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2c5fd91a7f
* first draft * update api name * fix bug * fix bug and * fix bug in c api * fix bug in c_api --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
114 lines
5.1 KiB
C++
Executable File
114 lines
5.1 KiB
C++
Executable File
// 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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#pragma once
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#include "fastdeploy/fastdeploy_model.h"
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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#include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h"
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#include "fastdeploy/vision/ocr/ppocr/structurev2_table_postprocessor.h"
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#include "fastdeploy/vision/ocr/ppocr/structurev2_table_preprocessor.h"
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#include "fastdeploy/utils/unique_ptr.h"
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namespace fastdeploy {
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namespace vision {
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/** \brief All OCR series model APIs are defined inside this namespace
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*
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*/
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namespace ocr {
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/*! @brief DBDetector object is used to load the detection model provided by PaddleOCR.
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*/
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class FASTDEPLOY_DECL StructureV2Table : public FastDeployModel {
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public:
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StructureV2Table();
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/** \brief Set path of model file, and the configuration of runtime
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*
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* \param[in] model_file Path of model file, e.g ./en_ppstructure_mobile_v2.0_SLANet_infer/model.pdmodel.
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* \param[in] params_file Path of parameter file, e.g ./en_ppstructure_mobile_v2.0_SLANet_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`.
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* \param[in] model_format Model format of the loaded model, default is Paddle format.
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*/
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StructureV2Table(const std::string& model_file,
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const std::string& params_file = "",
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const std::string& table_char_dict_path = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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/** \brief Clone a new StructureV2Table Recognizer with less memory usage when multiple instances of the same model are created
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*
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* \return new StructureV2Table* type unique pointer
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*/
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virtual std::unique_ptr<StructureV2Table> Clone() const;
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/// Get model's name
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std::string ModelName() const { return "ppocr/ocr_table"; }
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/** \brief Predict the input image and get OCR detection model result.
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*
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* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
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* \param[in] boxes_result The output of OCR detection model result will be writen to this structure.
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* \return true if the prediction is successed, otherwise false.
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*/
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virtual bool Predict(const cv::Mat& img,
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std::vector<std::array<int, 8>>* boxes_result,
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std::vector<std::string>* structure_result);
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/** \brief Predict the input image and get OCR detection model result.
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*
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* \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
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* \param[in] ocr_result The output of OCR detection model result will be writen to this structure.
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* \return true if the prediction is successed, otherwise false.
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*/
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virtual bool Predict(const cv::Mat& img, vision::OCRResult* ocr_result);
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/** \brief BatchPredict the input image and get OCR detection model result.
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*
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* \param[in] images The list input of image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
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* \param[in] det_results The output of OCR detection model result will be writen to this structure.
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* \return true if the prediction is successed, otherwise false.
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*/
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virtual bool BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<std::vector<std::array<int, 8>>>* det_results,
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std::vector<std::vector<std::string>>* structure_results);
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/** \brief BatchPredict the input image and get OCR detection model result.
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*
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* \param[in] images The list input of image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
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* \param[in] ocr_results The output of OCR detection model result will be writen to this structure.
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* \return true if the prediction is successed, otherwise false.
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*/
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virtual bool BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<vision::OCRResult>* ocr_results);
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/// Get preprocessor reference of StructureV2TablePreprocessor
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virtual StructureV2TablePreprocessor& GetPreprocessor() {
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return preprocessor_;
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}
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/// Get postprocessor reference of StructureV2TablePostprocessor
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virtual StructureV2TablePostprocessor& GetPostprocessor() {
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return postprocessor_;
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}
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private:
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bool Initialize();
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StructureV2TablePreprocessor preprocessor_;
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StructureV2TablePostprocessor postprocessor_;
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};
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} // namespace ocr
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} // namespace vision
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} // namespace fastdeploy
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