在C++上利用onnxruntime （CUDA）和 opencv 部署模型onnx
作者：互联网
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 = 1
std::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--->RGB
std::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 tensors
input_tensors.size(), //1
output_node_names.data(), //输出节点名
output_node_names.size()); //4
chrono::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：
- https://codechina.csdn.net/mirrors/tenglike1997/onnxruntime-projects/-/blob/master/Ubuntu/onnx_mobilenet
- https://blog.csdn.net/mightbxg/article/details/119237326
标签：imgSource,onnxruntime,onnx,cols,opencv,int,input,include,cv 来源： https://blog.csdn.net/qq_42995327/article/details/121051991