mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-30 03:22:05 +08:00
100 lines
3.2 KiB
C++
100 lines
3.2 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 <iostream>
|
|
#include <string>
|
|
#include "fastdeploy/vision.h"
|
|
|
|
void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
|
|
std::string model_file = model_dir + "/Portrait_PP_HumanSegV2_Lite_256x144_infer.onnx";
|
|
std::string params_file;
|
|
std::string config_file = model_dir + "/deploy.yaml";
|
|
auto option = fastdeploy::RuntimeOption();
|
|
option.UseCpu();
|
|
auto format = fastdeploy::ModelFormat::ONNX;
|
|
|
|
auto model = fastdeploy::vision::segmentation::PaddleSegModel(
|
|
model_file, params_file, config_file, option, format);
|
|
if (!model.Initialized()) {
|
|
std::cerr << "Failed to initialize." << std::endl;
|
|
return;
|
|
}
|
|
|
|
fastdeploy::TimeCounter tc;
|
|
tc.Start();
|
|
auto im = cv::imread(image_file);
|
|
fastdeploy::vision::SegmentationResult res;
|
|
if (!model.Predict(im, &res)) {
|
|
std::cerr << "Failed to predict." << std::endl;
|
|
return;
|
|
}
|
|
auto vis_im = fastdeploy::vision::VisSegmentation(im, res);
|
|
tc.End();
|
|
tc.PrintInfo("PPSeg in ONNX");
|
|
|
|
cv::imwrite("infer_onnx.jpg", vis_im);
|
|
std::cout
|
|
<< "Visualized result saved in ./infer_onnx.jpg"
|
|
<< std::endl;
|
|
}
|
|
|
|
void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
|
|
std::string model_file = model_dir + "/Portrait_PP_HumanSegV2_Lite_256x144_infer_rk3588.rknn";
|
|
std::string params_file;
|
|
std::string config_file = model_dir + "/deploy.yaml";
|
|
auto option = fastdeploy::RuntimeOption();
|
|
option.UseRKNPU2();
|
|
auto format = fastdeploy::ModelFormat::RKNN;
|
|
|
|
auto model = fastdeploy::vision::segmentation::PaddleSegModel(
|
|
model_file, params_file, config_file, option, format);
|
|
if (!model.Initialized()) {
|
|
std::cerr << "Failed to initialize." << std::endl;
|
|
return;
|
|
}
|
|
model.GetPreprocessor().DisablePermute();
|
|
model.GetPreprocessor().DisableNormalize();
|
|
|
|
fastdeploy::TimeCounter tc;
|
|
tc.Start();
|
|
auto im = cv::imread(image_file);
|
|
fastdeploy::vision::SegmentationResult res;
|
|
if (!model.Predict(im, &res)) {
|
|
std::cerr << "Failed to predict." << std::endl;
|
|
return;
|
|
}
|
|
auto vis_im = fastdeploy::vision::VisSegmentation(im, res);
|
|
tc.End();
|
|
tc.PrintInfo("PPSeg in RKNPU2");
|
|
|
|
cv::imwrite("infer_rknn.jpg", vis_im);
|
|
std::cout
|
|
<< "Visualized result saved in ./infer_rknn.jpg"
|
|
<< std::endl;
|
|
}
|
|
|
|
int main(int argc, char* argv[]) {
|
|
if (argc < 3) {
|
|
std::cout
|
|
<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
|
|
"e.g ./infer_model ./picodet_model_dir ./test.jpeg"
|
|
<< std::endl;
|
|
return -1;
|
|
}
|
|
|
|
RKNPU2Infer(argv[1], argv[2]);
|
|
// ONNXInfer(argv[1], argv[2]);
|
|
return 0;
|
|
}
|
|
|