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54 lines
2.0 KiB
C++
54 lines
2.0 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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class 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 CpuRun(Mat* mat);
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#ifdef ENABLE_OPENCV_CUDA
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bool GpuRun(Mat* mat);
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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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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::OPENCV_CPU);
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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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};
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} // namespace vision
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} // namespace fastdeploy
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