mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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89 lines
3.0 KiB
C++
89 lines
3.0 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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#include "fastdeploy/vision/common/processors/normalize.h"
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namespace fastdeploy {
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namespace vision {
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Normalize::Normalize(const std::vector<float>& mean,
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const std::vector<float>& std, bool is_scale,
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const std::vector<float>& min,
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const std::vector<float>& max) {
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FDASSERT(mean.size() == std.size(),
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"Normalize: requires the size of mean equal to the size of std.");
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std::vector<double> mean_(mean.begin(), mean.end());
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std::vector<double> std_(std.begin(), std.end());
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std::vector<double> min_(mean.size(), 0.0);
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std::vector<double> max_(mean.size(), 255.0);
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if (min.size() != 0) {
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FDASSERT(
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min.size() == mean.size(),
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"Normalize: while min is defined, requires the size of min equal to "
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"the size of mean.");
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min_.assign(min.begin(), min.end());
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}
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if (max.size() != 0) {
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FDASSERT(
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min.size() == mean.size(),
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"Normalize: while max is defined, requires the size of max equal to "
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"the size of mean.");
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max_.assign(max.begin(), max.end());
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}
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for (auto c = 0; c < mean_.size(); ++c) {
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double alpha = 1.0;
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if (is_scale) {
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alpha /= (max_[c] - min_[c]);
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}
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double beta = -1.0 * (mean_[c] + min_[c] * alpha) / std_[c];
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alpha /= std_[c];
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alpha_.push_back(alpha);
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beta_.push_back(beta);
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}
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}
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bool Normalize::CpuRun(Mat* mat) {
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cv::Mat* im = mat->GetCpuMat();
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std::vector<cv::Mat> split_im;
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cv::split(*im, split_im);
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for (int c = 0; c < im->channels(); c++) {
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split_im[c].convertTo(split_im[c], CV_32FC1, alpha_[c], beta_[c]);
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}
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cv::merge(split_im, *im);
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return true;
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}
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#ifdef ENABLE_OPENCV_CUDA
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bool Normalize::GpuRun(Mat* mat) {
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cv::cuda::GpuMat* im = mat->GetGpuMat();
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std::vector<cv::cuda::GpuMat> split_im;
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cv::cuda::split(*im, split_im);
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for (int c = 0; c < im->channels(); c++) {
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split_im[c].convertTo(split_im[c], CV_32FC1, alpha_[c], beta_[c]);
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}
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cv::cuda::merge(split_im, *im);
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return true;
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}
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#endif
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bool Normalize::Run(Mat* mat, const std::vector<float>& mean,
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const std::vector<float>& std, bool is_scale,
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const std::vector<float>& min,
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const std::vector<float>& max, ProcLib lib) {
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auto n = Normalize(mean, std, is_scale, min, max);
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return n(mat, lib);
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}
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} // namespace vision
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} // namespace fastdeploy
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