mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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24317e1a14
* Imporve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Improve OCR Readme * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Add Initialize function to PP-OCR * Make all the model links come from PaddleOCR * Improve OCR readme * Improve OCR readme * Improve OCR readme * Improve OCR readme * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add Readme for vision results * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add check for label file in postprocess of Rec model * Add comments to create API docs * Improve OCR comments * Rename OCR and add comments * Make sure previous python example works * Make sure previous python example works Co-authored-by: Jason <jiangjiajun@baidu.com>
302 lines
12 KiB
Python
302 lines
12 KiB
Python
# # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# #
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# # Licensed under the Apache License, Version 2.0 (the "License");
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# # you may not use this file except in compliance with the License.
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# # You may obtain a copy of the License at
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# #
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# # http://www.apache.org/licenses/LICENSE-2.0
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# #
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# # Unless required by applicable law or agreed to in writing, software
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# # distributed under the License is distributed on an "AS IS" BASIS,
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# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# # See the License for the specific language governing permissions and
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# # limitations under the License.
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from __future__ import absolute_import
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import logging
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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class DBDetector(FastDeployModel):
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def __init__(self,
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model_file="",
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load OCR detection model provided by PaddleOCR.
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:param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
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:param params_file: (str)Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
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"""
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super(DBDetector, self).__init__(runtime_option)
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if (len(model_file) == 0):
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self._model = C.vision.ocr.DBDetector()
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else:
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self._model = C.vision.ocr.DBDetector(
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model_file, params_file, self._runtime_option, model_format)
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assert self.initialized, "DBDetector initialize failed."
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# 一些跟DBDetector模型有关的属性封装
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@property
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def max_side_len(self):
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return self._model.max_side_len
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@property
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def det_db_thresh(self):
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return self._model.det_db_thresh
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@property
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def det_db_box_thresh(self):
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return self._model.det_db_box_thresh
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@property
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def det_db_unclip_ratio(self):
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return self._model.det_db_unclip_ratio
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@property
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def det_db_score_mode(self):
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return self._model.det_db_score_mode
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@property
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def use_dilation(self):
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return self._model.use_dilation
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@property
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def is_scale(self):
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return self._model.max_wh
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@max_side_len.setter
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def max_side_len(self, value):
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assert isinstance(
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value, int), "The value to set `max_side_len` must be type of int."
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self._model.max_side_len = value
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@det_db_thresh.setter
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def det_db_thresh(self, value):
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assert isinstance(
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value,
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float), "The value to set `det_db_thresh` must be type of float."
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self._model.det_db_thresh = value
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@det_db_box_thresh.setter
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def det_db_box_thresh(self, value):
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assert isinstance(
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value, float
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), "The value to set `det_db_box_thresh` must be type of float."
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self._model.det_db_box_thresh = value
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@det_db_unclip_ratio.setter
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def det_db_unclip_ratio(self, value):
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assert isinstance(
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value, float
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), "The value to set `det_db_unclip_ratio` must be type of float."
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self._model.det_db_unclip_ratio = value
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@det_db_score_mode.setter
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def det_db_score_mode(self, value):
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assert isinstance(
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value,
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str), "The value to set `det_db_score_mode` must be type of str."
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self._model.det_db_score_mode = value
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@use_dilation.setter
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def use_dilation(self, value):
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assert isinstance(
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value,
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bool), "The value to set `use_dilation` must be type of bool."
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self._model.use_dilation = value
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@is_scale.setter
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def is_scale(self, value):
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assert isinstance(
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value, bool), "The value to set `is_scale` must be type of bool."
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self._model.is_scale = value
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class Classifier(FastDeployModel):
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def __init__(self,
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model_file="",
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load OCR classification model provided by PaddleOCR.
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:param model_file: (str)Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
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:param params_file: (str)Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
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"""
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super(Classifier, self).__init__(runtime_option)
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if (len(model_file) == 0):
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self._model = C.vision.ocr.Classifier()
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else:
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self._model = C.vision.ocr.Classifier(
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model_file, params_file, self._runtime_option, model_format)
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assert self.initialized, "Classifier initialize failed."
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@property
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def cls_thresh(self):
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return self._model.cls_thresh
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@property
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def cls_image_shape(self):
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return self._model.cls_image_shape
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@property
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def cls_batch_num(self):
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return self._model.cls_batch_num
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@cls_thresh.setter
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def cls_thresh(self, value):
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assert isinstance(
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value,
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float), "The value to set `cls_thresh` must be type of float."
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self._model.cls_thresh = value
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@cls_image_shape.setter
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def cls_image_shape(self, value):
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assert isinstance(
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value, list), "The value to set `cls_thresh` must be type of list."
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self._model.cls_image_shape = value
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@cls_batch_num.setter
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def cls_batch_num(self, value):
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assert isinstance(
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value,
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int), "The value to set `cls_batch_num` must be type of int."
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self._model.cls_batch_num = value
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class Recognizer(FastDeployModel):
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def __init__(self,
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model_file="",
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params_file="",
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label_path="",
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load OCR recognition model provided by PaddleOCR
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:param model_file: (str)Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
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:param params_file: (str)Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
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:param label_path: (str)Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.txt
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU.
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model.
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"""
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super(Recognizer, self).__init__(runtime_option)
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if (len(model_file) == 0):
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self._model = C.vision.ocr.Recognizer()
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else:
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self._model = C.vision.ocr.Recognizer(
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model_file, params_file, label_path, self._runtime_option,
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model_format)
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assert self.initialized, "Recognizer initialize failed."
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@property
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def rec_img_h(self):
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return self._model.rec_img_h
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@property
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def rec_img_w(self):
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return self._model.rec_img_w
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@property
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def rec_batch_num(self):
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return self._model.rec_batch_num
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@rec_img_h.setter
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def rec_img_h(self, value):
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assert isinstance(
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value, int), "The value to set `rec_img_h` must be type of int."
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self._model.rec_img_h = value
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@rec_img_w.setter
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def rec_img_w(self, value):
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assert isinstance(
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value, int), "The value to set `rec_img_w` must be type of int."
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self._model.rec_img_w = value
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@rec_batch_num.setter
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def rec_batch_num(self, value):
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assert isinstance(
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value,
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int), "The value to set `rec_batch_num` must be type of int."
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self._model.rec_batch_num = value
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class PPOCRv3(FastDeployModel):
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def __init__(self, det_model=None, cls_model=None, rec_model=None):
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"""Consruct a pipeline with text detector, direction classifier and text recognizer models
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:param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
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:param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
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:param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
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"""
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assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
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if cls_model is None:
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self.system = C.vision.ocr.PPOCRv3(det_model._model,
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rec_model._model)
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else:
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self.system = C.vision.ocr.PPOCRv3(
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det_model._model, cls_model._model, rec_model._model)
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def predict(self, input_image):
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"""Predict an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: OCRResult
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"""
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return self.system.predict(input_image)
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class PPOCRSystemv3(PPOCRv3):
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def __init__(self, det_model=None, cls_model=None, rec_model=None):
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logging.warning(
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"DEPRECATED: fd.vision.ocr.PPOCRSystemv3 is deprecated, "
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"please use fd.vision.ocr.PPOCRv3 instead.")
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super(PPOCRSystemv3, self).__init__(det_model, cls_model, rec_model)
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def predict(self, input_image):
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return super(PPOCRSystemv3, self).predict(input_image)
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class PPOCRv2(FastDeployModel):
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def __init__(self, det_model=None, cls_model=None, rec_model=None):
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"""Consruct a pipeline with text detector, direction classifier and text recognizer models
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:param det_model: (FastDeployModel) The detection model object created by fastdeploy.vision.ocr.DBDetector.
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:param cls_model: (FastDeployModel) The classification model object created by fastdeploy.vision.ocr.Classifier.
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:param rec_model: (FastDeployModel) The recognition model object created by fastdeploy.vision.ocr.Recognizer.
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"""
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assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
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if cls_model is None:
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self.system = C.vision.ocr.PPOCRv2(det_model._model,
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rec_model._model)
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else:
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self.system = C.vision.ocr.PPOCRv2(
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det_model._model, cls_model._model, rec_model._model)
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def predict(self, input_image):
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"""Predict an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: OCRResult
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"""
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return self.system.predict(input_image)
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class PPOCRSystemv2(PPOCRv2):
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def __init__(self, det_model=None, cls_model=None, rec_model=None):
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logging.warning(
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"DEPRECATED: fd.vision.ocr.PPOCRSystemv2 is deprecated, "
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"please use fd.vision.ocr.PPOCRv2 instead.")
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super(PPOCRSystemv2, self).__init__(det_model, cls_model, rec_model)
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def predict(self, input_image):
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return super(PPOCRSystemv2, self).predict(input_image)
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