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# coding: utf-8
from enum import Enum
from pathlib import Path
from typing import Union, Optional, List, Dict, Any
from PIL import Image
from cnstd import LayoutAnalyzer
from cnstd.yolov7.consts import CATEGORY_DICT
from .utils import read_img, save_layout_img, select_device
class ElementType(Enum):
ABANDONED = -2 # 可以指定有些区域不做识别，如 Image 与 Image caption 中间地带
IGNORED = -1
UNKNOWN = 0
TEXT = 1
TITLE = 2
FIGURE = 3
TABLE = 4
FORMULA = 5
PLAIN_TEXT = 11 # 与 TEXT 类似，但是绝对不包含公式
def __repr__(self) -> str:
return self.name
def __str__(self) -> str:
return self.name
class LayoutParser(object):
def __init__(
self,
model_type: str = 'yolov7_tiny', # 当前仅支持 `yolov7_tiny`
model_backend: str = 'pytorch', # 当前仅支持 `pytorch`
device: str = None,
**kwargs
):
device = select_device(device)
device = device if device != 'mps' else 'cpu'
self.layout_model = LayoutAnalyzer(
model_name='layout',
model_type=model_type,
model_backend=model_backend,
device=device,
**kwargs,
)
self.ignored_types = {'_background_', 'Footer', 'Reference'}
self.type_mappings = {
'Header': ElementType.TEXT,
'Text': ElementType.TEXT,
'Title': ElementType.TITLE,
'Figure': ElementType.FIGURE,
'Figure caption': ElementType.TEXT,
'Table': ElementType.TABLE,
'Table caption': ElementType.TEXT,
'Reference': ElementType.TEXT,
'Equation': ElementType.FORMULA,
}
@classmethod
def from_config(cls, configs: Optional[dict] = None, device: str = None, **kwargs):
configs = configs or {}
device = select_device(device)
configs['device'] = device if device != 'mps' else 'cpu'
return cls(
model_type=configs.get('model_type', 'yolov7_tiny'),
model_backend=configs.get('model_backend', 'pytorch'),
device=device,
**kwargs,
)
def __call__(self, *args, **kwargs):
return self.parse(*args, **kwargs)
def parse(
self,
img: Union[str, Path, Image.Image],
resized_shape: int = 608,
table_as_image: bool = False,
**kwargs
) -> (List[Dict[str, Any]], Dict[str, Any]):
"""
Args:
img ():
resized_shape ():
table_as_image ():
**kwargs ():
Returns: parsed results & column meta information;
the parsed results is a list of dict with keys: 'type', 'position', 'score':
* type: ElementType
* position: np.ndarray, with shape of (4, 2)
* score: float
the column meta is a dict, with column number as its keys.
"""
if isinstance(img, Image.Image):
img0 = img.convert('RGB')
else:
img0 = read_img(img, return_type='Image')
layout_out = self.layout_model(img0.copy(), resized_shape=resized_shape)
if kwargs.get('save_layout_res'):
save_layout_img(
img0,
CATEGORY_DICT['layout'],
layout_out,
kwargs.get('save_layout_res'),
key='box',
)
final_out = []
for box_info in layout_out:
image_type = box_info['type']
if image_type in self.ignored_types:
continue
image_type = self.type_mappings.get(image_type, image_type)
if table_as_image and image_type == ElementType.TABLE:
image_type = ElementType.FIGURE
final_out.append(
{
'type': image_type,
'position': box_info['box'],
'score': box_info['score'],
}
)
return final_out, {}
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