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Python

import enum
from magic_pdf.config.model_block_type import ModelBlockTypeEnum
from magic_pdf.config.ocr_content_type import CategoryId, ContentType
from magic_pdf.data.dataset import Dataset
from magic_pdf.libs.boxbase import (_is_in, bbox_distance, bbox_relative_pos,
calculate_iou)
from magic_pdf.libs.coordinate_transform import get_scale_ratio
from magic_pdf.pre_proc.remove_bbox_overlap import _remove_overlap_between_bbox
CAPATION_OVERLAP_AREA_RATIO = 0.6
MERGE_BOX_OVERLAP_AREA_RATIO = 1.1
class PosRelationEnum(enum.Enum):
LEFT = 'left'
RIGHT = 'right'
UP = 'up'
BOTTOM = 'bottom'
ALL = 'all'
class MagicModel:
"""每个函数没有得到元素的时候返回空list."""
def __fix_axis(self):
for model_page_info in self.__model_list:
need_remove_list = []
page_no = model_page_info['page_info']['page_no']
horizontal_scale_ratio, vertical_scale_ratio = get_scale_ratio(
model_page_info, self.__docs.get_page(page_no)
)
layout_dets = model_page_info['layout_dets']
for layout_det in layout_dets:
if layout_det.get('bbox') is not None:
# 兼容直接输出bbox的模型数据,如paddle
x0, y0, x1, y1 = layout_det['bbox']
else:
# 兼容直接输出poly的模型数据如xxx
x0, y0, _, _, x1, y1, _, _ = layout_det['poly']
bbox = [
int(x0 / horizontal_scale_ratio),
int(y0 / vertical_scale_ratio),
int(x1 / horizontal_scale_ratio),
int(y1 / vertical_scale_ratio),
]
layout_det['bbox'] = bbox
# 删除高度或者宽度小于等于0的spans
if bbox[2] - bbox[0] <= 0 or bbox[3] - bbox[1] <= 0:
need_remove_list.append(layout_det)
for need_remove in need_remove_list:
layout_dets.remove(need_remove)
def __fix_by_remove_low_confidence(self):
for model_page_info in self.__model_list:
need_remove_list = []
layout_dets = model_page_info['layout_dets']
for layout_det in layout_dets:
if layout_det['score'] <= 0.05:
need_remove_list.append(layout_det)
else:
continue
for need_remove in need_remove_list:
layout_dets.remove(need_remove)
def __fix_by_remove_high_iou_and_low_confidence(self):
for model_page_info in self.__model_list:
need_remove_list = []
layout_dets = model_page_info['layout_dets']
for layout_det1 in layout_dets:
for layout_det2 in layout_dets:
if layout_det1 == layout_det2:
continue
if layout_det1['category_id'] in [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
] and layout_det2['category_id'] in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:
if (
calculate_iou(layout_det1['bbox'], layout_det2['bbox'])
> 0.9
):
if layout_det1['score'] < layout_det2['score']:
layout_det_need_remove = layout_det1
else:
layout_det_need_remove = layout_det2
if layout_det_need_remove not in need_remove_list:
need_remove_list.append(layout_det_need_remove)
else:
continue
else:
continue
for need_remove in need_remove_list:
layout_dets.remove(need_remove)
def __init__(self, model_list: list, docs: Dataset):
self.__model_list = model_list
self.__docs = docs
"""为所有模型数据添加bbox信息(缩放poly->bbox)"""
self.__fix_axis()
"""删除置信度特别低的模型数据(<0.05),提高质量"""
self.__fix_by_remove_low_confidence()
"""删除高iou(>0.9)数据中置信度较低的那个"""
self.__fix_by_remove_high_iou_and_low_confidence()
self.__fix_footnote()
def _bbox_distance(self, bbox1, bbox2):
left, right, bottom, top = bbox_relative_pos(bbox1, bbox2)
flags = [left, right, bottom, top]
count = sum([1 if v else 0 for v in flags])
if count > 1:
return float('inf')
if left or right:
l1 = bbox1[3] - bbox1[1]
l2 = bbox2[3] - bbox2[1]
else:
l1 = bbox1[2] - bbox1[0]
l2 = bbox2[2] - bbox2[0]
if l2 > l1 and (l2 - l1) / l1 > 0.3:
return float('inf')
return bbox_distance(bbox1, bbox2)
def __fix_footnote(self):
# 3: figure, 5: table, 7: footnote
for model_page_info in self.__model_list:
footnotes = []
figures = []
tables = []
for obj in model_page_info['layout_dets']:
if obj['category_id'] == 7:
footnotes.append(obj)
elif obj['category_id'] == 3:
figures.append(obj)
elif obj['category_id'] == 5:
tables.append(obj)
if len(footnotes) * len(figures) == 0:
continue
dis_figure_footnote = {}
dis_table_footnote = {}
for i in range(len(footnotes)):
for j in range(len(figures)):
pos_flag_count = sum(
list(
map(
lambda x: 1 if x else 0,
bbox_relative_pos(
footnotes[i]['bbox'], figures[j]['bbox']
),
)
)
)
if pos_flag_count > 1:
continue
dis_figure_footnote[i] = min(
self._bbox_distance(figures[j]['bbox'], footnotes[i]['bbox']),
dis_figure_footnote.get(i, float('inf')),
)
for i in range(len(footnotes)):
for j in range(len(tables)):
pos_flag_count = sum(
list(
map(
lambda x: 1 if x else 0,
bbox_relative_pos(
footnotes[i]['bbox'], tables[j]['bbox']
),
)
)
)
if pos_flag_count > 1:
continue
dis_table_footnote[i] = min(
self._bbox_distance(tables[j]['bbox'], footnotes[i]['bbox']),
dis_table_footnote.get(i, float('inf')),
)
for i in range(len(footnotes)):
if i not in dis_figure_footnote:
continue
if dis_table_footnote.get(i, float('inf')) > dis_figure_footnote[i]:
footnotes[i]['category_id'] = CategoryId.ImageFootnote
def __reduct_overlap(self, bboxes):
N = len(bboxes)
keep = [True] * N
for i in range(N):
for j in range(N):
if i == j:
continue
if _is_in(bboxes[i]['bbox'], bboxes[j]['bbox']):
keep[i] = False
return [bboxes[i] for i in range(N) if keep[i]]
def __tie_up_category_by_distance_v2(
self,
page_no: int,
subject_category_id: int,
object_category_id: int,
priority_pos: PosRelationEnum,
):
"""_summary_
Args:
page_no (int): _description_
subject_category_id (int): _description_
object_category_id (int): _description_
priority_pos (PosRelationEnum): _description_
Returns:
_type_: _description_
"""
AXIS_MULPLICITY = 0.5
subjects = self.__reduct_overlap(
list(
map(
lambda x: {'bbox': x['bbox'], 'score': x['score']},
filter(
lambda x: x['category_id'] == subject_category_id,
self.__model_list[page_no]['layout_dets'],
),
)
)
)
objects = self.__reduct_overlap(
list(
map(
lambda x: {'bbox': x['bbox'], 'score': x['score']},
filter(
lambda x: x['category_id'] == object_category_id,
self.__model_list[page_no]['layout_dets'],
),
)
)
)
M = len(objects)
subjects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)
objects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)
sub_obj_map_h = {i: [] for i in range(len(subjects))}
dis_by_directions = {
'top': [[-1, float('inf')]] * M,
'bottom': [[-1, float('inf')]] * M,
'left': [[-1, float('inf')]] * M,
'right': [[-1, float('inf')]] * M,
}
for i, obj in enumerate(objects):
l_x_axis, l_y_axis = (
obj['bbox'][2] - obj['bbox'][0],
obj['bbox'][3] - obj['bbox'][1],
)
axis_unit = min(l_x_axis, l_y_axis)
for j, sub in enumerate(subjects):
bbox1, bbox2, _ = _remove_overlap_between_bbox(
objects[i]['bbox'], subjects[j]['bbox']
)
left, right, bottom, top = bbox_relative_pos(bbox1, bbox2)
flags = [left, right, bottom, top]
if sum([1 if v else 0 for v in flags]) > 1:
continue
if left:
if dis_by_directions['left'][i][1] > bbox_distance(
obj['bbox'], sub['bbox']
):
dis_by_directions['left'][i] = [
j,
bbox_distance(obj['bbox'], sub['bbox']),
]
if right:
if dis_by_directions['right'][i][1] > bbox_distance(
obj['bbox'], sub['bbox']
):
dis_by_directions['right'][i] = [
j,
bbox_distance(obj['bbox'], sub['bbox']),
]
if bottom:
if dis_by_directions['bottom'][i][1] > bbox_distance(
obj['bbox'], sub['bbox']
):
dis_by_directions['bottom'][i] = [
j,
bbox_distance(obj['bbox'], sub['bbox']),
]
if top:
if dis_by_directions['top'][i][1] > bbox_distance(
obj['bbox'], sub['bbox']
):
dis_by_directions['top'][i] = [
j,
bbox_distance(obj['bbox'], sub['bbox']),
]
if (
dis_by_directions['top'][i][1] != float('inf')
and dis_by_directions['bottom'][i][1] != float('inf')
and priority_pos in (PosRelationEnum.BOTTOM, PosRelationEnum.UP)
):
RATIO = 3
if (
abs(
dis_by_directions['top'][i][1]
- dis_by_directions['bottom'][i][1]
)
< RATIO * axis_unit
):
if priority_pos == PosRelationEnum.BOTTOM:
sub_obj_map_h[dis_by_directions['bottom'][i][0]].append(i)
else:
sub_obj_map_h[dis_by_directions['top'][i][0]].append(i)
continue
if dis_by_directions['left'][i][1] != float('inf') or dis_by_directions[
'right'
][i][1] != float('inf'):
if dis_by_directions['left'][i][1] != float(
'inf'
) and dis_by_directions['right'][i][1] != float('inf'):
if AXIS_MULPLICITY * axis_unit >= abs(
dis_by_directions['left'][i][1]
- dis_by_directions['right'][i][1]
):
left_sub_bbox = subjects[dis_by_directions['left'][i][0]][
'bbox'
]
right_sub_bbox = subjects[dis_by_directions['right'][i][0]][
'bbox'
]
left_sub_bbox_y_axis = left_sub_bbox[3] - left_sub_bbox[1]
right_sub_bbox_y_axis = right_sub_bbox[3] - right_sub_bbox[1]
if (
abs(left_sub_bbox_y_axis - l_y_axis)
+ dis_by_directions['left'][i][0]
> abs(right_sub_bbox_y_axis - l_y_axis)
+ dis_by_directions['right'][i][0]
):
left_or_right = dis_by_directions['right'][i]
else:
left_or_right = dis_by_directions['left'][i]
else:
left_or_right = dis_by_directions['left'][i]
if left_or_right[1] > dis_by_directions['right'][i][1]:
left_or_right = dis_by_directions['right'][i]
else:
left_or_right = dis_by_directions['left'][i]
if left_or_right[1] == float('inf'):
left_or_right = dis_by_directions['right'][i]
else:
left_or_right = [-1, float('inf')]
if dis_by_directions['top'][i][1] != float('inf') or dis_by_directions[
'bottom'
][i][1] != float('inf'):
if dis_by_directions['top'][i][1] != float('inf') and dis_by_directions[
'bottom'
][i][1] != float('inf'):
if AXIS_MULPLICITY * axis_unit >= abs(
dis_by_directions['top'][i][1]
- dis_by_directions['bottom'][i][1]
):
top_bottom = subjects[dis_by_directions['bottom'][i][0]]['bbox']
bottom_top = subjects[dis_by_directions['top'][i][0]]['bbox']
top_bottom_x_axis = top_bottom[2] - top_bottom[0]
bottom_top_x_axis = bottom_top[2] - bottom_top[0]
if (
abs(top_bottom_x_axis - l_x_axis)
+ dis_by_directions['bottom'][i][1]
> abs(bottom_top_x_axis - l_x_axis)
+ dis_by_directions['top'][i][1]
):
top_or_bottom = dis_by_directions['top'][i]
else:
top_or_bottom = dis_by_directions['bottom'][i]
else:
top_or_bottom = dis_by_directions['top'][i]
if top_or_bottom[1] > dis_by_directions['bottom'][i][1]:
top_or_bottom = dis_by_directions['bottom'][i]
else:
top_or_bottom = dis_by_directions['top'][i]
if top_or_bottom[1] == float('inf'):
top_or_bottom = dis_by_directions['bottom'][i]
else:
top_or_bottom = [-1, float('inf')]
if left_or_right[1] != float('inf') or top_or_bottom[1] != float('inf'):
if left_or_right[1] != float('inf') and top_or_bottom[1] != float(
'inf'
):
if AXIS_MULPLICITY * axis_unit >= abs(
left_or_right[1] - top_or_bottom[1]
):
y_axis_bbox = subjects[left_or_right[0]]['bbox']
x_axis_bbox = subjects[top_or_bottom[0]]['bbox']
if (
abs((x_axis_bbox[2] - x_axis_bbox[0]) - l_x_axis) / l_x_axis
> abs((y_axis_bbox[3] - y_axis_bbox[1]) - l_y_axis)
/ l_y_axis
):
sub_obj_map_h[left_or_right[0]].append(i)
else:
sub_obj_map_h[top_or_bottom[0]].append(i)
else:
if left_or_right[1] > top_or_bottom[1]:
sub_obj_map_h[top_or_bottom[0]].append(i)
else:
sub_obj_map_h[left_or_right[0]].append(i)
else:
if left_or_right[1] != float('inf'):
sub_obj_map_h[left_or_right[0]].append(i)
else:
sub_obj_map_h[top_or_bottom[0]].append(i)
ret = []
for i in sub_obj_map_h.keys():
ret.append(
{
'sub_bbox': {
'bbox': subjects[i]['bbox'],
'score': subjects[i]['score'],
},
'obj_bboxes': [
{'score': objects[j]['score'], 'bbox': objects[j]['bbox']}
for j in sub_obj_map_h[i]
],
'sub_idx': i,
}
)
return ret
def __tie_up_category_by_distance_v3(
self,
page_no: int,
subject_category_id: int,
object_category_id: int,
priority_pos: PosRelationEnum,
):
subjects = self.__reduct_overlap(
list(
map(
lambda x: {'bbox': x['bbox'], 'score': x['score']},
filter(
lambda x: x['category_id'] == subject_category_id,
self.__model_list[page_no]['layout_dets'],
),
)
)
)
objects = self.__reduct_overlap(
list(
map(
lambda x: {'bbox': x['bbox'], 'score': x['score']},
filter(
lambda x: x['category_id'] == object_category_id,
self.__model_list[page_no]['layout_dets'],
),
)
)
)
ret = []
N, M = len(subjects), len(objects)
subjects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)
objects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2)
OBJ_IDX_OFFSET = 10000
SUB_BIT_KIND, OBJ_BIT_KIND = 0, 1
all_boxes_with_idx = [(i, SUB_BIT_KIND, sub['bbox'][0], sub['bbox'][1]) for i, sub in enumerate(subjects)] + [(i + OBJ_IDX_OFFSET , OBJ_BIT_KIND, obj['bbox'][0], obj['bbox'][1]) for i, obj in enumerate(objects)]
seen_idx = set()
seen_sub_idx = set()
while N > len(seen_sub_idx):
candidates = []
for idx, kind, x0, y0 in all_boxes_with_idx:
if idx in seen_idx:
continue
candidates.append((idx, kind, x0, y0))
if len(candidates) == 0:
break
left_x = min([v[2] for v in candidates])
top_y = min([v[3] for v in candidates])
candidates.sort(key=lambda x: (x[2]-left_x) ** 2 + (x[3] - top_y) ** 2)
fst_idx, fst_kind, left_x, top_y = candidates[0]
candidates.sort(key=lambda x: (x[2] - left_x) ** 2 + (x[3] - top_y)**2)
nxt = None
for i in range(1, len(candidates)):
if candidates[i][1] ^ fst_kind == 1:
nxt = candidates[i]
break
if nxt is None:
break
if fst_kind == SUB_BIT_KIND:
sub_idx, obj_idx = fst_idx, nxt[0] - OBJ_IDX_OFFSET
else:
sub_idx, obj_idx = nxt[0], fst_idx - OBJ_IDX_OFFSET
pair_dis = bbox_distance(subjects[sub_idx]['bbox'], objects[obj_idx]['bbox'])
nearest_dis = float('inf')
for i in range(N):
if i in seen_idx or i == sub_idx:continue
nearest_dis = min(nearest_dis, bbox_distance(subjects[i]['bbox'], objects[obj_idx]['bbox']))
if pair_dis >= 3*nearest_dis:
seen_idx.add(sub_idx)
continue
seen_idx.add(sub_idx)
seen_idx.add(obj_idx + OBJ_IDX_OFFSET)
seen_sub_idx.add(sub_idx)
ret.append(
{
'sub_bbox': {
'bbox': subjects[sub_idx]['bbox'],
'score': subjects[sub_idx]['score'],
},
'obj_bboxes': [
{'score': objects[obj_idx]['score'], 'bbox': objects[obj_idx]['bbox']}
],
'sub_idx': sub_idx,
}
)
for i in range(len(objects)):
j = i + OBJ_IDX_OFFSET
if j in seen_idx:
continue
seen_idx.add(j)
nearest_dis, nearest_sub_idx = float('inf'), -1
for k in range(len(subjects)):
dis = bbox_distance(objects[i]['bbox'], subjects[k]['bbox'])
if dis < nearest_dis:
nearest_dis = dis
nearest_sub_idx = k
for k in range(len(subjects)):
if k != nearest_sub_idx: continue
if k in seen_sub_idx:
for kk in range(len(ret)):
if ret[kk]['sub_idx'] == k:
ret[kk]['obj_bboxes'].append({'score': objects[i]['score'], 'bbox': objects[i]['bbox']})
break
else:
ret.append(
{
'sub_bbox': {
'bbox': subjects[k]['bbox'],
'score': subjects[k]['score'],
},
'obj_bboxes': [
{'score': objects[i]['score'], 'bbox': objects[i]['bbox']}
],
'sub_idx': k,
}
)
seen_sub_idx.add(k)
seen_idx.add(k)
for i in range(len(subjects)):
if i in seen_sub_idx:
continue
ret.append(
{
'sub_bbox': {
'bbox': subjects[i]['bbox'],
'score': subjects[i]['score'],
},
'obj_bboxes': [],
'sub_idx': i,
}
)
return ret
def get_imgs_v2(self, page_no: int):
with_captions = self.__tie_up_category_by_distance_v3(
page_no, 3, 4, PosRelationEnum.BOTTOM
)
with_footnotes = self.__tie_up_category_by_distance_v3(
page_no, 3, CategoryId.ImageFootnote, PosRelationEnum.ALL
)
ret = []
for v in with_captions:
record = {
'image_body': v['sub_bbox'],
'image_caption_list': v['obj_bboxes'],
}
filter_idx = v['sub_idx']
d = next(filter(lambda x: x['sub_idx'] == filter_idx, with_footnotes))
record['image_footnote_list'] = d['obj_bboxes']
ret.append(record)
return ret
def get_tables_v2(self, page_no: int) -> list:
with_captions = self.__tie_up_category_by_distance_v3(
page_no, 5, 6, PosRelationEnum.UP
)
with_footnotes = self.__tie_up_category_by_distance_v3(
page_no, 5, 7, PosRelationEnum.ALL
)
ret = []
for v in with_captions:
record = {
'table_body': v['sub_bbox'],
'table_caption_list': v['obj_bboxes'],
}
filter_idx = v['sub_idx']
d = next(filter(lambda x: x['sub_idx'] == filter_idx, with_footnotes))
record['table_footnote_list'] = d['obj_bboxes']
ret.append(record)
return ret
def get_imgs(self, page_no: int):
return self.get_imgs_v2(page_no)
def get_tables(
self, page_no: int
) -> list: # 3个坐标 caption, table主体table-note
return self.get_tables_v2(page_no)
def get_equations(self, page_no: int) -> list: # 有坐标,也有字
inline_equations = self.__get_blocks_by_type(
ModelBlockTypeEnum.EMBEDDING.value, page_no, ['latex']
)
interline_equations = self.__get_blocks_by_type(
ModelBlockTypeEnum.ISOLATED.value, page_no, ['latex']
)
interline_equations_blocks = self.__get_blocks_by_type(
ModelBlockTypeEnum.ISOLATE_FORMULA.value, page_no
)
return inline_equations, interline_equations, interline_equations_blocks
def get_discarded(self, page_no: int) -> list: # 自研模型,只有坐标
blocks = self.__get_blocks_by_type(ModelBlockTypeEnum.ABANDON.value, page_no)
return blocks
def get_text_blocks(self, page_no: int) -> list: # 自研模型搞的,只有坐标,没有字
blocks = self.__get_blocks_by_type(ModelBlockTypeEnum.PLAIN_TEXT.value, page_no)
return blocks
def get_title_blocks(self, page_no: int) -> list: # 自研模型,只有坐标,没字
blocks = self.__get_blocks_by_type(ModelBlockTypeEnum.TITLE.value, page_no)
return blocks
def get_ocr_text(self, page_no: int) -> list: # paddle 搞的,有字也有坐标
text_spans = []
model_page_info = self.__model_list[page_no]
layout_dets = model_page_info['layout_dets']
for layout_det in layout_dets:
if layout_det['category_id'] == '15':
span = {
'bbox': layout_det['bbox'],
'content': layout_det['text'],
}
text_spans.append(span)
return text_spans
def get_all_spans(self, page_no: int) -> list:
def remove_duplicate_spans(spans):
new_spans = []
for span in spans:
if not any(span == existing_span for existing_span in new_spans):
new_spans.append(span)
return new_spans
all_spans = []
model_page_info = self.__model_list[page_no]
layout_dets = model_page_info['layout_dets']
allow_category_id_list = [3, 5, 13, 14, 15]
"""当成span拼接的"""
# 3: 'image', # 图片
# 5: 'table', # 表格
# 13: 'inline_equation', # 行内公式
# 14: 'interline_equation', # 行间公式
# 15: 'text', # ocr识别文本
for layout_det in layout_dets:
category_id = layout_det['category_id']
if category_id in allow_category_id_list:
span = {'bbox': layout_det['bbox'], 'score': layout_det['score']}
if category_id == 3:
span['type'] = ContentType.Image
elif category_id == 5:
# 获取table模型结果
latex = layout_det.get('latex', None)
html = layout_det.get('html', None)
if latex:
span['latex'] = latex
elif html:
span['html'] = html
span['type'] = ContentType.Table
elif category_id == 13:
span['content'] = layout_det['latex']
span['type'] = ContentType.InlineEquation
elif category_id == 14:
span['content'] = layout_det['latex']
span['type'] = ContentType.InterlineEquation
elif category_id == 15:
span['content'] = layout_det['text']
span['type'] = ContentType.Text
all_spans.append(span)
return remove_duplicate_spans(all_spans)
def get_page_size(self, page_no: int): # 获取页面宽高
# 获取当前页的page对象
page = self.__docs.get_page(page_no).get_page_info()
# 获取当前页的宽高
page_w = page.w
page_h = page.h
return page_w, page_h
def __get_blocks_by_type(
self, type: int, page_no: int, extra_col: list[str] = []
) -> list:
blocks = []
for page_dict in self.__model_list:
layout_dets = page_dict.get('layout_dets', [])
page_info = page_dict.get('page_info', {})
page_number = page_info.get('page_no', -1)
if page_no != page_number:
continue
for item in layout_dets:
category_id = item.get('category_id', -1)
bbox = item.get('bbox', None)
if category_id == type:
block = {
'bbox': bbox,
'score': item.get('score'),
}
for col in extra_col:
block[col] = item.get(col, None)
blocks.append(block)
return blocks
def get_model_list(self, page_no):
return self.__model_list[page_no]