Merge branch 'update_requirements' of https://github.com/WenmuZhou/PaddleOCR...

Merge branch 'update_requirements' of https://github.com/WenmuZhou/PaddleOCR into update_requirements

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/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/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).

configs/e2e/e2e_r50_vd_pg.yml

@@ -62,20 +62,21 @@ PostProcess:
   mode: fast   # fast or slow two ways
 Metric:
   name: E2EMetric
-  gt_mat_dir:    # the dir of 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
-    label_file_list: [.././train_data/total_text/train/]
+    data_dir: ./train_data/total_text/train
+    label_file_list: [./train_data/total_text/train/]
     ratio_list: [1.0]
-    data_format: icdar #two data format: icdar/textnet
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
+      - E2ELabelEncode:
       - PGProcessTrain:
           batch_size: 14  # same as loader: batch_size_per_card
           min_crop_size: 24
@@ -92,13 +93,12 @@ Train:
 Eval:
   dataset:
     name: PGDataSet
-    data_dir: ./train_data/
+    data_dir: ./train_data/total_text/test
     label_file_list: [./train_data/total_text/test/]
     transforms:
       - DecodeImage: # load image
           img_mode: RGB
           channel_first: False
-      - E2ELabelEncode:
       - E2EResizeForTest:
           max_side_len: 768
       - NormalizeImage:
@@ -108,7 +108,7 @@ Eval:
           order: 'hwc'
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags', 'img_id']
+          keep_keys: [ 'image', 'shape', 'img_id']
   loader:
     shuffle: False
     drop_last: False

configs/rec/multi_language/generate_multi_language_configs.py

@@ -118,7 +118,6 @@ class ArgsParser(ArgumentParser):
         return config
 
     def _set_language(self, type):
-        print("type:", type)
         lang = type[0]
         assert (type), "please use -l or --language to choose language type"
         assert(

doc/doc_ch/models_list.md

@@ -113,7 +113,7 @@ python3 generate_multi_language_configs.py -l it \
 | 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](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)
+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

@@ -134,7 +134,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
 <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：
+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）
 
@@ -155,7 +155,7 @@ 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/korean.ttf')
+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')
 ```
@@ -240,7 +240,7 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
 |德文|german|german|
 |日文|japan|japan|
 |韩文|korean|korean|
-|中文繁体|chinese traditional |ch_tra|
+|中文繁体|chinese traditional |chinese_cht|
 |意大利文| Italian |it|
 |西班牙文|Spanish |es|
 |葡萄牙文| Portuguese|pt|
@@ -259,10 +259,9 @@ ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别
 |乌克兰文|Ukranian|uk|
 |白俄罗斯文|Belarusian|be|
 |泰卢固文|Telugu |te|
-|卡纳达文|Kannada |kn|
 |泰米尔文|Tamil |ta|
 |南非荷兰文 |Afrikaans |af|
-|阿塞拜疆文 |Azerbaijani    |az|
+|阿塞拜疆文 |Azerbaijani  |az|
 |波斯尼亚文|Bosnian|bs|
 |捷克文|Czech|cs|
 |威尔士文 |Welsh |cy|

doc/doc_en/models_list_en.md

@@ -111,7 +111,7 @@ python3 generate_multi_language_configs.py -l it \
 | 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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
+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

@@ -153,7 +153,7 @@ 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/korean.ttf')
+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')
 ```
@@ -232,7 +232,7 @@ For functions such as data annotation, you can read the complete [Document Tutor
 |german|german|
 |japan|japan|
 |korean|korean|
-|chinese traditional |ch_tra|
+|chinese traditional |chinese_cht|
 | Italian |it|
 |Spanish |es|
 | Portuguese|pt|
@@ -251,7 +251,6 @@ For functions such as data annotation, you can read the complete [Document Tutor
 |Ukranian|uk|
 |Belarusian|be|
 |Telugu |te|
-|Kannada |kn|
 |Tamil |ta|
 |Afrikaans |af|
 |Azerbaijani    |az|

paddleocr.py

@@ -30,6 +30,7 @@ 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']
 
@@ -117,7 +118,7 @@ model_urls = {
 }
 
 SUPPORT_DET_MODEL = ['DB']
-VERSION = 2.1
+VERSION = '2.1'
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
@@ -315,14 +316,13 @@ 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, det_lang))
+            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,

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)
@@ -187,29 +187,31 @@ class CTCLabelEncode(BaseRecLabelEncode):
         return dict_character
 
 
-class E2ELabelEncode(BaseRecLabelEncode):
-    def __init__(self,
-                 max_text_length,
-                 character_dict_path=None,
-                 character_type='EN',
-                 use_space_char=False,
-                 **kwargs):
-        super(E2ELabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
-        self.pad_num = len(self.dict)  # the length to pad
+class E2ELabelEncode(object):
+    def __init__(self, **kwargs):
+        pass
 
     def __call__(self, data):
-        texts = data['strs']
-        temp_texts = []
-        for text in texts:
-            text = text.lower()
-            text = self.encode(text)
-            if text is None:
-                return None
-            text = text + [self.pad_num] * (self.max_text_len - len(text))
-            temp_texts.append(text)
-        data['strs'] = np.array(temp_texts)
+        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
 
 

ppocr/data/imaug/pg_process.py

@@ -88,7 +88,7 @@ class PGProcessTrain(object):
 
         return min_area_quad
 
-    def check_and_validate_polys(self, polys, tags, xxx_todo_changeme):
+    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
@@ -96,7 +96,7 @@ class PGProcessTrain(object):
         :param tags:
         :return:
         """
-        (h, w) = xxx_todo_changeme
+        (h, w) = im_size
         if polys.shape[0] == 0:
             return polys, np.array([]), np.array([])
         polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
@@ -750,8 +750,8 @@ class PGProcessTrain(object):
         input_size = 512
         im = data['image']
         text_polys = data['polys']
-        text_tags = data['tags']
-        text_strs = data['strs']
+        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))

ppocr/data/pgnet_dataset.py

@@ -29,20 +29,20 @@ class PGDataSet(Dataset):
         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)
-        self.data_format = dataset_config.get('data_format', 'icdar')
         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_format)
+        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()
@@ -55,108 +55,40 @@ class PGDataSet(Dataset):
             random.shuffle(self.data_lines)
         return
 
-    def extract_polys(self, poly_txt_path):
-        """
-        Read text_polys, txt_tags, txts from give txt file.
-        """
-        text_polys, txt_tags, txts = [], [], []
-        with open(poly_txt_path) as f:
-            for line in f.readlines():
-                poly_str, txt = line.strip().split('\t')
-                poly = list(map(float, poly_str.split(',')))
-                text_polys.append(
-                    np.array(
-                        poly, dtype=np.float32).reshape(-1, 2))
-                txts.append(txt)
-                txt_tags.append(txt == '###')
-
-        return np.array(list(map(np.array, text_polys))), \
-               np.array(txt_tags, dtype=np.bool), txts
-
-    def extract_info_textnet(self, im_fn, img_dir=''):
-        """
-        Extract information from line in textnet format.
-        """
-        info_list = im_fn.split('\t')
-        img_path = ''
-        for ext in [
-                'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'JPG'
-        ]:
-            if os.path.exists(os.path.join(img_dir, info_list[0] + "." + ext)):
-                img_path = os.path.join(img_dir, info_list[0] + "." + ext)
-                break
-
-        if img_path == '':
-            print('Image {0} NOT found in {1}, and it will be ignored.'.format(
-                info_list[0], img_dir))
-
-        nBox = (len(info_list) - 1) // 9
-        wordBBs, txts, txt_tags = [], [], []
-        for n in range(0, nBox):
-            wordBB = list(map(float, info_list[n * 9 + 1:(n + 1) * 9]))
-            txt = info_list[(n + 1) * 9]
-            wordBBs.append([[wordBB[0], wordBB[1]], [wordBB[2], wordBB[3]],
-                            [wordBB[4], wordBB[5]], [wordBB[6], wordBB[7]]])
-            txts.append(txt)
-            if txt == '###':
-                txt_tags.append(True)
-            else:
-                txt_tags.append(False)
-        return img_path, np.array(wordBBs, dtype=np.float32), txt_tags, txts
-
-    def get_image_info_list(self, file_list, ratio_list, data_format='textnet'):
+    def get_image_info_list(self, file_list, ratio_list):
         if isinstance(file_list, str):
             file_list = [file_list]
         data_lines = []
-        for idx, data_source in enumerate(file_list):
-            image_files = []
-            if data_format == 'icdar':
-                image_files = [(data_source, x) for x in
-                               os.listdir(os.path.join(data_source, 'rgb'))
-                               if x.split('.')[-1] in [
-                                   'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif',
-                                   'tiff', 'gif', 'JPG'
-                               ]]
-            elif data_format == 'textnet':
-                with open(data_source) as f:
-                    image_files = [(data_source, x.strip())
-                                   for x in f.readlines()]
-            else:
-                print("Unrecognized data format...")
-                exit(-1)
-            random.seed(self.seed)
-            image_files = random.sample(
-                image_files, round(len(image_files) * ratio_list[idx]))
-            data_lines.extend(image_files)
+        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_path, data_line = self.data_lines[file_idx]
+        data_line = self.data_lines[file_idx]
         try:
-            if self.data_format == 'icdar':
-                im_path = os.path.join(data_path, 'rgb', data_line)
-                poly_path = os.path.join(data_path, 'poly',
-                                         data_line.split('.')[0] + '.txt')
-                text_polys, text_tags, text_strs = self.extract_polys(poly_path)
+            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':
+                img_id = int(data_line.split(".")[0][7:])
             else:
-                image_dir = os.path.join(os.path.dirname(data_path), 'image')
-                im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
-                    data_line, image_dir)
-            img_id = int(data_line.split(".")[0][3:])
-
-            data = {
-                'img_path': im_path,
-                'polys': text_polys,
-                'tags': text_tags,
-                'strs': text_strs,
-                'img_id': img_id
-            }
+                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(

ppocr/metrics/e2e_metric.py

@@ -35,11 +35,11 @@ class E2EMetric(object):
         self.reset()
 
     def __call__(self, preds, batch, **kwargs):
-        img_id = batch[5][0]
+        img_id = batch[2][0]
         e2e_info_list = [{
             'points': det_polyon,
-            'text': pred_str
-        } for det_polyon, pred_str in zip(preds['points'], preds['strs'])]
+            'texts': pred_str
+        } for det_polyon, pred_str in zip(preds['points'], preds['texts'])]
         result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
         self.results.append(result)
 

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)

ppocr/utils/e2e_metric/Deteval.py

@@ -26,7 +26,7 @@ def get_socre(gt_dir, img_id, pred_dict):
         n = len(pred_dict)
         for i in range(n):
             points = pred_dict[i]['points']
-            text = pred_dict[i]['text']
+            text = pred_dict[i]['texts']
             point = ",".join(map(str, points.reshape(-1, )))
             det.append([point, text])
         return det

ppocr/utils/e2e_utils/extract_textpoint_fast.py

@@ -21,6 +21,7 @@ import math
 
 import numpy as np
 from itertools import groupby
+from cv2.ximgproc import thinning as thin
 from skimage.morphology._skeletonize import thin
 
 

ppocr/utils/e2e_utils/pgnet_pp_utils.py

@@ -64,7 +64,7 @@ class PGNet_PostProcess(object):
                                                 src_w, src_h, self.valid_set)
         data = {
             'points': poly_list,
-            'strs': keep_str_list,
+            'texts': keep_str_list,
         }
         return data
 
@@ -176,6 +176,6 @@ class PGNet_PostProcess(object):
                 exit(-1)
         data = {
             'points': poly_list,
-            'strs': keep_str_list,
+            'texts': keep_str_list,
         }
         return data

tools/infer/predict_e2e.py

@@ -122,7 +122,7 @@ class TextE2E(object):
         else:
             raise NotImplementedError
         post_result = self.postprocess_op(preds, shape_list)
-        points, strs = post_result['points'], post_result['strs']
+        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

tools/infer_e2e.py

@@ -103,7 +103,7 @@ def main():
             images = paddle.to_tensor(images)
             preds = model(images)
             post_result = post_process_class(preds, shape_list)
-            points, strs = post_result['points'], post_result['strs']
+            points, strs = post_result['points'], post_result['texts']
             # write resule
             dt_boxes_json = []
             for poly, str in zip(points, strs):