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Unverified Commit 158e556b authored by Xiaomeng Zhao's avatar Xiaomeng Zhao Committed by GitHub
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Merge pull request #1063 from opendatalab/release-0.10.0 

Release 0.10.0
parents 038f48d3 30be5017
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Showing with 1576 additions and 1159 deletions
magic_pdf/libs/drop_tag.py deleted 100644 → 0
View file @ 038f48d3
COLOR_BG_HEADER_TXT_BLOCK = "color_background_header_txt_block"
PAGE_NO = "page-no" # 页码
CONTENT_IN_FOOT_OR_HEADER = 'in-foot-header-area' # 页眉页脚内的文本
VERTICAL_TEXT = 'vertical-text' # 垂直文本
ROTATE_TEXT = 'rotate-text' # 旋转文本
EMPTY_SIDE_BLOCK = 'empty-side-block' # 边缘上的空白没有任何内容的block
ON_IMAGE_TEXT = 'on-image-text' # 文本在图片上
ON_TABLE_TEXT = 'on-table-text' # 文本在表格上
class DropTag:
PAGE_NUMBER = "page_no"
HEADER = "header"
FOOTER = "footer"
FOOTNOTE = "footnote"
NOT_IN_LAYOUT = "not_in_layout"
SPAN_OVERLAP = "span_overlap"
BLOCK_OVERLAP = "block_overlap"
magic_pdf/libs/pdf_image_tools.py
View file @ 158e556b
from magic_pdf.rw.AbsReaderWriter import AbsReaderWriter
from magic_pdf.libs.commons import fitz
from magic_pdf.libs.commons import join_path
from io import BytesIO
import cv2
import numpy as np
from PIL import Image
from magic_pdf.data.data_reader_writer import DataWriter
from magic_pdf.libs.commons import fitz, join_path
from magic_pdf.libs.hash_utils import compute_sha256
def cut_image(bbox: tuple, page_num: int, page: fitz.Page, return_path, imageWriter: AbsReaderWriter):
"""
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。
"""
def cut_image(bbox: tuple, page_num: int, page: fitz.Page, return_path, imageWriter: DataWriter):
"""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。"""
# 拼接文件名
filename = f"{page_num}_{int(bbox[0])}_{int(bbox[1])}_{int(bbox[2])}_{int(bbox[3])}"
filename = f'{page_num}_{int(bbox[0])}_{int(bbox[1])}_{int(bbox[2])}_{int(bbox[3])}'
# 老版本返回不带bucket的路径
img_path = join_path(return_path, filename) if return_path is not None else None
# 新版本生成平铺路径
img_hash256_path = f"{compute_sha256(img_path)}.jpg"
img_hash256_path = f'{compute_sha256(img_path)}.jpg'
# 将坐标转换为fitz.Rect对象
rect = fitz.Rect(*bbox)
......@@ -28,6 +29,29 @@ def cut_image(bbox: tuple, page_num: int, page: fitz.Page, return_path, imageWri
byte_data = pix.tobytes(output='jpeg', jpg_quality=95)
imageWriter.write(byte_data, img_hash256_path, AbsReaderWriter.MODE_BIN)
imageWriter.write(img_hash256_path, byte_data)
return img_hash256_path
def cut_image_to_pil_image(bbox: tuple, page: fitz.Page, mode="pillow"):
# 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)
# 将字节数据转换为文件对象
image_file = BytesIO(pix.tobytes(output='png'))
# 使用 Pillow 打开图像
pil_image = Image.open(image_file)
if mode == "cv2":
image_result = cv2.cvtColor(np.asarray(pil_image), cv2.COLOR_RGB2BGR)
elif mode == "pillow":
image_result = pil_image
else:
raise ValueError(f"mode: {mode} is not supported.")
return image_result
\ No newline at end of file
magic_pdf/model/doc_analyze_by_custom_model.py
View file @ 158e556b
......@@ -163,7 +163,9 @@ def doc_analyze(pdf_bytes: bytes, ocr: bool = False, show_log: bool = False,
page_width = img_dict["width"]
page_height = img_dict["height"]
if start_page_id <= index <= end_page_id:
page_start = time.time()
result = custom_model(img)
logger.info(f'-----page_id : {index}, page total time: {round(time.time() - page_start, 2)}-----')
else:
result = []
page_info = {"page_no": index, "height": page_height, "width": page_width}
......
magic_pdf/model/magic_model.py
View file @ 158e556b
import enum
import json
from magic_pdf.config.model_block_type import ModelBlockTypeEnum
from magic_pdf.config.ocr_content_type import CategoryId, ContentType
from magic_pdf.data.data_reader_writer import (FileBasedDataReader,
FileBasedDataWriter)
from magic_pdf.data.dataset import Dataset
from magic_pdf.libs.boxbase import (_is_in, _is_part_overlap, bbox_distance,
bbox_relative_pos, box_area, calculate_iou,
......@@ -9,11 +13,7 @@ from magic_pdf.libs.boxbase import (_is_in, _is_part_overlap, bbox_distance,
from magic_pdf.libs.commons import fitz, join_path
from magic_pdf.libs.coordinate_transform import get_scale_ratio
from magic_pdf.libs.local_math import float_gt
from magic_pdf.libs.ModelBlockTypeEnum import ModelBlockTypeEnum
from magic_pdf.libs.ocr_content_type import CategoryId, ContentType
from magic_pdf.pre_proc.remove_bbox_overlap import _remove_overlap_between_bbox
from magic_pdf.rw.AbsReaderWriter import AbsReaderWriter
from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter
CAPATION_OVERLAP_AREA_RATIO = 0.6
MERGE_BOX_OVERLAP_AREA_RATIO = 1.1
......@@ -1050,27 +1050,27 @@ class MagicModel:
if __name__ == '__main__':
drw = DiskReaderWriter(r'D:/project/20231108code-clean')
drw = FileBasedDataReader(r'D:/project/20231108code-clean')
if 0:
pdf_file_path = r'linshixuqiu\19983-00.pdf'
model_file_path = r'linshixuqiu\19983-00_new.json'
pdf_bytes = drw.read(pdf_file_path, AbsReaderWriter.MODE_BIN)
model_json_txt = drw.read(model_file_path, AbsReaderWriter.MODE_TXT)
pdf_bytes = drw.read(pdf_file_path)
model_json_txt = drw.read(model_file_path).decode()
model_list = json.loads(model_json_txt)
write_path = r'D:\project\20231108code-clean\linshixuqiu\19983-00'
img_bucket_path = 'imgs'
img_writer = DiskReaderWriter(join_path(write_path, img_bucket_path))
img_writer = FileBasedDataWriter(join_path(write_path, img_bucket_path))
pdf_docs = fitz.open('pdf', pdf_bytes)
magic_model = MagicModel(model_list, pdf_docs)
if 1:
from magic_pdf.data.dataset import PymuDocDataset
model_list = json.loads(
drw.read('/opt/data/pdf/20240418/j.chroma.2009.03.042.json')
)
pdf_bytes = drw.read(
'/opt/data/pdf/20240418/j.chroma.2009.03.042.pdf', AbsReaderWriter.MODE_BIN
)
pdf_docs = fitz.open('pdf', pdf_bytes)
magic_model = MagicModel(model_list, pdf_docs)
pdf_bytes = drw.read('/opt/data/pdf/20240418/j.chroma.2009.03.042.pdf')
magic_model = MagicModel(model_list, PymuDocDataset(pdf_bytes))
for i in range(7):
print(magic_model.get_imgs(i))
magic_pdf/model/pdf_extract_kit.py
View file @ 158e556b
import numpy as np
import torch
from loguru import logger
# flake8: noqa
import os
import time
import cv2
import numpy as np
import torch
import yaml
from loguru import logger
from PIL import Image
os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1' # 禁止albumentations检查更新
......@@ -13,16 +15,18 @@ os.environ['YOLO_VERBOSE'] = 'False' # disable yolo logger
try:
import torchtext
if torchtext.__version__ >= "0.18.0":
if torchtext.__version__ >= '0.18.0':
torchtext.disable_torchtext_deprecation_warning()
except ImportError:
pass
from magic_pdf.libs.Constants import *
from magic_pdf.config.constants import *
from magic_pdf.model.model_list import AtomicModel
from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
from magic_pdf.model.sub_modules.model_utils import get_res_list_from_layout_res, crop_img, clean_vram
from magic_pdf.model.sub_modules.ocr.paddleocr.ocr_utils import get_adjusted_mfdetrec_res, get_ocr_result_list
from magic_pdf.model.sub_modules.model_utils import (
clean_vram, crop_img, get_res_list_from_layout_res)
from magic_pdf.model.sub_modules.ocr.paddleocr.ocr_utils import (
get_adjusted_mfdetrec_res, get_ocr_result_list)
class CustomPEKModel:
......@@ -41,42 +45,54 @@ class CustomPEKModel:
model_config_dir = os.path.join(root_dir, 'resources', 'model_config')
# 构建 model_configs.yaml 文件的完整路径
config_path = os.path.join(model_config_dir, 'model_configs.yaml')
with open(config_path, "r", encoding='utf-8') as f:
with open(config_path, 'r', encoding='utf-8') as f:
self.configs = yaml.load(f, Loader=yaml.FullLoader)
# 初始化解析配置
# layout config
self.layout_config = kwargs.get("layout_config")
self.layout_model_name = self.layout_config.get("model", MODEL_NAME.DocLayout_YOLO)
self.layout_config = kwargs.get('layout_config')
self.layout_model_name = self.layout_config.get(
'model', MODEL_NAME.DocLayout_YOLO
)
# formula config
self.formula_config = kwargs.get("formula_config")
self.mfd_model_name = self.formula_config.get("mfd_model", MODEL_NAME.YOLO_V8_MFD)
self.mfr_model_name = self.formula_config.get("mfr_model", MODEL_NAME.UniMerNet_v2_Small)
self.apply_formula = self.formula_config.get("enable", True)
self.formula_config = kwargs.get('formula_config')
self.mfd_model_name = self.formula_config.get(
'mfd_model', MODEL_NAME.YOLO_V8_MFD
)
self.mfr_model_name = self.formula_config.get(
'mfr_model', MODEL_NAME.UniMerNet_v2_Small
)
self.apply_formula = self.formula_config.get('enable', True)
# table config
self.table_config = kwargs.get("table_config")
self.apply_table = self.table_config.get("enable", False)
self.table_max_time = self.table_config.get("max_time", TABLE_MAX_TIME_VALUE)
self.table_model_name = self.table_config.get("model", MODEL_NAME.RAPID_TABLE)
self.table_config = kwargs.get('table_config')
self.apply_table = self.table_config.get('enable', False)
self.table_max_time = self.table_config.get('max_time', TABLE_MAX_TIME_VALUE)
self.table_model_name = self.table_config.get('model', MODEL_NAME.RAPID_TABLE)
# ocr config
self.apply_ocr = ocr
self.lang = kwargs.get("lang", None)
self.lang = kwargs.get('lang', None)
logger.info(
"DocAnalysis init, this may take some times, layout_model: {}, apply_formula: {}, apply_ocr: {}, "
"apply_table: {}, table_model: {}, lang: {}".format(
self.layout_model_name, self.apply_formula, self.apply_ocr, self.apply_table, self.table_model_name,
self.lang
'DocAnalysis init, this may take some times, layout_model: {}, apply_formula: {}, apply_ocr: {}, '
'apply_table: {}, table_model: {}, lang: {}'.format(
self.layout_model_name,
self.apply_formula,
self.apply_ocr,
self.apply_table,
self.table_model_name,
self.lang,
)
)
# 初始化解析方案
self.device = kwargs.get("device", "cpu")
logger.info("using device: {}".format(self.device))
models_dir = kwargs.get("models_dir", os.path.join(root_dir, "resources", "models"))
logger.info("using models_dir: {}".format(models_dir))
self.device = kwargs.get('device', 'cpu')
logger.info('using device: {}'.format(self.device))
models_dir = kwargs.get(
'models_dir', os.path.join(root_dir, 'resources', 'models')
)
logger.info('using models_dir: {}'.format(models_dir))
atom_model_manager = AtomModelSingleton()
......@@ -85,18 +101,24 @@ class CustomPEKModel:
# 初始化公式检测模型
self.mfd_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.MFD,
mfd_weights=str(os.path.join(models_dir, self.configs["weights"][self.mfd_model_name])),
device=self.device
mfd_weights=str(
os.path.join(
models_dir, self.configs['weights'][self.mfd_model_name]
)
),
device=self.device,
)
# 初始化公式解析模型
mfr_weight_dir = str(os.path.join(models_dir, self.configs["weights"][self.mfr_model_name]))
mfr_cfg_path = str(os.path.join(model_config_dir, "UniMERNet", "demo.yaml"))
mfr_weight_dir = str(
os.path.join(models_dir, self.configs['weights'][self.mfr_model_name])
)
mfr_cfg_path = str(os.path.join(model_config_dir, 'UniMERNet', 'demo.yaml'))
self.mfr_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.MFR,
mfr_weight_dir=mfr_weight_dir,
mfr_cfg_path=mfr_cfg_path,
device=self.device
device=self.device,
)
# 初始化layout模型
......@@ -104,42 +126,51 @@ class CustomPEKModel:
self.layout_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.Layout,
layout_model_name=MODEL_NAME.LAYOUTLMv3,
layout_weights=str(os.path.join(models_dir, self.configs['weights'][self.layout_model_name])),
layout_config_file=str(os.path.join(model_config_dir, "layoutlmv3", "layoutlmv3_base_inference.yaml")),
device=self.device
layout_weights=str(
os.path.join(
models_dir, self.configs['weights'][self.layout_model_name]
)
),
layout_config_file=str(
os.path.join(
model_config_dir, 'layoutlmv3', 'layoutlmv3_base_inference.yaml'
)
),
device=self.device,
)
elif self.layout_model_name == MODEL_NAME.DocLayout_YOLO:
self.layout_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.Layout,
layout_model_name=MODEL_NAME.DocLayout_YOLO,
doclayout_yolo_weights=str(os.path.join(models_dir, self.configs['weights'][self.layout_model_name])),
device=self.device
doclayout_yolo_weights=str(
os.path.join(
models_dir, self.configs['weights'][self.layout_model_name]
)
),
device=self.device,
)
# 初始化ocr
if self.apply_ocr:
self.ocr_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.OCR,
ocr_show_log=show_log,
det_db_box_thresh=0.3,
lang=self.lang
)
self.ocr_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.OCR,
ocr_show_log=show_log,
det_db_box_thresh=0.3,
lang=self.lang
)
# init table model
if self.apply_table:
table_model_dir = self.configs["weights"][self.table_model_name]
table_model_dir = self.configs['weights'][self.table_model_name]
self.table_model = atom_model_manager.get_atom_model(
atom_model_name=AtomicModel.Table,
table_model_name=self.table_model_name,
table_model_path=str(os.path.join(models_dir, table_model_dir)),
table_max_time=self.table_max_time,
device=self.device
device=self.device,
)
logger.info('DocAnalysis init done!')
def __call__(self, image):
page_start = time.time()
# layout检测
layout_start = time.time()
layout_res = []
......@@ -150,7 +181,7 @@ class CustomPEKModel:
# doclayout_yolo
layout_res = self.layout_model.predict(image)
layout_cost = round(time.time() - layout_start, 2)
logger.info(f"layout detection time: {layout_cost}")
logger.info(f'layout detection time: {layout_cost}')
pil_img = Image.fromarray(image)
......@@ -158,40 +189,47 @@ class CustomPEKModel:
# 公式检测
mfd_start = time.time()
mfd_res = self.mfd_model.predict(image)
logger.info(f"mfd time: {round(time.time() - mfd_start, 2)}")
logger.info(f'mfd time: {round(time.time() - mfd_start, 2)}')
# 公式识别
mfr_start = time.time()
formula_list = self.mfr_model.predict(mfd_res, image)
layout_res.extend(formula_list)
mfr_cost = round(time.time() - mfr_start, 2)
logger.info(f"formula nums: {len(formula_list)}, mfr time: {mfr_cost}")
logger.info(f'formula nums: {len(formula_list)}, mfr time: {mfr_cost}')
# 清理显存
clean_vram(self.device, vram_threshold=8)
# 从layout_res中获取ocr区域、表格区域、公式区域
ocr_res_list, table_res_list, single_page_mfdetrec_res = get_res_list_from_layout_res(layout_res)
ocr_res_list, table_res_list, single_page_mfdetrec_res = (
get_res_list_from_layout_res(layout_res)
)
# ocr识别
if self.apply_ocr:
ocr_start = time.time()
# Process each area that requires OCR processing
for res in ocr_res_list:
new_image, useful_list = crop_img(res, pil_img, crop_paste_x=50, crop_paste_y=50)
adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(single_page_mfdetrec_res, useful_list)
# OCR recognition
new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
ocr_start = time.time()
# Process each area that requires OCR processing
for res in ocr_res_list:
new_image, useful_list = crop_img(res, pil_img, crop_paste_x=50, crop_paste_y=50)
adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(single_page_mfdetrec_res, useful_list)
# OCR recognition
new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
if self.apply_ocr:
ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res)[0]
else:
ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res, rec=False)[0]
# Integration results
if ocr_res:
ocr_result_list = get_ocr_result_list(ocr_res, useful_list)
layout_res.extend(ocr_result_list)
# Integration results
if ocr_res:
ocr_result_list = get_ocr_result_list(ocr_res, useful_list)
layout_res.extend(ocr_result_list)
ocr_cost = round(time.time() - ocr_start, 2)
ocr_cost = round(time.time() - ocr_start, 2)
if self.apply_ocr:
logger.info(f"ocr time: {ocr_cost}")
else:
logger.info(f"det time: {ocr_cost}")
# 表格识别 table recognition
if self.apply_table:
......@@ -202,27 +240,35 @@ class CustomPEKModel:
html_code = None
if self.table_model_name == MODEL_NAME.STRUCT_EQTABLE:
with torch.no_grad():
table_result = self.table_model.predict(new_image, "html")
table_result = self.table_model.predict(new_image, 'html')
if len(table_result) > 0:
html_code = table_result[0]
elif self.table_model_name == MODEL_NAME.TABLE_MASTER:
html_code = self.table_model.img2html(new_image)
elif self.table_model_name == MODEL_NAME.RAPID_TABLE:
html_code, table_cell_bboxes, elapse = self.table_model.predict(new_image)
html_code, table_cell_bboxes, elapse = self.table_model.predict(
new_image
)
run_time = time.time() - single_table_start_time
if run_time > self.table_max_time:
logger.warning(f"table recognition processing exceeds max time {self.table_max_time}s")
logger.warning(
f'table recognition processing exceeds max time {self.table_max_time}s'
)
# 判断是否返回正常
if html_code:
expected_ending = html_code.strip().endswith('</html>') or html_code.strip().endswith('</table>')
expected_ending = html_code.strip().endswith(
'</html>'
) or html_code.strip().endswith('</table>')
if expected_ending:
res["html"] = html_code
res['html'] = html_code
else:
logger.warning(f"table recognition processing fails, not found expected HTML table end")
logger.warning(
'table recognition processing fails, not found expected HTML table end'
)
else:
logger.warning(f"table recognition processing fails, not get html return")
logger.info(f"table time: {round(time.time() - table_start, 2)}")
logger.info(f"-----page total time: {round(time.time() - page_start, 2)}-----")
logger.warning(
'table recognition processing fails, not get html return'
)
logger.info(f'table time: {round(time.time() - table_start, 2)}')
return layout_res
magic_pdf/model/sub_modules/model_init.py
View file @ 158e556b
from loguru import logger
from magic_pdf.libs.Constants import MODEL_NAME
from magic_pdf.config.constants import MODEL_NAME
from magic_pdf.model.model_list import AtomicModel
from magic_pdf.model.sub_modules.layout.doclayout_yolo.DocLayoutYOLO import DocLayoutYOLOModel
from magic_pdf.model.sub_modules.layout.layoutlmv3.model_init import Layoutlmv3_Predictor
from magic_pdf.model.sub_modules.layout.doclayout_yolo.DocLayoutYOLO import \
DocLayoutYOLOModel
from magic_pdf.model.sub_modules.layout.layoutlmv3.model_init import \
Layoutlmv3_Predictor
from magic_pdf.model.sub_modules.mfd.yolov8.YOLOv8 import YOLOv8MFDModel
from magic_pdf.model.sub_modules.mfr.unimernet.Unimernet import UnimernetModel
from magic_pdf.model.sub_modules.ocr.paddleocr.ppocr_273_mod import ModifiedPaddleOCR
from magic_pdf.model.sub_modules.ocr.paddleocr.ppocr_273_mod import \
ModifiedPaddleOCR
from magic_pdf.model.sub_modules.table.rapidtable.rapid_table import \
RapidTableModel
# from magic_pdf.model.sub_modules.ocr.paddleocr.ppocr_291_mod import ModifiedPaddleOCR
from magic_pdf.model.sub_modules.table.structeqtable.struct_eqtable import StructTableModel
from magic_pdf.model.sub_modules.table.tablemaster.tablemaster_paddle import TableMasterPaddleModel
from magic_pdf.model.sub_modules.table.rapidtable.rapid_table import RapidTableModel
from magic_pdf.model.sub_modules.table.structeqtable.struct_eqtable import \
StructTableModel
from magic_pdf.model.sub_modules.table.tablemaster.tablemaster_paddle import \
TableMasterPaddleModel
def table_model_init(table_model_type, model_path, max_time, _device_='cpu'):
......@@ -19,14 +24,14 @@ def table_model_init(table_model_type, model_path, max_time, _device_='cpu'):
table_model = StructTableModel(model_path, max_new_tokens=2048, max_time=max_time)
elif table_model_type == MODEL_NAME.TABLE_MASTER:
config = {
"model_dir": model_path,
"device": _device_
'model_dir': model_path,
'device': _device_
}
table_model = TableMasterPaddleModel(config)
elif table_model_type == MODEL_NAME.RAPID_TABLE:
table_model = RapidTableModel()
else:
logger.error("table model type not allow")
logger.error('table model type not allow')
exit(1)
return table_model
......@@ -58,7 +63,7 @@ def ocr_model_init(show_log: bool = False,
use_dilation=True,
det_db_unclip_ratio=1.8,
):
if lang is not None:
if lang is not None and lang != '':
model = ModifiedPaddleOCR(
show_log=show_log,
det_db_box_thresh=det_db_box_thresh,
......@@ -87,8 +92,8 @@ class AtomModelSingleton:
return cls._instance
def get_atom_model(self, atom_model_name: str, **kwargs):
lang = kwargs.get("lang", None)
layout_model_name = kwargs.get("layout_model_name", None)
lang = kwargs.get('lang', None)
layout_model_name = kwargs.get('layout_model_name', None)
key = (atom_model_name, layout_model_name, lang)
if key not in self._models:
self._models[key] = atom_model_init(model_name=atom_model_name, **kwargs)
......@@ -98,47 +103,47 @@ class AtomModelSingleton:
def atom_model_init(model_name: str, **kwargs):
atom_model = None
if model_name == AtomicModel.Layout:
if kwargs.get("layout_model_name") == MODEL_NAME.LAYOUTLMv3:
if kwargs.get('layout_model_name') == MODEL_NAME.LAYOUTLMv3:
atom_model = layout_model_init(
kwargs.get("layout_weights"),
kwargs.get("layout_config_file"),
kwargs.get("device")
kwargs.get('layout_weights'),
kwargs.get('layout_config_file'),
kwargs.get('device')
)
elif kwargs.get("layout_model_name") == MODEL_NAME.DocLayout_YOLO:
elif kwargs.get('layout_model_name') == MODEL_NAME.DocLayout_YOLO:
atom_model = doclayout_yolo_model_init(
kwargs.get("doclayout_yolo_weights"),
kwargs.get("device")
kwargs.get('doclayout_yolo_weights'),
kwargs.get('device')
)
elif model_name == AtomicModel.MFD:
atom_model = mfd_model_init(
kwargs.get("mfd_weights"),
kwargs.get("device")
kwargs.get('mfd_weights'),
kwargs.get('device')
)
elif model_name == AtomicModel.MFR:
atom_model = mfr_model_init(
kwargs.get("mfr_weight_dir"),
kwargs.get("mfr_cfg_path"),
kwargs.get("device")
kwargs.get('mfr_weight_dir'),
kwargs.get('mfr_cfg_path'),
kwargs.get('device')
)
elif model_name == AtomicModel.OCR:
atom_model = ocr_model_init(
kwargs.get("ocr_show_log"),
kwargs.get("det_db_box_thresh"),
kwargs.get("lang")
kwargs.get('ocr_show_log'),
kwargs.get('det_db_box_thresh'),
kwargs.get('lang')
)
elif model_name == AtomicModel.Table:
atom_model = table_model_init(
kwargs.get("table_model_name"),
kwargs.get("table_model_path"),
kwargs.get("table_max_time"),
kwargs.get("device")
kwargs.get('table_model_name'),
kwargs.get('table_model_path'),
kwargs.get('table_max_time'),
kwargs.get('device')
)
else:
logger.error("model name not allow")
logger.error('model name not allow')
exit(1)
if atom_model is None:
logger.error("model init failed")
logger.error('model init failed')
exit(1)
else:
return atom_model
magic_pdf/model/sub_modules/ocr/paddleocr/ocr_utils.py
View file @ 158e556b
......@@ -71,7 +71,13 @@ def remove_intervals(original, masks):
def update_det_boxes(dt_boxes, mfd_res):
new_dt_boxes = []
angle_boxes_list = []
for text_box in dt_boxes:
if calculate_is_angle(text_box):
angle_boxes_list.append(text_box)
continue
text_bbox = points_to_bbox(text_box)
masks_list = []
for mf_box in mfd_res:
......@@ -85,6 +91,9 @@ def update_det_boxes(dt_boxes, mfd_res):
temp_dt_box.append(bbox_to_points([text_remove_mask[0], text_bbox[1], text_remove_mask[1], text_bbox[3]]))
if len(temp_dt_box) > 0:
new_dt_boxes.extend(temp_dt_box)
new_dt_boxes.extend(angle_boxes_list)
return new_dt_boxes
......@@ -143,9 +152,11 @@ def merge_det_boxes(dt_boxes):
angle_boxes_list = []
for text_box in dt_boxes:
text_bbox = points_to_bbox(text_box)
if text_bbox[2] <= text_bbox[0] or text_bbox[3] <= text_bbox[1]:
if calculate_is_angle(text_box):
angle_boxes_list.append(text_box)
continue
text_box_dict = {
'bbox': text_bbox,
'type': 'text',
......@@ -200,15 +211,21 @@ def get_ocr_result_list(ocr_res, useful_list):
ocr_result_list = []
for box_ocr_res in ocr_res:
p1, p2, p3, p4 = box_ocr_res[0]
text, score = box_ocr_res[1]
average_angle_degrees = calculate_angle_degrees(box_ocr_res[0])
if average_angle_degrees > 0.5:
if len(box_ocr_res) == 2:
p1, p2, p3, p4 = box_ocr_res[0]
text, score = box_ocr_res[1]
else:
p1, p2, p3, p4 = box_ocr_res
text, score = "", 1
# average_angle_degrees = calculate_angle_degrees(box_ocr_res[0])
# if average_angle_degrees > 0.5:
poly = [p1, p2, p3, p4]
if calculate_is_angle(poly):
# logger.info(f"average_angle_degrees: {average_angle_degrees}, text: {text}")
# 与x轴的夹角超过0.5度，对边界做一下矫正
# 计算几何中心
x_center = sum(point[0] for point in box_ocr_res[0]) / 4
y_center = sum(point[1] for point in box_ocr_res[0]) / 4
x_center = sum(point[0] for point in poly) / 4
y_center = sum(point[1] for point in poly) / 4
new_height = ((p4[1] - p1[1]) + (p3[1] - p2[1])) / 2
new_width = p3[0] - p1[0]
p1 = [x_center - new_width / 2, y_center - new_height / 2]
......@@ -257,3 +274,12 @@ def calculate_angle_degrees(poly):
# logger.info(f"average_angle_degrees: {average_angle_degrees}")
return average_angle_degrees
def calculate_is_angle(poly):
p1, p2, p3, p4 = poly
height = ((p4[1] - p1[1]) + (p3[1] - p2[1])) / 2
if 0.8 * height <= (p3[1] - p1[1]) <= 1.2 * height:
return False
else:
# logger.info((p3[1] - p1[1])/height)
return True
\ No newline at end of file
magic_pdf/model/sub_modules/ocr/paddleocr/ppocr_273_mod.py
View file @ 158e556b
......@@ -78,9 +78,18 @@ class ModifiedPaddleOCR(PaddleOCR):
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, elapse = self.text_detector(img)
if not dt_boxes:
if dt_boxes is None:
ocr_res.append(None)
continue
dt_boxes = sorted_boxes(dt_boxes)
# merge_det_boxes 和 update_det_boxes 都会把poly转成bbox再转回poly，因此需要过滤所有倾斜程度较大的文本框
dt_boxes = merge_det_boxes(dt_boxes)
if mfd_res:
bef = time.time()
dt_boxes = update_det_boxes(dt_boxes, mfd_res)
aft = time.time()
logger.debug("split text box by formula, new dt_boxes num : {}, elapsed : {}".format(
len(dt_boxes), aft - bef))
tmp_res = [box.tolist() for box in dt_boxes]
ocr_res.append(tmp_res)
return ocr_res
......@@ -125,9 +134,8 @@ class ModifiedPaddleOCR(PaddleOCR):
dt_boxes = sorted_boxes(dt_boxes)
# @todo 目前是在bbox层merge，对倾斜文本行的兼容性不佳，需要修改成支持poly的merge
# dt_boxes = merge_det_boxes(dt_boxes)
# merge_det_boxes 和 update_det_boxes 都会把poly转成bbox再转回poly，因此需要过滤所有倾斜程度较大的文本框
dt_boxes = merge_det_boxes(dt_boxes)
if mfd_res:
bef = time.time()
......
magic_pdf/model/sub_modules/table/rapidtable/rapid_table.py
View file @ 158e556b
......@@ -10,5 +10,7 @@ class RapidTableModel(object):
def predict(self, image):
ocr_result, _ = self.ocr_engine(np.asarray(image))
if ocr_result is None:
return None, None, None
html_code, table_cell_bboxes, elapse = self.table_model(np.asarray(image), ocr_result)
return html_code, table_cell_bboxes, elapse
\ No newline at end of file
magic_pdf/model/sub_modules/table/tablemaster/tablemaster_paddle.py
View file @ 158e556b
import os
import cv2
import numpy as np
from paddleocr.ppstructure.table.predict_table import TableSystem
from paddleocr.ppstructure.utility import init_args
from magic_pdf.libs.Constants import *
import os
from PIL import Image
import numpy as np
from magic_pdf.config.constants import * # noqa: F403
class TableMasterPaddleModel(object):
"""
This class is responsible for converting image of table into HTML format using a pre-trained model.
"""This class is responsible for converting image of table into HTML format
using a pre-trained model.
Attributes:
- table_sys: An instance of TableSystem initialized with parsed arguments.
Attributes:
- table_sys: An instance of TableSystem initialized with parsed arguments.
Methods:
- __init__(config): Initializes the model with configuration parameters.
- img2html(image): Converts a PIL Image or NumPy array to HTML string.
- parse_args(**kwargs): Parses configuration arguments.
Methods:
- __init__(config): Initializes the model with configuration parameters.
- img2html(image): Converts a PIL Image or NumPy array to HTML string.
- parse_args(**kwargs): Parses configuration arguments.
"""
def __init__(self, config):
......@@ -40,30 +42,30 @@ class TableMasterPaddleModel(object):
image = np.asarray(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
pred_res, _ = self.table_sys(image)
pred_html = pred_res["html"]
pred_html = pred_res['html']
# res = '<td><table border="1">' + pred_html.replace("<html><body><table>", "").replace(
# "</table></body></html>","") + "</table></td>\n"
return pred_html
def parse_args(self, **kwargs):
parser = init_args()
model_dir = kwargs.get("model_dir")
table_model_dir = os.path.join(model_dir, TABLE_MASTER_DIR)
table_char_dict_path = os.path.join(model_dir, TABLE_MASTER_DICT)
det_model_dir = os.path.join(model_dir, DETECT_MODEL_DIR)
rec_model_dir = os.path.join(model_dir, REC_MODEL_DIR)
rec_char_dict_path = os.path.join(model_dir, REC_CHAR_DICT)
device = kwargs.get("device", "cpu")
use_gpu = True if device.startswith("cuda") else False
model_dir = kwargs.get('model_dir')
table_model_dir = os.path.join(model_dir, TABLE_MASTER_DIR) # noqa: F405
table_char_dict_path = os.path.join(model_dir, TABLE_MASTER_DICT) # noqa: F405
det_model_dir = os.path.join(model_dir, DETECT_MODEL_DIR) # noqa: F405
rec_model_dir = os.path.join(model_dir, REC_MODEL_DIR) # noqa: F405
rec_char_dict_path = os.path.join(model_dir, REC_CHAR_DICT) # noqa: F405
device = kwargs.get('device', 'cpu')
use_gpu = True if device.startswith('cuda') else False
config = {
"use_gpu": use_gpu,
"table_max_len": kwargs.get("table_max_len", TABLE_MAX_LEN),
"table_algorithm": "TableMaster",
"table_model_dir": table_model_dir,
"table_char_dict_path": table_char_dict_path,
"det_model_dir": det_model_dir,
"rec_model_dir": rec_model_dir,
"rec_char_dict_path": rec_char_dict_path,
'use_gpu': use_gpu,
'table_max_len': kwargs.get('table_max_len', TABLE_MAX_LEN), # noqa: F405
'table_algorithm': 'TableMaster',
'table_model_dir': table_model_dir,
'table_char_dict_path': table_char_dict_path,
'det_model_dir': det_model_dir,
'rec_model_dir': rec_model_dir,
'rec_char_dict_path': rec_char_dict_path,
}
parser.set_defaults(**config)
return parser.parse_args([])
magic_pdf/para/para_pipeline.py deleted 100644 → 0
View file @ 038f48d3
import os
import json
from magic_pdf.para.commons import *
from magic_pdf.para.raw_processor import RawBlockProcessor
from magic_pdf.para.layout_match_processor import LayoutFilterProcessor
from magic_pdf.para.stats import BlockStatisticsCalculator
from magic_pdf.para.stats import DocStatisticsCalculator
from magic_pdf.para.title_processor import TitleProcessor
from magic_pdf.para.block_termination_processor import BlockTerminationProcessor
from magic_pdf.para.block_continuation_processor import BlockContinuationProcessor
from magic_pdf.para.draw import DrawAnnos
from magic_pdf.para.exceptions import (
DenseSingleLineBlockException,
TitleDetectionException,
TitleLevelException,
ParaSplitException,
ParaMergeException,
DiscardByException,
)
if sys.version_info[0] >= 3:
sys.stdout.reconfigure(encoding="utf-8") # type: ignore
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
"""
# Find and replace the interline and inline equations, should be better done before the paragraph processing
# Create "para_blocks" for each page.
# equationProcessor = EquationsProcessor()
# pdf_dic = equationProcessor.batch_process_blocks(pdf_info_dict)
# 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)
pdf_dic = titleProcessor.batch_process_blocks_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_process_blocks_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)
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 any(
info is not None
for info in [
is_discard_by_single_line_block,
is_discard_by_title_detection,
is_discard_by_title_level,
is_discard_by_split_para,
is_discard_by_merge_para,
]
):
error_info = next(
(
info
for info in [
is_discard_by_single_line_block,
is_discard_by_title_detection,
is_discard_by_title_level,
is_discard_by_split_para,
is_discard_by_merge_para,
]
if info is not None
),
None,
)
return pdf_dic, error_info
if any(
info is not None
for info in [
is_discard_by_single_line_block,
is_discard_by_title_detection,
is_discard_by_title_level,
is_discard_by_split_para,
is_discard_by_merge_para,
]
):
error_info = next(
(
info
for info in [
is_discard_by_single_line_block,
is_discard_by_title_detection,
is_discard_by_title_level,
is_discard_by_split_para,
is_discard_by_merge_para,
]
if info is not None
),
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 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
magic_pdf/para/para_split.py
View file @ 158e556b
This diff is collapsed.
magic_pdf/para/para_split_v2.py
View file @ 158e556b
This diff is collapsed.
magic_pdf/para/para_split_v3.py
View file @ 158e556b
import copy
from loguru import logger
from magic_pdf.libs.Constants import LINES_DELETED, CROSS_PAGE
from magic_pdf.libs.ocr_content_type import BlockType, ContentType
LINE_STOP_FLAG = ('.', '!', '?', '。', '！', '？', ')', '）', '"', '”', ':', '：', ';', '；')
from magic_pdf.config.constants import CROSS_PAGE, LINES_DELETED
from magic_pdf.config.ocr_content_type import BlockType, ContentType
LINE_STOP_FLAG = (
'.',
'!',
'?',
'。',
'！',
'？',
')',
'）',
'"',
'”',
':',
'：',
';',
'；',
)
LIST_END_FLAG = ('.', '。', ';', '；')
class ListLineTag:
IS_LIST_START_LINE = "is_list_start_line"
IS_LIST_END_LINE = "is_list_end_line"
IS_LIST_START_LINE = 'is_list_start_line'
IS_LIST_END_LINE = 'is_list_end_line'
def __process_blocks(blocks):
......@@ -27,12 +40,14 @@ def __process_blocks(blocks):
# 如果当前块是 text 类型
if current_block['type'] == 'text':
current_block["bbox_fs"] = copy.deepcopy(current_block["bbox"])
if 'lines' in current_block and len(current_block["lines"]) > 0:
current_block['bbox_fs'] = [min([line['bbox'][0] for line in current_block['lines']]),
min([line['bbox'][1] for line in current_block['lines']]),
max([line['bbox'][2] for line in current_block['lines']]),
max([line['bbox'][3] for line in current_block['lines']])]
current_block['bbox_fs'] = copy.deepcopy(current_block['bbox'])
if 'lines' in current_block and len(current_block['lines']) > 0:
current_block['bbox_fs'] = [
min([line['bbox'][0] for line in current_block['lines']]),
min([line['bbox'][1] for line in current_block['lines']]),
max([line['bbox'][2] for line in current_block['lines']]),
max([line['bbox'][3] for line in current_block['lines']]),
]
current_group.append(current_block)
# 检查下一个块是否存在
......@@ -64,6 +79,7 @@ def __is_list_or_index_block(block):
line_height = first_line['bbox'][3] - first_line['bbox'][1]
block_weight = block['bbox_fs'][2] - block['bbox_fs'][0]
block_height = block['bbox_fs'][3] - block['bbox_fs'][1]
page_weight, page_height = block['page_size']
left_close_num = 0
left_not_close_num = 0
......@@ -75,10 +91,17 @@ def __is_list_or_index_block(block):
multiple_para_flag = False
last_line = block['lines'][-1]
if page_weight == 0:
block_weight_radio = 0
else:
block_weight_radio = block_weight / page_weight
# logger.info(f"block_weight_radio: {block_weight_radio}")
# 如果首行左边不顶格而右边顶格,末行左边顶格而右边不顶格 （第一行可能可以右边不顶格）
if (first_line['bbox'][0] - block['bbox_fs'][0] > line_height / 2 and
abs(last_line['bbox'][0] - block['bbox_fs'][0]) < line_height / 2 and
block['bbox_fs'][2] - last_line['bbox'][2] > line_height
if (
first_line['bbox'][0] - block['bbox_fs'][0] > line_height / 2
and abs(last_line['bbox'][0] - block['bbox_fs'][0]) < line_height / 2
and block['bbox_fs'][2] - last_line['bbox'][2] > line_height
):
multiple_para_flag = True
......@@ -86,14 +109,14 @@ def __is_list_or_index_block(block):
line_mid_x = (line['bbox'][0] + line['bbox'][2]) / 2
block_mid_x = (block['bbox_fs'][0] + block['bbox_fs'][2]) / 2
if (
line['bbox'][0] - block['bbox_fs'][0] > 0.8 * line_height and
block['bbox_fs'][2] - line['bbox'][2] > 0.8 * line_height
line['bbox'][0] - block['bbox_fs'][0] > 0.8 * line_height
and block['bbox_fs'][2] - line['bbox'][2] > 0.8 * line_height
):
external_sides_not_close_num += 1
if abs(line_mid_x - block_mid_x) < line_height / 2:
center_close_num += 1
line_text = ""
line_text = ''
for span in line['spans']:
span_type = span['type']
......@@ -114,7 +137,12 @@ def __is_list_or_index_block(block):
right_close_num += 1
else:
# 右侧不顶格情况下是否有一段距离，拍脑袋用0.3block宽度做阈值
closed_area = 0.26 * block_weight
# block宽的阈值可以小些，block窄的阈值要大
if block_weight_radio >= 0.5:
closed_area = 0.26 * block_weight
else:
closed_area = 0.36 * block_weight
if block['bbox_fs'][2] - line['bbox'][2] > closed_area:
right_not_close_num += 1
......@@ -136,15 +164,19 @@ def __is_list_or_index_block(block):
if line_text[-1].isdigit():
num_end_count += 1
if num_start_count / len(lines_text_list) >= 0.8 or num_end_count / len(lines_text_list) >= 0.8:
if (
num_start_count / len(lines_text_list) >= 0.8
or num_end_count / len(lines_text_list) >= 0.8
):
line_num_flag = True
if flag_end_count / len(lines_text_list) >= 0.8:
line_end_flag = True
# 有的目录右侧不贴边, 目前认为左边或者右边有一边全贴边，且符合数字规则极为index
if ((left_close_num / len(block['lines']) >= 0.8 or right_close_num / len(block['lines']) >= 0.8)
and line_num_flag
):
if (
left_close_num / len(block['lines']) >= 0.8
or right_close_num / len(block['lines']) >= 0.8
) and line_num_flag:
for line in block['lines']:
line[ListLineTag.IS_LIST_START_LINE] = True
return BlockType.Index
......@@ -152,17 +184,21 @@ def __is_list_or_index_block(block):
# 全部line都居中的特殊list识别，每行都需要换行，特征是多行，且大多数行都前后not_close,每line中点x坐标接近
# 补充条件block的长宽比有要求
elif (
external_sides_not_close_num >= 2 and
center_close_num == len(block['lines']) and
external_sides_not_close_num / len(block['lines']) >= 0.5 and
block_height / block_weight > 0.4
external_sides_not_close_num >= 2
and center_close_num == len(block['lines'])
and external_sides_not_close_num / len(block['lines']) >= 0.5
and block_height / block_weight > 0.4
):
for line in block['lines']:
line[ListLineTag.IS_LIST_START_LINE] = True
return BlockType.List
elif left_close_num >= 2 and (
right_not_close_num >= 2 or line_end_flag or left_not_close_num >= 2) and not multiple_para_flag:
elif (
left_close_num >= 2
and (right_not_close_num >= 2 or line_end_flag or left_not_close_num >= 2)
and not multiple_para_flag
# and block_weight_radio > 0.27
):
# 处理一种特殊的没有缩进的list，所有行都贴左边，通过右边的空隙判断是否是item尾
if left_close_num / len(block['lines']) > 0.8:
# 这种是每个item只有一行，且左边都贴边的短item list
......@@ -173,10 +209,15 @@ def __is_list_or_index_block(block):
# 这种是大部分line item 都有结束标识符的情况，按结束标识符区分不同item
elif line_end_flag:
for i, line in enumerate(block['lines']):
if len(lines_text_list[i]) > 0 and lines_text_list[i][-1] in LIST_END_FLAG:
if (
len(lines_text_list[i]) > 0
and lines_text_list[i][-1] in LIST_END_FLAG
):
line[ListLineTag.IS_LIST_END_LINE] = True
if i + 1 < len(block['lines']):
block['lines'][i + 1][ListLineTag.IS_LIST_START_LINE] = True
block['lines'][i + 1][
ListLineTag.IS_LIST_START_LINE
] = True
# line item基本没有结束标识符，而且也没有缩进，按右侧空隙判断哪些是item end
else:
line_start_flag = False
......@@ -185,7 +226,10 @@ def __is_list_or_index_block(block):
line[ListLineTag.IS_LIST_START_LINE] = True
line_start_flag = False
if abs(block['bbox_fs'][2] - line['bbox'][2]) > 0.1 * block_weight:
if (
abs(block['bbox_fs'][2] - line['bbox'][2])
> 0.1 * block_weight
):
line[ListLineTag.IS_LIST_END_LINE] = True
line_start_flag = True
# 一种有缩进的特殊有序list,start line 左侧不贴边且以数字开头，end line 以 IS_LIST_END_FLAG 结尾且数量和start line 一致
......@@ -223,18 +267,25 @@ def __merge_2_text_blocks(block1, block2):
if len(last_line['spans']) > 0:
last_span = last_line['spans'][-1]
line_height = last_line['bbox'][3] - last_line['bbox'][1]
if (abs(block2['bbox_fs'][2] - last_line['bbox'][2]) < line_height and
not last_span['content'].endswith(LINE_STOP_FLAG) and
# 两个block宽度差距超过2倍也不合并
abs(block1_weight - block2_weight) < min_block_weight
):
if block1['page_num'] != block2['page_num']:
for line in block1['lines']:
for span in line['spans']:
span[CROSS_PAGE] = True
block2['lines'].extend(block1['lines'])
block1['lines'] = []
block1[LINES_DELETED] = True
if len(first_line['spans']) > 0:
first_span = first_line['spans'][0]
if len(first_span['content']) > 0:
span_start_with_num = first_span['content'][0].isdigit()
if (
abs(block2['bbox_fs'][2] - last_line['bbox'][2])
< line_height
and not last_span['content'].endswith(LINE_STOP_FLAG)
# 两个block宽度差距超过2倍也不合并
and abs(block1_weight - block2_weight) < min_block_weight
and not span_start_with_num
):
if block1['page_num'] != block2['page_num']:
for line in block1['lines']:
for span in line['spans']:
span[CROSS_PAGE] = True
block2['lines'].extend(block1['lines'])
block1['lines'] = []
block1[LINES_DELETED] = True
return block1, block2
......@@ -263,7 +314,6 @@ def __is_list_group(text_blocks_group):
def __para_merge_page(blocks):
page_text_blocks_groups = __process_blocks(blocks)
for text_blocks_group in page_text_blocks_groups:
if len(text_blocks_group) > 0:
# 需要先在合并前对所有block判断是否为list or index block
for block in text_blocks_group:
......@@ -272,7 +322,6 @@ def __para_merge_page(blocks):
# logger.info(f"{block['type']}:{block}")
if len(text_blocks_group) > 1:
# 在合并前判断这个group 是否是一个 list group
is_list_group = __is_list_group(text_blocks_group)
......@@ -284,11 +333,18 @@ def __para_merge_page(blocks):
if i - 1 >= 0:
prev_block = text_blocks_group[i - 1]
if current_block['type'] == 'text' and prev_block['type'] == 'text' and not is_list_group:
if (
current_block['type'] == 'text'
and prev_block['type'] == 'text'
and not is_list_group
):
__merge_2_text_blocks(current_block, prev_block)
elif (
(current_block['type'] == BlockType.List and prev_block['type'] == BlockType.List) or
(current_block['type'] == BlockType.Index and prev_block['type'] == BlockType.Index)
current_block['type'] == BlockType.List
and prev_block['type'] == BlockType.List
) or (
current_block['type'] == BlockType.Index
and prev_block['type'] == BlockType.Index
):
__merge_2_list_blocks(current_block, prev_block)
......@@ -296,12 +352,13 @@ def __para_merge_page(blocks):
continue
def para_split(pdf_info_dict, debug_mode=False):
def para_split(pdf_info_dict):
all_blocks = []
for page_num, page in pdf_info_dict.items():
blocks = copy.deepcopy(page['preproc_blocks'])
for block in blocks:
block['page_num'] = page_num
block['page_size'] = page['page_size']
all_blocks.extend(blocks)
__para_merge_page(all_blocks)
......@@ -317,4 +374,4 @@ if __name__ == '__main__':
# 调用函数
groups = __process_blocks(input_blocks)
for group_index, group in enumerate(groups):
print(f"Group {group_index}: {group}")
print(f'Group {group_index}: {group}')
magic_pdf/pdf_parse_by_ocr.py
View file @ 158e556b
......@@ -9,6 +9,7 @@ def parse_pdf_by_ocr(pdf_bytes,
start_page_id=0,
end_page_id=None,
debug_mode=False,
lang=None,
):
dataset = PymuDocDataset(pdf_bytes)
return pdf_parse_union(dataset,
......@@ -18,4 +19,5 @@ def parse_pdf_by_ocr(pdf_bytes,
start_page_id=start_page_id,
end_page_id=end_page_id,
debug_mode=debug_mode,
lang=lang,
)
magic_pdf/pdf_parse_by_txt.py
View file @ 158e556b
......@@ -10,6 +10,7 @@ def parse_pdf_by_txt(
start_page_id=0,
end_page_id=None,
debug_mode=False,
lang=None,
):
dataset = PymuDocDataset(pdf_bytes)
return pdf_parse_union(dataset,
......@@ -19,4 +20,5 @@ def parse_pdf_by_txt(
start_page_id=start_page_id,
end_page_id=end_page_id,
debug_mode=debug_mode,
lang=lang,
)
magic_pdf/pdf_parse_union_core.py
View file @ 158e556b
This diff is collapsed.
magic_pdf/pdf_parse_union_core_v2.py
View file @ 158e556b
......@@ -7,18 +7,32 @@ from typing import List
import torch
from loguru import logger
from magic_pdf.config.drop_reason import DropReason
from magic_pdf.config.enums import SupportedPdfParseMethod
from magic_pdf.config.ocr_content_type import BlockType, ContentType
from magic_pdf.data.dataset import Dataset, PageableData
from magic_pdf.libs.boxbase import calculate_overlap_area_in_bbox1_area_ratio
from magic_pdf.libs.clean_memory import clean_memory
from magic_pdf.libs.commons import fitz, get_delta_time
from magic_pdf.libs.config_reader import get_local_layoutreader_model_dir
from magic_pdf.libs.convert_utils import dict_to_list
from magic_pdf.libs.drop_reason import DropReason
from magic_pdf.libs.hash_utils import compute_md5
from magic_pdf.libs.local_math import float_equal
from magic_pdf.libs.ocr_content_type import ContentType, BlockType
from magic_pdf.libs.pdf_image_tools import cut_image_to_pil_image
from magic_pdf.model.magic_model import MagicModel
os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1' # 禁止albumentations检查更新
os.environ['YOLO_VERBOSE'] = 'False' # disable yolo logger
try:
import torchtext
if torchtext.__version__ >= "0.18.0":
torchtext.disable_torchtext_deprecation_warning()
except ImportError:
pass
from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
from magic_pdf.para.para_split_v3 import para_split
from magic_pdf.pre_proc.citationmarker_remove import remove_citation_marker
from magic_pdf.pre_proc.construct_page_dict import \
......@@ -30,8 +44,8 @@ from magic_pdf.pre_proc.equations_replace import (
from magic_pdf.pre_proc.ocr_detect_all_bboxes import \
ocr_prepare_bboxes_for_layout_split_v2
from magic_pdf.pre_proc.ocr_dict_merge import (fill_spans_in_blocks,
fix_discarded_block,
fix_block_spans_v2)
fix_block_spans_v2,
fix_discarded_block)
from magic_pdf.pre_proc.ocr_span_list_modify import (
get_qa_need_list_v2, remove_overlaps_low_confidence_spans,
remove_overlaps_min_spans)
......@@ -74,7 +88,151 @@ def __replace_STX_ETX(text_str: str):
return text_str
def txt_spans_extract(pdf_page, inline_equations, interline_equations):
def chars_to_content(span):
# # 先给chars按char['bbox']的x坐标排序
# span['chars'] = sorted(span['chars'], key=lambda x: x['bbox'][0])
# 先给chars按char['bbox']的中心点的x坐标排序
span['chars'] = sorted(span['chars'], key=lambda x: (x['bbox'][0] + x['bbox'][2]) / 2)
content = ''
# 求char的平均宽度
if len(span['chars']) == 0:
span['content'] = content
del span['chars']
return
else:
char_width_sum = sum([char['bbox'][2] - char['bbox'][0] for char in span['chars']])
char_avg_width = char_width_sum / len(span['chars'])
for char in span['chars']:
# 如果下一个char的x0和上一个char的x1距离超过一个字符宽度，则需要在中间插入一个空格
if char['bbox'][0] - span['chars'][span['chars'].index(char) - 1]['bbox'][2] > char_avg_width:
content += ' '
content += char['c']
span['content'] = __replace_STX_ETX(content)
del span['chars']
LINE_STOP_FLAG = ('.', '!', '?', '。', '！', '？', ')', '）', '"', '”', ':', '：', ';', '；', ']', '】', '}', '}', '>', '》', '、', ',', '，', '-', '—', '–',)
def fill_char_in_spans(spans, all_chars):
for char in all_chars:
for span in spans:
# 判断char是否属于LINE_STOP_FLAG
if char['c'] in LINE_STOP_FLAG:
char_is_line_stop_flag = True
else:
char_is_line_stop_flag = False
if calculate_char_in_span(char['bbox'], span['bbox'], char_is_line_stop_flag):
span['chars'].append(char)
break
for span in spans:
chars_to_content(span)
# 使用鲁棒性更强的中心点坐标判断
def calculate_char_in_span(char_bbox, span_bbox, char_is_line_stop_flag):
char_center_x = (char_bbox[0] + char_bbox[2]) / 2
char_center_y = (char_bbox[1] + char_bbox[3]) / 2
span_center_y = (span_bbox[1] + span_bbox[3]) / 2
span_height = span_bbox[3] - span_bbox[1]
if (
span_bbox[0] < char_center_x < span_bbox[2]
and span_bbox[1] < char_center_y < span_bbox[3]
and abs(char_center_y - span_center_y) < span_height / 4 # 字符的中轴和span的中轴高度差不能超过1/4span高度
):
return True
else:
# 如果char是LINE_STOP_FLAG，就不用中心点判定，换一种方案（左边界在span区域内，高度判定和之前逻辑一致）
# 主要是给结尾符号一个进入span的机会，这个char还应该离span右边界较近
if char_is_line_stop_flag:
if (
(span_bbox[2] - span_height) < char_bbox[0] < span_bbox[2]
and char_center_x > span_bbox[0]
and span_bbox[1] < char_center_y < span_bbox[3]
and abs(char_center_y - span_center_y) < span_height / 4
):
return True
else:
return False
def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang):
useful_spans = []
unuseful_spans = []
for span in spans:
for block in all_bboxes:
if block[7] in [BlockType.ImageBody, BlockType.TableBody, BlockType.InterlineEquation]:
continue
else:
if calculate_overlap_area_in_bbox1_area_ratio(span['bbox'], block[0:4]) > 0.5:
useful_spans.append(span)
break
for block in all_discarded_blocks:
if calculate_overlap_area_in_bbox1_area_ratio(span['bbox'], block[0:4]) > 0.5:
unuseful_spans.append(span)
break
text_blocks = pdf_page.get_text('rawdict', flags=fitz.TEXTFLAGS_TEXT)['blocks']
# @todo: 拿到char之后把倾斜角度较大的先删一遍
all_pymu_chars = []
for block in text_blocks:
for line in block['lines']:
for span in line['spans']:
all_pymu_chars.extend(span['chars'])
new_spans = []
for span in useful_spans:
if span['type'] in [ContentType.Text]:
span['chars'] = []
new_spans.append(span)
for span in unuseful_spans:
if span['type'] in [ContentType.Text]:
span['chars'] = []
new_spans.append(span)
fill_char_in_spans(new_spans, all_pymu_chars)
empty_spans = []
for span in new_spans:
if len(span['content']) == 0:
empty_spans.append(span)
if len(empty_spans) > 0:
# 初始化ocr模型
atom_model_manager = AtomModelSingleton()
ocr_model = atom_model_manager.get_atom_model(
atom_model_name="ocr",
ocr_show_log=False,
det_db_box_thresh=0.3,
lang=lang
)
for span in empty_spans:
spans.remove(span)
# 对span的bbox截图
span_img = cut_image_to_pil_image(span['bbox'], pdf_page, mode="cv2")
ocr_res = ocr_model.ocr(span_img, det=False)
# logger.info(f"ocr_res: {ocr_res}")
# logger.info(f"empty_span: {span}")
if ocr_res and len(ocr_res) > 0:
if len(ocr_res[0]) > 0:
ocr_text, ocr_score = ocr_res[0][0]
if ocr_score > 0.5 and len(ocr_text) > 0:
span['content'] = ocr_text
spans.append(span)
return spans
def txt_spans_extract_v1(pdf_page, inline_equations, interline_equations):
text_raw_blocks = pdf_page.get_text('dict', flags=fitz.TEXTFLAGS_TEXT)['blocks']
char_level_text_blocks = pdf_page.get_text('rawdict', flags=fitz.TEXTFLAGS_TEXT)[
'blocks'
......@@ -164,8 +322,8 @@ class ModelSingleton:
def do_predict(boxes: List[List[int]], model) -> List[int]:
from magic_pdf.model.sub_modules.reading_oreder.layoutreader.helpers import (boxes2inputs, parse_logits,
prepare_inputs)
from magic_pdf.model.sub_modules.reading_oreder.layoutreader.helpers import (
boxes2inputs, parse_logits, prepare_inputs)
inputs = boxes2inputs(boxes)
inputs = prepare_inputs(inputs, model)
......@@ -206,7 +364,9 @@ def cal_block_index(fix_blocks, sorted_bboxes):
del block['real_lines']
import numpy as np
from magic_pdf.model.sub_modules.reading_oreder.layoutreader.xycut import recursive_xy_cut
from magic_pdf.model.sub_modules.reading_oreder.layoutreader.xycut import \
recursive_xy_cut
random_boxes = np.array(block_bboxes)
np.random.shuffle(random_boxes)
......@@ -291,7 +451,7 @@ def sort_lines_by_model(fix_blocks, page_w, page_h, line_height):
page_line_list.append(bbox)
elif block['type'] in [BlockType.ImageBody, BlockType.TableBody]:
bbox = block['bbox']
block["real_lines"] = copy.deepcopy(block['lines'])
block['real_lines'] = copy.deepcopy(block['lines'])
lines = insert_lines_into_block(bbox, line_height, page_w, page_h)
block['lines'] = []
for line in lines:
......@@ -462,18 +622,16 @@ def remove_outside_spans(spans, all_bboxes, all_discarded_blocks):
def parse_page_core(
page_doc: PageableData, magic_model, page_id, pdf_bytes_md5, imageWriter, parse_mode
page_doc: PageableData, magic_model, page_id, pdf_bytes_md5, imageWriter, parse_mode, lang
):
need_drop = False
drop_reason = []
"""从magic_model对象中获取后面会用到的区块信息"""
# img_blocks = magic_model.get_imgs(page_id)
# table_blocks = magic_model.get_tables(page_id)
img_groups = magic_model.get_imgs_v2(page_id)
table_groups = magic_model.get_tables_v2(page_id)
"""对image和table的区块分组"""
img_body_blocks, img_caption_blocks, img_footnote_blocks = process_groups(
img_groups, 'image_body', 'image_caption_list', 'image_footnote_list'
)
......@@ -517,38 +675,20 @@ def parse_page_core(
page_h,
)
"""获取所有的spans信息"""
spans = magic_model.get_all_spans(page_id)
"""根据parse_mode，构造spans"""
if parse_mode == SupportedPdfParseMethod.TXT:
"""ocr 中文本类的 span 用 pymu spans 替换！"""
pymu_spans = txt_spans_extract(page_doc, inline_equations, interline_equations)
spans = replace_text_span(pymu_spans, spans)
elif parse_mode == SupportedPdfParseMethod.OCR:
pass
else:
raise Exception('parse_mode must be txt or ocr')
"""在删除重复span之前，应该通过image_body和table_body的block过滤一下image和table的span"""
"""顺便删除大水印并保留abandon的span"""
spans = remove_outside_spans(spans, all_bboxes, all_discarded_blocks)
"""删除重叠spans中置信度较低的那些"""
spans, dropped_spans_by_confidence = remove_overlaps_low_confidence_spans(spans)
"""删除重叠spans中较小的那些"""
spans, dropped_spans_by_span_overlap = remove_overlaps_min_spans(spans)
"""对image和table截图"""
spans = ocr_cut_image_and_table(
spans, page_doc, page_id, pdf_bytes_md5, imageWriter
)
"""先处理不需要排版的discarded_blocks"""
discarded_block_with_spans, spans = fill_spans_in_blocks(
all_discarded_blocks, spans, 0.4
)
fix_discarded_blocks = fix_discarded_block(discarded_block_with_spans)
"""如果当前页面没有bbox则跳过"""
"""如果当前页面没有有效的bbox则跳过"""
if len(all_bboxes) == 0:
logger.warning(f'skip this page, not found useful bbox, page_id: {page_id}')
return ocr_construct_page_component_v2(
......@@ -566,7 +706,32 @@ def parse_page_core(
drop_reason,
)
"""将span填入blocks中"""
"""删除重叠spans中置信度较低的那些"""
spans, dropped_spans_by_confidence = remove_overlaps_low_confidence_spans(spans)
"""删除重叠spans中较小的那些"""
spans, dropped_spans_by_span_overlap = remove_overlaps_min_spans(spans)
"""根据parse_mode，构造spans，主要是文本类的字符填充"""
if parse_mode == SupportedPdfParseMethod.TXT:
"""之前的公式替换方案"""
# pymu_spans = txt_spans_extract_v1(page_doc, inline_equations, interline_equations)
# spans = replace_text_span(pymu_spans, spans)
"""ocr 中文本类的 span 用 pymu spans 替换！"""
spans = txt_spans_extract_v2(page_doc, spans, all_bboxes, all_discarded_blocks, lang)
elif parse_mode == SupportedPdfParseMethod.OCR:
pass
else:
raise Exception('parse_mode must be txt or ocr')
"""对image和table截图"""
spans = ocr_cut_image_and_table(
spans, page_doc, page_id, pdf_bytes_md5, imageWriter
)
"""span填充进block"""
block_with_spans, spans = fill_spans_in_blocks(all_bboxes, spans, 0.5)
"""对block进行fix操作"""
......@@ -616,6 +781,7 @@ def pdf_parse_union(
start_page_id=0,
end_page_id=None,
debug_mode=False,
lang=None,
):
pdf_bytes_md5 = compute_md5(dataset.data_bits())
......@@ -652,7 +818,7 @@ def pdf_parse_union(
"""解析pdf中的每一页"""
if start_page_id <= page_id <= end_page_id:
page_info = parse_page_core(
page, magic_model, page_id, pdf_bytes_md5, imageWriter, parse_mode
page, magic_model, page_id, pdf_bytes_md5, imageWriter, parse_mode, lang
)
else:
page_info = page.get_page_info()
......@@ -664,7 +830,7 @@ def pdf_parse_union(
pdf_info_dict[f'page_{page_id}'] = page_info
"""分段"""
para_split(pdf_info_dict, debug_mode=debug_mode)
para_split(pdf_info_dict)
"""dict转list"""
pdf_info_list = dict_to_list(pdf_info_dict)
......
magic_pdf/pipe/AbsPipe.py
View file @ 158e556b
This diff is collapsed.
magic_pdf/pipe/OCRPipe.py
View file @ 158e556b
from loguru import logger
from magic_pdf.libs.MakeContentConfig import DropMode, MakeMode
from magic_pdf.config.make_content_config import DropMode, MakeMode
from magic_pdf.data.data_reader_writer import DataWriter
from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
from magic_pdf.rw.AbsReaderWriter import AbsReaderWriter
from magic_pdf.pipe.AbsPipe import AbsPipe
from magic_pdf.user_api import parse_ocr_pdf
class OCRPipe(AbsPipe):
def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: AbsReaderWriter, is_debug: bool = False,
def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
start_page_id=0, end_page_id=None, lang=None,
layout_model=None, formula_enable=None, table_enable=None):
super().__init__(pdf_bytes, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
......@@ -32,10 +32,10 @@ class OCRPipe(AbsPipe):
def pipe_mk_uni_format(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF):
result = super().pipe_mk_uni_format(img_parent_path, drop_mode)
logger.info("ocr_pipe mk content list finished")
logger.info('ocr_pipe mk content list finished')
return result
def pipe_mk_markdown(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF, md_make_mode=MakeMode.MM_MD):
result = super().pipe_mk_markdown(img_parent_path, drop_mode, md_make_mode)
logger.info(f"ocr_pipe mk {md_make_mode} finished")
logger.info(f'ocr_pipe mk {md_make_mode} finished')
return result
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