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rec_preprocessor.h
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// 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 "fastdeploy/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/processors/manager.h"
#include "fastdeploy/vision/common/result.h"
namespace fastdeploy {
namespace vision {
namespace ocr {
/*! @brief Preprocessor object for PaddleClas serials model.
*/
class FASTDEPLOY_DECL RecognizerPreprocessor : public ProcessorManager {
public:
RecognizerPreprocessor();
using ProcessorManager::Run;
/** \brief Process the input image and prepare input tensors for runtime
*
* \param[in] images The input data list, all the elements are FDMat
* \param[in] outputs The output tensors which will be fed into runtime
* \return true if the preprocess successed, otherwise false
*/
bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
size_t start_index, size_t end_index,
const std::vector<int>& indices);
/** \brief Implement the virtual function of ProcessorManager, Apply() is the
* body of Run(). Apply() contains the main logic of preprocessing, Run() is
* called by users to execute preprocessing
*
* \param[in] image_batch The input image batch
* \param[in] outputs The output tensors which will feed in runtime
* \return true if the preprocess successed, otherwise false
*/
virtual bool Apply(FDMatBatch* image_batch, std::vector<FDTensor>* outputs);
/// Set static_shape_infer is true or not. When deploy PP-OCR
/// on hardware which can not support dynamic input shape very well,
/// like Huawei Ascned, static_shape_infer needs to to be true.
void SetStaticShapeInfer(bool static_shape_infer) {
static_shape_infer_ = static_shape_infer;
}
/// Get static_shape_infer of the recognition preprocess
bool GetStaticShapeInfer() const { return static_shape_infer_; }
/// Set preprocess normalize parameters, please call this API to customize
/// the normalize parameters, otherwise it will use the default normalize
/// parameters.
void SetNormalize(const std::vector<float>& mean,
const std::vector<float>& std,
bool is_scale) {
normalize_permute_op_ =
std::make_shared<NormalizeAndPermute>(mean, std, is_scale);
normalize_op_ = std::make_shared<Normalize>(mean, std, is_scale);
}
/// Set rec_image_shape for the recognition preprocess
void SetRecImageShape(const std::vector<int>& rec_image_shape) {
rec_image_shape_ = rec_image_shape;
}
/// Get rec_image_shape for the recognition preprocess
std::vector<int> GetRecImageShape() { return rec_image_shape_; }
/// This function will disable normalize in preprocessing step.
void DisableNormalize() { disable_normalize_ = true; }
/// This function will disable hwc2chw in preprocessing step.
void DisablePermute() { disable_permute_ = true; }
private:
void OcrRecognizerResizeImage(FDMat* mat, float max_wh_ratio,
const std::vector<int>& rec_image_shape,
bool static_shape_infer);
// for recording the switch of hwc2chw
bool disable_permute_ = false;
// for recording the switch of normalize
bool disable_normalize_ = false;
std::vector<int> rec_image_shape_ = {3, 48, 320};
bool static_shape_infer_ = false;
std::shared_ptr<Resize> resize_op_;
std::shared_ptr<Pad> pad_op_;
std::shared_ptr<NormalizeAndPermute> normalize_permute_op_;
std::shared_ptr<Normalize> normalize_op_;
std::shared_ptr<HWC2CHW> hwc2chw_op_;
std::shared_ptr<Cast> cast_op_;
};
} // namespace ocr
} // namespace vision
} // namespace fastdeploy