mirror of
https://github.com/RapidAI/RapidOCR.git
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74 lines
3.8 KiB
Markdown
74 lines
3.8 KiB
Markdown
## Rapid Layout
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<p align="left">
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<a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
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<a href="https://pypi.org/project/rapid-layout/"><img alt="PyPI" src="https://img.shields.io/pypi/v/rapid-layout"></a>
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<a href="https://pepy.tech/project/rapid-layout"><img src="https://static.pepy.tech/personalized-badge/rapid-layout?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads"></a>
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</p>
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#### 简介和说明
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- 该部分主要是做文档类图像的版面分析。模型来源：[PaddleOCR 版面分析](https://github.com/PaddlePaddle/PaddleOCR/blob/133d67f27dc8a241d6b2e30a9f047a0fb75bebbe/ppstructure/layout/README_ch.md)
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- 具体来说，就是分析给定的文档类别图像（论文截图等），定位其中类别和位置，如标题、段落、表格和图片等各个部分。
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- 目前支持三种类别的版面分析模型：中文、英文和表格版面分析模型，具体可参见下面表格：
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| 模型类型 | 模型名称 | 模型大小 | 支持种类 |
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|:---:|:---:|:---:|:---:|
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| 表格 | `layout_table.onnx` | 7.06M | `table` |
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| 英文 | `layout_publaynet.onnx` | 7.06M | `text title list table figure` |
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| 中文 | `layout_cdla.onnx` | 7.07M | `text title figure figure_caption table table_caption` <br> `header footer reference equation` |
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- 模型下载地址为：[百度网盘](https://pan.baidu.com/s/1PI9fksW6F6kQfJhwUkewWg?pwd=p29g) | [Google Drive](https://drive.google.com/drive/folders/1DAPWSN2zGQ-ED_Pz7RaJGTjfkN2-Mvsf?usp=sharing)
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#### 使用方式
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1. pip安装
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- 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
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```bash
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$ pip install rapid-layout
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```
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2. python脚本运行
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```python
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import cv2
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from rapid_layout import RapidLayout
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# RapidLayout类提供model_path参数，可以自行指定上述3个模型，默认是layout_cdla.onnx
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# layout_engine = RapidLayout(model_path='layout_publaynet.onnx')
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layout_engine = RapidLayout()
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img = cv2.imread('test_images/layout.png')
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layout_res, elapse = layout_engine(img)
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```
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3. 终端运行
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- 用法:
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```bash
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$ rapid_layout -h
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usage: rapid_layout [-h] [-v] -img IMG_PATH [-m MODEL_PATH]
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optional arguments:
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-h, --help show this help message and exit
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-v, --vis Wheter to visualize the layout results.
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-img IMG_PATH, --img_path IMG_PATH
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Path to image for layout.
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-m MODEL_PATH, --model_path MODEL_PATH
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The model path used for inference.
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```
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- 示例:
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```bash
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$ rapid_layout -v -img test_images/layout.png
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```
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4. 结果
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- 返回结果
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```python
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# bbox: [左上角x0,左上角y0, 右下角x0, 右上角x1]
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# label: 类别
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[
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{'bbox': array([321.4160495, 91.53214898, 562.06141263, 199.85522603]), 'label': 'text'},
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{'bbox': array([58.67292211, 107.29000663, 300.25448676, 199.68142]), 'label': 'table_caption'}
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]
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```
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- 可视化结果
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<div align="center">
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<img src="https://warehouse-camo.ingress.cmh1.psfhosted.org/bbf4e9220b4d38ccefad2f86d1d894d6f2e8e98e/68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f526170696441492f52617069644f43522f393437633639353864333066343763376337623031366637646333303866323335616365633365652f707974686f6e2f72617069645f7374727563747572652f746573745f696d616765732f6c61796f75745f726573756c742e6a7067" width="80%" height="80%">
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</div>
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