Merge pull request #2925 from WenmuZhou/Optimizing_parameters

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 @@ 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
+from tools.infer.utility import draw_ocr, init_args, str2bool
 
 __all__ = ['PaddleOCR']
 
@@ -167,106 +167,24 @@ def maybe_download(model_storage_directory, url):
         os.remove(tmp_path)
 
 
-def parse_args(mMain=True, add_help=True):
+def parse_args(mMain=True):
     import argparse
-
-    def str2bool(v):
-        return v.lower() in ("true", "t", "1")
-
+    parser = init_args()
+    parser.add_help = mMain
+    parser.add_argument("--lang", type=str, default='ch')
+    parser.add_argument("--det", type=str2bool, default=True)
+    parser.add_argument("--rec", type=str2bool, default=True)
+
+    for action in parser._actions:
+        if action.dest == 'rec_char_dict_path':
+            action.default = None
     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()
     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 action in parser._actions:
+            inference_args_dict[action.dest] = action.default
+        return argparse.Namespace(**inference_args_dict)
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -276,7 +194,7 @@ class PaddleOCR(predict_system.TextSystem):
         args:
             **kwargs: other params show in paddleocr --help
         """
-        postprocess_params = parse_args(mMain=False, add_help=False)
+        postprocess_params = parse_args(mMain=False)
         postprocess_params.__dict__.update(**kwargs)
         self.use_angle_cls = postprocess_params.use_angle_cls
         lang = postprocess_params.lang
@@ -346,7 +264,7 @@ class PaddleOCR(predict_system.TextSystem):
         # 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 +276,7 @@ class PaddleOCR(predict_system.TextSystem):
         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 +298,7 @@ class PaddleOCR(predict_system.TextSystem):
         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 +308,7 @@ class PaddleOCR(predict_system.TextSystem):
         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

tools/infer/predict_system.py

@@ -85,7 +85,7 @@ class TextSystem(object):
             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 @@ class TextSystem(object):
             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(

tools/infer/utility.py

@@ -23,13 +23,15 @@ import math
 from paddle import inference
 import time
 from ppocr.utils.logging import get_logger
+
 logger = get_logger()
 
 
-def parse_args():
-    def str2bool(v):
-        return v.lower() in ("true", "t", "1")
+def str2bool(v):
+    return v.lower() in ("true", "t", "1")
+
 
+def init_args():
     parser = argparse.ArgumentParser()
     # params for prediction engine
     parser.add_argument("--use_gpu", type=str2bool, default=True)
@@ -108,6 +110,11 @@ def parse_args():
     parser.add_argument("--total_process_num", type=int, default=1)
     parser.add_argument("--process_id", type=int, default=0)
 
+    return parser
+
+
+def parse_args():
+    parser = init_args()
     return parser.parse_args()
 
 
@@ -141,7 +148,7 @@ def create_predictor(args, mode, logger):
             config.enable_tensorrt_engine(
                 precision_mode=inference.PrecisionType.Float32,
                 max_batch_size=args.max_batch_size,
-                min_subgraph_size=3)  # skip the minmum trt subgraph 
+                min_subgraph_size=3)  # skip the minmum trt subgraph
         if mode == "det" and "mobile" in model_file_path:
             min_input_shape = {
                 "x": [1, 3, 50, 50],