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Commit eb4625a9 authored by zhaoxiaomeng's avatar zhaoxiaomeng
Browse files

Merge remote-tracking branch 'origin/master' 

# Conflicts:
#	magic_pdf/model/pdf_extract_kit.py
parents 4101c357 899c7918
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.github/workflows/benchmark.yml
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...@@ -18,15 +18,12 @@ on: ...@@ -18,15 +18,12 @@ on:
workflow_dispatch: workflow_dispatch:
jobs: jobs:
pdf-test: pdf-test:
runs-on: mineru runs-on: ubuntu-latest
timeout-minutes: 180 timeout-minutes: 180
strategy: strategy:
fail-fast: true fail-fast: true
steps: steps:
- name: config-net
run: |
source activate base
- name: PDF benchmark - name: PDF benchmark
uses: actions/checkout@v3 uses: actions/checkout@v3
with: with:
......
.github/workflows/cli.yml
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...@@ -18,15 +18,12 @@ on: ...@@ -18,15 +18,12 @@ on:
workflow_dispatch: workflow_dispatch:
jobs: jobs:
cli-test: cli-test:
runs-on: mineru runs-on: ubuntu-latest
timeout-minutes: 40 timeout-minutes: 40
strategy: strategy:
fail-fast: true fail-fast: true
steps: steps:
- name: config-net
run: |
source activate base
- name: PDF cli - name: PDF cli
uses: actions/checkout@v3 uses: actions/checkout@v3
with: with:
...@@ -34,19 +31,11 @@ jobs: ...@@ -34,19 +31,11 @@ jobs:
- name: check-requirements - name: check-requirements
run: | run: |
changed_files=$(git diff --name-only -r HEAD~1 HEAD) pip install -r requirements.txt
echo $changed_files pip install -r requirements-qa.txt
if [[ $changed_files =~ "requirements.txt" ]] || [[ $changed_files =~ "requirements-qa.txt" ]]; then
pip install -r requirements.txt
pip install -r requirements-qa.txt
fi
- name: config-net-reset
run: |
export http_proxy=""
export https_proxy=""
- name: test_cli - name: test_cli
run: | run: |
cp magic-pdf.template.json ~/magic-pdf.json
echo $GITHUB_WORKSPACE echo $GITHUB_WORKSPACE
cd $GITHUB_WORKSPACE && export PYTHONPATH=. && pytest -s -v tests/test_unit.py cd $GITHUB_WORKSPACE && export PYTHONPATH=. && pytest -s -v tests/test_unit.py
cd $GITHUB_WORKSPACE && pytest -s -v tests/test_cli/test_cli.py cd $GITHUB_WORKSPACE && pytest -s -v tests/test_cli/test_cli.py
......
others/README.md deleted 100644 → 0
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# pdf_toolbox
pdf 解析基础函数
## pdf是否是文字类型/扫描类型的区分
```shell
cat s3_pdf_path.example.pdf | parallel --colsep ' ' -j 10 "python pdf_meta_scan.py --s3-pdf-path {2} --s3-profile {1} >> {/}.jsonl"
find dir/to/jsonl/ -type f -name "*.jsonl" | parallel -j 10 "python pdf_classfy_by_type.py --json_file {} >> {/}.jsonl"
```
```shell
# 如果单独运行脚本，合并到code-clean之后需要运行，参考如下：
python -m pdf_meta_scan --s3-pdf-path "D:\pdf_files\内容排序测试_pdf\p3_图文混排 5.pdf" --s3-profile s2
```
## pdf
others/check_inline_formula.py deleted 100644 → 0
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# 最终版：把那种text_block有重叠，且inline_formula位置在重叠部分的，认定整个页面都有问题，所有的inline_formula都改成no_check
from magic_pdf.libs import fitz
def check_inline_formula(page, inline_formula_boxes):
"""
:param page :fitz读取的当前页的内容
:param inline_formula_boxes: list类型，每一个元素是一个元祖 (L, U, R, D)
:return: inline_formula_check: list类型，每一个元素是一个类别，其顺序对应输入的inline_formula_boxes，给每个行内公式打一个标签，包括：
- nocheck_inline_formula：这个公式框没有与任何span相交，有可能存在问题
- wrong_text_block：这个公式框同时存在多个block里，可能页面的text block存在问题
- false_inline_formula：只涉及一个span并且只占据这个span的小部分面积，判断可能不是公式
- true_inline_formula：两种情况判断为公式，一是横跨多个span，二是只涉及一个span但是几乎占据了这个span大部分的面积
"""
# count = defaultdict(int)
## ------------------------ Text --------------------------------------------
blocks = page.get_text(
"dict",
flags=fitz.TEXTFLAGS_TEXT,
#clip=clip,
)["blocks"]
# iterate over the bboxes
inline_formula_check = []
for result in inline_formula_boxes:
(x1, y1, x2, y2) = (result[0], result[1], result[2], result[3])
## 逐个block##
in_block = 0
for bbox in blocks:
# image = cv2.rectangle(image, (int(bbox['bbox'][0]), int(bbox['bbox'][1])), (int(bbox['bbox'][2]), int(bbox['bbox'][3])), (0, 255, 0), 1)
if (y1 >= bbox['bbox'][1] and y2 <= bbox['bbox'][3]) and (x1 >= bbox['bbox'][0] and x2 <= bbox['bbox'][2]): # 判定公式在哪一个block
in_block += 1
intersect = []
# ## 逐个span###
for line in bbox['lines']:
if line['bbox'][1] <= ((y2 - y1) / 2) + y1 <= line['bbox'][3]: # 判断公式在哪一行
for item in line['spans']:
(t_x1, t_y1, t_x2, t_y2) = item['bbox']
if not ((t_x1 < x1 and t_x2 < x1) or (t_x1 > x2 and t_x2 > x2) or (t_y1 < y1 and t_y2 < y1) or (t_y1 > y2 and t_y2 > y2)): # 判断是否相交
intersect.append(item['bbox'])
# image = cv2.rectangle(image, (int(t_x1), int(t_y1)), (int(t_x2), int(t_y2)), (0, 255, 0), 1) # 可视化涉及到的span
# 可视化公式的分类
if len(intersect) == 0: # 没有与任何一个span有相交，这个span或者这个inline_formula_box可能有问题
# print(f'Wrong location, check {img_path}')
inline_formula_check_result = "nocheck_inline_formula"
# count['not_in_line'] += 1
elif len(intersect) == 1:
if abs((intersect[0][2] - intersect[0][0]) - (x2 - x1)) < (x2 - x1)*0.5: # 只涉及一个span但是几乎占据了这个span大部分的面积，判定为公式
# image = cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 1)
inline_formula_check_result = "true_inline_formula"
# count['one_span_large'] += 1
else: # 只涉及一个span并且只占据这个span的小部分面积，判断可能不是公式
# image = cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 1)
inline_formula_check_result = "false_inline_formula"
# count['fail'] += 1
else: # 横跨多个span,判定为公式
# image = cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 1)
inline_formula_check_result = "true_inline_formula"
# count['multi_span'] += 1
if in_block == 0: # 这个公式没有在任何的block里，这个公式可能有问题
# image = cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (255, 255, 0), 1)
inline_formula_check_result = "nocheck_inline_formula"
# count['not_in_block'] += 1
elif in_block > 1: # 这个公式存在于多个block里，这个页面可能有问题
inline_formula_check_result = "wrong_text_block"
inline_formula_check.append(inline_formula_check_result)
return inline_formula_check
others/pdf2json_infer.py deleted 100644 → 0
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import sys
from typing import Tuple
import os
import boto3, json
from botocore.config import Config
from magic_pdf.libs import fitz
from loguru import logger
from pathlib import Path
from tqdm import tqdm
import numpy as np
# sys.path.insert(0, "/mnt/petrelfs/ouyanglinke/code-clean/")
# print(sys.path)
from validation import cal_edit_distance, format_gt_bbox, label_match, detect_val
# from pdf2text_recogFigure_20231107 import parse_images # 获取figures的bbox
# from pdf2text_recogTable_20231107 import parse_tables # 获取tables的bbox
# from pdf2text_recogEquation_20231108 import parse_equations # 获取equations的bbox
# from pdf2text_recogTitle_20231113 import parse_titles # 获取Title的bbox
# from pdf2text_recogPara import parse_blocks_per_page
# from bbox_sort import bbox_sort, CONTENT_IDX, CONTENT_TYPE_IDX
from magic_pdf.layout.bbox_sort import bbox_sort, CONTENT_IDX, CONTENT_TYPE_IDX
from magic_pdf.pre_proc import parse_images # 获取figures的bbox
from magic_pdf.pre_proc.detect_tables import parse_tables # 获取tables的bbox
from magic_pdf.pre_proc import parse_equations # 获取equations的bbox
# from pdf2text_recogFootnote import parse_footnotes # 获取footnotes的bbox
from magic_pdf.post_proc.detect_para import process_blocks_per_page
from magic_pdf.libs import parse_aws_param, parse_bucket_key, read_file, join_path
def cut_image(bbox: Tuple, page_num: int, page: fitz.Page, save_parent_path: str, s3_profile: str):
"""
从第page_num页的page中，根据bbox进行裁剪出一张jpg图片，返回图片路径
save_path：需要同时支持s3和本地, 图片存放在save_path下，文件名是: {page_num}_{bbox[0]}_{bbox[1]}_{bbox[2]}_{bbox[3]}.jpg , bbox内数字取整。
"""
# 拼接路径
image_save_path = join_path(save_parent_path, f"{page_num}_{int(bbox[0])}_{int(bbox[1])}_{int(bbox[2])}_{int(bbox[3])}.jpg")
try:
# 将坐标转换为fitz.Rect对象
rect = fitz.Rect(*bbox)
# 配置缩放倍数为3倍
zoom = fitz.Matrix(3, 3)
# 截取图片
pix = page.get_pixmap(clip=rect, matrix=zoom)
# 打印图片文件名
# print(f"Saved {image_save_path}")
if image_save_path.startswith("s3://"):
ak, sk, end_point, addressing_style = parse_aws_param(s3_profile)
cli = boto3.client(service_name="s3", aws_access_key_id=ak, aws_secret_access_key=sk, endpoint_url=end_point,
config=Config(s3={'addressing_style': addressing_style}))
bucket_name, bucket_key = parse_bucket_key(image_save_path)
# 将字节流上传到s3
cli.upload_fileobj(pix.tobytes(output='jpeg', jpg_quality=95), bucket_name, bucket_key)
else:
# 保存图片到本地
# 先检查一下image_save_path的父目录是否存在，如果不存在，就创建
parent_dir = os.path.dirname(image_save_path)
if not os.path.exists(parent_dir):
os.makedirs(parent_dir)
pix.save(image_save_path, jpg_quality=95)
# 为了直接能在markdown里看，这里把地址改为相对于mardown的地址
pth = Path(image_save_path)
image_save_path = f"{pth.parent.name}/{pth.name}"
return image_save_path
except Exception as e:
logger.exception(e)
return image_save_path
def get_images_by_bboxes(book_name:str, page_num:int, page: fitz.Page, save_path:str, s3_profile:str, image_bboxes:list, table_bboxes:list, equation_inline_bboxes:list, equation_interline_bboxes:list) -> dict:
"""
返回一个dict, key为bbox, 值是图片地址
"""
ret = {}
# 图片的保存路径组成是这样的： {s3_or_local_path}/{book_name}/{images|tables|equations}/{page_num}_{bbox[0]}_{bbox[1]}_{bbox[2]}_{bbox[3]}.jpg
image_save_path = join_path(save_path, book_name, "images")
table_save_path = join_path(save_path, book_name, "tables")
equation_inline_save_path = join_path(save_path, book_name, "equations_inline")
equation_interline_save_path = join_path(save_path, book_name, "equation_interline")
for bbox in image_bboxes:
image_path = cut_image(bbox, page_num, page, image_save_path, s3_profile)
ret[bbox] = (image_path, "image") # 第二个元素是"image"，表示是图片
for bbox in table_bboxes:
image_path = cut_image(bbox, page_num, page, table_save_path, s3_profile)
ret[bbox] = (image_path, "table")
# 对公式目前只截图，不返回
for bbox in equation_inline_bboxes:
cut_image(bbox, page_num, page, equation_inline_save_path, s3_profile)
for bbox in equation_interline_bboxes:
cut_image(bbox, page_num, page, equation_interline_save_path, s3_profile)
return ret
def reformat_bboxes(images_box_path_dict:list, paras_dict:dict):
"""
把bbox重新组装成一个list，每个元素[x0, y0, x1, y1, block_content, idx_x, idx_y], 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是段落的内容
"""
all_bboxes = []
for bbox, image_info in images_box_path_dict.items():
all_bboxes.append([bbox[0], bbox[1], bbox[2], bbox[3], image_info, None, None, 'image'])
paras_dict = paras_dict[f"page_{paras_dict['page_id']}"]
for block_id, kvpair in paras_dict.items():
bbox = kvpair['bbox']
content = kvpair
all_bboxes.append([bbox[0], bbox[1], bbox[2], bbox[3], content, None, None, 'text'])
return all_bboxes
def concat2markdown(all_bboxes:list):
"""
对排序后的bboxes拼接内容
"""
content_md = ""
for box in all_bboxes:
content_type = box[CONTENT_TYPE_IDX]
if content_type == 'image':
image_type = box[CONTENT_IDX][1]
image_path = box[CONTENT_IDX][0]
content_md += f"![{image_type}]({image_path})"
content_md += "\n\n"
elif content_type == 'text': # 组装文本
paras = box[CONTENT_IDX]['paras']
text_content = ""
for para_id, para in paras.items():# 拼装内部的段落文本
text_content += para['text']
text_content += "\n\n"
content_md += text_content
else:
raise Exception(f"ERROR: {content_type} is not supported!")
return content_md
def main(s3_pdf_path: str, s3_pdf_profile: str, pdf_model_path:str, pdf_model_profile:str, save_path: str, page_num: int):
"""
"""
pth = Path(s3_pdf_path)
book_name = pth.name
#book_name = "".join(os.path.basename(s3_pdf_path).split(".")[0:-1])
res_dir_path = None
exclude_bboxes = []
# text_content_save_path = f"{save_path}/{book_name}/book.md"
# metadata_save_path = f"{save_path}/{book_name}/metadata.json"
try:
pdf_bytes = read_file(s3_pdf_path, s3_pdf_profile)
pdf_docs = fitz.open("pdf", pdf_bytes)
page_id = page_num - 1
page = pdf_docs[page_id] # 验证集只需要读取特定页面即可
model_output_json = join_path(pdf_model_path, f"page_{page_num}.json") # 模型输出的页面编号从1开始的
json_from_docx = read_file(model_output_json, pdf_model_profile) # TODO 这个读取方法名字应该改一下，避免语义歧义
json_from_docx_obj = json.loads(json_from_docx)
# 解析图片
image_bboxes = parse_images(page_id, page, json_from_docx_obj)
# 解析表格
table_bboxes = parse_tables(page_id, page, json_from_docx_obj)
# 解析公式
equations_interline_bboxes, equations_inline_bboxes = parse_equations(page_id, page, json_from_docx_obj)
# # 解析标题
# title_bboxs = parse_titles(page_id, page, res_dir_path, json_from_docx_obj, exclude_bboxes)
# # 解析页眉
# header_bboxs = parse_headers(page_id, page, res_dir_path, json_from_docx_obj, exclude_bboxes)
# # 解析页码
# pageNo_bboxs = parse_pageNos(page_id, page, res_dir_path, json_from_docx_obj, exclude_bboxes)
# # 解析脚注
# footnote_bboxs = parse_footnotes(page_id, page, res_dir_path, json_from_docx_obj, exclude_bboxes)
# # 解析页脚
# footer_bboxs = parse_footers(page_id, page, res_dir_path, json_from_docx_obj, exclude_bboxes)
# # 评估Layout是否规整、简单
# isSimpleLayout_flag, fullColumn_cnt, subColumn_cnt, curPage_loss = evaluate_pdf_layout(page_id, page, res_dir_path, json_from_docx_obj, exclude_bboxes)
# 把图、表、公式都进行截图，保存到本地，返回图片路径作为内容
images_box_path_dict = get_images_by_bboxes(book_name, page_id, page, save_path, s3_pdf_profile, image_bboxes, table_bboxes, equations_inline_bboxes,
equations_interline_bboxes) # 只要表格和图片的截图
# 解析文字段落
footer_bboxes = []
header_bboxes = []
exclude_bboxes = image_bboxes + table_bboxes
paras_dict = process_blocks_per_page(page, page_id, image_bboxes, table_bboxes, equations_inline_bboxes, equations_interline_bboxes, footer_bboxes, header_bboxes)
# paras_dict = postprocess_paras_pipeline(paras_dict)
# 最后一步，根据bbox进行从左到右，从上到下的排序，之后拼接起来, 排序
all_bboxes = reformat_bboxes(images_box_path_dict, paras_dict) # 由于公式目前还没有，所以equation_bboxes是None，多数存在段落里，暂时不解析
# 返回的是一个数组，每个元素[x0, y0, x1, y1, block_content, idx_x, idx_y, type], 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是段落的内容
# sorted_bboxes = bbox_sort(all_bboxes)
# markdown_text = concat2markdown(sorted_bboxes)
# parent_dir = os.path.dirname(text_content_save_path)
# if not os.path.exists(parent_dir):
# os.makedirs(parent_dir)
# with open(text_content_save_path, "a") as f:
# f.write(markdown_text)
# f.write(chr(12)) #换页符
# end for
# 写一个小的json,记录元数据
# metadata = {"book_name": book_name, "pdf_path": s3_pdf_path, "pdf_model_path": pdf_model_path, "save_path": save_path}
# with open(metadata_save_path, "w") as f:
# json.dump(metadata, f, ensure_ascii=False, indent=4)
return all_bboxes
except Exception as e:
print(f"ERROR: {s3_pdf_path}, {e}", file=sys.stderr)
logger.exception(e)
# @click.command()
# @click.option('--pdf-file-sub-path', help='s3上pdf文件的路径')
# @click.option('--save-path', help='解析出来的图片，文本的保存父目录')
def validation(validation_dataset: str, pdf_bin_file_profile: str, pdf_model_dir: str, pdf_model_profile: str, save_path: str):
#pdf_bin_file_path = "s3://llm-raw-snew/llm-raw-scihub/scimag07865000-07865999/10.1007/"
# pdf_bin_file_parent_path = "s3://llm-raw-snew/llm-raw-scihub/"
# pdf_model_parent_dir = "s3://llm-pdf-text/layout_det/scihub/"
# p = Path(pdf_file_sub_path)
# pdf_parent_path = p.parent
# pdf_file_name = p.name # pdf文件名字，含后缀
# pdf_bin_file_path = join_path(pdf_bin_file_parent_path, pdf_parent_path)
with open(validation_dataset, 'r') as f:
samples = json.load(f)
labels = []
det_res = []
edit_distance_list = []
for sample in tqdm(samples):
pdf_name = sample['pdf_name']
s3_pdf_path = sample['s3_path']
page_num = sample['page']
gt_order = sample['order']
pre = main(s3_pdf_path, pdf_bin_file_profile, join_path(pdf_model_dir, pdf_name), pdf_model_profile, save_path, page_num)
pre_dict_list = []
for item in pre:
pre_sample = {
'box': [item[0],item[1],item[2],item[3]],
'type': item[7],
'score': 1
}
pre_dict_list.append(pre_sample)
det_res.append(pre_dict_list)
match_change_dict = { # 待确认
"figure": "image",
"svg_figure": "image",
"inline_fomula": "equations_inline",
"fomula": "equation_interline",
"figure_caption": "text",
"table_caption": "text",
"fomula_caption": "text"
}
gt_annos = sample['annotations']
matched_label = label_match(gt_annos, match_change_dict)
labels.append(matched_label)
# 判断排序函数的精度
# 目前不考虑caption与图表相同序号的问题
ignore_category = ['abandon', 'figure_caption', 'table_caption', 'formula_caption']
gt_bboxes = format_gt_bbox(gt_annos, ignore_category)
sorted_bboxes = bbox_sort(gt_bboxes)
edit_distance = cal_edit_distance(sorted_bboxes)
edit_distance_list.append(edit_distance)
label_classes = ["image", "text", "table", "equation_interline"]
detect_matrix = detect_val(labels, det_res, label_classes)
print('detect_matrix', detect_matrix)
edit_distance_mean = np.mean(edit_distance_list)
print('edit_distance_mean', edit_distance_mean)
if __name__ == '__main__':
# 输入可以用以下命令生成批量pdf
# aws s3 ls s3://llm-pdf-text/layout_det/scihub/ --profile langchao | tail -n 10 | awk '{print "s3://llm-pdf-text/layout_det/scihub/"$4}' | xargs -I{} aws s3 ls {} --recursive --profile langchao | awk '{print substr($4,19)}' | parallel -j 1 echo {//} | sort -u
pdf_bin_file_profile = "outsider"
pdf_model_dir = "s3://llm-pdf-text/eval_1k/layout_res/"
pdf_model_profile = "langchao"
# validation_dataset = "/mnt/petrelfs/share_data/ouyanglinke/OCR/OCR_validation_dataset.json"
validation_dataset = "/mnt/petrelfs/share_data/ouyanglinke/OCR/OCR_validation_dataset_subset.json" # 测试
save_path = "/mnt/petrelfs/share_data/ouyanglinke/OCR/OCR_val_result"
validation(validation_dataset, pdf_bin_file_profile, pdf_model_dir, pdf_model_profile, save_path)
others/pdf2text_evaluatePdfLayout.py deleted 100644 → 0
View file @ 4101c357
from magic_pdf.libs import fitz # pyMuPDF库
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 evaluate_pdf_layout(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 title ---------#
title_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)
# {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
LOSS_THRESHOLD = 2000 # 经验值
fullColumn_bboxs = []
subColumn_bboxs = []
plainText_bboxs = []
#### read information of plain text
for xf in xf_json['layout_dets']:
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, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if xf['category_id'] == 2:
plainText_bboxs.append((L, U, R, D))
#### read information of column
for xf in xf_json['subfield_dets']:
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, R = min(L, R), max(L, R)
U, D = min(U, D), max(U, D)
if xf['category_id'] == 11:
fullColumn_bboxs.append((L, U, R, D))
elif xf['category_id'] == 12:
subColumn_bboxs.append((L, U, R, D))
curPage_loss = 0 # 当前页的loss
fail_cnt = 0 # Text文本块没被圈到的情形。
for L, U, R, D in plainText_bboxs:
find = False
for L2, U2, R2, D2 in (fullColumn_bboxs + subColumn_bboxs):
ratio_1, _ = calculate_overlapRatio_between_rect1_and_rect2(L, U, R, D, L2, U2, R2, D2)
if ratio_1 >= 0.9:
loss_1 = (L + R) / 2 - (L2 + R2) / 2
loss_2 = L - L2
cur_loss = min(abs(loss_1), abs(loss_2))
curPage_loss += cur_loss
find = True
break
if find == False:
fail_cnt += 1
isSimpleLayout_flag = False
if fail_cnt == 0 and len(fullColumn_bboxs) <= 1 and len(subColumn_bboxs) <= 2:
if curPage_loss <= LOSS_THRESHOLD:
isSimpleLayout_flag = True
return isSimpleLayout_flag, len(fullColumn_bboxs), len(subColumn_bboxs), curPage_loss
others/pdf2text_getNumberOfColumn.py deleted 100644 → 0
View file @ 4101c357
from magic_pdf.libs import fitz
from typing import List
def show_image(item, title=""):
"""Display a pixmap.
Just to display Pixmap image of "item" - ignore the man behind the curtain.
Args:
item: any PyMuPDF object having a "get_pixmap" method.
title: a string to be used as image title
Generates an RGB Pixmap from item using a constant DPI and using matplotlib
to show it inline of the notebook.
"""
DPI = 150 # use this resolution
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
pix = item.get_pixmap(dpi=DPI)
img = np.ndarray([pix.h, pix.w, 3], dtype=np.uint8, buffer=pix.samples_mv)
plt.figure(dpi=DPI) # set the figure's DPI
plt.title(title) # set title of image
_ = plt.imshow(img, extent=(0, pix.w * 72 / DPI, pix.h * 72 / DPI, 0))
def calculate_overlapRatio_between_line1_and_line2(L1: float, R1: float, L2: float, R2: float) -> (float, float):
# 计算两个line，重叠line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)
def get_targetAxis_and_splitAxis(page_ID: int, page: fitz.Page, columnNumber: int, textBboxs: List[(float, float, float, float)]) -> (List[float], List[float]):
"""
param: page: fitz解析出来的格式
param: columnNumber: Text的列数
param: textBboxs: 文本块list。 [(L, U, R, D), ... ]
return:
"""
INF = 10 ** 9
pageL, pageU, pageR, pageD = INF, INF, 0, 0
for L, U, R, D in textBboxs:
assert L <= R and U <= D
pageL = min(pageL, L)
pageR = max(pageR, R)
pageU = min(pageU, U)
pageD = max(pageD, D)
pageWidth = pageR - pageL
pageHeight = pageD - pageU
pageL -= pageWidth / 10 # 10是经验值
pageR += pageWidth / 10
pageU -= pageHeight / 10
pageD += pageHeight / 10
pageWidth = pageR - pageL
pageHeight = pageD - pageU
x_targetAxis = []
x_splitAxis = []
for i in range(0, columnNumber * 2 + 1):
if i & 1:
x_targetAxis.append(pageL + pageWidth / (2 * columnNumber) * i)
else:
x_splitAxis.append(pageL + pageWidth / (2 * columnNumber) * i)
# # 可视化：分列的外框
# path_bbox = []
# N = len(x_targetAxis)
# for i in range(N):
# L, R = x_splitAxis[i], x_splitAxis[i + 1]
# path_bbox.append((L, pageU, R, pageD))
# shape = page.new_shape()
# # iterate over the bboxes
# color_map = [fitz.pdfcolor["red"], fitz.pdfcolor["blue"], fitz.pdfcolor["yellow"], fitz.pdfcolor["black"], fitz.pdfcolor["green"], fitz.pdfcolor["brown"]]
# for i, rect in enumerate(path_bbox):
# # if i < 20:
# # continue
# shape.draw_rect(rect) # draw a border
# shape.insert_text(Point(rect[0], rect[1])+(5, 15), str(i), color=fitz.pdfcolor["blue"])
# shape.finish(color=color_map[i%len(color_map)])
# # shape.finish(color=fitz.pdfcolor["blue"])
# shape.commit() # store to the page
# # if i == 3:
# # print(rect)
# # break
# # print(rect)
# show_image(page, f"Table & Header BBoxes")
return x_targetAxis, x_splitAxis
def calculate_loss(page_ID: int, x_targetAxis: List[float], x_splitAxis: List[float], textBboxs: List[(float, float, float, float)]) -> (float, bool):
INF = 10 ** 9
# page_artbox = page.artbox
# pageL, pageU, pageR, pageD = page_artbox[0], page_artbox[1], page_artbox[2], page_artbox[3]
pageL, pageU, pageR, pageD = INF, INF, 0, 0
for L, U, R, D in textBboxs:
assert L <= R and U <= D
pageL = min(pageL, L)
pageR = max(pageR, R)
pageU = min(pageU, U)
pageD = max(pageD, D)
pageWidth = pageR - pageL
pageHeight = pageD - pageU
pageL -= pageWidth / 10
pageR += pageWidth / 10
pageU -= pageHeight / 10
pageD += pageHeight / 10
pageWidth = pageR - pageL
pageHeight = pageD - pageU
col_N = len(x_targetAxis) # 列数
col_texts_mid = [[] for _ in range(col_N)]
col_texts_LR = [[] for _ in range(col_N)]
oneLocateLoss_mid = 0
oneLocateLoss_LR = 0
oneLocateCnt_mid = 0 # 完美在一列中的个数
oneLocateCnt_LR = 0
oneLocateSquare_mid = 0.0 # 完美在一列的面积
oneLocateSquare_LR = 0.0
multiLocateLoss_mid = 0
multiLocateLoss_LR = 0
multiLocateCnt_mid = 0 # 在多列中的个数
multiLocateCnt_LR = 0
multiLocateSquare_mid = 0.0 # 在多列中的面积
multiLocateSquare_LR = 0.0
allLocateLoss_mid = 0
allLocateLoss_LR = 0
allLocateCnt_mid = 0 # 横跨页面的大框的个数
allLocateCnt_LR = 0
allLocateSquare_mid = 0.0 # 横跨整个页面的个数
allLocateSquare_LR = 0.0
isSimpleCondition = True # 就1个。2种方式，只要有一种情况不规整，就是不规整。
colID_Textcnt_mid = [0 for _ in range(col_N)] # 每一列中有多少个Text块，根据mid判断的
colID_Textcnt_LR = [0 for _ in range(col_N)] # 每一列中有多少个Text块，根据区间边界判断
allLocateBboxs_mid = [] # 跨整页的，bbox
allLocateBboxs_LR = []
non_allLocateBboxs_mid = []
non_allLocateBboxs_LR = [] # 不在单独某一列，但又不是全列
for L, U, R, D in textBboxs:
if D - U < 40: # 现在还没拼接好。先简单这样过滤页眉。也会牺牲一些很窄的长条
continue
if R - L < 40:
continue
located_cols_mid = []
located_cols_LR = []
for col_ID in range(col_N):
if col_N == 1:
located_cols_mid.append(col_ID)
located_cols_LR.append(col_ID)
else:
if L <= x_targetAxis[col_ID] <= R:
located_cols_mid.append(col_ID)
if calculate_overlapRatio_between_line1_and_line2(x_splitAxis[col_ID], x_splitAxis[col_ID + 1], L, R)[0] >= 0.2:
located_cols_LR.append(col_ID)
if len(located_cols_mid) == col_N:
allLocateBboxs_mid.append((L, U, R, D))
else:
non_allLocateBboxs_mid.append((L, U, R, D))
if len(located_cols_LR) == col_N:
allLocateBboxs_LR.append((L, U, R, D))
else:
non_allLocateBboxs_LR.append((L, U, R, D))
allLocateBboxs_mid.sort(key=lambda LURD: (LURD[1], LURD[0]))
non_allLocateBboxs_mid.sort(key=lambda LURD: (LURD[1], LURD[0]))
allLocateBboxs_LR.sort(key=lambda LURD: (LURD[1], LURD[0]))
non_allLocateBboxs_LR.sort(key=lambda LURD: (LURD[1], LURD[0]))
# --------------------判断，是不是有标题类的小块，掺杂在一列的pdf页面里。-------------#
isOneClumn = False
under_cnt = 0
under_square = 0.0
before_cnt = 0
before_square = 0.0
for nL, nU, nR, nD in non_allLocateBboxs_mid:
cnt = 0
for L, U, R, D in allLocateBboxs_mid:
if nD <= U:
cnt += 1
if cnt >= 1:
before_cnt += cnt
before_square += (R - L) * (D - U) * cnt
else:
under_cnt += 1
under_square += (R - L) * (D - U) * cnt
if (before_square + under_square) != 0 and before_square / (before_square + under_square) >= 0.2:
isOneClumn = True
if isOneClumn == True and col_N != 1:
return INF, False
if isOneClumn == True and col_N == 1:
return 0, True
#### 根据边界的统计情况，再判断一次
isOneClumn = False
under_cnt = 0
under_square = 0.0
before_cnt = 0
before_square = 0.0
for nL, nU, nR, nD in non_allLocateBboxs_LR:
cnt = 0
for L, U, R, D in allLocateBboxs_LR:
if nD <= U:
cnt += 1
if cnt >= 1:
before_cnt += cnt
before_square += (R - L) * (D - U) * cnt
else:
under_cnt += 1
under_square += (R - L) * (D - U) * cnt
if (before_square + under_square) != 0 and before_square / (before_square + under_square) >= 0.2:
isOneClumn = True
if isOneClumn == True and col_N != 1:
return INF, False
if isOneClumn == True and col_N == 1:
return 0, True
for L, U, R, D in textBboxs:
assert L < R and U < D, 'There is an error on bbox of text when calculate loss!'
# 简单排除页眉、迷你小块
# if (D - U) < pageHeight / 15 < 40 or (R - L) < pageWidth / 8:
if (D - U) < 40:
continue
if (R - L) < 40:
continue
mid = (L + R) / 2
located_cols_mid = [] # 在哪一列里，根据中点来判断
located_cols_LR = [] # 在哪一列里，根据边界判断
for col_ID in range(col_N):
if col_N == 1:
located_cols_mid.append(col_ID)
else:
# 根据中点判断
if L <= x_targetAxis[col_ID] <= R:
located_cols_mid.append(col_ID)
# 根据边界判断
if calculate_overlapRatio_between_line1_and_line2(x_splitAxis[col_ID], x_splitAxis[col_ID + 1], L, R)[0] >= 0.2:
located_cols_LR.append(col_ID)
## 1列的情形
if col_N == 1:
oneLocateLoss_mid += abs(mid - x_targetAxis[located_cols_mid[0]]) * (D - U) * (R - L)
# oneLocateLoss_mid += abs(L - x_splitAxis[located_cols[0]]) * (D - U) * (R - L)
oneLocateLoss_LR += abs(L - x_splitAxis[located_cols_mid[0]]) * (D - U) * (R - L)
oneLocateCnt_mid += 1
oneLocateSquare_mid += (D - U) * (R - L)
## 多列的情形
else:
######## 根据mid判断
if len(located_cols_mid) == 1:
oneLocateLoss_mid += abs(mid - x_targetAxis[located_cols_mid[0]]) * (D - U) * (R - L)
# oneLocateLoss_mid += abs(L - x_splitAxis[located_cols[0]]) * (D - U) * (R - L)
oneLocateCnt_mid += 1
oneLocateSquare_mid += (D - U) * (R - L)
elif 1 <= len(located_cols_mid) < col_N:
ll, rr = located_cols_mid[0], located_cols_mid[-1]
# multiLocateLoss_mid += abs(mid - (x_targetAxis[ll] + x_targetAxis[rr]) / 2) * (D - U) * (R - L)
multiLocateLoss_mid += abs(mid - x_targetAxis[ll]) * (D - U) * (R - L)
# multiLocateLoss_mid += abs(mid - (pageL + pageR) / 2) * (D - U) * (R - L)
multiLocateCnt_mid += 1
multiLocateSquare_mid += (D - U) * (R - L)
isSimpleCondition = False
else:
allLocateLoss_mid += abs(mid - (pageR + pageL) / 2) * (D - U) * (R - L)
allLocateCnt_mid += 1
allLocateSquare_mid += (D - U) * (R - L)
isSimpleCondition = False
######## 根据区间的边界判断
if len(located_cols_LR) == 1:
oneLocateLoss_LR += abs(mid - x_targetAxis[located_cols_LR[0]]) * (D - U) * (R - L)
# oneLocateLoss_LR += abs(L - x_splitAxis[located_cols_LR[0]]) * (D - U) * (R - L)
oneLocateCnt_LR += 1
oneLocateSquare_LR += (D - U) * (R - L)
elif 1 <= len(located_cols_LR) < col_N:
ll, rr = located_cols_LR[0], located_cols_LR[-1]
# multiLocateLoss_LR += abs(mid - (x_targetAxis[ll] + x_targetAxis[rr]) / 2) * (D - U) * (R - L)
multiLocateLoss_LR += abs(mid - x_targetAxis[ll]) * (D - U) * (R - L)
# multiLocateLoss_LR += abs(mid - (pageL + pageR) / 2) * (D - U) * (R - L)
multiLocateCnt_LR += 1
multiLocateSquare_LR += (D - U) * (R - L)
isSimpleCondition = False
else:
allLocateLoss_LR += abs(mid - (pageR + pageL) / 2) * (D - U) * (R - L)
allLocateCnt_LR += 1
allLocateSquare_LR += (D - U) * (R - L)
isSimpleCondition = False
tot_TextCnt = oneLocateCnt_mid + multiLocateCnt_mid + allLocateCnt_mid
tot_TextSquare = oneLocateSquare_mid + multiLocateSquare_mid + allLocateSquare_mid
# 1列的情形
if tot_TextSquare != 0 and allLocateSquare_mid / tot_TextSquare >= 0.85 and col_N == 1:
return 0, True
# 多列的情形
# if col_N >= 2:
# if allLocateCnt >= 1:
# oneLocateLoss_mid += ((pageR - pageL)) * oneLocateCnt_mid
# multiLocateLoss_mid += ((pageR - pageL) ) * multiLocateCnt_mid
# else:
# if multiLocateCnt_mid >= 1:
# oneLocateLoss_mid += ((pageR - pageL)) * oneLocateCnt_mid
totLoss_mid = oneLocateLoss_mid + multiLocateLoss_mid + allLocateLoss_mid
totLoss_LR = oneLocateCnt_LR + multiLocateCnt_LR + allLocateLoss_LR
return totLoss_mid + totLoss_LR, isSimpleCondition
def get_columnNumber(page_ID: int, page: fitz.Page, textBboxs) -> (int, float):
columnNumber_loss = dict()
columnNumber_isSimpleCondition = dict()
#### 枚举列数
for columnNumber in range(1, 5):
# print('---------{}--------'.format(columnNumber))
x_targetAxis, x_splitAxis = get_targetAxis_and_splitAxis(page_ID, page, columnNumber, textBboxs)
loss, isSimpleCondition = calculate_loss(page_ID, x_targetAxis, x_splitAxis, textBboxs)
columnNumber_loss[columnNumber] = loss
columnNumber_isSimpleCondition[columnNumber] = isSimpleCondition
col_idxs = [i for i in range(1, len(columnNumber_loss) + 1)]
col_idxs.sort(key=lambda i: (columnNumber_loss[i], i))
return col_idxs, columnNumber_loss, columnNumber_isSimpleCondition
others/pdf2text_recogFootnoteLine.py deleted 100644 → 0
View file @ 4101c357
import re
from magic_pdf.libs import _is_in_or_part_overlap
from magic_pdf.libs import fitz
import collections
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)
def parse_footnoteLine(page_ID: int, page: fitz.Page, json_from_DocXchain_obj, exclude_bboxes):
"""
: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)
#---------------------- PyMuPDF解析text --------------------#
textSize_freq = collections.defaultdict(float) # text块中，textSize的频率
textBlock_bboxs = []
textLine_bboxs = []
text_blocks = page.get_text(
"dict",
flags=fitz.TEXTFLAGS_TEXT,
#clip=clip,
)["blocks"]
totText_list = []
for i in range(len(text_blocks)):
# print(blocks[i]) #### print
bbox = text_blocks[i]['bbox']
textBlock_bboxs.append(bbox)
# print(bbox)
cur_block_text_list = []
for tt in text_blocks[i]['lines']:
# 当前line
cur_line_text_list = []
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_bboxs.append((L, U, R, D))
cur_line_text_list.append(xf['text'])
textSize_freq[xf['size']] += len(xf['text'])
cur_lines_text = ' '.join(cur_line_text_list)
cur_block_text_list.append(cur_lines_text)
totText_list.append('\n'.join(cur_block_text_list))
totText = '\n'.join(totText_list)
# print(totText) # 打印Text
textLine_bboxs.sort(key = lambda LURD: (LURD[0], LURD[1]))
textBlock_bboxs.sort(key = lambda LURD: (LURD[0], LURD[1]))
# print('------------ textSize_freq -----------')
max_sizeFreq = 0 # 出现频率最高的textSize
textSize_withMaxFreq = 0
for x, f in textSize_freq.items():
# print(x, f)
if f > max_sizeFreq:
max_sizeFreq = f
textSize_withMaxFreq = x
#**********************************************************#
#------------------ PyMuPDF读取drawings -----------------#
horizon_lines = []
drawings = page.get_cdrawings()
for drawing in drawings:
try:
rect = drawing['rect']
L, U, R, D = rect
# if (L, U, R, D) in exclude_bboxes:
# continue # 如果是Fiugre, Table, Equation。注释掉是因为，可以暂时先不消，先自我对消。最后再判读需不需要排除。
# 如果是水平线
if U <= D and D - U <= 3:
# 如果长度够
if (pageR - pageL) / 15 <= R - L:
if not(80/800 * pageD <= U <= 750/800 * pageD):
continue # 很可能是页眉和页脚的线
horizon_lines.append((L, U, R, D))
# print((L, U, R, D))
except:
pass
horizon_lines.sort(key = lambda LURD: (LURD[1]))
#********************************************************#
#----------------- 两条线可能是在表格中 ------------------#
def has_text_below_line(L: float, U: float, R: float, D: float, inLowerArea: bool) -> bool:
"""
检查线下是否紧挨着text
"""
Uu, Du = U - textSize_withMaxFreq, U # 线上的一个矩形
Lu, Ru = L, R
Ud, Dd = U, U + textSize_withMaxFreq # 线下的一个矩形
Ld, Rd = L, R
find = 0 # 在线下的文字。统计面积。
leftTextCnt = 0 # 不在线底下的文字（整体在线左侧的文字），说明不是个脚注线。统计面积。
English_alpha_cnt = 0 # 英文字母个数
nonEnglish_alpha_cnt = 0 # 非英文字母个数
punctuation_mark_cnt = 0 # 常见标点符号个数
digit_cnt = 0 # 数字个数
distance_nearest_up_line = None
distance_nearest_down_line = None
for i in range(len(text_blocks)):
# print(blocks[i]) #### print
bbox = text_blocks[i]['bbox']
L0, U0, R0, D0 = bbox
if 0< (R0 - L0) < pageR / 6 and (D0 - U0) / (R0 - L0) > 10 :
continue # 一个很窄的，竖直的长条。比如，arXiv预印本，左侧的arXiv标志信息。
textBlock_bboxs.append(bbox)
# print(bbox)
cur_block_text_list = []
for tt in text_blocks[i]['lines']:
# 当前line
cur_line_text_list = []
cur_line_bbox = None # 当前line，最右侧的section的bbox
for xf in tt['spans']:
L2, U2, R2, D2 = xf['bbox']
L2, R2 = min(L2, R2), max(L2, R2)
U2, D2 = min(U2, D2), max(U2, D2)
textLine = xf['text']
if L>0 and L2 < L and (L - L2) / L > 0.2:
leftTextCnt += abs(R2 - L2) * abs(D2 - U2)
else:
## 线下的部分
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(Ud, Dd, U2, D2)
ratio_3, ratio_4 = calculate_overlapRatio_between_line1_and_line2(Ld, Rd, L2, R2)
if U < (U2 + D2) / 2 and ratio_1 > 0 and ratio_2 > 0:
if max(ratio_3, ratio_4) > 0.8:
# if 444 <= U1 < 445 and 55 <= L2 < 56:
# print('匹配的框', L2, U2, R2, D2)
# if xf['size'] > 1.2 * textSize_withMaxFreq:
# return False # 可能是个标题。不能这样卡
find += abs(R2 - L2) * abs(D2 - U2)
distance_nearest_down_line = (U2 + D2) / 2 - U
for c in textLine:
if c == ' ':
continue
elif c.isdigit() == True:
digit_cnt += 1
elif c in ',.:!?[]()%，。、！？：【】（）《》-':
punctuation_mark_cnt += 1
elif c.isalpha() == True:
English_alpha_cnt += 1
else:
nonEnglish_alpha_cnt += 1
## 线上的部分
ratio_5, ratio_6 = calculate_overlapRatio_between_line1_and_line2(Uu, Du, U2, D2)
ratio_7, ratio_8 = calculate_overlapRatio_between_line1_and_line2(Lu, Ru, L2, R2)
if (U2 + D2) / 2 < U and ratio_5 > 0 and ratio_6 > 0:
if max(ratio_7, ratio_8) > 0.8:
distance_nearest_up_line = U - (U2 + D2) / 2
# if distance_nearest_up_line < 0:
# print(Lu, Uu, Ru, Du, L2, U2, R2, D2)
# print(distance_nearest_up_line, distance_nearest_down_line)
if distance_nearest_up_line != None and distance_nearest_down_line != None:
if distance_nearest_up_line * 1.5 < distance_nearest_down_line:
return False # 如果，一根线。距离上面的文字line更近。说明是个下划线，而不是footnoteLine
## 在上面的线条，要考虑左侧的text块儿。在很靠下的线条，就暂时不考虑左侧text块儿了。
if inLowerArea == False:
if leftTextCnt >= 2000/500000 * pageR * pageD:
return False
return find >= 0 and (English_alpha_cnt + nonEnglish_alpha_cnt + digit_cnt) >= 10
## 最下面区域的线条，判断时。
# print(English_alpha_cnt, nonEnglish_alpha_cnt, digit_cnt)
if (English_alpha_cnt + nonEnglish_alpha_cnt + digit_cnt) == 0:
return False
if (English_alpha_cnt + digit_cnt) / (English_alpha_cnt + nonEnglish_alpha_cnt + digit_cnt) > 0.5:
if nonEnglish_alpha_cnt / (English_alpha_cnt + nonEnglish_alpha_cnt + digit_cnt) > 0.4:
return False
else:
return True
return True
visited = [False for _ in range(len(horizon_lines))]
for i, b1 in enumerate(horizon_lines):
for j in range(i + 1, len(horizon_lines)):
L1, U1, R1, D1 = horizon_lines[i]
L2, U2, R2, D2 = horizon_lines[j]
## 在一条水平线，且挨着
if L1 > L2:
L1, U1, R1, D1, L2, U2, R2, D2 = L2, U2, R2, D2, L1, U1, R1, D1
in_horizontal_line_flag = (max(U1, D1, U2, D2) - min(U1, D1, U2, D2) <= 5) and (L2 - R1 <= pageR/10)
if in_horizontal_line_flag == True:
visited[i] = True
visited[j] = True
## 在竖直方向上是一致的。(表格，或者有的文章就是喜欢划线）
L1, U1, R1, D1 = horizon_lines[i]
L2, U2, R2, D2 = horizon_lines[j]
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R1, L2, R2)
# print(L1, U1, R1, D1, L2, U2, R2, D2, ratio_1, ratio_2)
in_vertical_line_flag = (ratio_1 > 0.9 and ratio_2 > 0.9) or (max(ratio_1, ratio_2) > 0.95)
if in_vertical_line_flag == True:
visited[i] = True
# if (U2 < pageD * 0.8 or (U2 - U1) < pageD * 0.3) and has_text_below_line(L2, U2, R2, D2, False) == False:
# visited[j] = True # 最最底下的线先不要动
else:
if ratio_1 > 0 and (R2 - L2) / (R1 - L1) > 1:
visited[i] = True
# print(horizon_lines)
horizon_lines = [horizon_lines[i] for i in range(len(horizon_lines)) if visited[i] == False]
# print(horizon_lines)
#*****************************************************************#
#------- 靠上的，就不是脚注。用一个THRESHOLD直接卡掉位于上半页的 -------#
visited = [False for _ in range(len(horizon_lines))]
THRESHOLD = (pageD - pageU) * 0.5
for i, (L, U, R, D) in enumerate(horizon_lines):
if U < THRESHOLD:
visited[i] = True
horizon_lines = [horizon_lines[i] for i in range(len(horizon_lines)) if visited[i] == False]
#******************************************************#
#--------------- 此时，还有遮挡的，上面的丢弃 ---------------#
visited = [False for _ in range(len(horizon_lines))]
for i, (L1, U1, R1, D1) in enumerate(horizon_lines):
for j in range(i + 1, len(horizon_lines)):
L2, U2, R2, D2 = horizon_lines[j]
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R1, L2, R2)
if (ratio_1 > 0.2 and ratio_2 > 0.2) or max(ratio_1, ratio_2) > 0.7:
visited[i] = True
horizon_lines = [horizon_lines[i] for i in range(len(horizon_lines)) if visited[i] == False]
#********************************************************#
# print(horizon_lines)
## 检查，线下面有没有紧挨着的text
horizon_lines = [LURD for LURD in horizon_lines if has_text_below_line(*(LURD), True) == True]
# print(horizon_lines)
## 卡一下长度
# horizon_lines = [LURD for LURD in horizon_lines if (LURD[2] - LURD[0] >= pageR / 10)]
## 上面最多保留2条
horizon_lines = horizon_lines[max(-2, -len(horizon_lines)) :]
#----------------------------------------------------- 第2段 -----------------------------------------------------------#
#----------------------------------- 最下面的情形，用距离硬卡。还有在右侧的情形就被包含了 -----------------------------------#
#------------------ PyMuPDF读取drawings -----------------#
down_horizon_lines = []
drawings = page.get_cdrawings()
for drawing in drawings:
try:
rect = drawing['rect']
L, U, R, D = rect
# if (L, U, R, D) in exclude_bboxes:
# continue # 如果是Fiugre, Table, Equation。目前是Figure识别的比较好。但是Table和Equation识别的不好
# 如果是水平线
if U <= D and D - U <= 3 and U > pageD * 0.85:
# 如果长度够
if (pageR - pageL) / 15 <= R - L:
down_horizon_lines.append((L, U, R, D))
# print((L, U, R, D))
except:
pass
down_horizon_lines.sort(key = lambda LURD: (LURD[0], LURD[2], LURD[1]))
visited = [False for _ in range(len(down_horizon_lines))]
for i in range(len(down_horizon_lines) - 1):
L1, U1, R1, D1 = down_horizon_lines[i]
L2, U2, R2, D2 = down_horizon_lines[i + 1]
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R1, L2, R2)
if ratio_1 <= 0.1 and ratio_2 <= 0.1:
if L2 - R1 <= pageR / 3:
visited[i] = True
visited[i + 1] = True
down_horizon_lines = [down_horizon_lines[i] for i in range(len(down_horizon_lines)) if visited[i] == False]
down_horizon_lines = [LURD for LURD in down_horizon_lines if has_text_below_line(*(LURD), True) == True]
# for LURD in down_horizon_lines:
# print('第2阶段，LURD是： ', LURD)
# print(has_text_below_line(*(LURD), True))
footnoteLines = horizon_lines + down_horizon_lines
footnoteLines = list(set(footnoteLines))
footnoteLines = footnoteLines[max(-2, -len(footnoteLines)) : ]
#-------------------------- 最后再检查一遍。是否在图片、表格、公式中。 ------------------------------#
def line_in_specialBboxes(L: float, U: float, R: float, D: float, specialBboxes) -> bool:
L2, U2, R2, D2 = L, U, R, D # 当前这根线
for L1, U1, R1, D1 in specialBboxes:
if U1 <= U2 <= D2 < D1:
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R1, L2, R2)
if ratio_1 > 0 and ratio_2 > 0.6:
return True
# else:
# U1 -= min(textSize_withMaxFreq * 2, 20)
# D1 += min(textSize_withMaxFreq * 2, 20)
# if U1 <= U2 <= D2 < D1:
# ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R1, L2, R2)
# if ratio_1 > 0 and ratio_2 > 0.8:
# return True
return False
footnoteLines = [LURD for LURD in footnoteLines if line_in_specialBboxes(*(LURD), exclude_bboxes) == False]
#-------------------------- 检查，线，是否在当前column的左侧，而不是在一段文字的中间 （通过DocXChain识别的column或者徐超老师写的Layout识别）------------------------------#
# #--------- 通过json_from_DocXchain来获取 column ---------#
# column_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)
# # {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] / 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'] == 11 or xf['category_id'] == 12) and xf['score'] >= 0.3:
# column_bbox_from_DocXChain.append((L, U, R, D))
#---------------手写，检查，线是否是与某个column的左端对齐 ------------------#
def check_isOnTheLeftOfColumn(L: float, U: float, R: float, D: float) -> bool:
LL = L - textSize_withMaxFreq
RR = LL
UU = max(pageD * 0.02, U - 100/800 * pageD)
DD = min(U + 50/800 * pageD, pageD * 0.98)
# print(LL, UU, RR, DD)
cnt = 0
for bbox in textLine_bboxs:
L2, U2, R2, D2 = bbox
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(UU, DD, U2, D2)
ratio_3, ratio_4 = calculate_overlapRatio_between_line1_and_line2(L, R, L2, R2)
if ratio_1 > 0 and ratio_2 > 0:
if max(ratio_3, ratio_4) > 0.8:
if abs(LL - L2) <= 20/700 * pageR:
cnt += 1
# else:
# if (R2 - L2) >= 30/700 * pageR:
# print(LL, UU, RR, DD, L2, U2, R2, D2)
# return False # 不能这样卡。有些注释里面，单独的特殊符号就是一个textLineBbox
# print('cnt: ', cnt)
return cnt >= 4
# def check_isOnTheLeftOfColumn_considerLayout(L0: float, U0: float, R0: float, D0: float) -> bool:
# LL = L0 - textSize_withMaxFreq * 1.5
# RR = LL
# UU = 100/800 * pageD
# DD = 700/800 * pageD
# STEP = textSize_withMaxFreq / 2
# def check_ok(L: float, U: float, R: float, D: float) -> bool:
# for bbox in textBlock_bboxs:
# L2, U2, R2, D2 = bbox
# ratio_3, ratio_4 = calculate_overlapRatio_between_line1_and_line2(L, R, L2, R2)
# if max(ratio_3, ratio_4) > 0.8:
# if (R2 - L2) > 1/4 * pageR and L2 < LL <= RR < R2:
# if abs(LL - L2) < 50/700 * pageR or abs(RR - R2) < 50/700 * pageR:
# continue
# else:
# return False
# return True
# ## 先探上面
# u = UU
# d = U0
# while u + STEP/2 < d:
# mid = (u + d) / 2
# if check_ok(L0, mid, R0, U0) == True:
# d = mid
# else:
# u = mid + STEP
# print(mid)
# dist_up = U0 - u
# print(u)
# ## 再探下面
# u = D0
# d = DD
# while u + STEP/2 < d:
# mid = (u + d) / 2
# if check_ok(L0, mid, R0, D0) == True:
# u = mid
# else:
# d = mid - STEP
# print(u)
# print('^^^^^^^^^^^^^^')
# dist_down = u - D0
# if dist_up + dist_down < textSize_withMaxFreq * 10:
# return False
# return True
footnoteLines = [LURD for LURD in footnoteLines if check_isOnTheLeftOfColumn(*(LURD)) == True]
# footnoteLines = [LURD for LURD in footnoteLines if check_isOnTheLeftOfColumn_considerLayout(*(LURD)) == True] # 不具有泛化性。不用了。
#--------------------------------- 通过footnoteLine获取bbox -------------------------------#
def get_footnoteBbox(L: float, U: float, R: float, D: float) -> (float, float, float, float):
"""
检查线下是否紧挨着text
"""
L1, U1, R1, D1 = L, U, R, D
raw_bboxes = []
for i in range(len(text_blocks)):
bbox = text_blocks[i]['bbox']
L2, U2, R2, D2 = bbox
if (D2 - U2) / (R2 - L2) > 10 and (R2 - L2) < pageR / 6:
continue # 一个很窄的，竖直的长条。比如，arXiv预印本，左侧的arXiv标志信息。
if U2 < D2 < U1:
continue # 在线上面
under_THRESHOLD = min(D1 + textSize_withMaxFreq * 20, pageD * 0.98)
if U2 < under_THRESHOLD:
ratio_1, ratio_2 = calculate_overlapRatio_between_line1_and_line2(L1, R1, L2, R2)
if max(ratio_1, ratio_2) > 0.8:
raw_bboxes.append((L2, U2, R2, D2))
# print(L1, U1, R1, D1)
# print(raw_bboxes)
if len(raw_bboxes) == 0:
return []
raw_bboxes.sort(key = lambda LURD: (LURD[1], LURD[0]))
raw_bboxes = [LURD for LURD in raw_bboxes if (abs(LURD[0] - L1) < textSize_withMaxFreq * 6 or L1 < LURD[0])] # footnote的bbox，应该都是左端对齐的
if len(raw_bboxes) == 0:
return []
#------------------ full column和sub column混合，肯定也不行 ------------------#
LL, UU, RR, DD = raw_bboxes[0]
for L, U, R, D in raw_bboxes:
LL, UU, RR, DD = min(LL, L), min(UU, U), max(RR, R), max(DD, D)
for L, U, R, D in raw_bboxes:
if (RR - LL) > pageR*0.8 and (R - L) > pageR * 0.15 and (RR - LL) / (R - L) > 2:
return []
if abs(LL - L) > textSize_withMaxFreq * 3:
return []
#-------------------- 太高了的，full column的框。不行 ----------------------#
if UU < 650/800 * pageD and (RR - LL) > 0.5 * pageR:
return []
#-------------- 第一段字数很少。后面的段字数很多，也不行 ----------------#
if len(raw_bboxes) > 1:
bbox_square = []
for L, U, R, D in raw_bboxes:
cur_s = abs(R - L) * abs(D - U)
bbox_square.append(cur_s)
s0 = bbox_square[0]
s1n = sum(bbox_square[1: ]) / len(bbox_square[1: ])
if s1n / s0 > 10 or max(bbox_square) / s0 > 15:
return []
raw_bboxes += [(LL, UU, RR, DD)]
return raw_bboxes
# print(footnoteLines)
footnoteBboxes = []
for L, U, R, D in footnoteLines:
cur = get_footnoteBbox(L, U, R, D)
if len(cur) > 0:
footnoteBboxes.append((L, U, R, D))
footnoteBboxes += cur
footnoteBboxes = list(set(footnoteBboxes))
return footnoteBboxes
def __bbox_in(box1, box2):
"""
box1是否在box2中
"""
L1, U1, R1, D1 = box1
L2, U2, R2, D2 = box2
if int(L2) <= int(L1) and int(U2) <= int(U1) and int(R1) <= int(R2) and int(D1) <= int(D2):
return True
return False
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([__bbox_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
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'] = 'in-foot-header-area'
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
others/pdf2text_recogPara_v2.py deleted 100644 → 0
View file @ 4101c357
import os
import sys
import json
import re
import math
import unicodedata
from collections import Counter
import numpy as np
from termcolor import cprint
from magic_pdf.libs import fitz
from magic_pdf.libs import NLPModels
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
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
class LayoutFilterProcessor:
def __init__(self) -> None:
pass
def batch_process_blocks(self, pdf_dict):
"""
This function processes the blocks in batch.
Parameters
----------
self : object
The instance of the class.
pdf_dict : dict
pdf dictionary
Returns
-------
pdf_dict : dict
pdf dictionary
"""
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]
# Enlarge each value of x0, y0, x1, y1 for each layout_bbox to prevent loss of text.
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
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: # Handle the first line
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: # Handle the rest lines
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
"""
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.
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
class BlockStatisticsCalculator:
"""
This class calculates the statistics of the block.
"""
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".
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:
"""
This class calculates the statistics of the document.
"""
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
class TitleProcessor:
"""
This class processes the title.
"""
def __init__(self, *doc_statistics) -> None:
if len(doc_statistics) > 0:
self.doc_statistics = doc_statistics[0]
self.nlp_model = NLPModels()
self.MAX_TITLE_LEVEL = 3
self.numbered_title_pattern = r"""
^ # 行首
( # 开始捕获组
[\(\（]\d+[\)\）] # 括号内数字，支持中文和英文括号，例如：(1) 或 （1）
|\d+[\)\）]\s # 数字后跟右括号和空格，支持中文和英文括号，例如：2) 或 2）
|[\(\（][A-Z][\)\）] # 括号内大写字母，支持中文和英文括号，例如：(A) 或 （A）
|[A-Z][\)\）]\s # 大写字母后跟右括号和空格，例如：A) 或 A）
|[\(\（][IVXLCDM]+[\)\）] # 括号内罗马数字，支持中文和英文括号，例如：(I) 或 （I）
|[IVXLCDM]+[\)\）]\s # 罗马数字后跟右括号和空格，例如：I) 或 I）
|\d+(\.\d+)*\s # 数字或复合数字编号后跟空格，例如：1. 或 3.2.1
|[一二三四五六七八九十百千]+[、\s] # 中文序号后跟顿号和空格，例如：一、
|[\（|\(][一二三四五六七八九十百千]+[\）|\)]\s* # 中文括号内中文序号后跟空格，例如：（一）
|[A-Z]\.\d+(\.\d+)?\s # 大写字母后跟点和数字，例如：A.1 或 A.1.1
|[\(\（][a-z][\)\）] # 括号内小写字母，支持中文和英文括号，例如：(a) 或 （a）
|[a-z]\)\s # 小写字母后跟右括号和空格，例如：a)
|[A-Z]-\s # 大写字母后跟短横线和空格，例如：A-
|\w+:\s # 英文序号词后跟冒号和空格，例如：First:
|第[一二三四五六七八九十百千]+[章节部分条款]\s # 以“第”开头的中文标题后跟空格
|[IVXLCDM]+\. # 罗马数字后跟点，例如：I.
|\d+\.\s # 单个数字后跟点和空格，例如：1.
) # 结束捕获组
.+ # 标题的其余部分
"""
self.stage = (
0 # Used for distinguishing the stage of title detection, the number is occurred in paragraph process pipeline
)
def _is_potential_title(
self,
curr_line,
prev_line,
prev_line_is_title,
next_line,
avg_char_width,
avg_char_height,
median_font_size,
):
"""
This function checks if the line is a potential title.
Parameters
----------
curr_line : dict
current line
prev_line : dict
previous line
next_line : dict
next line
avg_char_width : float
average of char widths
avg_char_height : float
average of line heights
Returns
-------
bool
True if the line is a potential title, False otherwise.
"""
def __is_line_centered(line_bbox, page_bbox, avg_char_width):
"""
This function checks if the line is centered on the page
Parameters
----------
line_bbox : list
bbox of the line
page_bbox : list
bbox of the page
avg_char_width : float
average of char widths
Returns
-------
bool
True if the line is centered on the page, False otherwise.
"""
horizontal_ratio = 0.5
horizontal_thres = horizontal_ratio * avg_char_width
x0, _, x1, _ = line_bbox
_, _, page_x1, _ = page_bbox
return abs((x0 + x1) / 2 - page_x1 / 2) < horizontal_thres
def __is_bold_font_line(line):
"""
Check if a line contains any bold font style.
"""
def _is_bold_span(span):
# if span text is empty or only contains space, return False
if not span["text"].strip():
return False
return bool(span["flags"] & 2**4) # Check if the font is bold
for span in line["spans"]:
if not _is_bold_span(span):
return False
return True
def __is_italic_font_line(line):
"""
Check if a line contains any italic font style.
"""
def __is_italic_span(span):
return bool(span["flags"] & 2**1) # Check if the font is italic
for span in line["spans"]:
if not __is_italic_span(span):
return False
return True
def __is_punctuation_heavy(line_text):
"""
Check if the line contains a high ratio of punctuation marks, which may indicate
that the line is not a title.
Parameters:
line_text (str): Text of the line.
Returns:
bool: True if the line is heavy with punctuation, False otherwise.
"""
# Pattern for common title format like "X.Y. Title"
pattern = r"\b\d+\.\d+\..*\b"
# If the line matches the title format, return False
if re.match(pattern, line_text.strip()):
return False
# Find all punctuation marks in the line
punctuation_marks = re.findall(r"[^\w\s]", line_text)
number_of_punctuation_marks = len(punctuation_marks)
text_length = len(line_text)
if text_length == 0:
return False
punctuation_ratio = number_of_punctuation_marks / text_length
if punctuation_ratio >= 0.1:
return True
return False
def __has_mixed_font_styles(spans, strict_mode=False):
"""
This function checks if the line has mixed font styles, the strict mode will compare the font types
Parameters
----------
spans : list
spans of the line
strict_mode : bool
True for strict mode, the font types will be fully compared
False for non-strict mode, the font types will be compared by the most longest common prefix
Returns
-------
bool
True if the line has mixed font styles, False otherwise.
"""
if strict_mode:
font_styles = set()
for span in spans:
font_style = span["font"].lower()
font_styles.add(font_style)
return len(font_styles) > 1
else: # non-strict mode
font_styles = []
for span in spans:
font_style = span["font"].lower()
font_styles.append(font_style)
if len(font_styles) > 1:
longest_common_prefix = os.path.commonprefix(font_styles)
if len(longest_common_prefix) > 0:
return False
else:
return True
else:
return False
def __is_different_font_type_from_neighbors(curr_line_font_type, prev_line_font_type, next_line_font_type):
"""
This function checks if the current line has a different font type from the previous and next lines
Parameters
----------
curr_line_font_type : str
font type of the current line
prev_line_font_type : str
font type of the previous line
next_line_font_type : str
font type of the next line
Returns
-------
bool
True if the current line has a different font type from the previous and next lines, False otherwise.
"""
return all(
curr_line_font_type != other_font_type.lower()
for other_font_type in [prev_line_font_type, next_line_font_type]
if other_font_type is not None
)
def __is_larger_font_size_from_neighbors(curr_line_font_size, prev_line_font_size, next_line_font_size):
"""
This function checks if the current line has a larger font size than the previous and next lines
Parameters
----------
curr_line_font_size : float
font size of the current line
prev_line_font_size : float
font size of the previous line
next_line_font_size : float
font size of the next line
Returns
-------
bool
True if the current line has a larger font size than the previous and next lines, False otherwise.
"""
return all(
curr_line_font_size > other_font_size * 1.2
for other_font_size in [prev_line_font_size, next_line_font_size]
if other_font_size is not None
)
def __is_similar_to_pre_line(curr_line_font_type, prev_line_font_type, curr_line_font_size, prev_line_font_size):
"""
This function checks if the current line is similar to the previous line
Parameters
----------
curr_line : dict
current line
prev_line : dict
previous line
Returns
-------
bool
True if the current line is similar to the previous line, False otherwise.
"""
if curr_line_font_type == prev_line_font_type and curr_line_font_size == prev_line_font_size:
return True
else:
return False
def __is_same_font_type_of_docAvg(curr_line_font_type):
"""
This function checks if the current line has the same font type as the document average font type
Parameters
----------
curr_line_font_type : str
font type of the current line
Returns
-------
bool
True if the current line has the same font type as the document average font type, False otherwise.
"""
doc_most_common_font_type = safe_get(self.doc_statistics, "most_common_font_type", "").lower()
doc_second_most_common_font_type = safe_get(self.doc_statistics, "second_most_common_font_type", "").lower()
return curr_line_font_type.lower() in [doc_most_common_font_type, doc_second_most_common_font_type]
def __is_font_size_not_less_than_docAvg(curr_line_font_size, ratio: float = 1):
"""
This function checks if the current line has a large enough font size
Parameters
----------
curr_line_font_size : float
font size of the current line
ratio : float
ratio of the current line font size to the document average font size
Returns
-------
bool
True if the current line has a large enough font size, False otherwise.
"""
doc_most_common_font_size = safe_get(self.doc_statistics, "most_common_font_size", 0)
doc_second_most_common_font_size = safe_get(self.doc_statistics, "second_most_common_font_size", 0)
doc_avg_font_size = min(doc_most_common_font_size, doc_second_most_common_font_size)
return curr_line_font_size >= doc_avg_font_size * ratio
def __is_sufficient_spacing_above_and_below(
curr_line_bbox,
prev_line_bbox,
next_line_bbox,
avg_char_height,
median_font_size,
):
"""
This function checks if the current line has sufficient spacing above and below
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
avg_char_height : float
average of line heights
Returns
-------
bool
True if the current line has sufficient spacing above and below, False otherwise.
"""
vertical_ratio = 1.25
vertical_thres = vertical_ratio * median_font_size
_, y0, _, y1 = curr_line_bbox
sufficient_spacing_above = False
if prev_line_bbox:
vertical_spacing_above = min(y0 - prev_line_bbox[1], y1 - prev_line_bbox[3])
sufficient_spacing_above = vertical_spacing_above > vertical_thres
else:
sufficient_spacing_above = True
sufficient_spacing_below = False
if next_line_bbox:
vertical_spacing_below = min(next_line_bbox[1] - y0, next_line_bbox[3] - y1)
sufficient_spacing_below = vertical_spacing_below > vertical_thres
else:
sufficient_spacing_below = True
return (sufficient_spacing_above, sufficient_spacing_below)
def __is_word_list_line_by_rules(curr_line_text):
"""
This function checks if the current line is a word list
Parameters
----------
curr_line_text : str
text of the current line
Returns
-------
bool
True if the current line is a name list, False otherwise.
"""
# name_list_pattern = r"([a-zA-Z][a-zA-Z\s]{0,20}[a-zA-Z]|[\u4e00-\u9fa5·]{2,16})(?=[，,;；\s]|$)"
name_list_pattern = r"(?<![\u4e00-\u9fa5])([A-Z][a-z]{0,19}\s[A-Z][a-z]{0,19}|[\u4e00-\u9fa5]{2,6})(?=[，,;；\s]|$)"
compiled_pattern = re.compile(name_list_pattern)
if compiled_pattern.search(curr_line_text):
return True
else:
return False
def __get_text_catgr_by_nlp(curr_line_text):
"""
This function checks if the current line is a name list using nlp model, such as spacy
Parameters
----------
curr_line_text : str
text of the current line
Returns
-------
bool
True if the current line is a name list, False otherwise.
"""
result = self.nlp_model.detect_entity_catgr_using_nlp(curr_line_text)
return result
def __is_numbered_title(curr_line_text):
"""
This function checks if the current line is a numbered list
Parameters
----------
curr_line_text : str
text of the current line
Returns
-------
bool
True if the current line is a numbered list, False otherwise.
"""
compiled_pattern = re.compile(self.numbered_title_pattern, re.VERBOSE)
if compiled_pattern.search(curr_line_text):
return True
else:
return False
def __is_end_with_ending_puncs(line_text):
"""
This function checks if the current line ends with a ending punctuation mark
Parameters
----------
line_text : str
text of the current line
Returns
-------
bool
True if the current line ends with a punctuation mark, False otherwise.
"""
end_puncs = [".", "?", "!", "。", "？", "！", "…"]
line_text = line_text.rstrip()
if line_text[-1] in end_puncs:
return True
return False
def __contains_only_no_meaning_symbols(line_text):
"""
This function checks if the current line contains only symbols that have no meaning, if so, it is not a title.
Situation contains:
1. Only have punctuation marks
2. Only have other non-meaning symbols
Parameters
----------
line_text : str
text of the current line
Returns
-------
bool
True if the current line contains only symbols that have no meaning, False otherwise.
"""
punctuation_marks = re.findall(r"[^\w\s]", line_text) # find all punctuation marks
number_of_punctuation_marks = len(punctuation_marks)
text_length = len(line_text)
if text_length == 0:
return False
punctuation_ratio = number_of_punctuation_marks / text_length
if punctuation_ratio >= 0.9:
return True
return False
def __is_equation(line_text):
"""
This function checks if the current line is an equation.
Parameters
----------
line_text : str
Returns
-------
bool
True if the current line is an equation, False otherwise.
"""
equation_reg = r"\$.*?\\overline.*?\$" # to match interline equations
if re.search(equation_reg, line_text):
return True
else:
return False
def __is_title_by_len(text, max_length=200):
"""
This function checks if the current line is a title by length.
Parameters
----------
text : str
text of the current line
max_length : int
max length of the title
Returns
-------
bool
True if the current line is a title, False otherwise.
"""
text = text.strip()
return len(text) <= max_length
def __compute_line_font_type_and_size(curr_line):
"""
This function computes the font type and font size of the line.
Parameters
----------
line : dict
line
Returns
-------
font_type : str
font type of the line
font_size : float
font size of the line
"""
spans = curr_line["spans"]
max_accumulated_length = 0
max_span_font_size = curr_line["spans"][0]["size"] # default value, float type
max_span_font_type = curr_line["spans"][0]["font"].lower() # default value, string type
for span in spans:
if span["text"].isspace():
continue
span_length = span["bbox"][2] - span["bbox"][0]
if span_length > max_accumulated_length:
max_accumulated_length = span_length
max_span_font_size = span["size"]
max_span_font_type = span["font"].lower()
return max_span_font_type, max_span_font_size
def __is_a_consistent_sub_title(pre_line, curr_line):
"""
This function checks if the current line is a consistent sub title.
Parameters
----------
pre_line : dict
previous line
curr_line : dict
current line
Returns
-------
bool
True if the current line is a consistent sub title, False otherwise.
"""
if pre_line is None:
return False
start_letter_of_pre_line = pre_line["text"][0]
start_letter_of_curr_line = curr_line["text"][0]
has_same_prefix_digit = (
start_letter_of_pre_line.isdigit()
and start_letter_of_curr_line.isdigit()
and start_letter_of_pre_line == start_letter_of_curr_line
)
# prefix text of curr_line satisfies the following title format: x.x
prefix_text_pattern = r"^\d+\.\d+"
subtitle_format_match = re.match(prefix_text_pattern, curr_line["text"])
if subtitle_format_match:
has_subtitle_format = True
else:
has_subtitle_format = False
if has_same_prefix_digit or has_subtitle_format:
print("is a consistent sub title")
return True
"""
Title detecting main Process.
"""
"""
Basic features about the current line.
"""
curr_line_bbox = curr_line["bbox"]
curr_line_text = curr_line["text"]
curr_line_font_type, curr_line_font_size = __compute_line_font_type_and_size(curr_line)
if len(curr_line_text.strip()) == 0: # skip empty lines
return False, False
prev_line_bbox = prev_line["bbox"] if prev_line else None
if prev_line:
prev_line_font_type, prev_line_font_size = __compute_line_font_type_and_size(prev_line)
else:
prev_line_font_type, prev_line_font_size = None, None
next_line_bbox = next_line["bbox"] if next_line else None
if next_line:
next_line_font_type, next_line_font_size = __compute_line_font_type_and_size(next_line)
else:
next_line_font_type, next_line_font_size = None, None
"""
Aggregated features about the current line.
"""
is_italc_font = __is_italic_font_line(curr_line)
is_bold_font = __is_bold_font_line(curr_line)
is_font_size_little_less_than_doc_avg = __is_font_size_not_less_than_docAvg(curr_line_font_size, ratio=0.8)
is_font_size_not_less_than_doc_avg = __is_font_size_not_less_than_docAvg(curr_line_font_size, ratio=1)
is_much_larger_font_than_doc_avg = __is_font_size_not_less_than_docAvg(curr_line_font_size, ratio=1.6)
is_not_same_font_type_of_docAvg = not __is_same_font_type_of_docAvg(curr_line_font_type)
is_potential_title_font = is_bold_font or is_font_size_not_less_than_doc_avg or is_not_same_font_type_of_docAvg
is_mix_font_styles_strict = __has_mixed_font_styles(curr_line["spans"], strict_mode=True)
is_mix_font_styles_loose = __has_mixed_font_styles(curr_line["spans"], strict_mode=False)
is_punctuation_heavy = __is_punctuation_heavy(curr_line_text)
is_word_list_line_by_rules = __is_word_list_line_by_rules(curr_line_text)
is_person_or_org_list_line_by_nlp = __get_text_catgr_by_nlp(curr_line_text) in ["PERSON", "GPE", "ORG"]
is_font_size_larger_than_neighbors = __is_larger_font_size_from_neighbors(
curr_line_font_size, prev_line_font_size, next_line_font_size
)
is_font_type_diff_from_neighbors = __is_different_font_type_from_neighbors(
curr_line_font_type, prev_line_font_type, next_line_font_type
)
has_sufficient_spaces_above, has_sufficient_spaces_below = __is_sufficient_spacing_above_and_below(
curr_line_bbox, prev_line_bbox, next_line_bbox, avg_char_height, median_font_size
)
is_similar_to_pre_line = __is_similar_to_pre_line(
curr_line_font_type, prev_line_font_type, curr_line_font_size, prev_line_font_size
)
is_consistent_sub_title = __is_a_consistent_sub_title(prev_line, curr_line)
"""
Further aggregated features about the current line.
Attention:
Features that start with __ are for internal use.
"""
__is_line_left_aligned_from_neighbors = is_line_left_aligned_from_neighbors(
curr_line_bbox, prev_line_bbox, next_line_bbox, avg_char_width
)
__is_font_diff_from_neighbors = is_font_size_larger_than_neighbors or is_font_type_diff_from_neighbors
is_a_left_inline_title = (
is_mix_font_styles_strict and __is_line_left_aligned_from_neighbors and __is_font_diff_from_neighbors
)
is_title_by_check_prev_line = prev_line is None and has_sufficient_spaces_above and is_potential_title_font
is_title_by_check_next_line = next_line is None and has_sufficient_spaces_below and is_potential_title_font
is_title_by_check_pre_and_next_line = (
(prev_line is not None or next_line is not None)
and has_sufficient_spaces_above
and has_sufficient_spaces_below
and is_potential_title_font
)
is_numbered_title = __is_numbered_title(curr_line_text) and (
(has_sufficient_spaces_above or prev_line is None) and (has_sufficient_spaces_below or next_line is None)
)
is_not_end_with_ending_puncs = not __is_end_with_ending_puncs(curr_line_text)
is_not_only_no_meaning_symbols = not __contains_only_no_meaning_symbols(curr_line_text)
is_equation = __is_equation(curr_line_text)
is_title_by_len = __is_title_by_len(curr_line_text)
"""
Decide if the line is a title.
"""
is_title = (
is_not_end_with_ending_puncs # not end with ending punctuation marks
and is_not_only_no_meaning_symbols # not only have no meaning symbols
and is_title_by_len # is a title by length, default max length is 200
and not is_equation # an interline equation should never be a title
and is_potential_title_font # is a potential title font, which is bold or larger than the document average font size or not the same font type as the document average font type
and (
(is_not_same_font_type_of_docAvg and is_font_size_not_less_than_doc_avg)
or (is_bold_font and is_much_larger_font_than_doc_avg and is_not_same_font_type_of_docAvg)
or (
is_much_larger_font_than_doc_avg
and (is_title_by_check_prev_line or is_title_by_check_next_line or is_title_by_check_pre_and_next_line)
)
or (
is_font_size_little_less_than_doc_avg
and is_bold_font
and (is_title_by_check_prev_line or is_title_by_check_next_line or is_title_by_check_pre_and_next_line)
)
) # not the same font type as the document average font type, which includes the most common font type and the second most common font type
and (
(
not is_person_or_org_list_line_by_nlp
and (
is_much_larger_font_than_doc_avg
or (is_not_same_font_type_of_docAvg and is_font_size_not_less_than_doc_avg)
)
)
or (
not (is_word_list_line_by_rules and is_person_or_org_list_line_by_nlp)
and not is_a_left_inline_title
and not is_punctuation_heavy
and (is_title_by_check_prev_line or is_title_by_check_next_line or is_title_by_check_pre_and_next_line)
)
or (
is_person_or_org_list_line_by_nlp
and (is_bold_font and is_much_larger_font_than_doc_avg and is_not_same_font_type_of_docAvg)
and (is_bold_font and is_much_larger_font_than_doc_avg and is_not_same_font_type_of_docAvg)
)
or (is_numbered_title and not is_a_left_inline_title)
)
# )
) or (prev_line_is_title and is_consistent_sub_title)
is_name_or_org_list_to_be_removed = (
(is_person_or_org_list_line_by_nlp)
and is_punctuation_heavy
and (is_title_by_check_prev_line or is_title_by_check_next_line or is_title_by_check_pre_and_next_line)
) and not is_title
if is_name_or_org_list_to_be_removed:
is_author_or_org_list = True
else:
is_author_or_org_list = False
# return is_title, is_author_or_org_list
# """
"""
# print reason why the line is a title
if is_title:
print_green("This line is a title.")
print_green("↓" * 10)
print()
print("curr_line_text: ", curr_line_text)
print()
print(f"prev_line_is_title: {prev_line_is_title}")
print()
print(f"is_consistent_sub_title: {is_consistent_sub_title}")
"""
# print reason why the line is not a title
# line_text = curr_line_text.strip()
# test_text = "Career/Personal Life"
# text_content_condition = line_text == test_text
# if not is_title and text_content_condition: # Print specific line
"""
if not is_title: # Print each line
print_red("This line is not a title.")
print_red("↓" * 10)
print()
print("curr_line_text: ", curr_line_text)
print()
if is_not_end_with_ending_puncs:
print_green(f"is_not_end_with_ending_puncs")
else:
print_red(f"is_end_with_ending_puncs")
if is_not_only_no_meaning_symbols:
print_green(f"is_not_only_no_meaning_symbols")
else:
print_red(f"is_only_no_meaning_symbols")
if is_title_by_len:
print_green(f"is_title_by_len: {is_title_by_len}")
else:
print_red(f"is_not_title_by_len: {is_title_by_len}")
if is_equation:
print_red(f"is_equation")
else:
print_green(f"is_not_equation")
if is_potential_title_font:
print_green(f"is_potential_title_font")
else:
print_red(f"is_not_potential_title_font")
if is_punctuation_heavy:
print_red("is_punctuation_heavy")
else:
print_green("is_not_punctuation_heavy")
if is_bold_font:
print_green(f"is_bold_font")
else:
print_red(f"is_not_bold_font")
if is_font_size_not_less_than_doc_avg:
print_green(f"is_larger_font_than_doc_avg")
else:
print_red(f"is_not_larger_font_than_doc_avg")
if is_much_larger_font_than_doc_avg:
print_green(f"is_much_larger_font_than_doc_avg")
else:
print_red(f"is_not_much_larger_font_than_doc_avg")
if is_not_same_font_type_of_docAvg:
print_green(f"is_not_same_font_type_of_docAvg")
else:
print_red(f"is_same_font_type_of_docAvg")
if is_word_list_line_by_rules:
print_red("is_word_list_line_by_rules")
else:
print_green("is_not_name_list_by_rules")
if is_person_or_org_list_line_by_nlp:
print_red("is_person_or_org_list_line_by_nlp")
else:
print_green("is_not_person_or_org_list_line_by_nlp")
if not is_numbered_title:
print_red("is_not_numbered_title")
else:
print_green("is_numbered_title")
if is_a_left_inline_title:
print_red("is_a_left_inline_title")
else:
print_green("is_not_a_left_inline_title")
if not is_title_by_check_prev_line:
print_red("is_not_title_by_check_prev_line")
else:
print_green("is_title_by_check_prev_line")
if not is_title_by_check_next_line:
print_red("is_not_title_by_check_next_line")
else:
print_green("is_title_by_check_next_line")
if not is_title_by_check_pre_and_next_line:
print_red("is_not_title_by_check_pre_and_next_line")
else:
print_green("is_title_by_check_pre_and_next_line")
# print_green("Common features:")
# print_green("↓" * 10)
# print(f" curr_line_font_type: {curr_line_font_type}")
# print(f" curr_line_font_size: {curr_line_font_size}")
# print()
"""
# """
return is_title, is_author_or_org_list
def _detect_title(self, curr_block, pre_block):
"""
Use the functions 'is_potential_title' to detect titles of each paragraph block.
If a line is a title, then the value of key 'is_title' of the line will be set to True.
"""
raw_lines = curr_block["lines"]
blk_avg_char_width = curr_block["avg_char_width"]
blk_avg_char_height = curr_block["avg_char_height"]
blk_media_font_size = curr_block["median_font_size"]
if self.stage == 0:
is_prev_line_a_title = False
for i, curr_line in enumerate(raw_lines):
prev_line = raw_lines[i - 1] if i > 0 else None
next_line = raw_lines[i + 1] if i < len(raw_lines) - 1 else None
is_line_a_title, is_line_an_entities_list = self._is_potential_title(
curr_line,
prev_line,
is_prev_line_a_title,
next_line,
blk_avg_char_width,
blk_avg_char_height,
blk_media_font_size,
)
if is_line_a_title:
curr_line["is_title"] = is_line_a_title
is_prev_line_a_title = True # set the flag to True for the next line
else:
curr_line["is_title"] = False
is_prev_line_a_title = False # set the flag to False for the next line
if is_line_an_entities_list:
curr_line["is_author_or_org_list"] = is_line_an_entities_list
else:
curr_line["is_author_or_org_list"] = False
return curr_block
if self.stage == 1: # Check the block consistent titles.
if pre_block and "paras" in pre_block.keys():
print_red(f"Checking cross block title...")
last_para_content = None
paras_of_pre_block = pre_block["paras"]
last_key = sorted(paras_of_pre_block.keys())[-1]
last_para_content = paras_of_pre_block[last_key]
if last_para_content is not None:
last_line_of_last_para_of_last_block = pre_block["lines"][-1]
first_line_of_curr_block = raw_lines[0]
next_line_of_curr_block = raw_lines[1] if len(raw_lines) > 1 else None
is_line_a_title, is_line_an_entities_list = self._is_potential_title(
first_line_of_curr_block,
last_line_of_last_para_of_last_block,
last_line_of_last_para_of_last_block["is_title"],
next_line_of_curr_block,
blk_avg_char_width,
blk_avg_char_height,
blk_media_font_size,
)
if is_line_a_title:
first_line_of_curr_block["is_title"] = is_line_a_title
else:
first_line_of_curr_block["is_title"] = False
if is_line_an_entities_list:
first_line_of_curr_block["is_author_or_org_list"] = is_line_an_entities_list
else:
first_line_of_curr_block["is_author_or_org_list"] = False
# print(f"first_line_of_curr_block: {first_line_of_curr_block['text']}")
# print(f"last_line_of_pre_block: {last_line_of_last_para['text']}")
return curr_block
else:
print_red(f"last_para_content is None")
def batch_detect_titles(self, pdf_dic):
"""
This function batch process the blocks to detect titles.
Parameters
----------
pdf_dict : dict
result dictionary
Returns
-------
pdf_dict : dict
result dictionary
"""
num_titles = 0
for page_id, page_content in pdf_dic.items():
if page_id.startswith("page_"):
para_blocks = []
if "para_blocks" in page_content.keys():
para_blocks = page_content["para_blocks"]
all_single_line_blocks = []
for block in para_blocks:
if len(block["lines"]) == 1:
all_single_line_blocks.append(block)
new_para_blocks = []
if not len(all_single_line_blocks) == len(para_blocks): # Not all blocks are single line blocks.
for para_idx, para_block in enumerate(para_blocks):
print(f"______________________________________________________")
print(f"page_id: {page_id}")
print(f"para_block id: {para_block['block_id']}")
print(f"para_idx: {para_idx}")
pre_block = para_blocks[para_idx - 1] if para_idx > 0 else None
curr_block = para_block
print_yellow(f"text of current block: {curr_block['text'] if curr_block else None}")
print_green(f"text of previous block: {pre_block['text'] if pre_block else None}")
new_block = self._detect_title(curr_block, pre_block)
new_para_blocks.append(new_block)
# num_titles += sum([line.get("is_title", 0) for line in new_block["lines"]])
if new_block is not None:
num_titles += sum([line.get("is_title", 0) for line in new_block["lines"]])
else:
num_titles += 0
else: # All blocks are single line blocks.
for para_block in para_blocks:
new_para_blocks.append(para_block)
num_titles += sum([line.get("is_title", 0) for line in para_block["lines"]])
para_blocks = new_para_blocks
page_content["para_blocks"] = para_blocks
for para_block in para_blocks:
if para_block is not None:
all_titles = all(safe_get(line, "is_title", False) for line in para_block["lines"])
para_text_len = sum([len(line["text"]) for line in para_block["lines"]])
if (
all_titles and para_text_len < 200
): # total length of the paragraph is less than 200, more than this should not be a title
para_block["is_block_title"] = 1
else:
para_block["is_block_title"] = 0
all_name_or_org_list_to_be_removed = all(
safe_get(line, "is_author_or_org_list", False) for line in para_block["lines"]
)
if all_name_or_org_list_to_be_removed and page_id == "page_0":
para_block["is_block_an_author_or_org_list"] = 1
else:
para_block["is_block_an_author_or_org_list"] = 0
else:
all_titles = False
# para_block["is_block_title"] = 0
# para_block["is_block_an_author_or_org_list"] = 0
# page_content["para_blocks"] = para_blocks
pdf_dic["statistics"]["num_titles"] = num_titles
return pdf_dic
def _recog_title_level(self, title_blocks):
"""
This function determines the title level based on the font size of the title.
Parameters
----------
title_blocks : list
Returns
-------
title_blocks : list
"""
font_sizes = np.array([safe_get(tb["block"], "block_font_size", 0) for tb in title_blocks])
# Use the mean and std of font sizes to remove extreme values
mean_font_size = np.mean(font_sizes)
std_font_size = np.std(font_sizes)
min_extreme_font_size = mean_font_size - std_font_size # type: ignore
max_extreme_font_size = mean_font_size + std_font_size # type: ignore
# Compute the threshold for title level
middle_font_sizes = font_sizes[(font_sizes > min_extreme_font_size) & (font_sizes < max_extreme_font_size)]
if middle_font_sizes.size > 0:
middle_mean_font_size = np.mean(middle_font_sizes)
level_threshold = middle_mean_font_size
else:
level_threshold = mean_font_size
for tb in title_blocks:
title_block = tb["block"]
title_font_size = safe_get(title_block, "block_font_size", 0)
current_level = 1 # Initialize title level, the biggest level is 1
# print(f"Before adjustment by font size, {current_level}")
if title_font_size >= max_extreme_font_size:
current_level = 1
elif title_font_size <= min_extreme_font_size:
current_level = 3
elif float(title_font_size) >= float(level_threshold):
current_level = 2
else:
current_level = 3
# print(f"After adjustment by font size, {current_level}")
title_block["block_title_level"] = current_level
return title_blocks
def batch_recog_title_level(self, pdf_dic):
"""
This function batch process the blocks to recognize title level.
Parameters
----------
pdf_dict : dict
result dictionary
Returns
-------
pdf_dict : dict
result dictionary
"""
title_blocks = []
# Collect all titles
for page_id, blocks in pdf_dic.items():
if page_id.startswith("page_"):
para_blocks = blocks.get("para_blocks", [])
for block in para_blocks:
if block.get("is_block_title"):
title_obj = {"page_id": page_id, "block": block}
title_blocks.append(title_obj)
# Determine title level
if title_blocks:
# Determine title level based on font size
title_blocks = self._recog_title_level(title_blocks)
return pdf_dic
class BlockTerminationProcessor:
"""
This class is used to process the block termination.
"""
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_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
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_type_1, font_type_2, 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_type_1, list):
font_type_1 = font_type_1[0] if font_type_1 else ""
if isinstance(font_type_2, list):
font_type_2 = font_type_2[0] if font_type_2 else ""
if font_type_1 == font_type_2:
return True
# Find the length of the common prefix
common_prefix_length = len(os.path.commonprefix([font_type_1, font_type_2]))
# Calculate the minimum prefix length based on the ratio
min_prefix_length = int(min(len(font_type_1), len(font_type_2)) * prefix_length_ratio)
return common_prefix_length >= min_prefix_length
def __is_same_block_font(self, block_1, block_2):
"""
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(block_1, "block_font_type", "")
block_1_font_size = safe_get(block_1, "block_font_size", 0)
block_1_avg_char_width = safe_get(block_1, "avg_char_width", 0)
block_2_font_type = safe_get(block_2, "block_font_type", "")
block_2_font_size = safe_get(block_2, "block_font_size", 0)
block_2_avg_char_width = safe_get(block_2, "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(block_1, "text", "")
block_2_text = safe_get(block_2, "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_condition = 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_condition
)
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 _match_brackets(self, text):
# pattern = r"^[\(\)\[\]（）【】{}｛｝<>＜＞〔〕〘〙\"\'“”‘’]"
pattern = r"^[\(\)\]（）】{}｛｝>＞〕〙\"\'“”‘’]"
return bool(re.match(pattern, text))
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]))
or (self._match_brackets(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, block_1, block_2):
"""
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(block_1, block_2)
def _is_para_continued(self, para_1, para_2):
"""
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(para_1, para_2)
is_para_puncs_consistent = self._is_para_puncs_consistent(para_1, para_2)
return is_para_font_consistent and is_para_puncs_consistent
def _are_boundaries_of_block_consistent(self, block_1, block_2):
"""
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_block_1 = block_1["lines"][-1]
first_line_of_block_2 = block_2["lines"][0]
spans_of_last_line_of_block_1 = last_line_of_block_1["spans"]
spans_of_first_line_of_block_2 = first_line_of_block_2["spans"]
font_type_of_last_line_of_block_1 = spans_of_last_line_of_block_1[0]["font"].lower()
font_size_of_last_line_of_block_1 = spans_of_last_line_of_block_1[0]["size"]
font_color_of_last_line_of_block_1 = spans_of_last_line_of_block_1[0]["color"]
font_flags_of_last_line_of_block_1 = spans_of_last_line_of_block_1[0]["flags"]
font_type_of_first_line_of_block_2 = spans_of_first_line_of_block_2[0]["font"].lower()
font_size_of_first_line_of_block_2 = spans_of_first_line_of_block_2[0]["size"]
font_color_of_first_line_of_block_2 = spans_of_first_line_of_block_2[0]["color"]
font_flags_of_first_line_of_block_2 = spans_of_first_line_of_block_2[0]["flags"]
return (
self.__is_similar_font_type(font_type_of_last_line_of_block_1, font_type_of_first_line_of_block_2)
and abs(font_size_of_last_line_of_block_1 - font_size_of_first_line_of_block_2) < 1
# and font_color_of_last_line_of_block1 == font_color_of_first_line_of_block2
and font_flags_of_last_line_of_block_1 == font_flags_of_first_line_of_block_2
)
def should_merge_next_para(self, curr_para, next_para):
"""
This function checks if the next_para should be merged into the curr_para.
Parameters
----------
curr_para : dict
The current paragraph.
next_para : dict
The next paragraph.
Returns
-------
bool
True if the next_para should be merged into the curr_para, False otherwise.
"""
if self._is_para_continued(curr_para, next_para):
return True
else:
return False
def batch_tag_paras(self, pdf_dict):
"""
This function tags the paragraphs in the pdf_dict.
Parameters
----------
pdf_dict : dict
PDF dictionary.
Returns
-------
pdf_dict : dict
PDF dictionary with tagged paragraphs.
"""
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):
"""
This function finds a block by its id.
Parameters
----------
para_blocks : list
List of blocks.
block_id : int
Id of the block to find.
Returns
-------
block : dict
The block with the given id.
"""
for block in para_blocks:
if block.get("block_id") == block_id:
return block
return None
def batch_merge_paras(self, pdf_dict):
"""
This function merges the paragraphs in the pdf_dict.
Parameters
----------
pdf_dict : dict
PDF dictionary.
Returns
-------
pdf_dict : dict
PDF dictionary with merged paragraphs.
"""
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
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):
"""
This function draws annotations on the pdf file.
Parameters
----------
input_pdf_path : str
path to the input pdf file
pdf_dic : dict
pdf dictionary
output_pdf_path : str
path to the output pdf file
pdf_dic : dict
pdf dictionary
"""
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()
class ParaProcessPipeline:
def __init__(self) -> None:
pass
def para_process_pipeline(self, pdf_info_dict, para_debug_mode=None, input_pdf_path=None, output_pdf_path=None):
"""
This function processes the paragraphs, including:
1. Read raw input json file into pdf_dic
2. Detect and replace equations
3. Combine spans into a natural line
4. Check if the paragraphs are inside bboxes passed from "layout_bboxes" key
5. Compute statistics for each block
6. Detect titles in the document
7. Detect paragraphs inside each block
8. Divide the level of the titles
9. Detect and combine paragraphs from different blocks into one paragraph
10. Check whether the final results after checking headings, dividing paragraphs within blocks, and merging paragraphs between blocks are plausible and reasonable.
11. Draw annotations on the pdf file
Parameters
----------
pdf_dic_json_fpath : str
path to the pdf dictionary json file.
Notice: data noises, including overlap blocks, header, footer, watermark, vertical margin note have been removed already.
input_pdf_doc : str
path to the input pdf file
output_pdf_path : str
path to the output pdf file
Returns
-------
pdf_dict : dict
result dictionary
"""
error_info = None
output_json_file = ""
output_dir = ""
if input_pdf_path is not None:
input_pdf_path = os.path.abspath(input_pdf_path)
# print_green_on_red(f">>>>>>>>>>>>>>>>>>> Process the paragraphs of {input_pdf_path}")
if output_pdf_path is not None:
output_dir = os.path.dirname(output_pdf_path)
output_json_file = f"{output_dir}/pdf_dic.json"
def __save_pdf_dic(pdf_dic, output_pdf_path, stage="0", para_debug_mode=para_debug_mode):
"""
Save the pdf_dic to a json file
"""
output_pdf_file_name = os.path.basename(output_pdf_path)
# output_dir = os.path.dirname(output_pdf_path)
output_dir = "\\tmp\\pdf_parse"
output_pdf_file_name = output_pdf_file_name.replace(".pdf", f"_stage_{stage}.json")
pdf_dic_json_fpath = os.path.join(output_dir, output_pdf_file_name)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if para_debug_mode == "full":
with open(pdf_dic_json_fpath, "w", encoding="utf-8") as f:
json.dump(pdf_dic, f, indent=2, ensure_ascii=False)
# Validate the output already exists
if not os.path.exists(pdf_dic_json_fpath):
print_red(f"Failed to save the pdf_dic to {pdf_dic_json_fpath}")
return None
else:
print_green(f"Succeed to save the pdf_dic to {pdf_dic_json_fpath}")
return pdf_dic_json_fpath
"""
Preprocess the lines of block
"""
# Combine spans into a natural line
rawBlockProcessor = RawBlockProcessor()
pdf_dic = rawBlockProcessor.batch_process_blocks(pdf_info_dict)
# print(f"pdf_dic['page_0']['para_blocks'][0]: {pdf_dic['page_0']['para_blocks'][0]}", end="\n\n")
# Check if the paragraphs are inside bboxes passed from "layout_bboxes" key
layoutFilter = LayoutFilterProcessor()
pdf_dic = layoutFilter.batch_process_blocks(pdf_dic)
# Compute statistics for each block
blockStatisticsCalculator = BlockStatisticsCalculator()
pdf_dic = blockStatisticsCalculator.batch_process_blocks(pdf_dic)
# print(f"pdf_dic['page_0']['para_blocks'][0]: {pdf_dic['page_0']['para_blocks'][0]}", end="\n\n")
# Compute statistics for all blocks(namely this pdf document)
docStatisticsCalculator = DocStatisticsCalculator()
pdf_dic = docStatisticsCalculator.calc_stats_of_doc(pdf_dic)
# print(f"pdf_dic['statistics']: {pdf_dic['statistics']}", end="\n\n")
# Dump the first three stages of pdf_dic to a json file
if para_debug_mode == "full":
pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="0", para_debug_mode=para_debug_mode)
"""
Detect titles in the document
"""
doc_statistics = pdf_dic["statistics"]
titleProcessor = TitleProcessor(doc_statistics)
titleProcessor.stage = 0
pdf_dic = titleProcessor.batch_detect_titles(pdf_dic)
if para_debug_mode == "full":
pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="1", para_debug_mode=para_debug_mode)
"""
Detect and divide the level of the titles
"""
titleProcessor = TitleProcessor()
pdf_dic = titleProcessor.batch_recog_title_level(pdf_dic)
if para_debug_mode == "full":
pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="2", para_debug_mode=para_debug_mode)
"""
Detect and split paragraphs inside each block
"""
blockInnerParasProcessor = BlockTerminationProcessor()
pdf_dic = blockInnerParasProcessor.batch_process_blocks(pdf_dic)
if para_debug_mode == "full":
pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="3", para_debug_mode=para_debug_mode)
# pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="3", para_debug_mode="full")
# print_green(f"pdf_dic_json_fpath: {pdf_dic_json_fpath}")
"""
Detect and combine paragraphs from different blocks into one paragraph
"""
blockContinuationProcessor = BlockContinuationProcessor()
pdf_dic = blockContinuationProcessor.batch_tag_paras(pdf_dic)
pdf_dic = blockContinuationProcessor.batch_merge_paras(pdf_dic)
"""
Detect titles in the document again
"""
doc_statistics = pdf_dic["statistics"]
titleProcessor = TitleProcessor(doc_statistics)
titleProcessor.stage = 1
# pdf_dic = titleProcessor.batch_detect_titles(pdf_dic)
if para_debug_mode == "full":
pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="4", para_debug_mode=para_debug_mode)
# pdf_dic_json_fpath = __save_pdf_dic(pdf_dic, output_pdf_path, stage="4", para_debug_mode="full")
# print_green(f"pdf_dic_json_fpath: {pdf_dic_json_fpath}")
"""
Discard pdf files by checking exceptions and return the error info to the caller
"""
discardByException = DiscardByException()
is_discard_by_single_line_block = discardByException.discard_by_single_line_block(
pdf_dic, exception=DenseSingleLineBlockException()
)
is_discard_by_title_detection = discardByException.discard_by_title_detection(
pdf_dic, exception=TitleDetectionException()
)
is_discard_by_title_level = discardByException.discard_by_title_level(pdf_dic, exception=TitleLevelException())
is_discard_by_split_para = discardByException.discard_by_split_para(pdf_dic, exception=ParaSplitException())
is_discard_by_merge_para = discardByException.discard_by_merge_para(pdf_dic, exception=ParaMergeException())
if is_discard_by_single_line_block is not None:
error_info = is_discard_by_single_line_block
elif is_discard_by_title_detection is not None:
error_info = is_discard_by_title_detection
elif is_discard_by_title_level is not None:
error_info = is_discard_by_title_level
elif is_discard_by_split_para is not None:
error_info = is_discard_by_split_para
elif is_discard_by_merge_para is not None:
error_info = is_discard_by_merge_para
if error_info is not None:
return pdf_dic, error_info
"""
Dump the final pdf_dic to a json file
"""
if para_debug_mode is not None:
with open(output_json_file, "w", encoding="utf-8") as f:
json.dump(pdf_info_dict, f, ensure_ascii=False, indent=4)
"""
Draw the annotations
"""
if para_debug_mode is not None:
drawAnnos = DrawAnnos()
drawAnnos.draw_annos(input_pdf_path, pdf_dic, output_pdf_path)
"""
Remove the intermediate files which are generated in the process of paragraph processing if debug_mode is simple
"""
if para_debug_mode is not None:
for fpath in os.listdir(output_dir):
if fpath.endswith(".json") and "stage" in fpath:
os.remove(os.path.join(output_dir, fpath))
return pdf_dic, error_info
"""
Run this script to test the function with Command:
python detect_para.py [pdf_path] [output_pdf_path]
Params:
- pdf_path: the path of the pdf file
- output_pdf_path: the path of the output pdf file
"""
if __name__ == "__main__":
DEFAULT_PDF_PATH = (
"app/pdf_toolbox/tests/assets/paper/paper.pdf" if os.name != "nt" else "app\\pdf_toolbox\\tests\\assets\\paper\\paper.pdf"
)
input_pdf_path = sys.argv[1] if len(sys.argv) > 1 else DEFAULT_PDF_PATH
output_pdf_path = sys.argv[2] if len(sys.argv) > 2 else input_pdf_path.split(".")[0] + "_recogPara.pdf"
output_json_path = sys.argv[3] if len(sys.argv) > 3 else input_pdf_path.split(".")[0] + "_recogPara.json"
import stat
# Remove existing output file if it exists
if os.path.exists(output_pdf_path):
os.chmod(output_pdf_path, stat.S_IWRITE)
os.remove(output_pdf_path)
input_pdf_doc = open_pdf(input_pdf_path)
# postprocess the paragraphs
paraProcessPipeline = ParaProcessPipeline()
# parse paragraph and save to json file
pdf_dic = {}
blockInnerParasProcessor = BlockTerminationProcessor()
"""
Construct the pdf dictionary.
"""
for page_id, page in enumerate(input_pdf_doc): # type: ignore
# print(f"Processing page {page_id}")
# print(f"page: {page}")
raw_blocks = page.get_text("dict")["blocks"]
# Save text blocks to "preproc_blocks"
preproc_blocks = []
for block in raw_blocks:
if block["type"] == 0:
preproc_blocks.append(block)
layout_bboxes = []
# Construct the pdf dictionary as schema above
page_dict = {
"para_blocks": None,
"preproc_blocks": preproc_blocks,
"images": None,
"tables": None,
"interline_equations": None,
"inline_equations": None,
"layout_bboxes": None,
"pymu_raw_blocks": None,
"global_statistic": None,
"droped_text_block": None,
"droped_image_block": None,
"droped_table_block": None,
"image_backup": None,
"table_backup": None,
}
pdf_dic[f"page_{page_id}"] = page_dict
# print(f"pdf_dic: {pdf_dic}")
with open(output_json_path, "w", encoding="utf-8") as f:
json.dump(pdf_dic, f, ensure_ascii=False, indent=4)
pdf_dic = paraProcessPipeline.para_process_pipeline(output_json_path, input_pdf_doc, output_pdf_path)
others/pdf2text_recogTitle.py deleted 100644 → 0
View file @ 4101c357
from magic_pdf.libs.commons import fitz # pyMuPDF库
def parse_titles(page_ID: int, page: fitz.Page, json_from_DocXchain_obj: dict, exclude_bboxes):
"""
: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 title ---------#
title_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)
# {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] / 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'] == 0 and xf['score'] >= 0.3:
title_bbox_from_DocXChain.append((L, U, R, D))
title_final_names = []
title_final_bboxs = []
title_ID = 0
for L, U, R, D in title_bbox_from_DocXChain:
# cur_title = page.get_pixmap(clip=(L,U,R,D))
new_title_name = "title_{}_{}.png".format(page_ID, title_ID) # 标题name
# cur_title.save(res_dir_path + '/' + new_title_name) # 把标题存储在新建的文件夹，并命名
title_final_names.append(new_title_name) # 把标题的名字存在list中
title_final_bboxs.append((L, U, R, D))
title_ID += 1
title_final_bboxs.sort(key = lambda LURD: (LURD[1], LURD[0]))
curPage_all_title_bboxs = title_final_bboxs
return curPage_all_title_bboxs
others/vali_bbox_sort.py deleted 100644 → 0
View file @ 4101c357
import numpy as np
import tqdm
import json
from validation import cal_edit_distance, format_gt_bbox
from magic_pdf.layout.layout_sort import sort_with_layout
with open('/mnt/petrelfs/share_data/ouyanglinke/OCR/OCR_validation_dataset_final_rotated_formulafix_highdpi_scihub.json', 'r') as f:
samples = json.load(f)
# labels = []
# det_res = []
edit_distance_dict = []
edit_distance_list = []
for i, sample in tqdm.tqdm(enumerate(samples)):
pdf_name = sample['pdf_name']
s3_pdf_path = sample['s3_path']
page_num = sample['page']
page_width = sample['annotations']['width']
page_height = sample['annotations']['height']
# pre = main(s3_pdf_path, pdf_bin_file_profile, join_path(pdf_model_dir, pdf_name), pdf_model_profile, save_path, page_num)
# pre_dict_list = []
# for item in pre:
# pre_sample = {
# 'box': [item[0],item[1],item[2],item[3]],
# 'type': item[7],
# 'score': 1
# }
# pre_dict_list.append(pre_sample)
# det_res.append(pre_dict_list)
# match_change_dict = { # 待确认
# "figure": "image",
# "svg_figure": "image",
# "inline_fomula": "equations_inline",
# "fomula": "equation_interline",
# "figure_caption": "text",
# "table_caption": "text",
# "fomula_caption": "text"
# }
gt_annos = sample['annotations']
# matched_label = label_match(gt_annos, match_change_dict)
# labels.append(matched_label)
# 判断排序函数的精度
# 目前不考虑caption与图表相同序号的问题
ignore_category = ['abandon', 'figure_caption', 'table_caption', 'formula_caption', 'inline_fomula']
gt_bboxes = format_gt_bbox(gt_annos, ignore_category)
sorted_bboxes, _ = sort_with_layout(gt_bboxes, page_width, page_height)
if sorted_bboxes:
edit_distance = cal_edit_distance(sorted_bboxes)
edit_distance_list.append(edit_distance)
edit_distance_dict.append({
"sample_id": i,
"s3_path": s3_pdf_path,
"page_num": page_num,
"page_s2_path": sample['page_path'],
"edit_distance": edit_distance
})
# label_classes = ["image", "text", "table", "equation_interline"]
# detect_matrix = detect_val(labels, det_res, label_classes)
# print('detect_matrix', detect_matrix)
edit_distance_mean = np.mean(edit_distance_list)
print('edit_distance_mean', edit_distance_mean)
edit_distance_dict_sorted = sorted(edit_distance_dict, key=lambda x: x['edit_distance'], reverse=True)
# print(edit_distance_dict_sorted)
result = {
"edit_distance_mean": edit_distance_mean,
"edit_distance_dict_sorted": edit_distance_dict_sorted
}
with open('vali_bbox_sort_result.json', 'w') as f:
json.dump(result, f)
\ No newline at end of file
others/validation.py deleted 100644 → 0
View file @ 4101c357
import numpy as np
from mmeval import COCODetection
import distance
def reformat_gt_and_pred(labels, det_res, label_classes):
preds = []
gts = []
for idx, (ann, pred) in enumerate(zip(labels, det_res)):
# with open(label_path, "r") as f:
# ann = json.load(f)
gt_bboxes = []
gt_labels = []
for item in ann['step_1']['result']:
if item['attribute'] in label_classes:
gt_bboxes.append([item['x'], item['y'], item['x']+item['width'], item['y']+item['height']])
gt_labels.append(label_classes.index(item['attribute']))
gts.append({
'img_id': idx,
'width': ann['width'],
'height': ann['height'],
'bboxes': np.array(gt_bboxes),
'labels': np.array(gt_labels),
'ignore_flags': [False]*len(gt_labels),
})
bboxes = []
labels = []
scores = []
for item in pred:
bboxes.append(item['box'])
labels.append(label_classes.index(item['type']))
scores.append(item['score'])
preds.append({
'img_id': idx,
'bboxes': np.array(bboxes),
'scores': np.array(scores),
'labels': np.array(labels),
})
return gts, preds
def detect_val(labels, det_res, label_classes):
# label_classes = ['inline_formula', "formula"]
meta={'CLASSES':tuple(label_classes)}
coco_det_metric = COCODetection(dataset_meta=meta, metric=['bbox'])
gts, preds = reformat_gt_and_pred(labels, det_res, label_classes)
res = coco_det_metric(predictions=preds, groundtruths=gts)
return res
def label_match(annotations, match_change_dict):
for item in annotations['step_1']['result']:
if item['attribute'] in match_change_dict.keys():
item['attribute'] = match_change_dict[item['attribute']]
return annotations
def format_gt_bbox(annotations, ignore_category):
gt_bboxes = []
for item in annotations['step_1']['result']:
if item['textAttribute'] and item['attribute'] not in ignore_category:
x0 = item['x']
y0 = item['y']
x1 = item['x'] + item['width']
y1 = item['y'] + item['height']
order = item['textAttribute']
category = item['attribute']
gt_bboxes.append([x0, y0, x1, y1, order, None, None, category])
return gt_bboxes
def cal_edit_distance(sorted_bboxes):
# order_list = [int(bbox[4]) for bbox in sorted_bboxes]
# print(sorted_bboxes[0][0][12])
order_list = [int(bbox[12]) for bbox in sorted_bboxes]
sorted_order = sorted(order_list, key=int)
distance_cal = distance.levenshtein(order_list, sorted_order)
if len(order_list) > 0:
return distance_cal / len(order_list)
else:
return 0
\ No newline at end of file
requirements-qa.txt
View file @ eb4625a9
pytest
Levenshtein Levenshtein
nltk nltk
rapidfuzz rapidfuzz
...@@ -11,4 +12,5 @@ scipy ...@@ -11,4 +12,5 @@ scipy
scikit-learn scikit-learn
tqdm tqdm
htmltabletomd htmltabletomd
pypandoc pypandoc
\ No newline at end of file pyopenssl==24.0.0
\ No newline at end of file
tests/test_cli/conf/conf.py
View file @ eb4625a9
...@@ -2,7 +2,6 @@ import os ...@@ -2,7 +2,6 @@ import os
conf = { conf = {
"code_path": os.environ.get('GITHUB_WORKSPACE'), "code_path": os.environ.get('GITHUB_WORKSPACE'),
"pdf_dev_path" : os.environ.get('GITHUB_WORKSPACE') + "/tests/test_cli/pdf_dev", "pdf_dev_path" : os.environ.get('GITHUB_WORKSPACE') + "/tests/test_cli/pdf_dev",
"pdf_res_path": "/share/quyuan/mineru/data/mineru" "pdf_res_path": "/tmp"
} }
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