该项目主要是汇集全网开源的版面分析的项目，具体来说，就是分析给定的文档类别图像（论文截图、研报等），定位其中类别和位置，如标题、段落、表格和图片等各个部分。
目前支持已经支持的版面分析模型如下：
model_type |
版面类型 | 支持类别 |
|---|---|---|
pp_layout_table |
表格 | ["table"] |
pp_layout_publaynet |
英文 | ["text", "title", "list", "table", "figure"] |
pp_layout_cdla |
中文 | ['text', 'title', 'figure', 'figure_caption', 'table', 'table_caption', 'header', 'footer', 'reference', 'equation'] |
yolov8n_layout_paper |
论文 | ['Text', 'Title', 'Header', 'Footer', 'Figure', 'Table', 'Toc', 'Figure caption', 'Table caption'] |
yolov8n_layout_report |
研报 | ['Text', 'Title', 'Header', 'Footer', 'Figure', 'Table', 'Toc', 'Figure caption', 'Table caption'] |
yolov8n_layout_publaynet |
英文 | ["Text", "Title", "List", "Table", "Figure"] |
yolov8n_layout_general6 |
通用 | ["Text", "Title", "Figure", "Table", "Caption", "Equation"] |
🔥doclayout_docstructbench |
通用 | ['title', 'plain text', 'abandon', 'figure', 'figure_caption', 'table', 'table_caption', 'table_footnote', 'isolate_formula', 'formula_caption'] |
🔥doclayout_d4la |
通用 | ['DocTitle', 'ParaTitle', 'ParaText', 'ListText', 'RegionTitle', 'Date', 'LetterHead', 'LetterDear', 'LetterSign', 'Question', 'OtherText', 'RegionKV', 'RegionList', 'Abstract', 'Author', 'TableName', 'Table', 'Figure', 'FigureName', 'Equation', 'Reference', 'Footer', 'PageHeader', 'PageFooter', 'Number', 'Catalog', 'PageNumber'] |
🔥doclayout_docsynth |
通用 | ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'] |
PP模型来源：PaddleOCR 版面分析
yolov8n系列来源：360LayoutAnalysis
（推荐使用）🔥doclayout_yolo模型来源：DocLayout-YOLO，该模型是目前最为优秀的开源模型，挑选了3个基于不同训练集训练得到的模型。其中doclayout_docstructbench来自link，doclayout_d4la来自link，doclayout_docsynth来自link。
上述模型下载地址为：link
由于模型较小，预先将中文版面分析模型(layout_cdla.onnx)打包进了whl包内，如果做中文版面分析，可直接安装使用
pip install rapid-layoutimport cv2
from imread_from_url import imread_from_url # pip install imread_from_url
from rapid_layout import RapidLayout, VisLayout
# model_type类型参见上表。指定不同model_type时，会自动下载相应模型到安装目录下的。
layout_engine = RapidLayout(model_type="doclayout_docstructbench", conf_thres=0.2)
img_url = "https://raw.githubusercontent.com/opendatalab/DocLayout-YOLO/refs/heads/main/assets/example/financial.jpg"
img = imread_from_url(img_url)
boxes, scores, class_names, elapse = layout_engine(img)
ploted_img = VisLayout.draw_detections(img, boxes, scores, class_names)
if ploted_img is not None:
cv2.imwrite("layout_res.png", ploted_img)
$ rapid_layout -h
usage: rapid_layout [-h] -img IMG_PATH
[-m {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}]
[--conf_thres {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}]
[--iou_thres {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}]
[--use_cuda] [--use_dml] [-v]
options:
-h, --help show this help message and exit
-img IMG_PATH, --img_path IMG_PATH
Path to image for layout.
-m {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}, --model_type {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}
Support model type
--conf_thres {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}
Box threshold, the range is [0, 1]
--iou_thres {pp_layout_cdla,pp_layout_publaynet,pp_layout_table,yolov8n_layout_paper,yolov8n_layout_report,yolov8n_layout_publaynet,yolov8n_layout_general6,doclayout_docstructbench,doclayout_d4la,doclayout_docsynth}
IoU threshold, the range is [0, 1]
--use_cuda Whether to use cuda.
--use_dml Whether to use DirectML, which only works in Windows10+.
-v, --vis Wheter to visualize the layout results.-
示例:
rapid_layout -v -img test_images/layout.png
- 因为版面分析模型输入图像尺寸固定，故可使用
onnxruntime-gpu来提速。 - 因为
rapid_layout库默认依赖是CPU版onnxruntime，如果想要使用GPU推理，需要手动安装onnxruntime-gpu。 - 详细使用和评测可参见AI Studio
pip install rapid_layout
pip uninstall onnxruntime
# 这里一定要确定onnxruntime-gpu与GPU对应
# 可参见https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements
pip install onnxruntime-gpuimport cv2
from rapid_layout import RapidLayout
from pathlib import Path
# 注意：这里需要使用use_cuda指定参数
layout_engine = RapidLayout(model_type="doclayout_yolo", conf_thres=0.2, use_cuda=True)
# warm up
layout_engine("images/12027_5.png")
elapses = []
img_list = list(Path('images').iterdir())
for img_path in img_list:
boxes, scores, class_names, elapse = layout_engine(img_path)
print(f"{img_path}: {elapse}s")
elapses.append(elapse)
avg_elapse = sum(elapses) / len(elapses)
print(f'avg elapse: {avg_elapse:.4f}')