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https://github.com/PaddlePaddle/FastDeploy.git
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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>
94 lines
3.8 KiB
C++
Executable File
94 lines
3.8 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 <vector>
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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/structurev2_table.h"
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#include "fastdeploy/vision/ocr/ppocr/dbdetector.h"
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#include "fastdeploy/vision/ocr/ppocr/recognizer.h"
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#include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h"
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#include "fastdeploy/utils/unique_ptr.h"
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namespace fastdeploy {
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/** \brief This pipeline can launch detection model, classification model and recognition model sequentially. All OCR pipeline APIs are defined inside this namespace.
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*
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*/
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namespace pipeline {
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/*! @brief PPStructureV2Table is used to load PP-OCRv2 series models provided by PaddleOCR.
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*/
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class FASTDEPLOY_DECL PPStructureV2Table : public FastDeployModel {
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public:
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/** \brief Set up the detection model path, recognition model path and table model path respectively.
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*
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* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
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* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
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* \param[in] table_model Path of table recognition model, e.g ./en_ppstructure_mobile_v2.0_SLANet_infer
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*/
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PPStructureV2Table(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Recognizer* rec_model,
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fastdeploy::vision::ocr::StructureV2Table* table_model);
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/** \brief Clone a new PPStructureV2Table with less memory usage when multiple instances of the same model are created
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*
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* \return new PPStructureV2Table* type unique pointer
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*/
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std::unique_ptr<PPStructureV2Table> Clone() const;
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/** \brief Predict the input image and get OCR result.
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*
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
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* \param[in] result The output OCR result will be writen to this structure.
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* \return true if the prediction successed, otherwise false.
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*/
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virtual bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result);
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virtual bool Predict(const cv::Mat& img,
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fastdeploy::vision::OCRResult* result);
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/** \brief BatchPredict the input image and get OCR result.
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*
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* \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
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* \param[in] batch_result The output list of OCR result will be writen to this structure.
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* \return true if the prediction 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<fastdeploy::vision::OCRResult>* batch_result);
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bool Initialized() const override;
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bool SetRecBatchSize(int rec_batch_size);
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int GetRecBatchSize();
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protected:
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fastdeploy::vision::ocr::DBDetector* detector_ = nullptr;
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fastdeploy::vision::ocr::Recognizer* recognizer_ = nullptr;
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fastdeploy::vision::ocr::StructureV2Table* table_ = nullptr;
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private:
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int rec_batch_size_ = 6;
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};
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namespace application {
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namespace ocrsystem {
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typedef pipeline::PPStructureV2Table PPStructureV2TableSystem;
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} // namespace ocrsystem
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} // namespace application
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} // namespace pipeline
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} // namespace fastdeploy
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