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PaddleOCR
提交 c94428a8 编写于 作者: L LDOUBLEV
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fix conflicts

上级 390f240b 7efa3975
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Showing with 10871 addition and 10731 deletion
PPOCRLabel/PPOCRLabel.py
浏览文件 @ c94428a8
...@@ -106,7 +106,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -106,7 +106,7 @@ class MainWindow(QMainWindow, WindowMixin):
getStr = lambda strId: self.stringBundle.getString(strId) getStr = lambda strId: self.stringBundle.getString(strId)
self.defaultSaveDir = defaultSaveDir self.defaultSaveDir = defaultSaveDir
self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=True, lang=lang) self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=False, lang=lang)
if os.path.exists('./data/paddle.png'): if os.path.exists('./data/paddle.png'):
result = self.ocr.ocr('./data/paddle.png', cls=True, det=True) result = self.ocr.ocr('./data/paddle.png', cls=True, det=True)
...@@ -274,6 +274,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -274,6 +274,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.preButton.setIconSize(QSize(40, 100)) self.preButton.setIconSize(QSize(40, 100))
self.preButton.clicked.connect(self.openPrevImg) self.preButton.clicked.connect(self.openPrevImg)
self.preButton.setStyleSheet('border: none;') self.preButton.setStyleSheet('border: none;')
self.preButton.setShortcut('a')
self.iconlist = QListWidget() self.iconlist = QListWidget()
self.iconlist.setViewMode(QListView.IconMode) self.iconlist.setViewMode(QListView.IconMode)
self.iconlist.setFlow(QListView.TopToBottom) self.iconlist.setFlow(QListView.TopToBottom)
...@@ -289,12 +290,12 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -289,12 +290,12 @@ class MainWindow(QMainWindow, WindowMixin):
self.nextButton.setIconSize(QSize(40, 100)) self.nextButton.setIconSize(QSize(40, 100))
self.nextButton.setStyleSheet('border: none;') self.nextButton.setStyleSheet('border: none;')
self.nextButton.clicked.connect(self.openNextImg) self.nextButton.clicked.connect(self.openNextImg)
self.nextButton.setShortcut('d')
hlayout.addWidget(self.preButton) hlayout.addWidget(self.preButton)
hlayout.addWidget(self.iconlist) hlayout.addWidget(self.iconlist)
hlayout.addWidget(self.nextButton) hlayout.addWidget(self.nextButton)
# self.setLayout(hlayout)
iconListContainer = QWidget() iconListContainer = QWidget()
iconListContainer.setLayout(hlayout) iconListContainer.setLayout(hlayout)
...@@ -359,11 +360,6 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -359,11 +360,6 @@ class MainWindow(QMainWindow, WindowMixin):
opendir = action(getStr('openDir'), self.openDirDialog, opendir = action(getStr('openDir'), self.openDirDialog,
'Ctrl+u', 'open', getStr('openDir')) 'Ctrl+u', 'open', getStr('openDir'))
openNextImg = action(getStr('nextImg'), self.openNextImg,
'd', 'next', getStr('nextImgDetail'))
openPrevImg = action(getStr('prevImg'), self.openPrevImg,
'a', 'prev', getStr('prevImgDetail'))
save = action(getStr('save'), self.saveFile, save = action(getStr('save'), self.saveFile,
'Ctrl+V', 'verify', getStr('saveDetail'), enabled=False) 'Ctrl+V', 'verify', getStr('saveDetail'), enabled=False)
...@@ -371,7 +367,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -371,7 +367,7 @@ class MainWindow(QMainWindow, WindowMixin):
alcm = action(getStr('choosemodel'), self.autolcm, alcm = action(getStr('choosemodel'), self.autolcm,
'Ctrl+M', 'next', getStr('tipchoosemodel')) 'Ctrl+M', 'next', getStr('tipchoosemodel'))
deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+D', 'close', getStr('deleteImgDetail'), deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+Shift+D', 'close', getStr('deleteImgDetail'),
enabled=True) enabled=True)
resetAll = action(getStr('resetAll'), self.resetAll, None, 'resetall', getStr('resetAllDetail')) resetAll = action(getStr('resetAll'), self.resetAll, None, 'resetall', getStr('resetAllDetail'))
...@@ -388,7 +384,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -388,7 +384,7 @@ class MainWindow(QMainWindow, WindowMixin):
'w', 'new', getStr('crtBoxDetail'), enabled=False) 'w', 'new', getStr('crtBoxDetail'), enabled=False)
delete = action(getStr('delBox'), self.deleteSelectedShape, delete = action(getStr('delBox'), self.deleteSelectedShape,
'Delete', 'delete', getStr('delBoxDetail'), enabled=False) 'backspace', 'delete', getStr('delBoxDetail'), enabled=False)
copy = action(getStr('dupBox'), self.copySelectedShape, copy = action(getStr('dupBox'), self.copySelectedShape,
'Ctrl+C', 'copy', getStr('dupBoxDetail'), 'Ctrl+C', 'copy', getStr('dupBoxDetail'),
enabled=False) enabled=False)
...@@ -446,8 +442,11 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -446,8 +442,11 @@ class MainWindow(QMainWindow, WindowMixin):
reRec = action(getStr('reRecognition'), self.reRecognition, reRec = action(getStr('reRecognition'), self.reRecognition,
'Ctrl+Shift+R', 'reRec', getStr('reRecognition'), enabled=False) 'Ctrl+Shift+R', 'reRec', getStr('reRecognition'), enabled=False)
singleRere = action(getStr('singleRe'), self.singleRerecognition,
'Ctrl+R', 'reRec', getStr('singleRe'), enabled=False)
createpoly = action(getStr('creatPolygon'), self.createPolygon, createpoly = action(getStr('creatPolygon'), self.createPolygon,
'p', 'new', 'Creat Polygon', enabled=True) 'q', 'new', 'Creat Polygon', enabled=True)
saveRec = action(getStr('saveRec'), self.saveRecResult, saveRec = action(getStr('saveRec'), self.saveRecResult,
'', 'save', getStr('saveRec'), enabled=False) '', 'save', getStr('saveRec'), enabled=False)
...@@ -491,6 +490,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -491,6 +490,7 @@ class MainWindow(QMainWindow, WindowMixin):
icon='color', tip=getStr('shapeFillColorDetail'), icon='color', tip=getStr('shapeFillColorDetail'),
enabled=False) enabled=False)
# Label list context menu. # Label list context menu.
labelMenu = QMenu() labelMenu = QMenu()
addActions(labelMenu, (edit, delete)) addActions(labelMenu, (edit, delete))
...@@ -501,7 +501,6 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -501,7 +501,6 @@ class MainWindow(QMainWindow, WindowMixin):
# Draw squares/rectangles # Draw squares/rectangles
self.drawSquaresOption = QAction(getStr('drawSquares'), self) self.drawSquaresOption = QAction(getStr('drawSquares'), self)
self.drawSquaresOption.setShortcut('Ctrl+Shift+R')
self.drawSquaresOption.setCheckable(True) self.drawSquaresOption.setCheckable(True)
self.drawSquaresOption.setChecked(settings.get(SETTING_DRAW_SQUARE, False)) self.drawSquaresOption.setChecked(settings.get(SETTING_DRAW_SQUARE, False))
self.drawSquaresOption.triggered.connect(self.toogleDrawSquare) self.drawSquaresOption.triggered.connect(self.toogleDrawSquare)
...@@ -509,7 +508,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -509,7 +508,7 @@ class MainWindow(QMainWindow, WindowMixin):
# Store actions for further handling. # Store actions for further handling.
self.actions = struct(save=save, open=open, resetAll=resetAll, deleteImg=deleteImg, self.actions = struct(save=save, open=open, resetAll=resetAll, deleteImg=deleteImg,
lineColor=color1, create=create, delete=delete, edit=edit, copy=copy, lineColor=color1, create=create, delete=delete, edit=edit, copy=copy,
saveRec=saveRec, saveRec=saveRec, singleRere=singleRere,AutoRec=AutoRec,reRec=reRec,
createMode=createMode, editMode=editMode, createMode=createMode, editMode=editMode,
shapeLineColor=shapeLineColor, shapeFillColor=shapeFillColor, shapeLineColor=shapeLineColor, shapeFillColor=shapeFillColor,
zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg, zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg,
...@@ -518,9 +517,9 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -518,9 +517,9 @@ class MainWindow(QMainWindow, WindowMixin):
fileMenuActions=( fileMenuActions=(
open, opendir, saveLabel, resetAll, quit), open, opendir, saveLabel, resetAll, quit),
beginner=(), advanced=(), beginner=(), advanced=(),
editMenu=(createpoly, edit, copy, delete, editMenu=(createpoly, edit, copy, delete,singleRere,
None, color1, self.drawSquaresOption), None, color1, self.drawSquaresOption),
beginnerContext=(create, edit, copy, delete), beginnerContext=(create, edit, copy, delete, singleRere),
advancedContext=(createMode, editMode, edit, copy, advancedContext=(createMode, editMode, edit, copy,
delete, shapeLineColor, shapeFillColor), delete, shapeLineColor, shapeFillColor),
onLoadActive=( onLoadActive=(
...@@ -562,7 +561,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -562,7 +561,7 @@ class MainWindow(QMainWindow, WindowMixin):
zoomIn, zoomOut, zoomOrg, None, zoomIn, zoomOut, zoomOrg, None,
fitWindow, fitWidth)) fitWindow, fitWidth))
addActions(self.menus.autolabel, (alcm, None, help)) # addActions(self.menus.autolabel, (AutoRec, reRec, alcm, None, help)) #
self.menus.file.aboutToShow.connect(self.updateFileMenu) self.menus.file.aboutToShow.connect(self.updateFileMenu)
...@@ -572,6 +571,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -572,6 +571,7 @@ class MainWindow(QMainWindow, WindowMixin):
action('&Copy here', self.copyShape), action('&Copy here', self.copyShape),
action('&Move here', self.moveShape))) action('&Move here', self.moveShape)))
self.statusBar().showMessage('%s started.' % __appname__) self.statusBar().showMessage('%s started.' % __appname__)
self.statusBar().show() self.statusBar().show()
...@@ -919,6 +919,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -919,6 +919,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.actions.edit.setEnabled(selected) self.actions.edit.setEnabled(selected)
self.actions.shapeLineColor.setEnabled(selected) self.actions.shapeLineColor.setEnabled(selected)
self.actions.shapeFillColor.setEnabled(selected) self.actions.shapeFillColor.setEnabled(selected)
self.actions.singleRere.setEnabled(selected)
def addLabel(self, shape): def addLabel(self, shape):
shape.paintLabel = self.displayLabelOption.isChecked() shape.paintLabel = self.displayLabelOption.isChecked()
...@@ -988,6 +989,19 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -988,6 +989,19 @@ class MainWindow(QMainWindow, WindowMixin):
self.updateComboBox() self.updateComboBox()
self.canvas.loadShapes(s) self.canvas.loadShapes(s)
def singleLabel(self, shape):
if shape is None:
# print('rm empty label')
return
item = self.shapesToItems[shape]
item.setText(shape.label)
self.updateComboBox()
# ADD:
item = self.shapesToItemsbox[shape]
item.setText(str([(int(p.x()), int(p.y())) for p in shape.points]))
self.updateComboBox()
def updateComboBox(self): def updateComboBox(self):
# Get the unique labels and add them to the Combobox. # Get the unique labels and add them to the Combobox.
itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())] itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())]
...@@ -1441,6 +1455,8 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -1441,6 +1455,8 @@ class MainWindow(QMainWindow, WindowMixin):
self.haveAutoReced = False self.haveAutoReced = False
self.AutoRecognition.setEnabled(True) self.AutoRecognition.setEnabled(True)
self.reRecogButton.setEnabled(True) self.reRecogButton.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
self.actions.reRec.setEnabled(True)
self.actions.saveLabel.setEnabled(True) self.actions.saveLabel.setEnabled(True)
...@@ -1755,6 +1771,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -1755,6 +1771,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.loadFile(self.filePath) # ADD self.loadFile(self.filePath) # ADD
self.haveAutoReced = True self.haveAutoReced = True
self.AutoRecognition.setEnabled(False) self.AutoRecognition.setEnabled(False)
self.actions.AutoRec.setEnabled(False)
self.setDirty() self.setDirty()
self.saveCacheLabel() self.saveCacheLabel()
...@@ -1794,6 +1811,27 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -1794,6 +1811,27 @@ class MainWindow(QMainWindow, WindowMixin):
else: else:
QMessageBox.information(self, "Information", "Draw a box!") QMessageBox.information(self, "Information", "Draw a box!")
def singleRerecognition(self):
img = cv2.imread(self.filePath)
shape = self.canvas.selectedShape
box = [[int(p.x()), int(p.y())] for p in shape.points]
assert len(box) == 4
img_crop = get_rotate_crop_image(img, np.array(box, np.float32))
if img_crop is None:
msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
return
result = self.ocr.ocr(img_crop, cls=True, det=False)
if result[0][0] is not '':
result.insert(0, box)
print('result in reRec is ', result)
if result[1][0] == shape.label:
print('label no change')
else:
shape.label = result[1][0]
self.singleLabel(shape)
self.setDirty()
print(box)
def autolcm(self): def autolcm(self):
vbox = QVBoxLayout() vbox = QVBoxLayout()
...@@ -1825,6 +1863,7 @@ class MainWindow(QMainWindow, WindowMixin): ...@@ -1825,6 +1863,7 @@ class MainWindow(QMainWindow, WindowMixin):
self.dialog.exec_() self.dialog.exec_()
if self.filePath: if self.filePath:
self.AutoRecognition.setEnabled(True) self.AutoRecognition.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
def modelChoose(self): def modelChoose(self):
......
PPOCRLabel/README.md
浏览文件 @ c94428a8
...@@ -6,6 +6,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field. I ...@@ -6,6 +6,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field. I
<img src="./data/gif/steps_en.gif" width="100%"/> <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 ## Installation
### 1. Install PaddleOCR ### 1. Install PaddleOCR
...@@ -92,11 +96,30 @@ Therefore, if the recognition result has been manually changed before, it may ch ...@@ -92,11 +96,30 @@ Therefore, if the recognition result has been manually changed before, it may ch
## Explanation ## 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 ### 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. - 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. - 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) 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) - 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)
......
PPOCRLabel/README_ch.md
浏览文件 @ c94428a8
...@@ -6,6 +6,10 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，使用p ...@@ -6,6 +6,10 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，使用p
<img src="./data/gif/steps.gif" width="100%"/> <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 ## 安装
### 1. 安装PaddleOCR ### 1. 安装PaddleOCR
...@@ -72,6 +76,26 @@ python3 PPOCRLabel.py --lang ch ...@@ -72,6 +76,26 @@ python3 PPOCRLabel.py --lang ch
| crop_img | 识别数据。按照检测框切割后的图片。与rec_gt.txt同时产生。 | | crop_img | 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 ## 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 ### 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。 - 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。
......
PPOCRLabel/libs/autoDialog.py
浏览文件 @ c94428a8
...@@ -46,8 +46,9 @@ class Worker(QThread): ...@@ -46,8 +46,9 @@ class Worker(QThread):
chars = res[1][0] chars = res[1][0]
cond = res[1][1] cond = res[1][1]
posi = res[0] posi = res[0]
strs += "Transcription: " + chars + " Probability: " + str( strs += "Transcription: " + chars + " Probability: " + str(cond) + \
cond) + " Location: " + json.dumps(posi) + '\n' " Location: " + json.dumps(posi) +'\n'
# Sending large amounts of data repeatedly through pyqtSignal may affect the program efficiency
self.listValue.emit(strs) self.listValue.emit(strs)
self.mainThread.result_dic = self.result_dic self.mainThread.result_dic = self.result_dic
self.mainThread.filePath = Imgpath self.mainThread.filePath = Imgpath
......
PPOCRLabel/libs/resources.py
浏览文件 @ c94428a8
此差异已折叠。
PPOCRLabel/resources/strings/strings-zh-CN.properties
浏览文件 @ c94428a8
...@@ -94,4 +94,5 @@ ok=确认 ...@@ -94,4 +94,5 @@ ok=确认
autolabeling=自动标注中 autolabeling=自动标注中
hideBox=隐藏所有标注 hideBox=隐藏所有标注
showBox=显示所有标注 showBox=显示所有标注
saveLabel=保存标记结果 saveLabel=保存标记结果
\ No newline at end of file singleRe=重识别此区块
\ No newline at end of file
PPOCRLabel/resources/strings/strings-zh-TW.properties 已删除 100644 → 0
浏览文件 @ 390f240b
saveAsDetail=將標籤保存到其他文件
changeSaveDir=改變存放目錄
openFile=開啟檔案
shapeLineColorDetail=更改線條顏色
resetAll=重置
crtBox=創建區塊
crtBoxDetail=畫一個區塊
dupBoxDetail=複製區塊
verifyImg=驗證圖像
zoominDetail=放大
verifyImgDetail=驗證圖像
saveDetail=將標籤存到
openFileDetail=打開圖像
fitWidthDetail=調整到窗口寬度
tutorial=YouTube教學
editLabel=編輯標籤
openAnnotationDetail=打開標籤文件
quit=結束
shapeFillColorDetail=更改填充顏色
closeCurDetail=關閉目前檔案
closeCur=關閉
deleteImg=刪除圖像
deleteImgDetail=刪除目前圖像
fitWin=調整到跟窗口一樣大小
delBox=刪除選取區塊
boxLineColorDetail=選擇框線顏色
originalsize=原始大小
resetAllDetail=重設所有設定
zoomoutDetail=畫面放大
save=儲存
saveAs=另存為
fitWinDetail=縮放到窗口一樣
openDir=開啟目錄
copyPrevBounding=複製當前圖像中的上一個邊界框
showHide=顯示/隱藏標籤
changeSaveFormat=更改儲存格式
shapeFillColor=填充顏色
quitApp=離開本程式
dupBox=複製區塊
delBoxDetail=刪除區塊
zoomin=放大畫面
info=資訊
openAnnotation=開啟標籤
prevImgDetail=上一個圖像
fitWidth=縮放到跟畫面一樣寬
zoomout=縮小畫面
changeSavedAnnotationDir=更改預設標籤存的目錄
nextImgDetail=下一個圖像
originalsizeDetail=放大到原始大小
prevImg=上一個圖像
tutorialDetail=顯示示範內容
shapeLineColor=形狀線條顏色
boxLineColor=日期分隔線顏色
editLabelDetail=修改所選區塊的標籤
nextImg=下一張圖片
useDefaultLabel=使用預設標籤
useDifficult=有難度的
boxLabelText=區塊的標籤
labels=標籤
autoSaveMode=自動儲存模式
singleClsMode=單一類別模式
displayLabel=顯示類別
fileList=檔案清單
files=檔案
iconList=XX
icon=XX
advancedMode=進階模式
advancedModeDetail=切到進階模式
showAllBoxDetail=顯示所有區塊
hideAllBoxDetail=隱藏所有區塊
PPOCRLabel/resources/strings/strings.properties
浏览文件 @ c94428a8
...@@ -94,4 +94,5 @@ ok=OK ...@@ -94,4 +94,5 @@ ok=OK
autolabeling=Automatic Labeling autolabeling=Automatic Labeling
hideBox=Hide All Box hideBox=Hide All Box
showBox=Show All Box showBox=Show All Box
saveLabel=Save Label saveLabel=Save Label
\ No newline at end of file singleRe=Re-recognition RectBox
\ No newline at end of file
README.md
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...@@ -122,8 +122,7 @@ For a new language request, please refer to [Guideline for new language_requests ...@@ -122,8 +122,7 @@ For a new language request, please refer to [Guideline for new language_requests
<img src="./doc/ppocr_framework.png" width="800"> <img src="./doc/ppocr_framework.png" width="800">
</div> </div>
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. 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 and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim). 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) ## Visualization [more](./doc/doc_en/visualization_en.md)
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README_ch.md
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...@@ -115,7 +115,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式 ...@@ -115,7 +115,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
<img src="./doc/ppocr_framework.png" width="800"> <img src="./doc/ppocr_framework.png" width="800">
</div> </div>
PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。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裁剪器和PACT量化的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)。 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> <a name="效果展示"></a>
## 效果展示 [more](./doc/doc_ch/visualization.md) ## 效果展示 [more](./doc/doc_ch/visualization.md)
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StyleText/README.md
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...@@ -22,7 +22,7 @@ English | [简体中文](README_ch.md) ...@@ -22,7 +22,7 @@ English | [简体中文](README_ch.md)
</div> </div>
The Style-Text data synthesis tool is a tool based on Baidu's self-developed text editing algorithm "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047). 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: Different from the commonly used GAN-based data synthesis tools, the main framework of Style-Text includes:
* (1) Text foreground style transfer module. * (1) Text foreground style transfer module.
...@@ -69,10 +69,15 @@ fusion_generator: ...@@ -69,10 +69,15 @@ fusion_generator:
1. You can run `tools/synth_image` and generate the demo image, which is saved in the current folder. 1. You can run `tools/synth_image` and generate the demo image, which is saved in the current folder.
```python ```python
python3 -m tools.synth_image -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
``` ```
* Note: The language options is correspond to the corpus. Currently, the tool only supports English, Simplified Chinese and Korean. * 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`. For example, enter the following image and corpus `PaddleOCR`.
...@@ -136,9 +141,21 @@ We provide a general dataset containing Chinese, English and Korean (50,000 imag ...@@ -136,9 +141,21 @@ We provide a general dataset containing Chinese, English and Korean (50,000 imag
2. You can run the following command to start synthesis task: 2. You can run the following command to start synthesis task:
``` bash ``` bash
python3 -m tools.synth_dataset.py -c configs/dataset_config.yml 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> <a name="Applications"></a>
### Applications ### Applications
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StyleText/README_ch.md
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</div> </div>
Style-Text数据合成工具是基于百度自研的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047 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。 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。
...@@ -61,9 +61,13 @@ fusion_generator: ...@@ -61,9 +61,13 @@ fusion_generator:
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： 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 ```python
python3 -m tools.synth_image -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_images/2.jpg --text_corpus PaddleOCR --language en
``` ```
* 注意：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": 例如，输入如下图片和语料"PaddleOCR":
...@@ -124,8 +128,21 @@ python3 -m tools.synth_image -c configs/config.yml --style_image examples/style_ ...@@ -124,8 +128,21 @@ python3 -m tools.synth_image -c configs/config.yml --style_image examples/style_
2. 运行`tools/synth_dataset`合成数据： 2. 运行`tools/synth_dataset`合成数据：
``` bash ``` bash
python3 -m tools.synth_dataset -c configs/dataset_config.yml python3 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> <a name="应用案例"></a>
### 四、应用案例 ### 四、应用案例
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StyleText/configs/dataset_config.yml
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- 0.5 - 0.5
expand_result: false expand_result: false
bg_generator: bg_generator:
pretrain: models/style_text_rec/bg_generator pretrain: style_text_models/bg_generator
module_name: bg_generator module_name: bg_generator
generator_type: BgGeneratorWithMask generator_type: BgGeneratorWithMask
encode_dim: 64 encode_dim: 64
...@@ -43,7 +43,7 @@ Predictor: ...@@ -43,7 +43,7 @@ Predictor:
conv_block_dilation: true conv_block_dilation: true
output_factor: 1.05 output_factor: 1.05
text_generator: text_generator:
pretrain: models/style_text_rec/text_generator pretrain: style_text_models/text_generator
module_name: text_generator module_name: text_generator
generator_type: TextGenerator generator_type: TextGenerator
encode_dim: 64 encode_dim: 64
...@@ -52,7 +52,7 @@ Predictor: ...@@ -52,7 +52,7 @@ Predictor:
conv_block_dropout: false conv_block_dropout: false
conv_block_dilation: true conv_block_dilation: true
fusion_generator: fusion_generator:
pretrain: models/style_text_rec/fusion_generator pretrain: style_text_models/fusion_generator
module_name: fusion_generator module_name: fusion_generator
generator_type: FusionGeneratorSimple generator_type: FusionGeneratorSimple
encode_dim: 64 encode_dim: 64
......
StyleText/engine/predictors.py
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...@@ -28,6 +28,7 @@ class StyleTextRecPredictor(object): ...@@ -28,6 +28,7 @@ class StyleTextRecPredictor(object):
], "Generator {} not supported.".format(algorithm) ], "Generator {} not supported.".format(algorithm)
use_gpu = config["Global"]['use_gpu'] use_gpu = config["Global"]['use_gpu']
check_gpu(use_gpu) check_gpu(use_gpu)
paddle.set_device('gpu' if use_gpu else 'cpu')
self.logger = get_logger() self.logger = get_logger()
self.generator = getattr(style_text_rec, algorithm)(config) self.generator = getattr(style_text_rec, algorithm)(config)
self.height = config["Global"]["image_height"] self.height = config["Global"]["image_height"]
......
StyleText/examples/corpus/example.txt
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PaddleOCR Paddle
飞桨文字识别 飞桨文字识别
StyleText/tools/synth_dataset.py
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...@@ -11,6 +11,14 @@ ...@@ -11,6 +11,14 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
from engine.synthesisers import DatasetSynthesiser from engine.synthesisers import DatasetSynthesiser
......
StyleText/tools/synth_image.py
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...@@ -16,13 +16,13 @@ import cv2 ...@@ -16,13 +16,13 @@ import cv2
import sys import sys
import glob import glob
from utils.config import ArgsParser
from engine.synthesisers import ImageSynthesiser
__dir__ = os.path.dirname(os.path.abspath(__file__)) __dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__) sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..'))) sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
from utils.config import ArgsParser
from engine.synthesisers import ImageSynthesiser
def synth_image(): def synth_image():
args = ArgsParser().parse_args() args = ArgsParser().parse_args()
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configs/det/det_r50_vd_sast_totaltext.yml
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...@@ -60,7 +60,8 @@ Metric: ...@@ -60,7 +60,8 @@ Metric:
Train: Train:
dataset: dataset:
name: SimpleDataSet name: SimpleDataSet
label_file_path: [./train_data/art_latin_icdar_14pt/train_no_tt_test/train_label_json.txt, ./train_data/total_text_icdar_14pt/train_label_json.txt] data_dir: ./train_data/
label_file_list: [./train_data/art_latin_icdar_14pt/train_no_tt_test/train_label_json.txt, ./train_data/total_text_icdar_14pt/train_label_json.txt]
data_ratio_list: [0.5, 0.5] data_ratio_list: [0.5, 0.5]
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
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deploy/cpp_infer/tools/config.txt
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...@@ -11,7 +11,7 @@ max_side_len 960 ...@@ -11,7 +11,7 @@ max_side_len 960
det_db_thresh 0.3 det_db_thresh 0.3
det_db_box_thresh 0.5 det_db_box_thresh 0.5
det_db_unclip_ratio 2.0 det_db_unclip_ratio 2.0
det_model_dir ./inference/ch__ppocr_mobile_v2.0_det_infer/ det_model_dir ./inference/ch_ppocr_mobile_v2.0_det_infer/
# cls config # cls config
use_angle_cls 0 use_angle_cls 0
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doc/doc_ch/algorithm_overview.md
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...@@ -9,9 +9,9 @@ ...@@ -9,9 +9,9 @@
### 1.文本检测算法 ### 1.文本检测算法
PaddleOCR开源的文本检测算法列表： PaddleOCR开源的文本检测算法列表：
- [x] DB([paper]( https://arxiv.org/abs/1911.08947) )（ppocr推荐） - [x] DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐）
- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) - [x] EAST([paper](https://arxiv.org/abs/1704.03155))[1]
- [x] SAST([paper](https://arxiv.org/abs/1908.05498)) - [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4]
在ICDAR2015文本检测公开数据集上，算法效果如下： 在ICDAR2015文本检测公开数据集上，算法效果如下：
...@@ -38,13 +38,13 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训 ...@@ -38,13 +38,13 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训
### 2.文本识别算法 ### 2.文本识别算法
PaddleOCR基于动态图开源的文本识别算法列表： PaddleOCR基于动态图开源的文本识别算法列表：
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717) )（ppocr推荐） - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) coming soon - [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon - [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294)) coming soon - [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
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： 参考[DTRB][3](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| |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
|-|-|-|-|-| |-|-|-|-|-|
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...@@ -117,7 +117,7 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/ ...@@ -117,7 +117,7 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
``` ```
# 预测分类结果 # 预测分类结果
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
``` ```
预测图片： 预测图片：
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...@@ -120,16 +120,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat ...@@ -120,16 +120,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat
测试单张图像的检测效果 测试单张图像的检测效果
```shell ```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
``` ```
测试DB模型时，调整后处理阈值， 测试DB模型时，调整后处理阈值，
```shell ```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
``` ```
测试文件夹下所有图像的检测效果 测试文件夹下所有图像的检测效果
```shell ```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy" python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
``` ```
doc/doc_ch/inference.md
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...@@ -245,7 +245,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img ...@@ -245,7 +245,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
超轻量中文识别模型推理，可以执行如下命令： 超轻量中文识别模型推理，可以执行如下命令：
``` ```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/" # 下载超轻量中文识别模型：
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
``` ```
![](../imgs_words/ch/word_4.jpg) ![](../imgs_words/ch/word_4.jpg)
...@@ -266,7 +269,6 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) ...@@ -266,7 +269,6 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
``` ```
python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
``` ```
CRNN 文本识别模型推理，可以执行如下命令： CRNN 文本识别模型推理，可以执行如下命令：
...@@ -327,7 +329,10 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904) ...@@ -327,7 +329,10 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
方向分类模型推理，可以执行如下命令： 方向分类模型推理，可以执行如下命令：
``` ```
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/" # 下载超轻量中文方向分类器模型：
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
``` ```
![](../imgs_words/ch/word_1.jpg) ![](../imgs_words/ch/word_1.jpg)
......
doc/doc_ch/recognition.md
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...@@ -324,7 +324,6 @@ Eval: ...@@ -324,7 +324,6 @@ Eval:
评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。 评估数据集可以通过 `configs/rec/rec_icdar15_train.yml` 修改Eval中的 `label_file_path` 设置。
*注意* 评估时必须确保配置文件中 infer_img 字段为空
``` ```
# GPU 评估， Global.checkpoints 为待测权重 # GPU 评估， Global.checkpoints 为待测权重
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
...@@ -342,7 +341,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec ...@@ -342,7 +341,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec
``` ```
# 预测英文结果 # 预测英文结果
python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.png
``` ```
预测图片： 预测图片：
...@@ -361,7 +360,7 @@ infer_img: doc/imgs_words/en/word_1.png ...@@ -361,7 +360,7 @@ infer_img: doc/imgs_words/en/word_1.png
``` ```
# 预测中文结果 # 预测中文结果
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
``` ```
预测图片： 预测图片：
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doc/doc_ch/reference.md
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...@@ -11,11 +11,12 @@ ...@@ -11,11 +11,12 @@
} }
2. DB: 2. DB:
@article{liao2019real, @inproceedings{liao2020real,
title={Real-time Scene Text Detection with Differentiable Binarization}, title={Real-Time Scene Text Detection with Differentiable Binarization.},
author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang}, author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
journal={arXiv preprint arXiv:1911.08947}, booktitle={AAAI},
year={2019} pages={11474--11481},
year={2020}
} }
3. DTRB: 3. DTRB:
...@@ -37,10 +38,11 @@ ...@@ -37,10 +38,11 @@
} }
5. SRN: 5. SRN:
@article{yu2020towards, @inproceedings{yu2020towards,
title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks}, title={Towards accurate scene text recognition with semantic reasoning networks},
author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui}, author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Liu, Tao and Han, Junyu and Liu, Jingtuo and Ding, Errui},
journal={arXiv preprint arXiv:2003.12294}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={12113--12122},
year={2020} year={2020}
} }
...@@ -52,4 +54,62 @@ ...@@ -52,4 +54,62 @@
pages={9086--9095}, pages={9086--9095},
year={2019} year={2019}
} }
```
\ No newline at end of file 7. CRNN:
@article{shi2016end,
title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={39},
number={11},
pages={2298--2304},
year={2016},
publisher={IEEE}
}
8. FPGM:
@inproceedings{he2019filter,
title={Filter pruning via geometric median for deep convolutional neural networks acceleration},
author={He, Yang and Liu, Ping and Wang, Ziwei and Hu, Zhilan and Yang, Yi},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4340--4349},
year={2019}
}
9. PACT:
@article{choi2018pact,
title={Pact: Parameterized clipping activation for quantized neural networks},
author={Choi, Jungwook and Wang, Zhuo and Venkataramani, Swagath and Chuang, Pierce I-Jen and Srinivasan, Vijayalakshmi and Gopalakrishnan, Kailash},
journal={arXiv preprint arXiv:1805.06085},
year={2018}
}
10.Rosetta
@inproceedings{borisyuk2018rosetta,
title={Rosetta: Large scale system for text detection and recognition in images},
author={Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath},
booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={71--79},
year={2018}
}
11.STAR-Net
@inproceedings{liu2016star,
title={STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition.},
author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
booktitle={BMVC},
volume={2},
pages={7},
year={2016}
}
12.RARE
@inproceedings{shi2016robust,
title={Robust scene text recognition with automatic rectification},
author={Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4168--4176},
year={2016}
}
```
doc/doc_en/algorithm_overview_en.md
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...@@ -11,9 +11,9 @@ This tutorial lists the text detection algorithms and text recognition algorithm ...@@ -11,9 +11,9 @@ This tutorial lists the text detection algorithms and text recognition algorithm
### 1. Text Detection Algorithm ### 1. Text Detection Algorithm
PaddleOCR open source text detection algorithms list: PaddleOCR open source text detection algorithms list:
- [x] EAST([paper](https://arxiv.org/abs/1704.03155)) - [x] EAST([paper](https://arxiv.org/abs/1704.03155))[2]
- [x] DB([paper](https://arxiv.org/abs/1911.08947)) - [x] DB([paper](https://arxiv.org/abs/1911.08947))[1]
- [x] SAST([paper](https://arxiv.org/abs/1908.05498) )(Baidu Self-Research) - [x] SAST([paper](https://arxiv.org/abs/1908.05498))[4]
On the ICDAR2015 dataset, the text detection result is as follows: On the ICDAR2015 dataset, the text detection result is as follows:
...@@ -39,11 +39,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re ...@@ -39,11 +39,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re
### 2. Text Recognition Algorithm ### 2. Text Recognition Algorithm
PaddleOCR open-source text recognition algorithms list: PaddleOCR open-source text recognition algorithms list:
- [x] CRNN([paper](https://arxiv.org/abs/1507.05717)) - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085)) - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) coming soon - [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon
- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon - [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
- [ ] SRN([paper](https://arxiv.org/abs/2003.12294) )(Baidu Self-Research) coming soon - [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
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: 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:
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doc/doc_en/angle_class_en.md
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...@@ -119,7 +119,7 @@ Use `Global.infer_img` to specify the path of the predicted picture or folder, a ...@@ -119,7 +119,7 @@ Use `Global.infer_img` to specify the path of the predicted picture or folder, a
``` ```
# Predict English results # Predict English results
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_10.png python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words_en/word_10.png
``` ```
Input image: Input image:
......
doc/doc_en/detection_en.md
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...@@ -113,16 +113,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat ...@@ -113,16 +113,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat
Test the detection result on a single image: Test the detection result on a single image:
```shell ```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
``` ```
When testing the DB model, adjust the post-processing threshold: When testing the DB model, adjust the post-processing threshold:
```shell ```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
``` ```
Test the detection result on all images in the folder: Test the detection result on all images in the folder:
```shell ```shell
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy" python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
``` ```
doc/doc_en/inference_en.md
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...@@ -255,15 +255,18 @@ The following will introduce the lightweight Chinese recognition model inference ...@@ -255,15 +255,18 @@ The following will introduce the lightweight Chinese recognition model inference
For lightweight Chinese recognition model inference, you can execute the following commands: For lightweight Chinese recognition model inference, you can execute the following commands:
``` ```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/" # download CRNN text recognition inference model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
``` ```
![](../imgs_words/ch/word_4.jpg) ![](../imgs_words_en/word_10.png)
After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen. After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.
```bash ```bash
Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
``` ```
<a name="CTC-BASED_RECOGNITION"></a> <a name="CTC-BASED_RECOGNITION"></a>
...@@ -339,7 +342,12 @@ For angle classification model inference, you can execute the following commands ...@@ -339,7 +342,12 @@ For angle classification model inference, you can execute the following commands
``` ```
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/" python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/"
``` ```
```
# download text angle class inference model：
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
```
![](../imgs_words_en/word_10.png) ![](../imgs_words_en/word_10.png)
After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen. After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
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doc/doc_en/recognition_en.md
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...@@ -317,11 +317,11 @@ Eval: ...@@ -317,11 +317,11 @@ Eval:
<a name="EVALUATION"></a> <a name="EVALUATION"></a>
### EVALUATION ### EVALUATION
The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
``` ```
# GPU evaluation, Global.checkpoints is the weight to be tested # GPU evaluation, Global.checkpoints is the weight to be tested
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
``` ```
<a name="PREDICTION"></a> <a name="PREDICTION"></a>
...@@ -336,7 +336,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci ...@@ -336,7 +336,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci
``` ```
# Predict English results # Predict English results
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
``` ```
Input image: Input image:
...@@ -354,7 +354,7 @@ The configuration file used for prediction must be consistent with the training. ...@@ -354,7 +354,7 @@ The configuration file used for prediction must be consistent with the training.
``` ```
# Predict Chinese results # Predict Chinese results
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
``` ```
Input image: Input image:
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  • 2-up
  • Swipe
  • Onion skin
paddleocr.py
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...@@ -262,8 +262,8 @@ class PaddleOCR(predict_system.TextSystem): ...@@ -262,8 +262,8 @@ class PaddleOCR(predict_system.TextSystem):
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL)) logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
sys.exit(0) sys.exit(0)
postprocess_params.rec_char_dict_path = Path( postprocess_params.rec_char_dict_path = str(
__file__).parent / postprocess_params.rec_char_dict_path Path(__file__).parent / postprocess_params.rec_char_dict_path)
# init det_model and rec_model # init det_model and rec_model
super().__init__(postprocess_params) super().__init__(postprocess_params)
......
ppocr/losses/det_basic_loss.py
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...@@ -45,7 +45,6 @@ class BalanceLoss(nn.Layer): ...@@ -45,7 +45,6 @@ class BalanceLoss(nn.Layer):
self.balance_loss = balance_loss self.balance_loss = balance_loss
self.main_loss_type = main_loss_type self.main_loss_type = main_loss_type
self.negative_ratio = negative_ratio self.negative_ratio = negative_ratio
self.main_loss_type = main_loss_type
self.return_origin = return_origin self.return_origin = return_origin
self.eps = eps self.eps = eps
......
ppocr/utils/save_load.py
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...@@ -102,7 +102,6 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None): ...@@ -102,7 +102,6 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
best_model_dict = states_dict.get('best_model_dict', {}) best_model_dict = states_dict.get('best_model_dict', {})
if 'epoch' in states_dict: if 'epoch' in states_dict:
best_model_dict['start_epoch'] = states_dict['epoch'] + 1 best_model_dict['start_epoch'] = states_dict['epoch'] + 1
best_model_dict['start_epoch'] = best_model_dict['best_epoch'] + 1
logger.info("resume from {}".format(checkpoints)) logger.info("resume from {}".format(checkpoints))
elif pretrained_model: elif pretrained_model:
......
setup.py
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...@@ -32,7 +32,7 @@ setup( ...@@ -32,7 +32,7 @@ setup(
package_dir={'paddleocr': ''}, package_dir={'paddleocr': ''},
include_package_data=True, include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]}, entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='2.0.1', version='2.0.2',
install_requires=requirements, install_requires=requirements,
license='Apache License 2.0', license='Apache License 2.0',
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices', description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
......
tools/infer/predict_det.py
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...@@ -71,6 +71,9 @@ class TextDetector(object): ...@@ -71,6 +71,9 @@ class TextDetector(object):
postprocess_params["cover_thresh"] = args.det_east_cover_thresh postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh postprocess_params["nms_thresh"] = args.det_east_nms_thresh
elif self.det_algorithm == "SAST": elif self.det_algorithm == "SAST":
pre_process_list[0] = {
'DetResizeForTest': {'resize_long': args.det_limit_side_len}
}
postprocess_params['name'] = 'SASTPostProcess' postprocess_params['name'] = 'SASTPostProcess'
postprocess_params["score_thresh"] = args.det_sast_score_thresh postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
......
tools/infer/utility.py
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...@@ -34,7 +34,6 @@ def parse_args(): ...@@ -34,7 +34,6 @@ def parse_args():
parser.add_argument("--ir_optim", type=str2bool, default=True) parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False) parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--use_fp16", type=str2bool, default=False) parser.add_argument("--use_fp16", type=str2bool, default=False)
parser.add_argument("--max_batch_size", type=int, default=10)
parser.add_argument("--gpu_mem", type=int, default=8000) parser.add_argument("--gpu_mem", type=int, default=8000)
# params for text detector # params for text detector
......
tools/program.py
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...@@ -332,7 +332,7 @@ def eval(model, valid_dataloader, post_process_class, eval_class): ...@@ -332,7 +332,7 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
return metirc return metirc
def preprocess(): def preprocess(is_train=False):
FLAGS = ArgsParser().parse_args() FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config) config = load_config(FLAGS.config)
merge_config(FLAGS.opt) merge_config(FLAGS.opt)
...@@ -350,15 +350,17 @@ def preprocess(): ...@@ -350,15 +350,17 @@ def preprocess():
device = paddle.set_device(device) device = paddle.set_device(device)
config['Global']['distributed'] = dist.get_world_size() != 1 config['Global']['distributed'] = dist.get_world_size() != 1
if is_train:
# save_config # save_config
save_model_dir = config['Global']['save_model_dir'] save_model_dir = config['Global']['save_model_dir']
os.makedirs(save_model_dir, exist_ok=True) os.makedirs(save_model_dir, exist_ok=True)
with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f: with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
yaml.dump(dict(config), f, default_flow_style=False, sort_keys=False) yaml.dump(
dict(config), f, default_flow_style=False, sort_keys=False)
logger = get_logger( log_file = '{}/train.log'.format(save_model_dir)
name='root', log_file='{}/train.log'.format(save_model_dir)) else:
log_file = None
logger = get_logger(name='root', log_file=log_file)
if config['Global']['use_visualdl']: if config['Global']['use_visualdl']:
from visualdl import LogWriter from visualdl import LogWriter
vdl_writer_path = '{}/vdl/'.format(save_model_dir) vdl_writer_path = '{}/vdl/'.format(save_model_dir)
......
tools/train.py
浏览文件 @ c94428a8
...@@ -110,6 +110,6 @@ def test_reader(config, device, logger): ...@@ -110,6 +110,6 @@ def test_reader(config, device, logger):
if __name__ == '__main__': if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess() config, device, logger, vdl_writer = program.preprocess(is_train=True)
main(config, device, logger, vdl_writer) main(config, device, logger, vdl_writer)
# test_reader(config, device, logger) # test_reader(config, device, logger)
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