Merge pull request #1344 from WenmuZhou/update_angle_class_doc

paddleocr whl adaptation dygraph

MANIFEST.in

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

doc/doc_ch/whl.md

@@ -261,6 +261,61 @@ im_show.save('result.jpg')
 paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true
 ```
 
+### 使用网络图片或者numpy数组作为输入
+
+1. 网络图片
+
+代码使用
+```python
+from paddleocr import PaddleOCR, draw_ocr
+# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
+# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
+ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
+img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
+result = ocr.ocr(img_path, cls=True)
+for line in result:
+    print(line)
+
+# 显示结果
+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/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+命令行模式
+```bash
+paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
+```
+
+2. numpy数组
+仅通过代码使用时支持numpy数组作为输入
+```python
+from paddleocr import PaddleOCR, draw_ocr
+# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
+# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
+ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
+img_path = 'PaddleOCR/doc/imgs/11.jpg'
+img = cv2.imread(img_path)
+# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消
+result = ocr.ocr(img_path, cls=True)
+for line in result:
+    print(line)
+
+# 显示结果
+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/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
 ## 参数说明
 
 | 字段                    | 说明                                                                                                                                                                                                                 | 默认值                  |
@@ -285,6 +340,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
 | max_text_length         | 识别算法能识别的最大文字长度                                                                                                                                                                                         | 25                      |
 | rec_char_dict_path      | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径                                                                                                                                                | ./ppocr/utils/ppocr_keys_v1.txt                        |
 | use_space_char          | 是否识别空格                                                                                                                                                                                                         | TRUE                    |
+| drop_score          | 对输出按照分数(来自于识别模型)进行过滤，低于此分数的不返回                                                                                                                                                                                                         | 0.5                    |
 | use_angle_cls          | 是否加载分类模型                                                                                                                                                                                                         | FALSE                    |
 | cls_model_dir          | 分类模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/cls`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件                                                                                 | None                    |
 | cls_image_shape          | 分类算法的输入图片尺寸                                                                           | "3, 48, 192"                    |
@@ -295,4 +351,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
 | lang                     | 模型语言类型,目前支持 中文(ch)和英文(en)                                                                                                                                                                                                  | ch                    |
 | det                     | 前向时使用启动检测                                                                                                                                                                                                   | TRUE                    |
 | rec                     | 前向时是否启动识别                                                                                                                                                                                                   | TRUE                    |
-| cls                     | 前向时是否启动分类                                                                                                                                                                                                 | FALSE                    |
+| cls                     | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类)                                                                                                                                                                                                | FALSE                    |

doc/doc_en/whl_en.md

@@ -271,6 +271,59 @@ im_show.save('result.jpg')
 paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_dir} --rec_model_dir {your_rec_model_dir} --rec_char_dict_path {your_rec_char_dict_path} --cls_model_dir {your_cls_model_dir} --use_angle_cls true --cls true
 ```
 
+### Use web images or numpy array as input
+
+1. Web image
+
+Use by code
+```python
+from paddleocr import PaddleOCR, draw_ocr
+ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
+img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
+result = ocr.ocr(img_path, cls=True)
+for line in result:
+    print(line)
+
+# show result
+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/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+Use by command line
+```bash
+paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
+```
+
+2. Numpy array
+Support numpy array as input only when used by code
+
+```python
+from paddleocr import PaddleOCR, draw_ocr
+ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
+img_path = 'PaddleOCR/doc/imgs/11.jpg'
+img = cv2.imread(img_path)
+# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), If your own training model supports grayscale images, you can uncomment this line
+result = ocr.ocr(img_path, cls=True)
+for line in result:
+    print(line)
+
+# show result
+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/simfang.ttf')
+im_show = Image.fromarray(im_show)
+im_show.save('result.jpg')
+```
+
+
 ## Parameter Description
 
 | Parameter                    | Description                                                                                                                                                                                                                 | Default value                  |
@@ -295,6 +348,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
 | max_text_length         | The maximum text length that the recognition algorithm can recognize                                                                                                                                                                                         | 25                      |
 | rec_char_dict_path      | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2                                                                                                                                              | ./ppocr/utils/ppocr_keys_v1.txt                        |
 | use_space_char          | Whether to recognize spaces                                                                                                                                                                                                         | TRUE                    |
+| drop_score          | Filter the output by score (from the recognition model), and those below this score will not be returned                                                                                                                                                                                                        | 0.5                    |
 | use_angle_cls          | Whether to load classification model                                                                                                                                                                                                       | FALSE                    |
 | cls_model_dir           | the classification inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/cls`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
 | cls_image_shape         | image shape of classification algorithm                                                                                                                                                                                            | "3,48,192"              |
@@ -305,4 +359,4 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
 | lang                     | The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported                                                                                                                                                                                                  | ch                    |
 | det                     | Enable detction when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
 | rec                     | Enable recognition when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
-| cls                     | Enable classification when `ppocr.ocr` func exec                                                                                                                                                                                                   | FALSE                    |
+| cls                     | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction)                                                                                                                                                                                                   | FALSE                    |

paddleocr.py

@@ -26,17 +26,50 @@ import requests
 from tqdm import tqdm
 
 from tools.infer import predict_system
-from ppocr.utils.utility import initial_logger
+from ppocr.utils.logging import get_logger
 
-logger = initial_logger()
+logger = get_logger()
 from ppocr.utils.utility import check_and_read_gif, get_image_file_list
 
 __all__ = ['PaddleOCR']
 
-model_params = {
+model_urls = {
-    'det': 'https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar',
+    'det':
-    'rec':
+        'https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar',
-    'https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar',
+    'rec': {
+        'ch': {
+            'url':
+                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar',
+            'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
+        },
+        'en': {
+            'url':
+                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/en/en_ppocr_mobile_v1.1_rec_infer.tar',
+            'dict_path': './ppocr/utils/ic15_dict.txt'
+        },
+        'french': {
+            'url':
+                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/fr/french_ppocr_mobile_v1.1_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/french_dict.txt'
+        },
+        'german': {
+            'url':
+                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/ge/german_ppocr_mobile_v1.1_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/german_dict.txt'
+        },
+        'korean': {
+            'url':
+                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/kr/korean_ppocr_mobile_v1.1_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/korean_dict.txt'
+        },
+        'japan': {
+            'url':
+                'https://paddleocr.bj.bcebos.com/20-09-22/mobile/jp/japan_ppocr_mobile_v1.1_rec_infer.tar',
+            'dict_path': './ppocr/utils/dict/japan_dict.txt'
+        }
+    },
+    'cls':
+        'https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar'
 }
 
 SUPPORT_DET_MODEL = ['DB']
@@ -54,8 +87,8 @@ def download_with_progressbar(url, save_path):
             progress_bar.update(len(data))
             file.write(data)
     progress_bar.close()
-    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+    if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
-        logger.error("ERROR, something went wrong")
+        logger.error("Something went wrong while downloading models")
         sys.exit(0)
 
 
@@ -63,7 +96,7 @@ def maybe_download(model_storage_directory, url):
     # using custom model
     if not os.path.exists(os.path.join(
             model_storage_directory, 'model')) or not os.path.exists(
-                os.path.join(model_storage_directory, 'params')):
+        os.path.join(model_storage_directory, 'params')):
         tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
         print('download {} to {}'.format(url, tmp_path))
         os.makedirs(model_storage_directory, exist_ok=True)
@@ -84,53 +117,102 @@ def maybe_download(model_storage_directory, url):
         os.remove(tmp_path)
 
 
-def parse_args():
+def parse_args(mMain=True, add_help=True):
     import argparse
 
     def str2bool(v):
         return v.lower() in ("true", "t", "1")
 
-    parser = argparse.ArgumentParser()
+    if mMain:
-    # params for prediction engine
+        parser = argparse.ArgumentParser(add_help=add_help)
-    parser.add_argument("--use_gpu", type=str2bool, default=True)
+        # params for prediction engine
-    parser.add_argument("--ir_optim", type=str2bool, default=True)
+        parser.add_argument("--use_gpu", type=str2bool, default=True)
-    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
+        parser.add_argument("--ir_optim", type=str2bool, default=True)
-    parser.add_argument("--gpu_mem", type=int, default=8000)
+        parser.add_argument("--use_tensorrt", type=str2bool, default=False)
+        parser.add_argument("--gpu_mem", type=int, default=8000)
 
-    # params for text detector
+        # params for text detector
-    parser.add_argument("--image_dir", type=str)
+        parser.add_argument("--image_dir", type=str)
-    parser.add_argument("--det_algorithm", type=str, default='DB')
+        parser.add_argument("--det_algorithm", type=str, default='DB')
-    parser.add_argument("--det_model_dir", type=str, default=None)
+        parser.add_argument("--det_model_dir", type=str, default=None)
-    parser.add_argument("--det_max_side_len", type=float, default=960)
+        parser.add_argument("--det_limit_side_len", type=float, default=960)
+        parser.add_argument("--det_limit_type", type=str, default='max')
 
-    # DB parmas
+        # DB parmas
-    parser.add_argument("--det_db_thresh", type=float, default=0.3)
+        parser.add_argument("--det_db_thresh", type=float, default=0.3)
-    parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
+        parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
-    parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
+        parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
 
-    # EAST parmas
+        # EAST parmas
-    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
+        parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
-    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
+        parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
-    parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
+        parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
 
-    # params for text recognizer
+        # params for text recognizer
-    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
+        parser.add_argument("--rec_algorithm", type=str, default='CRNN')
-    parser.add_argument("--rec_model_dir", type=str, default=None)
+        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_image_shape", type=str, default="3, 32, 320")
-    parser.add_argument("--rec_char_type", type=str, default='ch')
+        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=30)
-    parser.add_argument("--max_text_length", type=int, default=25)
+        parser.add_argument("--max_text_length", type=int, default=25)
-    parser.add_argument(
+        parser.add_argument("--rec_char_dict_path", type=str, default=None)
-        "--rec_char_dict_path",
+        parser.add_argument("--use_space_char", type=bool, default=True)
-        type=str,
+        parser.add_argument("--drop_score", type=float, default=0.5)
-        default="./ppocr/utils/ppocr_keys_v1.txt")
-    parser.add_argument("--use_space_char", type=bool, default=True)
-    parser.add_argument("--enable_mkldnn", type=bool, default=False)
 
-    parser.add_argument("--det", type=str2bool, default=True)
+        # params for text classifier
-    parser.add_argument("--rec", type=str2bool, default=True)
+        parser.add_argument("--cls_model_dir", type=str, default=None)
-    parser.add_argument("--use_zero_copy_run", type=bool, default=False)
+        parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
-    return parser.parse_args()
+        parser.add_argument("--label_list", type=list, default=['0', '180'])
+        parser.add_argument("--cls_batch_num", type=int, default=30)
+        parser.add_argument("--cls_thresh", type=float, default=0.9)
+
+        parser.add_argument("--enable_mkldnn", type=bool, default=False)
+        parser.add_argument("--use_zero_copy_run", type=bool, default=False)
+        parser.add_argument("--use_pdserving", type=str2bool, default=False)
+
+        parser.add_argument("--lang", type=str, default='ch')
+        parser.add_argument("--det", type=str2bool, default=True)
+        parser.add_argument("--rec", type=str2bool, default=True)
+        parser.add_argument("--use_angle_cls", type=str2bool, default=False)
+        return parser.parse_args()
+    else:
+        return argparse.Namespace(use_gpu=True,
+                                  ir_optim=True,
+                                  use_tensorrt=False,
+                                  gpu_mem=8000,
+                                  image_dir='',
+                                  det_algorithm='DB',
+                                  det_model_dir=None,
+                                  det_limit_side_len=960,
+                                  det_limit_type='max',
+                                  det_db_thresh=0.3,
+                                  det_db_box_thresh=0.5,
+                                  det_db_unclip_ratio=2.0,
+                                  det_east_score_thresh=0.8,
+                                  det_east_cover_thresh=0.1,
+                                  det_east_nms_thresh=0.2,
+                                  rec_algorithm='CRNN',
+                                  rec_model_dir=None,
+                                  rec_image_shape="3, 32, 320",
+                                  rec_char_type='ch',
+                                  rec_batch_num=30,
+                                  max_text_length=25,
+                                  rec_char_dict_path=None,
+                                  use_space_char=True,
+                                  drop_score=0.5,
+                                  cls_model_dir=None,
+                                  cls_image_shape="3, 48, 192",
+                                  label_list=['0', '180'],
+                                  cls_batch_num=30,
+                                  cls_thresh=0.9,
+                                  enable_mkldnn=False,
+                                  use_zero_copy_run=False,
+                                  use_pdserving=False,
+                                  lang='ch',
+                                  det=True,
+                                  rec=True,
+                                  use_angle_cls=False
+                                  )
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -140,18 +222,31 @@ class PaddleOCR(predict_system.TextSystem):
         args:
             **kwargs: other params show in paddleocr --help
         """
-        postprocess_params = parse_args()
+        postprocess_params = parse_args(mMain=False, add_help=False)
         postprocess_params.__dict__.update(**kwargs)
+        self.use_angle_cls = postprocess_params.use_angle_cls
+        lang = postprocess_params.lang
+        assert lang in model_urls[
+            'rec'], 'param lang must in {}, but got {}'.format(
+            model_urls['rec'].keys(), lang)
+        if postprocess_params.rec_char_dict_path is None:
+            postprocess_params.rec_char_dict_path = model_urls['rec'][lang][
+                'dict_path']
 
         # init model dir
         if postprocess_params.det_model_dir is None:
             postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
         if postprocess_params.rec_model_dir is None:
-            postprocess_params.rec_model_dir = os.path.join(BASE_DIR, 'rec')
+            postprocess_params.rec_model_dir = os.path.join(
+                BASE_DIR, 'rec/{}'.format(lang))
+        if postprocess_params.cls_model_dir is None:
+            postprocess_params.cls_model_dir = os.path.join(BASE_DIR, 'cls')
         print(postprocess_params)
         # download model
-        maybe_download(postprocess_params.det_model_dir, model_params['det'])
+        maybe_download(postprocess_params.det_model_dir, model_urls['det'])
-        maybe_download(postprocess_params.rec_model_dir, model_params['rec'])
+        maybe_download(postprocess_params.rec_model_dir,
+                       model_urls['rec'][lang]['url'])
+        maybe_download(postprocess_params.cls_model_dir, model_urls['cls'])
 
         if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
             logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
@@ -166,7 +261,7 @@ class PaddleOCR(predict_system.TextSystem):
         # init det_model and rec_model
         super().__init__(postprocess_params)
 
-    def ocr(self, img, det=True, rec=True):
+    def ocr(self, img, det=True, rec=True, cls=False):
         """
         ocr with paddleocr
         args：
@@ -175,7 +270,16 @@ class PaddleOCR(predict_system.TextSystem):
             rec: use text recognition or not, if false, only det will be exec. default is True
         """
         assert isinstance(img, (np.ndarray, list, str))
+        if isinstance(img, list) and det == True:
+            logger.error('When input a list of images, det must be false')
+            exit(0)
+
+        self.use_angle_cls = cls
         if isinstance(img, str):
+            # download net image
+            if img.startswith('http'):
+                download_with_progressbar(img, 'tmp.jpg')
+                img = 'tmp.jpg'
             image_file = img
             img, flag = check_and_read_gif(image_file)
             if not flag:
@@ -183,6 +287,8 @@ class PaddleOCR(predict_system.TextSystem):
             if img is None:
                 logger.error("error in loading image:{}".format(image_file))
                 return None
+        if isinstance(img, np.ndarray) and len(img.shape) == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
         if det and rec:
             dt_boxes, rec_res = self.__call__(img)
             return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
@@ -194,20 +300,34 @@ class PaddleOCR(predict_system.TextSystem):
         else:
             if not isinstance(img, list):
                 img = [img]
+            if self.use_angle_cls:
+                img, cls_res, elapse = self.text_classifier(img)
+                if not rec:
+                    return cls_res
             rec_res, elapse = self.text_recognizer(img)
             return rec_res
 
 
 def main():
-    # for com
+    # for cmd
-    args = parse_args()
+    args = parse_args(mMain=True)
-    image_file_list = get_image_file_list(args.image_dir)
+    image_dir = args.image_dir
+    if image_dir.startswith('http'):
+        download_with_progressbar(image_dir, 'tmp.jpg')
+        image_file_list = ['tmp.jpg']
+    else:
+        image_file_list = get_image_file_list(args.image_dir)
     if len(image_file_list) == 0:
         logger.error('no images find in {}'.format(args.image_dir))
         return
-    ocr_engine = PaddleOCR()
+
+    ocr_engine = PaddleOCR(**(args.__dict__))
     for img_path in image_file_list:
-        print(img_path)
+        logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
-        result = ocr_engine.ocr(img_path, det=args.det, rec=args.rec)
+        result = ocr_engine.ocr(img_path,
-        for line in result:
+                                det=args.det,
-            print(line)
+                                rec=args.rec,
+                                cls=args.use_angle_cls)
+        if result is not None:
+            for line in result:
+                logger.info(line)

setup.py

@@ -32,7 +32,7 @@ setup(
     package_dir={'paddleocr': ''},
     include_package_data=True,
     entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
-    version='0.0.3',
+    version='2.0',
     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/infer/predict_system.py

@@ -13,6 +13,7 @@
 # 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__, '../..')))
@@ -30,12 +31,15 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
 from tools.infer.utility import draw_ocr_box_txt
 
+logger = get_logger()
+
 
 class TextSystem(object):
     def __init__(self, args):
         self.text_detector = predict_det.TextDetector(args)
         self.text_recognizer = predict_rec.TextRecognizer(args)
         self.use_angle_cls = args.use_angle_cls
+        self.drop_score = args.drop_score
         if self.use_angle_cls:
             self.text_classifier = predict_cls.TextClassifier(args)
 
@@ -81,7 +85,8 @@ class TextSystem(object):
     def __call__(self, img):
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
-        logger.info("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
+        logger.info("dt_boxes num : {}, elapse : {}".format(
+            len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
         img_crop_list = []
@@ -99,9 +104,16 @@ class TextSystem(object):
                 len(img_crop_list), elapse))
 
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        logger.info("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
+        logger.info("rec_res num  : {}, elapse : {}".format(
+            len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
-        return dt_boxes, rec_res
+        filter_boxes, filter_rec_res = [], []
+        for box, rec_reuslt in zip(dt_boxes, rec_res):
+            text, score = rec_reuslt
+            if score >= self.drop_score:
+                filter_boxes.append(box)
+                filter_rec_res.append(rec_reuslt)
+        return filter_boxes, filter_rec_res
 
 
 def sorted_boxes(dt_boxes):
@@ -117,8 +129,8 @@ def sorted_boxes(dt_boxes):
     _boxes = list(sorted_boxes)
 
     for i in range(num_boxes - 1):
-        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
+        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
-            (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
             tmp = _boxes[i]
             _boxes[i] = _boxes[i + 1]
             _boxes[i + 1] = tmp
@@ -143,12 +155,8 @@ def main(args):
         elapse = time.time() - starttime
         logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
 
-        dt_num = len(dt_boxes)
+        for text, score in rec_res:
-        for dno in range(dt_num):
+            logger.info("{}, {:.3f}".format(text, score))
-            text, score = rec_res[dno]
-            if score >= drop_score:
-                text_str = "%s, %.3f" % (text, score)
-                logger.info(text_str)
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -174,5 +182,4 @@ def main(args):
 
 
 if __name__ == "__main__":
-    logger = get_logger()
     main(utility.parse_args())