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---
name: Issue template
about: Issue template for code error.
title: ''
labels: ''
assignees: ''
---
请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem
- 系统环境/System Environment：
- 版本号/Version：Paddle： PaddleOCR： 问题相关组件/Related components：
- 运行指令/Command Code：
- 完整报错/Complete Error Message：
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# Byte-compiled / optimized / DLL files
__pycache__/
.ipynb_checkpoints/
*.py[cod]
*$py.class
# C extensions
*.so
inference/
inference_results/
output/
*.DS_Store
*.vs
*.user
*~
*.vscode
*.idea
*.log
.clang-format
.clang_format.hook
build/
dist/
paddleocr.egg-info/
/deploy/android_demo/app/OpenCV/
/deploy/android_demo/app/PaddleLite/
/deploy/android_demo/app/.cxx/
/deploy/android_demo/app/cache/
test_tipc/web/models/
test_tipc/web/node_modules/
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- repo: https://github.com/PaddlePaddle/mirrors-yapf.git
sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
hooks:
- id: yapf
files: \.py$
- repo: https://github.com/pre-commit/pre-commit-hooks
sha: a11d9314b22d8f8c7556443875b731ef05965464
hooks:
- id: check-merge-conflict
- id: check-symlinks
- id: detect-private-key
files: (?!.*paddle)^.*$
- id: end-of-file-fixer
files: \.md$
- id: trailing-whitespace
files: \.md$
- repo: https://github.com/Lucas-C/pre-commit-hooks
sha: v1.0.1
hooks:
- id: forbid-crlf
files: \.md$
- id: remove-crlf
files: \.md$
- id: forbid-tabs
files: \.md$
- id: remove-tabs
files: \.md$
- repo: local
hooks:
- id: clang-format
name: clang-format
description: Format files with ClangFormat
entry: bash .clang_format.hook -i
language: system
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
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[style]
based_on_style = pep8
column_limit = 80
LICENSE 0 → 100644
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include LICENSE
include README.md
recursive-include ppocr/utils *.*
recursive-include ppocr/data *.py
recursive-include ppocr/postprocess *.py
recursive-include tools/infer *.py
recursive-include tools __init__.py
recursive-include ppocr/utils/e2e_utils *.py
recursive-include ppstructure *.py
\ No newline at end of file
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# ex: set ts=8 noet:
all: qt5 test
test: testpy3
testpy2:
python -m unittest discover tests
testpy3:
python3 -m unittest discover tests
qt4: qt4py2
qt5: qt5py3
qt4py2:
pyrcc4 -py2 -o libs/resources.py resources.qrc
qt4py3:
pyrcc4 -py3 -o libs/resources.py resources.qrc
qt5py3:
pyrcc5 -o libs/resources.py resources.qrc
clean:
rm -rf ~/.labelImgSettings.pkl *.pyc dist labelImg.egg-info __pycache__ build
pip_upload:
python3 setup.py upload
long_description:
restview --long-description
.PHONY: all
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English | [简体中文](README_ch.md)
# PPOCRLabelv2
PPOCRLabelv2 is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PP-OCR model to automatically detect and re-recognize data. It is written in Python3 and PyQT5, supporting rectangular box, table, irregular text and key information annotation modes. Annotations can be directly used for the training of PP-OCR detection and recognition models.
| regular text annotation | table annotation |
| :-------------------------------------------------: | :--------------------------------------------: |
| <img src="./data/gif/steps_en.gif" width="80%"/> | <img src="./data/gif/table.gif" width="100%"/> |
| **irregular text annotation** | **key information annotation** |
| <img src="./data/gif/multi-point.gif" width="80%"/> | <img src="./data/gif/kie.gif" width="100%"/> |
### Recent Update
- 2022.05: Add table annotations, follow `2.2 Table Annotations` for more information （by [whjdark](https://github.com/peterh0323); [Evezerest](https://github.com/Evezerest))
- 2022.02:（by [PeterH0323](https://github.com/peterh0323) ）
- Add KIE Mode by using `--kie`, for [detection + identification + keyword extraction] labeling.
- Improve user experience: support using `C` or `X` to rotate box, prompt for the number of files and labels, optimize interaction.
- 2021.11.17:
- Support install and start PPOCRLabel through the whl package (by [d2623587501](https://github.com/d2623587501))
- Dataset segmentation: Divide the annotation file into training, verification and testing parts (refer to section 3.5 below, by [MrCuiHao](https://github.com/MrCuiHao))
- 2021.8.11:
- New functions: Open the dataset folder, image rotation (Note: Please delete the label box before rotating the image) (by [Wei-JL](https://github.com/Wei-JL))
- Added shortcut key description (Help-Shortcut Key), repaired the direction shortcut key movement function under batch processing (by [d2623587501](https://github.com/d2623587501))
- 2021.2.5: New batch processing and undo functions (by [Evezerest](https://github.com/Evezerest)):
- **Batch processing function**: Press and hold the Ctrl key to select the box, you can move, copy, and delete in batches.
- **Undo function**: In the process of drawing a four-point label box or after editing the box, press Ctrl+Z to undo the previous operation.
- Fix image rotation and size problems, optimize the process of editing the mark frame (by [ninetailskim](https://github.com/ninetailskim)、 [edencfc](https://github.com/edencfc)).
- 2021.1.11: Optimize the labeling experience (by [edencfc](https://github.com/edencfc)),
- Users can choose whether to pop up the label input dialog after drawing the detection box in "View - Pop-up Label Input Dialog".
- The recognition result scrolls synchronously when users click related detection box.
- Click to modify the recognition result.(If you can't change the result, please switch to the system default input method, or switch back to the original input method again)
- 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
## 1. Installation and Run
### 1.1 Install PaddlePaddle
```bash
pip3 install --upgrade pip
# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
# If you only have cpu on your machine, please run the following command to install
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
```
For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
### 1.2 Install and Run PPOCRLabel
PPOCRLabel can be started in two ways: whl package and Python script. The whl package form is more convenient to start, and the python script to start is convenient for secondary development.
#### Windows
```bash
pip install PPOCRLabel # install
# Select label mode and run
PPOCRLabel # [Normal mode] for [detection + recognition] labeling
PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
> If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file using http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely.
>
> Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
>
#### Ubuntu Linux
```bash
pip3 install PPOCRLabel
pip3 install trash-cli
# Select label mode and run
PPOCRLabel # [Normal mode] for [detection + recognition] labeling
PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
#### MacOS
```bash
pip3 install PPOCRLabel
pip3 install opencv-contrib-python-headless==4.2.0.32
# Select label mode and run
PPOCRLabel # [Normal mode] for [detection + recognition] labeling
PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
#### 1.2.2 Run PPOCRLabel by Python Script
If you modify the PPOCRLabel file (for example, specifying a new built-in model), it will be more convenient to see the results by running the Python script. If you still want to start with the whl package, you need to uninstall the whl package in the current environment and then recompile it according to the next section.
```bash
cd ./PPOCRLabel # Switch to the PPOCRLabel directory
# Select label mode and run
python PPOCRLabel.py # [Normal mode] for [detection + recognition] labeling
python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
```
#### 1.2.3 Build and Install the Whl Package Locally
Compile and install a new whl package, where 1.0.2 is the version number, you can specify the new version in 'setup.py'.
```bash
cd PaddleOCR/PPOCRLabel
python3 setup.py bdist_wheel
pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
```
## 2. Usage
### 2.1 Steps
1. Build and launch using the instructions above.
2. Click 'Open Dir' in Menu/File to select the folder of the picture.<sup>[1]</sup>
3. Click 'Auto recognition', use PP-OCR model to automatically annotate images which marked with 'X' <sup>[2]</sup>before the file name.
4. Create Box:
4.1 Click 'Create RectBox' or press 'W' in English keyboard mode to draw a new rectangle detection box. Click and release left mouse to select a region to annotate the text area.
4.2 Press 'Q' to enter four-point labeling mode which enables you to create any four-point shape by clicking four points with the left mouse button in succession and DOUBLE CLICK the left mouse as the signal of labeling completion.
5. After the marking frame is drawn, the user clicks "OK", and the detection frame will be pre-assigned a "TEMPORARY" label.
6. Click 're-Recognition', model will rewrite ALL recognition results in ALL detection box<sup>[3]</sup>.
7. Single click the result in 'recognition result' list to manually change inaccurate recognition results.
8. **Click "Check", the image status will switch to "√",then the program automatically jump to the next.**
9. Click "Delete Image", and the image will be deleted to the recycle bin.
10. Labeling result: the user can export the label result manually through the menu "File - Export Label", while the program will also export automatically if "File - Auto export Label Mode" is selected. The manually checked label will be stored in *Label.txt* under the opened picture folder. Click "File"-"Export Recognition Results" in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*<sup>[4]</sup>.
### 2.2 Table Annotation
The table annotation is aimed at extracting the structure of the table in a picture and converting it to Excel format,
so the annotation needs to be done simultaneously with external software to edit Excel.
In PPOCRLabel, complete the text information labeling (text and position), complete the table structure information
labeling in the Excel file, the recommended steps are:
1. Table annotation: After opening the table picture, click on the `Table Recognition` button in the upper right corner of PPOCRLabel, which will call the table recognition model in PP-Structure to automatically label
the table and pop up Excel at the same time.
2. Change the recognition result: **label each cell** (i.e. the text in a cell is marked as a box). Right click on the box and click on `Cell Re-recognition`.
You can use the model to automatically recognise the text within a cell.
3. Mark the table structure: for each cell contains the text, **mark as any identifier (such as `1`) in Excel**, to ensure that the merged cell structure is same as the original picture.
4. Export JSON format annotation: close all Excel files corresponding to table images, click `File`-`Export table JSON annotation` to obtain JSON annotation results.
### 2.3 Note
[1] PPOCRLabel uses the opened folder as the project. After opening the image folder, the picture will not be displayed in the dialog. Instead, the pictures under the folder will be directly imported into the program after clicking "Open Dir".
[2] The image status indicates whether the user has saved the image manually. If it has not been saved manually it is "X", otherwise it is "√", PPOCRLabel will not relabel pictures with a status of "√".
[3] After clicking "Re-recognize", the model will overwrite ALL recognition results in the picture. Therefore, if the recognition result has been manually changed before, it may change after re-recognition.
[4] The files produced by PPOCRLabel can be found under the opened picture folder including the following, please do not manually change the contents, otherwise it will cause the program to be abnormal.
| File name | Description |
| :-----------: | :----------------------------------------------------------: |
| Label.txt | The detection label file can be directly used for PP-OCR detection model training. After the user saves 5 label results, the file will be automatically exported. It will also be written when the user closes the application or changes the file folder. |
| fileState.txt | The picture status file save the image in the current folder that has been manually confirmed by the user. |
| Cache.cach | Cache files to save the results of model recognition. |
| rec_gt.txt | The recognition label file, which can be directly used for PP-OCR identification model training, is generated after the user clicks on the menu bar "File"-"Export recognition result". |
| crop_img | The recognition data, generated at the same time with *rec_gt.txt* |
## 3. Explanation
### 3.1 Shortcut keys
| Shortcut keys | Description |
|--------------------------|--------------------------------------------------|
| Ctrl + Shift + R | Re-recognize all the labels of the current image |
| W | Create a rect box |
| Q | Create a multi-points box |
| X | Rotate the box anti-clockwise |
| C | Rotate the box clockwise |
| Ctrl + E | Edit label of the selected box |
| Ctrl + X | Change key class of the box when enable `--kie` |
| Ctrl + R | Re-recognize the selected box |
| Ctrl + C | Copy and paste the selected box |
| Ctrl + Left Mouse Button | Multi select the label box |
| Backspace | Delete the selected box |
| Ctrl + V | Check image |
| Ctrl + Shift + d | Delete image |
| D | Next image |
| A | Previous image |
| Ctrl++ | Zoom in |
| Ctrl-- | Zoom out |
| ↑→↓← | Move selected box |
### 3.2 Built-in Model
- Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.
- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languages​include French, German, Korean, and Japanese.
For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)
- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/PPOCRLabel/PPOCRLabel.py#L97) :
add parameter `det_model_dir` in `self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang) `
### 3.3 Export Label Result
PPOCRLabel supports three ways to export Label.txt
- Automatically export: After selecting "File - Auto Export Label Mode", the program will automatically write the annotations into Label.txt every time the user confirms an image. If this option is not turned on, it will be automatically exported after detecting that the user has manually checked 5 images.
> The automatically export mode is turned off by default
- Manual export: Click "File-Export Marking Results" to manually export the label.
- Close application export
### 3.4 Export Partial Recognition Results
For some data that are difficult to recognize, the recognition results will not be exported by **unchecking** the corresponding tags in the recognition results checkbox. The unchecked recognition result is saved as `True` in the `difficult` variable in the label file `label.txt`.
> *Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.*
### 3.5 Dataset division
- Enter the following command in the terminal to execute the dataset division script:
```
cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data
```
Parameter Description:
- `trainValTestRatio` is the division ratio of the number of images in the training set, validation set, and test set, set according to your actual situation, the default is `6:2:2`
- `datasetRootPath` is the storage path of the complete dataset labeled by PPOCRLabel. The default path is `PaddleOCR/train_data` .
```
|-train_data
|-crop_img
|- word_001_crop_0.png
|- word_002_crop_0.jpg
|- word_003_crop_0.jpg
| ...
| Label.txt
| rec_gt.txt
|- word_001.png
|- word_002.jpg
|- word_003.jpg
| ...
```
### 3.6 Error message
- If paddleocr is installed with whl, it has a higher priority than calling PaddleOCR class with paddleocr.py, which may cause an exception if whl package is not updated.
- For Linux users, if you get an error starting with **objc[XXXXX]** when opening the software, it proves that your opencv version is too high. It is recommended to install version 4.2:
```
pip install opencv-python==4.2.0.32
```
- If you get an error starting with **Missing string id **,you need to recompile resources:
```
pyrcc5 -o libs/resources.py resources.qrc
```
- If you get an error ``` module 'cv2' has no attribute 'INTER_NEAREST'```, you need to delete all opencv related packages first, and then reinstall the 4.2.0.32 version of headless opencv
```
pip install opencv-contrib-python-headless==4.2.0.32
```
### 4. Related
1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
PPOCRLabel/README_ch.md 0 → 100644
View file @ 89eb5e4b
[English](README.md) | 简体中文
# PPOCRLabelv2
PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置PP-OCR模型对数据自动标注和重新识别。使用Python3和PyQT5编写，支持矩形框标注、表格标注、不规则文本标注、关键信息标注模式，导出格式可直接用于PaddleOCR检测和识别模型的训练。
| 常规标注 | 表格标注 |
| :---------------------------------------------------: | :----------------------------------------------: |
| <img src="./data/gif/steps_en.gif" width="80%"/> | <img src="./data/gif/table.gif" width="100%"/> |
| **不规则文本标注** | **关键信息标注** |
| <img src="./data/gif/multi-point.gif" width="80%"/> | <img src="./data/gif/kie.gif" width="100%"/> |
#### 近期更新
- 2022.05：新增表格标注，使用方法见下方`2.2 表格标注`（by [whjdark](https://github.com/peterh0323); [Evezerest](https://github.com/Evezerest))
- 2022.02：新增关键信息标注、优化标注体验（by [PeterH0323](https://github.com/peterh0323) ）
- 新增：使用 `--kie` 进入 KIE 功能，用于打【检测+识别+关键字提取】的标签
- 提升用户体验：新增文件与标记数目提示、优化交互、修复gpu使用等问题。
- 新增功能：使用 `C` 和 `X` 对标记框进行旋转。
- 2021.11.17：
- 新增支持通过whl包安装和启动PPOCRLabel（by [d2623587501](https://github.com/d2623587501)）
- 标注数据集切分：对标注数据进行训练、验证与测试集划分（参考下方3.5节，by [MrCuiHao](https://github.com/MrCuiHao)）
- 2021.8.11：
- 新增功能：打开数据所在文件夹、右键图像旋转90度（注意：旋转前的图片上不能存在标记框，by [Wei-JL](https://github.com/Wei-JL)）
- 新增快捷键说明（帮助-快捷键）、修复批处理下的方向快捷键移动功能（by [d2623587501](https://github.com/d2623587501)）
- 2021.2.5：新增批处理与撤销功能（by [Evezerest](https://github.com/Evezerest))
- **批处理功能**：按住Ctrl键选择标记框后可批量移动、复制、删除、重新识别。
- **撤销功能**：在绘制四点标注框过程中或对框进行编辑操作后，按下Ctrl+Z可撤销上一部操作。
- 修复图像旋转和尺寸问题、优化编辑标记框过程（by [ninetailskim](https://github.com/ninetailskim)、 [edencfc](https://github.com/edencfc)）
- 2021.1.11：优化标注体验（by [edencfc](https://github.com/edencfc)）：
- 用户可在“视图 - 弹出标记输入框”选择在画完检测框后标记输入框是否弹出。
- 识别结果与检测框同步滚动。
- 识别结果更改为单击修改。（如果无法修改，请切换为系统自带输入法，或再次切回原输入法）
- 2020.12.18： 支持对单个标记框进行重新识别（by [ninetailskim](https://github.com/ninetailskim)），完善快捷键。
如果您对完善工具有不一样的想法，欢迎通过[社区常规赛](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)报名相关更改，获得积分兑换奖励。
## 1. 安装与运行
### 1.1 安装PaddlePaddle
```bash
pip3 install --upgrade pip
# 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
# 如果您的机器是CPU，请运行以下命令安装
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
```
更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
### 1.2 安装与运行PPOCRLabel
PPOCRLabel可通过whl包与Python脚本两种方式启动，whl包形式启动更加方便，python脚本启动便于二次开发
#### 1.2.1 通过whl包安装与运行
##### Windows
```bash
pip install PPOCRLabel # 安装
# 选择标签模式来启动
PPOCRLabel --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
> 注意：通过whl包安装PPOCRLabel会自动下载 `paddleocr` whl包，其中shapely依赖可能会出现 `[winRrror 126] 找不到指定模块的问题。` 的错误，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载并安装
##### Ubuntu Linux
```bash
pip3 install PPOCRLabel
pip3 install trash-cli
# 选择标签模式来启动
PPOCRLabel --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
##### MacOS
```bash
pip3 install PPOCRLabel
pip3 install opencv-contrib-python-headless==4.2.0.32 # 如果下载过慢请添加"-i https://mirror.baidu.com/pypi/simple"
# 选择标签模式来启动
PPOCRLabel --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
```
> 如果上述安装出现问题，可以参考3.6节 错误提示
#### 1.2.2 通过Python脚本运行PPOCRLabel
如果您对PPOCRLabel文件有所更改（例如指定新的内置模型），通过Python脚本运行会更加方便的看到更改的结果。如果仍然需要通过whl包启动，则需要先卸载当前环境中的whl包，然后参考下节重新编译whl包。
```bash
cd ./PPOCRLabel # 切换到PPOCRLabel目录
python PPOCRLabel.py --lang ch
```
#### 1.2.3 本地构建whl包并安装
编译与安装新的whl包，其中1.0.2为版本号，可在 `setup.py` 中指定新版本。
```bash
cd PaddleOCR/PPOCRLabel
python3 setup.py bdist_wheel
pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
```
## 2. 使用
### 2.1 操作步骤
> 如果您只需要标注文字信息和位置，推荐按照以下步骤展开：
1. 安装与运行：使用上述命令安装与运行程序。
2. 打开文件夹：在菜单栏点击 “文件” - "打开目录" 选择待标记图片的文件夹<sup>[1]</sup>.
3. 自动标注：点击 ”自动标注“，使用PPOCR超轻量模型对图片文件名前图片状态<sup>[2]</sup>为 “X” 的图片进行自动标注。
4. 手动标注：点击 “矩形标注”（推荐直接在英文模式下点击键盘中的 “W”)，用户可对当前图片中模型未检出的部分进行手动绘制标记框。点击键盘Q，则使用四点标注模式（或点击“编辑” - “四点标注”），用户依次点击4个点后，双击左键表示标注完成。
5. 标记框绘制完成后，用户点击 “确认”，检测框会先被预分配一个 “待识别” 标签。
6. 重新识别：将图片中的所有检测画绘制/调整完成后，点击 “重新识别”，PPOCR模型会对当前图片中的**所有检测框**重新识别<sup>[3]</sup>。
7. 内容更改：单击识别结果，对不准确的识别结果进行手动更改。
8. **确认标记：点击 “确认”，图片状态切换为 “√”，跳转至下一张。**
9. 删除：点击 “删除图像”，图片将会被删除至回收站。
10. 导出结果：用户可以通过菜单中“文件-导出标记结果”手动导出，同时也可以点击“文件 - 自动导出标记结果”开启自动导出。手动确认过的标记将会被存放在所打开图片文件夹下的*Label.txt*中。在菜单栏点击 “文件” - "导出识别结果"后，会将此类图片的识别训练数据保存在*crop_img*文件夹下，识别标签保存在*rec_gt.txt*中<sup>[4]</sup>。
### 2.2 表格标注
表格标注针对表格的结构化提取，将图片中的表格转换为Excel格式，因此标注时需要配合外部软件打开Excel同时完成。
在PPOCRLabel软件中完成表格中的文字信息标注（文字与位置）、在Excel文件中完成表格结构信息标注，推荐的步骤为：
1. 表格识别：打开表格图片后，点击软件右上角 `表格识别` 按钮，软件调用PP-Structure中的表格识别模型，自动为表格打标签，同时弹出Excel
2. 更改识别结果：**以表格中的单元格为单位增加标注框**（即一个单元格内的文字都标记为一个框）。标注框上鼠标右键后点击 `单元格重识别`
可利用模型自动识别单元格内的文字。
3. 标注表格结构：将表格图像中有文字的单元格，**在Excel中标记为任意标识符（如`1`）**，保证Excel中的单元格合并情况与原图相同即可。
4. 导出JSON格式：关闭所有表格图像对应的Excel，点击 `文件`-`导出表格JSON标注` 获得JSON标注结果。
### 2.3 注意
[1] PPOCRLabel以**文件夹**为基本标记单位，打开待标记的图片文件夹后，不会在窗口栏中显示图片，而是在点击 "选择文件夹" 之后直接将文件夹下的图片导入到程序中。
[2] 图片状态表示本张图片用户是否手动保存过，未手动保存过即为 “X”，手动保存过为 “√”。点击 “自动标注”按钮后，PPOCRLabel不会对状态为 “√” 的图片重新标注。
[3] 点击“重新识别”后，模型会对图片中的识别结果进行覆盖。因此如果在此之前手动更改过识别结果，有可能在重新识别后产生变动。
[4] PPOCRLabel产生的文件放置于标记图片文件夹下，包括一下几种，请勿手动更改其中内容，否则会引起程序出现异常。
| 文件名 | 说明 |
| :-----------: | :----------------------------------------------------------: |
| Label.txt | 检测标签，可直接用于PPOCR检测模型训练。用户每确认5张检测结果后，程序会进行自动写入。当用户关闭应用程序或切换文件路径后同样会进行写入。 |
| fileState.txt | 图片状态标记文件，保存当前文件夹下已经被用户手动确认过的图片名称。 |
| Cache.cach | 缓存文件，保存模型自动识别的结果。 |
| rec_gt.txt | 识别标签。可直接用于PPOCR识别模型训练。需用户手动点击菜单栏“文件” - "导出识别结果"后产生。 |
| crop_img | 识别数据。按照检测框切割后的图片。与rec_gt.txt同时产生。 |
## 3. 说明
### 3.1 快捷键
| 快捷键 | 说明 |
|------------------|---------------------------------|
| Ctrl + shift + R | 对当前图片的所有标记重新识别 |
| W | 新建矩形框 |
| Q | 新建多点框 |
| X | 框逆时针旋转 |
| C | 框顺时针旋转 |
| Ctrl + E | 编辑所选框标签 |
| Ctrl + X | `--kie` 模式下，修改 Box 的关键字种类 |
| Ctrl + R | 重新识别所选标记 |
| Ctrl + C | 【复制并粘贴】选中的标记框 |
| Ctrl + 鼠标左键 | 多选标记框 |
| Backspace | 删除所选框 |
| Ctrl + V | 确认本张图片标记 |
| Ctrl + Shift + d | 删除本张图片 |
| D | 下一张图片 |
| A | 上一张图片 |
| Ctrl++ | 缩小 |
| Ctrl-- | 放大 |
| ↑→↓← | 移动标记框 |
### 3.2 内置模型
- 默认模型：PPOCRLabel默认使用PaddleOCR中的中英文超轻量OCR模型，支持中英文与数字识别，多种语言检测。
- 模型语言切换：用户可通过菜单栏中 "PaddleOCR" - "选择模型" 切换内置模型语言，目前支持的语言包括法文、德文、韩文、日文。具体模型下载链接可参考[PaddleOCR模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md).
- **自定义模型**：如果用户想将内置模型更换为自己的推理模型，可根据[自定义模型代码使用](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md#%E8%87%AA%E5%AE%9A%E4%B9%89%E6%A8%A1%E5%9E%8B)，通过修改PPOCRLabel.py中针对[PaddleOCR类的实例化](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/PPOCRLabel.py#L116) 实现，例如指定检测模型：`self.ocr = PaddleOCR(det=True, cls=True, use_gpu=gpu, lang=lang) `，在 `det_model_dir` 中传入 自己的模型即可。
### 3.3 导出标记结果
PPOCRLabel支持三种导出方式：
- 自动导出：点击“文件 - 自动导出标记结果”后，用户每确认过一张图片，程序自动将标记结果写入Label.txt中。若未开启此选项，则检测到用户手动确认过5张图片后进行自动导出。
> 默认情况下自动导出功能为关闭状态
- 手动导出：点击“文件 - 导出标记结果”手动导出标记。
- 关闭应用程序导出
### 3.4 导出部分识别结果
针对部分难以识别的数据，通过在识别结果的复选框中**取消勾选**相应的标记，其识别结果不会被导出。被取消勾选的识别结果在标记文件 `label.txt` 中的 `difficult` 变量保存为 `True` 。
> *注意：识别结果中的复选框状态仍需用户手动点击确认后才能保留*
### 3.5 数据集划分
在终端中输入以下命令执行数据集划分脚本：
```
cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data
```
参数说明：
- `trainValTestRatio` 是训练集、验证集、测试集的图像数量划分比例，根据实际情况设定，默认是`6:2:2`
- `datasetRootPath` 是PPOCRLabel标注的完整数据集存放路径。默认路径是 `PaddleOCR/train_data` 分割数据集前应有如下结构：
```
|-train_data
|-crop_img
|- word_001_crop_0.png
|- word_002_crop_0.jpg
|- word_003_crop_0.jpg
| ...
| Label.txt
| rec_gt.txt
|- word_001.png
|- word_002.jpg
|- word_003.jpg
| ...
```
### 3.6 错误提示
- 如果同时使用whl包安装了paddleocr，其优先级大于通过paddleocr.py调用PaddleOCR类，whl包未更新时会导致程序异常。
- PPOCRLabel**不支持对中文文件名**的图片进行自动标注。
- 针对Linux用户：如果您在打开软件过程中出现**objc[XXXXX]**开头的错误，证明您的opencv版本太高，建议安装4.2版本：
```
pip install opencv-python==4.2.0.32
```
- 如果出现 ```Missing string id``` 开头的错误，需要重新编译资源：
```
pyrcc5 -o libs/resources.py resources.qrc
```
- 如果出现``` module 'cv2' has no attribute 'INTER_NEAREST'```错误，需要首先删除所有opencv相关包，然后重新安装4.2.0.32版本的headless opencv
```
pip install opencv-contrib-python-headless==4.2.0.32
```
### 4. 参考资料
1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
PPOCRLabel/gen_ocr_train_val_test.py 0 → 100644
View file @ 89eb5e4b
# coding:utf8
import os
import shutil
import random
import argparse
# 删除划分的训练集、验证集、测试集文件夹，重新创建一个空的文件夹
def isCreateOrDeleteFolder(path, flag):
flagPath = os.path.join(path, flag)
if os.path.exists(flagPath):
shutil.rmtree(flagPath)
os.makedirs(flagPath)
flagAbsPath = os.path.abspath(flagPath)
return flagAbsPath
def splitTrainVal(root, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
# 按照指定的比例划分训练集、验证集、测试集
dataAbsPath = os.path.abspath(root)
if flag == "det":
labelFilePath = os.path.join(dataAbsPath, args.detLabelFileName)
elif flag == "rec":
labelFilePath = os.path.join(dataAbsPath, args.recLabelFileName)
labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
labelFileContent = labelFileRead.readlines()
random.shuffle(labelFileContent)
labelRecordLen = len(labelFileContent)
for index, labelRecordInfo in enumerate(labelFileContent):
imageRelativePath = labelRecordInfo.split('\t')[0]
imageLabel = labelRecordInfo.split('\t')[1]
imageName = os.path.basename(imageRelativePath)
if flag == "det":
imagePath = os.path.join(dataAbsPath, imageName)
elif flag == "rec":
imagePath = os.path.join(dataAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
# 按预设的比例划分训练集、验证集、测试集
trainValTestRatio = args.trainValTestRatio.split(":")
trainRatio = eval(trainValTestRatio[0]) / 10
valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
curRatio = index / labelRecordLen
if curRatio < trainRatio:
imageCopyPath = os.path.join(absTrainRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
elif curRatio >= trainRatio and curRatio < valRatio:
imageCopyPath = os.path.join(absValRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
else:
imageCopyPath = os.path.join(absTestRootPath, imageName)
shutil.copy(imagePath, imageCopyPath)
testTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
# 删掉存在的文件
def removeFile(path):
if os.path.exists(path):
os.remove(path)
def genDetRecTrainVal(args):
detAbsTrainRootPath = isCreateOrDeleteFolder(args.detRootPath, "train")
detAbsValRootPath = isCreateOrDeleteFolder(args.detRootPath, "val")
detAbsTestRootPath = isCreateOrDeleteFolder(args.detRootPath, "test")
recAbsTrainRootPath = isCreateOrDeleteFolder(args.recRootPath, "train")
recAbsValRootPath = isCreateOrDeleteFolder(args.recRootPath, "val")
recAbsTestRootPath = isCreateOrDeleteFolder(args.recRootPath, "test")
removeFile(os.path.join(args.detRootPath, "train.txt"))
removeFile(os.path.join(args.detRootPath, "val.txt"))
removeFile(os.path.join(args.detRootPath, "test.txt"))
removeFile(os.path.join(args.recRootPath, "train.txt"))
removeFile(os.path.join(args.recRootPath, "val.txt"))
removeFile(os.path.join(args.recRootPath, "test.txt"))
detTrainTxt = open(os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8")
detValTxt = open(os.path.join(args.detRootPath, "val.txt"), "a", encoding="UTF-8")
detTestTxt = open(os.path.join(args.detRootPath, "test.txt"), "a", encoding="UTF-8")
recTrainTxt = open(os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8")
recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
recTestTxt = open(os.path.join(args.recRootPath, "test.txt"), "a", encoding="UTF-8")
splitTrainVal(args.datasetRootPath, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
detTestTxt, "det")
for root, dirs, files in os.walk(args.datasetRootPath):
for dir in dirs:
if dir == 'crop_img':
splitTrainVal(root, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
recTestTxt, "rec")
else:
continue
break
if __name__ == "__main__":
# 功能描述：分别划分检测和识别的训练集、验证集、测试集
# 说明：可以根据自己的路径和需求调整参数，图像数据往往多人合作分批标注，每一批图像数据放在一个文件夹内用PPOCRLabel进行标注，
# 如此会有多个标注好的图像文件夹汇总并划分训练集、验证集、测试集的需求
parser = argparse.ArgumentParser()
parser.add_argument(
"--trainValTestRatio",
type=str,
default="6:2:2",
help="ratio of trainset:valset:testset")
parser.add_argument(
"--datasetRootPath",
type=str,
default="../train_data/",
help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
)
parser.add_argument(
"--detRootPath",
type=str,
default="../train_data/det",
help="the path where the divided detection dataset is placed")
parser.add_argument(
"--recRootPath",
type=str,
default="../train_data/rec",
help="the path where the divided recognition dataset is placed"
)
parser.add_argument(
"--detLabelFileName",
type=str,
default="Label.txt",
help="the name of the detection annotation file")
parser.add_argument(
"--recLabelFileName",
type=str,
default="rec_gt.txt",
help="the name of the recognition annotation file"
)
parser.add_argument(
"--recImageDirName",
type=str,
default="crop_img",
help="the name of the folder where the cropped recognition dataset is located"
)
args = parser.parse_args()
genDetRecTrainVal(args)
PPOCRLabel/libs/__init__.py 0 → 100644
View file @ 89eb5e4b
__version_info__ = ('1', '0', '0')
__version__ = '.'.join(__version_info__)
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