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643f41e711
* 添加paddleclas模型 * 更新README_CN * 更新README_CN * 更新README * update get_model.sh * update get_models.sh * update paddleseg models * update paddle_seg models * update paddle_seg models * modified test resources * update benchmark_gpu_trt.sh * add paddle detection * add paddledetection to benchmark * modified benchmark cmakelists * update benchmark scripts * modified benchmark function calling * modified paddledetection documents * upadte getmodels.sh * add PaddleDetectonModel * reset examples/paddledetection * resolve conflict * update pybind * resolve conflict * fix bug * delete debug mode * update checkarch log * update trt inputs example * Update README.md * add ppocr_v4 * update ppocr_v4 * update ocr_v4 * update ocr_v4 * update ocr_v4 * update ocr_v4 --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
81 lines
3.3 KiB
C++
Executable File
81 lines
3.3 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/vision/ocr/ppocr/ppocr_v3.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 PPOCRv4 is used to load PP-OCRv4 series models provided by PaddleOCR.
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*/
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class FASTDEPLOY_DECL PPOCRv4 : public PPOCRv3 {
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public:
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/** \brief Set up the detection model path, classification model path and recognition model path respectively.
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*
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* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv4_det_infer
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* \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
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* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv4_rec_infer
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*/
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PPOCRv4(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Classifier* cls_model,
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fastdeploy::vision::ocr::Recognizer* rec_model)
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: PPOCRv3(det_model, cls_model, rec_model) {
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// The only difference between v2 and v3
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auto preprocess_shape = recognizer_->GetPreprocessor().GetRecImageShape();
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preprocess_shape[1] = 48;
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recognizer_->GetPreprocessor().SetRecImageShape(preprocess_shape);
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}
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/** \brief Classification model is optional, so this function is set up the detection model path and recognition model path respectively.
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*
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* \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv4_det_infer
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* \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv4_rec_infer
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*/
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PPOCRv4(fastdeploy::vision::ocr::DBDetector* det_model,
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fastdeploy::vision::ocr::Recognizer* rec_model)
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: PPOCRv3(det_model, rec_model) {
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// The only difference between v2 and v4
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auto preprocess_shape = recognizer_->GetPreprocessor().GetRecImageShape();
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preprocess_shape[1] = 48;
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recognizer_->GetPreprocessor().SetRecImageShape(preprocess_shape);
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}
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/** \brief Clone a new PPOCRv4 with less memory usage when multiple instances of the same model are created
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*
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* \return new PPOCRv4* type unique pointer
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*/
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std::unique_ptr<PPOCRv4> Clone() const {
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std::unique_ptr<PPOCRv4> clone_model = utils::make_unique<PPOCRv4>(PPOCRv4(*this));
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clone_model->detector_ = detector_->Clone().release();
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if (classifier_ != nullptr) {
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clone_model->classifier_ = classifier_->Clone().release();
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}
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clone_model->recognizer_ = recognizer_->Clone().release();
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return clone_model;
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}
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};
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} // namespace pipeline
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namespace application {
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namespace ocrsystem {
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typedef pipeline::PPOCRv4 PPOCRSystemv4;
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} // namespace ocrsystem
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} // namespace application
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} // namespace fastdeploy
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