---
license: apache-2.0
language:
- zh
size_categories:
- 1K<n<10K
---

# CDLA: A Chinese document layout analysis (CDLA) dataset

### 介绍

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：

|正文|标题|图片|图片标题|表格|表格标题|页眉|页脚|注释|公式|
|---|---|---|---|---|---|---|---|---|---|
|Text|Title|Figure|Figure caption|Table|Table caption|Header|Footer|Reference|Equation|

共包含5000张训练集和1000张验证集，分别在train和val目录下。

整理自：[CDLA](https://github.com/buptlihang/CDLA)

标注可视化：
![](./assets/demo.png)


### 使用方式
```python
from datasets import load_dataset

dataset = load_dataset("SWHL/CDLA")

train_data = dataset["train"]
print(train_data[0])

val_data = dataset["validation"]
print(val_data[0])

# {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1240x1754 at 0x12FEE3DF0>,
# 'version': '4.5.6', 'flags': {},
# 'shapes': [
#     {'label': 'Header', 'points': [[118.0, 135.66666666666669]], 'group_id': None, 'shape_type': 'polygon', 'flags': {}}
# ],
# 'imagePath': 'train_0001.jpg', 'imageData': None, 'imageHeight': 1754, 'imageWidth': 1240}
```

### 下载链接

- 百度云下载：[link](https://pan.baidu.com/s/1449mhds2ze5JLk-88yKVAA), 提取码: tp0d
- Google Drive Download：[link](https://drive.google.com/file/d/14SUsp_TG8OPdK0VthRXBcAbYzIBjSNLm/view?usp=sharing)


### 标注格式

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:

- `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。
- `labels`: 类别。
- `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。
- `shape_type`: "polygon"
- `imagePath`: 图片路径/名
- `imageHeight`: 高
- `imageWidth`: 宽


展示一个完整的标注样例:

<details>
  
```json
{
  "version":"4.5.6",
  "flags":{},
  "shapes":[
    {
      "label":"Title",
      "points":[
        [
          553.1111111111111,
          166.59259259259258
        ],
        [
          553.1111111111111,
          198.59259259259258
        ],
        [
          686.1111111111111,
          198.59259259259258
        ],
        [
          686.1111111111111,
          166.59259259259258
        ]
      ],
      "group_id":null,
      "shape_type":"polygon",
      "flags":{}
    },
    {
      "label":"Text",
      "points":[
        [
          250.5925925925925,
          298.0740740740741
        ],
        [
          250.5925925925925,
          345.0740740740741
        ],
        [
          188.5925925925925,
          345.0740740740741
        ],
        [
          188.5925925925925,
          410.0740740740741
        ],
        [
          188.5925925925925,
          456.0740740740741
        ],
        [
          324.5925925925925,
          456.0740740740741
        ],
        [
          324.5925925925925,
          410.0740740740741
        ],
        [
          1051.5925925925926,
          410.0740740740741
        ],
        [
          1051.5925925925926,
          345.0740740740741
        ],
        [
          1052.5925925925926,
          345.0740740740741
        ],
        [
          1052.5925925925926,
          298.0740740740741
        ]
      ],
      "group_id":null,
      "shape_type":"polygon",
      "flags":{}
    },
    {
      "label":"Footer",
      "points":[
        [
          1033.7407407407406,
          1634.5185185185185
        ],
        [
          1033.7407407407406,
          1646.5185185185185
        ],
        [
          1052.7407407407406,
          1646.5185185185185
        ],
        [
          1052.7407407407406,
          1634.5185185185185
        ]
      ],
      "group_id":null,
      "shape_type":"polygon",
      "flags":{}
    }
  ],
  "imagePath":"val_0031.jpg",
  "imageData":null,
  "imageHeight":1754,
  "imageWidth":1240
}
```
</details>

### 转COCO格式
```bash
# train
python3 labelme2coco.py CDLA_dir/train train_save_path  --labels labels.txt

# val
python3 labelme2coco.py CDLA_dir/val val_save_path  --labels labels.txt
```

转换结果保存在train_save_path/val_save_path目录下。

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)