mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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a36d49a973
* [Backend] fix lite backend save model error * [Backend] fixed typos * [FlyCV] optimize the integration of FlyCV * [cmake] close some tests options * [cmake] close some test option * [FlyCV] remove un-need warnings * [FlyCV] remove un-need GetMat method * [FlyCV] optimize FlyCV codes * [cmake] remove un-need cmake function in examples/CMakelists * [cmake] support gflags for Android
125 lines
4.1 KiB
C++
125 lines
4.1 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/stride_pad.h"
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namespace fastdeploy {
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namespace vision {
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bool StridePad::ImplByOpenCV(Mat* mat) {
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if (mat->layout != Layout::HWC) {
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FDERROR << "StridePad: The input data must be Layout::HWC format!"
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<< std::endl;
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return false;
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}
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if (mat->Channels() > 4) {
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FDERROR << "StridePad: Only support channels <= 4." << std::endl;
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return false;
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}
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if (mat->Channels() != value_.size()) {
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FDERROR
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<< "StridePad: Require input channels equals to size of padding value, "
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"but now channels = "
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<< mat->Channels() << ", the size of padding values = " << value_.size()
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<< "." << std::endl;
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return false;
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}
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int origin_w = mat->Width();
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int origin_h = mat->Height();
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int pad_h = (mat->Height() / stride_) * stride_ +
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(mat->Height() % stride_ != 0) * stride_ - mat->Height();
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int pad_w = (mat->Width() / stride_) * stride_ +
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(mat->Width() % stride_ != 0) * stride_ - mat->Width();
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if (pad_h == 0 && pad_w == 0) {
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return true;
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}
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cv::Mat* im = mat->GetOpenCVMat();
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cv::Scalar value;
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if (value_.size() == 1) {
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value = cv::Scalar(value_[0]);
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} else if (value_.size() == 2) {
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value = cv::Scalar(value_[0], value_[1]);
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} else if (value_.size() == 3) {
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value = cv::Scalar(value_[0], value_[1], value_[2]);
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} else {
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value = cv::Scalar(value_[0], value_[1], value_[2], value_[3]);
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}
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// top, bottom, left, right
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cv::copyMakeBorder(*im, *im, 0, pad_h, 0, pad_w, cv::BORDER_CONSTANT, value);
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mat->SetHeight(origin_h + pad_h);
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mat->SetWidth(origin_w + pad_w);
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return true;
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}
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#ifdef ENABLE_FLYCV
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bool StridePad::ImplByFlyCV(Mat* mat) {
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if (mat->layout != Layout::HWC) {
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FDERROR << "StridePad: The input data must be Layout::HWC format!"
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<< std::endl;
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return false;
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}
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if (mat->Channels() > 4) {
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FDERROR << "StridePad: Only support channels <= 4." << std::endl;
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return false;
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}
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if (mat->Channels() != value_.size()) {
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FDERROR
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<< "StridePad: Require input channels equals to size of padding value, "
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"but now channels = "
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<< mat->Channels() << ", the size of padding values = " << value_.size()
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<< "." << std::endl;
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return false;
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}
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int origin_w = mat->Width();
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int origin_h = mat->Height();
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int pad_h = (mat->Height() / stride_) * stride_ +
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(mat->Height() % stride_ != 0) * stride_ - mat->Height();
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int pad_w = (mat->Width() / stride_) * stride_ +
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(mat->Width() % stride_ != 0) * stride_ - mat->Width();
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if (pad_h == 0 && pad_w == 0) {
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return true;
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}
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fcv::Mat* im = mat->GetFlyCVMat();
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fcv::Scalar value;
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if (value_.size() == 1) {
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value = fcv::Scalar(value_[0]);
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} else if (value_.size() == 2) {
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value = fcv::Scalar(value_[0], value_[1]);
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} else if (value_.size() == 3) {
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value = fcv::Scalar(value_[0], value_[1], value_[2]);
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} else {
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value = fcv::Scalar(value_[0], value_[1], value_[2], value_[3]);
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}
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fcv::Mat new_im;
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// top, bottom, left, right
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fcv::copy_make_border(*im, new_im, 0, pad_h, 0, pad_w,
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fcv::BorderTypes::BORDER_CONSTANT, value);
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mat->SetMat(new_im);
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mat->SetHeight(new_im.height());
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mat->SetWidth(new_im.width());
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return true;
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}
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#endif
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bool StridePad::Run(Mat* mat, int stride, const std::vector<float>& value,
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ProcLib lib) {
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auto p = StridePad(stride, value);
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return p(mat, lib);
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}
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} // namespace vision
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} // namespace fastdeploy
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