Keep in sync with the dygraph branch

Keep in sync with the dygraph branch

Keep in sync with the dygraph branch
ea1b76af · qq_25193841 · 1749464b · ea1b76af · ea1b76af · ea1b76af
6 changed file
--- a/PPOCRLabel/PPOCRLabel.py
+++ b/PPOCRLabel/PPOCRLabel.py
@@ -106,7 +106,7 @@ class MainWindow(QMainWindow, WindowMixin):
        getStr = lambda strId: self.stringBundle.getString(strId)
        self.defaultSaveDir = defaultSaveDir
-        self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=True, lang=lang)
+        self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=False, lang=lang)
        if os.path.exists('./data/paddle.png'):
            result = self.ocr.ocr('./data/paddle.png', cls=True, det=True)
@@ -257,9 +257,6 @@ class MainWindow(QMainWindow, WindowMixin):
        self.imgsliderDock.setObjectName(getStr('IR'))
        self.imgsliderDock.setWidget(self.imgsplider)
        self.imgsliderDock.setFeatures(QDockWidget.DockWidgetFloatable)
-        # op = QGraphicsOpacityEffect()
-        # op.setOpacity(0.2)
-        # self.imgsliderDock.setGraphicsEffect(op)
        self.imgsliderDock.setAttribute(Qt.WA_TranslucentBackground)
        self.addDockWidget(Qt.RightDockWidgetArea, self.imgsliderDock)
@@ -299,15 +296,10 @@ class MainWindow(QMainWindow, WindowMixin):
        hlayout.addWidget(self.iconlist)
        hlayout.addWidget(self.nextButton)
-        # self.setLayout(hlayout)
        iconListContainer = QWidget()
        iconListContainer.setLayout(hlayout)
        iconListContainer.setFixedHeight(100)
-        # iconListContainer.setFixedWidth(530)
-        # op = QGraphicsOpacityEffect()
-        # op.setOpacity(0.5)
-        # iconListContainer.setGraphicsEffect(op)
        ########### Canvas ###########
        self.canvas = Canvas(parent=self)
@@ -498,9 +490,6 @@ class MainWindow(QMainWindow, WindowMixin):
                                icon='color', tip=getStr('shapeFillColorDetail'),
                                enabled=False)
-        # labels = self.dock.toggleViewAction()
-        # labels.setText(getStr('showHide'))
-        # labels.setShortcut('Ctrl+Shift+L')
        # Label list context menu.
        labelMenu = QMenu()
@@ -582,16 +571,6 @@ class MainWindow(QMainWindow, WindowMixin):
            action('&Copy here', self.copyShape),
            action('&Move here', self.moveShape)))
-        # self.tools = self.toolbar('Tools')
-        # self.actions.beginner = (
-        #     open, opendir, openNextImg, openPrevImg, verify, save, None, create, copy, delete, None,
-        #     zoomIn, zoom, zoomOut, fitWindow, fitWidth)
-        #
-        # self.actions.advanced = (
-        #     open, opendir, openNextImg, openPrevImg, save, None,
-        #     createMode, editMode, None,
-        #     hideAll, showAll)
        self.statusBar().showMessage('%s started.' % __appname__)
        self.statusBar().show()

--- a/PPOCRLabel/README.md
+++ b/PPOCRLabel/README.md
-# **PPOCRLabel**
+English | [简体中文](README_ch.md)
-PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，使用python3和pyqt5编写，支持矩形框标注和四点标注模式，导出格式可直接用于PPOCR检测和识别模型的训练。
+# PPOCRLabel
-<img src="./data/gif/steps.gif" width="100%"/>
+PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field. It is written in python3 and pyqt5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models.
-#### 近期更新
+<img src="./data/gif/steps_en.gif" width="100%"/>
- 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。
+### Recent Update
-## 安装
+- 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
-### 1. 安装PaddleOCR
+## Installation
-参考[PaddleOCR安装文档](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/installation.md)准备好PaddleOCR
+### 1. Install PaddleOCR
+Refer to [PaddleOCR installation document](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/installation.md) to prepare PaddleOCR
+### 2. Install PPOCRLabel
-### 2. 安装PPOCRLabel
 #### Windows + Anaconda
+Download and install [Anaconda](https://www.anaconda.com/download/#download) (Python 3+)
 ```
 pip install pyqt5
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python PPOCRLabel.py --lang ch
+python PPOCRLabel.py
 ```
 #### Ubuntu Linux
@@ -27,98 +33,121 @@ python PPOCRLabel.py --lang ch
 ```
 pip3 install pyqt5
 pip3 install trash-cli
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python3 PPOCRLabel.py --lang ch
+python3 PPOCRLabel.py
 ```
 #### macOS
 ```
 pip3 install pyqt5
-pip3 uninstall opencv-python # 由于mac版本的opencv与pyqt有冲突，需先手动卸载opencv
+pip3 uninstall opencv-python # Uninstall opencv manually as it conflicts with pyqt
-pip3 install opencv-contrib-python-headless # 安装headless版本的open-cv
+pip3 install opencv-contrib-python-headless # Install the headless version of opencv
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python3 PPOCRLabel.py --lang ch
+python3 PPOCRLabel.py
 ```
-## 使用
+## Usage
+### 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 PPOCR 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. Double 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(The results will not be written directly to the file at this time).
-### 操作步骤
+9. Click "Delete Image" and the image will be deleted to the recycle bin.
-1. 安装与运行：使用上述命令安装与运行程序。
+10. Labeling result: the user can save manually through the menu "File - Save Label", while the program will also save automatically after every 10 images confirmed by the user.the manually checked label will be stored in *Label.txt* under the opened picture folder.
-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>.
+    Click "PaddleOCR"-"Save 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>.
-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P，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：双击识别结果，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（此时不会直接将结果写入文件）。
-9. 删除：点击 “删除图像”，图片将会被删除至回收站。
-10. 保存结果：用户可以通过菜单中“文件-保存标记结果”手动保存，同时程序也会在用户每确认10张图片后自动保存一次。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” - "保存识别结果"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>。
-### 注意
+### Note
-[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。
+[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] 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。
+[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] 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。
+[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] 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。
+[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.
-|    文件名     |                             说明                             |
+|   File name   |                         Description                          |
 | :-----------: | :----------------------------------------------------------: |
-|   Label.txt   | 检测标签，可直接用于PPOCR检测模型训练。用户每保存10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。 |
+|   Label.txt   | The detection label file can be directly used for PPOCR detection model training. After the user saves 10 label results, the file will be automatically saved. It will also be written when the user closes the application or changes the file folder. |
-| 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。 |
+| fileState.txt | The picture status file save the image in the current folder that has been manually confirmed by the user. |
-|  Cache.cach   |              缓存文件，保存模型自动识别的结果。              |
+|  Cache.cach   |    Cache files to save the results of model recognition.     |
-|  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” - "保存识别结果"后产生。 |
+|  rec_gt.txt   | The recognition label file, which can be directly used for PPOCR identification model training, is generated after the user clicks on the menu bar "File"-"Save recognition result". |
-|   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。   |
+|   crop_img    | The recognition data, generated at the same time with *rec_gt.txt* |
-## 说明
+## Explanation
-### 快捷键
+### Shortcut keys
-| 快捷键           | 说明                         |
+| Shortcut keys    | Description                                      |
-| ---------------- | ---------------------------- |
+| ---------------- | ------------------------------------------------ |
-| Ctrl + shift + A | 自动标注所有未确认过的图片   |
+| Ctrl + shift + A | Automatically label all unchecked images         |
-| Ctrl + shift + R | 对当前图片的所有标记重新识别 |
+| Ctrl + shift + R | Re-recognize all the labels of the current image |
-| W                | 新建矩形框                   |
+| W                | Create a rect box                                |
-| Q                | 新建四点框                   |
+| Q                | Create a four-points box                         |
-| Ctrl + E         | 编辑所选框标签               |
+| Ctrl + E         | Edit label of the selected box                   |
-| Ctrl + R         | 重新识别所选标记             |
+| Ctrl + R         | Re-recognize the selected box                    |
-| Backspace        | 删除所选框                   |
+| Backspace        | Delete the selected box                          |
-| Ctrl + V         | 确认本张图片标记             |
+| Ctrl + V         | Check image                                      |
-| Ctrl + Shift + d | 删除本张图片                 |
+| Ctrl + Shift + d | Delete image                                     |
-| D                | 下一张图片                   |
+| D                | Next image                                       |
-| A                | 上一张图片                   |
+| A                | Previous image                                   |
-| Ctrl++           | 缩小                         |
+| Ctrl++           | Zoom in                                          |
-| Ctrl--           | 放大                         |
+| Ctrl--           | Zoom out                                         |
-| ↑→↓←             | 移动标记框                   |
+| ↑→↓←             | Move selected box                                |
-### 内置模型
+### Built-in Model
- - 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。
+- 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.
- - 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).
+- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude 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)
- - 自定义模型：用户可根据[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/develop/PPOCRLabel/PPOCRLabel.py#L110)替换成自己训练的模型。
+- Custom model: The model trained by users can be replaced by modifying PPOCRLabel.py in [PaddleOCR class instantiation](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/PPOCRLabel.py#L110) referring [Custom Model Code](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md#use-custom-model)
-### 导出部分识别结果
+### Export partial recognition results
-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。
+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.
-*注意：识别结果中的复选框状态仍需用户手动点击保存后才能保留*
+*Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.*
-### 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。
+### Error message
- PPOCRLabel**不支持对中文文件名**的图片进行自动标注。
- 对于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：
+- 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.
-	```
-	pip install opencv-python==4.2.0.32
+- 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:
-	```
- 如果出现''Missing string id '开头的错误，需要重新编译资源：
+    ```
-	```
+    pip install opencv-python==4.2.0.32
-	pyrcc5 -o libs/resources.py resources.qrc
+    ```
-	```
+- If you get an error starting with **Missing string id **,you need to recompile resources:
-### 参考资料
+    ```
+    pyrcc5 -o libs/resources.py resources.qrc
-**1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)**
+    ```
+- 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 headless version of opencv
+    ```
+    pip install opencv-contrib-python-headless
+    ```
+### Related
+1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
--- a/PPOCRLabel/README_ch.md
+++ b/PPOCRLabel/README_ch.md
+[English](README.md) | 简体中文
+# PPOCRLabel
+PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，使用python3和pyqt5编写，支持矩形框标注和四点标注模式，导出格式可直接用于PPOCR检测和识别模型的训练。
+<img src="./data/gif/steps.gif" width="100%"/>
+#### 近期更新
+- 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。
+## 安装
+### 1. 安装PaddleOCR
+参考[PaddleOCR安装文档](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/installation.md)准备好PaddleOCR
+### 2. 安装PPOCRLabel
+#### Windows + Anaconda
+```
+pip install pyqt5
+cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+python PPOCRLabel.py --lang ch
+```
+#### Ubuntu Linux
+```
+pip3 install pyqt5
+pip3 install trash-cli
+cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+python3 PPOCRLabel.py --lang ch
+```
+#### macOS
+```
+pip3 install pyqt5
+pip3 uninstall opencv-python # 由于mac版本的opencv与pyqt有冲突，需先手动卸载opencv
+pip3 install opencv-contrib-python-headless # 安装headless版本的open-cv
+cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+python3 PPOCRLabel.py --lang ch
+```
+## 使用
+### 操作步骤
+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：双击识别结果，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（此时不会直接将结果写入文件）。
+9. 删除：点击 “删除图像”，图片将会被删除至回收站。
+10. 保存结果：用户可以通过菜单中“文件-保存标记结果”手动保存，同时程序也会在用户每确认10张图片后自动保存一次。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” - "保存识别结果"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>。
+### 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。用户每保存10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” - "保存识别结果"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。   |
+## 说明
+### 快捷键
+| 快捷键           | 说明                         |
+| ---------------- | ---------------------------- |
+| Ctrl + shift + A | 自动标注所有未确认过的图片   |
+| Ctrl + shift + R | 对当前图片的所有标记重新识别 |
+| W                | 新建矩形框                   |
+| Q                | 新建四点框                   |
+| Ctrl + E         | 编辑所选框标签               |
+| Ctrl + R         | 重新识别所选标记             |
+| 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                   |
+### 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：用户可根据[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/develop/PPOCRLabel/PPOCRLabel.py#L110)替换成自己训练的模型。
+### 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。
+*注意：识别结果中的复选框状态仍需用户手动点击保存后才能保留*
+### 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headless版本的opencv
+    ```
+    pip install opencv-contrib-python-headless
+    ```
+### 参考资料
+1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
--- a/PPOCRLabel/README_en.md
+++ b/PPOCRLabel/README_en.md
-# PPOCRLabel
-PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field. It is written in python3 and pyqt5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models.
-<img src="./data/gif/steps_en.gif" width="100%"/>
-### Recent Update
- 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
-## Installation
-### 1. Install PaddleOCR
-Refer to [PaddleOCR installation document](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/installation.md) to prepare PaddleOCR
-### 2. Install PPOCRLabel
-#### Windows + Anaconda
-Download and install [Anaconda](https://www.anaconda.com/download/#download) (Python 3+)
-```
-pip install pyqt5
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python PPOCRLabel.py
-```
-#### Ubuntu Linux
-```
-pip3 install pyqt5
-pip3 install trash-cli
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python3 PPOCRLabel.py
-```
-#### macOS
-```
-pip3 install pyqt5
-pip3 uninstall opencv-python # Uninstall opencv manually as it conflicts with pyqt
-pip3 install opencv-contrib-python-headless # Install the headless version of opencv
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python3 PPOCRLabel.py
-```
-## Usage
-### 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 PPOCR 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 'P' 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. Double 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(The results will not be written directly to the file at this time).
-9. Click "Delete Image" and the image will be deleted to the recycle bin.
-10. Labeling result: the user can save manually through the menu "File - Save Label", while the program will also save automatically after every 10 images confirmed by the user.the manually checked label will be stored in *Label.txt* under the opened picture folder.
-    Click "PaddleOCR"-"Save 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>.
-### 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 PPOCR detection model training. After the user saves 10 label results, the file will be automatically saved. 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 PPOCR identification model training, is generated after the user clicks on the menu bar "File"-"Save recognition result". |
-|   crop_img    | The recognition data, generated at the same time with *rec_gt.txt* |
-## Explanation
-### Shortcut keys
-| Shortcut keys    | Description                                      |
-| ---------------- | ------------------------------------------------ |
-| Ctrl + shift + A | Automatically label all unchecked images         |
-| Ctrl + shift + R | Re-recognize all the labels of the current image |
-| W                | Create a rect box                                |
-| Q                | Create a four-points box                         |
-| Ctrl + E         | Edit label of the selected box                   |
-| Ctrl + R         | Re-recognize the selected 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                                |
-### 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 languagesinclude 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: The model trained by users can be replaced by modifying PPOCRLabel.py in [PaddleOCR class instantiation](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/PPOCRLabel.py#L110) referring [Custom Model Code](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md#use-custom-model)
-### 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.
-*Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.*
-### 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
-	```
-### Related
-1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
--- a/PPOCRLabel/data/gif/steps_en.gif
+++ b/PPOCRLabel/data/gif/steps_en.gif
--- a/PPOCRLabel/libs/autoDialog.py
+++ b/PPOCRLabel/libs/autoDialog.py
@@ -46,7 +46,8 @@ class Worker(QThread):
                            chars = res[1][0]
                            cond = res[1][1]
                            posi = res[0]
-                            strs += "Transcription: " + chars + " Probability: " + str(cond) + " Location: " + json.dumps(posi)
+                            strs += "Transcription: " + chars + " Probability: " + str(cond) + \
+                                    " Location: " + json.dumps(posi) +'\n'
                        # Sending large amounts of data repeatedly through pyqtSignal may affect the program efficiency
                        self.listValue.emit(strs)
                        self.mainThread.result_dic = self.result_dic