// 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 "ultra_infer/ultra_infer_model.h" #include "ultra_infer/vision/common/processors/transform.h" #include "ultra_infer/vision/common/result.h" #include "ultra_infer/utils/unique_ptr.h" #include "ultra_infer/vision/ocr/ppocr/classifier.h" #include "ultra_infer/vision/ocr/ppocr/dbdetector.h" #include "ultra_infer/vision/ocr/ppocr/recognizer.h" #include "ultra_infer/vision/ocr/ppocr/utils/ocr_postprocess_op.h" namespace ultra_infer { /** \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 ULTRAINFER_DECL PPOCRv2 : public UltraInferModel { 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(ultra_infer::vision::ocr::DBDetector *det_model, ultra_infer::vision::ocr::Classifier *cls_model, ultra_infer::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(ultra_infer::vision::ocr::DBDetector *det_model, ultra_infer::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 written to this structure. \return true if the prediction succeeded, * otherwise false. */ virtual bool Predict(cv::Mat *img, ultra_infer::vision::OCRResult *result); virtual bool Predict(const cv::Mat &img, ultra_infer::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 written to this structure. \return true if the * prediction succeeded, 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: ultra_infer::vision::ocr::DBDetector *detector_ = nullptr; ultra_infer::vision::ocr::Classifier *classifier_ = nullptr; ultra_infer::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 ultra_infer