// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include #include "fastdeploy/fastdeploy_model.h" #include "fastdeploy/vision/common/processors/transform.h" #include "fastdeploy/vision/common/result.h" #include "fastdeploy/vision/ocr/ppocr/classifier.h" #include "fastdeploy/vision/ocr/ppocr/dbdetector.h" #include "fastdeploy/vision/ocr/ppocr/recognizer.h" #include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h" #include "fastdeploy/utils/unique_ptr.h" namespace fastdeploy { /** \brief This pipeline can launch detection model, classification model and recognition model sequentially. All OCR pipeline APIs are defined inside this namespace. * */ namespace pipeline { /*! @brief PPOCRv2 is used to load PP-OCRv2 series models provided by PaddleOCR. */ class FASTDEPLOY_DECL PPOCRv2 : public FastDeployModel { public: /** \brief Set up the detection model path, classification model path and recognition model path respectively. * * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer * \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer */ PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model, fastdeploy::vision::ocr::Classifier* cls_model, fastdeploy::vision::ocr::Recognizer* rec_model); /** \brief Classification model is optional, so this function is set up the detection model path and recognition model path respectively. * * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer */ PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model, fastdeploy::vision::ocr::Recognizer* rec_model); /** \brief Clone a new PPOCRv2 with less memory usage when multiple instances of the same model are created * * \return new PPOCRv2* type unique pointer */ std::unique_ptr Clone() const; /** \brief Predict the input image and get OCR result. * * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format. * \param[in] result The output OCR result will be writen to this structure. * \return true if the prediction successed, otherwise false. */ virtual bool Predict(cv::Mat* img, fastdeploy::vision::OCRResult* result); virtual bool Predict(const cv::Mat& img, fastdeploy::vision::OCRResult* result); /** \brief BatchPredict the input image and get OCR result. * * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format. * \param[in] batch_result The output list of OCR result will be writen to this structure. * \return true if the prediction successed, otherwise false. */ virtual bool BatchPredict(const std::vector& images, std::vector* batch_result); bool Initialized() const override; bool SetClsBatchSize(int cls_batch_size); int GetClsBatchSize(); bool SetRecBatchSize(int rec_batch_size); int GetRecBatchSize(); protected: fastdeploy::vision::ocr::DBDetector* detector_ = nullptr; fastdeploy::vision::ocr::Classifier* classifier_ = nullptr; fastdeploy::vision::ocr::Recognizer* recognizer_ = nullptr; private: int cls_batch_size_ = 1; int rec_batch_size_ = 6; }; namespace application { namespace ocrsystem { typedef pipeline::PPOCRv2 PPOCRSystemv2; } // namespace ocrsystem } // namespace application } // namespace pipeline } // namespace fastdeploy