mirror of https://github.com/open-mmlab/mmocr.git
151 lines
8.5 KiB
Markdown
151 lines
8.5 KiB
Markdown
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# 文字检测
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## 概览
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文字检测任务的数据集应按如下目录配置：
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```text
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├── ctw1500
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│ ├── annotations
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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├── icdar2015
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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├── icdar2017
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│ ├── imgs
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│ ├── instances_training.json
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│ └── instances_val.json
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├── synthtext
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│ ├── imgs
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│ └── instances_training.lmdb
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│ ├── data.mdb
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│ └── lock.mdb
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├── textocr
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│ ├── train
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│ ├── instances_training.json
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│ └── instances_val.json
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├── totaltext
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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```
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| 数据集名称 | 数据图片 | | 标注文件 | |
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| :---------: | :----------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :-------------------------------------: | :--------------------------------------------------------------------------------------------: |
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| | | 训练集 (training) | 验证集 (validation) | 测试集 (testing) | |
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| CTW1500 | [下载地址](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - |
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| ICDAR2015 | [下载地址](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) | - | [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) |
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| ICDAR2017 | [下载地址](https://rrc.cvc.uab.es/?ch=8&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json) | [instances_val.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json) | - | | |
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| Synthtext | [下载地址](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | instances_training.lmdb ([data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb), [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb)) | - | - |
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| TextOCR | [下载地址](https://textvqa.org/textocr/dataset) | - | - | -
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| Totaltext | [下载地址](https://github.com/cs-chan/Total-Text-Dataset) | - | - | -
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## 重要提醒
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:::{note}
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**若用户需要在 CTW1500, ICDAR 2015/2017 或 Totaltext 数据集上训练模型**, 请注意这些数据集中有部分图片的 EXIF 信息里保存着方向信息。MMCV 采用的 OpenCV 后端会默认根据方向信息对图片进行旋转；而由于数据集的标注是在原图片上进行的，这种冲突会使得部分训练样本失效。因此，用户应该在配置 pipeline 时使用 `dict(type='LoadImageFromFile', color_type='color_ignore_orientation')` 以避免 MMCV 的这一行为。（配置文件可参考 [DBNet](https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py)）
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:::
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## 准备步骤
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### ICDAR 2015
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- 第一步：从[下载地址](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载 `ch4_training_images.zip`、`ch4_test_images.zip`、`ch4_training_localization_transcription_gt.zip`、`Challenge4_Test_Task1_GT.zip` 四个文件，分别对应训练集数据、测试集数据、训练集标注、测试集标注。
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- 第二步：运行以下命令，移动数据集到对应文件夹
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```bash
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mkdir icdar2015 && cd icdar2015
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mkdir imgs && mkdir annotations
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# 移动数据到目录：
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mv ch4_training_images imgs/training
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mv ch4_test_images imgs/test
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# 移动标注到目录：
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mv ch4_training_localization_transcription_gt annotations/training
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mv Challenge4_Test_Task1_GT annotations/test
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```
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- 第三步：下载 [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) 和 [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json)，并放入 `icdar2015` 文件夹里。或者也可以用以下命令直接生成 `instances_training.json` 和 `instances_test.json`:
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```bash
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python tools/data/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test
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```
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### ICDAR 2017
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- 与上述步骤类似。
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### CTW1500
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- 第一步：执行以下命令，从 [下载地址](https://github.com/Yuliang-Liu/Curve-Text-Detector) 下载 `train_images.zip`，`test_images.zip`，`train_labels.zip`，`test_labels.zip` 四个文件并配置到对应目录：
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```bash
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mkdir ctw1500 && cd ctw1500
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mkdir imgs && mkdir annotations
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# 下载并配置标注
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cd annotations
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wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip
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wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download
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unzip train_labels.zip && mv ctw1500_train_labels training
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unzip test_labels.zip -d test
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cd ..
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# 下载并配置数据
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cd imgs
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wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip
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wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip
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unzip train_images.zip && mv train_images training
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unzip test_images.zip && mv test_images test
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```
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- 第二步：执行以下命令，生成 `instances_training.json` 和 `instances_test.json`。
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```bash
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python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
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```
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### SynthText
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- 下载 [data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb) 和 [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb) 并放置到 `synthtext/instances_training.lmdb/` 中.
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### TextOCR
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- 第一步：下载 [train_val_images.zip](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)，[TextOCR_0.1_train.json](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json) 和 [TextOCR_0.1_val.json](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json) 到 `textocr` 文件夹里。
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```bash
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mkdir textocr && cd textocr
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# 下载 TextOCR 数据集
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wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
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wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json
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wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json
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# 把图片移到对应目录
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unzip -q train_val_images.zip
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mv train_images train
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```
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- 第二步：生成 `instances_training.json` 和 `instances_val.json`:
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```bash
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python tools/data/textdet/textocr_converter.py /path/to/textocr
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```
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### Totaltext
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- 第一步：从 [github dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) 下载 `totaltext.zip`，从 [github Groundtruth](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text) 下载 `groundtruth_text.zip` 。（建议下载 `.mat` 格式的标注文件，因为我们提供的标注格式转换脚本 `totaltext_converter.py` 仅支持 `.mat` 格式。）
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```bash
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mkdir totaltext && cd totaltext
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mkdir imgs && mkdir annotations
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# 图像
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# 在 ./totaltext 中执行
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unzip totaltext.zip
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mv Images/Train imgs/training
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mv Images/Test imgs/test
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# 标注文件
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unzip groundtruth_text.zip
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cd Groundtruth
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mv Polygon/Train ../annotations/training
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mv Polygon/Test ../annotations/test
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```
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- 第二步：用以下命令生成 `instances_training.json` 和 `instances_test.json` ：
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```bash
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python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
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```
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