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--- |
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license: apache-2.0 |
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language: |
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- zh |
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size_categories: |
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- 1K<n<10K |
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--- |
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# CDLA: A Chinese document layout analysis (CDLA) dataset |
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### 介绍 |
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CDLA是一个中文文档版面分析数据集，面向中文文献类（论文）场景。包含以下10个label： |
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|正文|标题|图片|图片标题|表格|表格标题|页眉|页脚|注释|公式| |
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|---|---|---|---|---|---|---|---|---|---| |
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|Text|Title|Figure|Figure caption|Table|Table caption|Header|Footer|Reference|Equation| |
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共包含5000张训练集和1000张验证集，分别在train和val目录下。 |
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整理自：[CDLA](https://github.com/buptlihang/CDLA) |
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标注可视化： |
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### 使用方式 |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("SWHL/CDLA") |
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train_data = dataset["train"] |
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print(train_data[0]) |
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val_data = dataset["validation"] |
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print(val_data[0]) |
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# {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1240x1754 at 0x12FEE3DF0>, |
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# 'version': '4.5.6', 'flags': {}, |
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# 'shapes': [ |
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# {'label': 'Header', 'points': [[118.0, 135.66666666666669]], 'group_id': None, 'shape_type': 'polygon', 'flags': {}} |
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# ], |
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# 'imagePath': 'train_0001.jpg', 'imageData': None, 'imageHeight': 1754, 'imageWidth': 1240} |
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``` |
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### 下载链接 |
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- 百度云下载：[link](https://pan.baidu.com/s/1449mhds2ze5JLk-88yKVAA), 提取码: tp0d |
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- Google Drive Download：[link](https://drive.google.com/file/d/14SUsp_TG8OPdK0VthRXBcAbYzIBjSNLm/view?usp=sharing) |
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### 标注格式 |
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我们的标注工具是labelme，所以标注格式和labelme格式一致。这里说明一下比较重要的字段: |
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- `shapes`: shapes字段是一个list，里面有多个dict，每个dict代表一个标注实例。 |
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- `labels`: 类别。 |
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- `points`: 实例标注。因为我们的标注是Polygon形式，所以points里的坐标数量可能大于4。 |
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- `shape_type`: "polygon" |
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- `imagePath`: 图片路径/名 |
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- `imageHeight`: 高 |
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- `imageWidth`: 宽 |
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展示一个完整的标注样例: |
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<details> |
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```json |
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{ |
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"version":"4.5.6", |
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"flags":{}, |
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"shapes":[ |
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{ |
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"label":"Title", |
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"points":[ |
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[ |
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553.1111111111111, |
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166.59259259259258 |
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], |
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[ |
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553.1111111111111, |
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198.59259259259258 |
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], |
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[ |
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686.1111111111111, |
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198.59259259259258 |
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], |
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[ |
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686.1111111111111, |
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166.59259259259258 |
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] |
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], |
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"group_id":null, |
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"shape_type":"polygon", |
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"flags":{} |
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}, |
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{ |
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"label":"Text", |
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"points":[ |
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[ |
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250.5925925925925, |
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298.0740740740741 |
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], |
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[ |
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250.5925925925925, |
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345.0740740740741 |
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], |
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[ |
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188.5925925925925, |
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345.0740740740741 |
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], |
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[ |
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188.5925925925925, |
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410.0740740740741 |
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], |
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[ |
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188.5925925925925, |
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456.0740740740741 |
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], |
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[ |
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324.5925925925925, |
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456.0740740740741 |
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], |
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[ |
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324.5925925925925, |
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410.0740740740741 |
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], |
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[ |
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1051.5925925925926, |
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410.0740740740741 |
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], |
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[ |
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1051.5925925925926, |
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345.0740740740741 |
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], |
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[ |
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1052.5925925925926, |
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345.0740740740741 |
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], |
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[ |
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1052.5925925925926, |
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298.0740740740741 |
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] |
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], |
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"group_id":null, |
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"shape_type":"polygon", |
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"flags":{} |
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}, |
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{ |
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"label":"Footer", |
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"points":[ |
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[ |
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1033.7407407407406, |
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1634.5185185185185 |
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], |
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[ |
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1033.7407407407406, |
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1646.5185185185185 |
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], |
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[ |
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1052.7407407407406, |
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1646.5185185185185 |
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], |
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[ |
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1052.7407407407406, |
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1634.5185185185185 |
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] |
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], |
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"group_id":null, |
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"shape_type":"polygon", |
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"flags":{} |
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} |
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], |
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"imagePath":"val_0031.jpg", |
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"imageData":null, |
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"imageHeight":1754, |
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"imageWidth":1240 |
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} |
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``` |
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</details> |
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### 转COCO格式 |
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```bash |
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# train |
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python3 labelme2coco.py CDLA_dir/train train_save_path --labels labels.txt |
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# val |
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python3 labelme2coco.py CDLA_dir/val val_save_path --labels labels.txt |
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``` |
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转换结果保存在train_save_path/val_save_path目录下。 |
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labelme2coco.py取自labelme，更多信息请参考[labelme官方项目](https://github.com/wkentaro/labelme/tree/master/examples/instance_segmentation) |
