概述

将得到的模型转化为onnx模型，加载到c++中运行，来完成模型的部署，下载并安装onnxruntime;

CMakeLists.txt:

cmake_minimum_required(VERSION 2.8)
project(test)#使用clang++编译器
set(CMAKE_CXX_COMPILER clang++)
set(CMAKE_BUILD_TYPE "Release")
set(CMAKE_INCLUDE_CURRENT_DIR ON)#find the opencv and the qt5
find_package(OpenCV 4.5.1  REQUIRED)
#onnxruntime
set(ONNXRUNTIME_ROOT_PATH /home/zyl/ubuntu/tensorRT/onnxruntime-master)
set(ONNXRUNTIME_INCLUDE_DIRS ${ONNXRUNTIME_ROOT_PATH}/include/onnxruntime${ONNXRUNTIME_ROOT_PATH}/onnxruntime${ONNXRUNTIME_ROOT_PATH}/include/onnxruntime/core/session/)
set(ONNXRUNTIME_LIB ${ONNXRUNTIME_ROOT_PATH}/build/Linux/Release/libonnxruntime.so)
include_directories(${ONNXRUNTIME_INCLUDE_DIRS})add_executable(${PROJECT_NAME} "main.cpp")
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS} ${ONNXRUNTIME_LIB})

C++源码：

#include <iostream>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <chrono>
#include <string>//onnxruntime
#include <core/session/onnxruntime_cxx_api.h>
#include <core/providers/cuda/cuda_provider_factory.h>
#include <core/session/onnxruntime_c_api.h>
#include <core/providers/tensorrt/tensorrt_provider_factory.h>using namespace std;int main(int argc,char ** argv)
{//输入网络的维度static constexpr const int width = 600;static constexpr const int height = 600;static constexpr const int channel = 3;std::array<int64_t, 4> input_shape_{ 1,height, width,channel};//-------------------------------------------------------------onnxruntime-------------------------------------------------//图片和模型位置string img_path = argv[1];string model_path = "model.onnx";cv::Mat imgSource = cv::imread(img_path);Ort::Env env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, "Detection");Ort::SessionOptions session_options;//CUDA加速开启OrtSessionOptionsAppendExecutionProvider_CUDA(session_options, 0);session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);Ort::AllocatorWithDefaultOptions allocator;//加载ONNX模型Ort::Session session(env, model_path.c_str(), session_options);//获取输入输出的维度std::vector<int64_t> input_dims = session.GetInputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();std::vector<int64_t> output_dims = session.GetOutputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();/*session.GetOutputName(1, allocator);session.GetInputName(1, allocator);//输出模型输入节点的数量size_t num_input_nodes = session.GetInputCount();size_t num_output_nodes = session.GetOutputCount();*/std::vector<const char*> input_node_names = {"image_tensor:0"};std::vector<const char*> output_node_names = {"detection_boxes:0","detection_scores:0","detection_classes:0","num_detections:0" };input_dims[0] = output_dims[0] = 1;//batch size = 1std::vector<Ort::Value> input_tensors;auto memory_info = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);//将图像存储到uchar数组中，BGR--->RGBstd::array<uchar, width * height *channel> input_image_{};uchar* input =  input_image_.data();for (int i = 0; i < imgSource.rows; i++) {for (int j = 0; j < imgSource.cols; j++) {for (int c = 0; c < 3; c++){//NHWC 格式if(c==0)input[i*imgSource.cols*3+j*3+c] = imgSource.ptr<uchar>(i)[j*3+2];if(c==1)input[i*imgSource.cols*3+j*3+c] = imgSource.ptr<uchar>(i)[j*3+1];if(c==2)input[i*imgSource.cols*3+j*3+c] = imgSource.ptr<uchar>(i)[j*3+0];//NCHW 格式
//                if (c == 0)
//                     input[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + c];
//                if (c == 1)
//                     input[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + c];
//                if (c == 2)
//                     input[c*imgSource.rows*imgSource.cols + i * imgSource.cols + j] = imgSource.ptr<uchar>(i)[j * 3 + c];}}}input_tensors.push_back(Ort::Value::CreateTensor<uchar>(memory_info, input, input_image_.size(), input_shape_.data(), input_shape_.size()));//不知道输入维度时//input_tensors.push_back(Ort::Value::CreateTensor<uchar>(//        memory_info, input, input_image_.size(), input_dims.data(), input_dims.size()));chrono::steady_clock::time_point t1 = chrono::steady_clock::now();std::vector<Ort::Value> output_tensors;for(int i=0; i<100;i++)output_tensors = session.Run(Ort::RunOptions { nullptr },input_node_names.data(), //输入节点名input_tensors.data(),     //input tensorsinput_tensors.size(),     //1output_node_names.data(), //输出节点名output_node_names.size()); //4chrono::steady_clock::time_point t2 = chrono::steady_clock::now();chrono::duration<double> delay_time = chrono::duration_cast<chrono::duration<double>>(t2 - t1); //milliseconds 毫秒cout<<"前向传播平均耗时:"<<delay_time.count()*1000/100.0<<"ms"<<endl;float* boxes_ = output_tensors[0].GetTensorMutableData<float>();float* scores_ = output_tensors[1].GetTensorMutableData<float>();float* class_ = output_tensors[2].GetTensorMutableData<float>();float* num_detection = output_tensors[3].GetTensorMutableData<float>();//-------------------------------------------------------------onnxruntime-------------------------------------------------//------------------循环遍历显示检测框--------------------------cv::Mat frame(imgSource.clone());for (int i = 0; i < num_detection[0]; i++){float confidence = scores_[i];size_t objectClass = (size_t)class_[i];if (confidence >= 0.8){int xLeftBottom = static_cast<int>(boxes_[i*4 + 1] * frame.cols);int yLeftBottom = static_cast<int>(boxes_[i*4 + 0] * frame.rows);int xRightTop = static_cast<int>(boxes_[i*4 + 3] * frame.cols);int yRightTop = static_cast<int>(boxes_[i*4 + 2]* frame.rows);//显示检测框cv::Rect object((int)xLeftBottom, (int)yLeftBottom,(int)(xRightTop - xLeftBottom),(int)(yRightTop - yLeftBottom));cv::rectangle(frame, object, cv::Scalar(0,0,255), 2);cv::String label = cv::String("confidence :") +to_string(confidence);int baseLine = 0;cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.3, 1, &baseLine);cv::rectangle(frame, cv::Rect(cv::Point(xLeftBottom, yLeftBottom - labelSize.height),cv::Size(labelSize.width, labelSize.height + baseLine)),cv::Scalar(255, 255, 0), cv::FILLED);cv::putText(frame, label, cv::Point(xLeftBottom, yLeftBottom),cv::FONT_HERSHEY_SIMPLEX, 0.3, cv::Scalar(0, 0, 0));}}cv::imshow("frame",frame);cv::waitKey(0);return 0;}

 
 
参考链接：

1. https://codechina.csdn.net/mirrors/tenglike1997/onnxruntime-projects/-/blob/master/Ubuntu/onnx_mobilenet
2. https://blog.csdn.net/mightbxg/article/details/119237326