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Commit 9515c2aa authored by myhloli's avatar myhloli
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remove useless files

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magic_pdf/model/pdf_extract_kit.py
View file @ 9515c2aa
...@@ -88,12 +88,7 @@ class CustomPEKModel: ...@@ -88,12 +88,7 @@ class CustomPEKModel:
self.device = kwargs.get("device", self.configs["config"]["device"]) self.device = kwargs.get("device", self.configs["config"]["device"])
logger.info("using device: {}".format(self.device)) logger.info("using device: {}".format(self.device))
models_dir = kwargs.get("models_dir", os.path.join(root_dir, "resources", "models")) models_dir = kwargs.get("models_dir", os.path.join(root_dir, "resources", "models"))
# 初始化layout模型
self.layout_model = layout_model_init(
os.path.join(models_dir, self.configs['weights']['layout']),
os.path.join(model_config_dir, "layoutlmv3", "layoutlmv3_base_inference.yaml"),
device=self.device
)
# 初始化公式识别 # 初始化公式识别
if self.apply_formula: if self.apply_formula:
# 初始化公式检测模型 # 初始化公式检测模型
...@@ -106,6 +101,13 @@ class CustomPEKModel: ...@@ -106,6 +101,13 @@ class CustomPEKModel:
device=self.device device=self.device
) )
self.mfr_transform = transforms.Compose([mfr_vis_processors, ]) self.mfr_transform = transforms.Compose([mfr_vis_processors, ])
# 初始化layout模型
self.layout_model = layout_model_init(
os.path.join(models_dir, self.configs['weights']['layout']),
os.path.join(model_config_dir, "layoutlmv3", "layoutlmv3_base_inference.yaml"),
device=self.device
)
# 初始化ocr # 初始化ocr
if self.apply_ocr: if self.apply_ocr:
self.ocr_model = ModifiedPaddleOCR(show_log=show_log) self.ocr_model = ModifiedPaddleOCR(show_log=show_log)
......
others/README.md deleted 100644 → 0
View file @ 5d54bb04
# 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
View file @ 5d54bb04
# 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
View file @ 5d54bb04
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 @ 5d54bb04
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 @ 5d54bb04
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
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others/pdf2text_recogPara_v2.py deleted 100644 → 0
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others/pdf2text_recogTitle.py deleted 100644 → 0
View file @ 5d54bb04
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
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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 @ 5d54bb04
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
tools/magic-pdf.json deleted 100644 → 0
View file @ 5d54bb04
{
"temp-output-dir": "/tmp/"
}
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