Merge branch 'dygraph' into dygraph

PPOCRLabel/PPOCRLabel.py

@@ -152,16 +152,6 @@ class MainWindow(QMainWindow):
         self.fileListWidget.setIconSize(QSize(25, 25))
         filelistLayout.addWidget(self.fileListWidget)
 
-        self.AutoRecognition = QToolButton()
-        self.AutoRecognition.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
-        self.AutoRecognition.setIcon(newIcon('Auto'))
-        autoRecLayout = QHBoxLayout()
-        autoRecLayout.setContentsMargins(0, 0, 0, 0)
-        autoRecLayout.addWidget(self.AutoRecognition)
-        autoRecContainer = QWidget()
-        autoRecContainer.setLayout(autoRecLayout)
-        filelistLayout.addWidget(autoRecContainer)
-
         fileListContainer = QWidget()
         fileListContainer.setLayout(filelistLayout)
         self.fileListName = getStr('fileList')
@@ -172,17 +162,30 @@ class MainWindow(QMainWindow):
 
         #  ================== Key List  ==================
         if self.kie_mode:
-            # self.keyList = QListWidget()
             self.keyList = UniqueLabelQListWidget()
-            # self.keyList.itemSelectionChanged.connect(self.keyListSelectionChanged)
-            # self.keyList.itemDoubleClicked.connect(self.editBox)
-            # self.keyList.itemChanged.connect(self.keyListItemChanged)
+
+            # set key list height
+            key_list_height = int(QApplication.desktop().height() // 4)
+            if key_list_height < 50:
+                key_list_height = 50
+            self.keyList.setMaximumHeight(key_list_height)
+
             self.keyListDockName = getStr('keyListTitle')
             self.keyListDock = QDockWidget(self.keyListDockName, self)
             self.keyListDock.setWidget(self.keyList)
             self.keyListDock.setFeatures(QDockWidget.NoDockWidgetFeatures)
             filelistLayout.addWidget(self.keyListDock)
 
+        self.AutoRecognition = QToolButton()
+        self.AutoRecognition.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
+        self.AutoRecognition.setIcon(newIcon('Auto'))
+        autoRecLayout = QHBoxLayout()
+        autoRecLayout.setContentsMargins(0, 0, 0, 0)
+        autoRecLayout.addWidget(self.AutoRecognition)
+        autoRecContainer = QWidget()
+        autoRecContainer.setLayout(autoRecLayout)
+        filelistLayout.addWidget(autoRecContainer)
+
         #  ================== Right Area  ==================
         listLayout = QVBoxLayout()
         listLayout.setContentsMargins(0, 0, 0, 0)
@@ -431,8 +434,7 @@ class MainWindow(QMainWindow):
         #  ================== New Actions ==================
 
         edit = action(getStr('editLabel'), self.editLabel,
-                      'Ctrl+E', 'edit', getStr('editLabelDetail'),
-                      enabled=False)
+                      'Ctrl+E', 'edit', getStr('editLabelDetail'), enabled=False)
 
         AutoRec = action(getStr('autoRecognition'), self.autoRecognition,
                          '', 'Auto', getStr('autoRecognition'), enabled=False)
@@ -465,11 +467,10 @@ class MainWindow(QMainWindow):
                       'Ctrl+Z', "undo", getStr("undo"), enabled=False)
 
         change_cls = action(getStr("keyChange"), self.change_box_key,
-                            'Ctrl+B', "edit", getStr("keyChange"), enabled=False)
+                            'Ctrl+X', "edit", getStr("keyChange"), enabled=False)
 
         lock = action(getStr("lockBox"), self.lockSelectedShape,
-                      None, "lock", getStr("lockBoxDetail"),
-                      enabled=False)
+                      None, "lock", getStr("lockBoxDetail"), enabled=False)
 
         self.editButton.setDefaultAction(edit)
         self.newButton.setDefaultAction(create)
@@ -534,9 +535,10 @@ class MainWindow(QMainWindow):
                               fileMenuActions=(opendir, open_dataset_dir, saveLabel, resetAll, quit),
                               beginner=(), advanced=(),
                               editMenu=(createpoly, edit, copy, delete, singleRere, None, undo, undoLastPoint,
-                                        None, rotateLeft, rotateRight, None, color1, self.drawSquaresOption, lock),
+                                        None, rotateLeft, rotateRight, None, color1, self.drawSquaresOption, lock,
+                                        None, change_cls),
                               beginnerContext=(
-                              create, edit, copy, delete, singleRere, rotateLeft, rotateRight, lock, change_cls),
+                                  create, edit, copy, delete, singleRere, rotateLeft, rotateRight, lock, change_cls),
                               advancedContext=(createMode, editMode, edit, copy,
                                                delete, shapeLineColor, shapeFillColor),
                               onLoadActive=(create, createMode, editMode),
@@ -1105,7 +1107,9 @@ class MainWindow(QMainWindow):
             shapes = [format_shape(shape) for shape in self.canvas.shapes if shape.line_color != DEFAULT_LOCK_COLOR]
         # Can add differrent annotation formats here
         for box in self.result_dic:
-            trans_dic = {"label": box[1][0], "points": box[0], "difficult": False, "key_cls": "None"}
+            trans_dic = {"label": box[1][0], "points": box[0], "difficult": False}
+            if self.kie_mode:
+                trans_dic.update({"key_cls": "None"})
             if trans_dic["label"] == "" and mode == 'Auto':
                 continue
             shapes.append(trans_dic)
@@ -1113,8 +1117,10 @@ class MainWindow(QMainWindow):
         try:
             trans_dic = []
             for box in shapes:
-                trans_dic.append({"transcription": box['label'], "points": box['points'],
-                                  "difficult": box['difficult'], "key_cls": box['key_cls']})
+                trans_dict = {"transcription": box['label'], "points": box['points'], "difficult": box['difficult']}
+                if self.kie_mode:
+                    trans_dict.update({"key_cls": box['key_cls']})
+                trans_dic.append(trans_dict)
             self.PPlabel[annotationFilePath] = trans_dic
             if mode == 'Auto':
                 self.Cachelabel[annotationFilePath] = trans_dic
@@ -1424,15 +1430,17 @@ class MainWindow(QMainWindow):
         # box['ratio'] of the shapes saved in lockedShapes contains the ratio of the
         # four corner coordinates of the shapes to the height and width of the image
         for box in self.canvas.lockedShapes:
+            key_cls = None if not self.kie_mode else box['key_cls']
             if self.canvas.isInTheSameImage:
                 shapes.append((box['transcription'], [[s[0] * width, s[1] * height] for s in box['ratio']],
-                               DEFAULT_LOCK_COLOR, box['key_cls'], box['difficult']))
+                               DEFAULT_LOCK_COLOR, key_cls, box['difficult']))
             else:
                 shapes.append(('锁定框：待检测', [[s[0] * width, s[1] * height] for s in box['ratio']],
-                               DEFAULT_LOCK_COLOR, box['key_cls'], box['difficult']))
+                               DEFAULT_LOCK_COLOR, key_cls, box['difficult']))
         if imgidx in self.PPlabel.keys():
             for box in self.PPlabel[imgidx]:
-                shapes.append((box['transcription'], box['points'], None, box['key_cls'], box['difficult']))
+                key_cls = None if not self.kie_mode else box['key_cls']
+                shapes.append((box['transcription'], box['points'], None, key_cls, box['difficult']))
 
         self.loadLabels(shapes)
         self.canvas.verified = False
@@ -1460,6 +1468,7 @@ class MainWindow(QMainWindow):
     def adjustScale(self, initial=False):
         value = self.scalers[self.FIT_WINDOW if initial else self.zoomMode]()
         self.zoomWidget.setValue(int(100 * value))
+        self.imageSlider.setValue(self.zoomWidget.value())  # set zoom slider value
 
     def scaleFitWindow(self):
         """Figure out the size of the pixmap in order to fit the main widget."""
@@ -1600,7 +1609,6 @@ class MainWindow(QMainWindow):
         else:
             self.keyDialog.labelList.addItems(self.existed_key_cls_set)
 
-
     def importDirImages(self, dirpath, isDelete=False):
         if not self.mayContinue() or not dirpath:
             return
@@ -2238,13 +2246,22 @@ class MainWindow(QMainWindow):
             print('The program will automatically save once after confirming 5 images (default)')
 
     def change_box_key(self):
+        if not self.kie_mode:
+            return
         key_text, _ = self.keyDialog.popUp(self.key_previous_text)
         if key_text is None:
             return
         self.key_previous_text = key_text
         for shape in self.canvas.selectedShapes:
             shape.key_cls = key_text
+            if not self.keyList.findItemsByLabel(key_text):
+                item = self.keyList.createItemFromLabel(key_text)
+                self.keyList.addItem(item)
+                rgb = self._get_rgb_by_label(key_text, self.kie_mode)
+                self.keyList.setItemLabel(item, key_text, rgb)
+
             self._update_shape_color(shape)
+            self.keyDialog.addLabelHistory(key_text)
 
     def undoShapeEdit(self):
         self.canvas.restoreShape()
@@ -2288,9 +2305,10 @@ class MainWindow(QMainWindow):
             shapes = [format_shape(shape) for shape in self.canvas.selectedShapes]
             trans_dic = []
             for box in shapes:
-                trans_dic.append({"transcription": box['label'], "ratio": box['ratio'],
-                                  "difficult": box['difficult'],
-                                  "key_cls": "None" if "key_cls" not in box else box["key_cls"]})
+                trans_dict = {"transcription": box['label'], "ratio": box['ratio'], "difficult": box['difficult']}
+                if self.kie_mode:
+                    trans_dict.update({"key_cls": box["key_cls"]})
+                trans_dic.append(trans_dict)
             self.canvas.lockedShapes = trans_dic
             self.actions.save.setEnabled(True)
 

PPOCRLabel/README.md

@@ -9,7 +9,7 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w
 ### Recent Update
 
 - 2022.02：（by [PeterH0323](https://github.com/peterh0323) ）
-  - Added KIE mode, for [detection + identification + keyword extraction] labeling.
+  - Add KIE Mode by using `--kie`, for [detection + identification + keyword extraction] labeling.
 - 2022.01：（by [PeterH0323](https://github.com/peterh0323) ）
   - Improve user experience: prompt for the number of files and labels, optimize interaction, and fix bugs such as only use CPU when inference
 - 2021.11.17:
@@ -54,7 +54,10 @@ PPOCRLabel can be started in two ways: whl package and Python script. The whl pa
 
 ```bash
 pip install PPOCRLabel  # install
-PPOCRLabel  # run
+
+# Select label mode and run 
+PPOCRLabel  # [Normal mode] for [detection + recognition] labeling
+PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
 
 > If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file using http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely.
@@ -67,13 +70,18 @@ PPOCRLabel  # run
 ```bash
 pip3 install PPOCRLabel
 pip3 install trash-cli
-PPOCRLabel
+
+# Select label mode and run 
+PPOCRLabel  # [Normal mode] for [detection + recognition] labeling
+PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
 
 #### MacOS
 ```bash
 pip3 install PPOCRLabel
 pip3 install opencv-contrib-python-headless==4.2.0.32
+
+# Select label mode and run 
 PPOCRLabel  # [Normal mode] for [detection + recognition] labeling
 PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
@@ -90,6 +98,8 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
 
 ```bash
 cd ./PPOCRLabel  # Switch to the PPOCRLabel directory
+
+# Select label mode and run 
 python PPOCRLabel.py  # [Normal mode] for [detection + recognition] labeling
 python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
@@ -156,6 +166,7 @@ python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyw
 | X                        | Rotate the box anti-clockwise                    |
 | C                        | Rotate the box clockwise                         |
 | Ctrl + E                 | Edit label of the selected box                   |
+| Ctrl + X                 | Change key class of the box when enable `--kie`  |
 | Ctrl + R                 | Re-recognize the selected box                    |
 | Ctrl + C                 | Copy and paste the selected box                  |
 | Ctrl + Left Mouse Button | Multi select the label box                       |

PPOCRLabel/README_ch.md

@@ -9,7 +9,7 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P
 #### 近期更新
 
 - 2022.02：（by [PeterH0323](https://github.com/peterh0323) ）
-  - 新增：KIE 功能，用于打【检测+识别+关键字提取】的标签
+  - 新增：使用 `--kie` 进入 KIE 功能，用于打【检测+识别+关键字提取】的标签
 - 2022.01：（by [PeterH0323](https://github.com/peterh0323) ）
   - 提升用户体验：新增文件与标记数目提示、优化交互、修复gpu使用等问题
 - 2021.11.17：
@@ -57,7 +57,10 @@ PPOCRLabel可通过whl包与Python脚本两种方式启动，whl包形式启动
 
 ```bash
 pip install PPOCRLabel  # 安装
-PPOCRLabel --lang ch  # 运行
+
+# 选择标签模式来启动
+PPOCRLabel --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
+PPOCRLabel --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
 ```
 > 注意：通过whl包安装PPOCRLabel会自动下载 `paddleocr` whl包，其中shapely依赖可能会出现 `[winRrror 126] 找不到指定模块的问题。` 的错误，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载并安装
 ##### Ubuntu Linux
@@ -65,13 +68,18 @@ PPOCRLabel --lang ch  # 运行
 ```bash
 pip3 install PPOCRLabel
 pip3 install trash-cli
-PPOCRLabel --lang ch
+
+# 选择标签模式来启动
+PPOCRLabel --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
+PPOCRLabel --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
 ```
 
 ##### MacOS
 ```bash
 pip3 install PPOCRLabel
 pip3 install opencv-contrib-python-headless==4.2.0.32 # 如果下载过慢请添加"-i https://mirror.baidu.com/pypi/simple"
+
+# 选择标签模式来启动
 PPOCRLabel --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
 PPOCRLabel --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
 ```
@@ -92,6 +100,8 @@ pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.
 
 ```bash
 cd ./PPOCRLabel  # 切换到PPOCRLabel目录
+
+# 选择标签模式来启动
 python PPOCRLabel.py --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
 python PPOCRLabel.py --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
 ```
@@ -137,25 +147,27 @@ python PPOCRLabel.py --lang ch --kie True  # 启动 【KIE 模式】，用于打
 
 ### 3.1 快捷键
 
-| 快捷键              | 说明             |
-|------------------|----------------|
-| Ctrl + shift + R | 对当前图片的所有标记重新识别  |
-| W                | 新建矩形框                  |
-| Q                | 新建四点框                  |
-| X                | 框逆时针旋转                |
-| C                | 框顺时针旋转                |
-| Ctrl + E         | 编辑所选框标签              |
-| Ctrl + R         | 重新识别所选标记            |
-| Ctrl + C         | 复制并粘贴选中的标记框       |
-| Ctrl + 鼠标左键    | 多选标记框                 |
-| Backspace        | 删除所选框                 |
-| Ctrl + V         | 确认本张图片标记           |
-| Ctrl + Shift + d | 删除本张图片               |
-| D                | 下一张图片                |
-| A                | 上一张图片                |
-| Ctrl++           | 缩小                     |
-| Ctrl--           | 放大                     |
-| ↑→↓←             | 移动标记框                |
+| 快捷键              | 说明                              |
+|------------------|---------------------------------|
+| Ctrl + shift + R | 对当前图片的所有标记重新识别                  |
+| W                | 新建矩形框                           |
+| Q                | 新建四点框                           |
+| X                | 框逆时针旋转                          |
+| C                | 框顺时针旋转                          |
+| Ctrl + E         | 编辑所选框标签                         |
+| Ctrl + X         |  `--kie` 模式下，修改 Box 的关键字种类 |
+| Ctrl + R         | 重新识别所选标记                        |
+| Ctrl + C         | 复制并粘贴选中的标记框                     |
+| Ctrl + 鼠标左键    | 多选标记框                           |
+| Backspac         | 删除所选框                           |
+| Ctrl + V         | 确认本张图片标记                        |
+| Ctrl + Shift + d | 删除本张图片                          |
+| D                | 下一张图片                           |
+| A                | 上一张图片                           |
+| Ctrl++           | 缩小                              |
+| Ctrl--           | 放大                              |
+| ↑→↓←             | 移动标记框                           |
+
 
 ### 3.2 内置模型
 

PPOCRLabel/libs/canvas.py

@@ -546,7 +546,7 @@ class Canvas(QWidget):
         # Give up if both fail.
         for shape in shapes:
             point = shape[0]
-            offset = QPointF(2.0, 2.0)
+            offset = QPointF(5.0, 5.0)
             self.calculateOffsets(shape, point)
             self.prevPoint = point
             if not self.boundedMoveShape(shape, point - offset):

PPOCRLabel/libs/unique_label_qlist_widget.py

 # -*- encoding: utf-8 -*-
 
-from PyQt5.QtCore import Qt
+from PyQt5.QtCore import Qt, QSize
 from PyQt5 import QtWidgets
 
 
@@ -40,6 +40,7 @@ class UniqueLabelQListWidget(EscapableQListWidget):
             qlabel.setText('<font color="#{:02x}{:02x}{:02x}">●</font> {} '.format(*color, label))
         qlabel.setAlignment(Qt.AlignBottom)
 
-        item.setSizeHint(qlabel.sizeHint())
+        # item.setSizeHint(qlabel.sizeHint())
+        item.setSizeHint(QSize(25, 25))
 
         self.setItemWidget(item, qlabel)

README_ch.md

@@ -32,7 +32,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - PP-OCR系列高质量预训练模型，准确的识别效果
     - 超轻量PP-OCRv2系列：检测（3.1M）+ 方向分类器（1.4M）+ 识别（8.5M）= 13.0M
     - 超轻量PP-OCR 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
+    - 通用PP-OCR 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、法语等约80种语言
 - PP-Structure文档结构化系统

benchmark/run_benchmark_det.sh

 #!/usr/bin/env bash
-set -xe
 # 运行示例：CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode}
 # 参数说明
 function _set_params(){
@@ -34,11 +33,13 @@ function _train(){
         train_cmd="python tools/train.py "${train_cmd}""
         ;;
       mp)
+        rm -rf ./mylog
         train_cmd="python -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}"
         ;;
       *) echo "choose run_mode(sp or mp)"; exit 1;
     esac
 # 以下不用修改
+    echo ${train_cmd}
     timeout 15m ${train_cmd} > ${log_file} 2>&1
     if [ $? -ne 0 ];then
             echo -e "${model_name}, FAIL"

configs/det/det_mv3_pse.yml

@@ -56,7 +56,7 @@ PostProcess:
   thresh: 0
   box_thresh: 0.85
   min_area: 16
-  box_type: box # 'box' or 'poly'
+  box_type: quad # 'quad' or 'poly'
   scale: 1
 
 Metric:

configs/det/det_r50_vd_dcn_fce_ctw.yml

+Global:
+  use_gpu: true
+  epoch_num: 1500
+  log_smooth_window: 20
+  print_batch_step: 20
+  save_model_dir: ./output/det_r50_dcn_fce_ctw/
+  save_epoch_step: 100
+  # evaluation is run every 835 iterations
+  eval_batch_step: [0, 835]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained 
+  checkpoints: 
+  save_inference_dir: 
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_fce/predicts_fce.txt
+
+
+Architecture:
+  model_type: det
+  algorithm: FCE
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+    dcn_stage: [False, True, True, True]
+    out_indices: [1,2,3]
+  Neck:
+    name: FCEFPN
+    out_channels: 256
+    has_extra_convs: False
+    extra_stage: 0
+  Head:
+    name: FCEHead
+    fourier_degree: 5
+Loss:
+  name: FCELoss
+  fourier_degree: 5
+  num_sample: 50
+  
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0001
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: FCEPostProcess
+  scales: [8, 16, 32]
+  alpha: 1.0
+  beta: 1.0
+  fourier_degree: 5
+  box_type: 'poly'
+
+Metric:
+  name: DetFCEMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ctw1500/imgs/
+    label_file_list: 
+      - ./train_data/ctw1500/imgs/training.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+          ignore_orientation: True
+      - DetLabelEncode: # Class handling label
+      - ColorJitter: 
+          brightness: 0.142
+          saturation: 0.5
+          contrast: 0.5
+      - RandomScaling: 
+      - RandomCropFlip:
+          crop_ratio: 0.5
+      - RandomCropPolyInstances:
+          crop_ratio: 0.8
+          min_side_ratio: 0.3
+      - RandomRotatePolyInstances:
+          rotate_ratio: 0.5
+          max_angle: 30
+          pad_with_fixed_color: False
+      - SquareResizePad:
+          target_size: 800
+          pad_ratio: 0.6
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+      - FCENetTargets:
+          fourier_degree: 5
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'p3_maps', 'p4_maps', 'p5_maps'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 6
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ctw1500/imgs/
+    label_file_list:
+      - ./train_data/ctw1500/imgs/test.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+          ignore_orientation: True
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_type: 'min'
+          limit_side_len: 736
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - Pad: 
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2
\ No newline at end of file

configs/det/det_r50_vd_pse.yml

@@ -55,7 +55,7 @@ PostProcess:
   thresh: 0
   box_thresh: 0.85
   min_area: 16
-  box_type: box # 'box' or 'poly'
+  box_type: quad # 'quad' or 'poly'
   scale: 1
 
 Metric:

configs/rec/rec_efficientb3_fpn_pren.yml

+Global:
+  use_gpu: True
+  epoch_num: 8
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/rec/pren_new
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 2000]
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path:
+  max_text_length: &max_text_length 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_pren.txt
+
+Optimizer:
+  name: Adadelta
+  lr:
+    name: Piecewise
+    decay_epochs: [2, 5, 7]
+    values: [0.5, 0.1, 0.01, 0.001]
+
+Architecture:
+  model_type: rec
+  algorithm: PREN
+  in_channels: 3
+  Backbone:
+    name: EfficientNetb3_PREN
+  Neck:
+    name: PRENFPN
+    n_r: 5
+    d_model: 384
+    max_len: *max_text_length
+    dropout: 0.1
+  Head:
+    name: PRENHead
+
+Loss:
+  name: PRENLoss
+
+PostProcess:
+  name: PRENLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage:
+          img_mode: BGR
+          channel_first: False
+      - PRENLabelEncode:
+      - RecAug:
+      - PRENResizeImg: 
+          image_shape: [64, 256]  # h,w
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+  loader:
+    shuffle: True
+    batch_size_per_card: 128
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/validation/
+    transforms:
+      - DecodeImage:
+          img_mode: BGR
+          channel_first: False
+      - PRENLabelEncode:
+      - PRENResizeImg: 
+          image_shape: [64, 256]  # h,w
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 64
+    num_workers: 8

deploy/android_demo/README.md

-# 如何快速测试
-### 1. 安装最新版本的Android Studio
-可以从 https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
+- [Android Demo](#android-demo)
+  - [1. 简介](#1-简介)
+  - [2. 近期更新](#2-近期更新)
+  - [3. 快速使用](#3-快速使用)
+    - [3.1 环境准备](#31-环境准备)
+    - [3.2 导入项目](#32-导入项目)
+    - [3.3 运行demo](#33-运行demo)
+    - [3.4 运行模式](#34-运行模式)
+    - [3.5 设置](#35-设置)
+  - [4 更多支持](#4-更多支持)
 
-### 2. 按照NDK 20 以上版本
-Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
+# Android Demo
 
-如果您是初学者，可以用以下方式安装和测试NDK编译环境。
-点击 File -> New ->New Project，  新建  "Native C++" project
+## 1. 简介
+此为PaddleOCR的Android Demo，目前支持文本检测，文本方向分类器和文本识别模型的使用。使用 [PaddleLite v2.10](https://github.com/PaddlePaddle/Paddle-Lite/tree/release/v2.10) 进行开发。
+
+## 2. 近期更新
+* 2022.02.27
+    * 预测库更新到PaddleLite v2.10
+    * 支持6种运行模式：
+      * 检测+分类+识别
+      * 检测+识别
+      * 分类+识别
+      * 检测
+      * 识别
+      * 分类
+
+## 3. 快速使用
+
+### 3.1 环境准备
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
+2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`
+
+**注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake ](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 Paddle Lite 预测库版本一样的 NDK
+
+### 3.2 导入项目
 
-### 3. 导入项目
 点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
+导入完成后呈现如下界面
+![](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/import_demo.jpg)
+
+### 3.3 运行demo
+将手机连接上电脑后，点击Android Studio工具栏中的运行按钮即可运行demo。在此过程中，手机会弹出"允许从 USB 安装软件权限"的弹窗，点击允许即可。
+
+软件安转到手机上后会在手机主屏最后一页看到如下app
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/install_finish.jpeg" width="400">
+</div>
+
+点击app图标即可启动app，启动后app主页如下
+
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/main_page.jpg" width="400">
+</div>
+
+app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们的功能分别为
+
+* 运行模型：按照已选择的模式，运行对应的模型组合
+* 拍照识别：唤起手机相机拍照并获取拍照的图像，拍照完成后需要点击运行模型进行识别
+* 选取图片：唤起手机相册拍照选择图像，选择完成后需要点击运行模型进行识别
+* 清空绘图：清空当前显示图像上绘制的文本框，以便进行下一次识别(每次识别使用的图像都是当前显示的图像)
+* 下拉列表：进行运行模式的选择，目前包含6种运行模式，默认模式为**检测+分类+识别**详细说明见下一节。
+* 菜单按钮：点击后会进入菜单界面，进行模型和内置图像有关设置
+
+点击运行模型后，会按照所选择的模式运行对应的模型，**检测+分类+识别**模式下运行的模型结果如下所示：
+
+<img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400">
+
+模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model successed`表明模型运行成功。
+
+模型的运行结果显示在运行结果显示区，显示格式为
+```text
+序号：Det：(x1,y1)(x2,y2)(x3,y3)(x4,y4) Rec: 识别文本,识别置信度 Cls：分类类别,分类分时
+```
+
+### 3.4 运行模式
+
+PaddleOCR demo共提供了6种运行模式，如下图
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/select_mode.jpg" width="400">
+</div>
+
+每种模式的运行结果如下表所示
+
+| 检测+分类+识别                                                                                       | 检测+识别                                                                                      | 分类+识别                                                                                      |
+|------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
+| <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_cls_rec.jpg" width="400"> |
+
+
+| 检测                                                                                     | 识别                                                                                     | 分类                                                                                     |
+|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
+| <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_rec.jpg" width="400"> | <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_cls.jpg" width="400"> |
+
+### 3.5 设置
+
+设置界面如下
 
+<div align="left">
+    <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/settings.jpg" width="400">
+</div>
 
-# 获得更多支持
-前往[端计算模型生成平台EasyEdge](https://ai.baidu.com/easyedge/app/open_source_demo?referrerUrl=paddlelite)，获得更多开发支持：
+在设置界面可以进行如下几项设定：
+1. 普通设置
+   * Enable custom settings: 选中状态下才能更改设置
+   * Model Path: 所运行的模型地址，使用默认值就好
+   * Label Path: 识别模型的字典
+   * Image Path: 进行识别的内置图像名
+2. 模型运行态设置，此项设置更改后返回主界面时，会自动重新加载模型
+   * CPU Thread Num: 模型运行使用的CPU核心数量
+   * CPU Power Mode: 模型运行模式，大小核设定
+3. 输入设置
+   * det long size: DB模型预处理时图像的长边长度，超过此长度resize到该值，短边进行等比例缩放，小于此长度不进行处理。
+4. 输出设置
+   * Score Threshold: DB模型后处理box的阈值，低于此阈值的box进行过滤，不显示。
 
-- Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
-- SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发
+## 4 更多支持
+1. 实时识别，更新预测库可参考 https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/ocr/android/app/cxx/ppocr_demo
+2. 更多Paddle-Lite相关问题可前往[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) ，获得更多开发支持

deploy/android_demo/app/build.gradle

@@ -8,8 +8,8 @@ android {
         applicationId "com.baidu.paddle.lite.demo.ocr"
         minSdkVersion 23
         targetSdkVersion 29
-        versionCode 1
-        versionName "1.0"
+        versionCode 2
+        versionName "2.0"
         testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
         externalNativeBuild {
             cmake {
@@ -17,11 +17,6 @@ android {
                 arguments '-DANDROID_PLATFORM=android-23', '-DANDROID_STL=c++_shared' ,"-DANDROID_ARM_NEON=TRUE"
             }
         }
-        ndk {
-            // abiFilters "arm64-v8a", "armeabi-v7a"
-            abiFilters   "arm64-v8a", "armeabi-v7a"
-            ldLibs "jnigraphics"
-        }
     }
     buildTypes {
         release {
@@ -48,7 +43,7 @@ dependencies {
 
 def archives = [
         [
-                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/paddle_lite_libs_v2_9_0.tar.gz',
+                'src' : 'https://paddleocr.bj.bcebos.com/libs/paddle_lite_libs_v2_10.tar.gz',
                 'dest': 'PaddleLite'
         ],
         [
@@ -56,7 +51,7 @@ def archives = [
                 'dest': 'OpenCV'
         ],
         [
-                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ocr_v2_for_cpu.tar.gz',
+                'src' : 'https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2.tar.gz',
                 'dest' : 'src/main/assets/models'
         ],
         [

deploy/android_demo/app/src/main/AndroidManifest.xml

@@ -14,7 +14,6 @@
             android:roundIcon="@mipmap/ic_launcher_round"
             android:supportsRtl="true"
             android:theme="@style/AppTheme">
-        <!-- to test MiniActivity, change this to com.baidu.paddle.lite.demo.ocr.MiniActivity -->
         <activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
             <intent-filter>
                 <action android:name="android.intent.action.MAIN"/>

deploy/android_demo/app/src/main/cpp/native.cpp

@@ -13,7 +13,7 @@ static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
 extern "C" JNIEXPORT jlong JNICALL
 Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
     JNIEnv *env, jobject thiz, jstring j_det_model_path,
-    jstring j_rec_model_path, jstring j_cls_model_path, jint j_thread_num,
+    jstring j_rec_model_path, jstring j_cls_model_path, jint j_use_opencl, jint j_thread_num,
     jstring j_cpu_mode) {
   std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
   std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
@@ -21,6 +21,7 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(
   int thread_num = j_thread_num;
   std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
   ppredictor::OCR_Config conf;
+  conf.use_opencl = j_use_opencl;
   conf.thread_num = thread_num;
   conf.mode = str_to_cpu_mode(cpu_mode);
   ppredictor::OCR_PPredictor *orc_predictor =
@@ -57,32 +58,31 @@ str_to_cpu_mode(const std::string &cpu_mode) {
 
 extern "C" JNIEXPORT jfloatArray JNICALL
 Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
-    JNIEnv *env, jobject thiz, jlong java_pointer, jfloatArray buf,
-    jfloatArray ddims, jobject original_image) {
+    JNIEnv *env, jobject thiz, jlong java_pointer, jobject original_image,jint j_max_size_len, jint j_run_det, jint j_run_cls, jint j_run_rec) {
   LOGI("begin to run native forward");
   if (java_pointer == 0) {
     LOGE("JAVA pointer is NULL");
     return cpp_array_to_jfloatarray(env, nullptr, 0);
   }
+
   cv::Mat origin = bitmap_to_cv_mat(env, original_image);
   if (origin.size == 0) {
     LOGE("origin bitmap cannot convert to CV Mat");
     return cpp_array_to_jfloatarray(env, nullptr, 0);
   }
+
+  int max_size_len = j_max_size_len;
+  int run_det = j_run_det;
+  int run_cls = j_run_cls;
+  int run_rec = j_run_rec;
+
   ppredictor::OCR_PPredictor *ppredictor =
       (ppredictor::OCR_PPredictor *)java_pointer;
-  std::vector<float> dims_float_arr = jfloatarray_to_float_vector(env, ddims);
   std::vector<int64_t> dims_arr;
-  dims_arr.resize(dims_float_arr.size());
-  std::copy(dims_float_arr.cbegin(), dims_float_arr.cend(), dims_arr.begin());
-
-  // 这里值有点大，就不调用jfloatarray_to_float_vector了
-  int64_t buf_len = (int64_t)env->GetArrayLength(buf);
-  jfloat *buf_data = env->GetFloatArrayElements(buf, JNI_FALSE);
-  float *data = (jfloat *)buf_data;
   std::vector<ppredictor::OCRPredictResult> results =
-      ppredictor->infer_ocr(dims_arr, data, buf_len, NET_OCR, origin);
+      ppredictor->infer_ocr(origin, max_size_len, run_det, run_cls, run_rec);
   LOGI("infer_ocr finished with boxes %ld", results.size());
+
   // 这里将std::vector<ppredictor::OCRPredictResult> 序列化成
   // float数组，传输到java层再反序列化
   std::vector<float> float_arr;
@@ -90,13 +90,18 @@ Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(
     float_arr.push_back(r.points.size());
     float_arr.push_back(r.word_index.size());
     float_arr.push_back(r.score);
+    // add det point
     for (const std::vector<int> &point : r.points) {
       float_arr.push_back(point.at(0));
       float_arr.push_back(point.at(1));
     }
+    // add rec word idx
     for (int index : r.word_index) {
       float_arr.push_back(index);
     }
+    // add cls result
+    float_arr.push_back(r.cls_label);
+    float_arr.push_back(r.cls_score);
   }
   return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
 }

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.cpp

@@ -17,15 +17,15 @@ int OCR_PPredictor::init(const std::string &det_model_content,
                          const std::string &rec_model_content,
                          const std::string &cls_model_content) {
   _det_predictor = std::unique_ptr<PPredictor>(
-      new PPredictor{_config.thread_num, NET_OCR, _config.mode});
+      new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR, _config.mode});
   _det_predictor->init_nb(det_model_content);
 
   _rec_predictor = std::unique_ptr<PPredictor>(
-      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+      new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
   _rec_predictor->init_nb(rec_model_content);
 
   _cls_predictor = std::unique_ptr<PPredictor>(
-      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+      new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
   _cls_predictor->init_nb(cls_model_content);
   return RETURN_OK;
 }
@@ -34,15 +34,16 @@ int OCR_PPredictor::init_from_file(const std::string &det_model_path,
                                    const std::string &rec_model_path,
                                    const std::string &cls_model_path) {
   _det_predictor = std::unique_ptr<PPredictor>(
-      new PPredictor{_config.thread_num, NET_OCR, _config.mode});
+      new PPredictor{_config.use_opencl, _config.thread_num, NET_OCR, _config.mode});
   _det_predictor->init_from_file(det_model_path);
 
+
   _rec_predictor = std::unique_ptr<PPredictor>(
-      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+      new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
   _rec_predictor->init_from_file(rec_model_path);
 
   _cls_predictor = std::unique_ptr<PPredictor>(
-      new PPredictor{_config.thread_num, NET_OCR_INTERNAL, _config.mode});
+      new PPredictor{_config.use_opencl,_config.thread_num, NET_OCR_INTERNAL, _config.mode});
   _cls_predictor->init_from_file(cls_model_path);
   return RETURN_OK;
 }
@@ -77,90 +78,173 @@ visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
 }
 
 std::vector<OCRPredictResult>
-OCR_PPredictor::infer_ocr(const std::vector<int64_t> &dims,
-                          const float *input_data, int input_len, int net_flag,
-                          cv::Mat &origin) {
+OCR_PPredictor::infer_ocr(cv::Mat &origin,int max_size_len, int run_det, int run_cls, int run_rec) {
+  LOGI("ocr cpp start *****************");
+  LOGI("ocr cpp det: %d, cls: %d, rec: %d", run_det, run_cls, run_rec);
+  std::vector<OCRPredictResult> ocr_results;
+  if(run_det){
+    infer_det(origin, max_size_len, ocr_results);
+  }
+  if(run_rec){
+    if(ocr_results.size()==0){
+      OCRPredictResult res;
+      ocr_results.emplace_back(std::move(res));
+    }
+    for(int i = 0; i < ocr_results.size();i++) {
+      infer_rec(origin, run_cls, ocr_results[i]);
+    }
+  }else if(run_cls){
+    ClsPredictResult cls_res = infer_cls(origin);
+    OCRPredictResult res;
+    res.cls_score = cls_res.cls_score;
+    res.cls_label = cls_res.cls_label;
+    ocr_results.push_back(res);
+  }
+
+  LOGI("ocr cpp end *****************");
+  return ocr_results;
+}
+
+cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
+                     std::vector<float> &ratio_hw) {
+  int w = img.cols;
+  int h = img.rows;
+
+  float ratio = 1.f;
+  int max_wh = w >= h ? w : h;
+  if (max_wh > max_size_len) {
+    if (h > w) {
+      ratio = static_cast<float>(max_size_len) / static_cast<float>(h);
+    } else {
+      ratio = static_cast<float>(max_size_len) / static_cast<float>(w);
+    }
+  }
+
+  int resize_h = static_cast<int>(float(h) * ratio);
+  int resize_w = static_cast<int>(float(w) * ratio);
+  if (resize_h % 32 == 0)
+    resize_h = resize_h;
+  else if (resize_h / 32 < 1 + 1e-5)
+    resize_h = 32;
+  else
+    resize_h = (resize_h / 32 - 1) * 32;
+
+  if (resize_w % 32 == 0)
+    resize_w = resize_w;
+  else if (resize_w / 32 < 1 + 1e-5)
+    resize_w = 32;
+  else
+    resize_w = (resize_w / 32 - 1) * 32;
+
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
+
+  ratio_hw.push_back(static_cast<float>(resize_h) / static_cast<float>(h));
+  ratio_hw.push_back(static_cast<float>(resize_w) / static_cast<float>(w));
+  return resize_img;
+}
+
+void OCR_PPredictor::infer_det(cv::Mat &origin, int max_size_len, std::vector<OCRPredictResult> &ocr_results) {
+  std::vector<float> mean = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+
   PredictorInput input = _det_predictor->get_first_input();
-  input.set_dims(dims);
-  input.set_data(input_data, input_len);
+
+  std::vector<float> ratio_hw;
+  cv::Mat input_image = DetResizeImg(origin, max_size_len, ratio_hw);
+  input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+  const float *dimg = reinterpret_cast<const float *>(input_image.data);
+  int input_size = input_image.rows * input_image.cols;
+
+  input.set_dims({1, 3, input_image.rows, input_image.cols});
+
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+  LOGI("ocr cpp det shape %d,%d", input_image.rows,input_image.cols);
   std::vector<PredictorOutput> results = _det_predictor->infer();
   PredictorOutput &res = results.at(0);
   std::vector<std::vector<std::vector<int>>> filtered_box = calc_filtered_boxes(
-      res.get_float_data(), res.get_size(), (int)dims[2], (int)dims[3], origin);
-  LOGI("Filter_box size %ld", filtered_box.size());
-  return infer_rec(filtered_box, origin);
+          res.get_float_data(), res.get_size(), input_image.rows, input_image.cols, origin);
+  LOGI("ocr cpp det Filter_box size %ld", filtered_box.size());
+
+  for(int i = 0;i<filtered_box.size();i++){
+    LOGI("ocr cpp box  %d,%d,%d,%d,%d,%d,%d,%d", filtered_box[i][0][0],filtered_box[i][0][1], filtered_box[i][1][0],filtered_box[i][1][1], filtered_box[i][2][0],filtered_box[i][2][1], filtered_box[i][3][0],filtered_box[i][3][1]);
+    OCRPredictResult res;
+    res.points = filtered_box[i];
+    ocr_results.push_back(res);
+  }
 }
 
-std::vector<OCRPredictResult> OCR_PPredictor::infer_rec(
-    const std::vector<std::vector<std::vector<int>>> &boxes,
-    const cv::Mat &origin_img) {
+void OCR_PPredictor::infer_rec(const cv::Mat &origin_img, int run_cls, OCRPredictResult& ocr_result) {
   std::vector<float> mean = {0.5f, 0.5f, 0.5f};
   std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
   std::vector<int64_t> dims = {1, 3, 0, 0};
-  std::vector<OCRPredictResult> ocr_results;
 
   PredictorInput input = _rec_predictor->get_first_input();
-  for (auto bp = boxes.crbegin(); bp != boxes.crend(); ++bp) {
-    const std::vector<std::vector<int>> &box = *bp;
-    cv::Mat crop_img = get_rotate_crop_image(origin_img, box);
-    crop_img = infer_cls(crop_img);
 
-    float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
-    cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
-    input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
-    const float *dimg = reinterpret_cast<const float *>(input_image.data);
-    int input_size = input_image.rows * input_image.cols;
+  const std::vector<std::vector<int>> &box = ocr_result.points;
+  cv::Mat crop_img;
+  if(box.size()>0){
+    crop_img = get_rotate_crop_image(origin_img, box);
+  }
+  else{
+    crop_img = origin_img;
+  }
 
-    dims[2] = input_image.rows;
-    dims[3] = input_image.cols;
-    input.set_dims(dims);
+  if(run_cls){
+    ClsPredictResult cls_res = infer_cls(crop_img);
+    crop_img = cls_res.img;
+    ocr_result.cls_score = cls_res.cls_score;
+    ocr_result.cls_label = cls_res.cls_label;
+  }
 
-    neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
-                    scale);
 
-    std::vector<PredictorOutput> results = _rec_predictor->infer();
-    const float *predict_batch = results.at(0).get_float_data();
-    const std::vector<int64_t> predict_shape = results.at(0).get_shape();
+  float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
+  cv::Mat input_image = crnn_resize_img(crop_img, wh_ratio);
+  input_image.convertTo(input_image, CV_32FC3, 1 / 255.0f);
+  const float *dimg = reinterpret_cast<const float *>(input_image.data);
+  int input_size = input_image.rows * input_image.cols;
 
-    OCRPredictResult res;
+  dims[2] = input_image.rows;
+  dims[3] = input_image.cols;
+  input.set_dims(dims);
 
-    // ctc decode
-    int argmax_idx;
-    int last_index = 0;
-    float score = 0.f;
-    int count = 0;
-    float max_value = 0.0f;
-
-    for (int n = 0; n < predict_shape[1]; n++) {
-      argmax_idx = int(argmax(&predict_batch[n * predict_shape[2]],
-                              &predict_batch[(n + 1) * predict_shape[2]]));
-      max_value =
-          float(*std::max_element(&predict_batch[n * predict_shape[2]],
-                                  &predict_batch[(n + 1) * predict_shape[2]]));
-      if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
-        score += max_value;
-        count += 1;
-        res.word_index.push_back(argmax_idx);
-      }
-      last_index = argmax_idx;
-    }
-    score /= count;
-    if (res.word_index.empty()) {
-      continue;
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+
+  std::vector<PredictorOutput> results = _rec_predictor->infer();
+  const float *predict_batch = results.at(0).get_float_data();
+  const std::vector<int64_t> predict_shape = results.at(0).get_shape();
+
+  // ctc decode
+  int argmax_idx;
+  int last_index = 0;
+  float score = 0.f;
+  int count = 0;
+  float max_value = 0.0f;
+
+  for (int n = 0; n < predict_shape[1]; n++) {
+    argmax_idx = int(argmax(&predict_batch[n * predict_shape[2]],
+                            &predict_batch[(n + 1) * predict_shape[2]]));
+    max_value =
+        float(*std::max_element(&predict_batch[n * predict_shape[2]],
+                                &predict_batch[(n + 1) * predict_shape[2]]));
+    if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
+      score += max_value;
+      count += 1;
+      ocr_result.word_index.push_back(argmax_idx);
     }
-    res.score = score;
-    res.points = box;
-    ocr_results.emplace_back(std::move(res));
+    last_index = argmax_idx;
   }
-  LOGI("ocr_results finished %lu", ocr_results.size());
-  return ocr_results;
+  score /= count;
+  ocr_result.score = score;
+  LOGI("ocr cpp rec word size %ld", count);
 }
 
-cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
+ClsPredictResult OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
   std::vector<float> mean = {0.5f, 0.5f, 0.5f};
   std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
   std::vector<int64_t> dims = {1, 3, 0, 0};
-  std::vector<OCRPredictResult> ocr_results;
 
   PredictorInput input = _cls_predictor->get_first_input();
 
@@ -182,7 +266,7 @@ cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
   float score = 0;
   int label = 0;
   for (int64_t i = 0; i < results.at(0).get_size(); i++) {
-    LOGI("output scores [%f]", scores[i]);
+    LOGI("ocr cpp cls output scores [%f]", scores[i]);
     if (scores[i] > score) {
       score = scores[i];
       label = i;
@@ -193,7 +277,12 @@ cv::Mat OCR_PPredictor::infer_cls(const cv::Mat &img, float thresh) {
   if (label % 2 == 1 && score > thresh) {
     cv::rotate(srcimg, srcimg, 1);
   }
-  return srcimg;
+  ClsPredictResult res;
+  res.cls_label = label;
+  res.cls_score = score;
+  res.img = srcimg;
+  LOGI("ocr cpp cls word cls %ld, %f", label, score);
+  return res;
 }
 
 std::vector<std::vector<std::vector<int>>>

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h

@@ -15,7 +15,8 @@ namespace ppredictor {
  * Config
  */
 struct OCR_Config {
-  int thread_num = 4; // Thread num
+    int use_opencl = 0;
+    int thread_num = 4; // Thread num
   paddle::lite_api::PowerMode mode =
       paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
 };
@@ -27,8 +28,15 @@ struct OCRPredictResult {
   std::vector<int> word_index;
   std::vector<std::vector<int>> points;
   float score;
+  float cls_score;
+  int cls_label=-1;
 };
 
+struct ClsPredictResult {
+    float cls_score;
+    int cls_label=-1;
+    cv::Mat img;
+};
 /**
  * OCR there are 2 models
  * 1. First model（det），select polygones to show where are the texts
@@ -62,8 +70,7 @@ public:
    * @return
    */
   virtual std::vector<OCRPredictResult>
-  infer_ocr(const std::vector<int64_t> &dims, const float *input_data,
-            int input_len, int net_flag, cv::Mat &origin);
+  infer_ocr(cv::Mat &origin, int max_size_len, int run_det, int run_cls, int run_rec);
 
   virtual NET_TYPE get_net_flag() const;
 
@@ -80,25 +87,26 @@ private:
   calc_filtered_boxes(const float *pred, int pred_size, int output_height,
                       int output_width, const cv::Mat &origin);
 
+  void
+  infer_det(cv::Mat &origin, int max_side_len, std::vector<OCRPredictResult>& ocr_results);
   /**
-   * infer for second model
+   * infer for rec model
    *
    * @param boxes
    * @param origin
    * @return
    */
-  std::vector<OCRPredictResult>
-  infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
-            const cv::Mat &origin);
+  void
+  infer_rec(const cv::Mat &origin, int run_cls, OCRPredictResult& ocr_result);
 
-  /**
+    /**
   * infer for cls model
   *
   * @param boxes
   * @param origin
   * @return
   */
-  cv::Mat infer_cls(const cv::Mat &origin, float thresh = 0.9);
+  ClsPredictResult infer_cls(const cv::Mat &origin, float thresh = 0.9);
 
   /**
    * Postprocess or sencod model to extract text

deploy/android_demo/app/src/main/cpp/ppredictor.cpp

@@ -2,9 +2,9 @@
 #include "common.h"
 
 namespace ppredictor {
-PPredictor::PPredictor(int thread_num, int net_flag,
+PPredictor::PPredictor(int use_opencl, int thread_num, int net_flag,
                        paddle::lite_api::PowerMode mode)
-    : _thread_num(thread_num), _net_flag(net_flag), _mode(mode) {}
+    : _use_opencl(use_opencl), _thread_num(thread_num), _net_flag(net_flag), _mode(mode) {}
 
 int PPredictor::init_nb(const std::string &model_content) {
   paddle::lite_api::MobileConfig config;
@@ -19,10 +19,40 @@ int PPredictor::init_from_file(const std::string &model_content) {
 }
 
 template <typename ConfigT> int PPredictor::_init(ConfigT &config) {
+  bool is_opencl_backend_valid = paddle::lite_api::IsOpenCLBackendValid(/*check_fp16_valid = false*/);
+  if (is_opencl_backend_valid) {
+    if (_use_opencl != 0) {
+      // Make sure you have write permission of the binary path.
+      // We strongly recommend each model has a unique binary name.
+      const std::string bin_path = "/data/local/tmp/";
+      const std::string bin_name = "lite_opencl_kernel.bin";
+      config.set_opencl_binary_path_name(bin_path, bin_name);
+
+      // opencl tune option
+      // CL_TUNE_NONE: 0
+      // CL_TUNE_RAPID: 1
+      // CL_TUNE_NORMAL: 2
+      // CL_TUNE_EXHAUSTIVE: 3
+      const std::string tuned_path = "/data/local/tmp/";
+      const std::string tuned_name = "lite_opencl_tuned.bin";
+      config.set_opencl_tune(paddle::lite_api::CL_TUNE_NORMAL, tuned_path, tuned_name);
+
+      // opencl precision option
+      // CL_PRECISION_AUTO: 0, first fp16 if valid, default
+      // CL_PRECISION_FP32: 1, force fp32
+      // CL_PRECISION_FP16: 2, force fp16
+      config.set_opencl_precision(paddle::lite_api::CL_PRECISION_FP32);
+      LOGI("ocr cpp device: running on gpu.");
+    }
+  } else {
+    LOGI("ocr cpp device: running on cpu.");
+    // you can give backup cpu nb model instead
+    // config.set_model_from_file(cpu_nb_model_dir);
+  }
   config.set_threads(_thread_num);
   config.set_power_mode(_mode);
   _predictor = paddle::lite_api::CreatePaddlePredictor(config);
-  LOGI("paddle instance created");
+  LOGI("ocr cpp paddle instance created");
   return RETURN_OK;
 }
 
@@ -43,18 +73,18 @@ std::vector<PredictorInput> PPredictor::get_inputs(int num) {
 PredictorInput PPredictor::get_first_input() { return get_input(0); }
 
 std::vector<PredictorOutput> PPredictor::infer() {
-  LOGI("infer Run start %d", _net_flag);
+  LOGI("ocr cpp infer Run start %d", _net_flag);
   std::vector<PredictorOutput> results;
   if (!_is_input_get) {
     return results;
   }
   _predictor->Run();
-  LOGI("infer Run end");
+  LOGI("ocr cpp infer Run end");
 
   for (int i = 0; i < _predictor->GetOutputNames().size(); i++) {
     std::unique_ptr<const paddle::lite_api::Tensor> output_tensor =
         _predictor->GetOutput(i);
-    LOGI("output tensor[%d] size %ld", i, product(output_tensor->shape()));
+    LOGI("ocr cpp output tensor[%d] size %ld", i, product(output_tensor->shape()));
     PredictorOutput result{std::move(output_tensor), i, _net_flag};
     results.emplace_back(std::move(result));
   }

deploy/android_demo/app/src/main/cpp/ppredictor.h

@@ -22,7 +22,7 @@ public:
 class PPredictor : public PPredictor_Interface {
 public:
   PPredictor(
-      int thread_num, int net_flag = 0,
+          int use_opencl, int thread_num, int net_flag = 0,
       paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH);
 
   virtual ~PPredictor() {}
@@ -54,6 +54,7 @@ protected:
   template <typename ConfigT> int _init(ConfigT &config);
 
 private:
+    int _use_opencl;
   int _thread_num;
   paddle::lite_api::PowerMode _mode;
   std::shared_ptr<paddle::lite_api::PaddlePredictor> _predictor;

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java

@@ -13,6 +13,7 @@ import android.graphics.BitmapFactory;
 import android.graphics.drawable.BitmapDrawable;
 import android.media.ExifInterface;
 import android.content.res.AssetManager;
+import android.media.FaceDetector;
 import android.net.Uri;
 import android.os.Bundle;
 import android.os.Environment;
@@ -27,7 +28,9 @@ import android.view.Menu;
 import android.view.MenuInflater;
 import android.view.MenuItem;
 import android.view.View;
+import android.widget.CheckBox;
 import android.widget.ImageView;
+import android.widget.Spinner;
 import android.widget.TextView;
 import android.widget.Toast;
 
@@ -68,23 +71,24 @@ public class MainActivity extends AppCompatActivity {
     protected ImageView ivInputImage;
     protected TextView tvOutputResult;
     protected TextView tvInferenceTime;
+    protected CheckBox cbOpencl;
+    protected Spinner spRunMode;
 
-    // Model settings of object detection
+    // Model settings of ocr
     protected String modelPath = "";
     protected String labelPath = "";
     protected String imagePath = "";
     protected int cpuThreadNum = 1;
     protected String cpuPowerMode = "";
-    protected String inputColorFormat = "";
-    protected long[] inputShape = new long[]{};
-    protected float[] inputMean = new float[]{};
-    protected float[] inputStd = new float[]{};
+    protected int detLongSize = 960;
     protected float scoreThreshold = 0.1f;
     private String currentPhotoPath;
-    private AssetManager assetManager =null;
+    private AssetManager assetManager = null;
 
     protected Predictor predictor = new Predictor();
 
+    private Bitmap cur_predict_image = null;
+
     @Override
     protected void onCreate(Bundle savedInstanceState) {
         super.onCreate(savedInstanceState);
@@ -98,10 +102,12 @@ public class MainActivity extends AppCompatActivity {
 
         // Setup the UI components
         tvInputSetting = findViewById(R.id.tv_input_setting);
+        cbOpencl = findViewById(R.id.cb_opencl);
         tvStatus = findViewById(R.id.tv_model_img_status);
         ivInputImage = findViewById(R.id.iv_input_image);
         tvInferenceTime = findViewById(R.id.tv_inference_time);
         tvOutputResult = findViewById(R.id.tv_output_result);
+        spRunMode = findViewById(R.id.sp_run_mode);
         tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
         tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
 
@@ -111,26 +117,26 @@ public class MainActivity extends AppCompatActivity {
             public void handleMessage(Message msg) {
                 switch (msg.what) {
                     case RESPONSE_LOAD_MODEL_SUCCESSED:
-                        if(pbLoadModel!=null && pbLoadModel.isShowing()){
+                        if (pbLoadModel != null && pbLoadModel.isShowing()) {
                             pbLoadModel.dismiss();
                         }
                         onLoadModelSuccessed();
                         break;
                     case RESPONSE_LOAD_MODEL_FAILED:
-                        if(pbLoadModel!=null && pbLoadModel.isShowing()){
+                        if (pbLoadModel != null && pbLoadModel.isShowing()) {
                             pbLoadModel.dismiss();
                         }
                         Toast.makeText(MainActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
                         onLoadModelFailed();
                         break;
                     case RESPONSE_RUN_MODEL_SUCCESSED:
-                        if(pbRunModel!=null && pbRunModel.isShowing()){
+                        if (pbRunModel != null && pbRunModel.isShowing()) {
                             pbRunModel.dismiss();
                         }
                         onRunModelSuccessed();
                         break;
                     case RESPONSE_RUN_MODEL_FAILED:
-                        if(pbRunModel!=null && pbRunModel.isShowing()){
+                        if (pbRunModel != null && pbRunModel.isShowing()) {
                             pbRunModel.dismiss();
                         }
                         Toast.makeText(MainActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
@@ -175,71 +181,47 @@ public class MainActivity extends AppCompatActivity {
         super.onResume();
         SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
         boolean settingsChanged = false;
+        boolean model_settingsChanged = false;
         String model_path = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
                 getString(R.string.MODEL_PATH_DEFAULT));
         String label_path = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
                 getString(R.string.LABEL_PATH_DEFAULT));
         String image_path = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
                 getString(R.string.IMAGE_PATH_DEFAULT));
-        settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
+        model_settingsChanged |= !model_path.equalsIgnoreCase(modelPath);
         settingsChanged |= !label_path.equalsIgnoreCase(labelPath);
         settingsChanged |= !image_path.equalsIgnoreCase(imagePath);
         int cpu_thread_num = Integer.parseInt(sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
                 getString(R.string.CPU_THREAD_NUM_DEFAULT)));
-        settingsChanged |= cpu_thread_num != cpuThreadNum;
+        model_settingsChanged |= cpu_thread_num != cpuThreadNum;
         String cpu_power_mode =
                 sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
                         getString(R.string.CPU_POWER_MODE_DEFAULT));
-        settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
-        String input_color_format =
-                sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
-                        getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
-        settingsChanged |= !input_color_format.equalsIgnoreCase(inputColorFormat);
-        long[] input_shape =
-                Utils.parseLongsFromString(sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
-                        getString(R.string.INPUT_SHAPE_DEFAULT)), ",");
-        float[] input_mean =
-                Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
-                        getString(R.string.INPUT_MEAN_DEFAULT)), ",");
-        float[] input_std =
-                Utils.parseFloatsFromString(sharedPreferences.getString(getString(R.string.INPUT_STD_KEY)
-                        , getString(R.string.INPUT_STD_DEFAULT)), ",");
-        settingsChanged |= input_shape.length != inputShape.length;
-        settingsChanged |= input_mean.length != inputMean.length;
-        settingsChanged |= input_std.length != inputStd.length;
-        if (!settingsChanged) {
-            for (int i = 0; i < input_shape.length; i++) {
-                settingsChanged |= input_shape[i] != inputShape[i];
-            }
-            for (int i = 0; i < input_mean.length; i++) {
-                settingsChanged |= input_mean[i] != inputMean[i];
-            }
-            for (int i = 0; i < input_std.length; i++) {
-                settingsChanged |= input_std[i] != inputStd[i];
-            }
-        }
+        model_settingsChanged |= !cpu_power_mode.equalsIgnoreCase(cpuPowerMode);
+
+        int det_long_size = Integer.parseInt(sharedPreferences.getString(getString(R.string.DET_LONG_SIZE_KEY),
+                getString(R.string.DET_LONG_SIZE_DEFAULT)));
+        settingsChanged |= det_long_size != detLongSize;
         float score_threshold =
                 Float.parseFloat(sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
                         getString(R.string.SCORE_THRESHOLD_DEFAULT)));
         settingsChanged |= scoreThreshold != score_threshold;
         if (settingsChanged) {
-            modelPath = model_path;
             labelPath = label_path;
             imagePath = image_path;
+            detLongSize = det_long_size;
+            scoreThreshold = score_threshold;
+            set_img();
+        }
+        if (model_settingsChanged) {
+            modelPath = model_path;
             cpuThreadNum = cpu_thread_num;
             cpuPowerMode = cpu_power_mode;
-            inputColorFormat = input_color_format;
-            inputShape = input_shape;
-            inputMean = input_mean;
-            inputStd = input_std;
-            scoreThreshold = score_threshold;
             // Update UI
-            tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\n" + "CPU" +
-                    " Thread Num: " + Integer.toString(cpuThreadNum) + "\n" + "CPU Power Mode: " + cpuPowerMode);
+            tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
             tvInputSetting.scrollTo(0, 0);
             // Reload model if configure has been changed
-//            loadModel();
-            set_img();
+            loadModel();
         }
     }
 
@@ -254,20 +236,28 @@ public class MainActivity extends AppCompatActivity {
     }
 
     public boolean onLoadModel() {
-        return predictor.init(MainActivity.this, modelPath, labelPath, cpuThreadNum,
+        if (predictor.isLoaded()) {
+            predictor.releaseModel();
+        }
+        return predictor.init(MainActivity.this, modelPath, labelPath, cbOpencl.isChecked() ? 1 : 0, cpuThreadNum,
                 cpuPowerMode,
-                inputColorFormat,
-                inputShape, inputMean,
-                inputStd, scoreThreshold);
+                detLongSize, scoreThreshold);
     }
 
     public boolean onRunModel() {
-        return predictor.isLoaded() && predictor.runModel();
+        String run_mode = spRunMode.getSelectedItem().toString();
+        int run_det = run_mode.contains("检测") ? 1 : 0;
+        int run_cls = run_mode.contains("分类") ? 1 : 0;
+        int run_rec = run_mode.contains("识别") ? 1 : 0;
+        return predictor.isLoaded() && predictor.runModel(run_det, run_cls, run_rec);
     }
 
     public void onLoadModelSuccessed() {
         // Load test image from path and run model
+        tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
+        tvInputSetting.scrollTo(0, 0);
         tvStatus.setText("STATUS: load model successed");
+
     }
 
     public void onLoadModelFailed() {
@@ -290,20 +280,13 @@ public class MainActivity extends AppCompatActivity {
         tvStatus.setText("STATUS: run model failed");
     }
 
-    public void onImageChanged(Bitmap image) {
-        // Rerun model if users pick test image from gallery or camera
-        if (image != null && predictor.isLoaded()) {
-            predictor.setInputImage(image);
-            runModel();
-        }
-    }
-
     public void set_img() {
         // Load test image from path and run model
         try {
-            assetManager= getAssets();
-            InputStream in=assetManager.open(imagePath);
-            Bitmap bmp=BitmapFactory.decodeStream(in);
+            assetManager = getAssets();
+            InputStream in = assetManager.open(imagePath);
+            Bitmap bmp = BitmapFactory.decodeStream(in);
+            cur_predict_image = bmp;
             ivInputImage.setImageBitmap(bmp);
         } catch (IOException e) {
             Toast.makeText(MainActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
@@ -430,7 +413,7 @@ public class MainActivity extends AppCompatActivity {
                         Cursor cursor = managedQuery(uri, proj, null, null, null);
                         cursor.moveToFirst();
                         if (image != null) {
-//                            onImageChanged(image);
+                            cur_predict_image = image;
                             ivInputImage.setImageBitmap(image);
                         }
                     } catch (IOException e) {
@@ -451,7 +434,7 @@ public class MainActivity extends AppCompatActivity {
                         Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
                         image = Utils.rotateBitmap(image, orientation);
                         if (image != null) {
-//                            onImageChanged(image);
+                            cur_predict_image = image;
                             ivInputImage.setImageBitmap(image);
                         }
                     } else {
@@ -464,28 +447,28 @@ public class MainActivity extends AppCompatActivity {
         }
     }
 
-    public void btn_load_model_click(View view) {
-        if (predictor.isLoaded()){
-            tvStatus.setText("STATUS: model has been loaded");
-        }else{
-            tvStatus.setText("STATUS: load model ......");
-            loadModel();
-        }
+    public void btn_reset_img_click(View view) {
+        ivInputImage.setImageBitmap(cur_predict_image);
+    }
+
+    public void cb_opencl_click(View view) {
+        tvStatus.setText("STATUS: load model ......");
+        loadModel();
     }
 
     public void btn_run_model_click(View view) {
-        Bitmap image =((BitmapDrawable)ivInputImage.getDrawable()).getBitmap();
-        if(image == null) {
+        Bitmap image = ((BitmapDrawable) ivInputImage.getDrawable()).getBitmap();
+        if (image == null) {
             tvStatus.setText("STATUS: image is not exists");
-        }
-        else if (!predictor.isLoaded()){
+        } else if (!predictor.isLoaded()) {
             tvStatus.setText("STATUS: model is not loaded");
-        }else{
+        } else {
             tvStatus.setText("STATUS: run model ...... ");
             predictor.setInputImage(image);
             runModel();
         }
     }
+
     public void btn_choice_img_click(View view) {
         if (requestAllPermissions()) {
             openGallery();
@@ -506,4 +489,32 @@ public class MainActivity extends AppCompatActivity {
         worker.quit();
         super.onDestroy();
     }
+
+    public int get_run_mode() {
+        String run_mode = spRunMode.getSelectedItem().toString();
+        int mode;
+        switch (run_mode) {
+            case "检测+分类+识别":
+                mode = 1;
+                break;
+            case "检测+识别":
+                mode = 2;
+                break;
+            case "识别+分类":
+                mode = 3;
+                break;
+            case "检测":
+                mode = 4;
+                break;
+            case "识别":
+                mode = 5;
+                break;
+            case "分类":
+                mode = 6;
+                break;
+            default:
+                mode = 1;
+        }
+        return mode;
+    }
 }

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MiniActivity.java

-package com.baidu.paddle.lite.demo.ocr;
-
-import android.graphics.Bitmap;
-import android.graphics.BitmapFactory;
-import android.os.Build;
-import android.os.Bundle;
-import android.os.Handler;
-import android.os.HandlerThread;
-import android.os.Message;
-import android.util.Log;
-import android.view.View;
-import android.widget.Button;
-import android.widget.ImageView;
-import android.widget.TextView;
-import android.widget.Toast;
-
-import androidx.appcompat.app.AppCompatActivity;
-
-import java.io.IOException;
-import java.io.InputStream;
-
-public class MiniActivity extends AppCompatActivity {
-
-
-    public static final int REQUEST_LOAD_MODEL = 0;
-    public static final int REQUEST_RUN_MODEL = 1;
-    public static final int REQUEST_UNLOAD_MODEL = 2;
-    public static final int RESPONSE_LOAD_MODEL_SUCCESSED = 0;
-    public static final int RESPONSE_LOAD_MODEL_FAILED = 1;
-    public static final int RESPONSE_RUN_MODEL_SUCCESSED = 2;
-    public static final int RESPONSE_RUN_MODEL_FAILED = 3;
-
-    private static final String TAG = "MiniActivity";
-
-    protected Handler receiver = null; // Receive messages from worker thread
-    protected Handler sender = null; // Send command to worker thread
-    protected HandlerThread worker = null; // Worker thread to load&run model
-    protected volatile Predictor predictor = null;
-
-    private String assetModelDirPath = "models/ocr_v2_for_cpu";
-    private String assetlabelFilePath = "labels/ppocr_keys_v1.txt";
-
-    private Button button;
-    private ImageView imageView; // image result
-    private TextView textView; // text result
-
-    @Override
-    protected void onCreate(Bundle savedInstanceState) {
-        super.onCreate(savedInstanceState);
-        setContentView(R.layout.activity_mini);
-
-        Log.i(TAG, "SHOW in Logcat");
-
-        // Prepare the worker thread for mode loading and inference
-        worker = new HandlerThread("Predictor Worker");
-        worker.start();
-        sender = new Handler(worker.getLooper()) {
-            public void handleMessage(Message msg) {
-                switch (msg.what) {
-                    case REQUEST_LOAD_MODEL:
-                        // Load model and reload test image
-                        if (!onLoadModel()) {
-                            runOnUiThread(new Runnable() {
-                                @Override
-                                public void run() {
-                                    Toast.makeText(MiniActivity.this, "Load model failed!", Toast.LENGTH_SHORT).show();
-                                }
-                            });
-                        }
-                        break;
-                    case REQUEST_RUN_MODEL:
-                        // Run model if model is loaded
-                        final boolean isSuccessed = onRunModel();
-                        runOnUiThread(new Runnable() {
-                            @Override
-                            public void run() {
-                                if (isSuccessed){
-                                    onRunModelSuccessed();
-                                }else{
-                                    Toast.makeText(MiniActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
-                                }
-                            }
-                        });
-                        break;
-                }
-            }
-        };
-        sender.sendEmptyMessage(REQUEST_LOAD_MODEL); // corresponding to REQUEST_LOAD_MODEL， to call onLoadModel()
-
-        imageView = findViewById(R.id.imageView);
-        textView = findViewById(R.id.sample_text);
-        button = findViewById(R.id.button);
-        button.setOnClickListener(new View.OnClickListener() {
-            @Override
-            public void onClick(View v) {
-                sender.sendEmptyMessage(REQUEST_RUN_MODEL);
-            }
-        });
-
-
-    }
-
-    @Override
-    protected void onDestroy() {
-        onUnloadModel();
-        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.JELLY_BEAN_MR2) {
-            worker.quitSafely();
-        } else {
-            worker.quit();
-        }
-        super.onDestroy();
-    }
-
-    /**
-     * call in onCreate, model init
-     *
-     * @return
-     */
-    private boolean onLoadModel() {
-        if (predictor == null) {
-            predictor = new Predictor();
-        }
-        return predictor.init(this, assetModelDirPath, assetlabelFilePath);
-    }
-
-    /**
-     * init engine
-     * call in onCreate
-     *
-     * @return
-     */
-    private boolean onRunModel() {
-        try {
-            String assetImagePath = "images/0.jpg";
-            InputStream imageStream = getAssets().open(assetImagePath);
-            Bitmap image = BitmapFactory.decodeStream(imageStream);
-            // Input is Bitmap
-            predictor.setInputImage(image);
-            return predictor.isLoaded() && predictor.runModel();
-        } catch (IOException e) {
-            e.printStackTrace();
-            return false;
-        }
-    }
-
-    private void onRunModelSuccessed() {
-        Log.i(TAG, "onRunModelSuccessed");
-        textView.setText(predictor.outputResult);
-        imageView.setImageBitmap(predictor.outputImage);
-    }
-
-    private void onUnloadModel() {
-        if (predictor != null) {
-            predictor.releaseModel();
-        }
-    }
-}

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OCRPredictorNative.java

@@ -29,22 +29,22 @@ public class OCRPredictorNative {
     public OCRPredictorNative(Config config) {
         this.config = config;
         loadLibrary();
-        nativePointer = init(config.detModelFilename, config.recModelFilename,config.clsModelFilename,
+        nativePointer = init(config.detModelFilename, config.recModelFilename, config.clsModelFilename, config.useOpencl,
                 config.cpuThreadNum, config.cpuPower);
         Log.i("OCRPredictorNative", "load success " + nativePointer);
 
     }
 
 
-    public ArrayList<OcrResultModel> runImage(float[] inputData, int width, int height, int channels, Bitmap originalImage) {
-        Log.i("OCRPredictorNative", "begin to run image " + inputData.length + " " + width + " " + height);
-        float[] dims = new float[]{1, channels, height, width};
-        float[] rawResults = forward(nativePointer, inputData, dims, originalImage);
+    public ArrayList<OcrResultModel> runImage(Bitmap originalImage, int max_size_len, int run_det, int run_cls, int run_rec) {
+        Log.i("OCRPredictorNative", "begin to run image ");
+        float[] rawResults = forward(nativePointer, originalImage, max_size_len, run_det, run_cls, run_rec);
         ArrayList<OcrResultModel> results = postprocess(rawResults);
         return results;
     }
 
     public static class Config {
+        public int useOpencl;
         public int cpuThreadNum;
         public String cpuPower;
         public String detModelFilename;
@@ -53,16 +53,16 @@ public class OCRPredictorNative {
 
     }
 
-    public void destory(){
+    public void destory() {
         if (nativePointer > 0) {
             release(nativePointer);
             nativePointer = 0;
         }
     }
 
-    protected native long init(String detModelPath, String recModelPath,String clsModelPath, int threadNum, String cpuMode);
+    protected native long init(String detModelPath, String recModelPath, String clsModelPath, int useOpencl, int threadNum, String cpuMode);
 
-    protected native float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
+    protected native float[] forward(long pointer, Bitmap originalImage,int max_size_len, int run_det, int run_cls, int run_rec);
 
     protected native void release(long pointer);
 
@@ -73,9 +73,9 @@ public class OCRPredictorNative {
         while (begin < raw.length) {
             int point_num = Math.round(raw[begin]);
             int word_num = Math.round(raw[begin + 1]);
-            OcrResultModel model = parse(raw, begin + 2, point_num, word_num);
-            begin += 2 + 1 + point_num * 2 + word_num;
-            results.add(model);
+            OcrResultModel res = parse(raw, begin + 2, point_num, word_num);
+            begin += 2 + 1 + point_num * 2 + word_num + 2;
+            results.add(res);
         }
 
         return results;
@@ -83,19 +83,22 @@ public class OCRPredictorNative {
 
     private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) {
         int current = begin;
-        OcrResultModel model = new OcrResultModel();
-        model.setConfidence(raw[current]);
+        OcrResultModel res = new OcrResultModel();
+        res.setConfidence(raw[current]);
         current++;
         for (int i = 0; i < pointNum; i++) {
-            model.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
+            res.addPoints(Math.round(raw[current + i * 2]), Math.round(raw[current + i * 2 + 1]));
         }
         current += (pointNum * 2);
         for (int i = 0; i < wordNum; i++) {
             int index = Math.round(raw[current + i]);
-            model.addWordIndex(index);
+            res.addWordIndex(index);
         }
+        current += wordNum;
+        res.setClsIdx(raw[current]);
+        res.setClsConfidence(raw[current + 1]);
         Log.i("OCRPredictorNative", "word finished " + wordNum);
-        return model;
+        return res;
     }
 
 

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/OcrResultModel.java

@@ -10,6 +10,9 @@ public class OcrResultModel {
     private List<Integer> wordIndex;
     private String label;
     private float confidence;
+    private float cls_idx;
+    private String cls_label;
+    private float cls_confidence;
 
     public OcrResultModel() {
         super();
@@ -49,4 +52,28 @@ public class OcrResultModel {
     public void setConfidence(float confidence) {
         this.confidence = confidence;
     }
+
+    public float getClsIdx() {
+        return cls_idx;
+    }
+
+    public void setClsIdx(float idx) {
+        this.cls_idx = idx;
+    }
+
+    public String getClsLabel() {
+        return cls_label;
+    }
+
+    public void setClsLabel(String label) {
+        this.cls_label = label;
+    }
+
+    public float getClsConfidence() {
+        return cls_confidence;
+    }
+
+    public void setClsConfidence(float confidence) {
+        this.cls_confidence = confidence;
+    }
 }

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/Predictor.java

@@ -31,23 +31,19 @@ public class Predictor {
     protected float inferenceTime = 0;
     // Only for object detection
     protected Vector<String> wordLabels = new Vector<String>();
-    protected String inputColorFormat = "BGR";
-    protected long[] inputShape = new long[]{1, 3, 960};
-    protected float[] inputMean = new float[]{0.485f, 0.456f, 0.406f};
-    protected float[] inputStd = new float[]{1.0f / 0.229f, 1.0f / 0.224f, 1.0f / 0.225f};
+    protected int detLongSize = 960;
     protected float scoreThreshold = 0.1f;
     protected Bitmap inputImage = null;
     protected Bitmap outputImage = null;
     protected volatile String outputResult = "";
-    protected float preprocessTime = 0;
     protected float postprocessTime = 0;
 
 
     public Predictor() {
     }
 
-    public boolean init(Context appCtx, String modelPath, String labelPath) {
-        isLoaded = loadModel(appCtx, modelPath, cpuThreadNum, cpuPowerMode);
+    public boolean init(Context appCtx, String modelPath, String labelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode) {
+        isLoaded = loadModel(appCtx, modelPath, useOpencl, cpuThreadNum, cpuPowerMode);
         if (!isLoaded) {
             return false;
         }
@@ -56,49 +52,18 @@ public class Predictor {
     }
 
 
-    public boolean init(Context appCtx, String modelPath, String labelPath, int cpuThreadNum, String cpuPowerMode,
-                        String inputColorFormat,
-                        long[] inputShape, float[] inputMean,
-                        float[] inputStd, float scoreThreshold) {
-        if (inputShape.length != 3) {
-            Log.e(TAG, "Size of input shape should be: 3");
-            return false;
-        }
-        if (inputMean.length != inputShape[1]) {
-            Log.e(TAG, "Size of input mean should be: " + Long.toString(inputShape[1]));
-            return false;
-        }
-        if (inputStd.length != inputShape[1]) {
-            Log.e(TAG, "Size of input std should be: " + Long.toString(inputShape[1]));
-            return false;
-        }
-        if (inputShape[0] != 1) {
-            Log.e(TAG, "Only one batch is supported in the image classification demo, you can use any batch size in " +
-                    "your Apps!");
-            return false;
-        }
-        if (inputShape[1] != 1 && inputShape[1] != 3) {
-            Log.e(TAG, "Only one/three channels are supported in the image classification demo, you can use any " +
-                    "channel size in your Apps!");
-            return false;
-        }
-        if (!inputColorFormat.equalsIgnoreCase("BGR")) {
-            Log.e(TAG, "Only  BGR color format is supported.");
-            return false;
-        }
-        boolean isLoaded = init(appCtx, modelPath, labelPath);
+    public boolean init(Context appCtx, String modelPath, String labelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode,
+                        int detLongSize, float scoreThreshold) {
+        boolean isLoaded = init(appCtx, modelPath, labelPath, useOpencl, cpuThreadNum, cpuPowerMode);
         if (!isLoaded) {
             return false;
         }
-        this.inputColorFormat = inputColorFormat;
-        this.inputShape = inputShape;
-        this.inputMean = inputMean;
-        this.inputStd = inputStd;
+        this.detLongSize = detLongSize;
         this.scoreThreshold = scoreThreshold;
         return true;
     }
 
-    protected boolean loadModel(Context appCtx, String modelPath, int cpuThreadNum, String cpuPowerMode) {
+    protected boolean loadModel(Context appCtx, String modelPath, int useOpencl, int cpuThreadNum, String cpuPowerMode) {
         // Release model if exists
         releaseModel();
 
@@ -118,12 +83,13 @@ public class Predictor {
         }
 
         OCRPredictorNative.Config config = new OCRPredictorNative.Config();
+        config.useOpencl = useOpencl;
         config.cpuThreadNum = cpuThreadNum;
-        config.detModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_det_opt.nb";
-        config.recModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_rec_opt.nb";
-        config.clsModelFilename = realPath + File.separator + "ch_ppocr_mobile_v2.0_cls_opt.nb";
-        Log.e("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
         config.cpuPower = cpuPowerMode;
+        config.detModelFilename = realPath + File.separator + "det_db.nb";
+        config.recModelFilename = realPath + File.separator + "rec_crnn.nb";
+        config.clsModelFilename = realPath + File.separator + "cls.nb";
+        Log.i("Predictor", "model path" + config.detModelFilename + " ; " + config.recModelFilename + ";" + config.clsModelFilename);
         paddlePredictor = new OCRPredictorNative(config);
 
         this.cpuThreadNum = cpuThreadNum;
@@ -170,82 +136,29 @@ public class Predictor {
     }
 
 
-    public boolean runModel() {
+    public boolean runModel(int run_det, int run_cls, int run_rec) {
         if (inputImage == null || !isLoaded()) {
             return false;
         }
 
-        // Pre-process image, and feed input tensor with pre-processed data
-
-        Bitmap scaleImage = Utils.resizeWithStep(inputImage, Long.valueOf(inputShape[2]).intValue(), 32);
-
-        Date start = new Date();
-        int channels = (int) inputShape[1];
-        int width = scaleImage.getWidth();
-        int height = scaleImage.getHeight();
-        float[] inputData = new float[channels * width * height];
-        if (channels == 3) {
-            int[] channelIdx = null;
-            if (inputColorFormat.equalsIgnoreCase("RGB")) {
-                channelIdx = new int[]{0, 1, 2};
-            } else if (inputColorFormat.equalsIgnoreCase("BGR")) {
-                channelIdx = new int[]{2, 1, 0};
-            } else {
-                Log.i(TAG, "Unknown color format " + inputColorFormat + ", only RGB and BGR color format is " +
-                        "supported!");
-                return false;
-            }
-
-            int[] channelStride = new int[]{width * height, width * height * 2};
-            int[] pixels=new int[width*height];
-            scaleImage.getPixels(pixels,0,scaleImage.getWidth(),0,0,scaleImage.getWidth(),scaleImage.getHeight());
-            for (int i = 0; i < pixels.length; i++) {
-                int color = pixels[i];
-                float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
-                        (float) blue(color) / 255.0f};
-                inputData[i] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
-                inputData[i + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
-                inputData[i+ channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
-            }
-        } else if (channels == 1) {
-            int[] pixels=new int[width*height];
-            scaleImage.getPixels(pixels,0,scaleImage.getWidth(),0,0,scaleImage.getWidth(),scaleImage.getHeight());
-            for (int i = 0; i < pixels.length; i++) {
-                int color = pixels[i];
-                float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
-                inputData[i] = (gray - inputMean[0]) / inputStd[0];
-            }
-        } else {
-            Log.i(TAG, "Unsupported channel size " + Integer.toString(channels) + ",  only channel 1 and 3 is " +
-                    "supported!");
-            return false;
-        }
-        float[] pixels = inputData;
-        Log.i(TAG, "pixels " + pixels[0] + " " + pixels[1] + " " + pixels[2] + " " + pixels[3]
-                + " " + pixels[pixels.length / 2] + " " + pixels[pixels.length / 2 + 1] + " " + pixels[pixels.length - 2] + " " + pixels[pixels.length - 1]);
-        Date end = new Date();
-        preprocessTime = (float) (end.getTime() - start.getTime());
-
         // Warm up
         for (int i = 0; i < warmupIterNum; i++) {
-            paddlePredictor.runImage(inputData, width, height, channels, inputImage);
+            paddlePredictor.runImage(inputImage, detLongSize, run_det, run_cls, run_rec);
         }
         warmupIterNum = 0; // do not need warm
         // Run inference
-        start = new Date();
-        ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
-        end = new Date();
+        Date start = new Date();
+        ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputImage, detLongSize, run_det, run_cls, run_rec);
+        Date end = new Date();
         inferenceTime = (end.getTime() - start.getTime()) / (float) inferIterNum;
 
         results = postprocess(results);
-        Log.i(TAG, "[stat] Preprocess Time: " + preprocessTime
-                + " ; Inference Time: " + inferenceTime + " ;Box Size " + results.size());
+        Log.i(TAG, "[stat] Inference Time: " + inferenceTime + " ;Box Size " + results.size());
         drawResults(results);
 
         return true;
     }
 
-
     public boolean isLoaded() {
         return paddlePredictor != null && isLoaded;
     }
@@ -282,10 +195,6 @@ public class Predictor {
         return outputResult;
     }
 
-    public float preprocessTime() {
-        return preprocessTime;
-    }
-
     public float postprocessTime() {
         return postprocessTime;
     }
@@ -310,6 +219,7 @@ public class Predictor {
                 }
             }
             r.setLabel(word.toString());
+            r.setClsLabel(r.getClsIdx() == 1 ? "180" : "0");
         }
         return results;
     }
@@ -319,14 +229,22 @@ public class Predictor {
         for (int i = 0; i < results.size(); i++) {
             OcrResultModel result = results.get(i);
             StringBuilder sb = new StringBuilder("");
-            sb.append(result.getLabel());
-            sb.append(" ").append(result.getConfidence());
-            sb.append("; Points: ");
-            for (Point p : result.getPoints()) {
-                sb.append("(").append(p.x).append(",").append(p.y).append(") ");
+            if(result.getPoints().size()>0){
+                sb.append("Det: ");
+                for (Point p : result.getPoints()) {
+                    sb.append("(").append(p.x).append(",").append(p.y).append(") ");
+                }
+            }
+            if(result.getLabel().length() > 0){
+                sb.append("\n Rec: ").append(result.getLabel());
+                sb.append(",").append(result.getConfidence());
+            }
+            if(result.getClsIdx()!=-1){
+                sb.append(" Cls: ").append(result.getClsLabel());
+                sb.append(",").append(result.getClsConfidence());
             }
             Log.i(TAG, sb.toString()); // show LOG in Logcat panel
-            outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
+            outputResultSb.append(i + 1).append(": ").append(sb.toString()).append("\n");
         }
         outputResult = outputResultSb.toString();
         outputImage = inputImage;
@@ -344,6 +262,9 @@ public class Predictor {
         for (OcrResultModel result : results) {
             Path path = new Path();
             List<Point> points = result.getPoints();
+            if(points.size()==0){
+                continue;
+            }
             path.moveTo(points.get(0).x, points.get(0).y);
             for (int i = points.size() - 1; i >= 0; i--) {
                 Point p = points.get(i);

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/SettingsActivity.java

@@ -20,16 +20,13 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
     ListPreference etImagePath = null;
     ListPreference lpCPUThreadNum = null;
     ListPreference lpCPUPowerMode = null;
-    ListPreference lpInputColorFormat = null;
-    EditTextPreference etInputShape = null;
-    EditTextPreference etInputMean = null;
-    EditTextPreference etInputStd = null;
+    EditTextPreference etDetLongSize = null;
     EditTextPreference etScoreThreshold = null;
 
     List<String> preInstalledModelPaths = null;
     List<String> preInstalledLabelPaths = null;
     List<String> preInstalledImagePaths = null;
-    List<String> preInstalledInputShapes = null;
+    List<String> preInstalledDetLongSizes = null;
     List<String> preInstalledCPUThreadNums = null;
     List<String> preInstalledCPUPowerModes = null;
     List<String> preInstalledInputColorFormats = null;
@@ -50,7 +47,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
         preInstalledModelPaths = new ArrayList<String>();
         preInstalledLabelPaths = new ArrayList<String>();
         preInstalledImagePaths = new ArrayList<String>();
-        preInstalledInputShapes = new ArrayList<String>();
+        preInstalledDetLongSizes = new ArrayList<String>();
         preInstalledCPUThreadNums = new ArrayList<String>();
         preInstalledCPUPowerModes = new ArrayList<String>();
         preInstalledInputColorFormats = new ArrayList<String>();
@@ -63,10 +60,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
         preInstalledImagePaths.add(getString(R.string.IMAGE_PATH_DEFAULT));
         preInstalledCPUThreadNums.add(getString(R.string.CPU_THREAD_NUM_DEFAULT));
         preInstalledCPUPowerModes.add(getString(R.string.CPU_POWER_MODE_DEFAULT));
-        preInstalledInputColorFormats.add(getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
-        preInstalledInputShapes.add(getString(R.string.INPUT_SHAPE_DEFAULT));
-        preInstalledInputMeans.add(getString(R.string.INPUT_MEAN_DEFAULT));
-        preInstalledInputStds.add(getString(R.string.INPUT_STD_DEFAULT));
+        preInstalledDetLongSizes.add(getString(R.string.DET_LONG_SIZE_DEFAULT));
         preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_DEFAULT));
 
         // Setup UI components
@@ -89,11 +83,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
                 (ListPreference) findPreference(getString(R.string.CPU_THREAD_NUM_KEY));
         lpCPUPowerMode =
                 (ListPreference) findPreference(getString(R.string.CPU_POWER_MODE_KEY));
-        lpInputColorFormat =
-                (ListPreference) findPreference(getString(R.string.INPUT_COLOR_FORMAT_KEY));
-        etInputShape = (EditTextPreference) findPreference(getString(R.string.INPUT_SHAPE_KEY));
-        etInputMean = (EditTextPreference) findPreference(getString(R.string.INPUT_MEAN_KEY));
-        etInputStd = (EditTextPreference) findPreference(getString(R.string.INPUT_STD_KEY));
+        etDetLongSize = (EditTextPreference) findPreference(getString(R.string.DET_LONG_SIZE_KEY));
         etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
     }
 
@@ -112,11 +102,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
                 editor.putString(getString(R.string.IMAGE_PATH_KEY), preInstalledImagePaths.get(modelIdx));
                 editor.putString(getString(R.string.CPU_THREAD_NUM_KEY), preInstalledCPUThreadNums.get(modelIdx));
                 editor.putString(getString(R.string.CPU_POWER_MODE_KEY), preInstalledCPUPowerModes.get(modelIdx));
-                editor.putString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
-                        preInstalledInputColorFormats.get(modelIdx));
-                editor.putString(getString(R.string.INPUT_SHAPE_KEY), preInstalledInputShapes.get(modelIdx));
-                editor.putString(getString(R.string.INPUT_MEAN_KEY), preInstalledInputMeans.get(modelIdx));
-                editor.putString(getString(R.string.INPUT_STD_KEY), preInstalledInputStds.get(modelIdx));
+                editor.putString(getString(R.string.DET_LONG_SIZE_KEY), preInstalledDetLongSizes.get(modelIdx));
                 editor.putString(getString(R.string.SCORE_THRESHOLD_KEY),
                         preInstalledScoreThresholds.get(modelIdx));
                 editor.apply();
@@ -129,10 +115,7 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
         etImagePath.setEnabled(enableCustomSettings);
         lpCPUThreadNum.setEnabled(enableCustomSettings);
         lpCPUPowerMode.setEnabled(enableCustomSettings);
-        lpInputColorFormat.setEnabled(enableCustomSettings);
-        etInputShape.setEnabled(enableCustomSettings);
-        etInputMean.setEnabled(enableCustomSettings);
-        etInputStd.setEnabled(enableCustomSettings);
+        etDetLongSize.setEnabled(enableCustomSettings);
         etScoreThreshold.setEnabled(enableCustomSettings);
         modelPath = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
                 getString(R.string.MODEL_PATH_DEFAULT));
@@ -144,14 +127,8 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
                 getString(R.string.CPU_THREAD_NUM_DEFAULT));
         String cpuPowerMode = sharedPreferences.getString(getString(R.string.CPU_POWER_MODE_KEY),
                 getString(R.string.CPU_POWER_MODE_DEFAULT));
-        String inputColorFormat = sharedPreferences.getString(getString(R.string.INPUT_COLOR_FORMAT_KEY),
-                getString(R.string.INPUT_COLOR_FORMAT_DEFAULT));
-        String inputShape = sharedPreferences.getString(getString(R.string.INPUT_SHAPE_KEY),
-                getString(R.string.INPUT_SHAPE_DEFAULT));
-        String inputMean = sharedPreferences.getString(getString(R.string.INPUT_MEAN_KEY),
-                getString(R.string.INPUT_MEAN_DEFAULT));
-        String inputStd = sharedPreferences.getString(getString(R.string.INPUT_STD_KEY),
-                getString(R.string.INPUT_STD_DEFAULT));
+        String detLongSize = sharedPreferences.getString(getString(R.string.DET_LONG_SIZE_KEY),
+                getString(R.string.DET_LONG_SIZE_DEFAULT));
         String scoreThreshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
                 getString(R.string.SCORE_THRESHOLD_DEFAULT));
         etModelPath.setSummary(modelPath);
@@ -164,14 +141,8 @@ public class SettingsActivity extends AppCompatPreferenceActivity implements Sha
         lpCPUThreadNum.setSummary(cpuThreadNum);
         lpCPUPowerMode.setValue(cpuPowerMode);
         lpCPUPowerMode.setSummary(cpuPowerMode);
-        lpInputColorFormat.setValue(inputColorFormat);
-        lpInputColorFormat.setSummary(inputColorFormat);
-        etInputShape.setSummary(inputShape);
-        etInputShape.setText(inputShape);
-        etInputMean.setSummary(inputMean);
-        etInputMean.setText(inputMean);
-        etInputStd.setSummary(inputStd);
-        etInputStd.setText(inputStd);
+        etDetLongSize.setSummary(detLongSize);
+        etDetLongSize.setText(detLongSize);
         etScoreThreshold.setText(scoreThreshold);
         etScoreThreshold.setSummary(scoreThreshold);
     }

deploy/android_demo/app/src/main/res/layout/activity_main.xml

@@ -23,13 +23,7 @@
                 android:layout_height="wrap_content"
                 android:orientation="horizontal">
 
-                <Button
-                    android:id="@+id/btn_load_model"
-                    android:layout_width="0dp"
-                    android:layout_height="wrap_content"
-                    android:layout_weight="1"
-                    android:onClick="btn_load_model_click"
-                    android:text="加载模型" />
+
                 <Button
                     android:id="@+id/btn_run_model"
                     android:layout_width="0dp"
@@ -52,7 +46,45 @@
                     android:onClick="btn_choice_img_click"
                     android:text="选取图片" />
 
+                <Button
+                    android:id="@+id/btn_reset_img"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_reset_img_click"
+                    android:text="清空绘图" />
+
             </LinearLayout>
+            <LinearLayout
+                android:id="@+id/run_mode_layout"
+                android:layout_width="fill_parent"
+                android:layout_height="wrap_content"
+                android:orientation="horizontal">
+
+                <CheckBox
+                    android:id="@+id/cb_opencl"
+                    android:layout_width="0dp"
+                    android:layout_weight="1"
+                    android:layout_height="wrap_content"
+                    android:text="开启OPENCL"
+                    android:onClick="cb_opencl_click"
+                    android:visibility="gone"/>
+                <TextView
+                    android:layout_width="0dp"
+                    android:layout_weight="0.5"
+                    android:layout_height="wrap_content"
+                    android:text="运行模式:"/>
+
+                <Spinner
+                    android:id="@+id/sp_run_mode"
+                    android:layout_width="0dp"
+                    android:layout_weight="1.5"
+                    android:layout_height="wrap_content"
+                    android:entries="@array/run_Model"
+                    />
+
+            </LinearLayout>
+
             <TextView
                     android:id="@+id/tv_input_setting"
                     android:layout_width="wrap_content"
@@ -60,7 +92,7 @@
                     android:scrollbars="vertical"
                     android:layout_marginLeft="12dp"
                     android:layout_marginRight="12dp"
-                    android:layout_marginTop="10dp"
+                    android:layout_marginTop="5dp"
                     android:layout_marginBottom="5dp"
                     android:lineSpacingExtra="4dp"
                     android:singleLine="false"

deploy/android_demo/app/src/main/res/layout/activity_mini.xml

-<?xml version="1.0" encoding="utf-8"?>
-<!-- for MiniActivity Use Only -->
-<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
-    xmlns:app="http://schemas.android.com/apk/res-auto"
-    xmlns:tools="http://schemas.android.com/tools"
-    android:layout_width="match_parent"
-    android:layout_height="match_parent"
-    app:layout_constraintLeft_toLeftOf="parent"
-    app:layout_constraintLeft_toRightOf="parent"
-    tools:context=".MainActivity">
-
-    <TextView
-        android:id="@+id/sample_text"
-        android:layout_width="0dp"
-        android:layout_height="wrap_content"
-        android:text="Hello World!"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent"
-        app:layout_constraintTop_toBottomOf="@id/imageView"
-        android:scrollbars="vertical"
-        />
-
-    <ImageView
-        android:id="@+id/imageView"
-        android:layout_width="wrap_content"
-        android:layout_height="wrap_content"
-        android:paddingTop="20dp"
-        android:paddingBottom="20dp"
-        app:layout_constraintBottom_toTopOf="@id/imageView"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent"
-        app:layout_constraintTop_toTopOf="parent"
-        tools:srcCompat="@tools:sample/avatars" />
-
-    <Button
-        android:id="@+id/button"
-        android:layout_width="wrap_content"
-        android:layout_height="wrap_content"
-        android:layout_marginBottom="4dp"
-        android:text="Button"
-        app:layout_constraintBottom_toBottomOf="parent"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent"
-        tools:layout_editor_absoluteX="161dp" />
-
-</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file

deploy/android_demo/app/src/main/res/values/arrays.xml

 <?xml version="1.0" encoding="utf-8"?>
 <resources>
     <string-array name="image_name_entries">
-        <item>0.jpg</item>
-        <item>90.jpg</item>
-        <item>180.jpg</item>
-        <item>270.jpg</item>
+        <item>det_0.jpg</item>
+        <item>det_90.jpg</item>
+        <item>det_180.jpg</item>
+        <item>det_270.jpg</item>
+        <item>rec_0.jpg</item>
+        <item>rec_0_180.jpg</item>
+        <item>rec_1.jpg</item>
+        <item>rec_1_180.jpg</item>
     </string-array>
     <string-array name="image_name_values">
-        <item>images/0.jpg</item>
-        <item>images/90.jpg</item>
-        <item>images/180.jpg</item>
-        <item>images/270.jpg</item>
+        <item>images/det_0.jpg</item>
+        <item>images/det_90.jpg</item>
+        <item>images/det_180.jpg</item>
+        <item>images/det_270.jpg</item>
+        <item>images/rec_0.jpg</item>
+        <item>images/rec_0_180.jpg</item>
+        <item>images/rec_1.jpg</item>
+        <item>images/rec_1_180.jpg</item>
     </string-array>
     <string-array name="cpu_thread_num_entries">
         <item>1 threads</item>
@@ -48,4 +56,12 @@
         <item>BGR</item>
         <item>RGB</item>
     </string-array>
+    <string-array name="run_Model">
+        <item>检测+分类+识别</item>
+        <item>检测+识别</item>
+        <item>分类+识别</item>
+        <item>检测</item>
+        <item>识别</item>
+        <item>分类</item>
+    </string-array>
 </resources>
\ No newline at end of file

deploy/android_demo/app/src/main/res/values/strings.xml

 <resources>
-    <string name="app_name">OCR Chinese</string>
+    <string name="app_name">PaddleOCR</string>
     <string name="CHOOSE_PRE_INSTALLED_MODEL_KEY">CHOOSE_PRE_INSTALLED_MODEL_KEY</string>
     <string name="ENABLE_CUSTOM_SETTINGS_KEY">ENABLE_CUSTOM_SETTINGS_KEY</string>
     <string name="MODEL_PATH_KEY">MODEL_PATH_KEY</string>
@@ -7,20 +7,14 @@
     <string name="IMAGE_PATH_KEY">IMAGE_PATH_KEY</string>
     <string name="CPU_THREAD_NUM_KEY">CPU_THREAD_NUM_KEY</string>
     <string name="CPU_POWER_MODE_KEY">CPU_POWER_MODE_KEY</string>
-    <string name="INPUT_COLOR_FORMAT_KEY">INPUT_COLOR_FORMAT_KEY</string>
-    <string name="INPUT_SHAPE_KEY">INPUT_SHAPE_KEY</string>
-    <string name="INPUT_MEAN_KEY">INPUT_MEAN_KEY</string>
-    <string name="INPUT_STD_KEY">INPUT_STD_KEY</string>
+    <string name="DET_LONG_SIZE_KEY">DET_LONG_SIZE_KEY</string>
     <string name="SCORE_THRESHOLD_KEY">SCORE_THRESHOLD_KEY</string>
-    <string name="MODEL_PATH_DEFAULT">models/ocr_v2_for_cpu</string>
+    <string name="MODEL_PATH_DEFAULT">models/ch_PP-OCRv2</string>
     <string name="LABEL_PATH_DEFAULT">labels/ppocr_keys_v1.txt</string>
-    <string name="IMAGE_PATH_DEFAULT">images/0.jpg</string>
+    <string name="IMAGE_PATH_DEFAULT">images/det_0.jpg</string>
     <string name="CPU_THREAD_NUM_DEFAULT">4</string>
     <string name="CPU_POWER_MODE_DEFAULT">LITE_POWER_HIGH</string>
-    <string name="INPUT_COLOR_FORMAT_DEFAULT">BGR</string>
-    <string name="INPUT_SHAPE_DEFAULT">1,3,960</string>
-    <string name="INPUT_MEAN_DEFAULT">0.485, 0.456, 0.406</string>
-    <string name="INPUT_STD_DEFAULT">0.229,0.224,0.225</string>
+    <string name="DET_LONG_SIZE_DEFAULT">960</string>
     <string name="SCORE_THRESHOLD_DEFAULT">0.1</string>
 </resources>
 

deploy/android_demo/app/src/main/res/xml/settings.xml

@@ -47,26 +47,10 @@
                 android:entryValues="@array/cpu_power_mode_values"/>
     </PreferenceCategory>
     <PreferenceCategory android:title="Input Settings">
-        <ListPreference
-                android:defaultValue="@string/INPUT_COLOR_FORMAT_DEFAULT"
-                android:key="@string/INPUT_COLOR_FORMAT_KEY"
-                android:negativeButtonText="@null"
-                android:positiveButtonText="@null"
-                android:title="Input Color Format: BGR or RGB"
-                android:entries="@array/input_color_format_entries"
-                android:entryValues="@array/input_color_format_values"/>
-        <EditTextPreference
-                android:key="@string/INPUT_SHAPE_KEY"
-                android:defaultValue="@string/INPUT_SHAPE_DEFAULT"
-                android:title="Input Shape: (1,1,max_width_height) or (1,3,max_width_height)" />
-        <EditTextPreference
-                android:key="@string/INPUT_MEAN_KEY"
-                android:defaultValue="@string/INPUT_MEAN_DEFAULT"
-                android:title="Input Mean: (channel/255-mean)/std" />
         <EditTextPreference
-                android:key="@string/INPUT_STD_KEY"
-                android:defaultValue="@string/INPUT_STD_DEFAULT"
-                android:title="Input Std: (channel/255-mean)/std" />
+                android:key="@string/DET_LONG_SIZE_KEY"
+                android:defaultValue="@string/DET_LONG_SIZE_DEFAULT"
+                android:title="det long size" />
     </PreferenceCategory>
     <PreferenceCategory android:title="Output Settings">
         <EditTextPreference

deploy/cpp_infer/include/ocr_det.h

@@ -45,8 +45,9 @@ public:
                       const double &det_db_thresh,
                       const double &det_db_box_thresh,
                       const double &det_db_unclip_ratio,
-                      const bool &use_polygon_score, const bool &visualize,
-                      const bool &use_tensorrt, const std::string &precision) {
+                      const bool &use_polygon_score, const bool &use_dilation,
+                      const bool &visualize, const bool &use_tensorrt,
+                      const std::string &precision) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
     this->gpu_mem_ = gpu_mem;
@@ -59,6 +60,7 @@ public:
     this->det_db_box_thresh_ = det_db_box_thresh;
     this->det_db_unclip_ratio_ = det_db_unclip_ratio;
     this->use_polygon_score_ = use_polygon_score;
+    this->use_dilation_ = use_dilation;
 
     this->visualize_ = visualize;
     this->use_tensorrt_ = use_tensorrt;
@@ -71,7 +73,8 @@ public:
   void LoadModel(const std::string &model_dir);
 
   // Run predictor
-  void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes, std::vector<double> *times);
+  void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes,
+           std::vector<double> *times);
 
 private:
   std::shared_ptr<Predictor> predictor_;
@@ -88,6 +91,7 @@ private:
   double det_db_box_thresh_ = 0.5;
   double det_db_unclip_ratio_ = 2.0;
   bool use_polygon_score_ = false;
+  bool use_dilation_ = false;
 
   bool visualize_ = true;
   bool use_tensorrt_ = false;

deploy/cpp_infer/readme.md

@@ -4,16 +4,20 @@
 C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成
 PaddleOCR模型部署。
 
-* [1. 准备环境](#1)
-  + [1.0 运行准备](#10)
-  + [1.1 编译opencv库](#11)
-  + [1.2 下载或者编译Paddle预测库](#12)
-    - [1.2.1 直接下载安装](#121)
-    - [1.2.2 预测库源码编译](#122)
-* [2 开始运行](#2)
-  + [2.1 将模型导出为inference model](#21)
-  + [2.2 编译PaddleOCR C++预测demo](#22)
-  + [2.3运行demo](#23)
+- [服务器端C++预测](#服务器端c预测)
+  - [1. 准备环境](#1-准备环境)
+    - [1.0 运行准备](#10-运行准备)
+    - [1.1 编译opencv库](#11-编译opencv库)
+    - [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库)
+      - [1.2.1 直接下载安装](#121-直接下载安装)
+      - [1.2.2 预测库源码编译](#122-预测库源码编译)
+  - [2 开始运行](#2-开始运行)
+    - [2.1 将模型导出为inference model](#21-将模型导出为inference-model)
+    - [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo)
+    - [2.3 运行demo](#23-运行demo)
+        - [1. 只调用检测：](#1-只调用检测)
+        - [2. 只调用识别：](#2-只调用识别)
+        - [3. 调用串联：](#3-调用串联)
 
 <a name="1"></a>
 
@@ -103,7 +107,7 @@ opencv3/
 
 #### 1.2.1 直接下载安装
 
-* [Paddle预测库官网](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
+* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。
 
 * 下载之后使用下面的方法解压。
 
@@ -249,7 +253,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 |gpu_id|int|0|GPU id，使用GPU时有效|
 |gpu_mem|int|4000|申请的GPU内存|
 |cpu_math_library_num_threads|int|10|CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快|
-|use_mkldnn|bool|true|是否使用mkldnn库|
+|enable_mkldnn|bool|true|是否使用mkldnn库|
 
 - 检测模型相关
 

deploy/cpp_infer/readme_en.md

@@ -78,7 +78,7 @@ opencv3/
 
 #### 1.2.1 Direct download and installation
 
-[Paddle inference library official website](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html). You can review and select the appropriate version of the inference library on the official website.
+[Paddle inference library official website](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux). You can review and select the appropriate version of the inference library on the official website.
 
 
 * After downloading, use the following command to extract files.
@@ -231,7 +231,7 @@ More parameters are as follows,
 |gpu_id|int|0|GPU id when use_gpu is true|
 |gpu_mem|int|4000|GPU memory requested|
 |cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
-|use_mkldnn|bool|true|Whether to use mkdlnn library|
+|enable_mkldnn|bool|true|Whether to use mkdlnn library|
 
 - Detection related parameters
 

deploy/cpp_infer/src/ocr_det.cpp

@@ -14,7 +14,6 @@
 
 #include <include/ocr_det.h>
 
-
 namespace PaddleOCR {
 
 void DBDetector::LoadModel(const std::string &model_dir) {
@@ -30,13 +29,10 @@ void DBDetector::LoadModel(const std::string &model_dir) {
       if (this->precision_ == "fp16") {
         precision = paddle_infer::Config::Precision::kHalf;
       }
-     if (this->precision_ == "int8") {
+      if (this->precision_ == "int8") {
         precision = paddle_infer::Config::Precision::kInt8;
-      } 
-      config.EnableTensorRtEngine(
-          1 << 20, 10, 3,
-          precision,
-          false, false);
+      }
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
       std::map<std::string, std::vector<int>> min_input_shape = {
           {"x", {1, 3, 50, 50}},
           {"conv2d_92.tmp_0", {1, 96, 20, 20}},
@@ -105,7 +101,7 @@ void DBDetector::Run(cv::Mat &img,
   cv::Mat srcimg;
   cv::Mat resize_img;
   img.copyTo(srcimg);
-  
+
   auto preprocess_start = std::chrono::steady_clock::now();
   this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
                        this->use_tensorrt_);
@@ -116,16 +112,16 @@ void DBDetector::Run(cv::Mat &img,
   std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
   this->permute_op_.Run(&resize_img, input.data());
   auto preprocess_end = std::chrono::steady_clock::now();
-    
+
   // Inference.
   auto input_names = this->predictor_->GetInputNames();
   auto input_t = this->predictor_->GetInputHandle(input_names[0]);
   input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
   auto inference_start = std::chrono::steady_clock::now();
   input_t->CopyFromCpu(input.data());
-  
+
   this->predictor_->Run();
-    
+
   std::vector<float> out_data;
   auto output_names = this->predictor_->GetOutputNames();
   auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
@@ -136,7 +132,7 @@ void DBDetector::Run(cv::Mat &img,
   out_data.resize(out_num);
   output_t->CopyToCpu(out_data.data());
   auto inference_end = std::chrono::steady_clock::now();
-  
+
   auto postprocess_start = std::chrono::steady_clock::now();
   int n2 = output_shape[2];
   int n3 = output_shape[3];
@@ -157,24 +153,29 @@ void DBDetector::Run(cv::Mat &img,
   const double maxvalue = 255;
   cv::Mat bit_map;
   cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
-  cv::Mat dilation_map;
-  cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
-  cv::dilate(bit_map, dilation_map, dila_ele);
+  if (this->use_dilation_) {
+    cv::Mat dila_ele =
+        cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
+    cv::dilate(bit_map, bit_map, dila_ele);
+  }
+
   boxes = post_processor_.BoxesFromBitmap(
-      pred_map, dilation_map, this->det_db_box_thresh_,
-      this->det_db_unclip_ratio_, this->use_polygon_score_);
+      pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
+      this->use_polygon_score_);
 
   boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
   auto postprocess_end = std::chrono::steady_clock::now();
   std::cout << "Detected boxes num: " << boxes.size() << endl;
 
-  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
   times->push_back(double(preprocess_diff.count() * 1000));
   std::chrono::duration<float> inference_diff = inference_end - inference_start;
   times->push_back(double(inference_diff.count() * 1000));
-  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
   times->push_back(double(postprocess_diff.count() * 1000));
-    
+
   //// visualization
   if (this->visualize_) {
     Utility::VisualizeBboxes(srcimg, boxes);

deploy/cpp_infer/src/ocr_rec.cpp

@@ -15,108 +15,115 @@
 #include <include/ocr_rec.h>
 
 namespace PaddleOCR {
-    
-void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *times) {
-    std::chrono::duration<float> preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
-    std::chrono::duration<float> inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
-    std::chrono::duration<float> postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
-
-    int img_num = img_list.size();
-    std::vector<float> width_list;
-    for (int i = 0; i < img_num; i++) {
-        width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
+
+void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
+                         std::vector<double> *times) {
+  std::chrono::duration<float> preprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> inference_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> postprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+
+  int img_num = img_list.size();
+  std::vector<float> width_list;
+  for (int i = 0; i < img_num; i++) {
+    width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
+  }
+  std::vector<int> indices = Utility::argsort(width_list);
+
+  for (int beg_img_no = 0; beg_img_no < img_num;
+       beg_img_no += this->rec_batch_num_) {
+    auto preprocess_start = std::chrono::steady_clock::now();
+    int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
+    float max_wh_ratio = 0;
+    for (int ino = beg_img_no; ino < end_img_no; ino++) {
+      int h = img_list[indices[ino]].rows;
+      int w = img_list[indices[ino]].cols;
+      float wh_ratio = w * 1.0 / h;
+      max_wh_ratio = max(max_wh_ratio, wh_ratio);
     }
-    std::vector<int> indices = Utility::argsort(width_list);
-
-    for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) {
-        auto preprocess_start = std::chrono::steady_clock::now();
-        int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
-        float max_wh_ratio = 0;
-        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
-            int h = img_list[indices[ino]].rows;
-            int w = img_list[indices[ino]].cols;
-            float wh_ratio = w * 1.0 / h;
-            max_wh_ratio = max(max_wh_ratio, wh_ratio);
-        }
-        std::vector<cv::Mat> norm_img_batch;
-        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
-            cv::Mat srcimg;
-            img_list[indices[ino]].copyTo(srcimg);
-            cv::Mat resize_img;
-            this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_);
-            this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
-            norm_img_batch.push_back(resize_img);
-        }
-        
-        int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
-        std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
-        this->permute_op_.Run(norm_img_batch, input.data());
-        auto preprocess_end = std::chrono::steady_clock::now();
-        preprocess_diff += preprocess_end - preprocess_start;
-
-        // Inference.
-        auto input_names = this->predictor_->GetInputNames();
-        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
-        input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
-        auto inference_start = std::chrono::steady_clock::now();
-        input_t->CopyFromCpu(input.data());
-        this->predictor_->Run();
-
-        std::vector<float> predict_batch;
-        auto output_names = this->predictor_->GetOutputNames();
-        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
-        auto predict_shape = output_t->shape();
-
-        int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
-                                std::multiplies<int>());
-        predict_batch.resize(out_num);
-
-        output_t->CopyToCpu(predict_batch.data());
-        auto inference_end = std::chrono::steady_clock::now();
-        inference_diff += inference_end - inference_start;
-        
-        // ctc decode
-        auto postprocess_start = std::chrono::steady_clock::now();
-        for (int m = 0; m < predict_shape[0]; m++) {
-            std::vector<std::string> str_res;
-            int argmax_idx;
-            int last_index = 0;
-            float score = 0.f;
-            int count = 0;
-            float max_value = 0.0f;
-
-            for (int n = 0; n < predict_shape[1]; n++) {
-                argmax_idx =
-                    int(Utility::argmax(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
-                                        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
-                max_value =
-                    float(*std::max_element(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
-                                            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
-
-                if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
-                    score += max_value;
-                    count += 1;
-                    str_res.push_back(label_list_[argmax_idx]);
-                }
-                last_index = argmax_idx;
-            }
-            score /= count;
-            if (isnan(score))
-                continue;
-            for (int i = 0; i < str_res.size(); i++) {
-                std::cout << str_res[i];
-            }
-            std::cout << "\tscore: " << score << std::endl;
+    int batch_width = 0;
+    std::vector<cv::Mat> norm_img_batch;
+    for (int ino = beg_img_no; ino < end_img_no; ino++) {
+      cv::Mat srcimg;
+      img_list[indices[ino]].copyTo(srcimg);
+      cv::Mat resize_img;
+      this->resize_op_.Run(srcimg, resize_img, max_wh_ratio,
+                           this->use_tensorrt_);
+      this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                              this->is_scale_);
+      norm_img_batch.push_back(resize_img);
+      batch_width = max(resize_img.cols, batch_width);
+    }
+
+    std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
+    this->permute_op_.Run(norm_img_batch, input.data());
+    auto preprocess_end = std::chrono::steady_clock::now();
+    preprocess_diff += preprocess_end - preprocess_start;
+
+    // Inference.
+    auto input_names = this->predictor_->GetInputNames();
+    auto input_t = this->predictor_->GetInputHandle(input_names[0]);
+    input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
+    auto inference_start = std::chrono::steady_clock::now();
+    input_t->CopyFromCpu(input.data());
+    this->predictor_->Run();
+
+    std::vector<float> predict_batch;
+    auto output_names = this->predictor_->GetOutputNames();
+    auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
+    auto predict_shape = output_t->shape();
+
+    int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
+                                  std::multiplies<int>());
+    predict_batch.resize(out_num);
+
+    output_t->CopyToCpu(predict_batch.data());
+    auto inference_end = std::chrono::steady_clock::now();
+    inference_diff += inference_end - inference_start;
+
+    // ctc decode
+    auto postprocess_start = std::chrono::steady_clock::now();
+    for (int m = 0; m < predict_shape[0]; m++) {
+      std::vector<std::string> str_res;
+      int argmax_idx;
+      int last_index = 0;
+      float score = 0.f;
+      int count = 0;
+      float max_value = 0.0f;
+
+      for (int n = 0; n < predict_shape[1]; n++) {
+        argmax_idx = int(Utility::argmax(
+            &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+        max_value = float(*std::max_element(
+            &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+
+        if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
+          score += max_value;
+          count += 1;
+          str_res.push_back(label_list_[argmax_idx]);
         }
-        auto postprocess_end = std::chrono::steady_clock::now();
-        postprocess_diff += postprocess_end - postprocess_start;
+        last_index = argmax_idx;
+      }
+      score /= count;
+      if (isnan(score))
+        continue;
+      for (int i = 0; i < str_res.size(); i++) {
+        std::cout << str_res[i];
+      }
+      std::cout << "\tscore: " << score << std::endl;
     }
-    times->push_back(double(preprocess_diff.count() * 1000));
-    times->push_back(double(inference_diff.count() * 1000));
-    times->push_back(double(postprocess_diff.count() * 1000));
+    auto postprocess_end = std::chrono::steady_clock::now();
+    postprocess_diff += postprocess_end - postprocess_start;
+  }
+  times->push_back(double(preprocess_diff.count() * 1000));
+  times->push_back(double(inference_diff.count() * 1000));
+  times->push_back(double(postprocess_diff.count() * 1000));
 }
 
-    
 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   //   AnalysisConfig config;
   paddle_infer::Config config;
@@ -130,23 +137,17 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
       if (this->precision_ == "fp16") {
         precision = paddle_infer::Config::Precision::kHalf;
       }
-     if (this->precision_ == "int8") {
+      if (this->precision_ == "int8") {
         precision = paddle_infer::Config::Precision::kInt8;
-      } 
-      config.EnableTensorRtEngine(
-          1 << 20, 10, 3,
-          precision,
-          false, false);
+      }
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
 
       std::map<std::string, std::vector<int>> min_input_shape = {
-          {"x", {1, 3, 32, 10}},
-          {"lstm_0.tmp_0", {10, 1, 96}}};
+          {"x", {1, 3, 32, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}};
       std::map<std::string, std::vector<int>> max_input_shape = {
-          {"x", {1, 3, 32, 2000}},
-          {"lstm_0.tmp_0", {1000, 1, 96}}};
+          {"x", {1, 3, 32, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}};
       std::map<std::string, std::vector<int>> opt_input_shape = {
-          {"x", {1, 3, 32, 320}},
-          {"lstm_0.tmp_0", {25, 1, 96}}};
+          {"x", {1, 3, 32, 320}}, {"lstm_0.tmp_0", {25, 1, 96}}};
 
       config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                     opt_input_shape);
@@ -168,7 +169,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   config.SwitchIrOptim(true);
 
   config.EnableMemoryOptim();
-//   config.DisableGlogInfo();
+  //   config.DisableGlogInfo();
 
   this->predictor_ = CreatePredictor(config);
 }

deploy/lite/readme.md

+- [端侧部署](#端侧部署)
+  - [1. 准备环境](#1-准备环境)
+    - [运行准备](#运行准备)
+    - [1.1 准备交叉编译环境](#11-准备交叉编译环境)
+    - [1.2 准备预测库](#12-准备预测库)
+  - [2 开始运行](#2-开始运行)
+    - [2.1 模型优化](#21-模型优化)
+    - [2.2 与手机联调](#22-与手机联调)
+      - [注意：](#注意)
+  - [FAQ](#faq)
+  
 # 端侧部署
 
 本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleOCR超轻量中文检测、识别模型的详细步骤。
@@ -26,17 +37,17 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 
       | 平台 | 预测库下载链接 |
       |---|---|
-      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
-      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
+      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
 
-      注：1. 上述预测库为PaddleLite 2.9分支编译得到，有关PaddleLite 2.9 详细信息可参考 [链接](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.9) 。
+      注：1. 上述预测库为PaddleLite 2.10分支编译得到，有关PaddleLite 2.10 详细信息可参考 [链接](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.10) 。
 
 - 2. [推荐]编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-# 切换到Paddle-Lite release/v2.9 稳定分支
-git checkout release/v2.9
+# 切换到Paddle-Lite release/v2.10 稳定分支
+git checkout release/v2.10
 ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
 ```
 
@@ -85,8 +96,8 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 
 |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
 |---|---|---|---|---|---|---|
-|V2.0|超轻量中文OCR 移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
-|V2.0(slim)|超轻量中文OCR 移动端模型|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
+|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
 
 如果直接使用上述表格中的模型进行部署，可略过下述步骤，直接阅读 [2.2节](#2.2与手机联调)。
 
@@ -97,7 +108,7 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括
 # 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-git checkout release/v2.9
+git checkout release/v2.10
 # 启动编译
 ./lite/tools/build.sh build_optimize_tool
 ```
@@ -123,15 +134,15 @@ cd build.opt/lite/api/
 下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。
 
 ```
-# 【推荐】 下载PaddleOCR V2.0版本的中英文 inference模型
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_det_slim_infer.tar
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_rec_slim_infer.tar
+# 【推荐】 下载 PP-OCRv2版本的中英文 inference模型
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_det_slim_quant_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_rec_slim_quant_infer.tar
 wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_cls_slim_infer.tar
-# 转换V2.0检测模型
-./opt --model_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# 转换V2.0识别模型
-./opt --model_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# 转换V2.0方向分类器模型
+# 转换检测模型
+./opt --model_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+# 转换识别模型
+./opt --model_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+# 转换方向分类器模型
 ./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
 
 ```
@@ -186,15 +197,15 @@ wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls
  ```
 
  准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。
- 准备lite opt工具优化后的模型文件，比如使用`ch_ppocr_mobile_v2.0_det_slim_opt.nb，ch_ppocr_mobile_v2.0_rec_slim_opt.nb, ch_ppocr_mobile_v2.0_cls_slim_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。
+ 准备lite opt工具优化后的模型文件，比如使用`ch_PP-OCRv2_det_slim_opt.ch_PP-OCRv2_rec_slim_rec.nb, ch_ppocr_mobile_v2.0_cls_slim_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。
 
  执行完成后，ocr文件夹下将有如下文件格式：
 
 ```
 demo/cxx/ocr/
 |-- debug/  
-|   |--ch_ppocr_mobile_v2.0_det_slim_opt.nb           优化后的检测模型文件
-|   |--ch_ppocr_mobile_v2.0_rec_slim_opt.nb           优化后的识别模型文件
+|   |--ch_PP-OCRv2_det_slim_opt.nb           优化后的检测模型文件
+|   |--ch_PP-OCRv2_rec_slim_opt.nb           优化后的识别模型文件
 |   |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb           优化后的文字方向分类器模型文件
 |   |--11.jpg                           待测试图像
 |   |--ppocr_keys_v1.txt                中文字典文件
@@ -250,7 +261,7 @@ use_direction_classify  0  # 是否使用方向分类器，0表示不使用，1
  export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
  # 开始使用，ocr_db_crnn可执行文件的使用方式为:
  # ./ocr_db_crnn  检测模型文件 方向分类器模型文件  识别模型文件  测试图像路径  字典文件路径
- ./ocr_db_crnn ch_ppocr_mobile_v2.0_det_slim_opt.nb  ch_ppocr_mobile_v2.0_rec_slim_opt.nb  ch_ppocr_mobile_v2.0_cls_slim_opt.nb  ./11.jpg  ppocr_keys_v1.txt
+ ./ocr_db_crnn ch_PP-OCRv2_det_slim_opt.nb  ch_PP-OCRv2_rec_slim_opt.nb  ch_ppocr_mobile_v2.0_cls_slim_opt.nb  ./11.jpg  ppocr_keys_v1.txt
  ```
 
  如果对代码做了修改，则需要重新编译并push到手机上。

deploy/lite/readme_en.md

+- [Tutorial of PaddleOCR Mobile deployment](#tutorial-of-paddleocr-mobile-deployment)
+  - [1. Preparation](#1-preparation)
+    - [Preparation environment](#preparation-environment)
+    - [1.1 Prepare the cross-compilation environment](#11-prepare-the-cross-compilation-environment)
+    - [1.2 Prepare Paddle-Lite library](#12-prepare-paddle-lite-library)
+  - [2 Run](#2-run)
+    - [2.1 Inference Model Optimization](#21-inference-model-optimization)
+    - [2.2 Run optimized model on Phone](#22-run-optimized-model-on-phone)
+      - [注意：](#注意)
+  - [FAQ](#faq)
+
 # Tutorial of PaddleOCR Mobile deployment
 
 This tutorial will introduce how to use [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) to deploy PaddleOCR ultra-lightweight Chinese and English detection models on mobile phones.
@@ -28,17 +39,17 @@ There are two ways to obtain the Paddle-Lite library：
 
       | Platform | Paddle-Lite library download link |
       |---|---|
-      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
-      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.gcc.c++_shared.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz)|
+      |IOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.with_log.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.with_log.tiny_publish.tar.gz)|
 
-      Note: 1. The above Paddle-Lite library is compiled from the Paddle-Lite 2.9 branch. For more information about Paddle-Lite 2.9, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.9).
+      Note: 1. The above Paddle-Lite library is compiled from the Paddle-Lite 2.10 branch. For more information about Paddle-Lite 2.10, please refer to [link](https://github.com/PaddlePaddle/Paddle-Lite/releases/tag/v2.10).
 
 - 2. [Recommended] Compile Paddle-Lite to get the prediction library. The compilation method of Paddle-Lite is as follows：
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-# Switch to Paddle-Lite release/v2.8 stable branch
-git checkout release/v2.8
+# Switch to Paddle-Lite release/v2.10 stable branch
+git checkout release/v2.10
 ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
 ```
 
@@ -87,10 +98,10 @@ The following table also provides a series of models that can be deployed on mob
 
 |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
 |---|---|---|---|---|---|---|
-|V2.0|extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
-|V2.0(slim)|extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
+|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.6M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
 
-If you directly use the model in the above table for deployment, you can skip the following steps and directly read [Section 2.2](#2.2 Run optimized model on Phone).
+If you directly use the model in the above table for deployment, you can skip the following steps and directly read [Section 2.2](#2.2-Run-optimized-model-on-Phone).
 
 If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model.
 
@@ -98,7 +109,7 @@ The `opt` tool can be obtained by compiling Paddle Lite.
 ```
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-git checkout release/v2.9
+git checkout release/v2.10
 ./lite/tools/build.sh build_optimize_tool
 ```
 
@@ -124,22 +135,22 @@ cd build.opt/lite/api/
 The following takes the ultra-lightweight Chinese model of PaddleOCR as an example to introduce the use of the compiled opt file to complete the conversion of the inference model to the Paddle-Lite optimized model
 
 ```
-# [Recommendation] Download the Chinese and English inference model of PaddleOCR V2.0
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_det_slim_infer.tar
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_rec_slim_infer.tar
+# 【[Recommendation] Download the Chinese and English inference model of PP-OCRv2
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_det_slim_quant_infer.tar
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf  ch_PP-OCRv2_rec_slim_quant_infer.tar
 wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_cls_slim_infer.tar && tar xf  ch_ppocr_mobile_v2.0_cls_slim_infer.tar
-# Convert V2.0 detection model
-./opt --model_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_det_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# Convert V2.0 recognition model
-./opt --model_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_rec_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
-# Convert V2.0 angle classifier model
+# Convert detection model
+./opt --model_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_det_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_det_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+# Convert recognition model
+./opt --model_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdmodel  --param_file=./ch_PP-OCRv2_rec_slim_quant_infer/inference.pdiparams  --optimize_out=./ch_PP-OCRv2_rec_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
+# Convert angle classifier model
 ./opt --model_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdmodel  --param_file=./ch_ppocr_mobile_v2.0_cls_slim_infer/inference.pdiparams  --optimize_out=./ch_ppocr_mobile_v2.0_cls_slim_opt --valid_targets=arm  --optimize_out_type=naive_buffer
 
 ```
 
 After the conversion is successful, there will be more files ending with `.nb` in the inference model directory, which is the successfully converted model file.
 
-<a name="2.2 Run optimized model on Phone"></a>
+<a name="2.2-Run-optimized-model-on-Phone"></a>
 ### 2.2 Run optimized model on Phone
 
 Some preparatory work is required first.
@@ -194,8 +205,8 @@ The structure of the OCR demo is as follows after the above command is executed:
 ```
 demo/cxx/ocr/
 |-- debug/  
-|   |--ch_ppocr_mobile_v2.0_det_slim_opt.nb           Detection model
-|   |--ch_ppocr_mobile_v2.0_rec_slim_opt.nb           Recognition model
+|   |--ch_PP-OCRv2_det_slim_opt.nb           Detection model
+|   |--ch_PP-OCRv2_rec_slim_opt.nb           Recognition model
 |   |--ch_ppocr_mobile_v2.0_cls_slim_opt.nb           Text direction classification model
 |   |--11.jpg                           Image for OCR
 |   |--ppocr_keys_v1.txt                Dictionary file
@@ -249,7 +260,7 @@ After the above steps are completed, you can use adb to push the file to the pho
  export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
  # The use of ocr_db_crnn is:
  # ./ocr_db_crnn Detection model file Orientation classifier model file Recognition model file Test image path Dictionary file path
- ./ocr_db_crnn ch_ppocr_mobile_v2.0_det_opt.nb  ch_ppocr_mobile_v2.0_rec_opt.nb  ch_ppocr_mobile_v2.0_cls_opt.nb  ./11.jpg  ppocr_keys_v1.txt
+ ./ocr_db_crnn ch_PP-OCRv2_det_slim_opt.nb  ch_PP-OCRv2_rec_slim_opt.nb  ch_ppocr_mobile_v2.0_cls_opt.nb  ./11.jpg  ppocr_keys_v1.txt
  ```
 
 If you modify the code, you need to recompile and push to the phone.

deploy/paddle2onnx/readme.md

 # paddle2onnx 模型转化与预测
 
-本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNX 引擎预测。
+本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNXRuntime 引擎预测。
 
 ## 1. 环境准备
 
-需要准备 Paddle2ONNX 模型转化环境，和 ONNX 模型预测环境
+需要准备 PaddleOCR、Paddle2ONNX 模型转化环境，和 ONNXRuntime 预测环境
+
+###  PaddleOCR
+
+克隆PaddleOCR的仓库，使用release/2.4分支，并进行安装，由于PaddleOCR仓库比较大，git clone速度比较慢，所以本教程已下载
+
+```
+git clone  -b release/2.4 https://github.com/PaddlePaddle/PaddleOCR.git
+cd PaddleOCR && python3.7 setup.py install
+```
 
 ###  Paddle2ONNX
 
@@ -16,7 +25,7 @@ Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式，算
 python3.7 -m pip install paddle2onnx
 ```
 
-- 安装 ONNX
+- 安装 ONNXRuntime
 ```
 # 建议安装 1.9.0 版本，可根据环境更换版本号
 python3.7 -m pip install onnxruntime==1.9.0
@@ -30,11 +39,17 @@ python3.7 -m pip install onnxruntime==1.9.0
 有两种方式获取Paddle静态图模型：在 [model_list](../../doc/doc_ch/models_list.md) 中下载PaddleOCR提供的预测模型；
 参考[模型导出说明](../../doc/doc_ch/inference.md#训练模型转inference模型)把训练好的权重转为 inference_model。
 
-以 ppocr 检测模型为例：
+以 ppocr 中文检测、识别、分类模型为例：
 
 ```
-wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ..
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+cd ./inference && tar xf ch_PP-OCRv2_det_infer.tar && cd ..
+
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+cd ./inference && tar xf ch_PP-OCRv2_rec_infer.tar && cd ..
+
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
 ```
 
 - 模型转换
@@ -42,35 +57,160 @@ cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ..
 使用 Paddle2ONNX 将Paddle静态图模型转换为ONNX模型格式：
 
 ```
-paddle2onnx --model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ \
---model_filename=inference.pdmodel \
---params_filename=inference.pdiparams \
---save_file=./inference/det_mobile_onnx/model.onnx \
---opset_version=10 \
---input_shape_dict="{'x': [-1, 3, -1, -1]}" \
---enable_onnx_checker=True
+paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
+--model_filename inference.pdmodel \
+--params_filename inference.pdiparams \
+--save_file ./inference/det_onnx/model.onnx \
+--opset_version 10 \
+--input_shape_dict="{'x':[-1,3,-1,-1]}" \
+--enable_onnx_checker True
+
+paddle2onnx --model_dir ./inference/ch_PP-OCRv2_rec_infer \
+--model_filename inference.pdmodel \
+--params_filename inference.pdiparams \
+--save_file ./inference/rec_onnx/model.onnx \
+--opset_version 10 \
+--input_shape_dict="{'x':[-1,3,-1,-1]}" \
+--enable_onnx_checker True
+
+paddle2onnx --model_dir ./inference/ch_ppocr_mobile_v2.0_cls_infer \
+--model_filename ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel \
+--params_filename ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams \
+--save_file ./inferencecls_onnx/model.onnx \
+--opset_version 10 \
+--input_shape_dict="{'x':[-1,3,-1,-1]}" \
+--enable_onnx_checker True
 ```
 
-执行完毕后，ONNX 模型会被保存在 `./inference/det_mobile_onnx/` 路径下
+执行完毕后，ONNX 模型会被分别保存在 `./inference/det_onnx/`，`./inference/rec_onnx/`，`./inference/cls_onnx/`路径下
 
 * 注意：对于OCR模型，转化过程中必须采用动态shape的形式，即加入选项--input_shape_dict="{'x': [-1, 3, -1, -1]}"，否则预测结果可能与直接使用Paddle预测有细微不同。
   另外，以下几个模型暂不支持转换为 ONNX 模型：
   NRTR、SAR、RARE、SRN
 
-## 3. onnx 预测
+## 3. 推理预测
+
+以中文OCR模型为例，使用 ONNXRuntime 预测可执行如下命令：
+
+```
+python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \
+--det_model_dir=./inference/det_onnx/model.onnx  \
+--rec_model_dir=./inference/rec_onnx/model.onnx  \
+--cls_model_dir=./inference/cls_onnx/model.onnx  \
+--image_dir=./deploy/lite/imgs/lite_demo.png
+```
+
+以中文OCR模型为例，使用 Paddle Inference 预测可执行如下命令：
+
+```
+python3.7 tools/infer/predict_system.py --use_gpu=False \
+--cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \
+--rec_model_dir=./inference/ch_PP-OCRv2_rec_infer \
+--det_model_dir=./inference/ch_PP-OCRv2_det_infer \
+--image_dir=./deploy/lite/imgs/lite_demo.png
+```
+
+
+执行命令后在终端会打印出预测的识别信息，并在 `./inference_results/` 下保存可视化结果。
+
+ONNXRuntime 执行效果：
+
+<div align="center">
+    <img src="./images/lite_demo_onnx.png" width=800">
+</div>
+
+Paddle Inference 执行效果：
+
+<div align="center">
+    <img src="./images/lite_demo_paddle.png" width=800">
+</div>
 
-以检测模型为例，使用 ONNX 预测可执行如下命令：
 
+使用 ONNXRuntime 预测，终端输出：
 ```
-python3.7 ../../tools/infer/predict_det.py --use_gpu=False --use_onnx=True \
---det_model_dir=./inference/det_mobile_onnx/model.onnx \
---image_dir=../../doc/imgs/1.jpg
+[2022/02/22 17:48:27] root DEBUG: dt_boxes num : 38, elapse : 0.043187856674194336
+[2022/02/22 17:48:27] root DEBUG: rec_res num  : 38, elapse : 0.592170000076294
+[2022/02/22 17:48:27] root DEBUG: 0  Predict time of ./deploy/lite/imgs/lite_demo.png: 0.642s
+[2022/02/22 17:48:27] root DEBUG: The, 0.984
+[2022/02/22 17:48:27] root DEBUG: visualized, 0.882
+[2022/02/22 17:48:27] root DEBUG: etect18片, 0.720
+[2022/02/22 17:48:27] root DEBUG: image saved in./vis.jpg, 0.947
+[2022/02/22 17:48:27] root DEBUG: 纯臻营养护发素0.993604, 0.996
+[2022/02/22 17:48:27] root DEBUG: 产品信息/参数, 0.922
+[2022/02/22 17:48:27] root DEBUG: 0.992728, 0.914
+[2022/02/22 17:48:27] root DEBUG: （45元／每公斤，100公斤起订）, 0.926
+[2022/02/22 17:48:27] root DEBUG: 0.97417, 0.977
+[2022/02/22 17:48:27] root DEBUG: 每瓶22元，1000瓶起订）0.993976, 0.962
+[2022/02/22 17:48:27] root DEBUG: 【品牌】：代加工方式/0EMODM, 0.945
+[2022/02/22 17:48:27] root DEBUG: 0.985133, 0.980
+[2022/02/22 17:48:27] root DEBUG: 【品名】：纯臻营养护发素, 0.921
+[2022/02/22 17:48:27] root DEBUG: 0.995007, 0.883
+[2022/02/22 17:48:27] root DEBUG: 【产品编号】：YM-X-30110.96899, 0.955
+[2022/02/22 17:48:27] root DEBUG: 【净含量】：220ml, 0.943
+[2022/02/22 17:48:27] root DEBUG: Q.996577, 0.932
+[2022/02/22 17:48:27] root DEBUG: 【适用人群】：适合所有肤质, 0.913
+[2022/02/22 17:48:27] root DEBUG: 0.995842, 0.969
+[2022/02/22 17:48:27] root DEBUG: 【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
+[2022/02/22 17:48:27] root DEBUG: 0.961928, 0.964
+[2022/02/22 17:48:27] root DEBUG: 10, 0.812
+[2022/02/22 17:48:27] root DEBUG: 糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
+[2022/02/22 17:48:27] root DEBUG: 0.925898, 0.943
+[2022/02/22 17:48:27] root DEBUG: （成品包材）, 0.974
+[2022/02/22 17:48:27] root DEBUG: 0.972573, 0.961
+[2022/02/22 17:48:27] root DEBUG: 【主要功能】：可紧致头发磷层，从而达到, 0.936
+[2022/02/22 17:48:27] root DEBUG: 0.994448, 0.952
+[2022/02/22 17:48:27] root DEBUG: 13, 0.998
+[2022/02/22 17:48:27] root DEBUG: 即时持久改善头发光泽的效果，给干燥的头, 0.994
+[2022/02/22 17:48:27] root DEBUG: 0.990198, 0.975
+[2022/02/22 17:48:27] root DEBUG: 14, 0.977
+[2022/02/22 17:48:27] root DEBUG: 发足够的滋养, 0.991
+[2022/02/22 17:48:27] root DEBUG: 0.997668, 0.918
+[2022/02/22 17:48:27] root DEBUG: 花费了0.457335秒, 0.901
+[2022/02/22 17:48:27] root DEBUG: The visualized image saved in ./inference_results/lite_demo.png
+[2022/02/22 17:48:27] root INFO: The predict total time is 0.7003889083862305
 ```
 
-执行命令后在终端会打印出预测的检测框坐标，并在 `./inference_results/` 下保存可视化结果。
+使用 Paddle Inference 预测，终端输出：
 
 ```
-root INFO: 1.jpg  [[[291, 295], [334, 292], [348, 844], [305, 847]], [[344, 296], [379, 294], [387, 669], [353, 671]]]
-The predict time of ../../doc/imgs/1.jpg: 0.06162881851196289
-The visualized image saved in ./inference_results/det_res_1.jpg
+[2022/02/22 17:47:25] root DEBUG: dt_boxes num : 38, elapse : 0.11791276931762695
+[2022/02/22 17:47:27] root DEBUG: rec_res num  : 38, elapse : 2.6206860542297363
+[2022/02/22 17:47:27] root DEBUG: 0  Predict time of ./deploy/lite/imgs/lite_demo.png: 2.746s
+[2022/02/22 17:47:27] root DEBUG: The, 0.984
+[2022/02/22 17:47:27] root DEBUG: visualized, 0.882
+[2022/02/22 17:47:27] root DEBUG: etect18片, 0.720
+[2022/02/22 17:47:27] root DEBUG: image saved in./vis.jpg, 0.947
+[2022/02/22 17:47:27] root DEBUG: 纯臻营养护发素0.993604, 0.996
+[2022/02/22 17:47:27] root DEBUG: 产品信息/参数, 0.922
+[2022/02/22 17:47:27] root DEBUG: 0.992728, 0.914
+[2022/02/22 17:47:27] root DEBUG: （45元／每公斤，100公斤起订）, 0.926
+[2022/02/22 17:47:27] root DEBUG: 0.97417, 0.977
+[2022/02/22 17:47:27] root DEBUG: 每瓶22元，1000瓶起订）0.993976, 0.962
+[2022/02/22 17:47:27] root DEBUG: 【品牌】：代加工方式/0EMODM, 0.945
+[2022/02/22 17:47:27] root DEBUG: 0.985133, 0.980
+[2022/02/22 17:47:27] root DEBUG: 【品名】：纯臻营养护发素, 0.921
+[2022/02/22 17:47:27] root DEBUG: 0.995007, 0.883
+[2022/02/22 17:47:27] root DEBUG: 【产品编号】：YM-X-30110.96899, 0.955
+[2022/02/22 17:47:27] root DEBUG: 【净含量】：220ml, 0.943
+[2022/02/22 17:47:27] root DEBUG: Q.996577, 0.932
+[2022/02/22 17:47:27] root DEBUG: 【适用人群】：适合所有肤质, 0.913
+[2022/02/22 17:47:27] root DEBUG: 0.995842, 0.969
+[2022/02/22 17:47:27] root DEBUG: 【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
+[2022/02/22 17:47:27] root DEBUG: 0.961928, 0.964
+[2022/02/22 17:47:27] root DEBUG: 10, 0.812
+[2022/02/22 17:47:27] root DEBUG: 糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
+[2022/02/22 17:47:27] root DEBUG: 0.925898, 0.943
+[2022/02/22 17:47:27] root DEBUG: （成品包材）, 0.974
+[2022/02/22 17:47:27] root DEBUG: 0.972573, 0.961
+[2022/02/22 17:47:27] root DEBUG: 【主要功能】：可紧致头发磷层，从而达到, 0.936
+[2022/02/22 17:47:27] root DEBUG: 0.994448, 0.952
+[2022/02/22 17:47:27] root DEBUG: 13, 0.998
+[2022/02/22 17:47:27] root DEBUG: 即时持久改善头发光泽的效果，给干燥的头, 0.994
+[2022/02/22 17:47:27] root DEBUG: 0.990198, 0.975
+[2022/02/22 17:47:27] root DEBUG: 14, 0.977
+[2022/02/22 17:47:27] root DEBUG: 发足够的滋养, 0.991
+[2022/02/22 17:47:27] root DEBUG: 0.997668, 0.918
+[2022/02/22 17:47:27] root DEBUG: 花费了0.457335秒, 0.901
+[2022/02/22 17:47:27] root DEBUG: The visualized image saved in ./inference_results/lite_demo.png
+[2022/02/22 17:47:27] root INFO: The predict total time is 2.8338775634765625
 ```

deploy/pdserving/README.md

@@ -34,35 +34,26 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
 PaddleOCR operating environment and Paddle Serving operating environment are needed.
 
 1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
-   Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.0.1.
+   Download the corresponding paddlepaddle whl package according to the environment, it is recommended to install version 2.2.2.
 
 
 2. The steps of PaddleServing operating environment prepare are as follows:
 
-    Install serving which used to start the service
-    ```
-    pip3 install paddle-serving-server==0.6.1 # for CPU
-    pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
-    # Other GPU environments need to confirm the environment and then choose to execute the following commands
-    pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
-    pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
-    ```
-
-3. Install the client to send requests to the service
 
 ```bash
-# 安装serving，用于启动服务
+# Install serving which used to start the service
 wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
 pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
-# 如果是cuda10.1环境，可以使用下面的命令安装paddle-serving-server
+
+# Install paddle-serving-server for cuda10.1
 # wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
 # pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
 
-# 安装client，用于向服务发送请求
+# Install serving which used to start the service
 wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
 pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
 
-# 安装serving-app
+# Install serving-app
 wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
 pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
 ```
@@ -87,27 +78,27 @@ Then, you can use installed paddle_serving_client tool to convert inference mode
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_det_serving/ \
-                                         --serving_client ./ppocrv2_det_client/
+                                         --serving_server ./ppocr_det_mobile_2.0_serving/ \
+                                         --serving_client ./ppocr_det_mobile_2.0_client/
 
 #  Recognition model conversion
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_rec_serving/  \
-                                         --serving_client ./ppocrv2_rec_client/
+                                         --serving_server ./ppocr_rec_mobile_2.0_serving/  \
+                                         --serving_client ./ppocr_rec_mobile_2.0_client/
 
 ```
 
 After the detection model is converted, there will be additional folders of `ppocr_det_mobile_2.0_serving` and `ppocr_det_mobile_2.0_client` in the current folder, with the following format:
 ```
-|- ppocrv2_det_serving/
+|- ppocr_det_mobile_2.0_serving/
   |- __model__  
   |- __params__
   |- serving_server_conf.prototxt  
   |- serving_server_conf.stream.prototxt
 
-|- ppocrv2_det_client
+|- ppocr_det_mobile_2.0_client
   |- serving_client_conf.prototxt  
   |- serving_client_conf.stream.prototxt
 

deploy/pdserving/README_CN.md

@@ -31,7 +31,7 @@ PaddleOCR提供2种服务部署方式：
 需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
 
 - 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
-  根据环境下载对应的paddle whl包，推荐安装2.0.1版本
+  根据环境下载对应的paddlepaddle whl包，推荐安装2.2.2版本
 
 - 准备PaddleServing的运行环境，步骤如下
 
@@ -75,26 +75,26 @@ wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_det_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_det_serving/ \
-                                         --serving_client ./ppocrv2_det_client/
+                                         --serving_server ./ppocr_det_mobile_2.0_serving/ \
+                                         --serving_client ./ppocr_det_mobile_2.0_client/
 
 # 转换识别模型
 python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
                                          --model_filename inference.pdmodel          \
                                          --params_filename inference.pdiparams       \
-                                         --serving_server ./ppocrv2_rec_serving/  \
-                                         --serving_client ./ppocrv2_rec_client/
+                                         --serving_server ./ppocr_rec_mobile_2.0_serving/  \
+                                         --serving_client ./ppocr_rec_mobile_2.0_client/
 ```
 
-检测模型转换完成后，会在当前文件夹多出`ppocrv2_det_serving` 和`ppocrv2_det_client`的文件夹，具备如下格式：
+检测模型转换完成后，会在当前文件夹多出`ppocr_det_mobile_2.0_serving` 和`ppocr_det_mobile_2.0_client`的文件夹，具备如下格式：
 ```
-|- ppocrv2_det_serving/
+|- ppocr_det_mobile_2.0_serving/
   |- __model__  
   |- __params__
   |- serving_server_conf.prototxt  
   |- serving_server_conf.stream.prototxt
 
-|- ppocrv2_det_client
+|- ppocr_det_mobile_2.0_client
   |- serving_client_conf.prototxt  
   |- serving_client_conf.stream.prototxt
 

deploy/pdserving/config.yml

@@ -34,7 +34,7 @@ op:
             client_type: local_predictor
 
             #det模型路径
-            model_config: ./ppocrv2_det_serving
+            model_config: ./ppocr_det_mobile_2.0_serving
 
             #Fetch结果列表，以client_config中fetch_var的alias_name为准
             fetch_list: ["save_infer_model/scale_0.tmp_1"]
@@ -60,7 +60,7 @@ op:
             client_type: local_predictor
 
             #rec模型路径
-            model_config: ./ppocrv2_rec_serving
+            model_config: ./ppocr_rec_mobile_2.0_serving
 
             #Fetch结果列表，以client_config中fetch_var的alias_name为准
             fetch_list: ["save_infer_model/scale_0.tmp_1"]  

deploy/pdserving/ocr_reader.py

@@ -433,3 +433,54 @@ class OCRReader(object):
         text = self.label_ops.decode(
             preds_idx, preds_prob, is_remove_duplicate=True)
         return text
+
+from argparse import ArgumentParser,RawDescriptionHelpFormatter
+import yaml
+class ArgsParser(ArgumentParser):
+    def __init__(self):
+        super(ArgsParser, self).__init__(
+            formatter_class=RawDescriptionHelpFormatter)
+        self.add_argument("-c", "--config", help="configuration file to use")
+        self.add_argument(
+            "-o", "--opt", nargs='+', help="set configuration options")
+
+    def parse_args(self, argv=None):
+        args = super(ArgsParser, self).parse_args(argv)
+        assert args.config is not None, \
+            "Please specify --config=configure_file_path."
+        args.conf_dict = self._parse_opt(args.opt, args.config)
+        print("args config:", args.conf_dict)
+        return args
+
+    def _parse_helper(self, v):
+        if v.isnumeric():
+            if "." in v:
+                v = float(v)
+            else:
+                v = int(v)
+        elif v == "True" or v == "False":
+            v = (v == "True")
+        return v
+
+    def _parse_opt(self, opts, conf_path):
+        f = open(conf_path)
+        config = yaml.load(f, Loader=yaml.Loader)
+        if not opts:
+            return config
+        for s in opts:
+            s = s.strip()
+            k, v = s.split('=')
+            v = self._parse_helper(v)
+            print(k,v, type(v))
+            cur = config
+            parent = cur
+            for kk in k.split("."):
+                if kk not in cur:
+                     cur[kk] = {}
+                     parent = cur
+                     cur = cur[kk]
+                else:
+                     parent = cur
+                     cur = cur[kk]
+            parent[k.split(".")[-1]] = v
+        return config
\ No newline at end of file

deploy/pdserving/web_service_det.py

@@ -18,7 +18,7 @@ import numpy as np
 import cv2
 import base64
 # from paddle_serving_app.reader import OCRReader
-from ocr_reader import OCRReader, DetResizeForTest
+from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
 from paddle_serving_app.reader import Sequential, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
@@ -73,5 +73,6 @@ class OcrService(WebService):
 
 
 uci_service = OcrService(name="ocr")
-uci_service.prepare_pipeline_config("config.yml")
+FLAGS = ArgsParser().parse_args()
+uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
 uci_service.run_service()

deploy/pdserving/web_service_rec.py

@@ -18,7 +18,7 @@ import numpy as np
 import cv2
 import base64
 # from paddle_serving_app.reader import OCRReader
-from ocr_reader import OCRReader, DetResizeForTest
+from ocr_reader import OCRReader, DetResizeForTest, ArgsParser
 from paddle_serving_app.reader import Sequential, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 
@@ -82,5 +82,6 @@ class OcrService(WebService):
 
 
 uci_service = OcrService(name="ocr")
-uci_service.prepare_pipeline_config("config.yml")
+FLAGS = ArgsParser().parse_args()
+uci_service.prepare_pipeline_config(yml_dict=FLAGS.conf_dict)
 uci_service.run_service()

doc/doc_ch/FAQ.md

@@ -349,7 +349,7 @@ A：PaddleOCR已完成Windows和Mac系统适配，运行时注意两点：
 
 #### Q：训练文字识别模型，真实数据有30w，合成数据有500w，需要做样本均衡吗？
 
-A：需要，一般需要保证一个batch中真实数据样本和合成数据样本的比例是1：1~1：3左右效果比较理想。如果合成数据过大，会过拟合到合成数据，预测效果往往不佳。还有一种启发性的尝试是可以先用大量合成数据训练一个base模型，然后再用真实数据微调，在一些简单场景效果也是会有提升的。
+A：需要，一般需要保证一个batch中真实数据样本和合成数据样本的比例是5：1~10：1左右效果比较理想。如果合成数据过大，会过拟合到合成数据，预测效果往往不佳。还有一种启发性的尝试是可以先用大量合成数据训练一个base模型，然后再用真实数据微调，在一些简单场景效果也是会有提升的。
 
 #### Q: 当训练数据量少时，如何获取更多的数据？
 

doc/doc_ch/algorithm_overview.md

 # 两阶段算法
 
-- [两阶段算法](#-----)
-  * [1. 算法介绍](#1)
-    + [1.1 文本检测算法](#11)
-    + [1.2 文本识别算法](#12)
-  * [2. 模型训练](#2)
-  * [3. 模型推理](#3)
+- [两阶段算法](#两阶段算法)
+  - [1. 算法介绍](#1-算法介绍)
+    - [1.1 文本检测算法](#11-文本检测算法)
+    - [1.2 文本识别算法](#12-文本识别算法)
+  - [2. 模型训练](#2-模型训练)
+  - [3. 模型推理](#3-模型推理)
 
 <a name="1"></a>
 
@@ -21,6 +21,7 @@ PaddleOCR开源的文本检测算法列表：
 - [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
 - [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
 - [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
+- [x]  FCENet([paper](https://arxiv.org/abs/2104.10442))
 
 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|
@@ -39,6 +40,12 @@ PaddleOCR开源的文本检测算法列表：
 | --- | --- | --- | --- | --- | --- |
 |SAST|ResNet50_vd|89.63%|78.44%|83.66%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
 
+在CTW1500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|
+| --- | --- | --- | --- | --- | --- |         
+|FCE|ResNet50_dcn|88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.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)
 * [Google Drive下载地址](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing)

doc/doc_ch/detection.md

@@ -10,6 +10,7 @@
   * [2.1 启动训练](#21-----)
   * [2.2 断点训练](#22-----)
   * [2.3 更换Backbone 训练](#23---backbone---)
+  * [2.4 知识蒸馏训练](#24---distill---)
 - [3. 模型评估与预测](#3--------)
   * [3.1 指标评估](#31-----)
   * [3.2 测试检测效果](#32-------)
@@ -182,6 +183,15 @@ args1: args1
 
 **注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
 
+
+<a name="24---distill---"></a>
+
+## 2.4 知识蒸馏训练
+
+PaddleOCR支持了基于知识蒸馏的检测模型训练过程，更多内容可以参考[知识蒸馏说明文档](./knowledge_distillation.md)。
+
+
+
 <a name="3--------"></a>
 # 3. 模型评估与预测
 

doc/doc_ch/models_list.md

@@ -6,13 +6,14 @@
 > 3. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
 
 
-- [1. 文本检测模型](#文本检测模型)
-- [2. 文本识别模型](#文本识别模型)
-    - [2.1 中文识别模型](#中文识别模型)
-    - [2.2 英文识别模型](#英文识别模型)
-    - [2.3 多语言识别模型](#多语言识别模型)
-- [3. 文本方向分类模型](#文本方向分类模型)
-- [4. Paddle-Lite 模型](#Paddle-Lite模型)
+- [PP-OCR系列模型列表（V2.1，2021年9月6日更新）](#pp-ocr系列模型列表v212021年9月6日更新)
+  - [1. 文本检测模型](#1-文本检测模型)
+  - [2. 文本识别模型](#2-文本识别模型)
+    - [2.1 中文识别模型](#21-中文识别模型)
+    - [2.2 英文识别模型](#22-英文识别模型)
+    - [2.3 多语言识别模型（更多语言持续更新中...）](#23-多语言识别模型更多语言持续更新中)
+  - [3. 文本方向分类模型](#3-文本方向分类模型)
+  - [4. Paddle-Lite 模型](#4-paddle-lite-模型)
 
 PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
 
@@ -100,6 +101,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 
 |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
 |---|---|---|---|---|---|---|
+|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.6M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
 |PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
 |PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.9M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
 |V2.0|ppocr_v2.0超轻量中文OCR移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|

doc/doc_ch/quickstart.md

 # PaddleOCR快速开始
 
-
-- [PaddleOCR快速开始](#paddleocr)
-
-  + [1. 安装PaddleOCR whl包](#1)
-  * [2. 便捷使用](#2)
-    + [2.1 命令行使用](#21)
+- [1. 安装PaddleOCR whl包](#1)
+- [2. 便捷使用](#2)
+  - [2.1 命令行使用](#21)
       - [2.1.1 中英文模型](#211)
       - [2.1.2 多语言模型](#212)
       - [2.1.3 版面分析](#213)
-    + [2.2 Python脚本使用](#22)
+  - [2.2 Python脚本使用](#22)
       - [2.2.1 中英文与多语言使用](#221)
       - [2.2.2 版面分析](#222)
 

doc/doc_ch/recognition.md

@@ -11,6 +11,7 @@
     - [2.1 数据增强](#数据增强)
     - [2.2 通用模型训练](#通用模型训练)
     - [2.3 多语言模型训练](#多语言模型训练)
+    - [2.4 知识蒸馏训练](#知识蒸馏训练)
 - [3 评估](#评估)
 - [4 预测](#预测)
 - [5 转Inference模型测试](#Inference)
@@ -368,6 +369,13 @@ Eval:
     label_file_list: ["./train_data/french_val.txt"]
     ...
 ```
+
+<a name="知识蒸馏训练"></a>
+
+### 2.4 知识蒸馏训练
+
+PaddleOCR支持了基于知识蒸馏的文本识别模型训练过程，更多内容可以参考[知识蒸馏说明文档](./knowledge_distillation.md)。
+
 <a name="评估"></a>
 ## 3 评估
 

doc/doc_en/detection_en.md

@@ -9,6 +9,7 @@ This section uses the icdar2015 dataset as an example to introduce the training,
   * [2.1 Start Training](#21-start-training)
   * [2.2 Load Trained Model and Continue Training](#22-load-trained-model-and-continue-training)
   * [2.3 Training with New Backbone](#23-training-with-new-backbone)
+  * [2.4 Training with knowledge distillation](#24)
 - [3. Evaluation and Test](#3-evaluation-and-test)
   * [3.1 Evaluation](#31-evaluation)
   * [3.2 Test](#32-test)
@@ -174,6 +175,11 @@ After adding the four-part modules of the network, you only need to configure th
 
 **NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md).
 
+
+### 2.4 Training with knowledge distillation
+
+Knowledge distillation is supported in PaddleOCR for text detection training process. For more details, please refer to [doc](./knowledge_distillation_en.md).
+
 ## 3. Evaluation and Test
 
 ### 3.1 Evaluation

doc/doc_en/models_list_en.md

@@ -94,6 +94,8 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
 ## 4. Paddle-Lite Model
 |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
 |---|---|---|---|---|---|---|
+|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
+|PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.6M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_slim_opt.nb)|v2.10|
 |PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
 |PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.9M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
 |V2.0|ppocr_v2.0 extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|

doc/doc_en/quickstart_en.md

 
 # PaddleOCR Quick Start
 
-[PaddleOCR Quick Start](#paddleocr-quick-start)
-
 + [1. Install PaddleOCR Whl Package](#1-install-paddleocr-whl-package)
 * [2. Easy-to-Use](#2-easy-to-use)
   + [2.1 Use by Command Line](#21-use-by-command-line)

doc/doc_en/recognition_en.md

@@ -10,6 +10,7 @@
     - [2.1 Data Augmentation](#Data_Augmentation)
     - [2.2 General Training](#Training)
     - [2.3 Multi-language Training](#Multi_language)
+    - [2.4 Training with Knowledge Distillation](#kd)
 
 - [3. Evaluation](#EVALUATION)
 
@@ -361,6 +362,12 @@ Eval:
     ...
 ```
 
+<a name="kd"></a>
+
+### 2.4 Training with Knowledge Distillation
+
+Knowledge distillation is supported in PaddleOCR for text recognition training process. For more details, please refer to [doc](./knowledge_distillation_en.md).
+
 <a name="EVALUATION"></a>
 
 ## 3. Evalution

ppocr/data/imaug/__init__.py

@@ -22,7 +22,8 @@ from .make_shrink_map import MakeShrinkMap
 from .random_crop_data import EastRandomCropData, RandomCropImgMask
 from .make_pse_gt import MakePseGt
 
-from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg
+from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, \
+    SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg
 from .randaugment import RandAugment
 from .copy_paste import CopyPaste
 from .ColorJitter import ColorJitter
@@ -36,6 +37,9 @@ from .gen_table_mask import *
 
 from .vqa import *
 
+from .fce_aug import *
+from .fce_targets import FCENetTargets
+
 
 def transform(data, ops=None):
     """ transform """

ppocr/data/imaug/label_ops.py

@@ -785,6 +785,53 @@ class SARLabelEncode(BaseRecLabelEncode):
         return [self.padding_idx]
 
 
+class PRENLabelEncode(BaseRecLabelEncode):
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path,
+                 use_space_char=False,
+                 **kwargs):
+        super(PRENLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
+
+    def add_special_char(self, dict_character):
+        padding_str = '<PAD>'  # 0 
+        end_str = '<EOS>'  # 1
+        unknown_str = '<UNK>'  # 2
+
+        dict_character = [padding_str, end_str, unknown_str] + dict_character
+        self.padding_idx = 0
+        self.end_idx = 1
+        self.unknown_idx = 2
+
+        return dict_character
+
+    def encode(self, text):
+        if len(text) == 0 or len(text) >= self.max_text_len:
+            return None
+        if self.lower:
+            text = text.lower()
+        text_list = []
+        for char in text:
+            if char not in self.dict:
+                text_list.append(self.unknown_idx)
+            else:
+                text_list.append(self.dict[char])
+        text_list.append(self.end_idx)
+        if len(text_list) < self.max_text_len:
+            text_list += [self.padding_idx] * (
+                self.max_text_len - len(text_list))
+        return text_list
+
+    def __call__(self, data):
+        text = data['label']
+        encoded_text = self.encode(text)
+        if encoded_text is None:
+            return None
+        data['label'] = np.array(encoded_text)
+        return data
+
+
 class VQATokenLabelEncode(object):
     """
     Label encode for NLP VQA methods

ppocr/data/imaug/operators.py

@@ -23,14 +23,20 @@ import sys
 import six
 import cv2
 import numpy as np
+import math
 
 
 class DecodeImage(object):
     """ decode image """
 
-    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+    def __init__(self,
+                 img_mode='RGB',
+                 channel_first=False,
+                 ignore_orientation=False,
+                 **kwargs):
         self.img_mode = img_mode
         self.channel_first = channel_first
+        self.ignore_orientation = ignore_orientation
 
     def __call__(self, data):
         img = data['image']
@@ -41,7 +47,11 @@ class DecodeImage(object):
             assert type(img) is bytes and len(
                 img) > 0, "invalid input 'img' in DecodeImage"
         img = np.frombuffer(img, dtype='uint8')
-        img = cv2.imdecode(img, 1)
+        if self.ignore_orientation:
+            img = cv2.imdecode(img, cv2.IMREAD_IGNORE_ORIENTATION |
+                               cv2.IMREAD_COLOR)
+        else:
+            img = cv2.imdecode(img, 1)
         if img is None:
             return None
         if self.img_mode == 'GRAY':
@@ -156,6 +166,44 @@ class KeepKeys(object):
         return data_list
 
 
+class Pad(object):
+    def __init__(self, size=None, size_div=32, **kwargs):
+        if size is not None and not isinstance(size, (int, list, tuple)):
+            raise TypeError("Type of target_size is invalid. Now is {}".format(
+                type(size)))
+        if isinstance(size, int):
+            size = [size, size]
+        self.size = size
+        self.size_div = size_div
+
+    def __call__(self, data):
+
+        img = data['image']
+        img_h, img_w = img.shape[0], img.shape[1]
+        if self.size:
+            resize_h2, resize_w2 = self.size
+            assert (
+                img_h < resize_h2 and img_w < resize_w2
+            ), '(h, w) of target size should be greater than (img_h, img_w)'
+        else:
+            resize_h2 = max(
+                int(math.ceil(img.shape[0] / self.size_div) * self.size_div),
+                self.size_div)
+            resize_w2 = max(
+                int(math.ceil(img.shape[1] / self.size_div) * self.size_div),
+                self.size_div)
+        img = cv2.copyMakeBorder(
+            img,
+            0,
+            resize_h2 - img_h,
+            0,
+            resize_w2 - img_w,
+            cv2.BORDER_CONSTANT,
+            value=0)
+        data['image'] = img
+        return data
+
+
 class Resize(object):
     def __init__(self, size=(640, 640), **kwargs):
         self.size = size

ppocr/data/simple_dataset.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import numpy as np
 import os
+import json
 import random
 import traceback
 from paddle.io import Dataset