update hubserving
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@ -12,7 +12,7 @@ def read_params():
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cfg = Config()
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#params for text classifier
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cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/"
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cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v2.0_cls_infer/"
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cfg.cls_image_shape = "3, 48, 192"
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cfg.label_list = ['0', '180']
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cfg.cls_batch_num = 30
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@ -13,7 +13,7 @@ def read_params():
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#params for text detector
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cfg.det_algorithm = "DB"
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cfg.det_model_dir = "./inference/ch_ppocr_mobile_v1.1_det_infer/"
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cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/"
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cfg.det_limit_side_len = 960
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cfg.det_limit_type = 'max'
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@ -27,16 +27,6 @@ def read_params():
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# cfg.det_east_cover_thresh = 0.1
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# cfg.det_east_nms_thresh = 0.2
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# #params for text recognizer
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# cfg.rec_algorithm = "CRNN"
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# cfg.rec_model_dir = "./inference/ch_det_mv3_crnn/"
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# cfg.rec_image_shape = "3, 32, 320"
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# cfg.rec_char_type = 'ch'
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# cfg.rec_batch_num = 30
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# cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
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# cfg.use_space_char = True
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cfg.use_zero_copy_run = False
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cfg.use_pdserving = False
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@ -13,7 +13,7 @@ def read_params():
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#params for text detector
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cfg.det_algorithm = "DB"
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cfg.det_model_dir = "./inference/ch_ppocr_mobile_v1.1_det_infer/"
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cfg.det_model_dir = "./inference/ch_ppocr_mobile_v2.0_det_infer/"
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cfg.det_limit_side_len = 960
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cfg.det_limit_type = 'max'
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@ -29,7 +29,7 @@ def read_params():
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#params for text recognizer
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cfg.rec_algorithm = "CRNN"
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cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v1.1_rec_infer/"
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cfg.rec_model_dir = "./inference/ch_ppocr_mobile_v2.0_rec_infer/"
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cfg.rec_image_shape = "3, 32, 320"
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cfg.rec_char_type = 'ch'
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@ -41,7 +41,7 @@ def read_params():
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#params for text classifier
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cfg.use_angle_cls = True
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cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/"
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cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v2.0_cls_infer/"
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cfg.cls_image_shape = "3, 48, 192"
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cfg.label_list = ['0', '180']
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cfg.cls_batch_num = 30
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@ -49,5 +49,6 @@ def read_params():
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cfg.use_zero_copy_run = False
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cfg.use_pdserving = False
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cfg.drop_score = 0.5
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return cfg
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@ -33,11 +33,11 @@ pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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### 2. 下载推理模型
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安装服务模块前,需要准备推理模型并放到正确路径。默认使用的是v1.1版的超轻量模型,默认模型路径为:
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安装服务模块前,需要准备推理模型并放到正确路径。默认使用的是v2.0版的超轻量模型,默认模型路径为:
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```
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检测模型:./inference/ch_ppocr_mobile_v1.1_det_infer/
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识别模型:./inference/ch_ppocr_mobile_v1.1_rec_infer/
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方向分类器:./inference/ch_ppocr_mobile_v1.1_cls_infer/
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检测模型:./inference/ch_ppocr_mobile_v2.0_det_infer/
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识别模型:./inference/ch_ppocr_mobile_v2.0_rec_infer/
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方向分类器:./inference/ch_ppocr_mobile_v2.0_cls_infer/
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```
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**模型路径可在`params.py`中查看和修改。** 更多模型可以从PaddleOCR提供的[模型库](../../doc/doc_ch/models_list.md)下载,也可以替换成自己训练转换好的模型。
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@ -34,11 +34,11 @@ pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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### 2. Download inference model
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Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v1.1 is used, and the default model path is:
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Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v2.0 is used, and the default model path is:
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```
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detection model: ./inference/ch_ppocr_mobile_v1.1_det_infer/
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recognition model: ./inference/ch_ppocr_mobile_v1.1_rec_infer/
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text direction classifier: ./inference/ch_ppocr_mobile_v1.1_cls_infer/
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detection model: ./inference/ch_ppocr_mobile_v2.0_det_infer/
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recognition model: ./inference/ch_ppocr_mobile_v2.0_rec_infer/
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text direction classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/
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```
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**The model path can be found and modified in `params.py`.** More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself.
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@ -17,8 +17,9 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from ppocr.utils.logging import get_logger
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logger = get_logger()
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import cv2
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import numpy as np
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import time
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