// 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/base.h" namespace fastdeploy { namespace vision { class FASTDEPLOY_DECL Normalize : public Processor { public: Normalize(const std::vector& mean, const std::vector& std, bool is_scale = true, const std::vector& min = std::vector(), const std::vector& max = std::vector()); bool ImplByOpenCV(Mat* mat); #ifdef ENABLE_FLYCV bool ImplByFalconCV(Mat* mat); #endif std::string Name() { return "Normalize"; } // While use normalize, it is more recommend not use this function // this function will need to compute result = ((mat / 255) - mean) / std // if we use the following method // ``` // auto norm = Normalize(...) // norm(mat) // ``` // There will be some precomputation in contruct function // and the `norm(mat)` only need to compute result = mat * alpha + beta // which will reduce lots of time static bool Run(Mat* mat, const std::vector& mean, const std::vector& std, bool is_scale = true, const std::vector& min = std::vector(), const std::vector& max = std::vector(), ProcLib lib = ProcLib::OPENCV); std::vector GetAlpha() const { return alpha_; } std::vector GetBeta() const { return beta_; } private: std::vector alpha_; std::vector beta_; }; } // namespace vision } // namespace fastdeploy