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https://github.com/PaddlePaddle/FastDeploy.git
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f601d076e4
* Fix links in readme * Fix links in readme * Update PPOCRv2/v3 examples * Update auto compression configs * Add neww quantization support for paddleclas model * Update quantized Yolov6s model download link * Improve PPOCR comments
81 lines
3.7 KiB
C++
Executable File
81 lines
3.7 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/cls_postprocessor.h"
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#include "fastdeploy/vision/ocr/ppocr/cls_preprocessor.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 Classifier object is used to load the classification model provided by PaddleOCR.
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*/
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class FASTDEPLOY_DECL Classifier : public FastDeployModel {
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public:
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Classifier();
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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 ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
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* \param[in] params_file Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_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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Classifier(const std::string& model_file, const std::string& params_file = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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/// Get model's name
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std::string ModelName() const { return "ppocr/ocr_cls"; }
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/** \brief Predict the input image and get OCR classification model cls_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] cls_label The label result of cls model will be written in to this param.
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* \param[in] cls_score The score result of cls model will be written in to this param.
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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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int32_t* cls_label, float* cls_score);
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/** \brief BatchPredict the input image and get OCR classification model cls_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] cls_labels The label results of cls model will be written in to this vector.
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* \param[in] cls_scores The score results of cls model will be written in to this vector.
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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<int32_t>* cls_labels,
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std::vector<float>* cls_scores);
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virtual bool BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<int32_t>* cls_labels,
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std::vector<float>* cls_scores,
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size_t start_index, size_t end_index);
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ClassifierPreprocessor preprocessor_;
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ClassifierPostprocessor postprocessor_;
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private:
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bool Initialize();
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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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