mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 03:46:40 +08:00
89 lines
3.0 KiB
C++
89 lines
3.0 KiB
C++
// 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.
|
|
|
|
#include "fastdeploy/vision/common/processors/normalize.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
Normalize::Normalize(const std::vector<float>& mean,
|
|
const std::vector<float>& std, bool is_scale,
|
|
const std::vector<float>& min,
|
|
const std::vector<float>& max) {
|
|
FDASSERT(mean.size() == std.size(),
|
|
"Normalize: requires the size of mean equal to the size of std.");
|
|
std::vector<double> mean_(mean.begin(), mean.end());
|
|
std::vector<double> std_(std.begin(), std.end());
|
|
std::vector<double> min_(mean.size(), 0.0);
|
|
std::vector<double> max_(mean.size(), 255.0);
|
|
if (min.size() != 0) {
|
|
FDASSERT(
|
|
min.size() == mean.size(),
|
|
"Normalize: while min is defined, requires the size of min equal to "
|
|
"the size of mean.");
|
|
min_.assign(min.begin(), min.end());
|
|
}
|
|
if (max.size() != 0) {
|
|
FDASSERT(
|
|
min.size() == mean.size(),
|
|
"Normalize: while max is defined, requires the size of max equal to "
|
|
"the size of mean.");
|
|
max_.assign(max.begin(), max.end());
|
|
}
|
|
for (auto c = 0; c < mean_.size(); ++c) {
|
|
double alpha = 1.0;
|
|
if (is_scale) {
|
|
alpha /= (max_[c] - min_[c]);
|
|
}
|
|
double beta = -1.0 * (mean_[c] + min_[c] * alpha) / std_[c];
|
|
alpha /= std_[c];
|
|
alpha_.push_back(alpha);
|
|
beta_.push_back(beta);
|
|
}
|
|
}
|
|
|
|
bool Normalize::CpuRun(Mat* mat) {
|
|
cv::Mat* im = mat->GetCpuMat();
|
|
std::vector<cv::Mat> split_im;
|
|
cv::split(*im, split_im);
|
|
for (int c = 0; c < im->channels(); c++) {
|
|
split_im[c].convertTo(split_im[c], CV_32FC1, alpha_[c], beta_[c]);
|
|
}
|
|
cv::merge(split_im, *im);
|
|
return true;
|
|
}
|
|
|
|
#ifdef ENABLE_OPENCV_CUDA
|
|
bool Normalize::GpuRun(Mat* mat) {
|
|
cv::cuda::GpuMat* im = mat->GetGpuMat();
|
|
std::vector<cv::cuda::GpuMat> split_im;
|
|
cv::cuda::split(*im, split_im);
|
|
for (int c = 0; c < im->channels(); c++) {
|
|
split_im[c].convertTo(split_im[c], CV_32FC1, alpha_[c], beta_[c]);
|
|
}
|
|
cv::cuda::merge(split_im, *im);
|
|
return true;
|
|
}
|
|
#endif
|
|
|
|
bool Normalize::Run(Mat* mat, const std::vector<float>& mean,
|
|
const std::vector<float>& std, bool is_scale,
|
|
const std::vector<float>& min,
|
|
const std::vector<float>& max, ProcLib lib) {
|
|
auto n = Normalize(mean, std, is_scale, min, max);
|
|
return n(mat, lib);
|
|
}
|
|
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|