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4d57b62f83
* add vision processors cpp api doc * add base class Processor in api doc * Update base.h --------- Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
95 lines
3.2 KiB
C++
95 lines
3.2 KiB
C++
// 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/common/processors/base.h"
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namespace fastdeploy {
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namespace vision {
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/*! @brief Processor for Normalize images with given paramters.
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*/
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class FASTDEPLOY_DECL Normalize : public Processor {
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public:
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Normalize(const std::vector<float>& mean, const std::vector<float>& std,
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bool is_scale = true,
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const std::vector<float>& min = std::vector<float>(),
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const std::vector<float>& max = std::vector<float>(),
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bool swap_rb = false);
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bool ImplByOpenCV(Mat* mat);
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#ifdef ENABLE_FLYCV
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bool ImplByFlyCV(Mat* mat);
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#endif
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#ifdef WITH_GPU
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bool ImplByCuda(FDMat* mat);
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bool ImplByCuda(FDMatBatch* mat_batch);
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#endif
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#ifdef ENABLE_CVCUDA
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bool ImplByCvCuda(FDMat* mat);
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bool ImplByCvCuda(FDMatBatch* mat_batch);
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#endif
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std::string Name() { return "Normalize"; }
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// While use normalize, it is more recommend not use this function
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// this function will need to compute result = ((mat / 255) - mean) / std
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// if we use the following method
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// ```
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// auto norm = Normalize(...)
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// norm(mat)
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// ```
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// There will be some precomputation in contruct function
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// and the `norm(mat)` only need to compute result = mat * alpha + beta
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// which will reduce lots of time
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/** \brief Process the input images
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*
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* \param[in] mat The input image data, `result = mat * alpha + beta`
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* \param[in] mean target mean vector of output images
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* \param[in] std target std vector of output images
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* \param[in] max max value vector to be in target image
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* \param[in] min min value vector to be in target image
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* \param[in] lib to define OpenCV or FlyCV or CVCUDA will be used.
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* \param[in] swap_rb to define whether to swap r and b channel order
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* \return true if the process successed, otherwise false
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*/
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static bool Run(Mat* mat, const std::vector<float>& mean,
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const std::vector<float>& std, bool is_scale = true,
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const std::vector<float>& min = std::vector<float>(),
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const std::vector<float>& max = std::vector<float>(),
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ProcLib lib = ProcLib::DEFAULT, bool swap_rb = false);
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std::vector<float> GetAlpha() const { return alpha_; }
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std::vector<float> GetBeta() const { return beta_; }
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bool GetSwapRB() {
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return swap_rb_;
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}
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/** \brief Process the input images
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*
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* \param[in] swap_rb set the value of the swap_rb parameter
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*/
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void SetSwapRB(bool swap_rb) {
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swap_rb_ = swap_rb;
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}
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private:
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std::vector<float> alpha_;
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std::vector<float> beta_;
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FDTensor gpu_alpha_;
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FDTensor gpu_beta_;
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bool swap_rb_;
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};
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} // namespace vision
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} // namespace fastdeploy
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