_top
if text_block and text_block.get("_table_caption", False) is False:
first_text_line = " ".join(span['text'] for line in text_block['lines'] for span in line['spans'])
if __check_table_title_pattern(first_text_line) and text_block.get("_table", False) is False:
tb[0] = min(tb[0], text_block['bbox'][0])
tb[1] = min(tb[1], text_block['bbox'][1])
tb[2] = max(tb[2], text_block['bbox'][2])
tb[3] = max(tb[3], text_block['bbox'][3])
text_block['_table_caption'] = True
continue
text_block = text_block_bottom
if text_block and text_block.get("_table_caption", False) is False:
first_text_line, _ = __get_table_caption_text(text_block)
if __check_table_title_pattern(first_text_line) and text_block.get("_table", False) is False:
tb[0] = min(tb[0], text_block['bbox'][0])
tb[1] = min(tb[1], text_block['bbox'][1])
tb[2] = max(tb[2], text_block['bbox'][2])
tb[3] = max(tb[3], text_block['bbox'][3])
text_block['_table_caption'] = True
continue
"""向左、向右寻找,暂时只寻找一次"""
left_text_block = find_left_nearest_text_bbox(pymu_blocks, tb)
if left_text_block and left_text_block.get("_image_caption", False) is False:
first_text_line, _ = __get_table_caption_text(left_text_block)
if __check_table_title_pattern(first_text_line):
tb[0] = min(tb[0], left_text_block['bbox'][0])
tb[1] = min(tb[1], left_text_block['bbox'][1])
tb[2] = max(tb[2], left_text_block['bbox'][2])
tb[3] = max(tb[3], left_text_block['bbox'][3])
left_text_block['_image_caption'] = True
continue
right_text_block = find_right_nearest_text_bbox(pymu_blocks, tb)
if right_text_block and right_text_block.get("_image_caption", False) is False:
first_text_line, _ = __get_table_caption_text(right_text_block)
if __check_table_title_pattern(first_text_line):
tb[0] = min(tb[0], right_text_block['bbox'][0])
tb[1] = min(tb[1], right_text_block['bbox'][1])
tb[2] = max(tb[2], right_text_block['bbox'][2])
tb[3] = max(tb[3], right_text_block['bbox'][3])
right_text_block['_image_caption'] = True
continue
return table_bboxes
-23
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@@ -1,23 +0,0 @@
import collections
def get_main_text_font(pdf_docs):
font_names = collections.Counter()
for page in pdf_docs:
blocks = page.get_text('dict')['blocks']
if blocks is not None:
for block in blocks:
lines = block.get('lines')
if lines is not None:
for line in lines:
span_font = [(span['font'], len(span['text'])) for span in line['spans'] if
'font' in span and len(span['text']) > 0]
if span_font:
# main_text_font应该用基于字数最多的字体而不是span级别的统计
# font_names.append(font_name for font_name in span_font)
# block_fonts.append(font_name for font_name in span_font)
for font, count in span_font:
font_names[font] += count
main_text_font = font_names.most_common(1)[0][0]
return main_text_font
-133
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@@ -1,133 +0,0 @@
import fitz
from magic_pdf.layout.layout_sort import get_bboxes_layout
from magic_pdf.libs.boxbase import _is_part_overlap, _is_in
from magic_pdf.libs.coordinate_transform import get_scale_ratio
def get_center_point(bbox):
"""
根据边界框坐标信息,计算出该边界框的中心点坐标。
Args:
bbox (list): 边界框坐标信息,包含四个元素,分别为左上角x坐标、左上角y坐标、右下角x坐标、右下角y坐标。
Returns:
list: 中心点坐标信息,包含两个元素,分别为x坐标和y坐标。
"""
return [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
def get_area(bbox):
"""
根据边界框坐标信息,计算出该边界框的面积。
Args:
bbox (list): 边界框坐标信息,包含四个元素,分别为左上角x坐标、左上角y坐标、右下角x坐标、右下角y坐标。
Returns:
float: 该边界框的面积。
"""
return (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
def adjust_layouts(layout_bboxes, page_boundry, page_id):
# 遍历所有布局框
for i in range(len(layout_bboxes)):
# 遍历当前布局框之后的布局框
for j in range(i + 1, len(layout_bboxes)):
# 判断两个布局框是否重叠
if _is_part_overlap(layout_bboxes[i], layout_bboxes[j]):
# 计算每个布局框的中心点坐标和面积
area_i = get_area(layout_bboxes[i])
area_j = get_area(layout_bboxes[j])
# 较大布局框和较小布局框的赋值
if area_i > area_j:
larger_layout, smaller_layout = layout_bboxes[i], layout_bboxes[j]
else:
larger_layout, smaller_layout = layout_bboxes[j], layout_bboxes[i]
center_large = get_center_point(larger_layout)
center_small = get_center_point(smaller_layout)
# 计算横向和纵向的距离差
distance_x = center_large[0] - center_small[0]
distance_y = center_large[1] - center_small[1]
# 根据距离差判断重叠方向并修正边界
if abs(distance_x) > abs(distance_y): # 左右重叠
if distance_x > 0 and larger_layout[0] < smaller_layout[2]:
larger_layout[0] = smaller_layout[2]+1
if distance_x < 0 and larger_layout[2] > smaller_layout[0]:
larger_layout[2] = smaller_layout[0]-1
else: # 上下重叠
if distance_y > 0 and larger_layout[1] < smaller_layout[3]:
larger_layout[1] = smaller_layout[3]+1
if distance_y < 0 and larger_layout[3] > smaller_layout[1]:
larger_layout[3] = smaller_layout[1]-1
# 排序调整布局边界框列表
new_bboxes = []
for layout_bbox in layout_bboxes:
new_bboxes.append([layout_bbox[0], layout_bbox[1], layout_bbox[2], layout_bbox[3], None, None, None, None, None, None, None, None, None])
layout_bboxes, layout_tree = get_bboxes_layout(new_bboxes, page_boundry, page_id)
# 返回排序调整后的布局边界框列表
return layout_bboxes, layout_tree
def layout_detect(layout_info, page: fitz.Page, ocr_page_info):
"""
对输入的布局信息进行解析,提取出每个子布局的边界框,并对所有子布局进行排序调整。
Args:
layout_info (list): 包含子布局信息的列表,每个子布局信息为字典类型,包含'poly'字段,表示子布局的边界框坐标信息。
Returns:
list: 经过排序调整后的所有子布局边界框信息的列表,每个边界框信息为字典类型,包含'layout_bbox'字段,表示边界框的坐标信息。
"""
page_id = ocr_page_info['page_info']['page_no']-1
horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(ocr_page_info, page)
# 初始化布局边界框列表
layout_bboxes = []
# 遍历每个子布局
for sub_layout in layout_info:
# 提取子布局的边界框坐标信息
x0, y0, _, _, x1, y1, _, _ = sub_layout['poly']
bbox = [int(x0 / horizontal_scale_ratio), int(y0 / vertical_scale_ratio),
int(x1 / horizontal_scale_ratio), int(y1 / vertical_scale_ratio)]
# 将子布局的边界框添加到列表中
layout_bboxes.append(bbox)
# 初始化新的布局边界框列表
new_layout_bboxes = []
# 遍历每个布局边界框
for i in range(len(layout_bboxes)):
# 初始化标记变量,用于判断当前边界框是否需要保留
keep = True
# 获取当前边界框的坐标信息
box_i = layout_bboxes[i]
# 遍历其他边界框
for j in range(len(layout_bboxes)):
# 排除当前边界框自身
if i != j:
# 获取其他边界框的坐标信息
box_j = layout_bboxes[j]
# 检测box_i是否被box_j包含
if _is_in(box_i, box_j):
# 如果当前边界框被其他边界框包含,则标记为不需要保留
keep = False
# 跳出内层循环
break
# 如果当前边界框需要保留,则添加到新的布局边界框列表中
if keep:
new_layout_bboxes.append(layout_bboxes[i])
# 对新的布局边界框列表进行排序调整
page_width = page.rect.width
page_height = page.rect.height
page_boundry = [0, 0, page_width, page_height]
layout_bboxes, layout_tree = adjust_layouts(new_layout_bboxes, page_boundry, page_id)
# 返回排序调整后的布局边界框列表
return layout_bboxes, layout_tree
-78
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@@ -1,78 +0,0 @@
from magic_pdf.config.drop_reason import DropReason
from magic_pdf.libs.boxbase import _is_in, _is_in_or_part_overlap
from magic_pdf.libs.commons import fitz
def __area(box):
return (box[2] - box[0]) * (box[3] - box[1])
def __is_contain_color_background_rect(
page: fitz.Page, text_blocks, image_bboxes
) -> bool:
"""检查page是包含有颜色背景的矩形."""
color_bg_rect = []
p_width, p_height = page.rect.width, page.rect.height
# 先找到最大的带背景矩形
blocks = page.get_cdrawings()
for block in blocks:
if 'fill' in block and block['fill']: # 过滤掉透明的
fill = list(block['fill'])
fill[0], fill[1], fill[2] = int(fill[0]), int(fill[1]), int(fill[2])
if fill == (1.0, 1.0, 1.0):
continue
rect = block['rect']
# 过滤掉特别小的矩形
if __area(rect) < 10 * 10:
continue
# 为了防止是svg图片上的色块,这里过滤掉这类
if any(
[_is_in_or_part_overlap(rect, img_bbox) for img_bbox in image_bboxes]
):
continue
color_bg_rect.append(rect)
# 找到最大的背景矩形
if len(color_bg_rect) > 0:
max_rect = max(color_bg_rect, key=lambda x: __area(x))
max_rect_int = (
int(max_rect[0]),
int(max_rect[1]),
int(max_rect[2]),
int(max_rect[3]),
)
# 判断最大的背景矩形是否包含超过3行文字,或者50个字 TODO
if (
max_rect[2] - max_rect[0] > 0.2 * p_width
and max_rect[3] - max_rect[1] > 0.1 * p_height
): # 宽度符合
# 看是否有文本块落入到这个矩形中
for text_block in text_blocks:
box = text_block['bbox']
box_int = (int(box[0]), int(box[1]), int(box[2]), int(box[3]))
if _is_in(box_int, max_rect_int):
return True
return False
def __is_table_overlap_text_block(text_blocks, table_bbox):
"""检查table_bbox是否覆盖了text_blocks里的文本块 TODO."""
for text_block in text_blocks:
box = text_block['bbox']
if _is_in_or_part_overlap(table_bbox, box):
return True
return False
def pdf_filter(page: fitz.Page, text_blocks, table_bboxes, image_bboxes) -> tuple:
"""return:(True|False, err_msg) True, 如果pdf符合要求 False, 如果pdf不符合要求."""
if __is_contain_color_background_rect(page, text_blocks, image_bboxes):
return False, {
'_need_drop': True,
'_drop_reason': DropReason.COLOR_BACKGROUND_TEXT_BOX,
}
return True, None
@@ -1,101 +0,0 @@
from loguru import logger
from magic_pdf.config.drop_tag import COLOR_BG_HEADER_TXT_BLOCK
from magic_pdf.libs.boxbase import (_is_in, _is_in_or_part_overlap,
calculate_overlap_area_2_minbox_area_ratio)
def __area(box):
return (box[2] - box[0]) * (box[3] - box[1])
def rectangle_position_determination(rect, p_width):
"""判断矩形是否在页面中轴线附近。
Args:
rect (list): 矩形坐标,格式为[x1, y1, x2, y2]。
p_width (int): 页面宽度。
Returns:
bool: 若矩形在页面中轴线附近则返回True,否则返回False。
"""
# 页面中轴线x坐标
x_axis = p_width / 2
# 矩形是否跨越中轴线
is_span = rect[0] < x_axis and rect[2] > x_axis
if is_span:
return True
else:
# 矩形与中轴线的距离,只算近的那一边
distance = rect[0] - x_axis if rect[0] > x_axis else x_axis - rect[2]
# 判断矩形与中轴线的距离是否小于页面宽度的20%
if distance < p_width * 0.2:
return True
else:
return False
def remove_colored_strip_textblock(remain_text_blocks, page):
"""根据页面中特定颜色和大小过滤文本块,将符合条件的文本块从remain_text_blocks中移除,并返回移除的文本块列表colored_str
ip_textblock。
Args:
remain_text_blocks (list): 剩余文本块列表。
page (Page): 页面对象。
Returns:
tuple: 剩余文本块列表和移除的文本块列表。
"""
colored_strip_textblocks = [] # 先构造一个空的返回
if len(remain_text_blocks) > 0:
p_width, p_height = page.rect.width, page.rect.height
blocks = page.get_cdrawings()
colored_strip_bg_rect = []
for block in blocks:
is_filled = (
'fill' in block and block['fill'] and block['fill'] != (1.0, 1.0, 1.0)
) # 过滤掉透明的
rect = block['rect']
area_is_large_enough = __area(rect) > 100 # 过滤掉特别小的矩形
rectangle_position_determination_result = rectangle_position_determination(
rect, p_width
)
in_upper_half_page = (
rect[3] < p_height * 0.3
) # 找到位于页面上半部分的矩形,下边界小于页面高度的30%
aspect_ratio_exceeds_4 = (rect[2] - rect[0]) > (
rect[3] - rect[1]
) * 4 # 找到长宽比超过4的矩形
if (
is_filled
and area_is_large_enough
and rectangle_position_determination_result
and in_upper_half_page
and aspect_ratio_exceeds_4
):
colored_strip_bg_rect.append(rect)
if len(colored_strip_bg_rect) > 0:
for colored_strip_block_bbox in colored_strip_bg_rect:
for text_block in remain_text_blocks:
text_bbox = text_block['bbox']
if _is_in(text_bbox, colored_strip_block_bbox) or (
_is_in_or_part_overlap(text_bbox, colored_strip_block_bbox)
and calculate_overlap_area_2_minbox_area_ratio(
text_bbox, colored_strip_block_bbox
)
> 0.6
):
logger.info(
f'remove_colored_strip_textblock: {text_bbox}, {colored_strip_block_bbox}'
)
text_block['tag'] = COLOR_BG_HEADER_TXT_BLOCK
colored_strip_textblocks.append(text_block)
if len(colored_strip_textblocks) > 0:
for colored_strip_textblock in colored_strip_textblocks:
if colored_strip_textblock in remain_text_blocks:
remain_text_blocks.remove(colored_strip_textblock)
return remain_text_blocks, colored_strip_textblocks
@@ -1,114 +0,0 @@
import re
from magic_pdf.config.drop_tag import CONTENT_IN_FOOT_OR_HEADER, PAGE_NO
from magic_pdf.libs.boxbase import _is_in_or_part_overlap
def remove_headder_footer_one_page(text_raw_blocks, image_bboxes, table_bboxes, header_bboxs, footer_bboxs,
page_no_bboxs, page_w, page_h):
"""删除页眉页脚,页码 从line级别进行删除,删除之后观察这个text-block是否是空的,如果是空的,则移动到remove_list中."""
header = []
footer = []
if len(header) == 0:
model_header = header_bboxs
if model_header:
x0 = min([x for x, _, _, _ in model_header])
y0 = min([y for _, y, _, _ in model_header])
x1 = max([x1 for _, _, x1, _ in model_header])
y1 = max([y1 for _, _, _, y1 in model_header])
header = [x0, y0, x1, y1]
if len(footer) == 0:
model_footer = footer_bboxs
if model_footer:
x0 = min([x for x, _, _, _ in model_footer])
y0 = min([y for _, y, _, _ in model_footer])
x1 = max([x1 for _, _, x1, _ in model_footer])
y1 = max([y1 for _, _, _, y1 in model_footer])
footer = [x0, y0, x1, y1]
header_y0 = 0 if len(header) == 0 else header[3]
footer_y0 = page_h if len(footer) == 0 else footer[1]
if page_no_bboxs:
top_part = [b for b in page_no_bboxs if b[3] < page_h / 2]
btn_part = [b for b in page_no_bboxs if b[1] > page_h / 2]
top_max_y0 = max([b[1] for b in top_part]) if top_part else 0
btn_min_y1 = min([b[3] for b in btn_part]) if btn_part else page_h
header_y0 = max(header_y0, top_max_y0)
footer_y0 = min(footer_y0, btn_min_y1)
content_boundry = [0, header_y0, page_w, footer_y0]
header = [0, 0, page_w, header_y0]
footer = [0, footer_y0, page_w, page_h]
"""以上计算出来了页眉页脚的边界,下面开始进行删除"""
text_block_to_remove = []
# 首先检查每个textblock
for blk in text_raw_blocks:
if len(blk['lines']) > 0:
for line in blk['lines']:
line_del = []
for span in line['spans']:
span_del = []
if span['bbox'][3] < header_y0:
span_del.append(span)
elif _is_in_or_part_overlap(span['bbox'], header) or _is_in_or_part_overlap(span['bbox'], footer):
span_del.append(span)
for span in span_del:
line['spans'].remove(span)
if not line['spans']:
line_del.append(line)
for line in line_del:
blk['lines'].remove(line)
else:
# if not blk['lines']:
blk['tag'] = CONTENT_IN_FOOT_OR_HEADER
text_block_to_remove.append(blk)
"""有的时候由于pageNo太小了,总是会有一点和content_boundry重叠一点,被放入正文,因此对于pageNo,进行span粒度的删除"""
page_no_block_2_remove = []
if page_no_bboxs:
for pagenobox in page_no_bboxs:
for block in text_raw_blocks:
if _is_in_or_part_overlap(pagenobox, block['bbox']): # 在span级别删除页码
for line in block['lines']:
for span in line['spans']:
if _is_in_or_part_overlap(pagenobox, span['bbox']):
# span['text'] = ''
span['tag'] = PAGE_NO
# 检查这个block是否只有这一个span,如果是,那么就把这个block也删除
if len(line['spans']) == 1 and len(block['lines']) == 1:
page_no_block_2_remove.append(block)
else:
# 测试最后一个是不是页码:规则是,最后一个block仅有1个line,一个span,且text是数字,空格,符号组成,不含字母,并且包含数字
if len(text_raw_blocks) > 0:
text_raw_blocks.sort(key=lambda x: x['bbox'][1], reverse=True)
last_block = text_raw_blocks[0]
if len(last_block['lines']) == 1:
last_line = last_block['lines'][0]
if len(last_line['spans']) == 1:
last_span = last_line['spans'][0]
if last_span['text'].strip() and not re.search('[a-zA-Z]', last_span['text']) and re.search('[0-9]',
last_span[
'text']):
last_span['tag'] = PAGE_NO
page_no_block_2_remove.append(last_block)
for b in page_no_block_2_remove:
text_block_to_remove.append(b)
for blk in text_block_to_remove:
if blk in text_raw_blocks:
text_raw_blocks.remove(blk)
text_block_remain = text_raw_blocks
image_bbox_to_remove = [bbox for bbox in image_bboxes if not _is_in_or_part_overlap(bbox, content_boundry)]
image_bbox_remain = [bbox for bbox in image_bboxes if _is_in_or_part_overlap(bbox, content_boundry)]
table_bbox_to_remove = [bbox for bbox in table_bboxes if not _is_in_or_part_overlap(bbox, content_boundry)]
table_bbox_remain = [bbox for bbox in table_bboxes if _is_in_or_part_overlap(bbox, content_boundry)]
return image_bbox_remain, table_bbox_remain, text_block_remain, text_block_to_remove, image_bbox_to_remove, table_bbox_to_remove
@@ -1,236 +0,0 @@
import math
import re
from magic_pdf.config.drop_tag import (EMPTY_SIDE_BLOCK, ROTATE_TEXT,
VERTICAL_TEXT)
from magic_pdf.libs.boxbase import is_vbox_on_side
def detect_non_horizontal_texts(result_dict):
"""This function detects watermarks and vertical margin notes in the
document.
Watermarks are identified by finding blocks with the same coordinates and frequently occurring identical texts across multiple pages.
If these conditions are met, the blocks are highly likely to be watermarks, as opposed to headers or footers, which can change from page to page.
If the direction of these blocks is not horizontal, they are definitely considered to be watermarks.
Vertical margin notes are identified by finding blocks with the same coordinates and frequently occurring identical texts across multiple pages.
If these conditions are met, the blocks are highly likely to be vertical margin notes, which typically appear on the left and right sides of the page. # noqa: E501
If the direction of these blocks is vertical, they are definitely considered to be vertical margin notes.
Parameters
----------
result_dict : dict
The result dictionary.
Returns
-------
result_dict : dict
The updated result dictionary.
"""
# Dictionary to store information about potential watermarks
potential_watermarks = {}
potential_margin_notes = {}
for page_id, page_content in result_dict.items():
if page_id.startswith('page_'):
for block_id, block_data in page_content.items():
if block_id.startswith('block_'):
if 'dir' in block_data:
coordinates_text = (
block_data['bbox'],
block_data['text'],
) # Tuple of coordinates and text
angle = math.atan2(block_data['dir'][1], block_data['dir'][0])
angle = abs(math.degrees(angle))
if angle > 5 and angle < 85: # Check if direction is watermarks
if coordinates_text in potential_watermarks:
potential_watermarks[coordinates_text] += 1
else:
potential_watermarks[coordinates_text] = 1
if angle > 85 and angle < 105: # Check if direction is vertical
if coordinates_text in potential_margin_notes:
potential_margin_notes[coordinates_text] += (
1 # Increment count
)
else:
potential_margin_notes[coordinates_text] = (
1 # Initialize count
)
# Identify watermarks by finding entries with counts higher than a threshold (e.g., appearing on more than half of the pages)
watermark_threshold = len(result_dict) // 2
watermarks = {
k: v for k, v in potential_watermarks.items() if v > watermark_threshold
}
# Identify margin notes by finding entries with counts higher than a threshold (e.g., appearing on more than half of the pages)
margin_note_threshold = len(result_dict) // 2
margin_notes = {
k: v for k, v in potential_margin_notes.items() if v > margin_note_threshold
}
# Add watermark information to the result dictionary
for page_id, blocks in result_dict.items():
if page_id.startswith('page_'):
for block_id, block_data in blocks.items():
coordinates_text = (block_data['bbox'], block_data['text'])
if coordinates_text in watermarks:
block_data['is_watermark'] = 1
else:
block_data['is_watermark'] = 0
if coordinates_text in margin_notes:
block_data['is_vertical_margin_note'] = 1
else:
block_data['is_vertical_margin_note'] = 0
return result_dict
"""
1. 当一个block里全部文字都不是dir=(1,0),这个block整体去掉
2. 当一个block里全部文字都是dir=(1,0),但是每行只有一个字,这个block整体去掉。这个block必须出现在页面的四周,否则不去掉
"""
def __is_a_word(sentence):
# 如果输入是中文并且长度为1,则返回True
if re.fullmatch(r'[\u4e00-\u9fa5]', sentence):
return True
# 判断是否为单个英文单词或字符(包括ASCII标点)
elif re.fullmatch(r'[a-zA-Z0-9]+', sentence) and len(sentence) <= 2:
return True
else:
return False
def __get_text_color(num):
"""获取字体的颜色RGB值."""
blue = num & 255
green = (num >> 8) & 255
red = (num >> 16) & 255
return red, green, blue
def __is_empty_side_box(text_block):
"""是否是边缘上的空白没有任何内容的block."""
for line in text_block['lines']:
for span in line['spans']:
font_color = span['color']
r, g, b = __get_text_color(font_color)
if len(span['text'].strip()) > 0 and (r, g, b) != (255, 255, 255):
return False
return True
def remove_rotate_side_textblock(pymu_text_block, page_width, page_height):
"""返回删除了垂直,水印,旋转的textblock 删除的内容打上tag返回."""
removed_text_block = []
for i, block in enumerate(
pymu_text_block
): # 格式参考test/assets/papre/pymu_textblocks.json
lines = block['lines']
block_bbox = block['bbox']
if not is_vbox_on_side(
block_bbox, page_width, page_height, 0.2
): # 保证这些box必须在页面的两边
continue
if (
all(
[
__is_a_word(line['spans'][0]['text'])
for line in lines
if len(line['spans']) > 0
]
)
and len(lines) > 1
and all([len(line['spans']) == 1 for line in lines])
):
is_box_valign = (
(
len(
set(
[
int(line['spans'][0]['bbox'][0])
for line in lines
if len(line['spans']) > 0
]
)
)
== 1
)
and (
len(
[
int(line['spans'][0]['bbox'][0])
for line in lines
if len(line['spans']) > 0
]
)
> 1
)
) # 测试bbox在垂直方向是不是x0都相等,也就是在垂直方向排列.同时必须大于等于2个字
if is_box_valign:
block['tag'] = VERTICAL_TEXT
removed_text_block.append(block)
continue
for line in lines:
if line['dir'] != (1, 0):
block['tag'] = ROTATE_TEXT
removed_text_block.append(
block
) # 只要有一个line不是dir=(1,0),就把整个block都删掉
break
for block in removed_text_block:
pymu_text_block.remove(block)
return pymu_text_block, removed_text_block
def get_side_boundry(rotate_bbox, page_width, page_height):
"""根据rotate_bbox,返回页面的左右正文边界."""
left_x = 0
right_x = page_width
for x in rotate_bbox:
box = x['bbox']
if box[2] < page_width / 2:
left_x = max(left_x, box[2])
else:
right_x = min(right_x, box[0])
return left_x + 1, right_x - 1
def remove_side_blank_block(pymu_text_block, page_width, page_height):
"""删除页面两侧的空白block."""
removed_text_block = []
for i, block in enumerate(
pymu_text_block
): # 格式参考test/assets/papre/pymu_textblocks.json
block_bbox = block['bbox']
if not is_vbox_on_side(
block_bbox, page_width, page_height, 0.2
): # 保证这些box必须在页面的两边
continue
if __is_empty_side_box(block):
block['tag'] = EMPTY_SIDE_BLOCK
removed_text_block.append(block)
continue
for block in removed_text_block:
pymu_text_block.remove(block)
return pymu_text_block, removed_text_block
@@ -1,184 +0,0 @@
"""
从pdf里提取出来api给出的bbox,然后根据重叠情况做出取舍
1. 首先去掉出现在图片上的bbox,图片包括表格和图片
2. 然后去掉出现在文字blcok上的图片bbox
"""
from magic_pdf.config.drop_tag import ON_IMAGE_TEXT, ON_TABLE_TEXT
from magic_pdf.libs.boxbase import (_is_in, _is_in_or_part_overlap,
_is_left_overlap)
def resolve_bbox_overlap_conflict(images: list, tables: list, interline_equations: list, inline_equations: list,
text_raw_blocks: list):
"""
text_raw_blocks结构是从pymupdf里直接取到的结构,具体样例参考test/assets/papre/pymu_textblocks.json
当下采用一种粗暴的方式:
1. 去掉图片上的公式
2. 去掉table上的公式
2. 图片和文字block部分重叠,首先丢弃图片
3. 图片和图片重叠,修改图片的bbox,使得图片不重叠(暂时没这么做,先把图片都扔掉)
4. 去掉文字bbox里位于图片、表格上的文字(一定要完全在图、表内部)
5. 去掉表格上的文字
"""
text_block_removed = []
images_backup = []
# 去掉位于图片上的文字block
for image_box in images:
for text_block in text_raw_blocks:
text_bbox = text_block['bbox']
if _is_in(text_bbox, image_box):
text_block['tag'] = ON_IMAGE_TEXT
text_block_removed.append(text_block)
# 去掉table上的文字block
for table_box in tables:
for text_block in text_raw_blocks:
text_bbox = text_block['bbox']
if _is_in(text_bbox, table_box):
text_block['tag'] = ON_TABLE_TEXT
text_block_removed.append(text_block)
for text_block in text_block_removed:
if text_block in text_raw_blocks:
text_raw_blocks.remove(text_block)
# 第一步去掉在图片上出现的公式box
temp = []
for image_box in images:
for eq1 in interline_equations:
if _is_in_or_part_overlap(image_box, eq1[:4]):
temp.append(eq1)
for eq2 in inline_equations:
if _is_in_or_part_overlap(image_box, eq2[:4]):
temp.append(eq2)
for eq in temp:
if eq in interline_equations:
interline_equations.remove(eq)
if eq in inline_equations:
inline_equations.remove(eq)
# 第二步去掉在表格上出现的公式box
temp = []
for table_box in tables:
for eq1 in interline_equations:
if _is_in_or_part_overlap(table_box, eq1[:4]):
temp.append(eq1)
for eq2 in inline_equations:
if _is_in_or_part_overlap(table_box, eq2[:4]):
temp.append(eq2)
for eq in temp:
if eq in interline_equations:
interline_equations.remove(eq)
if eq in inline_equations:
inline_equations.remove(eq)
# 图片和文字重叠,丢掉图片
for image_box in images:
for text_block in text_raw_blocks:
text_bbox = text_block['bbox']
if _is_in_or_part_overlap(image_box, text_bbox):
images_backup.append(image_box)
break
for image_box in images_backup:
images.remove(image_box)
# 图片和图片重叠,两张都暂时不参与版面计算
images_dup_index = []
for i in range(len(images)):
for j in range(i + 1, len(images)):
if _is_in_or_part_overlap(images[i], images[j]):
images_dup_index.append(i)
images_dup_index.append(j)
dup_idx = set(images_dup_index)
for img_id in dup_idx:
images_backup.append(images[img_id])
images[img_id] = None
images = [img for img in images if img is not None]
# 如果行间公式和文字block重叠,放到临时的数据里,防止这些文字box影响到layout计算。通过计算IOU合并行间公式和文字block
# 对于这样的文本块删除,然后保留行间公式的大小不变。
# 当计算完毕layout,这部分再合并回来
text_block_removed_2 = []
# for text_block in text_raw_blocks:
# text_bbox = text_block["bbox"]
# for eq in interline_equations:
# ratio = calculate_overlap_area_2_minbox_area_ratio(text_bbox, eq[:4])
# if ratio>0.05:
# text_block['tag'] = "belong-to-interline-equation"
# text_block_removed_2.append(text_block)
# break
# for tb in text_block_removed_2:
# if tb in text_raw_blocks:
# text_raw_blocks.remove(tb)
# text_block_removed = text_block_removed + text_block_removed_2
return images, tables, interline_equations, inline_equations, text_raw_blocks, text_block_removed, images_backup, text_block_removed_2
def check_text_block_horizontal_overlap(text_blocks: list, header, footer) -> bool:
"""检查文本block之间的水平重叠情况,这种情况如果发生,那么这个pdf就不再继续处理了。 因为这种情况大概率发生了公式没有被检测出来。"""
if len(text_blocks) == 0:
return False
page_min_y = 0
page_max_y = max(yy['bbox'][3] for yy in text_blocks)
def __max_y(lst: list):
if len(lst) > 0:
return max([item[1] for item in lst])
return page_min_y
def __min_y(lst: list):
if len(lst) > 0:
return min([item[3] for item in lst])
return page_max_y
clip_y0 = __max_y(header)
clip_y1 = __min_y(footer)
txt_bboxes = []
for text_block in text_blocks:
bbox = text_block['bbox']
if bbox[1] >= clip_y0 and bbox[3] <= clip_y1:
txt_bboxes.append(bbox)
for i in range(len(txt_bboxes)):
for j in range(i + 1, len(txt_bboxes)):
if _is_left_overlap(txt_bboxes[i], txt_bboxes[j]) or _is_left_overlap(txt_bboxes[j], txt_bboxes[i]):
return True
return False
def check_useful_block_horizontal_overlap(useful_blocks: list) -> bool:
"""检查文本block之间的水平重叠情况,这种情况如果发生,那么这个pdf就不再继续处理了。 因为这种情况大概率发生了公式没有被检测出来。"""
if len(useful_blocks) == 0:
return False
page_min_y = 0
page_max_y = max(yy['bbox'][3] for yy in useful_blocks)
useful_bboxes = []
for text_block in useful_blocks:
bbox = text_block['bbox']
if bbox[1] >= page_min_y and bbox[3] <= page_max_y:
useful_bboxes.append(bbox)
for i in range(len(useful_bboxes)):
for j in range(i + 1, len(useful_bboxes)):
area_i = (useful_bboxes[i][2] - useful_bboxes[i][0]) * (useful_bboxes[i][3] - useful_bboxes[i][1])
area_j = (useful_bboxes[j][2] - useful_bboxes[j][0]) * (useful_bboxes[j][3] - useful_bboxes[j][1])
if _is_left_overlap(useful_bboxes[i], useful_bboxes[j]) or _is_left_overlap(useful_bboxes[j], useful_bboxes[i]):
if area_i > area_j:
return True, useful_bboxes[j], useful_bboxes[i]
else:
return True, useful_bboxes[i], useful_bboxes[j]
return False, None, None
@@ -1,29 +0,0 @@
def solve_inline_too_large_interval(pdf_info_dict: dict) -> dict: # text_block -> json中的preproc_block
"""解决行内文本间距过大问题"""
for i in range(len(pdf_info_dict)):
text_blocks = pdf_info_dict[f'page_{i}']['preproc_blocks']
for block in text_blocks:
x_pre_1, y_pre_1, x_pre_2, y_pre_2 = 0, 0, 0, 0
for line in block['lines']:
x_cur_1, y_cur_1, x_cur_2, y_cur_2 = line['bbox']
# line_box = [x1, y1, x2, y2]
if int(y_cur_1) == int(y_pre_1) and int(y_cur_2) == int(y_pre_2):
# if len(line['spans']) == 1:
line['spans'][0]['text'] = ' ' + line['spans'][0]['text']
x_pre_1, y_pre_1, x_pre_2, y_pre_2 = line['bbox']
return pdf_info_dict
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"""
统计处需要跨页、全局性的数据
- 统计出字号从大到小
- 正文区域占比最高的前5
- 正文平均行间距
- 正文平均字间距
- 正文平均字符宽度
- 正文平均字符高度
"""