Merge pull request #2102 from Evezerest/release2.0-rc1

Change the path of joinus.png to absolute path

MANIFEST.in

@@ -1,8 +1,7 @@
 include LICENSE.txt
 include README.md
 
-recursive-include ppocr/utils *.txt utility.py character.py check.py
-recursive-include ppocr/data/det *.py
+recursive-include ppocr/utils *.txt utility.py logging.py
+recursive-include ppocr/data/ *.py
 recursive-include ppocr/postprocess *.py
-recursive-include ppocr/postprocess/lanms *.*
-recursive-include tools/infer *.py
+recursive-include tools/infer *.py

PPOCRLabel/Makefile

@@ -0,0 +1,35 @@
+# 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

PPOCRLabel/README.md

@@ -0,0 +1,153 @@
+English | [简体中文](README_ch.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 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: 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
+    ```
+- 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)

PPOCRLabel/README_ch.md

@@ -0,0 +1,134 @@
+[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
+
+[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)

PPOCRLabel/combobox.py

@@ -0,0 +1,46 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+import sys
+try:
+    from PyQt5.QtWidgets import QWidget, QHBoxLayout, QComboBox
+except ImportError:
+    # needed for py3+qt4
+    # Ref:
+    # http://pyqt.sourceforge.net/Docs/PyQt4/incompatible_apis.html
+    # http://stackoverflow.com/questions/21217399/pyqt4-qtcore-qvariant-object-instead-of-a-string
+    if sys.version_info.major >= 3:
+        import sip
+        sip.setapi('QVariant', 2)
+    from PyQt4.QtGui import QWidget, QHBoxLayout, QComboBox
+
+
+class ComboBox(QWidget):
+    def __init__(self, parent=None, items=[]):
+        super(ComboBox, self).__init__(parent)
+
+        layout = QHBoxLayout()
+        self.cb = QComboBox()
+        self.items = items
+        self.cb.addItems(self.items)
+
+        self.cb.currentIndexChanged.connect(parent.comboSelectionChanged)
+
+        layout.addWidget(self.cb)
+        self.setLayout(layout)
+
+    def update_items(self, items):
+        self.items = items
+
+        self.cb.clear()
+        self.cb.addItems(self.items)

PPOCRLabel/libs/__init__.py

@@ -0,0 +1,2 @@
+__version_info__ = ('1', '0', '0')
+__version__ = '.'.join(__version_info__)

PPOCRLabel/libs/autoDialog.py

@@ -0,0 +1,155 @@
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+import json
+
+from libs.utils import newIcon
+
+BB = QDialogButtonBox
+
+
+class Worker(QThread):
+    progressBarValue = pyqtSignal(int)
+    listValue = pyqtSignal(str)
+    endsignal = pyqtSignal(int, str)
+    handle = 0
+
+    def __init__(self, ocr, mImgList, mainThread, model):
+        super(Worker, self).__init__()
+        self.ocr = ocr
+        self.mImgList = mImgList
+        self.mainThread = mainThread
+        self.model = model
+        self.setStackSize(1024*1024)
+
+    def run(self):
+        try:
+            findex = 0
+            for Imgpath in self.mImgList:
+                if self.handle == 0:
+                    self.listValue.emit(Imgpath)
+                    if self.model == 'paddle':
+                        self.result_dic = self.ocr.ocr(Imgpath, cls=True, det=True)
+
+                    # 结果保存
+                    if self.result_dic is None or len(self.result_dic) == 0:
+                        print('Can not recognise file  is :  ', Imgpath)
+                        pass
+                    else:
+                        strs = ''
+                        for res in self.result_dic:
+                            chars = res[1][0]
+                            cond = res[1][1]
+                            posi = res[0]
+                            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
+                        self.mainThread.filePath = Imgpath
+                        # 保存
+                        self.mainThread.saveFile(mode='Auto')
+                    findex += 1
+                    self.progressBarValue.emit(findex)
+                else:
+                    break
+            self.endsignal.emit(0, "readAll")
+            self.exec()
+        except Exception as e:
+            print(e)
+            raise
+
+
+class AutoDialog(QDialog):
+
+    def __init__(self, text="Enter object label", parent=None, ocr=None, mImgList=None, lenbar=0):
+        super(AutoDialog, self).__init__(parent)
+        self.setFixedWidth(1000)
+        self.parent = parent
+        self.ocr = ocr
+        self.mImgList = mImgList
+        self.pb = QProgressBar()
+        self.pb.setRange(0, lenbar)
+        self.pb.setValue(0)
+
+        layout = QVBoxLayout()
+        layout.addWidget(self.pb)
+        self.model = 'paddle'
+        self.listWidget = QListWidget(self)
+        layout.addWidget(self.listWidget)
+
+        self.buttonBox = bb = BB(BB.Ok | BB.Cancel, Qt.Horizontal, self)
+        bb.button(BB.Ok).setIcon(newIcon('done'))
+        bb.button(BB.Cancel).setIcon(newIcon('undo'))
+        bb.accepted.connect(self.validate)
+        bb.rejected.connect(self.reject)
+        layout.addWidget(bb)
+        bb.button(BB.Ok).setEnabled(False)
+
+        self.setLayout(layout)
+        # self.setWindowTitle("自动标注中")
+        self.setWindowModality(Qt.ApplicationModal)
+
+        # self.setWindowFlags(Qt.WindowCloseButtonHint)
+
+        self.thread_1 = Worker(self.ocr, self.mImgList, self.parent, 'paddle')
+        self.thread_1.progressBarValue.connect(self.handleProgressBarSingal)
+        self.thread_1.listValue.connect(self.handleListWidgetSingal)
+        self.thread_1.endsignal.connect(self.handleEndsignalSignal)
+
+    def handleProgressBarSingal(self, i):
+        self.pb.setValue(i)
+
+    def handleListWidgetSingal(self, i):
+        self.listWidget.addItem(i)
+        titem = self.listWidget.item(self.listWidget.count() - 1)
+        self.listWidget.scrollToItem(titem)
+
+    def handleEndsignalSignal(self, i, str):
+        if i == 0 and str == "readAll":
+            self.buttonBox.button(BB.Ok).setEnabled(True)
+            self.buttonBox.button(BB.Cancel).setEnabled(False)
+
+    def reject(self):
+        print("reject")
+        self.thread_1.handle = -1
+        self.thread_1.quit()
+        # del self.thread_1
+        # if self.thread_1.isRunning():
+        #     self.thread_1.terminate()
+        # self.thread_1.quit()
+        # super(AutoDialog,self).reject()
+        while not self.thread_1.isFinished():
+            pass
+        self.accept()
+
+    def validate(self):
+        self.accept()
+
+    def postProcess(self):
+        try:
+            self.edit.setText(self.edit.text().trimmed())
+            # print(self.edit.text())
+        except AttributeError:
+            # PyQt5: AttributeError: 'str' object has no attribute 'trimmed'
+            self.edit.setText(self.edit.text())
+            print(self.edit.text())
+
+    def popUp(self):
+        self.thread_1.start()
+        return 1 if self.exec_() else None
+
+    def closeEvent(self, event):
+        print("???")
+        # if self.thread_1.isRunning():
+        #     self.thread_1.quit()
+        #
+        #     # self._thread.terminate()
+        # # del self.thread_1
+        # super(AutoDialog, self).closeEvent(event)
+        self.reject()

PPOCRLabel/libs/canvas.py

@@ -0,0 +1,798 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+#from PyQt4.QtOpenGL import *
+
+from libs.shape import Shape
+from libs.utils import distance
+
+CURSOR_DEFAULT = Qt.ArrowCursor
+CURSOR_POINT = Qt.PointingHandCursor
+CURSOR_DRAW = Qt.CrossCursor
+CURSOR_MOVE = Qt.ClosedHandCursor
+CURSOR_GRAB = Qt.OpenHandCursor
+
+# class Canvas(QGLWidget):
+
+
+class Canvas(QWidget):
+    zoomRequest = pyqtSignal(int)
+    scrollRequest = pyqtSignal(int, int)
+    newShape = pyqtSignal()
+    selectionChanged = pyqtSignal(bool)
+    shapeMoved = pyqtSignal()
+    drawingPolygon = pyqtSignal(bool)
+
+    CREATE, EDIT = list(range(2))
+    _fill_drawing = False # draw shadows
+
+    epsilon = 11.0
+
+    def __init__(self, *args, **kwargs):
+        super(Canvas, self).__init__(*args, **kwargs)
+        # Initialise local state.
+        self.mode = self.EDIT
+        self.shapes = []
+        self.current = None
+        self.selectedShape = None  # save the selected shape here
+        self.selectedShapeCopy = None
+        self.drawingLineColor = QColor(0, 0, 255)
+        self.drawingRectColor = QColor(0, 0, 255)
+        self.line = Shape(line_color=self.drawingLineColor)
+        self.prevPoint = QPointF()
+        self.offsets = QPointF(), QPointF()
+        self.scale = 1.0
+        self.pixmap = QPixmap()
+        self.visible = {}
+        self._hideBackround = False
+        self.hideBackround = False
+        self.hShape = None
+        self.hVertex = None
+        self._painter = QPainter()
+        self._cursor = CURSOR_DEFAULT
+        # Menus:
+        self.menus = (QMenu(), QMenu())
+        # Set widget options.
+        self.setMouseTracking(True)
+        self.setFocusPolicy(Qt.WheelFocus)
+        self.verified = False
+        self.drawSquare = False
+        self.fourpoint = True # ADD
+        self.pointnum = 0
+
+        #initialisation for panning
+        self.pan_initial_pos = QPoint()
+
+    def setDrawingColor(self, qColor):
+        self.drawingLineColor = qColor
+        self.drawingRectColor = qColor
+
+    def enterEvent(self, ev):
+        self.overrideCursor(self._cursor)
+
+    def leaveEvent(self, ev):
+        self.restoreCursor()
+
+    def focusOutEvent(self, ev):
+        self.restoreCursor()
+
+    def isVisible(self, shape):
+        return self.visible.get(shape, True)
+
+    def drawing(self):
+        return self.mode == self.CREATE
+
+    def editing(self):
+        return self.mode == self.EDIT
+
+    def setEditing(self, value=True):
+        self.mode = self.EDIT if value else self.CREATE
+        if not value:  # Create
+            self.unHighlight()
+            self.deSelectShape()
+        self.prevPoint = QPointF()
+        self.repaint()
+
+    def unHighlight(self):
+        if self.hShape:
+            self.hShape.highlightClear()
+        self.hVertex = self.hShape = None
+
+    def selectedVertex(self):
+        return self.hVertex is not None
+
+
+    def mouseMoveEvent(self, ev):
+        """Update line with last point and current coordinates."""
+        pos = self.transformPos(ev.pos())
+
+        # Update coordinates in status bar if image is opened
+        window = self.parent().window()
+        if window.filePath is not None:
+            self.parent().window().labelCoordinates.setText(
+                'X: %d; Y: %d' % (pos.x(), pos.y()))
+
+        # Polygon drawing.
+        if self.drawing():
+            self.overrideCursor(CURSOR_DRAW) # ?
+            if self.current:
+                # Display annotation width and height while drawing
+                currentWidth = abs(self.current[0].x() - pos.x())
+                currentHeight = abs(self.current[0].y() - pos.y())
+                self.parent().window().labelCoordinates.setText(
+                        'Width: %d, Height: %d / X: %d; Y: %d' % (currentWidth, currentHeight, pos.x(), pos.y()))
+
+                color = self.drawingLineColor
+                if self.outOfPixmap(pos):
+                    # Don't allow the user to draw outside the pixmap.
+                    # Clip the coordinates to 0 or max,
+                    # if they are outside the range [0, max]
+                    size = self.pixmap.size()
+                    clipped_x = min(max(0, pos.x()), size.width())
+                    clipped_y = min(max(0, pos.y()), size.height())
+                    pos = QPointF(clipped_x, clipped_y)
+                elif len(self.current) > 1 and self.closeEnough(pos, self.current[0]) and not self.fourpoint:
+                    # Attract line to starting point and colorise to alert the
+                    # user:
+                    pos = self.current[0]
+                    color = self.current.line_color
+                    self.overrideCursor(CURSOR_POINT)
+                    self.current.highlightVertex(0, Shape.NEAR_VERTEX)
+                elif ( # ADD
+                        len(self.current) > 1
+                        and self.fourpoint
+                        and self.closeEnough(pos, self.current[0])
+                ):
+                    # Attract line to starting point and
+                    # colorise to alert the user.
+                    pos = self.current[0]
+                    self.overrideCursor(CURSOR_POINT)
+                    self.current.highlightVertex(0, Shape.NEAR_VERTEX)
+
+
+                if self.drawSquare:
+                    initPos = self.current[0]
+                    minX = initPos.x()
+                    minY = initPos.y()
+                    min_size = min(abs(pos.x() - minX), abs(pos.y() - minY))
+                    directionX = -1 if pos.x() - minX < 0 else 1
+                    directionY = -1 if pos.y() - minY < 0 else 1
+                    self.line[1] = QPointF(minX + directionX * min_size, minY + directionY * min_size)
+
+                elif self.fourpoint:
+                    # self.line[self.pointnum] = pos # OLD
+
+                    self.line[0] = self.current[-1]
+                    self.line[1] = pos
+
+                else:
+                    self.line[1] = pos # pos is the mouse's current position
+
+                self.line.line_color = color
+                self.prevPoint = QPointF() # ？
+                self.current.highlightClear()
+            else:
+                self.prevPoint = pos
+            self.repaint()
+            return
+
+        # Polygon copy moving.
+        if Qt.RightButton & ev.buttons():
+            if self.selectedShapeCopy and self.prevPoint:
+                self.overrideCursor(CURSOR_MOVE)
+                self.boundedMoveShape(self.selectedShapeCopy, pos)
+                self.repaint()
+            elif self.selectedShape:
+                self.selectedShapeCopy = self.selectedShape.copy()
+                self.repaint()
+            return
+
+        # Polygon/Vertex moving.
+        if Qt.LeftButton & ev.buttons():
+            if self.selectedVertex():
+                self.boundedMoveVertex(pos)
+                self.shapeMoved.emit()
+                self.repaint()
+            elif self.selectedShape and self.prevPoint:
+                self.overrideCursor(CURSOR_MOVE)
+                self.boundedMoveShape(self.selectedShape, pos)
+                self.shapeMoved.emit()
+                self.repaint()
+            else:
+                #pan
+                delta_x = pos.x() - self.pan_initial_pos.x()
+                delta_y = pos.y() - self.pan_initial_pos.y()
+                self.scrollRequest.emit(delta_x, Qt.Horizontal)
+                self.scrollRequest.emit(delta_y, Qt.Vertical)
+                self.update()
+            return
+
+        # Just hovering over the canvas, 2 posibilities:
+        # - Highlight shapes
+        # - Highlight vertex
+        # Update shape/vertex fill and tooltip value accordingly.
+        self.setToolTip("Image")
+        for shape in reversed([s for s in self.shapes if self.isVisible(s)]):
+            # Look for a nearby vertex to highlight. If that fails,
+            # check if we happen to be inside a shape.
+            index = shape.nearestVertex(pos, self.epsilon)
+            if index is not None:
+                if self.selectedVertex():
+                    self.hShape.highlightClear()
+                self.hVertex, self.hShape = index, shape
+                shape.highlightVertex(index, shape.MOVE_VERTEX)
+                self.overrideCursor(CURSOR_POINT)
+                self.setToolTip("Click & drag to move point")
+                self.setStatusTip(self.toolTip())
+                self.update()
+                break
+            elif shape.containsPoint(pos):
+                if self.selectedVertex():
+                    self.hShape.highlightClear()
+                self.hVertex, self.hShape = None, shape
+                self.setToolTip(
+                    "Click & drag to move shape '%s'" % shape.label)
+                self.setStatusTip(self.toolTip())
+                self.overrideCursor(CURSOR_GRAB)
+                self.update()
+                break
+        else:  # Nothing found, clear highlights, reset state.
+            if self.hShape:
+                self.hShape.highlightClear()
+                self.update()
+            self.hVertex, self.hShape = None, None
+            self.overrideCursor(CURSOR_DEFAULT)
+
+    def mousePressEvent(self, ev):
+        pos = self.transformPos(ev.pos())
+
+        if ev.button() == Qt.LeftButton:
+            if self.drawing():
+                # self.handleDrawing(pos) # OLD
+
+
+                if self.current and self.fourpoint: # ADD IF
+                    # Add point to existing shape.
+                    print('Adding points in mousePressEvent is ', self.line[1])
+                    self.current.addPoint(self.line[1])
+                    self.line[0] = self.current[-1]
+                    if self.current.isClosed():
+                        # print('1111')
+                        self.finalise()
+                elif not self.outOfPixmap(pos):
+                    # Create new shape.
+                    self.current = Shape()# self.current = Shape(shape_type=self.createMode)
+                    self.current.addPoint(pos)
+                    # if self.createMode == "point":
+                    #     self.finalise()
+                    # else:
+                    #     if self.createMode == "circle":
+                    #         self.current.shape_type = "circle"
+                    self.line.points = [pos, pos]
+                    self.setHiding()
+                    self.drawingPolygon.emit(True)
+                    self.update()
+
+
+            else:
+                selection = self.selectShapePoint(pos)
+                self.prevPoint = pos
+
+                if selection is None:
+                    #pan
+                    QApplication.setOverrideCursor(QCursor(Qt.OpenHandCursor))
+                    self.pan_initial_pos = pos
+
+        elif ev.button() == Qt.RightButton and self.editing():
+            self.selectShapePoint(pos)
+            self.prevPoint = pos
+        self.update()
+
+    def mouseReleaseEvent(self, ev):
+        if ev.button() == Qt.RightButton:
+            menu = self.menus[bool(self.selectedShapeCopy)]
+            self.restoreCursor()
+            if not menu.exec_(self.mapToGlobal(ev.pos()))\
+               and self.selectedShapeCopy:
+                # Cancel the move by deleting the shadow copy.
+                self.selectedShapeCopy = None
+                self.repaint()
+        elif ev.button() == Qt.LeftButton and self.selectedShape: # OLD
+            if self.selectedVertex():
+                self.overrideCursor(CURSOR_POINT)
+            else:
+                self.overrideCursor(CURSOR_GRAB)
+
+
+        elif ev.button() == Qt.LeftButton and not self.fourpoint:
+            pos = self.transformPos(ev.pos())
+            if self.drawing():
+                self.handleDrawing(pos)
+            else:
+                #pan
+                QApplication.restoreOverrideCursor() # ?
+
+
+    def endMove(self, copy=False):
+        assert self.selectedShape and self.selectedShapeCopy
+        shape = self.selectedShapeCopy
+        #del shape.fill_color
+        #del shape.line_color
+        if copy:
+            self.shapes.append(shape)
+            self.selectedShape.selected = False
+            self.selectedShape = shape
+            self.repaint()
+        else:
+            self.selectedShape.points = [p for p in shape.points]
+        self.selectedShapeCopy = None
+
+    def hideBackroundShapes(self, value):
+        self.hideBackround = value
+        if self.selectedShape:
+            # Only hide other shapes if there is a current selection.
+            # Otherwise the user will not be able to select a shape.
+            self.setHiding(True)
+            self.repaint()
+
+    def handleDrawing(self, pos):
+        if self.current and self.current.reachMaxPoints() is False:
+            if self.fourpoint:
+                targetPos = self.line[self.pointnum]
+                self.current.addPoint(targetPos)
+                print('current points in handleDrawing is ', self.line[self.pointnum])
+                self.update()
+                if self.pointnum == 3:
+                    self.finalise()
+
+            else: # 按住送掉后跳到这里
+                initPos = self.current[0]
+                print('initPos', self.current[0])
+                minX = initPos.x()
+                minY = initPos.y()
+                targetPos = self.line[1]
+                maxX = targetPos.x()
+                maxY = targetPos.y()
+                self.current.addPoint(QPointF(maxX, minY))
+                self.current.addPoint(targetPos)
+                self.current.addPoint(QPointF(minX, maxY))
+                self.finalise()
+
+        elif not self.outOfPixmap(pos):
+            print('release')
+            self.current = Shape()
+            self.current.addPoint(pos)
+            self.line.points = [pos, pos]
+            self.setHiding()
+            self.drawingPolygon.emit(True)
+            self.update()
+
+    def setHiding(self, enable=True):
+        self._hideBackround = self.hideBackround if enable else False
+
+    def canCloseShape(self):
+        return self.drawing() and self.current and len(self.current) > 2
+
+    def mouseDoubleClickEvent(self, ev):
+        # We need at least 4 points here, since the mousePress handler
+        # adds an extra one before this handler is called.
+        if self.canCloseShape() and len(self.current) > 3:
+            if not self.fourpoint:
+                self.current.popPoint()
+            self.finalise()
+
+    def selectShape(self, shape):
+        self.deSelectShape()
+        shape.selected = True
+        self.selectedShape = shape
+        self.setHiding()
+        self.selectionChanged.emit(True)
+        self.update()
+
+    def selectShapePoint(self, point):
+        """Select the first shape created which contains this point."""
+        self.deSelectShape()
+        if self.selectedVertex():  # A vertex is marked for selection.
+            index, shape = self.hVertex, self.hShape
+            shape.highlightVertex(index, shape.MOVE_VERTEX)
+            self.selectShape(shape)
+            return self.hVertex
+        for shape in reversed(self.shapes):
+            if self.isVisible(shape) and shape.containsPoint(point):
+                self.selectShape(shape)
+                self.calculateOffsets(shape, point)
+                return self.selectedShape
+        return None
+
+    def calculateOffsets(self, shape, point):
+        rect = shape.boundingRect()
+        x1 = rect.x() - point.x()
+        y1 = rect.y() - point.y()
+        x2 = (rect.x() + rect.width()) - point.x()
+        y2 = (rect.y() + rect.height()) - point.y()
+        self.offsets = QPointF(x1, y1), QPointF(x2, y2)
+
+    def snapPointToCanvas(self, x, y):
+        """
+        Moves a point x,y to within the boundaries of the canvas.
+        :return: (x,y,snapped) where snapped is True if x or y were changed, False if not.
+        """
+        if x < 0 or x > self.pixmap.width() or y < 0 or y > self.pixmap.height():
+            x = max(x, 0)
+            y = max(y, 0)
+            x = min(x, self.pixmap.width())
+            y = min(y, self.pixmap.height())
+            return x, y, True
+
+        return x, y, False
+
+    def boundedMoveVertex(self, pos):
+        index, shape = self.hVertex, self.hShape
+        point = shape[index]
+        if self.outOfPixmap(pos):
+            size = self.pixmap.size()
+            clipped_x = min(max(0, pos.x()), size.width())
+            clipped_y = min(max(0, pos.y()), size.height())
+            pos = QPointF(clipped_x, clipped_y)
+
+        if self.drawSquare:
+            opposite_point_index = (index + 2) % 4
+            opposite_point = shape[opposite_point_index]
+
+            min_size = min(abs(pos.x() - opposite_point.x()), abs(pos.y() - opposite_point.y()))
+            directionX = -1 if pos.x() - opposite_point.x() < 0 else 1
+            directionY = -1 if pos.y() - opposite_point.y() < 0 else 1
+            shiftPos = QPointF(opposite_point.x() + directionX * min_size - point.x(),
+                               opposite_point.y() + directionY * min_size - point.y())
+        else:
+            shiftPos = pos - point
+
+        shape.moveVertexBy(index, shiftPos)
+
+        lindex = (index + 1) % 4
+        rindex = (index + 3) % 4
+        lshift = None
+        rshift = None
+        if index % 2 == 0:
+            rshift = QPointF(shiftPos.x(), 0)
+            lshift = QPointF(0, shiftPos.y())
+        else:
+            lshift = QPointF(shiftPos.x(), 0)
+            rshift = QPointF(0, shiftPos.y())
+        shape.moveVertexBy(rindex, rshift)
+        shape.moveVertexBy(lindex, lshift)
+
+    def boundedMoveShape(self, shape, pos):
+        if self.outOfPixmap(pos):
+            return False  # No need to move
+        o1 = pos + self.offsets[0]
+        if self.outOfPixmap(o1):
+            pos -= QPointF(min(0, o1.x()), min(0, o1.y()))
+        o2 = pos + self.offsets[1]
+        if self.outOfPixmap(o2):
+            pos += QPointF(min(0, self.pixmap.width() - o2.x()),
+                           min(0, self.pixmap.height() - o2.y()))
+        # The next line tracks the new position of the cursor
+        # relative to the shape, but also results in making it
+        # a bit "shaky" when nearing the border and allows it to
+        # go outside of the shape's area for some reason. XXX
+        #self.calculateOffsets(self.selectedShape, pos)
+        dp = pos - self.prevPoint
+        if dp:
+            shape.moveBy(dp)
+            self.prevPoint = pos
+            return True
+        return False
+
+    def deSelectShape(self):
+        if self.selectedShape:
+            self.selectedShape.selected = False
+            self.selectedShape = None
+            self.setHiding(False)
+            self.selectionChanged.emit(False)
+            self.update()
+
+    def deleteSelected(self):
+        if self.selectedShape:
+            shape = self.selectedShape
+            self.shapes.remove(self.selectedShape)
+            self.selectedShape = None
+            self.update()
+            return shape
+
+    def copySelectedShape(self):
+        if self.selectedShape:
+            shape = self.selectedShape.copy()
+            self.deSelectShape()
+            self.shapes.append(shape)
+            shape.selected = True
+            self.selectedShape = shape
+            self.boundedShiftShape(shape)
+            return shape
+
+    def boundedShiftShape(self, shape):
+        # Try to move in one direction, and if it fails in another.
+        # Give up if both fail.
+        point = shape[0]
+        offset = QPointF(2.0, 2.0)
+        self.calculateOffsets(shape, point)
+        self.prevPoint = point
+        if not self.boundedMoveShape(shape, point - offset):
+            self.boundedMoveShape(shape, point + offset)
+
+    def paintEvent(self, event):
+        if not self.pixmap:
+            return super(Canvas, self).paintEvent(event)
+
+        p = self._painter
+        p.begin(self)
+        p.setRenderHint(QPainter.Antialiasing)
+        p.setRenderHint(QPainter.HighQualityAntialiasing)
+        p.setRenderHint(QPainter.SmoothPixmapTransform)
+
+        p.scale(self.scale, self.scale)
+        p.translate(self.offsetToCenter())
+
+        p.drawPixmap(0, 0, self.pixmap)
+        Shape.scale = self.scale
+        for shape in self.shapes:
+            if (shape.selected or not self._hideBackround) and self.isVisible(shape):
+                shape.fill = shape.selected or shape == self.hShape
+                shape.paint(p)
+        if self.current:
+            self.current.paint(p)
+            self.line.paint(p)
+        if self.selectedShapeCopy:
+            self.selectedShapeCopy.paint(p)
+
+        # Paint rect
+        if self.current is not None and len(self.line) == 2 and not self.fourpoint:
+            # print('Drawing rect')
+            leftTop = self.line[0]
+            rightBottom = self.line[1]
+            rectWidth = rightBottom.x() - leftTop.x()
+            rectHeight = rightBottom.y() - leftTop.y()
+            p.setPen(self.drawingRectColor)
+            brush = QBrush(Qt.BDiagPattern)
+            p.setBrush(brush)
+            p.drawRect(leftTop.x(), leftTop.y(), rectWidth, rectHeight)
+
+
+        # ADD：
+        if (
+                self.fillDrawing()
+                and self.fourpoint
+                and self.current is not None
+                and len(self.current.points) >= 2
+        ):
+            print('paint event')
+            drawing_shape = self.current.copy()
+            drawing_shape.addPoint(self.line[1])
+            drawing_shape.fill = True
+            drawing_shape.paint(p)
+
+        if self.drawing() and not self.prevPoint.isNull() and not self.outOfPixmap(self.prevPoint):
+            p.setPen(QColor(0, 0, 0))
+            p.drawLine(self.prevPoint.x(), 0, self.prevPoint.x(), self.pixmap.height())
+            p.drawLine(0, self.prevPoint.y(), self.pixmap.width(), self.prevPoint.y())
+
+        self.setAutoFillBackground(True)
+        if self.verified:
+            pal = self.palette()
+            pal.setColor(self.backgroundRole(), QColor(184, 239, 38, 128))
+            self.setPalette(pal)
+        else:
+            pal = self.palette()
+            pal.setColor(self.backgroundRole(), QColor(232, 232, 232, 255))
+            self.setPalette(pal)
+
+        p.end()
+
+    def fillDrawing(self):
+        return self._fill_drawing
+
+    def transformPos(self, point):
+        """Convert from widget-logical coordinates to painter-logical coordinates."""
+        return point / self.scale - self.offsetToCenter()
+
+    def offsetToCenter(self):
+        s = self.scale
+        area = super(Canvas, self).size()
+        w, h = self.pixmap.width() * s, self.pixmap.height() * s
+        aw, ah = area.width(), area.height()
+        x = (aw - w) / (2 * s) if aw > w else 0
+        y = (ah - h) / (2 * s) if ah > h else 0
+        return QPointF(x, y)
+
+    def outOfPixmap(self, p):
+        w, h = self.pixmap.width(), self.pixmap.height()
+        return not (0 <= p.x() <= w and 0 <= p.y() <= h)
+
+    def finalise(self):
+        assert self.current
+        if self.current.points[0] == self.current.points[-1]:
+            # print('finalse')
+            self.current = None
+            self.drawingPolygon.emit(False)
+            self.update()
+            return
+
+        self.current.close()
+        self.shapes.append(self.current)
+        self.current = None
+        self.setHiding(False)
+        self.newShape.emit()
+        self.update()
+
+    def closeEnough(self, p1, p2):
+        #d = distance(p1 - p2)
+        #m = (p1-p2).manhattanLength()
+        # print "d %.2f, m %d, %.2f" % (d, m, d - m)
+        return distance(p1 - p2) < self.epsilon
+
+    # These two, along with a call to adjustSize are required for the
+    # scroll area.
+    def sizeHint(self):
+        return self.minimumSizeHint()
+
+    def minimumSizeHint(self):
+        if self.pixmap:
+            return self.scale * self.pixmap.size()
+        return super(Canvas, self).minimumSizeHint()
+
+    def wheelEvent(self, ev):
+        qt_version = 4 if hasattr(ev, "delta") else 5
+        if qt_version == 4:
+            if ev.orientation() == Qt.Vertical:
+                v_delta = ev.delta()
+                h_delta = 0
+            else:
+                h_delta = ev.delta()
+                v_delta = 0
+        else:
+            delta = ev.angleDelta()
+            h_delta = delta.x()
+            v_delta = delta.y()
+
+        mods = ev.modifiers()
+        if Qt.ControlModifier == int(mods) and v_delta:
+            self.zoomRequest.emit(v_delta)
+        else:
+            v_delta and self.scrollRequest.emit(v_delta, Qt.Vertical)
+            h_delta and self.scrollRequest.emit(h_delta, Qt.Horizontal)
+        ev.accept()
+
+    def keyPressEvent(self, ev):
+        key = ev.key()
+        if key == Qt.Key_Escape and self.current:
+            print('ESC press')
+            self.current = None
+            self.drawingPolygon.emit(False)
+            self.update()
+        elif key == Qt.Key_Return and self.canCloseShape():
+            self.finalise()
+        elif key == Qt.Key_Left and self.selectedShape:
+            self.moveOnePixel('Left')
+        elif key == Qt.Key_Right and self.selectedShape:
+            self.moveOnePixel('Right')
+        elif key == Qt.Key_Up and self.selectedShape:
+            self.moveOnePixel('Up')
+        elif key == Qt.Key_Down and self.selectedShape:
+            self.moveOnePixel('Down')
+
+    def moveOnePixel(self, direction):
+        # print(self.selectedShape.points)
+        if direction == 'Left' and not self.moveOutOfBound(QPointF(-1.0, 0)):
+            # print("move Left one pixel")
+            self.selectedShape.points[0] += QPointF(-1.0, 0)
+            self.selectedShape.points[1] += QPointF(-1.0, 0)
+            self.selectedShape.points[2] += QPointF(-1.0, 0)
+            self.selectedShape.points[3] += QPointF(-1.0, 0)
+        elif direction == 'Right' and not self.moveOutOfBound(QPointF(1.0, 0)):
+            # print("move Right one pixel")
+            self.selectedShape.points[0] += QPointF(1.0, 0)
+            self.selectedShape.points[1] += QPointF(1.0, 0)
+            self.selectedShape.points[2] += QPointF(1.0, 0)
+            self.selectedShape.points[3] += QPointF(1.0, 0)
+        elif direction == 'Up' and not self.moveOutOfBound(QPointF(0, -1.0)):
+            # print("move Up one pixel")
+            self.selectedShape.points[0] += QPointF(0, -1.0)
+            self.selectedShape.points[1] += QPointF(0, -1.0)
+            self.selectedShape.points[2] += QPointF(0, -1.0)
+            self.selectedShape.points[3] += QPointF(0, -1.0)
+        elif direction == 'Down' and not self.moveOutOfBound(QPointF(0, 1.0)):
+            # print("move Down one pixel")
+            self.selectedShape.points[0] += QPointF(0, 1.0)
+            self.selectedShape.points[1] += QPointF(0, 1.0)
+            self.selectedShape.points[2] += QPointF(0, 1.0)
+            self.selectedShape.points[3] += QPointF(0, 1.0)
+        self.shapeMoved.emit()
+        self.repaint()
+
+    def moveOutOfBound(self, step):
+        points = [p1+p2 for p1, p2 in zip(self.selectedShape.points, [step]*4)]
+        return True in map(self.outOfPixmap, points)
+
+    def setLastLabel(self, text, line_color  = None, fill_color = None):
+        assert text
+        self.shapes[-1].label = text
+        if line_color:
+            self.shapes[-1].line_color = line_color
+
+        if fill_color:
+            self.shapes[-1].fill_color = fill_color
+
+        return self.shapes[-1]
+
+    def undoLastLine(self):
+        assert self.shapes
+        self.current = self.shapes.pop()
+        self.current.setOpen()
+        self.line.points = [self.current[-1], self.current[0]]
+        self.drawingPolygon.emit(True)
+
+    def resetAllLines(self):
+        assert self.shapes
+        self.current = self.shapes.pop()
+        self.current.setOpen()
+        self.line.points = [self.current[-1], self.current[0]]
+        self.drawingPolygon.emit(True)
+        self.current = None
+        self.drawingPolygon.emit(False)
+        self.update()
+
+    def loadPixmap(self, pixmap):
+        self.pixmap = pixmap
+        self.shapes = []
+        self.repaint() # 这函数在哪
+
+    def loadShapes(self, shapes):
+        self.shapes = list(shapes)
+        self.current = None
+        self.repaint()
+
+    def setShapeVisible(self, shape, value):
+        self.visible[shape] = value
+        self.repaint()
+
+    def currentCursor(self):
+        cursor = QApplication.overrideCursor()
+        if cursor is not None:
+            cursor = cursor.shape()
+        return cursor
+
+    def overrideCursor(self, cursor):
+        self._cursor = cursor
+        if self.currentCursor() is None:
+            QApplication.setOverrideCursor(cursor)
+        else:
+            QApplication.changeOverrideCursor(cursor)
+
+    def restoreCursor(self):
+        QApplication.restoreOverrideCursor()
+
+    def resetState(self):
+        self.restoreCursor()
+        self.pixmap = None
+        self.update()
+
+    def setDrawingShapeToSquare(self, status):
+        self.drawSquare = status

PPOCRLabel/libs/colorDialog.py

@@ -0,0 +1,49 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import QColorDialog, QDialogButtonBox
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+BB = QDialogButtonBox
+
+
+class ColorDialog(QColorDialog):
+
+    def __init__(self, parent=None):
+        super(ColorDialog, self).__init__(parent)
+        self.setOption(QColorDialog.ShowAlphaChannel)
+        # The Mac native dialog does not support our restore button.
+        self.setOption(QColorDialog.DontUseNativeDialog)
+        # Add a restore defaults button.
+        # The default is set at invocation time, so that it
+        # works across dialogs for different elements.
+        self.default = None
+        self.bb = self.layout().itemAt(1).widget()
+        self.bb.addButton(BB.RestoreDefaults)
+        self.bb.clicked.connect(self.checkRestore)
+
+    def getColor(self, value=None, title=None, default=None):
+        self.default = default
+        if title:
+            self.setWindowTitle(title)
+        if value:
+            self.setCurrentColor(value)
+        return self.currentColor() if self.exec_() else None
+
+    def checkRestore(self, button):
+        if self.bb.buttonRole(button) & BB.ResetRole and self.default:
+            self.setCurrentColor(self.default)

PPOCRLabel/libs/constants.py

@@ -0,0 +1,31 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+SETTING_FILENAME = 'filename'
+SETTING_RECENT_FILES = 'recentFiles'
+SETTING_WIN_SIZE = 'window/size'
+SETTING_WIN_POSE = 'window/position'
+SETTING_WIN_GEOMETRY = 'window/geometry'
+SETTING_LINE_COLOR = 'line/color'
+SETTING_FILL_COLOR = 'fill/color'
+SETTING_ADVANCE_MODE = 'advanced'
+SETTING_WIN_STATE = 'window/state'
+SETTING_SAVE_DIR = 'savedir'
+SETTING_PAINT_LABEL = 'paintlabel'
+SETTING_LAST_OPEN_DIR = 'lastOpenDir'
+SETTING_AUTO_SAVE = 'autosave'
+SETTING_SINGLE_CLASS = 'singleclass'
+FORMAT_PASCALVOC='PascalVOC'
+FORMAT_YOLO='YOLO'
+SETTING_DRAW_SQUARE = 'draw/square'
+SETTING_LABEL_FILE_FORMAT= 'labelFileFormat'
+DEFAULT_ENCODING = 'utf-8'

PPOCRLabel/libs/create_ml_io.py

@@ -0,0 +1,143 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#!/usr/bin/env python
+# -*- coding: utf8 -*-
+import json
+from pathlib import Path
+
+from libs.constants import DEFAULT_ENCODING
+import os
+
+JSON_EXT = '.json'
+ENCODE_METHOD = DEFAULT_ENCODING
+
+
+class CreateMLWriter:
+    def __init__(self, foldername, filename, imgsize, shapes, outputfile, databasesrc='Unknown', localimgpath=None):
+        self.foldername = foldername
+        self.filename = filename
+        self.databasesrc = databasesrc
+        self.imgsize = imgsize
+        self.boxlist = []
+        self.localimgpath = localimgpath
+        self.verified = False
+        self.shapes = shapes
+        self.outputfile = outputfile
+
+    def write(self):
+        if os.path.isfile(self.outputfile):
+            with open(self.outputfile, "r") as file:
+                input_data = file.read()
+                outputdict = json.loads(input_data)
+        else:
+            outputdict = []
+
+        outputimagedict = {
+            "image": self.filename,
+            "annotations": []
+        }
+
+        for shape in self.shapes:
+            points = shape["points"]
+
+            x1 = points[0][0]
+            y1 = points[0][1]
+            x2 = points[1][0]
+            y2 = points[2][1]
+
+            height, width, x, y = self.calculate_coordinates(x1, x2, y1, y2)
+
+            shapedict = {
+                "label": shape["label"],
+                "coordinates": {
+                    "x": x,
+                    "y": y,
+                    "width": width,
+                    "height": height
+                }
+            }
+            outputimagedict["annotations"].append(shapedict)
+
+        # check if image already in output
+        exists = False
+        for i in range(0, len(outputdict)):
+            if outputdict[i]["image"] == outputimagedict["image"]:
+                exists = True
+                outputdict[i] = outputimagedict
+                break
+
+        if not exists:
+            outputdict.append(outputimagedict)
+
+        Path(self.outputfile).write_text(json.dumps(outputdict), ENCODE_METHOD)
+
+    def calculate_coordinates(self, x1, x2, y1, y2):
+        if x1 < x2:
+            xmin = x1
+            xmax = x2
+        else:
+            xmin = x2
+            xmax = x1
+        if y1 < y2:
+            ymin = y1
+            ymax = y2
+        else:
+            ymin = y2
+            ymax = y1
+        width = xmax - xmin
+        if width < 0:
+            width = width * -1
+        height = ymax - ymin
+        # x and y from center of rect
+        x = xmin + width / 2
+        y = ymin + height / 2
+        return height, width, x, y
+
+
+class CreateMLReader:
+    def __init__(self, jsonpath, filepath):
+        self.jsonpath = jsonpath
+        self.shapes = []
+        self.verified = False
+        self.filename = filepath.split("/")[-1:][0]
+        try:
+            self.parse_json()
+        except ValueError:
+            print("JSON decoding failed")
+
+    def parse_json(self):
+        with open(self.jsonpath, "r") as file:
+            inputdata = file.read()
+
+        outputdict = json.loads(inputdata)
+        self.verified = True
+
+        if len(self.shapes) > 0:
+            self.shapes = []
+        for image in outputdict:
+            if image["image"] == self.filename:
+                for shape in image["annotations"]:
+                    self.add_shape(shape["label"], shape["coordinates"])
+
+    def add_shape(self, label, bndbox):
+        xmin = bndbox["x"] - (bndbox["width"] / 2)
+        ymin = bndbox["y"] - (bndbox["height"] / 2)
+
+        xmax = bndbox["x"] + (bndbox["width"] / 2)
+        ymax = bndbox["y"] + (bndbox["height"] / 2)
+
+        points = [(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)]
+        self.shapes.append((label, points, None, None, True))
+
+    def get_shapes(self):
+        return self.shapes

PPOCRLabel/libs/hashableQListWidgetItem.py

@@ -0,0 +1,40 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import sys
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    # needed for py3+qt4
+    # Ref:
+    # http://pyqt.sourceforge.net/Docs/PyQt4/incompatible_apis.html
+    # http://stackoverflow.com/questions/21217399/pyqt4-qtcore-qvariant-object-instead-of-a-string
+    if sys.version_info.major >= 3:
+        import sip
+        sip.setapi('QVariant', 2)
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+# PyQt5: TypeError: unhashable type: 'QListWidgetItem'
+
+
+class HashableQListWidgetItem(QListWidgetItem):
+
+    def __init__(self, *args):
+        super(HashableQListWidgetItem, self).__init__(*args)
+
+    def __hash__(self):
+        return hash(id(self))

PPOCRLabel/libs/labelDialog.py

@@ -0,0 +1,107 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+from libs.utils import newIcon, labelValidator
+
+BB = QDialogButtonBox
+
+
+class LabelDialog(QDialog):
+
+    def __init__(self, text="Enter object label", parent=None, listItem=None):
+        super(LabelDialog, self).__init__(parent)
+
+        self.edit = QLineEdit()  # OLD
+        # self.edit = QTextEdit()
+        self.edit.setText(text)
+        # self.edit.setValidator(labelValidator()) # 验证有效性
+        self.edit.editingFinished.connect(self.postProcess)
+
+        model = QStringListModel()
+        model.setStringList(listItem)
+        completer = QCompleter()
+        completer.setModel(model)
+        self.edit.setCompleter(completer)
+
+        layout = QVBoxLayout()
+        layout.addWidget(self.edit)
+        self.buttonBox = bb = BB(BB.Ok | BB.Cancel, Qt.Horizontal, self)
+        bb.button(BB.Ok).setIcon(newIcon('done'))
+        bb.button(BB.Cancel).setIcon(newIcon('undo'))
+        bb.accepted.connect(self.validate)
+        bb.rejected.connect(self.reject)
+        layout.addWidget(bb)
+
+        # if listItem is not None and len(listItem) > 0:
+        #     self.listWidget = QListWidget(self)
+        #     for item in listItem:
+        #         self.listWidget.addItem(item)
+        #     self.listWidget.itemClicked.connect(self.listItemClick)
+        #     self.listWidget.itemDoubleClicked.connect(self.listItemDoubleClick)
+        #     layout.addWidget(self.listWidget)
+
+        self.setLayout(layout)
+
+    def validate(self):
+        try:
+            if self.edit.text().trimmed():
+                self.accept()
+        except AttributeError:
+            # PyQt5: AttributeError: 'str' object has no attribute 'trimmed'
+            if self.edit.text().strip():
+                self.accept()
+
+    def postProcess(self):
+        try:
+            self.edit.setText(self.edit.text().trimmed())
+            # print(self.edit.text())
+        except AttributeError:
+            # PyQt5: AttributeError: 'str' object has no attribute 'trimmed'
+            self.edit.setText(self.edit.text())
+            print(self.edit.text())
+
+    def popUp(self, text='', move=True):
+        self.edit.setText(text)
+        self.edit.setSelection(0, len(text))
+        self.edit.setFocus(Qt.PopupFocusReason)
+        if move:
+            cursor_pos = QCursor.pos()
+            parent_bottomRight = self.parentWidget().geometry()
+            max_x = parent_bottomRight.x() + parent_bottomRight.width() - self.sizeHint().width()
+            max_y = parent_bottomRight.y() + parent_bottomRight.height() - self.sizeHint().height()
+            max_global = self.parentWidget().mapToGlobal(QPoint(max_x, max_y))
+            if cursor_pos.x() > max_global.x():
+                cursor_pos.setX(max_global.x())
+            if cursor_pos.y() > max_global.y():
+                cursor_pos.setY(max_global.y())
+            self.move(cursor_pos)
+        return self.edit.text() if self.exec_() else None
+
+    def listItemClick(self, tQListWidgetItem):
+        try:
+            text = tQListWidgetItem.text().trimmed()
+        except AttributeError:
+            # PyQt5: AttributeError: 'str' object has no attribute 'trimmed'
+            text = tQListWidgetItem.text().strip()
+        self.edit.setText(text)
+
+    def listItemDoubleClick(self, tQListWidgetItem):
+        self.listItemClick(tQListWidgetItem)
+        self.validate()

PPOCRLabel/libs/settings.py

@@ -0,0 +1,60 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+import pickle
+import os
+import sys
+
+
+class Settings(object):
+    def __init__(self):
+        # Be default, the home will be in the same folder as labelImg
+        home = os.path.expanduser("~")
+        self.data = {}
+        # self.path = os.path.join(home, '.labelImgSettings.pkl')
+        self.path = os.path.join(home, '.autoOCRSettings.pkl')
+
+    def __setitem__(self, key, value):
+        self.data[key] = value
+
+    def __getitem__(self, key):
+        return self.data[key]
+
+    def get(self, key, default=None):
+        if key in self.data:
+            return self.data[key]
+        return default
+
+    def save(self):
+        if self.path:
+            with open(self.path, 'wb') as f:
+                pickle.dump(self.data, f, pickle.HIGHEST_PROTOCOL)
+                return True
+        return False
+
+    def load(self):
+        try:
+            if os.path.exists(self.path):
+                with open(self.path, 'rb') as f:
+                    self.data = pickle.load(f)
+                    return True
+        except:
+            print('Loading setting failed')
+        return False
+
+    def reset(self):
+        if os.path.exists(self.path):
+            os.remove(self.path)
+            print('Remove setting pkl file ${0}'.format(self.path))
+        self.data = {}
+        self.path = None

PPOCRLabel/libs/shape.py

@@ -0,0 +1,217 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+from libs.utils import distance
+import sys
+
+DEFAULT_LINE_COLOR = QColor(0, 255, 0, 128)
+DEFAULT_FILL_COLOR = QColor(255, 0, 0, 128)
+DEFAULT_SELECT_LINE_COLOR = QColor(255, 255, 255)
+DEFAULT_SELECT_FILL_COLOR = QColor(0, 128, 255, 155)
+DEFAULT_VERTEX_FILL_COLOR = QColor(0, 255, 0, 255)
+DEFAULT_HVERTEX_FILL_COLOR = QColor(255, 0, 0)
+MIN_Y_LABEL = 10
+
+
+class Shape(object):
+    P_SQUARE, P_ROUND = range(2)
+
+    MOVE_VERTEX, NEAR_VERTEX = range(2)
+
+    # The following class variables influence the drawing
+    # of _all_ shape objects.
+    line_color = DEFAULT_LINE_COLOR
+    fill_color = DEFAULT_FILL_COLOR
+    select_line_color = DEFAULT_SELECT_LINE_COLOR
+    select_fill_color = DEFAULT_SELECT_FILL_COLOR
+    vertex_fill_color = DEFAULT_VERTEX_FILL_COLOR
+    hvertex_fill_color = DEFAULT_HVERTEX_FILL_COLOR
+    point_type = P_ROUND
+    point_size = 8
+    scale = 1.0
+
+    def __init__(self, label=None, line_color=None, difficult=False, paintLabel=False):
+        self.label = label
+        self.points = []
+        self.fill = False
+        self.selected = False
+        self.difficult = difficult
+        self.paintLabel = paintLabel
+
+        self._highlightIndex = None
+        self._highlightMode = self.NEAR_VERTEX
+        self._highlightSettings = {
+            self.NEAR_VERTEX: (4, self.P_ROUND),
+            self.MOVE_VERTEX: (1.5, self.P_SQUARE),
+        }
+
+        self._closed = False
+
+        if line_color is not None:
+            # Override the class line_color attribute
+            # with an object attribute. Currently this
+            # is used for drawing the pending line a different color.
+            self.line_color = line_color
+
+    def close(self):
+        self._closed = True
+
+    def reachMaxPoints(self):
+        if len(self.points) >= 4:
+            return True
+        return False
+
+    def addPoint(self, point):
+        if not self.reachMaxPoints():
+            self.points.append(point)
+
+    def popPoint(self):
+        if self.points:
+            return self.points.pop()
+        return None
+
+    def isClosed(self):
+        return self._closed
+
+    def setOpen(self):
+        self._closed = False
+
+    def paint(self, painter):
+        if self.points:
+            color = self.select_line_color if self.selected else self.line_color
+            pen = QPen(color)
+            # Try using integer sizes for smoother drawing(?)
+            pen.setWidth(max(1, int(round(2.0 / self.scale))))
+            painter.setPen(pen)
+
+            line_path = QPainterPath()
+            vrtx_path = QPainterPath()
+
+            line_path.moveTo(self.points[0])
+            # Uncommenting the following line will draw 2 paths
+            # for the 1st vertex, and make it non-filled, which
+            # may be desirable.
+            #self.drawVertex(vrtx_path, 0)
+
+            for i, p in enumerate(self.points):
+                line_path.lineTo(p)
+                self.drawVertex(vrtx_path, i)
+            if self.isClosed():
+                line_path.lineTo(self.points[0])
+
+            painter.drawPath(line_path)
+            painter.drawPath(vrtx_path)
+            painter.fillPath(vrtx_path, self.vertex_fill_color)
+
+            # Draw text at the top-left
+            if self.paintLabel:
+                min_x = sys.maxsize
+                min_y = sys.maxsize
+                for point in self.points:
+                    min_x = min(min_x, point.x())
+                    min_y = min(min_y, point.y())
+                if min_x != sys.maxsize and min_y != sys.maxsize:
+                    font = QFont()
+                    font.setPointSize(8)
+                    font.setBold(True)
+                    painter.setFont(font)
+                    if(self.label == None):
+                        self.label = ""
+                    if(min_y < MIN_Y_LABEL):
+                        min_y += MIN_Y_LABEL
+                    painter.drawText(min_x, min_y, self.label)
+
+            if self.fill:
+                color = self.select_fill_color if self.selected else self.fill_color
+                painter.fillPath(line_path, color)
+
+    def drawVertex(self, path, i):
+        d = self.point_size / self.scale
+        shape = self.point_type
+        point = self.points[i]
+        if i == self._highlightIndex:
+            size, shape = self._highlightSettings[self._highlightMode]
+            d *= size
+        if self._highlightIndex is not None:
+            self.vertex_fill_color = self.hvertex_fill_color
+        else:
+            self.vertex_fill_color = Shape.vertex_fill_color
+        if shape == self.P_SQUARE:
+            path.addRect(point.x() - d / 2, point.y() - d / 2, d, d)
+        elif shape == self.P_ROUND:
+            path.addEllipse(point, d / 2.0, d / 2.0)
+        else:
+            assert False, "unsupported vertex shape"
+
+    def nearestVertex(self, point, epsilon):
+        for i, p in enumerate(self.points):
+            if distance(p - point) <= epsilon:
+                return i
+        return None
+
+    def containsPoint(self, point):
+        return self.makePath().contains(point)
+
+    def makePath(self):
+        path = QPainterPath(self.points[0])
+        for p in self.points[1:]:
+            path.lineTo(p)
+        return path
+
+    def boundingRect(self):
+        return self.makePath().boundingRect()
+
+    def moveBy(self, offset):
+        self.points = [p + offset for p in self.points]
+
+    def moveVertexBy(self, i, offset):
+        self.points[i] = self.points[i] + offset
+
+    def highlightVertex(self, i, action):
+        self._highlightIndex = i
+        self._highlightMode = action
+
+    def highlightClear(self):
+        self._highlightIndex = None
+
+    def copy(self):
+        shape = Shape("%s" % self.label)
+        shape.points = [p for p in self.points]
+        shape.fill = self.fill
+        shape.selected = self.selected
+        shape._closed = self._closed
+        if self.line_color != Shape.line_color:
+            shape.line_color = self.line_color
+        if self.fill_color != Shape.fill_color:
+            shape.fill_color = self.fill_color
+        shape.difficult = self.difficult
+        return shape
+
+    def __len__(self):
+        return len(self.points)
+
+    def __getitem__(self, key):
+        return self.points[key]
+
+    def __setitem__(self, key, value):
+        self.points[key] = value

PPOCRLabel/libs/stringBundle.py

@@ -0,0 +1,86 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import re
+import os
+import sys
+import locale
+from libs.ustr import ustr
+
+try:
+    from PyQt5.QtCore import *
+except ImportError:
+    if sys.version_info.major >= 3:
+        import sip
+        sip.setapi('QVariant', 2)
+    from PyQt4.QtCore import *
+
+
+class StringBundle:
+
+    __create_key = object()
+
+    def __init__(self, create_key, localeStr):
+        assert(create_key == StringBundle.__create_key), "StringBundle must be created using StringBundle.getBundle"
+        self.idToMessage = {}
+        paths = self.__createLookupFallbackList(localeStr)
+        for path in paths:
+            self.__loadBundle(path)
+
+    @classmethod
+    def getBundle(cls, localeStr=None):
+        if localeStr is None:
+            try:
+                localeStr = locale.getlocale()[0] if locale.getlocale() and len(
+                    locale.getlocale()) > 0 else os.getenv('LANG')
+            except:
+                print('Invalid locale')
+                localeStr = 'en'
+
+        return StringBundle(cls.__create_key, localeStr)
+
+    def getString(self, stringId):
+        assert(stringId in self.idToMessage), "Missing string id : " + stringId
+        return self.idToMessage[stringId]
+
+    def __createLookupFallbackList(self, localeStr):
+        resultPaths = []
+        basePath = ":/strings"
+        resultPaths.append(basePath)
+        if localeStr is not None:
+            # Don't follow standard BCP47. Simple fallback
+            tags = re.split('[^a-zA-Z]', localeStr)
+            for tag in tags:
+                lastPath = resultPaths[-1]
+                resultPaths.append(lastPath + '-' + tag)
+
+        return resultPaths
+
+    def __loadBundle(self, path):
+        PROP_SEPERATOR = '='
+        f = QFile(path)
+        if f.exists():
+            if f.open(QIODevice.ReadOnly | QFile.Text):
+                text = QTextStream(f)
+                text.setCodec("UTF-8")
+
+            while not text.atEnd():
+                line = ustr(text.readLine())
+                key_value = line.split(PROP_SEPERATOR)
+                key = key_value[0].strip()
+                value = PROP_SEPERATOR.join(key_value[1:]).strip().strip('"')
+                self.idToMessage[key] = value
+
+            f.close()

PPOCRLabel/libs/toolBar.py

@@ -0,0 +1,51 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+
+class ToolBar(QToolBar):
+
+    def __init__(self, title):
+        super(ToolBar, self).__init__(title)
+        layout = self.layout()
+        m = (0, 0, 0, 0)
+        layout.setSpacing(0)
+        layout.setContentsMargins(*m)
+        self.setContentsMargins(*m)
+        self.setWindowFlags(self.windowFlags() | Qt.FramelessWindowHint)
+
+    def addAction(self, action):
+        if isinstance(action, QWidgetAction):
+            return super(ToolBar, self).addAction(action)
+        btn = ToolButton()
+        btn.setDefaultAction(action)
+        btn.setToolButtonStyle(self.toolButtonStyle())
+        self.addWidget(btn)
+
+
+class ToolButton(QToolButton):
+    """ToolBar companion class which ensures all buttons have the same size."""
+    minSize = (60, 60)
+
+    def minimumSizeHint(self):
+        ms = super(ToolButton, self).minimumSizeHint()
+        w1, h1 = ms.width(), ms.height()
+        w2, h2 = self.minSize
+        ToolButton.minSize = max(w1, w2), max(h1, h2)
+        return QSize(*ToolButton.minSize)

PPOCRLabel/libs/ustr.py

@@ -0,0 +1,29 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+import sys
+from libs.constants import DEFAULT_ENCODING
+
+def ustr(x):
+    '''py2/py3 unicode helper'''
+
+    if sys.version_info < (3, 0, 0):
+        from PyQt4.QtCore import QString
+        if type(x) == str:
+            return x.decode(DEFAULT_ENCODING)
+        if type(x) == QString:
+            #https://blog.csdn.net/friendan/article/details/51088476
+            #https://blog.csdn.net/xxm524/article/details/74937308
+            return unicode(x.toUtf8(), DEFAULT_ENCODING, 'ignore')
+        return x
+    else:
+        return x

PPOCRLabel/libs/utils.py

@@ -0,0 +1,182 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+from math import sqrt
+from libs.ustr import ustr
+import hashlib
+import re
+import sys
+import cv2
+import numpy as np
+
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+
+def newIcon(icon, iconSize=None):
+    if iconSize is not None:
+        return QIcon(QIcon(':/' + icon).pixmap(iconSize,iconSize))
+    else:
+        return QIcon(':/' + icon)
+
+
+def newButton(text, icon=None, slot=None):
+    b = QPushButton(text)
+    if icon is not None:
+        b.setIcon(newIcon(icon))
+    if slot is not None:
+        b.clicked.connect(slot)
+    return b
+
+
+def newAction(parent, text, slot=None, shortcut=None, icon=None,
+              tip=None, checkable=False, enabled=True, iconSize=None):
+    """Create a new action and assign callbacks, shortcuts, etc."""
+    a = QAction(text, parent)
+    if icon is not None:
+        if iconSize is not None:
+            a.setIcon(newIcon(icon, iconSize))
+        else:
+            a.setIcon(newIcon(icon))
+    if shortcut is not None:
+        if isinstance(shortcut, (list, tuple)):
+            a.setShortcuts(shortcut)
+        else:
+            a.setShortcut(shortcut)
+    if tip is not None:
+        a.setToolTip(tip)
+        a.setStatusTip(tip)
+    if slot is not None:
+        a.triggered.connect(slot)
+    if checkable:
+        a.setCheckable(True)
+    a.setEnabled(enabled)
+    return a
+
+
+def addActions(widget, actions):
+    for action in actions:
+        if action is None:
+            widget.addSeparator()
+        elif isinstance(action, QMenu):
+            widget.addMenu(action)
+        else:
+            widget.addAction(action)
+
+
+def labelValidator():
+    return QRegExpValidator(QRegExp(r'^[^ \t].+'), None)
+
+
+class struct(object):
+
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+
+
+def distance(p):
+    return sqrt(p.x() * p.x() + p.y() * p.y())
+
+
+def fmtShortcut(text):
+    mod, key = text.split('+', 1)
+    return '<b>%s</b>+<b>%s</b>' % (mod, key)
+
+
+def generateColorByText(text):
+    s = ustr(text)
+    hashCode = int(hashlib.sha256(s.encode('utf-8')).hexdigest(), 16)
+    r = int((hashCode / 255) % 255)
+    g = int((hashCode / 65025)  % 255)
+    b = int((hashCode / 16581375)  % 255)
+    return QColor(r, g, b, 100)
+
+def have_qstring():
+    '''p3/qt5 get rid of QString wrapper as py3 has native unicode str type'''
+    return not (sys.version_info.major >= 3 or QT_VERSION_STR.startswith('5.'))
+
+def util_qt_strlistclass():
+    return QStringList if have_qstring() else list
+
+def natural_sort(list, key=lambda s:s):
+    """
+    Sort the list into natural alphanumeric order.
+    """
+    def get_alphanum_key_func(key):
+        convert = lambda text: int(text) if text.isdigit() else text
+        return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
+    sort_key = get_alphanum_key_func(key)
+    list.sort(key=sort_key)
+
+
+def get_rotate_crop_image(img, points):
+
+    try:
+        img_crop_width = int(
+            max(
+                np.linalg.norm(points[0] - points[1]),
+                np.linalg.norm(points[2] - points[3])))
+        img_crop_height = int(
+            max(
+                np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[1] - points[2])))
+        pts_std = np.float32([[0, 0], [img_crop_width, 0],
+                              [img_crop_width, img_crop_height],
+                              [0, img_crop_height]])
+        M = cv2.getPerspectiveTransform(points, pts_std)
+        dst_img = cv2.warpPerspective(
+            img,
+            M, (img_crop_width, img_crop_height),
+            borderMode=cv2.BORDER_REPLICATE,
+            flags=cv2.INTER_CUBIC)
+        dst_img_height, dst_img_width = dst_img.shape[0:2]
+        if dst_img_height * 1.0 / dst_img_width >= 1.5:
+            dst_img = np.rot90(dst_img)
+        return dst_img
+    except Exception as e:
+        print(e)
+
+def stepsInfo(lang='en'):
+    if lang == 'ch':
+        msg = "1. 安装与运行：使用上述命令安装与运行程序。\n" \
+              "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.\n"\
+              "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为 “X” 的图片进行自动标注。\n" \
+              "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。\n" \
+              "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。\n" \
+              "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。\n" \
+              "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。\n" \
+              "8. 保存：点击 “保存”，图片状态切换为 “√”，跳转至下一张。\n" \
+              "9. 删除：点击 “删除图像”，图片将会被删除至回收站。\n" \
+              "10. 标注结果：关闭应用程序或切换文件路径后，手动保存过的标签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 “PaddleOCR” - 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。\n"
+    else:
+        msg = "1. Build and launch using the instructions above.\n" \
+              "2. Click 'Open Dir' in Menu/File to select the folder of the picture.\n"\
+              "3. Click 'Auto recognition', use PPOCR model to automatically annotate images which marked with 'X' before the file name."\
+              "4. Create Box:\n"\
+                   "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.\n"\
+                   "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.\n"\
+              "5. After the marking frame is drawn, the user clicks 'OK', and the detection frame will be pre-assigned a TEMPORARY label.\n"\
+              "6. Click re-Recognition, model will rewrite ALL recognition results in ALL detection box.\n"\
+              "7. Double click the result in 'recognition result' list to manually change inaccurate recognition results.\n"\
+              "8. Click 'Save', the image status will switch to '√',then the program automatically jump to the next.\n"\
+              "9. Click 'Delete Image' and the image will be deleted to the recycle bin.\n"\
+              "10. Labeling result: After closing the application or switching the file path, the manually saved label will be stored in *Label.txt* under the opened picture folder.\n"\
+              "    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*.\n"
+    return msg

PPOCRLabel/libs/zoomWidget.py

@@ -0,0 +1,38 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+try:
+    from PyQt5.QtGui import *
+    from PyQt5.QtCore import *
+    from PyQt5.QtWidgets import *
+except ImportError:
+    from PyQt4.QtGui import *
+    from PyQt4.QtCore import *
+
+
+class ZoomWidget(QSpinBox):
+
+    def __init__(self, value=100):
+        super(ZoomWidget, self).__init__()
+        self.setButtonSymbols(QAbstractSpinBox.NoButtons)
+        self.setRange(1, 500)
+        self.setSuffix(' %')
+        self.setValue(value)
+        self.setToolTip(u'Zoom Level')
+        self.setStatusTip(self.toolTip())
+        self.setAlignment(Qt.AlignCenter)
+
+    def minimumSizeHint(self):
+        height = super(ZoomWidget, self).minimumSizeHint().height()
+        fm = QFontMetrics(self.font())
+        width = fm.width(str(self.maximum()))
+        return QSize(width, height)

PPOCRLabel/requirements/requirements-linux-python3.txt

@@ -0,0 +1,2 @@
+pyqt5==5.10.1
+lxml==4.2.4

PPOCRLabel/resources.qrc

@@ -0,0 +1,39 @@
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+<qresource>
+
+<file alias="help">resources/icons/help.png</file>
+<file alias="app">resources/icons/app.png</file>
+<file alias="Auto">resources/icons/Auto.png</file>
+<file alias="reRec">resources/icons/reRec.png</file>
+<file alias="expert">resources/icons/expert2.png</file>
+<file alias="done">resources/icons/done.png</file>
+<file alias="file">resources/icons/file.png</file>
+<file alias="labels">resources/icons/labels.png</file>
+<file alias="new">resources/icons/objects.png</file>
+<file alias="close">resources/icons/close.png</file>
+<file alias="fit-width">resources/icons/fit-width.png</file>
+<file alias="fit-window">resources/icons/fit-window.png</file>
+<file alias="undo">resources/icons/undo.png</file>
+<file alias="hide">resources/icons/eye.png</file>
+<file alias="quit">resources/icons/quit.png</file>
+<file alias="copy">resources/icons/copy.png</file>
+<file alias="edit">resources/icons/edit.png</file>
+<file alias="open">resources/icons/open.png</file>
+<file alias="save">resources/icons/save.png</file>
+<file alias="format_voc">resources/icons/format_voc.png</file>
+<file alias="format_yolo">resources/icons/format_yolo.png</file>
+<file alias="save-as">resources/icons/save-as.png</file>
+<file alias="color">resources/icons/color.png</file>
+<file alias="color_line">resources/icons/color_line.png</file>
+<file alias="zoom">resources/icons/zoom.png</file>
+<file alias="zoom-in">resources/icons/zoom-in.png</file>
+<file alias="zoom-out">resources/icons/zoom-out.png</file>
+<file alias="delete">resources/icons/cancel.png</file>
+<file alias="next">resources/icons/next.png</file>
+<file alias="prev">resources/icons/prev.png</file>
+<file alias="resetall">resources/icons/resetall.png</file>
+<file alias="verify">resources/icons/verify.png</file>
+<file alias="strings">resources/strings/strings.properties</file>
+<file alias="strings-zh-CN">resources/strings/strings-zh-CN.properties</file>
+</qresource>
+</RCC>

PPOCRLabel/resources/icons/app.svg

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PPOCRLabel/resources/strings/strings-zh-CN.properties

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+saveAsDetail=將标签保存到其他文件
+changeSaveDir=改变存放目录
+openFile=打开文件
+shapeLineColorDetail=更改线条颜色
+resetAll=重置界面与保存地址
+crtBox=矩形标注
+crtBoxDetail=创建一个新的区块
+dupBoxDetail=复制区块
+verifyImg=验证图像
+zoominDetail=放大
+verifyImgDetail=验证图像
+saveDetail=保存标签文件
+openFileDetail=打开图像文件
+fitWidthDetail=调整宽度适应到窗口宽度
+tutorial=PaddleOCR地址
+editLabel=编辑标签
+openAnnotationDetail=打开标签文件
+quit=退出
+shapeFillColorDetail=更改填充颜色
+closeCurDetail=关闭当前文件
+closeCur=关闭文件
+deleteImg=删除图像
+deleteImgDetail=删除当前图像
+fitWin=调整到窗口大小
+delBox=删除选择的区块
+boxLineColorDetail=选择线框颜色
+originalsize=原始大小
+resetAllDetail=重置所有设定
+zoomoutDetail=放大画面
+save=确认
+saveAs=另存为
+fitWinDetail=缩放到当前窗口大小
+openDir=打开目录
+copyPrevBounding=复制当前图像中的上一个边界框
+showHide=显示/隐藏标签
+changeSaveFormat=更改存储格式
+shapeFillColor=填充颜色
+quitApp=退出程序
+dupBox=复制区块
+delBoxDetail=删除区块
+zoomin=放大画面
+info=信息
+openAnnotation=开启标签
+prevImgDetail=上一个图像
+fitWidth=缩放到跟当前画面一样宽
+zoomout=缩小画面
+changeSavedAnnotationDir=更改保存标签文件的预设目录
+nextImgDetail=下一个图像
+originalsizeDetail=放大到原始大小
+prevImg=上一张
+tutorialDetail=显示示范内容
+shapeLineColor=形状线条颜色
+boxLineColor=区块线条颜色
+editLabelDetail=修改当前所选的区块颜色
+nextImg=下一张
+useDefaultLabel=使用预设标签
+useDifficult=有难度的
+boxLabelText=区块的标签
+labels=标签
+autoSaveMode=自动保存模式
+singleClsMode=单一类别模式
+displayLabel=显示类别
+fileList=文件列表
+files=文件
+advancedMode=专家模式
+advancedModeDetail=切换到专家模式
+showAllBoxDetail=显示所有区块
+hideAllBoxDetail=隐藏所有区块
+annoPanel=标注面板
+anno=标注
+addNewBbox=新框
+reLabel=重标注
+choosemodel=选择模型
+tipchoosemodel=选择OCR模型
+ImageResize=图片缩放
+IR=图片缩放
+autoRecognition=自动标注
+reRecognition=重新识别
+mfile=文件
+medit=编辑
+mview=视图
+mhelp=帮助
+iconList=缩略图
+detectionBoxposition=检测框位置
+recognitionResult=识别结果
+creatPolygon=四点标注
+drawSquares=正方形标注
+saveRec=保存识别结果
+tempLabel=待识别
+steps=操作步骤
+choseModelLg=选择模型语言
+cancel=取消
+ok=确认
+autolabeling=自动标注中
+hideBox=隐藏所有标注
+showBox=显示所有标注
+saveLabel=保存标记结果
+singleRe=重识别此区块

PPOCRLabel/resources/strings/strings.properties

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+openFileDetail=Open image or label file
+quit=Quit
+quitApp=Quit application
+openDir=Open Dir
+copyPrevBounding=Copy previous Bounding Boxes in the current image 
+changeSavedAnnotationDir=Change default saved Annotation dir
+openAnnotation=Open Annotation
+openAnnotationDetail=Open an annotation file
+changeSaveDir=Change Save Dir
+nextImg=Next Image
+nextImgDetail=Open the next Image
+prevImg=Prev Image
+prevImgDetail=Open the previous Image
+verifyImg=Verify Image
+verifyImgDetail=Verify Image
+save=Check
+saveDetail=Save the labels to a file
+changeSaveFormat=Change save format
+saveAs=Save As
+saveAsDetail=Save the labels to a different file
+closeCur=Close
+closeCurDetail=Close the current file
+deleteImg=Delete current image
+deleteImgDetail=Delete the current image
+resetAll=Reset Interface and Save Dir
+resetAllDetail=Reset All
+boxLineColor=Box Line Color
+boxLineColorDetail=Choose Box line color
+crtBox=Create RectBox
+crtBoxDetail=Draw a new box
+delBox=Delete RectBox
+delBoxDetail=Remove the box
+dupBox=Duplicate RectBox
+dupBoxDetail=Create a duplicate of the selected box
+tutorial=PaddleOCR url
+tutorialDetail=Show demo
+info=Information
+zoomin=Zoom In
+zoominDetail=Increase zoom level
+zoomout=Zoom Out
+zoomoutDetail=Decrease zoom level
+originalsize=Original size
+originalsizeDetail=Zoom to original size
+fitWin=Fit Window
+fitWinDetail=Zoom follows window size
+fitWidth=Fit Width
+fitWidthDetail=Zoom follows window width
+editLabel=Edit Label
+editLabelDetail=Modify the label of the selected Box
+shapeLineColor=Shape Line Color
+shapeLineColorDetail=Change the line color for this specific shape
+shapeFillColor=Shape Fill Color
+shapeFillColorDetail=Change the fill color for this specific shape
+showHide=Show/Hide Label Panel
+useDefaultLabel=Use default label
+useDifficult=Difficult
+boxLabelText=Box Labels
+labels=Labels
+autoSaveMode=Auto Save mode
+singleClsMode=Single Class Mode
+displayLabel=Display Labels
+fileList=File List
+files=Files
+advancedMode=Advanced Mode
+advancedModeDetail=Swtich to advanced mode
+showAllBoxDetail=Show all bounding boxes
+hideAllBoxDetail=Hide all bounding boxes
+annoPanel=anno Panel
+anno=anno
+addNewBbox=new bbox
+reLabel=reLabel
+choosemodel=Choose OCR model
+tipchoosemodel=Choose OCR model from dir
+ImageResize=Image Resize
+IR=Image Resize
+autoRecognition=Auto Recognition
+reRecognition=Re-recognition
+mfile=File
+medit=Eidt
+mview=View
+mhelp=Help
+iconList=Icon List
+detectionBoxposition=Detection box position
+recognitionResult=Recognition result
+creatPolygon=Create Quadrilateral
+drawSquares=Draw Squares
+saveRec=Save Recognition Result
+tempLabel=TEMPORARY
+steps=Steps
+choseModelLg=Choose Model Language
+cancel=Cancel
+ok=OK
+autolabeling=Automatic Labeling
+hideBox=Hide All Box
+showBox=Show All Box
+saveLabel=Save Label
+singleRe=Re-recognition RectBox

PPOCRLabel/setup.cfg

@@ -0,0 +1,8 @@
+[bumpversion]
+commit = True
+tag = True
+
+[bumpversion:file:setup.py]
+
+[bdist_wheel]
+universal = 1

PPOCRLabel/setup.py

@@ -0,0 +1,139 @@
+# Copyright (c) <2015-Present> Tzutalin
+# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
+# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction, including without
+# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
+# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
+# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from setuptools import setup, find_packages, Command
+from sys import platform as _platform
+from shutil import rmtree
+import sys
+import os
+
+here = os.path.abspath(os.path.dirname(__file__))
+NAME = 'labelImg'
+REQUIRES_PYTHON = '>=3.0.0'
+REQUIRED_DEP = ['pyqt5', 'lxml']
+about = {}
+
+with open(os.path.join(here, 'libs', '__init__.py')) as f:
+    exec(f.read(), about)
+
+with open('README.rst') as readme_file:
+    readme = readme_file.read()
+
+with open('HISTORY.rst') as history_file:
+    history = history_file.read()
+
+
+# OS specific settings
+SET_REQUIRES = []
+if _platform == "linux" or _platform == "linux2":
+   # linux
+   print('linux')
+elif _platform == "darwin":
+   # MAC OS X
+   SET_REQUIRES.append('py2app')
+
+required_packages = find_packages()
+required_packages.append('labelImg')
+
+APP = [NAME + '.py']
+OPTIONS = {
+    'argv_emulation': True,
+    'iconfile': 'resources/icons/app.icns'
+}
+
+class UploadCommand(Command):
+    """Support setup.py upload."""
+
+    description=readme + '\n\n' + history,
+
+    user_options = []
+
+    @staticmethod
+    def status(s):
+        """Prints things in bold."""
+        print('\033[1m{0}\033[0m'.format(s))
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        try:
+            self.status('Removing previous builds…')
+            rmtree(os.path.join(here, 'dist'))
+        except OSError:
+            self.status('Fail to remove previous builds..')
+            pass
+
+        self.status('Building Source and Wheel (universal) distribution…')
+        os.system(
+            '{0} setup.py sdist bdist_wheel --universal'.format(sys.executable))
+
+        self.status('Uploading the package to PyPI via Twine…')
+        os.system('twine upload dist/*')
+
+        self.status('Pushing git tags…')
+        os.system('git tag -d v{0}'.format(about['__version__']))
+        os.system('git tag v{0}'.format(about['__version__']))
+        # os.system('git push --tags')
+
+        sys.exit()
+
+
+setup(
+    app=APP,
+    name=NAME,
+    version=about['__version__'],
+    description="LabelImg is a graphical image annotation tool and label object bounding boxes in images",
+    long_description=readme + '\n\n' + history,
+    author="TzuTa Lin",
+    author_email='tzu.ta.lin@gmail.com',
+    url='https://github.com/tzutalin/labelImg',
+    python_requires=REQUIRES_PYTHON,
+    package_dir={'labelImg': '.'},
+    packages=required_packages,
+    entry_points={
+        'console_scripts': [
+            'labelImg=labelImg.labelImg:main'
+        ]
+    },
+    include_package_data=True,
+    install_requires=REQUIRED_DEP,
+    license="MIT license",
+    zip_safe=False,
+    keywords='labelImg labelTool development annotation deeplearning',
+    classifiers=[
+        'Development Status :: 5 - Production/Stable',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: MIT License',
+        'Natural Language :: English',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.3',
+        'Programming Language :: Python :: 3.4',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
+    ],
+    package_data={'data/predefined_classes.txt': ['data/predefined_classes.txt']},
+    options={'py2app': OPTIONS},
+    setup_requires=SET_REQUIRES,
+    # $ setup.py publish support.
+    cmdclass={
+        'upload': UploadCommand,
+    }
+)

README.md

@@ -1,209 +1,185 @@
-[English](README_en.md) | 简体中文
+English | [简体中文](README_ch.md)
 
-## 简介
-PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
+## Introduction
+PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
 
-**近期更新**
-- 2020.8.26 更新OCR相关的84个常见问题及解答，具体参考[FAQ](./doc/doc_ch/FAQ.md)
-- 2020.8.24 支持通过whl包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./doc/doc_ch/whl.md)
-- 2020.8.21 更新8月18日B站直播课回放和PPT，课节2，易学易用的OCR工具大礼包，[获取地址](https://aistudio.baidu.com/aistudio/education/group/info/1519)
-- 2020.8.16 开源文本检测算法[SAST](https://arxiv.org/abs/1908.05498)和文本识别算法[SRN](https://arxiv.org/abs/2003.12294)
-- 2020.7.23 发布7月21日B站直播课回放和PPT，课节1，PaddleOCR开源大礼包全面解读，[获取地址](https://aistudio.baidu.com/aistudio/course/introduce/1519)
-- 2020.7.15 添加基于EasyEdge和Paddle-Lite的移动端DEMO，支持iOS和Android系统
-- [more](./doc/doc_ch/update.md)
+## Notice
+PaddleOCR supports both dynamic graph and static graph programming paradigm
+- Dynamic graph: dygraph branch (default), **supported by paddle 2.0rc1+ ([installation](./doc/doc_en/installation_en.md))**
+- Static graph: develop branch
 
+**Recent updates**
+- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
+- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
+- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
+- [more](./doc/doc_en/update_en.md)
 
-## 特性
-- 超轻量级中文OCR模型，总模型仅8.6M
-    - 单模型支持中英文数字组合识别、竖排文本识别、长文本识别
-    - 检测模型DB（4.1M）+识别模型CRNN（4.5M）
-- 实用通用中文OCR模型
-- 多种预测推理部署方案，包括服务部署和端侧部署
-- 多种文本检测训练算法，EAST、DB、SAST
-- 多种文本识别训练算法，Rosetta、CRNN、STAR-Net、RARE、SRN
-- 可运行于Linux、Windows、MacOS等多种系统
+## Features
+- PPOCR series of high-quality pre-trained models, comparable to commercial effects
+    - Ultra lightweight ppocr_mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M
+    - General ppocr_server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M
+    - Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
+    - Support multi-language recognition: Korean, Japanese, German, French
+- Rich toolkits related to the OCR areas
+    - Semi-automatic data annotation tool, i.e., PPOCRLabel: support fast and efficient data annotation
+    - Data synthesis tool, i.e., Style-Text: easy to synthesize a large number of images which are similar to the target scene image
+- Support user-defined training, provides rich predictive inference deployment solutions
+- Support PIP installation, easy to use
+- Support Linux, Windows, MacOS and other systems
 
-## 快速体验
+## Visualization
 
 <div align="center">
-    <img src="doc/imgs_results/11.jpg" width="800">
+    <img src="doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
+    <img src="doc/imgs_results/ch_ppocr_mobile_v2.0/00018069.jpg" width="800">
 </div>
 
-上图是超轻量级中文OCR模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
+The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see [More visualizations](./doc/doc_en/visualization_en.md).
 
-- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
-- 移动端DEMO体验(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)：[安装包二维码获取地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
+<a name="Community"></a>
+## Community
+- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
 
-   Android手机也可以扫描下面二维码安装体验。
+<div align="center">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/joinus.PNG"  width = "200" height = "200" />
+</div>
+
+
+## Quick Experience
+
+You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
+
+Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in to the website to obtain the QR code for  installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
+
+ Also, you can scan the QR code below to install the App (**Android support only**)
 
 <div align="center">
 <img src="./doc/ocr-android-easyedge.png"  width = "200" height = "200" />
 </div>
 
+- [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)
 
-## 中文OCR模型列表
-
-|模型名称|模型简介|检测模型地址|识别模型地址|支持空格的识别模型地址|
-|-|-|-|-|-|
-|chinese_db_crnn_mobile|超轻量级中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
-|chinese_db_crnn_server|通用中文OCR模型|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
-
-## 文档教程
-- [快速安装](./doc/doc_ch/installation.md)
-- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
-- 算法介绍
-    - [文本检测](#文本检测算法)
-    - [文本识别](#文本识别算法)
-- 模型训练/评估
-    - [文本检测](./doc/doc_ch/detection.md)
-    - [文本识别](./doc/doc_ch/recognition.md)
-    - [yml参数配置文件介绍](./doc/doc_ch/config.md)
-    - [中文OCR训练预测技巧](./doc/doc_ch/tricks.md)
-- 预测部署
-    - [基于Python预测引擎推理](./doc/doc_ch/inference.md)
-    - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
-    - [服务化部署](./doc/doc_ch/serving.md)
-    - [端侧部署](./deploy/lite/readme.md)
-    - 模型量化压缩（coming soon）
-    - [Benchmark](./doc/doc_ch/benchmark.md)
-- 数据集
-    - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
-    - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
-    - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
-    - [常用数据标注工具](./doc/doc_ch/data_annotation.md)
-    - [常用数据合成工具](./doc/doc_ch/data_synthesis.md)
-- 效果展示
-    - [超轻量级中文OCR效果展示](#超轻量级中文OCR效果展示)
-    - [通用中文OCR效果展示](#通用中文OCR效果展示)
-    - [支持空格的中文OCR效果展示](#支持空格的中文OCR效果展示)
-- FAQ
-    - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
-    - [【理论篇】OCR通用21个问题](./doc/doc_ch/FAQ.md)
-    - [【实战篇】PaddleOCR实战53个问题](./doc/doc_ch/FAQ.md)
-- [技术交流群](#欢迎加入PaddleOCR技术交流群)
-- [参考文献](./doc/doc_ch/reference.md)
-- [许可证书](#许可证书)
-- [贡献代码](#贡献代码)
-
-<a name="算法介绍"></a>
-## 算法介绍
-<a name="文本检测算法"></a>
-### 1.文本检测算法
-
-PaddleOCR开源的文本检测算法列表：
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
-- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))(百度自研)
-
-在ICDAR2015文本检测公开数据集上，算法效果如下：
-
-|模型|骨干网络|precision|recall|Hmean|下载链接|
-|-|-|-|-|-|-|
-|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
-|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
-|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
-|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
-|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
-
-在Total-text文本检测公开数据集上，算法效果如下：
-
-|模型|骨干网络|precision|recall|Hmean|下载链接|
-|-|-|-|-|-|-|
-|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
-
-**说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
+<a name="Supported-Chinese-model-list"></a>
 
 
-使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集共3w张数据，训练中文检测模型的相关配置和预训练文件如下：
+## PP-OCR 2.0 series model list（Update on Dec 15）
+**Note** : Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
 
-|模型|骨干网络|配置文件|预训练模型|
-|-|-|-|-|
-|超轻量中文模型|MobileNetV3|det_mv3_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
-|通用中文OCR模型|ResNet50_vd|det_r50_vd_db.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
+| Model introduction                                           | Model name                   | Recommended scene | Detection model                                              | Direction classifier                                         | Recognition model                                            |
+| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| Chinese and English ultra-lightweight OCR model (9.4M)       | ch_ppocr_mobile_v2.0_xx      | Mobile & server   |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar)      |
+| Chinese and English general OCR model (143.4M)               | ch_ppocr_server_v2.0_xx      | Server            |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_traingit.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar)  |  
 
-* 注： 上述DB模型的训练和评估，需设置后处理参数box_thresh=0.6，unclip_ratio=1.5，使用不同数据集、不同模型训练，可调整这两个参数进行优化
 
-PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./doc/doc_ch/detection.md)。
+For more model downloads (including multiple languages), please refer to [PP-OCR v2.0 series model downloads](./doc/doc_en/models_list_en.md).
 
-<a name="文本识别算法"></a>
-### 2.文本识别算法
+For a new language request, please refer to [Guideline for new language_requests](#language_requests).
 
-PaddleOCR开源的文本识别算法列表：
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研)
+## Tutorials
+- [Installation](./doc/doc_en/installation_en.md)
+- [Quick Start](./doc/doc_en/quickstart_en.md)
+- [Code Structure](./doc/doc_en/tree_en.md)
+- Algorithm Introduction
+    - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
+    - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
+    - [PP-OCR Pipeline](#PP-OCR-Pipeline)
+- Model Training/Evaluation
+    - [Text Detection](./doc/doc_en/detection_en.md)
+    - [Text Recognition](./doc/doc_en/recognition_en.md)
+    - [Direction Classification](./doc/doc_en/angle_class_en.md)
+    - [Yml Configuration](./doc/doc_en/config_en.md)
+- Inference and Deployment
+    - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
+    - [Python Inference](./doc/doc_en/inference_en.md)
+    - [C++ Inference](./deploy/cpp_infer/readme_en.md)
+    - [Serving](./deploy/hubserving/readme_en.md)
+    - [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md)
+    - [Benchmark](./doc/doc_en/benchmark_en.md)  
+- Data Annotation and Synthesis
+    - [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
+    - [Data Synthesis Tool: Style-Text](./StyleText/README.md)
+    - [Other Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
+    - [Other Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
+- Datasets
+    - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
+    - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
+    - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
+- [Visualization](#Visualization)
+- [New language requests](#language_requests)
+- [FAQ](./doc/doc_en/FAQ_en.md)
+- [Community](#Community)
+- [References](./doc/doc_en/reference_en.md)
+- [License](#LICENSE)
+- [Contribution](#CONTRIBUTION)
 
-参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
 
-|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
-|-|-|-|-|-|
-|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
-|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
-|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
-|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
-|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
-|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
-|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
-|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
-|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
 
-**说明：** SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广，增广后的数据可以在[百度网盘](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA)上下载，提取码: y3ry。
-原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
+<a name="PP-OCR-Pipeline"></a>
 
-使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：  
-
-|模型|骨干网络|配置文件|预训练模型|
-|-|-|-|-|
-|超轻量中文模型|MobileNetV3|rec_chinese_lite_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
-|通用中文OCR模型|Resnet34_vd|rec_chinese_common_train.yml|[下载链接](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
-
-PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./doc/doc_ch/recognition.md)。
-
-## 效果展示
-
-<a name="超轻量级中文OCR效果展示"></a>
-### 1.超轻量级中文OCR效果展示  [more](./doc/doc_ch/visualization.md)
+## PP-OCR Pipeline
 
 <div align="center">
-    <img src="doc/imgs_results/1.jpg" width="800">
+    <img src="./doc/ppocr_framework.png" width="800">
 </div>
 
-<a name="通用中文OCR效果展示"></a>
-### 2.通用中文OCR效果展示  [more](./doc/doc_ch/visualization.md)
+PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner [8] and PACT quantization [9] is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
 
+
+## Visualization [more](./doc/doc_en/visualization_en.md)
+- Chinese OCR model
 <div align="center">
-    <img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00015504.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/rotate_00052204.jpg" width="800">
 </div>
 
-<a name="支持空格的中文OCR效果展示"></a>
-### 3.支持空格的中文OCR效果展示  [more](./doc/doc_ch/visualization.md)
-
+- English OCR model
 <div align="center">
-    <img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/img_12.jpg" width="800">
 </div>
 
-<a name="欢迎加入PaddleOCR技术交流群"></a>
-## 欢迎加入PaddleOCR技术交流群
-请扫描下面二维码，完成问卷填写，获取加群二维码和OCR方向的炼丹秘籍
-
+- Multilingual OCR model
 <div align="center">
-<img src="./doc/joinus.jpg"  width = "200" height = "200" />
+    <img src="./doc/imgs_results/french_0.jpg" width="800">
+    <img src="./doc/imgs_results/korean.jpg" width="800">
 </div>
 
-<a name="许可证书"></a>
-## 许可证书
-本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。
 
-<a name="贡献代码"></a>
-## 贡献代码
-我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
+<a name="language_requests"></a>
+## Guideline for new language requests
 
-- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
-- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
-- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
-- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集
-- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
-- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
-- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。
+If you want to request a new language support, a PR with 2 following files are needed：
+
+1. In folder [ppocr/utils/dict](./ppocr/utils/dict),
+it is necessary to submit the dict text to this path and name it with `{language}_dict.txt` that contains a list of all characters. Please see the format example from other files in that folder.
+
+2. In folder [ppocr/utils/corpus](./ppocr/utils/corpus),
+it is necessary to submit the corpus to this path and name it with `{language}_corpus.txt` that contains a list of words in your language.
+Maybe, 50000 words per language is necessary at least.
+Of course, the more, the better.
+
+If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.
+
+More details, please refer to [Multilingual OCR Development Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
+
+
+<a name="LICENSE"></a>
+## License
+This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
+
+<a name="CONTRIBUTION"></a>
+## Contribution
+We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
+
+- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing and revising the English documentation.
+- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
+- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
+- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
+- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo  and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
+- Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
+- Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.
+- Thanks [lijinhan](https://github.com/lijinhan) for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment.
+- Thanks [Mejans](https://github.com/Mejans) for contributing the Occitan corpus and character set.
+- Thanks [LKKlein](https://github.com/LKKlein) for contributing a new deploying package with the Golang program language.
+- Thanks [Evezerest](https://github.com/Evezerest), [ninetailskim](https://github.com/ninetailskim), [edencfc](https://github.com/edencfc), [BeyondYourself](https://github.com/BeyondYourself) and [1084667371](https://github.com/1084667371) for contributing a new data annotation tool, i.e., PPOCRLabel。

README_ch.md

@@ -0,0 +1,160 @@
+[English](README.md) | 简体中文
+
+## 简介
+PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
+## 注意
+PaddleOCR同时支持动态图与静态图两种编程范式
+- 动态图版本：dygraph分支（默认），需将paddle版本升级至2.0rc1+（[快速安装](./doc/doc_ch/installation.md)）
+- 静态图版本：develop分支
+
+**近期更新**
+- 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
+- 2020.12.14 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数127个，每周一都会更新，欢迎大家持续关注。
+- 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
+- 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
+- [More](./doc/doc_ch/update.md)
+
+
+
+## 特性
+
+- PPOCR系列高质量预训练模型，准确的识别效果
+    - 超轻量ppocr_mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M
+    - 通用ppocr_server系列：检测（47.1M）+方向分类器（1.4M）+ 识别（94.9M）= 143.4M
+    - 支持中英文数字组合识别、竖排文本识别、长文本识别
+    - 支持多语言识别：韩语、日语、德语、法语
+- 丰富易用的OCR相关工具组件
+    - 半自动数据标注工具PPOCRLabel：支持快速高效的数据标注
+    - 数据合成工具Style-Text：批量合成大量与目标场景类似的图像
+- 支持用户自定义训练，提供丰富的预测推理部署方案
+- 支持PIP快速安装使用
+- 可运行于Linux、Windows、MacOS等多种系统
+
+## 效果展示
+
+<div align="center">
+    <img src="doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
+    <img src="doc/imgs_results/ch_ppocr_mobile_v2.0/00018069.jpg" width="800">
+</div>
+
+上图是通用ppocr_server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
+
+<a name="欢迎加入PaddleOCR技术交流群"></a>
+## 欢迎加入PaddleOCR技术交流群
+- 微信扫描二维码加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/joinus.PNG"  width = "200" height = "200" />
+</div>
+
+## 快速体验
+- PC端：超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
+
+- 移动端：[安装包DEMO下载地址](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)(基于EasyEdge和Paddle-Lite, 支持iOS和Android系统)，Android手机也可以直接扫描下面二维码安装体验。
+
+
+<div align="center">
+<img src="./doc/ocr-android-easyedge.png"  width = "200" height = "200" />
+</div>
+
+- 代码体验：从[快速安装](./doc/doc_ch/quickstart.md) 开始
+
+<a name="模型下载"></a>
+## PP-OCR 2.0系列模型列表（更新中）
+**说明** ：2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md)的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
+| 模型简介     | 模型名称     |推荐场景          | 检测模型 | 方向分类器 | 识别模型 |
+| ------------ | --------------- | ----------------|---- | ---------- | -------- |
+| 中英文超轻量OCR模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar)      |
+| 中英文通用OCR模型（143.4M）   |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar)  |  
+
+更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
+
+## 文档教程
+- [快速安装](./doc/doc_ch/installation.md)
+- [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
+- [代码组织结构](./doc/doc_ch/tree.md)
+- 算法介绍
+    - [文本检测](./doc/doc_ch/algorithm_overview.md)
+    - [文本识别](./doc/doc_ch/algorithm_overview.md)
+    - [PP-OCR Pipline](#PP-OCR)
+- 模型训练/评估
+    - [文本检测](./doc/doc_ch/detection.md)
+    - [文本识别](./doc/doc_ch/recognition.md)
+    - [方向分类器](./doc/doc_ch/angle_class.md)
+    - [yml参数配置文件介绍](./doc/doc_ch/config.md)
+- 预测部署
+    - [基于pip安装whl包快速推理](./doc/doc_ch/whl.md)
+    - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
+    - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
+    - [服务化部署](./deploy/hubserving/readme.md)
+    - [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
+    - [Benchmark](./doc/doc_ch/benchmark.md)
+- 数据集
+    - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
+    - [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
+    - [垂类多语言OCR数据集](./doc/doc_ch/vertical_and_multilingual_datasets.md)
+- 数据标注与合成
+    - [半自动标注工具PPOCRLabel](./PPOCRLabel/README_ch.md)
+    - [数据合成工具Style-Text](./StyleText/README_ch.md)
+    - [其它数据标注工具](./doc/doc_ch/data_annotation.md)
+    - [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
+- [效果展示](#效果展示)
+- FAQ
+    - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
+    - [【理论篇】OCR通用30个问题](./doc/doc_ch/FAQ.md)
+    - [【实战篇】PaddleOCR实战84个问题](./doc/doc_ch/FAQ.md)
+- [技术交流群](#欢迎加入PaddleOCR技术交流群)
+- [参考文献](./doc/doc_ch/reference.md)
+- [许可证书](#许可证书)
+- [贡献代码](#贡献代码)
+
+
+<a name="PP-OCR"></a>
+## PP-OCR Pipline
+<div align="center">
+    <img src="./doc/ppocr_framework.png" width="800">
+</div>
+
+PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测[2]、检测框矫正和CRNN文本识别三部分组成[7]。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 。其中FPGM裁剪器[8]和PACT量化[9]的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)。
+
+<a name="效果展示"></a>
+## 效果展示 [more](./doc/doc_ch/visualization.md)
+- 中文模型
+<div align="center">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00015504.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/rotate_00052204.jpg" width="800">
+</div>
+
+- 英文模型
+<div align="center">
+    <img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/img_12.jpg" width="800">
+</div>
+
+- 其他语言模型
+<div align="center">
+    <img src="./doc/imgs_results/french_0.jpg" width="800">
+    <img src="./doc/imgs_results/korean.jpg" width="800">
+</div>
+
+
+<a name="许可证书"></a>
+## 许可证书
+本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>许可认证。
+
+<a name="贡献代码"></a>
+## 贡献代码
+我们非常欢迎你为PaddleOCR贡献代码，也十分感谢你的反馈。
+
+
+- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档
+- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题
+- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码
+- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集
+- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码
+- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格。
+- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务。
+- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用。
+- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料。
+- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了PPOCRLabel的完整代码。

README_en.md

@@ -1,231 +0,0 @@
-English | [简体中文](README.md)
-
-## Introduction
-PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
-
-**Recent updates**
-- 2020.8.24 Support the use of PaddleOCR through whl package installation，pelease refer  [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md)
-- 2020.8.16, Release text detection algorithm [SAST](https://arxiv.org/abs/1908.05498) and text recognition algorithm [SRN](https://arxiv.org/abs/2003.12294)
-- 2020.7.23, Release the playback and PPT of live class on BiliBili station, PaddleOCR Introduction, [address](https://aistudio.baidu.com/aistudio/course/introduce/1519)
-- 2020.7.15, Add mobile App demo , support both iOS and  Android  ( based on easyedge and Paddle Lite)
-- 2020.7.15, Improve the  deployment ability, add the C + +  inference , serving deployment. In addition, the benchmarks of the ultra-lightweight OCR model are provided.
-- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
-- [more](./doc/doc_en/update_en.md)
-
-## Features
-- Ultra-lightweight OCR model, total model size is only 8.6M
-    - Single model supports Chinese/English numbers combination recognition, vertical text recognition, long text recognition
-    - Detection model DB (4.1M) + recognition model CRNN (4.5M)
-- Various text detection algorithms: EAST, DB
-- Various text recognition algorithms: Rosetta, CRNN, STAR-Net, RARE
-- Support Linux, Windows, macOS and other systems.
-
-## Visualization
-
-![](doc/imgs_results/11.jpg)
-
-![](doc/imgs_results/img_10.jpg)
-
-[More visualization](./doc/doc_en/visualization_en.md)
-
-You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
-
-Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in to the website to obtain the QR code for  installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
-
- Also, you can scan the QR code below to install the App (**Android support only**)
-
-<div align="center">
-<img src="./doc/ocr-android-easyedge.png"  width = "200" height = "200" />
-</div>
-
-- [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)
-
-<a name="Supported-Chinese-model-list"></a>
-
-### Supported Models:
-
-|Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
-|-|-|-|-|-|
-|db_crnn_mobile|ultra-lightweight OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
-|db_crnn_server|General OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) / [pre-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
-
-
-## Tutorials
-- [Installation](./doc/doc_en/installation_en.md)
-- [Quick Start](./doc/doc_en/quickstart_en.md)
-- Algorithm introduction
-    - [Text Detection Algorithm](#TEXTDETECTIONALGORITHM)
-    - [Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
-    - [END-TO-END OCR Algorithm](#ENDENDOCRALGORITHM)
-- Model training/evaluation
-    - [Text Detection](./doc/doc_en/detection_en.md)
-    - [Text Recognition](./doc/doc_en/recognition_en.md)
-    - [Yml Configuration](./doc/doc_en/config_en.md)
-    - [Tricks](./doc/doc_en/tricks_en.md)
-- Deployment
-    - [Python Inference](./doc/doc_en/inference_en.md)
-    - [C++ Inference](./deploy/cpp_infer/readme_en.md)
-    - [Serving](./doc/doc_en/serving_en.md)
-    - [Mobile](./deploy/lite/readme_en.md)
-    - Model Quantization and Compression (coming soon)
-    - [Benchmark](./doc/doc_en/benchmark_en.md)
-- Datasets
-    - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
-    - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
-    - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
-    - [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
-    - [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
-- [FAQ](#FAQ)
-- Visualization
-    - [Ultra-lightweight Chinese/English OCR Visualization](#UCOCRVIS)
-    - [General Chinese/English OCR Visualization](#GeOCRVIS)
-    - [Chinese/English OCR Visualization (Support Space Recognition )](#SpaceOCRVIS)
-- [Community](#Community)
-- [References](./doc/doc_en/reference_en.md)
-- [License](#LICENSE)
-- [Contribution](#CONTRIBUTION)
-
-<a name="TEXTDETECTIONALGORITHM"></a>
-## Text Detection Algorithm
-
-PaddleOCR open source text detection algorithms list:
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
-- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))(Baidu Self-Research)
-
-On the ICDAR2015 dataset, the text detection result is as follows:
-
-|Model|Backbone|precision|recall|Hmean|Download link|
-|-|-|-|-|-|-|
-|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)|
-|EAST|MobileNetV3|81.67%|79.83%|80.74%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_east.tar)|
-|DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)|
-|DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)|
-|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[Download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)|
-
-On Total-Text dataset, the text detection result is as follows:
-
-|Model|Backbone|precision|recall|Hmean|Download link|
-|-|-|-|-|-|-|
-|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[Download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)|
-
-**Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
-
-For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for text detection task are as follows:  
-|Model|Backbone|Configuration file|Pre-trained model|
-|-|-|-|-|
-|ultra-lightweight OCR model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|
-|General OCR model|ResNet50_vd|det_r50_vd_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|
-
-* Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
-
-For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.md)
-
-<a name="TEXTRECOGNITIONALGORITHM"></a>
-## Text Recognition Algorithm
-
-PaddleOCR open-source text recognition algorithms list:
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))(Baidu Self-Research)
-
-Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
-
-|Model|Backbone|Avg Accuracy|Module combination|Download link|
-|-|-|-|-|-|
-|Rosetta|Resnet34_vd|80.24%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_none_ctc.tar)|
-|Rosetta|MobileNetV3|78.16%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_none_ctc.tar)|
-|CRNN|Resnet34_vd|82.20%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_none_bilstm_ctc.tar)|
-|CRNN|MobileNetV3|79.37%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar)|
-|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)|
-|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
-|RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
-|RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
-|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[Download link](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
-
-**Note：** SRN model uses data expansion method to expand the two training sets mentioned above, and the expanded data can be downloaded from [Baidu Drive](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA) (download code: y3ry).
-
-The average accuracy of the two-stage training in the original paper is 89.74%, and that of one stage training in paddleocr is 88.33%. Both pre-trained weights can be downloaded [here](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar).
-
-We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w  training data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the model. The related configuration and pre-trained models are as follows:
-
-|Model|Backbone|Configuration file|Pre-trained model|
-|-|-|-|-|
-|ultra-lightweight OCR model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)|
-|General OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)|
-
-Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
-
-<a name="ENDENDOCRALGORITHM"></a>
-## END-TO-END OCR Algorithm
-- [ ]  [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, coming soon)
-
-## Visualization
-
-<a name="UCOCRVIS"></a>
-### 1.Ultra-lightweight Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
-
-<div align="center">
-    <img src="doc/imgs_results/1.jpg" width="800">
-</div>
-
-<a name="GeOCRVIS"></a>
-### 2. General Chinese/English OCR Visualization [more](./doc/doc_en/visualization_en.md)
-
-<div align="center">
-    <img src="doc/imgs_results/chinese_db_crnn_server/11.jpg" width="800">
-</div>
-
-<a name="SpaceOCRVIS"></a>
-### 3.Chinese/English OCR Visualization (Space_support) [more](./doc/doc_en/visualization_en.md)
-
-<div align="center">
-    <img src="doc/imgs_results/chinese_db_crnn_server/en_paper.jpg" width="800">
-</div>
-
-<a name="FAQ"></a>
-
-## FAQ
-1. Error when using attention-based recognition model: KeyError: 'predict'
-
-    The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
-
-2. About inference speed
-
-    When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
-
-3. Service deployment and mobile deployment
-
-    It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
-
-4. Release time of self-developed algorithm
-
-    Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
-
-[more](./doc/doc_en/FAQ_en.md)
-
-<a name="Community"></a>
-## Community
-Scan  the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
-
-<div align="center">
-<img src="./doc/joinus.jpg"  width = "200" height = "200" />
-</div>
-
-<a name="LICENSE"></a>
-## License
-This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
-
-<a name="CONTRIBUTION"></a>
-## Contribution
-We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
-
-- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing and revising the English documentation.
-- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
-- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
-- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
-- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo  and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
-- Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
-- Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.

StyleText/README.md

@@ -0,0 +1,220 @@
+English | [简体中文](README_ch.md)
+
+## Style Text
+
+### Contents
+- [1. Introduction](#Introduction)
+- [2. Preparation](#Preparation)
+- [3. Quick Start](#Quick_Start)
+- [4. Applications](#Applications)
+- [5. Code Structure](#Code_structure)
+
+
+<a name="Introduction"></a>
+### Introduction
+
+<div align="center">
+    <img src="doc/images/3.png" width="800">
+</div>
+
+<div align="center">
+    <img src="doc/images/9.png" width="600">
+</div>
+
+
+The Style-Text data synthesis tool is a tool based on Baidu and HUST cooperation research work, "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
+
+Different from the commonly used GAN-based data synthesis tools, the main framework of Style-Text includes:
+* (1) Text foreground style transfer module.
+* (2) Background extraction module.
+* (3) Fusion module.
+
+After these three steps, you can quickly realize the image text style transfer. The following figure is some results of the data synthesis tool.
+
+<div align="center">
+    <img src="doc/images/10.png" width="1000">
+</div>
+
+
+<a name="Preparation"></a>
+#### Preparation
+
+1. Please refer the [QUICK INSTALLATION](../doc/doc_en/installation_en.md) to install PaddlePaddle. Python3 environment is strongly recommended.
+2. Download the pretrained models and unzip:
+
+```bash
+cd StyleText
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/style_text_models.zip
+unzip style_text_models.zip
+```
+
+If you save the model in another location, please modify the address of the model file in `configs/config.yml`, and you need to modify these three configurations at the same time:
+
+```
+bg_generator:
+  pretrain: style_text_rec/bg_generator
+...
+text_generator:
+  pretrain: style_text_models/text_generator
+...
+fusion_generator:
+  pretrain: style_text_models/fusion_generator
+```
+
+<a name="Quick_Start"></a>
+### Quick Start
+
+#### Synthesis single image
+
+1. You can run `tools/synth_image` and generate the demo image, which is saved in the current folder.
+
+```python
+python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
+```
+
+* Note 1: The language options is correspond to the corpus. Currently, the tool only supports English, Simplified Chinese and Korean.
+* Note 2: Synth-Text is mainly used to generate images for OCR recognition models.
+  So the height of style images should be around 32 pixels. Images in other sizes may behave poorly.
+* Note 3: You can modify `use_gpu` in `configs/config.yml` to determine whether to use GPU for prediction.
+
+
+
+For example, enter the following image and corpus `PaddleOCR`.
+
+<div align="center">
+    <img src="examples/style_images/2.jpg" width="300">
+</div>
+
+The result `fake_fusion.jpg` will be generated.
+
+<div align="center">
+    <img src="doc/images/4.jpg" width="300">
+</div>
+
+What's more, the medium result `fake_bg.jpg` will also be saved, which is the background output.
+
+<div align="center">
+    <img src="doc/images/7.jpg" width="300">
+</div>
+
+
+`fake_text.jpg` * `fake_text.jpg` is the generated image with the same font style as `Style Input`.
+
+
+<div align="center">
+    <img src="doc/images/8.jpg" width="300">
+</div>
+
+
+#### Batch synthesis
+
+In actual application scenarios, it is often necessary to synthesize pictures in batches and add them to the training set. StyleText can use a batch of style pictures and corpus to synthesize data in batches. The synthesis process is as follows:
+
+1. The referenced dataset can be specifed in `configs/dataset_config.yml`:
+
+   * `Global`：
+     * `output_dir:`：Output synthesis data path.
+   * `StyleSampler`：
+     * `image_home`：style images' folder.
+     * `label_file`：Style images' file list. If label is provided, then it is the label file path.
+     * `with_label`：Whether the `label_file` is label file list.
+   * `CorpusGenerator`：
+     * `method`：Method of CorpusGenerator，supports `FileCorpus` and `EnNumCorpus`. If `EnNumCorpus` is used，No other configuration is needed，otherwise you need to set `corpus_file` and `language`.
+     * `language`：Language of the corpus.
+     * `corpus_file`: Filepath of the corpus. Corpus file should be a text file which will be split by line-endings（'\n'）. Corpus generator samples one line each time.
+
+
+Example of corpus file: 
+```
+PaddleOCR
+飞桨文字识别
+StyleText
+风格文本图像数据合成
+```
+
+We provide a general dataset containing Chinese, English and Korean (50,000 images in all) for your trial ([download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/chkoen_5w.tar)), some examples are given below :
+
+<div align="center">
+     <img src="doc/images/5.png" width="800">
+</div>
+
+2. You can run the following command to start synthesis task:
+
+   ``` bash
+   python3 tools/synth_dataset.py -c configs/dataset_config.yml
+   ```
+
+We also provide example corpus and images in `examples` folder.
+    <div align="center">
+        <img src="examples/style_images/1.jpg" width="300">
+        <img src="examples/style_images/2.jpg" width="300">
+    </div>
+If you run the code above directly, you will get example output data in `output_data` folder.
+You will get synthesis images and labels as below:
+   <div align="center">
+       <img src="doc/images/12.png" width="800">
+   </div>
+There will be some cache under the `label` folder. If the program exit unexpectedly, you can find cached labels there.
+When the program finish normally, you will find all the labels in `label.txt` which give the final results.
+
+<a name="Applications"></a>
+### Applications
+We take two scenes as examples, which are metal surface English number recognition and general Korean recognition, to illustrate practical cases of using StyleText to synthesize data to improve text recognition. The following figure shows some examples of real scene images and composite images:
+
+<div align="center">
+    <img src="doc/images/11.png" width="800">
+</div>
+
+
+After adding the above synthetic data for training, the accuracy of the recognition model is improved, which is shown in the following table:
+
+
+| Scenario | Characters | Raw Data | Test Data | Only Use Raw Data</br>Recognition Accuracy | New Synthetic Data | Simultaneous Use of Synthetic Data</br>Recognition Accuracy | Index Improvement |
+| -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
+| Metal surface | English and numbers | 2203     | 650      | 0.5938                     | 20000        | 0.7546                 | 16%      |
+| Random background | Korean       | 5631     | 1230     | 0.3012                     | 100000       | 0.5057                 | 20%      |
+
+
+<a name="Code_structure"></a>
+### Code Structure
+
+```
+StyleText
+|-- arch                        // Network module files.
+|   |-- base_module.py
+|   |-- decoder.py
+|   |-- encoder.py
+|   |-- spectral_norm.py
+|   `-- style_text_rec.py
+|-- configs                     // Config files.
+|   |-- config.yml
+|   `-- dataset_config.yml
+|-- engine                      // Synthesis engines.
+|   |-- corpus_generators.py    // Sample corpus from file or generate random corpus.
+|   |-- predictors.py           // Predict using network.
+|   |-- style_samplers.py       // Sample style images.
+|   |-- synthesisers.py         // Manage other engines to synthesis images.
+|   |-- text_drawers.py         // Generate standard input text images.
+|   `-- writers.py              // Write synthesis images and labels into files.
+|-- examples                    // Example files.
+|   |-- corpus
+|   |   `-- example.txt
+|   |-- image_list.txt
+|   `-- style_images
+|       |-- 1.jpg
+|       `-- 2.jpg
+|-- fonts                       // Font files.
+|   |-- ch_standard.ttf
+|   |-- en_standard.ttf
+|   `-- ko_standard.ttf
+|-- tools                       // Program entrance.
+|   |-- __init__.py
+|   |-- synth_dataset.py        // Synthesis dataset.
+|   `-- synth_image.py          // Synthesis image.
+`-- utils                       // Module of basic functions.
+    |-- config.py
+    |-- load_params.py
+    |-- logging.py
+    |-- math_functions.py
+    `-- sys_funcs.py
+```

StyleText/README_ch.md

@@ -0,0 +1,205 @@
+简体中文 | [English](README.md)
+
+## Style Text
+
+
+### 目录
+- [一、工具简介](#工具简介)
+- [二、环境配置](#环境配置)
+- [三、快速上手](#快速上手)
+- [四、应用案例](#应用案例)
+- [五、代码结构](#代码结构)
+
+<a name="工具简介"></a>
+### 一、工具简介
+<div align="center">
+    <img src="doc/images/3.png" width="800">
+</div>
+
+<div align="center">
+    <img src="doc/images/1.png" width="600">
+</div>
+
+
+Style-Text数据合成工具是基于百度和华科合作研发的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047
+
+不同于常用的基于GAN的数据合成工具，Style-Text主要框架包括：1.文本前景风格迁移模块 2.背景抽取模块 3.融合模块。经过这样三步，就可以迅速实现图像文本风格迁移。下图是一些该数据合成工具效果图。
+
+<div align="center">
+    <img src="doc/images/2.png" width="1000">
+</div>
+
+<a name="环境配置"></a>
+### 二、环境配置
+
+1. 参考[快速安装](../doc/doc_ch/installation.md)，安装PaddleOCR。
+2. 进入`StyleText`目录，下载模型，并解压：
+
+```bash
+cd StyleText
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/style_text_models.zip
+unzip style_text_models.zip
+```
+
+如果您将模型保存再其他位置，请在`configs/config.yml`中修改模型文件的地址，修改时需要同时修改这三个配置：
+
+```
+bg_generator:
+  pretrain: style_text_models/bg_generator
+...
+text_generator:
+  pretrain: style_text_models/text_generator
+...
+fusion_generator:
+  pretrain: style_text_models/fusion_generator
+```
+
+<a name="快速上手"></a>
+### 三、快速上手
+
+#### 合成单张图
+输入一张风格图和一段文字语料，运行tools/synth_image，合成单张图片，结果图像保存在当前目录下：
+
+```python
+python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
+```
+* 注1：语言选项和语料相对应，目前该工具只支持英文、简体中文和韩语。
+* 注2：Style-Text生成的数据主要应用于OCR识别场景。基于当前PaddleOCR识别模型的设计，我们主要支持高度在32左右的风格图像。
+  如果输入图像尺寸相差过多，效果可能不佳。
+* 注3：可以通过修改配置文件中的`use_gpu`(true或者false)参数来决定是否使用GPU进行预测。
+
+
+例如，输入如下图片和语料"PaddleOCR":
+
+<div align="center">
+    <img src="examples/style_images/2.jpg" width="300">
+</div>
+
+生成合成数据`fake_fusion.jpg`：
+<div align="center">
+    <img src="doc/images/4.jpg" width="300">
+</div>
+
+除此之外，程序还会生成并保存中间结果`fake_bg.jpg`：为风格参考图去掉文字后的背景；
+
+<div align="center">
+    <img src="doc/images/7.jpg" width="300">
+</div>
+
+`fake_text.jpg`：是用提供的字符串，仿照风格参考图中文字的风格，生成在灰色背景上的文字图片。
+
+<div align="center">
+    <img src="doc/images/8.jpg" width="300">
+</div>
+
+#### 批量合成
+在实际应用场景中，经常需要批量合成图片，补充到训练集中。Style-Text可以使用一批风格图片和语料，批量合成数据。合成过程如下：
+
+1. 在`configs/dataset_config.yml`中配置目标场景风格图像和语料的路径，具体如下：
+
+   * `Global`：
+     * `output_dir:`：保存合成数据的目录。
+   * `StyleSampler`：
+     * `image_home`：风格图片目录；
+     * `label_file`：风格图片路径列表文件，如果所用数据集有label，则label_file为label文件路径；
+     * `with_label`：标志`label_file`是否为label文件。
+   * `CorpusGenerator`：
+     * `method`：语料生成方法，目前有`FileCorpus`和`EnNumCorpus`可选。如果使用`EnNumCorpus`，则不需要填写其他配置，否则需要修改`corpus_file`和`language`；
+     * `language`：语料的语种；
+     * `corpus_file`: 语料文件路径。语料文件应使用文本文件。语料生成器首先会将语料按行切分，之后每次随机选取一行。
+
+   语料文件格式示例：
+   ```
+   PaddleOCR
+   飞桨文字识别
+   StyleText
+   风格文本图像数据合成
+   ...
+   ```
+
+   Style-Text也提供了一批中英韩5万张通用场景数据用作文本风格图像，便于合成场景丰富的文本图像，下图给出了一些示例。
+
+   中英韩5万张通用场景数据: [下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/style_text/chkoen_5w.tar)
+
+<div align="center">
+    <img src="doc/images/5.png" width="800">
+</div>
+
+2. 运行`tools/synth_dataset`合成数据：
+
+   ``` bash
+   python tools/synth_dataset.py -c configs/dataset_config.yml
+   ```
+   我们在examples目录下提供了样例图片和语料。
+    <div align="center">
+        <img src="examples/style_images/1.jpg" width="300">
+        <img src="examples/style_images/2.jpg" width="300">
+    </div>
+
+   直接运行上述命令，可以在output_data中产生样例输出，包括图片和用于训练识别模型的标注文件：
+   <div align="center">
+       <img src="doc/images/12.png" width="800">
+   </div>
+
+   其中label目录下的标注文件为程序运行过程中产生的缓存，如果程序在中途异常终止，可以使用缓存的标注文件。
+   如果程序正常运行完毕，则会在output_data下生成label.txt，为最终的标注结果。
+
+<a name="应用案例"></a>
+### 四、应用案例
+下面以金属表面英文数字识别和通用韩语识别两个场景为例，说明使用Style-Text合成数据，来提升文本识别效果的实际案例。下图给出了一些真实场景图像和合成图像的示例：
+
+<div align="center">
+    <img src="doc/images/6.png" width="800">
+</div>
+
+在添加上述合成数据进行训练后，识别模型的效果提升，如下表所示：
+
+| 场景     | 字符       | 原始数据 | 测试数据 | 只使用原始数据</br>识别准确率 | 新增合成数据 | 同时使用合成数据</br>识别准确率 | 指标提升 |
+| -------- | ---------- | -------- | -------- | -------------------------- | ------------ | ---------------------- | -------- |
+| 金属表面 | 英文和数字 | 2203     | 650      | 0.5938                     | 20000        | 0.7546                 | 16%      |
+| 随机背景 | 韩语       | 5631     | 1230     | 0.3012                     | 100000       | 0.5057                 | 20%      |
+
+
+<a name="代码结构"></a>
+### 五、代码结构
+
+```
+StyleText
+|-- arch                        // 网络结构定义文件
+|   |-- base_module.py
+|   |-- decoder.py
+|   |-- encoder.py
+|   |-- spectral_norm.py
+|   `-- style_text_rec.py
+|-- configs                     // 配置文件
+|   |-- config.yml
+|   `-- dataset_config.yml
+|-- engine                      // 数据合成引擎
+|   |-- corpus_generators.py    // 从文本采样或随机生成语料
+|   |-- predictors.py           // 调用网络生成数据
+|   |-- style_samplers.py       // 采样风格图片
+|   |-- synthesisers.py         // 调度各个模块，合成数据
+|   |-- text_drawers.py         // 生成标准文字图片，用作输入
+|   `-- writers.py              // 将合成的图片和标签写入本地目录
+|-- examples                    // 示例文件
+|   |-- corpus
+|   |   `-- example.txt
+|   |-- image_list.txt
+|   `-- style_images
+|       |-- 1.jpg
+|       `-- 2.jpg
+|-- fonts                       // 字体文件
+|   |-- ch_standard.ttf
+|   |-- en_standard.ttf
+|   `-- ko_standard.ttf
+|-- tools                       // 程序入口
+|   |-- __init__.py
+|   |-- synth_dataset.py        // 批量合成数据
+|   `-- synth_image.py          // 合成单张图片
+`-- utils                       // 其他基础功能模块
+    |-- config.py
+    |-- load_params.py
+    |-- logging.py
+    |-- math_functions.py
+    `-- sys_funcs.py
+```

StyleText/arch/base_module.py

@@ -0,0 +1,255 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import paddle
+import paddle.nn as nn
+
+from arch.spectral_norm import spectral_norm
+
+
+class CBN(nn.Layer):
+    def __init__(self,
+                 name,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 use_bias=False,
+                 norm_layer=None,
+                 act=None,
+                 act_attr=None):
+        super(CBN, self).__init__()
+        if use_bias:
+            bias_attr = paddle.ParamAttr(name=name + "_bias")
+        else:
+            bias_attr = None
+        self._conv = paddle.nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            weight_attr=paddle.ParamAttr(name=name + "_weights"),
+            bias_attr=bias_attr)
+        if norm_layer:
+            self._norm_layer = getattr(paddle.nn, norm_layer)(
+                num_features=out_channels, name=name + "_bn")
+        else:
+            self._norm_layer = None
+        if act:
+            if act_attr:
+                self._act = getattr(paddle.nn, act)(**act_attr,
+                                                    name=name + "_" + act)
+            else:
+                self._act = getattr(paddle.nn, act)(name=name + "_" + act)
+        else:
+            self._act = None
+
+    def forward(self, x):
+        out = self._conv(x)
+        if self._norm_layer:
+            out = self._norm_layer(out)
+        if self._act:
+            out = self._act(out)
+        return out
+
+
+class SNConv(nn.Layer):
+    def __init__(self,
+                 name,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 use_bias=False,
+                 norm_layer=None,
+                 act=None,
+                 act_attr=None):
+        super(SNConv, self).__init__()
+        if use_bias:
+            bias_attr = paddle.ParamAttr(name=name + "_bias")
+        else:
+            bias_attr = None
+        self._sn_conv = spectral_norm(
+            paddle.nn.Conv2D(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                weight_attr=paddle.ParamAttr(name=name + "_weights"),
+                bias_attr=bias_attr))
+        if norm_layer:
+            self._norm_layer = getattr(paddle.nn, norm_layer)(
+                num_features=out_channels, name=name + "_bn")
+        else:
+            self._norm_layer = None
+        if act:
+            if act_attr:
+                self._act = getattr(paddle.nn, act)(**act_attr,
+                                                    name=name + "_" + act)
+            else:
+                self._act = getattr(paddle.nn, act)(name=name + "_" + act)
+        else:
+            self._act = None
+
+    def forward(self, x):
+        out = self._sn_conv(x)
+        if self._norm_layer:
+            out = self._norm_layer(out)
+        if self._act:
+            out = self._act(out)
+        return out
+
+
+class SNConvTranspose(nn.Layer):
+    def __init__(self,
+                 name,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 output_padding=0,
+                 dilation=1,
+                 groups=1,
+                 use_bias=False,
+                 norm_layer=None,
+                 act=None,
+                 act_attr=None):
+        super(SNConvTranspose, self).__init__()
+        if use_bias:
+            bias_attr = paddle.ParamAttr(name=name + "_bias")
+        else:
+            bias_attr = None
+        self._sn_conv_transpose = spectral_norm(
+            paddle.nn.Conv2DTranspose(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                output_padding=output_padding,
+                dilation=dilation,
+                groups=groups,
+                weight_attr=paddle.ParamAttr(name=name + "_weights"),
+                bias_attr=bias_attr))
+        if norm_layer:
+            self._norm_layer = getattr(paddle.nn, norm_layer)(
+                num_features=out_channels, name=name + "_bn")
+        else:
+            self._norm_layer = None
+        if act:
+            if act_attr:
+                self._act = getattr(paddle.nn, act)(**act_attr,
+                                                    name=name + "_" + act)
+            else:
+                self._act = getattr(paddle.nn, act)(name=name + "_" + act)
+        else:
+            self._act = None
+
+    def forward(self, x):
+        out = self._sn_conv_transpose(x)
+        if self._norm_layer:
+            out = self._norm_layer(out)
+        if self._act:
+            out = self._act(out)
+        return out
+
+
+class MiddleNet(nn.Layer):
+    def __init__(self, name, in_channels, mid_channels, out_channels,
+                 use_bias):
+        super(MiddleNet, self).__init__()
+        self._sn_conv1 = SNConv(
+            name=name + "_sn_conv1",
+            in_channels=in_channels,
+            out_channels=mid_channels,
+            kernel_size=1,
+            use_bias=use_bias,
+            norm_layer=None,
+            act=None)
+        self._pad2d = nn.Pad2D(padding=[1, 1, 1, 1], mode="replicate")
+        self._sn_conv2 = SNConv(
+            name=name + "_sn_conv2",
+            in_channels=mid_channels,
+            out_channels=mid_channels,
+            kernel_size=3,
+            use_bias=use_bias)
+        self._sn_conv3 = SNConv(
+            name=name + "_sn_conv3",
+            in_channels=mid_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            use_bias=use_bias)
+
+    def forward(self, x):
+
+        sn_conv1 = self._sn_conv1.forward(x)
+        pad_2d = self._pad2d.forward(sn_conv1)
+        sn_conv2 = self._sn_conv2.forward(pad_2d)
+        sn_conv3 = self._sn_conv3.forward(sn_conv2)
+        return sn_conv3
+
+
+class ResBlock(nn.Layer):
+    def __init__(self, name, channels, norm_layer, use_dropout, use_dilation,
+                 use_bias):
+        super(ResBlock, self).__init__()
+        if use_dilation:
+            padding_mat = [1, 1, 1, 1]
+        else:
+            padding_mat = [0, 0, 0, 0]
+        self._pad1 = nn.Pad2D(padding_mat, mode="replicate")
+
+        self._sn_conv1 = SNConv(
+            name=name + "_sn_conv1",
+            in_channels=channels,
+            out_channels=channels,
+            kernel_size=3,
+            padding=0,
+            norm_layer=norm_layer,
+            use_bias=use_bias,
+            act="ReLU",
+            act_attr=None)
+        if use_dropout:
+            self._dropout = nn.Dropout(0.5)
+        else:
+            self._dropout = None
+        self._pad2 = nn.Pad2D([1, 1, 1, 1], mode="replicate")
+        self._sn_conv2 = SNConv(
+            name=name + "_sn_conv2",
+            in_channels=channels,
+            out_channels=channels,
+            kernel_size=3,
+            norm_layer=norm_layer,
+            use_bias=use_bias,
+            act="ReLU",
+            act_attr=None)
+
+    def forward(self, x):
+        pad1 = self._pad1.forward(x)
+        sn_conv1 = self._sn_conv1.forward(pad1)
+        pad2 = self._pad2.forward(sn_conv1)
+        sn_conv2 = self._sn_conv2.forward(pad2)
+        return sn_conv2 + x

StyleText/arch/decoder.py

@@ -0,0 +1,251 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import paddle
+import paddle.nn as nn
+
+from arch.base_module import SNConv, SNConvTranspose, ResBlock
+
+
+class Decoder(nn.Layer):
+    def __init__(self, name, encode_dim, out_channels, use_bias, norm_layer,
+                 act, act_attr, conv_block_dropout, conv_block_num,
+                 conv_block_dilation, out_conv_act, out_conv_act_attr):
+        super(Decoder, self).__init__()
+        conv_blocks = []
+        for i in range(conv_block_num):
+            conv_blocks.append(
+                ResBlock(
+                    name="{}_conv_block_{}".format(name, i),
+                    channels=encode_dim * 8,
+                    norm_layer=norm_layer,
+                    use_dropout=conv_block_dropout,
+                    use_dilation=conv_block_dilation,
+                    use_bias=use_bias))
+        self.conv_blocks = nn.Sequential(*conv_blocks)
+        self._up1 = SNConvTranspose(
+            name=name + "_up1",
+            in_channels=encode_dim * 8,
+            out_channels=encode_dim * 4,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up2 = SNConvTranspose(
+            name=name + "_up2",
+            in_channels=encode_dim * 4,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up3 = SNConvTranspose(
+            name=name + "_up3",
+            in_channels=encode_dim * 2,
+            out_channels=encode_dim,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._pad2d = paddle.nn.Pad2D([1, 1, 1, 1], mode="replicate")
+        self._out_conv = SNConv(
+            name=name + "_out_conv",
+            in_channels=encode_dim,
+            out_channels=out_channels,
+            kernel_size=3,
+            use_bias=use_bias,
+            norm_layer=None,
+            act=out_conv_act,
+            act_attr=out_conv_act_attr)
+
+    def forward(self, x):
+        if isinstance(x, (list, tuple)):
+            x = paddle.concat(x, axis=1)
+        output_dict = dict()
+        output_dict["conv_blocks"] = self.conv_blocks.forward(x)
+        output_dict["up1"] = self._up1.forward(output_dict["conv_blocks"])
+        output_dict["up2"] = self._up2.forward(output_dict["up1"])
+        output_dict["up3"] = self._up3.forward(output_dict["up2"])
+        output_dict["pad2d"] = self._pad2d.forward(output_dict["up3"])
+        output_dict["out_conv"] = self._out_conv.forward(output_dict["pad2d"])
+        return output_dict
+
+
+class DecoderUnet(nn.Layer):
+    def __init__(self, name, encode_dim, out_channels, use_bias, norm_layer,
+                 act, act_attr, conv_block_dropout, conv_block_num,
+                 conv_block_dilation, out_conv_act, out_conv_act_attr):
+        super(DecoderUnet, self).__init__()
+        conv_blocks = []
+        for i in range(conv_block_num):
+            conv_blocks.append(
+                ResBlock(
+                    name="{}_conv_block_{}".format(name, i),
+                    channels=encode_dim * 8,
+                    norm_layer=norm_layer,
+                    use_dropout=conv_block_dropout,
+                    use_dilation=conv_block_dilation,
+                    use_bias=use_bias))
+        self._conv_blocks = nn.Sequential(*conv_blocks)
+        self._up1 = SNConvTranspose(
+            name=name + "_up1",
+            in_channels=encode_dim * 8,
+            out_channels=encode_dim * 4,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up2 = SNConvTranspose(
+            name=name + "_up2",
+            in_channels=encode_dim * 8,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up3 = SNConvTranspose(
+            name=name + "_up3",
+            in_channels=encode_dim * 4,
+            out_channels=encode_dim,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._pad2d = paddle.nn.Pad2D([1, 1, 1, 1], mode="replicate")
+        self._out_conv = SNConv(
+            name=name + "_out_conv",
+            in_channels=encode_dim,
+            out_channels=out_channels,
+            kernel_size=3,
+            use_bias=use_bias,
+            norm_layer=None,
+            act=out_conv_act,
+            act_attr=out_conv_act_attr)
+
+    def forward(self, x, y, feature2, feature1):
+        output_dict = dict()
+        output_dict["conv_blocks"] = self._conv_blocks(
+            paddle.concat(
+                (x, y), axis=1))
+        output_dict["up1"] = self._up1.forward(output_dict["conv_blocks"])
+        output_dict["up2"] = self._up2.forward(
+            paddle.concat(
+                (output_dict["up1"], feature2), axis=1))
+        output_dict["up3"] = self._up3.forward(
+            paddle.concat(
+                (output_dict["up2"], feature1), axis=1))
+        output_dict["pad2d"] = self._pad2d.forward(output_dict["up3"])
+        output_dict["out_conv"] = self._out_conv.forward(output_dict["pad2d"])
+        return output_dict
+
+
+class SingleDecoder(nn.Layer):
+    def __init__(self, name, encode_dim, out_channels, use_bias, norm_layer,
+                 act, act_attr, conv_block_dropout, conv_block_num,
+                 conv_block_dilation, out_conv_act, out_conv_act_attr):
+        super(SingleDecoder, self).__init__()
+        conv_blocks = []
+        for i in range(conv_block_num):
+            conv_blocks.append(
+                ResBlock(
+                    name="{}_conv_block_{}".format(name, i),
+                    channels=encode_dim * 4,
+                    norm_layer=norm_layer,
+                    use_dropout=conv_block_dropout,
+                    use_dilation=conv_block_dilation,
+                    use_bias=use_bias))
+        self._conv_blocks = nn.Sequential(*conv_blocks)
+        self._up1 = SNConvTranspose(
+            name=name + "_up1",
+            in_channels=encode_dim * 4,
+            out_channels=encode_dim * 4,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up2 = SNConvTranspose(
+            name=name + "_up2",
+            in_channels=encode_dim * 8,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up3 = SNConvTranspose(
+            name=name + "_up3",
+            in_channels=encode_dim * 4,
+            out_channels=encode_dim,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            output_padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._pad2d = paddle.nn.Pad2D([1, 1, 1, 1], mode="replicate")
+        self._out_conv = SNConv(
+            name=name + "_out_conv",
+            in_channels=encode_dim,
+            out_channels=out_channels,
+            kernel_size=3,
+            use_bias=use_bias,
+            norm_layer=None,
+            act=out_conv_act,
+            act_attr=out_conv_act_attr)
+
+    def forward(self, x, feature2, feature1):
+        output_dict = dict()
+        output_dict["conv_blocks"] = self._conv_blocks.forward(x)
+        output_dict["up1"] = self._up1.forward(output_dict["conv_blocks"])
+        output_dict["up2"] = self._up2.forward(
+            paddle.concat(
+                (output_dict["up1"], feature2), axis=1))
+        output_dict["up3"] = self._up3.forward(
+            paddle.concat(
+                (output_dict["up2"], feature1), axis=1))
+        output_dict["pad2d"] = self._pad2d.forward(output_dict["up3"])
+        output_dict["out_conv"] = self._out_conv.forward(output_dict["pad2d"])
+        return output_dict

StyleText/arch/encoder.py

@@ -0,0 +1,186 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import paddle
+import paddle.nn as nn
+
+from arch.base_module import SNConv, SNConvTranspose, ResBlock
+
+
+class Encoder(nn.Layer):
+    def __init__(self, name, in_channels, encode_dim, use_bias, norm_layer,
+                 act, act_attr, conv_block_dropout, conv_block_num,
+                 conv_block_dilation):
+        super(Encoder, self).__init__()
+        self._pad2d = paddle.nn.Pad2D([3, 3, 3, 3], mode="replicate")
+        self._in_conv = SNConv(
+            name=name + "_in_conv",
+            in_channels=in_channels,
+            out_channels=encode_dim,
+            kernel_size=7,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down1 = SNConv(
+            name=name + "_down1",
+            in_channels=encode_dim,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down2 = SNConv(
+            name=name + "_down2",
+            in_channels=encode_dim * 2,
+            out_channels=encode_dim * 4,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down3 = SNConv(
+            name=name + "_down3",
+            in_channels=encode_dim * 4,
+            out_channels=encode_dim * 4,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        conv_blocks = []
+        for i in range(conv_block_num):
+            conv_blocks.append(
+                ResBlock(
+                    name="{}_conv_block_{}".format(name, i),
+                    channels=encode_dim * 4,
+                    norm_layer=norm_layer,
+                    use_dropout=conv_block_dropout,
+                    use_dilation=conv_block_dilation,
+                    use_bias=use_bias))
+        self._conv_blocks = nn.Sequential(*conv_blocks)
+
+    def forward(self, x):
+        out_dict = dict()
+        x = self._pad2d(x)
+        out_dict["in_conv"] = self._in_conv.forward(x)
+        out_dict["down1"] = self._down1.forward(out_dict["in_conv"])
+        out_dict["down2"] = self._down2.forward(out_dict["down1"])
+        out_dict["down3"] = self._down3.forward(out_dict["down2"])
+        out_dict["res_blocks"] = self._conv_blocks.forward(out_dict["down3"])
+        return out_dict
+
+
+class EncoderUnet(nn.Layer):
+    def __init__(self, name, in_channels, encode_dim, use_bias, norm_layer,
+                 act, act_attr):
+        super(EncoderUnet, self).__init__()
+        self._pad2d = paddle.nn.Pad2D([3, 3, 3, 3], mode="replicate")
+        self._in_conv = SNConv(
+            name=name + "_in_conv",
+            in_channels=in_channels,
+            out_channels=encode_dim,
+            kernel_size=7,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down1 = SNConv(
+            name=name + "_down1",
+            in_channels=encode_dim,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down2 = SNConv(
+            name=name + "_down2",
+            in_channels=encode_dim * 2,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down3 = SNConv(
+            name=name + "_down3",
+            in_channels=encode_dim * 2,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._down4 = SNConv(
+            name=name + "_down4",
+            in_channels=encode_dim * 2,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up1 = SNConvTranspose(
+            name=name + "_up1",
+            in_channels=encode_dim * 2,
+            out_channels=encode_dim * 2,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+        self._up2 = SNConvTranspose(
+            name=name + "_up2",
+            in_channels=encode_dim * 4,
+            out_channels=encode_dim * 4,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act=act,
+            act_attr=act_attr)
+
+    def forward(self, x):
+        output_dict = dict()
+        x = self._pad2d(x)
+        output_dict['in_conv'] = self._in_conv.forward(x)
+        output_dict['down1'] = self._down1.forward(output_dict['in_conv'])
+        output_dict['down2'] = self._down2.forward(output_dict['down1'])
+        output_dict['down3'] = self._down3.forward(output_dict['down2'])
+        output_dict['down4'] = self._down4.forward(output_dict['down3'])
+        output_dict['up1'] = self._up1.forward(output_dict['down4'])
+        output_dict['up2'] = self._up2.forward(
+            paddle.concat(
+                (output_dict['down3'], output_dict['up1']), axis=1))
+        output_dict['concat'] = paddle.concat(
+            (output_dict['down2'], output_dict['up2']), axis=1)
+        return output_dict

StyleText/arch/spectral_norm.py

@@ -0,0 +1,150 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+def normal_(x, mean=0., std=1.):
+    temp_value = paddle.normal(mean, std, shape=x.shape)
+    x.set_value(temp_value)
+    return x
+
+
+class SpectralNorm(object):
+    def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):
+        self.name = name
+        self.dim = dim
+        if n_power_iterations <= 0:
+            raise ValueError('Expected n_power_iterations to be positive, but '
+                             'got n_power_iterations={}'.format(
+                                 n_power_iterations))
+        self.n_power_iterations = n_power_iterations
+        self.eps = eps
+
+    def reshape_weight_to_matrix(self, weight):
+        weight_mat = weight
+        if self.dim != 0:
+            # transpose dim to front
+            weight_mat = weight_mat.transpose([
+                self.dim,
+                * [d for d in range(weight_mat.dim()) if d != self.dim]
+            ])
+
+        height = weight_mat.shape[0]
+
+        return weight_mat.reshape([height, -1])
+
+    def compute_weight(self, module, do_power_iteration):
+        weight = getattr(module, self.name + '_orig')
+        u = getattr(module, self.name + '_u')
+        v = getattr(module, self.name + '_v')
+        weight_mat = self.reshape_weight_to_matrix(weight)
+
+        if do_power_iteration:
+            with paddle.no_grad():
+                for _ in range(self.n_power_iterations):
+                    v.set_value(
+                        F.normalize(
+                            paddle.matmul(
+                                weight_mat,
+                                u,
+                                transpose_x=True,
+                                transpose_y=False),
+                            axis=0,
+                            epsilon=self.eps, ))
+
+                    u.set_value(
+                        F.normalize(
+                            paddle.matmul(weight_mat, v),
+                            axis=0,
+                            epsilon=self.eps, ))
+                if self.n_power_iterations > 0:
+                    u = u.clone()
+                    v = v.clone()
+
+        sigma = paddle.dot(u, paddle.mv(weight_mat, v))
+        weight = weight / sigma
+        return weight
+
+    def remove(self, module):
+        with paddle.no_grad():
+            weight = self.compute_weight(module, do_power_iteration=False)
+        delattr(module, self.name)
+        delattr(module, self.name + '_u')
+        delattr(module, self.name + '_v')
+        delattr(module, self.name + '_orig')
+
+        module.add_parameter(self.name, weight.detach())
+
+    def __call__(self, module, inputs):
+        setattr(
+            module,
+            self.name,
+            self.compute_weight(
+                module, do_power_iteration=module.training))
+
+    @staticmethod
+    def apply(module, name, n_power_iterations, dim, eps):
+        for k, hook in module._forward_pre_hooks.items():
+            if isinstance(hook, SpectralNorm) and hook.name == name:
+                raise RuntimeError(
+                    "Cannot register two spectral_norm hooks on "
+                    "the same parameter {}".format(name))
+
+        fn = SpectralNorm(name, n_power_iterations, dim, eps)
+        weight = module._parameters[name]
+
+        with paddle.no_grad():
+            weight_mat = fn.reshape_weight_to_matrix(weight)
+            h, w = weight_mat.shape
+
+            # randomly initialize u and v
+            u = module.create_parameter([h])
+            u = normal_(u, 0., 1.)
+            v = module.create_parameter([w])
+            v = normal_(v, 0., 1.)
+            u = F.normalize(u, axis=0, epsilon=fn.eps)
+            v = F.normalize(v, axis=0, epsilon=fn.eps)
+
+        # delete fn.name form parameters, otherwise you can not set attribute
+        del module._parameters[fn.name]
+        module.add_parameter(fn.name + "_orig", weight)
+        # still need to assign weight back as fn.name because all sorts of
+        # things may assume that it exists, e.g., when initializing weights.
+        # However, we can't directly assign as it could be an Parameter and
+        # gets added as a parameter. Instead, we register weight * 1.0 as a plain
+        # attribute.
+        setattr(module, fn.name, weight * 1.0)
+        module.register_buffer(fn.name + "_u", u)
+        module.register_buffer(fn.name + "_v", v)
+
+        module.register_forward_pre_hook(fn)
+        return fn
+
+
+def spectral_norm(module,
+                  name='weight',
+                  n_power_iterations=1,
+                  eps=1e-12,
+                  dim=None):
+
+    if dim is None:
+        if isinstance(module, (nn.Conv1DTranspose, nn.Conv2DTranspose,
+                               nn.Conv3DTranspose, nn.Linear)):
+            dim = 1
+        else:
+            dim = 0
+    SpectralNorm.apply(module, name, n_power_iterations, dim, eps)
+    return module

StyleText/arch/style_text_rec.py

@@ -0,0 +1,285 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import paddle
+import paddle.nn as nn
+
+from arch.base_module import MiddleNet, ResBlock
+from arch.encoder import Encoder
+from arch.decoder import Decoder, DecoderUnet, SingleDecoder
+from utils.load_params import load_dygraph_pretrain
+from utils.logging import get_logger
+
+
+class StyleTextRec(nn.Layer):
+    def __init__(self, config):
+        super(StyleTextRec, self).__init__()
+        self.logger = get_logger()
+        self.text_generator = TextGenerator(config["Predictor"][
+            "text_generator"])
+        self.bg_generator = BgGeneratorWithMask(config["Predictor"][
+            "bg_generator"])
+        self.fusion_generator = FusionGeneratorSimple(config["Predictor"][
+            "fusion_generator"])
+        bg_generator_pretrain = config["Predictor"]["bg_generator"]["pretrain"]
+        text_generator_pretrain = config["Predictor"]["text_generator"][
+            "pretrain"]
+        fusion_generator_pretrain = config["Predictor"]["fusion_generator"][
+            "pretrain"]
+        load_dygraph_pretrain(
+            self.bg_generator,
+            self.logger,
+            path=bg_generator_pretrain,
+            load_static_weights=False)
+        load_dygraph_pretrain(
+            self.text_generator,
+            self.logger,
+            path=text_generator_pretrain,
+            load_static_weights=False)
+        load_dygraph_pretrain(
+            self.fusion_generator,
+            self.logger,
+            path=fusion_generator_pretrain,
+            load_static_weights=False)
+
+    def forward(self, style_input, text_input):
+        text_gen_output = self.text_generator.forward(style_input, text_input)
+        fake_text = text_gen_output["fake_text"]
+        fake_sk = text_gen_output["fake_sk"]
+        bg_gen_output = self.bg_generator.forward(style_input)
+        bg_encode_feature = bg_gen_output["bg_encode_feature"]
+        bg_decode_feature1 = bg_gen_output["bg_decode_feature1"]
+        bg_decode_feature2 = bg_gen_output["bg_decode_feature2"]
+        fake_bg = bg_gen_output["fake_bg"]
+
+        fusion_gen_output = self.fusion_generator.forward(fake_text, fake_bg)
+        fake_fusion = fusion_gen_output["fake_fusion"]
+        return {
+            "fake_fusion": fake_fusion,
+            "fake_text": fake_text,
+            "fake_sk": fake_sk,
+            "fake_bg": fake_bg,
+        }
+
+
+class TextGenerator(nn.Layer):
+    def __init__(self, config):
+        super(TextGenerator, self).__init__()
+        name = config["module_name"]
+        encode_dim = config["encode_dim"]
+        norm_layer = config["norm_layer"]
+        conv_block_dropout = config["conv_block_dropout"]
+        conv_block_num = config["conv_block_num"]
+        conv_block_dilation = config["conv_block_dilation"]
+        if norm_layer == "InstanceNorm2D":
+            use_bias = True
+        else:
+            use_bias = False
+        self.encoder_text = Encoder(
+            name=name + "_encoder_text",
+            in_channels=3,
+            encode_dim=encode_dim,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation)
+        self.encoder_style = Encoder(
+            name=name + "_encoder_style",
+            in_channels=3,
+            encode_dim=encode_dim,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation)
+        self.decoder_text = Decoder(
+            name=name + "_decoder_text",
+            encode_dim=encode_dim,
+            out_channels=int(encode_dim / 2),
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation,
+            out_conv_act="Tanh",
+            out_conv_act_attr=None)
+        self.decoder_sk = Decoder(
+            name=name + "_decoder_sk",
+            encode_dim=encode_dim,
+            out_channels=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation,
+            out_conv_act="Sigmoid",
+            out_conv_act_attr=None)
+
+        self.middle = MiddleNet(
+            name=name + "_middle_net",
+            in_channels=int(encode_dim / 2) + 1,
+            mid_channels=encode_dim,
+            out_channels=3,
+            use_bias=use_bias)
+
+    def forward(self, style_input, text_input):
+        style_feature = self.encoder_style.forward(style_input)["res_blocks"]
+        text_feature = self.encoder_text.forward(text_input)["res_blocks"]
+        fake_c_temp = self.decoder_text.forward([text_feature,
+                                                 style_feature])["out_conv"]
+        fake_sk = self.decoder_sk.forward([text_feature,
+                                           style_feature])["out_conv"]
+        fake_text = self.middle(paddle.concat((fake_c_temp, fake_sk), axis=1))
+        return {"fake_sk": fake_sk, "fake_text": fake_text}
+
+
+class BgGeneratorWithMask(nn.Layer):
+    def __init__(self, config):
+        super(BgGeneratorWithMask, self).__init__()
+        name = config["module_name"]
+        encode_dim = config["encode_dim"]
+        norm_layer = config["norm_layer"]
+        conv_block_dropout = config["conv_block_dropout"]
+        conv_block_num = config["conv_block_num"]
+        conv_block_dilation = config["conv_block_dilation"]
+        self.output_factor = config.get("output_factor", 1.0)
+
+        if norm_layer == "InstanceNorm2D":
+            use_bias = True
+        else:
+            use_bias = False
+
+        self.encoder_bg = Encoder(
+            name=name + "_encoder_bg",
+            in_channels=3,
+            encode_dim=encode_dim,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation)
+
+        self.decoder_bg = SingleDecoder(
+            name=name + "_decoder_bg",
+            encode_dim=encode_dim,
+            out_channels=3,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation,
+            out_conv_act="Tanh",
+            out_conv_act_attr=None)
+
+        self.decoder_mask = Decoder(
+            name=name + "_decoder_mask",
+            encode_dim=encode_dim // 2,
+            out_channels=1,
+            use_bias=use_bias,
+            norm_layer=norm_layer,
+            act="ReLU",
+            act_attr=None,
+            conv_block_dropout=conv_block_dropout,
+            conv_block_num=conv_block_num,
+            conv_block_dilation=conv_block_dilation,
+            out_conv_act="Sigmoid",
+            out_conv_act_attr=None)
+
+        self.middle = MiddleNet(
+            name=name + "_middle_net",
+            in_channels=3 + 1,
+            mid_channels=encode_dim,
+            out_channels=3,
+            use_bias=use_bias)
+
+    def forward(self, style_input):
+        encode_bg_output = self.encoder_bg(style_input)
+        decode_bg_output = self.decoder_bg(encode_bg_output["res_blocks"],
+                                           encode_bg_output["down2"],
+                                           encode_bg_output["down1"])
+
+        fake_c_temp = decode_bg_output["out_conv"]
+        fake_bg_mask = self.decoder_mask.forward(encode_bg_output[
+            "res_blocks"])["out_conv"]
+        fake_bg = self.middle(
+            paddle.concat(
+                (fake_c_temp, fake_bg_mask), axis=1))
+        return {
+            "bg_encode_feature": encode_bg_output["res_blocks"],
+            "bg_decode_feature1": decode_bg_output["up1"],
+            "bg_decode_feature2": decode_bg_output["up2"],
+            "fake_bg": fake_bg,
+            "fake_bg_mask": fake_bg_mask,
+        }
+
+
+class FusionGeneratorSimple(nn.Layer):
+    def __init__(self, config):
+        super(FusionGeneratorSimple, self).__init__()
+        name = config["module_name"]
+        encode_dim = config["encode_dim"]
+        norm_layer = config["norm_layer"]
+        conv_block_dropout = config["conv_block_dropout"]
+        conv_block_dilation = config["conv_block_dilation"]
+        if norm_layer == "InstanceNorm2D":
+            use_bias = True
+        else:
+            use_bias = False
+
+        self._conv = nn.Conv2D(
+            in_channels=6,
+            out_channels=encode_dim,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=paddle.ParamAttr(name=name + "_conv_weights"),
+            bias_attr=False)
+
+        self._res_block = ResBlock(
+            name="{}_conv_block".format(name),
+            channels=encode_dim,
+            norm_layer=norm_layer,
+            use_dropout=conv_block_dropout,
+            use_dilation=conv_block_dilation,
+            use_bias=use_bias)
+
+        self._reduce_conv = nn.Conv2D(
+            in_channels=encode_dim,
+            out_channels=3,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=paddle.ParamAttr(name=name + "_reduce_conv_weights"),
+            bias_attr=False)
+
+    def forward(self, fake_text, fake_bg):
+        fake_concat = paddle.concat((fake_text, fake_bg), axis=1)
+        fake_concat_tmp = self._conv(fake_concat)
+        output_res = self._res_block(fake_concat_tmp)
+        fake_fusion = self._reduce_conv(output_res)
+        return {"fake_fusion": fake_fusion}

StyleText/configs/config.yml

@@ -0,0 +1,54 @@
+Global:
+  output_num: 10
+  output_dir: output_data
+  use_gpu: false
+  image_height: 32
+  image_width: 320
+TextDrawer:
+  fonts:
+    en: fonts/en_standard.ttf
+    ch: fonts/ch_standard.ttf
+    ko: fonts/ko_standard.ttf
+Predictor:
+  method: StyleTextRecPredictor
+  algorithm: StyleTextRec
+  scale: 0.00392156862745098
+  mean:
+  - 0.5
+  - 0.5
+  - 0.5
+  std:
+  - 0.5
+  - 0.5
+  - 0.5
+  expand_result: false
+  bg_generator:
+    pretrain: style_text_models/bg_generator
+    module_name: bg_generator
+    generator_type: BgGeneratorWithMask
+    encode_dim: 64
+    norm_layer: null
+    conv_block_num: 4
+    conv_block_dropout: false
+    conv_block_dilation: true
+    output_factor: 1.05
+  text_generator:
+    pretrain: style_text_models/text_generator
+    module_name: text_generator
+    generator_type: TextGenerator
+    encode_dim: 64
+    norm_layer: InstanceNorm2D
+    conv_block_num: 4
+    conv_block_dropout: false
+    conv_block_dilation: true
+  fusion_generator:
+    pretrain: style_text_models/fusion_generator
+    module_name: fusion_generator
+    generator_type: FusionGeneratorSimple
+    encode_dim: 64
+    norm_layer: null
+    conv_block_num: 4
+    conv_block_dropout: false
+    conv_block_dilation: true
+Writer:
+  method: SimpleWriter

StyleText/configs/dataset_config.yml

@@ -0,0 +1,64 @@
+Global:
+  output_num: 10
+  output_dir: output_data
+  use_gpu: false
+  image_height: 32
+  image_width: 320
+  standard_font: fonts/en_standard.ttf
+TextDrawer:
+  fonts:
+    en: fonts/en_standard.ttf
+    ch: fonts/ch_standard.ttf
+    ko: fonts/ko_standard.ttf
+StyleSampler:
+  method: DatasetSampler
+  image_home: examples
+  label_file: examples/image_list.txt
+  with_label: true
+CorpusGenerator:
+  method: FileCorpus
+  language: ch
+  corpus_file: examples/corpus/example.txt
+Predictor:
+  method: StyleTextRecPredictor
+  algorithm: StyleTextRec
+  scale: 0.00392156862745098
+  mean:
+  - 0.5
+  - 0.5
+  - 0.5
+  std:
+  - 0.5
+  - 0.5
+  - 0.5
+  expand_result: false
+  bg_generator:
+    pretrain: style_text_models/bg_generator
+    module_name: bg_generator
+    generator_type: BgGeneratorWithMask
+    encode_dim: 64
+    norm_layer: null
+    conv_block_num: 4
+    conv_block_dropout: false
+    conv_block_dilation: true
+    output_factor: 1.05
+  text_generator:
+    pretrain: style_text_models/text_generator
+    module_name: text_generator
+    generator_type: TextGenerator
+    encode_dim: 64
+    norm_layer: InstanceNorm2D
+    conv_block_num: 4
+    conv_block_dropout: false
+    conv_block_dilation: true
+  fusion_generator:
+    pretrain: style_text_models/fusion_generator
+    module_name: fusion_generator
+    generator_type: FusionGeneratorSimple
+    encode_dim: 64
+    norm_layer: null
+    conv_block_num: 4
+    conv_block_dropout: false
+    conv_block_dilation: true
+Writer:
+  method: SimpleWriter