combine args in paddleocr and ppocr/infer/utility

doc/doc_ch/whl.md

@@ -59,7 +59,7 @@ im_show.save('result.jpg')
 from paddleocr import PaddleOCR, draw_ocr
 ocr = PaddleOCR() # need to run only once to download and load model into memory
 img_path = 'PaddleOCR/doc/imgs/11.jpg'
-result = ocr.ocr(img_path)
+result = ocr.ocr(img_path,cls=False)
 for line in result:
  print(line)
 

doc/doc_en/whl_en.md

@@ -59,7 +59,7 @@ Visualization of results
 from paddleocr import PaddleOCR,draw_ocr
 ocr = PaddleOCR(lang='en') # need to run only once to download and load model into memory
 img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
-result = ocr.ocr(img_path)
+result = ocr.ocr(img_path, cls=False)
 for line in result:
  print(line)
 

paddleocr.py

@@ -30,7 +30,7 @@
 
 logger = get_logger()
 from ppocr.utils.utility import check_and_read_gif, get_image_file_list
-from tools.infer.utility import draw_ocr
+from tools.infer.utility import draw_ocr, inference_args_list, str2bool, parse_args
 
 __all__ = ['PaddleOCR']
 
@@ -167,106 +167,36 @@ def maybe_download(model_storage_directory, url):
  os.remove(tmp_path)
 
 
-def parse_args(mMain=True, add_help=True):
+def parse_args_whl(mMain=True):
  import argparse
-
- def str2bool(v):
- return v.lower() in ("true", "t", "1")
-
+ extend_args_list = [
+ {
+ 'name': 'lang',
+ 'type': str,
+ 'default': 'ch'
+ },
+ {
+ 'name': 'det',
+ 'type': str2bool,
+ 'default': True
+ },
+ {
+ 'name': 'rec',
+ 'type': str2bool,
+ 'default': True
+ },
+ ]
+ for item in inference_args_list:
+ if item['name'] == 'rec_char_dict_path':
+ item['default'] = None
+ inference_args_list.extend(extend_args_list)
  if mMain:
- parser = argparse.ArgumentParser(add_help=add_help)
- # params for prediction engine
- parser.add_argument("--use_gpu", type=str2bool, default=True)
- parser.add_argument("--ir_optim", type=str2bool, default=True)
- parser.add_argument("--use_tensorrt", type=str2bool, default=False)
- parser.add_argument("--gpu_mem", type=int, default=8000)
-
- # params for text detector
- parser.add_argument("--image_dir", type=str)
- parser.add_argument("--det_algorithm", type=str, default='DB')
- parser.add_argument("--det_model_dir", type=str, default=None)
- parser.add_argument("--det_limit_side_len", type=float, default=960)
- parser.add_argument("--det_limit_type", type=str, default='max')
-
- # DB parmas
- 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_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)
- parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
- parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
-
- # params for text recognizer
- parser.add_argument("--rec_algorithm", type=str, default='CRNN')
- 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=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)
- parser.add_argument("--drop_score", type=float, default=0.5)
-
- # params for text classifier
- 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=6)
- 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()
+ return 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=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,
- rec_algorithm='CRNN',
- rec_model_dir=None,
- rec_image_shape="3, 32, 320",
- rec_char_type='ch',
- rec_batch_num=6,
- 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=6,
- 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)
+ inference_args_dict = {}
+ for item in inference_args_list:
+ inference_args_dict[item['name']] = item['default']
+ return argparse.Namespace(**inference_args_dict)
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -276,7 +206,7 @@ def __init__(self, **kwargs):
  args:
  **kwargs: other params show in paddleocr --help
  """
- postprocess_params = parse_args(mMain=False, add_help=False)
+ postprocess_params = parse_args_whl(mMain=False)
  postprocess_params.__dict__.update(**kwargs)
  self.use_angle_cls = postprocess_params.use_angle_cls
  lang = postprocess_params.lang
@@ -346,7 +276,7 @@ def __init__(self, **kwargs):
  # init det_model and rec_model
  super().__init__(postprocess_params)
 
- def ocr(self, img, det=True, rec=True, cls=False):
+ def ocr(self, img, det=True, rec=True, cls=True):
  """
  ocr with paddleocr
  args：
@@ -358,9 +288,7 @@ def ocr(self, img, det=True, rec=True, cls=False):
  if isinstance(img, list) and det == True:
  logger.error('When input a list of images, det must be false')
  exit(0)
- if cls == False:
- self.use_angle_cls = False
- elif cls == True and self.use_angle_cls == False:
+ if cls == True and self.use_angle_cls == False:
  logger.warning(
  'Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process'
  )
@@ -382,7 +310,7 @@ def ocr(self, img, det=True, rec=True, cls=False):
  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)
+ dt_boxes, rec_res = self.__call__(img, cls)
  return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
  elif det and not rec:
  dt_boxes, elapse = self.text_detector(img)
@@ -392,7 +320,7 @@ def ocr(self, img, det=True, rec=True, cls=False):
  else:
  if not isinstance(img, list):
  img = [img]
- if self.use_angle_cls:
+ if self.use_angle_cls and cls:
  img, cls_res, elapse = self.text_classifier(img)
  if not rec:
  return cls_res
@@ -402,7 +330,7 @@ def ocr(self, img, det=True, rec=True, cls=False):
 
 def main():
  # for cmd
- args = parse_args(mMain=True)
+ args = parse_args_whl(mMain=True)
  image_dir = args.image_dir
  if image_dir.startswith('http'):
  download_with_progressbar(image_dir, 'tmp.jpg')

tools/infer/predict_system.py

@@ -85,7 +85,7 @@ def print_draw_crop_rec_res(self, img_crop_list, rec_res):
  cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
  logger.info(bno, rec_res[bno])
 
- def __call__(self, img):
+ def __call__(self, img, cls=True):
  ori_im = img.copy()
  dt_boxes, elapse = self.text_detector(img)
  logger.info("dt_boxes num : {}, elapse : {}".format(
@@ -100,7 +100,7 @@ def __call__(self, img):
  tmp_box = copy.deepcopy(dt_boxes[bno])
  img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
  img_crop_list.append(img_crop)
- if self.use_angle_cls:
+ if self.use_angle_cls and cls:
  img_crop_list, angle_list, elapse = self.text_classifier(
  img_crop_list)
  logger.info("cls num : {}, elapse : {}".format(