Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph

.gitignore

@@ -24,4 +24,8 @@ output/
 
 build/
 dist/
-paddleocr.egg-info/
+paddleocr.egg-info/
\ No newline at end of file
+/deploy/android_demo/app/OpenCV/
+/deploy/android_demo/app/PaddleLite/
+/deploy/android_demo/app/.cxx/
+/deploy/android_demo/app/cache/

MANIFEST.in

@@ -4,4 +4,5 @@ include README.md
 recursive-include ppocr/utils *.txt utility.py logging.py
 recursive-include ppocr/data/ *.py
 recursive-include ppocr/postprocess *.py
-recursive-include tools/infer *.py
+recursive-include tools/infer *.py
\ No newline at end of file
+recursive-include ppocr/utils/e2e_utils/ *.py
\ No newline at end of file

PPOCRLabel/PPOCRLabel.py

@@ -147,6 +147,7 @@ class MainWindow(QMainWindow, WindowMixin):
         self.itemsToShapesbox = {}
         self.shapesToItemsbox = {}
         self.prevLabelText = getStr('tempLabel')
+        self.noLabelText = getStr('nullLabel')
         self.model = 'paddle'
         self.PPreader = None
         self.autoSaveNum = 5
@@ -1020,7 +1021,7 @@ class MainWindow(QMainWindow, WindowMixin):
         item.setText(str([(int(p.x()), int(p.y())) for p in shape.points]))
         self.updateComboBox()
 
-    def updateComboBox(self): # TODO：貌似没用
+    def updateComboBox(self):
         # Get the unique labels and add them to the Combobox.
         itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())]
 
@@ -1040,7 +1041,7 @@ class MainWindow(QMainWindow, WindowMixin):
             return dict(label=s.label,  # str
                         line_color=s.line_color.getRgb(),
                         fill_color=s.fill_color.getRgb(),
-                        points=[(p.x(), p.y()) for p in s.points],  # QPonitF
+                        points=[(int(p.x()), int(p.y())) for p in s.points],  # QPonitF
                        # add chris
                         difficult=s.difficult)  # bool
 
@@ -1069,7 +1070,7 @@ class MainWindow(QMainWindow, WindowMixin):
             # print('Image:{0} -> Annotation:{1}'.format(self.filePath, annotationFilePath))
             return True
         except:
-            self.errorMessage(u'Error saving label data')
+            self.errorMessage(u'Error saving label data', u'Error saving label data')
             return False
 
     def copySelectedShape(self):
@@ -1802,10 +1803,14 @@ class MainWindow(QMainWindow, WindowMixin):
                     result.insert(0, box)
                     print('result in reRec is ', result)
                     self.result_dic.append(result)
-                    if result[1][0] == shape.label:
-                        print('label no change')
-                    else:
-                        rec_flag += 1
+                else:
+                    print('Can not recognise the box')
+                    self.result_dic.append([box,(self.noLabelText,0)])
+
+                if self.noLabelText == shape.label or result[1][0] == shape.label:
+                    print('label no change')
+                else:
+                    rec_flag += 1
 
             if len(self.result_dic) > 0 and rec_flag > 0:
                 self.saveFile(mode='Auto')
@@ -1836,9 +1841,14 @@ class MainWindow(QMainWindow, WindowMixin):
                     print('label no change')
                 else:
                     shape.label = result[1][0]
-                self.singleLabel(shape)
-                self.setDirty()
-            print(box)
+            else:
+                print('Can not recognise the box')
+                if self.noLabelText == shape.label:
+                    print('label no change')
+                else:
+                    shape.label = self.noLabelText
+            self.singleLabel(shape)
+            self.setDirty()
 
     def autolcm(self):
         vbox = QVBoxLayout()

PPOCRLabel/README.md

@@ -29,9 +29,7 @@ PaddleOCR models has been built in PPOCRLabel, please refer to [PaddleOCR instal
 
 ### 2. Install PPOCRLabel
 
-#### Windows + Anaconda
-
-Download and install [Anaconda](https://www.anaconda.com/download/#download) (Python 3+)
+#### Windows
 
 ```
 pip install pyqt5

PPOCRLabel/README_ch.md

@@ -31,7 +31,7 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P
 PPOCRLabel内置PaddleOCR模型，故请参考[PaddleOCR安装文档](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/installation.md)准备好PaddleOCR，并确保PaddleOCR安装成功。
 
 ### 2. 安装PPOCRLabel
-#### Windows + Anaconda
+#### Windows
 
 ```
 pip install pyqt5

PPOCRLabel/libs/canvas.py

@@ -45,7 +45,7 @@ class Canvas(QWidget):
     CREATE, EDIT = list(range(2))
     _fill_drawing = False # draw shadows
 
-    epsilon = 11.0
+    epsilon = 5.0
 
     def __init__(self, *args, **kwargs):
         super(Canvas, self).__init__(*args, **kwargs)

PPOCRLabel/libs/utils.py

@@ -124,6 +124,15 @@ def natural_sort(list, key=lambda s:s):
 
 
 def get_rotate_crop_image(img, points):
+    # Use Green's theory to judge clockwise or counterclockwise
+    # author: biyanhua
+    d = 0.0
+    for index in range(-1, 3):
+        d += -0.5 * (points[index + 1][1] + points[index][1]) * (
+                    points[index + 1][0] - points[index][0])
+    if d < 0: # counterclockwise
+        tmp = np.array(points)
+        points[1], points[3] = tmp[3], tmp[1]
 
     try:
         img_crop_width = int(

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

@@ -87,6 +87,7 @@ creatPolygon=四点标注
 drawSquares=正方形标注
 saveRec=保存识别结果
 tempLabel=待识别
+nullLabel=无法识别
 steps=操作步骤
 choseModelLg=选择模型语言
 cancel=取消

PPOCRLabel/resources/strings/strings.properties

@@ -77,7 +77,7 @@ IR=Image Resize
 autoRecognition=Auto Recognition
 reRecognition=Re-recognition
 mfile=File
-medit=Eidt
+medit=Edit
 mview=View
 mhelp=Help
 iconList=Icon List
@@ -87,6 +87,7 @@ creatPolygon=Create Quadrilateral
 drawSquares=Draw Squares
 saveRec=Save Recognition Result
 tempLabel=TEMPORARY
+nullLabel=NULL
 steps=Steps
 choseModelLg=Choose Model Language
 cancel=Cancel

README.md

@@ -32,7 +32,8 @@ PaddleOCR supports both dynamic graph and static graph programming paradigm
 
 <div align="center">
     <img src="doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
-    <img src="doc/imgs_results/ch_ppocr_mobile_v2.0/00018069.jpg" width="800">
+    <img src="doc/imgs_results/multi_lang/img_01.jpg" width="800">
+    <img src="doc/imgs_results/multi_lang/img_02.jpg" width="800">
 </div>
 
 The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see [More visualizations](./doc/doc_en/visualization_en.md).

README_ch.md

@@ -8,9 +8,9 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 - 静态图版本：develop分支
 
 **近期更新**
+- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](./doc/doc_ch/pgnet.md)开源，[多语言模型](./doc/doc_ch/multi_languages.md)支持种类增加到80+。
 - 2021.2.1 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数162个，每周一都会更新，欢迎大家持续关注。
-- 2021.1.26,28,29 PaddleOCR官方研发团队带来技术深入解读三日直播课，1月26日、28日、29日晚上19:30，[直播地址](https://live.bilibili.com/21689802)
-- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，[多语言模型下载](./doc/doc_ch/models_list.md)，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)
+- 2021.1.21 更新多语言识别模型，目前支持语种超过27种，包括中文简体、中文繁体、英文、法文、德文、韩文、日文、意大利文、西班牙文、葡萄牙文、俄罗斯文、阿拉伯文等，后续计划可以参考[多语言研发计划](https://github.com/PaddlePaddle/PaddleOCR/issues/1048)
 - 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
 - 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
 - 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
@@ -74,11 +74,13 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 ## 文档教程
 - [快速安装](./doc/doc_ch/installation.md)
 - [中文OCR模型快速使用](./doc/doc_ch/quickstart.md)
+- [多语言OCR模型快速使用](./doc/doc_ch/multi_languages.md)
 - [代码组织结构](./doc/doc_ch/tree.md)
 - 算法介绍
     - [文本检测](./doc/doc_ch/algorithm_overview.md)
     - [文本识别](./doc/doc_ch/algorithm_overview.md)
-    - [PP-OCR Pipline](#PP-OCR)
+    - [PP-OCR Pipeline](#PP-OCR)
+    - [端到端PGNet算法](./doc/doc_ch/pgnet.md)
 - 模型训练/评估
     - [文本检测](./doc/doc_ch/detection.md)
     - [文本识别](./doc/doc_ch/recognition.md)
@@ -112,7 +114,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 
 
 <a name="PP-OCR"></a>
-## PP-OCR Pipline
+## PP-OCR Pipeline
 <div align="center">
     <img src="./doc/ppocr_framework.png" width="800">
 </div>

configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1200
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [3000, 2000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
   checkpoints:

configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1200
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [3000, 2000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet18_vd_pretrained
   checkpoints:

configs/det/det_mv3_db.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1200
   # evaluation is run every 2000 iterations
   eval_batch_step: [0, 2000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
   checkpoints:

configs/det/det_mv3_east.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1000
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
   checkpoints: 

configs/det/det_r50_vd_db.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1200
   # evaluation is run every 2000 iterations
   eval_batch_step: [0,2000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
   checkpoints:

configs/det/det_r50_vd_east.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1000
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/
   checkpoints: 

configs/det/det_r50_vd_sast_icdar15.yml

@@ -7,19 +7,15 @@ Global:
   save_epoch_step: 1000
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
-  checkpoints: 
+  checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: 
+  infer_img:
   save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
 
+
 Architecture:
   model_type: det
   algorithm: SAST

configs/det/det_r50_vd_sast_totaltext.yml

@@ -7,11 +7,6 @@ Global:
   save_epoch_step: 1000
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
-  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
-  #    from static branch, load_static_weights must be set as True.
-  # 2. If you want to finetune the pretrained models we provide in the docs,
-  #    you should set load_static_weights as False.
-  load_static_weights: True
   cal_metric_during_train: False
   pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
   checkpoints: 

configs/e2e/e2e_r50_vd_pg.yml

+Global:
+  use_gpu: True
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/pgnet_r50_vd_totaltext/
+  save_epoch_step: 10
+  # evaluation is run every 0 iterationss after the 1000th iteration
+  eval_batch_step: [ 0, 1000 ]
+  cal_metric_during_train: False
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
+  save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
+  character_dict_path: ppocr/utils/ic15_dict.txt
+  character_type: EN
+  max_text_length: 50 # the max length in seq
+  max_text_nums: 30 # the max seq nums in a pic
+  tcl_len: 64
+
+Architecture:
+  model_type: e2e
+  algorithm: PGNet
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: PGFPN
+  Head:
+    name: PGHead
+
+Loss:
+  name: PGLoss
+  tcl_bs: 64
+  max_text_length: 50 # the same as Global: max_text_length
+  max_text_nums: 30 # the same as Global：max_text_nums
+  pad_num: 36 # the length of dict for pad
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+
+PostProcess:
+  name: PGPostProcess
+  score_thresh: 0.5
+  mode: fast   # fast or slow two ways
+
+Metric:
+  name: E2EMetric
+  mode: A   # two ways for eval, A: label from txt,  B: label from gt_mat
+  gt_mat_dir:  ./train_data/total_text/gt  # the dir of gt_mat
+  character_dict_path: ppocr/utils/ic15_dict.txt
+  main_indicator: f_score_e2e
+
+Train:
+  dataset:
+    name: PGDataSet
+    data_dir: ./train_data/total_text/train
+    label_file_list: [./train_data/total_text/train/train.txt]
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - E2ELabelEncodeTrain:
+      - PGProcessTrain:
+          batch_size: 14  # same as loader: batch_size_per_card
+          min_crop_size: 24
+          min_text_size: 4
+          max_text_size: 512
+      - KeepKeys:
+          keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: True
+    batch_size_per_card: 14
+    num_workers: 16
+
+Eval:
+  dataset:
+    name: PGDataSet
+    data_dir: ./train_data/total_text/test
+    label_file_list: [./train_data/total_text/test/test.txt]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - E2ELabelEncodeTest:
+      - E2EResizeForTest:
+          max_side_len: 768
+      - 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', 'shape', 'polys', 'texts', 'ignore_tags', 'img_id']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2
\ No newline at end of file

configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_common_v2.0.txt
 
 
 Optimizer:

configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
 
 
 Optimizer:

configs/rec/multi_language/generate_multi_language_configs.py

@@ -19,21 +19,56 @@ import logging
 logging.basicConfig(level=logging.INFO)
 
 support_list = {
-    'it':'italian', 'xi':'spanish', 'pu':'portuguese', 'ru':'russian', 'ar':'arabic',
-    'ta':'tamil', 'ug':'uyghur', 'fa':'persian', 'ur':'urdu', 'rs':'serbian latin',
-    'oc':'occitan', 'rsc':'serbian cyrillic', 'bg':'bulgarian', 'uk':'ukranian', 'be':'belarusian',
-    'te':'telugu', 'ka':'kannada', 'chinese_cht':'chinese tradition','hi':'hindi','mr':'marathi',
-    'ne':'nepali',
+    'it': 'italian',
+    'xi': 'spanish',
+    'pu': 'portuguese',
+    'ru': 'russian',
+    'ar': 'arabic',
+    'ta': 'tamil',
+    'ug': 'uyghur',
+    'fa': 'persian',
+    'ur': 'urdu',
+    'rs': 'serbian latin',
+    'oc': 'occitan',
+    'rsc': 'serbian cyrillic',
+    'bg': 'bulgarian',
+    'uk': 'ukranian',
+    'be': 'belarusian',
+    'te': 'telugu',
+    'ka': 'kannada',
+    'chinese_cht': 'chinese tradition',
+    'hi': 'hindi',
+    'mr': 'marathi',
+    'ne': 'nepali',
 }
-assert(
-    os.path.isfile("./rec_multi_language_lite_train.yml")
-    ),"Loss basic configuration file rec_multi_language_lite_train.yml.\
+
+latin_lang = [
+    'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
+    'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
+    'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
+    'sw', 'tl', 'tr', 'uz', 'vi', 'latin'
+]
+arabic_lang = ['ar', 'fa', 'ug', 'ur']
+cyrillic_lang = [
+    'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava',
+    'dar', 'inh', 'che', 'lbe', 'lez', 'tab', 'cyrillic'
+]
+devanagari_lang = [
+    'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom',
+    'sa', 'bgc', 'devanagari'
+]
+multi_lang = latin_lang + arabic_lang + cyrillic_lang + devanagari_lang
+
+assert (os.path.isfile("./rec_multi_language_lite_train.yml")
+        ), "Loss basic configuration file rec_multi_language_lite_train.yml.\
 You can download it from \
 https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/configs/rec/multi_language/"
- 
-global_config = yaml.load(open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
+
+global_config = yaml.load(
+    open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
 project_path = os.path.abspath(os.path.join(os.getcwd(), "../../../"))
 
+
 class ArgsParser(ArgumentParser):
     def __init__(self):
         super(ArgsParser, self).__init__(
@@ -41,15 +76,30 @@ class ArgsParser(ArgumentParser):
         self.add_argument(
             "-o", "--opt", nargs='+', help="set configuration options")
         self.add_argument(
-            "-l", "--language", nargs='+', help="set language type, support {}".format(support_list))
+            "-l",
+            "--language",
+            nargs='+',
+            help="set language type, support {}".format(support_list))
         self.add_argument(
-            "--train",type=str,help="you can use this command to change the train dataset default path")
+            "--train",
+            type=str,
+            help="you can use this command to change the train dataset default path"
+        )
         self.add_argument(
-            "--val",type=str,help="you can use this command to change the eval dataset default path")
+            "--val",
+            type=str,
+            help="you can use this command to change the eval dataset default path"
+        )
         self.add_argument(
-            "--dict",type=str,help="you can use this command to change the dictionary default path")
+            "--dict",
+            type=str,
+            help="you can use this command to change the dictionary default path"
+        )
         self.add_argument(
-            "--data_dir",type=str,help="you can use this command to change the dataset default root path")
+            "--data_dir",
+            type=str,
+            help="you can use this command to change the dataset default root path"
+        )
 
     def parse_args(self, argv=None):
         args = super(ArgsParser, self).parse_args(argv)
@@ -68,21 +118,37 @@ class ArgsParser(ArgumentParser):
         return config
 
     def _set_language(self, type):
-        assert(type),"please use -l or --language to choose language type"
+        lang = type[0]
+        assert (type), "please use -l or --language to choose language type"
         assert(
-                type[0] in support_list.keys()
+                lang in support_list.keys() or lang in multi_lang
                ),"the sub_keys(-l or --language) can only be one of support list: \n{},\nbut get: {}, " \
-                 "please check your running command".format(support_list, type)
-        global_config['Global']['character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(type[0])
-        global_config['Global']['save_model_dir'] = './output/rec_{}_lite'.format(type[0])
-        global_config['Train']['dataset']['label_file_list'] = ["train_data/{}_train.txt".format(type[0])]
-        global_config['Eval']['dataset']['label_file_list'] = ["train_data/{}_val.txt".format(type[0])]
-        global_config['Global']['character_type'] = type[0]
-        assert(
-                os.path.isfile(os.path.join(project_path,global_config['Global']['character_dict_path']))
-              ),"Loss default dictionary file {}_dict.txt.You can download it from \
-https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(type[0])
-        return type[0]
+                 "please check your running command".format(multi_lang, type)
+        if lang in latin_lang:
+            lang = "latin"
+        elif lang in arabic_lang:
+            lang = "arabic"
+        elif lang in cyrillic_lang:
+            lang = "cyrillic"
+        elif lang in devanagari_lang:
+            lang = "devanagari"
+        global_config['Global'][
+            'character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(lang)
+        global_config['Global'][
+            'save_model_dir'] = './output/rec_{}_lite'.format(lang)
+        global_config['Train']['dataset'][
+            'label_file_list'] = ["train_data/{}_train.txt".format(lang)]
+        global_config['Eval']['dataset'][
+            'label_file_list'] = ["train_data/{}_val.txt".format(lang)]
+        global_config['Global']['character_type'] = lang
+        assert (
+            os.path.isfile(
+                os.path.join(project_path, global_config['Global'][
+                    'character_dict_path']))
+        ), "Loss default dictionary file {}_dict.txt.You can download it from \
+https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(
+            lang)
+        return lang
 
 
 def merge_config(config):
@@ -110,43 +176,51 @@ def merge_config(config):
                     cur[sub_key] = value
                 else:
                     cur = cur[sub_key]
-                    
+
+
 def loss_file(path):
-    assert(
-            os.path.exists(path)
-          ),"There is no such file:{},Please do not forget to put in the specified file".format(path)
+    assert (
+        os.path.exists(path)
+    ), "There is no such file:{},Please do not forget to put in the specified file".format(
+        path)
+
 
-        
 if __name__ == '__main__':
     FLAGS = ArgsParser().parse_args()
     merge_config(FLAGS.opt)
     save_file_path = 'rec_{}_lite_train.yml'.format(FLAGS.language)
     if os.path.isfile(save_file_path):
         os.remove(save_file_path)
-        
+
     if FLAGS.train:
         global_config['Train']['dataset']['label_file_list'] = [FLAGS.train]
-        train_label_path = os.path.join(project_path,FLAGS.train)
+        train_label_path = os.path.join(project_path, FLAGS.train)
         loss_file(train_label_path)
     if FLAGS.val:
         global_config['Eval']['dataset']['label_file_list'] = [FLAGS.val]
-        eval_label_path = os.path.join(project_path,FLAGS.val)
+        eval_label_path = os.path.join(project_path, FLAGS.val)
         loss_file(eval_label_path)
     if FLAGS.dict:
         global_config['Global']['character_dict_path'] = FLAGS.dict
-        dict_path = os.path.join(project_path,FLAGS.dict)
+        dict_path = os.path.join(project_path, FLAGS.dict)
         loss_file(dict_path)
     if FLAGS.data_dir:
         global_config['Eval']['dataset']['data_dir'] = FLAGS.data_dir
         global_config['Train']['dataset']['data_dir'] = FLAGS.data_dir
-        data_dir = os.path.join(project_path,FLAGS.data_dir)
+        data_dir = os.path.join(project_path, FLAGS.data_dir)
         loss_file(data_dir)
-        
+
     with open(save_file_path, 'w') as f:
-        yaml.dump(dict(global_config), f, default_flow_style=False, sort_keys=False)
+        yaml.dump(
+            dict(global_config), f, default_flow_style=False, sort_keys=False)
     logging.info("Project path is          :{}".format(project_path))
-    logging.info("Train list path set to   :{}".format(global_config['Train']['dataset']['label_file_list'][0]))
-    logging.info("Eval list path set to    :{}".format(global_config['Eval']['dataset']['label_file_list'][0]))
-    logging.info("Dataset root path set to :{}".format(global_config['Eval']['dataset']['data_dir']))
-    logging.info("Dict path set to         :{}".format(global_config['Global']['character_dict_path']))
-    logging.info("Config file set to       :configs/rec/multi_language/{}".format(save_file_path))
+    logging.info("Train list path set to   :{}".format(global_config['Train'][
+        'dataset']['label_file_list'][0]))
+    logging.info("Eval list path set to    :{}".format(global_config['Eval'][
+        'dataset']['label_file_list'][0]))
+    logging.info("Dataset root path set to :{}".format(global_config['Eval'][
+        'dataset']['data_dir']))
+    logging.info("Dict path set to         :{}".format(global_config['Global'][
+        'character_dict_path']))
+    logging.info("Config file set to       :configs/rec/multi_language/{}".
+                 format(save_file_path))

configs/rec/multi_language/rec_arabic_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_arabic_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/arabic_dict.txt
+  character_type: arabic
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/arabic_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/arabic_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_cyrillic_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_cyrillic_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/cyrillic_dict.txt
+  character_type: cyrillic
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/cyrillic_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/cyrillic_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_devanagari_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_devanagari_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/devanagari_dict.txt
+  character_type: devanagari
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/devanagari_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/devanagari_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_en_number_lite_train.yml

@@ -15,11 +15,11 @@ Global:
   use_visualdl: False
   infer_img:
   # for data or label process
-  character_dict_path: ppocr/utils/dict/en_dict.txt
+  character_dict_path: ppocr/utils/en_dict.txt
   character_type: EN
   max_text_length: 25
   infer_mode: False
-  use_space_char: False
+  use_space_char: True
 
 
 Optimizer:

configs/rec/multi_language/rec_latin_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_latin_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/latin_dict.txt
+  character_type: latin
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/latin_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/latin_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/rec_icdar15_train.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_ic15.txt
 
 Optimizer:
   name: Adam
@@ -81,7 +82,7 @@ Eval:
   dataset:
     name: SimpleDataSet
     data_dir: ./train_data/
-    label_file_list: ["./train_data/train_list.txt"]
+    label_file_list: ["./train_data/val_list.txt"]
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_none_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_mv3_none_none_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_none_none_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_mv3_tps_bilstm_att.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_tps_bilstm_att.txt
 
 
 Optimizer:

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_tps_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_none_bilstm_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_none_none_ctc.txt
 
 Optimizer:
   name: Adam

configs/rec/rec_r34_vd_tps_bilstm_att.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_b3_rare_r34_none_gru.txt
 
 
 Optimizer:

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -19,6 +19,7 @@ Global:
   max_text_length: 25
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_tps_bilstm_ctc.txt
 
 Optimizer:
   name: Adam
@@ -37,7 +38,7 @@ Architecture:
     name: TPS
     num_fiducial: 20
     loc_lr: 0.1
-    model_name: small
+    model_name: large
   Backbone:
     name: ResNet
     layers: 34

configs/rec/rec_r50_fpn_srn.yml

@@ -20,6 +20,7 @@ Global:
   num_heads: 8
   infer_mode: False
   use_space_char: False
+  save_res_path: ./output/rec/predicts_srn.txt
 
 
 Optimizer:

deploy/android_demo/.gitignore

+*.iml
+.gradle
+/local.properties
+/.idea/*
+.DS_Store
+/build
+/captures
+.externalNativeBuild
+

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。
+
+### 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。
+
+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
+
+### 3. 导入项目
+点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
+
+
+# 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：
+
+- 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

deploy/android_demo/app/.gitignore

+/build

deploy/android_demo/app/build.gradle

+import java.security.MessageDigest
+
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 29
+    defaultConfig {
+        applicationId "com.baidu.paddle.lite.demo.ocr"
+        minSdkVersion 23
+        targetSdkVersion 29
+        versionCode 1
+        versionName "1.0"
+        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
+        externalNativeBuild {
+            cmake {
+                cppFlags "-std=c++11 -frtti -fexceptions -Wno-format"
+                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 {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path "src/main/cpp/CMakeLists.txt"
+            version "3.10.2"
+        }
+    }
+}
+
+dependencies {
+    implementation fileTree(include: ['*.jar'], dir: 'libs')
+    implementation 'androidx.appcompat:appcompat:1.1.0'
+    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
+    testImplementation 'junit:junit:4.12'
+    androidTestImplementation 'com.android.support.test:runner:1.0.2'
+    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
+}
+
+def archives = [
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/paddle_lite_libs_v2_9_0.tar.gz',
+                'dest': 'PaddleLite'
+        ],
+        [
+                'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/opencv-4.2.0-android-sdk.tar.gz',
+                'dest': 'OpenCV'
+        ],
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ocr_v2_for_cpu.tar.gz',
+                'dest' : 'src/main/assets/models'
+        ],
+        [
+                'src' : 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_dict.tar.gz',
+                'dest' : 'src/main/assets/labels'
+        ]
+]
+
+task downloadAndExtractArchives(type: DefaultTask) {
+    doFirst {
+        println "Downloading and extracting archives including libs and models"
+    }
+    doLast {
+        // Prepare cache folder for archives
+        String cachePath = "cache"
+        if (!file("${cachePath}").exists()) {
+            mkdir "${cachePath}"
+        }
+        archives.eachWithIndex { archive, index ->
+            MessageDigest messageDigest = MessageDigest.getInstance('MD5')
+            messageDigest.update(archive.src.bytes)
+            String cacheName = new BigInteger(1, messageDigest.digest()).toString(32)
+            // Download the target archive if not exists
+            boolean copyFiles = !file("${archive.dest}").exists()
+            if (!file("${cachePath}/${cacheName}.tar.gz").exists()) {
+                ant.get(src: archive.src, dest: file("${cachePath}/${cacheName}.tar.gz"))
+                copyFiles = true; // force to copy files from the latest archive files
+            }
+            // Extract the target archive if its dest path does not exists
+            if (copyFiles) {
+                copy {
+                    from tarTree("${cachePath}/${cacheName}.tar.gz")
+                    into "${archive.dest}"
+                }
+            }
+        }
+    }
+}
+preBuild.dependsOn downloadAndExtractArchives
\ No newline at end of file

deploy/android_demo/app/proguard-rules.pro

+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile

deploy/android_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ocr/ExampleInstrumentedTest.java

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.support.test.InstrumentationRegistry;
+import android.support.test.runner.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getTargetContext();
+
+        assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
+    }
+}

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

+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+          package="com.baidu.paddle.lite.demo.ocr">
+
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.CAMERA"/>
+
+
+    <application
+            android:allowBackup="true"
+            android:icon="@mipmap/ic_launcher"
+            android:label="@string/app_name"
+            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"/>
+                <category android:name="android.intent.category.LAUNCHER"/>
+            </intent-filter>
+        </activity>
+        <activity
+                android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
+                android:label="Settings">
+        </activity>
+        <provider
+            android:name="androidx.core.content.FileProvider"
+            android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
+            android:exported="false"
+            android:grantUriPermissions="true">
+            <meta-data
+                android:name="android.support.FILE_PROVIDER_PATHS"
+                android:resource="@xml/file_paths"></meta-data>
+        </provider>
+    </application>
+
+</manifest>
\ No newline at end of file

deploy/android_demo/app/src/main/cpp/CMakeLists.txt

+# For more information about using CMake with Android Studio, read the
+# documentation: https://d.android.com/studio/projects/add-native-code.html
+
+# Sets the minimum version of CMake required to build the native library.
+
+cmake_minimum_required(VERSION 3.4.1)
+
+# Creates and names a library, sets it as either STATIC or SHARED, and provides
+# the relative paths to its source code. You can define multiple libraries, and
+# CMake builds them for you. Gradle automatically packages shared libraries with
+# your APK.
+
+set(PaddleLite_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../PaddleLite")
+include_directories(${PaddleLite_DIR}/cxx/include)
+
+set(OpenCV_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../OpenCV/sdk/native/jni")
+message(STATUS "opencv dir: ${OpenCV_DIR}")
+find_package(OpenCV REQUIRED)
+message(STATUS "OpenCV libraries: ${OpenCV_LIBS}")
+include_directories(${OpenCV_INCLUDE_DIRS})
+aux_source_directory(. SOURCES)
+set(CMAKE_CXX_FLAGS
+        "${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os"
+        )
+set(CMAKE_CXX_FLAGS
+        "${CMAKE_CXX_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden -fdata-sections -ffunction-sections"
+        )
+set(CMAKE_SHARED_LINKER_FLAGS
+        "${CMAKE_SHARED_LINKER_FLAGS} -Wl,--gc-sections -Wl,-z,nocopyreloc")
+
+add_library(
+        # Sets the name of the library.
+        Native
+        # Sets the library as a shared library.
+        SHARED
+        # Provides a relative path to your source file(s).
+        ${SOURCES})
+
+find_library(
+        # Sets the name of the path variable.
+        log-lib
+        # Specifies the name of the NDK library that you want CMake to locate.
+        log)
+
+add_library(
+        # Sets the name of the library.
+        paddle_light_api_shared
+        # Sets the library as a shared library.
+        SHARED
+        # Provides a relative path to your source file(s).
+        IMPORTED)
+
+set_target_properties(
+        # Specifies the target library.
+        paddle_light_api_shared
+        # Specifies the parameter you want to define.
+        PROPERTIES
+        IMPORTED_LOCATION
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
+        # Provides the path to the library you want to import.
+)
+
+
+# Specifies libraries CMake should link to your target library. You can link
+# multiple libraries, such as libraries you define in this build script,
+# prebuilt third-party libraries, or system libraries.
+
+target_link_libraries(
+        # Specifies the target library.
+        Native
+        paddle_light_api_shared
+        ${OpenCV_LIBS}
+        GLESv2
+        EGL
+        jnigraphics
+        ${log-lib}
+)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libc++_shared.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libc++_shared.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libpaddle_light_api_shared.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir.so)
+
+add_custom_command(
+        TARGET Native
+        POST_BUILD
+        COMMAND
+        ${CMAKE_COMMAND} -E copy
+        ${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir_build.so
+        ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir_build.so)
\ No newline at end of file

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

+//
+// Created by fu on 4/25/18.
+//
+
+#pragma once
+#import <numeric>
+#import <vector>
+
+#ifdef __ANDROID__
+
+#include <android/log.h>
+
+#define LOG_TAG "OCR_NDK"
+
+#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
+#define LOGW(...) __android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__)
+#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)
+#else
+#include <stdio.h>
+#define LOGI(format, ...)                                                      \
+  fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
+#define LOGW(format, ...)                                                      \
+  fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
+#define LOGE(format, ...)                                                      \
+  fprintf(stderr, "[" LOG_TAG "]Error: " format "\n", ##__VA_ARGS__)
+#endif
+
+enum RETURN_CODE { RETURN_OK = 0 };
+
+enum NET_TYPE { NET_OCR = 900100, NET_OCR_INTERNAL = 991008 };
+
+template <typename T> inline T product(const std::vector<T> &vec) {
+  if (vec.empty()) {
+    return 0;
+  }
+  return std::accumulate(vec.begin(), vec.end(), 1, std::multiplies<T>());
+}

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

+//
+// Created by fujiayi on 2020/7/5.
+//
+
+#include "native.h"
+#include "ocr_ppredictor.h"
+#include <algorithm>
+#include <paddle_api.h>
+#include <string>
+
+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_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);
+  std::string cls_model_path = jstring_to_cpp_string(env, j_cls_model_path);
+  int thread_num = j_thread_num;
+  std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
+  ppredictor::OCR_Config conf;
+  conf.thread_num = thread_num;
+  conf.mode = str_to_cpu_mode(cpu_mode);
+  ppredictor::OCR_PPredictor *orc_predictor =
+      new ppredictor::OCR_PPredictor{conf};
+  orc_predictor->init_from_file(det_model_path, rec_model_path, cls_model_path);
+  return reinterpret_cast<jlong>(orc_predictor);
+}
+
+/**
+ * "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH
+ * @param cpu_mode
+ * @return
+ */
+static paddle::lite_api::PowerMode
+str_to_cpu_mode(const std::string &cpu_mode) {
+  static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
+      {"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH},
+      {"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH},
+      {"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL},
+      {"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND},
+      {"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
+      {"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}};
+  std::string upper_key;
+  std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(),
+                 ::toupper);
+  auto index = cpu_mode_map.find(upper_key);
+  if (index == cpu_mode_map.end()) {
+    LOGE("cpu_mode not found %s", upper_key.c_str());
+    return paddle::lite_api::LITE_POWER_HIGH;
+  } else {
+    return index->second;
+  }
+}
+
+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) {
+  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);
+  }
+  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);
+  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;
+  for (const ppredictor::OCRPredictResult &r : results) {
+    float_arr.push_back(r.points.size());
+    float_arr.push_back(r.word_index.size());
+    float_arr.push_back(r.score);
+    for (const std::vector<int> &point : r.points) {
+      float_arr.push_back(point.at(0));
+      float_arr.push_back(point.at(1));
+    }
+    for (int index : r.word_index) {
+      float_arr.push_back(index);
+    }
+  }
+  return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
+}
+
+extern "C" JNIEXPORT void JNICALL
+Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(
+    JNIEnv *env, jobject thiz, jlong java_pointer) {
+  if (java_pointer == 0) {
+    LOGE("JAVA pointer is NULL");
+    return;
+  }
+  ppredictor::OCR_PPredictor *ppredictor =
+      (ppredictor::OCR_PPredictor *)java_pointer;
+  delete ppredictor;
+}
\ No newline at end of file

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

+//
+// Created by fujiayi on 2020/7/5.
+//
+
+#pragma once
+
+#include "common.h"
+#include <android/bitmap.h>
+#include <jni.h>
+#include <opencv2/opencv.hpp>
+#include <string>
+#include <vector>
+
+inline std::string jstring_to_cpp_string(JNIEnv *env, jstring jstr) {
+  // In java, a unicode char will be encoded using 2 bytes (utf16).
+  // so jstring will contain characters utf16. std::string in c++ is
+  // essentially a string of bytes, not characters, so if we want to
+  // pass jstring from JNI to c++, we have convert utf16 to bytes.
+  if (!jstr) {
+    return "";
+  }
+  const jclass stringClass = env->GetObjectClass(jstr);
+  const jmethodID getBytes =
+      env->GetMethodID(stringClass, "getBytes", "(Ljava/lang/String;)[B");
+  const jbyteArray stringJbytes = (jbyteArray)env->CallObjectMethod(
+      jstr, getBytes, env->NewStringUTF("UTF-8"));
+
+  size_t length = (size_t)env->GetArrayLength(stringJbytes);
+  jbyte *pBytes = env->GetByteArrayElements(stringJbytes, NULL);
+
+  std::string ret = std::string(reinterpret_cast<char *>(pBytes), length);
+  env->ReleaseByteArrayElements(stringJbytes, pBytes, JNI_ABORT);
+
+  env->DeleteLocalRef(stringJbytes);
+  env->DeleteLocalRef(stringClass);
+  return ret;
+}
+
+inline jstring cpp_string_to_jstring(JNIEnv *env, std::string str) {
+  auto *data = str.c_str();
+  jclass strClass = env->FindClass("java/lang/String");
+  jmethodID strClassInitMethodID =
+      env->GetMethodID(strClass, "<init>", "([BLjava/lang/String;)V");
+
+  jbyteArray bytes = env->NewByteArray(strlen(data));
+  env->SetByteArrayRegion(bytes, 0, strlen(data),
+                          reinterpret_cast<const jbyte *>(data));
+
+  jstring encoding = env->NewStringUTF("UTF-8");
+  jstring res = (jstring)(
+      env->NewObject(strClass, strClassInitMethodID, bytes, encoding));
+
+  env->DeleteLocalRef(strClass);
+  env->DeleteLocalRef(encoding);
+  env->DeleteLocalRef(bytes);
+
+  return res;
+}
+
+inline jfloatArray cpp_array_to_jfloatarray(JNIEnv *env, const float *buf,
+                                            int64_t len) {
+  if (len == 0) {
+    return env->NewFloatArray(0);
+  }
+  jfloatArray result = env->NewFloatArray(len);
+  env->SetFloatArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jintArray cpp_array_to_jintarray(JNIEnv *env, const int *buf,
+                                        int64_t len) {
+  jintArray result = env->NewIntArray(len);
+  env->SetIntArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jbyteArray cpp_array_to_jbytearray(JNIEnv *env, const int8_t *buf,
+                                          int64_t len) {
+  jbyteArray result = env->NewByteArray(len);
+  env->SetByteArrayRegion(result, 0, len, buf);
+  return result;
+}
+
+inline jlongArray int64_vector_to_jlongarray(JNIEnv *env,
+                                             const std::vector<int64_t> &vec) {
+  jlongArray result = env->NewLongArray(vec.size());
+  jlong *buf = new jlong[vec.size()];
+  for (size_t i = 0; i < vec.size(); ++i) {
+    buf[i] = (jlong)vec[i];
+  }
+  env->SetLongArrayRegion(result, 0, vec.size(), buf);
+  delete[] buf;
+  return result;
+}
+
+inline std::vector<int64_t> jlongarray_to_int64_vector(JNIEnv *env,
+                                                       jlongArray data) {
+  int data_size = env->GetArrayLength(data);
+  jlong *data_ptr = env->GetLongArrayElements(data, nullptr);
+  std::vector<int64_t> data_vec(data_ptr, data_ptr + data_size);
+  env->ReleaseLongArrayElements(data, data_ptr, 0);
+  return data_vec;
+}
+
+inline std::vector<float> jfloatarray_to_float_vector(JNIEnv *env,
+                                                      jfloatArray data) {
+  int data_size = env->GetArrayLength(data);
+  jfloat *data_ptr = env->GetFloatArrayElements(data, nullptr);
+  std::vector<float> data_vec(data_ptr, data_ptr + data_size);
+  env->ReleaseFloatArrayElements(data, data_ptr, 0);
+  return data_vec;
+}
+
+inline cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
+  AndroidBitmapInfo info;
+  int result = AndroidBitmap_getInfo(env, bitmap, &info);
+  if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
+    LOGE("AndroidBitmap_getInfo failed, result: %d", result);
+    return cv::Mat{};
+  }
+  if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
+    LOGE("Bitmap format is not RGBA_8888 !");
+    return cv::Mat{};
+  }
+  unsigned char *srcData = NULL;
+  AndroidBitmap_lockPixels(env, bitmap, (void **)&srcData);
+  cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
+  memcpy(mat.data, srcData, info.height * info.width * 4);
+  AndroidBitmap_unlockPixels(env, bitmap);
+  cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
+  /**
+  if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
+      LOGE("Write image failed " );
+  }
+   */
+  return mat;
+}

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

+/*******************************************************************************
+*                                                                              *
+* Author    :  Angus Johnson                                                   *
+* Version   :  6.4.2                                                           *
+* Date      :  27 February 2017                                                *
+* Website   :  http://www.angusj.com                                           *
+* Copyright :  Angus Johnson 2010-2017                                         *
+*                                                                              *
+* License:                                                                     *
+* Use, modification & distribution is subject to Boost Software License Ver 1. *
+* http://www.boost.org/LICENSE_1_0.txt                                         *
+*                                                                              *
+* Attributions:                                                                *
+* The code in this library is an extension of Bala Vatti's clipping algorithm: *
+* "A generic solution to polygon clipping"                                     *
+* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63.             *
+* http://portal.acm.org/citation.cfm?id=129906                                 *
+*                                                                              *
+* Computer graphics and geometric modeling: implementation and algorithms      *
+* By Max K. Agoston                                                            *
+* Springer; 1 edition (January 4, 2005)                                        *
+* http://books.google.com/books?q=vatti+clipping+agoston                       *
+*                                                                              *
+* See also:                                                                    *
+* "Polygon Offsetting by Computing Winding Numbers"                            *
+* Paper no. DETC2005-85513 pp. 565-575                                         *
+* ASME 2005 International Design Engineering Technical Conferences             *
+* and Computers and Information in Engineering Conference (IDETC/CIE2005)      *
+* September 24-28, 2005 , Long Beach, California, USA                          *
+* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf              *
+*                                                                              *
+*******************************************************************************/
+
+/*******************************************************************************
+*                                                                              *
+* This is a translation of the Delphi Clipper library and the naming style     *
+* used has retained a Delphi flavour.                                          *
+*                                                                              *
+*******************************************************************************/
+
+#include "ocr_clipper.hpp"
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <ostream>
+#include <stdexcept>
+#include <vector>
+
+namespace ClipperLib {
+
+static double const pi = 3.141592653589793238;
+static double const two_pi = pi * 2;
+static double const def_arc_tolerance = 0.25;
+
+enum Direction { dRightToLeft, dLeftToRight };
+
+static int const Unassigned = -1; // edge not currently 'owning' a solution
+static int const Skip = -2;       // edge that would otherwise close a path
+
+#define HORIZONTAL (-1.0E+40)
+#define TOLERANCE (1.0e-20)
+#define NEAR_ZERO(val) (((val) > -TOLERANCE) && ((val) < TOLERANCE))
+
+struct TEdge {
+  IntPoint Bot;
+  IntPoint Curr; // current (updated for every new scanbeam)
+  IntPoint Top;
+  double Dx;
+  PolyType PolyTyp;
+  EdgeSide Side; // side only refers to current side of solution poly
+  int WindDelta; // 1 or -1 depending on winding direction
+  int WindCnt;
+  int WindCnt2; // winding count of the opposite polytype
+  int OutIdx;
+  TEdge *Next;
+  TEdge *Prev;
+  TEdge *NextInLML;
+  TEdge *NextInAEL;
+  TEdge *PrevInAEL;
+  TEdge *NextInSEL;
+  TEdge *PrevInSEL;
+};
+
+struct IntersectNode {
+  TEdge *Edge1;
+  TEdge *Edge2;
+  IntPoint Pt;
+};
+
+struct LocalMinimum {
+  cInt Y;
+  TEdge *LeftBound;
+  TEdge *RightBound;
+};
+
+struct OutPt;
+
+// OutRec: contains a path in the clipping solution. Edges in the AEL will
+// carry a pointer to an OutRec when they are part of the clipping solution.
+struct OutRec {
+  int Idx;
+  bool IsHole;
+  bool IsOpen;
+  OutRec *FirstLeft; // see comments in clipper.pas
+  PolyNode *PolyNd;
+  OutPt *Pts;
+  OutPt *BottomPt;
+};
+
+struct OutPt {
+  int Idx;
+  IntPoint Pt;
+  OutPt *Next;
+  OutPt *Prev;
+};
+
+struct Join {
+  OutPt *OutPt1;
+  OutPt *OutPt2;
+  IntPoint OffPt;
+};
+
+struct LocMinSorter {
+  inline bool operator()(const LocalMinimum &locMin1,
+                         const LocalMinimum &locMin2) {
+    return locMin2.Y < locMin1.Y;
+  }
+};
+
+//------------------------------------------------------------------------------
+//------------------------------------------------------------------------------
+
+inline cInt Round(double val) {
+  if ((val < 0))
+    return static_cast<cInt>(val - 0.5);
+  else
+    return static_cast<cInt>(val + 0.5);
+}
+//------------------------------------------------------------------------------
+
+inline cInt Abs(cInt val) { return val < 0 ? -val : val; }
+
+//------------------------------------------------------------------------------
+// PolyTree methods ...
+//------------------------------------------------------------------------------
+
+void PolyTree::Clear() {
+  for (PolyNodes::size_type i = 0; i < AllNodes.size(); ++i)
+    delete AllNodes[i];
+  AllNodes.resize(0);
+  Childs.resize(0);
+}
+//------------------------------------------------------------------------------
+
+PolyNode *PolyTree::GetFirst() const {
+  if (!Childs.empty())
+    return Childs[0];
+  else
+    return 0;
+}
+//------------------------------------------------------------------------------
+
+int PolyTree::Total() const {
+  int result = (int)AllNodes.size();
+  // with negative offsets, ignore the hidden outer polygon ...
+  if (result > 0 && Childs[0] != AllNodes[0])
+    result--;
+  return result;
+}
+
+//------------------------------------------------------------------------------
+// PolyNode methods ...
+//------------------------------------------------------------------------------
+
+PolyNode::PolyNode() : Parent(0), Index(0), m_IsOpen(false) {}
+//------------------------------------------------------------------------------
+
+int PolyNode::ChildCount() const { return (int)Childs.size(); }
+//------------------------------------------------------------------------------
+
+void PolyNode::AddChild(PolyNode &child) {
+  unsigned cnt = (unsigned)Childs.size();
+  Childs.push_back(&child);
+  child.Parent = this;
+  child.Index = cnt;
+}
+//------------------------------------------------------------------------------
+
+PolyNode *PolyNode::GetNext() const {
+  if (!Childs.empty())
+    return Childs[0];
+  else
+    return GetNextSiblingUp();
+}
+//------------------------------------------------------------------------------
+
+PolyNode *PolyNode::GetNextSiblingUp() const {
+  if (!Parent) // protects against PolyTree.GetNextSiblingUp()
+    return 0;
+  else if (Index == Parent->Childs.size() - 1)
+    return Parent->GetNextSiblingUp();
+  else
+    return Parent->Childs[Index + 1];
+}
+//------------------------------------------------------------------------------
+
+bool PolyNode::IsHole() const {
+  bool result = true;
+  PolyNode *node = Parent;
+  while (node) {
+    result = !result;
+    node = node->Parent;
+  }
+  return result;
+}
+//------------------------------------------------------------------------------
+
+bool PolyNode::IsOpen() const { return m_IsOpen; }
+//------------------------------------------------------------------------------
+
+#ifndef use_int32
+
+//------------------------------------------------------------------------------
+// Int128 class (enables safe math on signed 64bit integers)
+// eg Int128 val1((long64)9223372036854775807); //ie 2^63 -1
+//    Int128 val2((long64)9223372036854775807);
+//    Int128 val3 = val1 * val2;
+//    val3.AsString => "85070591730234615847396907784232501249" (8.5e+37)
+//------------------------------------------------------------------------------
+
+class Int128 {
+public:
+  ulong64 lo;
+  long64 hi;
+
+  Int128(long64 _lo = 0) {
+    lo = (ulong64)_lo;
+    if (_lo < 0)
+      hi = -1;
+    else
+      hi = 0;
+  }
+
+  Int128(const Int128 &val) : lo(val.lo), hi(val.hi) {}
+
+  Int128(const long64 &_hi, const ulong64 &_lo) : lo(_lo), hi(_hi) {}
+
+  Int128 &operator=(const long64 &val) {
+    lo = (ulong64)val;
+    if (val < 0)
+      hi = -1;
+    else
+      hi = 0;
+    return *this;
+  }
+
+  bool operator==(const Int128 &val) const {
+    return (hi == val.hi && lo == val.lo);
+  }
+
+  bool operator!=(const Int128 &val) const { return !(*this == val); }
+
+  bool operator>(const Int128 &val) const {
+    if (hi != val.hi)
+      return hi > val.hi;
+    else
+      return lo > val.lo;
+  }
+
+  bool operator<(const Int128 &val) const {
+    if (hi != val.hi)
+      return hi < val.hi;
+    else
+      return lo < val.lo;
+  }
+
+  bool operator>=(const Int128 &val) const { return !(*this < val); }
+
+  bool operator<=(const Int128 &val) const { return !(*this > val); }
+
+  Int128 &operator+=(const Int128 &rhs) {
+    hi += rhs.hi;
+    lo += rhs.lo;
+    if (lo < rhs.lo)
+      hi++;
+    return *this;
+  }
+
+  Int128 operator+(const Int128 &rhs) const {
+    Int128 result(*this);
+    result += rhs;
+    return result;
+  }
+
+  Int128 &operator-=(const Int128 &rhs) {
+    *this += -rhs;
+    return *this;
+  }
+
+  Int128 operator-(const Int128 &rhs) const {
+    Int128 result(*this);
+    result -= rhs;
+    return result;
+  }
+
+  Int128 operator-() const // unary negation
+  {
+    if (lo == 0)
+      return Int128(-hi, 0);
+    else
+      return Int128(~hi, ~lo + 1);
+  }
+
+  operator double() const {
+    const double shift64 = 18446744073709551616.0; // 2^64
+    if (hi < 0) {
+      if (lo == 0)
+        return (double)hi * shift64;
+      else
+        return -(double)(~lo + ~hi * shift64);
+    } else
+      return (double)(lo + hi * shift64);
+  }
+};
+//------------------------------------------------------------------------------
+
+Int128 Int128Mul(long64 lhs, long64 rhs) {
+  bool negate = (lhs < 0) != (rhs < 0);
+
+  if (lhs < 0)
+    lhs = -lhs;
+  ulong64 int1Hi = ulong64(lhs) >> 32;
+  ulong64 int1Lo = ulong64(lhs & 0xFFFFFFFF);
+
+  if (rhs < 0)
+    rhs = -rhs;
+  ulong64 int2Hi = ulong64(rhs) >> 32;
+  ulong64 int2Lo = ulong64(rhs & 0xFFFFFFFF);
+
+  // nb: see comments in clipper.pas
+  ulong64 a = int1Hi * int2Hi;
+  ulong64 b = int1Lo * int2Lo;
+  ulong64 c = int1Hi * int2Lo + int1Lo * int2Hi;
+
+  Int128 tmp;
+  tmp.hi = long64(a + (c >> 32));
+  tmp.lo = long64(c << 32);
+  tmp.lo += long64(b);
+  if (tmp.lo < b)
+    tmp.hi++;
+  if (negate)
+    tmp = -tmp;
+  return tmp;
+};
+#endif
+
+//------------------------------------------------------------------------------
+// Miscellaneous global functions
+//------------------------------------------------------------------------------
+
+bool Orientation(const Path &poly) { return Area(poly) >= 0; }
+//------------------------------------------------------------------------------
+
+double Area(const Path &poly) {
+  int size = (int)poly.size();
+  if (size < 3)
+    return 0;
+
+  double a = 0;
+  for (int i = 0, j = size - 1; i < size; ++i) {
+    a += ((double)poly[j].X + poly[i].X) * ((double)poly[j].Y - poly[i].Y);
+    j = i;
+  }
+  return -a * 0.5;
+}
+//------------------------------------------------------------------------------
+
+double Area(const OutPt *op) {
+  const OutPt *startOp = op;
+  if (!op)
+    return 0;
+  double a = 0;
+  do {
+    a += (double)(op->Prev->Pt.X + op->Pt.X) *
+         (double)(op->Prev->Pt.Y - op->Pt.Y);
+    op = op->Next;
+  } while (op != startOp);
+  return a * 0.5;
+}
+//------------------------------------------------------------------------------
+
+double Area(const OutRec &outRec) { return Area(outRec.Pts); }
+//------------------------------------------------------------------------------
+
+bool PointIsVertex(const IntPoint &Pt, OutPt *pp) {
+  OutPt *pp2 = pp;
+  do {
+    if (pp2->Pt == Pt)
+      return true;
+    pp2 = pp2->Next;
+  } while (pp2 != pp);
+  return false;
+}
+//------------------------------------------------------------------------------
+
+// See "The Point in Polygon Problem for Arbitrary Polygons" by Hormann &
+// Agathos
+// http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.5498&rep=rep1&type=pdf
+int PointInPolygon(const IntPoint &pt, const Path &path) {
+  // returns 0 if false, +1 if true, -1 if pt ON polygon boundary
+  int result = 0;
+  size_t cnt = path.size();
+  if (cnt < 3)
+    return 0;
+  IntPoint ip = path[0];
+  for (size_t i = 1; i <= cnt; ++i) {
+    IntPoint ipNext = (i == cnt ? path[0] : path[i]);
+    if (ipNext.Y == pt.Y) {
+      if ((ipNext.X == pt.X) ||
+          (ip.Y == pt.Y && ((ipNext.X > pt.X) == (ip.X < pt.X))))
+        return -1;
+    }
+    if ((ip.Y < pt.Y) != (ipNext.Y < pt.Y)) {
+      if (ip.X >= pt.X) {
+        if (ipNext.X > pt.X)
+          result = 1 - result;
+        else {
+          double d = (double)(ip.X - pt.X) * (ipNext.Y - pt.Y) -
+                     (double)(ipNext.X - pt.X) * (ip.Y - pt.Y);
+          if (!d)
+            return -1;
+          if ((d > 0) == (ipNext.Y > ip.Y))
+            result = 1 - result;
+        }
+      } else {
+        if (ipNext.X > pt.X) {
+          double d = (double)(ip.X - pt.X) * (ipNext.Y - pt.Y) -
+                     (double)(ipNext.X - pt.X) * (ip.Y - pt.Y);
+          if (!d)
+            return -1;
+          if ((d > 0) == (ipNext.Y > ip.Y))
+            result = 1 - result;
+        }
+      }
+    }
+    ip = ipNext;
+  }
+  return result;
+}
+//------------------------------------------------------------------------------
+
+int PointInPolygon(const IntPoint &pt, OutPt *op) {
+  // returns 0 if false, +1 if true, -1 if pt ON polygon boundary
+  int result = 0;
+  OutPt *startOp = op;
+  for (;;) {
+    if (op->Next->Pt.Y == pt.Y) {
+      if ((op->Next->Pt.X == pt.X) ||
+          (op->Pt.Y == pt.Y && ((op->Next->Pt.X > pt.X) == (op->Pt.X < pt.X))))
+        return -1;
+    }
+    if ((op->Pt.Y < pt.Y) != (op->Next->Pt.Y < pt.Y)) {
+      if (op->Pt.X >= pt.X) {
+        if (op->Next->Pt.X > pt.X)
+          result = 1 - result;
+        else {
+          double d = (double)(op->Pt.X - pt.X) * (op->Next->Pt.Y - pt.Y) -
+                     (double)(op->Next->Pt.X - pt.X) * (op->Pt.Y - pt.Y);
+          if (!d)
+            return -1;
+          if ((d > 0) == (op->Next->Pt.Y > op->Pt.Y))
+            result = 1 - result;
+        }
+      } else {
+        if (op->Next->Pt.X > pt.X) {
+          double d = (double)(op->Pt.X - pt.X) * (op->Next->Pt.Y - pt.Y) -
+                     (double)(op->Next->Pt.X - pt.X) * (op->Pt.Y - pt.Y);
+          if (!d)
+            return -1;
+          if ((d > 0) == (op->Next->Pt.Y > op->Pt.Y))
+            result = 1 - result;
+        }
+      }
+    }
+    op = op->Next;
+    if (startOp == op)
+      break;
+  }
+  return result;
+}
+//------------------------------------------------------------------------------
+
+bool Poly2ContainsPoly1(OutPt *OutPt1, OutPt *OutPt2) {
+  OutPt *op = OutPt1;
+  do {
+    // nb: PointInPolygon returns 0 if false, +1 if true, -1 if pt on polygon
+    int res = PointInPolygon(op->Pt, OutPt2);
+    if (res >= 0)
+      return res > 0;
+    op = op->Next;
+  } while (op != OutPt1);
+  return true;
+}
+//----------------------------------------------------------------------
+
+bool SlopesEqual(const TEdge &e1, const TEdge &e2, bool UseFullInt64Range) {
+#ifndef use_int32
+  if (UseFullInt64Range)
+    return Int128Mul(e1.Top.Y - e1.Bot.Y, e2.Top.X - e2.Bot.X) ==
+           Int128Mul(e1.Top.X - e1.Bot.X, e2.Top.Y - e2.Bot.Y);
+  else
+#endif
+    return (e1.Top.Y - e1.Bot.Y) * (e2.Top.X - e2.Bot.X) ==
+           (e1.Top.X - e1.Bot.X) * (e2.Top.Y - e2.Bot.Y);
+}
+//------------------------------------------------------------------------------
+
+bool SlopesEqual(const IntPoint pt1, const IntPoint pt2, const IntPoint pt3,
+                 bool UseFullInt64Range) {
+#ifndef use_int32
+  if (UseFullInt64Range)
+    return Int128Mul(pt1.Y - pt2.Y, pt2.X - pt3.X) ==
+           Int128Mul(pt1.X - pt2.X, pt2.Y - pt3.Y);
+  else
+#endif
+    return (pt1.Y - pt2.Y) * (pt2.X - pt3.X) ==
+           (pt1.X - pt2.X) * (pt2.Y - pt3.Y);
+}
+//------------------------------------------------------------------------------
+
+bool SlopesEqual(const IntPoint pt1, const IntPoint pt2, const IntPoint pt3,
+                 const IntPoint pt4, bool UseFullInt64Range) {
+#ifndef use_int32
+  if (UseFullInt64Range)
+    return Int128Mul(pt1.Y - pt2.Y, pt3.X - pt4.X) ==
+           Int128Mul(pt1.X - pt2.X, pt3.Y - pt4.Y);
+  else
+#endif
+    return (pt1.Y - pt2.Y) * (pt3.X - pt4.X) ==
+           (pt1.X - pt2.X) * (pt3.Y - pt4.Y);
+}
+//------------------------------------------------------------------------------
+
+inline bool IsHorizontal(TEdge &e) { return e.Dx == HORIZONTAL; }
+//------------------------------------------------------------------------------
+
+inline double GetDx(const IntPoint pt1, const IntPoint pt2) {
+  return (pt1.Y == pt2.Y) ? HORIZONTAL
+                          : (double)(pt2.X - pt1.X) / (pt2.Y - pt1.Y);
+}
+//---------------------------------------------------------------------------
+
+inline void SetDx(TEdge &e) {
+  cInt dy = (e.Top.Y - e.Bot.Y);
+  if (dy == 0)
+    e.Dx = HORIZONTAL;
+  else
+    e.Dx = (double)(e.Top.X - e.Bot.X) / dy;
+}
+//---------------------------------------------------------------------------
+
+inline void SwapSides(TEdge &Edge1, TEdge &Edge2) {
+  EdgeSide Side = Edge1.Side;
+  Edge1.Side = Edge2.Side;
+  Edge2.Side = Side;
+}
+//------------------------------------------------------------------------------
+
+inline void SwapPolyIndexes(TEdge &Edge1, TEdge &Edge2) {
+  int OutIdx = Edge1.OutIdx;
+  Edge1.OutIdx = Edge2.OutIdx;
+  Edge2.OutIdx = OutIdx;
+}
+//------------------------------------------------------------------------------
+
+inline cInt TopX(TEdge &edge, const cInt currentY) {
+  return (currentY == edge.Top.Y)
+             ? edge.Top.X
+             : edge.Bot.X + Round(edge.Dx * (currentY - edge.Bot.Y));
+}
+//------------------------------------------------------------------------------
+
+void IntersectPoint(TEdge &Edge1, TEdge &Edge2, IntPoint &ip) {
+#ifdef use_xyz
+  ip.Z = 0;
+#endif
+
+  double b1, b2;
+  if (Edge1.Dx == Edge2.Dx) {
+    ip.Y = Edge1.Curr.Y;
+    ip.X = TopX(Edge1, ip.Y);
+    return;
+  } else if (Edge1.Dx == 0) {
+    ip.X = Edge1.Bot.X;
+    if (IsHorizontal(Edge2))
+      ip.Y = Edge2.Bot.Y;
+    else {
+      b2 = Edge2.Bot.Y - (Edge2.Bot.X / Edge2.Dx);
+      ip.Y = Round(ip.X / Edge2.Dx + b2);
+    }
+  } else if (Edge2.Dx == 0) {
+    ip.X = Edge2.Bot.X;
+    if (IsHorizontal(Edge1))
+      ip.Y = Edge1.Bot.Y;
+    else {
+      b1 = Edge1.Bot.Y - (Edge1.Bot.X / Edge1.Dx);
+      ip.Y = Round(ip.X / Edge1.Dx + b1);
+    }
+  } else {
+    b1 = Edge1.Bot.X - Edge1.Bot.Y * Edge1.Dx;
+    b2 = Edge2.Bot.X - Edge2.Bot.Y * Edge2.Dx;
+    double q = (b2 - b1) / (Edge1.Dx - Edge2.Dx);
+    ip.Y = Round(q);
+    if (std::fabs(Edge1.Dx) < std::fabs(Edge2.Dx))
+      ip.X = Round(Edge1.Dx * q + b1);
+    else
+      ip.X = Round(Edge2.Dx * q + b2);
+  }
+
+  if (ip.Y < Edge1.Top.Y || ip.Y < Edge2.Top.Y) {
+    if (Edge1.Top.Y > Edge2.Top.Y)
+      ip.Y = Edge1.Top.Y;
+    else
+      ip.Y = Edge2.Top.Y;
+    if (std::fabs(Edge1.Dx) < std::fabs(Edge2.Dx))
+      ip.X = TopX(Edge1, ip.Y);
+    else
+      ip.X = TopX(Edge2, ip.Y);
+  }
+  // finally, don't allow 'ip' to be BELOW curr.Y (ie bottom of scanbeam) ...
+  if (ip.Y > Edge1.Curr.Y) {
+    ip.Y = Edge1.Curr.Y;
+    // use the more vertical edge to derive X ...
+    if (std::fabs(Edge1.Dx) > std::fabs(Edge2.Dx))
+      ip.X = TopX(Edge2, ip.Y);
+    else
+      ip.X = TopX(Edge1, ip.Y);
+  }
+}
+//------------------------------------------------------------------------------
+
+void ReversePolyPtLinks(OutPt *pp) {
+  if (!pp)
+    return;
+  OutPt *pp1, *pp2;
+  pp1 = pp;
+  do {
+    pp2 = pp1->Next;
+    pp1->Next = pp1->Prev;
+    pp1->Prev = pp2;
+    pp1 = pp2;
+  } while (pp1 != pp);
+}
+//------------------------------------------------------------------------------
+
+void DisposeOutPts(OutPt *&pp) {
+  if (pp == 0)
+    return;
+  pp->Prev->Next = 0;
+  while (pp) {
+    OutPt *tmpPp = pp;
+    pp = pp->Next;
+    delete tmpPp;
+  }
+}
+//------------------------------------------------------------------------------
+
+inline void InitEdge(TEdge *e, TEdge *eNext, TEdge *ePrev, const IntPoint &Pt) {
+  std::memset(e, 0, sizeof(TEdge));
+  e->Next = eNext;
+  e->Prev = ePrev;
+  e->Curr = Pt;
+  e->OutIdx = Unassigned;
+}
+//------------------------------------------------------------------------------
+
+void InitEdge2(TEdge &e, PolyType Pt) {
+  if (e.Curr.Y >= e.Next->Curr.Y) {
+    e.Bot = e.Curr;
+    e.Top = e.Next->Curr;
+  } else {
+    e.Top = e.Curr;
+    e.Bot = e.Next->Curr;
+  }
+  SetDx(e);
+  e.PolyTyp = Pt;
+}
+//------------------------------------------------------------------------------
+
+TEdge *RemoveEdge(TEdge *e) {
+  // removes e from double_linked_list (but without removing from memory)
+  e->Prev->Next = e->Next;
+  e->Next->Prev = e->Prev;
+  TEdge *result = e->Next;
+  e->Prev = 0; // flag as removed (see ClipperBase.Clear)
+  return result;
+}
+//------------------------------------------------------------------------------
+
+inline void ReverseHorizontal(TEdge &e) {
+  // swap horizontal edges' Top and Bottom x's so they follow the natural
+  // progression of the bounds - ie so their xbots will align with the
+  // adjoining lower edge. [Helpful in the ProcessHorizontal() method.]
+  std::swap(e.Top.X, e.Bot.X);
+#ifdef use_xyz
+  std::swap(e.Top.Z, e.Bot.Z);
+#endif
+}
+//------------------------------------------------------------------------------
+
+void SwapPoints(IntPoint &pt1, IntPoint &pt2) {
+  IntPoint tmp = pt1;
+  pt1 = pt2;
+  pt2 = tmp;
+}
+//------------------------------------------------------------------------------
+
+bool GetOverlapSegment(IntPoint pt1a, IntPoint pt1b, IntPoint pt2a,
+                       IntPoint pt2b, IntPoint &pt1, IntPoint &pt2) {
+  // precondition: segments are Collinear.
+  if (Abs(pt1a.X - pt1b.X) > Abs(pt1a.Y - pt1b.Y)) {
+    if (pt1a.X > pt1b.X)
+      SwapPoints(pt1a, pt1b);
+    if (pt2a.X > pt2b.X)
+      SwapPoints(pt2a, pt2b);
+    if (pt1a.X > pt2a.X)
+      pt1 = pt1a;
+    else
+      pt1 = pt2a;
+    if (pt1b.X < pt2b.X)
+      pt2 = pt1b;
+    else
+      pt2 = pt2b;
+    return pt1.X < pt2.X;
+  } else {
+    if (pt1a.Y < pt1b.Y)
+      SwapPoints(pt1a, pt1b);
+    if (pt2a.Y < pt2b.Y)
+      SwapPoints(pt2a, pt2b);
+    if (pt1a.Y < pt2a.Y)
+      pt1 = pt1a;
+    else
+      pt1 = pt2a;
+    if (pt1b.Y > pt2b.Y)
+      pt2 = pt1b;
+    else
+      pt2 = pt2b;
+    return pt1.Y > pt2.Y;
+  }
+}
+//------------------------------------------------------------------------------
+
+bool FirstIsBottomPt(const OutPt *btmPt1, const OutPt *btmPt2) {
+  OutPt *p = btmPt1->Prev;
+  while ((p->Pt == btmPt1->Pt) && (p != btmPt1))
+    p = p->Prev;
+  double dx1p = std::fabs(GetDx(btmPt1->Pt, p->Pt));
+  p = btmPt1->Next;
+  while ((p->Pt == btmPt1->Pt) && (p != btmPt1))
+    p = p->Next;
+  double dx1n = std::fabs(GetDx(btmPt1->Pt, p->Pt));
+
+  p = btmPt2->Prev;
+  while ((p->Pt == btmPt2->Pt) && (p != btmPt2))
+    p = p->Prev;
+  double dx2p = std::fabs(GetDx(btmPt2->Pt, p->Pt));
+  p = btmPt2->Next;
+  while ((p->Pt == btmPt2->Pt) && (p != btmPt2))
+    p = p->Next;
+  double dx2n = std::fabs(GetDx(btmPt2->Pt, p->Pt));
+
+  if (std::max(dx1p, dx1n) == std::max(dx2p, dx2n) &&
+      std::min(dx1p, dx1n) == std::min(dx2p, dx2n))
+    return Area(btmPt1) > 0; // if otherwise identical use orientation
+  else
+    return (dx1p >= dx2p && dx1p >= dx2n) || (dx1n >= dx2p && dx1n >= dx2n);
+}
+//------------------------------------------------------------------------------
+
+OutPt *GetBottomPt(OutPt *pp) {
+  OutPt *dups = 0;
+  OutPt *p = pp->Next;
+  while (p != pp) {
+    if (p->Pt.Y > pp->Pt.Y) {
+      pp = p;
+      dups = 0;
+    } else if (p->Pt.Y == pp->Pt.Y && p->Pt.X <= pp->Pt.X) {
+      if (p->Pt.X < pp->Pt.X) {
+        dups = 0;
+        pp = p;
+      } else {
+        if (p->Next != pp && p->Prev != pp)
+          dups = p;
+      }
+    }
+    p = p->Next;
+  }
+  if (dups) {
+    // there appears to be at least 2 vertices at BottomPt so ...
+    while (dups != p) {
+      if (!FirstIsBottomPt(p, dups))
+        pp = dups;
+      dups = dups->Next;
+      while (dups->Pt != pp->Pt)
+        dups = dups->Next;
+    }
+  }
+  return pp;
+}
+//------------------------------------------------------------------------------
+
+bool Pt2IsBetweenPt1AndPt3(const IntPoint pt1, const IntPoint pt2,
+                           const IntPoint pt3) {
+  if ((pt1 == pt3) || (pt1 == pt2) || (pt3 == pt2))
+    return false;
+  else if (pt1.X != pt3.X)
+    return (pt2.X > pt1.X) == (pt2.X < pt3.X);
+  else
+    return (pt2.Y > pt1.Y) == (pt2.Y < pt3.Y);
+}
+//------------------------------------------------------------------------------
+
+bool HorzSegmentsOverlap(cInt seg1a, cInt seg1b, cInt seg2a, cInt seg2b) {
+  if (seg1a > seg1b)
+    std::swap(seg1a, seg1b);
+  if (seg2a > seg2b)
+    std::swap(seg2a, seg2b);
+  return (seg1a < seg2b) && (seg2a < seg1b);
+}
+
+//------------------------------------------------------------------------------
+// ClipperBase class methods ...
+//------------------------------------------------------------------------------
+
+ClipperBase::ClipperBase() // constructor
+{
+  m_CurrentLM = m_MinimaList.begin(); // begin() == end() here
+  m_UseFullRange = false;
+}
+//------------------------------------------------------------------------------
+
+ClipperBase::~ClipperBase() // destructor
+{
+  Clear();
+}
+//------------------------------------------------------------------------------
+
+void RangeTest(const IntPoint &Pt, bool &useFullRange) {
+  if (useFullRange) {
+    if (Pt.X > hiRange || Pt.Y > hiRange || -Pt.X > hiRange || -Pt.Y > hiRange)
+      throw clipperException("Coordinate outside allowed range");
+  } else if (Pt.X > loRange || Pt.Y > loRange || -Pt.X > loRange ||
+             -Pt.Y > loRange) {
+    useFullRange = true;
+    RangeTest(Pt, useFullRange);
+  }
+}
+//------------------------------------------------------------------------------
+
+TEdge *FindNextLocMin(TEdge *E) {
+  for (;;) {
+    while (E->Bot != E->Prev->Bot || E->Curr == E->Top)
+      E = E->Next;
+    if (!IsHorizontal(*E) && !IsHorizontal(*E->Prev))
+      break;
+    while (IsHorizontal(*E->Prev))
+      E = E->Prev;
+    TEdge *E2 = E;
+    while (IsHorizontal(*E))
+      E = E->Next;
+    if (E->Top.Y == E->Prev->Bot.Y)
+      continue; // ie just an intermediate horz.
+    if (E2->Prev->Bot.X < E->Bot.X)
+      E = E2;
+    break;
+  }
+  return E;
+}
+//------------------------------------------------------------------------------
+
+TEdge *ClipperBase::ProcessBound(TEdge *E, bool NextIsForward) {
+  TEdge *Result = E;
+  TEdge *Horz = 0;
+
+  if (E->OutIdx == Skip) {
+    // if edges still remain in the current bound beyond the skip edge then
+    // create another LocMin and call ProcessBound once more
+    if (NextIsForward) {
+      while (E->Top.Y == E->Next->Bot.Y)
+        E = E->Next;
+      // don't include top horizontals when parsing a bound a second time,
+      // they will be contained in the opposite bound ...
+      while (E != Result && IsHorizontal(*E))
+        E = E->Prev;
+    } else {
+      while (E->Top.Y == E->Prev->Bot.Y)
+        E = E->Prev;
+      while (E != Result && IsHorizontal(*E))
+        E = E->Next;
+    }
+
+    if (E == Result) {
+      if (NextIsForward)
+        Result = E->Next;
+      else
+        Result = E->Prev;
+    } else {
+      // there are more edges in the bound beyond result starting with E
+      if (NextIsForward)
+        E = Result->Next;
+      else
+        E = Result->Prev;
+      MinimaList::value_type locMin;
+      locMin.Y = E->Bot.Y;
+      locMin.LeftBound = 0;
+      locMin.RightBound = E;
+      E->WindDelta = 0;
+      Result = ProcessBound(E, NextIsForward);
+      m_MinimaList.push_back(locMin);
+    }
+    return Result;
+  }
+
+  TEdge *EStart;
+
+  if (IsHorizontal(*E)) {
+    // We need to be careful with open paths because this may not be a
+    // true local minima (ie E may be following a skip edge).
+    // Also, consecutive horz. edges may start heading left before going right.
+    if (NextIsForward)
+      EStart = E->Prev;
+    else
+      EStart = E->Next;
+    if (IsHorizontal(*EStart)) // ie an adjoining horizontal skip edge
+    {
+      if (EStart->Bot.X != E->Bot.X && EStart->Top.X != E->Bot.X)
+        ReverseHorizontal(*E);
+    } else if (EStart->Bot.X != E->Bot.X)
+      ReverseHorizontal(*E);
+  }
+
+  EStart = E;
+  if (NextIsForward) {
+    while (Result->Top.Y == Result->Next->Bot.Y && Result->Next->OutIdx != Skip)
+      Result = Result->Next;
+    if (IsHorizontal(*Result) && Result->Next->OutIdx != Skip) {
+      // nb: at the top of a bound, horizontals are added to the bound
+      // only when the preceding edge attaches to the horizontal's left vertex
+      // unless a Skip edge is encountered when that becomes the top divide
+      Horz = Result;
+      while (IsHorizontal(*Horz->Prev))
+        Horz = Horz->Prev;
+      if (Horz->Prev->Top.X > Result->Next->Top.X)
+        Result = Horz->Prev;
+    }
+    while (E != Result) {
+      E->NextInLML = E->Next;
+      if (IsHorizontal(*E) && E != EStart && E->Bot.X != E->Prev->Top.X)
+        ReverseHorizontal(*E);
+      E = E->Next;
+    }
+    if (IsHorizontal(*E) && E != EStart && E->Bot.X != E->Prev->Top.X)
+      ReverseHorizontal(*E);
+    Result = Result->Next; // move to the edge just beyond current bound
+  } else {
+    while (Result->Top.Y == Result->Prev->Bot.Y && Result->Prev->OutIdx != Skip)
+      Result = Result->Prev;
+    if (IsHorizontal(*Result) && Result->Prev->OutIdx != Skip) {
+      Horz = Result;
+      while (IsHorizontal(*Horz->Next))
+        Horz = Horz->Next;
+      if (Horz->Next->Top.X == Result->Prev->Top.X ||
+          Horz->Next->Top.X > Result->Prev->Top.X)
+        Result = Horz->Next;
+    }
+
+    while (E != Result) {
+      E->NextInLML = E->Prev;
+      if (IsHorizontal(*E) && E != EStart && E->Bot.X != E->Next->Top.X)
+        ReverseHorizontal(*E);
+      E = E->Prev;
+    }
+    if (IsHorizontal(*E) && E != EStart && E->Bot.X != E->Next->Top.X)
+      ReverseHorizontal(*E);
+    Result = Result->Prev; // move to the edge just beyond current bound
+  }
+
+  return Result;
+}
+//------------------------------------------------------------------------------
+
+bool ClipperBase::AddPath(const Path &pg, PolyType PolyTyp, bool Closed) {
+#ifdef use_lines
+  if (!Closed && PolyTyp == ptClip)
+    throw clipperException("AddPath: Open paths must be subject.");
+#else
+  if (!Closed)
+    throw clipperException("AddPath: Open paths have been disabled.");
+#endif
+
+  int highI = (int)pg.size() - 1;
+  if (Closed)
+    while (highI > 0 && (pg[highI] == pg[0]))
+      --highI;
+  while (highI > 0 && (pg[highI] == pg[highI - 1]))
+    --highI;
+  if ((Closed && highI < 2) || (!Closed && highI < 1))
+    return false;
+
+  // create a new edge array ...
+  TEdge *edges = new TEdge[highI + 1];
+
+  bool IsFlat = true;
+  // 1. Basic (first) edge initialization ...
+  try {
+    edges[1].Curr = pg[1];
+    RangeTest(pg[0], m_UseFullRange);
+    RangeTest(pg[highI], m_UseFullRange);
+    InitEdge(&edges[0], &edges[1], &edges[highI], pg[0]);
+    InitEdge(&edges[highI], &edges[0], &edges[highI - 1], pg[highI]);
+    for (int i = highI - 1; i >= 1; --i) {
+      RangeTest(pg[i], m_UseFullRange);
+      InitEdge(&edges[i], &edges[i + 1], &edges[i - 1], pg[i]);
+    }
+  } catch (...) {
+    delete[] edges;
+    throw; // range test fails
+  }
+  TEdge *eStart = &edges[0];
+
+  // 2. Remove duplicate vertices, and (when closed) collinear edges ...
+  TEdge *E = eStart, *eLoopStop = eStart;
+  for (;;) {
+    // nb: allows matching start and end points when not Closed ...
+    if (E->Curr == E->Next->Curr && (Closed || E->Next != eStart)) {
+      if (E == E->Next)
+        break;
+      if (E == eStart)
+        eStart = E->Next;
+      E = RemoveEdge(E);
+      eLoopStop = E;
+      continue;
+    }
+    if (E->Prev == E->Next)
+      break; // only two vertices
+    else if (Closed && SlopesEqual(E->Prev->Curr, E->Curr, E->Next->Curr,
+                                   m_UseFullRange) &&
+             (!m_PreserveCollinear ||
+              !Pt2IsBetweenPt1AndPt3(E->Prev->Curr, E->Curr, E->Next->Curr))) {
+      // Collinear edges are allowed for open paths but in closed paths
+      // the default is to merge adjacent collinear edges into a single edge.
+      // However, if the PreserveCollinear property is enabled, only overlapping
+      // collinear edges (ie spikes) will be removed from closed paths.
+      if (E == eStart)
+        eStart = E->Next;
+      E = RemoveEdge(E);
+      E = E->Prev;
+      eLoopStop = E;
+      continue;
+    }
+    E = E->Next;
+    if ((E == eLoopStop) || (!Closed && E->Next == eStart))
+      break;
+  }
+
+  if ((!Closed && (E == E->Next)) || (Closed && (E->Prev == E->Next))) {
+    delete[] edges;
+    return false;
+  }
+
+  if (!Closed) {
+    m_HasOpenPaths = true;
+    eStart->Prev->OutIdx = Skip;
+  }
+
+  // 3. Do second stage of edge initialization ...
+  E = eStart;
+  do {
+    InitEdge2(*E, PolyTyp);
+    E = E->Next;
+    if (IsFlat && E->Curr.Y != eStart->Curr.Y)
+      IsFlat = false;
+  } while (E != eStart);
+
+  // 4. Finally, add edge bounds to LocalMinima list ...
+
+  // Totally flat paths must be handled differently when adding them
+  // to LocalMinima list to avoid endless loops etc ...
+  if (IsFlat) {
+    if (Closed) {
+      delete[] edges;
+      return false;
+    }
+    E->Prev->OutIdx = Skip;
+    MinimaList::value_type locMin;
+    locMin.Y = E->Bot.Y;
+    locMin.LeftBound = 0;
+    locMin.RightBound = E;
+    locMin.RightBound->Side = esRight;
+    locMin.RightBound->WindDelta = 0;
+    for (;;) {
+      if (E->Bot.X != E->Prev->Top.X)
+        ReverseHorizontal(*E);
+      if (E->Next->OutIdx == Skip)
+        break;
+      E->NextInLML = E->Next;
+      E = E->Next;
+    }
+    m_MinimaList.push_back(locMin);
+    m_edges.push_back(edges);
+    return true;
+  }
+
+  m_edges.push_back(edges);
+  bool leftBoundIsForward;
+  TEdge *EMin = 0;
+
+  // workaround to avoid an endless loop in the while loop below when
+  // open paths have matching start and end points ...
+  if (E->Prev->Bot == E->Prev->Top)
+    E = E->Next;
+
+  for (;;) {
+    E = FindNextLocMin(E);
+    if (E == EMin)
+      break;
+    else if (!EMin)
+      EMin = E;
+
+    // E and E.Prev now share a local minima (left aligned if horizontal).
+    // Compare their slopes to find which starts which bound ...
+    MinimaList::value_type locMin;
+    locMin.Y = E->Bot.Y;
+    if (E->Dx < E->Prev->Dx) {
+      locMin.LeftBound = E->Prev;
+      locMin.RightBound = E;
+      leftBoundIsForward = false; // Q.nextInLML = Q.prev
+    } else {
+      locMin.LeftBound = E;
+      locMin.RightBound = E->Prev;
+      leftBoundIsForward = true; // Q.nextInLML = Q.next
+    }
+
+    if (!Closed)
+      locMin.LeftBound->WindDelta = 0;
+    else if (locMin.LeftBound->Next == locMin.RightBound)
+      locMin.LeftBound->WindDelta = -1;
+    else
+      locMin.LeftBound->WindDelta = 1;
+    locMin.RightBound->WindDelta = -locMin.LeftBound->WindDelta;
+
+    E = ProcessBound(locMin.LeftBound, leftBoundIsForward);
+    if (E->OutIdx == Skip)
+      E = ProcessBound(E, leftBoundIsForward);
+
+    TEdge *E2 = ProcessBound(locMin.RightBound, !leftBoundIsForward);
+    if (E2->OutIdx == Skip)
+      E2 = ProcessBound(E2, !leftBoundIsForward);
+
+    if (locMin.LeftBound->OutIdx == Skip)
+      locMin.LeftBound = 0;
+    else if (locMin.RightBound->OutIdx == Skip)
+      locMin.RightBound = 0;
+    m_MinimaList.push_back(locMin);
+    if (!leftBoundIsForward)
+      E = E2;
+  }
+  return true;
+}
+//------------------------------------------------------------------------------
+
+bool ClipperBase::AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed) {
+  bool result = false;
+  for (Paths::size_type i = 0; i < ppg.size(); ++i)
+    if (AddPath(ppg[i], PolyTyp, Closed))
+      result = true;
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::Clear() {
+  DisposeLocalMinimaList();
+  for (EdgeList::size_type i = 0; i < m_edges.size(); ++i) {
+    TEdge *edges = m_edges[i];
+    delete[] edges;
+  }
+  m_edges.clear();
+  m_UseFullRange = false;
+  m_HasOpenPaths = false;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::Reset() {
+  m_CurrentLM = m_MinimaList.begin();
+  if (m_CurrentLM == m_MinimaList.end())
+    return; // ie nothing to process
+  std::sort(m_MinimaList.begin(), m_MinimaList.end(), LocMinSorter());
+
+  m_Scanbeam = ScanbeamList(); // clears/resets priority_queue
+  // reset all edges ...
+  for (MinimaList::iterator lm = m_MinimaList.begin(); lm != m_MinimaList.end();
+       ++lm) {
+    InsertScanbeam(lm->Y);
+    TEdge *e = lm->LeftBound;
+    if (e) {
+      e->Curr = e->Bot;
+      e->Side = esLeft;
+      e->OutIdx = Unassigned;
+    }
+
+    e = lm->RightBound;
+    if (e) {
+      e->Curr = e->Bot;
+      e->Side = esRight;
+      e->OutIdx = Unassigned;
+    }
+  }
+  m_ActiveEdges = 0;
+  m_CurrentLM = m_MinimaList.begin();
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::DisposeLocalMinimaList() {
+  m_MinimaList.clear();
+  m_CurrentLM = m_MinimaList.begin();
+}
+//------------------------------------------------------------------------------
+
+bool ClipperBase::PopLocalMinima(cInt Y, const LocalMinimum *&locMin) {
+  if (m_CurrentLM == m_MinimaList.end() || (*m_CurrentLM).Y != Y)
+    return false;
+  locMin = &(*m_CurrentLM);
+  ++m_CurrentLM;
+  return true;
+}
+//------------------------------------------------------------------------------
+
+IntRect ClipperBase::GetBounds() {
+  IntRect result;
+  MinimaList::iterator lm = m_MinimaList.begin();
+  if (lm == m_MinimaList.end()) {
+    result.left = result.top = result.right = result.bottom = 0;
+    return result;
+  }
+  result.left = lm->LeftBound->Bot.X;
+  result.top = lm->LeftBound->Bot.Y;
+  result.right = lm->LeftBound->Bot.X;
+  result.bottom = lm->LeftBound->Bot.Y;
+  while (lm != m_MinimaList.end()) {
+    // todo - needs fixing for open paths
+    result.bottom = std::max(result.bottom, lm->LeftBound->Bot.Y);
+    TEdge *e = lm->LeftBound;
+    for (;;) {
+      TEdge *bottomE = e;
+      while (e->NextInLML) {
+        if (e->Bot.X < result.left)
+          result.left = e->Bot.X;
+        if (e->Bot.X > result.right)
+          result.right = e->Bot.X;
+        e = e->NextInLML;
+      }
+      result.left = std::min(result.left, e->Bot.X);
+      result.right = std::max(result.right, e->Bot.X);
+      result.left = std::min(result.left, e->Top.X);
+      result.right = std::max(result.right, e->Top.X);
+      result.top = std::min(result.top, e->Top.Y);
+      if (bottomE == lm->LeftBound)
+        e = lm->RightBound;
+      else
+        break;
+    }
+    ++lm;
+  }
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::InsertScanbeam(const cInt Y) { m_Scanbeam.push(Y); }
+//------------------------------------------------------------------------------
+
+bool ClipperBase::PopScanbeam(cInt &Y) {
+  if (m_Scanbeam.empty())
+    return false;
+  Y = m_Scanbeam.top();
+  m_Scanbeam.pop();
+  while (!m_Scanbeam.empty() && Y == m_Scanbeam.top()) {
+    m_Scanbeam.pop();
+  } // Pop duplicates.
+  return true;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::DisposeAllOutRecs() {
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i)
+    DisposeOutRec(i);
+  m_PolyOuts.clear();
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::DisposeOutRec(PolyOutList::size_type index) {
+  OutRec *outRec = m_PolyOuts[index];
+  if (outRec->Pts)
+    DisposeOutPts(outRec->Pts);
+  delete outRec;
+  m_PolyOuts[index] = 0;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::DeleteFromAEL(TEdge *e) {
+  TEdge *AelPrev = e->PrevInAEL;
+  TEdge *AelNext = e->NextInAEL;
+  if (!AelPrev && !AelNext && (e != m_ActiveEdges))
+    return; // already deleted
+  if (AelPrev)
+    AelPrev->NextInAEL = AelNext;
+  else
+    m_ActiveEdges = AelNext;
+  if (AelNext)
+    AelNext->PrevInAEL = AelPrev;
+  e->NextInAEL = 0;
+  e->PrevInAEL = 0;
+}
+//------------------------------------------------------------------------------
+
+OutRec *ClipperBase::CreateOutRec() {
+  OutRec *result = new OutRec;
+  result->IsHole = false;
+  result->IsOpen = false;
+  result->FirstLeft = 0;
+  result->Pts = 0;
+  result->BottomPt = 0;
+  result->PolyNd = 0;
+  m_PolyOuts.push_back(result);
+  result->Idx = (int)m_PolyOuts.size() - 1;
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::SwapPositionsInAEL(TEdge *Edge1, TEdge *Edge2) {
+  // check that one or other edge hasn't already been removed from AEL ...
+  if (Edge1->NextInAEL == Edge1->PrevInAEL ||
+      Edge2->NextInAEL == Edge2->PrevInAEL)
+    return;
+
+  if (Edge1->NextInAEL == Edge2) {
+    TEdge *Next = Edge2->NextInAEL;
+    if (Next)
+      Next->PrevInAEL = Edge1;
+    TEdge *Prev = Edge1->PrevInAEL;
+    if (Prev)
+      Prev->NextInAEL = Edge2;
+    Edge2->PrevInAEL = Prev;
+    Edge2->NextInAEL = Edge1;
+    Edge1->PrevInAEL = Edge2;
+    Edge1->NextInAEL = Next;
+  } else if (Edge2->NextInAEL == Edge1) {
+    TEdge *Next = Edge1->NextInAEL;
+    if (Next)
+      Next->PrevInAEL = Edge2;
+    TEdge *Prev = Edge2->PrevInAEL;
+    if (Prev)
+      Prev->NextInAEL = Edge1;
+    Edge1->PrevInAEL = Prev;
+    Edge1->NextInAEL = Edge2;
+    Edge2->PrevInAEL = Edge1;
+    Edge2->NextInAEL = Next;
+  } else {
+    TEdge *Next = Edge1->NextInAEL;
+    TEdge *Prev = Edge1->PrevInAEL;
+    Edge1->NextInAEL = Edge2->NextInAEL;
+    if (Edge1->NextInAEL)
+      Edge1->NextInAEL->PrevInAEL = Edge1;
+    Edge1->PrevInAEL = Edge2->PrevInAEL;
+    if (Edge1->PrevInAEL)
+      Edge1->PrevInAEL->NextInAEL = Edge1;
+    Edge2->NextInAEL = Next;
+    if (Edge2->NextInAEL)
+      Edge2->NextInAEL->PrevInAEL = Edge2;
+    Edge2->PrevInAEL = Prev;
+    if (Edge2->PrevInAEL)
+      Edge2->PrevInAEL->NextInAEL = Edge2;
+  }
+
+  if (!Edge1->PrevInAEL)
+    m_ActiveEdges = Edge1;
+  else if (!Edge2->PrevInAEL)
+    m_ActiveEdges = Edge2;
+}
+//------------------------------------------------------------------------------
+
+void ClipperBase::UpdateEdgeIntoAEL(TEdge *&e) {
+  if (!e->NextInLML)
+    throw clipperException("UpdateEdgeIntoAEL: invalid call");
+
+  e->NextInLML->OutIdx = e->OutIdx;
+  TEdge *AelPrev = e->PrevInAEL;
+  TEdge *AelNext = e->NextInAEL;
+  if (AelPrev)
+    AelPrev->NextInAEL = e->NextInLML;
+  else
+    m_ActiveEdges = e->NextInLML;
+  if (AelNext)
+    AelNext->PrevInAEL = e->NextInLML;
+  e->NextInLML->Side = e->Side;
+  e->NextInLML->WindDelta = e->WindDelta;
+  e->NextInLML->WindCnt = e->WindCnt;
+  e->NextInLML->WindCnt2 = e->WindCnt2;
+  e = e->NextInLML;
+  e->Curr = e->Bot;
+  e->PrevInAEL = AelPrev;
+  e->NextInAEL = AelNext;
+  if (!IsHorizontal(*e))
+    InsertScanbeam(e->Top.Y);
+}
+//------------------------------------------------------------------------------
+
+bool ClipperBase::LocalMinimaPending() {
+  return (m_CurrentLM != m_MinimaList.end());
+}
+
+//------------------------------------------------------------------------------
+// TClipper methods ...
+//------------------------------------------------------------------------------
+
+Clipper::Clipper(int initOptions)
+    : ClipperBase() // constructor
+{
+  m_ExecuteLocked = false;
+  m_UseFullRange = false;
+  m_ReverseOutput = ((initOptions & ioReverseSolution) != 0);
+  m_StrictSimple = ((initOptions & ioStrictlySimple) != 0);
+  m_PreserveCollinear = ((initOptions & ioPreserveCollinear) != 0);
+  m_HasOpenPaths = false;
+#ifdef use_xyz
+  m_ZFill = 0;
+#endif
+}
+//------------------------------------------------------------------------------
+
+#ifdef use_xyz
+void Clipper::ZFillFunction(ZFillCallback zFillFunc) { m_ZFill = zFillFunc; }
+//------------------------------------------------------------------------------
+#endif
+
+bool Clipper::Execute(ClipType clipType, Paths &solution,
+                      PolyFillType fillType) {
+  return Execute(clipType, solution, fillType, fillType);
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::Execute(ClipType clipType, PolyTree &polytree,
+                      PolyFillType fillType) {
+  return Execute(clipType, polytree, fillType, fillType);
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::Execute(ClipType clipType, Paths &solution,
+                      PolyFillType subjFillType, PolyFillType clipFillType) {
+  if (m_ExecuteLocked)
+    return false;
+  if (m_HasOpenPaths)
+    throw clipperException(
+        "Error: PolyTree struct is needed for open path clipping.");
+  m_ExecuteLocked = true;
+  solution.resize(0);
+  m_SubjFillType = subjFillType;
+  m_ClipFillType = clipFillType;
+  m_ClipType = clipType;
+  m_UsingPolyTree = false;
+  bool succeeded = ExecuteInternal();
+  if (succeeded)
+    BuildResult(solution);
+  DisposeAllOutRecs();
+  m_ExecuteLocked = false;
+  return succeeded;
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::Execute(ClipType clipType, PolyTree &polytree,
+                      PolyFillType subjFillType, PolyFillType clipFillType) {
+  if (m_ExecuteLocked)
+    return false;
+  m_ExecuteLocked = true;
+  m_SubjFillType = subjFillType;
+  m_ClipFillType = clipFillType;
+  m_ClipType = clipType;
+  m_UsingPolyTree = true;
+  bool succeeded = ExecuteInternal();
+  if (succeeded)
+    BuildResult2(polytree);
+  DisposeAllOutRecs();
+  m_ExecuteLocked = false;
+  return succeeded;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::FixHoleLinkage(OutRec &outrec) {
+  // skip OutRecs that (a) contain outermost polygons or
+  //(b) already have the correct owner/child linkage ...
+  if (!outrec.FirstLeft ||
+      (outrec.IsHole != outrec.FirstLeft->IsHole && outrec.FirstLeft->Pts))
+    return;
+
+  OutRec *orfl = outrec.FirstLeft;
+  while (orfl && ((orfl->IsHole == outrec.IsHole) || !orfl->Pts))
+    orfl = orfl->FirstLeft;
+  outrec.FirstLeft = orfl;
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::ExecuteInternal() {
+  bool succeeded = true;
+  try {
+    Reset();
+    m_Maxima = MaximaList();
+    m_SortedEdges = 0;
+
+    succeeded = true;
+    cInt botY, topY;
+    if (!PopScanbeam(botY))
+      return false;
+    InsertLocalMinimaIntoAEL(botY);
+    while (PopScanbeam(topY) || LocalMinimaPending()) {
+      ProcessHorizontals();
+      ClearGhostJoins();
+      if (!ProcessIntersections(topY)) {
+        succeeded = false;
+        break;
+      }
+      ProcessEdgesAtTopOfScanbeam(topY);
+      botY = topY;
+      InsertLocalMinimaIntoAEL(botY);
+    }
+  } catch (...) {
+    succeeded = false;
+  }
+
+  if (succeeded) {
+    // fix orientations ...
+    for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i) {
+      OutRec *outRec = m_PolyOuts[i];
+      if (!outRec->Pts || outRec->IsOpen)
+        continue;
+      if ((outRec->IsHole ^ m_ReverseOutput) == (Area(*outRec) > 0))
+        ReversePolyPtLinks(outRec->Pts);
+    }
+
+    if (!m_Joins.empty())
+      JoinCommonEdges();
+
+    // unfortunately FixupOutPolygon() must be done after JoinCommonEdges()
+    for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i) {
+      OutRec *outRec = m_PolyOuts[i];
+      if (!outRec->Pts)
+        continue;
+      if (outRec->IsOpen)
+        FixupOutPolyline(*outRec);
+      else
+        FixupOutPolygon(*outRec);
+    }
+
+    if (m_StrictSimple)
+      DoSimplePolygons();
+  }
+
+  ClearJoins();
+  ClearGhostJoins();
+  return succeeded;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::SetWindingCount(TEdge &edge) {
+  TEdge *e = edge.PrevInAEL;
+  // find the edge of the same polytype that immediately preceeds 'edge' in AEL
+  while (e && ((e->PolyTyp != edge.PolyTyp) || (e->WindDelta == 0)))
+    e = e->PrevInAEL;
+  if (!e) {
+    if (edge.WindDelta == 0) {
+      PolyFillType pft =
+          (edge.PolyTyp == ptSubject ? m_SubjFillType : m_ClipFillType);
+      edge.WindCnt = (pft == pftNegative ? -1 : 1);
+    } else
+      edge.WindCnt = edge.WindDelta;
+    edge.WindCnt2 = 0;
+    e = m_ActiveEdges; // ie get ready to calc WindCnt2
+  } else if (edge.WindDelta == 0 && m_ClipType != ctUnion) {
+    edge.WindCnt = 1;
+    edge.WindCnt2 = e->WindCnt2;
+    e = e->NextInAEL; // ie get ready to calc WindCnt2
+  } else if (IsEvenOddFillType(edge)) {
+    // EvenOdd filling ...
+    if (edge.WindDelta == 0) {
+      // are we inside a subj polygon ...
+      bool Inside = true;
+      TEdge *e2 = e->PrevInAEL;
+      while (e2) {
+        if (e2->PolyTyp == e->PolyTyp && e2->WindDelta != 0)
+          Inside = !Inside;
+        e2 = e2->PrevInAEL;
+      }
+      edge.WindCnt = (Inside ? 0 : 1);
+    } else {
+      edge.WindCnt = edge.WindDelta;
+    }
+    edge.WindCnt2 = e->WindCnt2;
+    e = e->NextInAEL; // ie get ready to calc WindCnt2
+  } else {
+    // nonZero, Positive or Negative filling ...
+    if (e->WindCnt * e->WindDelta < 0) {
+      // prev edge is 'decreasing' WindCount (WC) toward zero
+      // so we're outside the previous polygon ...
+      if (Abs(e->WindCnt) > 1) {
+        // outside prev poly but still inside another.
+        // when reversing direction of prev poly use the same WC
+        if (e->WindDelta * edge.WindDelta < 0)
+          edge.WindCnt = e->WindCnt;
+        // otherwise continue to 'decrease' WC ...
+        else
+          edge.WindCnt = e->WindCnt + edge.WindDelta;
+      } else
+        // now outside all polys of same polytype so set own WC ...
+        edge.WindCnt = (edge.WindDelta == 0 ? 1 : edge.WindDelta);
+    } else {
+      // prev edge is 'increasing' WindCount (WC) away from zero
+      // so we're inside the previous polygon ...
+      if (edge.WindDelta == 0)
+        edge.WindCnt = (e->WindCnt < 0 ? e->WindCnt - 1 : e->WindCnt + 1);
+      // if wind direction is reversing prev then use same WC
+      else if (e->WindDelta * edge.WindDelta < 0)
+        edge.WindCnt = e->WindCnt;
+      // otherwise add to WC ...
+      else
+        edge.WindCnt = e->WindCnt + edge.WindDelta;
+    }
+    edge.WindCnt2 = e->WindCnt2;
+    e = e->NextInAEL; // ie get ready to calc WindCnt2
+  }
+
+  // update WindCnt2 ...
+  if (IsEvenOddAltFillType(edge)) {
+    // EvenOdd filling ...
+    while (e != &edge) {
+      if (e->WindDelta != 0)
+        edge.WindCnt2 = (edge.WindCnt2 == 0 ? 1 : 0);
+      e = e->NextInAEL;
+    }
+  } else {
+    // nonZero, Positive or Negative filling ...
+    while (e != &edge) {
+      edge.WindCnt2 += e->WindDelta;
+      e = e->NextInAEL;
+    }
+  }
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::IsEvenOddFillType(const TEdge &edge) const {
+  if (edge.PolyTyp == ptSubject)
+    return m_SubjFillType == pftEvenOdd;
+  else
+    return m_ClipFillType == pftEvenOdd;
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::IsEvenOddAltFillType(const TEdge &edge) const {
+  if (edge.PolyTyp == ptSubject)
+    return m_ClipFillType == pftEvenOdd;
+  else
+    return m_SubjFillType == pftEvenOdd;
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::IsContributing(const TEdge &edge) const {
+  PolyFillType pft, pft2;
+  if (edge.PolyTyp == ptSubject) {
+    pft = m_SubjFillType;
+    pft2 = m_ClipFillType;
+  } else {
+    pft = m_ClipFillType;
+    pft2 = m_SubjFillType;
+  }
+
+  switch (pft) {
+  case pftEvenOdd:
+    // return false if a subj line has been flagged as inside a subj polygon
+    if (edge.WindDelta == 0 && edge.WindCnt != 1)
+      return false;
+    break;
+  case pftNonZero:
+    if (Abs(edge.WindCnt) != 1)
+      return false;
+    break;
+  case pftPositive:
+    if (edge.WindCnt != 1)
+      return false;
+    break;
+  default: // pftNegative
+    if (edge.WindCnt != -1)
+      return false;
+  }
+
+  switch (m_ClipType) {
+  case ctIntersection:
+    switch (pft2) {
+    case pftEvenOdd:
+    case pftNonZero:
+      return (edge.WindCnt2 != 0);
+    case pftPositive:
+      return (edge.WindCnt2 > 0);
+    default:
+      return (edge.WindCnt2 < 0);
+    }
+    break;
+  case ctUnion:
+    switch (pft2) {
+    case pftEvenOdd:
+    case pftNonZero:
+      return (edge.WindCnt2 == 0);
+    case pftPositive:
+      return (edge.WindCnt2 <= 0);
+    default:
+      return (edge.WindCnt2 >= 0);
+    }
+    break;
+  case ctDifference:
+    if (edge.PolyTyp == ptSubject)
+      switch (pft2) {
+      case pftEvenOdd:
+      case pftNonZero:
+        return (edge.WindCnt2 == 0);
+      case pftPositive:
+        return (edge.WindCnt2 <= 0);
+      default:
+        return (edge.WindCnt2 >= 0);
+      }
+    else
+      switch (pft2) {
+      case pftEvenOdd:
+      case pftNonZero:
+        return (edge.WindCnt2 != 0);
+      case pftPositive:
+        return (edge.WindCnt2 > 0);
+      default:
+        return (edge.WindCnt2 < 0);
+      }
+    break;
+  case ctXor:
+    if (edge.WindDelta == 0) // XOr always contributing unless open
+      switch (pft2) {
+      case pftEvenOdd:
+      case pftNonZero:
+        return (edge.WindCnt2 == 0);
+      case pftPositive:
+        return (edge.WindCnt2 <= 0);
+      default:
+        return (edge.WindCnt2 >= 0);
+      }
+    else
+      return true;
+    break;
+  default:
+    return true;
+  }
+}
+//------------------------------------------------------------------------------
+
+OutPt *Clipper::AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &Pt) {
+  OutPt *result;
+  TEdge *e, *prevE;
+  if (IsHorizontal(*e2) || (e1->Dx > e2->Dx)) {
+    result = AddOutPt(e1, Pt);
+    e2->OutIdx = e1->OutIdx;
+    e1->Side = esLeft;
+    e2->Side = esRight;
+    e = e1;
+    if (e->PrevInAEL == e2)
+      prevE = e2->PrevInAEL;
+    else
+      prevE = e->PrevInAEL;
+  } else {
+    result = AddOutPt(e2, Pt);
+    e1->OutIdx = e2->OutIdx;
+    e1->Side = esRight;
+    e2->Side = esLeft;
+    e = e2;
+    if (e->PrevInAEL == e1)
+      prevE = e1->PrevInAEL;
+    else
+      prevE = e->PrevInAEL;
+  }
+
+  if (prevE && prevE->OutIdx >= 0 && prevE->Top.Y < Pt.Y && e->Top.Y < Pt.Y) {
+    cInt xPrev = TopX(*prevE, Pt.Y);
+    cInt xE = TopX(*e, Pt.Y);
+    if (xPrev == xE && (e->WindDelta != 0) && (prevE->WindDelta != 0) &&
+        SlopesEqual(IntPoint(xPrev, Pt.Y), prevE->Top, IntPoint(xE, Pt.Y),
+                    e->Top, m_UseFullRange)) {
+      OutPt *outPt = AddOutPt(prevE, Pt);
+      AddJoin(result, outPt, e->Top);
+    }
+  }
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &Pt) {
+  AddOutPt(e1, Pt);
+  if (e2->WindDelta == 0)
+    AddOutPt(e2, Pt);
+  if (e1->OutIdx == e2->OutIdx) {
+    e1->OutIdx = Unassigned;
+    e2->OutIdx = Unassigned;
+  } else if (e1->OutIdx < e2->OutIdx)
+    AppendPolygon(e1, e2);
+  else
+    AppendPolygon(e2, e1);
+}
+//------------------------------------------------------------------------------
+
+void Clipper::AddEdgeToSEL(TEdge *edge) {
+  // SEL pointers in PEdge are reused to build a list of horizontal edges.
+  // However, we don't need to worry about order with horizontal edge
+  // processing.
+  if (!m_SortedEdges) {
+    m_SortedEdges = edge;
+    edge->PrevInSEL = 0;
+    edge->NextInSEL = 0;
+  } else {
+    edge->NextInSEL = m_SortedEdges;
+    edge->PrevInSEL = 0;
+    m_SortedEdges->PrevInSEL = edge;
+    m_SortedEdges = edge;
+  }
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::PopEdgeFromSEL(TEdge *&edge) {
+  if (!m_SortedEdges)
+    return false;
+  edge = m_SortedEdges;
+  DeleteFromSEL(m_SortedEdges);
+  return true;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::CopyAELToSEL() {
+  TEdge *e = m_ActiveEdges;
+  m_SortedEdges = e;
+  while (e) {
+    e->PrevInSEL = e->PrevInAEL;
+    e->NextInSEL = e->NextInAEL;
+    e = e->NextInAEL;
+  }
+}
+//------------------------------------------------------------------------------
+
+void Clipper::AddJoin(OutPt *op1, OutPt *op2, const IntPoint OffPt) {
+  Join *j = new Join;
+  j->OutPt1 = op1;
+  j->OutPt2 = op2;
+  j->OffPt = OffPt;
+  m_Joins.push_back(j);
+}
+//------------------------------------------------------------------------------
+
+void Clipper::ClearJoins() {
+  for (JoinList::size_type i = 0; i < m_Joins.size(); i++)
+    delete m_Joins[i];
+  m_Joins.resize(0);
+}
+//------------------------------------------------------------------------------
+
+void Clipper::ClearGhostJoins() {
+  for (JoinList::size_type i = 0; i < m_GhostJoins.size(); i++)
+    delete m_GhostJoins[i];
+  m_GhostJoins.resize(0);
+}
+//------------------------------------------------------------------------------
+
+void Clipper::AddGhostJoin(OutPt *op, const IntPoint OffPt) {
+  Join *j = new Join;
+  j->OutPt1 = op;
+  j->OutPt2 = 0;
+  j->OffPt = OffPt;
+  m_GhostJoins.push_back(j);
+}
+//------------------------------------------------------------------------------
+
+void Clipper::InsertLocalMinimaIntoAEL(const cInt botY) {
+  const LocalMinimum *lm;
+  while (PopLocalMinima(botY, lm)) {
+    TEdge *lb = lm->LeftBound;
+    TEdge *rb = lm->RightBound;
+
+    OutPt *Op1 = 0;
+    if (!lb) {
+      // nb: don't insert LB into either AEL or SEL
+      InsertEdgeIntoAEL(rb, 0);
+      SetWindingCount(*rb);
+      if (IsContributing(*rb))
+        Op1 = AddOutPt(rb, rb->Bot);
+    } else if (!rb) {
+      InsertEdgeIntoAEL(lb, 0);
+      SetWindingCount(*lb);
+      if (IsContributing(*lb))
+        Op1 = AddOutPt(lb, lb->Bot);
+      InsertScanbeam(lb->Top.Y);
+    } else {
+      InsertEdgeIntoAEL(lb, 0);
+      InsertEdgeIntoAEL(rb, lb);
+      SetWindingCount(*lb);
+      rb->WindCnt = lb->WindCnt;
+      rb->WindCnt2 = lb->WindCnt2;
+      if (IsContributing(*lb))
+        Op1 = AddLocalMinPoly(lb, rb, lb->Bot);
+      InsertScanbeam(lb->Top.Y);
+    }
+
+    if (rb) {
+      if (IsHorizontal(*rb)) {
+        AddEdgeToSEL(rb);
+        if (rb->NextInLML)
+          InsertScanbeam(rb->NextInLML->Top.Y);
+      } else
+        InsertScanbeam(rb->Top.Y);
+    }
+
+    if (!lb || !rb)
+      continue;
+
+    // if any output polygons share an edge, they'll need joining later ...
+    if (Op1 && IsHorizontal(*rb) && m_GhostJoins.size() > 0 &&
+        (rb->WindDelta != 0)) {
+      for (JoinList::size_type i = 0; i < m_GhostJoins.size(); ++i) {
+        Join *jr = m_GhostJoins[i];
+        // if the horizontal Rb and a 'ghost' horizontal overlap, then convert
+        // the 'ghost' join to a real join ready for later ...
+        if (HorzSegmentsOverlap(jr->OutPt1->Pt.X, jr->OffPt.X, rb->Bot.X,
+                                rb->Top.X))
+          AddJoin(jr->OutPt1, Op1, jr->OffPt);
+      }
+    }
+
+    if (lb->OutIdx >= 0 && lb->PrevInAEL &&
+        lb->PrevInAEL->Curr.X == lb->Bot.X && lb->PrevInAEL->OutIdx >= 0 &&
+        SlopesEqual(lb->PrevInAEL->Bot, lb->PrevInAEL->Top, lb->Curr, lb->Top,
+                    m_UseFullRange) &&
+        (lb->WindDelta != 0) && (lb->PrevInAEL->WindDelta != 0)) {
+      OutPt *Op2 = AddOutPt(lb->PrevInAEL, lb->Bot);
+      AddJoin(Op1, Op2, lb->Top);
+    }
+
+    if (lb->NextInAEL != rb) {
+
+      if (rb->OutIdx >= 0 && rb->PrevInAEL->OutIdx >= 0 &&
+          SlopesEqual(rb->PrevInAEL->Curr, rb->PrevInAEL->Top, rb->Curr,
+                      rb->Top, m_UseFullRange) &&
+          (rb->WindDelta != 0) && (rb->PrevInAEL->WindDelta != 0)) {
+        OutPt *Op2 = AddOutPt(rb->PrevInAEL, rb->Bot);
+        AddJoin(Op1, Op2, rb->Top);
+      }
+
+      TEdge *e = lb->NextInAEL;
+      if (e) {
+        while (e != rb) {
+          // nb: For calculating winding counts etc, IntersectEdges() assumes
+          // that param1 will be to the Right of param2 ABOVE the intersection
+          // ...
+          IntersectEdges(rb, e, lb->Curr); // order important here
+          e = e->NextInAEL;
+        }
+      }
+    }
+  }
+}
+//------------------------------------------------------------------------------
+
+void Clipper::DeleteFromSEL(TEdge *e) {
+  TEdge *SelPrev = e->PrevInSEL;
+  TEdge *SelNext = e->NextInSEL;
+  if (!SelPrev && !SelNext && (e != m_SortedEdges))
+    return; // already deleted
+  if (SelPrev)
+    SelPrev->NextInSEL = SelNext;
+  else
+    m_SortedEdges = SelNext;
+  if (SelNext)
+    SelNext->PrevInSEL = SelPrev;
+  e->NextInSEL = 0;
+  e->PrevInSEL = 0;
+}
+//------------------------------------------------------------------------------
+
+#ifdef use_xyz
+void Clipper::SetZ(IntPoint &pt, TEdge &e1, TEdge &e2) {
+  if (pt.Z != 0 || !m_ZFill)
+    return;
+  else if (pt == e1.Bot)
+    pt.Z = e1.Bot.Z;
+  else if (pt == e1.Top)
+    pt.Z = e1.Top.Z;
+  else if (pt == e2.Bot)
+    pt.Z = e2.Bot.Z;
+  else if (pt == e2.Top)
+    pt.Z = e2.Top.Z;
+  else
+    (*m_ZFill)(e1.Bot, e1.Top, e2.Bot, e2.Top, pt);
+}
+//------------------------------------------------------------------------------
+#endif
+
+void Clipper::IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &Pt) {
+  bool e1Contributing = (e1->OutIdx >= 0);
+  bool e2Contributing = (e2->OutIdx >= 0);
+
+#ifdef use_xyz
+  SetZ(Pt, *e1, *e2);
+#endif
+
+#ifdef use_lines
+  // if either edge is on an OPEN path ...
+  if (e1->WindDelta == 0 || e2->WindDelta == 0) {
+    // ignore subject-subject open path intersections UNLESS they
+    // are both open paths, AND they are both 'contributing maximas' ...
+    if (e1->WindDelta == 0 && e2->WindDelta == 0)
+      return;
+
+    // if intersecting a subj line with a subj poly ...
+    else if (e1->PolyTyp == e2->PolyTyp && e1->WindDelta != e2->WindDelta &&
+             m_ClipType == ctUnion) {
+      if (e1->WindDelta == 0) {
+        if (e2Contributing) {
+          AddOutPt(e1, Pt);
+          if (e1Contributing)
+            e1->OutIdx = Unassigned;
+        }
+      } else {
+        if (e1Contributing) {
+          AddOutPt(e2, Pt);
+          if (e2Contributing)
+            e2->OutIdx = Unassigned;
+        }
+      }
+    } else if (e1->PolyTyp != e2->PolyTyp) {
+      // toggle subj open path OutIdx on/off when Abs(clip.WndCnt) == 1 ...
+      if ((e1->WindDelta == 0) && abs(e2->WindCnt) == 1 &&
+          (m_ClipType != ctUnion || e2->WindCnt2 == 0)) {
+        AddOutPt(e1, Pt);
+        if (e1Contributing)
+          e1->OutIdx = Unassigned;
+      } else if ((e2->WindDelta == 0) && (abs(e1->WindCnt) == 1) &&
+                 (m_ClipType != ctUnion || e1->WindCnt2 == 0)) {
+        AddOutPt(e2, Pt);
+        if (e2Contributing)
+          e2->OutIdx = Unassigned;
+      }
+    }
+    return;
+  }
+#endif
+
+  // update winding counts...
+  // assumes that e1 will be to the Right of e2 ABOVE the intersection
+  if (e1->PolyTyp == e2->PolyTyp) {
+    if (IsEvenOddFillType(*e1)) {
+      int oldE1WindCnt = e1->WindCnt;
+      e1->WindCnt = e2->WindCnt;
+      e2->WindCnt = oldE1WindCnt;
+    } else {
+      if (e1->WindCnt + e2->WindDelta == 0)
+        e1->WindCnt = -e1->WindCnt;
+      else
+        e1->WindCnt += e2->WindDelta;
+      if (e2->WindCnt - e1->WindDelta == 0)
+        e2->WindCnt = -e2->WindCnt;
+      else
+        e2->WindCnt -= e1->WindDelta;
+    }
+  } else {
+    if (!IsEvenOddFillType(*e2))
+      e1->WindCnt2 += e2->WindDelta;
+    else
+      e1->WindCnt2 = (e1->WindCnt2 == 0) ? 1 : 0;
+    if (!IsEvenOddFillType(*e1))
+      e2->WindCnt2 -= e1->WindDelta;
+    else
+      e2->WindCnt2 = (e2->WindCnt2 == 0) ? 1 : 0;
+  }
+
+  PolyFillType e1FillType, e2FillType, e1FillType2, e2FillType2;
+  if (e1->PolyTyp == ptSubject) {
+    e1FillType = m_SubjFillType;
+    e1FillType2 = m_ClipFillType;
+  } else {
+    e1FillType = m_ClipFillType;
+    e1FillType2 = m_SubjFillType;
+  }
+  if (e2->PolyTyp == ptSubject) {
+    e2FillType = m_SubjFillType;
+    e2FillType2 = m_ClipFillType;
+  } else {
+    e2FillType = m_ClipFillType;
+    e2FillType2 = m_SubjFillType;
+  }
+
+  cInt e1Wc, e2Wc;
+  switch (e1FillType) {
+  case pftPositive:
+    e1Wc = e1->WindCnt;
+    break;
+  case pftNegative:
+    e1Wc = -e1->WindCnt;
+    break;
+  default:
+    e1Wc = Abs(e1->WindCnt);
+  }
+  switch (e2FillType) {
+  case pftPositive:
+    e2Wc = e2->WindCnt;
+    break;
+  case pftNegative:
+    e2Wc = -e2->WindCnt;
+    break;
+  default:
+    e2Wc = Abs(e2->WindCnt);
+  }
+
+  if (e1Contributing && e2Contributing) {
+    if ((e1Wc != 0 && e1Wc != 1) || (e2Wc != 0 && e2Wc != 1) ||
+        (e1->PolyTyp != e2->PolyTyp && m_ClipType != ctXor)) {
+      AddLocalMaxPoly(e1, e2, Pt);
+    } else {
+      AddOutPt(e1, Pt);
+      AddOutPt(e2, Pt);
+      SwapSides(*e1, *e2);
+      SwapPolyIndexes(*e1, *e2);
+    }
+  } else if (e1Contributing) {
+    if (e2Wc == 0 || e2Wc == 1) {
+      AddOutPt(e1, Pt);
+      SwapSides(*e1, *e2);
+      SwapPolyIndexes(*e1, *e2);
+    }
+  } else if (e2Contributing) {
+    if (e1Wc == 0 || e1Wc == 1) {
+      AddOutPt(e2, Pt);
+      SwapSides(*e1, *e2);
+      SwapPolyIndexes(*e1, *e2);
+    }
+  } else if ((e1Wc == 0 || e1Wc == 1) && (e2Wc == 0 || e2Wc == 1)) {
+    // neither edge is currently contributing ...
+
+    cInt e1Wc2, e2Wc2;
+    switch (e1FillType2) {
+    case pftPositive:
+      e1Wc2 = e1->WindCnt2;
+      break;
+    case pftNegative:
+      e1Wc2 = -e1->WindCnt2;
+      break;
+    default:
+      e1Wc2 = Abs(e1->WindCnt2);
+    }
+    switch (e2FillType2) {
+    case pftPositive:
+      e2Wc2 = e2->WindCnt2;
+      break;
+    case pftNegative:
+      e2Wc2 = -e2->WindCnt2;
+      break;
+    default:
+      e2Wc2 = Abs(e2->WindCnt2);
+    }
+
+    if (e1->PolyTyp != e2->PolyTyp) {
+      AddLocalMinPoly(e1, e2, Pt);
+    } else if (e1Wc == 1 && e2Wc == 1)
+      switch (m_ClipType) {
+      case ctIntersection:
+        if (e1Wc2 > 0 && e2Wc2 > 0)
+          AddLocalMinPoly(e1, e2, Pt);
+        break;
+      case ctUnion:
+        if (e1Wc2 <= 0 && e2Wc2 <= 0)
+          AddLocalMinPoly(e1, e2, Pt);
+        break;
+      case ctDifference:
+        if (((e1->PolyTyp == ptClip) && (e1Wc2 > 0) && (e2Wc2 > 0)) ||
+            ((e1->PolyTyp == ptSubject) && (e1Wc2 <= 0) && (e2Wc2 <= 0)))
+          AddLocalMinPoly(e1, e2, Pt);
+        break;
+      case ctXor:
+        AddLocalMinPoly(e1, e2, Pt);
+      }
+    else
+      SwapSides(*e1, *e2);
+  }
+}
+//------------------------------------------------------------------------------
+
+void Clipper::SetHoleState(TEdge *e, OutRec *outrec) {
+  TEdge *e2 = e->PrevInAEL;
+  TEdge *eTmp = 0;
+  while (e2) {
+    if (e2->OutIdx >= 0 && e2->WindDelta != 0) {
+      if (!eTmp)
+        eTmp = e2;
+      else if (eTmp->OutIdx == e2->OutIdx)
+        eTmp = 0;
+    }
+    e2 = e2->PrevInAEL;
+  }
+  if (!eTmp) {
+    outrec->FirstLeft = 0;
+    outrec->IsHole = false;
+  } else {
+    outrec->FirstLeft = m_PolyOuts[eTmp->OutIdx];
+    outrec->IsHole = !outrec->FirstLeft->IsHole;
+  }
+}
+//------------------------------------------------------------------------------
+
+OutRec *GetLowermostRec(OutRec *outRec1, OutRec *outRec2) {
+  // work out which polygon fragment has the correct hole state ...
+  if (!outRec1->BottomPt)
+    outRec1->BottomPt = GetBottomPt(outRec1->Pts);
+  if (!outRec2->BottomPt)
+    outRec2->BottomPt = GetBottomPt(outRec2->Pts);
+  OutPt *OutPt1 = outRec1->BottomPt;
+  OutPt *OutPt2 = outRec2->BottomPt;
+  if (OutPt1->Pt.Y > OutPt2->Pt.Y)
+    return outRec1;
+  else if (OutPt1->Pt.Y < OutPt2->Pt.Y)
+    return outRec2;
+  else if (OutPt1->Pt.X < OutPt2->Pt.X)
+    return outRec1;
+  else if (OutPt1->Pt.X > OutPt2->Pt.X)
+    return outRec2;
+  else if (OutPt1->Next == OutPt1)
+    return outRec2;
+  else if (OutPt2->Next == OutPt2)
+    return outRec1;
+  else if (FirstIsBottomPt(OutPt1, OutPt2))
+    return outRec1;
+  else
+    return outRec2;
+}
+//------------------------------------------------------------------------------
+
+bool OutRec1RightOfOutRec2(OutRec *outRec1, OutRec *outRec2) {
+  do {
+    outRec1 = outRec1->FirstLeft;
+    if (outRec1 == outRec2)
+      return true;
+  } while (outRec1);
+  return false;
+}
+//------------------------------------------------------------------------------
+
+OutRec *Clipper::GetOutRec(int Idx) {
+  OutRec *outrec = m_PolyOuts[Idx];
+  while (outrec != m_PolyOuts[outrec->Idx])
+    outrec = m_PolyOuts[outrec->Idx];
+  return outrec;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::AppendPolygon(TEdge *e1, TEdge *e2) {
+  // get the start and ends of both output polygons ...
+  OutRec *outRec1 = m_PolyOuts[e1->OutIdx];
+  OutRec *outRec2 = m_PolyOuts[e2->OutIdx];
+
+  OutRec *holeStateRec;
+  if (OutRec1RightOfOutRec2(outRec1, outRec2))
+    holeStateRec = outRec2;
+  else if (OutRec1RightOfOutRec2(outRec2, outRec1))
+    holeStateRec = outRec1;
+  else
+    holeStateRec = GetLowermostRec(outRec1, outRec2);
+
+  // get the start and ends of both output polygons and
+  // join e2 poly onto e1 poly and delete pointers to e2 ...
+
+  OutPt *p1_lft = outRec1->Pts;
+  OutPt *p1_rt = p1_lft->Prev;
+  OutPt *p2_lft = outRec2->Pts;
+  OutPt *p2_rt = p2_lft->Prev;
+
+  // join e2 poly onto e1 poly and delete pointers to e2 ...
+  if (e1->Side == esLeft) {
+    if (e2->Side == esLeft) {
+      // z y x a b c
+      ReversePolyPtLinks(p2_lft);
+      p2_lft->Next = p1_lft;
+      p1_lft->Prev = p2_lft;
+      p1_rt->Next = p2_rt;
+      p2_rt->Prev = p1_rt;
+      outRec1->Pts = p2_rt;
+    } else {
+      // x y z a b c
+      p2_rt->Next = p1_lft;
+      p1_lft->Prev = p2_rt;
+      p2_lft->Prev = p1_rt;
+      p1_rt->Next = p2_lft;
+      outRec1->Pts = p2_lft;
+    }
+  } else {
+    if (e2->Side == esRight) {
+      // a b c z y x
+      ReversePolyPtLinks(p2_lft);
+      p1_rt->Next = p2_rt;
+      p2_rt->Prev = p1_rt;
+      p2_lft->Next = p1_lft;
+      p1_lft->Prev = p2_lft;
+    } else {
+      // a b c x y z
+      p1_rt->Next = p2_lft;
+      p2_lft->Prev = p1_rt;
+      p1_lft->Prev = p2_rt;
+      p2_rt->Next = p1_lft;
+    }
+  }
+
+  outRec1->BottomPt = 0;
+  if (holeStateRec == outRec2) {
+    if (outRec2->FirstLeft != outRec1)
+      outRec1->FirstLeft = outRec2->FirstLeft;
+    outRec1->IsHole = outRec2->IsHole;
+  }
+  outRec2->Pts = 0;
+  outRec2->BottomPt = 0;
+  outRec2->FirstLeft = outRec1;
+
+  int OKIdx = e1->OutIdx;
+  int ObsoleteIdx = e2->OutIdx;
+
+  e1->OutIdx =
+      Unassigned; // nb: safe because we only get here via AddLocalMaxPoly
+  e2->OutIdx = Unassigned;
+
+  TEdge *e = m_ActiveEdges;
+  while (e) {
+    if (e->OutIdx == ObsoleteIdx) {
+      e->OutIdx = OKIdx;
+      e->Side = e1->Side;
+      break;
+    }
+    e = e->NextInAEL;
+  }
+
+  outRec2->Idx = outRec1->Idx;
+}
+//------------------------------------------------------------------------------
+
+OutPt *Clipper::AddOutPt(TEdge *e, const IntPoint &pt) {
+  if (e->OutIdx < 0) {
+    OutRec *outRec = CreateOutRec();
+    outRec->IsOpen = (e->WindDelta == 0);
+    OutPt *newOp = new OutPt;
+    outRec->Pts = newOp;
+    newOp->Idx = outRec->Idx;
+    newOp->Pt = pt;
+    newOp->Next = newOp;
+    newOp->Prev = newOp;
+    if (!outRec->IsOpen)
+      SetHoleState(e, outRec);
+    e->OutIdx = outRec->Idx;
+    return newOp;
+  } else {
+    OutRec *outRec = m_PolyOuts[e->OutIdx];
+    // OutRec.Pts is the 'Left-most' point & OutRec.Pts.Prev is the 'Right-most'
+    OutPt *op = outRec->Pts;
+
+    bool ToFront = (e->Side == esLeft);
+    if (ToFront && (pt == op->Pt))
+      return op;
+    else if (!ToFront && (pt == op->Prev->Pt))
+      return op->Prev;
+
+    OutPt *newOp = new OutPt;
+    newOp->Idx = outRec->Idx;
+    newOp->Pt = pt;
+    newOp->Next = op;
+    newOp->Prev = op->Prev;
+    newOp->Prev->Next = newOp;
+    op->Prev = newOp;
+    if (ToFront)
+      outRec->Pts = newOp;
+    return newOp;
+  }
+}
+//------------------------------------------------------------------------------
+
+OutPt *Clipper::GetLastOutPt(TEdge *e) {
+  OutRec *outRec = m_PolyOuts[e->OutIdx];
+  if (e->Side == esLeft)
+    return outRec->Pts;
+  else
+    return outRec->Pts->Prev;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::ProcessHorizontals() {
+  TEdge *horzEdge;
+  while (PopEdgeFromSEL(horzEdge))
+    ProcessHorizontal(horzEdge);
+}
+//------------------------------------------------------------------------------
+
+inline bool IsMinima(TEdge *e) {
+  return e && (e->Prev->NextInLML != e) && (e->Next->NextInLML != e);
+}
+//------------------------------------------------------------------------------
+
+inline bool IsMaxima(TEdge *e, const cInt Y) {
+  return e && e->Top.Y == Y && !e->NextInLML;
+}
+//------------------------------------------------------------------------------
+
+inline bool IsIntermediate(TEdge *e, const cInt Y) {
+  return e->Top.Y == Y && e->NextInLML;
+}
+//------------------------------------------------------------------------------
+
+TEdge *GetMaximaPair(TEdge *e) {
+  if ((e->Next->Top == e->Top) && !e->Next->NextInLML)
+    return e->Next;
+  else if ((e->Prev->Top == e->Top) && !e->Prev->NextInLML)
+    return e->Prev;
+  else
+    return 0;
+}
+//------------------------------------------------------------------------------
+
+TEdge *GetMaximaPairEx(TEdge *e) {
+  // as GetMaximaPair() but returns 0 if MaxPair isn't in AEL (unless it's
+  // horizontal)
+  TEdge *result = GetMaximaPair(e);
+  if (result &&
+      (result->OutIdx == Skip ||
+       (result->NextInAEL == result->PrevInAEL && !IsHorizontal(*result))))
+    return 0;
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::SwapPositionsInSEL(TEdge *Edge1, TEdge *Edge2) {
+  if (!(Edge1->NextInSEL) && !(Edge1->PrevInSEL))
+    return;
+  if (!(Edge2->NextInSEL) && !(Edge2->PrevInSEL))
+    return;
+
+  if (Edge1->NextInSEL == Edge2) {
+    TEdge *Next = Edge2->NextInSEL;
+    if (Next)
+      Next->PrevInSEL = Edge1;
+    TEdge *Prev = Edge1->PrevInSEL;
+    if (Prev)
+      Prev->NextInSEL = Edge2;
+    Edge2->PrevInSEL = Prev;
+    Edge2->NextInSEL = Edge1;
+    Edge1->PrevInSEL = Edge2;
+    Edge1->NextInSEL = Next;
+  } else if (Edge2->NextInSEL == Edge1) {
+    TEdge *Next = Edge1->NextInSEL;
+    if (Next)
+      Next->PrevInSEL = Edge2;
+    TEdge *Prev = Edge2->PrevInSEL;
+    if (Prev)
+      Prev->NextInSEL = Edge1;
+    Edge1->PrevInSEL = Prev;
+    Edge1->NextInSEL = Edge2;
+    Edge2->PrevInSEL = Edge1;
+    Edge2->NextInSEL = Next;
+  } else {
+    TEdge *Next = Edge1->NextInSEL;
+    TEdge *Prev = Edge1->PrevInSEL;
+    Edge1->NextInSEL = Edge2->NextInSEL;
+    if (Edge1->NextInSEL)
+      Edge1->NextInSEL->PrevInSEL = Edge1;
+    Edge1->PrevInSEL = Edge2->PrevInSEL;
+    if (Edge1->PrevInSEL)
+      Edge1->PrevInSEL->NextInSEL = Edge1;
+    Edge2->NextInSEL = Next;
+    if (Edge2->NextInSEL)
+      Edge2->NextInSEL->PrevInSEL = Edge2;
+    Edge2->PrevInSEL = Prev;
+    if (Edge2->PrevInSEL)
+      Edge2->PrevInSEL->NextInSEL = Edge2;
+  }
+
+  if (!Edge1->PrevInSEL)
+    m_SortedEdges = Edge1;
+  else if (!Edge2->PrevInSEL)
+    m_SortedEdges = Edge2;
+}
+//------------------------------------------------------------------------------
+
+TEdge *GetNextInAEL(TEdge *e, Direction dir) {
+  return dir == dLeftToRight ? e->NextInAEL : e->PrevInAEL;
+}
+//------------------------------------------------------------------------------
+
+void GetHorzDirection(TEdge &HorzEdge, Direction &Dir, cInt &Left,
+                      cInt &Right) {
+  if (HorzEdge.Bot.X < HorzEdge.Top.X) {
+    Left = HorzEdge.Bot.X;
+    Right = HorzEdge.Top.X;
+    Dir = dLeftToRight;
+  } else {
+    Left = HorzEdge.Top.X;
+    Right = HorzEdge.Bot.X;
+    Dir = dRightToLeft;
+  }
+}
+//------------------------------------------------------------------------
+
+/*******************************************************************************
+* Notes: Horizontal edges (HEs) at scanline intersections (ie at the Top or    *
+* Bottom of a scanbeam) are processed as if layered. The order in which HEs    *
+* are processed doesn't matter. HEs intersect with other HE Bot.Xs only [#]    *
+* (or they could intersect with Top.Xs only, ie EITHER Bot.Xs OR Top.Xs),      *
+* and with other non-horizontal edges [*]. Once these intersections are        *
+* processed, intermediate HEs then 'promote' the Edge above (NextInLML) into   *
+* the AEL. These 'promoted' edges may in turn intersect [%] with other HEs.    *
+*******************************************************************************/
+
+void Clipper::ProcessHorizontal(TEdge *horzEdge) {
+  Direction dir;
+  cInt horzLeft, horzRight;
+  bool IsOpen = (horzEdge->WindDelta == 0);
+
+  GetHorzDirection(*horzEdge, dir, horzLeft, horzRight);
+
+  TEdge *eLastHorz = horzEdge, *eMaxPair = 0;
+  while (eLastHorz->NextInLML && IsHorizontal(*eLastHorz->NextInLML))
+    eLastHorz = eLastHorz->NextInLML;
+  if (!eLastHorz->NextInLML)
+    eMaxPair = GetMaximaPair(eLastHorz);
+
+  MaximaList::const_iterator maxIt;
+  MaximaList::const_reverse_iterator maxRit;
+  if (m_Maxima.size() > 0) {
+    // get the first maxima in range (X) ...
+    if (dir == dLeftToRight) {
+      maxIt = m_Maxima.begin();
+      while (maxIt != m_Maxima.end() && *maxIt <= horzEdge->Bot.X)
+        maxIt++;
+      if (maxIt != m_Maxima.end() && *maxIt >= eLastHorz->Top.X)
+        maxIt = m_Maxima.end();
+    } else {
+      maxRit = m_Maxima.rbegin();
+      while (maxRit != m_Maxima.rend() && *maxRit > horzEdge->Bot.X)
+        maxRit++;
+      if (maxRit != m_Maxima.rend() && *maxRit <= eLastHorz->Top.X)
+        maxRit = m_Maxima.rend();
+    }
+  }
+
+  OutPt *op1 = 0;
+
+  for (;;) // loop through consec. horizontal edges
+  {
+
+    bool IsLastHorz = (horzEdge == eLastHorz);
+    TEdge *e = GetNextInAEL(horzEdge, dir);
+    while (e) {
+
+      // this code block inserts extra coords into horizontal edges (in output
+      // polygons) whereever maxima touch these horizontal edges. This helps
+      //'simplifying' polygons (ie if the Simplify property is set).
+      if (m_Maxima.size() > 0) {
+        if (dir == dLeftToRight) {
+          while (maxIt != m_Maxima.end() && *maxIt < e->Curr.X) {
+            if (horzEdge->OutIdx >= 0 && !IsOpen)
+              AddOutPt(horzEdge, IntPoint(*maxIt, horzEdge->Bot.Y));
+            maxIt++;
+          }
+        } else {
+          while (maxRit != m_Maxima.rend() && *maxRit > e->Curr.X) {
+            if (horzEdge->OutIdx >= 0 && !IsOpen)
+              AddOutPt(horzEdge, IntPoint(*maxRit, horzEdge->Bot.Y));
+            maxRit++;
+          }
+        }
+      };
+
+      if ((dir == dLeftToRight && e->Curr.X > horzRight) ||
+          (dir == dRightToLeft && e->Curr.X < horzLeft))
+        break;
+
+      // Also break if we've got to the end of an intermediate horizontal edge
+      // ...
+      // nb: Smaller Dx's are to the right of larger Dx's ABOVE the horizontal.
+      if (e->Curr.X == horzEdge->Top.X && horzEdge->NextInLML &&
+          e->Dx < horzEdge->NextInLML->Dx)
+        break;
+
+      if (horzEdge->OutIdx >= 0 && !IsOpen) // note: may be done multiple times
+      {
+#ifdef use_xyz
+        if (dir == dLeftToRight)
+          SetZ(e->Curr, *horzEdge, *e);
+        else
+          SetZ(e->Curr, *e, *horzEdge);
+#endif
+        op1 = AddOutPt(horzEdge, e->Curr);
+        TEdge *eNextHorz = m_SortedEdges;
+        while (eNextHorz) {
+          if (eNextHorz->OutIdx >= 0 &&
+              HorzSegmentsOverlap(horzEdge->Bot.X, horzEdge->Top.X,
+                                  eNextHorz->Bot.X, eNextHorz->Top.X)) {
+            OutPt *op2 = GetLastOutPt(eNextHorz);
+            AddJoin(op2, op1, eNextHorz->Top);
+          }
+          eNextHorz = eNextHorz->NextInSEL;
+        }
+        AddGhostJoin(op1, horzEdge->Bot);
+      }
+
+      // OK, so far we're still in range of the horizontal Edge  but make sure
+      // we're at the last of consec. horizontals when matching with eMaxPair
+      if (e == eMaxPair && IsLastHorz) {
+        if (horzEdge->OutIdx >= 0)
+          AddLocalMaxPoly(horzEdge, eMaxPair, horzEdge->Top);
+        DeleteFromAEL(horzEdge);
+        DeleteFromAEL(eMaxPair);
+        return;
+      }
+
+      if (dir == dLeftToRight) {
+        IntPoint Pt = IntPoint(e->Curr.X, horzEdge->Curr.Y);
+        IntersectEdges(horzEdge, e, Pt);
+      } else {
+        IntPoint Pt = IntPoint(e->Curr.X, horzEdge->Curr.Y);
+        IntersectEdges(e, horzEdge, Pt);
+      }
+      TEdge *eNext = GetNextInAEL(e, dir);
+      SwapPositionsInAEL(horzEdge, e);
+      e = eNext;
+    } // end while(e)
+
+    // Break out of loop if HorzEdge.NextInLML is not also horizontal ...
+    if (!horzEdge->NextInLML || !IsHorizontal(*horzEdge->NextInLML))
+      break;
+
+    UpdateEdgeIntoAEL(horzEdge);
+    if (horzEdge->OutIdx >= 0)
+      AddOutPt(horzEdge, horzEdge->Bot);
+    GetHorzDirection(*horzEdge, dir, horzLeft, horzRight);
+
+  } // end for (;;)
+
+  if (horzEdge->OutIdx >= 0 && !op1) {
+    op1 = GetLastOutPt(horzEdge);
+    TEdge *eNextHorz = m_SortedEdges;
+    while (eNextHorz) {
+      if (eNextHorz->OutIdx >= 0 &&
+          HorzSegmentsOverlap(horzEdge->Bot.X, horzEdge->Top.X,
+                              eNextHorz->Bot.X, eNextHorz->Top.X)) {
+        OutPt *op2 = GetLastOutPt(eNextHorz);
+        AddJoin(op2, op1, eNextHorz->Top);
+      }
+      eNextHorz = eNextHorz->NextInSEL;
+    }
+    AddGhostJoin(op1, horzEdge->Top);
+  }
+
+  if (horzEdge->NextInLML) {
+    if (horzEdge->OutIdx >= 0) {
+      op1 = AddOutPt(horzEdge, horzEdge->Top);
+      UpdateEdgeIntoAEL(horzEdge);
+      if (horzEdge->WindDelta == 0)
+        return;
+      // nb: HorzEdge is no longer horizontal here
+      TEdge *ePrev = horzEdge->PrevInAEL;
+      TEdge *eNext = horzEdge->NextInAEL;
+      if (ePrev && ePrev->Curr.X == horzEdge->Bot.X &&
+          ePrev->Curr.Y == horzEdge->Bot.Y && ePrev->WindDelta != 0 &&
+          (ePrev->OutIdx >= 0 && ePrev->Curr.Y > ePrev->Top.Y &&
+           SlopesEqual(*horzEdge, *ePrev, m_UseFullRange))) {
+        OutPt *op2 = AddOutPt(ePrev, horzEdge->Bot);
+        AddJoin(op1, op2, horzEdge->Top);
+      } else if (eNext && eNext->Curr.X == horzEdge->Bot.X &&
+                 eNext->Curr.Y == horzEdge->Bot.Y && eNext->WindDelta != 0 &&
+                 eNext->OutIdx >= 0 && eNext->Curr.Y > eNext->Top.Y &&
+                 SlopesEqual(*horzEdge, *eNext, m_UseFullRange)) {
+        OutPt *op2 = AddOutPt(eNext, horzEdge->Bot);
+        AddJoin(op1, op2, horzEdge->Top);
+      }
+    } else
+      UpdateEdgeIntoAEL(horzEdge);
+  } else {
+    if (horzEdge->OutIdx >= 0)
+      AddOutPt(horzEdge, horzEdge->Top);
+    DeleteFromAEL(horzEdge);
+  }
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::ProcessIntersections(const cInt topY) {
+  if (!m_ActiveEdges)
+    return true;
+  try {
+    BuildIntersectList(topY);
+    size_t IlSize = m_IntersectList.size();
+    if (IlSize == 0)
+      return true;
+    if (IlSize == 1 || FixupIntersectionOrder())
+      ProcessIntersectList();
+    else
+      return false;
+  } catch (...) {
+    m_SortedEdges = 0;
+    DisposeIntersectNodes();
+    throw clipperException("ProcessIntersections error");
+  }
+  m_SortedEdges = 0;
+  return true;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::DisposeIntersectNodes() {
+  for (size_t i = 0; i < m_IntersectList.size(); ++i)
+    delete m_IntersectList[i];
+  m_IntersectList.clear();
+}
+//------------------------------------------------------------------------------
+
+void Clipper::BuildIntersectList(const cInt topY) {
+  if (!m_ActiveEdges)
+    return;
+
+  // prepare for sorting ...
+  TEdge *e = m_ActiveEdges;
+  m_SortedEdges = e;
+  while (e) {
+    e->PrevInSEL = e->PrevInAEL;
+    e->NextInSEL = e->NextInAEL;
+    e->Curr.X = TopX(*e, topY);
+    e = e->NextInAEL;
+  }
+
+  // bubblesort ...
+  bool isModified;
+  do {
+    isModified = false;
+    e = m_SortedEdges;
+    while (e->NextInSEL) {
+      TEdge *eNext = e->NextInSEL;
+      IntPoint Pt;
+      if (e->Curr.X > eNext->Curr.X) {
+        IntersectPoint(*e, *eNext, Pt);
+        if (Pt.Y < topY)
+          Pt = IntPoint(TopX(*e, topY), topY);
+        IntersectNode *newNode = new IntersectNode;
+        newNode->Edge1 = e;
+        newNode->Edge2 = eNext;
+        newNode->Pt = Pt;
+        m_IntersectList.push_back(newNode);
+
+        SwapPositionsInSEL(e, eNext);
+        isModified = true;
+      } else
+        e = eNext;
+    }
+    if (e->PrevInSEL)
+      e->PrevInSEL->NextInSEL = 0;
+    else
+      break;
+  } while (isModified);
+  m_SortedEdges = 0; // important
+}
+//------------------------------------------------------------------------------
+
+void Clipper::ProcessIntersectList() {
+  for (size_t i = 0; i < m_IntersectList.size(); ++i) {
+    IntersectNode *iNode = m_IntersectList[i];
+    {
+      IntersectEdges(iNode->Edge1, iNode->Edge2, iNode->Pt);
+      SwapPositionsInAEL(iNode->Edge1, iNode->Edge2);
+    }
+    delete iNode;
+  }
+  m_IntersectList.clear();
+}
+//------------------------------------------------------------------------------
+
+bool IntersectListSort(IntersectNode *node1, IntersectNode *node2) {
+  return node2->Pt.Y < node1->Pt.Y;
+}
+//------------------------------------------------------------------------------
+
+inline bool EdgesAdjacent(const IntersectNode &inode) {
+  return (inode.Edge1->NextInSEL == inode.Edge2) ||
+         (inode.Edge1->PrevInSEL == inode.Edge2);
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::FixupIntersectionOrder() {
+  // pre-condition: intersections are sorted Bottom-most first.
+  // Now it's crucial that intersections are made only between adjacent edges,
+  // so to ensure this the order of intersections may need adjusting ...
+  CopyAELToSEL();
+  std::sort(m_IntersectList.begin(), m_IntersectList.end(), IntersectListSort);
+  size_t cnt = m_IntersectList.size();
+  for (size_t i = 0; i < cnt; ++i) {
+    if (!EdgesAdjacent(*m_IntersectList[i])) {
+      size_t j = i + 1;
+      while (j < cnt && !EdgesAdjacent(*m_IntersectList[j]))
+        j++;
+      if (j == cnt)
+        return false;
+      std::swap(m_IntersectList[i], m_IntersectList[j]);
+    }
+    SwapPositionsInSEL(m_IntersectList[i]->Edge1, m_IntersectList[i]->Edge2);
+  }
+  return true;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::DoMaxima(TEdge *e) {
+  TEdge *eMaxPair = GetMaximaPairEx(e);
+  if (!eMaxPair) {
+    if (e->OutIdx >= 0)
+      AddOutPt(e, e->Top);
+    DeleteFromAEL(e);
+    return;
+  }
+
+  TEdge *eNext = e->NextInAEL;
+  while (eNext && eNext != eMaxPair) {
+    IntersectEdges(e, eNext, e->Top);
+    SwapPositionsInAEL(e, eNext);
+    eNext = e->NextInAEL;
+  }
+
+  if (e->OutIdx == Unassigned && eMaxPair->OutIdx == Unassigned) {
+    DeleteFromAEL(e);
+    DeleteFromAEL(eMaxPair);
+  } else if (e->OutIdx >= 0 && eMaxPair->OutIdx >= 0) {
+    if (e->OutIdx >= 0)
+      AddLocalMaxPoly(e, eMaxPair, e->Top);
+    DeleteFromAEL(e);
+    DeleteFromAEL(eMaxPair);
+  }
+#ifdef use_lines
+  else if (e->WindDelta == 0) {
+    if (e->OutIdx >= 0) {
+      AddOutPt(e, e->Top);
+      e->OutIdx = Unassigned;
+    }
+    DeleteFromAEL(e);
+
+    if (eMaxPair->OutIdx >= 0) {
+      AddOutPt(eMaxPair, e->Top);
+      eMaxPair->OutIdx = Unassigned;
+    }
+    DeleteFromAEL(eMaxPair);
+  }
+#endif
+  else
+    throw clipperException("DoMaxima error");
+}
+//------------------------------------------------------------------------------
+
+void Clipper::ProcessEdgesAtTopOfScanbeam(const cInt topY) {
+  TEdge *e = m_ActiveEdges;
+  while (e) {
+    // 1. process maxima, treating them as if they're 'bent' horizontal edges,
+    //   but exclude maxima with horizontal edges. nb: e can't be a horizontal.
+    bool IsMaximaEdge = IsMaxima(e, topY);
+
+    if (IsMaximaEdge) {
+      TEdge *eMaxPair = GetMaximaPairEx(e);
+      IsMaximaEdge = (!eMaxPair || !IsHorizontal(*eMaxPair));
+    }
+
+    if (IsMaximaEdge) {
+      if (m_StrictSimple)
+        m_Maxima.push_back(e->Top.X);
+      TEdge *ePrev = e->PrevInAEL;
+      DoMaxima(e);
+      if (!ePrev)
+        e = m_ActiveEdges;
+      else
+        e = ePrev->NextInAEL;
+    } else {
+      // 2. promote horizontal edges, otherwise update Curr.X and Curr.Y ...
+      if (IsIntermediate(e, topY) && IsHorizontal(*e->NextInLML)) {
+        UpdateEdgeIntoAEL(e);
+        if (e->OutIdx >= 0)
+          AddOutPt(e, e->Bot);
+        AddEdgeToSEL(e);
+      } else {
+        e->Curr.X = TopX(*e, topY);
+        e->Curr.Y = topY;
+#ifdef use_xyz
+        e->Curr.Z =
+            topY == e->Top.Y ? e->Top.Z : (topY == e->Bot.Y ? e->Bot.Z : 0);
+#endif
+      }
+
+      // When StrictlySimple and 'e' is being touched by another edge, then
+      // make sure both edges have a vertex here ...
+      if (m_StrictSimple) {
+        TEdge *ePrev = e->PrevInAEL;
+        if ((e->OutIdx >= 0) && (e->WindDelta != 0) && ePrev &&
+            (ePrev->OutIdx >= 0) && (ePrev->Curr.X == e->Curr.X) &&
+            (ePrev->WindDelta != 0)) {
+          IntPoint pt = e->Curr;
+#ifdef use_xyz
+          SetZ(pt, *ePrev, *e);
+#endif
+          OutPt *op = AddOutPt(ePrev, pt);
+          OutPt *op2 = AddOutPt(e, pt);
+          AddJoin(op, op2, pt); // StrictlySimple (type-3) join
+        }
+      }
+
+      e = e->NextInAEL;
+    }
+  }
+
+  // 3. Process horizontals at the Top of the scanbeam ...
+  m_Maxima.sort();
+  ProcessHorizontals();
+  m_Maxima.clear();
+
+  // 4. Promote intermediate vertices ...
+  e = m_ActiveEdges;
+  while (e) {
+    if (IsIntermediate(e, topY)) {
+      OutPt *op = 0;
+      if (e->OutIdx >= 0)
+        op = AddOutPt(e, e->Top);
+      UpdateEdgeIntoAEL(e);
+
+      // if output polygons share an edge, they'll need joining later ...
+      TEdge *ePrev = e->PrevInAEL;
+      TEdge *eNext = e->NextInAEL;
+      if (ePrev && ePrev->Curr.X == e->Bot.X && ePrev->Curr.Y == e->Bot.Y &&
+          op && ePrev->OutIdx >= 0 && ePrev->Curr.Y > ePrev->Top.Y &&
+          SlopesEqual(e->Curr, e->Top, ePrev->Curr, ePrev->Top,
+                      m_UseFullRange) &&
+          (e->WindDelta != 0) && (ePrev->WindDelta != 0)) {
+        OutPt *op2 = AddOutPt(ePrev, e->Bot);
+        AddJoin(op, op2, e->Top);
+      } else if (eNext && eNext->Curr.X == e->Bot.X &&
+                 eNext->Curr.Y == e->Bot.Y && op && eNext->OutIdx >= 0 &&
+                 eNext->Curr.Y > eNext->Top.Y &&
+                 SlopesEqual(e->Curr, e->Top, eNext->Curr, eNext->Top,
+                             m_UseFullRange) &&
+                 (e->WindDelta != 0) && (eNext->WindDelta != 0)) {
+        OutPt *op2 = AddOutPt(eNext, e->Bot);
+        AddJoin(op, op2, e->Top);
+      }
+    }
+    e = e->NextInAEL;
+  }
+}
+//------------------------------------------------------------------------------
+
+void Clipper::FixupOutPolyline(OutRec &outrec) {
+  OutPt *pp = outrec.Pts;
+  OutPt *lastPP = pp->Prev;
+  while (pp != lastPP) {
+    pp = pp->Next;
+    if (pp->Pt == pp->Prev->Pt) {
+      if (pp == lastPP)
+        lastPP = pp->Prev;
+      OutPt *tmpPP = pp->Prev;
+      tmpPP->Next = pp->Next;
+      pp->Next->Prev = tmpPP;
+      delete pp;
+      pp = tmpPP;
+    }
+  }
+
+  if (pp == pp->Prev) {
+    DisposeOutPts(pp);
+    outrec.Pts = 0;
+    return;
+  }
+}
+//------------------------------------------------------------------------------
+
+void Clipper::FixupOutPolygon(OutRec &outrec) {
+  // FixupOutPolygon() - removes duplicate points and simplifies consecutive
+  // parallel edges by removing the middle vertex.
+  OutPt *lastOK = 0;
+  outrec.BottomPt = 0;
+  OutPt *pp = outrec.Pts;
+  bool preserveCol = m_PreserveCollinear || m_StrictSimple;
+
+  for (;;) {
+    if (pp->Prev == pp || pp->Prev == pp->Next) {
+      DisposeOutPts(pp);
+      outrec.Pts = 0;
+      return;
+    }
+
+    // test for duplicate points and collinear edges ...
+    if ((pp->Pt == pp->Next->Pt) || (pp->Pt == pp->Prev->Pt) ||
+        (SlopesEqual(pp->Prev->Pt, pp->Pt, pp->Next->Pt, m_UseFullRange) &&
+         (!preserveCol ||
+          !Pt2IsBetweenPt1AndPt3(pp->Prev->Pt, pp->Pt, pp->Next->Pt)))) {
+      lastOK = 0;
+      OutPt *tmp = pp;
+      pp->Prev->Next = pp->Next;
+      pp->Next->Prev = pp->Prev;
+      pp = pp->Prev;
+      delete tmp;
+    } else if (pp == lastOK)
+      break;
+    else {
+      if (!lastOK)
+        lastOK = pp;
+      pp = pp->Next;
+    }
+  }
+  outrec.Pts = pp;
+}
+//------------------------------------------------------------------------------
+
+int PointCount(OutPt *Pts) {
+  if (!Pts)
+    return 0;
+  int result = 0;
+  OutPt *p = Pts;
+  do {
+    result++;
+    p = p->Next;
+  } while (p != Pts);
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::BuildResult(Paths &polys) {
+  polys.reserve(m_PolyOuts.size());
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i) {
+    if (!m_PolyOuts[i]->Pts)
+      continue;
+    Path pg;
+    OutPt *p = m_PolyOuts[i]->Pts->Prev;
+    int cnt = PointCount(p);
+    if (cnt < 2)
+      continue;
+    pg.reserve(cnt);
+    for (int i = 0; i < cnt; ++i) {
+      pg.push_back(p->Pt);
+      p = p->Prev;
+    }
+    polys.push_back(pg);
+  }
+}
+//------------------------------------------------------------------------------
+
+void Clipper::BuildResult2(PolyTree &polytree) {
+  polytree.Clear();
+  polytree.AllNodes.reserve(m_PolyOuts.size());
+  // add each output polygon/contour to polytree ...
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); i++) {
+    OutRec *outRec = m_PolyOuts[i];
+    int cnt = PointCount(outRec->Pts);
+    if ((outRec->IsOpen && cnt < 2) || (!outRec->IsOpen && cnt < 3))
+      continue;
+    FixHoleLinkage(*outRec);
+    PolyNode *pn = new PolyNode();
+    // nb: polytree takes ownership of all the PolyNodes
+    polytree.AllNodes.push_back(pn);
+    outRec->PolyNd = pn;
+    pn->Parent = 0;
+    pn->Index = 0;
+    pn->Contour.reserve(cnt);
+    OutPt *op = outRec->Pts->Prev;
+    for (int j = 0; j < cnt; j++) {
+      pn->Contour.push_back(op->Pt);
+      op = op->Prev;
+    }
+  }
+
+  // fixup PolyNode links etc ...
+  polytree.Childs.reserve(m_PolyOuts.size());
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); i++) {
+    OutRec *outRec = m_PolyOuts[i];
+    if (!outRec->PolyNd)
+      continue;
+    if (outRec->IsOpen) {
+      outRec->PolyNd->m_IsOpen = true;
+      polytree.AddChild(*outRec->PolyNd);
+    } else if (outRec->FirstLeft && outRec->FirstLeft->PolyNd)
+      outRec->FirstLeft->PolyNd->AddChild(*outRec->PolyNd);
+    else
+      polytree.AddChild(*outRec->PolyNd);
+  }
+}
+//------------------------------------------------------------------------------
+
+void SwapIntersectNodes(IntersectNode &int1, IntersectNode &int2) {
+  // just swap the contents (because fIntersectNodes is a single-linked-list)
+  IntersectNode inode = int1; // gets a copy of Int1
+  int1.Edge1 = int2.Edge1;
+  int1.Edge2 = int2.Edge2;
+  int1.Pt = int2.Pt;
+  int2.Edge1 = inode.Edge1;
+  int2.Edge2 = inode.Edge2;
+  int2.Pt = inode.Pt;
+}
+//------------------------------------------------------------------------------
+
+inline bool E2InsertsBeforeE1(TEdge &e1, TEdge &e2) {
+  if (e2.Curr.X == e1.Curr.X) {
+    if (e2.Top.Y > e1.Top.Y)
+      return e2.Top.X < TopX(e1, e2.Top.Y);
+    else
+      return e1.Top.X > TopX(e2, e1.Top.Y);
+  } else
+    return e2.Curr.X < e1.Curr.X;
+}
+//------------------------------------------------------------------------------
+
+bool GetOverlap(const cInt a1, const cInt a2, const cInt b1, const cInt b2,
+                cInt &Left, cInt &Right) {
+  if (a1 < a2) {
+    if (b1 < b2) {
+      Left = std::max(a1, b1);
+      Right = std::min(a2, b2);
+    } else {
+      Left = std::max(a1, b2);
+      Right = std::min(a2, b1);
+    }
+  } else {
+    if (b1 < b2) {
+      Left = std::max(a2, b1);
+      Right = std::min(a1, b2);
+    } else {
+      Left = std::max(a2, b2);
+      Right = std::min(a1, b1);
+    }
+  }
+  return Left < Right;
+}
+//------------------------------------------------------------------------------
+
+inline void UpdateOutPtIdxs(OutRec &outrec) {
+  OutPt *op = outrec.Pts;
+  do {
+    op->Idx = outrec.Idx;
+    op = op->Prev;
+  } while (op != outrec.Pts);
+}
+//------------------------------------------------------------------------------
+
+void Clipper::InsertEdgeIntoAEL(TEdge *edge, TEdge *startEdge) {
+  if (!m_ActiveEdges) {
+    edge->PrevInAEL = 0;
+    edge->NextInAEL = 0;
+    m_ActiveEdges = edge;
+  } else if (!startEdge && E2InsertsBeforeE1(*m_ActiveEdges, *edge)) {
+    edge->PrevInAEL = 0;
+    edge->NextInAEL = m_ActiveEdges;
+    m_ActiveEdges->PrevInAEL = edge;
+    m_ActiveEdges = edge;
+  } else {
+    if (!startEdge)
+      startEdge = m_ActiveEdges;
+    while (startEdge->NextInAEL &&
+           !E2InsertsBeforeE1(*startEdge->NextInAEL, *edge))
+      startEdge = startEdge->NextInAEL;
+    edge->NextInAEL = startEdge->NextInAEL;
+    if (startEdge->NextInAEL)
+      startEdge->NextInAEL->PrevInAEL = edge;
+    edge->PrevInAEL = startEdge;
+    startEdge->NextInAEL = edge;
+  }
+}
+//----------------------------------------------------------------------
+
+OutPt *DupOutPt(OutPt *outPt, bool InsertAfter) {
+  OutPt *result = new OutPt;
+  result->Pt = outPt->Pt;
+  result->Idx = outPt->Idx;
+  if (InsertAfter) {
+    result->Next = outPt->Next;
+    result->Prev = outPt;
+    outPt->Next->Prev = result;
+    outPt->Next = result;
+  } else {
+    result->Prev = outPt->Prev;
+    result->Next = outPt;
+    outPt->Prev->Next = result;
+    outPt->Prev = result;
+  }
+  return result;
+}
+//------------------------------------------------------------------------------
+
+bool JoinHorz(OutPt *op1, OutPt *op1b, OutPt *op2, OutPt *op2b,
+              const IntPoint Pt, bool DiscardLeft) {
+  Direction Dir1 = (op1->Pt.X > op1b->Pt.X ? dRightToLeft : dLeftToRight);
+  Direction Dir2 = (op2->Pt.X > op2b->Pt.X ? dRightToLeft : dLeftToRight);
+  if (Dir1 == Dir2)
+    return false;
+
+  // When DiscardLeft, we want Op1b to be on the Left of Op1, otherwise we
+  // want Op1b to be on the Right. (And likewise with Op2 and Op2b.)
+  // So, to facilitate this while inserting Op1b and Op2b ...
+  // when DiscardLeft, make sure we're AT or RIGHT of Pt before adding Op1b,
+  // otherwise make sure we're AT or LEFT of Pt. (Likewise with Op2b.)
+  if (Dir1 == dLeftToRight) {
+    while (op1->Next->Pt.X <= Pt.X && op1->Next->Pt.X >= op1->Pt.X &&
+           op1->Next->Pt.Y == Pt.Y)
+      op1 = op1->Next;
+    if (DiscardLeft && (op1->Pt.X != Pt.X))
+      op1 = op1->Next;
+    op1b = DupOutPt(op1, !DiscardLeft);
+    if (op1b->Pt != Pt) {
+      op1 = op1b;
+      op1->Pt = Pt;
+      op1b = DupOutPt(op1, !DiscardLeft);
+    }
+  } else {
+    while (op1->Next->Pt.X >= Pt.X && op1->Next->Pt.X <= op1->Pt.X &&
+           op1->Next->Pt.Y == Pt.Y)
+      op1 = op1->Next;
+    if (!DiscardLeft && (op1->Pt.X != Pt.X))
+      op1 = op1->Next;
+    op1b = DupOutPt(op1, DiscardLeft);
+    if (op1b->Pt != Pt) {
+      op1 = op1b;
+      op1->Pt = Pt;
+      op1b = DupOutPt(op1, DiscardLeft);
+    }
+  }
+
+  if (Dir2 == dLeftToRight) {
+    while (op2->Next->Pt.X <= Pt.X && op2->Next->Pt.X >= op2->Pt.X &&
+           op2->Next->Pt.Y == Pt.Y)
+      op2 = op2->Next;
+    if (DiscardLeft && (op2->Pt.X != Pt.X))
+      op2 = op2->Next;
+    op2b = DupOutPt(op2, !DiscardLeft);
+    if (op2b->Pt != Pt) {
+      op2 = op2b;
+      op2->Pt = Pt;
+      op2b = DupOutPt(op2, !DiscardLeft);
+    };
+  } else {
+    while (op2->Next->Pt.X >= Pt.X && op2->Next->Pt.X <= op2->Pt.X &&
+           op2->Next->Pt.Y == Pt.Y)
+      op2 = op2->Next;
+    if (!DiscardLeft && (op2->Pt.X != Pt.X))
+      op2 = op2->Next;
+    op2b = DupOutPt(op2, DiscardLeft);
+    if (op2b->Pt != Pt) {
+      op2 = op2b;
+      op2->Pt = Pt;
+      op2b = DupOutPt(op2, DiscardLeft);
+    };
+  };
+
+  if ((Dir1 == dLeftToRight) == DiscardLeft) {
+    op1->Prev = op2;
+    op2->Next = op1;
+    op1b->Next = op2b;
+    op2b->Prev = op1b;
+  } else {
+    op1->Next = op2;
+    op2->Prev = op1;
+    op1b->Prev = op2b;
+    op2b->Next = op1b;
+  }
+  return true;
+}
+//------------------------------------------------------------------------------
+
+bool Clipper::JoinPoints(Join *j, OutRec *outRec1, OutRec *outRec2) {
+  OutPt *op1 = j->OutPt1, *op1b;
+  OutPt *op2 = j->OutPt2, *op2b;
+
+  // There are 3 kinds of joins for output polygons ...
+  // 1. Horizontal joins where Join.OutPt1 & Join.OutPt2 are vertices anywhere
+  // along (horizontal) collinear edges (& Join.OffPt is on the same
+  // horizontal).
+  // 2. Non-horizontal joins where Join.OutPt1 & Join.OutPt2 are at the same
+  // location at the Bottom of the overlapping segment (& Join.OffPt is above).
+  // 3. StrictSimple joins where edges touch but are not collinear and where
+  // Join.OutPt1, Join.OutPt2 & Join.OffPt all share the same point.
+  bool isHorizontal = (j->OutPt1->Pt.Y == j->OffPt.Y);
+
+  if (isHorizontal && (j->OffPt == j->OutPt1->Pt) &&
+      (j->OffPt == j->OutPt2->Pt)) {
+    // Strictly Simple join ...
+    if (outRec1 != outRec2)
+      return false;
+    op1b = j->OutPt1->Next;
+    while (op1b != op1 && (op1b->Pt == j->OffPt))
+      op1b = op1b->Next;
+    bool reverse1 = (op1b->Pt.Y > j->OffPt.Y);
+    op2b = j->OutPt2->Next;
+    while (op2b != op2 && (op2b->Pt == j->OffPt))
+      op2b = op2b->Next;
+    bool reverse2 = (op2b->Pt.Y > j->OffPt.Y);
+    if (reverse1 == reverse2)
+      return false;
+    if (reverse1) {
+      op1b = DupOutPt(op1, false);
+      op2b = DupOutPt(op2, true);
+      op1->Prev = op2;
+      op2->Next = op1;
+      op1b->Next = op2b;
+      op2b->Prev = op1b;
+      j->OutPt1 = op1;
+      j->OutPt2 = op1b;
+      return true;
+    } else {
+      op1b = DupOutPt(op1, true);
+      op2b = DupOutPt(op2, false);
+      op1->Next = op2;
+      op2->Prev = op1;
+      op1b->Prev = op2b;
+      op2b->Next = op1b;
+      j->OutPt1 = op1;
+      j->OutPt2 = op1b;
+      return true;
+    }
+  } else if (isHorizontal) {
+    // treat horizontal joins differently to non-horizontal joins since with
+    // them we're not yet sure where the overlapping is. OutPt1.Pt & OutPt2.Pt
+    // may be anywhere along the horizontal edge.
+    op1b = op1;
+    while (op1->Prev->Pt.Y == op1->Pt.Y && op1->Prev != op1b &&
+           op1->Prev != op2)
+      op1 = op1->Prev;
+    while (op1b->Next->Pt.Y == op1b->Pt.Y && op1b->Next != op1 &&
+           op1b->Next != op2)
+      op1b = op1b->Next;
+    if (op1b->Next == op1 || op1b->Next == op2)
+      return false; // a flat 'polygon'
+
+    op2b = op2;
+    while (op2->Prev->Pt.Y == op2->Pt.Y && op2->Prev != op2b &&
+           op2->Prev != op1b)
+      op2 = op2->Prev;
+    while (op2b->Next->Pt.Y == op2b->Pt.Y && op2b->Next != op2 &&
+           op2b->Next != op1)
+      op2b = op2b->Next;
+    if (op2b->Next == op2 || op2b->Next == op1)
+      return false; // a flat 'polygon'
+
+    cInt Left, Right;
+    // Op1 --> Op1b & Op2 --> Op2b are the extremites of the horizontal edges
+    if (!GetOverlap(op1->Pt.X, op1b->Pt.X, op2->Pt.X, op2b->Pt.X, Left, Right))
+      return false;
+
+    // DiscardLeftSide: when overlapping edges are joined, a spike will created
+    // which needs to be cleaned up. However, we don't want Op1 or Op2 caught up
+    // on the discard Side as either may still be needed for other joins ...
+    IntPoint Pt;
+    bool DiscardLeftSide;
+    if (op1->Pt.X >= Left && op1->Pt.X <= Right) {
+      Pt = op1->Pt;
+      DiscardLeftSide = (op1->Pt.X > op1b->Pt.X);
+    } else if (op2->Pt.X >= Left && op2->Pt.X <= Right) {
+      Pt = op2->Pt;
+      DiscardLeftSide = (op2->Pt.X > op2b->Pt.X);
+    } else if (op1b->Pt.X >= Left && op1b->Pt.X <= Right) {
+      Pt = op1b->Pt;
+      DiscardLeftSide = op1b->Pt.X > op1->Pt.X;
+    } else {
+      Pt = op2b->Pt;
+      DiscardLeftSide = (op2b->Pt.X > op2->Pt.X);
+    }
+    j->OutPt1 = op1;
+    j->OutPt2 = op2;
+    return JoinHorz(op1, op1b, op2, op2b, Pt, DiscardLeftSide);
+  } else {
+    // nb: For non-horizontal joins ...
+    //    1. Jr.OutPt1.Pt.Y == Jr.OutPt2.Pt.Y
+    //    2. Jr.OutPt1.Pt > Jr.OffPt.Y
+
+    // make sure the polygons are correctly oriented ...
+    op1b = op1->Next;
+    while ((op1b->Pt == op1->Pt) && (op1b != op1))
+      op1b = op1b->Next;
+    bool Reverse1 = ((op1b->Pt.Y > op1->Pt.Y) ||
+                     !SlopesEqual(op1->Pt, op1b->Pt, j->OffPt, m_UseFullRange));
+    if (Reverse1) {
+      op1b = op1->Prev;
+      while ((op1b->Pt == op1->Pt) && (op1b != op1))
+        op1b = op1b->Prev;
+      if ((op1b->Pt.Y > op1->Pt.Y) ||
+          !SlopesEqual(op1->Pt, op1b->Pt, j->OffPt, m_UseFullRange))
+        return false;
+    };
+    op2b = op2->Next;
+    while ((op2b->Pt == op2->Pt) && (op2b != op2))
+      op2b = op2b->Next;
+    bool Reverse2 = ((op2b->Pt.Y > op2->Pt.Y) ||
+                     !SlopesEqual(op2->Pt, op2b->Pt, j->OffPt, m_UseFullRange));
+    if (Reverse2) {
+      op2b = op2->Prev;
+      while ((op2b->Pt == op2->Pt) && (op2b != op2))
+        op2b = op2b->Prev;
+      if ((op2b->Pt.Y > op2->Pt.Y) ||
+          !SlopesEqual(op2->Pt, op2b->Pt, j->OffPt, m_UseFullRange))
+        return false;
+    }
+
+    if ((op1b == op1) || (op2b == op2) || (op1b == op2b) ||
+        ((outRec1 == outRec2) && (Reverse1 == Reverse2)))
+      return false;
+
+    if (Reverse1) {
+      op1b = DupOutPt(op1, false);
+      op2b = DupOutPt(op2, true);
+      op1->Prev = op2;
+      op2->Next = op1;
+      op1b->Next = op2b;
+      op2b->Prev = op1b;
+      j->OutPt1 = op1;
+      j->OutPt2 = op1b;
+      return true;
+    } else {
+      op1b = DupOutPt(op1, true);
+      op2b = DupOutPt(op2, false);
+      op1->Next = op2;
+      op2->Prev = op1;
+      op1b->Prev = op2b;
+      op2b->Next = op1b;
+      j->OutPt1 = op1;
+      j->OutPt2 = op1b;
+      return true;
+    }
+  }
+}
+//----------------------------------------------------------------------
+
+static OutRec *ParseFirstLeft(OutRec *FirstLeft) {
+  while (FirstLeft && !FirstLeft->Pts)
+    FirstLeft = FirstLeft->FirstLeft;
+  return FirstLeft;
+}
+//------------------------------------------------------------------------------
+
+void Clipper::FixupFirstLefts1(OutRec *OldOutRec, OutRec *NewOutRec) {
+  // tests if NewOutRec contains the polygon before reassigning FirstLeft
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i) {
+    OutRec *outRec = m_PolyOuts[i];
+    OutRec *firstLeft = ParseFirstLeft(outRec->FirstLeft);
+    if (outRec->Pts && firstLeft == OldOutRec) {
+      if (Poly2ContainsPoly1(outRec->Pts, NewOutRec->Pts))
+        outRec->FirstLeft = NewOutRec;
+    }
+  }
+}
+//----------------------------------------------------------------------
+
+void Clipper::FixupFirstLefts2(OutRec *InnerOutRec, OutRec *OuterOutRec) {
+  // A polygon has split into two such that one is now the inner of the other.
+  // It's possible that these polygons now wrap around other polygons, so check
+  // every polygon that's also contained by OuterOutRec's FirstLeft container
+  //(including 0) to see if they've become inner to the new inner polygon ...
+  OutRec *orfl = OuterOutRec->FirstLeft;
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i) {
+    OutRec *outRec = m_PolyOuts[i];
+
+    if (!outRec->Pts || outRec == OuterOutRec || outRec == InnerOutRec)
+      continue;
+    OutRec *firstLeft = ParseFirstLeft(outRec->FirstLeft);
+    if (firstLeft != orfl && firstLeft != InnerOutRec &&
+        firstLeft != OuterOutRec)
+      continue;
+    if (Poly2ContainsPoly1(outRec->Pts, InnerOutRec->Pts))
+      outRec->FirstLeft = InnerOutRec;
+    else if (Poly2ContainsPoly1(outRec->Pts, OuterOutRec->Pts))
+      outRec->FirstLeft = OuterOutRec;
+    else if (outRec->FirstLeft == InnerOutRec ||
+             outRec->FirstLeft == OuterOutRec)
+      outRec->FirstLeft = orfl;
+  }
+}
+//----------------------------------------------------------------------
+void Clipper::FixupFirstLefts3(OutRec *OldOutRec, OutRec *NewOutRec) {
+  // reassigns FirstLeft WITHOUT testing if NewOutRec contains the polygon
+  for (PolyOutList::size_type i = 0; i < m_PolyOuts.size(); ++i) {
+    OutRec *outRec = m_PolyOuts[i];
+    OutRec *firstLeft = ParseFirstLeft(outRec->FirstLeft);
+    if (outRec->Pts && firstLeft == OldOutRec)
+      outRec->FirstLeft = NewOutRec;
+  }
+}
+//----------------------------------------------------------------------
+
+void Clipper::JoinCommonEdges() {
+  for (JoinList::size_type i = 0; i < m_Joins.size(); i++) {
+    Join *join = m_Joins[i];
+
+    OutRec *outRec1 = GetOutRec(join->OutPt1->Idx);
+    OutRec *outRec2 = GetOutRec(join->OutPt2->Idx);
+
+    if (!outRec1->Pts || !outRec2->Pts)
+      continue;
+    if (outRec1->IsOpen || outRec2->IsOpen)
+      continue;
+
+    // get the polygon fragment with the correct hole state (FirstLeft)
+    // before calling JoinPoints() ...
+    OutRec *holeStateRec;
+    if (outRec1 == outRec2)
+      holeStateRec = outRec1;
+    else if (OutRec1RightOfOutRec2(outRec1, outRec2))
+      holeStateRec = outRec2;
+    else if (OutRec1RightOfOutRec2(outRec2, outRec1))
+      holeStateRec = outRec1;
+    else
+      holeStateRec = GetLowermostRec(outRec1, outRec2);
+
+    if (!JoinPoints(join, outRec1, outRec2))
+      continue;
+
+    if (outRec1 == outRec2) {
+      // instead of joining two polygons, we've just created a new one by
+      // splitting one polygon into two.
+      outRec1->Pts = join->OutPt1;
+      outRec1->BottomPt = 0;
+      outRec2 = CreateOutRec();
+      outRec2->Pts = join->OutPt2;
+
+      // update all OutRec2.Pts Idx's ...
+      UpdateOutPtIdxs(*outRec2);
+
+      if (Poly2ContainsPoly1(outRec2->Pts, outRec1->Pts)) {
+        // outRec1 contains outRec2 ...
+        outRec2->IsHole = !outRec1->IsHole;
+        outRec2->FirstLeft = outRec1;
+
+        if (m_UsingPolyTree)
+          FixupFirstLefts2(outRec2, outRec1);
+
+        if ((outRec2->IsHole ^ m_ReverseOutput) == (Area(*outRec2) > 0))
+          ReversePolyPtLinks(outRec2->Pts);
+
+      } else if (Poly2ContainsPoly1(outRec1->Pts, outRec2->Pts)) {
+        // outRec2 contains outRec1 ...
+        outRec2->IsHole = outRec1->IsHole;
+        outRec1->IsHole = !outRec2->IsHole;
+        outRec2->FirstLeft = outRec1->FirstLeft;
+        outRec1->FirstLeft = outRec2;
+
+        if (m_UsingPolyTree)
+          FixupFirstLefts2(outRec1, outRec2);
+
+        if ((outRec1->IsHole ^ m_ReverseOutput) == (Area(*outRec1) > 0))
+          ReversePolyPtLinks(outRec1->Pts);
+      } else {
+        // the 2 polygons are completely separate ...
+        outRec2->IsHole = outRec1->IsHole;
+        outRec2->FirstLeft = outRec1->FirstLeft;
+
+        // fixup FirstLeft pointers that may need reassigning to OutRec2
+        if (m_UsingPolyTree)
+          FixupFirstLefts1(outRec1, outRec2);
+      }
+
+    } else {
+      // joined 2 polygons together ...
+
+      outRec2->Pts = 0;
+      outRec2->BottomPt = 0;
+      outRec2->Idx = outRec1->Idx;
+
+      outRec1->IsHole = holeStateRec->IsHole;
+      if (holeStateRec == outRec2)
+        outRec1->FirstLeft = outRec2->FirstLeft;
+      outRec2->FirstLeft = outRec1;
+
+      if (m_UsingPolyTree)
+        FixupFirstLefts3(outRec2, outRec1);
+    }
+  }
+}
+
+//------------------------------------------------------------------------------
+// ClipperOffset support functions ...
+//------------------------------------------------------------------------------
+
+DoublePoint GetUnitNormal(const IntPoint &pt1, const IntPoint &pt2) {
+  if (pt2.X == pt1.X && pt2.Y == pt1.Y)
+    return DoublePoint(0, 0);
+
+  double Dx = (double)(pt2.X - pt1.X);
+  double dy = (double)(pt2.Y - pt1.Y);
+  double f = 1 * 1.0 / std::sqrt(Dx * Dx + dy * dy);
+  Dx *= f;
+  dy *= f;
+  return DoublePoint(dy, -Dx);
+}
+
+//------------------------------------------------------------------------------
+// ClipperOffset class
+//------------------------------------------------------------------------------
+
+ClipperOffset::ClipperOffset(double miterLimit, double arcTolerance) {
+  this->MiterLimit = miterLimit;
+  this->ArcTolerance = arcTolerance;
+  m_lowest.X = -1;
+}
+//------------------------------------------------------------------------------
+
+ClipperOffset::~ClipperOffset() { Clear(); }
+//------------------------------------------------------------------------------
+
+void ClipperOffset::Clear() {
+  for (int i = 0; i < m_polyNodes.ChildCount(); ++i)
+    delete m_polyNodes.Childs[i];
+  m_polyNodes.Childs.clear();
+  m_lowest.X = -1;
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::AddPath(const Path &path, JoinType joinType,
+                            EndType endType) {
+  int highI = (int)path.size() - 1;
+  if (highI < 0)
+    return;
+  PolyNode *newNode = new PolyNode();
+  newNode->m_jointype = joinType;
+  newNode->m_endtype = endType;
+
+  // strip duplicate points from path and also get index to the lowest point ...
+  if (endType == etClosedLine || endType == etClosedPolygon)
+    while (highI > 0 && path[0] == path[highI])
+      highI--;
+  newNode->Contour.reserve(highI + 1);
+  newNode->Contour.push_back(path[0]);
+  int j = 0, k = 0;
+  for (int i = 1; i <= highI; i++)
+    if (newNode->Contour[j] != path[i]) {
+      j++;
+      newNode->Contour.push_back(path[i]);
+      if (path[i].Y > newNode->Contour[k].Y ||
+          (path[i].Y == newNode->Contour[k].Y &&
+           path[i].X < newNode->Contour[k].X))
+        k = j;
+    }
+  if (endType == etClosedPolygon && j < 2) {
+    delete newNode;
+    return;
+  }
+  m_polyNodes.AddChild(*newNode);
+
+  // if this path's lowest pt is lower than all the others then update m_lowest
+  if (endType != etClosedPolygon)
+    return;
+  if (m_lowest.X < 0)
+    m_lowest = IntPoint(m_polyNodes.ChildCount() - 1, k);
+  else {
+    IntPoint ip = m_polyNodes.Childs[(int)m_lowest.X]->Contour[(int)m_lowest.Y];
+    if (newNode->Contour[k].Y > ip.Y ||
+        (newNode->Contour[k].Y == ip.Y && newNode->Contour[k].X < ip.X))
+      m_lowest = IntPoint(m_polyNodes.ChildCount() - 1, k);
+  }
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::AddPaths(const Paths &paths, JoinType joinType,
+                             EndType endType) {
+  for (Paths::size_type i = 0; i < paths.size(); ++i)
+    AddPath(paths[i], joinType, endType);
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::FixOrientations() {
+  // fixup orientations of all closed paths if the orientation of the
+  // closed path with the lowermost vertex is wrong ...
+  if (m_lowest.X >= 0 &&
+      !Orientation(m_polyNodes.Childs[(int)m_lowest.X]->Contour)) {
+    for (int i = 0; i < m_polyNodes.ChildCount(); ++i) {
+      PolyNode &node = *m_polyNodes.Childs[i];
+      if (node.m_endtype == etClosedPolygon ||
+          (node.m_endtype == etClosedLine && Orientation(node.Contour)))
+        ReversePath(node.Contour);
+    }
+  } else {
+    for (int i = 0; i < m_polyNodes.ChildCount(); ++i) {
+      PolyNode &node = *m_polyNodes.Childs[i];
+      if (node.m_endtype == etClosedLine && !Orientation(node.Contour))
+        ReversePath(node.Contour);
+    }
+  }
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::Execute(Paths &solution, double delta) {
+  solution.clear();
+  FixOrientations();
+  DoOffset(delta);
+
+  // now clean up 'corners' ...
+  Clipper clpr;
+  clpr.AddPaths(m_destPolys, ptSubject, true);
+  if (delta > 0) {
+    clpr.Execute(ctUnion, solution, pftPositive, pftPositive);
+  } else {
+    IntRect r = clpr.GetBounds();
+    Path outer(4);
+    outer[0] = IntPoint(r.left - 10, r.bottom + 10);
+    outer[1] = IntPoint(r.right + 10, r.bottom + 10);
+    outer[2] = IntPoint(r.right + 10, r.top - 10);
+    outer[3] = IntPoint(r.left - 10, r.top - 10);
+
+    clpr.AddPath(outer, ptSubject, true);
+    clpr.ReverseSolution(true);
+    clpr.Execute(ctUnion, solution, pftNegative, pftNegative);
+    if (solution.size() > 0)
+      solution.erase(solution.begin());
+  }
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::Execute(PolyTree &solution, double delta) {
+  solution.Clear();
+  FixOrientations();
+  DoOffset(delta);
+
+  // now clean up 'corners' ...
+  Clipper clpr;
+  clpr.AddPaths(m_destPolys, ptSubject, true);
+  if (delta > 0) {
+    clpr.Execute(ctUnion, solution, pftPositive, pftPositive);
+  } else {
+    IntRect r = clpr.GetBounds();
+    Path outer(4);
+    outer[0] = IntPoint(r.left - 10, r.bottom + 10);
+    outer[1] = IntPoint(r.right + 10, r.bottom + 10);
+    outer[2] = IntPoint(r.right + 10, r.top - 10);
+    outer[3] = IntPoint(r.left - 10, r.top - 10);
+
+    clpr.AddPath(outer, ptSubject, true);
+    clpr.ReverseSolution(true);
+    clpr.Execute(ctUnion, solution, pftNegative, pftNegative);
+    // remove the outer PolyNode rectangle ...
+    if (solution.ChildCount() == 1 && solution.Childs[0]->ChildCount() > 0) {
+      PolyNode *outerNode = solution.Childs[0];
+      solution.Childs.reserve(outerNode->ChildCount());
+      solution.Childs[0] = outerNode->Childs[0];
+      solution.Childs[0]->Parent = outerNode->Parent;
+      for (int i = 1; i < outerNode->ChildCount(); ++i)
+        solution.AddChild(*outerNode->Childs[i]);
+    } else
+      solution.Clear();
+  }
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::DoOffset(double delta) {
+  m_destPolys.clear();
+  m_delta = delta;
+
+  // if Zero offset, just copy any CLOSED polygons to m_p and return ...
+  if (NEAR_ZERO(delta)) {
+    m_destPolys.reserve(m_polyNodes.ChildCount());
+    for (int i = 0; i < m_polyNodes.ChildCount(); i++) {
+      PolyNode &node = *m_polyNodes.Childs[i];
+      if (node.m_endtype == etClosedPolygon)
+        m_destPolys.push_back(node.Contour);
+    }
+    return;
+  }
+
+  // see offset_triginometry3.svg in the documentation folder ...
+  if (MiterLimit > 2)
+    m_miterLim = 2 / (MiterLimit * MiterLimit);
+  else
+    m_miterLim = 0.5;
+
+  double y;
+  if (ArcTolerance <= 0.0)
+    y = def_arc_tolerance;
+  else if (ArcTolerance > std::fabs(delta) * def_arc_tolerance)
+    y = std::fabs(delta) * def_arc_tolerance;
+  else
+    y = ArcTolerance;
+  // see offset_triginometry2.svg in the documentation folder ...
+  double steps = pi / std::acos(1 - y / std::fabs(delta));
+  if (steps > std::fabs(delta) * pi)
+    steps = std::fabs(delta) * pi; // ie excessive precision check
+  m_sin = std::sin(two_pi / steps);
+  m_cos = std::cos(two_pi / steps);
+  m_StepsPerRad = steps / two_pi;
+  if (delta < 0.0)
+    m_sin = -m_sin;
+
+  m_destPolys.reserve(m_polyNodes.ChildCount() * 2);
+  for (int i = 0; i < m_polyNodes.ChildCount(); i++) {
+    PolyNode &node = *m_polyNodes.Childs[i];
+    m_srcPoly = node.Contour;
+
+    int len = (int)m_srcPoly.size();
+    if (len == 0 ||
+        (delta <= 0 && (len < 3 || node.m_endtype != etClosedPolygon)))
+      continue;
+
+    m_destPoly.clear();
+    if (len == 1) {
+      if (node.m_jointype == jtRound) {
+        double X = 1.0, Y = 0.0;
+        for (cInt j = 1; j <= steps; j++) {
+          m_destPoly.push_back(IntPoint(Round(m_srcPoly[0].X + X * delta),
+                                        Round(m_srcPoly[0].Y + Y * delta)));
+          double X2 = X;
+          X = X * m_cos - m_sin * Y;
+          Y = X2 * m_sin + Y * m_cos;
+        }
+      } else {
+        double X = -1.0, Y = -1.0;
+        for (int j = 0; j < 4; ++j) {
+          m_destPoly.push_back(IntPoint(Round(m_srcPoly[0].X + X * delta),
+                                        Round(m_srcPoly[0].Y + Y * delta)));
+          if (X < 0)
+            X = 1;
+          else if (Y < 0)
+            Y = 1;
+          else
+            X = -1;
+        }
+      }
+      m_destPolys.push_back(m_destPoly);
+      continue;
+    }
+    // build m_normals ...
+    m_normals.clear();
+    m_normals.reserve(len);
+    for (int j = 0; j < len - 1; ++j)
+      m_normals.push_back(GetUnitNormal(m_srcPoly[j], m_srcPoly[j + 1]));
+    if (node.m_endtype == etClosedLine || node.m_endtype == etClosedPolygon)
+      m_normals.push_back(GetUnitNormal(m_srcPoly[len - 1], m_srcPoly[0]));
+    else
+      m_normals.push_back(DoublePoint(m_normals[len - 2]));
+
+    if (node.m_endtype == etClosedPolygon) {
+      int k = len - 1;
+      for (int j = 0; j < len; ++j)
+        OffsetPoint(j, k, node.m_jointype);
+      m_destPolys.push_back(m_destPoly);
+    } else if (node.m_endtype == etClosedLine) {
+      int k = len - 1;
+      for (int j = 0; j < len; ++j)
+        OffsetPoint(j, k, node.m_jointype);
+      m_destPolys.push_back(m_destPoly);
+      m_destPoly.clear();
+      // re-build m_normals ...
+      DoublePoint n = m_normals[len - 1];
+      for (int j = len - 1; j > 0; j--)
+        m_normals[j] = DoublePoint(-m_normals[j - 1].X, -m_normals[j - 1].Y);
+      m_normals[0] = DoublePoint(-n.X, -n.Y);
+      k = 0;
+      for (int j = len - 1; j >= 0; j--)
+        OffsetPoint(j, k, node.m_jointype);
+      m_destPolys.push_back(m_destPoly);
+    } else {
+      int k = 0;
+      for (int j = 1; j < len - 1; ++j)
+        OffsetPoint(j, k, node.m_jointype);
+
+      IntPoint pt1;
+      if (node.m_endtype == etOpenButt) {
+        int j = len - 1;
+        pt1 = IntPoint((cInt)Round(m_srcPoly[j].X + m_normals[j].X * delta),
+                       (cInt)Round(m_srcPoly[j].Y + m_normals[j].Y * delta));
+        m_destPoly.push_back(pt1);
+        pt1 = IntPoint((cInt)Round(m_srcPoly[j].X - m_normals[j].X * delta),
+                       (cInt)Round(m_srcPoly[j].Y - m_normals[j].Y * delta));
+        m_destPoly.push_back(pt1);
+      } else {
+        int j = len - 1;
+        k = len - 2;
+        m_sinA = 0;
+        m_normals[j] = DoublePoint(-m_normals[j].X, -m_normals[j].Y);
+        if (node.m_endtype == etOpenSquare)
+          DoSquare(j, k);
+        else
+          DoRound(j, k);
+      }
+
+      // re-build m_normals ...
+      for (int j = len - 1; j > 0; j--)
+        m_normals[j] = DoublePoint(-m_normals[j - 1].X, -m_normals[j - 1].Y);
+      m_normals[0] = DoublePoint(-m_normals[1].X, -m_normals[1].Y);
+
+      k = len - 1;
+      for (int j = k - 1; j > 0; --j)
+        OffsetPoint(j, k, node.m_jointype);
+
+      if (node.m_endtype == etOpenButt) {
+        pt1 = IntPoint((cInt)Round(m_srcPoly[0].X - m_normals[0].X * delta),
+                       (cInt)Round(m_srcPoly[0].Y - m_normals[0].Y * delta));
+        m_destPoly.push_back(pt1);
+        pt1 = IntPoint((cInt)Round(m_srcPoly[0].X + m_normals[0].X * delta),
+                       (cInt)Round(m_srcPoly[0].Y + m_normals[0].Y * delta));
+        m_destPoly.push_back(pt1);
+      } else {
+        k = 1;
+        m_sinA = 0;
+        if (node.m_endtype == etOpenSquare)
+          DoSquare(0, 1);
+        else
+          DoRound(0, 1);
+      }
+      m_destPolys.push_back(m_destPoly);
+    }
+  }
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::OffsetPoint(int j, int &k, JoinType jointype) {
+  // cross product ...
+  m_sinA = (m_normals[k].X * m_normals[j].Y - m_normals[j].X * m_normals[k].Y);
+  if (std::fabs(m_sinA * m_delta) < 1.0) {
+    // dot product ...
+    double cosA =
+        (m_normals[k].X * m_normals[j].X + m_normals[j].Y * m_normals[k].Y);
+    if (cosA > 0) // angle => 0 degrees
+    {
+      m_destPoly.push_back(
+          IntPoint(Round(m_srcPoly[j].X + m_normals[k].X * m_delta),
+                   Round(m_srcPoly[j].Y + m_normals[k].Y * m_delta)));
+      return;
+    }
+    // else angle => 180 degrees
+  } else if (m_sinA > 1.0)
+    m_sinA = 1.0;
+  else if (m_sinA < -1.0)
+    m_sinA = -1.0;
+
+  if (m_sinA * m_delta < 0) {
+    m_destPoly.push_back(
+        IntPoint(Round(m_srcPoly[j].X + m_normals[k].X * m_delta),
+                 Round(m_srcPoly[j].Y + m_normals[k].Y * m_delta)));
+    m_destPoly.push_back(m_srcPoly[j]);
+    m_destPoly.push_back(
+        IntPoint(Round(m_srcPoly[j].X + m_normals[j].X * m_delta),
+                 Round(m_srcPoly[j].Y + m_normals[j].Y * m_delta)));
+  } else
+    switch (jointype) {
+    case jtMiter: {
+      double r = 1 + (m_normals[j].X * m_normals[k].X +
+                      m_normals[j].Y * m_normals[k].Y);
+      if (r >= m_miterLim)
+        DoMiter(j, k, r);
+      else
+        DoSquare(j, k);
+      break;
+    }
+    case jtSquare:
+      DoSquare(j, k);
+      break;
+    case jtRound:
+      DoRound(j, k);
+      break;
+    }
+  k = j;
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::DoSquare(int j, int k) {
+  double dx = std::tan(std::atan2(m_sinA, m_normals[k].X * m_normals[j].X +
+                                              m_normals[k].Y * m_normals[j].Y) /
+                       4);
+  m_destPoly.push_back(IntPoint(
+      Round(m_srcPoly[j].X + m_delta * (m_normals[k].X - m_normals[k].Y * dx)),
+      Round(m_srcPoly[j].Y +
+            m_delta * (m_normals[k].Y + m_normals[k].X * dx))));
+  m_destPoly.push_back(IntPoint(
+      Round(m_srcPoly[j].X + m_delta * (m_normals[j].X + m_normals[j].Y * dx)),
+      Round(m_srcPoly[j].Y +
+            m_delta * (m_normals[j].Y - m_normals[j].X * dx))));
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::DoMiter(int j, int k, double r) {
+  double q = m_delta / r;
+  m_destPoly.push_back(
+      IntPoint(Round(m_srcPoly[j].X + (m_normals[k].X + m_normals[j].X) * q),
+               Round(m_srcPoly[j].Y + (m_normals[k].Y + m_normals[j].Y) * q)));
+}
+//------------------------------------------------------------------------------
+
+void ClipperOffset::DoRound(int j, int k) {
+  double a = std::atan2(m_sinA, m_normals[k].X * m_normals[j].X +
+                                    m_normals[k].Y * m_normals[j].Y);
+  int steps = std::max((int)Round(m_StepsPerRad * std::fabs(a)), 1);
+
+  double X = m_normals[k].X, Y = m_normals[k].Y, X2;
+  for (int i = 0; i < steps; ++i) {
+    m_destPoly.push_back(IntPoint(Round(m_srcPoly[j].X + X * m_delta),
+                                  Round(m_srcPoly[j].Y + Y * m_delta)));
+    X2 = X;
+    X = X * m_cos - m_sin * Y;
+    Y = X2 * m_sin + Y * m_cos;
+  }
+  m_destPoly.push_back(
+      IntPoint(Round(m_srcPoly[j].X + m_normals[j].X * m_delta),
+               Round(m_srcPoly[j].Y + m_normals[j].Y * m_delta)));
+}
+
+//------------------------------------------------------------------------------
+// Miscellaneous public functions
+//------------------------------------------------------------------------------
+
+void Clipper::DoSimplePolygons() {
+  PolyOutList::size_type i = 0;
+  while (i < m_PolyOuts.size()) {
+    OutRec *outrec = m_PolyOuts[i++];
+    OutPt *op = outrec->Pts;
+    if (!op || outrec->IsOpen)
+      continue;
+    do // for each Pt in Polygon until duplicate found do ...
+    {
+      OutPt *op2 = op->Next;
+      while (op2 != outrec->Pts) {
+        if ((op->Pt == op2->Pt) && op2->Next != op && op2->Prev != op) {
+          // split the polygon into two ...
+          OutPt *op3 = op->Prev;
+          OutPt *op4 = op2->Prev;
+          op->Prev = op4;
+          op4->Next = op;
+          op2->Prev = op3;
+          op3->Next = op2;
+
+          outrec->Pts = op;
+          OutRec *outrec2 = CreateOutRec();
+          outrec2->Pts = op2;
+          UpdateOutPtIdxs(*outrec2);
+          if (Poly2ContainsPoly1(outrec2->Pts, outrec->Pts)) {
+            // OutRec2 is contained by OutRec1 ...
+            outrec2->IsHole = !outrec->IsHole;
+            outrec2->FirstLeft = outrec;
+            if (m_UsingPolyTree)
+              FixupFirstLefts2(outrec2, outrec);
+          } else if (Poly2ContainsPoly1(outrec->Pts, outrec2->Pts)) {
+            // OutRec1 is contained by OutRec2 ...
+            outrec2->IsHole = outrec->IsHole;
+            outrec->IsHole = !outrec2->IsHole;
+            outrec2->FirstLeft = outrec->FirstLeft;
+            outrec->FirstLeft = outrec2;
+            if (m_UsingPolyTree)
+              FixupFirstLefts2(outrec, outrec2);
+          } else {
+            // the 2 polygons are separate ...
+            outrec2->IsHole = outrec->IsHole;
+            outrec2->FirstLeft = outrec->FirstLeft;
+            if (m_UsingPolyTree)
+              FixupFirstLefts1(outrec, outrec2);
+          }
+          op2 = op; // ie get ready for the Next iteration
+        }
+        op2 = op2->Next;
+      }
+      op = op->Next;
+    } while (op != outrec->Pts);
+  }
+}
+//------------------------------------------------------------------------------
+
+void ReversePath(Path &p) { std::reverse(p.begin(), p.end()); }
+//------------------------------------------------------------------------------
+
+void ReversePaths(Paths &p) {
+  for (Paths::size_type i = 0; i < p.size(); ++i)
+    ReversePath(p[i]);
+}
+//------------------------------------------------------------------------------
+
+void SimplifyPolygon(const Path &in_poly, Paths &out_polys,
+                     PolyFillType fillType) {
+  Clipper c;
+  c.StrictlySimple(true);
+  c.AddPath(in_poly, ptSubject, true);
+  c.Execute(ctUnion, out_polys, fillType, fillType);
+}
+//------------------------------------------------------------------------------
+
+void SimplifyPolygons(const Paths &in_polys, Paths &out_polys,
+                      PolyFillType fillType) {
+  Clipper c;
+  c.StrictlySimple(true);
+  c.AddPaths(in_polys, ptSubject, true);
+  c.Execute(ctUnion, out_polys, fillType, fillType);
+}
+//------------------------------------------------------------------------------
+
+void SimplifyPolygons(Paths &polys, PolyFillType fillType) {
+  SimplifyPolygons(polys, polys, fillType);
+}
+//------------------------------------------------------------------------------
+
+inline double DistanceSqrd(const IntPoint &pt1, const IntPoint &pt2) {
+  double Dx = ((double)pt1.X - pt2.X);
+  double dy = ((double)pt1.Y - pt2.Y);
+  return (Dx * Dx + dy * dy);
+}
+//------------------------------------------------------------------------------
+
+double DistanceFromLineSqrd(const IntPoint &pt, const IntPoint &ln1,
+                            const IntPoint &ln2) {
+  // The equation of a line in general form (Ax + By + C = 0)
+  // given 2 points (x�,y�) & (x�,y�) is ...
+  //(y� - y�)x + (x� - x�)y + (y� - y�)x� - (x� - x�)y� = 0
+  // A = (y� - y�); B = (x� - x�); C = (y� - y�)x� - (x� - x�)y�
+  // perpendicular distance of point (x�,y�) = (Ax� + By� + C)/Sqrt(A� + B�)
+  // see http://en.wikipedia.org/wiki/Perpendicular_distance
+  double A = double(ln1.Y - ln2.Y);
+  double B = double(ln2.X - ln1.X);
+  double C = A * ln1.X + B * ln1.Y;
+  C = A * pt.X + B * pt.Y - C;
+  return (C * C) / (A * A + B * B);
+}
+//---------------------------------------------------------------------------
+
+bool SlopesNearCollinear(const IntPoint &pt1, const IntPoint &pt2,
+                         const IntPoint &pt3, double distSqrd) {
+  // this function is more accurate when the point that's geometrically
+  // between the other 2 points is the one that's tested for distance.
+  // ie makes it more likely to pick up 'spikes' ...
+  if (Abs(pt1.X - pt2.X) > Abs(pt1.Y - pt2.Y)) {
+    if ((pt1.X > pt2.X) == (pt1.X < pt3.X))
+      return DistanceFromLineSqrd(pt1, pt2, pt3) < distSqrd;
+    else if ((pt2.X > pt1.X) == (pt2.X < pt3.X))
+      return DistanceFromLineSqrd(pt2, pt1, pt3) < distSqrd;
+    else
+      return DistanceFromLineSqrd(pt3, pt1, pt2) < distSqrd;
+  } else {
+    if ((pt1.Y > pt2.Y) == (pt1.Y < pt3.Y))
+      return DistanceFromLineSqrd(pt1, pt2, pt3) < distSqrd;
+    else if ((pt2.Y > pt1.Y) == (pt2.Y < pt3.Y))
+      return DistanceFromLineSqrd(pt2, pt1, pt3) < distSqrd;
+    else
+      return DistanceFromLineSqrd(pt3, pt1, pt2) < distSqrd;
+  }
+}
+//------------------------------------------------------------------------------
+
+bool PointsAreClose(IntPoint pt1, IntPoint pt2, double distSqrd) {
+  double Dx = (double)pt1.X - pt2.X;
+  double dy = (double)pt1.Y - pt2.Y;
+  return ((Dx * Dx) + (dy * dy) <= distSqrd);
+}
+//------------------------------------------------------------------------------
+
+OutPt *ExcludeOp(OutPt *op) {
+  OutPt *result = op->Prev;
+  result->Next = op->Next;
+  op->Next->Prev = result;
+  result->Idx = 0;
+  return result;
+}
+//------------------------------------------------------------------------------
+
+void CleanPolygon(const Path &in_poly, Path &out_poly, double distance) {
+  // distance = proximity in units/pixels below which vertices
+  // will be stripped. Default ~= sqrt(2).
+
+  size_t size = in_poly.size();
+
+  if (size == 0) {
+    out_poly.clear();
+    return;
+  }
+
+  OutPt *outPts = new OutPt[size];
+  for (size_t i = 0; i < size; ++i) {
+    outPts[i].Pt = in_poly[i];
+    outPts[i].Next = &outPts[(i + 1) % size];
+    outPts[i].Next->Prev = &outPts[i];
+    outPts[i].Idx = 0;
+  }
+
+  double distSqrd = distance * distance;
+  OutPt *op = &outPts[0];
+  while (op->Idx == 0 && op->Next != op->Prev) {
+    if (PointsAreClose(op->Pt, op->Prev->Pt, distSqrd)) {
+      op = ExcludeOp(op);
+      size--;
+    } else if (PointsAreClose(op->Prev->Pt, op->Next->Pt, distSqrd)) {
+      ExcludeOp(op->Next);
+      op = ExcludeOp(op);
+      size -= 2;
+    } else if (SlopesNearCollinear(op->Prev->Pt, op->Pt, op->Next->Pt,
+                                   distSqrd)) {
+      op = ExcludeOp(op);
+      size--;
+    } else {
+      op->Idx = 1;
+      op = op->Next;
+    }
+  }
+
+  if (size < 3)
+    size = 0;
+  out_poly.resize(size);
+  for (size_t i = 0; i < size; ++i) {
+    out_poly[i] = op->Pt;
+    op = op->Next;
+  }
+  delete[] outPts;
+}
+//------------------------------------------------------------------------------
+
+void CleanPolygon(Path &poly, double distance) {
+  CleanPolygon(poly, poly, distance);
+}
+//------------------------------------------------------------------------------
+
+void CleanPolygons(const Paths &in_polys, Paths &out_polys, double distance) {
+  out_polys.resize(in_polys.size());
+  for (Paths::size_type i = 0; i < in_polys.size(); ++i)
+    CleanPolygon(in_polys[i], out_polys[i], distance);
+}
+//------------------------------------------------------------------------------
+
+void CleanPolygons(Paths &polys, double distance) {
+  CleanPolygons(polys, polys, distance);
+}
+//------------------------------------------------------------------------------
+
+void Minkowski(const Path &poly, const Path &path, Paths &solution, bool isSum,
+               bool isClosed) {
+  int delta = (isClosed ? 1 : 0);
+  size_t polyCnt = poly.size();
+  size_t pathCnt = path.size();
+  Paths pp;
+  pp.reserve(pathCnt);
+  if (isSum)
+    for (size_t i = 0; i < pathCnt; ++i) {
+      Path p;
+      p.reserve(polyCnt);
+      for (size_t j = 0; j < poly.size(); ++j)
+        p.push_back(IntPoint(path[i].X + poly[j].X, path[i].Y + poly[j].Y));
+      pp.push_back(p);
+    }
+  else
+    for (size_t i = 0; i < pathCnt; ++i) {
+      Path p;
+      p.reserve(polyCnt);
+      for (size_t j = 0; j < poly.size(); ++j)
+        p.push_back(IntPoint(path[i].X - poly[j].X, path[i].Y - poly[j].Y));
+      pp.push_back(p);
+    }
+
+  solution.clear();
+  solution.reserve((pathCnt + delta) * (polyCnt + 1));
+  for (size_t i = 0; i < pathCnt - 1 + delta; ++i)
+    for (size_t j = 0; j < polyCnt; ++j) {
+      Path quad;
+      quad.reserve(4);
+      quad.push_back(pp[i % pathCnt][j % polyCnt]);
+      quad.push_back(pp[(i + 1) % pathCnt][j % polyCnt]);
+      quad.push_back(pp[(i + 1) % pathCnt][(j + 1) % polyCnt]);
+      quad.push_back(pp[i % pathCnt][(j + 1) % polyCnt]);
+      if (!Orientation(quad))
+        ReversePath(quad);
+      solution.push_back(quad);
+    }
+}
+//------------------------------------------------------------------------------
+
+void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution,
+                  bool pathIsClosed) {
+  Minkowski(pattern, path, solution, true, pathIsClosed);
+  Clipper c;
+  c.AddPaths(solution, ptSubject, true);
+  c.Execute(ctUnion, solution, pftNonZero, pftNonZero);
+}
+//------------------------------------------------------------------------------
+
+void TranslatePath(const Path &input, Path &output, const IntPoint delta) {
+  // precondition: input != output
+  output.resize(input.size());
+  for (size_t i = 0; i < input.size(); ++i)
+    output[i] = IntPoint(input[i].X + delta.X, input[i].Y + delta.Y);
+}
+//------------------------------------------------------------------------------
+
+void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution,
+                  bool pathIsClosed) {
+  Clipper c;
+  for (size_t i = 0; i < paths.size(); ++i) {
+    Paths tmp;
+    Minkowski(pattern, paths[i], tmp, true, pathIsClosed);
+    c.AddPaths(tmp, ptSubject, true);
+    if (pathIsClosed) {
+      Path tmp2;
+      TranslatePath(paths[i], tmp2, pattern[0]);
+      c.AddPath(tmp2, ptClip, true);
+    }
+  }
+  c.Execute(ctUnion, solution, pftNonZero, pftNonZero);
+}
+//------------------------------------------------------------------------------
+
+void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution) {
+  Minkowski(poly1, poly2, solution, false, true);
+  Clipper c;
+  c.AddPaths(solution, ptSubject, true);
+  c.Execute(ctUnion, solution, pftNonZero, pftNonZero);
+}
+//------------------------------------------------------------------------------
+
+enum NodeType { ntAny, ntOpen, ntClosed };
+
+void AddPolyNodeToPaths(const PolyNode &polynode, NodeType nodetype,
+                        Paths &paths) {
+  bool match = true;
+  if (nodetype == ntClosed)
+    match = !polynode.IsOpen();
+  else if (nodetype == ntOpen)
+    return;
+
+  if (!polynode.Contour.empty() && match)
+    paths.push_back(polynode.Contour);
+  for (int i = 0; i < polynode.ChildCount(); ++i)
+    AddPolyNodeToPaths(*polynode.Childs[i], nodetype, paths);
+}
+//------------------------------------------------------------------------------
+
+void PolyTreeToPaths(const PolyTree &polytree, Paths &paths) {
+  paths.resize(0);
+  paths.reserve(polytree.Total());
+  AddPolyNodeToPaths(polytree, ntAny, paths);
+}
+//------------------------------------------------------------------------------
+
+void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths) {
+  paths.resize(0);
+  paths.reserve(polytree.Total());
+  AddPolyNodeToPaths(polytree, ntClosed, paths);
+}
+//------------------------------------------------------------------------------
+
+void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths) {
+  paths.resize(0);
+  paths.reserve(polytree.Total());
+  // Open paths are top level only, so ...
+  for (int i = 0; i < polytree.ChildCount(); ++i)
+    if (polytree.Childs[i]->IsOpen())
+      paths.push_back(polytree.Childs[i]->Contour);
+}
+//------------------------------------------------------------------------------
+
+std::ostream &operator<<(std::ostream &s, const IntPoint &p) {
+  s << "(" << p.X << "," << p.Y << ")";
+  return s;
+}
+//------------------------------------------------------------------------------
+
+std::ostream &operator<<(std::ostream &s, const Path &p) {
+  if (p.empty())
+    return s;
+  Path::size_type last = p.size() - 1;
+  for (Path::size_type i = 0; i < last; i++)
+    s << "(" << p[i].X << "," << p[i].Y << "), ";
+  s << "(" << p[last].X << "," << p[last].Y << ")\n";
+  return s;
+}
+//------------------------------------------------------------------------------
+
+std::ostream &operator<<(std::ostream &s, const Paths &p) {
+  for (Paths::size_type i = 0; i < p.size(); i++)
+    s << p[i];
+  s << "\n";
+  return s;
+}
+//------------------------------------------------------------------------------
+
+} // ClipperLib namespace

deploy/android_demo/app/src/main/cpp/ocr_clipper.hpp

+/*******************************************************************************
+*                                                                              *
+* Author    :  Angus Johnson                                                   *
+* Version   :  6.4.2                                                           *
+* Date      :  27 February 2017                                                *
+* Website   :  http://www.angusj.com                                           *
+* Copyright :  Angus Johnson 2010-2017                                         *
+*                                                                              *
+* License:                                                                     *
+* Use, modification & distribution is subject to Boost Software License Ver 1. *
+* http://www.boost.org/LICENSE_1_0.txt                                         *
+*                                                                              *
+* Attributions:                                                                *
+* The code in this library is an extension of Bala Vatti's clipping algorithm: *
+* "A generic solution to polygon clipping"                                     *
+* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63.             *
+* http://portal.acm.org/citation.cfm?id=129906                                 *
+*                                                                              *
+* Computer graphics and geometric modeling: implementation and algorithms      *
+* By Max K. Agoston                                                            *
+* Springer; 1 edition (January 4, 2005)                                        *
+* http://books.google.com/books?q=vatti+clipping+agoston                       *
+*                                                                              *
+* See also:                                                                    *
+* "Polygon Offsetting by Computing Winding Numbers"                            *
+* Paper no. DETC2005-85513 pp. 565-575                                         *
+* ASME 2005 International Design Engineering Technical Conferences             *
+* and Computers and Information in Engineering Conference (IDETC/CIE2005)      *
+* September 24-28, 2005 , Long Beach, California, USA                          *
+* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf              *
+*                                                                              *
+*******************************************************************************/
+
+#ifndef clipper_hpp
+#define clipper_hpp
+
+#define CLIPPER_VERSION "6.4.2"
+
+// use_int32: When enabled 32bit ints are used instead of 64bit ints. This
+// improve performance but coordinate values are limited to the range +/- 46340
+//#define use_int32
+
+// use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
+//#define use_xyz
+
+// use_lines: Enables line clipping. Adds a very minor cost to performance.
+#define use_lines
+
+// use_deprecated: Enables temporary support for the obsolete functions
+//#define use_deprecated
+
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <list>
+#include <ostream>
+#include <queue>
+#include <set>
+#include <stdexcept>
+#include <vector>
+
+namespace ClipperLib {
+
+enum ClipType { ctIntersection, ctUnion, ctDifference, ctXor };
+enum PolyType { ptSubject, ptClip };
+// By far the most widely used winding rules for polygon filling are
+// EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
+// Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
+// see http://glprogramming.com/red/chapter11.html
+enum PolyFillType { pftEvenOdd, pftNonZero, pftPositive, pftNegative };
+
+#ifdef use_int32
+typedef int cInt;
+static cInt const loRange = 0x7FFF;
+static cInt const hiRange = 0x7FFF;
+#else
+typedef signed long long cInt;
+static cInt const loRange = 0x3FFFFFFF;
+static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
+typedef signed long long long64; // used by Int128 class
+typedef unsigned long long ulong64;
+
+#endif
+
+struct IntPoint {
+  cInt X;
+  cInt Y;
+#ifdef use_xyz
+  cInt Z;
+  IntPoint(cInt x = 0, cInt y = 0, cInt z = 0) : X(x), Y(y), Z(z){};
+#else
+
+  IntPoint(cInt x = 0, cInt y = 0) : X(x), Y(y){};
+#endif
+
+  friend inline bool operator==(const IntPoint &a, const IntPoint &b) {
+    return a.X == b.X && a.Y == b.Y;
+  }
+
+  friend inline bool operator!=(const IntPoint &a, const IntPoint &b) {
+    return a.X != b.X || a.Y != b.Y;
+  }
+};
+//------------------------------------------------------------------------------
+
+typedef std::vector<IntPoint> Path;
+typedef std::vector<Path> Paths;
+
+inline Path &operator<<(Path &poly, const IntPoint &p) {
+  poly.push_back(p);
+  return poly;
+}
+
+inline Paths &operator<<(Paths &polys, const Path &p) {
+  polys.push_back(p);
+  return polys;
+}
+
+std::ostream &operator<<(std::ostream &s, const IntPoint &p);
+
+std::ostream &operator<<(std::ostream &s, const Path &p);
+
+std::ostream &operator<<(std::ostream &s, const Paths &p);
+
+struct DoublePoint {
+  double X;
+  double Y;
+
+  DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
+
+  DoublePoint(IntPoint ip) : X((double)ip.X), Y((double)ip.Y) {}
+};
+//------------------------------------------------------------------------------
+
+#ifdef use_xyz
+typedef void (*ZFillCallback)(IntPoint &e1bot, IntPoint &e1top, IntPoint &e2bot,
+                              IntPoint &e2top, IntPoint &pt);
+#endif
+
+enum InitOptions {
+  ioReverseSolution = 1,
+  ioStrictlySimple = 2,
+  ioPreserveCollinear = 4
+};
+enum JoinType { jtSquare, jtRound, jtMiter };
+enum EndType {
+  etClosedPolygon,
+  etClosedLine,
+  etOpenButt,
+  etOpenSquare,
+  etOpenRound
+};
+
+class PolyNode;
+
+typedef std::vector<PolyNode *> PolyNodes;
+
+class PolyNode {
+public:
+  PolyNode();
+
+  virtual ~PolyNode(){};
+  Path Contour;
+  PolyNodes Childs;
+  PolyNode *Parent;
+
+  PolyNode *GetNext() const;
+
+  bool IsHole() const;
+
+  bool IsOpen() const;
+
+  int ChildCount() const;
+
+private:
+  // PolyNode& operator =(PolyNode& other);
+  unsigned Index; // node index in Parent.Childs
+  bool m_IsOpen;
+  JoinType m_jointype;
+  EndType m_endtype;
+
+  PolyNode *GetNextSiblingUp() const;
+
+  void AddChild(PolyNode &child);
+
+  friend class Clipper; // to access Index
+  friend class ClipperOffset;
+};
+
+class PolyTree : public PolyNode {
+public:
+  ~PolyTree() { Clear(); };
+
+  PolyNode *GetFirst() const;
+
+  void Clear();
+
+  int Total() const;
+
+private:
+  // PolyTree& operator =(PolyTree& other);
+  PolyNodes AllNodes;
+
+  friend class Clipper; // to access AllNodes
+};
+
+bool Orientation(const Path &poly);
+
+double Area(const Path &poly);
+
+int PointInPolygon(const IntPoint &pt, const Path &path);
+
+void SimplifyPolygon(const Path &in_poly, Paths &out_polys,
+                     PolyFillType fillType = pftEvenOdd);
+
+void SimplifyPolygons(const Paths &in_polys, Paths &out_polys,
+                      PolyFillType fillType = pftEvenOdd);
+
+void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
+
+void CleanPolygon(const Path &in_poly, Path &out_poly, double distance = 1.415);
+
+void CleanPolygon(Path &poly, double distance = 1.415);
+
+void CleanPolygons(const Paths &in_polys, Paths &out_polys,
+                   double distance = 1.415);
+
+void CleanPolygons(Paths &polys, double distance = 1.415);
+
+void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution,
+                  bool pathIsClosed);
+
+void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution,
+                  bool pathIsClosed);
+
+void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution);
+
+void PolyTreeToPaths(const PolyTree &polytree, Paths &paths);
+
+void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths);
+
+void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths);
+
+void ReversePath(Path &p);
+
+void ReversePaths(Paths &p);
+
+struct IntRect {
+  cInt left;
+  cInt top;
+  cInt right;
+  cInt bottom;
+};
+
+// enums that are used internally ...
+enum EdgeSide { esLeft = 1, esRight = 2 };
+
+// forward declarations (for stuff used internally) ...
+struct TEdge;
+struct IntersectNode;
+struct LocalMinimum;
+struct OutPt;
+struct OutRec;
+struct Join;
+
+typedef std::vector<OutRec *> PolyOutList;
+typedef std::vector<TEdge *> EdgeList;
+typedef std::vector<Join *> JoinList;
+typedef std::vector<IntersectNode *> IntersectList;
+
+//------------------------------------------------------------------------------
+
+// ClipperBase is the ancestor to the Clipper class. It should not be
+// instantiated directly. This class simply abstracts the conversion of sets of
+// polygon coordinates into edge objects that are stored in a LocalMinima list.
+class ClipperBase {
+public:
+  ClipperBase();
+
+  virtual ~ClipperBase();
+
+  virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
+
+  bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
+
+  virtual void Clear();
+
+  IntRect GetBounds();
+
+  bool PreserveCollinear() { return m_PreserveCollinear; };
+
+  void PreserveCollinear(bool value) { m_PreserveCollinear = value; };
+
+protected:
+  void DisposeLocalMinimaList();
+
+  TEdge *AddBoundsToLML(TEdge *e, bool IsClosed);
+
+  virtual void Reset();
+
+  TEdge *ProcessBound(TEdge *E, bool IsClockwise);
+
+  void InsertScanbeam(const cInt Y);
+
+  bool PopScanbeam(cInt &Y);
+
+  bool LocalMinimaPending();
+
+  bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
+
+  OutRec *CreateOutRec();
+
+  void DisposeAllOutRecs();
+
+  void DisposeOutRec(PolyOutList::size_type index);
+
+  void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
+
+  void DeleteFromAEL(TEdge *e);
+
+  void UpdateEdgeIntoAEL(TEdge *&e);
+
+  typedef std::vector<LocalMinimum> MinimaList;
+  MinimaList::iterator m_CurrentLM;
+  MinimaList m_MinimaList;
+
+  bool m_UseFullRange;
+  EdgeList m_edges;
+  bool m_PreserveCollinear;
+  bool m_HasOpenPaths;
+  PolyOutList m_PolyOuts;
+  TEdge *m_ActiveEdges;
+
+  typedef std::priority_queue<cInt> ScanbeamList;
+  ScanbeamList m_Scanbeam;
+};
+//------------------------------------------------------------------------------
+
+class Clipper : public virtual ClipperBase {
+public:
+  Clipper(int initOptions = 0);
+
+  bool Execute(ClipType clipType, Paths &solution,
+               PolyFillType fillType = pftEvenOdd);
+
+  bool Execute(ClipType clipType, Paths &solution, PolyFillType subjFillType,
+               PolyFillType clipFillType);
+
+  bool Execute(ClipType clipType, PolyTree &polytree,
+               PolyFillType fillType = pftEvenOdd);
+
+  bool Execute(ClipType clipType, PolyTree &polytree, PolyFillType subjFillType,
+               PolyFillType clipFillType);
+
+  bool ReverseSolution() { return m_ReverseOutput; };
+
+  void ReverseSolution(bool value) { m_ReverseOutput = value; };
+
+  bool StrictlySimple() { return m_StrictSimple; };
+
+  void StrictlySimple(bool value) { m_StrictSimple = value; };
+// set the callback function for z value filling on intersections (otherwise Z
+// is 0)
+#ifdef use_xyz
+  void ZFillFunction(ZFillCallback zFillFunc);
+#endif
+protected:
+  virtual bool ExecuteInternal();
+
+private:
+  JoinList m_Joins;
+  JoinList m_GhostJoins;
+  IntersectList m_IntersectList;
+  ClipType m_ClipType;
+  typedef std::list<cInt> MaximaList;
+  MaximaList m_Maxima;
+  TEdge *m_SortedEdges;
+  bool m_ExecuteLocked;
+  PolyFillType m_ClipFillType;
+  PolyFillType m_SubjFillType;
+  bool m_ReverseOutput;
+  bool m_UsingPolyTree;
+  bool m_StrictSimple;
+#ifdef use_xyz
+  ZFillCallback m_ZFill; // custom callback
+#endif
+
+  void SetWindingCount(TEdge &edge);
+
+  bool IsEvenOddFillType(const TEdge &edge) const;
+
+  bool IsEvenOddAltFillType(const TEdge &edge) const;
+
+  void InsertLocalMinimaIntoAEL(const cInt botY);
+
+  void InsertEdgeIntoAEL(TEdge *edge, TEdge *startEdge);
+
+  void AddEdgeToSEL(TEdge *edge);
+
+  bool PopEdgeFromSEL(TEdge *&edge);
+
+  void CopyAELToSEL();
+
+  void DeleteFromSEL(TEdge *e);
+
+  void SwapPositionsInSEL(TEdge *edge1, TEdge *edge2);
+
+  bool IsContributing(const TEdge &edge) const;
+
+  bool IsTopHorz(const cInt XPos);
+
+  void DoMaxima(TEdge *e);
+
+  void ProcessHorizontals();
+
+  void ProcessHorizontal(TEdge *horzEdge);
+
+  void AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
+
+  OutPt *AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
+
+  OutRec *GetOutRec(int idx);
+
+  void AppendPolygon(TEdge *e1, TEdge *e2);
+
+  void IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &pt);
+
+  OutPt *AddOutPt(TEdge *e, const IntPoint &pt);
+
+  OutPt *GetLastOutPt(TEdge *e);
+
+  bool ProcessIntersections(const cInt topY);
+
+  void BuildIntersectList(const cInt topY);
+
+  void ProcessIntersectList();
+
+  void ProcessEdgesAtTopOfScanbeam(const cInt topY);
+
+  void BuildResult(Paths &polys);
+
+  void BuildResult2(PolyTree &polytree);
+
+  void SetHoleState(TEdge *e, OutRec *outrec);
+
+  void DisposeIntersectNodes();
+
+  bool FixupIntersectionOrder();
+
+  void FixupOutPolygon(OutRec &outrec);
+
+  void FixupOutPolyline(OutRec &outrec);
+
+  bool IsHole(TEdge *e);
+
+  bool FindOwnerFromSplitRecs(OutRec &outRec, OutRec *&currOrfl);
+
+  void FixHoleLinkage(OutRec &outrec);
+
+  void AddJoin(OutPt *op1, OutPt *op2, const IntPoint offPt);
+
+  void ClearJoins();
+
+  void ClearGhostJoins();
+
+  void AddGhostJoin(OutPt *op, const IntPoint offPt);
+
+  bool JoinPoints(Join *j, OutRec *outRec1, OutRec *outRec2);
+
+  void JoinCommonEdges();
+
+  void DoSimplePolygons();
+
+  void FixupFirstLefts1(OutRec *OldOutRec, OutRec *NewOutRec);
+
+  void FixupFirstLefts2(OutRec *InnerOutRec, OutRec *OuterOutRec);
+
+  void FixupFirstLefts3(OutRec *OldOutRec, OutRec *NewOutRec);
+
+#ifdef use_xyz
+  void SetZ(IntPoint &pt, TEdge &e1, TEdge &e2);
+#endif
+};
+//------------------------------------------------------------------------------
+
+class ClipperOffset {
+public:
+  ClipperOffset(double miterLimit = 2.0, double roundPrecision = 0.25);
+
+  ~ClipperOffset();
+
+  void AddPath(const Path &path, JoinType joinType, EndType endType);
+
+  void AddPaths(const Paths &paths, JoinType joinType, EndType endType);
+
+  void Execute(Paths &solution, double delta);
+
+  void Execute(PolyTree &solution, double delta);
+
+  void Clear();
+
+  double MiterLimit;
+  double ArcTolerance;
+
+private:
+  Paths m_destPolys;
+  Path m_srcPoly;
+  Path m_destPoly;
+  std::vector<DoublePoint> m_normals;
+  double m_delta, m_sinA, m_sin, m_cos;
+  double m_miterLim, m_StepsPerRad;
+  IntPoint m_lowest;
+  PolyNode m_polyNodes;
+
+  void FixOrientations();
+
+  void DoOffset(double delta);
+
+  void OffsetPoint(int j, int &k, JoinType jointype);
+
+  void DoSquare(int j, int k);
+
+  void DoMiter(int j, int k, double r);
+
+  void DoRound(int j, int k);
+};
+//------------------------------------------------------------------------------
+
+class clipperException : public std::exception {
+public:
+  clipperException(const char *description) : m_descr(description) {}
+
+  virtual ~clipperException() throw() {}
+
+  virtual const char *what() const throw() { return m_descr.c_str(); }
+
+private:
+  std::string m_descr;
+};
+//------------------------------------------------------------------------------
+
+} // ClipperLib namespace
+
+#endif // clipper_hpp

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

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ocr_cls_process.h"
+#include <cmath>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <iostream>
+#include <vector>
+
+const std::vector<int> CLS_IMAGE_SHAPE = {3, 48, 192};
+
+cv::Mat cls_resize_img(const cv::Mat &img) {
+  int imgC = CLS_IMAGE_SHAPE[0];
+  int imgW = CLS_IMAGE_SHAPE[2];
+  int imgH = CLS_IMAGE_SHAPE[1];
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_CUBIC);
+
+  if (resize_w < imgW) {
+    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(imgW - resize_w),
+                       cv::BORDER_CONSTANT, {0, 0, 0});
+  }
+  return resize_img;
+}
\ No newline at end of file

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

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "common.h"
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+extern const std::vector<int> CLS_IMAGE_SHAPE;
+
+cv::Mat cls_resize_img(const cv::Mat &img);
\ No newline at end of file

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

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ocr_crnn_process.h"
+#include <cmath>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <iostream>
+#include <vector>
+
+const std::string CHARACTER_TYPE = "ch";
+const int MAX_DICT_LENGTH = 6624;
+const std::vector<int> REC_IMAGE_SHAPE = {3, 32, 320};
+
+static cv::Mat crnn_resize_norm_img(cv::Mat img, float wh_ratio) {
+  int imgC = REC_IMAGE_SHAPE[0];
+  int imgW = REC_IMAGE_SHAPE[2];
+  int imgH = REC_IMAGE_SHAPE[1];
+
+  if (CHARACTER_TYPE == "ch")
+    imgW = int(32 * wh_ratio);
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_CUBIC);
+
+  resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
+
+  for (int h = 0; h < resize_img.rows; h++) {
+    for (int w = 0; w < resize_img.cols; w++) {
+      resize_img.at<cv::Vec3f>(h, w)[0] =
+          (resize_img.at<cv::Vec3f>(h, w)[0] - 0.5) * 2;
+      resize_img.at<cv::Vec3f>(h, w)[1] =
+          (resize_img.at<cv::Vec3f>(h, w)[1] - 0.5) * 2;
+      resize_img.at<cv::Vec3f>(h, w)[2] =
+          (resize_img.at<cv::Vec3f>(h, w)[2] - 0.5) * 2;
+    }
+  }
+
+  cv::Mat dist;
+  cv::copyMakeBorder(resize_img, dist, 0, 0, 0, int(imgW - resize_w),
+                     cv::BORDER_CONSTANT, {0, 0, 0});
+
+  return dist;
+}
+
+cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio) {
+  int imgC = REC_IMAGE_SHAPE[0];
+  int imgW = REC_IMAGE_SHAPE[2];
+  int imgH = REC_IMAGE_SHAPE[1];
+
+  if (CHARACTER_TYPE == "ch") {
+    imgW = int(32 * wh_ratio);
+  }
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w = 0;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH));
+  return resize_img;
+}
+
+cv::Mat get_rotate_crop_image(const cv::Mat &srcimage,
+                              const std::vector<std::vector<int>> &box) {
+
+  std::vector<std::vector<int>> points = box;
+
+  int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
+  int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
+  int left = int(*std::min_element(x_collect, x_collect + 4));
+  int right = int(*std::max_element(x_collect, x_collect + 4));
+  int top = int(*std::min_element(y_collect, y_collect + 4));
+  int bottom = int(*std::max_element(y_collect, y_collect + 4));
+
+  cv::Mat img_crop;
+  srcimage(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
+
+  for (int i = 0; i < points.size(); i++) {
+    points[i][0] -= left;
+    points[i][1] -= top;
+  }
+
+  int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
+                                pow(points[0][1] - points[1][1], 2)));
+  int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
+                                 pow(points[0][1] - points[3][1], 2)));
+
+  cv::Point2f pts_std[4];
+  pts_std[0] = cv::Point2f(0., 0.);
+  pts_std[1] = cv::Point2f(img_crop_width, 0.);
+  pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
+  pts_std[3] = cv::Point2f(0.f, img_crop_height);
+
+  cv::Point2f pointsf[4];
+  pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
+  pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
+  pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
+  pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
+
+  cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
+
+  cv::Mat dst_img;
+  cv::warpPerspective(img_crop, dst_img, M,
+                      cv::Size(img_crop_width, img_crop_height),
+                      cv::BORDER_REPLICATE);
+
+  if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
+    /*
+    cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
+    cv::transpose(dst_img, srcCopy);
+    cv::flip(srcCopy, srcCopy, 0);
+    return srcCopy;
+    */
+    cv::transpose(dst_img, dst_img);
+    cv::flip(dst_img, dst_img, 0);
+    return dst_img;
+  } else {
+    return dst_img;
+  }
+}

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

+//
+// Created by fujiayi on 2020/7/3.
+//
+#pragma once
+
+#include "common.h"
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+extern const std::vector<int> REC_IMAGE_SHAPE;
+
+cv::Mat get_rotate_crop_image(const cv::Mat &srcimage,
+                              const std::vector<std::vector<int>> &box);
+
+cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio);
+
+template <class ForwardIterator>
+inline size_t argmax(ForwardIterator first, ForwardIterator last) {
+  return std::distance(first, std::max_element(first, last));
+}
\ No newline at end of file

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

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ocr_clipper.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <iostream>
+#include <math.h>
+#include <vector>
+
+static void getcontourarea(float **box, float unclip_ratio, float &distance) {
+  int pts_num = 4;
+  float area = 0.0f;
+  float dist = 0.0f;
+  for (int i = 0; i < pts_num; i++) {
+    area += box[i][0] * box[(i + 1) % pts_num][1] -
+            box[i][1] * box[(i + 1) % pts_num][0];
+    dist += sqrtf((box[i][0] - box[(i + 1) % pts_num][0]) *
+                      (box[i][0] - box[(i + 1) % pts_num][0]) +
+                  (box[i][1] - box[(i + 1) % pts_num][1]) *
+                      (box[i][1] - box[(i + 1) % pts_num][1]));
+  }
+  area = fabs(float(area / 2.0));
+
+  distance = area * unclip_ratio / dist;
+}
+
+static cv::RotatedRect unclip(float **box) {
+  float unclip_ratio = 2.0;
+  float distance = 1.0;
+
+  getcontourarea(box, unclip_ratio, distance);
+
+  ClipperLib::ClipperOffset offset;
+  ClipperLib::Path p;
+  p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
+    << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1]))
+    << ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
+    << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
+  offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
+
+  ClipperLib::Paths soln;
+  offset.Execute(soln, distance);
+  std::vector<cv::Point2f> points;
+
+  for (int j = 0; j < soln.size(); j++) {
+    for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
+      points.emplace_back(soln[j][i].X, soln[j][i].Y);
+    }
+  }
+  cv::RotatedRect res = cv::minAreaRect(points);
+
+  return res;
+}
+
+static float **Mat2Vec(cv::Mat mat) {
+  auto **array = new float *[mat.rows];
+  for (int i = 0; i < mat.rows; ++i) {
+    array[i] = new float[mat.cols];
+  }
+  for (int i = 0; i < mat.rows; ++i) {
+    for (int j = 0; j < mat.cols; ++j) {
+      array[i][j] = mat.at<float>(i, j);
+    }
+  }
+
+  return array;
+}
+
+static void quickSort(float **s, int l, int r) {
+  if (l < r) {
+    int i = l, j = r;
+    float x = s[l][0];
+    float *xp = s[l];
+    while (i < j) {
+      while (i < j && s[j][0] >= x) {
+        j--;
+      }
+      if (i < j) {
+        std::swap(s[i++], s[j]);
+      }
+      while (i < j && s[i][0] < x) {
+        i++;
+      }
+      if (i < j) {
+        std::swap(s[j--], s[i]);
+      }
+    }
+    s[i] = xp;
+    quickSort(s, l, i - 1);
+    quickSort(s, i + 1, r);
+  }
+}
+
+static void quickSort_vector(std::vector<std::vector<int>> &box, int l, int r,
+                             int axis) {
+  if (l < r) {
+    int i = l, j = r;
+    int x = box[l][axis];
+    std::vector<int> xp(box[l]);
+    while (i < j) {
+      while (i < j && box[j][axis] >= x) {
+        j--;
+      }
+      if (i < j) {
+        std::swap(box[i++], box[j]);
+      }
+      while (i < j && box[i][axis] < x) {
+        i++;
+      }
+      if (i < j) {
+        std::swap(box[j--], box[i]);
+      }
+    }
+    box[i] = xp;
+    quickSort_vector(box, l, i - 1, axis);
+    quickSort_vector(box, i + 1, r, axis);
+  }
+}
+
+static std::vector<std::vector<int>>
+order_points_clockwise(std::vector<std::vector<int>> pts) {
+  std::vector<std::vector<int>> box = pts;
+  quickSort_vector(box, 0, int(box.size() - 1), 0);
+  std::vector<std::vector<int>> leftmost = {box[0], box[1]};
+  std::vector<std::vector<int>> rightmost = {box[2], box[3]};
+
+  if (leftmost[0][1] > leftmost[1][1]) {
+    std::swap(leftmost[0], leftmost[1]);
+  }
+
+  if (rightmost[0][1] > rightmost[1][1]) {
+    std::swap(rightmost[0], rightmost[1]);
+  }
+
+  std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1],
+                                        leftmost[1]};
+  return rect;
+}
+
+static float **get_mini_boxes(cv::RotatedRect box, float &ssid) {
+  ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
+
+  cv::Mat points;
+  cv::boxPoints(box, points);
+  // sorted box points
+  auto array = Mat2Vec(points);
+  quickSort(array, 0, 3);
+
+  float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
+  if (array[3][1] <= array[2][1]) {
+    idx2 = array[3];
+    idx3 = array[2];
+  } else {
+    idx2 = array[2];
+    idx3 = array[3];
+  }
+  if (array[1][1] <= array[0][1]) {
+    idx1 = array[1];
+    idx4 = array[0];
+  } else {
+    idx1 = array[0];
+    idx4 = array[1];
+  }
+
+  array[0] = idx1;
+  array[1] = idx2;
+  array[2] = idx3;
+  array[3] = idx4;
+
+  return array;
+}
+
+template <class T> T clamp(T x, T min, T max) {
+  if (x > max) {
+    return max;
+  }
+  if (x < min) {
+    return min;
+  }
+  return x;
+}
+
+static float clampf(float x, float min, float max) {
+  if (x > max)
+    return max;
+  if (x < min)
+    return min;
+  return x;
+}
+
+float box_score_fast(float **box_array, cv::Mat pred) {
+  auto array = box_array;
+  int width = pred.cols;
+  int height = pred.rows;
+
+  float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
+  float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
+
+  int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0,
+                   width - 1);
+  int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0,
+                   width - 1);
+  int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0,
+                   height - 1);
+  int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0,
+                   height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point root_point[4];
+  root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
+  root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
+  root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
+  root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
+  const cv::Point *ppt[1] = {root_point};
+  int npt[] = {4};
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
+
+  auto score = cv::mean(croppedImg, mask)[0];
+  return score;
+}
+
+std::vector<std::vector<std::vector<int>>>
+boxes_from_bitmap(const cv::Mat &pred, const cv::Mat &bitmap) {
+  const int min_size = 3;
+  const int max_candidates = 1000;
+  const float box_thresh = 0.5;
+
+  int width = bitmap.cols;
+  int height = bitmap.rows;
+
+  std::vector<std::vector<cv::Point>> contours;
+  std::vector<cv::Vec4i> hierarchy;
+
+  cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST,
+                   cv::CHAIN_APPROX_SIMPLE);
+
+  int num_contours =
+      contours.size() >= max_candidates ? max_candidates : contours.size();
+
+  std::vector<std::vector<std::vector<int>>> boxes;
+
+  for (int _i = 0; _i < num_contours; _i++) {
+    float ssid;
+    cv::RotatedRect box = cv::minAreaRect(contours[_i]);
+    auto array = get_mini_boxes(box, ssid);
+
+    auto box_for_unclip = array;
+    // end get_mini_box
+
+    if (ssid < min_size) {
+      continue;
+    }
+
+    float score;
+    score = box_score_fast(array, pred);
+    // end box_score_fast
+    if (score < box_thresh) {
+      continue;
+    }
+
+    // start for unclip
+    cv::RotatedRect points = unclip(box_for_unclip);
+    // end for unclip
+
+    cv::RotatedRect clipbox = points;
+    auto cliparray = get_mini_boxes(clipbox, ssid);
+
+    if (ssid < min_size + 2)
+      continue;
+
+    int dest_width = pred.cols;
+    int dest_height = pred.rows;
+    std::vector<std::vector<int>> intcliparray;
+
+    for (int num_pt = 0; num_pt < 4; num_pt++) {
+      std::vector<int> a{int(clampf(roundf(cliparray[num_pt][0] / float(width) *
+                                           float(dest_width)),
+                                    0, float(dest_width))),
+                         int(clampf(roundf(cliparray[num_pt][1] /
+                                           float(height) * float(dest_height)),
+                                    0, float(dest_height)))};
+      intcliparray.emplace_back(std::move(a));
+    }
+    boxes.emplace_back(std::move(intcliparray));
+
+  } // end for
+  return boxes;
+}
+
+int _max(int a, int b) { return a >= b ? a : b; }
+
+int _min(int a, int b) { return a >= b ? b : a; }
+
+std::vector<std::vector<std::vector<int>>>
+filter_tag_det_res(const std::vector<std::vector<std::vector<int>>> &o_boxes,
+                   float ratio_h, float ratio_w, const cv::Mat &srcimg) {
+  int oriimg_h = srcimg.rows;
+  int oriimg_w = srcimg.cols;
+  std::vector<std::vector<std::vector<int>>> boxes{o_boxes};
+  std::vector<std::vector<std::vector<int>>> root_points;
+  for (int n = 0; n < boxes.size(); n++) {
+    boxes[n] = order_points_clockwise(boxes[n]);
+    for (int m = 0; m < boxes[0].size(); m++) {
+      boxes[n][m][0] /= ratio_w;
+      boxes[n][m][1] /= ratio_h;
+
+      boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
+    }
+  }
+
+  for (int n = 0; n < boxes.size(); n++) {
+    int rect_width, rect_height;
+    rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
+                          pow(boxes[n][0][1] - boxes[n][1][1], 2)));
+    rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
+                           pow(boxes[n][0][1] - boxes[n][3][1], 2)));
+    if (rect_width <= 10 || rect_height <= 10)
+      continue;
+    root_points.push_back(boxes[n]);
+  }
+  return root_points;
+}
\ No newline at end of file

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

+//
+// Created by fujiayi on 2020/7/2.
+//
+#pragma once
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+std::vector<std::vector<std::vector<int>>>
+boxes_from_bitmap(const cv::Mat &pred, const cv::Mat &bitmap);
+
+std::vector<std::vector<std::vector<int>>>
+filter_tag_det_res(const std::vector<std::vector<std::vector<int>>> &o_boxes,
+                   float ratio_h, float ratio_w, const cv::Mat &srcimg);
\ No newline at end of file

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

+//
+// Created by fujiayi on 2020/7/1.
+//
+
+#include "ocr_ppredictor.h"
+#include "common.h"
+#include "ocr_cls_process.h"
+#include "ocr_crnn_process.h"
+#include "ocr_db_post_process.h"
+#include "preprocess.h"
+
+namespace ppredictor {
+
+OCR_PPredictor::OCR_PPredictor(const OCR_Config &config) : _config(config) {}
+
+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});
+  _det_predictor->init_nb(det_model_content);
+
+  _rec_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_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});
+  _cls_predictor->init_nb(cls_model_content);
+  return RETURN_OK;
+}
+
+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});
+  _det_predictor->init_from_file(det_model_path);
+
+  _rec_predictor = std::unique_ptr<PPredictor>(
+      new PPredictor{_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});
+  _cls_predictor->init_from_file(cls_model_path);
+  return RETURN_OK;
+}
+/**
+ * for debug use, show result of First Step
+ * @param filter_boxes
+ * @param boxes
+ * @param srcimg
+ */
+static void
+visual_img(const std::vector<std::vector<std::vector<int>>> &filter_boxes,
+           const std::vector<std::vector<std::vector<int>>> &boxes,
+           const cv::Mat &srcimg) {
+  // visualization
+  cv::Point rook_points[filter_boxes.size()][4];
+  for (int n = 0; n < filter_boxes.size(); n++) {
+    for (int m = 0; m < filter_boxes[0].size(); m++) {
+      rook_points[n][m] =
+          cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
+    }
+  }
+
+  cv::Mat img_vis;
+  srcimg.copyTo(img_vis);
+  for (int n = 0; n < boxes.size(); n++) {
+    const cv::Point *ppt[1] = {rook_points[n]};
+    int npt[] = {4};
+    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
+  }
+  // 调试用，自行替换需要修改的路径
+  cv::imwrite("/sdcard/1/vis.png", img_vis);
+}
+
+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) {
+  PredictorInput input = _det_predictor->get_first_input();
+  input.set_dims(dims);
+  input.set_data(input_data, input_len);
+  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);
+}
+
+std::vector<OCRPredictResult> OCR_PPredictor::infer_rec(
+    const std::vector<std::vector<std::vector<int>>> &boxes,
+    const cv::Mat &origin_img) {
+  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;
+
+    dims[2] = input_image.rows;
+    dims[3] = input_image.cols;
+    input.set_dims(dims);
+
+    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();
+
+    OCRPredictResult res;
+
+    // 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;
+    }
+    res.score = score;
+    res.points = box;
+    ocr_results.emplace_back(std::move(res));
+  }
+  LOGI("ocr_results finished %lu", ocr_results.size());
+  return ocr_results;
+}
+
+cv::Mat 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();
+
+  cv::Mat input_image = cls_resize_img(img);
+  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;
+
+  dims[2] = input_image.rows;
+  dims[3] = input_image.cols;
+  input.set_dims(dims);
+
+  neon_mean_scale(dimg, input.get_mutable_float_data(), input_size, mean,
+                  scale);
+
+  std::vector<PredictorOutput> results = _cls_predictor->infer();
+
+  const float *scores = results.at(0).get_float_data();
+  float score = 0;
+  int label = 0;
+  for (int64_t i = 0; i < results.at(0).get_size(); i++) {
+    LOGI("output scores [%f]", scores[i]);
+    if (scores[i] > score) {
+      score = scores[i];
+      label = i;
+    }
+  }
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  if (label % 2 == 1 && score > thresh) {
+    cv::rotate(srcimg, srcimg, 1);
+  }
+  return srcimg;
+}
+
+std::vector<std::vector<std::vector<int>>>
+OCR_PPredictor::calc_filtered_boxes(const float *pred, int pred_size,
+                                    int output_height, int output_width,
+                                    const cv::Mat &origin) {
+  const double threshold = 0.3;
+  const double maxvalue = 1;
+
+  cv::Mat pred_map = cv::Mat::zeros(output_height, output_width, CV_32F);
+  memcpy(pred_map.data, pred, pred_size * sizeof(float));
+  cv::Mat cbuf_map;
+  pred_map.convertTo(cbuf_map, CV_8UC1);
+
+  cv::Mat bit_map;
+  cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
+
+  std::vector<std::vector<std::vector<int>>> boxes =
+      boxes_from_bitmap(pred_map, bit_map);
+  float ratio_h = output_height * 1.0f / origin.rows;
+  float ratio_w = output_width * 1.0f / origin.cols;
+  std::vector<std::vector<std::vector<int>>> filter_boxes =
+      filter_tag_det_res(boxes, ratio_h, ratio_w, origin);
+  return filter_boxes;
+}
+
+std::vector<int>
+OCR_PPredictor::postprocess_rec_word_index(const PredictorOutput &res) {
+  const int *rec_idx = res.get_int_data();
+  const std::vector<std::vector<uint64_t>> rec_idx_lod = res.get_lod();
+
+  std::vector<int> pred_idx;
+  for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
+    pred_idx.emplace_back(rec_idx[n]);
+  }
+  return pred_idx;
+}
+
+float OCR_PPredictor::postprocess_rec_score(const PredictorOutput &res) {
+  const float *predict_batch = res.get_float_data();
+  const std::vector<int64_t> predict_shape = res.get_shape();
+  const std::vector<std::vector<uint64_t>> predict_lod = res.get_lod();
+  int blank = predict_shape[1];
+  float score = 0.f;
+  int count = 0;
+  for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
+    int argmax_idx = argmax(predict_batch + n * predict_shape[1],
+                            predict_batch + (n + 1) * predict_shape[1]);
+    float max_value = predict_batch[n * predict_shape[1] + argmax_idx];
+    if (blank - 1 - argmax_idx > 1e-5) {
+      score += max_value;
+      count += 1;
+    }
+  }
+  if (count == 0) {
+    LOGE("calc score count 0");
+  } else {
+    score /= count;
+  }
+  LOGI("calc score: %f", score);
+  return score;
+}
+
+NET_TYPE OCR_PPredictor::get_net_flag() const { return NET_OCR; }
+}
\ No newline at end of file

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

+//
+// Created by fujiayi on 2020/7/1.
+//
+
+#pragma once
+
+#include "ppredictor.h"
+#include <opencv2/opencv.hpp>
+#include <paddle_api.h>
+#include <string>
+
+namespace ppredictor {
+
+/**
+ * Config
+ */
+struct OCR_Config {
+  int thread_num = 4; // Thread num
+  paddle::lite_api::PowerMode mode =
+      paddle::lite_api::LITE_POWER_HIGH; // PaddleLite Mode
+};
+
+/**
+ * PolyGone Result
+ */
+struct OCRPredictResult {
+  std::vector<int> word_index;
+  std::vector<std::vector<int>> points;
+  float score;
+};
+
+/**
+ * OCR there are 2 models
+ * 1. First model（det），select polygones to show where are the texts
+ * 2. crop from the origin images, use these polygones to infer
+ */
+class OCR_PPredictor : public PPredictor_Interface {
+public:
+  OCR_PPredictor(const OCR_Config &config);
+
+  virtual ~OCR_PPredictor() {}
+
+  /**
+   * 初始化二个模型的Predictor
+   * @param det_model_content
+   * @param rec_model_content
+   * @return
+   */
+  int init(const std::string &det_model_content,
+           const std::string &rec_model_content,
+           const std::string &cls_model_content);
+  int init_from_file(const std::string &det_model_path,
+                     const std::string &rec_model_path,
+                     const std::string &cls_model_path);
+  /**
+   * Return OCR result
+   * @param dims
+   * @param input_data
+   * @param input_len
+   * @param net_flag
+   * @param origin
+   * @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);
+
+  virtual NET_TYPE get_net_flag() const;
+
+private:
+  /**
+   * calcul Polygone from the result image of first model
+   * @param pred
+   * @param output_height
+   * @param output_width
+   * @param origin
+   * @return
+   */
+  std::vector<std::vector<std::vector<int>>>
+  calc_filtered_boxes(const float *pred, int pred_size, int output_height,
+                      int output_width, const cv::Mat &origin);
+
+  /**
+   * infer for second model
+   *
+   * @param boxes
+   * @param origin
+   * @return
+   */
+  std::vector<OCRPredictResult>
+  infer_rec(const std::vector<std::vector<std::vector<int>>> &boxes,
+            const cv::Mat &origin);
+
+  /**
+  * infer for cls model
+  *
+  * @param boxes
+  * @param origin
+  * @return
+  */
+  cv::Mat infer_cls(const cv::Mat &origin, float thresh = 0.9);
+
+  /**
+   * Postprocess or sencod model to extract text
+   * @param res
+   * @return
+   */
+  std::vector<int> postprocess_rec_word_index(const PredictorOutput &res);
+
+  /**
+   * calculate confidence of second model text result
+   * @param res
+   * @return
+   */
+  float postprocess_rec_score(const PredictorOutput &res);
+
+  std::unique_ptr<PPredictor> _det_predictor;
+  std::unique_ptr<PPredictor> _rec_predictor;
+  std::unique_ptr<PPredictor> _cls_predictor;
+  OCR_Config _config;
+};
+}

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

+#include "ppredictor.h"
+#include "common.h"
+
+namespace ppredictor {
+PPredictor::PPredictor(int thread_num, int net_flag,
+                       paddle::lite_api::PowerMode mode)
+    : _thread_num(thread_num), _net_flag(net_flag), _mode(mode) {}
+
+int PPredictor::init_nb(const std::string &model_content) {
+  paddle::lite_api::MobileConfig config;
+  config.set_model_from_buffer(model_content);
+  return _init(config);
+}
+
+int PPredictor::init_from_file(const std::string &model_content) {
+  paddle::lite_api::MobileConfig config;
+  config.set_model_from_file(model_content);
+  return _init(config);
+}
+
+template <typename ConfigT> int PPredictor::_init(ConfigT &config) {
+  config.set_threads(_thread_num);
+  config.set_power_mode(_mode);
+  _predictor = paddle::lite_api::CreatePaddlePredictor(config);
+  LOGI("paddle instance created");
+  return RETURN_OK;
+}
+
+PredictorInput PPredictor::get_input(int index) {
+  PredictorInput input{_predictor->GetInput(index), index, _net_flag};
+  _is_input_get = true;
+  return input;
+}
+
+std::vector<PredictorInput> PPredictor::get_inputs(int num) {
+  std::vector<PredictorInput> results;
+  for (int i = 0; i < num; i++) {
+    results.emplace_back(get_input(i));
+  }
+  return results;
+}
+
+PredictorInput PPredictor::get_first_input() { return get_input(0); }
+
+std::vector<PredictorOutput> PPredictor::infer() {
+  LOGI("infer Run start %d", _net_flag);
+  std::vector<PredictorOutput> results;
+  if (!_is_input_get) {
+    return results;
+  }
+  _predictor->Run();
+  LOGI("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()));
+    PredictorOutput result{std::move(output_tensor), i, _net_flag};
+    results.emplace_back(std::move(result));
+  }
+  return results;
+}
+
+NET_TYPE PPredictor::get_net_flag() const { return (NET_TYPE)_net_flag; }
+}
\ No newline at end of file

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

+#pragma once
+
+#include "paddle_api.h"
+#include "predictor_input.h"
+#include "predictor_output.h"
+
+namespace ppredictor {
+
+/**
+ * PaddleLite Preditor Common Interface
+ */
+class PPredictor_Interface {
+public:
+  virtual ~PPredictor_Interface() {}
+
+  virtual NET_TYPE get_net_flag() const = 0;
+};
+
+/**
+ * Common Predictor
+ */
+class PPredictor : public PPredictor_Interface {
+public:
+  PPredictor(
+      int thread_num, int net_flag = 0,
+      paddle::lite_api::PowerMode mode = paddle::lite_api::LITE_POWER_HIGH);
+
+  virtual ~PPredictor() {}
+
+  /**
+   * init paddlitelite opt model，nb format ，or use ini_paddle
+   * @param model_content
+   * @return 0
+   */
+  virtual int init_nb(const std::string &model_content);
+
+  virtual int init_from_file(const std::string &model_content);
+
+  std::vector<PredictorOutput> infer();
+
+  std::shared_ptr<paddle::lite_api::PaddlePredictor> get_predictor() {
+    return _predictor;
+  }
+
+  virtual std::vector<PredictorInput> get_inputs(int num);
+
+  virtual PredictorInput get_input(int index);
+
+  virtual PredictorInput get_first_input();
+
+  virtual NET_TYPE get_net_flag() const;
+
+protected:
+  template <typename ConfigT> int _init(ConfigT &config);
+
+private:
+  int _thread_num;
+  paddle::lite_api::PowerMode _mode;
+  std::shared_ptr<paddle::lite_api::PaddlePredictor> _predictor;
+  bool _is_input_get = false;
+  int _net_flag;
+};
+}

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

+#include "predictor_input.h"
+
+namespace ppredictor {
+
+void PredictorInput::set_dims(std::vector<int64_t> dims) {
+  // yolov3
+  if (_net_flag == 101 && _index == 1) {
+    _tensor->Resize({1, 2});
+    _tensor->mutable_data<int>()[0] = (int)dims.at(2);
+    _tensor->mutable_data<int>()[1] = (int)dims.at(3);
+  } else {
+    _tensor->Resize(dims);
+  }
+  _is_dims_set = true;
+}
+
+float *PredictorInput::get_mutable_float_data() {
+  if (!_is_dims_set) {
+    LOGE("PredictorInput::set_dims is not called");
+  }
+  return _tensor->mutable_data<float>();
+}
+
+void PredictorInput::set_data(const float *input_data, int input_float_len) {
+  float *input_raw_data = get_mutable_float_data();
+  memcpy(input_raw_data, input_data, input_float_len * sizeof(float));
+}
+}
\ No newline at end of file

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

+#pragma once
+
+#include "common.h"
+#include <paddle_api.h>
+#include <vector>
+
+namespace ppredictor {
+class PredictorInput {
+public:
+  PredictorInput(std::unique_ptr<paddle::lite_api::Tensor> &&tensor, int index,
+                 int net_flag)
+      : _tensor(std::move(tensor)), _index(index), _net_flag(net_flag) {}
+
+  void set_dims(std::vector<int64_t> dims);
+
+  float *get_mutable_float_data();
+
+  void set_data(const float *input_data, int input_float_len);
+
+private:
+  std::unique_ptr<paddle::lite_api::Tensor> _tensor;
+  bool _is_dims_set = false;
+  int _index;
+  int _net_flag;
+};
+}

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

+#include "predictor_output.h"
+namespace ppredictor {
+const float *PredictorOutput::get_float_data() const {
+  return _tensor->data<float>();
+}
+
+const int *PredictorOutput::get_int_data() const {
+  return _tensor->data<int>();
+}
+
+const std::vector<std::vector<uint64_t>> PredictorOutput::get_lod() const {
+  return _tensor->lod();
+}
+
+int64_t PredictorOutput::get_size() const {
+  if (_net_flag == NET_OCR) {
+    return _tensor->shape().at(2) * _tensor->shape().at(3);
+  } else {
+    return product(_tensor->shape());
+  }
+}
+
+const std::vector<int64_t> PredictorOutput::get_shape() const {
+  return _tensor->shape();
+}
+}
\ No newline at end of file

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

+#pragma once
+
+#include "common.h"
+#include <paddle_api.h>
+#include <vector>
+
+namespace ppredictor {
+class PredictorOutput {
+public:
+  PredictorOutput() {}
+  PredictorOutput(std::unique_ptr<const paddle::lite_api::Tensor> &&tensor,
+                  int index, int net_flag)
+      : _tensor(std::move(tensor)), _index(index), _net_flag(net_flag) {}
+
+  const float *get_float_data() const;
+  const int *get_int_data() const;
+  int64_t get_size() const;
+  const std::vector<std::vector<uint64_t>> get_lod() const;
+  const std::vector<int64_t> get_shape() const;
+
+  std::vector<float> data;    // return float, or use data_int
+  std::vector<int> data_int;  // several layers return int ，or use data
+  std::vector<int64_t> shape; // PaddleLite output shape
+  std::vector<std::vector<uint64_t>> lod; // PaddleLite output lod
+
+private:
+  std::unique_ptr<const paddle::lite_api::Tensor> _tensor;
+  int _index;
+  int _net_flag;
+};
+}

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

+#include "preprocess.h"
+#include <android/bitmap.h>
+
+cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
+  AndroidBitmapInfo info;
+  int result = AndroidBitmap_getInfo(env, bitmap, &info);
+  if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
+    LOGE("AndroidBitmap_getInfo failed, result: %d", result);
+    return cv::Mat{};
+  }
+  if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
+    LOGE("Bitmap format is not RGBA_8888 !");
+    return cv::Mat{};
+  }
+  unsigned char *srcData = NULL;
+  AndroidBitmap_lockPixels(env, bitmap, (void **)&srcData);
+  cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
+  memcpy(mat.data, srcData, info.height * info.width * 4);
+  AndroidBitmap_unlockPixels(env, bitmap);
+  cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
+  /**
+  if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
+      LOGE("Write image failed " );
+  }
+   */
+
+  return mat;
+}
+
+cv::Mat resize_img(const cv::Mat &img, int height, int width) {
+  if (img.rows == height && img.cols == width) {
+    return img;
+  }
+  cv::Mat new_img;
+  cv::resize(img, new_img, cv::Size(height, width));
+  return new_img;
+}
+
+// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
+void neon_mean_scale(const float *din, float *dout, int size,
+                     const std::vector<float> &mean,
+                     const std::vector<float> &scale) {
+  if (mean.size() != 3 || scale.size() != 3) {
+    LOGE("[ERROR] mean or scale size must equal to 3");
+    return;
+  }
+
+  float32x4_t vmean0 = vdupq_n_f32(mean[0]);
+  float32x4_t vmean1 = vdupq_n_f32(mean[1]);
+  float32x4_t vmean2 = vdupq_n_f32(mean[2]);
+  float32x4_t vscale0 = vdupq_n_f32(scale[0]);
+  float32x4_t vscale1 = vdupq_n_f32(scale[1]);
+  float32x4_t vscale2 = vdupq_n_f32(scale[2]);
+
+  float *dout_c0 = dout;
+  float *dout_c1 = dout + size;
+  float *dout_c2 = dout + size * 2;
+
+  int i = 0;
+  for (; i < size - 3; i += 4) {
+    float32x4x3_t vin3 = vld3q_f32(din);
+    float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
+    float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
+    float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
+    float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
+    float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
+    float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
+    vst1q_f32(dout_c0, vs0);
+    vst1q_f32(dout_c1, vs1);
+    vst1q_f32(dout_c2, vs2);
+
+    din += 12;
+    dout_c0 += 4;
+    dout_c1 += 4;
+    dout_c2 += 4;
+  }
+  for (; i < size; i++) {
+    *(dout_c0++) = (*(din++) - mean[0]) * scale[0];
+    *(dout_c1++) = (*(din++) - mean[1]) * scale[1];
+    *(dout_c2++) = (*(din++) - mean[2]) * scale[2];
+  }
+}
\ No newline at end of file

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

+#pragma once
+
+#include "common.h"
+#include <jni.h>
+#include <opencv2/opencv.hpp>
+cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap);
+
+cv::Mat resize_img(const cv::Mat &img, int height, int width);
+
+void neon_mean_scale(const float *din, float *dout, int size,
+                     const std::vector<float> &mean,
+                     const std::vector<float> &scale);

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

+/*
+ * Copyright (C) 2014 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.res.Configuration;
+import android.os.Bundle;
+import android.preference.PreferenceActivity;
+import android.view.MenuInflater;
+import android.view.View;
+import android.view.ViewGroup;
+
+import androidx.annotation.LayoutRes;
+import androidx.annotation.Nullable;
+import androidx.appcompat.app.ActionBar;
+import androidx.appcompat.app.AppCompatDelegate;
+import androidx.appcompat.widget.Toolbar;
+
+/**
+ * A {@link PreferenceActivity} which implements and proxies the necessary calls
+ * to be used with AppCompat.
+ * <p>
+ * This technique can be used with an {@link android.app.Activity} class, not just
+ * {@link PreferenceActivity}.
+ */
+public abstract class AppCompatPreferenceActivity extends PreferenceActivity {
+    private AppCompatDelegate mDelegate;
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        getDelegate().installViewFactory();
+        getDelegate().onCreate(savedInstanceState);
+        super.onCreate(savedInstanceState);
+    }
+
+    @Override
+    protected void onPostCreate(Bundle savedInstanceState) {
+        super.onPostCreate(savedInstanceState);
+        getDelegate().onPostCreate(savedInstanceState);
+    }
+
+    public ActionBar getSupportActionBar() {
+        return getDelegate().getSupportActionBar();
+    }
+
+    public void setSupportActionBar(@Nullable Toolbar toolbar) {
+        getDelegate().setSupportActionBar(toolbar);
+    }
+
+    @Override
+    public MenuInflater getMenuInflater() {
+        return getDelegate().getMenuInflater();
+    }
+
+    @Override
+    public void setContentView(@LayoutRes int layoutResID) {
+        getDelegate().setContentView(layoutResID);
+    }
+
+    @Override
+    public void setContentView(View view) {
+        getDelegate().setContentView(view);
+    }
+
+    @Override
+    public void setContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().setContentView(view, params);
+    }
+
+    @Override
+    public void addContentView(View view, ViewGroup.LayoutParams params) {
+        getDelegate().addContentView(view, params);
+    }
+
+    @Override
+    protected void onPostResume() {
+        super.onPostResume();
+        getDelegate().onPostResume();
+    }
+
+    @Override
+    protected void onTitleChanged(CharSequence title, int color) {
+        super.onTitleChanged(title, color);
+        getDelegate().setTitle(title);
+    }
+
+    @Override
+    public void onConfigurationChanged(Configuration newConfig) {
+        super.onConfigurationChanged(newConfig);
+        getDelegate().onConfigurationChanged(newConfig);
+    }
+
+    @Override
+    protected void onStop() {
+        super.onStop();
+        getDelegate().onStop();
+    }
+
+    @Override
+    protected void onDestroy() {
+        super.onDestroy();
+        getDelegate().onDestroy();
+    }
+
+    public void invalidateOptionsMenu() {
+        getDelegate().invalidateOptionsMenu();
+    }
+
+    private AppCompatDelegate getDelegate() {
+        if (mDelegate == null) {
+            mDelegate = AppCompatDelegate.create(this, null);
+        }
+        return mDelegate;
+    }
+}

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

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.Manifest;
+import android.app.ProgressDialog;
+import android.content.ContentResolver;
+import android.content.Context;
+import android.content.Intent;
+import android.content.SharedPreferences;
+import android.content.pm.PackageManager;
+import android.database.Cursor;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.graphics.drawable.BitmapDrawable;
+import android.media.ExifInterface;
+import android.content.res.AssetManager;
+import android.net.Uri;
+import android.os.Bundle;
+import android.os.Environment;
+import android.os.Handler;
+import android.os.HandlerThread;
+import android.os.Message;
+import android.preference.PreferenceManager;
+import android.provider.MediaStore;
+import android.text.method.ScrollingMovementMethod;
+import android.util.Log;
+import android.view.Menu;
+import android.view.MenuInflater;
+import android.view.MenuItem;
+import android.view.View;
+import android.widget.ImageView;
+import android.widget.TextView;
+import android.widget.Toast;
+
+import androidx.annotation.NonNull;
+import androidx.appcompat.app.AppCompatActivity;
+import androidx.core.app.ActivityCompat;
+import androidx.core.content.ContextCompat;
+import androidx.core.content.FileProvider;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.InputStream;
+import java.text.SimpleDateFormat;
+import java.util.Date;
+
+public class MainActivity extends AppCompatActivity {
+    private static final String TAG = MainActivity.class.getSimpleName();
+    public static final int OPEN_GALLERY_REQUEST_CODE = 0;
+    public static final int TAKE_PHOTO_REQUEST_CODE = 1;
+
+    public static final int REQUEST_LOAD_MODEL = 0;
+    public static final int REQUEST_RUN_MODEL = 1;
+    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;
+
+    protected ProgressDialog pbLoadModel = null;
+    protected ProgressDialog pbRunModel = null;
+
+    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
+
+    // UI components of object detection
+    protected TextView tvInputSetting;
+    protected TextView tvStatus;
+    protected ImageView ivInputImage;
+    protected TextView tvOutputResult;
+    protected TextView tvInferenceTime;
+
+    // Model settings of object detection
+    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 float scoreThreshold = 0.1f;
+    private String currentPhotoPath;
+    private AssetManager assetManager =null;
+
+    protected Predictor predictor = new Predictor();
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+
+        // Clear all setting items to avoid app crashing due to the incorrect settings
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        SharedPreferences.Editor editor = sharedPreferences.edit();
+        editor.clear();
+        editor.apply();
+
+        // Setup the UI components
+        tvInputSetting = findViewById(R.id.tv_input_setting);
+        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);
+        tvInputSetting.setMovementMethod(ScrollingMovementMethod.getInstance());
+        tvOutputResult.setMovementMethod(ScrollingMovementMethod.getInstance());
+
+        // Prepare the worker thread for mode loading and inference
+        receiver = new Handler() {
+            @Override
+            public void handleMessage(Message msg) {
+                switch (msg.what) {
+                    case RESPONSE_LOAD_MODEL_SUCCESSED:
+                        if(pbLoadModel!=null && pbLoadModel.isShowing()){
+                            pbLoadModel.dismiss();
+                        }
+                        onLoadModelSuccessed();
+                        break;
+                    case RESPONSE_LOAD_MODEL_FAILED:
+                        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()){
+                            pbRunModel.dismiss();
+                        }
+                        onRunModelSuccessed();
+                        break;
+                    case RESPONSE_RUN_MODEL_FAILED:
+                        if(pbRunModel!=null && pbRunModel.isShowing()){
+                            pbRunModel.dismiss();
+                        }
+                        Toast.makeText(MainActivity.this, "Run model failed!", Toast.LENGTH_SHORT).show();
+                        onRunModelFailed();
+                        break;
+                    default:
+                        break;
+                }
+            }
+        };
+
+        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()) {
+                            receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_SUCCESSED);
+                        } else {
+                            receiver.sendEmptyMessage(RESPONSE_LOAD_MODEL_FAILED);
+                        }
+                        break;
+                    case REQUEST_RUN_MODEL:
+                        // Run model if model is loaded
+                        if (onRunModel()) {
+                            receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_SUCCESSED);
+                        } else {
+                            receiver.sendEmptyMessage(RESPONSE_RUN_MODEL_FAILED);
+                        }
+                        break;
+                    default:
+                        break;
+                }
+            }
+        };
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        SharedPreferences sharedPreferences = PreferenceManager.getDefaultSharedPreferences(this);
+        boolean 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);
+        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;
+        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];
+            }
+        }
+        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;
+            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.scrollTo(0, 0);
+            // Reload model if configure has been changed
+//            loadModel();
+            set_img();
+        }
+    }
+
+    public void loadModel() {
+        pbLoadModel = ProgressDialog.show(this, "", "loading model...", false, false);
+        sender.sendEmptyMessage(REQUEST_LOAD_MODEL);
+    }
+
+    public void runModel() {
+        pbRunModel = ProgressDialog.show(this, "", "running model...", false, false);
+        sender.sendEmptyMessage(REQUEST_RUN_MODEL);
+    }
+
+    public boolean onLoadModel() {
+        return predictor.init(MainActivity.this, modelPath, labelPath, cpuThreadNum,
+                cpuPowerMode,
+                inputColorFormat,
+                inputShape, inputMean,
+                inputStd, scoreThreshold);
+    }
+
+    public boolean onRunModel() {
+        return predictor.isLoaded() && predictor.runModel();
+    }
+
+    public void onLoadModelSuccessed() {
+        // Load test image from path and run model
+        tvStatus.setText("STATUS: load model successed");
+    }
+
+    public void onLoadModelFailed() {
+        tvStatus.setText("STATUS: load model failed");
+    }
+
+    public void onRunModelSuccessed() {
+        tvStatus.setText("STATUS: run model successed");
+        // Obtain results and update UI
+        tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
+        Bitmap outputImage = predictor.outputImage();
+        if (outputImage != null) {
+            ivInputImage.setImageBitmap(outputImage);
+        }
+        tvOutputResult.setText(predictor.outputResult());
+        tvOutputResult.scrollTo(0, 0);
+    }
+
+    public void onRunModelFailed() {
+        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);
+            ivInputImage.setImageBitmap(bmp);
+        } catch (IOException e) {
+            Toast.makeText(MainActivity.this, "Load image failed!", Toast.LENGTH_SHORT).show();
+            e.printStackTrace();
+        }
+    }
+
+    public void onSettingsClicked() {
+        startActivity(new Intent(MainActivity.this, SettingsActivity.class));
+    }
+
+    @Override
+    public boolean onCreateOptionsMenu(Menu menu) {
+        MenuInflater inflater = getMenuInflater();
+        inflater.inflate(R.menu.menu_action_options, menu);
+        return true;
+    }
+
+    public boolean onPrepareOptionsMenu(Menu menu) {
+        boolean isLoaded = predictor.isLoaded();
+        return super.onPrepareOptionsMenu(menu);
+    }
+
+    @Override
+    public boolean onOptionsItemSelected(MenuItem item) {
+        switch (item.getItemId()) {
+            case android.R.id.home:
+                finish();
+                break;
+            case R.id.settings:
+                if (requestAllPermissions()) {
+                    // Make sure we have SDCard r&w permissions to load model from SDCard
+                    onSettingsClicked();
+                }
+                break;
+        }
+        return super.onOptionsItemSelected(item);
+    }
+
+    @Override
+    public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions,
+                                           @NonNull int[] grantResults) {
+        super.onRequestPermissionsResult(requestCode, permissions, grantResults);
+        if (grantResults[0] != PackageManager.PERMISSION_GRANTED || grantResults[1] != PackageManager.PERMISSION_GRANTED) {
+            Toast.makeText(this, "Permission Denied", Toast.LENGTH_SHORT).show();
+        }
+    }
+
+    private boolean requestAllPermissions() {
+        if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE)
+                != PackageManager.PERMISSION_GRANTED || ContextCompat.checkSelfPermission(this,
+                Manifest.permission.CAMERA)
+                != PackageManager.PERMISSION_GRANTED) {
+            ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE,
+                            Manifest.permission.CAMERA},
+                    0);
+            return false;
+        }
+        return true;
+    }
+
+    private void openGallery() {
+        Intent intent = new Intent(Intent.ACTION_PICK, null);
+        intent.setDataAndType(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, "image/*");
+        startActivityForResult(intent, OPEN_GALLERY_REQUEST_CODE);
+    }
+
+    private void takePhoto() {
+        Intent takePictureIntent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE);
+        // Ensure that there's a camera activity to handle the intent
+        if (takePictureIntent.resolveActivity(getPackageManager()) != null) {
+            // Create the File where the photo should go
+            File photoFile = null;
+            try {
+                photoFile = createImageFile();
+            } catch (IOException ex) {
+                Log.e("MainActitity", ex.getMessage(), ex);
+                Toast.makeText(MainActivity.this,
+                        "Create Camera temp file failed: " + ex.getMessage(), Toast.LENGTH_SHORT).show();
+            }
+            // Continue only if the File was successfully created
+            if (photoFile != null) {
+                Log.i(TAG, "FILEPATH " + getExternalFilesDir("Pictures").getAbsolutePath());
+                Uri photoURI = FileProvider.getUriForFile(this,
+                        "com.baidu.paddle.lite.demo.ocr.fileprovider",
+                        photoFile);
+                currentPhotoPath = photoFile.getAbsolutePath();
+                takePictureIntent.putExtra(MediaStore.EXTRA_OUTPUT, photoURI);
+                startActivityForResult(takePictureIntent, TAKE_PHOTO_REQUEST_CODE);
+                Log.i(TAG, "startActivityForResult finished");
+            }
+        }
+
+    }
+
+    private File createImageFile() throws IOException {
+        // Create an image file name
+        String timeStamp = new SimpleDateFormat("yyyyMMdd_HHmmss").format(new Date());
+        String imageFileName = "JPEG_" + timeStamp + "_";
+        File storageDir = getExternalFilesDir(Environment.DIRECTORY_PICTURES);
+        File image = File.createTempFile(
+                imageFileName,  /* prefix */
+                ".bmp",         /* suffix */
+                storageDir      /* directory */
+        );
+
+        return image;
+    }
+
+    @Override
+    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
+        super.onActivityResult(requestCode, resultCode, data);
+        if (resultCode == RESULT_OK) {
+            switch (requestCode) {
+                case OPEN_GALLERY_REQUEST_CODE:
+                    if (data == null) {
+                        break;
+                    }
+                    try {
+                        ContentResolver resolver = getContentResolver();
+                        Uri uri = data.getData();
+                        Bitmap image = MediaStore.Images.Media.getBitmap(resolver, uri);
+                        String[] proj = {MediaStore.Images.Media.DATA};
+                        Cursor cursor = managedQuery(uri, proj, null, null, null);
+                        cursor.moveToFirst();
+                        if (image != null) {
+//                            onImageChanged(image);
+                            ivInputImage.setImageBitmap(image);
+                        }
+                    } catch (IOException e) {
+                        Log.e(TAG, e.toString());
+                    }
+                    break;
+                case TAKE_PHOTO_REQUEST_CODE:
+                    if (currentPhotoPath != null) {
+                        ExifInterface exif = null;
+                        try {
+                            exif = new ExifInterface(currentPhotoPath);
+                        } catch (IOException e) {
+                            e.printStackTrace();
+                        }
+                        int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION,
+                                ExifInterface.ORIENTATION_UNDEFINED);
+                        Log.i(TAG, "rotation " + orientation);
+                        Bitmap image = BitmapFactory.decodeFile(currentPhotoPath);
+                        image = Utils.rotateBitmap(image, orientation);
+                        if (image != null) {
+//                            onImageChanged(image);
+                            ivInputImage.setImageBitmap(image);
+                        }
+                    } else {
+                        Log.e(TAG, "currentPhotoPath is null");
+                    }
+                    break;
+                default:
+                    break;
+            }
+        }
+    }
+
+    public void btn_load_model_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) {
+            tvStatus.setText("STATUS: image is not exists");
+        }
+        else if (!predictor.isLoaded()){
+            tvStatus.setText("STATUS: model is not loaded");
+        }else{
+            tvStatus.setText("STATUS: run model ...... ");
+            predictor.setInputImage(image);
+            runModel();
+        }
+    }
+    public void btn_choice_img_click(View view) {
+        if (requestAllPermissions()) {
+            openGallery();
+        }
+    }
+
+    public void btn_take_photo_click(View view) {
+        if (requestAllPermissions()) {
+            takePhoto();
+        }
+    }
+
+    @Override
+    protected void onDestroy() {
+        if (predictor != null) {
+            predictor.releaseModel();
+        }
+        worker.quit();
+        super.onDestroy();
+    }
+}

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

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.graphics.Bitmap;
+import android.util.Log;
+
+import java.util.ArrayList;
+import java.util.concurrent.atomic.AtomicBoolean;
+
+public class OCRPredictorNative {
+
+    private static final AtomicBoolean isSOLoaded = new AtomicBoolean();
+
+    public static void loadLibrary() throws RuntimeException {
+        if (!isSOLoaded.get() && isSOLoaded.compareAndSet(false, true)) {
+            try {
+                System.loadLibrary("Native");
+            } catch (Throwable e) {
+                RuntimeException exception = new RuntimeException(
+                        "Load libNative.so failed, please check it exists in apk file.", e);
+                throw exception;
+            }
+        }
+    }
+
+    private Config config;
+
+    private long nativePointer = 0;
+
+    public OCRPredictorNative(Config config) {
+        this.config = config;
+        loadLibrary();
+        nativePointer = init(config.detModelFilename, config.recModelFilename,config.clsModelFilename,
+                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);
+        ArrayList<OcrResultModel> results = postprocess(rawResults);
+        return results;
+    }
+
+    public static class Config {
+        public int cpuThreadNum;
+        public String cpuPower;
+        public String detModelFilename;
+        public String recModelFilename;
+        public String clsModelFilename;
+
+    }
+
+    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 float[] forward(long pointer, float[] buf, float[] ddims, Bitmap originalImage);
+
+    protected native void release(long pointer);
+
+    private ArrayList<OcrResultModel> postprocess(float[] raw) {
+        ArrayList<OcrResultModel> results = new ArrayList<OcrResultModel>();
+        int begin = 0;
+
+        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);
+        }
+
+        return results;
+    }
+
+    private OcrResultModel parse(float[] raw, int begin, int pointNum, int wordNum) {
+        int current = begin;
+        OcrResultModel model = new OcrResultModel();
+        model.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]));
+        }
+        current += (pointNum * 2);
+        for (int i = 0; i < wordNum; i++) {
+            int index = Math.round(raw[current + i]);
+            model.addWordIndex(index);
+        }
+        Log.i("OCRPredictorNative", "word finished " + wordNum);
+        return model;
+    }
+
+
+}

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

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.graphics.Point;
+
+import java.util.ArrayList;
+import java.util.List;
+
+public class OcrResultModel {
+    private List<Point> points;
+    private List<Integer> wordIndex;
+    private String label;
+    private float confidence;
+
+    public OcrResultModel() {
+        super();
+        points = new ArrayList<>();
+        wordIndex = new ArrayList<>();
+    }
+
+    public void addPoints(int x, int y) {
+        Point point = new Point(x, y);
+        points.add(point);
+    }
+
+    public void addWordIndex(int index) {
+        wordIndex.add(index);
+    }
+
+    public List<Point> getPoints() {
+        return points;
+    }
+
+    public List<Integer> getWordIndex() {
+        return wordIndex;
+    }
+
+    public String getLabel() {
+        return label;
+    }
+
+    public void setLabel(String label) {
+        this.label = label;
+    }
+
+    public float getConfidence() {
+        return confidence;
+    }
+
+    public void setConfidence(float confidence) {
+        this.confidence = confidence;
+    }
+}

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

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.graphics.Canvas;
+import android.graphics.Color;
+import android.graphics.Paint;
+import android.graphics.Path;
+import android.graphics.Point;
+import android.util.Log;
+
+import java.io.File;
+import java.io.InputStream;
+import java.util.ArrayList;
+import java.util.Date;
+import java.util.List;
+import java.util.Vector;
+
+import static android.graphics.Color.*;
+
+public class Predictor {
+    private static final String TAG = Predictor.class.getSimpleName();
+    public boolean isLoaded = false;
+    public int warmupIterNum = 1;
+    public int inferIterNum = 1;
+    public int cpuThreadNum = 4;
+    public String cpuPowerMode = "LITE_POWER_HIGH";
+    public String modelPath = "";
+    public String modelName = "";
+    protected OCRPredictorNative paddlePredictor = null;
+    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 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);
+        if (!isLoaded) {
+            return false;
+        }
+        isLoaded = loadLabel(appCtx, labelPath);
+        return isLoaded;
+    }
+
+
+    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);
+        if (!isLoaded) {
+            return false;
+        }
+        this.inputColorFormat = inputColorFormat;
+        this.inputShape = inputShape;
+        this.inputMean = inputMean;
+        this.inputStd = inputStd;
+        this.scoreThreshold = scoreThreshold;
+        return true;
+    }
+
+    protected boolean loadModel(Context appCtx, String modelPath, int cpuThreadNum, String cpuPowerMode) {
+        // Release model if exists
+        releaseModel();
+
+        // Load model
+        if (modelPath.isEmpty()) {
+            return false;
+        }
+        String realPath = modelPath;
+        if (!modelPath.substring(0, 1).equals("/")) {
+            // Read model files from custom path if the first character of mode path is '/'
+            // otherwise copy model to cache from assets
+            realPath = appCtx.getCacheDir() + "/" + modelPath;
+            Utils.copyDirectoryFromAssets(appCtx, modelPath, realPath);
+        }
+        if (realPath.isEmpty()) {
+            return false;
+        }
+
+        OCRPredictorNative.Config config = new OCRPredictorNative.Config();
+        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;
+        paddlePredictor = new OCRPredictorNative(config);
+
+        this.cpuThreadNum = cpuThreadNum;
+        this.cpuPowerMode = cpuPowerMode;
+        this.modelPath = realPath;
+        this.modelName = realPath.substring(realPath.lastIndexOf("/") + 1);
+        return true;
+    }
+
+    public void releaseModel() {
+        if (paddlePredictor != null) {
+            paddlePredictor.destory();
+            paddlePredictor = null;
+        }
+        isLoaded = false;
+        cpuThreadNum = 1;
+        cpuPowerMode = "LITE_POWER_HIGH";
+        modelPath = "";
+        modelName = "";
+    }
+
+    protected boolean loadLabel(Context appCtx, String labelPath) {
+        wordLabels.clear();
+        wordLabels.add("black");
+        // Load word labels from file
+        try {
+            InputStream assetsInputStream = appCtx.getAssets().open(labelPath);
+            int available = assetsInputStream.available();
+            byte[] lines = new byte[available];
+            assetsInputStream.read(lines);
+            assetsInputStream.close();
+            String words = new String(lines);
+            String[] contents = words.split("\n");
+            for (String content : contents) {
+                wordLabels.add(content);
+            }
+            Log.i(TAG, "Word label size: " + wordLabels.size());
+        } catch (Exception e) {
+            Log.e(TAG, e.getMessage());
+            return false;
+        }
+        return true;
+    }
+
+
+    public boolean runModel() {
+        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 p = scaleImage.getPixel(scaleImage.getWidth() - 1, scaleImage.getHeight() - 1);
+            for (int y = 0; y < height; y++) {
+                for (int x = 0; x < width; x++) {
+                    int color = scaleImage.getPixel(x, y);
+                    float[] rgb = new float[]{(float) red(color) / 255.0f, (float) green(color) / 255.0f,
+                            (float) blue(color) / 255.0f};
+                    inputData[y * width + x] = (rgb[channelIdx[0]] - inputMean[0]) / inputStd[0];
+                    inputData[y * width + x + channelStride[0]] = (rgb[channelIdx[1]] - inputMean[1]) / inputStd[1];
+                    inputData[y * width + x + channelStride[1]] = (rgb[channelIdx[2]] - inputMean[2]) / inputStd[2];
+
+                }
+            }
+        } else if (channels == 1) {
+            for (int y = 0; y < height; y++) {
+                for (int x = 0; x < width; x++) {
+                    int color = inputImage.getPixel(x, y);
+                    float gray = (float) (red(color) + green(color) + blue(color)) / 3.0f / 255.0f;
+                    inputData[y * width + x] = (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);
+        }
+        warmupIterNum = 0; // do not need warm
+        // Run inference
+        start = new Date();
+        ArrayList<OcrResultModel> results = paddlePredictor.runImage(inputData, width, height, channels, inputImage);
+        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());
+        drawResults(results);
+
+        return true;
+    }
+
+
+    public boolean isLoaded() {
+        return paddlePredictor != null && isLoaded;
+    }
+
+    public String modelPath() {
+        return modelPath;
+    }
+
+    public String modelName() {
+        return modelName;
+    }
+
+    public int cpuThreadNum() {
+        return cpuThreadNum;
+    }
+
+    public String cpuPowerMode() {
+        return cpuPowerMode;
+    }
+
+    public float inferenceTime() {
+        return inferenceTime;
+    }
+
+    public Bitmap inputImage() {
+        return inputImage;
+    }
+
+    public Bitmap outputImage() {
+        return outputImage;
+    }
+
+    public String outputResult() {
+        return outputResult;
+    }
+
+    public float preprocessTime() {
+        return preprocessTime;
+    }
+
+    public float postprocessTime() {
+        return postprocessTime;
+    }
+
+
+    public void setInputImage(Bitmap image) {
+        if (image == null) {
+            return;
+        }
+        this.inputImage = image.copy(Bitmap.Config.ARGB_8888, true);
+    }
+
+    private ArrayList<OcrResultModel> postprocess(ArrayList<OcrResultModel> results) {
+        for (OcrResultModel r : results) {
+            StringBuffer word = new StringBuffer();
+            for (int index : r.getWordIndex()) {
+                if (index >= 0 && index < wordLabels.size()) {
+                    word.append(wordLabels.get(index));
+                } else {
+                    Log.e(TAG, "Word index is not in label list:" + index);
+                    word.append("×");
+                }
+            }
+            r.setLabel(word.toString());
+        }
+        return results;
+    }
+
+    private void drawResults(ArrayList<OcrResultModel> results) {
+        StringBuffer outputResultSb = new StringBuffer("");
+        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(") ");
+            }
+            Log.i(TAG, sb.toString()); // show LOG in Logcat panel
+            outputResultSb.append(i + 1).append(": ").append(result.getLabel()).append("\n");
+        }
+        outputResult = outputResultSb.toString();
+        outputImage = inputImage;
+        Canvas canvas = new Canvas(outputImage);
+        Paint paintFillAlpha = new Paint();
+        paintFillAlpha.setStyle(Paint.Style.FILL);
+        paintFillAlpha.setColor(Color.parseColor("#3B85F5"));
+        paintFillAlpha.setAlpha(50);
+
+        Paint paint = new Paint();
+        paint.setColor(Color.parseColor("#3B85F5"));
+        paint.setStrokeWidth(5);
+        paint.setStyle(Paint.Style.STROKE);
+
+        for (OcrResultModel result : results) {
+            Path path = new Path();
+            List<Point> points = result.getPoints();
+            path.moveTo(points.get(0).x, points.get(0).y);
+            for (int i = points.size() - 1; i >= 0; i--) {
+                Point p = points.get(i);
+                path.lineTo(p.x, p.y);
+            }
+            canvas.drawPath(path, paint);
+            canvas.drawPath(path, paintFillAlpha);
+        }
+    }
+
+}

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

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.SharedPreferences;
+import android.os.Bundle;
+import android.preference.CheckBoxPreference;
+import android.preference.EditTextPreference;
+import android.preference.ListPreference;
+
+import androidx.appcompat.app.ActionBar;
+
+import java.util.ArrayList;
+import java.util.List;
+
+
+public class SettingsActivity extends AppCompatPreferenceActivity implements SharedPreferences.OnSharedPreferenceChangeListener {
+    ListPreference lpChoosePreInstalledModel = null;
+    CheckBoxPreference cbEnableCustomSettings = null;
+    EditTextPreference etModelPath = null;
+    EditTextPreference etLabelPath = null;
+    ListPreference etImagePath = null;
+    ListPreference lpCPUThreadNum = null;
+    ListPreference lpCPUPowerMode = null;
+    ListPreference lpInputColorFormat = null;
+    EditTextPreference etInputShape = null;
+    EditTextPreference etInputMean = null;
+    EditTextPreference etInputStd = null;
+    EditTextPreference etScoreThreshold = null;
+
+    List<String> preInstalledModelPaths = null;
+    List<String> preInstalledLabelPaths = null;
+    List<String> preInstalledImagePaths = null;
+    List<String> preInstalledInputShapes = null;
+    List<String> preInstalledCPUThreadNums = null;
+    List<String> preInstalledCPUPowerModes = null;
+    List<String> preInstalledInputColorFormats = null;
+    List<String> preInstalledInputMeans = null;
+    List<String> preInstalledInputStds = null;
+    List<String> preInstalledScoreThresholds = null;
+
+    @Override
+    public void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        addPreferencesFromResource(R.xml.settings);
+        ActionBar supportActionBar = getSupportActionBar();
+        if (supportActionBar != null) {
+            supportActionBar.setDisplayHomeAsUpEnabled(true);
+        }
+
+        // Initialized pre-installed models
+        preInstalledModelPaths = new ArrayList<String>();
+        preInstalledLabelPaths = new ArrayList<String>();
+        preInstalledImagePaths = new ArrayList<String>();
+        preInstalledInputShapes = new ArrayList<String>();
+        preInstalledCPUThreadNums = new ArrayList<String>();
+        preInstalledCPUPowerModes = new ArrayList<String>();
+        preInstalledInputColorFormats = new ArrayList<String>();
+        preInstalledInputMeans = new ArrayList<String>();
+        preInstalledInputStds = new ArrayList<String>();
+        preInstalledScoreThresholds = new ArrayList<String>();
+        // Add ssd_mobilenet_v1_pascalvoc_for_cpu
+        preInstalledModelPaths.add(getString(R.string.MODEL_PATH_DEFAULT));
+        preInstalledLabelPaths.add(getString(R.string.LABEL_PATH_DEFAULT));
+        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));
+        preInstalledScoreThresholds.add(getString(R.string.SCORE_THRESHOLD_DEFAULT));
+
+        // Setup UI components
+        lpChoosePreInstalledModel =
+                (ListPreference) findPreference(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY));
+        String[] preInstalledModelNames = new String[preInstalledModelPaths.size()];
+        for (int i = 0; i < preInstalledModelPaths.size(); i++) {
+            preInstalledModelNames[i] =
+                    preInstalledModelPaths.get(i).substring(preInstalledModelPaths.get(i).lastIndexOf("/") + 1);
+        }
+        lpChoosePreInstalledModel.setEntries(preInstalledModelNames);
+        lpChoosePreInstalledModel.setEntryValues(preInstalledModelPaths.toArray(new String[preInstalledModelPaths.size()]));
+        cbEnableCustomSettings =
+                (CheckBoxPreference) findPreference(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY));
+        etModelPath = (EditTextPreference) findPreference(getString(R.string.MODEL_PATH_KEY));
+        etModelPath.setTitle("Model Path (SDCard: " + Utils.getSDCardDirectory() + ")");
+        etLabelPath = (EditTextPreference) findPreference(getString(R.string.LABEL_PATH_KEY));
+        etImagePath = (ListPreference) findPreference(getString(R.string.IMAGE_PATH_KEY));
+        lpCPUThreadNum =
+                (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));
+        etScoreThreshold = (EditTextPreference) findPreference(getString(R.string.SCORE_THRESHOLD_KEY));
+    }
+
+    private void reloadPreferenceAndUpdateUI() {
+        SharedPreferences sharedPreferences = getPreferenceScreen().getSharedPreferences();
+        boolean enableCustomSettings =
+                sharedPreferences.getBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
+        String modelPath = sharedPreferences.getString(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        int modelIdx = lpChoosePreInstalledModel.findIndexOfValue(modelPath);
+        if (modelIdx >= 0 && modelIdx < preInstalledModelPaths.size()) {
+            if (!enableCustomSettings) {
+                SharedPreferences.Editor editor = sharedPreferences.edit();
+                editor.putString(getString(R.string.MODEL_PATH_KEY), preInstalledModelPaths.get(modelIdx));
+                editor.putString(getString(R.string.LABEL_PATH_KEY), preInstalledLabelPaths.get(modelIdx));
+                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.SCORE_THRESHOLD_KEY),
+                        preInstalledScoreThresholds.get(modelIdx));
+                editor.apply();
+            }
+            lpChoosePreInstalledModel.setSummary(modelPath);
+        }
+        cbEnableCustomSettings.setChecked(enableCustomSettings);
+        etModelPath.setEnabled(enableCustomSettings);
+        etLabelPath.setEnabled(enableCustomSettings);
+        etImagePath.setEnabled(enableCustomSettings);
+        lpCPUThreadNum.setEnabled(enableCustomSettings);
+        lpCPUPowerMode.setEnabled(enableCustomSettings);
+        lpInputColorFormat.setEnabled(enableCustomSettings);
+        etInputShape.setEnabled(enableCustomSettings);
+        etInputMean.setEnabled(enableCustomSettings);
+        etInputStd.setEnabled(enableCustomSettings);
+        etScoreThreshold.setEnabled(enableCustomSettings);
+        modelPath = sharedPreferences.getString(getString(R.string.MODEL_PATH_KEY),
+                getString(R.string.MODEL_PATH_DEFAULT));
+        String labelPath = sharedPreferences.getString(getString(R.string.LABEL_PATH_KEY),
+                getString(R.string.LABEL_PATH_DEFAULT));
+        String imagePath = sharedPreferences.getString(getString(R.string.IMAGE_PATH_KEY),
+                getString(R.string.IMAGE_PATH_DEFAULT));
+        String cpuThreadNum = sharedPreferences.getString(getString(R.string.CPU_THREAD_NUM_KEY),
+                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 scoreThreshold = sharedPreferences.getString(getString(R.string.SCORE_THRESHOLD_KEY),
+                getString(R.string.SCORE_THRESHOLD_DEFAULT));
+        etModelPath.setSummary(modelPath);
+        etModelPath.setText(modelPath);
+        etLabelPath.setSummary(labelPath);
+        etLabelPath.setText(labelPath);
+        etImagePath.setSummary(imagePath);
+        etImagePath.setValue(imagePath);
+        lpCPUThreadNum.setValue(cpuThreadNum);
+        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);
+        etScoreThreshold.setText(scoreThreshold);
+        etScoreThreshold.setSummary(scoreThreshold);
+    }
+
+    @Override
+    protected void onResume() {
+        super.onResume();
+        getPreferenceScreen().getSharedPreferences().registerOnSharedPreferenceChangeListener(this);
+        reloadPreferenceAndUpdateUI();
+    }
+
+    @Override
+    protected void onPause() {
+        super.onPause();
+        getPreferenceScreen().getSharedPreferences().unregisterOnSharedPreferenceChangeListener(this);
+    }
+
+    @Override
+    public void onSharedPreferenceChanged(SharedPreferences sharedPreferences, String key) {
+        if (key.equals(getString(R.string.CHOOSE_PRE_INSTALLED_MODEL_KEY))) {
+            SharedPreferences.Editor editor = sharedPreferences.edit();
+            editor.putBoolean(getString(R.string.ENABLE_CUSTOM_SETTINGS_KEY), false);
+            editor.commit();
+        }
+        reloadPreferenceAndUpdateUI();
+    }
+}

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

+package com.baidu.paddle.lite.demo.ocr;
+
+import android.content.Context;
+import android.graphics.Bitmap;
+import android.graphics.Matrix;
+import android.media.ExifInterface;
+import android.os.Environment;
+
+import java.io.*;
+
+public class Utils {
+    private static final String TAG = Utils.class.getSimpleName();
+
+    public static void copyFileFromAssets(Context appCtx, String srcPath, String dstPath) {
+        if (srcPath.isEmpty() || dstPath.isEmpty()) {
+            return;
+        }
+        InputStream is = null;
+        OutputStream os = null;
+        try {
+            is = new BufferedInputStream(appCtx.getAssets().open(srcPath));
+            os = new BufferedOutputStream(new FileOutputStream(new File(dstPath)));
+            byte[] buffer = new byte[1024];
+            int length = 0;
+            while ((length = is.read(buffer)) != -1) {
+                os.write(buffer, 0, length);
+            }
+        } catch (FileNotFoundException e) {
+            e.printStackTrace();
+        } catch (IOException e) {
+            e.printStackTrace();
+        } finally {
+            try {
+                os.close();
+                is.close();
+            } catch (IOException e) {
+                e.printStackTrace();
+            }
+        }
+    }
+
+    public static void copyDirectoryFromAssets(Context appCtx, String srcDir, String dstDir) {
+        if (srcDir.isEmpty() || dstDir.isEmpty()) {
+            return;
+        }
+        try {
+            if (!new File(dstDir).exists()) {
+                new File(dstDir).mkdirs();
+            }
+            for (String fileName : appCtx.getAssets().list(srcDir)) {
+                String srcSubPath = srcDir + File.separator + fileName;
+                String dstSubPath = dstDir + File.separator + fileName;
+                if (new File(srcSubPath).isDirectory()) {
+                    copyDirectoryFromAssets(appCtx, srcSubPath, dstSubPath);
+                } else {
+                    copyFileFromAssets(appCtx, srcSubPath, dstSubPath);
+                }
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    public static float[] parseFloatsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        float[] floats = new float[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            floats[i] = Float.parseFloat(pieces[i].trim());
+        }
+        return floats;
+    }
+
+    public static long[] parseLongsFromString(String string, String delimiter) {
+        String[] pieces = string.trim().toLowerCase().split(delimiter);
+        long[] longs = new long[pieces.length];
+        for (int i = 0; i < pieces.length; i++) {
+            longs[i] = Long.parseLong(pieces[i].trim());
+        }
+        return longs;
+    }
+
+    public static String getSDCardDirectory() {
+        return Environment.getExternalStorageDirectory().getAbsolutePath();
+    }
+
+    public static boolean isSupportedNPU() {
+        return false;
+        // String hardware = android.os.Build.HARDWARE;
+        // return hardware.equalsIgnoreCase("kirin810") || hardware.equalsIgnoreCase("kirin990");
+    }
+
+    public static Bitmap resizeWithStep(Bitmap bitmap, int maxLength, int step) {
+        int width = bitmap.getWidth();
+        int height = bitmap.getHeight();
+        int maxWH = Math.max(width, height);
+        float ratio = 1;
+        int newWidth = width;
+        int newHeight = height;
+        if (maxWH > maxLength) {
+            ratio = maxLength * 1.0f / maxWH;
+            newWidth = (int) Math.floor(ratio * width);
+            newHeight = (int) Math.floor(ratio * height);
+        }
+
+        newWidth = newWidth - newWidth % step;
+        if (newWidth == 0) {
+            newWidth = step;
+        }
+        newHeight = newHeight - newHeight % step;
+        if (newHeight == 0) {
+            newHeight = step;
+        }
+        return Bitmap.createScaledBitmap(bitmap, newWidth, newHeight, true);
+    }
+
+    public static Bitmap rotateBitmap(Bitmap bitmap, int orientation) {
+
+        Matrix matrix = new Matrix();
+        switch (orientation) {
+            case ExifInterface.ORIENTATION_NORMAL:
+                return bitmap;
+            case ExifInterface.ORIENTATION_FLIP_HORIZONTAL:
+                matrix.setScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_180:
+                matrix.setRotate(180);
+                break;
+            case ExifInterface.ORIENTATION_FLIP_VERTICAL:
+                matrix.setRotate(180);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_TRANSPOSE:
+                matrix.setRotate(90);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_90:
+                matrix.setRotate(90);
+                break;
+            case ExifInterface.ORIENTATION_TRANSVERSE:
+                matrix.setRotate(-90);
+                matrix.postScale(-1, 1);
+                break;
+            case ExifInterface.ORIENTATION_ROTATE_270:
+                matrix.setRotate(-90);
+                break;
+            default:
+                return bitmap;
+        }
+        try {
+            Bitmap bmRotated = Bitmap.createBitmap(bitmap, 0, 0, bitmap.getWidth(), bitmap.getHeight(), matrix, true);
+            bitmap.recycle();
+            return bmRotated;
+        }
+        catch (OutOfMemoryError e) {
+            e.printStackTrace();
+            return null;
+        }
+    }
+}

deploy/android_demo/app/src/main/res/drawable-v24/ic_launcher_foreground.xml

+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:aapt="http://schemas.android.com/aapt"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillType="evenOdd"
+        android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000">
+        <aapt:attr name="android:fillColor">
+            <gradient
+                android:endX="78.5885"
+                android:endY="90.9159"
+                android:startX="48.7653"
+                android:startY="61.0927"
+                android:type="linear">
+                <item
+                    android:color="#44000000"
+                    android:offset="0.0" />
+                <item
+                    android:color="#00000000"
+                    android:offset="1.0" />
+            </gradient>
+        </aapt:attr>
+    </path>
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="nonZero"
+        android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>

deploy/android_demo/app/src/main/res/drawable/ic_launcher_background.xml

+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillColor="#008577"
+        android:pathData="M0,0h108v108h-108z" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M9,0L9,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,0L19,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,0L29,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,0L39,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,0L49,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,0L59,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,0L69,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,0L79,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M89,0L89,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M99,0L99,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,9L108,9"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,19L108,19"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,29L108,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,39L108,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,49L108,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,59L108,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,69L108,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,79L108,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,89L108,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,99L108,99"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,29L89,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,39L89,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,49L89,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,59L89,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,69L89,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,79L89,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,19L29,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,19L39,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,19L49,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,19L59,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,19L69,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,19L79,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+</vector>

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

+<?xml version="1.0" encoding="utf-8"?>
+<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"
+                                             tools:context=".MainActivity">
+
+    <RelativeLayout
+            android:layout_width="match_parent"
+            android:layout_height="match_parent">
+
+        <LinearLayout
+                android:id="@+id/v_input_info"
+                android:layout_width="fill_parent"
+                android:layout_height="wrap_content"
+                android:layout_alignParentTop="true"
+                android:orientation="vertical">
+
+            <LinearLayout
+                android:id="@+id/btn_layout"
+                android:layout_width="fill_parent"
+                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"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_run_model_click"
+                    android:text="运行模型" />
+                <Button
+                    android:id="@+id/btn_take_photo"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_take_photo_click"
+                    android:text="拍照识别" />
+                <Button
+                    android:id="@+id/btn_choice_img"
+                    android:layout_width="0dp"
+                    android:layout_height="wrap_content"
+                    android:layout_weight="1"
+                    android:onClick="btn_choice_img_click"
+                    android:text="选取图片" />
+
+            </LinearLayout>
+            <TextView
+                    android:id="@+id/tv_input_setting"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:scrollbars="vertical"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="10dp"
+                    android:layout_marginBottom="5dp"
+                    android:lineSpacingExtra="4dp"
+                    android:singleLine="false"
+                    android:maxLines="6"
+                    android:text=""/>
+            <TextView
+                android:id="@+id/tv_model_img_status"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:scrollbars="vertical"
+                android:layout_marginLeft="12dp"
+                android:layout_marginRight="12dp"
+                android:layout_marginTop="-5dp"
+                android:layout_marginBottom="5dp"
+                android:lineSpacingExtra="4dp"
+                android:singleLine="false"
+                android:maxLines="6"
+                android:text="STATUS: ok"/>
+
+        </LinearLayout>
+
+        <RelativeLayout
+                android:layout_width="match_parent"
+                android:layout_height="match_parent"
+                android:layout_above="@+id/v_output_info"
+                android:layout_below="@+id/v_input_info">
+
+            <ImageView
+                    android:id="@+id/iv_input_image"
+                    android:layout_width="400dp"
+                    android:layout_height="400dp"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:adjustViewBounds="true"
+                    android:scaleType="fitCenter"/>
+        </RelativeLayout>
+
+
+        <RelativeLayout
+                android:id="@+id/v_output_info"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_alignParentBottom="true"
+                android:layout_centerHorizontal="true">
+
+            <TextView
+                    android:id="@+id/tv_output_result"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:layout_alignParentTop="true"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:scrollbars="vertical"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="5dp"
+                    android:textAlignment="center"
+                    android:lineSpacingExtra="5dp"
+                    android:singleLine="false"
+                    android:maxLines="5"
+                    android:text=""/>
+
+            <TextView
+                    android:id="@+id/tv_inference_time"
+                    android:layout_width="wrap_content"
+                    android:layout_height="wrap_content"
+                    android:layout_below="@+id/tv_output_result"
+                    android:layout_centerHorizontal="true"
+                    android:layout_centerVertical="true"
+                    android:textAlignment="center"
+                    android:layout_marginLeft="12dp"
+                    android:layout_marginRight="12dp"
+                    android:layout_marginTop="5dp"
+                    android:layout_marginBottom="10dp"
+                    android:text=""/>
+
+        </RelativeLayout>
+
+    </RelativeLayout>
+</androidx.constraintlayout.widget.ConstraintLayout>

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/menu/menu_action_options.xml

+<menu xmlns:android="http://schemas.android.com/apk/res/android"
+      xmlns:app="http://schemas.android.com/apk/res-auto">
+
+    <group>
+        <item
+                android:id="@+id/settings"
+                android:title="Settings..."
+                app:showAsAction="withText"/>
+    </group>
+</menu>

deploy/android_demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml

+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
\ No newline at end of file

deploy/android_demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml

+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
\ 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>
+    </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>
+    </string-array>
+    <string-array name="cpu_thread_num_entries">
+        <item>1 threads</item>
+        <item>2 threads</item>
+        <item>4 threads</item>
+        <item>8 threads</item>
+    </string-array>
+    <string-array name="cpu_thread_num_values">
+        <item>1</item>
+        <item>2</item>
+        <item>4</item>
+        <item>8</item>
+    </string-array>
+    <string-array name="cpu_power_mode_entries">
+        <item>HIGH(only big cores)</item>
+        <item>LOW(only LITTLE cores)</item>
+        <item>FULL(all cores)</item>
+        <item>NO_BIND(depends on system)</item>
+        <item>RAND_HIGH</item>
+        <item>RAND_LOW</item>
+    </string-array>
+    <string-array name="cpu_power_mode_values">
+        <item>LITE_POWER_HIGH</item>
+        <item>LITE_POWER_LOW</item>
+        <item>LITE_POWER_FULL</item>
+        <item>LITE_POWER_NO_BIND</item>
+        <item>LITE_POWER_RAND_HIGH</item>
+        <item>LITE_POWER_RAND_LOW</item>
+    </string-array>
+    <string-array name="input_color_format_entries">
+        <item>BGR color format</item>
+        <item>RGB color format</item>
+    </string-array>
+    <string-array name="input_color_format_values">
+        <item>BGR</item>
+        <item>RGB</item>
+    </string-array>
+</resources>
\ No newline at end of file

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

+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <color name="colorPrimary">#008577</color>
+    <color name="colorPrimaryDark">#00574B</color>
+    <color name="colorAccent">#D81B60</color>
+</resources>

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

+<resources>
+    <string name="app_name">OCR Chinese</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>
+    <string name="LABEL_PATH_KEY">LABEL_PATH_KEY</string>
+    <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="SCORE_THRESHOLD_KEY">SCORE_THRESHOLD_KEY</string>
+    <string name="MODEL_PATH_DEFAULT">models/ocr_v2_for_cpu</string>
+    <string name="LABEL_PATH_DEFAULT">labels/ppocr_keys_v1.txt</string>
+    <string name="IMAGE_PATH_DEFAULT">images/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="SCORE_THRESHOLD_DEFAULT">0.1</string>
+</resources>
+

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

+<resources>
+
+    <!-- Base application theme. -->
+    <style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
+        <!-- Customize your theme here. -->
+        <item name="colorPrimary">@color/colorPrimary</item>
+        <item name="colorPrimaryDark">@color/colorPrimaryDark</item>
+        <item name="colorAccent">@color/colorAccent</item>
+        <item name="actionOverflowMenuStyle">@style/OverflowMenuStyle</item>
+    </style>
+
+    <style name="OverflowMenuStyle" parent="Widget.AppCompat.Light.PopupMenu.Overflow">
+        <item name="overlapAnchor">false</item>
+    </style>
+
+    <style name="AppTheme.NoActionBar">
+        <item name="windowActionBar">false</item>
+        <item name="windowNoTitle">true</item>
+    </style>
+
+    <style name="AppTheme.AppBarOverlay" parent="ThemeOverlay.AppCompat.Dark.ActionBar"/>
+
+    <style name="AppTheme.PopupOverlay" parent="ThemeOverlay.AppCompat.Light"/>
+
+</resources>

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

+<?xml version="1.0" encoding="utf-8"?>
+<paths xmlns:android="http://schemas.android.com/apk/res/android">
+    <external-files-path name="my_images" path="Pictures" />
+</paths>
\ No newline at end of file

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

+<?xml version="1.0" encoding="utf-8"?>
+<PreferenceScreen xmlns:android="http://schemas.android.com/apk/res/android" >
+    <PreferenceCategory android:title="Model Settings">
+        <ListPreference
+                android:defaultValue="@string/MODEL_PATH_DEFAULT"
+                android:key="@string/CHOOSE_PRE_INSTALLED_MODEL_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="Choose pre-installed models" />
+        <CheckBoxPreference
+                android:defaultValue="false"
+                android:key="@string/ENABLE_CUSTOM_SETTINGS_KEY"
+                android:summaryOn="Enable"
+                android:summaryOff="Disable"
+                android:title="Enable custom settings"/>
+        <EditTextPreference
+                android:key="@string/MODEL_PATH_KEY"
+                android:defaultValue="@string/MODEL_PATH_DEFAULT"
+                android:title="Model Path" />
+        <EditTextPreference
+                android:key="@string/LABEL_PATH_KEY"
+                android:defaultValue="@string/LABEL_PATH_DEFAULT"
+                android:title="Label Path" />
+        <ListPreference
+                android:key="@string/IMAGE_PATH_KEY"
+                android:defaultValue="@string/IMAGE_PATH_DEFAULT"
+                android:entries="@array/image_name_entries"
+                android:entryValues="@array/image_name_values"
+                android:title="Image Path" />
+    </PreferenceCategory>
+    <PreferenceCategory android:title="CPU Settings">
+        <ListPreference
+                android:defaultValue="@string/CPU_THREAD_NUM_DEFAULT"
+                android:key="@string/CPU_THREAD_NUM_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="CPU Thread Num"
+                android:entries="@array/cpu_thread_num_entries"
+                android:entryValues="@array/cpu_thread_num_values"/>
+        <ListPreference
+                android:defaultValue="@string/CPU_POWER_MODE_DEFAULT"
+                android:key="@string/CPU_POWER_MODE_KEY"
+                android:negativeButtonText="@null"
+                android:positiveButtonText="@null"
+                android:title="CPU Power Mode"
+                android:entries="@array/cpu_power_mode_entries"
+                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" />
+    </PreferenceCategory>
+    <PreferenceCategory android:title="Output Settings">
+        <EditTextPreference
+                android:key="@string/SCORE_THRESHOLD_KEY"
+                android:defaultValue="@string/SCORE_THRESHOLD_DEFAULT"
+                android:title="Score Threshold" />
+    </PreferenceCategory>
+</PreferenceScreen>

deploy/android_demo/app/src/test/java/com/baidu/paddle/lite/demo/ocr/ExampleUnitTest.java

+package com.baidu.paddle.lite.demo.ocr;
+
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+/**
+ * Example local unit test, which will execute on the development machine (host).
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+public class ExampleUnitTest {
+    @Test
+    public void addition_isCorrect() {
+        assertEquals(4, 2 + 2);
+    }
+}
\ No newline at end of file

deploy/android_demo/build.gradle

+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+
+buildscript {
+    repositories {
+        google()
+        jcenter()
+        
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:4.1.2'
+        
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        google()
+        jcenter()
+        
+    }
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

deploy/android_demo/gradle.properties

+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx1536m
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true
+
+android.useAndroidX=true

deploy/android_demo/gradle/wrapper/gradle-wrapper.properties

+#Thu Feb 04 20:28:08 CST 2021
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-6.5-bin.zip

deploy/android_demo/gradlew

+#!/usr/bin/env sh
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=$(save "$@")
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
+if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
+  cd "$(dirname "$0")"
+fi
+
+exec "$JAVACMD" "$@"

deploy/android_demo/gradlew.bat

+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windows variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

deploy/android_demo/settings.gradle

+include ':app'

deploy/cpp_infer/CMakeLists.txt

@@ -40,6 +40,7 @@ endif()
 if (WIN32)
   include_directories("${PADDLE_LIB}/paddle/fluid/inference")
   include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/lib")
   link_directories("${PADDLE_LIB}/paddle/fluid/inference")
   find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
 
@@ -140,22 +141,22 @@ else()
   endif ()
 endif()
 
-# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
+# Note: libpaddle_inference_api.so/a must put before libpaddle_inference.so/a
 if(WITH_STATIC_LIB)
   if(WIN32)
     set(DEPS
-        ${PADDLE_LIB}/paddle/lib/paddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+        ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
   else()
     set(DEPS
-        ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+        ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
   endif()
 else()
   if(WIN32)
     set(DEPS
-        ${PADDLE_LIB}/paddle/lib/paddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
+        ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
   else()
     set(DEPS
-        ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
+        ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
   endif()
 endif(WITH_STATIC_LIB)
 

deploy/cpp_infer/include/config.h

@@ -49,6 +49,8 @@ public:
 
     this->det_db_unclip_ratio = stod(config_map_["det_db_unclip_ratio"]);
 
+    this->use_polygon_score = bool(stoi(config_map_["use_polygon_score"]));
+
     this->det_model_dir.assign(config_map_["det_model_dir"]);
 
     this->rec_model_dir.assign(config_map_["rec_model_dir"]);
@@ -86,6 +88,8 @@ public:
 
   double det_db_unclip_ratio = 2.0;
 
+  bool use_polygon_score = false;
+
   std::string det_model_dir;
 
   std::string rec_model_dir;

deploy/cpp_infer/include/ocr_det.h

@@ -44,7 +44,8 @@ public:
                       const bool &use_mkldnn, const int &max_side_len,
                       const double &det_db_thresh,
                       const double &det_db_box_thresh,
-                      const double &det_db_unclip_ratio, const bool &visualize,
+                      const double &det_db_unclip_ratio,
+                      const bool &use_polygon_score, const bool &visualize,
                       const bool &use_tensorrt, const bool &use_fp16) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
@@ -57,6 +58,7 @@ public:
     this->det_db_thresh_ = det_db_thresh;
     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->visualize_ = visualize;
     this->use_tensorrt_ = use_tensorrt;
@@ -85,6 +87,7 @@ private:
   double det_db_thresh_ = 0.3;
   double det_db_box_thresh_ = 0.5;
   double det_db_unclip_ratio_ = 2.0;
+  bool use_polygon_score_ = false;
 
   bool visualize_ = true;
   bool use_tensorrt_ = false;

deploy/cpp_infer/include/postprocess_op.h

@@ -51,10 +51,12 @@ public:
                                                float &ssid);
 
   float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred);
+  float PolygonScoreAcc(std::vector<cv::Point> contour, cv::Mat pred);
 
   std::vector<std::vector<std::vector<int>>>
   BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
-                  const float &box_thresh, const float &det_db_unclip_ratio);
+                  const float &box_thresh, const float &det_db_unclip_ratio,
+                  const bool &use_polygon_score);
 
   std::vector<std::vector<std::vector<int>>>
   FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes,

deploy/cpp_infer/readme.md

@@ -74,9 +74,10 @@ opencv3/
 
 * 有2种方式获取Paddle预测库，下面进行详细介绍。
 
+
 #### 1.2.1 直接下载安装
 
-* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.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。
+* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.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* ）。
 
 * 下载之后使用下面的方法解压。
 
@@ -130,8 +131,6 @@ build/paddle_inference_install_dir/
 其中`paddle`就是C++预测所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
 
 
-
-
 ## 2 开始运行
 
 ### 2.1 将模型导出为inference model
@@ -184,7 +183,7 @@ cmake .. \
 make -j
 ```
 
-`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中；为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
+`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
 
 
 * 编译完成之后，会在`build`文件夹下生成一个名为`ocr_system`的可执行文件。
@@ -212,6 +211,7 @@ max_side_len  960 # 输入图像长宽大于960时，等比例缩放图像，使
 det_db_thresh  0.3 # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显
 det_db_box_thresh  0.5 # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
 det_db_unclip_ratio  1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本
+use_polygon_score 1 # 是否使用多边形框计算bbox score，0表示使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。
 det_model_dir  ./inference/det_db # 检测模型inference model地址
 
 # cls config
@@ -232,7 +232,7 @@ visualize 1 # 是否对结果进行可视化，为1时，会在当前文件夹
 最终屏幕上会输出检测结果如下。
 
 <div align="center">
-    <img src="../imgs/cpp_infer_pred_12.png" width="600">
+    <img src="./imgs/cpp_infer_pred_12.png" width="600">
 </div>
 
 

deploy/cpp_infer/readme_en.md

@@ -91,8 +91,8 @@ tar -xf paddle_inference.tgz
 Finally you can see the following files in the folder of `paddle_inference/`.
 
 #### 1.2.2 Compile from the source code
-* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code.
-* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
+* If you want to get the latest Paddle inference library features, you can download the latest code from Paddle github repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.
+* You can refer to [Paddle inference library] (https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
 
 
 ```shell
@@ -217,6 +217,7 @@ max_side_len  960 #  Limit the maximum image height and width to 960
 det_db_thresh  0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
 det_db_box_thresh  0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
 det_db_unclip_ratio  1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
+use_polygon_score 1 # Whether to use polygon box to calculate bbox score, 0 means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.
 det_model_dir  ./inference/det_db # Address of detection inference model
 
 # cls config
@@ -238,7 +239,7 @@ visualize 1 # Whether to visualize the results，when it is set as 1, The predic
 The detection results will be shown on the screen, which is as follows.
 
 <div align="center">
-    <img src="../imgs/cpp_infer_pred_12.png" width="600">
+    <img src="./imgs/cpp_infer_pred_12.png" width="600">
 </div>
 
 

deploy/cpp_infer/src/main.cpp

@@ -59,7 +59,8 @@ int main(int argc, char **argv) {
                  config.gpu_mem, config.cpu_math_library_num_threads,
                  config.use_mkldnn, config.max_side_len, config.det_db_thresh,
                  config.det_db_box_thresh, config.det_db_unclip_ratio,
-                 config.visualize, config.use_tensorrt, config.use_fp16);
+                 config.use_polygon_score, config.visualize,
+                 config.use_tensorrt, config.use_fp16);
 
   Classifier *cls = nullptr;
   if (config.use_angle_cls == true) {

deploy/cpp_infer/src/ocr_det.cpp

@@ -109,9 +109,9 @@ void DBDetector::Run(cv::Mat &img,
   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);
-  boxes = post_processor_.BoxesFromBitmap(pred_map, dilation_map,
-                                          this->det_db_box_thresh_,
-                                          this->det_db_unclip_ratio_);
+  boxes = post_processor_.BoxesFromBitmap(
+      pred_map, dilation_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);
 

deploy/cpp_infer/src/postprocess_op.cpp

@@ -159,6 +159,53 @@ std::vector<std::vector<float>> PostProcessor::GetMiniBoxes(cv::RotatedRect box,
   return array;
 }
 
+float PostProcessor::PolygonScoreAcc(std::vector<cv::Point> contour,
+                                     cv::Mat pred) {
+  int width = pred.cols;
+  int height = pred.rows;
+  std::vector<float> box_x;
+  std::vector<float> box_y;
+  for (int i = 0; i < contour.size(); ++i) {
+    box_x.push_back(contour[i].x);
+    box_y.push_back(contour[i].y);
+  }
+
+  int xmin =
+      clamp(int(std::floor(*(std::min_element(box_x.begin(), box_x.end())))), 0,
+            width - 1);
+  int xmax =
+      clamp(int(std::ceil(*(std::max_element(box_x.begin(), box_x.end())))), 0,
+            width - 1);
+  int ymin =
+      clamp(int(std::floor(*(std::min_element(box_y.begin(), box_y.end())))), 0,
+            height - 1);
+  int ymax =
+      clamp(int(std::ceil(*(std::max_element(box_y.begin(), box_y.end())))), 0,
+            height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+
+  cv::Point* rook_point = new cv::Point[contour.size()];
+   
+  for (int i = 0; i < contour.size(); ++i) {
+    rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
+  }
+  const cv::Point *ppt[1] = {rook_point};
+  int npt[] = {int(contour.size())};
+
+
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
+  float score = cv::mean(croppedImg, mask)[0];
+
+  delete []rook_point;
+  return score;
+}
+
 float PostProcessor::BoxScoreFast(std::vector<std::vector<float>> box_array,
                                   cv::Mat pred) {
   auto array = box_array;
@@ -197,10 +244,9 @@ float PostProcessor::BoxScoreFast(std::vector<std::vector<float>> box_array,
   return score;
 }
 
-std::vector<std::vector<std::vector<int>>>
-PostProcessor::BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
-                               const float &box_thresh,
-                               const float &det_db_unclip_ratio) {
+std::vector<std::vector<std::vector<int>>> PostProcessor::BoxesFromBitmap(
+    const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh,
+    const float &det_db_unclip_ratio, const bool &use_polygon_score) {
   const int min_size = 3;
   const int max_candidates = 1000;
 
@@ -234,7 +280,12 @@ PostProcessor::BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
     }
 
     float score;
-    score = BoxScoreFast(array, pred);
+    if (use_polygon_score)
+      /* compute using polygon*/
+      score = PolygonScoreAcc(contours[_i], pred);
+    else
+      score = BoxScoreFast(array, pred);
+
     if (score < box_thresh)
       continue;
 

deploy/cpp_infer/src/preprocess_op.cpp

@@ -77,19 +77,10 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
 
   int resize_h = int(float(h) * ratio);
   int resize_w = 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) * 32;
+  
+  resize_h = max(int(round(float(resize_h) / 32) * 32), 32);
+  resize_w = max(int(round(float(resize_w) / 32) * 32), 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) * 32;
   if (!use_tensorrt) {
     cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
     ratio_h = float(resize_h) / float(h);

deploy/cpp_infer/tools/config.txt

@@ -10,6 +10,7 @@ max_side_len  960
 det_db_thresh  0.3
 det_db_box_thresh  0.5
 det_db_unclip_ratio  1.6
+use_polygon_score 1
 det_model_dir  ./inference/ch_ppocr_mobile_v2.0_det_infer/
 
 # cls config

deploy/hubserving/ocr_cls/module.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import os
 import sys
 sys.path.insert(0, ".")
+import copy
 
 from paddlehub.common.logger import logger
 from paddlehub.module.module import moduleinfo, runnable, serving
@@ -14,6 +15,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_cls import TextClassifier
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_cls.params import read_params
 
 
 @moduleinfo(
@@ -28,8 +31,7 @@ class OCRCls(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_cls.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -48,6 +50,20 @@ class OCRCls(hub.Module):
 
         self.text_classifier = TextClassifier(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_det/module.py

@@ -7,6 +7,8 @@ import os
 import sys
 sys.path.insert(0, ".")
 
+import copy
+
 from paddlehub.common.logger import logger
 from paddlehub.module.module import moduleinfo, runnable, serving
 import cv2
@@ -15,6 +17,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_det import TextDetector
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_system.params import read_params
 
 
 @moduleinfo(
@@ -29,8 +33,7 @@ class OCRDet(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_det.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -49,6 +52,20 @@ class OCRDet(hub.Module):
 
         self.text_detector = TextDetector(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_det/params.py

@@ -22,6 +22,7 @@ def read_params():
     cfg.det_db_box_thresh = 0.5
     cfg.det_db_unclip_ratio = 1.6
     cfg.use_dilation = False
+    cfg.det_db_score_mode = "fast"
 
     # #EAST parmas
     # cfg.det_east_score_thresh = 0.8

deploy/hubserving/ocr_rec/module.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import os
 import sys
 sys.path.insert(0, ".")
+import copy
 
 from paddlehub.common.logger import logger
 from paddlehub.module.module import moduleinfo, runnable, serving
@@ -14,6 +15,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_rec import TextRecognizer
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_rec.params import read_params
 
 
 @moduleinfo(
@@ -28,8 +31,7 @@ class OCRRec(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_rec.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -48,6 +50,20 @@ class OCRRec(hub.Module):
 
         self.text_recognizer = TextRecognizer(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_system/module.py

@@ -6,6 +6,7 @@ from __future__ import print_function
 import os
 import sys
 sys.path.insert(0, ".")
+import copy
 
 import time
 
@@ -17,6 +18,8 @@ import paddlehub as hub
 
 from tools.infer.utility import base64_to_cv2
 from tools.infer.predict_system import TextSystem
+from tools.infer.utility import parse_args
+from deploy.hubserving.ocr_system.params import read_params
 
 
 @moduleinfo(
@@ -31,8 +34,7 @@ class OCRSystem(hub.Module):
         """
         initialize with the necessary elements
         """
-        from ocr_system.params import read_params
-        cfg = read_params()
+        cfg = self.merge_configs()
 
         cfg.use_gpu = use_gpu
         if use_gpu:
@@ -51,6 +53,20 @@ class OCRSystem(hub.Module):
 
         self.text_sys = TextSystem(cfg)
 
+    def merge_configs(self, ):
+        # deafult cfg
+        backup_argv = copy.deepcopy(sys.argv)
+        sys.argv = sys.argv[:1]
+        cfg = parse_args()
+
+        update_cfg_map = vars(read_params())
+
+        for key in update_cfg_map:
+            cfg.__setattr__(key, update_cfg_map[key])
+
+        sys.argv = copy.deepcopy(backup_argv)
+        return cfg
+
     def read_images(self, paths=[]):
         images = []
         for img_path in paths:

deploy/hubserving/ocr_system/params.py

@@ -22,6 +22,7 @@ def read_params():
     cfg.det_db_box_thresh = 0.5
     cfg.det_db_unclip_ratio = 1.6
     cfg.use_dilation = False
+    cfg.det_db_score_mode = "fast"
 
     #EAST parmas
     cfg.det_east_score_thresh = 0.8

deploy/lite/Makefile

+ARM_ABI = arm8
+export ARM_ABI
+
+include ../Makefile.def
+
+LITE_ROOT=../../../
+
+THIRD_PARTY_DIR=${LITE_ROOT}/third_party
+
+OPENCV_VERSION=opencv4.1.0
+
+OPENCV_LIBS = ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgcodecs.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgproc.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_core.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtegra_hal.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjpeg-turbo.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibwebp.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibpng.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjasper.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibtiff.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libIlmImf.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtbb.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libcpufeatures.a
+
+OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/arm64-v8a/include
+
+CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
+
+CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
+
+###############################################################
+# How to use one of static libaray:                           #
+#  `libpaddle_api_full_bundled.a`                             #
+#  `libpaddle_api_light_bundled.a`                            #
+###############################################################
+# Note: default use lite's shared library.                    #
+###############################################################
+# 1. Comment above line using `libpaddle_light_api_shared.so`
+# 2. Undo comment below line using `libpaddle_api_light_bundled.a`
+
+#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
+
+ocr_db_crnn: fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o
+	$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o cls_process.o -o ocr_db_crnn  $(CXX_LIBS) $(LDFLAGS)
+
+ocr_db_crnn.o: ocr_db_crnn.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ocr_db_crnn.o -c ocr_db_crnn.cc
+
+crnn_process.o: fetch_opencv crnn_process.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o crnn_process.o -c crnn_process.cc
+
+cls_process.o: fetch_opencv cls_process.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o cls_process.o -c cls_process.cc
+
+db_post_process.o: fetch_clipper fetch_opencv db_post_process.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o db_post_process.o -c db_post_process.cc
+
+clipper.o: fetch_clipper
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o clipper.o -c clipper.cpp
+
+fetch_clipper:
+	@test -e clipper.hpp || \
+        ( echo "Fetch clipper " && \
+        wget -c https://paddle-inference-dist.cdn.bcebos.com/PaddleLite/Clipper/clipper.hpp)
+	@ test -e clipper.cpp || \
+        wget -c https://paddle-inference-dist.cdn.bcebos.com/PaddleLite/Clipper/clipper.cpp
+
+fetch_opencv:
+	@ test -d ${THIRD_PARTY_DIR} ||  mkdir ${THIRD_PARTY_DIR}
+	@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
+      (echo "fetch opencv libs" && \
+      wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz)
+	@ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \
+      tar -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
+
+
+.PHONY: clean
+clean:
+	rm -f ocr_db_crnn.o clipper.o db_post_process.o crnn_process.o cls_process.o
+	rm -f ocr_db_crnn

deploy/lite/cls_process.cc

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "cls_process.h" //NOLINT
+#include <algorithm>
+#include <memory>
+#include <string>
+
+const std::vector<int> rec_image_shape{3, 48, 192};
+
+cv::Mat ClsResizeImg(cv::Mat img) {
+  int imgC, imgH, imgW;
+  imgC = rec_image_shape[0];
+  imgH = rec_image_shape[1];
+  imgW = rec_image_shape[2];
+
+  float ratio = static_cast<float>(img.cols) / static_cast<float>(img.rows);
+
+  int resize_w, resize_h;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_LINEAR);
+  if (resize_w < imgW) {
+    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w,
+                       cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
+  }
+  return resize_img;
+}
\ No newline at end of file

deploy/lite/cls_process.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "math.h" //NOLINT
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+
+cv::Mat ClsResizeImg(cv::Mat img);
\ No newline at end of file

deploy/lite/config.txt

+max_side_len  960
+det_db_thresh  0.3
+det_db_box_thresh  0.5
+det_db_unclip_ratio  1.6
+det_db_use_dilate 0
+det_use_polygon_score 1
+use_direction_classify  1
\ No newline at end of file

deploy/lite/crnn_process.cc

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "crnn_process.h" //NOLINT
+#include <algorithm>
+#include <memory>
+#include <string>
+
+const std::vector<int> rec_image_shape{3, 32, 320};
+
+cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio) {
+  int imgC, imgH, imgW;
+  imgC = rec_image_shape[0];
+  imgW = rec_image_shape[2];
+  imgH = rec_image_shape[1];
+
+  imgW = int(32 * wh_ratio);
+
+  float ratio = static_cast<float>(img.cols) / static_cast<float>(img.rows);
+  int resize_w, resize_h;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = static_cast<int>(ceilf(imgH * ratio));
+  cv::Mat resize_img;
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_LINEAR);
+
+  return resize_img;
+}
+
+std::vector<std::string> ReadDict(std::string path) {
+  std::ifstream in(path);
+  std::string filename;
+  std::string line;
+  std::vector<std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      m_vec.push_back(line);
+    }
+  } else {
+    std::cout << "no such file" << std::endl;
+  }
+  return m_vec;
+}
+
+cv::Mat GetRotateCropImage(cv::Mat srcimage,
+                           std::vector<std::vector<int>> box) {
+  cv::Mat image;
+  srcimage.copyTo(image);
+  std::vector<std::vector<int>> points = box;
+
+  int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
+  int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
+  int left = int(*std::min_element(x_collect, x_collect + 4));
+  int right = int(*std::max_element(x_collect, x_collect + 4));
+  int top = int(*std::min_element(y_collect, y_collect + 4));
+  int bottom = int(*std::max_element(y_collect, y_collect + 4));
+
+  cv::Mat img_crop;
+  image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
+
+  for (int i = 0; i < points.size(); i++) {
+    points[i][0] -= left;
+    points[i][1] -= top;
+  }
+
+  int img_crop_width =
+      static_cast<int>(sqrt(pow(points[0][0] - points[1][0], 2) +
+                            pow(points[0][1] - points[1][1], 2)));
+  int img_crop_height =
+      static_cast<int>(sqrt(pow(points[0][0] - points[3][0], 2) +
+                            pow(points[0][1] - points[3][1], 2)));
+
+  cv::Point2f pts_std[4];
+  pts_std[0] = cv::Point2f(0., 0.);
+  pts_std[1] = cv::Point2f(img_crop_width, 0.);
+  pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
+  pts_std[3] = cv::Point2f(0.f, img_crop_height);
+
+  cv::Point2f pointsf[4];
+  pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
+  pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
+  pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
+  pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
+
+  cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
+
+  cv::Mat dst_img;
+  cv::warpPerspective(img_crop, dst_img, M,
+                      cv::Size(img_crop_width, img_crop_height),
+                      cv::BORDER_REPLICATE);
+
+  const float ratio = 1.5;
+  if (static_cast<float>(dst_img.rows) >=
+      static_cast<float>(dst_img.cols) * ratio) {
+    cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
+    cv::transpose(dst_img, srcCopy);
+    cv::flip(srcCopy, srcCopy, 0);
+    return srcCopy;
+  } else {
+    return dst_img;
+  }
+}

deploy/lite/crnn_process.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "math.h" //NOLINT
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+
+cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio);
+
+std::vector<std::string> ReadDict(std::string path);
+
+cv::Mat GetRotateCropImage(cv::Mat srcimage, std::vector<std::vector<int>> box);
+
+template <class ForwardIterator>
+inline size_t Argmax(ForwardIterator first, ForwardIterator last) {
+  return std::distance(first, std::max_element(first, last));
+}

deploy/lite/db_post_process.cc

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "db_post_process.h" // NOLINT
+#include <algorithm>
+#include <utility>
+
+void GetContourArea(std::vector<std::vector<float>> box, float unclip_ratio,
+                    float &distance) {
+  int pts_num = 4;
+  float area = 0.0f;
+  float dist = 0.0f;
+  for (int i = 0; i < pts_num; i++) {
+    area += box[i][0] * box[(i + 1) % pts_num][1] -
+            box[i][1] * box[(i + 1) % pts_num][0];
+    dist += sqrtf((box[i][0] - box[(i + 1) % pts_num][0]) *
+                      (box[i][0] - box[(i + 1) % pts_num][0]) +
+                  (box[i][1] - box[(i + 1) % pts_num][1]) *
+                      (box[i][1] - box[(i + 1) % pts_num][1]));
+  }
+  area = fabs(float(area / 2.0));
+
+  distance = area * unclip_ratio / dist;
+}
+
+cv::RotatedRect Unclip(std::vector<std::vector<float>> box,
+                       float unclip_ratio) {
+  float distance = 1.0;
+
+  GetContourArea(box, unclip_ratio, distance);
+
+  ClipperLib::ClipperOffset offset;
+  ClipperLib::Path p;
+  p << ClipperLib::IntPoint(static_cast<int>(box[0][0]),
+                            static_cast<int>(box[0][1]))
+    << ClipperLib::IntPoint(static_cast<int>(box[1][0]),
+                            static_cast<int>(box[1][1]))
+    << ClipperLib::IntPoint(static_cast<int>(box[2][0]),
+                            static_cast<int>(box[2][1]))
+    << ClipperLib::IntPoint(static_cast<int>(box[3][0]),
+                            static_cast<int>(box[3][1]));
+  offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
+
+  ClipperLib::Paths soln;
+  offset.Execute(soln, distance);
+  std::vector<cv::Point2f> points;
+
+  for (int j = 0; j < soln.size(); j++) {
+    for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
+      points.emplace_back(soln[j][i].X, soln[j][i].Y);
+    }
+  }
+  cv::RotatedRect res = cv::minAreaRect(points);
+
+  return res;
+}
+
+std::vector<std::vector<float>> Mat2Vector(cv::Mat mat) {
+  std::vector<std::vector<float>> img_vec;
+  std::vector<float> tmp;
+
+  for (int i = 0; i < mat.rows; ++i) {
+    tmp.clear();
+    for (int j = 0; j < mat.cols; ++j) {
+      tmp.push_back(mat.at<float>(i, j));
+    }
+    img_vec.push_back(tmp);
+  }
+  return img_vec;
+}
+
+bool XsortFp32(std::vector<float> a, std::vector<float> b) {
+  if (a[0] != b[0])
+    return a[0] < b[0];
+  return false;
+}
+
+bool XsortInt(std::vector<int> a, std::vector<int> b) {
+  if (a[0] != b[0])
+    return a[0] < b[0];
+  return false;
+}
+
+std::vector<std::vector<int>>
+OrderPointsClockwise(std::vector<std::vector<int>> pts) {
+  std::vector<std::vector<int>> box = pts;
+  std::sort(box.begin(), box.end(), XsortInt);
+
+  std::vector<std::vector<int>> leftmost = {box[0], box[1]};
+  std::vector<std::vector<int>> rightmost = {box[2], box[3]};
+
+  if (leftmost[0][1] > leftmost[1][1])
+    std::swap(leftmost[0], leftmost[1]);
+
+  if (rightmost[0][1] > rightmost[1][1])
+    std::swap(rightmost[0], rightmost[1]);
+
+  std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1],
+                                        leftmost[1]};
+  return rect;
+}
+
+std::vector<std::vector<float>> GetMiniBoxes(cv::RotatedRect box, float &ssid) {
+  ssid = std::min(box.size.width, box.size.height);
+
+  cv::Mat points;
+  cv::boxPoints(box, points);
+
+  auto array = Mat2Vector(points);
+  std::sort(array.begin(), array.end(), XsortFp32);
+
+  std::vector<float> idx1 = array[0], idx2 = array[1], idx3 = array[2],
+                     idx4 = array[3];
+  if (array[3][1] <= array[2][1]) {
+    idx2 = array[3];
+    idx3 = array[2];
+  } else {
+    idx2 = array[2];
+    idx3 = array[3];
+  }
+  if (array[1][1] <= array[0][1]) {
+    idx1 = array[1];
+    idx4 = array[0];
+  } else {
+    idx1 = array[0];
+    idx4 = array[1];
+  }
+
+  array[0] = idx1;
+  array[1] = idx2;
+  array[2] = idx3;
+  array[3] = idx4;
+
+  return array;
+}
+
+float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred) {
+  auto array = box_array;
+  int width = pred.cols;
+  int height = pred.rows;
+
+  float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
+  float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
+
+  int xmin = clamp(
+      static_cast<int>(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0,
+      width - 1);
+  int xmax =
+      clamp(static_cast<int>(std::ceilf(*(std::max_element(box_x, box_x + 4)))),
+            0, width - 1);
+  int ymin = clamp(
+      static_cast<int>(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0,
+      height - 1);
+  int ymax =
+      clamp(static_cast<int>(std::ceilf(*(std::max_element(box_y, box_y + 4)))),
+            0, height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point root_point[4];
+  root_point[0] = cv::Point(static_cast<int>(array[0][0]) - xmin,
+                            static_cast<int>(array[0][1]) - ymin);
+  root_point[1] = cv::Point(static_cast<int>(array[1][0]) - xmin,
+                            static_cast<int>(array[1][1]) - ymin);
+  root_point[2] = cv::Point(static_cast<int>(array[2][0]) - xmin,
+                            static_cast<int>(array[2][1]) - ymin);
+  root_point[3] = cv::Point(static_cast<int>(array[3][0]) - xmin,
+                            static_cast<int>(array[3][1]) - ymin);
+  const cv::Point *ppt[1] = {root_point};
+  int npt[] = {4};
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
+
+  auto score = cv::mean(croppedImg, mask)[0];
+  return score;
+}
+
+float PolygonScoreAcc(std::vector<cv::Point> contour, cv::Mat pred) {
+  int width = pred.cols;
+  int height = pred.rows;
+  std::vector<float> box_x;
+  std::vector<float> box_y;
+  for (int i = 0; i < contour.size(); ++i) {
+    box_x.push_back(contour[i].x);
+    box_y.push_back(contour[i].y);
+  }
+
+  int xmin =
+      clamp(int(std::floor(*(std::min_element(box_x.begin(), box_x.end())))), 0,
+            width - 1);
+  int xmax =
+      clamp(int(std::ceil(*(std::max_element(box_x.begin(), box_x.end())))), 0,
+            width - 1);
+  int ymin =
+      clamp(int(std::floor(*(std::min_element(box_y.begin(), box_y.end())))), 0,
+            height - 1);
+  int ymax =
+      clamp(int(std::ceil(*(std::max_element(box_y.begin(), box_y.end())))), 0,
+            height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point *rook_point = new cv::Point[contour.size()];
+
+  for (int i = 0; i < contour.size(); ++i) {
+    rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
+  }
+  const cv::Point *ppt[1] = {rook_point};
+  int npt[] = {int(contour.size())};
+
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
+  float score = cv::mean(croppedImg, mask)[0];
+
+  delete[] rook_point;
+  return score;
+}
+
+std::vector<std::vector<std::vector<int>>>
+BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
+                std::map<std::string, double> Config) {
+  const int min_size = 3;
+  const int max_candidates = 1000;
+  const float box_thresh = static_cast<float>(Config["det_db_box_thresh"]);
+  const float unclip_ratio = static_cast<float>(Config["det_db_unclip_ratio"]);
+  const int det_use_polygon_score = int(Config["det_use_polygon_score"]);
+
+  int width = bitmap.cols;
+  int height = bitmap.rows;
+
+  std::vector<std::vector<cv::Point>> contours;
+  std::vector<cv::Vec4i> hierarchy;
+
+  cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST,
+                   cv::CHAIN_APPROX_SIMPLE);
+
+  int num_contours =
+      contours.size() >= max_candidates ? max_candidates : contours.size();
+
+  std::vector<std::vector<std::vector<int>>> boxes;
+
+  for (int i = 0; i < num_contours; i++) {
+    float ssid;
+    if (contours[i].size() <= 2)
+      continue;
+
+    cv::RotatedRect box = cv::minAreaRect(contours[i]);
+    auto array = GetMiniBoxes(box, ssid);
+
+    auto box_for_unclip = array;
+    // end get_mini_box
+
+    if (ssid < min_size) {
+      continue;
+    }
+
+    float score;
+    if (det_use_polygon_score) {
+      score = PolygonScoreAcc(contours[i], pred);
+    } else {
+      score = BoxScoreFast(array, pred);
+    }
+    // end box_score_fast
+    if (score < box_thresh)
+      continue;
+
+    // start for unclip
+    cv::RotatedRect points = Unclip(box_for_unclip, unclip_ratio);
+    if (points.size.height < 1.001 && points.size.width < 1.001)
+      continue;
+    // end for unclip
+
+    cv::RotatedRect clipbox = points;
+    auto cliparray = GetMiniBoxes(clipbox, ssid);
+
+    if (ssid < min_size + 2)
+      continue;
+
+    int dest_width = pred.cols;
+    int dest_height = pred.rows;
+    std::vector<std::vector<int>> intcliparray;
+
+    for (int num_pt = 0; num_pt < 4; num_pt++) {
+      std::vector<int> a{
+          static_cast<int>(clamp(
+              roundf(cliparray[num_pt][0] / float(width) * float(dest_width)),
+              float(0), float(dest_width))),
+          static_cast<int>(clamp(
+              roundf(cliparray[num_pt][1] / float(height) * float(dest_height)),
+              float(0), float(dest_height)))};
+      intcliparray.push_back(a);
+    }
+    boxes.push_back(intcliparray);
+
+  } // end for
+  return boxes;
+}
+
+std::vector<std::vector<std::vector<int>>>
+FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
+                float ratio_w, cv::Mat srcimg) {
+  int oriimg_h = srcimg.rows;
+  int oriimg_w = srcimg.cols;
+
+  std::vector<std::vector<std::vector<int>>> root_points;
+  for (int n = 0; n < static_cast<int>(boxes.size()); n++) {
+    boxes[n] = OrderPointsClockwise(boxes[n]);
+    for (int m = 0; m < static_cast<int>(boxes[0].size()); m++) {
+      boxes[n][m][0] /= ratio_w;
+      boxes[n][m][1] /= ratio_h;
+
+      boxes[n][m][0] =
+          static_cast<int>(std::min(std::max(boxes[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][1] =
+          static_cast<int>(std::min(std::max(boxes[n][m][1], 0), oriimg_h - 1));
+    }
+  }
+
+  for (int n = 0; n < boxes.size(); n++) {
+    int rect_width, rect_height;
+    rect_width =
+        static_cast<int>(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
+                              pow(boxes[n][0][1] - boxes[n][1][1], 2)));
+    rect_height =
+        static_cast<int>(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
+                              pow(boxes[n][0][1] - boxes[n][3][1], 2)));
+    if (rect_width <= 4 || rect_height <= 4)
+      continue;
+    root_points.push_back(boxes[n]);
+  }
+  return root_points;
+}

deploy/lite/db_post_process.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <math.h>
+
+#include <iostream>
+#include <map>
+#include <vector>
+
+#include "clipper.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+
+template <class T> T clamp(T x, T min, T max) {
+  if (x > max)
+    return max;
+  if (x < min)
+    return min;
+  return x;
+}
+
+std::vector<std::vector<float>> Mat2Vector(cv::Mat mat);
+
+void GetContourArea(std::vector<std::vector<float>> box, float unclip_ratio,
+                    float &distance);
+
+cv::RotatedRect Unclip(std::vector<std::vector<float>> box, float unclip_ratio);
+
+std::vector<std::vector<float>> Mat2Vector(cv::Mat mat);
+
+bool XsortFp32(std::vector<float> a, std::vector<float> b);
+
+bool XsortInt(std::vector<int> a, std::vector<int> b);
+
+std::vector<std::vector<int>>
+OrderPointsClockwise(std::vector<std::vector<int>> pts);
+
+std::vector<std::vector<float>> GetMiniBoxes(cv::RotatedRect box, float &ssid);
+
+float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred);
+
+std::vector<std::vector<std::vector<int>>>
+BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
+                std::map<std::string, double> Config);
+
+std::vector<std::vector<std::vector<int>>>
+FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
+                float ratio_w, cv::Mat srcimg);

deploy/lite/ocr_db_crnn.cc

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle_api.h" // NOLINT
+#include <chrono>
+
+#include "cls_process.h"
+#include "crnn_process.h"
+#include "db_post_process.h"
+
+using namespace paddle::lite_api; // NOLINT
+using namespace std;
+
+// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
+void NeonMeanScale(const float *din, float *dout, int size,
+                   const std::vector<float> mean,
+                   const std::vector<float> scale) {
+  if (mean.size() != 3 || scale.size() != 3) {
+    std::cerr << "[ERROR] mean or scale size must equal to 3\n";
+    exit(1);
+  }
+  float32x4_t vmean0 = vdupq_n_f32(mean[0]);
+  float32x4_t vmean1 = vdupq_n_f32(mean[1]);
+  float32x4_t vmean2 = vdupq_n_f32(mean[2]);
+  float32x4_t vscale0 = vdupq_n_f32(scale[0]);
+  float32x4_t vscale1 = vdupq_n_f32(scale[1]);
+  float32x4_t vscale2 = vdupq_n_f32(scale[2]);
+
+  float *dout_c0 = dout;
+  float *dout_c1 = dout + size;
+  float *dout_c2 = dout + size * 2;
+
+  int i = 0;
+  for (; i < size - 3; i += 4) {
+    float32x4x3_t vin3 = vld3q_f32(din);
+    float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
+    float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
+    float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
+    float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
+    float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
+    float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
+    vst1q_f32(dout_c0, vs0);
+    vst1q_f32(dout_c1, vs1);
+    vst1q_f32(dout_c2, vs2);
+
+    din += 12;
+    dout_c0 += 4;
+    dout_c1 += 4;
+    dout_c2 += 4;
+  }
+  for (; i < size; i++) {
+    *(dout_c0++) = (*(din++) - mean[0]) * scale[0];
+    *(dout_c1++) = (*(din++) - mean[1]) * scale[1];
+    *(dout_c2++) = (*(din++) - mean[2]) * scale[2];
+  }
+}
+
+// resize image to a size multiple of 32 which is required by the network
+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;
+}
+
+cv::Mat RunClsModel(cv::Mat img, std::shared_ptr<PaddlePredictor> predictor_cls,
+                    const float thresh = 0.9) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat crop_img;
+  img.copyTo(crop_img);
+  cv::Mat resize_img;
+
+  int index = 0;
+  float wh_ratio =
+      static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);
+
+  resize_img = ClsResizeImg(crop_img);
+  resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
+
+  const float *dimg = reinterpret_cast<const float *>(resize_img.data);
+
+  std::unique_ptr<Tensor> input_tensor0(std::move(predictor_cls->GetInput(0)));
+  input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
+  auto *data0 = input_tensor0->mutable_data<float>();
+
+  NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
+  // Run CLS predictor
+  predictor_cls->Run();
+
+  // Get output and run postprocess
+  std::unique_ptr<const Tensor> softmax_out(
+      std::move(predictor_cls->GetOutput(0)));
+  auto *softmax_scores = softmax_out->mutable_data<float>();
+  auto softmax_out_shape = softmax_out->shape();
+  float score = 0;
+  int label = 0;
+  for (int i = 0; i < softmax_out_shape[1]; i++) {
+    if (softmax_scores[i] > score) {
+      score = softmax_scores[i];
+      label = i;
+    }
+  }
+  if (label % 2 == 1 && score > thresh) {
+    cv::rotate(srcimg, srcimg, 1);
+  }
+  return srcimg;
+}
+
+void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
+                 std::shared_ptr<PaddlePredictor> predictor_crnn,
+                 std::vector<std::string> &rec_text,
+                 std::vector<float> &rec_text_score,
+                 std::vector<std::string> charactor_dict,
+                 std::shared_ptr<PaddlePredictor> predictor_cls,
+                 int use_direction_classify) {
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat crop_img;
+  cv::Mat resize_img;
+
+  int index = 0;
+  for (int i = boxes.size() - 1; i >= 0; i--) {
+    crop_img = GetRotateCropImage(srcimg, boxes[i]);
+    if (use_direction_classify >= 1) {
+      crop_img = RunClsModel(crop_img, predictor_cls);
+    }
+    float wh_ratio =
+        static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);
+
+    resize_img = CrnnResizeImg(crop_img, wh_ratio);
+    resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
+
+    const float *dimg = reinterpret_cast<const float *>(resize_img.data);
+
+    std::unique_ptr<Tensor> input_tensor0(
+        std::move(predictor_crnn->GetInput(0)));
+    input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
+    auto *data0 = input_tensor0->mutable_data<float>();
+
+    NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
+    //// Run CRNN predictor
+    predictor_crnn->Run();
+
+    // Get output and run postprocess
+    std::unique_ptr<const Tensor> output_tensor0(
+        std::move(predictor_crnn->GetOutput(0)));
+    auto *predict_batch = output_tensor0->data<float>();
+    auto predict_shape = output_tensor0->shape();
+
+    // ctc decode
+    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(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;
+        str_res += charactor_dict[argmax_idx];
+      }
+      last_index = argmax_idx;
+    }
+    score /= count;
+    rec_text.push_back(str_res);
+    rec_text_score.push_back(score);
+  }
+}
+
+std::vector<std::vector<std::vector<int>>>
+RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
+            std::map<std::string, double> Config) {
+  // Read img
+  int max_side_len = int(Config["max_side_len"]);
+  int det_db_use_dilate = int(Config["det_db_use_dilate"]);
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+
+  std::vector<float> ratio_hw;
+  img = DetResizeImg(img, max_side_len, ratio_hw);
+  cv::Mat img_fp;
+  img.convertTo(img_fp, CV_32FC3, 1.0 / 255.f);
+
+  // Prepare input data from image
+  std::unique_ptr<Tensor> input_tensor0(std::move(predictor->GetInput(0)));
+  input_tensor0->Resize({1, 3, img_fp.rows, img_fp.cols});
+  auto *data0 = input_tensor0->mutable_data<float>();
+
+  std::vector<float> mean = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+  const float *dimg = reinterpret_cast<const float *>(img_fp.data);
+  NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
+
+  // Run predictor
+  predictor->Run();
+
+  // Get output and post process
+  std::unique_ptr<const Tensor> output_tensor(
+      std::move(predictor->GetOutput(0)));
+  auto *outptr = output_tensor->data<float>();
+  auto shape_out = output_tensor->shape();
+
+  // Save output
+  float pred[shape_out[2] * shape_out[3]];
+  unsigned char cbuf[shape_out[2] * shape_out[3]];
+
+  for (int i = 0; i < int(shape_out[2] * shape_out[3]); i++) {
+    pred[i] = static_cast<float>(outptr[i]);
+    cbuf[i] = static_cast<unsigned char>((outptr[i]) * 255);
+  }
+
+  cv::Mat cbuf_map(shape_out[2], shape_out[3], CV_8UC1,
+                   reinterpret_cast<unsigned char *>(cbuf));
+  cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F,
+                   reinterpret_cast<float *>(pred));
+
+  const double threshold = double(Config["det_db_thresh"]) * 255;
+  const double max_value = 255;
+  cv::Mat bit_map;
+  cv::threshold(cbuf_map, bit_map, threshold, max_value, cv::THRESH_BINARY);
+  if (det_db_use_dilate == 1) {
+    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);
+    bit_map = dilation_map;
+  }
+  auto boxes = BoxesFromBitmap(pred_map, bit_map, Config);
+
+  std::vector<std::vector<std::vector<int>>> filter_boxes =
+      FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg);
+
+  return filter_boxes;
+}
+
+std::shared_ptr<PaddlePredictor> loadModel(std::string model_file) {
+  MobileConfig config;
+  config.set_model_from_file(model_file);
+
+  std::shared_ptr<PaddlePredictor> predictor =
+      CreatePaddlePredictor<MobileConfig>(config);
+  return predictor;
+}
+
+cv::Mat Visualization(cv::Mat srcimg,
+                      std::vector<std::vector<std::vector<int>>> boxes) {
+  cv::Point rook_points[boxes.size()][4];
+  for (int n = 0; n < boxes.size(); n++) {
+    for (int m = 0; m < boxes[0].size(); m++) {
+      rook_points[n][m] = cv::Point(static_cast<int>(boxes[n][m][0]),
+                                    static_cast<int>(boxes[n][m][1]));
+    }
+  }
+  cv::Mat img_vis;
+  srcimg.copyTo(img_vis);
+  for (int n = 0; n < boxes.size(); n++) {
+    const cv::Point *ppt[1] = {rook_points[n]};
+    int npt[] = {4};
+    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
+  }
+
+  cv::imwrite("./vis.jpg", img_vis);
+  std::cout << "The detection visualized image saved in ./vis.jpg" << std::endl;
+  return img_vis;
+}
+
+std::vector<std::string> split(const std::string &str,
+                               const std::string &delim) {
+  std::vector<std::string> res;
+  if ("" == str)
+    return res;
+  char *strs = new char[str.length() + 1];
+  std::strcpy(strs, str.c_str());
+
+  char *d = new char[delim.length() + 1];
+  std::strcpy(d, delim.c_str());
+
+  char *p = std::strtok(strs, d);
+  while (p) {
+    string s = p;
+    res.push_back(s);
+    p = std::strtok(NULL, d);
+  }
+
+  return res;
+}
+
+std::map<std::string, double> LoadConfigTxt(std::string config_path) {
+  auto config = ReadDict(config_path);
+
+  std::map<std::string, double> dict;
+  for (int i = 0; i < config.size(); i++) {
+    std::vector<std::string> res = split(config[i], " ");
+    dict[res[0]] = stod(res[1]);
+  }
+  return dict;
+}
+
+int main(int argc, char **argv) {
+  if (argc < 5) {
+    std::cerr << "[ERROR] usage: " << argv[0]
+              << " det_model_file cls_model_file rec_model_file image_path "
+                 "charactor_dict\n";
+    exit(1);
+  }
+  std::string det_model_file = argv[1];
+  std::string rec_model_file = argv[2];
+  std::string cls_model_file = argv[3];
+  std::string img_path = argv[4];
+  std::string dict_path = argv[5];
+
+  //// load config from txt file
+  auto Config = LoadConfigTxt("./config.txt");
+  int use_direction_classify = int(Config["use_direction_classify"]);
+
+  auto start = std::chrono::system_clock::now();
+
+  auto det_predictor = loadModel(det_model_file);
+  auto rec_predictor = loadModel(rec_model_file);
+  auto cls_predictor = loadModel(cls_model_file);
+
+  auto charactor_dict = ReadDict(dict_path);
+  charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
+  charactor_dict.push_back(" ");
+
+  cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
+  auto boxes = RunDetModel(det_predictor, srcimg, Config);
+
+  std::vector<std::string> rec_text;
+  std::vector<float> rec_text_score;
+
+  RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
+              charactor_dict, cls_predictor, use_direction_classify);
+
+  auto end = std::chrono::system_clock::now();
+  auto duration =
+      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+
+  //// visualization
+  auto img_vis = Visualization(srcimg, boxes);
+
+  //// print recognized text
+  for (int i = 0; i < rec_text.size(); i++) {
+    std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
+              << std::endl;
+  }
+
+  std::cout << "花费了"
+            << double(duration.count()) *
+                   std::chrono::microseconds::period::num /
+                   std::chrono::microseconds::period::den
+            << "秒" << std::endl;
+
+  return 0;
+}
\ No newline at end of file

deploy/lite/prepare.sh

+#!/bin/bash
+
+mkdir -p  $1/demo/cxx/ocr/debug/
+cp  ../../ppocr/utils/ppocr_keys_v1.txt  $1/demo/cxx/ocr/debug/
+cp -r  ./*   $1/demo/cxx/ocr/
+cp ./config.txt  $1/demo/cxx/ocr/debug/
+cp ../../doc/imgs/11.jpg  $1/demo/cxx/ocr/debug/
+
+echo "Prepare Done"

deploy/lite/readme.md

+# 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。
+
+Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。
+
+
+## 1. 准备环境
+
+### 运行准备
+- 电脑（编译Paddle Lite）
+- 安卓手机（armv7或armv8）
+
+### 1.1 准备交叉编译环境
+交叉编译环境用于编译 Paddle Lite 和 PaddleOCR 的C++ demo。
+支持多种开发环境，不同开发环境的编译流程请参考对应文档。
+
+1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
+2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
+3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
+
+### 1.2 准备预测库
+
+预测库有两种获取方式：
+- 1. 直接下载，预测库下载链接如下：
+
+      | 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)|
+
+      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) 。
+
+- 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
+./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
+```
+
+注意：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，
+更多编译命令
+介绍请参考 [链接](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html) 。
+
+直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8/`文件夹，通过编译Paddle-Lite得到的预测库位于
+`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
+预测库的文件目录如下：
+```
+inference_lite_lib.android.armv8/
+|-- cxx                                        C++ 预测库和头文件
+|   |-- include                                C++ 头文件
+|   |   |-- paddle_api.h
+|   |   |-- paddle_image_preprocess.h
+|   |   |-- paddle_lite_factory_helper.h
+|   |   |-- paddle_place.h
+|   |   |-- paddle_use_kernels.h
+|   |   |-- paddle_use_ops.h
+|   |   `-- paddle_use_passes.h
+|   `-- lib                                           C++预测库
+|       |-- libpaddle_api_light_bundled.a             C++静态库
+|       `-- libpaddle_light_api_shared.so             C++动态库
+|-- java                                     Java预测库
+|   |-- jar
+|   |   `-- PaddlePredictor.jar
+|   |-- so
+|   |   `-- libpaddle_lite_jni.so
+|   `-- src
+|-- demo                                     C++和Java示例代码
+|   |-- cxx                                  C++  预测库demo
+|   `-- java                                 Java 预测库demo
+```
+
+## 2 开始运行
+
+### 2.1 模型优化
+
+Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-lite的opt工具可以自动
+对inference模型进行优化，优化后的模型更轻量，模型运行速度更快。
+
+如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。
+
+下述表格中也提供了一系列中文移动端模型：
+
+|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|
+
+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)。
+
+如果要部署的模型不在上述表格中，则需要按照如下步骤获得优化后的模型。
+
+模型优化需要Paddle-Lite的opt可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下：
+```
+# 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
+# 启动编译
+./lite/tools/build.sh build_optimize_tool
+```
+
+编译完成后，opt文件位于`build.opt/lite/api/`下，可通过如下方式查看opt的运行选项和使用方式；
+```
+cd build.opt/lite/api/
+./opt
+```
+
+|选项|说明|
+|---|---|
+|--model_dir|待优化的PaddlePaddle模型（非combined形式）的路径|
+|--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径|
+|--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径|
+|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现。若您需要在mobile端执行模型预测，请将此选项设置为naive_buffer。默认为protobuf|
+|--optimize_out|优化模型的输出路径|
+|--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm|
+|--record_tailoring_info|当使用 根据模型裁剪库文件 功能时，则设置该选项为true，以记录优化后模型含有的kernel和OP信息，默认为false|
+
+`--model_dir`适用于待优化的模型是非combined方式，PaddleOCR的inference模型是combined方式，即模型结构和模型参数使用单独一个文件存储。
+
+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_nfer.tar && tar xf  ch_ppocr_mobile_v2.0_rec_slim_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
+./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
+
+```
+
+转换成功后，inference模型目录下会多出`.nb`结尾的文件，即是转换成功的模型文件。
+
+注意：使用paddle-lite部署时，需要使用opt工具优化后的模型。 opt 工具的输入模型是paddle保存的inference模型
+
+<a name="2.2与手机联调"></a>
+### 2.2 与手机联调
+
+首先需要进行一些准备工作。
+ 1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI = arm7`。
+ 2. 打开手机的USB调试选项，选择文件传输模式，连接电脑。
+ 3. 电脑上安装adb工具，用于调试。 adb安装方式如下：
+
+    3.1. MAC电脑安装ADB:
+    ```
+    brew cask install android-platform-tools
+    ```
+    3.2. Linux安装ADB
+    ```
+    sudo apt update
+    sudo apt install -y wget adb
+    ```
+    3.3. Window安装ADB
+
+    win上安装需要去谷歌的安卓平台下载adb软件包进行安装：[链接](https://developer.android.com/studio)
+
+    打开终端，手机连接电脑，在终端中输入
+    ```
+    adb devices
+    ```
+    如果有device输出，则表示安装成功。
+    ```
+       List of devices attached
+       744be294    device
+    ```
+
+ 4. 准备优化后的模型、预测库文件、测试图像和使用的字典文件。
+ ```
+ git clone https://github.com/PaddlePaddle/PaddleOCR.git
+ cd PaddleOCR/deploy/lite/
+ # 运行prepare.sh，准备预测库文件、测试图像和使用的字典文件，并放置在预测库中的demo/cxx/ocr文件夹下
+ sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
+
+ # 进入OCR demo的工作目录
+ cd /{lite prediction library path}/inference_lite_lib.android.armv8/
+ cd demo/cxx/ocr/
+ # 将C++预测动态库so文件复制到debug文件夹中
+ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+ ```
+
+ 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。
+
+ 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_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
+|   |--libpaddle_light_api_shared.so    C++预测库文件
+|   |--config.txt                       超参数配置
+|-- config.txt                  超参数配置
+|-- cls_process.cc              方向分类器的预处理和后处理文件
+|-- cls_process.h
+|-- crnn_process.cc             识别模型CRNN的预处理和后处理文件
+|-- crnn_process.h
+|-- db_post_process.cc          检测模型DB的后处理文件
+|-- db_post_process.h
+|-- Makefile                    编译文件
+|-- ocr_db_crnn.cc              C++预测源文件
+```
+
+#### 注意：
+1. ppocr_keys_v1.txt是中文字典文件，如果使用的 nb 模型是英文数字或其他语言的模型，需要更换为对应语言的字典。
+PaddleOCR 在ppocr/utils/下存放了多种字典，包括：
+```
+dict/french_dict.txt     # 法语字典
+dict/german_dict.txt     # 德语字典
+ic15_dict.txt       # 英文字典
+dict/japan_dict.txt      # 日语字典
+dict/korean_dict.txt     # 韩语字典
+ppocr_keys_v1.txt   # 中文字典
+...
+```
+
+2.  `config.txt` 包含了检测器、分类器的超参数，如下：
+```
+max_side_len  960         # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960
+det_db_thresh  0.3        # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显
+det_db_box_thresh  0.5    # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小
+det_db_unclip_ratio  1.6  # 表示文本框的紧致程度，越小则文本框更靠近文本
+use_direction_classify  0  # 是否使用方向分类器，0表示不使用，1表示使用
+```
+
+ 5. 启动调试
+
+ 上述步骤完成后就可以使用adb将文件push到手机上运行，步骤如下：
+
+ ```
+ # 执行编译，得到可执行文件ocr_db_crnn
+ make -j
+
+ # 将编译的可执行文件移动到debug文件夹中
+ mv ocr_db_crnn ./debug/
+ # 将debug文件夹push到手机上
+ adb push debug /data/local/tmp/
+ adb shell
+ cd /data/local/tmp/debug
+ 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
+ ```
+
+ 如果对代码做了修改，则需要重新编译并push到手机上。
+
+ 运行效果如下：
+
+<div align="center">
+    <img src="imgs/lite_demo.png" width="600">
+</div>
+
+
+## FAQ
+Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？
+
+A1：如果已经走通了上述步骤，更换模型只需要替换 .nb 模型文件即可，同时要注意更新字典
+
+Q2：换一个图测试怎么做？
+
+A2：替换debug下的.jpg测试图像为你想要测试的图像，adb push 到手机上即可
+
+Q3：如何封装到手机APP中？
+
+A3：此demo旨在提供能在手机上运行OCR的核心算法部分，PaddleOCR/deploy/android_demo是将这个demo封装到手机app的示例，供参考

deploy/lite/readme_en.md

+# 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.
+
+paddle-lite is a lightweight inference engine for PaddlePaddle. It provides efficient inference capabilities for mobile phones and IoTs, and extensively integrates cross-platform hardware to provide lightweight deployment solutions for end-side deployment issues.
+
+## 1. Preparation
+
+### 运行准备
+
+- Computer (for Compiling Paddle Lite)
+- Mobile phone (arm7 or arm8)
+
+### 1.1 Prepare the cross-compilation environment
+The cross-compilation environment is used to compile C++ demos of Paddle Lite and PaddleOCR.
+Supports multiple development environments.
+
+For the compilation process of different development environments, please refer to the corresponding documents.
+
+1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
+2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
+3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
+
+### 1.2 Prepare Paddle-Lite library
+
+There are two ways to obtain the Paddle-Lite library：
+- 1. Download directly, the download link of the Paddle-Lite library is as follows：
+
+      | 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)|
+
+      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).
+
+- 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
+./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
+```
+
+Note: When compiling Paddle-Lite to obtain the Paddle-Lite library, you need to turn on the two options `--with_cv=ON --with_extra=ON`, `--arch` means the `arm` version, here is designated as armv8,
+
+More compilation commands refer to the introduction [link](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_andriod.html) 。
+
+After directly downloading the Paddle-Lite library and decompressing it, you can get the `inference_lite_lib.android.armv8/` folder, and the Paddle-Lite library obtained by compiling Paddle-Lite is located
+`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/` folder.
+
+The structure of the prediction library is as follows:
+```
+inference_lite_lib.android.armv8/
+|-- cxx                                        C++ prebuild library
+|   |-- include                                C++
+|   |   |-- paddle_api.h
+|   |   |-- paddle_image_preprocess.h
+|   |   |-- paddle_lite_factory_helper.h
+|   |   |-- paddle_place.h
+|   |   |-- paddle_use_kernels.h
+|   |   |-- paddle_use_ops.h
+|   |   `-- paddle_use_passes.h
+|   `-- lib                                           C++ library
+|       |-- libpaddle_api_light_bundled.a             C++ static library
+|       `-- libpaddle_light_api_shared.so             C++ dynamic library
+|-- java                                     Java library
+|   |-- jar
+|   |   `-- PaddlePredictor.jar
+|   |-- so
+|   |   `-- libpaddle_lite_jni.so
+|   `-- src
+|-- demo                                     C++ and Java demo
+|   |-- cxx                                  C++ demo
+|   `-- java                                 Java demo
+```
+
+## 2 Run
+
+### 2.1 Inference Model Optimization
+
+Paddle Lite provides a variety of strategies to automatically optimize the original training model, including quantization, sub-graph fusion, hybrid scheduling, Kernel optimization and so on. In order to make the optimization process more convenient and easy to use, Paddle Lite provide opt tools to automatically complete the optimization steps and output a lightweight, optimal executable model.
+
+If you have prepared the model file ending in .nb, you can skip this step.
+
+The following table also provides a series of models that can be deployed on mobile phones to recognize Chinese. You can directly download the optimized model.
+
+|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 lin](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download lin](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|[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|
+
+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.
+
+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
+./lite/tools/build.sh build_optimize_tool
+```
+
+After the compilation is complete, the opt file is located under build.opt/lite/api/, You can view the operating options and usage of opt in the following ways:
+
+```
+cd build.opt/lite/api/
+./opt
+```
+
+|Options|Description|
+|---|---|
+|--model_dir|The path of the PaddlePaddle model to be optimized (non-combined form)|
+|--model_file|The network structure file path of the PaddlePaddle model (combined form) to be optimized|
+|--param_file|The weight file path of the PaddlePaddle model (combined form) to be optimized|
+|--optimize_out_type|Output model type, currently supports two types: protobuf and naive_buffer, among which naive_buffer is a more lightweight serialization/deserialization implementation. If you need to perform model prediction on the mobile side, please set this option to naive_buffer. The default is protobuf|
+|--optimize_out|The output path of the optimized model|
+|--valid_targets|The executable backend of the model, the default is arm. Currently it supports x86, arm, opencl, npu, xpu, multiple backends can be specified at the same time (separated by spaces), and Model Optimize Tool will automatically select the best method. If you need to support Huawei NPU (DaVinci architecture NPU equipped with Kirin 810/990 Soc), it should be set to npu, arm|
+|--record_tailoring_info|When using the function of cutting library files according to the model, set this option to true to record the kernel and OP information contained in the optimized model. The default is false|
+
+`--model_dir` is suitable for the non-combined mode of the model to be optimized, and the inference model of PaddleOCR is the combined mode, that is, the model structure and model parameters are stored in a single file.
+
+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
+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
+./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>
+### 2.2 Run optimized model on Phone
+
+Some preparatory work is required first.
+ 1. Prepare an Android phone with arm8. If the compiled prediction library and opt file are armv7, you need an arm7 phone and modify ARM_ABI = arm7 in the Makefile.
+ 2. Make sure the phone is connected to the computer, open the USB debugging option of the phone, and select the file transfer mode.
+ 3. Install the adb tool on the computer.
+
+    3.1. Install ADB for MAC:
+    ```
+    brew cask install android-platform-tools
+    ```
+    3.2. Install ADB for Linux
+    ```
+    sudo apt update
+    sudo apt install -y wget adb
+    ```
+    3.3. Install ADB for windows
+
+    To install on win, you need to go to Google's Android platform to download the adb package for installation：[link](https://developer.android.com/studio)
+
+    Verify whether adb is installed successfully
+     ```
+    adb devices
+    ```
+    If there is device output, it means the installation is successful。
+    ```
+       List of devices attached
+       744be294    device
+    ```
+
+ 4. Prepare optimized models, prediction library files, test images and dictionary files used.
+ ```
+ git clone https://github.com/PaddlePaddle/PaddleOCR.git
+ cd PaddleOCR/deploy/lite/
+ # run prepare.sh
+ sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
+
+ #
+ cd /{lite prediction library path}/inference_lite_lib.android.armv8/
+ cd demo/cxx/ocr/
+ # copy paddle-lite C++ .so file to debug/ directory
+ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+
+ cd inference_lite_lib.android.armv8/demo/cxx/ocr/
+ cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+ ```
+
+Prepare the test image, taking PaddleOCR/doc/imgs/11.jpg as an example, copy the image file to the demo/cxx/ocr/debug/ folder. Prepare the model files optimized by the lite opt tool, ch_det_mv3_db_opt.nb, ch_rec_mv3_crnn_opt.nb, and place them under the demo/cxx/ocr/debug/ folder.
+
+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_ppocr_mobile_v2.0_cls_slim_opt.nb           Text direction classification model
+|   |--11.jpg                           Image for OCR
+|   |--ppocr_keys_v1.txt                Dictionary file
+|   |--libpaddle_light_api_shared.so    C++ .so file
+|   |--config.txt                       Config file
+|-- config.txt                  Config file
+|-- cls_process.cc              Pre-processing and post-processing files for the angle classifier
+|-- cls_process.h
+|-- crnn_process.cc             Pre-processing and post-processing files for the CRNN model
+|-- crnn_process.h
+|-- db_post_process.cc          Pre-processing and post-processing files for the DB model
+|-- db_post_process.h
+|-- Makefile  
+|-- ocr_db_crnn.cc              C++ main code
+```
+
+#### 注意：
+1. `ppocr_keys_v1.txt` is a Chinese dictionary file. If the nb model is used for English recognition or other language recognition, dictionary file should be replaced with a dictionary of the corresponding language. PaddleOCR provides a variety of dictionaries under ppocr/utils/, including:
+```
+dict/french_dict.txt     # french
+dict/german_dict.txt     # german
+ic15_dict.txt       # english
+dict/japan_dict.txt      # japan
+dict/korean_dict.txt     # korean
+ppocr_keys_v1.txt   # chinese
+```
+
+2.  `config.txt` of the detector and classifier, as shown below:
+```
+max_side_len  960         #  Limit the maximum image height and width to 960
+det_db_thresh  0.3        # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
+det_db_box_thresh  0.5    # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
+det_db_unclip_ratio  1.6  # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
+use_direction_classify  0  # Whether to use the direction classifier, 0 means not to use, 1 means to use
+```
+
+ 5. Run Model on phone
+
+After the above steps are completed, you can use adb to push the file to the phone to run, the steps are as follows:
+
+ ```
+ # Execute the compilation and get the executable file ocr_db_crnn
+ make -j
+ # Move the compiled executable file to the debug folder
+ mv ocr_db_crnn ./debug/
+ # Push the debug folder to the phone
+ adb push debug /data/local/tmp/
+ adb shell
+ cd /data/local/tmp/debug
+ 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
+ ```
+
+If you modify the code, you need to recompile and push to the phone.
+
+The outputs are as follows:
+
+<div align="center">
+    <img src="imgs/lite_demo.png" width="600">
+</div>
+
+## FAQ
+
+Q1: What if I want to change the model, do I need to run it again according to the process?
+
+A1: If you have performed the above steps, you only need to replace the .nb model file to complete the model replacement.
+
+Q2: How to test with another picture?
+
+A2: Replace the .jpg test image under ./debug with the image you want to test, and run adb push to push new image to the phone.
+
+Q3: How to package it into the mobile APP?
+
+A3: This demo aims to provide the core algorithm part that can run OCR on mobile phones. Further, PaddleOCR/deploy/android_demo is an example of encapsulating this demo into a mobile app for reference.

doc/doc_ch/detection.md

@@ -45,26 +45,17 @@ json.dumps编码前的图像标注信息是包含多个字典的list，字典中
 ## 快速启动训练
 
 首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列，
-您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
+您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/develop/ppcls/modeling/architectures)中的模型更换backbone，
+对应的backbone预训练模型可以从[PaddleClas repo 主页中找到下载链接](https://github.com/PaddlePaddle/PaddleClas#mobile-series)。
 ```shell
 cd PaddleOCR/
+# 根据backbone的不同选择下载对应的预训练模型
 # 下载MobileNetV3的预训练模型
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
 # 或，下载ResNet18_vd的预训练模型
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
 # 或，下载ResNet50_vd的预训练模型
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
-
-# 解压预训练模型文件，以MobileNetV3为例
-tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
-
-# 注：正确解压backbone预训练权重文件后，文件夹下包含众多以网络层命名的权重文件，格式如下：
-./pretrain_models/MobileNetV3_large_x0_5_pretrained/
-  └─ conv_last_bn_mean
-  └─ conv_last_bn_offset
-  └─ conv_last_bn_scale
-  └─ conv_last_bn_variance
-  └─ ......
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
 
 ```
 
@@ -120,16 +111,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{pat
 
 测试单张图像的检测效果
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"
 ```
 
 测试DB模型时，调整后处理阈值，
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"  PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 
 测试文件夹下所有图像的检测效果
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
 ```

doc/doc_ch/inference.md

@@ -12,7 +12,7 @@ inference 模型（`paddle.jit.save`保存的模型）
 - [一、训练模型转inference模型](#训练模型转inference模型)
     - [检测模型转inference模型](#检测模型转inference模型)
     - [识别模型转inference模型](#识别模型转inference模型)  
-    - [方向分类模型转inference模型](#方向分类模型转inference模型)  
+    - [方向分类模型转inference模型](#方向分类模型转inference模型)
 
 - [二、文本检测模型推理](#文本检测模型推理)
     - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
@@ -49,10 +49,9 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobi
 # -c 后面设置训练算法的yml配置文件
 # -o 配置可选参数
 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
-# Global.load_static_weights 参数需要设置为 False。
 # Global.save_inference_dir参数设置转换的模型将保存的地址。
 
-python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
+python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/
 ```
 转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.pretrained_model`参数，其指向训练中保存的模型参数文件。
 转换成功后，在模型保存目录下有三个文件：
@@ -76,10 +75,9 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobi
 # -c 后面设置训练算法的yml配置文件
 # -o 配置可选参数
 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
-# Global.load_static_weights 参数需要设置为 False。
 # Global.save_inference_dir参数设置转换的模型将保存的地址。
 
-python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
+python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy  Global.save_inference_dir=./inference/rec_crnn/
 ```
 
 **注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
@@ -105,10 +103,9 @@ wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobi
 # -c 后面设置训练算法的yml配置文件
 # -o 配置可选参数
 # Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
-# Global.load_static_weights 参数需要设置为 False。
 # Global.save_inference_dir参数设置转换的模型将保存的地址。
 
-python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy  Global.save_inference_dir=./inference/cls/
 ```
 
 转换成功后，在目录下有三个文件：
@@ -164,7 +161,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
 首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_db
 ```
 
 DB文本检测模型推理，可以执行如下命令：
@@ -185,7 +182,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
 首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar) )，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
+python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_east
 ```
 
 **EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令：
@@ -205,7 +202,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
 #### (1). 四边形文本检测模型（ICDAR2015）  
 首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar))，可以使用如下命令进行转换：
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_sast_ic15
 
 ```
 **SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令：
@@ -220,7 +217,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
 首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar))，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_sast_tt
 
 ```
 
@@ -270,7 +267,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
 的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) )，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
+python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/rec_crnn
 ```
 
 CRNN 文本识别模型推理，可以执行如下命令：
@@ -362,17 +359,18 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
 <a name="超轻量中文OCR模型推理"></a>
 ### 1. 超轻量中文OCR模型推理
 
-在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。可视化识别结果默认保存到 ./inference_results 文件夹里面。
+在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。`use_mp`表示是否使用多进程。`total_process_num`表示在使用多进程时的进程数。可视化识别结果默认保存到 ./inference_results 文件夹里面。
 
-```
+```shell
 # 使用方向分类器
 python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
 
 # 不使用方向分类器
 python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false
-```
-
 
+# 使用多进程
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+```
 
 
 

doc/doc_ch/models_list.md

@@ -104,27 +104,16 @@ python3 generate_multi_language_configs.py -l it \
 | german_mobile_v2.0_rec |德文识别|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
 | korean_mobile_v2.0_rec |韩文识别|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
 | japan_mobile_v2.0_rec |日文识别|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
-| it_mobile_v2.0_rec |意大利文识别|rec_it_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |
-| xi_mobile_v2.0_rec |西班牙文识别|rec_xi_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_train.tar) |
-| pu_mobile_v2.0_rec |葡萄牙文识别|rec_pu_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_train.tar) |
-| ru_mobile_v2.0_rec |俄罗斯文识别|rec_ru_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_train.tar) |
-| ar_mobile_v2.0_rec |阿拉伯文识别|rec_ar_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_train.tar) |
-| hi_mobile_v2.0_rec |印地文识别|rec_hi_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_train.tar) |
 | chinese_cht_mobile_v2.0_rec |中文繁体识别|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
-| ug_mobile_v2.0_rec |维吾尔文识别|rec_ug_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_train.tar) |
-| fa_mobile_v2.0_rec |波斯文识别|rec_fa_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_train.tar) |
-| ur_mobile_v2.0_rec |乌尔都文识别|rec_ur_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_train.tar) |
-| rs_mobile_v2.0_rec |塞尔维亚文（latin）识别|rec_rs_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_train.tar) |
-| oc_mobile_v2.0_rec |欧西坦文识别|rec_oc_lite_train.yml|2.53M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_train.tar) |
-| mr_mobile_v2.0_rec |马拉地文识别|rec_mr_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_train.tar) |
-| ne_mobile_v2.0_rec |尼泊尔文识别|rec_ne_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_train.tar) |
-| rsc_mobile_v2.0_rec |塞尔维亚文（cyrillic）识别|rec_rsc_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_train.tar) |
-| bg_mobile_v2.0_rec |保加利亚文识别|rec_bg_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_train.tar) |
-| uk_mobile_v2.0_rec |乌克兰文识别|rec_uk_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_train.tar) |
-| be_mobile_v2.0_rec |白俄罗斯文识别|rec_be_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_train.tar) |
 | te_mobile_v2.0_rec |泰卢固文识别|rec_te_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
 | ka_mobile_v2.0_rec |卡纳达文识别|rec_ka_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
 | ta_mobile_v2.0_rec |泰米尔文识别|rec_ta_lite_train.yml|2.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
+| latin_mobile_v2.0_rec |  拉丁文识别 |  [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
+| arabic_mobile_v2.0_rec |  阿拉伯字母 | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
+| cyrillic_mobile_v2.0_rec | 斯拉夫字母 | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
+| devanagari_mobile_v2.0_rec | 梵文字母 | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
+
+更多支持语种请参考: [多语言模型](./multi_languages.md)
 
 
 <a name="文本方向分类模型"></a>

doc/doc_ch/multi_languages.md

+# 多语言模型
+
+**近期更新**
+
+- 2021.4.9 支持**80种**语言的检测和识别
+- 2021.4.9 支持**轻量高精度**英文模型检测识别
+
+PaddleOCR 旨在打造一套丰富、领先、且实用的OCR工具库，不仅提供了通用场景下的中英文模型，也提供了专门在英文场景下训练的模型，
+和覆盖[80个语言](#语种缩写)的小语种模型。
+
+其中英文模型支持，大小写字母和常见标点的检测识别，并优化了空格字符的识别：
+
+<div align="center">
+    <img src="../imgs_results/multi_lang/img_12.jpg" width="900" height="300">
+</div>
+
+小语种模型覆盖了拉丁语系、阿拉伯语系、中文繁体、韩语、日语等等：
+
+<div align="center">
+    <img src="../imgs_results/multi_lang/japan_2.jpg" width="600" height="300">
+    <img src="../imgs_results/multi_lang/french_0.jpg" width="300" height="300">
+    <img src="../imgs_results/multi_lang/korean_0.jpg" width="500" height="300">
+    <img src="../imgs_results/multi_lang/arabic_0.jpg" width="300" height="300">
+</div>
+
+
+本文档将简要介绍小语种模型的使用方法。
+
+- [1 安装](#安装)
+    - [1.1 paddle 安装](#paddle安装)
+    - [1.2 paddleocr package 安装](#paddleocr_package_安装)  
+
+- [2 快速使用](#快速使用)
+    - [2.1 命令行运行](#命令行运行)
+    - [2.2 python 脚本运行](#python_脚本运行)
+- [3 自定义训练](#自定义训练)
+- [4 预测部署](#预测部署)
+- [4 支持语种及缩写](#语种缩写)
+
+<a name="安装"></a>
+## 1 安装
+
+<a name="paddle安装"></a>
+### 1.1 paddle 安装
+```
+# cpu
+pip install paddlepaddle
+
+# gpu
+pip install paddlepaddle-gpu
+```
+
+<a name="paddleocr_package_安装"></a>
+### 1.2 paddleocr package 安装
+
+
+pip 安装
+```
+pip install "paddleocr>=2.0.6" # 推荐使用2.0.6版本
+```
+本地构建并安装
+```
+python3 setup.py bdist_wheel
+pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号
+```
+
+<a name="快速使用"></a>
+## 2 快速使用
+
+<a name="命令行运行"></a>
+### 2.1 命令行运行
+
+查看帮助信息
+
+```
+paddleocr -h
+```
+
+* 整图预测（检测+识别）
+
+Paddleocr目前支持80个语种，可以通过修改--lang参数进行切换，具体支持的[语种](#语种缩写)可查看表格。
+
+``` bash
+
+paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
+```
+
+<div align="center">
+    <img src="../imgs_en/254.jpg" width="300" height="600">
+    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
+</div>
+
+
+结果是一个list，每个item包含了文本框，文字和识别置信度
+```text
+[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
+[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
+[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
+[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
+[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
+[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
+[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
+......
+```
+
+* 识别预测
+
+```bash
+paddleocr --image_dir doc/imgs_words_en/word_308.png --det false --lang=en
+```
+
+结果是一个tuple，返回识别结果和识别置信度
+
+```text
+(0.99879867, 'LITTLE')
+```
+
+* 检测预测
+
+```
+paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
+```
+
+结果是一个list，每个item只包含文本框
+
+```
+[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
+[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
+[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
+......
+```
+
+<a name="python_脚本运行"></a>
+### 2.2 python 脚本运行
+
+ppocr 也支持在python脚本中运行，便于嵌入到您自己的代码中 ：
+
+* 整图预测（检测+识别）
+
+```
+from paddleocr import PaddleOCR, draw_ocr
+
+# 同样也是通过修改 lang 参数切换语种
+ocr = PaddleOCR(lang="korean") # 首次执行会自动下载模型文件
+img_path = 'doc/imgs/korean_1.jpg '
+result = ocr.ocr(img_path)
+# 可通过参数控制单独执行识别、检测
+# result = ocr.ocr(img_path, det=False) 只执行识别
+# result = ocr.ocr(img_path, rec=False) 只执行检测
+# 打印检测框和识别结果
+for line in result:
+    print(line)
+
+# 可视化
+from PIL import Image
+image = Image.open(img_path).convert('RGB')
+boxes = [line[0] for line in result]
+txts = [line[1][0] for line in result]
+scores = [line[1][1] for line in result]
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+结果可视化:
+
+<div align="center">
+    <img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/korean.jpg" width="800">
+</div>
+
+ppocr 还支持方向分类， 更多使用方式请参考：[whl包使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md)
+
+<a name="自定义训练"></a>
+## 3 自定义训练
+
+ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别模型可以参考 [法语配置文件](../../configs/rec/multi_language/rec_french_lite_train.yml)
+修改训练数据路径、字典等参数。
+
+具体数据准备、训练过程可参考：[文本检测](../doc_ch/detection.md)、[文本识别](../doc_ch/recognition.md)，更多功能如预测部署、
+数据标注等功能可以阅读完整的[文档教程](../../README_ch.md)。
+
+<a name="预测部署"></a>
+## 4 预测部署
+
+除了安装whl包进行快速预测，ppocr 也提供了多种预测部署方式，如有需求可阅读相关文档：
+- [基于Python脚本预测引擎推理](./inference.md)
+- [基于C++预测引擎推理](../../deploy/cpp_infer/readme.md)
+- [服务化部署](../../deploy/hubserving/readme.md)
+- [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
+- [Benchmark](./benchmark.md)
+
+
+
+<a name="语种缩写"></a>
+## 5 支持语种及缩写
+
+| 语种 | 描述 | 缩写 | | 语种 | 描述 | 缩写 |
+| --- | --- | --- | ---|--- | --- | --- |
+|中文|chinese and english|ch| |保加利亚文|Bulgarian |bg|
+|英文|english|en| |乌克兰文|Ukranian|uk|
+|法文|french|fr| |白俄罗斯文|Belarusian|be|
+|德文|german|german| |泰卢固文|Telugu |te|
+|日文|japan|japan| | |阿巴扎文|Abaza |abq|
+|韩文|korean|korean| |泰米尔文|Tamil |ta|
+|中文繁体|chinese traditional |ch_tra| |南非荷兰文 |Afrikaans |af|
+|意大利文| Italian |it| |阿塞拜疆文 |Azerbaijani    |az|
+|西班牙文|Spanish |es| |波斯尼亚文|Bosnian|bs|
+|葡萄牙文| Portuguese|pt| |捷克文|Czech|cs|
+|俄罗斯文|Russia|ru| |威尔士文 |Welsh |cy|
+|阿拉伯文|Arabic|ar| |丹麦文 |Danish|da|
+|印地文|Hindi|hi| |爱沙尼亚文 |Estonian |et|
+|维吾尔|Uyghur|ug| |爱尔兰文 |Irish |ga|
+|波斯文|Persian|fa| |克罗地亚文|Croatian |hr|
+|乌尔都文|Urdu|ur| |匈牙利文|Hungarian |hu|
+|塞尔维亚文（latin)| Serbian(latin) |rs_latin| |印尼文|Indonesian|id|
+|欧西坦文|Occitan |oc| |冰岛文 |Icelandic|is|
+|马拉地文|Marathi|mr| |库尔德文 |Kurdish|ku|
+|尼泊尔文|Nepali|ne| |立陶宛文|Lithuanian |lt|
+|塞尔维亚文（cyrillic)|Serbian(cyrillic)|rs_cyrillic| |拉脱维亚文 |Latvian |lv|
+|毛利文|Maori|mi| | 达尔瓦文|Dargwa |dar|
+|马来文 |Malay|ms| | 因古什文|Ingush |inh|
+|马耳他文 |Maltese |mt| | 拉克文|Lak |lbe|
+|荷兰文 |Dutch |nl| | 莱兹甘文|Lezghian |lez|
+|挪威文 |Norwegian |no| |塔巴萨兰文 |Tabassaran |tab|
+|波兰文|Polish |pl| | 比尔哈文|Bihari |bh|
+| 罗马尼亚文|Romanian |ro| | 迈蒂利文|Maithili |mai|
+| 斯洛伐克文|Slovak |sk| | 昂加文|Angika |ang|
+| 斯洛文尼亚文|Slovenian |sl| | 孟加拉文|Bhojpuri |bho|
+| 阿尔巴尼亚文|Albanian |sq| | 摩揭陀文 |Magahi |mah|
+| 瑞典文|Swedish |sv| | 那格浦尔文|Nagpur |sck|
+| 西瓦希里文|Swahili |sw| | 尼瓦尔文|Newari |new|
+| 塔加洛文|Tagalog |tl| | 保加利亚文 |Goan Konkani|gom|
+| 土耳其文|Turkish |tr| | 沙特阿拉伯文|Saudi Arabia|sa|
+| 乌兹别克文|Uzbek |uz| | 阿瓦尔文|Avar |ava|
+| 越南文|Vietnamese |vi| | 阿瓦尔文|Avar |ava|
+| 蒙古文|Mongolian |mn| | 阿迪赫文|Adyghe |ady|

doc/doc_ch/pgnet.md

+# 端对端OCR算法-PGNet
+- [一、简介](#简介)
+- [二、环境配置](#环境配置)
+- [三、快速使用](#快速使用)
+- [四、模型训练、评估、推理](#模型训练、评估、推理)
+
+<a name="简介"></a>
+## 一、简介
+OCR算法可以分为两阶段算法和端对端的算法。二阶段OCR算法一般分为两个部分，文本检测和文本识别算法，文件检测算法从图像中得到文本行的检测框，然后识别算法去识别文本框中的内容。而端对端OCR算法可以在一个算法中完成文字检测和文字识别，其基本思想是设计一个同时具有检测单元和识别模块的模型，共享其中两者的CNN特征，并联合训练。由于一个算法即可完成文字识别，端对端模型更小，速度更快。
+
+### PGNet算法介绍
+近些年来，端对端OCR算法得到了良好的发展，包括MaskTextSpotter系列、TextSnake、TextDragon、PGNet系列等算法。在这些算法中，PGNet算法具备其他算法不具备的优势，包括：
+- 设计PGNet loss指导训练，不需要字符级别的标注
+- 不需要NMS和ROI相关操作，加速预测
+- 提出预测文本行内的阅读顺序模块；
+- 提出基于图的修正模块（GRM）来进一步提高模型识别性能
+- 精度更高，预测速度更快
+
+PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) ,算法原理图如下所示：
+![](../pgnet_framework.png)
+输入图像经过特征提取送入四个分支，分别是：文本边缘偏移量预测TBO模块，文本中心线预测TCL模块，文本方向偏移量预测TDO模块，以及文本字符分类图预测TCC模块。
+其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果，TCL、TDO、TCC负责文本识别。
+
+其检测识别效果图如下：
+
+![](../imgs_results/e2e_res_img293_pgnet.png)
+![](../imgs_results/e2e_res_img295_pgnet.png)
+
+### 性能指标
+
+测试集: Total Text
+
+测试环境: NVIDIA Tesla V100-SXM2-16GB
+
+|PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|下载|
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
+|Ours|87.03|82.48|84.69|61.71|58.43|60.03|48.73 (size=768)|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
+
+*note：PaddleOCR里的PGNet实现针对预测速度做了优化，在精度下降可接受范围内，可以显著提升端对端预测速度*
+
+
+
+<a name="环境配置"></a>
+## 二、环境配置
+请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
+
+<a name="快速使用"></a>
+## 三、快速使用
+### inference模型下载
+本节以训练好的端到端模型为例，快速使用模型预测，首先下载训练好的端到端inference模型[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar)
+```
+mkdir inference && cd inference
+# 下载英文端到端模型并解压
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar && tar xf e2e_server_pgnetA_infer.tar
+```
+* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
+
+解压完毕后应有如下文件结构：
+```
+├── e2e_server_pgnetA_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+### 单张图像或者图像集合预测
+```bash
+# 预测image_dir指定的单张图像
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
+
+# 预测image_dir指定的图像集合
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
+
+# 如果想使用CPU进行预测，需设置use_gpu参数为False
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True --use_gpu=False
+```
+### 可视化结果
+可视化文本检测结果默认保存到./inference_results文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
+![](../imgs_results/e2e_res_img623_pgnet.jpg)
+
+<a name="模型训练、评估、推理"></a>
+## 四、模型训练、评估、推理
+本节以totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
+
+###  准备数据
+下载解压[totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) 数据集到PaddleOCR/train_data/目录，数据集组织结构：
+```
+/PaddleOCR/train_data/total_text/train/
+  |- rgb/            # total_text数据集的训练数据
+      |- img11.jpg
+      | ...  
+  |- train.txt       # total_text数据集的训练标注
+```
+
+total_text.txt标注文件格式如下，文件名和标注信息中间用"\t"分隔：
+```
+" 图像文件名                    json.dumps编码的图像标注信息"
+rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
+```
+json.dumps编码前的图像标注信息是包含多个字典的list，字典中的 `points` 表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。
+`transcription` 表示当前文本框的文字，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
+如果您想在其他数据集上训练，可以按照上述形式构建标注文件。
+
+### 启动训练
+
+PGNet训练分为两个步骤：step1: 在合成数据上训练，得到预训练模型，此时模型精度依然较低；step2: 加载预训练模型，在totaltext数据集上训练；为快速训练，我们直接提供了step1的预训练模型。
+```shell
+cd PaddleOCR/
+下载step1 预训练模型
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar
+可以得到以下的文件格式
+./pretrain_models/train_step1/
+  └─ best_accuracy.pdopt
+  └─ best_accuracy.states
+  └─ best_accuracy.pdparams
+```
+*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
+
+```shell
+# 单机单卡训练 e2e 模型
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False
+# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy  Global.load_static_weights=False
+```
+
+上述指令中，通过-c 选择训练使用configs/e2e/e2e_r50_vd_pg.yml配置文件。
+有关配置文件的详细解释，请参考[链接](./config.md)。
+
+您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
+```shell
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001
+```
+
+#### 断点训练
+如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
+```shell
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model
+```
+
+**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
+
+PaddleOCR计算三个OCR端到端相关的指标，分别是：Precision、Recall、Hmean。
+
+运行如下代码，根据配置文件`e2e_r50_vd_pg.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
+
+评估时设置后处理参数`max_side_len=768`，使用不同数据集、不同模型训练，可调整参数进行优化
+训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。
+```shell
+python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy"
+```
+
+### 模型预测
+测试单张图像的端到端识别效果
+```shell
+python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
+```
+
+测试文件夹下所有图像的端到端识别效果
+```shell
+python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
+```
+
+### 预测推理
+#### (1). 四边形文本检测模型（ICDAR2015）  
+首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，以英文数据集训练的模型为例[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar) ，可以使用如下命令进行转换：
+```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
+python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
+```
+**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`**，可以执行如下命令：
+```
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/"  --e2e_pgnet_polygon=False
+```
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
+
+![](../imgs_results/e2e_res_img_10_pgnet.jpg)
+
+#### (2). 弯曲文本检测模型（Total-Text）
+对于弯曲文本样例
+
+**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`，同时，还需要增加参数`--e2e_pgnet_polygon=True`，**可以执行如下命令：
+```
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
+```
+可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
+
+![](../imgs_results/e2e_res_img623_pgnet.jpg)

doc/doc_ch/recognition.md

@@ -138,7 +138,7 @@ PaddleOCR内置了一部分字典，可以按需使用。
 
 `ppocr/utils/dict/german_dict.txt` 是一个包含131个字符的德文字典
 
-`ppocr/utils/dict/en_dict.txt` 是一个包含63个字符的英文字典
+`ppocr/utils/en_dict.txt` 是一个包含96个字符的英文字典
 
 
 
@@ -285,7 +285,7 @@ Eval:
 <a name="小语种"></a>
 #### 2.3 小语种
 
-PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
+PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi_languages` 路径下提供了一个多语言的配置文件模版: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
 
 您有两种方式创建所需的配置文件：
 
@@ -368,26 +368,12 @@ PaddleOCR目前已支持26种（除中文外）语种识别，`configs/rec/multi
 | rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   | german |
 | rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  | japan |
 | rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  | korean |
-| rec_it_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 意大利语  | it |
-| rec_xi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 西班牙语 |  xi |
-| rec_pu_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 葡萄牙语   | pu |
-| rec_ru_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 俄罗斯语  | ru |
-| rec_ar_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯语  | ar |
-| rec_hi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 印地语 |  hi |
-| rec_ug_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 维吾尔语  | ug |
-| rec_fa_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 波斯语  | fa |
-| rec_ur_ite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 乌尔都语  | ur |
-| rec_rs_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 塞尔维亚(latin)语 | rs |
-| rec_oc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 欧西坦语   | oc |
-| rec_mr_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 马拉地语  | mr |
-| rec_ne_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 尼泊尔语  | ne |
-| rec_rsc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 塞尔维亚(cyrillic)语 |  rsc |
-| rec_bg_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 保加利亚语  | bg |
-| rec_uk_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 乌克兰语  | uk |
-| rec_be_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 白俄罗斯语   | be |
-| rec_te_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 泰卢固语  | te |
-| rec_ka_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 卡纳达语  | ka |
-| rec_ta_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 泰米尔语 |  ta |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 拉丁字母  | latin |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯字母 |  ar |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 斯拉夫字母  | cyrillic |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 梵文字母  | devanagari |
+
+更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)
 
 多语言模型训练方式与中文模型一致，训练数据集均为100w的合成数据，少量的字体可以在 [百度网盘](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) 上下载，提取码：frgi。
 

doc/doc_ch/whl.md

@@ -36,7 +36,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -69,7 +69,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -114,7 +114,7 @@ for line in result:
 from PIL import Image
 
 image = Image.open(img_path).convert('RGB')
-im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -253,7 +253,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -285,7 +285,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -314,7 +314,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```

doc/doc_en/detection_en.md

@@ -38,28 +38,17 @@ If you want to train PaddleOCR on other datasets, please build the annotation fi
 
 ## TRAINING
 
-First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures) to replace backbone according to your needs.
+First download the pretrained model. The detection model of PaddleOCR currently supports 3 backbones, namely MobileNetV3, ResNet18_vd and ResNet50_vd. You can use the model in [PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/develop/ppcls/modeling/architectures) to replace backbone according to your needs.
+And the responding download link of backbone pretrain weights can be found in [PaddleClas repo](https://github.com/PaddlePaddle/PaddleClas#mobile-series).
 ```shell
 cd PaddleOCR/
 # Download the pre-trained model of MobileNetV3
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
 # or, download the pre-trained model of ResNet18_vd
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
 # or, download the pre-trained model of ResNet50_vd
-wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
 
-# decompressing the pre-training model file, take MobileNetV3 as an example
-tar -xf ./pretrain_models/MobileNetV3_large_x0_5_pretrained.tar ./pretrain_models/
-
-# Note: After decompressing the backbone pre-training weight file correctly, the file list in the folder is as follows:
-./pretrain_models/MobileNetV3_large_x0_5_pretrained/
-  └─ conv_last_bn_mean
-  └─ conv_last_bn_offset
-  └─ conv_last_bn_scale
-  └─ conv_last_bn_variance
-  └─ ......
-
-```
 
 #### START TRAINING
 *If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
@@ -113,16 +102,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{pat
 
 Test the detection result on a single image:
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"
 ```
 
 When testing the DB model, adjust the post-processing threshold:
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy"  PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```
 
 
 Test the detection result on all images in the folder:
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
 ```

doc/doc_en/inference_en.md

@@ -52,10 +52,9 @@ The above model is a DB algorithm trained with MobileNetV3 as the backbone. To c
 # -c Set the training algorithm yml configuration file
 # -o Set optional parameters
 # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
-# Global.load_static_weights needs to be set to False
 # Global.save_inference_dir Set the address where the converted model will be saved.
 
-python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
+python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy  Global.save_inference_dir=./inference/det_db/
 ```
 
 When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.pretrained_model` parameter in the configuration file.
@@ -80,10 +79,9 @@ The recognition model is converted to the inference model in the same way as the
 # -c Set the training algorithm yml configuration file
 # -o Set optional parameters
 # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
-# Global.load_static_weights needs to be set to False
 # Global.save_inference_dir Set the address where the converted model will be saved.
 
-python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
+python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy  Global.save_inference_dir=./inference/rec_crnn/
 ```
 
 If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
@@ -109,10 +107,9 @@ The angle classification model is converted to the inference model in the same w
 # -c Set the training algorithm yml configuration file
 # -o Set optional parameters
 # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
-# Global.load_static_weights needs to be set to False
 # Global.save_inference_dir Set the address where the converted model will be saved.
 
-python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy  Global.save_inference_dir=./inference/cls/
 ```
 
 After the conversion is successful, there are two files in the directory:
@@ -171,7 +168,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_d
 First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_db
 ```
 
 DB text detection model inference, you can execute the following command:
@@ -192,7 +189,7 @@ The visualized text detection results are saved to the `./inference_results` fol
 First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
+python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_east
 ```
 **For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command:
 
@@ -213,7 +210,7 @@ The visualized text detection results are saved to the `./inference_results` fol
 First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_sast_ic15
 ```
 
 **For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command:
@@ -230,7 +227,7 @@ The visualized text detection results are saved to the `./inference_results` fol
 First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_sast_tt
 ```
 
 **For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
@@ -279,7 +276,7 @@ Taking CRNN as an example, we introduce the recognition model inference based on
 First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)). It can be converted as follow:
 
 ```
-python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
+python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/rec_crnn
 ```
 
 For CRNN text recognition model inference, execute the following commands:
@@ -379,14 +376,18 @@ After executing the command, the prediction results (classification angle and sc
 <a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
 ### 1. LIGHTWEIGHT CHINESE MODEL
 
-When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model.The visualized recognition results are saved to the `./inference_results` folder by default.
+When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
 
-```
+```shell
 # use direction classifier
 python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
 
 # not use use direction classifier
 python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+
+# use multi-process
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+```
 ```
 
 After executing the command, the recognition result image is as follows:

doc/doc_en/models_list_en.md

@@ -102,27 +102,16 @@ python3 generate_multi_language_configs.py -l it \
 | german_mobile_v2.0_rec |Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) |
 | korean_mobile_v2.0_rec |Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) |
 | japan_mobile_v2.0_rec |Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) |
-| it_mobile_v2.0_rec |Lightweight model for Italian recognition|rec_it_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/it_mobile_v2.0_rec_train.tar) |
-| xi_mobile_v2.0_rec |Lightweight model for Spanish recognition|rec_xi_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/xi_mobile_v2.0_rec_train.tar) |
-| pu_mobile_v2.0_rec |Lightweight model for Portuguese recognition|rec_pu_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/pu_mobile_v2.0_rec_train.tar) |
-| ru_mobile_v2.0_rec |Lightweight model for Russia recognition|rec_ru_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ru_mobile_v2.0_rec_train.tar) |
-| ar_mobile_v2.0_rec |Lightweight model for Arabic recognition|rec_ar_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ar_mobile_v2.0_rec_train.tar) |
-| hi_mobile_v2.0_rec |Lightweight model for Hindi recognition|rec_hi_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/hi_mobile_v2.0_rec_train.tar) |
-| chinese_cht_mobile_v2.0_rec |Lightweight model for chinese traditional recognition|rec_chinese_cht_lite_train.yml|5.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
-| ug_mobile_v2.0_rec |Lightweight model for Uyghur recognition|rec_ug_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ug_mobile_v2.0_rec_train.tar) |
-| fa_mobile_v2.0_rec |Lightweight model for Persian recognition|rec_fa_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/fa_mobile_v2.0_rec_train.tar) |
-| ur_mobile_v2.0_rec |Lightweight model for Urdu recognition|rec_ur_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ur_mobile_v2.0_rec_train.tar) |
-| rs_mobile_v2.0_rec |Lightweight model for Serbian(latin) recognition|rec_rs_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rs_mobile_v2.0_rec_train.tar) |
-| oc_mobile_v2.0_rec |Lightweight model for Occitan recognition|rec_oc_lite_train.yml|2.53M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/oc_mobile_v2.0_rec_train.tar) |
-| mr_mobile_v2.0_rec |Lightweight model for Marathi recognition|rec_mr_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/mr_mobile_v2.0_rec_train.tar) |
-| ne_mobile_v2.0_rec |Lightweight model for Nepali recognition|rec_ne_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ne_mobile_v2.0_rec_train.tar) |
-| rsc_mobile_v2.0_rec |Lightweight model for Serbian(cyrillic) recognition|rec_rsc_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/rsc_mobile_v2.0_rec_train.tar) |
-| bg_mobile_v2.0_rec |Lightweight model for Bulgarian recognition|rec_bg_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/bg_mobile_v2.0_rec_train.tar) |
-| uk_mobile_v2.0_rec |Lightweight model for Ukranian recognition|rec_uk_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/uk_mobile_v2.0_rec_train.tar) |
-| be_mobile_v2.0_rec |Lightweight model for Belarusian recognition|rec_be_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/be_mobile_v2.0_rec_train.tar) |
+| chinese_cht_mobile_v2.0_rec |Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) |
 | te_mobile_v2.0_rec |Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) |
 | ka_mobile_v2.0_rec |Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) |
 | ta_mobile_v2.0_rec |Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) |
+| latin_mobile_v2.0_rec |  Lightweight model for latin recognition |  [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) |
+| arabic_mobile_v2.0_rec |  Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) |
+| cyrillic_mobile_v2.0_rec | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) |
+| devanagari_mobile_v2.0_rec | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) |
+
+For more supported languages, please refer to : [Multi-language model](./multi_languages_en.md)
 
 
 <a name="Angle"></a>

doc/doc_en/multi_languages_en.md

+# Multi-language model
+
+**Recent Update**
+
+- 2021.4.9 supports the detection and recognition of 80 languages
+- 2021.4.9 supports **lightweight high-precision** English model detection and recognition
+
+PaddleOCR aims to create a rich, leading, and practical OCR tool library, which not only provides
+Chinese and English models in general scenarios, but also provides models specifically trained
+in English scenarios. And multilingual models covering [80 languages](#language_abbreviations).
+
+Among them, the English model supports the detection and recognition of uppercase and lowercase
+letters and common punctuation, and the recognition of space characters is optimized:
+
+<div align="center">
+    <img src="../imgs_results/multi_lang/img_12.jpg" width="900" height="300">
+</div>
+
+The multilingual models cover Latin, Arabic, Traditional Chinese, Korean, Japanese, etc.:
+
+<div align="center">
+    <img src="../imgs_results/multi_lang/japan_2.jpg" width="600" height="300">
+    <img src="../imgs_results/multi_lang/french_0.jpg" width="300" height="300">
+    <img src="../imgs_results/multi_lang/korean_0.jpg" width="500" height="300">
+    <img src="../imgs_results/multi_lang/arabic_0.jpg" width="300" height="300">
+</div>
+
+This document will briefly introduce how to use the multilingual model.
+
+- [1 Installation](#Install)
+    - [1.1 paddle installation](#paddleinstallation)
+    - [1.2 paddleocr package installation](#paddleocr_package_install)
+
+- [2 Quick Use](#Quick_Use)
+    - [2.1 Command line operation](#Command_line_operation)
+    - [2.2 python script running](#python_Script_running)
+- [3 Custom Training](#Custom_Training)
+- [4 Inference and Deployment](#inference)
+- [4 Supported languages and abbreviations](#language_abbreviations)
+
+<a name="Install"></a>
+## 1 Installation
+
+<a name="paddle_install"></a>
+### 1.1 paddle installation
+```
+# cpu
+pip install paddlepaddle
+
+# gpu
+pip install paddlepaddle-gpu
+```
+
+<a name="paddleocr_package_install"></a>
+### 1.2 paddleocr package installation
+
+
+pip install
+```
+pip install "paddleocr>=2.0.6" # 2.0.6 version is recommended
+```
+Build and install locally
+```
+python3 setup.py bdist_wheel
+pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version number of paddleocr
+```
+
+<a name="Quick_use"></a>
+## 2 Quick use
+
+<a name="Command_line_operation"></a>
+### 2.1 Command line operation
+
+View help information
+
+```
+paddleocr -h
+```
+
+* Whole image prediction (detection + recognition)
+
+Paddleocr currently supports 80 languages, which can be switched by modifying the --lang parameter.
+The specific supported [language] (#language_abbreviations) can be viewed in the table.
+
+``` bash
+paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
+```
+<div align="center">
+    <img src="../imgs_en/254.jpg" width="300" height="600">
+    <img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
+</div>
+
+The result is a list, each item contains a text box, text and recognition confidence
+```text
+[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
+[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
+[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
+[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
+[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
+[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
+[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
+......
+```
+
+* Recognition
+
+```bash
+paddleocr --image_dir doc/imgs_words_en/word_308.png --det false --lang=en
+```
+
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_words_en/word_308.png)
+
+The result is a tuple, which returns the recognition result and recognition confidence
+
+```text
+(0.99879867, 'LITTLE')
+```
+
+* Detection
+
+```
+paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
+```
+
+The result is a list, each item contains only text boxes
+
+```
+[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
+[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
+[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
+......
+```
+
+<a name="python_script_running"></a>
+### 2.2 python script running
+
+ppocr also supports running in python scripts for easy embedding in your own code:
+
+* Whole image prediction (detection + recognition)
+
+```
+from paddleocr import PaddleOCR, draw_ocr
+
+# Also switch the language by modifying the lang parameter
+ocr = PaddleOCR(lang="korean") # The model file will be downloaded automatically when executed for the first time
+img_path ='doc/imgs/korean_1.jpg'
+result = ocr.ocr(img_path)
+# Recognition and detection can be performed separately through parameter control
+# result = ocr.ocr(img_path, det=False)  Only perform recognition
+# result = ocr.ocr(img_path, rec=False)  Only perform detection
+# Print detection frame and recognition result
+for line in result:
+    print(line)
+
+# Visualization
+from PIL import Image
+image = Image.open(img_path).convert('RGB')
+boxes = [line[0] for line in result]
+txts = [line[1][0] for line in result]
+scores = [line[1][1] for line in result]
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/korean.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+Visualization of results:
+![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.1/doc/imgs_results/korean.jpg)
+
+
+ppocr also supports direction classification. For more usage methods, please refer to: [whl package instructions](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md).
+
+<a name="Custom_training"></a>
+## 3 Custom training
+
+ppocr supports using your own data for custom training or finetune, where the recognition model can refer to [French configuration file](../../configs/rec/multi_language/rec_french_lite_train.yml)
+Modify the training data path, dictionary and other parameters.
+
+For specific data preparation and training process, please refer to: [Text Detection](../doc_en/detection_en.md), [Text Recognition](../doc_en/recognition_en.md), more functions such as predictive deployment,
+For functions such as data annotation, you can read the complete [Document Tutorial](../../README.md).
+
+
+<a name="inference"></a>
+## 4 Inference and Deployment
+
+In addition to installing the whl package for quick forecasting,
+ppocr also provides a variety of forecasting deployment methods.
+If necessary, you can read related documents:
+
+- [Python Inference](./inference_en.md)
+- [C++ Inference](../../deploy/cpp_infer/readme_en.md)
+- [Serving](../../deploy/hubserving/readme_en.md)
+- [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md)
+- [Benchmark](./benchmark_en.md)
+
+
+<a name="language_abbreviations"></a>
+## 5 Support languages and abbreviations
+
+| Language  | Abbreviation | | Language  | Abbreviation |
+| ---  | --- | --- | ---  | --- |
+|chinese and english|ch| |Arabic|ar|
+|english|en| |Hindi|hi|
+|french|fr| |Uyghur|ug|
+|german|german| |Persian|fa|
+|japan|japan| |Urdu|ur|
+|korean|korean| | Serbian(latin) |rs_latin|
+|chinese traditional |ch_tra| |Occitan |oc|
+| Italian |it| |Marathi|mr|
+|Spanish |es| |Nepali|ne|
+| Portuguese|pt| |Serbian(cyrillic)|rs_cyrillic|
+|Russia|ru||Bulgarian |bg|
+|Ukranian|uk| |Estonian |et|
+|Belarusian|be| |Irish |ga|
+|Telugu |te| |Croatian |hr|
+|Saudi Arabia|sa| |Hungarian |hu|
+|Tamil |ta| |Indonesian|id|
+|Afrikaans |af| |Icelandic|is|
+|Azerbaijani  |az||Kurdish|ku|
+|Bosnian|bs| |Lithuanian |lt|
+|Czech|cs| |Latvian |lv|
+|Welsh |cy| |Maori|mi|
+|Danish|da| |Malay|ms|
+|Maltese |mt| |Adyghe |ady|
+|Dutch |nl| |Kabardian |kbd|
+|Norwegian |no| |Avar |ava|
+|Polish |pl| |Dargwa |dar|
+|Romanian |ro| |Ingush |inh|
+|Slovak |sk| |Lak |lbe|
+|Slovenian |sl| |Lezghian |lez|
+|Albanian |sq| |Tabassaran |tab|
+|Swedish |sv| |Bihari |bh|
+|Swahili |sw| |Maithili |mai|
+|Tagalog |tl| |Angika |ang|
+|Turkish |tr| |Bhojpuri |bho|
+|Uzbek |uz| |Magahi |mah|
+|Vietnamese |vi| |Nagpur |sck|
+|Mongolian |mn| |Newari |new|
+|Abaza |abq| |Goan Konkani|gom|

doc/doc_en/pgnet_en.md

+# End-to-end OCR Algorithm-PGNet
+- [1. Brief Introduction](#Brief_Introduction)
+- [2. Environment Configuration](#Environment_Configuration)
+- [3. Quick Use](#Quick_Use)
+- [4. Model Training,Evaluation And Inference](#Model_Training_Evaluation_And_Inference)
+
+<a name="Brief_Introduction"></a>
+## 1. Brief Introduction
+OCR algorithm can be divided into two-stage algorithm and end-to-end algorithm. The two-stage OCR algorithm is generally divided into two parts, text detection and text recognition algorithm. The text detection algorithm gets the detection box of the text line from the image, and then the recognition algorithm identifies the content of the text box. The end-to-end OCR algorithm can complete text detection and recognition in one algorithm. Its basic idea is to design a model with both detection unit and recognition module, share the CNN features of both and train them together. Because one algorithm can complete character recognition, the end-to-end model is smaller and faster.
+### Introduction Of PGNet Algorithm
+In recent years, the end-to-end OCR algorithm has been well developed, including MaskTextSpotter series, TextSnake, TextDragon, PGNet series and so on. Among these algorithms, PGNet algorithm has the advantages that other algorithms do not
+- Pgnet loss is designed to guide training, and no character-level annotations is needed
+- NMS and ROI related operations are not needed, It can accelerate the prediction
+- The reading order prediction module is proposed
+- A graph based modification module (GRM) is proposed to further improve the performance of model recognition
+- Higher accuracy and faster prediction speed
+
+For details of PGNet algorithm, please refer to [paper](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) ,The schematic diagram of the algorithm is as follows:
+![](../pgnet_framework.png)
+After feature extraction, the input image is sent to four branches: TBO module for text edge offset prediction, TCL module for text centerline prediction, TDO module for text direction offset prediction, and TCC module for text character classification graph prediction.
+The output of TBO and TCL can get text detection results after post-processing, and TCL, TDO and TCC are responsible for text recognition.
+
+The results of detection and recognition are as follows:
+![](../imgs_results/e2e_res_img293_pgnet.png)
+![](../imgs_results/e2e_res_img295_pgnet.png)
+### Performance
+####Test set: Total Text
+
+####Test environment: NVIDIA Tesla V100-SXM2-16GB
+|PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|download|
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
+|Ours|87.03|82.48|84.69|61.71|58.43|60.03|48.73 (size=768)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)|
+
+*note：PGNet in PaddleOCR optimizes the prediction speed, and can significantly improve the end-to-end prediction speed within the acceptable range of accuracy reduction*
+
+<a name="Environment_Configuration"></a>
+## 2. Environment Configuration
+Please refer to [Quick Installation](./installation_en.md) Configure the PaddleOCR running environment.
+
+<a name="Quick_Use"></a>
+## 3. Quick Use
+### inference model download
+This section takes the trained end-to-end model as an example to quickly use the model prediction. First, download the trained end-to-end inference model [download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar)
+```
+mkdir inference && cd inference
+# Download the English end-to-end model and unzip it
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar && tar xf e2e_server_pgnetA_infer.tar
+```
+* In Windows environment, if 'wget' is not installed, the link can be copied to the browser when downloading the model, and decompressed and placed in the corresponding directory
+
+After decompression, there should be the following file structure:
+```
+├── e2e_server_pgnetA_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+```
+### Single image or image set prediction
+```bash
+# Prediction single image specified by image_dir
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
+
+# Prediction the collection of images specified by image_dir
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True
+
+# If you want to use CPU for prediction, you need to set use_gpu parameter is false
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e_server_pgnetA_infer/" --e2e_pgnet_polygon=True --use_gpu=False
+```
+### Visualization results
+The visualized end-to-end results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows:
+![](../imgs_results/e2e_res_img623_pgnet.jpg)
+
+<a name="Model_Training_Evaluation_And_Inference"></a>
+## 4. Model Training,Evaluation And Inference
+This section takes the totaltext dataset as an example to introduce the training, evaluation and testing of the end-to-end model in PaddleOCR.
+
+###  Data Preparation
+Download and unzip [totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) dataset to PaddleOCR/train_data/, dataset organization structure is as follow：
+```
+/PaddleOCR/train_data/total_text/train/
+  |- rgb/            # total_text training data of dataset
+      |- img11.png
+      | ...  
+  |- train.txt       # total_text training annotation of dataset
+```
+
+total_text.txt: the format of dimension file is as follows，the file name and annotation information are separated by "\t"：
+```
+" Image file name             Image annotation information encoded by json.dumps"
+rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
+```
+The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
+
+The `points` in the dictionary represent the coordinates (x, y) of the four points of the text box, arranged clockwise from the point at the upper left corner.
+
+`transcription` represents the text of the current text box. **When its content is "###" it means that the text box is invalid and will be skipped during training.**
+
+If you want to train PaddleOCR on other datasets, please build the annotation file according to the above format.
+
+
+### Start Training
+
+PGNet training is divided into two steps: Step 1: training on the synthetic data to get the pretrain_model, and the accuracy of the model is still low; step 2: loading the pretrain_model and training on the totaltext data set; for fast training, we directly provide the pre training model of step 1[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar).
+```shell
+cd PaddleOCR/
+download step1 pretrain_models
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar
+You can get the following file format
+./pretrain_models/train_step1/
+  └─ best_accuracy.pdopt
+  └─ best_accuracy.states
+  └─ best_accuracy.pdparams
+```
+*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
+
+```shell
+# single GPU training
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False
+# multi-GPU training
+# Set the GPU ID used by the '--gpus' parameter.
+python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy  Global.load_static_weights=False
+```
+
+In the above instruction, use `-c` to select the training to use the `configs/e2e/e2e_r50_vd_pg.yml` configuration file.
+For a detailed explanation of the configuration file, please refer to [config](./config_en.md).
+
+You can also use `-o` to change the training parameters without modifying the yml file. For example, adjust the training learning rate to 0.0001
+```shell
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001
+```
+
+#### Load trained model and continue training
+If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
+```shell
+python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model
+```
+
+**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
+
+PaddleOCR calculates three indicators for evaluating performance of OCR end-to-end task: Precision, Recall, and Hmean.
+
+
+Run the following code to calculate the evaluation indicators. The result will be saved in the test result file specified by `save_res_path` in the configuration file `e2e_r50_vd_pg.yml`
+When evaluating, set post-processing parameters `max_side_len=768`. If you use different datasets, different models for training.
+The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file.
+```shell
+python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml  -o Global.checkpoints="{path/to/weights}/best_accuracy"
+```
+
+### Model Test
+Test the end-to-end result on a single image:
+```shell
+python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
+```
+
+Test the end-to-end result on all images in the folder:
+```shell
+python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/e2e_pgnet/best_accuracy" Global.load_static_weights=false
+```
+
+### Model inference
+#### (1).Quadrangle text detection model (ICDAR2015)
+First, convert the model saved in the PGNet end-to-end training process into an inference model. In the first stage of training based on composite dataset, the model of English data set training is taken as an example[model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar), you can use the following command to convert:
+```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
+python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
+```
+**For PGNet quadrangle end-to-end model inference, you need to set the parameter `--e2e_algorithm="PGNet"`**, run the following command:
+```
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/"  --e2e_pgnet_polygon=False
+```
+The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows:
+
+![](../imgs_results/e2e_res_img_10_pgnet.jpg)
+
+#### (2). Curved text detection model (Total-Text)
+For the curved text example, we use the same model as the quadrilateral
+**For PGNet end-to-end curved text detection model inference, you need to set the parameter `--e2e_algorithm="PGNet"` and `--e2e_pgnet_polygon=True`**, run the following command:
+```
+python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
+```
+The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'e2e_res'. Examples of results are as follows:
+
+![](../imgs_results/e2e_res_img623_pgnet.jpg)

doc/doc_en/recognition_en.md

@@ -131,7 +131,7 @@ PaddleOCR has built-in dictionaries, which can be used on demand.
 
 `ppocr/utils/dict/german_dict.txt` is a German dictionary with 131 characters
 
-`ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
+`ppocr/utils/en_dict.txt` is a English dictionary with 96 characters
 
 
 The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
@@ -279,7 +279,7 @@ Eval:
 <a name="Multi_language"></a>
 #### 2.3 Multi-language
 
-PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is
+PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
 provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
 
 There are two ways to create the required configuration file:：
@@ -368,27 +368,12 @@ Currently, the multi-language algorithms supported by PaddleOCR are:
 | rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
 | rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
 | rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
-| rec_it_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Italian  | it |
-| rec_xi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Spanish |  xi |
-| rec_pu_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Portuguese   | pu |
-| rec_ru_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Russia  | ru |
-| rec_ar_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Arabic  | ar |
-| rec_hi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Hindi |  hi |
-| rec_ug_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Uyghur  | ug |
-| rec_fa_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Persian(Farsi)  | fa |
-| rec_ur_ite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Urdu  | ur |
-| rec_rs_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Serbian(latin) | rs |
-| rec_oc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Occitan  | oc |
-| rec_mr_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Marathi  | mr |
-| rec_ne_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Nepali  | ne |
-| rec_rsc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Serbian(cyrillic) |  rsc |
-| rec_bg_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Bulgarian  | bg |
-| rec_uk_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Ukranian  | uk |
-| rec_be_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Belarusian   | be |
-| rec_te_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Telugu  | te |
-| rec_ka_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Kannada  | ka |
-| rec_ta_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Tamil |  ta |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  | latin |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |  ar |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   | cyrillic |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  | devanagari |
 
+For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
 
 The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
 

doc/doc_en/whl_en.md

@@ -35,7 +35,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -69,7 +69,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -116,7 +116,7 @@ for line in result:
 from PIL import Image
 
 image = Image.open(img_path).convert('RGB')
-im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -262,7 +262,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -292,7 +292,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```
@@ -320,7 +320,7 @@ image = Image.open(img_path).convert('RGB')
 boxes = [line[0] for line in result]
 txts = [line[1][0] for line in result]
 scores = [line[1][1] for line in result]
-im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/simfang.ttf')
+im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
 im_show = Image.fromarray(im_show)
 im_show.save('result.jpg')
 ```

paddleocr.py

@@ -30,12 +30,17 @@ from ppocr.utils.logging import get_logger
 
 logger = get_logger()
 from ppocr.utils.utility import check_and_read_gif, get_image_file_list
+from tools.infer.utility import draw_ocr
 
 __all__ = ['PaddleOCR']
 
 model_urls = {
-    'det':
-    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+    'det': {
+        'ch':
+        'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+        'en':
+        'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
+    },
     'rec': {
         'ch': {
             'url':
@@ -45,7 +50,7 @@ model_urls = {
         'en': {
             'url':
             'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
-            'dict_path': './ppocr/utils/dict/en_dict.txt'
+            'dict_path': './ppocr/utils/en_dict.txt'
         },
         'french': {
             'url':
@@ -66,6 +71,46 @@ model_urls = {
             'url':
             'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
             'dict_path': './ppocr/utils/dict/japan_dict.txt'
+        },
+        'chinese_cht': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
+        },
+        'ta': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/ta_dict.txt'
+        },
+        'te': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/te_dict.txt'
+        },
+        'ka': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/ka_dict.txt'
+        },
+        'latin': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/latin_dict.txt'
+        },
+        'arabic': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/arabic_dict.txt'
+        },
+        'cyrillic': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
+        },
+        'devanagari': {
+            'url':
+            'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
         }
     },
     'cls':
@@ -73,7 +118,7 @@ model_urls = {
 }
 
 SUPPORT_DET_MODEL = ['DB']
-VERSION = 2.0
+VERSION = '2.1'
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
@@ -148,6 +193,7 @@ def parse_args(mMain=True, add_help=True):
         parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
         parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
         parser.add_argument("--use_dilation", type=bool, default=False)
+        parser.add_argument("--det_db_score_mode", type=str, default="fast")
 
         # EAST parmas
         parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
@@ -159,7 +205,7 @@ def parse_args(mMain=True, add_help=True):
         parser.add_argument("--rec_model_dir", type=str, default=None)
         parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
         parser.add_argument("--rec_char_type", type=str, default='ch')
-        parser.add_argument("--rec_batch_num", type=int, default=30)
+        parser.add_argument("--rec_batch_num", type=int, default=6)
         parser.add_argument("--max_text_length", type=int, default=25)
         parser.add_argument("--rec_char_dict_path", type=str, default=None)
         parser.add_argument("--use_space_char", type=bool, default=True)
@@ -169,7 +215,7 @@ def parse_args(mMain=True, add_help=True):
         parser.add_argument("--cls_model_dir", type=str, default=None)
         parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
         parser.add_argument("--label_list", type=list, default=['0', '180'])
-        parser.add_argument("--cls_batch_num", type=int, default=30)
+        parser.add_argument("--cls_batch_num", type=int, default=6)
         parser.add_argument("--cls_thresh", type=float, default=0.9)
 
         parser.add_argument("--enable_mkldnn", type=bool, default=False)
@@ -196,6 +242,7 @@ def parse_args(mMain=True, add_help=True):
             det_db_box_thresh=0.5,
             det_db_unclip_ratio=1.6,
             use_dilation=False,
+            det_db_score_mode="fast",
             det_east_score_thresh=0.8,
             det_east_cover_thresh=0.1,
             det_east_nms_thresh=0.2,
@@ -203,7 +250,7 @@ def parse_args(mMain=True, add_help=True):
             rec_model_dir=None,
             rec_image_shape="3, 32, 320",
             rec_char_type='ch',
-            rec_batch_num=30,
+            rec_batch_num=6,
             max_text_length=25,
             rec_char_dict_path=None,
             use_space_char=True,
@@ -211,7 +258,7 @@ def parse_args(mMain=True, add_help=True):
             cls_model_dir=None,
             cls_image_shape="3, 48, 192",
             label_list=['0', '180'],
-            cls_batch_num=30,
+            cls_batch_num=6,
             cls_thresh=0.9,
             enable_mkldnn=False,
             use_zero_copy_run=False,
@@ -233,9 +280,36 @@ class PaddleOCR(predict_system.TextSystem):
         postprocess_params.__dict__.update(**kwargs)
         self.use_angle_cls = postprocess_params.use_angle_cls
         lang = postprocess_params.lang
+        latin_lang = [
+            'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
+            'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
+            'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
+            'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
+        ]
+        arabic_lang = ['ar', 'fa', 'ug', 'ur']
+        cyrillic_lang = [
+            'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
+            'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
+        ]
+        devanagari_lang = [
+            'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
+            'gom', 'sa', 'bgc'
+        ]
+        if lang in latin_lang:
+            lang = "latin"
+        elif lang in arabic_lang:
+            lang = "arabic"
+        elif lang in cyrillic_lang:
+            lang = "cyrillic"
+        elif lang in devanagari_lang:
+            lang = "devanagari"
         assert lang in model_urls[
             'rec'], 'param lang must in {}, but got {}'.format(
                 model_urls['rec'].keys(), lang)
+        if lang == "ch":
+            det_lang = "ch"
+        else:
+            det_lang = "en"
         use_inner_dict = False
         if postprocess_params.rec_char_dict_path is None:
             use_inner_dict = True
@@ -244,17 +318,17 @@ class PaddleOCR(predict_system.TextSystem):
 
         # init model dir
         if postprocess_params.det_model_dir is None:
-            postprocess_params.det_model_dir = os.path.join(
-                BASE_DIR, '{}/det'.format(VERSION))
+            postprocess_params.det_model_dir = os.path.join(BASE_DIR, VERSION,
+                                                            'det', det_lang)
         if postprocess_params.rec_model_dir is None:
-            postprocess_params.rec_model_dir = os.path.join(
-                BASE_DIR, '{}/rec/{}'.format(VERSION, lang))
+            postprocess_params.rec_model_dir = os.path.join(BASE_DIR, VERSION,
+                                                            'rec', lang)
         if postprocess_params.cls_model_dir is None:
-            postprocess_params.cls_model_dir = os.path.join(
-                BASE_DIR, '{}/cls'.format(VERSION))
+            postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
         print(postprocess_params)
         # download model
-        maybe_download(postprocess_params.det_model_dir, model_urls['det'])
+        maybe_download(postprocess_params.det_model_dir,
+                       model_urls['det'][det_lang])
         maybe_download(postprocess_params.rec_model_dir,
                        model_urls['rec'][lang]['url'])
         maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])

ppocr/data/__init__.py

@@ -34,6 +34,7 @@ import paddle.distributed as dist
 from ppocr.data.imaug import transform, create_operators
 from ppocr.data.simple_dataset import SimpleDataSet
 from ppocr.data.lmdb_dataset import LMDBDataSet
+from ppocr.data.pgnet_dataset import PGDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -54,7 +55,7 @@ signal.signal(signal.SIGTERM, term_mp)
 def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDataSet']
+    support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))
@@ -72,14 +73,14 @@ def build_dataloader(config, mode, device, logger, seed=None):
     else:
         use_shared_memory = True
     if mode == "Train":
-        #Distribute data to multiple cards
+        # Distribute data to multiple cards
         batch_sampler = DistributedBatchSampler(
             dataset=dataset,
             batch_size=batch_size,
             shuffle=shuffle,
             drop_last=drop_last)
     else:
-        #Distribute data to single card
+        # Distribute data to single card
         batch_sampler = BatchSampler(
             dataset=dataset,
             batch_size=batch_size,

ppocr/data/imaug/__init__.py

@@ -28,6 +28,7 @@ from .label_ops import *
 
 from .east_process import *
 from .sast_process import *
+from .pg_process import *
 
 
 def transform(data, ops=None):

ppocr/data/imaug/label_ops.py

@@ -96,7 +96,7 @@ class BaseRecLabelEncode(object):
             'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
             'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs',
             'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi',
-            'mr', 'ne'
+            'mr', 'ne', 'latin', 'arabic', 'cyrillic', 'devanagari'
         ]
         assert character_type in support_character_type, "Only {} are supported now but get {}".format(
             support_character_type, character_type)
@@ -187,6 +187,78 @@ class CTCLabelEncode(BaseRecLabelEncode):
         return dict_character
 
 
+class E2ELabelEncodeTest(BaseRecLabelEncode):
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='EN',
+                 use_space_char=False,
+                 **kwargs):
+        super(E2ELabelEncodeTest,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+
+    def __call__(self, data):
+        import json
+        padnum = len(self.dict)
+        label = data['label']
+        label = json.loads(label)
+        nBox = len(label)
+        boxes, txts, txt_tags = [], [], []
+        for bno in range(0, nBox):
+            box = label[bno]['points']
+            txt = label[bno]['transcription']
+            boxes.append(box)
+            txts.append(txt)
+            if txt in ['*', '###']:
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        boxes = np.array(boxes, dtype=np.float32)
+        txt_tags = np.array(txt_tags, dtype=np.bool)
+        data['polys'] = boxes
+        data['ignore_tags'] = txt_tags
+        temp_texts = []
+        for text in txts:
+            text = text.lower()
+            text = self.encode(text)
+            if text is None:
+                return None
+            text = text + [padnum] * (self.max_text_len - len(text)
+                                      )  # use 36 to pad
+            temp_texts.append(text)
+        data['texts'] = np.array(temp_texts)
+        return data
+
+
+class E2ELabelEncodeTrain(object):
+    def __init__(self, **kwargs):
+        pass
+
+    def __call__(self, data):
+        import json
+        label = data['label']
+        label = json.loads(label)
+        nBox = len(label)
+        boxes, txts, txt_tags = [], [], []
+        for bno in range(0, nBox):
+            box = label[bno]['points']
+            txt = label[bno]['transcription']
+            boxes.append(box)
+            txts.append(txt)
+            if txt in ['*', '###']:
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        boxes = np.array(boxes, dtype=np.float32)
+        txt_tags = np.array(txt_tags, dtype=np.bool)
+
+        data['polys'] = boxes
+        data['texts'] = txts
+        data['ignore_tags'] = txt_tags
+        return data
+
+
 class AttnLabelEncode(BaseRecLabelEncode):
     """ Convert between text-label and text-index """
 

ppocr/data/imaug/operators.py

@@ -197,7 +197,6 @@ class DetResizeForTest(object):
             sys.exit(0)
         ratio_h = resize_h / float(h)
         ratio_w = resize_w / float(w)
-        # return img, np.array([h, w])
         return img, [ratio_h, ratio_w]
 
     def resize_image_type2(self, img):
@@ -206,7 +205,6 @@ class DetResizeForTest(object):
         resize_w = w
         resize_h = h
 
-        # Fix the longer side
         if resize_h > resize_w:
             ratio = float(self.resize_long) / resize_h
         else:
@@ -223,3 +221,72 @@ class DetResizeForTest(object):
         ratio_w = resize_w / float(w)
 
         return img, [ratio_h, ratio_w]
+
+
+class E2EResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(E2EResizeForTest, self).__init__()
+        self.max_side_len = kwargs['max_side_len']
+        self.valid_set = kwargs['valid_set']
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if self.valid_set == 'totaltext':
+            im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
+                img, max_side_len=self.max_side_len)
+        else:
+            im_resized, (ratio_h, ratio_w) = self.resize_image(
+                img, max_side_len=self.max_side_len)
+        data['image'] = im_resized
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def resize_image_for_totaltext(self, im, max_side_len=512):
+
+        h, w, _ = im.shape
+        resize_w = w
+        resize_h = h
+        ratio = 1.25
+        if h * ratio > max_side_len:
+            ratio = float(max_side_len) / resize_h
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def resize_image(self, im, max_side_len=512):
+        """
+        resize image to a size multiple of max_stride which is required by the network
+        :param im: the resized image
+        :param max_side_len: limit of max image size to avoid out of memory in gpu
+        :return: the resized image and the resize ratio
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(max_side_len) / resize_h
+        else:
+            ratio = float(max_side_len) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return im, (ratio_h, ratio_w)

ppocr/data/imaug/pg_process.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import cv2
+import numpy as np
+
+__all__ = ['PGProcessTrain']
+
+
+class PGProcessTrain(object):
+    def __init__(self,
+                 character_dict_path,
+                 max_text_length,
+                 max_text_nums,
+                 tcl_len,
+                 batch_size=14,
+                 min_crop_size=24,
+                 min_text_size=4,
+                 max_text_size=512,
+                 **kwargs):
+        self.tcl_len = tcl_len
+        self.max_text_length = max_text_length
+        self.max_text_nums = max_text_nums
+        self.batch_size = batch_size
+        self.min_crop_size = min_crop_size
+        self.min_text_size = min_text_size
+        self.max_text_size = max_text_size
+        self.Lexicon_Table = self.get_dict(character_dict_path)
+        self.pad_num = len(self.Lexicon_Table)
+        self.img_id = 0
+
+    def get_dict(self, character_dict_path):
+        character_str = ""
+        with open(character_dict_path, "rb") as fin:
+            lines = fin.readlines()
+            for line in lines:
+                line = line.decode('utf-8').strip("\n").strip("\r\n")
+                character_str += line
+            dict_character = list(character_str)
+        return dict_character
+
+    def quad_area(self, poly):
+        """
+        compute area of a polygon
+        :param poly:
+        :return:
+        """
+        edge = [(poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]),
+                (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]),
+                (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]),
+                (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])]
+        return np.sum(edge) / 2.
+
+    def gen_quad_from_poly(self, poly):
+        """
+        Generate min area quad from poly.
+        """
+        point_num = poly.shape[0]
+        min_area_quad = np.zeros((4, 2), dtype=np.float32)
+        rect = cv2.minAreaRect(poly.astype(
+            np.int32))  # (center (x,y), (width, height), angle of rotation)
+        box = np.array(cv2.boxPoints(rect))
+
+        first_point_idx = 0
+        min_dist = 1e4
+        for i in range(4):
+            dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
+                   np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
+                   np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
+                   np.linalg.norm(box[(i + 3) % 4] - poly[-1])
+            if dist < min_dist:
+                min_dist = dist
+                first_point_idx = i
+        for i in range(4):
+            min_area_quad[i] = box[(first_point_idx + i) % 4]
+
+        return min_area_quad
+
+    def check_and_validate_polys(self, polys, tags, im_size):
+        """
+        check so that the text poly is in the same direction,
+        and also filter some invalid polygons
+        :param polys:
+        :param tags:
+        :return:
+        """
+        (h, w) = im_size
+        if polys.shape[0] == 0:
+            return polys, np.array([]), np.array([])
+        polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
+        polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1)
+
+        validated_polys = []
+        validated_tags = []
+        hv_tags = []
+        for poly, tag in zip(polys, tags):
+            quad = self.gen_quad_from_poly(poly)
+            p_area = self.quad_area(quad)
+            if abs(p_area) < 1:
+                print('invalid poly')
+                continue
+            if p_area > 0:
+                if tag == False:
+                    print('poly in wrong direction')
+                    tag = True  # reversed cases should be ignore
+                poly = poly[(0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2,
+                             1), :]
+                quad = quad[(0, 3, 2, 1), :]
+
+            len_w = np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[3] -
+                                                                       quad[2])
+            len_h = np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] -
+                                                                       quad[2])
+            hv_tag = 1
+
+            if len_w * 2.0 < len_h:
+                hv_tag = 0
+
+            validated_polys.append(poly)
+            validated_tags.append(tag)
+            hv_tags.append(hv_tag)
+        return np.array(validated_polys), np.array(validated_tags), np.array(
+            hv_tags)
+
+    def crop_area(self,
+                  im,
+                  polys,
+                  tags,
+                  hv_tags,
+                  txts,
+                  crop_background=False,
+                  max_tries=25):
+        """
+        make random crop from the input image
+        :param im:
+        :param polys:  [b,4,2]
+        :param tags:
+        :param crop_background:
+        :param max_tries: 50 -> 25
+        :return:
+        """
+        h, w, _ = im.shape
+        pad_h = h // 10
+        pad_w = w // 10
+        h_array = np.zeros((h + pad_h * 2), dtype=np.int32)
+        w_array = np.zeros((w + pad_w * 2), dtype=np.int32)
+        for poly in polys:
+            poly = np.round(poly, decimals=0).astype(np.int32)
+            minx = np.min(poly[:, 0])
+            maxx = np.max(poly[:, 0])
+            w_array[minx + pad_w:maxx + pad_w] = 1
+            miny = np.min(poly[:, 1])
+            maxy = np.max(poly[:, 1])
+            h_array[miny + pad_h:maxy + pad_h] = 1
+        # ensure the cropped area not across a text
+        h_axis = np.where(h_array == 0)[0]
+        w_axis = np.where(w_array == 0)[0]
+        if len(h_axis) == 0 or len(w_axis) == 0:
+            return im, polys, tags, hv_tags, txts
+        for i in range(max_tries):
+            xx = np.random.choice(w_axis, size=2)
+            xmin = np.min(xx) - pad_w
+            xmax = np.max(xx) - pad_w
+            xmin = np.clip(xmin, 0, w - 1)
+            xmax = np.clip(xmax, 0, w - 1)
+            yy = np.random.choice(h_axis, size=2)
+            ymin = np.min(yy) - pad_h
+            ymax = np.max(yy) - pad_h
+            ymin = np.clip(ymin, 0, h - 1)
+            ymax = np.clip(ymax, 0, h - 1)
+            if xmax - xmin < self.min_crop_size or \
+                    ymax - ymin < self.min_crop_size:
+                continue
+            if polys.shape[0] != 0:
+                poly_axis_in_area = (polys[:, :, 0] >= xmin) & (polys[:, :, 0] <= xmax) \
+                                    & (polys[:, :, 1] >= ymin) & (polys[:, :, 1] <= ymax)
+                selected_polys = np.where(
+                    np.sum(poly_axis_in_area, axis=1) == 4)[0]
+            else:
+                selected_polys = []
+            if len(selected_polys) == 0:
+                # no text in this area
+                if crop_background:
+                    txts_tmp = []
+                    for selected_poly in selected_polys:
+                        txts_tmp.append(txts[selected_poly])
+                    txts = txts_tmp
+                    return im[ymin: ymax + 1, xmin: xmax + 1, :], \
+                           polys[selected_polys], tags[selected_polys], hv_tags[selected_polys], txts
+                else:
+                    continue
+            im = im[ymin:ymax + 1, xmin:xmax + 1, :]
+            polys = polys[selected_polys]
+            tags = tags[selected_polys]
+            hv_tags = hv_tags[selected_polys]
+            txts_tmp = []
+            for selected_poly in selected_polys:
+                txts_tmp.append(txts[selected_poly])
+            txts = txts_tmp
+            polys[:, :, 0] -= xmin
+            polys[:, :, 1] -= ymin
+            return im, polys, tags, hv_tags, txts
+
+        return im, polys, tags, hv_tags, txts
+
+    def fit_and_gather_tcl_points_v2(self,
+                                     min_area_quad,
+                                     poly,
+                                     max_h,
+                                     max_w,
+                                     fixed_point_num=64,
+                                     img_id=0,
+                                     reference_height=3):
+        """
+        Find the center point of poly as key_points, then fit and gather.
+        """
+        key_point_xys = []
+        point_num = poly.shape[0]
+        for idx in range(point_num // 2):
+            center_point = (poly[idx] + poly[point_num - 1 - idx]) / 2.0
+            key_point_xys.append(center_point)
+
+        tmp_image = np.zeros(
+            shape=(
+                max_h,
+                max_w, ), dtype='float32')
+        cv2.polylines(tmp_image, [np.array(key_point_xys).astype('int32')],
+                      False, 1.0)
+        ys, xs = np.where(tmp_image > 0)
+        xy_text = np.array(list(zip(xs, ys)), dtype='float32')
+
+        left_center_pt = (
+            (min_area_quad[0] - min_area_quad[1]) / 2.0).reshape(1, 2)
+        right_center_pt = (
+            (min_area_quad[1] - min_area_quad[2]) / 2.0).reshape(1, 2)
+        proj_unit_vec = (right_center_pt - left_center_pt) / (
+            np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
+        proj_unit_vec_tile = np.tile(proj_unit_vec,
+                                     (xy_text.shape[0], 1))  # (n, 2)
+        left_center_pt_tile = np.tile(left_center_pt,
+                                      (xy_text.shape[0], 1))  # (n, 2)
+        xy_text_to_left_center = xy_text - left_center_pt_tile
+        proj_value = np.sum(xy_text_to_left_center * proj_unit_vec_tile, axis=1)
+        xy_text = xy_text[np.argsort(proj_value)]
+
+        # convert to np and keep the num of point not greater then fixed_point_num
+        pos_info = np.array(xy_text).reshape(-1, 2)[:, ::-1]  # xy-> yx
+        point_num = len(pos_info)
+        if point_num > fixed_point_num:
+            keep_ids = [
+                int((point_num * 1.0 / fixed_point_num) * x)
+                for x in range(fixed_point_num)
+            ]
+            pos_info = pos_info[keep_ids, :]
+
+        keep = int(min(len(pos_info), fixed_point_num))
+        if np.random.rand() < 0.2 and reference_height >= 3:
+            dl = (np.random.rand(keep) - 0.5) * reference_height * 0.3
+            random_float = np.array([1, 0]).reshape([1, 2]) * dl.reshape(
+                [keep, 1])
+            pos_info += random_float
+            pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1)
+            pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1)
+
+        # padding to fixed length
+        pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32)
+        pos_l[:, 0] = np.ones((self.tcl_len, )) * img_id
+        pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32)
+        pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32)
+        pos_m[:keep] = 1.0
+        return pos_l, pos_m
+
+    def generate_direction_map(self, poly_quads, n_char, direction_map):
+        """
+        """
+        width_list = []
+        height_list = []
+        for quad in poly_quads:
+            quad_w = (np.linalg.norm(quad[0] - quad[1]) +
+                      np.linalg.norm(quad[2] - quad[3])) / 2.0
+            quad_h = (np.linalg.norm(quad[0] - quad[3]) +
+                      np.linalg.norm(quad[2] - quad[1])) / 2.0
+            width_list.append(quad_w)
+            height_list.append(quad_h)
+        norm_width = max(sum(width_list) / n_char, 1.0)
+        average_height = max(sum(height_list) / len(height_list), 1.0)
+        k = 1
+        for quad in poly_quads:
+            direct_vector_full = (
+                (quad[1] + quad[2]) - (quad[0] + quad[3])) / 2.0
+            direct_vector = direct_vector_full / (
+                np.linalg.norm(direct_vector_full) + 1e-6) * norm_width
+            direction_label = tuple(
+                map(float,
+                    [direct_vector[0], direct_vector[1], 1.0 / average_height]))
+            cv2.fillPoly(direction_map,
+                         quad.round().astype(np.int32)[np.newaxis, :, :],
+                         direction_label)
+            k += 1
+        return direction_map
+
+    def calculate_average_height(self, poly_quads):
+        """
+        """
+        height_list = []
+        for quad in poly_quads:
+            quad_h = (np.linalg.norm(quad[0] - quad[3]) +
+                      np.linalg.norm(quad[2] - quad[1])) / 2.0
+            height_list.append(quad_h)
+        average_height = max(sum(height_list) / len(height_list), 1.0)
+        return average_height
+
+    def generate_tcl_ctc_label(self,
+                               h,
+                               w,
+                               polys,
+                               tags,
+                               text_strs,
+                               ds_ratio,
+                               tcl_ratio=0.3,
+                               shrink_ratio_of_width=0.15):
+        """
+        Generate polygon.
+        """
+        score_map_big = np.zeros(
+            (
+                h,
+                w, ), dtype=np.float32)
+        h, w = int(h * ds_ratio), int(w * ds_ratio)
+        polys = polys * ds_ratio
+
+        score_map = np.zeros(
+            (
+                h,
+                w, ), dtype=np.float32)
+        score_label_map = np.zeros(
+            (
+                h,
+                w, ), dtype=np.float32)
+        tbo_map = np.zeros((h, w, 5), dtype=np.float32)
+        training_mask = np.ones(
+            (
+                h,
+                w, ), dtype=np.float32)
+        direction_map = np.ones((h, w, 3)) * np.array([0, 0, 1]).reshape(
+            [1, 1, 3]).astype(np.float32)
+
+        label_idx = 0
+        score_label_map_text_label_list = []
+        pos_list, pos_mask, label_list = [], [], []
+        for poly_idx, poly_tag in enumerate(zip(polys, tags)):
+            poly = poly_tag[0]
+            tag = poly_tag[1]
+
+            # generate min_area_quad
+            min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly)
+            min_area_quad_h = 0.5 * (
+                np.linalg.norm(min_area_quad[0] - min_area_quad[3]) +
+                np.linalg.norm(min_area_quad[1] - min_area_quad[2]))
+            min_area_quad_w = 0.5 * (
+                np.linalg.norm(min_area_quad[0] - min_area_quad[1]) +
+                np.linalg.norm(min_area_quad[2] - min_area_quad[3]))
+
+            if min(min_area_quad_h, min_area_quad_w) < self.min_text_size * ds_ratio \
+                    or min(min_area_quad_h, min_area_quad_w) > self.max_text_size * ds_ratio:
+                continue
+
+            if tag:
+                cv2.fillPoly(training_mask,
+                             poly.astype(np.int32)[np.newaxis, :, :], 0.15)
+            else:
+                text_label = text_strs[poly_idx]
+                text_label = self.prepare_text_label(text_label,
+                                                     self.Lexicon_Table)
+
+                text_label_index_list = [[self.Lexicon_Table.index(c_)]
+                                         for c_ in text_label
+                                         if c_ in self.Lexicon_Table]
+                if len(text_label_index_list) < 1:
+                    continue
+
+                tcl_poly = self.poly2tcl(poly, tcl_ratio)
+                tcl_quads = self.poly2quads(tcl_poly)
+                poly_quads = self.poly2quads(poly)
+
+                stcl_quads, quad_index = self.shrink_poly_along_width(
+                    tcl_quads,
+                    shrink_ratio_of_width=shrink_ratio_of_width,
+                    expand_height_ratio=1.0 / tcl_ratio)
+
+                cv2.fillPoly(score_map,
+                             np.round(stcl_quads).astype(np.int32), 1.0)
+                cv2.fillPoly(score_map_big,
+                             np.round(stcl_quads / ds_ratio).astype(np.int32),
+                             1.0)
+
+                for idx, quad in enumerate(stcl_quads):
+                    quad_mask = np.zeros((h, w), dtype=np.float32)
+                    quad_mask = cv2.fillPoly(
+                        quad_mask,
+                        np.round(quad[np.newaxis, :, :]).astype(np.int32), 1.0)
+                    tbo_map = self.gen_quad_tbo(poly_quads[quad_index[idx]],
+                                                quad_mask, tbo_map)
+
+                # score label map and score_label_map_text_label_list for refine
+                if label_idx == 0:
+                    text_pos_list_ = [[len(self.Lexicon_Table)], ]
+                    score_label_map_text_label_list.append(text_pos_list_)
+
+                label_idx += 1
+                cv2.fillPoly(score_label_map,
+                             np.round(poly_quads).astype(np.int32), label_idx)
+                score_label_map_text_label_list.append(text_label_index_list)
+
+                # direction info, fix-me
+                n_char = len(text_label_index_list)
+                direction_map = self.generate_direction_map(poly_quads, n_char,
+                                                            direction_map)
+
+                # pos info
+                average_shrink_height = self.calculate_average_height(
+                    stcl_quads)
+                pos_l, pos_m = self.fit_and_gather_tcl_points_v2(
+                    min_area_quad,
+                    poly,
+                    max_h=h,
+                    max_w=w,
+                    fixed_point_num=64,
+                    img_id=self.img_id,
+                    reference_height=average_shrink_height)
+
+                label_l = text_label_index_list
+                if len(text_label_index_list) < 2:
+                    continue
+
+                pos_list.append(pos_l)
+                pos_mask.append(pos_m)
+                label_list.append(label_l)
+
+        # use big score_map for smooth tcl lines
+        score_map_big_resized = cv2.resize(
+            score_map_big, dsize=None, fx=ds_ratio, fy=ds_ratio)
+        score_map = np.array(score_map_big_resized > 1e-3, dtype='float32')
+
+        return score_map, score_label_map, tbo_map, direction_map, training_mask, \
+               pos_list, pos_mask, label_list, score_label_map_text_label_list
+
+    def adjust_point(self, poly):
+        """
+        adjust point order.
+        """
+        point_num = poly.shape[0]
+        if point_num == 4:
+            len_1 = np.linalg.norm(poly[0] - poly[1])
+            len_2 = np.linalg.norm(poly[1] - poly[2])
+            len_3 = np.linalg.norm(poly[2] - poly[3])
+            len_4 = np.linalg.norm(poly[3] - poly[0])
+
+            if (len_1 + len_3) * 1.5 < (len_2 + len_4):
+                poly = poly[[1, 2, 3, 0], :]
+
+        elif point_num > 4:
+            vector_1 = poly[0] - poly[1]
+            vector_2 = poly[1] - poly[2]
+            cos_theta = np.dot(vector_1, vector_2) / (
+                np.linalg.norm(vector_1) * np.linalg.norm(vector_2) + 1e-6)
+            theta = np.arccos(np.round(cos_theta, decimals=4))
+
+            if abs(theta) > (70 / 180 * math.pi):
+                index = list(range(1, point_num)) + [0]
+                poly = poly[np.array(index), :]
+        return poly
+
+    def gen_min_area_quad_from_poly(self, poly):
+        """
+        Generate min area quad from poly.
+        """
+        point_num = poly.shape[0]
+        min_area_quad = np.zeros((4, 2), dtype=np.float32)
+        if point_num == 4:
+            min_area_quad = poly
+            center_point = np.sum(poly, axis=0) / 4
+        else:
+            rect = cv2.minAreaRect(poly.astype(
+                np.int32))  # (center (x,y), (width, height), angle of rotation)
+            center_point = rect[0]
+            box = np.array(cv2.boxPoints(rect))
+
+            first_point_idx = 0
+            min_dist = 1e4
+            for i in range(4):
+                dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
+                       np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
+                       np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
+                       np.linalg.norm(box[(i + 3) % 4] - poly[-1])
+                if dist < min_dist:
+                    min_dist = dist
+                    first_point_idx = i
+
+            for i in range(4):
+                min_area_quad[i] = box[(first_point_idx + i) % 4]
+
+        return min_area_quad, center_point
+
+    def shrink_quad_along_width(self,
+                                quad,
+                                begin_width_ratio=0.,
+                                end_width_ratio=1.):
+        """
+        Generate shrink_quad_along_width.
+        """
+        ratio_pair = np.array(
+            [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+        p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+        p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+        return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+
+    def shrink_poly_along_width(self,
+                                quads,
+                                shrink_ratio_of_width,
+                                expand_height_ratio=1.0):
+        """
+        shrink poly with given length.
+        """
+        upper_edge_list = []
+
+        def get_cut_info(edge_len_list, cut_len):
+            for idx, edge_len in enumerate(edge_len_list):
+                cut_len -= edge_len
+                if cut_len <= 0.000001:
+                    ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx]
+                    return idx, ratio
+
+        for quad in quads:
+            upper_edge_len = np.linalg.norm(quad[0] - quad[1])
+            upper_edge_list.append(upper_edge_len)
+
+        # length of left edge and right edge.
+        left_length = np.linalg.norm(quads[0][0] - quads[0][
+            3]) * expand_height_ratio
+        right_length = np.linalg.norm(quads[-1][1] - quads[-1][
+            2]) * expand_height_ratio
+
+        shrink_length = min(left_length, right_length,
+                            sum(upper_edge_list)) * shrink_ratio_of_width
+        # shrinking length
+        upper_len_left = shrink_length
+        upper_len_right = sum(upper_edge_list) - shrink_length
+
+        left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left)
+        left_quad = self.shrink_quad_along_width(
+            quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1)
+        right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right)
+        right_quad = self.shrink_quad_along_width(
+            quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio)
+
+        out_quad_list = []
+        if left_idx == right_idx:
+            out_quad_list.append(
+                [left_quad[0], right_quad[1], right_quad[2], left_quad[3]])
+        else:
+            out_quad_list.append(left_quad)
+            for idx in range(left_idx + 1, right_idx):
+                out_quad_list.append(quads[idx])
+            out_quad_list.append(right_quad)
+
+        return np.array(out_quad_list), list(range(left_idx, right_idx + 1))
+
+    def prepare_text_label(self, label_str, Lexicon_Table):
+        """
+        Prepare text lablel by given Lexicon_Table.
+        """
+        if len(Lexicon_Table) == 36:
+            return label_str.lower()
+        else:
+            return label_str
+
+    def vector_angle(self, A, B):
+        """
+        Calculate the angle between vector AB and x-axis positive direction.
+        """
+        AB = np.array([B[1] - A[1], B[0] - A[0]])
+        return np.arctan2(*AB)
+
+    def theta_line_cross_point(self, theta, point):
+        """
+        Calculate the line through given point and angle in ax + by + c =0 form.
+        """
+        x, y = point
+        cos = np.cos(theta)
+        sin = np.sin(theta)
+        return [sin, -cos, cos * y - sin * x]
+
+    def line_cross_two_point(self, A, B):
+        """
+        Calculate the line through given point A and B in ax + by + c =0 form.
+        """
+        angle = self.vector_angle(A, B)
+        return self.theta_line_cross_point(angle, A)
+
+    def average_angle(self, poly):
+        """
+        Calculate the average angle between left and right edge in given poly.
+        """
+        p0, p1, p2, p3 = poly
+        angle30 = self.vector_angle(p3, p0)
+        angle21 = self.vector_angle(p2, p1)
+        return (angle30 + angle21) / 2
+
+    def line_cross_point(self, line1, line2):
+        """
+        line1 and line2 in  0=ax+by+c form, compute the cross point of line1 and line2
+        """
+        a1, b1, c1 = line1
+        a2, b2, c2 = line2
+        d = a1 * b2 - a2 * b1
+
+        if d == 0:
+            print('Cross point does not exist')
+            return np.array([0, 0], dtype=np.float32)
+        else:
+            x = (b1 * c2 - b2 * c1) / d
+            y = (a2 * c1 - a1 * c2) / d
+
+        return np.array([x, y], dtype=np.float32)
+
+    def quad2tcl(self, poly, ratio):
+        """
+        Generate center line by poly clock-wise point. (4, 2)
+        """
+        ratio_pair = np.array(
+            [[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
+        p0_3 = poly[0] + (poly[3] - poly[0]) * ratio_pair
+        p1_2 = poly[1] + (poly[2] - poly[1]) * ratio_pair
+        return np.array([p0_3[0], p1_2[0], p1_2[1], p0_3[1]])
+
+    def poly2tcl(self, poly, ratio):
+        """
+        Generate center line by poly clock-wise point.
+        """
+        ratio_pair = np.array(
+            [[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
+        tcl_poly = np.zeros_like(poly)
+        point_num = poly.shape[0]
+
+        for idx in range(point_num // 2):
+            point_pair = poly[idx] + (poly[point_num - 1 - idx] - poly[idx]
+                                      ) * ratio_pair
+            tcl_poly[idx] = point_pair[0]
+            tcl_poly[point_num - 1 - idx] = point_pair[1]
+        return tcl_poly
+
+    def gen_quad_tbo(self, quad, tcl_mask, tbo_map):
+        """
+        Generate tbo_map for give quad.
+        """
+        # upper and lower line function: ax + by + c = 0;
+        up_line = self.line_cross_two_point(quad[0], quad[1])
+        lower_line = self.line_cross_two_point(quad[3], quad[2])
+
+        quad_h = 0.5 * (np.linalg.norm(quad[0] - quad[3]) +
+                        np.linalg.norm(quad[1] - quad[2]))
+        quad_w = 0.5 * (np.linalg.norm(quad[0] - quad[1]) +
+                        np.linalg.norm(quad[2] - quad[3]))
+
+        # average angle of left and right line.
+        angle = self.average_angle(quad)
+
+        xy_in_poly = np.argwhere(tcl_mask == 1)
+        for y, x in xy_in_poly:
+            point = (x, y)
+            line = self.theta_line_cross_point(angle, point)
+            cross_point_upper = self.line_cross_point(up_line, line)
+            cross_point_lower = self.line_cross_point(lower_line, line)
+            ##FIX, offset reverse
+            upper_offset_x, upper_offset_y = cross_point_upper - point
+            lower_offset_x, lower_offset_y = cross_point_lower - point
+            tbo_map[y, x, 0] = upper_offset_y
+            tbo_map[y, x, 1] = upper_offset_x
+            tbo_map[y, x, 2] = lower_offset_y
+            tbo_map[y, x, 3] = lower_offset_x
+            tbo_map[y, x, 4] = 1.0 / max(min(quad_h, quad_w), 1.0) * 2
+        return tbo_map
+
+    def poly2quads(self, poly):
+        """
+        Split poly into quads.
+        """
+        quad_list = []
+        point_num = poly.shape[0]
+
+        # point pair
+        point_pair_list = []
+        for idx in range(point_num // 2):
+            point_pair = [poly[idx], poly[point_num - 1 - idx]]
+            point_pair_list.append(point_pair)
+
+        quad_num = point_num // 2 - 1
+        for idx in range(quad_num):
+            # reshape and adjust to clock-wise
+            quad_list.append((np.array(point_pair_list)[[idx, idx + 1]]
+                              ).reshape(4, 2)[[0, 2, 3, 1]])
+
+        return np.array(quad_list)
+
+    def rotate_im_poly(self, im, text_polys):
+        """
+        rotate image with 90 / 180 / 270 degre
+        """
+        im_w, im_h = im.shape[1], im.shape[0]
+        dst_im = im.copy()
+        dst_polys = []
+        rand_degree_ratio = np.random.rand()
+        rand_degree_cnt = 1
+        if rand_degree_ratio > 0.5:
+            rand_degree_cnt = 3
+        for i in range(rand_degree_cnt):
+            dst_im = np.rot90(dst_im)
+        rot_degree = -90 * rand_degree_cnt
+        rot_angle = rot_degree * math.pi / 180.0
+        n_poly = text_polys.shape[0]
+        cx, cy = 0.5 * im_w, 0.5 * im_h
+        ncx, ncy = 0.5 * dst_im.shape[1], 0.5 * dst_im.shape[0]
+        for i in range(n_poly):
+            wordBB = text_polys[i]
+            poly = []
+            for j in range(4):  # 16->4
+                sx, sy = wordBB[j][0], wordBB[j][1]
+                dx = math.cos(rot_angle) * (sx - cx) - math.sin(rot_angle) * (
+                    sy - cy) + ncx
+                dy = math.sin(rot_angle) * (sx - cx) + math.cos(rot_angle) * (
+                    sy - cy) + ncy
+                poly.append([dx, dy])
+            dst_polys.append(poly)
+        return dst_im, np.array(dst_polys, dtype=np.float32)
+
+    def __call__(self, data):
+        input_size = 512
+        im = data['image']
+        text_polys = data['polys']
+        text_tags = data['ignore_tags']
+        text_strs = data['texts']
+        h, w, _ = im.shape
+        text_polys, text_tags, hv_tags = self.check_and_validate_polys(
+            text_polys, text_tags, (h, w))
+        if text_polys.shape[0] <= 0:
+            return None
+        # set aspect ratio and keep area fix
+        asp_scales = np.arange(1.0, 1.55, 0.1)
+        asp_scale = np.random.choice(asp_scales)
+        if np.random.rand() < 0.5:
+            asp_scale = 1.0 / asp_scale
+        asp_scale = math.sqrt(asp_scale)
+
+        asp_wx = asp_scale
+        asp_hy = 1.0 / asp_scale
+        im = cv2.resize(im, dsize=None, fx=asp_wx, fy=asp_hy)
+        text_polys[:, :, 0] *= asp_wx
+        text_polys[:, :, 1] *= asp_hy
+
+        h, w, _ = im.shape
+        if max(h, w) > 2048:
+            rd_scale = 2048.0 / max(h, w)
+            im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale)
+            text_polys *= rd_scale
+        h, w, _ = im.shape
+        if min(h, w) < 16:
+            return None
+
+        # no background
+        im, text_polys, text_tags, hv_tags, text_strs = self.crop_area(
+            im,
+            text_polys,
+            text_tags,
+            hv_tags,
+            text_strs,
+            crop_background=False)
+
+        if text_polys.shape[0] == 0:
+            return None
+        # # continue for all ignore case
+        if np.sum((text_tags * 1.0)) >= text_tags.size:
+            return None
+        new_h, new_w, _ = im.shape
+        if (new_h is None) or (new_w is None):
+            return None
+        # resize image
+        std_ratio = float(input_size) / max(new_w, new_h)
+        rand_scales = np.array(
+            [0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0])
+        rz_scale = std_ratio * np.random.choice(rand_scales)
+        im = cv2.resize(im, dsize=None, fx=rz_scale, fy=rz_scale)
+        text_polys[:, :, 0] *= rz_scale
+        text_polys[:, :, 1] *= rz_scale
+
+        # add gaussian blur
+        if np.random.rand() < 0.1 * 0.5:
+            ks = np.random.permutation(5)[0] + 1
+            ks = int(ks / 2) * 2 + 1
+            im = cv2.GaussianBlur(im, ksize=(ks, ks), sigmaX=0, sigmaY=0)
+        # add brighter
+        if np.random.rand() < 0.1 * 0.5:
+            im = im * (1.0 + np.random.rand() * 0.5)
+            im = np.clip(im, 0.0, 255.0)
+        # add darker
+        if np.random.rand() < 0.1 * 0.5:
+            im = im * (1.0 - np.random.rand() * 0.5)
+            im = np.clip(im, 0.0, 255.0)
+
+        # Padding the im to [input_size, input_size]
+        new_h, new_w, _ = im.shape
+        if min(new_w, new_h) < input_size * 0.5:
+            return None
+        im_padded = np.ones((input_size, input_size, 3), dtype=np.float32)
+        im_padded[:, :, 2] = 0.485 * 255
+        im_padded[:, :, 1] = 0.456 * 255
+        im_padded[:, :, 0] = 0.406 * 255
+
+        # Random the start position
+        del_h = input_size - new_h
+        del_w = input_size - new_w
+        sh, sw = 0, 0
+        if del_h > 1:
+            sh = int(np.random.rand() * del_h)
+        if del_w > 1:
+            sw = int(np.random.rand() * del_w)
+
+        # Padding
+        im_padded[sh:sh + new_h, sw:sw + new_w, :] = im.copy()
+        text_polys[:, :, 0] += sw
+        text_polys[:, :, 1] += sh
+
+        score_map, score_label_map, border_map, direction_map, training_mask, \
+        pos_list, pos_mask, label_list, score_label_map_text_label = self.generate_tcl_ctc_label(input_size,
+                                                                                                 input_size,
+                                                                                                 text_polys,
+                                                                                                 text_tags,
+                                                                                                 text_strs, 0.25)
+        if len(label_list) <= 0:  # eliminate negative samples
+            return None
+        pos_list_temp = np.zeros([64, 3])
+        pos_mask_temp = np.zeros([64, 1])
+        label_list_temp = np.zeros([self.max_text_length, 1]) + self.pad_num
+
+        for i, label in enumerate(label_list):
+            n = len(label)
+            if n > self.max_text_length:
+                label_list[i] = label[:self.max_text_length]
+                continue
+            while n < self.max_text_length:
+                label.append([self.pad_num])
+                n += 1
+
+        for i in range(len(label_list)):
+            label_list[i] = np.array(label_list[i])
+
+        if len(pos_list) <= 0 or len(pos_list) > self.max_text_nums:
+            return None
+        for __ in range(self.max_text_nums - len(pos_list), 0, -1):
+            pos_list.append(pos_list_temp)
+            pos_mask.append(pos_mask_temp)
+            label_list.append(label_list_temp)
+
+        if self.img_id == self.batch_size - 1:
+            self.img_id = 0
+        else:
+            self.img_id += 1
+
+        im_padded[:, :, 2] -= 0.485 * 255
+        im_padded[:, :, 1] -= 0.456 * 255
+        im_padded[:, :, 0] -= 0.406 * 255
+        im_padded[:, :, 2] /= (255.0 * 0.229)
+        im_padded[:, :, 1] /= (255.0 * 0.224)
+        im_padded[:, :, 0] /= (255.0 * 0.225)
+        im_padded = im_padded.transpose((2, 0, 1))
+        images = im_padded[::-1, :, :]
+        tcl_maps = score_map[np.newaxis, :, :]
+        tcl_label_maps = score_label_map[np.newaxis, :, :]
+        border_maps = border_map.transpose((2, 0, 1))
+        direction_maps = direction_map.transpose((2, 0, 1))
+        training_masks = training_mask[np.newaxis, :, :]
+        pos_list = np.array(pos_list)
+        pos_mask = np.array(pos_mask)
+        label_list = np.array(label_list)
+        data['images'] = images
+        data['tcl_maps'] = tcl_maps
+        data['tcl_label_maps'] = tcl_label_maps
+        data['border_maps'] = border_maps
+        data['direction_maps'] = direction_maps
+        data['training_masks'] = training_masks
+        data['label_list'] = label_list
+        data['pos_list'] = pos_list
+        data['pos_mask'] = pos_mask
+        return data

ppocr/data/pgnet_dataset.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import os
+from paddle.io import Dataset
+from .imaug import transform, create_operators
+import random
+
+
+class PGDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(PGDataSet, self).__init__()
+
+        self.logger = logger
+        self.seed = seed
+        self.mode = mode
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        self.delimiter = dataset_config.get('delimiter', '\t')
+        label_file_list = dataset_config.pop('label_file_list')
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+        self.data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+
+        logger.info("Initialize indexs of datasets:%s" % label_file_list)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
+
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+
+    def shuffle_data_random(self):
+        if self.do_shuffle:
+            random.seed(self.seed)
+            random.shuffle(self.data_lines)
+        return
+
+    def get_image_info_list(self, file_list, ratio_list):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        data_lines = []
+        for idx, file in enumerate(file_list):
+            with open(file, "rb") as f:
+                lines = f.readlines()
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
+        return data_lines
+
+    def __getitem__(self, idx):
+        file_idx = self.data_idx_order_list[idx]
+        data_line = self.data_lines[file_idx]
+        img_id = 0
+        try:
+            data_line = data_line.decode('utf-8')
+            substr = data_line.strip("\n").split(self.delimiter)
+            file_name = substr[0]
+            label = substr[1]
+            img_path = os.path.join(self.data_dir, file_name)
+            if self.mode.lower() == 'eval':
+                try:
+                    img_id = int(data_line.split(".")[0][7:])
+                except:
+                    img_id = 0
+            data = {'img_path': img_path, 'label': label, 'img_id': img_id}
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            outs = transform(data, self.ops)
+        except Exception as e:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    self.data_idx_order_list[idx], e))
+            outs = None
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/data/simple_dataset.py

@@ -23,6 +23,7 @@ class SimpleDataSet(Dataset):
     def __init__(self, config, mode, logger, seed=None):
         super(SimpleDataSet, self).__init__()
         self.logger = logger
+        self.mode = mode.lower()
 
         global_config = config['Global']
         dataset_config = config[mode]['dataset']
@@ -45,7 +46,7 @@ class SimpleDataSet(Dataset):
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
         self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
         self.data_idx_order_list = list(range(len(self.data_lines)))
-        if mode.lower() == "train":
+        if self.mode == "train" and self.do_shuffle:
             self.shuffle_data_random()
         self.ops = create_operators(dataset_config['transforms'], global_config)
 
@@ -56,16 +57,16 @@ class SimpleDataSet(Dataset):
         for idx, file in enumerate(file_list):
             with open(file, "rb") as f:
                 lines = f.readlines()
-                random.seed(self.seed)
-                lines = random.sample(lines,
-                                      round(len(lines) * ratio_list[idx]))
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
                 data_lines.extend(lines)
         return data_lines
 
     def shuffle_data_random(self):
-        if self.do_shuffle:
-            random.seed(self.seed)
-            random.shuffle(self.data_lines)
+        random.seed(self.seed)
+        random.shuffle(self.data_lines)
         return
 
     def __getitem__(self, idx):
@@ -90,7 +91,10 @@ class SimpleDataSet(Dataset):
                     data_line, e))
             outs = None
         if outs is None:
-            return self.__getitem__(np.random.randint(self.__len__()))
+            # during evaluation, we should fix the idx to get same results for many times of evaluation.
+            rnd_idx = np.random.randint(self.__len__(
+            )) if self.mode == "train" else (idx + 1) % self.__len__()
+            return self.__getitem__(rnd_idx)
         return outs
 
     def __len__(self):

ppocr/losses/__init__.py

@@ -29,10 +29,11 @@ def build_loss(config):
     # cls loss
     from .cls_loss import ClsLoss
 
+    # e2e loss
+    from .e2e_pg_loss import PGLoss
     support_dict = [
         'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss'
-    ]
+        'SRNLoss', 'PGLoss']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/e2e_pg_loss.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import nn
+import paddle
+
+from .det_basic_loss import DiceLoss
+from ppocr.utils.e2e_utils.extract_batchsize import pre_process
+
+
+class PGLoss(nn.Layer):
+    def __init__(self,
+                 tcl_bs,
+                 max_text_length,
+                 max_text_nums,
+                 pad_num,
+                 eps=1e-6,
+                 **kwargs):
+        super(PGLoss, self).__init__()
+        self.tcl_bs = tcl_bs
+        self.max_text_nums = max_text_nums
+        self.max_text_length = max_text_length
+        self.pad_num = pad_num
+        self.dice_loss = DiceLoss(eps=eps)
+
+    def border_loss(self, f_border, l_border, l_score, l_mask):
+        l_border_split, l_border_norm = paddle.tensor.split(
+            l_border, num_or_sections=[4, 1], axis=1)
+        f_border_split = f_border
+        b, c, h, w = l_border_norm.shape
+        l_border_norm_split = paddle.expand(
+            x=l_border_norm, shape=[b, 4 * c, h, w])
+        b, c, h, w = l_score.shape
+        l_border_score = paddle.expand(x=l_score, shape=[b, 4 * c, h, w])
+        b, c, h, w = l_mask.shape
+        l_border_mask = paddle.expand(x=l_mask, shape=[b, 4 * c, h, w])
+        border_diff = l_border_split - f_border_split
+        abs_border_diff = paddle.abs(border_diff)
+        border_sign = abs_border_diff < 1.0
+        border_sign = paddle.cast(border_sign, dtype='float32')
+        border_sign.stop_gradient = True
+        border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \
+                         (abs_border_diff - 0.5) * (1.0 - border_sign)
+        border_out_loss = l_border_norm_split * border_in_loss
+        border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \
+                      (paddle.sum(l_border_score * l_border_mask) + 1e-5)
+        return border_loss
+
+    def direction_loss(self, f_direction, l_direction, l_score, l_mask):
+        l_direction_split, l_direction_norm = paddle.tensor.split(
+            l_direction, num_or_sections=[2, 1], axis=1)
+        f_direction_split = f_direction
+        b, c, h, w = l_direction_norm.shape
+        l_direction_norm_split = paddle.expand(
+            x=l_direction_norm, shape=[b, 2 * c, h, w])
+        b, c, h, w = l_score.shape
+        l_direction_score = paddle.expand(x=l_score, shape=[b, 2 * c, h, w])
+        b, c, h, w = l_mask.shape
+        l_direction_mask = paddle.expand(x=l_mask, shape=[b, 2 * c, h, w])
+        direction_diff = l_direction_split - f_direction_split
+        abs_direction_diff = paddle.abs(direction_diff)
+        direction_sign = abs_direction_diff < 1.0
+        direction_sign = paddle.cast(direction_sign, dtype='float32')
+        direction_sign.stop_gradient = True
+        direction_in_loss = 0.5 * abs_direction_diff * abs_direction_diff * direction_sign + \
+                            (abs_direction_diff - 0.5) * (1.0 - direction_sign)
+        direction_out_loss = l_direction_norm_split * direction_in_loss
+        direction_loss = paddle.sum(direction_out_loss * l_direction_score * l_direction_mask) / \
+                         (paddle.sum(l_direction_score * l_direction_mask) + 1e-5)
+        return direction_loss
+
+    def ctcloss(self, f_char, tcl_pos, tcl_mask, tcl_label, label_t):
+        f_char = paddle.transpose(f_char, [0, 2, 3, 1])
+        tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
+        tcl_pos = paddle.cast(tcl_pos, dtype=int)
+        f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
+        f_tcl_char = paddle.reshape(f_tcl_char,
+                                    [-1, 64, 37])  # len(Lexicon_Table)+1
+        f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 1], axis=2)
+        f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
+        b, c, l = tcl_mask.shape
+        tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, 36 * l])
+        tcl_mask_fg.stop_gradient = True
+        f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
+            -20.0)
+        f_tcl_char_mask = paddle.concat([f_tcl_char_fg, f_tcl_char_bg], axis=2)
+        f_tcl_char_ld = paddle.transpose(f_tcl_char_mask, (1, 0, 2))
+        N, B, _ = f_tcl_char_ld.shape
+        input_lengths = paddle.to_tensor([N] * B, dtype='int64')
+        cost = paddle.nn.functional.ctc_loss(
+            log_probs=f_tcl_char_ld,
+            labels=tcl_label,
+            input_lengths=input_lengths,
+            label_lengths=label_t,
+            blank=self.pad_num,
+            reduction='none')
+        cost = cost.mean()
+        return cost
+
+    def forward(self, predicts, labels):
+        images, tcl_maps, tcl_label_maps, border_maps \
+            , direction_maps, training_masks, label_list, pos_list, pos_mask = labels
+        # for all the batch_size
+        pos_list, pos_mask, label_list, label_t = pre_process(
+            label_list, pos_list, pos_mask, self.max_text_length,
+            self.max_text_nums, self.pad_num, self.tcl_bs)
+
+        f_score, f_border, f_direction, f_char = predicts['f_score'], predicts['f_border'], predicts['f_direction'], \
+                                                 predicts['f_char']
+        score_loss = self.dice_loss(f_score, tcl_maps, training_masks)
+        border_loss = self.border_loss(f_border, border_maps, tcl_maps,
+                                       training_masks)
+        direction_loss = self.direction_loss(f_direction, direction_maps,
+                                             tcl_maps, training_masks)
+        ctc_loss = self.ctcloss(f_char, pos_list, pos_mask, label_list, label_t)
+        loss_all = score_loss + border_loss + direction_loss + 5 * ctc_loss
+
+        losses = {
+            'loss': loss_all,
+            "score_loss": score_loss,
+            "border_loss": border_loss,
+            "direction_loss": direction_loss,
+            "ctc_loss": ctc_loss
+        }
+        return losses

ppocr/metrics/__init__.py

@@ -26,8 +26,9 @@ def build_metric(config):
     from .det_metric import DetMetric
     from .rec_metric import RecMetric
     from .cls_metric import ClsMetric
+    from .e2e_metric import E2EMetric
 
-    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric']
+    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric', 'E2EMetric']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/metrics/e2e_metric.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+__all__ = ['E2EMetric']
+
+from ppocr.utils.e2e_metric.Deteval import get_socre_A, get_socre_B, combine_results
+from ppocr.utils.e2e_utils.extract_textpoint_slow import get_dict
+
+
+class E2EMetric(object):
+    def __init__(self,
+                 mode,
+                 gt_mat_dir,
+                 character_dict_path,
+                 main_indicator='f_score_e2e',
+                 **kwargs):
+        self.mode = mode
+        self.gt_mat_dir = gt_mat_dir
+        self.label_list = get_dict(character_dict_path)
+        self.max_index = len(self.label_list)
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        if self.mode == 'A':
+            gt_polyons_batch = batch[2]
+            temp_gt_strs_batch = batch[3][0]
+            ignore_tags_batch = batch[4]
+            gt_strs_batch = []
+
+            for temp_list in temp_gt_strs_batch:
+                t = ""
+                for index in temp_list:
+                    if index < self.max_index:
+                        t += self.label_list[index]
+                gt_strs_batch.append(t)
+
+            for pred, gt_polyons, gt_strs, ignore_tags in zip(
+                [preds], gt_polyons_batch, [gt_strs_batch], ignore_tags_batch):
+                # prepare gt
+                gt_info_list = [{
+                    'points': gt_polyon,
+                    'text': gt_str,
+                    'ignore': ignore_tag
+                } for gt_polyon, gt_str, ignore_tag in
+                                zip(gt_polyons, gt_strs, ignore_tags)]
+                # prepare det
+                e2e_info_list = [{
+                    'points': det_polyon,
+                    'texts': pred_str
+                } for det_polyon, pred_str in
+                                 zip(pred['points'], pred['texts'])]
+
+                result = get_socre_A(gt_info_list, e2e_info_list)
+                self.results.append(result)
+        else:
+            img_id = batch[5][0]
+            e2e_info_list = [{
+                'points': det_polyon,
+                'texts': pred_str
+            } for det_polyon, pred_str in zip(preds['points'], preds['texts'])]
+            result = get_socre_B(self.gt_mat_dir, img_id, e2e_info_list)
+            self.results.append(result)
+
+    def get_metric(self):
+        metircs = combine_results(self.results)
+        self.reset()
+        return metircs
+
+    def reset(self):
+        self.results = []  # clear results

ppocr/metrics/eval_det_iou.py

@@ -150,7 +150,7 @@ class DetectionIoUEvaluator(object):
                             pairs.append({'gt': gtNum, 'det': detNum})
                             detMatchedNums.append(detNum)
                             evaluationLog += "Match GT #" + \
-                                str(gtNum) + " with Det #" + str(detNum) + "\n"
+                                             str(gtNum) + " with Det #" + str(detNum) + "\n"
 
         numGtCare = (len(gtPols) - len(gtDontCarePolsNum))
         numDetCare = (len(detPols) - len(detDontCarePolsNum))
@@ -162,7 +162,7 @@ class DetectionIoUEvaluator(object):
             precision = 0 if numDetCare == 0 else float(detMatched) / numDetCare
 
         hmean = 0 if (precision + recall) == 0 else 2.0 * \
-            precision * recall / (precision + recall)
+                                                    precision * recall / (precision + recall)
 
         matchedSum += detMatched
         numGlobalCareGt += numGtCare
@@ -200,7 +200,8 @@ class DetectionIoUEvaluator(object):
         methodPrecision = 0 if numGlobalCareDet == 0 else float(
             matchedSum) / numGlobalCareDet
         methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
-            methodRecall * methodPrecision / (methodRecall + methodPrecision)
+                                                                    methodRecall * methodPrecision / (
+                                                                            methodRecall + methodPrecision)
         # print(methodRecall, methodPrecision, methodHmean)
         # sys.exit(-1)
         methodMetrics = {

ppocr/modeling/backbones/__init__.py

@@ -26,6 +26,9 @@ def build_backbone(config, model_type):
         from .rec_resnet_vd import ResNet
         from .rec_resnet_fpn import ResNetFPN
         support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
+    elif model_type == 'e2e':
+        from .e2e_resnet_vd_pg import ResNet
+        support_dict = ['ResNet']
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/e2e_resnet_vd_pg.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+__all__ = ["ResNet"]
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        self.conv2 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels * 4,
+            kernel_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels * 4,
+                kernel_size=1,
+                stride=stride,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
+        return y
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            act=None,
+            name=name + "_branch2b")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self, in_channels=3, layers=50, **kwargs):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            # depth = [3, 4, 6, 3]
+            depth = [3, 4, 6, 3, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512, 1024,
+                        2048] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512, 512]
+
+        self.conv1_1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=7,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+        self.stages = []
+        self.out_channels = [3, 64]
+        # num_filters = [64, 128, 256, 512, 512]
+        if layers >= 50:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(bottleneck_block)
+                self.out_channels.append(num_filters[block] * 4)
+                self.stages.append(nn.Sequential(*block_list))
+        else:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(basic_block)
+                self.out_channels.append(num_filters[block])
+                self.stages.append(nn.Sequential(*block_list))
+
+    def forward(self, inputs):
+        out = [inputs]
+        y = self.conv1_1(inputs)
+        out.append(y)
+        y = self.pool2d_max(y)
+        for block in self.stages:
+            y = block(y)
+            out.append(y)
+        return out

ppocr/modeling/backbones/rec_resnet_vd.py

@@ -249,7 +249,7 @@ class ResNet(nn.Layer):
                             name=conv_name))
                     shortcut = True
                     self.block_list.append(bottleneck_block)
-                self.out_channels = num_filters[block]
+                self.out_channels = num_filters[block] * 4
         else:
             for block in range(len(depth)):
                 shortcut = False

ppocr/modeling/heads/__init__.py

@@ -20,6 +20,7 @@ def build_head(config):
     from .det_db_head import DBHead
     from .det_east_head import EASTHead
     from .det_sast_head import SASTHead
+    from .e2e_pg_head import PGHead
 
     # rec head
     from .rec_ctc_head import CTCHead
@@ -30,8 +31,8 @@ def build_head(config):
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead'
-    ]
+        'SRNHead', 'PGHead']
+
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/e2e_pg_head.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 padding,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+        self.if_act = if_act
+        self.act = act
+        self.conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name="bn_" + name + "_scale"),
+            bias_attr=ParamAttr(name="bn_" + name + "_offset"),
+            moving_mean_name="bn_" + name + "_mean",
+            moving_variance_name="bn_" + name + "_variance",
+            use_global_stats=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class PGHead(nn.Layer):
+    """
+    """
+
+    def __init__(self, in_channels, **kwargs):
+        super(PGHead, self).__init__()
+        self.conv_f_score1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_score{}".format(1))
+        self.conv_f_score2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_score{}".format(2))
+        self.conv_f_score3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_score{}".format(3))
+
+        self.conv1 = nn.Conv2D(
+            in_channels=128,
+            out_channels=1,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_score{}".format(4)),
+            bias_attr=False)
+
+        self.conv_f_boder1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_boder{}".format(1))
+        self.conv_f_boder2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_boder{}".format(2))
+        self.conv_f_boder3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_boder{}".format(3))
+        self.conv2 = nn.Conv2D(
+            in_channels=128,
+            out_channels=4,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_boder{}".format(4)),
+            bias_attr=False)
+        self.conv_f_char1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(1))
+        self.conv_f_char2 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_char{}".format(2))
+        self.conv_f_char3 = ConvBNLayer(
+            in_channels=128,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(3))
+        self.conv_f_char4 = ConvBNLayer(
+            in_channels=256,
+            out_channels=256,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_char{}".format(4))
+        self.conv_f_char5 = ConvBNLayer(
+            in_channels=256,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(5))
+        self.conv3 = nn.Conv2D(
+            in_channels=256,
+            out_channels=37,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_char{}".format(6)),
+            bias_attr=False)
+
+        self.conv_f_direc1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_direc{}".format(1))
+        self.conv_f_direc2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_direc{}".format(2))
+        self.conv_f_direc3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_direc{}".format(3))
+        self.conv4 = nn.Conv2D(
+            in_channels=128,
+            out_channels=2,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_direc{}".format(4)),
+            bias_attr=False)
+
+    def forward(self, x):
+        f_score = self.conv_f_score1(x)
+        f_score = self.conv_f_score2(f_score)
+        f_score = self.conv_f_score3(f_score)
+        f_score = self.conv1(f_score)
+        f_score = F.sigmoid(f_score)
+
+        # f_border
+        f_border = self.conv_f_boder1(x)
+        f_border = self.conv_f_boder2(f_border)
+        f_border = self.conv_f_boder3(f_border)
+        f_border = self.conv2(f_border)
+
+        f_char = self.conv_f_char1(x)
+        f_char = self.conv_f_char2(f_char)
+        f_char = self.conv_f_char3(f_char)
+        f_char = self.conv_f_char4(f_char)
+        f_char = self.conv_f_char5(f_char)
+        f_char = self.conv3(f_char)
+
+        f_direction = self.conv_f_direc1(x)
+        f_direction = self.conv_f_direc2(f_direction)
+        f_direction = self.conv_f_direc3(f_direction)
+        f_direction = self.conv4(f_direction)
+
+        predicts = {}
+        predicts['f_score'] = f_score
+        predicts['f_border'] = f_border
+        predicts['f_char'] = f_char
+        predicts['f_direction'] = f_direction
+        return predicts

ppocr/modeling/heads/self_attention.py

@@ -285,8 +285,7 @@ class PrePostProcessLayer(nn.Layer):
             elif cmd == "n":  # add layer normalization
                 self.functors.append(
                     self.add_sublayer(
-                        "layer_norm_%d" % len(
-                            self.sublayers(include_sublayers=False)),
+                        "layer_norm_%d" % len(self.sublayers()),
                         paddle.nn.LayerNorm(
                             normalized_shape=d_model,
                             weight_attr=fluid.ParamAttr(
@@ -320,9 +319,7 @@ class PrepareEncoder(nn.Layer):
         self.src_emb_dim = src_emb_dim
         self.src_max_len = src_max_len
         self.emb = paddle.nn.Embedding(
-            num_embeddings=self.src_max_len,
-            embedding_dim=self.src_emb_dim,
-            sparse=True)
+            num_embeddings=self.src_max_len, embedding_dim=self.src_emb_dim)
         self.dropout_rate = dropout_rate
 
     def forward(self, src_word, src_pos):

ppocr/modeling/necks/__init__.py

@@ -14,12 +14,14 @@
 
 __all__ = ['build_neck']
 
+
 def build_neck(config):
     from .db_fpn import DBFPN
     from .east_fpn import EASTFPN
     from .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder']
+    from .pg_fpn import PGFPN
+    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/pg_fpn.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 groups=1,
+                 is_vd_mode=False,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance',
+            use_global_stats=False)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class DeConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=4,
+                 stride=2,
+                 padding=1,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(DeConvBNLayer, self).__init__()
+
+        self.if_act = if_act
+        self.act = act
+        self.deconv = nn.Conv2DTranspose(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name="bn_" + name + "_scale"),
+            bias_attr=ParamAttr(name="bn_" + name + "_offset"),
+            moving_mean_name="bn_" + name + "_mean",
+            moving_variance_name="bn_" + name + "_variance",
+            use_global_stats=False)
+
+    def forward(self, x):
+        x = self.deconv(x)
+        x = self.bn(x)
+        return x
+
+
+class PGFPN(nn.Layer):
+    def __init__(self, in_channels, **kwargs):
+        super(PGFPN, self).__init__()
+        num_inputs = [2048, 2048, 1024, 512, 256]
+        num_outputs = [256, 256, 192, 192, 128]
+        self.out_channels = 128
+        self.conv_bn_layer_1 = ConvBNLayer(
+            in_channels=3,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            act=None,
+            name='FPN_d1')
+        self.conv_bn_layer_2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act=None,
+            name='FPN_d2')
+        self.conv_bn_layer_3 = ConvBNLayer(
+            in_channels=256,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            act=None,
+            name='FPN_d3')
+        self.conv_bn_layer_4 = ConvBNLayer(
+            in_channels=32,
+            out_channels=64,
+            kernel_size=3,
+            stride=2,
+            act=None,
+            name='FPN_d4')
+        self.conv_bn_layer_5 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name='FPN_d5')
+        self.conv_bn_layer_6 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=3,
+            stride=2,
+            act=None,
+            name='FPN_d6')
+        self.conv_bn_layer_7 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name='FPN_d7')
+        self.conv_bn_layer_8 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name='FPN_d8')
+
+        self.conv_h0 = ConvBNLayer(
+            in_channels=num_inputs[0],
+            out_channels=num_outputs[0],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(0))
+        self.conv_h1 = ConvBNLayer(
+            in_channels=num_inputs[1],
+            out_channels=num_outputs[1],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(1))
+        self.conv_h2 = ConvBNLayer(
+            in_channels=num_inputs[2],
+            out_channels=num_outputs[2],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(2))
+        self.conv_h3 = ConvBNLayer(
+            in_channels=num_inputs[3],
+            out_channels=num_outputs[3],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(3))
+        self.conv_h4 = ConvBNLayer(
+            in_channels=num_inputs[4],
+            out_channels=num_outputs[4],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_h{}".format(4))
+
+        self.dconv0 = DeConvBNLayer(
+            in_channels=num_outputs[0],
+            out_channels=num_outputs[0 + 1],
+            name="dconv_{}".format(0))
+        self.dconv1 = DeConvBNLayer(
+            in_channels=num_outputs[1],
+            out_channels=num_outputs[1 + 1],
+            act=None,
+            name="dconv_{}".format(1))
+        self.dconv2 = DeConvBNLayer(
+            in_channels=num_outputs[2],
+            out_channels=num_outputs[2 + 1],
+            act=None,
+            name="dconv_{}".format(2))
+        self.dconv3 = DeConvBNLayer(
+            in_channels=num_outputs[3],
+            out_channels=num_outputs[3 + 1],
+            act=None,
+            name="dconv_{}".format(3))
+        self.conv_g1 = ConvBNLayer(
+            in_channels=num_outputs[1],
+            out_channels=num_outputs[1],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(1))
+        self.conv_g2 = ConvBNLayer(
+            in_channels=num_outputs[2],
+            out_channels=num_outputs[2],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(2))
+        self.conv_g3 = ConvBNLayer(
+            in_channels=num_outputs[3],
+            out_channels=num_outputs[3],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(3))
+        self.conv_g4 = ConvBNLayer(
+            in_channels=num_outputs[4],
+            out_channels=num_outputs[4],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(4))
+        self.convf = ConvBNLayer(
+            in_channels=num_outputs[4],
+            out_channels=num_outputs[4],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_f{}".format(4))
+
+    def forward(self, x):
+        c0, c1, c2, c3, c4, c5, c6 = x
+        # FPN_Down_Fusion
+        f = [c0, c1, c2]
+        g = [None, None, None]
+        h = [None, None, None]
+        h[0] = self.conv_bn_layer_1(f[0])
+        h[1] = self.conv_bn_layer_2(f[1])
+        h[2] = self.conv_bn_layer_3(f[2])
+
+        g[0] = self.conv_bn_layer_4(h[0])
+        g[1] = paddle.add(g[0], h[1])
+        g[1] = F.relu(g[1])
+        g[1] = self.conv_bn_layer_5(g[1])
+        g[1] = self.conv_bn_layer_6(g[1])
+
+        g[2] = paddle.add(g[1], h[2])
+        g[2] = F.relu(g[2])
+        g[2] = self.conv_bn_layer_7(g[2])
+        f_down = self.conv_bn_layer_8(g[2])
+
+        # FPN UP Fusion
+        f1 = [c6, c5, c4, c3, c2]
+        g = [None, None, None, None, None]
+        h = [None, None, None, None, None]
+        h[0] = self.conv_h0(f1[0])
+        h[1] = self.conv_h1(f1[1])
+        h[2] = self.conv_h2(f1[2])
+        h[3] = self.conv_h3(f1[3])
+        h[4] = self.conv_h4(f1[4])
+
+        g[0] = self.dconv0(h[0])
+        g[1] = paddle.add(g[0], h[1])
+        g[1] = F.relu(g[1])
+        g[1] = self.conv_g1(g[1])
+        g[1] = self.dconv1(g[1])
+
+        g[2] = paddle.add(g[1], h[2])
+        g[2] = F.relu(g[2])
+        g[2] = self.conv_g2(g[2])
+        g[2] = self.dconv2(g[2])
+
+        g[3] = paddle.add(g[2], h[3])
+        g[3] = F.relu(g[3])
+        g[3] = self.conv_g3(g[3])
+        g[3] = self.dconv3(g[3])
+
+        g[4] = paddle.add(x=g[3], y=h[4])
+        g[4] = F.relu(g[4])
+        g[4] = self.conv_g4(g[4])
+        f_up = self.convf(g[4])
+        f_common = paddle.add(f_down, f_up)
+        f_common = F.relu(f_common)
+        return f_common

ppocr/postprocess/__init__.py

@@ -28,10 +28,11 @@ def build_post_process(config, global_config=None):
     from .sast_postprocess import SASTPostProcess
     from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
     from .cls_postprocess import ClsPostProcess
+    from .pg_postprocess import PGPostProcess
 
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
-        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
+        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess'
     ]
 
     config = copy.deepcopy(config)

ppocr/postprocess/db_postprocess.py

@@ -34,12 +34,18 @@ class DBPostProcess(object):
                  max_candidates=1000,
                  unclip_ratio=2.0,
                  use_dilation=False,
+                 score_mode="fast",
                  **kwargs):
         self.thresh = thresh
         self.box_thresh = box_thresh
         self.max_candidates = max_candidates
         self.unclip_ratio = unclip_ratio
         self.min_size = 3
+        self.score_mode = score_mode
+        assert score_mode in [
+            "slow", "fast"
+        ], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
+
         self.dilation_kernel = None if not use_dilation else np.array(
             [[1, 1], [1, 1]])
 
@@ -69,7 +75,10 @@ class DBPostProcess(object):
             if sside < self.min_size:
                 continue
             points = np.array(points)
-            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.score_mode == "fast":
+                score = self.box_score_fast(pred, points.reshape(-1, 2))
+            else:
+                score = self.box_score_slow(pred, contour)
             if self.box_thresh > score:
                 continue
 
@@ -120,6 +129,9 @@ class DBPostProcess(object):
         return box, min(bounding_box[1])
 
     def box_score_fast(self, bitmap, _box):
+        '''
+        box_score_fast: use bbox mean score as the mean score
+        '''
         h, w = bitmap.shape[:2]
         box = _box.copy()
         xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
@@ -133,6 +145,27 @@ class DBPostProcess(object):
         cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
+    def box_score_slow(self, bitmap, contour):
+        '''
+        box_score_slow: use polyon mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        contour = contour.copy()
+        contour = np.reshape(contour, (-1, 2))
+
+        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
+        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
+        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
+        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+
+        contour[:, 0] = contour[:, 0] - xmin
+        contour[:, 1] = contour[:, 1] - ymin
+
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
     def __call__(self, outs_dict, shape_list):
         pred = outs_dict['maps']
         if isinstance(pred, paddle.Tensor):

ppocr/postprocess/pg_postprocess.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+from ppocr.utils.e2e_utils.pgnet_pp_utils import PGNet_PostProcess
+
+
+class PGPostProcess(object):
+    """
+    The post process for PGNet.
+    """
+
+    def __init__(self, character_dict_path, valid_set, score_thresh, mode,
+                 **kwargs):
+        self.character_dict_path = character_dict_path
+        self.valid_set = valid_set
+        self.score_thresh = score_thresh
+        self.mode = mode
+
+        # c++ la-nms is faster, but only support python 3.5
+        self.is_python35 = False
+        if sys.version_info.major == 3 and sys.version_info.minor == 5:
+            self.is_python35 = True
+
+    def __call__(self, outs_dict, shape_list):
+        post = PGNet_PostProcess(self.character_dict_path, self.valid_set,
+                                 self.score_thresh, outs_dict, shape_list)
+        if self.mode == 'fast':
+            data = post.pg_postprocess_fast()
+        else:
+            data = post.pg_postprocess_slow()
+        return data

ppocr/postprocess/rec_postprocess.py

@@ -28,7 +28,7 @@ class BaseRecLabelDecode(object):
             'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
             'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
             'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
-            'ne', 'EN'
+            'ne', 'EN', 'latin', 'arabic', 'cyrillic', 'devanagari'
         ]
         assert character_type in support_character_type, "Only {} are supported now but get {}".format(
             support_character_type, character_type)
@@ -218,6 +218,7 @@ class SRNLabelDecode(BaseRecLabelDecode):
                  **kwargs):
         super(SRNLabelDecode, self).__init__(character_dict_path,
                                              character_type, use_space_char)
+        self.max_text_length = kwargs.get('max_text_length', 25)
 
     def __call__(self, preds, label=None, *args, **kwargs):
         pred = preds['predict']
@@ -229,9 +230,9 @@ class SRNLabelDecode(BaseRecLabelDecode):
         preds_idx = np.argmax(pred, axis=1)
         preds_prob = np.max(pred, axis=1)
 
-        preds_idx = np.reshape(preds_idx, [-1, 25])
+        preds_idx = np.reshape(preds_idx, [-1, self.max_text_length])
 
-        preds_prob = np.reshape(preds_prob, [-1, 25])
+        preds_prob = np.reshape(preds_prob, [-1, self.max_text_length])
 
         text = self.decode(preds_idx, preds_prob)
 

ppocr/postprocess/sast_postprocess.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import os
 import sys
+
 __dir__ = os.path.dirname(__file__)
 sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..'))
@@ -49,12 +50,12 @@ class SASTPostProcess(object):
         self.shrink_ratio_of_width = shrink_ratio_of_width
         self.expand_scale = expand_scale
         self.tcl_map_thresh = tcl_map_thresh
-        
+
         # c++ la-nms is faster, but only support python 3.5
         self.is_python35 = False
         if sys.version_info.major == 3 and sys.version_info.minor == 5:
             self.is_python35 = True
-            
+
     def point_pair2poly(self, point_pair_list):
         """
         Transfer vertical point_pairs into poly point in clockwise.
@@ -66,31 +67,42 @@ class SASTPostProcess(object):
             point_list[idx] = point_pair[0]
             point_list[point_num - 1 - idx] = point_pair[1]
         return np.array(point_list).reshape(-1, 2)
-    
-    def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.):
+
+    def shrink_quad_along_width(self,
+                                quad,
+                                begin_width_ratio=0.,
+                                end_width_ratio=1.):
         """ 
         Generate shrink_quad_along_width.
         """
-        ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+        ratio_pair = np.array(
+            [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
         p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
         p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
         return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
-    
+
     def expand_poly_along_width(self, poly, shrink_ratio_of_width=0.3):
         """
         expand poly along width.
         """
         point_num = poly.shape[0]
-        left_quad = np.array([poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+        left_quad = np.array(
+            [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
         left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
-                    (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
-        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio, 1.0)
-        right_quad = np.array([poly[point_num // 2 - 2], poly[point_num // 2 - 1],
-                            poly[point_num // 2], poly[point_num // 2 + 1]], dtype=np.float32)
+                     (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio,
+                                                        1.0)
+        right_quad = np.array(
+            [
+                poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+                poly[point_num // 2], poly[point_num // 2 + 1]
+            ],
+            dtype=np.float32)
         right_ratio = 1.0 + \
-                    shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
-                    (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
-        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0, right_ratio)
+                      shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                      (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0,
+                                                         right_ratio)
         poly[0] = left_quad_expand[0]
         poly[-1] = left_quad_expand[-1]
         poly[point_num // 2 - 1] = right_quad_expand[1]
@@ -100,7 +112,7 @@ class SASTPostProcess(object):
     def restore_quad(self, tcl_map, tcl_map_thresh, tvo_map):
         """Restore quad."""
         xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
-        xy_text = xy_text[:, ::-1] # (n, 2)
+        xy_text = xy_text[:, ::-1]  # (n, 2)
 
         # Sort the text boxes via the y axis
         xy_text = xy_text[np.argsort(xy_text[:, 1])]
@@ -112,7 +124,7 @@ class SASTPostProcess(object):
         point_num = int(tvo_map.shape[-1] / 2)
         assert point_num == 4
         tvo_map = tvo_map[xy_text[:, 1], xy_text[:, 0], :]
-        xy_text_tile = np.tile(xy_text, (1, point_num)) # (n, point_num * 2)
+        xy_text_tile = np.tile(xy_text, (1, point_num))  # (n, point_num * 2)
         quads = xy_text_tile - tvo_map
 
         return scores, quads, xy_text
@@ -121,14 +133,12 @@ class SASTPostProcess(object):
         """
         compute area of a quad.
         """
-        edge = [
-            (quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
-            (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
-            (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
-            (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])
-        ]
+        edge = [(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
+                (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
+                (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
+                (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])]
         return np.sum(edge) / 2.
-        
+
     def nms(self, dets):
         if self.is_python35:
             import lanms
@@ -141,7 +151,7 @@ class SASTPostProcess(object):
         """
         Cluster pixels in tcl_map based on quads.
         """
-        instance_count = quads.shape[0] + 1 # contain background
+        instance_count = quads.shape[0] + 1  # contain background
         instance_label_map = np.zeros(tcl_map.shape[:2], dtype=np.int32)
         if instance_count == 1:
             return instance_count, instance_label_map
@@ -149,18 +159,19 @@ class SASTPostProcess(object):
         # predict text center
         xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
         n = xy_text.shape[0]
-        xy_text = xy_text[:, ::-1] # (n, 2)
-        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :] # (n, 2)
+        xy_text = xy_text[:, ::-1]  # (n, 2)
+        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :]  # (n, 2)
         pred_tc = xy_text - tco
-        
+
         # get gt text center
         m = quads.shape[0]
-        gt_tc = np.mean(quads, axis=1) # (m, 2)
+        gt_tc = np.mean(quads, axis=1)  # (m, 2)
 
-        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2)
-        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2)
-        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m)
-        xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,)
+        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :],
+                               (1, m, 1))  # (n, m, 2)
+        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1))  # (n, m, 2)
+        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2)  # (n, m)
+        xy_text_assign = np.argmin(dist_mat, axis=1) + 1  # (n,)
 
         instance_label_map[xy_text[:, 1], xy_text[:, 0]] = xy_text_assign
         return instance_count, instance_label_map
@@ -169,26 +180,47 @@ class SASTPostProcess(object):
         """
         Estimate sample points number.
         """
-        eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0
-        ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0
+        eh = (np.linalg.norm(quad[0] - quad[3]) +
+              np.linalg.norm(quad[1] - quad[2])) / 2.0
+        ew = (np.linalg.norm(quad[0] - quad[1]) +
+              np.linalg.norm(quad[2] - quad[3])) / 2.0
 
         dense_sample_pts_num = max(2, int(ew))
-        dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
-
-        dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1]
-        estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1))
+        dense_xy_center_line = xy_text[np.linspace(
+            0,
+            xy_text.shape[0] - 1,
+            dense_sample_pts_num,
+            endpoint=True,
+            dtype=np.float32).astype(np.int32)]
+
+        dense_xy_center_line_diff = dense_xy_center_line[
+            1:] - dense_xy_center_line[:-1]
+        estimate_arc_len = np.sum(
+            np.linalg.norm(
+                dense_xy_center_line_diff, axis=1))
 
         sample_pts_num = max(2, int(estimate_arc_len / eh))
         return sample_pts_num
 
-    def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h, 
-                shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0):
+    def detect_sast(self,
+                    tcl_map,
+                    tvo_map,
+                    tbo_map,
+                    tco_map,
+                    ratio_w,
+                    ratio_h,
+                    src_w,
+                    src_h,
+                    shrink_ratio_of_width=0.3,
+                    tcl_map_thresh=0.5,
+                    offset_expand=1.0,
+                    out_strid=4.0):
         """
         first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
         """
         # restore quad
-        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map)
+        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh,
+                                                   tvo_map)
         dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
         dets = self.nms(dets)
         if dets.shape[0] == 0:
@@ -202,7 +234,8 @@ class SASTPostProcess(object):
 
         # instance segmentation
         # instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
-        instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map)
+        instance_count, instance_label_map = self.cluster_by_quads_tco(
+            tcl_map, tcl_map_thresh, quads, tco_map)
 
         # restore single poly with tcl instance.
         poly_list = []
@@ -212,10 +245,10 @@ class SASTPostProcess(object):
             q_area = quad_areas[instance_idx - 1]
             if q_area < 5:
                 continue
-            
+
             #
-            len1 = float(np.linalg.norm(quad[0] -quad[1]))
-            len2 = float(np.linalg.norm(quad[1] -quad[2]))
+            len1 = float(np.linalg.norm(quad[0] - quad[1]))
+            len2 = float(np.linalg.norm(quad[1] - quad[2]))
             min_len = min(len1, len2)
             if min_len < 3:
                 continue
@@ -225,16 +258,18 @@ class SASTPostProcess(object):
                 continue
 
             # filter low confidence instance
-            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0] 
+            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0]
             if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.1:
-            # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
+                # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
                 continue
 
             # sort xy_text
-            left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0,
-                                        (quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2)
-            right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0,
-                                        (quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2)
+            left_center_pt = np.array(
+                [[(quad[0, 0] + quad[-1, 0]) / 2.0,
+                  (quad[0, 1] + quad[-1, 1]) / 2.0]])  # (1, 2)
+            right_center_pt = np.array(
+                [[(quad[1, 0] + quad[2, 0]) / 2.0,
+                  (quad[1, 1] + quad[2, 1]) / 2.0]])  # (1, 2)
             proj_unit_vec = (right_center_pt - left_center_pt) / \
                             (np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
             proj_value = np.sum(xy_text * proj_unit_vec, axis=1)
@@ -245,33 +280,45 @@ class SASTPostProcess(object):
                 sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
             else:
                 sample_pts_num = self.sample_pts_num
-            xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
+            xy_center_line = xy_text[np.linspace(
+                0,
+                xy_text.shape[0] - 1,
+                sample_pts_num,
+                endpoint=True,
+                dtype=np.float32).astype(np.int32)]
 
             point_pair_list = []
             for x, y in xy_center_line:
                 # get corresponding offset
                 offset = tbo_map[y, x, :].reshape(2, 2)
                 if offset_expand != 1.0:
-                    offset_length = np.linalg.norm(offset, axis=1, keepdims=True)
-                    expand_length = np.clip(offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0)
+                    offset_length = np.linalg.norm(
+                        offset, axis=1, keepdims=True)
+                    expand_length = np.clip(
+                        offset_length * (offset_expand - 1),
+                        a_min=0.5,
+                        a_max=3.0)
                     offset_detal = offset / offset_length * expand_length
-                    offset = offset + offset_detal                
-                # original point
+                    offset = offset + offset_detal
+                    # original point
                 ori_yx = np.array([y, x], dtype=np.float32)
-                point_pair = (ori_yx +  offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2) 
+                point_pair = (ori_yx + offset)[:, ::-1] * out_strid / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
                 point_pair_list.append(point_pair)
 
             # ndarry: (x, 2), expand poly along width
             detected_poly = self.point_pair2poly(point_pair_list)
-            detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width)
-            detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
-            detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
+            detected_poly = self.expand_poly_along_width(detected_poly,
+                                                         shrink_ratio_of_width)
+            detected_poly[:, 0] = np.clip(
+                detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(
+                detected_poly[:, 1], a_min=0, a_max=src_h)
             poly_list.append(detected_poly)
 
         return poly_list
 
-    def __call__(self, outs_dict, shape_list):                
+    def __call__(self, outs_dict, shape_list):
         score_list = outs_dict['f_score']
         border_list = outs_dict['f_border']
         tvo_list = outs_dict['f_tvo']
@@ -281,20 +328,28 @@ class SASTPostProcess(object):
             border_list = border_list.numpy()
             tvo_list = tvo_list.numpy()
             tco_list = tco_list.numpy()
-                    
+
         img_num = len(shape_list)
         poly_lists = []
         for ino in range(img_num):
-            p_score = score_list[ino].transpose((1,2,0))
-            p_border = border_list[ino].transpose((1,2,0))
-            p_tvo = tvo_list[ino].transpose((1,2,0))
-            p_tco = tco_list[ino].transpose((1,2,0))
+            p_score = score_list[ino].transpose((1, 2, 0))
+            p_border = border_list[ino].transpose((1, 2, 0))
+            p_tvo = tvo_list[ino].transpose((1, 2, 0))
+            p_tco = tco_list[ino].transpose((1, 2, 0))
             src_h, src_w, ratio_h, ratio_w = shape_list[ino]
 
-            poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h, 
-                                         shrink_ratio_of_width=self.shrink_ratio_of_width, 
-                                         tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale)
+            poly_list = self.detect_sast(
+                p_score,
+                p_tvo,
+                p_border,
+                p_tco,
+                ratio_w,
+                ratio_h,
+                src_w,
+                src_h,
+                shrink_ratio_of_width=self.shrink_ratio_of_width,
+                tcl_map_thresh=self.tcl_map_thresh,
+                offset_expand=self.expand_scale)
             poly_lists.append({'points': np.array(poly_list)})
 
         return poly_lists
-

ppocr/utils/dict/arabic_dict.txt

+ 
+!
+#
+$
+%
+&
+'
+(
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+_
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+É
+é
+ء
+آ
+أ
+ؤ
+إ
+ئ
+ا
+ب
+ة
+ت
+ث
+ج
+ح
+خ
+د
+ذ
+ر
+ز
+س
+ش
+ص
+ض
+ط
+ظ
+ع
+غ
+ف
+ق
+ك
+ل
+م
+ن
+ه
+و
+ى
+ي
+ً
+ٌ
+ٍ
+َ
+ُ
+ِ
+ّ
+ْ
+ٓ
+ٔ
+ٰ
+ٱ
+ٹ
+پ
+چ
+ڈ
+ڑ
+ژ
+ک
+ڭ
+گ
+ں
+ھ
+ۀ
+ہ
+ۂ
+ۃ
+ۆ
+ۇ
+ۈ
+ۋ
+ی
+ې
+ے
+ۓ
+ە
+١
+٢
+٣
+٤
+٥
+٦
+٧
+٨
+٩

ppocr/utils/dict/cyrillic_dict.txt

+ 
+!
+#
+$
+%
+&
+'
+(
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+_
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+É
+é
+Ё
+Є
+І
+Ј
+Љ
+Ў
+А
+Б
+В
+Г
+Д
+Е
+Ж
+З
+И
+Й
+К
+Л
+М
+Н
+О
+П
+Р
+С
+Т
+У
+Ф
+Х
+Ц
+Ч
+Ш
+Щ
+Ъ
+Ы
+Ь
+Э
+Ю
+Я
+а
+б
+в
+г
+д
+е
+ж
+з
+и
+й
+к
+л
+м
+н
+о
+п
+р
+с
+т
+у
+ф
+х
+ц
+ч
+ш
+щ
+ъ
+ы
+ь
+э
+ю
+я
+ё
+ђ
+є
+і
+ј
+љ
+њ
+ћ
+ў
+џ
+Ґ
+ґ

ppocr/utils/dict/devanagari_dict.txt

+ 
+!
+#
+$
+%
+&
+'
+(
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+_
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+É
+é
+ँ
+ं
+ः
+अ
+आ
+इ
+ई
+उ
+ऊ
+ऋ
+ए
+ऐ
+ऑ
+ओ
+औ
+क
+ख
+ग
+घ
+ङ
+च
+छ
+ज
+झ
+ञ
+ट
+ठ
+ड
+ढ
+ण
+त
+थ
+द
+ध
+न
+ऩ
+प
+फ
+ब
+भ
+म
+य
+र
+ऱ
+ल
+ळ
+व
+श
+ष
+स
+ह
+़
+ा
+ि
+ी
+ु
+ू
+ृ
+ॅ
+े
+ै
+ॉ
+ो
+ौ
+्
+॒
+क़
+ख़
+ग़
+ज़
+ड़
+ढ़
+फ़
+ॠ
+।
+०
+१
+२
+३
+४
+५
+६
+७
+८
+९
+॰

ppocr/utils/dict/latin_dict.txt

+ 
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+<
+=
+>
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+[
+]
+_
+`
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+{
+}
+¡
+£
+§
+ª
+«
+­
+°
+²
+³
+´
+µ
+·
+º
+»
+¿
+À
+Á
+Â
+Ä
+Å
+Ç
+È
+É
+Ê
+Ë
+Ì
+Í
+Î
+Ï
+Ò
+Ó
+Ô
+Õ
+Ö
+Ú
+Ü
+Ý
+ß
+à
+á
+â
+ã
+ä
+å
+æ
+ç
+è
+é
+ê
+ë
+ì
+í
+î
+ï
+ñ
+ò
+ó
+ô
+õ
+ö
+ø
+ù
+ú
+û
+ü
+ý
+ą
+Ć
+ć
+Č
+č
+Đ
+đ
+ę
+ı
+Ł
+ł
+ō
+Œ
+œ
+Š
+š
+Ÿ
+Ž
+ž
+ʒ
+β
+δ
+ε
+з
+Ṡ
+‘
+€
+™

ppocr/utils/e2e_metric/Deteval.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import scipy.io as io
+from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
+
+
+def get_socre_A(gt_dir, pred_dict):
+    allInputs = 1
+
+    def input_reading_mod(pred_dict):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]['points']
+            text = pred_dict[i]['texts']
+            point = ",".join(map(str, points.reshape(-1, )))
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dict):
+        """This helper reads groundtruths from mat files"""
+        gt = []
+        n = len(gt_dict)
+        for i in range(n):
+            points = gt_dict[i]['points'].tolist()
+            h = len(points)
+            text = gt_dict[i]['text']
+            xx = [
+                np.array(
+                    ['x:'], dtype='<U2'), 0, np.array(
+                        ['y:'], dtype='<U2'), 0, np.array(
+                            ['#'], dtype='<U1'), np.array(
+                                ['#'], dtype='<U1')
+            ]
+            t_x, t_y = [], []
+            for j in range(h):
+                t_x.append(points[j][0])
+                t_y.append(points[j][1])
+            xx[1] = np.array([t_x], dtype='int16')
+            xx[3] = np.array([t_y], dtype='int16')
+            if text != "":
+                xx[4] = np.array([text], dtype='U{}'.format(len(text)))
+                xx[5] = np.array(['c'], dtype='<U1')
+            gt.append(xx)
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            if (gt[5] == '#') and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(',')]
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(gt_x, gt_y)), 2)
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(det_x, det_y)), 2)
+
+    ##############################Initialization###################################
+    # global_sigma = []
+    # global_tau = []
+    # global_pred_str = []
+    # global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
+                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
+                input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
+                and (input_id != 'Deteval_result_non_curved.txt'):
+            detections = input_reading_mod(pred_dict)
+            groundtruths = gt_reading_mod(gt_dir)
+            detections = detection_filtering(
+                detections,
+                groundtruths)  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == '#':
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(',')]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma = local_sigma_table
+            global_tau = local_tau_table
+            global_pred_str = local_pred_str
+            global_gt_str = local_gt_str
+
+    single_data = {}
+    single_data['sigma'] = global_sigma
+    single_data['global_tau'] = global_tau
+    single_data['global_pred_str'] = global_pred_str
+    single_data['global_gt_str'] = global_gt_str
+    return single_data
+
+
+def get_socre_B(gt_dir, img_id, pred_dict):
+    allInputs = 1
+
+    def input_reading_mod(pred_dict):
+        """This helper reads input from txt files"""
+        det = []
+        n = len(pred_dict)
+        for i in range(n):
+            points = pred_dict[i]['points']
+            text = pred_dict[i]['texts']
+            point = ",".join(map(str, points.reshape(-1, )))
+            det.append([point, text])
+        return det
+
+    def gt_reading_mod(gt_dir, gt_id):
+        gt = io.loadmat('%s/poly_gt_img%s.mat' % (gt_dir, gt_id))
+        gt = gt['polygt']
+        return gt
+
+    def detection_filtering(detections, groundtruths, threshold=0.5):
+        for gt_id, gt in enumerate(groundtruths):
+            if (gt[5] == '#') and (gt[1].shape[1] > 1):
+                gt_x = list(map(int, np.squeeze(gt[1])))
+                gt_y = list(map(int, np.squeeze(gt[3])))
+                for det_id, detection in enumerate(detections):
+                    detection_orig = detection
+                    detection = [float(x) for x in detection[0].split(',')]
+                    detection = list(map(int, detection))
+                    det_x = detection[0::2]
+                    det_y = detection[1::2]
+                    det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
+                    if det_gt_iou > threshold:
+                        detections[det_id] = []
+
+                detections[:] = [item for item in detections if item != []]
+        return detections
+
+    def sigma_calculation(det_x, det_y, gt_x, gt_y):
+        """
+        sigma = inter_area / gt_area
+        """
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(gt_x, gt_y)), 2)
+
+    def tau_calculation(det_x, det_y, gt_x, gt_y):
+        if area(det_x, det_y) == 0.0:
+            return 0
+        return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
+                         area(det_x, det_y)), 2)
+
+    ##############################Initialization###################################
+    # global_sigma = []
+    # global_tau = []
+    # global_pred_str = []
+    # global_gt_str = []
+    ###############################################################################
+
+    for input_id in range(allInputs):
+        if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
+                input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
+                input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
+                and (input_id != 'Deteval_result_non_curved.txt'):
+            detections = input_reading_mod(pred_dict)
+            groundtruths = gt_reading_mod(gt_dir, img_id).tolist()
+            detections = detection_filtering(
+                detections,
+                groundtruths)  # filters detections overlapping with DC area
+            dc_id = []
+            for i in range(len(groundtruths)):
+                if groundtruths[i][5] == '#':
+                    dc_id.append(i)
+            cnt = 0
+            for a in dc_id:
+                num = a - cnt
+                del groundtruths[num]
+                cnt += 1
+
+            local_sigma_table = np.zeros((len(groundtruths), len(detections)))
+            local_tau_table = np.zeros((len(groundtruths), len(detections)))
+            local_pred_str = {}
+            local_gt_str = {}
+
+            for gt_id, gt in enumerate(groundtruths):
+                if len(detections) > 0:
+                    for det_id, detection in enumerate(detections):
+                        detection_orig = detection
+                        detection = [float(x) for x in detection[0].split(',')]
+                        detection = list(map(int, detection))
+                        pred_seq_str = detection_orig[1].strip()
+                        det_x = detection[0::2]
+                        det_y = detection[1::2]
+                        gt_x = list(map(int, np.squeeze(gt[1])))
+                        gt_y = list(map(int, np.squeeze(gt[3])))
+                        gt_seq_str = str(gt[4].tolist()[0])
+
+                        local_sigma_table[gt_id, det_id] = sigma_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_tau_table[gt_id, det_id] = tau_calculation(
+                            det_x, det_y, gt_x, gt_y)
+                        local_pred_str[det_id] = pred_seq_str
+                        local_gt_str[gt_id] = gt_seq_str
+
+            global_sigma = local_sigma_table
+            global_tau = local_tau_table
+            global_pred_str = local_pred_str
+            global_gt_str = local_gt_str
+
+    single_data = {}
+    single_data['sigma'] = global_sigma
+    single_data['global_tau'] = global_tau
+    single_data['global_pred_str'] = global_pred_str
+    single_data['global_gt_str'] = global_gt_str
+    return single_data
+
+
+def combine_results(all_data):
+    tr = 0.7
+    tp = 0.6
+    fsc_k = 0.8
+    k = 2
+    global_sigma = []
+    global_tau = []
+    global_pred_str = []
+    global_gt_str = []
+    for data in all_data:
+        global_sigma.append(data['sigma'])
+        global_tau.append(data['global_tau'])
+        global_pred_str.append(data['global_pred_str'])
+        global_gt_str.append(data['global_gt_str'])
+
+    global_accumulative_recall = 0
+    global_accumulative_precision = 0
+    total_num_gt = 0
+    total_num_det = 0
+    hit_str_count = 0
+    hit_count = 0
+
+    def one_to_one(local_sigma_table, local_tau_table,
+                   local_accumulative_recall, local_accumulative_precision,
+                   global_accumulative_recall, global_accumulative_precision,
+                   gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            gt_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[gt_id, :] > tr)
+            gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
+                0].shape[0]
+            gt_matching_qualified_tau_candidates = np.where(
+                local_tau_table[gt_id, :] > tp)
+            gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            det_matching_qualified_sigma_candidates = np.where(
+                local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
+                > tr)
+            det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
+                0].shape[0]
+            det_matching_qualified_tau_candidates = np.where(
+                local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
+                tp)
+            det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
+                0].shape[0]
+
+            if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
+                    (det_matching_num_qualified_sigma_candidates == 1) and (
+                    det_matching_num_qualified_tau_candidates == 1):
+                global_accumulative_recall = global_accumulative_recall + 1.0
+                global_accumulative_precision = global_accumulative_precision + 1.0
+                local_accumulative_recall = local_accumulative_recall + 1.0
+                local_accumulative_precision = local_accumulative_precision + 1.0
+
+                gt_flag[0, gt_id] = 1
+                matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
+                # recg start
+                gt_str_cur = global_gt_str[idy][gt_id]
+                pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
+                    0]]
+                if pred_str_cur == gt_str_cur:
+                    hit_str_num += 1
+                else:
+                    if pred_str_cur.lower() == gt_str_cur.lower():
+                        hit_str_num += 1
+                # recg end
+                det_flag[0, matched_det_id] = 1
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def one_to_many(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for gt_id in range(num_gt):
+            # skip the following if the groundtruth was matched
+            if gt_flag[0, gt_id] > 0:
+                continue
+
+            non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
+            num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
+
+            if num_non_zero_in_sigma >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_tau_candidates = np.where((local_tau_table[
+                    gt_id, :] >= tp) & (det_flag[0, :] == 0))
+                num_qualified_tau_candidates = qualified_tau_candidates[
+                    0].shape[0]
+
+                if num_qualified_tau_candidates == 1:
+                    if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
+                            and
+                        (local_sigma_table[gt_id, qualified_tau_candidates] >=
+                         tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, gt_id] = 1
+                        det_flag[0, qualified_tau_candidates] = 1
+                        # recg start
+                        gt_str_cur = global_gt_str[idy][gt_id]
+                        pred_str_cur = global_pred_str[idy][
+                            qualified_tau_candidates[0].tolist()[0]]
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                        # recg end
+                elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
+                      >= tr):
+                    gt_flag[0, gt_id] = 1
+                    det_flag[0, qualified_tau_candidates] = 1
+                    # recg start
+                    gt_str_cur = global_gt_str[idy][gt_id]
+                    pred_str_cur = global_pred_str[idy][
+                        qualified_tau_candidates[0].tolist()[0]]
+                    if pred_str_cur == gt_str_cur:
+                        hit_str_num += 1
+                    else:
+                        if pred_str_cur.lower() == gt_str_cur.lower():
+                            hit_str_num += 1
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + fsc_k
+                    global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + fsc_k
+                    local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
+
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    def many_to_one(local_sigma_table, local_tau_table,
+                    local_accumulative_recall, local_accumulative_precision,
+                    global_accumulative_recall, global_accumulative_precision,
+                    gt_flag, det_flag, idy):
+        hit_str_num = 0
+        for det_id in range(num_det):
+            # skip the following if the detection was matched
+            if det_flag[0, det_id] > 0:
+                continue
+
+            non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
+            num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
+
+            if num_non_zero_in_tau >= k:
+                ####search for all detections that overlaps with this groundtruth
+                qualified_sigma_candidates = np.where((
+                    local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
+                num_qualified_sigma_candidates = qualified_sigma_candidates[
+                    0].shape[0]
+
+                if num_qualified_sigma_candidates == 1:
+                    if ((local_tau_table[qualified_sigma_candidates, det_id] >=
+                         tp) and
+                        (local_sigma_table[qualified_sigma_candidates, det_id]
+                         >= tr)):
+                        # became an one-to-one case
+                        global_accumulative_recall = global_accumulative_recall + 1.0
+                        global_accumulative_precision = global_accumulative_precision + 1.0
+                        local_accumulative_recall = local_accumulative_recall + 1.0
+                        local_accumulative_precision = local_accumulative_precision + 1.0
+
+                        gt_flag[0, qualified_sigma_candidates] = 1
+                        det_flag[0, det_id] = 1
+                        # recg start
+                        pred_str_cur = global_pred_str[idy][det_id]
+                        gt_len = len(qualified_sigma_candidates[0])
+                        for idx in range(gt_len):
+                            ele_gt_id = qualified_sigma_candidates[0].tolist()[
+                                idx]
+                            if ele_gt_id not in global_gt_str[idy]:
+                                continue
+                            gt_str_cur = global_gt_str[idy][ele_gt_id]
+                            if pred_str_cur == gt_str_cur:
+                                hit_str_num += 1
+                                break
+                            else:
+                                if pred_str_cur.lower() == gt_str_cur.lower():
+                                    hit_str_num += 1
+                                break
+                        # recg end
+                elif (np.sum(local_tau_table[qualified_sigma_candidates,
+                                             det_id]) >= tp):
+                    det_flag[0, det_id] = 1
+                    gt_flag[0, qualified_sigma_candidates] = 1
+                    # recg start
+                    pred_str_cur = global_pred_str[idy][det_id]
+                    gt_len = len(qualified_sigma_candidates[0])
+                    for idx in range(gt_len):
+                        ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
+                        if ele_gt_id not in global_gt_str[idy]:
+                            continue
+                        gt_str_cur = global_gt_str[idy][ele_gt_id]
+                        if pred_str_cur == gt_str_cur:
+                            hit_str_num += 1
+                            break
+                        else:
+                            if pred_str_cur.lower() == gt_str_cur.lower():
+                                hit_str_num += 1
+                                break
+                    # recg end
+
+                    global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    global_accumulative_precision = global_accumulative_precision + fsc_k
+
+                    local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
+                    local_accumulative_precision = local_accumulative_precision + fsc_k
+        return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
+
+    for idx in range(len(global_sigma)):
+        local_sigma_table = np.array(global_sigma[idx])
+        local_tau_table = global_tau[idx]
+
+        num_gt = local_sigma_table.shape[0]
+        num_det = local_sigma_table.shape[1]
+
+        total_num_gt = total_num_gt + num_gt
+        total_num_det = total_num_det + num_det
+
+        local_accumulative_recall = 0
+        local_accumulative_precision = 0
+        gt_flag = np.zeros((1, num_gt))
+        det_flag = np.zeros((1, num_det))
+
+        #######first check for one-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
+                                                    local_accumulative_recall, local_accumulative_precision,
+                                                    global_accumulative_recall, global_accumulative_precision,
+                                                    gt_flag, det_flag, idx)
+
+        hit_str_count += hit_str_num
+        #######then check for one-to-many case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+        #######then check for many-to-one case##########
+        local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
+        gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
+                                                     local_accumulative_recall, local_accumulative_precision,
+                                                     global_accumulative_recall, global_accumulative_precision,
+                                                     gt_flag, det_flag, idx)
+        hit_str_count += hit_str_num
+
+    try:
+        recall = global_accumulative_recall / total_num_gt
+    except ZeroDivisionError:
+        recall = 0
+
+    try:
+        precision = global_accumulative_precision / total_num_det
+    except ZeroDivisionError:
+        precision = 0
+
+    try:
+        f_score = 2 * precision * recall / (precision + recall)
+    except ZeroDivisionError:
+        f_score = 0
+
+    try:
+        seqerr = 1 - float(hit_str_count) / global_accumulative_recall
+    except ZeroDivisionError:
+        seqerr = 1
+
+    try:
+        recall_e2e = float(hit_str_count) / total_num_gt
+    except ZeroDivisionError:
+        recall_e2e = 0
+
+    try:
+        precision_e2e = float(hit_str_count) / total_num_det
+    except ZeroDivisionError:
+        precision_e2e = 0
+
+    try:
+        f_score_e2e = 2 * precision_e2e * recall_e2e / (
+            precision_e2e + recall_e2e)
+    except ZeroDivisionError:
+        f_score_e2e = 0
+
+    final = {
+        'total_num_gt': total_num_gt,
+        'total_num_det': total_num_det,
+        'global_accumulative_recall': global_accumulative_recall,
+        'hit_str_count': hit_str_count,
+        'recall': recall,
+        'precision': precision,
+        'f_score': f_score,
+        'seqerr': seqerr,
+        'recall_e2e': recall_e2e,
+        'precision_e2e': precision_e2e,
+        'f_score_e2e': f_score_e2e
+    }
+    return final

ppocr/utils/e2e_metric/polygon_fast.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+from shapely.geometry import Polygon
+"""
+:param det_x: [1, N] Xs of detection's vertices 
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area(det_x, det_y) + 1.0)

ppocr/utils/e2e_utils/extract_batchsize.py

+import paddle
+import numpy as np
+import copy
+
+
+def org_tcl_rois(batch_size, pos_lists, pos_masks, label_lists, tcl_bs):
+    """
+    """
+    pos_lists_, pos_masks_, label_lists_ = [], [], []
+    img_bs = batch_size
+    ngpu = int(batch_size / img_bs)
+    img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
+    pos_lists_split, pos_masks_split, label_lists_split = [], [], []
+    for i in range(ngpu):
+        pos_lists_split.append([])
+        pos_masks_split.append([])
+        label_lists_split.append([])
+
+    for i in range(img_ids.shape[0]):
+        img_id = img_ids[i]
+        gpu_id = int(img_id / img_bs)
+        img_id = img_id % img_bs
+        pos_list = pos_lists[i].copy()
+        pos_list[:, 0] = img_id
+        pos_lists_split[gpu_id].append(pos_list)
+        pos_masks_split[gpu_id].append(pos_masks[i].copy())
+        label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
+    # repeat or delete
+    for i in range(ngpu):
+        vp_len = len(pos_lists_split[i])
+        if vp_len <= tcl_bs:
+            for j in range(0, tcl_bs - vp_len):
+                pos_list = pos_lists_split[i][j].copy()
+                pos_lists_split[i].append(pos_list)
+                pos_mask = pos_masks_split[i][j].copy()
+                pos_masks_split[i].append(pos_mask)
+                label_list = copy.deepcopy(label_lists_split[i][j])
+                label_lists_split[i].append(label_list)
+        else:
+            for j in range(0, vp_len - tcl_bs):
+                c_len = len(pos_lists_split[i])
+                pop_id = np.random.permutation(c_len)[0]
+                pos_lists_split[i].pop(pop_id)
+                pos_masks_split[i].pop(pop_id)
+                label_lists_split[i].pop(pop_id)
+    # merge
+    for i in range(ngpu):
+        pos_lists_.extend(pos_lists_split[i])
+        pos_masks_.extend(pos_masks_split[i])
+        label_lists_.extend(label_lists_split[i])
+    return pos_lists_, pos_masks_, label_lists_
+
+
+def pre_process(label_list, pos_list, pos_mask, max_text_length, max_text_nums,
+                pad_num, tcl_bs):
+    label_list = label_list.numpy()
+    batch, _, _, _ = label_list.shape
+    pos_list = pos_list.numpy()
+    pos_mask = pos_mask.numpy()
+    pos_list_t = []
+    pos_mask_t = []
+    label_list_t = []
+    for i in range(batch):
+        for j in range(max_text_nums):
+            if pos_mask[i, j].any():
+                pos_list_t.append(pos_list[i][j])
+                pos_mask_t.append(pos_mask[i][j])
+                label_list_t.append(label_list[i][j])
+    pos_list, pos_mask, label_list = org_tcl_rois(batch, pos_list_t, pos_mask_t,
+                                                  label_list_t, tcl_bs)
+    label = []
+    tt = [l.tolist() for l in label_list]
+    for i in range(tcl_bs):
+        k = 0
+        for j in range(max_text_length):
+            if tt[i][j][0] != pad_num:
+                k += 1
+            else:
+                break
+        label.append(k)
+    label = paddle.to_tensor(label)
+    label = paddle.cast(label, dtype='int64')
+    pos_list = paddle.to_tensor(pos_list)
+    pos_mask = paddle.to_tensor(pos_mask)
+    label_list = paddle.squeeze(paddle.to_tensor(label_list), axis=2)
+    label_list = paddle.cast(label_list, dtype='int32')
+    return pos_list, pos_mask, label_list, label

ppocr/utils/e2e_utils/extract_textpoint_fast.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains various CTC decoders."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import cv2
+import math
+
+import numpy as np
+from itertools import groupby
+from skimage.morphology._skeletonize import thin
+
+
+def get_dict(character_dict_path):
+    character_str = ""
+    with open(character_dict_path, "rb") as fin:
+        lines = fin.readlines()
+        for line in lines:
+            line = line.decode('utf-8').strip("\n").strip("\r\n")
+            character_str += line
+        dict_character = list(character_str)
+    return dict_character
+
+
+def softmax(logits):
+    """
+    logits: N x d
+    """
+    max_value = np.max(logits, axis=1, keepdims=True)
+    exp = np.exp(logits - max_value)
+    exp_sum = np.sum(exp, axis=1, keepdims=True)
+    dist = exp / exp_sum
+    return dist
+
+
+def get_keep_pos_idxs(labels, remove_blank=None):
+    """
+    Remove duplicate and get pos idxs of keep items.
+    The value of keep_blank should be [None, 95].
+    """
+    duplicate_len_list = []
+    keep_pos_idx_list = []
+    keep_char_idx_list = []
+    for k, v_ in groupby(labels):
+        current_len = len(list(v_))
+        if k != remove_blank:
+            current_idx = int(sum(duplicate_len_list) + current_len // 2)
+            keep_pos_idx_list.append(current_idx)
+            keep_char_idx_list.append(k)
+        duplicate_len_list.append(current_len)
+    return keep_char_idx_list, keep_pos_idx_list
+
+
+def remove_blank(labels, blank=0):
+    new_labels = [x for x in labels if x != blank]
+    return new_labels
+
+
+def insert_blank(labels, blank=0):
+    new_labels = [blank]
+    for l in labels:
+        new_labels += [l, blank]
+    return new_labels
+
+
+def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
+    """
+    CTC greedy (best path) decoder.
+    """
+    raw_str = np.argmax(np.array(probs_seq), axis=1)
+    remove_blank_in_pos = None if keep_blank_in_idxs else blank
+    dedup_str, keep_idx_list = get_keep_pos_idxs(
+        raw_str, remove_blank=remove_blank_in_pos)
+    dst_str = remove_blank(dedup_str, blank=blank)
+    return dst_str, keep_idx_list
+
+
+def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
+    _, _, C = logits_map.shape
+    ys, xs = zip(*gather_info)
+    logits_seq = logits_map[list(ys), list(xs)]
+    probs_seq = logits_seq
+    labels = np.argmax(probs_seq, axis=1)
+    dst_str = [k for k, v_ in groupby(labels) if k != C - 1]
+    detal = len(gather_info) // (pts_num - 1)
+    keep_idx_list = [0] + [detal * (i + 1) for i in range(pts_num - 2)] + [-1]
+    keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
+    return dst_str, keep_gather_list
+
+
+def ctc_decoder_for_image(gather_info_list,
+                          logits_map,
+                          Lexicon_Table,
+                          pts_num=6):
+    """
+    CTC decoder using multiple processes.
+    """
+    decoder_str = []
+    decoder_xys = []
+    for gather_info in gather_info_list:
+        if len(gather_info) < pts_num:
+            continue
+        dst_str, xys_list = instance_ctc_greedy_decoder(
+            gather_info, logits_map, pts_num=pts_num)
+        dst_str_readable = ''.join([Lexicon_Table[idx] for idx in dst_str])
+        if len(dst_str_readable) < 2:
+            continue
+        decoder_str.append(dst_str_readable)
+        decoder_xys.append(xys_list)
+    return decoder_str, decoder_xys
+
+
+def sort_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list, point_direction):
+        pos_list = np.array(pos_list).reshape(-1, 2)
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 2)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point, np.array(sorted_direction)
+
+
+def add_id(pos_list, image_id=0):
+    """
+    Add id for gather feature, for inference.
+    """
+    new_list = []
+    for item in pos_list:
+        new_list.append((image_id, item[0], item[1]))
+    return new_list
+
+
+def sort_and_expand_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    left_list = []
+    right_list = []
+    for i in range(append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            left_list.append((ly, lx))
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            right_list.append((ry, rx))
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    binary_tcl_map: h x w
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    max_append_num = 2 * append_num
+
+    left_list = []
+    right_list = []
+    for i in range(max_append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            if binary_tcl_map[ly, lx] > 0.5:
+                left_list.append((ly, lx))
+            else:
+                break
+
+    for i in range(max_append_num):
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            if binary_tcl_map[ry, rx] > 0.5:
+                right_list.append((ry, rx))
+            else:
+                break
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    point_num = len(point_pair_list) * 2
+    point_list = [0] * point_num
+    for idx, point_pair in enumerate(point_pair_list):
+        point_list[idx] = point_pair[0]
+        point_list[point_num - 1 - idx] = point_pair[1]
+    return np.array(point_list).reshape(-1, 2)
+
+
+def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
+    ratio_pair = np.array(
+        [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+    p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+    p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+    return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+
+
+def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
+    """
+    expand poly along width.
+    """
+    point_num = poly.shape[0]
+    left_quad = np.array(
+        [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+    left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
+                 (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+    left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
+    right_quad = np.array(
+        [
+            poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+            poly[point_num // 2], poly[point_num // 2 + 1]
+        ],
+        dtype=np.float32)
+    right_ratio = 1.0 + shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                  (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+    right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
+    poly[0] = left_quad_expand[0]
+    poly[-1] = left_quad_expand[-1]
+    poly[point_num // 2 - 1] = right_quad_expand[1]
+    poly[point_num // 2] = right_quad_expand[2]
+    return poly
+
+
+def restore_poly(instance_yxs_list, seq_strs, p_border, ratio_w, ratio_h, src_w,
+                 src_h, valid_set):
+    poly_list = []
+    keep_str_list = []
+    for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+        if len(keep_str) < 2:
+            print('--> too short, {}'.format(keep_str))
+            continue
+
+        offset_expand = 1.0
+        if valid_set == 'totaltext':
+            offset_expand = 1.2
+
+        point_pair_list = []
+        for y, x in yx_center_line:
+            offset = p_border[:, y, x].reshape(2, 2) * offset_expand
+            ori_yx = np.array([y, x], dtype=np.float32)
+            point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                [ratio_w, ratio_h]).reshape(-1, 2)
+            point_pair_list.append(point_pair)
+
+        detected_poly = point_pair2poly(point_pair_list)
+        detected_poly = expand_poly_along_width(
+            detected_poly, shrink_ratio_of_width=0.2)
+        detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
+        detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
+
+        keep_str_list.append(keep_str)
+        if valid_set == 'partvgg':
+            middle_point = len(detected_poly) // 2
+            detected_poly = detected_poly[
+                [0, middle_point - 1, middle_point, -1], :]
+            poly_list.append(detected_poly)
+        elif valid_set == 'totaltext':
+            poly_list.append(detected_poly)
+        else:
+            print('--> Not supported format.')
+            exit(-1)
+    return poly_list, keep_str_list
+
+
+def generate_pivot_list_fast(p_score,
+                             p_char_maps,
+                             f_direction,
+                             Lexicon_Table,
+                             score_thresh=0.5):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map.astype(np.uint8))
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            if len(pos_list) < 3:
+                continue
+
+            pos_list_sorted = sort_and_expand_with_direction_v2(
+                pos_list, f_direction, p_tcl_map)
+            all_pos_yxs.append(pos_list_sorted)
+
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decoded_str, keep_yxs_list = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, Lexicon_Table=Lexicon_Table)
+    return keep_yxs_list, decoded_str
+
+
+def extract_main_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    pos_list = np.array(pos_list)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    average_direction = average_direction / (
+        np.linalg.norm(average_direction) + 1e-6)
+    return average_direction
+
+
+def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
+    """
+    pos_list_full = np.array(pos_list).reshape(-1, 3)
+    pos_list = pos_list_full[:, 1:]
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+    sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+    return sorted_list
+
+
+def sort_by_direction_with_image_id(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list_full, point_direction):
+        pos_list_full = np.array(pos_list_full).reshape(-1, 3)
+        pos_list = pos_list_full[:, 1:]
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 3)
+    point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point

ppocr/utils/e2e_utils/extract_textpoint_slow.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains various CTC decoders."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import cv2
+import math
+
+import numpy as np
+from itertools import groupby
+from skimage.morphology._skeletonize import thin
+
+
+def get_dict(character_dict_path):
+    character_str = ""
+    with open(character_dict_path, "rb") as fin:
+        lines = fin.readlines()
+        for line in lines:
+            line = line.decode('utf-8').strip("\n").strip("\r\n")
+            character_str += line
+        dict_character = list(character_str)
+    return dict_character
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    pair_length_list = []
+    for point_pair in point_pair_list:
+        pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
+        pair_length_list.append(pair_length)
+    pair_length_list = np.array(pair_length_list)
+    pair_info = (pair_length_list.max(), pair_length_list.min(),
+                 pair_length_list.mean())
+
+    point_num = len(point_pair_list) * 2
+    point_list = [0] * point_num
+    for idx, point_pair in enumerate(point_pair_list):
+        point_list[idx] = point_pair[0]
+        point_list[point_num - 1 - idx] = point_pair[1]
+    return np.array(point_list).reshape(-1, 2), pair_info
+
+
+def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
+    """
+    Generate shrink_quad_along_width.
+    """
+    ratio_pair = np.array(
+        [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+    p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+    p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+    return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+
+
+def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
+    """
+    expand poly along width.
+    """
+    point_num = poly.shape[0]
+    left_quad = np.array(
+        [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+    left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
+                 (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+    left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
+    right_quad = np.array(
+        [
+            poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+            poly[point_num // 2], poly[point_num // 2 + 1]
+        ],
+        dtype=np.float32)
+    right_ratio = 1.0 + \
+                  shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                  (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+    right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
+    poly[0] = left_quad_expand[0]
+    poly[-1] = left_quad_expand[-1]
+    poly[point_num // 2 - 1] = right_quad_expand[1]
+    poly[point_num // 2] = right_quad_expand[2]
+    return poly
+
+
+def softmax(logits):
+    """
+    logits: N x d
+    """
+    max_value = np.max(logits, axis=1, keepdims=True)
+    exp = np.exp(logits - max_value)
+    exp_sum = np.sum(exp, axis=1, keepdims=True)
+    dist = exp / exp_sum
+    return dist
+
+
+def get_keep_pos_idxs(labels, remove_blank=None):
+    """
+    Remove duplicate and get pos idxs of keep items.
+    The value of keep_blank should be [None, 95].
+    """
+    duplicate_len_list = []
+    keep_pos_idx_list = []
+    keep_char_idx_list = []
+    for k, v_ in groupby(labels):
+        current_len = len(list(v_))
+        if k != remove_blank:
+            current_idx = int(sum(duplicate_len_list) + current_len // 2)
+            keep_pos_idx_list.append(current_idx)
+            keep_char_idx_list.append(k)
+        duplicate_len_list.append(current_len)
+    return keep_char_idx_list, keep_pos_idx_list
+
+
+def remove_blank(labels, blank=0):
+    new_labels = [x for x in labels if x != blank]
+    return new_labels
+
+
+def insert_blank(labels, blank=0):
+    new_labels = [blank]
+    for l in labels:
+        new_labels += [l, blank]
+    return new_labels
+
+
+def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
+    """
+    CTC greedy (best path) decoder.
+    """
+    raw_str = np.argmax(np.array(probs_seq), axis=1)
+    remove_blank_in_pos = None if keep_blank_in_idxs else blank
+    dedup_str, keep_idx_list = get_keep_pos_idxs(
+        raw_str, remove_blank=remove_blank_in_pos)
+    dst_str = remove_blank(dedup_str, blank=blank)
+    return dst_str, keep_idx_list
+
+
+def instance_ctc_greedy_decoder(gather_info,
+                                logits_map,
+                                keep_blank_in_idxs=True):
+    """
+    gather_info: [[x, y], [x, y] ...]
+    logits_map: H x W X (n_chars + 1)
+    """
+    _, _, C = logits_map.shape
+    ys, xs = zip(*gather_info)
+    logits_seq = logits_map[list(ys), list(xs)]  # n x 96
+    probs_seq = softmax(logits_seq)
+    dst_str, keep_idx_list = ctc_greedy_decoder(
+        probs_seq, blank=C - 1, keep_blank_in_idxs=keep_blank_in_idxs)
+    keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
+    return dst_str, keep_gather_list
+
+
+def ctc_decoder_for_image(gather_info_list, logits_map,
+                          keep_blank_in_idxs=True):
+    """
+    CTC decoder using multiple processes.
+    """
+    decoder_results = []
+    for gather_info in gather_info_list:
+        res = instance_ctc_greedy_decoder(
+            gather_info, logits_map, keep_blank_in_idxs=keep_blank_in_idxs)
+        decoder_results.append(res)
+    return decoder_results
+
+
+def sort_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list, point_direction):
+        pos_list = np.array(pos_list).reshape(-1, 2)
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 2)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point, np.array(sorted_direction)
+
+
+def add_id(pos_list, image_id=0):
+    """
+    Add id for gather feature, for inference.
+    """
+    new_list = []
+    for item in pos_list:
+        new_list.append((image_id, item[0], item[1]))
+    return new_list
+
+
+def sort_and_expand_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    left_list = []
+    right_list = []
+    for i in range(append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            left_list.append((ly, lx))
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            right_list.append((ry, rx))
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    binary_tcl_map: h x w
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    max_append_num = 2 * append_num
+
+    left_list = []
+    right_list = []
+    for i in range(max_append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            if binary_tcl_map[ly, lx] > 0.5:
+                left_list.append((ly, lx))
+            else:
+                break
+
+    for i in range(max_append_num):
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            if binary_tcl_map[ry, rx] > 0.5:
+                right_list.append((ry, rx))
+            else:
+                break
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def generate_pivot_list_curved(p_score,
+                               p_char_maps,
+                               f_direction,
+                               score_thresh=0.5,
+                               is_expand=True,
+                               is_backbone=False,
+                               image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map)
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    center_pos_yxs = []
+    end_points_yxs = []
+    instance_center_pos_yxs = []
+    pred_strs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                continue
+
+            if is_expand:
+                pos_list_sorted = sort_and_expand_with_direction_v2(
+                    pos_list, f_direction, p_tcl_map)
+            else:
+                pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decode_res = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
+    for decoded_str, keep_yxs_list in decode_res:
+        if is_backbone:
+            keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
+            instance_center_pos_yxs.append(keep_yxs_list_with_id)
+            pred_strs.append(decoded_str)
+        else:
+            end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
+            center_pos_yxs.extend(keep_yxs_list)
+
+    if is_backbone:
+        return pred_strs, instance_center_pos_yxs
+    else:
+        return center_pos_yxs, end_points_yxs
+
+
+def generate_pivot_list_horizontal(p_score,
+                                   p_char_maps,
+                                   f_direction,
+                                   score_thresh=0.5,
+                                   is_backbone=False,
+                                   image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map_bi = (p_score > score_thresh) * 1.0
+    instance_count, instance_label_map = cv2.connectedComponents(
+        p_tcl_map_bi.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    center_pos_yxs = []
+    end_points_yxs = []
+    instance_center_pos_yxs = []
+
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 5:
+                continue
+
+            # add rule here
+            main_direction = extract_main_direction(pos_list,
+                                                    f_direction)  # y x
+            reference_directin = np.array([0, 1]).reshape([-1, 2])  # y x
+            is_h_angle = abs(np.sum(
+                main_direction * reference_directin)) < math.cos(math.pi / 180 *
+                                                                 70)
+
+            point_yxs = np.array(pos_list)
+            max_y, max_x = np.max(point_yxs, axis=0)
+            min_y, min_x = np.min(point_yxs, axis=0)
+            is_h_len = (max_y - min_y) < 1.5 * (max_x - min_x)
+
+            pos_list_final = []
+            if is_h_len:
+                xs = np.unique(xs)
+                for x in xs:
+                    ys = instance_label_map[:, x].copy().reshape((-1, ))
+                    y = int(np.where(ys == instance_id)[0].mean())
+                    pos_list_final.append((y, x))
+            else:
+                ys = np.unique(ys)
+                for y in ys:
+                    xs = instance_label_map[y, :].copy().reshape((-1, ))
+                    x = int(np.where(xs == instance_id)[0].mean())
+                    pos_list_final.append((y, x))
+
+            pos_list_sorted, _ = sort_with_direction(pos_list_final,
+                                                     f_direction)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decode_res = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
+    for decoded_str, keep_yxs_list in decode_res:
+        if is_backbone:
+            keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
+            instance_center_pos_yxs.append(keep_yxs_list_with_id)
+        else:
+            end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
+            center_pos_yxs.extend(keep_yxs_list)
+
+    if is_backbone:
+        return instance_center_pos_yxs
+    else:
+        return center_pos_yxs, end_points_yxs
+
+
+def generate_pivot_list_slow(p_score,
+                             p_char_maps,
+                             f_direction,
+                             score_thresh=0.5,
+                             is_backbone=False,
+                             is_curved=True,
+                             image_id=0):
+    """
+    Warp all the function together.
+    """
+    if is_curved:
+        return generate_pivot_list_curved(
+            p_score,
+            p_char_maps,
+            f_direction,
+            score_thresh=score_thresh,
+            is_expand=True,
+            is_backbone=is_backbone,
+            image_id=image_id)
+    else:
+        return generate_pivot_list_horizontal(
+            p_score,
+            p_char_maps,
+            f_direction,
+            score_thresh=score_thresh,
+            is_backbone=is_backbone,
+            image_id=image_id)
+
+
+# for refine module
+def extract_main_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    pos_list = np.array(pos_list)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    average_direction = average_direction / (
+        np.linalg.norm(average_direction) + 1e-6)
+    return average_direction
+
+
+def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
+    """
+    pos_list_full = np.array(pos_list).reshape(-1, 3)
+    pos_list = pos_list_full[:, 1:]
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+    sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+    return sorted_list
+
+
+def sort_by_direction_with_image_id(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list_full, point_direction):
+        pos_list_full = np.array(pos_list_full).reshape(-1, 3)
+        pos_list = pos_list_full[:, 1:]
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 3)
+    point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point
+
+
+def generate_pivot_list_tt_inference(p_score,
+                                     p_char_maps,
+                                     f_direction,
+                                     score_thresh=0.5,
+                                     is_backbone=False,
+                                     is_curved=True,
+                                     image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map)
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                continue
+            pos_list_sorted = sort_and_expand_with_direction_v2(
+                pos_list, f_direction, p_tcl_map)
+            pos_list_sorted_with_id = add_id(pos_list_sorted, image_id=image_id)
+            all_pos_yxs.append(pos_list_sorted_with_id)
+    return all_pos_yxs

ppocr/utils/e2e_utils/pgnet_pp_utils.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+from extract_textpoint_slow import *
+from extract_textpoint_fast import generate_pivot_list_fast, restore_poly
+
+
+class PGNet_PostProcess(object):
+    # two different post-process
+    def __init__(self, character_dict_path, valid_set, score_thresh, outs_dict,
+                 shape_list):
+        self.Lexicon_Table = get_dict(character_dict_path)
+        self.valid_set = valid_set
+        self.score_thresh = score_thresh
+        self.outs_dict = outs_dict
+        self.shape_list = shape_list
+
+    def pg_postprocess_fast(self):
+        p_score = self.outs_dict['f_score']
+        p_border = self.outs_dict['f_border']
+        p_char = self.outs_dict['f_char']
+        p_direction = self.outs_dict['f_direction']
+        if isinstance(p_score, paddle.Tensor):
+            p_score = p_score[0].numpy()
+            p_border = p_border[0].numpy()
+            p_direction = p_direction[0].numpy()
+            p_char = p_char[0].numpy()
+        else:
+            p_score = p_score[0]
+            p_border = p_border[0]
+            p_direction = p_direction[0]
+            p_char = p_char[0]
+
+        src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
+        instance_yxs_list, seq_strs = generate_pivot_list_fast(
+            p_score,
+            p_char,
+            p_direction,
+            self.Lexicon_Table,
+            score_thresh=self.score_thresh)
+        poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs,
+                                                p_border, ratio_w, ratio_h,
+                                                src_w, src_h, self.valid_set)
+        data = {
+            'points': poly_list,
+            'texts': keep_str_list,
+        }
+        return data
+
+    def pg_postprocess_slow(self):
+        p_score = self.outs_dict['f_score']
+        p_border = self.outs_dict['f_border']
+        p_char = self.outs_dict['f_char']
+        p_direction = self.outs_dict['f_direction']
+        if isinstance(p_score, paddle.Tensor):
+            p_score = p_score[0].numpy()
+            p_border = p_border[0].numpy()
+            p_direction = p_direction[0].numpy()
+            p_char = p_char[0].numpy()
+        else:
+            p_score = p_score[0]
+            p_border = p_border[0]
+            p_direction = p_direction[0]
+            p_char = p_char[0]
+        src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
+        is_curved = self.valid_set == "totaltext"
+        char_seq_idx_set, instance_yxs_list = generate_pivot_list_slow(
+            p_score,
+            p_char,
+            p_direction,
+            score_thresh=self.score_thresh,
+            is_backbone=True,
+            is_curved=is_curved)
+        seq_strs = []
+        for char_idx_set in char_seq_idx_set:
+            pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
+            seq_strs.append(pr_str)
+        poly_list = []
+        keep_str_list = []
+        all_point_list = []
+        all_point_pair_list = []
+        for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+            if len(yx_center_line) == 1:
+                yx_center_line.append(yx_center_line[-1])
+
+            offset_expand = 1.0
+            if self.valid_set == 'totaltext':
+                offset_expand = 1.2
+
+            point_pair_list = []
+            for batch_id, y, x in yx_center_line:
+                offset = p_border[:, y, x].reshape(2, 2)
+                if offset_expand != 1.0:
+                    offset_length = np.linalg.norm(
+                        offset, axis=1, keepdims=True)
+                    expand_length = np.clip(
+                        offset_length * (offset_expand - 1),
+                        a_min=0.5,
+                        a_max=3.0)
+                    offset_detal = offset / offset_length * expand_length
+                    offset = offset + offset_detal
+                ori_yx = np.array([y, x], dtype=np.float32)
+                point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
+                point_pair_list.append(point_pair)
+
+                all_point_list.append([
+                    int(round(x * 4.0 / ratio_w)),
+                    int(round(y * 4.0 / ratio_h))
+                ])
+                all_point_pair_list.append(point_pair.round().astype(np.int32)
+                                           .tolist())
+
+            detected_poly, pair_length_info = point_pair2poly(point_pair_list)
+            detected_poly = expand_poly_along_width(
+                detected_poly, shrink_ratio_of_width=0.2)
+            detected_poly[:, 0] = np.clip(
+                detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(
+                detected_poly[:, 1], a_min=0, a_max=src_h)
+
+            if len(keep_str) < 2:
+                continue
+
+            keep_str_list.append(keep_str)
+            detected_poly = np.round(detected_poly).astype('int32')
+            if self.valid_set == 'partvgg':
+                middle_point = len(detected_poly) // 2
+                detected_poly = detected_poly[
+                    [0, middle_point - 1, middle_point, -1], :]
+                poly_list.append(detected_poly)
+            elif self.valid_set == 'totaltext':
+                poly_list.append(detected_poly)
+            else:
+                print('--> Not supported format.')
+                exit(-1)
+        data = {
+            'points': poly_list,
+            'texts': keep_str_list,
+        }
+        return data

ppocr/utils/e2e_utils/visual.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import cv2
+import time
+
+
+def resize_image(im, max_side_len=512):
+    """
+    resize image to a size multiple of max_stride which is required by the network
+    :param im: the resized image
+    :param max_side_len: limit of max image size to avoid out of memory in gpu
+    :return: the resized image and the resize ratio
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    if resize_h > resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_min(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    if resize_h < resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_for_totaltext(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+    ratio = 1.25
+    if h * ratio > max_side_len:
+        ratio = float(max_side_len) / resize_h
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    pair_length_list = []
+    for point_pair in point_pair_list:
+        pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
+        pair_length_list.append(pair_length)
+    pair_length_list = np.array(pair_length_list)
+    pair_info = (pair_length_list.max(), pair_length_list.min(),
+                 pair_length_list.mean())
+
+    point_num = len(point_pair_list) * 2
+    point_list = [0] * point_num
+    for idx, point_pair in enumerate(point_pair_list):
+        point_list[idx] = point_pair[0]
+        point_list[point_num - 1 - idx] = point_pair[1]
+    return np.array(point_list).reshape(-1, 2), pair_info
+
+
+def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
+    """
+    Generate shrink_quad_along_width.
+    """
+    ratio_pair = np.array(
+        [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+    p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+    p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+    return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+
+
+def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
+    """
+    expand poly along width.
+    """
+    point_num = poly.shape[0]
+    left_quad = np.array(
+        [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+    left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
+                 (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+    left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
+    right_quad = np.array(
+        [
+            poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+            poly[point_num // 2], poly[point_num // 2 + 1]
+        ],
+        dtype=np.float32)
+    right_ratio = 1.0 + \
+                  shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                  (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+    right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
+    poly[0] = left_quad_expand[0]
+    poly[-1] = left_quad_expand[-1]
+    poly[point_num // 2 - 1] = right_quad_expand[1]
+    poly[point_num // 2] = right_quad_expand[2]
+    return poly
+
+
+def norm2(x, axis=None):
+    if axis:
+        return np.sqrt(np.sum(x**2, axis=axis))
+    return np.sqrt(np.sum(x**2))
+
+
+def cos(p1, p2):
+    return (p1 * p2).sum() / (norm2(p1) * norm2(p2))

ppocr/utils/en_dict.txt

+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+<
+=
+>
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+[
+\
+]
+^
+_
+`
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+{
+|
+}
+~
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+ 

ppocr/utils/save_load.py

@@ -121,7 +121,7 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
     return best_model_dict
 
 
-def save_model(net,
+def save_model(model,
                optimizer,
                model_path,
                logger,
@@ -133,7 +133,7 @@ def save_model(net,
     """
     _mkdir_if_not_exist(model_path, logger)
     model_prefix = os.path.join(model_path, prefix)
-    paddle.save(net.state_dict(), model_prefix + '.pdparams')
+    paddle.save(model.state_dict(), model_prefix + '.pdparams')
     paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')
 
     # save metric and config

ppocr/utils/utility.py

@@ -61,6 +61,7 @@ def get_image_file_list(img_file):
                 imgs_lists.append(file_path)
     if len(imgs_lists) == 0:
         raise Exception("not found any img file in {}".format(img_file))
+    imgs_lists = sorted(imgs_lists)
     return imgs_lists
 
 

requirements.txt

@@ -3,8 +3,8 @@ scikit-image==0.17.2
 imgaug==0.4.0
 pyclipper
 lmdb
-opencv-python==4.2.0.32
 tqdm
 numpy
 visualdl
-python-Levenshtein
+python-Levenshtein
\ No newline at end of file
+opencv-contrib-python==4.2.0.32
\ No newline at end of file

setup.py

@@ -32,7 +32,7 @@ setup(
     package_dir={'paddleocr': ''},
     include_package_data=True,
     entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
-    version='2.0.3',
+    version='2.0.6',
     install_requires=requirements,
     license='Apache License 2.0',
     description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',

tools/eval.py

@@ -59,10 +59,10 @@ def main():
     eval_class = build_metric(config['Metric'])
 
     # start eval
-    metirc = program.eval(model, valid_dataloader, post_process_class,
+    metric = program.eval(model, valid_dataloader, post_process_class,
                           eval_class, use_srn)
     logger.info('metric eval ***************')
-    for k, v in metirc.items():
+    for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))
 
 

tools/export_model.py

@@ -31,14 +31,6 @@ from ppocr.utils.logging import get_logger
 from tools.program import load_config, merge_config, ArgsParser
 
 
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-c", "--config", help="configuration file to use")
-    parser.add_argument(
-        "-o", "--output_path", type=str, default='./output/infer/')
-    return parser.parse_args()
-
-
 def main():
     FLAGS = ArgsParser().parse_args()
     config = load_config(FLAGS.config)
@@ -61,17 +53,19 @@ def main():
     save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
 
     if config['Architecture']['algorithm'] == "SRN":
+        max_text_length = config['Architecture']['Head']['max_text_length']
         other_shape = [
             paddle.static.InputSpec(
                 shape=[None, 1, 64, 256], dtype='float32'), [
                     paddle.static.InputSpec(
                         shape=[None, 256, 1],
                         dtype="int64"), paddle.static.InputSpec(
-                            shape=[None, 25, 1],
-                            dtype="int64"), paddle.static.InputSpec(
-                                shape=[None, 8, 25, 25], dtype="int64"),
+                            shape=[None, max_text_length, 1], dtype="int64"),
                     paddle.static.InputSpec(
-                        shape=[None, 8, 25, 25], dtype="int64")
+                        shape=[None, 8, max_text_length, max_text_length],
+                        dtype="int64"), paddle.static.InputSpec(
+                            shape=[None, 8, max_text_length, max_text_length],
+                            dtype="int64")
                 ]
         ]
         model = to_static(model, input_spec=other_shape)

tools/infer/predict_det.py

@@ -39,7 +39,10 @@ class TextDetector(object):
         self.args = args
         self.det_algorithm = args.det_algorithm
         pre_process_list = [{
-            'DetResizeForTest': None
+            'DetResizeForTest': {
+                'limit_side_len': args.det_limit_side_len,
+                'limit_type': args.det_limit_type
+            }
         }, {
             'NormalizeImage': {
                 'std': [0.229, 0.224, 0.225],
@@ -62,6 +65,7 @@ class TextDetector(object):
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             postprocess_params["use_dilation"] = args.use_dilation
+            postprocess_params["score_mode"] = args.det_db_score_mode
         elif self.det_algorithm == "EAST":
             postprocess_params['name'] = 'EASTPostProcess'
             postprocess_params["score_thresh"] = args.det_east_score_thresh

tools/infer/predict_e2e.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import numpy as np
+import time
+import sys
+
+import tools.infer.utility as utility
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+from ppocr.data import create_operators, transform
+from ppocr.postprocess import build_post_process
+
+logger = get_logger()
+
+
+class TextE2E(object):
+    def __init__(self, args):
+        self.args = args
+        self.e2e_algorithm = args.e2e_algorithm
+        pre_process_list = [{
+            'E2EResizeForTest': {}
+        }, {
+            'NormalizeImage': {
+                'std': [0.229, 0.224, 0.225],
+                'mean': [0.485, 0.456, 0.406],
+                'scale': '1./255.',
+                'order': 'hwc'
+            }
+        }, {
+            'ToCHWImage': None
+        }, {
+            'KeepKeys': {
+                'keep_keys': ['image', 'shape']
+            }
+        }]
+        postprocess_params = {}
+        if self.e2e_algorithm == "PGNet":
+            pre_process_list[0] = {
+                'E2EResizeForTest': {
+                    'max_side_len': args.e2e_limit_side_len,
+                    'valid_set': 'totaltext'
+                }
+            }
+            postprocess_params['name'] = 'PGPostProcess'
+            postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
+            postprocess_params["character_dict_path"] = args.e2e_char_dict_path
+            postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
+            postprocess_params["mode"] = args.e2e_pgnet_mode
+            self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
+        else:
+            logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
+            sys.exit(0)
+
+        self.preprocess_op = create_operators(pre_process_list)
+        self.postprocess_op = build_post_process(postprocess_params)
+        self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
+            args, 'e2e', logger)  # paddle.jit.load(args.det_model_dir)
+        # self.predictor.eval()
+
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points
+
+    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.clip_det_res(box, img_height, img_width)
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def __call__(self, img):
+
+        ori_im = img.copy()
+        data = {'image': img}
+        data = transform(data, self.preprocess_op)
+        img, shape_list = data
+        if img is None:
+            return None, 0
+        img = np.expand_dims(img, axis=0)
+        shape_list = np.expand_dims(shape_list, axis=0)
+        img = img.copy()
+        starttime = time.time()
+
+        self.input_tensor.copy_from_cpu(img)
+        self.predictor.run()
+        outputs = []
+        for output_tensor in self.output_tensors:
+            output = output_tensor.copy_to_cpu()
+            outputs.append(output)
+
+        preds = {}
+        if self.e2e_algorithm == 'PGNet':
+            preds['f_border'] = outputs[0]
+            preds['f_char'] = outputs[1]
+            preds['f_direction'] = outputs[2]
+            preds['f_score'] = outputs[3]
+        else:
+            raise NotImplementedError
+        post_result = self.postprocess_op(preds, shape_list)
+        points, strs = post_result['points'], post_result['texts']
+        dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
+        elapse = time.time() - starttime
+        return dt_boxes, strs, elapse
+
+
+if __name__ == "__main__":
+    args = utility.parse_args()
+    image_file_list = get_image_file_list(args.image_dir)
+    text_detector = TextE2E(args)
+    count = 0
+    total_time = 0
+    draw_img_save = "./inference_results"
+    if not os.path.exists(draw_img_save):
+        os.makedirs(draw_img_save)
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        points, strs, elapse = text_detector(img)
+        if count > 0:
+            total_time += elapse
+        count += 1
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
+        src_im = utility.draw_e2e_res(points, strs, image_file)
+        img_name_pure = os.path.split(image_file)[-1]
+        img_path = os.path.join(draw_img_save,
+                                "e2e_res_{}".format(img_name_pure))
+        cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
+    if count > 1:
+        logger.info("Avg Time: {}".format(total_time / (count - 1)))

tools/infer/predict_rec.py

@@ -41,6 +41,7 @@ class TextRecognizer(object):
         self.character_type = args.rec_char_type
         self.rec_batch_num = args.rec_batch_num
         self.rec_algorithm = args.rec_algorithm
+        self.max_text_length = args.max_text_length
         postprocess_params = {
             'name': 'CTCLabelDecode',
             "character_type": args.rec_char_type,
@@ -186,8 +187,9 @@ class TextRecognizer(object):
                     norm_img = norm_img[np.newaxis, :]
                     norm_img_batch.append(norm_img)
                 else:
-                    norm_img = self.process_image_srn(
-                        img_list[indices[ino]], self.rec_image_shape, 8, 25)
+                    norm_img = self.process_image_srn(img_list[indices[ino]],
+                                                      self.rec_image_shape, 8,
+                                                      self.max_text_length)
                     encoder_word_pos_list = []
                     gsrm_word_pos_list = []
                     gsrm_slf_attn_bias1_list = []

tools/infer/predict_system.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 import sys
+import subprocess
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
@@ -141,6 +142,7 @@ def sorted_boxes(dt_boxes):
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
+    image_file_list = image_file_list[args.process_id::args.total_process_num]
     text_sys = TextSystem(args)
     is_visualize = True
     font_path = args.vis_font_path
@@ -184,4 +186,18 @@ def main(args):
 
 
 if __name__ == "__main__":
-    main(utility.parse_args())
+    args = utility.parse_args()
+    if args.use_mp:
+        p_list = []
+        total_process_num = args.total_process_num
+        for process_id in range(total_process_num):
+            cmd = [sys.executable, "-u"] + sys.argv + [
+                "--process_id={}".format(process_id),
+                "--use_mp={}".format(False)
+            ]
+            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
+            p_list.append(p)
+        for p in p_list:
+            p.wait()
+    else:
+        main(args)

tools/infer/utility.py

@@ -48,6 +48,7 @@ def parse_args():
     parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
     parser.add_argument("--max_batch_size", type=int, default=10)
     parser.add_argument("--use_dilation", type=bool, default=False)
+    parser.add_argument("--det_db_score_mode", type=str, default="fast")
     # EAST parmas
     parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
     parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
@@ -74,6 +75,20 @@ def parse_args():
         "--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
     parser.add_argument("--drop_score", type=float, default=0.5)
 
+    # params for e2e
+    parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
+    parser.add_argument("--e2e_model_dir", type=str)
+    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
+    parser.add_argument("--e2e_limit_type", type=str, default='max')
+
+    # PGNet parmas
+    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
+    parser.add_argument(
+        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
+    parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
+    parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
+    parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
+
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)
     parser.add_argument("--cls_model_dir", type=str)
@@ -85,6 +100,10 @@ def parse_args():
     parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
 
+    parser.add_argument("--use_mp", type=str2bool, default=False)
+    parser.add_argument("--total_process_num", type=int, default=1)
+    parser.add_argument("--process_id", type=int, default=0)
+
     return parser.parse_args()
 
 
@@ -93,8 +112,10 @@ def create_predictor(args, mode, logger):
         model_dir = args.det_model_dir
     elif mode == 'cls':
         model_dir = args.cls_model_dir
-    else:
+    elif mode == 'rec':
         model_dir = args.rec_model_dir
+    else:
+        model_dir = args.e2e_model_dir
 
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
@@ -148,6 +169,22 @@ def create_predictor(args, mode, logger):
     return predictor, input_tensor, output_tensors
 
 
+def draw_e2e_res(dt_boxes, strs, img_path):
+    src_im = cv2.imread(img_path)
+    for box, str in zip(dt_boxes, strs):
+        box = box.astype(np.int32).reshape((-1, 1, 2))
+        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+        cv2.putText(
+            src_im,
+            str,
+            org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+            fontFace=cv2.FONT_HERSHEY_COMPLEX,
+            fontScale=0.7,
+            color=(0, 255, 0),
+            thickness=1)
+    return src_im
+
+
 def draw_text_det_res(dt_boxes, img_path):
     src_im = cv2.imread(img_path)
     for box in dt_boxes:

tools/infer_e2e.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import json
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+
+
+def draw_e2e_res(dt_boxes, strs, config, img, img_name):
+    if len(dt_boxes) > 0:
+        src_im = img
+        for box, str in zip(dt_boxes, strs):
+            box = box.astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
+            cv2.putText(
+                src_im,
+                str,
+                org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
+                fontFace=cv2.FONT_HERSHEY_COMPLEX,
+                fontScale=0.7,
+                color=(0, 255, 0),
+                thickness=1)
+        save_det_path = os.path.dirname(config['Global'][
+            'save_res_path']) + "/e2e_results/"
+        if not os.path.exists(save_det_path):
+            os.makedirs(save_det_path)
+        save_path = os.path.join(save_det_path, os.path.basename(img_name))
+        cv2.imwrite(save_path, src_im)
+        logger.info("The e2e Image saved in {}".format(save_path))
+
+
+def main():
+    global_config = config['Global']
+
+    # build model
+    model = build_model(config['Architecture'])
+
+    init_model(config, model, logger)
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        elif op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['image', 'shape']
+        transforms.append(op)
+
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global']['save_res_path']
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    with open(save_res_path, "wb") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            images = np.expand_dims(batch[0], axis=0)
+            shape_list = np.expand_dims(batch[1], axis=0)
+            images = paddle.to_tensor(images)
+            preds = model(images)
+            post_result = post_process_class(preds, shape_list)
+            points, strs = post_result['points'], post_result['texts']
+            # write resule
+            dt_boxes_json = []
+            for poly, str in zip(points, strs):
+                tmp_json = {"transcription": str}
+                tmp_json['points'] = poly.tolist()
+                dt_boxes_json.append(tmp_json)
+            otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
+            fout.write(otstr.encode())
+            src_img = cv2.imread(file)
+            draw_e2e_res(points, strs, config, src_img, file)
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()

tools/infer_rec.py

@@ -73,35 +73,45 @@ def main():
     global_config['infer_mode'] = True
     ops = create_operators(transforms, global_config)
 
+    save_res_path = config['Global'].get('save_res_path',
+                                         "./output/rec/predicts_rec.txt")
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
     model.eval()
-    for file in get_image_file_list(config['Global']['infer_img']):
-        logger.info("infer_img: {}".format(file))
-        with open(file, 'rb') as f:
-            img = f.read()
-            data = {'image': img}
-        batch = transform(data, ops)
-        if config['Architecture']['algorithm'] == "SRN":
-            encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
-            gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
-            gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
-            gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
-
-            others = [
-                paddle.to_tensor(encoder_word_pos_list),
-                paddle.to_tensor(gsrm_word_pos_list),
-                paddle.to_tensor(gsrm_slf_attn_bias1_list),
-                paddle.to_tensor(gsrm_slf_attn_bias2_list)
-            ]
-
-        images = np.expand_dims(batch[0], axis=0)
-        images = paddle.to_tensor(images)
-        if config['Architecture']['algorithm'] == "SRN":
-            preds = model(images, others)
-        else:
-            preds = model(images)
-        post_result = post_process_class(preds)
-        for rec_reuslt in post_result:
-            logger.info('\t result: {}'.format(rec_reuslt))
+
+    with open(save_res_path, "w") as fout:
+        for file in get_image_file_list(config['Global']['infer_img']):
+            logger.info("infer_img: {}".format(file))
+            with open(file, 'rb') as f:
+                img = f.read()
+                data = {'image': img}
+            batch = transform(data, ops)
+            if config['Architecture']['algorithm'] == "SRN":
+                encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
+                gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
+                gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
+                gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
+
+                others = [
+                    paddle.to_tensor(encoder_word_pos_list),
+                    paddle.to_tensor(gsrm_word_pos_list),
+                    paddle.to_tensor(gsrm_slf_attn_bias1_list),
+                    paddle.to_tensor(gsrm_slf_attn_bias2_list)
+                ]
+
+            images = np.expand_dims(batch[0], axis=0)
+            images = paddle.to_tensor(images)
+            if config['Architecture']['algorithm'] == "SRN":
+                preds = model(images, others)
+            else:
+                preds = model(images)
+            post_result = post_process_class(preds)
+            for rec_reuslt in post_result:
+                logger.info('\t result: {}'.format(rec_reuslt))
+                if len(rec_reuslt) >= 2:
+                    fout.write(file + "\t" + rec_reuslt[0] + "\t" + str(
+                        rec_reuslt[1]) + "\n")
     logger.info("success!")
 
 

tools/program.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import os
 import sys
+import platform
 import yaml
 import time
 import shutil
@@ -159,6 +160,8 @@ def train(config,
     eval_batch_step = config['Global']['eval_batch_step']
 
     global_step = 0
+    if 'global_step' in pre_best_model_dict:
+        global_step = pre_best_model_dict['global_step']
     start_eval_step = 0
     if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
         start_eval_step = eval_batch_step[0]
@@ -196,9 +199,11 @@ def train(config,
         train_reader_cost = 0.0
         batch_sum = 0
         batch_start = time.time()
+        max_iter = len(train_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(train_dataloader)
         for idx, batch in enumerate(train_dataloader):
             train_reader_cost += time.time() - batch_start
-            if idx >= len(train_dataloader):
+            if idx >= max_iter:
                 break
             lr = optimizer.get_lr()
             images = batch[0]
@@ -287,7 +292,8 @@ def train(config,
                         is_best=True,
                         prefix='best_accuracy',
                         best_model_dict=best_model_dict,
-                        epoch=epoch)
+                        epoch=epoch,
+                        global_step=global_step)
                 best_str = 'best metric, {}'.format(', '.join([
                     '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                 ]))
@@ -309,7 +315,8 @@ def train(config,
                 is_best=False,
                 prefix='latest',
                 best_model_dict=best_model_dict,
-                epoch=epoch)
+                epoch=epoch,
+                global_step=global_step)
         if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
             save_model(
                 model,
@@ -319,7 +326,8 @@ def train(config,
                 is_best=False,
                 prefix='iter_epoch_{}'.format(epoch),
                 best_model_dict=best_model_dict,
-                epoch=epoch)
+                epoch=epoch,
+                global_step=global_step)
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
     logger.info(best_str)
@@ -335,8 +343,10 @@ def eval(model, valid_dataloader, post_process_class, eval_class,
         total_frame = 0.0
         total_time = 0.0
         pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
+        max_iter = len(valid_dataloader) - 1 if platform.system(
+        ) == "Windows" else len(valid_dataloader)
         for idx, batch in enumerate(valid_dataloader):
-            if idx >= len(valid_dataloader):
+            if idx >= max_iter:
                 break
             images = batch[0]
             start = time.time()
@@ -375,7 +385,8 @@ def preprocess(is_train=False):
 
     alg = config['Architecture']['algorithm']
     assert alg in [
-        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
+        'CLS', 'PGNet'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'